Also available in Stata and Python versions
Load libraries
library(wooldridge)
library(AER)
library(stargazer)
library(pglm)
library(sandwich)
library(mfx)
library(haven)
library(dplyr)
#library(lme4)
library(GJRM)Married Women´s Labor Force Participation
lpm <- lm(inlf ~ nwifeinc + exper + expersq + educ + age + kidslt6 + kidsge6, data=mroz)
lpmr <- coeftest(lpm, vcovHC(lpm, type="HC1"))
stargazer(lpm, lpmr, column.labels=c("LPM", "LPM_robust"), no.space=TRUE, type="text")##
## =======================================================
## Dependent variable:
## -----------------------------------
## inlf
## OLS coefficient
## test
## LPM LPM
## (1) (2)
## -------------------------------------------------------
## nwifeinc -0.003** -0.003**
## (0.001) (0.002)
## exper 0.039*** 0.039***
## (0.006) (0.006)
## expersq -0.001*** -0.001***
## (0.0002) (0.0002)
## educ 0.038*** 0.038***
## (0.007) (0.007)
## age -0.016*** -0.016***
## (0.002) (0.002)
## kidslt6 -0.262*** -0.262***
## (0.034) (0.032)
## kidsge6 0.013 0.013
## (0.013) (0.014)
## Constant 0.586*** 0.586***
## (0.154) (0.152)
## -------------------------------------------------------
## Observations 753
## R2 0.264
## Adjusted R2 0.257
## Residual Std. Error 0.427 (df = 745)
## F Statistic 38.218*** (df = 7; 745)
## =======================================================
## Note: *p<0.1; **p<0.05; ***p<0.01Married Women´s Labor Force Participation
LPM <- lm(inlf ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, data = mroz)
Logit <- glm(inlf ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, family=binomial(link="logit"), data = mroz)
Probit <- glm(inlf ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6, family=binomial(link="probit"), data = mroz)
stargazer(LPM, Logit, Probit, no.space=TRUE, type="text", title = "Table 15.1 LPM, Logit, and Probit Estimates of Labor Force Participation: (inlf)")##
## Table 15.1 LPM, Logit, and Probit Estimates of Labor Force Participation: (inlf)
## ===============================================================
## Dependent variable:
## -------------------------------------------
## inlf
## OLS logistic probit
## (1) (2) (3)
## ---------------------------------------------------------------
## nwifeinc -0.003** -0.021** -0.012**
## (0.001) (0.008) (0.005)
## educ 0.038*** 0.221*** 0.131***
## (0.007) (0.043) (0.025)
## exper 0.039*** 0.206*** 0.123***
## (0.006) (0.032) (0.019)
## expersq -0.001*** -0.003*** -0.002***
## (0.0002) (0.001) (0.001)
## age -0.016*** -0.088*** -0.053***
## (0.002) (0.015) (0.008)
## kidslt6 -0.262*** -1.443*** -0.868***
## (0.034) (0.204) (0.118)
## kidsge6 0.013 0.060 0.036
## (0.013) (0.075) (0.044)
## Constant 0.586*** 0.425 0.270
## (0.154) (0.860) (0.508)
## ---------------------------------------------------------------
## Observations 753 753 753
## R2 0.264
## Adjusted R2 0.257
## Log Likelihood -401.765 -401.302
## Akaike Inf. Crit. 819.530 818.604
## Residual Std. Error 0.427 (df = 745)
## F Statistic 38.218*** (df = 7; 745)
## ===============================================================
## Note: *p<0.1; **p<0.05; ***p<0.01Testing Exogeniety of Education in the Women´s LFP Model
v2 <- resid(lm(educ ~ nwifeinc + exper + expersq + age + kidslt6 + kidsge6 + motheduc + fatheduc + huseduc, data=mroz))
summary(Probit <- glm(inlf ~ nwifeinc + educ + exper + expersq + age + kidslt6 + kidsge6 + v2, family=binomial(link="probit"), data = mroz))##
## Call:
## glm(formula = inlf ~ nwifeinc + educ + exper + expersq + age +
## kidslt6 + kidsge6 + v2, family = binomial(link = "probit"),
## data = mroz)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2341 -0.9134 0.4252 0.8611 2.3711
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6209080 0.6507213 0.954 0.33999
## nwifeinc -0.0102850 0.0053524 -1.922 0.05466 .
## educ 0.1035744 0.0406773 2.546 0.01089 *
## exper 0.1262473 0.0190483 6.628 3.41e-11 ***
## expersq -0.0019431 0.0006023 -3.226 0.00126 **
## age -0.0543806 0.0086517 -6.286 3.27e-10 ***
## kidslt6 -0.8630815 0.1185043 -7.283 3.26e-13 ***
## kidsge6 0.0313809 0.0443001 0.708 0.47872
## v2 0.0433656 0.0502575 0.863 0.38821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1029.75 on 752 degrees of freedom
## Residual deviance: 801.85 on 744 degrees of freedom
## AIC: 819.85
##
## Number of Fisher Scoring iterations: 4Endogeniety of Nonwife income in the Women´s LFP Model
v2 <- resid(nwife <- lm(nwifeinc ~ huseduc + exper + expersq + educ + age + kidslt6 + kidsge6, data = mroz))
coeftest(nwife, vcovHC(nwife, type="HC1"))##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4720e+01 3.6161e+00 -4.0708 5.186e-05 ***
## huseduc 1.1782e+00 1.7019e-01 6.9224 9.588e-12 ***
## exper -3.1299e-01 1.3616e-01 -2.2987 0.021798 *
## expersq -4.7756e-04 3.9865e-03 -0.1198 0.904678
## educ 6.7470e-01 2.5156e-01 2.6821 0.007479 **
## age 3.4015e-01 5.4086e-02 6.2891 5.440e-10 ***
## kidslt6 8.2627e-01 1.0441e+00 0.7913 0.428994
## kidsge6 4.3553e-01 2.8859e-01 1.5091 0.131686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(Probit <- glm(inlf ~ nwifeinc+ exper + expersq + educ + age + kidslt6 + kidsge6 + v2, family=binomial(link="probit"), data = mroz))##
## Call:
## glm(formula = inlf ~ nwifeinc + exper + expersq + educ + age +
## kidslt6 + kidsge6 + v2, family = binomial(link = "probit"),
## data = mroz)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2523 -0.9078 0.4204 0.8566 2.2803
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0171183 0.5380339 0.032 0.97462
## nwifeinc -0.0368639 0.0183848 -2.005 0.04495 *
## exper 0.1163118 0.0193869 6.000 1.98e-09 ***
## expersq -0.0019458 0.0005999 -3.244 0.00118 **
## educ 0.1702142 0.0377615 4.508 6.56e-06 ***
## age -0.0449529 0.0101351 -4.435 9.19e-06 ***
## kidslt6 -0.8444319 0.1197268 -7.053 1.75e-12 ***
## kidsge6 0.0477912 0.0449431 1.063 0.28761
## v2 0.0267092 0.0191539 1.394 0.16318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1029.75 on 752 degrees of freedom
## Residual deviance: 800.61 on 744 degrees of freedom
## AIC: 818.61
##
## Number of Fisher Scoring iterations: 4probitmfx(Probit, data = mroz, atmean = F)## Call:
## probitmfx(formula = Probit, data = mroz, atmean = F)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## nwifeinc -0.01105760 0.00547445 -2.0199 0.0433983 *
## exper 0.03488859 0.00541166 6.4469 1.141e-10 ***
## expersq -0.00058367 0.00017639 -3.3089 0.0009365 ***
## educ 0.05105699 0.01090377 4.6825 2.834e-06 ***
## age -0.01348394 0.00293039 -4.6014 4.196e-06 ***
## kidslt6 -0.25329353 0.03248353 -7.7976 6.310e-15 ***
## kidsge6 0.01433531 0.01345082 1.0658 0.2865334
## v2 0.00801162 0.00572509 1.3994 0.1616970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Women’s Labor Force Participation and Having More than Two Children
df <- read.csv("labsup.csv")
df$agesq <- df$age*df$age
summary(LPM_OLS <- lm(worked ~ morekids + nonmomi + educ + age + agesq + black + hispan, data=df))##
## Call:
## lm(formula = worked ~ morekids + nonmomi + educ + age + agesq +
## black + hispan, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9455 -0.5002 0.2280 0.3924 0.9112
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4486450 0.1649612 -2.720 0.006538 **
## morekids -0.1091198 0.0054742 -19.933 < 2e-16 ***
## nonmomi -0.0011675 0.0001354 -8.625 < 2e-16 ***
## educ 0.0206475 0.0009053 22.808 < 2e-16 ***
## age 0.0562704 0.0112458 5.004 5.65e-07 ***
## agesq -0.0007829 0.0001932 -4.052 5.10e-05 ***
## black 0.0176482 0.0338829 0.521 0.602469
## hispan -0.1285859 0.0339362 -3.789 0.000152 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4714 on 31849 degrees of freedom
## Multiple R-squared: 0.08175, Adjusted R-squared: 0.08154
## F-statistic: 405 on 7 and 31849 DF, p-value: < 2.2e-16summary(Probit <- glm(worked ~ morekids + nonmomi + educ + age + agesq + black + hispan, family=binomial(link="probit"), data=df))##
## Call:
## glm(formula = worked ~ morekids + nonmomi + educ + age + agesq +
## black + hispan, family = binomial(link = "probit"), data = df)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0869 -1.1769 0.7224 0.9868 2.0069
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.4964755 0.4520026 -5.523 3.33e-08 ***
## morekids -0.2986554 0.0150534 -19.840 < 2e-16 ***
## nonmomi -0.0031412 0.0003719 -8.446 < 2e-16 ***
## educ 0.0554282 0.0025044 22.132 < 2e-16 ***
## age 0.1479387 0.0308659 4.793 1.64e-06 ***
## agesq -0.0020364 0.0005309 -3.836 0.000125 ***
## black 0.0412891 0.0915437 0.451 0.651966
## hispan -0.3586388 0.0917648 -3.908 9.30e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 43130 on 31856 degrees of freedom
## Residual deviance: 40436 on 31849 degrees of freedom
## AIC: 40452
##
## Number of Fisher Scoring iterations: 4probitmfx(Probit, data=df, atmean = F)## Call:
## probitmfx(formula = Probit, data = df, atmean = F)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## morekids -0.10946602 0.00550420 -19.8877 < 2.2e-16 ***
## nonmomi -0.00113875 0.00013437 -8.4745 < 2.2e-16 ***
## educ 0.02009384 0.00088671 22.6611 < 2.2e-16 ***
## age 0.05363076 0.01117731 4.7982 1.601e-06 ***
## agesq -0.00073825 0.00019231 -3.8389 0.0001236 ***
## black 0.01502517 0.03343145 0.4494 0.6531200
## hispan -0.13215805 0.03374329 -3.9166 8.982e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## dF/dx is for discrete change for the following variables:
##
## [1] "morekids" "black" "hispan"summary(LPM_IV <- ivreg(worked ~ morekids + nonmomi + educ + age + agesq + black + hispan | samesex + nonmomi + educ + age + agesq + black + hispan, data=df))##
## Call:
## ivreg(formula = worked ~ morekids + nonmomi + educ + age + agesq +
## black + hispan | samesex + nonmomi + educ + age + agesq +
## black + hispan, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9713 -0.4784 0.1867 0.3907 0.9460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4549690 0.1658195 -2.744 0.006077 **
## morekids -0.2008320 0.0964721 -2.082 0.037372 *
## nonmomi -0.0012600 0.0001671 -7.541 4.78e-14 ***
## educ 0.0175522 0.0033755 5.200 2.01e-07 ***
## age 0.0603517 0.0120811 4.996 5.90e-07 ***
## agesq -0.0008178 0.0001975 -4.140 3.48e-05 ***
## black 0.0168118 0.0340432 0.494 0.621424
## hispan -0.1308112 0.0341655 -3.829 0.000129 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4735 on 31849 degrees of freedom
## Multiple R-Squared: 0.07365, Adjusted R-squared: 0.07345
## Wald test: 345.9 on 7 and 31849 DF, p-value: < 2.2e-16# Bivariate probit
eq1 <- worked ~ morekids + nonmomi + educ + age + agesq + black + hispan
eq2 <- morekids ~ samesex + nonmomi + educ + age + agesq + black + hispan
f.list <- list(eq1, eq2)
mr <- c("probit", "probit")
# library(GJRM)
summary(biprobit <- gjrm(f.list, data=df, Model="B", margins=mr))##
## COPULA: Gaussian
## MARGIN 1: Bernoulli
## MARGIN 2: Bernoulli
##
## EQUATION 1
## Link function for mu.1: probit
## Formula: worked ~ morekids + nonmomi + educ + age + agesq + black + hispan
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.4753259 0.4496307 -5.505 3.69e-08 ***
## morekids -0.7024746 0.2040666 -3.442 0.000577 ***
## nonmomi -0.0034902 0.0003950 -8.836 < 2e-16 ***
## educ 0.0405659 0.0085401 4.750 2.03e-06 ***
## age 0.1632221 0.0312418 5.224 1.75e-07 ***
## agesq -0.0021523 0.0005277 -4.078 4.53e-05 ***
## black 0.0367334 0.0910000 0.404 0.686460
## hispan -0.3614829 0.0912099 -3.963 7.40e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## EQUATION 2
## Link function for mu.2: probit
## Formula: morekids ~ samesex + nonmomi + educ + age + agesq + black + hispan
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5725583 0.4514336 -3.483 0.000495 ***
## samesex 0.1446575 0.0144320 10.023 < 2e-16 ***
## nonmomi -0.0027063 0.0003685 -7.345 2.06e-13 ***
## educ -0.0907147 0.0024968 -36.333 < 2e-16 ***
## age 0.1190243 0.0307613 3.869 0.000109 ***
## agesq -0.0010280 0.0005284 -1.946 0.051693 .
## black -0.0277802 0.0921479 -0.301 0.763053
## hispan -0.0690522 0.0922843 -0.748 0.454306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## n = 31857 theta = 0.254(0.0345,0.432) tau = 0.164(0.022,0.284)
## total edf = 17Panel Data Models for Women’s Labor Force Participation
#library(haven)
lfp <- read_dta("lfp.dta")
lfpP <- pdata.frame(lfp, index = c("id", "period"))
summary(FE_Linear <- plm(lfp ~ kids + lhinc + educ + black + age + agesq + factor(period), data=lfpP, model = "within"))## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = lfp ~ kids + lhinc + educ + black + age + agesq +
## factor(period), data = lfpP, model = "within")
##
## Balanced Panel: n = 5663, T = 5, N = 28315
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.8410962 -0.0079438 0.0015233 0.0080867 0.8438588
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## kids -0.0388976 0.0060283 -6.4525 1.123e-10 ***
## lhinc -0.0089439 0.0039088 -2.2882 0.022138 *
## factor(period)2 -0.0042799 0.0040162 -1.0657 0.286590
## factor(period)3 -0.0108953 0.0040151 -2.7136 0.006662 **
## factor(period)4 -0.0123002 0.0040174 -3.0618 0.002203 **
## factor(period)5 -0.0176797 0.0040152 -4.4032 1.072e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1036.8
## Residual Sum of Squares: 1033.6
## R-Squared: 0.0031198
## Adj. R-Squared: -0.24639
## F-statistic: 11.8119 on 6 and 22646 DF, p-value: 2.8458e-13summary(Probit_Pooled <- glm(lfp ~ kids + lhinc + educ + black + age + agesq + factor(period), data=lfpP, family=binomial(link="probit")))##
## Call:
## glm(formula = lfp ~ kids + lhinc + educ + black + age + agesq +
## factor(period), family = binomial(link = "probit"), data = lfpP)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0040 -1.2284 0.6943 0.8688 2.7039
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.064e+00 1.373e-01 -7.751 9.13e-15 ***
## kids -1.989e-01 7.549e-03 -26.348 < 2e-16 ***
## lhinc -2.111e-01 1.330e-02 -15.870 < 2e-16 ***
## educ 7.969e-02 3.218e-03 24.763 < 2e-16 ***
## black 2.209e-01 3.348e-02 6.600 4.11e-11 ***
## age 1.449e-01 6.158e-03 23.533 < 2e-16 ***
## agesq -1.991e-03 7.576e-05 -26.284 < 2e-16 ***
## factor(period)2 -1.242e-02 2.535e-02 -0.490 0.6241
## factor(period)3 -3.252e-02 2.530e-02 -1.285 0.1987
## factor(period)4 -4.610e-02 2.530e-02 -1.822 0.0684 .
## factor(period)5 -5.778e-02 2.525e-02 -2.288 0.0221 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 35418 on 28314 degrees of freedom
## Residual deviance: 33113 on 28304 degrees of freedom
## AIC: 33135
##
## Number of Fisher Scoring iterations: 4probitmfx(Probit_Pooled, data=lfp, atmean = F)## Call:
## probitmfx(formula = Probit_Pooled, data = lfp, atmean = F)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## kids -6.6018e-02 2.4187e-03 -27.2948 < 2.2e-16 ***
## lhinc -7.0054e-02 4.3617e-03 -16.0611 < 2.2e-16 ***
## educ 2.6447e-02 1.0359e-03 25.5304 < 2.2e-16 ***
## black 6.9883e-02 1.0013e-02 6.9792 2.969e-12 ***
## age 4.8097e-02 1.9857e-03 24.2220 < 2.2e-16 ***
## agesq -6.6085e-04 2.4247e-05 -27.2544 < 2.2e-16 ***
## factor(period)2 -4.1304e-03 8.4420e-03 -0.4893 0.62466
## factor(period)3 -1.0839e-02 8.4694e-03 -1.2798 0.20062
## factor(period)4 -1.5392e-02 8.4963e-03 -1.8116 0.07004 .
## factor(period)5 -1.9322e-02 8.5061e-03 -2.2716 0.02311 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## dF/dx is for discrete change for the following variables:
##
## [1] "black" "factor(period)2" "factor(period)3" "factor(period)4"
## [5] "factor(period)5"#library(dplyr)
lfp <- lfp %>% group_by(id) %>%
mutate(lfp1 = lfp[1], lfp_1 = lag(lfp, n = 1),
kidsbar=mean(kids), lhincbar=mean(lhinc))
#lfp <- ddply(lfp, ~id, lfp1=lfp[1], ... ) #if using plyr, instead of dplyr
lfpP <- pdata.frame(lfp, index = c("id", "period"))summary(RE_Probit <- glm(lfp ~ kids + lhinc + kidsbar + lhincbar + educ + black + age + agesq + factor(period), data=lfpP, family=binomial("probit")))##
## Call:
## glm(formula = lfp ~ kids + lhinc + kidsbar + lhincbar + educ +
## black + age + agesq + factor(period), family = binomial("probit"),
## data = lfpP)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3160 -1.2207 0.6885 0.8683 2.6359
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.261e-01 1.428e-01 -5.084 3.70e-07 ***
## kids -1.174e-01 3.773e-02 -3.111 0.00187 **
## lhinc -2.881e-02 2.520e-02 -1.143 0.25293
## kidsbar -8.569e-02 3.849e-02 -2.227 0.02597 *
## lhincbar -2.502e-01 2.948e-02 -8.486 < 2e-16 ***
## educ 8.413e-02 3.268e-03 25.742 < 2e-16 ***
## black 2.031e-01 3.357e-02 6.049 1.45e-09 ***
## age 1.516e-01 6.216e-03 24.397 < 2e-16 ***
## agesq -2.067e-03 7.641e-05 -27.053 < 2e-16 ***
## factor(period)2 -1.357e-02 2.538e-02 -0.535 0.59291
## factor(period)3 -3.320e-02 2.533e-02 -1.311 0.18997
## factor(period)4 -3.903e-02 2.534e-02 -1.540 0.12344
## factor(period)5 -5.524e-02 2.529e-02 -2.185 0.02890 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 35418 on 28314 degrees of freedom
## Residual deviance: 33033 on 28302 degrees of freedom
## AIC: 33059
##
## Number of Fisher Scoring iterations: 4RE_APE<- probitmfx(RE_Probit, data=lfpP, atmean = F)
RE_APE## Call:
## probitmfx(formula = RE_Probit, data = lfpP, atmean = F)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## kids -3.8852e-02 1.2484e-02 -3.1120 0.001858 **
## lhinc -9.5363e-03 8.3409e-03 -1.1433 0.252902
## kidsbar -2.8364e-02 1.2736e-02 -2.2272 0.025937 *
## lhincbar -8.2811e-02 9.7246e-03 -8.5156 < 2.2e-16 ***
## educ 2.7849e-02 1.0467e-03 26.6061 < 2.2e-16 ***
## black 6.4344e-02 1.0116e-02 6.3607 2.009e-10 ***
## age 5.0195e-02 1.9947e-03 25.1642 < 2.2e-16 ***
## agesq -6.8428e-04 2.4342e-05 -28.1113 < 2.2e-16 ***
## factor(period)2 -4.4999e-03 8.4318e-03 -0.5337 0.593564
## factor(period)3 -1.1038e-02 8.4571e-03 -1.3051 0.191850
## factor(period)4 -1.2987e-02 8.4719e-03 -1.5329 0.125302
## factor(period)5 -1.8420e-02 8.4891e-03 -2.1698 0.030022 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## dF/dx is for discrete change for the following variables:
##
## [1] "black" "factor(period)2" "factor(period)3" "factor(period)4"
## [5] "factor(period)5"#library(pglm)
summary(CRE_Probit <- pglm(lfp ~ kids + lhinc + kidsbar + lhincbar + educ + black + age + agesq + factor(period), data=lfpP, model="random", effect = "individual", family=binomial("probit"))) ## --------------------------------------------
## Maximum Likelihood estimation
## Newton-Raphson maximisation, 7 iterations
## Return code 2: successive function values within tolerance limit
## Log-Likelihood: -8717.271
## 14 free parameters
## Estimates:
## Estimate Std. error t value Pr(> t)
## (Intercept) -3.2619293 0.7839086 -4.161 3.17e-05 ***
## kids -0.3705209 0.0670346 -5.527 3.25e-08 ***
## lhinc -0.0969143 0.0452282 -2.143 0.032130 *
## kidsbar -0.3280459 0.0863456 -3.799 0.000145 ***
## lhincbar -0.8722061 0.0817363 -10.671 < 2e-16 ***
## educ 0.2931048 0.0212183 13.814 < 2e-16 ***
## black 1.0279369 0.1612094 6.376 1.81e-10 ***
## age 0.5564136 0.0381874 14.571 < 2e-16 ***
## agesq -0.0074894 0.0004929 -15.193 < 2e-16 ***
## factor(period)2 -0.0362766 0.0477579 -0.760 0.447498
## factor(period)3 -0.1119849 0.0478704 -2.339 0.019318 *
## factor(period)4 -0.1211022 0.0478184 -2.533 0.011324 *
## factor(period)5 -0.1815886 0.0473095 -3.838 0.000124 ***
## sigma 4.4862132 0.0783991 57.223 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## --------------------------------------------probitmfx(CRE_Probit, data=lfpP, atmean = F)## Call:
## probitmfx(formula = CRE_Probit, data = lfpP, atmean = F)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## kids -3.8852e-02 1.2484e-02 -3.1120 0.001858 **
## lhinc -9.5363e-03 8.3409e-03 -1.1433 0.252902
## kidsbar -2.8364e-02 1.2736e-02 -2.2272 0.025937 *
## lhincbar -8.2811e-02 9.7246e-03 -8.5156 < 2.2e-16 ***
## educ 2.7849e-02 1.0467e-03 26.6061 < 2.2e-16 ***
## black 6.4344e-02 1.0116e-02 6.3607 2.009e-10 ***
## age 5.0195e-02 1.9947e-03 25.1642 < 2.2e-16 ***
## agesq -6.8428e-04 2.4342e-05 -28.1113 < 2.2e-16 ***
## factor(period)2 -4.4999e-03 8.4318e-03 -0.5337 0.593564
## factor(period)3 -1.1038e-02 8.4571e-03 -1.3051 0.191850
## factor(period)4 -1.2987e-02 8.4719e-03 -1.5329 0.125302
## factor(period)5 -1.8420e-02 8.4891e-03 -2.1698 0.030022 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## dF/dx is for discrete change for the following variables:
##
## [1] "black" "factor(period)2" "factor(period)3" "factor(period)4"
## [5] "factor(period)5"summary(FE_Logit <- pglm(lfp ~ kids + lhinc, data=lfpP, effect = "time", family=binomial("logit")))## --------------------------------------------
## Maximum Likelihood estimation
## Newton-Raphson maximisation, 7 iterations
## Return code 2: successive function values within tolerance limit
## Log-Likelihood: -9025.528
## 4 free parameters
## Estimates:
## Estimate Std. error t value Pr(> t)
## (Intercept) 5.63516 0.56080 10.048 < 2e-16 ***
## kids -0.66352 0.04743 -13.989 < 2e-16 ***
## lhinc -0.24869 0.07131 -3.488 0.000487 ***
## sigma 8.66079 0.19558 44.283 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## --------------------------------------------Note: Something is wrong here. CRE_Probit and FE_Logit are not exactly the same as in the textbook or the Stata version here..
Dynamic Women’s LFP Equation
lfp <- lfp %>% group_by(id) %>%
mutate(kids2 = kids[2], kids3=kids[3], kids4 = kids[4], kids5 = kids[5],
lhinc2 = lhinc[2], lhinc3=lhinc[3], lhinc4 = lhinc[4], lhinc5 = lhinc[5])
lfpP <- pdata.frame(lfp, index = c("id", "period"))
# summary(RE_Probit <- pglm(lfp ~ lfp1 + lfp_1 + kids + lhinc + kidsbar + lhincbar + kids2 + kids3 + kids4 + kids5 + lhinc2+ lhinc3 + lhinc4 + lhinc5 + educ + black + age + agesq + per3 + per4 + per5, data=lfpP, model="random", family=binomial(link="probit")))
# probitmfx(RE_Probit, data=lfpP, atmean = F)
summary(Probit_Pooled <- glm(lfp ~ lfp_1 + kids + lhinc + kidsbar + lhincbar + educ + black + age + agesq, data=lfp, family=binomial(link="probit")))##
## Call:
## glm(formula = lfp ~ lfp_1 + kids + lhinc + kidsbar + lhincbar +
## educ + black + age + agesq, family = binomial(link = "probit"),
## data = lfp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8325 -0.4037 0.2895 0.3316 2.6913
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.0808656 0.2326162 -8.945 < 2e-16 ***
## lfp_1 2.8732966 0.0269968 106.431 < 2e-16 ***
## kids -0.0408845 0.0629982 -0.649 0.516
## lhinc -0.0596502 0.0404712 -1.474 0.141
## kidsbar -0.0209056 0.0641473 -0.326 0.745
## lhincbar -0.0763569 0.0473610 -1.612 0.107
## educ 0.0306517 0.0053277 5.753 8.75e-09 ***
## black 0.0741814 0.0545141 1.361 0.174
## age 0.0866667 0.0101908 8.504 < 2e-16 ***
## agesq -0.0011241 0.0001253 -8.969 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 28409 on 22651 degrees of freedom
## Residual deviance: 10664 on 22642 degrees of freedom
## (5663 observations deleted due to missingness)
## AIC: 10684
##
## Number of Fisher Scoring iterations: 5probitmfx(Probit_Pooled, data=lfp, atmean = F)## Call:
## probitmfx(formula = Probit_Pooled, data = lfp, atmean = F)
##
## Marginal Effects:
## dF/dx Std. Err. z P>|z|
## lfp_1 8.3684e-01 4.4786e-03 186.8545 < 2.2e-16 ***
## kids -5.0871e-03 7.8389e-03 -0.6490 0.5164
## lhinc -7.4221e-03 5.0366e-03 -1.4736 0.1406
## kidsbar -2.6012e-03 7.9817e-03 -0.3259 0.7445
## lhincbar -9.5008e-03 5.8944e-03 -1.6118 0.1070
## educ 3.8139e-03 6.6489e-04 5.7361 9.689e-09 ***
## black 9.1341e-03 6.6477e-03 1.3740 0.1694
## age 1.0784e-02 1.2761e-03 8.4504 < 2.2e-16 ***
## agesq -1.3987e-04 1.5706e-05 -8.9055 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## dF/dx is for discrete change for the following variables:
##
## [1] "lfp_1" "black"