Homework2: Gender and Race Wage Gap Analysis
Niraj Prasad Koirala
library(haven)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(magrittr)
library(gapminder)
library(tidyverse)## -- Attaching packages ------------------------ tidyverse 1.2.1 --## v tibble 1.4.2 v purrr 0.2.4
## v tidyr 0.8.0 v stringr 1.3.0
## v readr 1.1.1 v forcats 0.3.0## -- Conflicts --------------------------- tidyverse_conflicts() --
## x tidyr::extract() masks magrittr::extract()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::set_names() masks magrittr::set_names()library(tinytex)
library(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, unitsdf.nlsy<- read_dta("C:\\Users\\t420\\Desktop\\NLSY.dta")str(df.nlsy)## Classes 'tbl_df', 'tbl' and 'data.frame': 5398 obs. of 15 variables:
## $ female : atomic 0 0 1 0 1 1 0 0 1 1 ...
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ union : atomic 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ yrseduc : atomic 16 12 13 16 18 13 13 13 17 16 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ wgt : atomic 779383 816610 796575 956985 857771 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ wage : atomic 20.9 13.1 13.2 27.8 22.6 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ age : atomic 39 35 41 40 36 41 43 42 40 38 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ nwhite : atomic 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ yrswrkd : atomic 18.9 7.73 19.98 20.08 17.19 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ fracpart : atomic 0.01323 0.06169 0.02153 0.00916 0.01837 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ famresp : atomic 0 1 1 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ s_agric : atomic 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ s_minman : atomic 0 1 0 0 0 0 0 0 1 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ s_other : atomic 1 0 1 1 0 0 1 1 0 1 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ s_hltedusoc: atomic 0 0 0 0 1 1 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ hazard : atomic 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"female.n= factor(df.nlsy$female, labels= c("men", "women"))
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## Levels: men womenDifference between male and female in terms of wage
ggplot(df.nlsy, aes(x = wage, y = ..density.., weight = wgt, fill = female.n)) + scale_fill_manual(name="", values= c("green", "yellow"))+
geom_histogram(bins = 20, position = position_dodge(width = 0), alpha = 1) +
labs(x = "log wage", y = "density", title = "log(wage) by gender") +
scale_x_log10() +
theme_minimal()
Years of work experience by gender
ggplot(df.nlsy, aes(x = yrswrkd, y = ..density.., weight = wgt, fill = female.n)) + scale_fill_manual(name="", values= c("green", "yellow"))+
geom_histogram(bins = 20, position = position_dodge(width = 0), alpha = 1) +
labs(x = "years of work experience", y = "density", title = "Years of experience according to gender") 
Fraction of time worked part time by gender
ggplot(df.nlsy, aes(x= fracpart, y=..density.., weight= wgt, fill=female.n))+ scale_fill_manual(name="", values=c("black","red"))+ geom_histogram(bins=20, position=position_dodge(width=0), alpha=1)+ labs(x="fraction of labor market experience", y= "density", title="fraction of labor market experience")
Years of education by gender
ggplot(df.nlsy, aes(x=yrseduc, y=..density.., weight=wgt, fill=female.n))+ scale_fill_manual(name="", values= c("red", "green"))+ geom_histogram(bins=20, position=position_dodge(width=0), alpha=1)+labs(x="years of education according to gender", y="density", title="years of education according to gender")## Warning: Removed 66 rows containing non-finite values (stat_bin).
D. Creating Overlapping Density Plots according to race.
race = factor(df.nlsy$nwhite, labels= c("white", "nwhite"))
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## Levels: white nwhiteDifference between whites and nonwhites in terms of wage.
ggplot(df.nlsy, aes(x = wage, y = ..density.., weight = wgt, fill = race)) + scale_fill_manual(name="", values= c("black", "pink"))+
geom_histogram(bins = 20, position = position_dodge(width = 0), alpha = 1) +
labs(x = "log wage", y = "density", title = "log(wage) by race") +
scale_x_log10() +
theme_minimal()
Difference between whites and nonwhites in terms of years of labor market experience
ggplot(df.nlsy, aes(x = yrswrkd, y = ..density.., weight = wgt, fill = race)) + scale_fill_manual(name="", values= c("black", "pink"))+
geom_histogram(bins = 20, position = position_dodge(width = 0), alpha = 1) +
labs(x = "yrswrkd", y = "density", title = "years of work experiences according to race") 
# Difference between whites and nonwhites in terms of fraction of time worked part time
ggplot(df.nlsy, aes(x = fracpart, y = ..density.., weight = wgt, fill = race)) + scale_fill_manual(name="", values= c("black", "pink"))+
geom_histogram(bins = 20, position = position_dodge(width = 0), alpha = 1) +
labs(x = "fraction of total time worked as part time according to race", y = "density", title = "Race and fraction of time worked part time") 
Race and years of education
ggplot(df.nlsy, aes(x = yrseduc ,y = ..density.., weight = wgt, fill = race)) + scale_fill_manual(name="", values= c("black", "pink"))+
geom_histogram(bins = 20, position = position_dodge(width = 0), alpha = 1) +
labs(x = "years of education", y = "density", title = "years of education according to race") ## Warning: Removed 66 rows containing non-finite values (stat_bin).
f) Calculation of average wage for men and women
g) Weighted OLS regression
lm(log(wage) ~ yrseduc + age+ yrswrkd+fracpart+female+union+female.n, weight = wgt, data = df.nlsy)##
## Call:
## lm(formula = log(wage) ~ yrseduc + age + yrswrkd + fracpart +
## female + union + female.n, data = df.nlsy, weights = wgt)
##
## Coefficients:
## (Intercept) yrseduc age yrswrkd fracpart
## 1.81297 0.10026 -0.02505 0.03670 -0.36074
## female union female.nwomen
## -0.18581 0.07104 NA