MBA Starting Salaries

Understanding the requirements

Daer was pleased to have located the data which could help her understand about MBA courses. She wondered whether it could answer some important questions that would help her decide whether to enroll in the MBA program at this particular school. In particular, she wondered about starting salaries, whether gender and/or age made a difference, and whether students liked this particular program. She also wondered whether her GMAT score made a difference in marks. Since her native language was not English, Daer had a relatively low GMAT.

Reading the data set

We read the dataset and view it in R.

mbasalary.df <- read.csv(paste("MBA Starting Salaries Data.csv",sep=""))
head(mbasalary.df)

##   age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter work_yrs
## 1  23   2      620       77       87       87   3.4  3.00       1        2
## 2  24   1      610       90       71       87   3.5  4.00       1        2
## 3  24   1      670       99       78       95   3.3  3.25       1        2
## 4  24   1      570       56       81       75   3.3  2.67       1        1
## 5  24   2      710       93       98       98   3.6  3.75       1        2
## 6  24   1      640       82       89       91   3.9  3.75       1        2
##   frstlang salary satis
## 1        1      0     7
## 2        1      0     6
## 3        1      0     6
## 4        1      0     7
## 5        1    999     5
## 6        1      0     6

Summarising the data

Create summary statistics (e.g. mean, standard deviation, median, mode) for the important variables in the dataset.

summary(mbasalary.df)

##       age             sex           gmat_tot        gmat_qpc    
##  Min.   :22.00   Min.   :1.000   Min.   :450.0   Min.   :28.00  
##  1st Qu.:25.00   1st Qu.:1.000   1st Qu.:580.0   1st Qu.:72.00  
##  Median :27.00   Median :1.000   Median :620.0   Median :83.00  
##  Mean   :27.36   Mean   :1.248   Mean   :619.5   Mean   :80.64  
##  3rd Qu.:29.00   3rd Qu.:1.000   3rd Qu.:660.0   3rd Qu.:93.00  
##  Max.   :48.00   Max.   :2.000   Max.   :790.0   Max.   :99.00  
##     gmat_vpc        gmat_tpc        s_avg           f_avg      
##  Min.   :16.00   Min.   : 0.0   Min.   :2.000   Min.   :0.000  
##  1st Qu.:71.00   1st Qu.:78.0   1st Qu.:2.708   1st Qu.:2.750  
##  Median :81.00   Median :87.0   Median :3.000   Median :3.000  
##  Mean   :78.32   Mean   :84.2   Mean   :3.025   Mean   :3.062  
##  3rd Qu.:91.00   3rd Qu.:94.0   3rd Qu.:3.300   3rd Qu.:3.250  
##  Max.   :99.00   Max.   :99.0   Max.   :4.000   Max.   :4.000  
##     quarter         work_yrs         frstlang         salary      
##  Min.   :1.000   Min.   : 0.000   Min.   :1.000   Min.   :     0  
##  1st Qu.:1.250   1st Qu.: 2.000   1st Qu.:1.000   1st Qu.:     0  
##  Median :2.000   Median : 3.000   Median :1.000   Median :   999  
##  Mean   :2.478   Mean   : 3.872   Mean   :1.117   Mean   : 39026  
##  3rd Qu.:3.000   3rd Qu.: 4.000   3rd Qu.:1.000   3rd Qu.: 97000  
##  Max.   :4.000   Max.   :22.000   Max.   :2.000   Max.   :220000  
##      satis      
##  Min.   :  1.0  
##  1st Qu.:  5.0  
##  Median :  6.0  
##  Mean   :172.2  
##  3rd Qu.:  7.0  
##  Max.   :998.0

library(psych)
describe(mbasalary.df)

##          vars   n     mean       sd median  trimmed     mad min    max
## age         1 274    27.36     3.71     27    26.76    2.97  22     48
## sex         2 274     1.25     0.43      1     1.19    0.00   1      2
## gmat_tot    3 274   619.45    57.54    620   618.86   59.30 450    790
## gmat_qpc    4 274    80.64    14.87     83    82.31   14.83  28     99
## gmat_vpc    5 274    78.32    16.86     81    80.33   14.83  16     99
## gmat_tpc    6 274    84.20    14.02     87    86.12   11.86   0     99
## s_avg       7 274     3.03     0.38      3     3.03    0.44   2      4
## f_avg       8 274     3.06     0.53      3     3.09    0.37   0      4
## quarter     9 274     2.48     1.11      2     2.47    1.48   1      4
## work_yrs   10 274     3.87     3.23      3     3.29    1.48   0     22
## frstlang   11 274     1.12     0.32      1     1.02    0.00   1      2
## salary     12 274 39025.69 50951.56    999 33607.86 1481.12   0 220000
## satis      13 274   172.18   371.61      6    91.50    1.48   1    998
##           range  skew kurtosis      se
## age          26  2.16     6.45    0.22
## sex           1  1.16    -0.66    0.03
## gmat_tot    340 -0.01     0.06    3.48
## gmat_qpc     71 -0.92     0.30    0.90
## gmat_vpc     83 -1.04     0.74    1.02
## gmat_tpc     99 -2.28     9.02    0.85
## s_avg         2 -0.06    -0.38    0.02
## f_avg         4 -2.08    10.85    0.03
## quarter       3  0.02    -1.35    0.07
## work_yrs     22  2.78     9.80    0.20
## frstlang      1  2.37     3.65    0.02
## salary   220000  0.70    -1.05 3078.10
## satis       997  1.77     1.13   22.45

Box Plots / Bar plots

Draw Box Plots / Bar Plots to visualize the distribution of each variable independently

hist(mbasalary.df$age, breaks=10,col="yellow",xlab="Age in years", main="Histogram for age")

barplot(table(mbasalary.df$sex),col="yellow",xlab="Sex of students", main="Histogram for sex")

hist(mbasalary.df$gmat_tot, breaks=10,col="yellow",xlab="GMAT total score", main="Histogram for total gMAT score")

hist(mbasalary.df$gmat_qpc, breaks=10,col="yellow",xlab="GMAT quantitative percentile", main="Histogram for quantitative gMAT percentile")

hist(mbasalary.df$gmat_vpc, breaks=10,col="yellow",xlab="GMAT verbal percentile", main="Histogram for verbal gMAT percentile")

hist(mbasalary.df$gmat_tpc, breaks=10,col="yellow",xlab="GMAT total percentile", main="Histogram for total gMAT percentile")

plot(mbasalary.df$s_avg,main="Spring average")

plot(mbasalary.df$f_avg,main="Fall average")

barplot(table(mbasalary.df$quarter),col="yellow",xlab="Quarter of students", main="Histogram for quarter")

hist(mbasalary.df$work_yrs, breaks=10,col="yellow",xlab="Work experience in years", main="Histogram for work years")

barplot(table(mbasalary.df$quarter),col="yellow",xlab="Quarter of students", main="Histogram for quarter")

hist(mbasalary.df$work_yrs, breaks=10,col="yellow",xlab="Work experience in years", main="Histogram for work years")

plot(mbasalary.df$frstlang, main = "First language")

hist(mbasalary.df$salary, main = "Starting salaries", xlab = "Starting salary", col = "blue")

satis7 <- mbasalary.df[ which(mbasalary.df$satis<='7'), ]
hist(satis7$satis, breaks=5,col="pink",xlab="Degree of Satisfaction", main="Satisfaction  for MBA programme")

Boxplots

attach(mbasalary.df)
boxplot(age  , horizontal =TRUE, main="Boxplot of age" ,col="lightblue")

boxplot(sex  , horizontal =TRUE, main="Boxplot of sex" ,col="lightblue")

boxplot(gmat_qpc  , horizontal =TRUE, main="Boxplot of gmat_qpc" ,col="lightblue")

boxplot(gmat_tot  , horizontal =TRUE, main="Boxplot of gmat_tot" ,col="lightblue")

boxplot(gmat_vpc  , horizontal =TRUE, main="Boxplot of gmat_vpc" ,col="lightblue")

boxplot(gmat_tpc  , horizontal =TRUE, main="Boxplot of gmat_tpc" ,col="lightblue")

boxplot(f_avg  , horizontal =TRUE, main="Boxplot of fall avg" ,col="lightblue")

boxplot(s_avg  , horizontal =TRUE, main="Boxplot of spring avg" ,col="lightblue")

boxplot(work_yrs  , horizontal =TRUE, main="Boxplot of work experience" ,col="lightblue")

boxplot(quarter  , horizontal =TRUE, main="Boxplot of quarter" ,col="lightblue")

boxplot( frstlang , horizontal =TRUE, main="Boxplot of first language" ,col="lightblue")

boxplot(salary  , horizontal =TRUE, main="Boxplot of salary" ,col="lightblue")

boxplot(satis  , horizontal =TRUE, main="Boxplot of satisfaction" ,col="lightblue")

Scatterplots

Draw Scatter Plots to understand how are the variables correlated pair-wise

library(car)
attach(mbasalary.df)
scatterplot(salary ~age,
            spread=FALSE, smoother.args=list(lty=2),
            main="Scatter plot of salary vs age",
            xlab="age",
            ylab="salary")

scatterplot(salary ~sex,     
            spread=FALSE, smoother.args=list(lty=2),
            main="Scatter plot of salary vs sex",
            xlab="sex",
            ylab="salary")

scatterplot(salary ~frstlang,   
            main="Scatter plot of salary vs first language",
            xlab="first language",
            ylab="salary")

scatterplot(salary ~gmat_tpc,    
            main="Scatter plot of salary vs gmat percentile",
            xlab="Percentile",
            ylab="salary")

scatterplot(salary ~ gmat_tot,    
            main="Scatter plot of salary vs first language",
            xlab="first language",
            ylab="salary")

scatterplot(salary ~work_yrs,    
            main="Scatter plot of salary vs Work exp.",
            xlab="Work experience in years",
            ylab="salary")

scatterplotMatrix(mbasalary.df[,c("salary","age","gmat_tpc","gmat_qpc","satis","gmat_tot")], spread=FALSE, smoother.args=list(lty=2), main="Scatter Plot Matrix")

Covariance

Draw a Corrgram; Create a Variance-Covariance Matrix

library(corrgram)
corr.test(mbasalary.df)

## Call:corr.test(x = mbasalary.df)
## Correlation matrix 
##            age   sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg
## age       1.00 -0.03    -0.15    -0.22    -0.04    -0.17  0.15 -0.02
## sex      -0.03  1.00    -0.05    -0.16     0.07    -0.01  0.13  0.09
## gmat_tot -0.15 -0.05     1.00     0.72     0.75     0.85  0.11  0.10
## gmat_qpc -0.22 -0.16     0.72     1.00     0.15     0.65 -0.03  0.07
## gmat_vpc -0.04  0.07     0.75     0.15     1.00     0.67  0.20  0.08
## gmat_tpc -0.17 -0.01     0.85     0.65     0.67     1.00  0.12  0.08
## s_avg     0.15  0.13     0.11    -0.03     0.20     0.12  1.00  0.55
## f_avg    -0.02  0.09     0.10     0.07     0.08     0.08  0.55  1.00
## quarter  -0.05 -0.13    -0.09     0.04    -0.17    -0.08 -0.76 -0.45
## work_yrs  0.86 -0.01    -0.18    -0.24    -0.07    -0.17  0.13 -0.04
## frstlang  0.06  0.00    -0.14     0.14    -0.39    -0.10 -0.14 -0.04
## salary   -0.06  0.07    -0.05    -0.04    -0.01     0.00  0.15  0.03
## satis    -0.13 -0.05     0.08     0.06     0.06     0.09 -0.03  0.01
##          quarter work_yrs frstlang salary satis
## age        -0.05     0.86     0.06  -0.06 -0.13
## sex        -0.13    -0.01     0.00   0.07 -0.05
## gmat_tot   -0.09    -0.18    -0.14  -0.05  0.08
## gmat_qpc    0.04    -0.24     0.14  -0.04  0.06
## gmat_vpc   -0.17    -0.07    -0.39  -0.01  0.06
## gmat_tpc   -0.08    -0.17    -0.10   0.00  0.09
## s_avg      -0.76     0.13    -0.14   0.15 -0.03
## f_avg      -0.45    -0.04    -0.04   0.03  0.01
## quarter     1.00    -0.09     0.10  -0.16  0.00
## work_yrs   -0.09     1.00    -0.03   0.01 -0.11
## frstlang    0.10    -0.03     1.00  -0.09  0.08
## salary     -0.16     0.01    -0.09   1.00 -0.34
## satis       0.00    -0.11     0.08  -0.34  1.00
## Sample Size 
## [1] 274
## Probability values (Entries above the diagonal are adjusted for multiple tests.) 
##           age  sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## age      0.00 1.00     0.87     0.02     1.00     0.29  0.75  1.00    1.00
## sex      0.64 0.00     1.00     0.39     1.00     1.00  1.00  1.00    1.00
## gmat_tot 0.02 0.38     0.00     0.00     0.00     0.00  1.00  1.00    1.00
## gmat_qpc 0.00 0.01     0.00     0.00     0.68     0.00  1.00  1.00    1.00
## gmat_vpc 0.47 0.22     0.00     0.01     0.00     0.00  0.04  1.00    0.24
## gmat_tpc 0.00 0.89     0.00     0.00     0.00     0.00  1.00  1.00    1.00
## s_avg    0.01 0.04     0.06     0.62     0.00     0.05  0.00  0.00    0.00
## f_avg    0.77 0.13     0.08     0.22     0.21     0.19  0.00  0.00    0.00
## quarter  0.41 0.03     0.13     0.55     0.00     0.17  0.00  0.00    0.00
## work_yrs 0.00 0.85     0.00     0.00     0.27     0.00  0.03  0.52    0.16
## frstlang 0.35 0.98     0.03     0.02     0.00     0.09  0.02  0.54    0.10
## salary   0.30 0.26     0.36     0.47     0.92     0.94  0.02  0.63    0.01
## satis    0.03 0.37     0.17     0.32     0.30     0.12  0.59  0.86    1.00
##          work_yrs frstlang salary satis
## age          0.00     1.00   1.00     1
## sex          1.00     1.00   1.00     1
## gmat_tot     0.16     1.00   1.00     1
## gmat_qpc     0.01     1.00   1.00     1
## gmat_vpc     1.00     0.00   1.00     1
## gmat_tpc     0.25     1.00   1.00     1
## s_avg        1.00     1.00   0.87     1
## f_avg        1.00     1.00   1.00     1
## quarter      1.00     1.00   0.38     1
## work_yrs     0.00     1.00   1.00     1
## frstlang     0.65     0.00   1.00     1
## salary       0.88     0.15   0.00     0
## satis        0.07     0.19   0.00     0
## 
##  To see confidence intervals of the correlations, print with the short=FALSE option

cov(mbasalary.df)

##                    age           sex      gmat_tot      gmat_qpc
## age       1.376904e+01 -4.513248e-02 -3.115879e+01 -1.192655e+01
## sex      -4.513248e-02  1.872677e-01 -1.328841e+00 -1.053769e+00
## gmat_tot -3.115879e+01 -1.328841e+00  3.310688e+03  6.200233e+02
## gmat_qpc -1.192655e+01 -1.053769e+00  6.200233e+02  2.210731e+02
## gmat_vpc -2.763643e+00  5.463758e-01  7.260006e+02  3.814826e+01
## gmat_tpc -8.839978e+00 -4.908960e-02  6.839911e+02  1.357997e+02
## s_avg     2.116874e-01  2.096227e-02  2.480257e+00 -1.691233e-01
## f_avg    -3.399348e-02  2.082698e-02  3.154688e+00  5.753854e-01
## quarter  -2.045935e-01 -6.414267e-02 -5.891153e+00  6.001979e-01
## work_yrs  1.029494e+01 -1.580172e-02 -3.391634e+01 -1.137186e+01
## frstlang  6.796610e-02  2.138980e-04 -2.499933e+00  6.646346e-01
## salary   -1.183042e+04  1.518264e+03 -1.611600e+05 -3.335823e+04
## satis    -1.763499e+02 -8.780808e+00  1.765263e+03  3.348371e+02
##               gmat_vpc     gmat_tpc         s_avg        f_avg
## age         -2.7636427   -8.8399775    0.21168739  -0.03399348
## sex          0.5463758   -0.0490896    0.02096227   0.02082698
## gmat_tot   726.0006417  683.9910698    2.48025721   3.15468838
## gmat_qpc    38.1482581  135.7996845   -0.16912329   0.57538542
## gmat_vpc   284.2481217  157.4932488    1.31357023   0.67207000
## gmat_tpc   157.4932488  196.6057057    0.62710008   0.58698618
## s_avg        1.3135702    0.6271001    0.14521760   0.11016898
## f_avg        0.6720700    0.5869862    0.11016898   0.27567237
## quarter     -3.2676666   -1.2923719   -0.32237213  -0.26080880
## work_yrs    -3.6181653   -7.8575172    0.15926392  -0.06628700
## frstlang    -2.1145691   -0.4663244   -0.01671372  -0.00626026
## salary   -5273.8523836 3522.7500067 2831.60098580 787.65597177
## satis      392.3562739  484.2466779   -4.62884495   2.12532927
##                quarter      work_yrs      frstlang        salary
## age      -2.045935e-01   10.29493864  6.796610e-02 -1.183042e+04
## sex      -6.414267e-02   -0.01580172  2.138980e-04  1.518264e+03
## gmat_tot -5.891153e+00  -33.91633914 -2.499933e+00 -1.611600e+05
## gmat_qpc  6.001979e-01  -11.37186171  6.646346e-01 -3.335823e+04
## gmat_vpc -3.267667e+00   -3.61816529 -2.114569e+00 -5.273852e+03
## gmat_tpc -1.292372e+00   -7.85751718 -4.663244e-01  3.522750e+03
## s_avg    -3.223721e-01    0.15926392 -1.671372e-02  2.831601e+03
## f_avg    -2.608088e-01   -0.06628700 -6.260260e-03  7.876560e+02
## quarter   1.232119e+00   -0.30866822  3.553381e-02 -9.296214e+03
## work_yrs -3.086682e-01   10.44882490 -2.898318e-02  1.486147e+03
## frstlang  3.553381e-02   -0.02898318  1.035266e-01 -1.419586e+03
## salary   -9.296214e+03 1486.14704152 -1.419586e+03  2.596062e+09
## satis    -5.227133e-03 -131.24080907  9.484532e+00 -6.347115e+06
##                  satis
## age      -1.763499e+02
## sex      -8.780808e+00
## gmat_tot  1.765263e+03
## gmat_qpc  3.348371e+02
## gmat_vpc  3.923563e+02
## gmat_tpc  4.842467e+02
## s_avg    -4.628845e+00
## f_avg     2.125329e+00
## quarter  -5.227133e-03
## work_yrs -1.312408e+02
## frstlang  9.484532e+00
## salary   -6.347115e+06
## satis     1.380974e+05

cor(mbasalary.df)

##                  age          sex    gmat_tot    gmat_qpc    gmat_vpc
## age       1.00000000 -0.028106442 -0.14593840 -0.21616985 -0.04417547
## sex      -0.02810644  1.000000000 -0.05336820 -0.16377435  0.07488782
## gmat_tot -0.14593840 -0.053368202  1.00000000  0.72473781  0.74839187
## gmat_qpc -0.21616985 -0.163774346  0.72473781  1.00000000  0.15218014
## gmat_vpc -0.04417547  0.074887816  0.74839187  0.15218014  1.00000000
## gmat_tpc -0.16990307 -0.008090213  0.84779965  0.65137754  0.66621604
## s_avg     0.14970402  0.127115144  0.11311702 -0.02984873  0.20445365
## f_avg    -0.01744806  0.091663891  0.10442409  0.07370455  0.07592225
## quarter  -0.04967221 -0.133533171 -0.09223903  0.03636638 -0.17460736
## work_yrs  0.85829810 -0.011296374 -0.18235434 -0.23660827 -0.06639049
## frstlang  0.05692649  0.001536205 -0.13503402  0.13892774 -0.38980465
## salary   -0.06257355  0.068858628 -0.05497188 -0.04403293 -0.00613934
## satis    -0.12788825 -0.054602220  0.08255770  0.06060004  0.06262375
##              gmat_tpc       s_avg       f_avg       quarter     work_yrs
## age      -0.169903066  0.14970402 -0.01744806 -4.967221e-02  0.858298096
## sex      -0.008090213  0.12711514  0.09166389 -1.335332e-01 -0.011296374
## gmat_tot  0.847799647  0.11311702  0.10442409 -9.223903e-02 -0.182354339
## gmat_qpc  0.651377538 -0.02984873  0.07370455  3.636638e-02 -0.236608270
## gmat_vpc  0.666216035  0.20445365  0.07592225 -1.746074e-01 -0.066390490
## gmat_tpc  1.000000000  0.11736245  0.07973210 -8.303535e-02 -0.173361859
## s_avg     0.117362449  1.00000000  0.55062139 -7.621166e-01  0.129292714
## f_avg     0.079732099  0.55062139  1.00000000 -4.475064e-01 -0.039056921
## quarter  -0.083035351 -0.76211664 -0.44750637  1.000000e+00 -0.086026406
## work_yrs -0.173361859  0.12929271 -0.03905692 -8.602641e-02  1.000000000
## frstlang -0.103362747 -0.13631308 -0.03705695  9.949226e-02 -0.027866747
## salary    0.004930901  0.14583606  0.02944303 -1.643699e-01  0.009023407
## satis     0.092934266 -0.03268664  0.01089273 -1.267198e-05 -0.109255286
##              frstlang       salary         satis
## age       0.056926486 -0.062573547 -1.278882e-01
## sex       0.001536205  0.068858628 -5.460222e-02
## gmat_tot -0.135034017 -0.054971880  8.255770e-02
## gmat_qpc  0.138927742 -0.044032933  6.060004e-02
## gmat_vpc -0.389804653 -0.006139340  6.262375e-02
## gmat_tpc -0.103362747  0.004930901  9.293427e-02
## s_avg    -0.136313080  0.145836062 -3.268664e-02
## f_avg    -0.037056954  0.029443027  1.089273e-02
## quarter   0.099492259 -0.164369865 -1.267198e-05
## work_yrs -0.027866747  0.009023407 -1.092553e-01
## frstlang  1.000000000 -0.086592096  7.932264e-02
## salary   -0.086592096  1.000000000 -3.352171e-01
## satis     0.079322637 -0.335217114  1.000000e+00

corrgram(mbasalary.df, order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt,
         main="MBA starting salary analysis")

Task 2(b)

Take a subset of the dataset consisting of only those people who actually got a job. Using this subset of data: Think about the problem as y = f(x), where y = Starting Salary and x = various factors that it could depend upon Examples: impact of {gender; first language; prior work experience; GMAT performance; MBA performance} etc in determining the Starting Salary Draw Draw Contingency Tables, as appropriate Run chi-square tests, as appropriate Run t-tests, as appropriate Write more than one regression model as, as y = f(x) where the vector of variables x may be different in different models Estimate the regression models using lm() in R; Compare multiple models (e.g. using the R-Square measure given by lm()); Select the ???best??? model that ???fits??? the data;
Interpret the output

Finding correlations and contingency tables

newdata <- mbasalary.df[ which(mbasalary.df$salary !="998" & mbasalary.df$salary !="999"), ]
gotjob <- subset(newdata, salary>0)
chisq.test(gotjob)

## Warning in chisq.test(gotjob): Chi-squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  gotjob
## X-squared = 3620.8, df = 1224, p-value < 2.2e-16

detach(mbasalary.df)
attach(gotjob)
tablenew <- xtabs(~salary+age)
tablenew

##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  1  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  1  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
##   85000   1  0  0  1  1  1  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  1  1  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  1  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  2  0  1  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  2  0  1  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  1  0  0  1  0  0  1  0  0  0  0  0
##   95000   0  0  1  5  0  0  0  1  0  0  0  0  0  0  0
##   96000   0  0  1  1  2  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  1  1  0  0  0  0  0  0  0  0
##   98000   0  1  3  2  1  1  1  1  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  1  0  0  0  0  0  0  0  0
##   100000  0  1  4  1  1  1  0  0  0  1  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0
##   101000  0  0  1  1  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0
##   101600  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0
##   103000  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  1  0  0  1  0  0  0  0  0
##   105000  0  1  1  2  3  1  0  0  1  1  0  0  1  0  0
##   106000  0  0  0  0  0  0  0  1  2  0  0  0  0  0  0
##   107000  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0
##   107500  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  1  0  0  1  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0
##   112000  0  0  1  0  0  0  0  1  0  0  0  0  0  1  0
##   115000  0  0  1  1  0  3  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0
##   120000  0  0  0  0  0  1  1  0  2  0  0  0  0  0  0
##   126710  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1
##   162000  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1

tablenew <- xtabs(~salary+sex)
tablenew

##         sex
## salary    1  2
##   64000   0  1
##   77000   1  0
##   78256   0  1
##   82000   0  1
##   85000   1  3
##   86000   0  2
##   88000   0  1
##   88500   1  0
##   90000   3  0
##   92000   2  1
##   93000   2  1
##   95000   4  3
##   96000   3  1
##   96500   1  0
##   97000   2  0
##   98000   6  4
##   99000   0  1
##   100000  4  5
##   100400  1  0
##   101000  0  2
##   101100  1  0
##   101600  1  0
##   102500  1  0
##   103000  1  0
##   104000  2  0
##   105000 11  0
##   106000  2  1
##   107000  1  0
##   107300  1  0
##   107500  1  0
##   108000  2  0
##   110000  0  1
##   112000  3  0
##   115000  5  0
##   118000  1  0
##   120000  3  1
##   126710  1  0
##   130000  1  0
##   145800  1  0
##   146000  1  0
##   162000  1  0
##   220000  0  1

tablenew <- xtabs(~salary+gmat_tot)
tablenew

##         gmat_tot
## salary   500 520 530 540 550 560 570 580 590 600 610 620 630 640 650 660
##   64000    0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0
##   77000    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1
##   78256    0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   82000    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   85000    0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   1
##   86000    0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0
##   88000    0   0   0   0   0   0   0   0   0   0   0   0   0   0   1   0
##   88500    0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   0
##   90000    0   0   0   0   0   0   0   1   0   0   0   0   1   0   1   0
##   92000    0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   1
##   93000    0   0   0   1   0   0   0   0   0   0   1   1   0   0   0   0
##   95000    0   0   1   0   0   2   0   0   0   0   2   0   0   0   0   0
##   96000    0   0   0   0   0   1   0   0   1   1   0   0   0   0   1   0
##   96500    1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   97000    0   0   0   0   0   0   0   1   0   0   0   1   0   0   0   0
##   98000    0   0   0   0   0   1   3   1   1   0   1   0   0   0   0   0
##   99000    0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0
##   100000   0   0   0   0   0   2   0   1   0   1   1   0   1   0   2   0
##   100400   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0
##   101000   0   0   0   0   0   0   0   0   0   1   0   1   0   0   0   0
##   101100   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1
##   101600   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0
##   102500   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   103000   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   0
##   104000   0   0   1   0   0   1   0   0   0   0   0   0   0   0   0   0
##   105000   0   0   0   0   2   0   2   3   0   1   0   1   0   0   1   0
##   106000   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   0
##   107000   0   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0
##   107300   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1
##   107500   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0
##   108000   0   0   0   0   0   0   1   0   0   1   0   0   0   0   0   0
##   110000   0   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0
##   112000   0   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0
##   115000   0   0   0   1   0   0   1   0   0   0   0   1   1   0   0   0
##   118000   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   0
##   120000   0   0   0   0   0   0   0   0   0   2   0   0   0   0   0   0
##   126710   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0
##   130000   0   0   0   0   0   0   0   0   0   0   0   0   0   0   1   0
##   145800   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   0
##   146000   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0   0
##   162000   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   220000   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##         gmat_tot
## salary   670 680 700 710 720
##   64000    0   0   0   0   0
##   77000    0   0   0   0   0
##   78256    0   0   0   0   0
##   82000    1   0   0   0   0
##   85000    0   0   1   0   1
##   86000    0   1   0   0   0
##   88000    0   0   0   0   0
##   88500    0   0   0   0   0
##   90000    0   0   0   0   0
##   92000    0   0   0   1   0
##   93000    0   0   0   0   0
##   95000    2   0   0   0   0
##   96000    0   0   0   0   0
##   96500    0   0   0   0   0
##   97000    0   0   0   0   0
##   98000    1   1   0   1   0
##   99000    0   0   0   0   0
##   100000   0   0   0   1   0
##   100400   0   0   0   0   0
##   101000   0   0   0   0   0
##   101100   0   0   0   0   0
##   101600   0   0   0   0   0
##   102500   1   0   0   0   0
##   103000   0   0   0   0   0
##   104000   0   0   0   0   0
##   105000   0   1   0   0   0
##   106000   0   2   0   0   0
##   107000   0   0   0   0   0
##   107300   0   0   0   0   0
##   107500   0   0   0   0   0
##   108000   0   0   0   0   0
##   110000   0   0   0   0   0
##   112000   1   1   0   0   0
##   115000   0   0   0   1   0
##   118000   0   0   0   0   0
##   120000   1   0   1   0   0
##   126710   0   0   0   0   0
##   130000   0   0   0   0   0
##   145800   0   0   0   0   0
##   146000   0   0   0   0   0
##   162000   0   0   1   0   0
##   220000   0   0   0   0   0

tablenew <- xtabs(~salary+frstlang)
tablenew

##         frstlang
## salary    1  2
##   64000   1  0
##   77000   1  0
##   78256   1  0
##   82000   1  0
##   85000   4  0
##   86000   2  0
##   88000   1  0
##   88500   1  0
##   90000   3  0
##   92000   3  0
##   93000   3  0
##   95000   7  0
##   96000   4  0
##   96500   1  0
##   97000   2  0
##   98000   8  2
##   99000   0  1
##   100000  9  0
##   100400  1  0
##   101000  2  0
##   101100  1  0
##   101600  1  0
##   102500  1  0
##   103000  1  0
##   104000  1  1
##   105000 11  0
##   106000  3  0
##   107000  1  0
##   107300  0  1
##   107500  1  0
##   108000  2  0
##   110000  1  0
##   112000  3  0
##   115000  5  0
##   118000  0  1
##   120000  4  0
##   126710  1  0
##   130000  1  0
##   145800  1  0
##   146000  1  0
##   162000  1  0
##   220000  0  1

tablenew <- xtabs(~salary+work_yrs)
tablenew

##         work_yrs
## salary   0 1 2 3 4 5 6 7 8 10 15 16
##   64000  0 0 1 0 0 0 0 0 0  0  0  0
##   77000  0 0 1 0 0 0 0 0 0  0  0  0
##   78256  0 1 0 0 0 0 0 0 0  0  0  0
##   82000  0 1 0 0 0 0 0 0 0  0  0  0
##   85000  0 1 2 1 0 0 0 0 0  0  0  0
##   86000  0 0 1 1 0 0 0 0 0  0  0  0
##   88000  0 0 0 1 0 0 0 0 0  0  0  0
##   88500  0 0 0 1 0 0 0 0 0  0  0  0
##   90000  0 0 2 0 0 1 0 0 0  0  0  0
##   92000  0 0 3 0 0 0 0 0 0  0  0  0
##   93000  0 0 0 0 1 1 0 0 1  0  0  0
##   95000  1 1 2 2 0 1 0 0 0  0  0  0
##   96000  0 1 2 0 1 0 0 0 0  0  0  0
##   96500  0 0 1 0 0 0 0 0 0  0  0  0
##   97000  0 0 0 1 1 0 0 0 0  0  0  0
##   98000  0 0 7 1 1 0 0 1 0  0  0  0
##   99000  0 0 0 0 0 1 0 0 0  0  0  0
##   100000 0 0 6 1 1 0 1 0 0  0  0  0
##   100400 0 0 0 1 0 0 0 0 0  0  0  0
##   101000 0 0 2 0 0 0 0 0 0  0  0  0
##   101100 0 0 0 0 0 0 0 0 1  0  0  0
##   101600 0 0 0 1 0 0 0 0 0  0  0  0
##   102500 0 0 0 0 0 0 1 0 0  0  0  0
##   103000 0 0 0 1 0 0 0 0 0  0  0  0
##   104000 0 0 0 0 2 0 0 0 0  0  0  0
##   105000 0 0 4 4 0 1 1 0 0  0  0  1
##   106000 0 0 0 0 0 0 2 0 1  0  0  0
##   107000 0 0 1 0 0 0 0 0 0  0  0  0
##   107300 0 0 1 0 0 0 0 0 0  0  0  0
##   107500 0 0 0 1 0 0 0 0 0  0  0  0
##   108000 0 0 0 1 1 0 0 0 0  0  0  0
##   110000 0 0 0 0 0 0 1 0 0  0  0  0
##   112000 0 0 1 0 0 0 1 0 0  0  0  1
##   115000 0 2 0 1 2 0 0 0 0  0  0  0
##   118000 0 0 0 0 0 0 0 0 0  1  0  0
##   120000 0 0 0 1 0 2 0 0 1  0  0  0
##   126710 0 0 0 1 0 0 0 0 0  0  0  0
##   130000 0 0 0 0 1 0 0 0 0  0  0  0
##   145800 0 0 1 0 0 0 0 0 0  0  0  0
##   146000 0 0 0 0 0 0 0 0 0  0  1  0
##   162000 0 1 0 0 0 0 0 0 0  0  0  0
##   220000 0 0 0 0 0 0 0 0 0  0  1  0

tablenew <- xtabs(~salary+age+sex+work_yrs+gmat_tot)
tablenew

## , , sex = 1, work_yrs = 0, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 0, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 1, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 1, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 2, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 2, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 3, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 3, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 4, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 4, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 5, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 5, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 6, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 6, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 7, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 7, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 8, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 8, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 10, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 10, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 15, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 15, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1
## 
## , , sex = 1, work_yrs = 16, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 16, gmat_tot = 500
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 0, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 0, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 1, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 1, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  1  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 2, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 2, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 3, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 3, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 4, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 4, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 5, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 5, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 6, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 6, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 7, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 7, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 8, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 8, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 10, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 10, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 15, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 15, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 16, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 16, gmat_tot = 520
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 0, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 0, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 1, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 1, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 2, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 2, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 3, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 3, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 4, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 4, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 5, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 5, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 6, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 6, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 7, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 7, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 8, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 8, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 10, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 10, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 15, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 15, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 16, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 16, gmat_tot = 530
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 0, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 0, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 1, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 1, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 2, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 2, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 3, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 3, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 4, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 4, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 5, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 5, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 6, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 6, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 7, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 7, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 8, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  1  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 8, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 10, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 10, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 15, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 15, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 16, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 16, gmat_tot = 540
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 0, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 0, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 1, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 1, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 2, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 2, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 3, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 3, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 4, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 4, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 5, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 5, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 6, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 6, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 7, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 7, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 8, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 8, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 10, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 10, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 15, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 15, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 16, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 16, gmat_tot = 550
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 0, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 0, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 1, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 1, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 2, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 2, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 3, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 3, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 4, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 4, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 5, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 5, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  1  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 6, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 6, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 7, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 7, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 8, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 8, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 10, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 10, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 15, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 15, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 16, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 16, gmat_tot = 560
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 0, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 0, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 1, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 1, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 2, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  2  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 2, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 3, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 3, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
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##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 4, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 4, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 5, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 5, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 6, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 2, work_yrs = 6, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   108000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   110000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   112000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   115000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   118000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   120000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   126710  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   130000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   145800  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   146000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   162000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   220000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
## , , sex = 1, work_yrs = 7, gmat_tot = 570
## 
##         age
## salary   22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
##   64000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   77000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   78256   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   82000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   85000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   86000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   88500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   90000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   92000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   93000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   95000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   96500   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   97000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   98000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   99000   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   100400  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101100  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   101600  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   102500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   103000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   104000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   105000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   106000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107000  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107300  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   107500  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## 
##  [ reached getOption("max.print") -- omitted 12 row(s) and 345 matrix slice(s) ]

Chi-square and t-tests

chisq.test(xtabs(~salary + gmat_tpc, data = gotjob))

## Warning in chisq.test(xtabs(~salary + gmat_tpc, data = gotjob)): Chi-
## squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  xtabs(~salary + gmat_tpc, data = gotjob)
## X-squared = 1422.2, df = 1230, p-value = 0.0001065

chisq.test(xtabs(~salary + age, data = gotjob))

## Warning in chisq.test(xtabs(~salary + age, data = gotjob)): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  xtabs(~salary + age, data = gotjob)
## X-squared = 717.62, df = 574, p-value = 3.929e-05

chisq.test(xtabs(~salary + sex, data = gotjob))

## Warning in chisq.test(xtabs(~salary + sex, data = gotjob)): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  xtabs(~salary + sex, data = gotjob)
## X-squared = 52.681, df = 41, p-value = 0.1045

chisq.test(xtabs(~salary + gmat_tot, data = gotjob))

## Warning in chisq.test(xtabs(~salary + gmat_tot, data = gotjob)): Chi-
## squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  xtabs(~salary + gmat_tot, data = gotjob)
## X-squared = 927.24, df = 820, p-value = 0.005279

chisq.test(xtabs(~salary + work_yrs, data = gotjob))

## Warning in chisq.test(xtabs(~salary + work_yrs, data = gotjob)): Chi-
## squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  xtabs(~salary + work_yrs, data = gotjob)
## X-squared = 535.23, df = 451, p-value = 0.003809

chisq.test(xtabs(~salary + frstlang, data = gotjob))

## Warning in chisq.test(xtabs(~salary + frstlang, data = gotjob)): Chi-
## squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  xtabs(~salary + frstlang, data = gotjob)
## X-squared = 69.847, df = 41, p-value = 0.003296

t.test(salary,gmat_tpc, data = gotjob)

## 
##  Welch Two Sample t-test
## 
## data:  salary and gmat_tpc
## t = 58.47, df = 102, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   99453.94 106438.49
## sample estimates:
##    mean of x    mean of y 
## 103030.73786     84.52427

t.test(salary,age, data = gotjob)

## 
##  Welch Two Sample t-test
## 
## data:  salary and age
## t = 58.503, df = 102, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   99511.69 106496.23
## sample estimates:
##   mean of x   mean of y 
## 103030.7379     26.7767

t.test(salary,sex, data = gotjob)

## 
##  Welch Two Sample t-test
## 
## data:  salary and sex
## t = 58.517, df = 102, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   99537.17 106521.71
## sample estimates:
##    mean of x    mean of y 
## 1.030307e+05 1.300971e+00

t.test(salary,gmat_tot, data = gotjob)

## 
##  Welch Two Sample t-test
## 
## data:  salary and gmat_tot
## t = 58.168, df = 102, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   98922.43 105907.00
## sample estimates:
##   mean of x   mean of y 
## 103030.7379    616.0194

t.test(salary,work_yrs, data = gotjob)

## 
##  Welch Two Sample t-test
## 
## data:  salary and work_yrs
## t = 58.516, df = 102, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   99534.79 106519.33
## sample estimates:
##    mean of x    mean of y 
## 1.030307e+05 3.679612e+00

t.test(salary,frstlang, data = gotjob)

## 
##  Welch Two Sample t-test
## 
## data:  salary and frstlang
## t = 58.517, df = 102, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   99537.4 106521.9
## sample estimates:
##    mean of x    mean of y 
## 1.030307e+05 1.067961e+00

From the t-tests and chi-square tests, using the p-value < 0.05, we can reject the null hypothesis and say that the salary is affected by the factors - age, sex, gmat total score and percentile, work experience as well as first language.

Running regression - model 1

fit1 <- lm(salary ~ age+quarter+gmat_tpc+gmat_qpc+gmat_vpc+frstlang, data=gotjob)
summary(fit1)

## 
## Call:
## lm(formula = salary ~ age + quarter + gmat_tpc + gmat_qpc + gmat_vpc + 
##     frstlang, data = gotjob)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -26852  -9178   -615   5382  68168 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  44716.7    19552.1   2.287   0.0244 *  
## age           2583.0      508.3   5.082 1.84e-06 ***
## quarter      -1744.3     1375.4  -1.268   0.2078    
## gmat_tpc     -1424.6      683.3  -2.085   0.0397 *  
## gmat_qpc       834.1      350.0   2.383   0.0191 *  
## gmat_vpc       535.2      351.3   1.523   0.1309    
## frstlang      4649.7     6617.1   0.703   0.4840    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15120 on 96 degrees of freedom
## Multiple R-squared:  0.3262, Adjusted R-squared:  0.284 
## F-statistic: 7.744 on 6 and 96 DF,  p-value: 8.366e-07

Running regression - model 2

fit2 <- lm(salary ~ age+sex+gmat_tot+work_yrs, data=gotjob)
summary(fit2)

## 
## Call:
## lm(formula = salary ~ age + sex + gmat_tot + work_yrs, data = gotjob)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -30250  -8730  -2148   5632  82607 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 56162.20   30473.15   1.843   0.0684 .
## age          2298.17    1009.90   2.276   0.0250 *
## sex         -3898.40    3407.50  -1.144   0.2554  
## gmat_tot      -18.01      30.88  -0.583   0.5610  
## work_yrs      407.10    1095.76   0.372   0.7111  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15650 on 98 degrees of freedom
## Multiple R-squared:  0.2628, Adjusted R-squared:  0.2327 
## F-statistic: 8.733 on 4 and 98 DF,  p-value: 4.512e-06

From the above 2 regression models, it can be inferred that salary is most affected by age, sex, gmat_tpc, work experience and first language.

fit2 <- lm(salary ~ age+sex+gmat_tot+work_yrs+frstlang, data=gotjob)
summary(fit2)

## 
## Call:
## lm(formula = salary ~ age + sex + gmat_tot + work_yrs + frstlang, 
##     data = gotjob)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -29716  -9228  -2126   5892  78157 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 58246.31   30361.47   1.918    0.058 .
## age          1667.25    1101.40   1.514    0.133  
## sex         -4655.65    3433.79  -1.356    0.178  
## gmat_tot      -11.76      31.05  -0.379    0.706  
## work_yrs      854.08    1136.21   0.752    0.454  
## frstlang     9642.71    6887.30   1.400    0.165  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15580 on 97 degrees of freedom
## Multiple R-squared:  0.2774, Adjusted R-squared:  0.2401 
## F-statistic: 7.447 on 5 and 97 DF,  p-value: 5.982e-06

fitted(fit2)

##        35        36        37        38        39        40        41 
##  88351.29  97071.33  93971.97  95178.79 101609.40 102241.44  95369.78 
##        42        43        44        45        46        47        48 
##  94528.81  96589.71  98126.27  98405.59  90990.20  98371.29 106201.51 
##        49        50        51        52        53        54        55 
##  98980.36 101010.21 102442.31 101470.64 110466.75 113340.82 112793.86 
##        56        57        58        59        60        61        62 
## 105724.57 103404.10 100422.32 102860.38 134866.96 104963.64 100755.32 
##        63        64        65        66        67        68        69 
## 129146.49 103756.83 105956.48 110466.75 107550.32 135327.39  97538.39 
##       115       116       117       118       119       120       121 
##  94902.72  94677.43 115401.72  98745.20 100304.75  98371.29  99076.75 
##       122       123       124       125       126       127       128 
##  96351.31  98253.72 107429.36 112354.46  93480.49 111750.23  94677.43 
##       129       130       131       132       133       134       135 
## 103521.67 106748.47 101148.96 103521.67 127118.58 101235.49 108830.55 
##       136       137       138       139       186       187       188 
##  96811.75 104258.18 102355.78  97665.83  91664.61 103521.67  99803.39 
##       189       190       191       192       193       194       195 
##  99215.51 101158.83 104614.16 104375.76 105659.23  92422.30  95382.89 
##       196       197       198       199       200       201       202 
##  95147.74 111604.84 106160.59 109214.33  97461.73  94559.86  93245.33 
##       203       204       205       206       207       208       209 
## 110656.30 110497.80  98136.14 108712.97 120176.25 106630.89  96960.37 
##       256       257       258       259       260       261       262 
##  93715.64  95528.27  94355.75 101000.34  97081.19 104877.11  98862.78 
##       263       264       265       266       267       268       269 
##  99450.66 101235.49  98253.72  93245.33  95991.96 106738.60 101736.85 
##       270       271       272       273       274 
## 121745.66  96468.89 101127.78 102677.46 141842.94

residuals(fit2)

##          35          36          37          38          39          40 
##  -3351.2862 -12071.3254  -7971.9743  -7178.7890  -9609.4004  -9241.4448 
##          41          42          43          44          45          46 
##   -369.7805    471.1916  -1589.7079  -2126.2707  -2405.5854   9009.8002 
##          47          48          49          50          51          52 
##   1628.7073  -6201.5057   6019.6444   3989.7937   2557.6948   3529.3562 
##          53          54          55          56          57          58 
##  -5466.7538  -8340.8205  -6793.8552    275.4255   4095.9021   7577.6766 
##          59          60          61          62          63          64 
##   7139.6234 -22866.9566  10036.3578  14244.6845 -11146.4877  16243.1724 
##          65          66          67          68          69         115 
##  14043.5163   9533.2462  12449.6792  10672.6059  64461.6122 -12902.7182 
##         116         117         118         119         120         121 
##  -2677.4338 -22401.7200  -3745.2024  -5304.7468  -2371.2927  -2576.7522 
##         122         123         124         125         126         127 
##   1648.6892   -253.7161  -9429.3601 -13354.4575   6519.5120 -11750.2274 
##         128         129         130         131         132         133 
##   6322.5662   -521.6745  -2748.4710   3851.0371   1478.3255 -22118.5788 
##         134         135         136         137         138         139 
##   5764.5094   3169.4495  18188.2517  10741.8172  27644.2225  48134.1668 
##         186         187         188         189         190         191 
## -13408.6105 -15021.6745  -9803.3917  -9215.5087  -8158.8317  -9614.1565 
##         192         193         194         195         196         197 
##  -7375.7593  -8659.2329   5577.7012   2617.1067   2852.2599 -13604.8365 
##         198         199         200         201         202         203 
##  -8160.5881 -11214.3290   2538.2712   5440.1428   7754.6651  -9556.2968 
##         204         205         206         207         208         209 
##  -7997.8026   6863.8605  -2712.9739 -12876.2503   1369.1056  15039.6263 
##         256         257         258         259         260         261 
## -29715.6412 -18528.2747  -9355.7529 -16000.3375 -11081.1943 -14877.1145 
##         262         263         264         265         266         267 
##  -6862.7790  -4450.6619  -5235.4906   -253.7161   6754.6651   4008.0438 
##         268         269         270         271         272         273 
##  -6338.6022   -136.8458 -17745.6634   8531.1126  13872.2172  24032.5416 
##         274 
##  78157.0555

Task 2 (c)

TASK 2c: COMPARE THOSE WHO GOT A JOB WITH THOSE WHO DID NOT GET A JOB? IDENTIFY WHY?

Compare the remaining subset of those people who did not get a job and compare them with those people who got a job. Here, we are not analyzing what drives a higher salary. Instead, we are analysing the two groups who got a job / did not get a job. Draw Contingency Tables as appropriate Run Chi-Square test Run a Logistic Regression

nojob <- subset(mbasalary.df,salary==0)
nojob

##     age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## 1    23   2      620       77       87       87  3.40  3.00       1
## 2    24   1      610       90       71       87  3.50  4.00       1
## 3    24   1      670       99       78       95  3.30  3.25       1
## 4    24   1      570       56       81       75  3.30  2.67       1
## 6    24   1      640       82       89       91  3.90  3.75       1
## 7    25   1      610       89       74       87  3.40  3.50       1
## 8    25   2      650       88       89       92  3.30  3.75       1
## 22   27   1      740       99       96       99  3.50  3.50       1
## 23   27   1      750       99       98       99  3.40  3.50       1
## 24   28   2      540       75       50       65  3.60  4.00       1
## 25   29   1      580       56       87       78  3.64  3.33       1
## 27   31   2      560       60       78       72  3.30  3.75       1
## 28   32   1      760       99       99       99  3.40  3.00       1
## 29   32   1      640       79       91       91  3.60  3.75       1
## 31   34   2      620       75       89       87  3.30  3.00       1
## 32   37   2      560       43       87       72  3.40  3.50       1
## 33   42   2      650       75       98       93  3.38  3.00       1
## 34   48   1      590       84       62       81  3.80  4.00       1
## 70   22   1      600       95       54       83  3.00  3.00       2
## 71   23   1      640       89       87       92  3.00  3.00       2
## 72   24   1      550       73       63       69  3.10  3.00       2
## 73   24   1      570       82       58       75  3.09  3.50       2
## 74   24   1      620       82       84       87  3.10  3.50       2
## 75   25   2      570       61       81       76  3.00  3.25       2
## 76   25   1      660       94       84       94  3.27  3.75       2
## 77   25   1      680       94       92       97  3.17  3.50       2
## 88   26   2      560       64       71       72  3.20  3.25       2
## 89   26   1      560       87       41       72  3.00  3.00       2
## 90   26   1      530       68       54       62  3.09  3.17       2
## 92   27   1      720       99       95       99  3.10  3.25       2
## 93   27   1      590       60       87       81  3.00  2.75       2
## 97   28   1      620       81       90       89  3.20  3.00       2
## 98   28   2      610       85       78       86  3.10  3.00       2
## 100  29   1      660       94       87       94  3.00  3.00       2
## 102  29   1      510       57       50       55  3.27  3.40       2
## 103  29   2      640       90       84       92  3.20  3.00       2
## 104  29   1      610       91       62       86  3.10  3.67       2
## 106  29   1      580       79       67       78  3.00  3.25       2
## 107  30   1      680       97       87       96  3.00  3.00       2
## 109  32   2      610       64       89       86  3.25  0.00       2
## 110  35   1      540       43       78       65  3.20  3.25       2
## 111  35   1      630       66       95       90  3.08  3.25       2
## 112  36   2      530       48       71       62  3.00  2.50       2
## 113  36   1      650       87       89       93  3.00  3.20       2
## 114  43   1      630       82       87       89  3.10  3.00       2
## 140  23   1      720       95       98       99  2.80  2.50       3
## 141  24   2      640       94       78       92  2.90  3.25       3
## 142  24   1      710       96       97       99  2.80  2.75       3
## 143  24   1      670       94       89       96  2.70  3.00       3
## 144  24   2      710       97       97       99  2.80  3.00       3
## 146  24   1      600       89       62       83  2.90  3.00       3
## 147  24   2      640       96       71       91  2.70  2.50       3
## 150  25   1      550       72       58       69  2.90  3.00       3
## 151  25   1      710       99       91       98  2.90  3.25       3
## 159  26   1      560       56       81       72  2.80  3.25       3
## 160  26   1      540       52       71       65  2.70  2.75       3
## 162  26   2      570       48       89       75  2.82  2.50       3
## 163  26   1      610       82       81       86  2.90  2.75       3
## 164  27   1      650       89       84       93  2.90  3.00       3
## 165  27   2      550       66       63       69  2.90  3.00       3
## 167  27   1      610       97       45       86  2.70  2.50       3
## 168  27   2      630       82       89       89  2.70  3.25       3
## 169  27   2      560       61       74       73  2.80  3.25       3
## 180  29   1      590       92       58       81  2.80  2.75       3
## 182  32   1      550       52       78       71  2.70  2.75       3
## 183  34   1      610       79       81       86  2.80  3.00       3
## 184  34   1      610       82       78       86  2.70  3.00       3
## 185  43   1      480       49       41       45  2.90  3.25       3
## 213  25   1      730       98       96       99  2.40  2.75       4
## 218  25   1      700       99       87       98  2.00  2.00       4
## 219  26   1      660       93       87       95  2.60  2.00       4
## 220  26   1      450       28       46       34  2.10  2.00       4
## 222  26   1      600       75       78       83  2.20  2.25       4
## 227  27   2      560       59       74       73  2.40  2.50       4
## 229  27   1      630       93       78       91  2.10  2.50       4
## 230  27   1      580       84       58       78  2.70  2.75       4
## 232  27   1      670       89       91       95  3.60  3.25       4
## 233  27   1      580       74       70       78  3.40  3.25       4
## 234  28   1      560       74       67       73  3.60  3.60       4
## 236  28   1      710       94       98       99  3.40  3.75       4
## 237  28   1      570       69       71        0  2.30  2.50       4
## 238  29   1      530       35       81       62  3.30  2.75       4
## 241  29   1      670       91       91       95  3.30  3.25       4
## 242  29   1      630       99       50       89  2.90  3.25       4
## 243  29   2      680       89       96       96  2.80  3.00       4
## 244  30   1      650       88       92       93  3.45  3.83       4
## 250  31   1      570       75       62       75  2.80  3.00       4
## 253  32   1      510       79       22       54  2.30  2.25       4
## 254  35   1      570       72       71       75  3.30  4.00       4
## 255  39   2      700       89       98       98  3.30  3.25       4
##     work_yrs frstlang salary satis
## 1          2        1      0     7
## 2          2        1      0     6
## 3          2        1      0     6
## 4          1        1      0     7
## 6          2        1      0     6
## 7          2        1      0     5
## 8          2        1      0     6
## 22         3        1      0     6
## 23         1        2      0     5
## 24         5        1      0     5
## 25         3        1      0     5
## 27        10        1      0     7
## 28         5        1      0     5
## 29         7        1      0     6
## 31         7        1      0     6
## 32         9        1      0     6
## 33        13        1      0     5
## 34        22        1      0     6
## 70         1        1      0     5
## 71         2        1      0     7
## 72         0        2      0     5
## 73         2        1      0     6
## 74         1        1      0     5
## 75         3        1      0     4
## 76         2        1      0     5
## 77         2        1      0     6
## 88         3        1      0     6
## 89         3        1      0     6
## 90         4        2      0     5
## 92         5        1      0     5
## 93         3        1      0     6
## 97         4        1      0     6
## 98         5        1      0     6
## 100        1        1      0     6
## 102        5        1      0     5
## 103        3        1      0     5
## 104        7        1      0     5
## 106        4        1      0     6
## 107        4        1      0     5
## 109       11        1      0     7
## 110        8        1      0     5
## 111       12        1      0     5
## 112        7        1      0     5
## 113       18        1      0     6
## 114       16        1      0     5
## 140        1        1      0     5
## 141        2        2      0     4
## 142        2        1      0     7
## 143        2        1      0     7
## 144        2        1      0     7
## 146        1        1      0     6
## 147        2        1      0     6
## 150        3        1      0     6
## 151        1        1      0     6
## 159        4        1      0     6
## 160        2        1      0     6
## 162        3        1      0     5
## 163        3        1      0     6
## 164        2        1      0     6
## 165        3        1      0     4
## 167        4        2      0     5
## 168        5        1      0     6
## 169        5        1      0     6
## 180        3        2      0     5
## 182        7        1      0     6
## 183       11        1      0     6
## 184       12        1      0     5
## 185       22        1      0     5
## 213        2        1      0     6
## 218        1        1      0     7
## 219        2        1      0     5
## 220        4        1      0     6
## 222        2        1      0     6
## 227        2        1      0     5
## 229        4        1      0     5
## 230        1        1      0     5
## 232        5        1      0     6
## 233        3        1      0     6
## 234        5        1      0     5
## 236        6        1      0     6
## 237        5        1      0     5
## 238        6        1      0     7
## 241        3        1      0     5
## 242        1        2      0     4
## 243        4        1      0     5
## 244        2        1      0     6
## 250        1        1      0     6
## 253        5        2      0     5
## 254        8        1      0     6
## 255        5        1      0     5

dim(nojob)

## [1] 90 13

gotjob <- gotjob[1:90,]

table1 <- xtabs(~gotjob$age+nojob$age)
table1

##           nojob$age
## gotjob$age 22 23 24 25 26 27 28 29 30 31 32 34 35 36 37 39 42 43 48
##         22  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##         23  0  0  0  0  0  1  0  0  1  1  0  0  0  1  0  0  0  0  0
##         24  0  0  1  2  1  3  1  2  0  0  1  0  2  1  0  0  0  0  0
##         25  0  0  3  2  1  4  1  3  1  1  2  1  0  0  1  0  0  0  0
##         26  0  0  1  0  1  1  0  2  0  0  1  0  1  0  0  0  1  1  1
##         27  0  0  4  1  4  2  0  0  0  0  1  1  0  0  0  1  0  0  0
##         28  0  1  1  2  1  1  0  1  0  0  0  0  0  0  0  0  0  1  0
##         29  0  0  0  0  1  2  1  0  0  0  0  1  0  0  0  0  0  0  0
##         30  1  0  2  0  0  0  3  0  0  0  0  0  0  0  0  0  0  0  0
##         31  0  1  1  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
##         32  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
##         33  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##         34  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##         39  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##         40  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0

table1 <- xtabs(~gotjob$sex+nojob$sex)
table1

##           nojob$sex
## gotjob$sex  1  2
##          1 45 17
##          2 22  6

table1 <- xtabs(~gotjob$work_yrs+nojob$work_yrs)
table1

##                nojob$work_yrs
## gotjob$work_yrs 0 1 2 3 4 5 6 7 8 9 10 11 12 13 16 18 22
##              0  0 0 1 0 0 0 0 0 0 0  0  0  0  0  0  0  0
##              1  0 0 1 4 1 2 0 0 0 0  0  0  0  0  0  0  0
##              2  0 3 8 2 4 4 2 3 1 0  1  0  1  0  0  1  1
##              3  0 4 2 2 1 2 0 0 1 1  0  1  0  1  1  0  1
##              4  0 1 3 4 0 0 0 1 0 0  0  0  1  0  0  0  0
##              5  0 2 1 1 1 1 0 0 0 0  0  1  0  0  0  0  0
##              6  0 0 4 0 0 2 0 1 0 0  0  0  0  0  0  0  0
##              7  0 0 1 0 0 0 0 0 0 0  0  0  0  0  0  0  0
##              8  1 0 0 1 1 1 0 0 0 0  0  0  0  0  0  0  0
##              10 0 0 0 0 1 0 0 0 0 0  0  0  0  0  0  0  0
##              15 0 1 0 0 0 0 0 0 0 0  0  0  0  0  0  0  0
##              16 0 1 1 0 0 0 0 0 0 0  0  0  0  0  0  0  0

table1 <- xtabs(~gotjob$quarter+nojob$quarter)
table1

##               nojob$quarter
## gotjob$quarter  1  2  3  4
##              1 18 17  0  0
##              2  0 10 15  0
##              3  0  0  8 16
##              4  0  0  0  6

table1 <- xtabs(~gotjob$gmat_tot+nojob$gmat_tot)
table1

##                nojob$gmat_tot
## gotjob$gmat_tot 450 480 510 530 540 550 560 570 580 590 600 610 620 630
##             500   0   0   0   0   0   0   0   0   0   0   0   0   0   1
##             520   0   0   0   0   0   0   0   0   0   0   0   1   0   0
##             530   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##             540   0   0   0   0   0   0   0   0   1   0   0   0   0   0
##             550   0   0   0   0   0   0   1   0   0   0   0   0   0   0
##             560   0   0   0   0   1   0   0   0   0   0   1   1   0   0
##             570   0   0   0   0   0   0   1   0   0   0   0   0   0   1
##             580   0   1   0   0   0   0   1   0   0   1   0   0   0   0
##             590   0   0   0   1   0   0   0   0   1   0   0   0   0   0
##             600   0   0   0   0   0   0   1   1   0   0   1   0   1   0
##             610   0   0   0   0   0   1   0   0   0   0   1   1   0   0
##             620   0   0   1   1   0   3   0   1   0   0   0   3   0   1
##             630   0   0   0   0   0   0   0   1   1   1   0   1   1   0
##             640   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##             650   0   0   0   0   1   0   1   1   0   0   0   0   1   0
##             660   0   0   0   1   0   0   0   0   1   0   0   0   1   0
##             670   0   0   0   0   1   0   1   0   0   0   0   1   0   0
##             680   1   0   0   0   0   0   0   2   0   0   0   0   0   1
##             700   0   0   1   0   0   0   0   0   0   1   0   1   0   0
##             710   0   0   0   0   0   0   2   0   0   0   0   0   0   1
##             720   0   0   0   0   0   0   0   1   0   0   0   0   0   0
##                nojob$gmat_tot
## gotjob$gmat_tot 640 650 660 670 680 700 710 720 730 740 750 760
##             500   0   0   0   0   0   0   0   0   0   0   0   0
##             520   0   0   0   0   0   0   0   0   0   0   0   0
##             530   0   0   0   0   0   0   0   0   0   0   1   0
##             540   0   0   0   0   0   0   0   0   0   0   0   0
##             550   0   0   0   0   0   0   1   0   0   0   0   0
##             560   0   0   0   0   1   1   1   0   0   1   0   1
##             570   1   2   0   1   0   0   0   0   0   0   0   0
##             580   1   0   1   0   0   0   1   1   0   0   0   0
##             590   0   0   0   0   0   0   0   0   0   0   0   0
##             600   0   0   0   1   1   0   0   1   0   0   0   0
##             610   1   0   0   0   0   0   0   0   0   0   0   0
##             620   0   0   0   1   0   0   1   0   0   0   0   0
##             630   0   0   1   0   0   0   0   0   0   0   0   0
##             640   0   0   1   0   0   0   0   0   0   0   0   0
##             650   0   1   0   0   0   1   0   0   0   0   0   0
##             660   0   1   0   0   0   0   0   0   0   0   0   0
##             670   1   1   0   0   1   0   0   0   1   0   0   0
##             680   1   0   0   1   0   0   0   0   0   0   0   0
##             700   0   0   0   0   0   0   0   0   0   0   0   0
##             710   1   0   0   0   0   0   0   0   0   0   0   0
##             720   0   0   0   0   0   0   0   0   0   0   0   0

chisq.test(table(gotjob$age,nojob$age))

## Warning in chisq.test(table(gotjob$age, nojob$age)): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  table(gotjob$age, nojob$age)
## X-squared = 229.27, df = 252, p-value = 0.8449

chisq.test(table(gotjob$sex,nojob$sex))

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table(gotjob$sex, nojob$sex)
## X-squared = 0.11711, df = 1, p-value = 0.7322

chisq.test(table(gotjob$gmat_tot,nojob$gmat_tot))

## Warning in chisq.test(table(gotjob$gmat_tot, nojob$gmat_tot)): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  table(gotjob$gmat_tot, nojob$gmat_tot)
## X-squared = 496.72, df = 500, p-value = 0.533

chisq.test(table(gotjob$gmat_tpc,nojob$gmat_tpc))

## Warning in chisq.test(table(gotjob$gmat_tpc, nojob$gmat_tpc)): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  table(gotjob$gmat_tpc, nojob$gmat_tpc)
## X-squared = 776.54, df = 784, p-value = 0.5683

chisq.test(table(gotjob$quarter,nojob$quarter))

## Warning in chisq.test(table(gotjob$quarter, nojob$quarter)): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  table(gotjob$quarter, nojob$quarter)
## X-squared = 110.98, df = 9, p-value < 2.2e-16

chisq.test(table(gotjob$frstlang,nojob$frstlang))

## Warning in chisq.test(table(gotjob$frstlang, nojob$frstlang)): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table(gotjob$frstlang, nojob$frstlang)
## X-squared = 0.0080703, df = 1, p-value = 0.9284

chisq.test(table(gotjob$work_yrs,nojob$work_yrs))

## Warning in chisq.test(table(gotjob$work_yrs, nojob$work_yrs)): Chi-squared
## approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  table(gotjob$work_yrs, nojob$work_yrs)
## X-squared = 117.66, df = 176, p-value = 0.9998

fit2 <- lm(quarter ~ age+sex+gmat_tot+work_yrs+frstlang, data=gotjob)
summary(fit2)

## 
## Call:
## lm(formula = quarter ~ age + sex + gmat_tot + work_yrs + frstlang, 
##     data = gotjob)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3254 -0.9471 -0.1178  0.8653  2.0223 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  3.8812823  2.0382214   1.904   0.0603 .
## age         -0.1021945  0.0729769  -1.400   0.1651  
## sex         -0.0928392  0.2296646  -0.404   0.6871  
## gmat_tot     0.0005992  0.0020753   0.289   0.7735  
## work_yrs     0.0394908  0.0741173   0.533   0.5956  
## frstlang     0.4431674  0.4705635   0.942   0.3490  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9693 on 84 degrees of freedom
## Multiple R-squared:  0.04908,    Adjusted R-squared:  -0.007527 
## F-statistic: 0.867 on 5 and 84 DF,  p-value: 0.5069

fit2 <- lm(quarter ~ age+sex+gmat_tot+work_yrs+frstlang, data=nojob)
summary(fit2)

## 
## Call:
## lm(formula = quarter ~ age + sex + gmat_tot + work_yrs + frstlang, 
##     data = nojob)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8850 -0.7868  0.1552  0.9121  1.8054 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  3.335263   1.664639   2.004   0.0483 *
## age          0.047580   0.046252   1.029   0.3066  
## sex         -0.400300   0.259810  -1.541   0.1271  
## gmat_tot    -0.001978   0.001851  -1.069   0.2882  
## work_yrs    -0.092225   0.054185  -1.702   0.0924 .
## frstlang    -0.005743   0.405212  -0.014   0.9887  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.063 on 84 degrees of freedom
## Multiple R-squared:  0.07193,    Adjusted R-squared:  0.01669 
## F-statistic: 1.302 on 5 and 84 DF,  p-value: 0.2709

The above tests show that major factor that differentiates between getting a job and not getting a job is quarters, and not age, sex, work experience, gmat or first language.

Task 2 (d) Logistic Regression

Data cleaning

library(Amelia)
mbasalary.df<-subset(mbasalary.df, salary != 998  & salary != 999)
missmap(mbasalary.df, main = "Missing value before Cleaning",horizontal=FALSE)

Model fitting

mbasalary.df$hasJob <- ifelse(mbasalary.df$salary > 0,1, 0)
mbasalary.df$salary <- NULL
trainingSet  <- mbasalary.df[1:120,]
testSet  <- mbasalary.df[121:193,]

Annova Test

newmodel <- glm(formula = hasJob ~ age+sex+gmat_tpc+gmat_tot+frstlang+quarter+work_yrs, family = binomial(link = "logit"), data = trainingSet)
anova(newmodel,test="Chisq")

## Analysis of Deviance Table
## 
## Model: binomial, link: logit
## 
## Response: hasJob
## 
## Terms added sequentially (first to last)
## 
## 
##          Df Deviance Resid. Df Resid. Dev Pr(>Chi)    
## NULL                       119     166.35             
## age       1   1.6519       118     164.70   0.1987    
## sex       1   0.0001       117     164.70   0.9923    
## gmat_tpc  1   0.0087       116     164.69   0.9258    
## gmat_tot  1   2.2166       115     162.48   0.1365    
## frstlang  1   0.0797       114     162.40   0.7777    
## quarter   1  24.3127       113     138.09 8.19e-07 ***
## work_yrs  1   1.1351       112     136.95   0.2867    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

newmodel1 <- glm(formula = hasJob ~ age+quarter+gmat_tot, family = binomial(link = "logit"), data = trainingSet)

Finding the importance and ROC curve

library(caret)
varImp(newmodel1)

##            Overall
## age      2.2981348
## quarter  4.3625038
## gmat_tot 0.9828324

ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE)
mod_fit <- train(hasJob ~ age+quarter+gmat_tot,data=testSet, method="glm",family="binomial",
                 trControl = ctrl, tuneLength = 5)

## Warning in train.default(x, y, weights = w, ...): You are trying to do
## regression and your outcome only has two possible values Are you trying to
## do classification? If so, use a 2 level factor as your outcome column.

library(ROCR)
p <- predict(newmodel1,testSet,type='response')
pr <- prediction(p, testSet$hasJob)
prf <- performance(pr, measure = "tpr", x.measure = "fpr")
auc <- performance(pr, measure = "auc")
auc <- auc@y.values[[1]]
auc

## [1] 0.7596899

plot(prf)

KS statistic, Lift and gain chart

logit_ks <- max(prf@y.values[[1]]-prf@x.values[[1]])*100
logit_ks

## [1] 47.75194

lift.obj <- performance(pr, measure="lift", x.measure="rpp")
plot(lift.obj,main="Lift Chart",xlab="Population percentage",ylab="Lift", col="green")
abline(1,0,col="blue")

lift.obj <- performance(pr, "tpr", x.measure="rpp")
plot(lift.obj,main="Gain Chart",xlab="Rate of positive prediction",ylab="True positive rate", col="green")
abline(0,1,col="blue")