Approach 2: gendered models
FOR WOMEN
- Voluntariness
##
## Call:
## lm(formula = voluntary ~ careertype + age + education + sector,
## data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8565 -0.1858 0.0131 0.2744 0.5758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.29330 0.07323 -4.01 6.7e-05 ***
## careertypeHT 0.07110 0.03514 2.02 0.0433 *
## careertypeIN 0.14857 0.02675 5.55 3.6e-08 ***
## careertypeMX 0.00136 0.03260 0.04 0.9667
## careertypeUN -0.09489 0.04854 -1.95 0.0509 .
## age 0.00327 0.00106 3.09 0.0021 **
## education 0.00163 0.00344 0.47 0.6349
## sector1 -0.04092 0.05736 -0.71 0.4758
## sector2 -0.18778 0.20951 -0.90 0.3703
## sector3 -0.02882 0.03327 -0.87 0.3867
## sector4 -0.12570 0.12285 -1.02 0.3065
## sector5 -0.10266 0.10105 -1.02 0.3099
## sector6 -0.02811 0.03212 -0.88 0.3816
## sector7 0.05939 0.05626 1.06 0.2914
## sector8 0.11645 0.06466 1.80 0.0720 .
## sector9 0.06453 0.05140 1.26 0.2096
## sector10 0.08945 0.12275 0.73 0.4664
## sector11 0.08545 0.04254 2.01 0.0449 *
## sector12 0.02269 0.03848 0.59 0.5556
## sector13 0.05441 0.03364 1.62 0.1061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.317 on 950 degrees of freedom
## (316 observations deleted due to missingness)
## Multiple R-squared: 0.0811, Adjusted R-squared: 0.0628
## F-statistic: 4.41 on 19 and 950 DF, p-value: 1.2e-09
- Age - included as control = SKIP
SUBJECTIVE CAREER SUCCESS INDICATORS
- Career satisfaction
##
## Call:
## lm(formula = satisfaction ~ careertype + age + education + sector,
## data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.529 -0.372 0.019 0.550 1.443
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.65365 0.26041 -2.51 0.0123 *
## careertypeHT -0.18762 0.11221 -1.67 0.0949 .
## careertypeIN -0.36446 0.06759 -5.39 9.1e-08 ***
## careertypeMX -0.09916 0.08510 -1.17 0.2443
## careertypeUN -0.53020 0.11640 -4.56 6.0e-06 ***
## age 0.01015 0.00285 3.55 0.0004 ***
## education 0.00183 0.00912 0.20 0.8412
## sectorManufacturing -0.12387 0.14560 -0.85 0.3951
## sectorUtilities 0.37872 0.40479 0.94 0.3498
## sectorConstruction 0.16721 0.31665 0.53 0.5976
## sectorWholesale&retail 0.06929 0.14565 0.48 0.6344
## sectorHoreca -0.14845 0.20340 -0.73 0.4657
## sectorTransport&communication 0.04006 0.21632 0.19 0.8531
## sectorFinancial intermediation 0.30065 0.18802 1.60 0.1102
## sectorRestate, rent&business 0.03107 0.36302 0.09 0.9318
## sectorPublic administration 0.04967 0.17492 0.28 0.7765
## sectorEducation 0.25085 0.15463 1.62 0.1051
## sectorHealth and social work -0.02082 0.14979 -0.14 0.8895
## sectorOther -0.15477 0.15697 -0.99 0.3244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.752 on 834 degrees of freedom
## (433 observations deleted due to missingness)
## Multiple R-squared: 0.123, Adjusted R-squared: 0.104
## F-statistic: 6.47 on 18 and 834 DF, p-value: 3.09e-15
- Career disappointment
##
## Call:
## lm(formula = disappointment ~ careertype + age + education +
## sector, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7807 -0.8231 -0.0461 0.5790 2.2632
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.721984 0.303473 2.38 0.01758 *
## careertypeHT 0.215121 0.130760 1.65 0.10031
## careertypeIN 0.013593 0.078769 0.17 0.86304
## careertypeMX -0.016270 0.099173 -0.16 0.86973
## careertypeUN 0.303212 0.135644 2.24 0.02566 *
## age -0.011580 0.003327 -3.48 0.00053 ***
## education 0.001856 0.010629 0.17 0.86140
## sectorManufacturing -0.044767 0.169673 -0.26 0.79196
## sectorUtilities 0.362324 0.471735 0.77 0.44266
## sectorConstruction 0.553545 0.369010 1.50 0.13397
## sectorWholesale&retail -0.000116 0.169732 0.00 0.99945
## sectorHoreca 0.301097 0.237032 1.27 0.20434
## sectorTransport&communication -0.067525 0.252093 -0.27 0.78888
## sectorFinancial intermediation 0.061761 0.219110 0.28 0.77811
## sectorRestate, rent&business 0.140095 0.423048 0.33 0.74061
## sectorPublic administration -0.035728 0.203841 -0.18 0.86091
## sectorEducation 0.074070 0.180200 0.41 0.68115
## sectorHealth and social work 0.123387 0.174565 0.71 0.47987
## sectorOther 0.305439 0.182928 1.67 0.09535 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.877 on 834 degrees of freedom
## (433 observations deleted due to missingness)
## Multiple R-squared: 0.0472, Adjusted R-squared: 0.0266
## F-statistic: 2.29 on 18 and 834 DF, p-value: 0.00168
- Career achievements
##
## Call:
## lm(formula = achievements ~ careertype + age + education + sector,
## data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.218 -0.260 -0.088 0.614 1.240
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.27372 0.22358 -1.22 0.2212
## careertypeHT -0.12822 0.09634 -1.33 0.1836
## careertypeIN -0.10052 0.05804 -1.73 0.0836 .
## careertypeMX -0.00698 0.07307 -0.10 0.9239
## careertypeUN -0.31842 0.09994 -3.19 0.0015 **
## age 0.00383 0.00245 1.56 0.1190
## education 0.00573 0.00783 0.73 0.4648
## sectorManufacturing -0.08054 0.12500 -0.64 0.5195
## sectorUtilities 0.14028 0.34754 0.40 0.6866
## sectorConstruction 0.27063 0.27186 1.00 0.3198
## sectorWholesale&retail 0.02491 0.12505 0.20 0.8422
## sectorHoreca -0.15195 0.17463 -0.87 0.3845
## sectorTransport&communication 0.12184 0.18572 0.66 0.5120
## sectorFinancial intermediation 0.16407 0.16143 1.02 0.3097
## sectorRestate, rent&business -0.46136 0.31167 -1.48 0.1392
## sectorPublic administration 0.10319 0.15018 0.69 0.4922
## sectorEducation 0.16438 0.13281 1.24 0.2162
## sectorHealth and social work -0.05544 0.12861 -0.43 0.6665
## sectorOther -0.06626 0.13477 -0.49 0.6231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.646 on 833 degrees of freedom
## (434 observations deleted due to missingness)
## Multiple R-squared: 0.0569, Adjusted R-squared: 0.0366
## F-statistic: 2.79 on 18 and 833 DF, p-value: 9.61e-05
- Career sacrifices
##
## Call:
## lm(formula = sacrifices ~ careertype + age + education + sector,
## data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.458 -0.294 -0.051 0.214 2.153
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10693 0.26273 0.41 0.68412
## careertypeHT 0.05319 0.11317 0.47 0.63844
## careertypeIN -0.24359 0.06817 -3.57 0.00037 ***
## careertypeMX 0.01091 0.08583 0.13 0.89892
## careertypeUN -0.16612 0.11739 -1.42 0.15741
## age -0.00309 0.00288 -1.07 0.28418
## education 0.00357 0.00921 0.39 0.69869
## sectorManufacturing 0.10671 0.14682 0.73 0.46754
## sectorUtilities -0.10583 0.40819 -0.26 0.79550
## sectorConstruction 0.84177 0.31931 2.64 0.00854 **
## sectorWholesale&retail 0.09342 0.14687 0.64 0.52489
## sectorHoreca 0.39208 0.20511 1.91 0.05627 .
## sectorTransport&communication -0.02974 0.21814 -0.14 0.89160
## sectorFinancial intermediation 0.14548 0.18960 0.77 0.44313
## sectorRestate, rent&business -0.73789 0.36606 -2.02 0.04415 *
## sectorPublic administration 0.05591 0.17638 0.32 0.75133
## sectorEducation 0.21959 0.15607 1.41 0.15982
## sectorHealth and social work 0.10346 0.15105 0.68 0.49359
## sectorOther 0.06767 0.15829 0.43 0.66913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.759 on 833 degrees of freedom
## (434 observations deleted due to missingness)
## Multiple R-squared: 0.0579, Adjusted R-squared: 0.0375
## F-statistic: 2.84 on 18 and 833 DF, p-value: 7.2e-05
- Health suffered due to career
##
## Call:
## lm(formula = healthsuffered ~ careertype + age + education +
## sector, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8386 -0.6915 -0.0735 0.5463 2.3867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.98470 0.30665 3.21 0.0014 **
## careertypeHT 0.12155 0.13213 0.92 0.3579
## careertypeIN -0.36993 0.07959 -4.65 3.9e-06 ***
## careertypeMX -0.01425 0.10021 -0.14 0.8869
## careertypeUN 0.00452 0.13706 0.03 0.9737
## age -0.01526 0.00336 -4.54 6.5e-06 ***
## education -0.02227 0.01074 -2.07 0.0384 *
## sectorManufacturing 0.15597 0.17145 0.91 0.3632
## sectorUtilities 0.07130 0.47667 0.15 0.8811
## sectorConstruction 0.09907 0.37287 0.27 0.7905
## sectorWholesale&retail 0.14137 0.17151 0.82 0.4100
## sectorHoreca 0.56427 0.23951 2.36 0.0187 *
## sectorTransport&communication -0.05629 0.25473 -0.22 0.8252
## sectorFinancial intermediation -0.21276 0.22140 -0.96 0.3368
## sectorRestate, rent&business -0.11806 0.42747 -0.28 0.7825
## sectorPublic administration 0.08735 0.20597 0.42 0.6716
## sectorEducation 0.26342 0.18208 1.45 0.1484
## sectorHealth and social work 0.24226 0.17639 1.37 0.1700
## sectorOther 0.11967 0.18484 0.65 0.5175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.886 on 834 degrees of freedom
## (433 observations deleted due to missingness)
## Multiple R-squared: 0.0856, Adjusted R-squared: 0.0659
## F-statistic: 4.34 on 18 and 834 DF, p-value: 5.44e-09
MARITAL INDICATORS
- Number of marriages
##
## Call:
## lm(formula = marriages ~ careertype + age + education + sector,
## data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2234 -0.1210 -0.0570 0.0198 2.8101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.35630 0.08588 4.15 3.6e-05 ***
## careertypeHT 0.09155 0.04352 2.10 0.036 *
## careertypeIN 0.05350 0.03236 1.65 0.099 .
## careertypeMX 0.05135 0.04025 1.28 0.202
## careertypeUN -0.03856 0.05657 -0.68 0.496
## age -0.00574 0.00124 -4.62 4.3e-06 ***
## education -0.01123 0.00408 -2.75 0.006 **
## sector1 -0.00917 0.06704 -0.14 0.891
## sector2 0.41938 0.26859 1.56 0.119
## sector3 -0.02055 0.04153 -0.49 0.621
## sector4 -0.24847 0.14609 -1.70 0.089 .
## sector5 0.03739 0.12324 0.30 0.762
## sector6 0.01069 0.03994 0.27 0.789
## sector7 0.06611 0.07178 0.92 0.357
## sector8 -0.10029 0.08263 -1.21 0.225
## sector9 -0.11472 0.06118 -1.88 0.061 .
## sector10 -0.10342 0.14591 -0.71 0.479
## sector11 0.03227 0.05206 0.62 0.535
## sector12 -0.02944 0.04276 -0.69 0.491
## sector13 0.01943 0.04005 0.49 0.628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.407 on 1129 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.0446, Adjusted R-squared: 0.0285
## F-statistic: 2.77 on 19 and 1129 DF, p-value: 6.96e-05
- Number of children
##
## Call:
## lm(formula = children ~ careertype + age + education + sector,
## data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.756 -0.873 -0.138 0.584 9.705
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.52798 0.28437 -5.37 9.6e-08 ***
## careertypeHT -0.21874 0.14858 -1.47 0.1413
## careertypeIN 0.42990 0.10694 4.02 6.3e-05 ***
## careertypeMX 0.03235 0.13606 0.24 0.8121
## careertypeUN 0.52890 0.18550 2.85 0.0044 **
## age 0.01988 0.00416 4.78 2.0e-06 ***
## education -0.00590 0.01351 -0.44 0.6624
## sector1 0.49613 0.22041 2.25 0.0246 *
## sector2 -0.04997 0.83949 -0.06 0.9525
## sector3 0.00400 0.13668 0.03 0.9767
## sector4 -0.39229 0.53750 -0.73 0.4657
## sector5 0.20588 0.43045 0.48 0.6325
## sector6 0.00539 0.13264 0.04 0.9676
## sector7 0.13465 0.23186 0.58 0.5615
## sector8 -0.20739 0.26977 -0.77 0.4422
## sector9 -0.00780 0.20876 -0.04 0.9702
## sector10 -0.48846 0.49177 -0.99 0.3208
## sector11 -0.07251 0.17402 -0.42 0.6770
## sector12 0.17833 0.14158 1.26 0.2081
## sector13 0.06832 0.13275 0.51 0.6069
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.27 on 1000 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.0857, Adjusted R-squared: 0.0683
## F-statistic: 4.93 on 19 and 1000 DF, p-value: 2.88e-11
- Number of divorces
##
## Call:
## lm(formula = divorces ~ careertype + age + education + sector,
## data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.617 -0.240 -0.148 -0.041 3.744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.48186 0.09392 5.13 3.4e-07 ***
## careertypeHT 0.18252 0.04760 3.83 0.00013 ***
## careertypeIN -0.01026 0.03539 -0.29 0.77191
## careertypeMX 0.02795 0.04402 0.64 0.52556
## careertypeUN 0.08852 0.06188 1.43 0.15281
## age -0.00747 0.00136 -5.50 4.7e-08 ***
## education -0.00490 0.00446 -1.10 0.27235
## sector1 -0.01052 0.07332 -0.14 0.88590
## sector2 0.26936 0.29376 0.92 0.35937
## sector3 -0.04826 0.04542 -1.06 0.28828
## sector4 -0.14119 0.15978 -0.88 0.37706
## sector5 0.06767 0.13479 0.50 0.61574
## sector6 -0.01073 0.04368 -0.25 0.80601
## sector7 0.05747 0.07850 0.73 0.46427
## sector8 -0.10504 0.09037 -1.16 0.24536
## sector9 -0.09197 0.06691 -1.37 0.16955
## sector10 -0.23998 0.15958 -1.50 0.13289
## sector11 0.13334 0.05694 2.34 0.01937 *
## sector12 -0.00948 0.04677 -0.20 0.83937
## sector13 0.03251 0.04381 0.74 0.45822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.445 on 1129 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.0701, Adjusted R-squared: 0.0545
## F-statistic: 4.48 on 19 and 1129 DF, p-value: 6.32e-10
FINANCIALS
- Wage Main Job
##
## Call:
## lm(formula = WageMain ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -1237 -219 -40 227 6304
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1989.99 433.06 4.60 6.4e-06 ***
## careertypeHT 427.60 136.54 3.13 0.00191 **
## careertypeIN -481.83 179.04 -2.69 0.00753 **
## careertypeMX -242.62 119.15 -2.04 0.04261 *
## careertypeUN -187.09 188.30 -0.99 0.32124
## age -14.82 3.87 -3.82 0.00016 ***
## education 28.87 10.80 2.67 0.00792 **
## sectorManufacturing 122.88 307.07 0.40 0.68933
## sectorUtilities 225.30 457.96 0.49 0.62312
## sectorConstruction -46.83 453.03 -0.10 0.91774
## sectorWholesale&retail 52.02 306.59 0.17 0.86539
## sectorHoreca -38.90 339.60 -0.11 0.90888
## sectorTransport&communication 235.97 348.19 0.68 0.49850
## sectorFinancial intermediation 330.41 337.65 0.98 0.32860
## sectorRestate, rent&business -276.87 456.45 -0.61 0.54461
## sectorPublic administration 242.00 326.13 0.74 0.45867
## sectorEducation 114.24 312.36 0.37 0.71484
## sectorHealth and social work 283.69 310.74 0.91 0.36201
## sectorOther 162.15 311.63 0.52 0.60323
## divorces 24.44 80.22 0.30 0.76088
## children -22.28 27.73 -0.80 0.42244
## activecareer 12.95 5.82 2.22 0.02688 *
## voluntary 70.15 106.50 0.66 0.51059
## satisfaction 84.03 43.23 1.94 0.05289 .
## disappointment 44.26 40.14 1.10 0.27110
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 576 on 294 degrees of freedom
## (967 observations deleted due to missingness)
## Multiple R-squared: 0.478, Adjusted R-squared: 0.435
## F-statistic: 11.2 on 24 and 294 DF, p-value: <2e-16
- Last Salary
##
## Call:
## lm(formula = lastsalary ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.845 -1.530 -0.276 1.574 6.487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.64694 1.74593 3.23 0.0014 **
## careertypeHT 1.08364 0.54452 1.99 0.0475 *
## careertypeIN -1.62877 0.71654 -2.27 0.0237 *
## careertypeMX 0.76364 0.53353 1.43 0.1534
## careertypeUN 1.00727 0.77478 1.30 0.1946
## age -0.08283 0.01949 -4.25 2.9e-05 ***
## education 0.08098 0.04617 1.75 0.0805 .
## sectorManufacturing -0.92499 1.02887 -0.90 0.3694
## sectorUtilities -2.72295 1.73231 -1.57 0.1171
## sectorConstruction -1.99381 2.62600 -0.76 0.4483
## sectorWholesale&retail -0.33203 1.02323 -0.32 0.7458
## sectorHoreca -0.87611 1.25662 -0.70 0.4862
## sectorTransport&communication 0.56047 1.23953 0.45 0.6515
## sectorFinancial intermediation -0.60574 1.17022 -0.52 0.6051
## sectorRestate, rent&business -1.21103 1.96464 -0.62 0.5381
## sectorPublic administration 0.49201 1.12600 0.44 0.6625
## sectorEducation 0.70524 1.06945 0.66 0.5101
## sectorHealth and social work 0.98653 1.03104 0.96 0.3394
## sectorOther 0.67579 1.06766 0.63 0.5272
## divorces 0.77919 0.37882 2.06 0.0406 *
## children 0.04332 0.10674 0.41 0.6851
## activecareer -0.00552 0.02352 -0.23 0.8144
## voluntary -0.13065 0.45855 -0.28 0.7759
## satisfaction 0.06574 0.19278 0.34 0.7333
## disappointment -0.06559 0.18189 -0.36 0.7186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.35 on 294 degrees of freedom
## (967 observations deleted due to missingness)
## Multiple R-squared: 0.363, Adjusted R-squared: 0.311
## F-statistic: 6.99 on 24 and 294 DF, p-value: <2e-16
- Home ownership
## Error: contrasts can be applied only to factors with 2 or more levels
##
## Call:
## lm(formula = lastsalary ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.845 -1.530 -0.276 1.574 6.487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.64694 1.74593 3.23 0.0014 **
## careertypeHT 1.08364 0.54452 1.99 0.0475 *
## careertypeIN -1.62877 0.71654 -2.27 0.0237 *
## careertypeMX 0.76364 0.53353 1.43 0.1534
## careertypeUN 1.00727 0.77478 1.30 0.1946
## age -0.08283 0.01949 -4.25 2.9e-05 ***
## education 0.08098 0.04617 1.75 0.0805 .
## sectorManufacturing -0.92499 1.02887 -0.90 0.3694
## sectorUtilities -2.72295 1.73231 -1.57 0.1171
## sectorConstruction -1.99381 2.62600 -0.76 0.4483
## sectorWholesale&retail -0.33203 1.02323 -0.32 0.7458
## sectorHoreca -0.87611 1.25662 -0.70 0.4862
## sectorTransport&communication 0.56047 1.23953 0.45 0.6515
## sectorFinancial intermediation -0.60574 1.17022 -0.52 0.6051
## sectorRestate, rent&business -1.21103 1.96464 -0.62 0.5381
## sectorPublic administration 0.49201 1.12600 0.44 0.6625
## sectorEducation 0.70524 1.06945 0.66 0.5101
## sectorHealth and social work 0.98653 1.03104 0.96 0.3394
## sectorOther 0.67579 1.06766 0.63 0.5272
## divorces 0.77919 0.37882 2.06 0.0406 *
## children 0.04332 0.10674 0.41 0.6851
## activecareer -0.00552 0.02352 -0.23 0.8144
## voluntary -0.13065 0.45855 -0.28 0.7759
## satisfaction 0.06574 0.19278 0.34 0.7333
## disappointment -0.06559 0.18189 -0.36 0.7186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.35 on 294 degrees of freedom
## (967 observations deleted due to missingness)
## Multiple R-squared: 0.363, Adjusted R-squared: 0.311
## F-statistic: 6.99 on 24 and 294 DF, p-value: <2e-16
CAREER ACTIVITY
- Retirement age
##
## Call:
## lm(formula = AgePension ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.396 -1.294 0.316 1.654 13.092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58.026314 1.826210 31.77 < 2e-16 ***
## careertypeHT 3.217309 0.688795 4.67 4.0e-06 ***
## careertypeIN 9.959827 0.848062 11.74 < 2e-16 ***
## careertypeMX 5.827389 0.629690 9.25 < 2e-16 ***
## careertypeUN 6.744486 0.916509 7.36 8.7e-13 ***
## age -0.000802 0.021773 -0.04 0.9706
## education 0.038257 0.055991 0.68 0.4948
## sectorManufacturing -0.717992 0.809952 -0.89 0.3758
## sectorUtilities -2.008362 2.195960 -0.91 0.3609
## sectorConstruction -8.157382 3.630410 -2.25 0.0251 *
## sectorWholesale&retail 0.677701 0.819259 0.83 0.4086
## sectorHoreca 0.157827 1.246709 0.13 0.8993
## sectorTransport&communication -2.094359 1.288397 -1.63 0.1047
## sectorFinancial intermediation 0.345842 1.148284 0.30 0.7634
## sectorRestate, rent&business 2.111644 2.591266 0.81 0.4156
## sectorPublic administration -1.845928 0.997253 -1.85 0.0648 .
## sectorEducation -0.490214 0.904606 -0.54 0.5881
## sectorHealth and social work 0.221072 0.853415 0.26 0.7957
## sectorOther 0.827802 0.883057 0.94 0.3490
## divorces -0.703874 0.445994 -1.58 0.1152
## children 0.290574 0.113695 2.56 0.0109 *
## activecareer 0.188476 0.026923 7.00 9.2e-12 ***
## voluntary -0.904990 0.529629 -1.71 0.0882 .
## satisfaction -0.613071 0.232170 -2.64 0.0086 **
## disappointment -0.273351 0.203952 -1.34 0.1808
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.46 on 455 degrees of freedom
## (806 observations deleted due to missingness)
## Multiple R-squared: 0.404, Adjusted R-squared: 0.372
## F-statistic: 12.8 on 24 and 455 DF, p-value: <2e-16
- Career active years
##
## Call:
## lm(formula = activecareer ~ careertype + age + education + sector +
## divorces + children + voluntary + satisfaction + disappointment,
## data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.364 -3.602 -0.168 3.675 23.676
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.2643 2.3644 0.96 0.339
## careertypeHT 0.0463 1.0472 0.04 0.965
## careertypeIN -26.9416 0.6417 -41.98 < 2e-16 ***
## careertypeMX -10.8169 0.7721 -14.01 < 2e-16 ***
## careertypeUN -20.5021 1.0496 -19.53 < 2e-16 ***
## age 0.0449 0.0262 1.72 0.087 .
## education -0.1378 0.0821 -1.68 0.094 .
## sectorManufacturing -2.3676 1.3157 -1.80 0.072 .
## sectorUtilities 1.0984 3.7476 0.29 0.770
## sectorConstruction -4.4612 3.0091 -1.48 0.139
## sectorWholesale&retail -1.9652 1.3227 -1.49 0.138
## sectorHoreca -1.6484 1.7651 -0.93 0.351
## sectorTransport&communication -1.5626 1.8603 -0.84 0.401
## sectorFinancial intermediation -3.0477 1.7150 -1.78 0.076 .
## sectorRestate, rent&business -6.0741 3.2698 -1.86 0.064 .
## sectorPublic administration -2.1987 1.5931 -1.38 0.168
## sectorEducation -2.3028 1.4708 -1.57 0.118
## sectorHealth and social work -2.2463 1.3709 -1.64 0.102
## sectorOther -0.0256 1.4132 -0.02 0.986
## divorces -0.6828 0.5353 -1.28 0.203
## children -0.8962 0.1733 -5.17 3.1e-07 ***
## voluntary 1.3918 0.7592 1.83 0.067 .
## satisfaction 0.3661 0.3269 1.12 0.263
## disappointment -0.0715 0.2882 -0.25 0.804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6 on 646 degrees of freedom
## (616 observations deleted due to missingness)
## Multiple R-squared: 0.81, Adjusted R-squared: 0.803
## F-statistic: 120 on 23 and 646 DF, p-value: <2e-16
- Career length
##
## Call:
## lm(formula = lastsalary ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.845 -1.530 -0.276 1.574 6.487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.64694 1.74593 3.23 0.0014 **
## careertypeHT 1.08364 0.54452 1.99 0.0475 *
## careertypeIN -1.62877 0.71654 -2.27 0.0237 *
## careertypeMX 0.76364 0.53353 1.43 0.1534
## careertypeUN 1.00727 0.77478 1.30 0.1946
## age -0.08283 0.01949 -4.25 2.9e-05 ***
## education 0.08098 0.04617 1.75 0.0805 .
## sectorManufacturing -0.92499 1.02887 -0.90 0.3694
## sectorUtilities -2.72295 1.73231 -1.57 0.1171
## sectorConstruction -1.99381 2.62600 -0.76 0.4483
## sectorWholesale&retail -0.33203 1.02323 -0.32 0.7458
## sectorHoreca -0.87611 1.25662 -0.70 0.4862
## sectorTransport&communication 0.56047 1.23953 0.45 0.6515
## sectorFinancial intermediation -0.60574 1.17022 -0.52 0.6051
## sectorRestate, rent&business -1.21103 1.96464 -0.62 0.5381
## sectorPublic administration 0.49201 1.12600 0.44 0.6625
## sectorEducation 0.70524 1.06945 0.66 0.5101
## sectorHealth and social work 0.98653 1.03104 0.96 0.3394
## sectorOther 0.67579 1.06766 0.63 0.5272
## divorces 0.77919 0.37882 2.06 0.0406 *
## children 0.04332 0.10674 0.41 0.6851
## activecareer -0.00552 0.02352 -0.23 0.8144
## voluntary -0.13065 0.45855 -0.28 0.7759
## satisfaction 0.06574 0.19278 0.34 0.7333
## disappointment -0.06559 0.18189 -0.36 0.7186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.35 on 294 degrees of freedom
## (967 observations deleted due to missingness)
## Multiple R-squared: 0.363, Adjusted R-squared: 0.311
## F-statistic: 6.99 on 24 and 294 DF, p-value: <2e-16
Experimenting with health
- Health general (W2)
##
## Call:
## lm(formula = w2health ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1198 -0.6147 0.0343 0.7092 2.4528
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.97216 0.39147 10.15 <2e-16 ***
## careertypeHT -0.23991 0.17326 -1.38 0.1666
## careertypeIN -0.37773 0.20501 -1.84 0.0659 .
## careertypeMX -0.16969 0.14587 -1.16 0.2451
## careertypeUN -0.28682 0.21902 -1.31 0.1908
## age 0.01322 0.00434 3.05 0.0024 **
## education -0.05498 0.01361 -4.04 6e-05 ***
## sectorManufacturing 0.28214 0.21823 1.29 0.1965
## sectorUtilities 0.37146 0.62008 0.60 0.5493
## sectorConstruction 0.07641 0.49869 0.15 0.8783
## sectorWholesale&retail 0.12978 0.21921 0.59 0.5540
## sectorHoreca 0.05996 0.29223 0.21 0.8375
## sectorTransport&communication -0.02494 0.30795 -0.08 0.9355
## sectorFinancial intermediation 0.56349 0.28443 1.98 0.0480 *
## sectorRestate, rent&business 0.59462 0.54242 1.10 0.2734
## sectorPublic administration 0.03660 0.26396 0.14 0.8898
## sectorEducation 0.25466 0.24380 1.04 0.2966
## sectorHealth and social work 0.13757 0.22728 0.61 0.5452
## sectorOther 0.24419 0.23381 1.04 0.2967
## divorces 0.25507 0.08867 2.88 0.0042 **
## children -0.02356 0.02926 -0.81 0.4210
## activecareer -0.01232 0.00651 -1.89 0.0588 .
## voluntary -0.02125 0.12593 -0.17 0.8661
## satisfaction -0.12176 0.05413 -2.25 0.0248 *
## disappointment 0.06595 0.04769 1.38 0.1672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.992 on 645 degrees of freedom
## (616 observations deleted due to missingness)
## Multiple R-squared: 0.0915, Adjusted R-squared: 0.0577
## F-statistic: 2.71 on 24 and 645 DF, p-value: 2.41e-05
- Long-term ilness (W2)
##
## Call:
## lm(formula = w2ilness ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.43e-16 -1.76e-17 2.00e-19 1.35e-17 2.71e-15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.00e+00 9.28e-17 3.23e+16 < 2e-16 ***
## careertypeHT -5.31e-18 4.67e-17 -1.10e-01 0.90949
## careertypeIN 3.93e-17 5.49e-17 7.20e-01 0.47467
## careertypeMX 7.99e-18 3.91e-17 2.00e-01 0.83838
## careertypeUN 2.89e-17 5.90e-17 4.90e-01 0.62427
## age -4.01e-20 1.11e-18 -4.00e-02 0.97122
## education 5.51e-19 3.46e-18 1.60e-01 0.87355
## sectorManufacturing -5.48e-18 4.94e-17 -1.10e-01 0.91170
## sectorUtilities 2.20e-17 1.81e-16 1.20e-01 0.90331
## sectorConstruction 5.32e-19 1.32e-16 0.00e+00 0.99677
## sectorWholesale&retail -2.79e-18 4.86e-17 -6.00e-02 0.95434
## sectorHoreca -7.45e-18 7.32e-17 -1.00e-01 0.91900
## sectorTransport&communication -5.54e-18 7.00e-17 -8.00e-02 0.93691
## sectorFinancial intermediation -3.70e-18 6.17e-17 -6.00e-02 0.95223
## sectorRestate, rent&business -2.53e-18 1.85e-16 -1.00e-02 0.98911
## sectorPublic administration 2.16e-16 6.34e-17 3.41e+00 0.00074 ***
## sectorEducation 9.99e-18 5.59e-17 1.80e-01 0.85841
## sectorHealth and social work -5.49e-19 5.03e-17 -1.00e-02 0.99130
## sectorOther -4.64e-18 5.18e-17 -9.00e-02 0.92872
## divorces -1.27e-17 2.06e-17 -6.20e-01 0.53712
## children 1.58e-18 7.06e-18 2.20e-01 0.82298
## activecareer 2.36e-19 1.76e-18 1.30e-01 0.89348
## voluntary 1.18e-17 3.29e-17 3.60e-01 0.71880
## satisfaction 6.84e-19 1.43e-17 5.00e-02 0.96199
## disappointment -4.72e-19 1.20e-17 -4.00e-02 0.96857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.69e-16 on 279 degrees of freedom
## (982 observations deleted due to missingness)
## Multiple R-squared: 0.491, Adjusted R-squared: 0.447
## F-statistic: 11.2 on 24 and 279 DF, p-value: <2e-16
- Problems limiting work (W2)
##
## Call:
## lm(formula = w2worklim ~ careertype + age + education + sector +
## divorces + children + activecareer + voluntary + satisfaction +
## disappointment, data = mydata, subset = gender == "female")
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9210 -0.0084 0.0047 0.0177 0.0798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.089408 0.065535 47.14 <2e-16 ***
## careertypeHT 0.006185 0.036749 0.17 0.867
## careertypeIN 0.023945 0.039313 0.61 0.544
## careertypeMX 0.018678 0.025316 0.74 0.462
## careertypeUN 0.042678 0.041145 1.04 0.302
## age -0.001443 0.000774 -1.86 0.065 .
## education -0.004101 0.002558 -1.60 0.111
## sectorManufacturing -0.022775 0.043168 -0.53 0.599
## sectorWholesale&retail -0.000798 0.044141 -0.02 0.986
## sectorHoreca -0.008569 0.053301 -0.16 0.873
## sectorTransport&communication -0.009176 0.059030 -0.16 0.877
## sectorFinancial intermediation 0.019998 0.055101 0.36 0.717
## sectorRestate, rent&business -0.072881 0.102501 -0.71 0.478
## sectorPublic administration 0.006474 0.047008 0.14 0.891
## sectorEducation 0.024686 0.048923 0.50 0.615
## sectorHealth and social work 0.011439 0.043865 0.26 0.795
## sectorOther 0.004408 0.043947 0.10 0.920
## divorces -0.004325 0.012911 -0.34 0.738
## children 0.002336 0.004901 0.48 0.634
## activecareer 0.001072 0.001174 0.91 0.363
## voluntary 0.016647 0.024567 0.68 0.499
## satisfaction 0.013674 0.010000 1.37 0.174
## disappointment 0.006684 0.008071 0.83 0.409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0835 on 132 degrees of freedom
## (1131 observations deleted due to missingness)
## Multiple R-squared: 0.073, Adjusted R-squared: -0.0815
## F-statistic: 0.473 on 22 and 132 DF, p-value: 0.978