The following is a replica of the findings of Green and Winik, 2010.

library(haven)
## Warning: package 'haven' was built under R version 3.5.3
library(foreign)
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.5.3
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(stargazer)
## 
## Please cite as:
##  Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
##  R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
data<-read.dta("C:/Users/PCMcC/Documents/Causal Inference/Homeworks/Assignment 4/GreenWinik_Criminology_2010_LimitedAnonymizedDataset.2.dta")
names(data)
##  [1] "pickupdate"          "birthyear"           "gender"             
##  [4] "race"                "disposition"         "dispdate"           
##  [7] "sentdate"            "felonycharges"       "highestcharge"      
## [10] "nondrug"             "drug1"               "drug2"              
## [13] "incarc"              "suspend"             "probat"             
## [16] "priorarr"            "priorfelarr"         "priordrugarr"       
## [19] "priorfeldrugarr"     "priorcon"            "priorfelcon"        
## [22] "priordrugcon"        "priorfeldrugcon"     "laterarr"           
## [25] "laterarrdate"        "laterfelarr"         "laterfelarrdate"    
## [28] "laterdrugarr"        "laterdrugarrdate"    "laterfeldrugarr"    
## [31] "laterfeldrugarrdate" "latercon"            "latercondate"       
## [34] "laterfelcon"         "laterfelcondate"     "laterdrugcon"       
## [37] "laterdrugcondate"    "laterfeldrugcon"     "laterfeldrugcondate"
## [40] "probrevoke"          "probrevokedate"      "codef"              
## [43] "clusterid"           "ccn"                 "datacoder"          
## [46] "cutoffdate1"         "cutoffdate2"         "cutoffdate3"        
## [49] "toserve"             "fullreleasetorecid"  "female"             
## [52] "nonblack"            "pickupdatecode"      "age"                
## [55] "agesq"               "incjudge"            "calendar1"          
## [58] "calendar2"           "calendar3"           "calendar4"          
## [61] "calendar5"           "calendar6"           "calendar7"          
## [64] "calendar8"           "calendar9"           "calendar"           
## [67] "incarcerate"         "marijuana"           "cocaine"            
## [70] "crack"               "heroin"              "pcp"                
## [73] "otherdrug"           "pwid"                "dist"               
## [76] "conviction"          "probatnonzero"       "probsuspend"

RECODING OF VARIABLES

#Filtering Data
data2<-filter(data, incjudge==1)
data2<-as.data.frame(data2)

#Probation Level
data2$genproblevel<-ifelse (data2$probat==0,1,0) 
data2$probatlevel1<-ifelse (data2$probat==1:12,1,0)
## Warning in data2$probat == 1:12: longer object length is not a multiple of
## shorter object length
data2$probatlevel2<-ifelse (data2$probat==13:24,1,0)
## Warning in data2$probat == 13:24: longer object length is not a multiple of
## shorter object length
data2$probatlevel3<-ifelse (data2$probat==25:36,1,0)
## Warning in data2$probat == 25:36: longer object length is not a multiple of
## shorter object length
data2$probatlevel4<-ifelse (data2$probat>=37, 1,0)
#Prison Level
data2$genprisonlevel<-ifelse (data2$toserve==0,1,0)
data2$prisonlevel1<-ifelse (data2$toserve==1:12,1,0)
## Warning in data2$toserve == 1:12: longer object length is not a multiple of
## shorter object length
data2$prisonlevel2<-ifelse (data2$toserve==13:24,1,0)
## Warning in data2$toserve == 13:24: longer object length is not a multiple
## of shorter object length
data2$prisonlevel3<-ifelse (data2$toserve==25:36,1,0)
## Warning in data2$toserve == 25:36: longer object length is not a multiple
## of shorter object length
data2$prisonlevel4<-ifelse (data2$toserve>=37,1,0)

table(data2$genproblevel, data2$genprisonlevel)
##    
##       0   1
##   0 124 312
##   1 295 272

TABLE 2: Means of Variables by Year

tablex<-aggregate(cbind(age, agesq, female, nonblack, priorarr, priordrugarr, priorfelarr, priorfeldrugarr, priorcon, priordrugcon, priorfelcon, priorfeldrugcon, pwid ,dist ,marijuana ,cocaine, crack ,heroin ,pcp ,otherdrug ,nondrug) ~ calendar, data = data2, mean,na.rm = TRUE)
table2<-(round(tablex, digits=2))
print(table2)
##   calendar   age   agesq female nonblack priorarr priordrugarr priorfelarr
## 1        1 31.92 1149.57   0.13     0.04     0.81         0.68        0.63
## 2        2 35.12 1370.64   0.07     0.04     0.87         0.74        0.73
## 3        3 33.18 1234.21   0.08     0.02     0.86         0.75        0.70
## 4        4 32.79 1190.19   0.10     0.03     0.83         0.72        0.74
## 5        5 33.75 1262.22   0.09     0.01     0.87         0.80        0.76
## 6        6 32.18 1157.95   0.09     0.02     0.82         0.65        0.68
## 7        7 32.33 1172.59   0.10     0.02     0.79         0.66        0.71
## 8        8 34.22 1301.33   0.09     0.01     0.91         0.74        0.73
## 9        9 32.35 1156.91   0.12     0.03     0.94         0.75        0.79
##   priorfeldrugarr priorcon priordrugcon priorfelcon priorfeldrugcon pwid
## 1            0.54     0.60         0.50        0.43            0.35 0.49
## 2            0.59     0.70         0.54        0.59            0.45 0.40
## 3            0.59     0.64         0.53        0.55            0.47 0.49
## 4            0.57     0.71         0.58        0.54            0.44 0.41
## 5            0.59     0.72         0.66        0.59            0.50 0.56
## 6            0.48     0.68         0.55        0.51            0.40 0.51
## 7            0.46     0.62         0.48        0.50            0.35 0.52
## 8            0.57     0.67         0.54        0.55            0.44 0.43
## 9            0.56     0.71         0.57        0.59            0.44 0.39
##   dist marijuana cocaine crack heroin  pcp otherdrug nondrug
## 1 0.60      0.22    0.39  0.15   0.24 0.07      0.05    0.11
## 2 0.69      0.17    0.38  0.15   0.31 0.07      0.00    0.09
## 3 0.62      0.17    0.46  0.19   0.30 0.04      0.03    0.18
## 4 0.69      0.17    0.40  0.19   0.26 0.02      0.04    0.13
## 5 0.53      0.23    0.34  0.21   0.30 0.06      0.03    0.13
## 6 0.59      0.18    0.41  0.24   0.29 0.01      0.02    0.11
## 7 0.54      0.17    0.44  0.25   0.16 0.06      0.05    0.16
## 8 0.62      0.18    0.33  0.22   0.29 0.04      0.03    0.12
## 9 0.67      0.21    0.44  0.19   0.23 0.04      0.04    0.15

TABLE 3: Means of Variables by Year

tablexy<-aggregate(cbind( incarcerate, toserve, probat, probatnonzero, laterarr) ~ calendar, data = data2, mean,na.rm = TRUE)
table3<-(round(tablexy, digits=2))
print(table3)
##   calendar incarcerate toserve probat probatnonzero laterarr
## 1        1        0.34    5.12  12.46          0.50     0.48
## 2        2        0.25    7.63  11.46          0.57     0.46
## 3        3        0.65   11.92  11.69          0.42     0.58
## 4        4        0.56    7.79   6.83          0.31     0.49
## 5        5        0.34    5.82  13.69          0.43     0.56
## 6        6        0.48    5.07  14.88          0.60     0.58
## 7        7        0.43    5.55   6.36          0.29     0.50
## 8        8        0.23    5.49   8.74          0.48     0.44
## 9        9        0.45    7.12   7.05          0.33     0.65

TABLE 4: F Tests of the Joint Significance of Calendar Assignment on Incarceration and Probation Sentences

#incarcerate
reg1<-lm(incarcerate~calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9, data=data2)
summary(reg1)$fstatistic
##      value      numdf      dendf 
##   9.795386   8.000000 994.000000
reg2<-lm(incarcerate~calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9 + age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug,  data=data2)
summary(reg2)$fstatistic
##     value     numdf     dendf 
##   8.44161  29.00000 973.00000
#to serve
reg3<-lm(toserve~calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9, data=data2)
summary(reg3)$fstatistic
##      value      numdf      dendf 
##   3.187805   8.000000 994.000000
reg4<-lm(toserve~calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9 + age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug,  data=data2)
summary(reg4)$fstatistic
##      value      numdf      dendf 
##   4.602083  29.000000 973.000000
#probatnonzero
reg5<-lm(probatnonzero~calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9, data=data2)
summary(reg5)$fstatistic
##      value      numdf      dendf 
##   5.648596   8.000000 994.000000
reg6<-lm(probatnonzero~calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9 + age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug,  data=data2)
summary(reg6)$fstatistic
##     value     numdf     dendf 
##   5.20496  29.00000 973.00000
#probat
reg7<-lm(probat~calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9, data=data2)
summary(reg5)$fstatistic
##      value      numdf      dendf 
##   5.648596   8.000000 994.000000
reg8<-lm(probat~calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9 + age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug,  data=data2)
summary(reg6)$fstatistic
##     value     numdf     dendf 
##   5.20496  29.00000 973.00000

TABLE 5: Estimates of the Effects of Length of Incarceration and Probation on Recidivism

library(AER)
## Warning: package 'AER' was built under R version 3.5.3
## Loading required package: car
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
## 
##     recode
## Loading required package: lmtest
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## Loading required package: sandwich
## Loading required package: survival
#toserve
iv1 = ivreg((laterarr==1) ~ toserve | calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9 , data = data2)
summary(iv1)
## 
## Call:
## ivreg(formula = (laterarr == 1) ~ toserve | calendar1 + calendar2 + 
##     calendar3 + calendar4 + calendar5 + calendar7 + calendar8 + 
##     calendar9, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9078 -0.4713  0.3372  0.5287  0.5287 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.471286   0.057247   8.233 5.68e-16 ***
## toserve     0.007981   0.007933   1.006    0.315    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5239 on 1001 degrees of freedom
## Multiple R-Squared: -0.0986, Adjusted R-squared: -0.0997 
## Wald test: 1.012 on 1 and 1001 DF,  p-value: 0.3147
ic1 = ivreg((laterarr==1) ~  toserve + age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug  | age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug + calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9   , data = data2)
summary(ic1)
## 
## Call:
## ivreg(formula = (laterarr == 1) ~ toserve + age + agesq + female + 
##     nonblack + priorarr + priordrugarr + priorfelarr + priorfeldrugarr + 
##     priorcon + priordrugcon + priorfelcon + priorfeldrugcon + 
##     pwid + dist + marijuana + cocaine + crack + heroin + pcp + 
##     otherdrug + nondrug | age + agesq + female + nonblack + priorarr + 
##     priordrugarr + priorfelarr + priorfeldrugarr + priorcon + 
##     priordrugcon + priorfelcon + priorfeldrugcon + pwid + dist + 
##     marijuana + cocaine + crack + heroin + pcp + otherdrug + 
##     nondrug + calendar1 + calendar2 + calendar3 + calendar4 + 
##     calendar5 + calendar7 + calendar8 + calendar9, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8860 -0.4825  0.2041  0.4608  0.9049 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.0123798  0.1904306   5.316 1.31e-07 ***
## toserve          0.0086508  0.0082367   1.050   0.2938    
## age             -0.0254644  0.0100055  -2.545   0.0111 *  
## agesq            0.0002126  0.0001324   1.605   0.1088    
## female          -0.0012192  0.0636057  -0.019   0.9847    
## nonblack        -0.2190621  0.1093432  -2.003   0.0454 *  
## priorarr        -0.0600934  0.0771971  -0.778   0.4365    
## priordrugarr     0.0073863  0.0692132   0.107   0.9150    
## priorfelarr      0.1044293  0.0711928   1.467   0.1427    
## priorfeldrugarr -0.1004298  0.0728534  -1.379   0.1684    
## priorcon         0.0204244  0.0757842   0.270   0.7876    
## priordrugcon     0.0405106  0.0775742   0.522   0.6016    
## priorfelcon     -0.1001847  0.0769295  -1.302   0.1931    
## priorfeldrugcon  0.0555271  0.0849087   0.654   0.5133    
## pwid             0.0113328  0.0618777   0.183   0.8547    
## dist             0.0109205  0.0653119   0.167   0.8672    
## marijuana        0.1000156  0.0576449   1.735   0.0830 .  
## cocaine         -0.0001741  0.0584119  -0.003   0.9976    
## crack            0.0399155  0.0655350   0.609   0.5426    
## heroin           0.0837925  0.0624773   1.341   0.1802    
## pcp              0.0817318  0.0968362   0.844   0.3989    
## otherdrug       -0.0402090  0.1071726  -0.375   0.7076    
## nondrug          0.0014532  0.0501801   0.029   0.9769    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.514 on 980 degrees of freedom
## Multiple R-Squared: -0.03562,    Adjusted R-squared: -0.05887 
## Wald test: 3.142 on 22 and 980 DF,  p-value: 1.645e-06
#probat
iv2 = ivreg((laterarr==1) ~ probat | calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9 , data = data2)
summary(iv2)
## 
## Call:
## ivreg(formula = (laterarr == 1) ~ probat | calendar1 + calendar2 + 
##     calendar3 + calendar4 + calendar5 + calendar7 + calendar8 + 
##     calendar9, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6350 -0.5039  0.4174  0.4961  0.4961 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.503897   0.056997   8.841   <2e-16 ***
## probat      0.002186   0.005315   0.411    0.681    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4992 on 1001 degrees of freedom
## Multiple R-Squared: 0.002492,    Adjusted R-squared: 0.001496 
## Wald test: 0.1691 on 1 and 1001 DF,  p-value: 0.681
ic2 = ivreg((laterarr==1) ~  probat + age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug  | age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug + calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9   , data = data2)
summary(ic2)
## 
## Call:
## ivreg(formula = (laterarr == 1) ~ probat + age + agesq + female + 
##     nonblack + priorarr + priordrugarr + priorfelarr + priorfeldrugarr + 
##     priorcon + priordrugcon + priorfelcon + priorfeldrugcon + 
##     pwid + dist + marijuana + cocaine + crack + heroin + pcp + 
##     otherdrug + nondrug | age + agesq + female + nonblack + priorarr + 
##     priordrugarr + priorfelarr + priorfeldrugarr + priorcon + 
##     priordrugcon + priorfelcon + priorfeldrugcon + pwid + dist + 
##     marijuana + cocaine + crack + heroin + pcp + otherdrug + 
##     nondrug + calendar1 + calendar2 + calendar3 + calendar4 + 
##     calendar5 + calendar7 + calendar8 + calendar9, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7976 -0.4752  0.2322  0.4431  0.8624 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.0147071  0.1961545   5.173 2.79e-07 ***
## probat           0.0012358  0.0051850   0.238   0.8117    
## age             -0.0252338  0.0101412  -2.488   0.0130 *  
## agesq            0.0002094  0.0001334   1.570   0.1168    
## female          -0.0318686  0.0555817  -0.573   0.5665    
## nonblack        -0.1910977  0.1050883  -1.818   0.0693 .  
## priorarr        -0.0697442  0.0726339  -0.960   0.3372    
## priordrugarr     0.0029892  0.0653524   0.046   0.9635    
## priorfelarr      0.1368515  0.0673954   2.031   0.0426 *  
## priorfeldrugarr -0.1191658  0.0680028  -1.752   0.0800 .  
## priorcon         0.0309100  0.0711503   0.434   0.6641    
## priordrugcon     0.0562407  0.0719406   0.782   0.4345    
## priorfelcon     -0.0825914  0.0716554  -1.153   0.2493    
## priorfeldrugcon  0.0875194  0.0804564   1.088   0.2770    
## pwid             0.0145487  0.0590992   0.246   0.8056    
## dist             0.0225452  0.0682875   0.330   0.7414    
## marijuana        0.0866665  0.0588066   1.474   0.1409    
## cocaine         -0.0036400  0.0599389  -0.061   0.9516    
## crack            0.0311841  0.0681925   0.457   0.6476    
## heroin           0.0753797  0.0628826   1.199   0.2309    
## pcp              0.1109518  0.0877993   1.264   0.2066    
## otherdrug       -0.0577620  0.1027145  -0.562   0.5740    
## nondrug          0.0113237  0.0470993   0.240   0.8101    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4863 on 980 degrees of freedom
## Multiple R-Squared: 0.07314, Adjusted R-squared: 0.05233 
## Wald test: 3.457 on 22 and 980 DF,  p-value: 1.536e-07
#toserve and probat
iv3 = ivreg((laterarr==1) ~ probat +toserve | calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9 , data = data2)
summary(iv3)
## 
## Call:
## ivreg(formula = (laterarr == 1) ~ probat + toserve | calendar1 + 
##     calendar2 + calendar3 + calendar4 + calendar5 + calendar7 + 
##     calendar8 + calendar9, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9238 -0.5048  0.3491  0.4952  0.5569 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.443064   0.083754   5.290  1.5e-07 ***
## probat      0.002574   0.005583   0.461    0.645    
## toserve     0.008226   0.007940   1.036    0.300    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5231 on 1000 degrees of freedom
## Multiple R-Squared: -0.09437,    Adjusted R-squared: -0.09656 
## Wald test: 0.6138 on 2 and 1000 DF,  p-value: 0.5415
ic3 = ivreg((laterarr==1) ~  probat + toserve+ age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug  | age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug + calendar1 + calendar2 + calendar3 + calendar4 + calendar5  + calendar7 + calendar8 +calendar9   , data = data2)
summary(ic3)
## 
## Call:
## ivreg(formula = (laterarr == 1) ~ probat + toserve + age + agesq + 
##     female + nonblack + priorarr + priordrugarr + priorfelarr + 
##     priorfeldrugarr + priorcon + priordrugcon + priorfelcon + 
##     priorfeldrugcon + pwid + dist + marijuana + cocaine + crack + 
##     heroin + pcp + otherdrug + nondrug | age + agesq + female + 
##     nonblack + priorarr + priordrugarr + priorfelarr + priorfeldrugarr + 
##     priorcon + priordrugcon + priorfelcon + priorfeldrugcon + 
##     pwid + dist + marijuana + cocaine + crack + heroin + pcp + 
##     otherdrug + nondrug + calendar1 + calendar2 + calendar3 + 
##     calendar4 + calendar5 + calendar7 + calendar8 + calendar9, 
##     data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9026 -0.4783  0.2097  0.4626  0.9182 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.9860869  0.2090100   4.718 2.73e-06 ***
## probat           0.0016750  0.0054947   0.305   0.7606    
## toserve          0.0088388  0.0082577   1.070   0.2847    
## age             -0.0242595  0.0107556  -2.256   0.0243 *  
## agesq            0.0001974  0.0001414   1.396   0.1629    
## female          -0.0040103  0.0642458  -0.062   0.9502    
## nonblack        -0.2108590  0.1125796  -1.873   0.0614 .  
## priorarr        -0.0607938  0.0772123  -0.787   0.4313    
## priordrugarr     0.0077784  0.0692081   0.112   0.9105    
## priorfelarr      0.1116499  0.0750131   1.488   0.1370    
## priorfeldrugarr -0.1031926  0.0733972  -1.406   0.1601    
## priorcon         0.0208340  0.0757774   0.275   0.7834    
## priordrugcon     0.0390094  0.0777113   0.502   0.6158    
## priorfelcon     -0.1051171  0.0785941  -1.337   0.1814    
## priorfeldrugcon  0.0627369  0.0881210   0.712   0.4767    
## pwid             0.0081431  0.0627411   0.130   0.8968    
## dist            -0.0005208  0.0753138  -0.007   0.9945    
## marijuana        0.0927199  0.0624025   1.486   0.1376    
## cocaine         -0.0077443  0.0634583  -0.122   0.9029    
## crack            0.0307665  0.0720657   0.427   0.6695    
## heroin           0.0768647  0.0664678   1.156   0.2478    
## pcp              0.0769635  0.0980678   0.785   0.4328    
## otherdrug       -0.0464154  0.1090633  -0.426   0.6705    
## nondrug          0.0029006  0.0503920   0.058   0.9541    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5139 on 979 degrees of freedom
## Multiple R-Squared: -0.03405,    Adjusted R-squared: -0.05835 
## Wald test: 3.011 on 23 and 979 DF,  p-value: 2.819e-06

TABLE 6: OLS Estimates of the Effects of Length of Incarceration and Probation on Recidivism

#toserve
reg9<-lm(laterarr~toserve, data=data2)
summary(reg9)
## 
## Call:
## lm(formula = laterarr ~ toserve, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5601 -0.5601  0.4399  0.4399  0.6737 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.560067   0.017669  31.698  < 2e-16 ***
## toserve     -0.004870   0.001187  -4.103 4.42e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4957 on 1001 degrees of freedom
## Multiple R-squared:  0.01654,    Adjusted R-squared:  0.01555 
## F-statistic: 16.83 on 1 and 1001 DF,  p-value: 4.417e-05
reg10<-lm(laterarr~toserve+ age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug , data=data2)
summary(reg10)
## 
## Call:
## lm(formula = laterarr ~ toserve + age + agesq + female + nonblack + 
##     priorarr + priordrugarr + priorfelarr + priorfeldrugarr + 
##     priorcon + priordrugcon + priorfelcon + priorfeldrugcon + 
##     pwid + dist + marijuana + cocaine + crack + heroin + pcp + 
##     otherdrug + nondrug, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8095 -0.4715  0.2293  0.4350  0.8204 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.0474995  0.1773836   5.905 4.85e-09 ***
## toserve         -0.0055579  0.0012145  -4.576 5.34e-06 ***
## age             -0.0265286  0.0093549  -2.836  0.00467 ** 
## agesq            0.0002254  0.0001239   1.820  0.06904 .  
## female          -0.0474369  0.0541816  -0.876  0.38151    
## nonblack        -0.1836394  0.1006475  -1.825  0.06837 .  
## priorarr        -0.0748591  0.0718775  -1.041  0.29791    
## priordrugarr    -0.0001894  0.0647061  -0.003  0.99767    
## priorfelarr      0.1482301  0.0624147   2.375  0.01774 *  
## priorfeldrugarr -0.1274226  0.0666907  -1.911  0.05634 .  
## priorcon         0.0368865  0.0704377   0.524  0.60062    
## priordrugcon     0.0677295  0.0711845   0.951  0.34160    
## priorfelcon     -0.0658616  0.0696714  -0.945  0.34473    
## priorfeldrugcon  0.0986459  0.0761008   1.296  0.19519    
## pwid             0.0203356  0.0577608   0.352  0.72486    
## dist             0.0433576  0.0586543   0.739  0.45996    
## marijuana        0.0871372  0.0535538   1.627  0.10404    
## cocaine          0.0032515  0.0546848   0.059  0.95260    
## crack            0.0367121  0.0613640   0.598  0.54980    
## heroin           0.0784551  0.0584538   1.342  0.17985    
## pcp              0.1346543  0.0861550   1.563  0.11839    
## otherdrug       -0.0611825  0.0997589  -0.613  0.53982    
## nondrug          0.0156832  0.0463813   0.338  0.73533    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4815 on 980 degrees of freedom
## Multiple R-squared:  0.0913, Adjusted R-squared:  0.0709 
## F-statistic: 4.475 on 22 and 980 DF,  p-value: 5.22e-11
#probat
reg11<-lm(laterarr~probat, data=data2)
summary(reg11)
## 
## Call:
## lm(formula = laterarr ~ probat, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6214 -0.5067  0.4245  0.4933  0.4933 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.506724   0.020008  25.326   <2e-16 ***
## probat      0.001911   0.001196   1.598     0.11    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4992 on 1001 degrees of freedom
## Multiple R-squared:  0.002545,   Adjusted R-squared:  0.001548 
## F-statistic: 2.554 on 1 and 1001 DF,  p-value: 0.1103
reg12<-lm(laterarr~probat+ age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug , data=data2)
summary(reg12)
## 
## Call:
## lm(formula = laterarr ~ probat + age + agesq + female + nonblack + 
##     priorarr + priordrugarr + priorfelarr + priorfeldrugarr + 
##     priorcon + priordrugcon + priorfelcon + priorfeldrugcon + 
##     pwid + dist + marijuana + cocaine + crack + heroin + pcp + 
##     otherdrug + nondrug, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8009 -0.4750  0.2315  0.4445  0.8638 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.0119151  0.1801007   5.619 2.51e-08 ***
## probat           0.0014168  0.0012175   1.164  0.24483    
## age             -0.0251051  0.0094870  -2.646  0.00827 ** 
## agesq            0.0002078  0.0001256   1.655  0.09831 .  
## female          -0.0322364  0.0546301  -0.590  0.55527    
## nonblack        -0.1901603  0.1017963  -1.868  0.06205 .  
## priorarr        -0.0698410  0.0725830  -0.962  0.33618    
## priordrugarr     0.0030208  0.0653458   0.046  0.96314    
## priorfelarr      0.1376947  0.0631760   2.180  0.02953 *  
## priorfeldrugarr -0.1195031  0.0673509  -1.774  0.07632 .  
## priorcon         0.0309778  0.0711245   0.436  0.66326    
## priordrugcon     0.0561173  0.0718578   0.781  0.43502    
## priorfelcon     -0.0830754  0.0703763  -1.180  0.23811    
## priorfeldrugcon  0.0883605  0.0769744   1.148  0.25128    
## pwid             0.0142168  0.0583719   0.244  0.80763    
## dist             0.0213548  0.0597095   0.358  0.72069    
## marijuana        0.0858595  0.0543459   1.580  0.11446    
## cocaine         -0.0044534  0.0554973  -0.080  0.93606    
## crack            0.0301905  0.0623319   0.484  0.62825    
## heroin           0.0746232  0.0592513   1.259  0.20817    
## pcp              0.1105120  0.0869409   1.271  0.20399    
## otherdrug       -0.0584629  0.1008436  -0.580  0.56222    
## nondrug          0.0115005  0.0468409   0.246  0.80610    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4863 on 980 degrees of freedom
## Multiple R-squared:  0.07316,    Adjusted R-squared:  0.05235 
## F-statistic: 3.516 on 22 and 980 DF,  p-value: 9.789e-08
#to serve and probat
reg13<-lm(laterarr~probat+toserve, data=data2)
summary(reg13)
## 
## Call:
## lm(formula = laterarr ~ probat + toserve, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6030 -0.5495  0.4184  0.4505  0.6749 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.549533   0.022781  24.123  < 2e-16 ***
## probat       0.000892   0.001217   0.733 0.463813    
## toserve     -0.004676   0.001217  -3.843 0.000129 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4958 on 1000 degrees of freedom
## Multiple R-squared:  0.01706,    Adjusted R-squared:  0.0151 
## F-statistic:  8.68 on 2 and 1000 DF,  p-value: 0.000183
reg14<-lm(laterarr~probat+toserve+ age+ agesq+ female+ nonblack+ priorarr+ priordrugarr+ priorfelarr+priorfeldrugarr+ priorcon+ priordrugcon+ priorfelcon+ priorfeldrugcon+ pwid +dist +marijuana +cocaine+crack +heroin + pcp + otherdrug + nondrug , data=data2)
summary(reg14)
## 
## Call:
## lm(formula = laterarr ~ probat + toserve + age + agesq + female + 
##     nonblack + priorarr + priordrugarr + priorfelarr + priorfeldrugarr + 
##     priorcon + priordrugcon + priorfelcon + priorfeldrugcon + 
##     pwid + dist + marijuana + cocaine + crack + heroin + pcp + 
##     otherdrug + nondrug, data = data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.8145 -0.4726  0.2300  0.4351  0.8232 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      1.0421059  0.1785440   5.837 7.24e-09 ***
## probat           0.0003390  0.0012304   0.275  0.78300    
## toserve         -0.0054904  0.0012396  -4.429 1.05e-05 ***
## age             -0.0262826  0.0094019  -2.795  0.00528 ** 
## agesq            0.0002224  0.0001244   1.787  0.07423 .  
## female          -0.0479058  0.0542339  -0.883  0.37728    
## nonblack        -0.1820529  0.1008595  -1.805  0.07138 .  
## priorarr        -0.0749702  0.0719125  -1.043  0.29743    
## priordrugarr    -0.0000943  0.0647375  -0.001  0.99884    
## priorfelarr      0.1496004  0.0626419   2.388  0.01712 *  
## priorfeldrugarr -0.1279256  0.0667472  -1.917  0.05558 .  
## priorcon         0.0369352  0.0704711   0.524  0.60031    
## priordrugcon     0.0673692  0.0712301   0.946  0.34449    
## priorfelcon     -0.0669310  0.0698123  -0.959  0.33793    
## priorfeldrugcon  0.1000154  0.0762988   1.311  0.19022    
## pwid             0.0196714  0.0578383   0.340  0.73385    
## dist             0.0409750  0.0593159   0.691  0.48986    
## marijuana        0.0856875  0.0538369   1.592  0.11179    
## cocaine          0.0017124  0.0549951   0.031  0.97517    
## crack            0.0348673  0.0617571   0.565  0.57248    
## heroin           0.0770642  0.0586989   1.313  0.18953    
## pcp              0.1335795  0.0862839   1.548  0.12191    
## otherdrug       -0.0623949  0.0999030  -0.625  0.53241    
## nondrug          0.0159466  0.0464131   0.344  0.73124    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4817 on 979 degrees of freedom
## Multiple R-squared:  0.09137,    Adjusted R-squared:  0.07002 
## F-statistic:  4.28 on 23 and 979 DF,  p-value: 1.098e-10