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id,female,s,married,earnings,catgov,collbarg 5687,0,8,1,23.33,0,0 5726,0,8,1,10.25,0,0 4463,0,11,0,22,0,1 6316,0,17,0,15,0,0 5790,0,18,0,10,0,0 3715,0,8,0,30,0,0 3055,0,13,0,12,0,1...

1 answer below »
id,female,s,ma
ied,earnings,catgov,collbarg
5687,0,8,1,23.33,0,0
5726,0,8,1,10.25,0,0
4463,0,11,0,22,0,1
6316,0,17,0,15,0,0
5790,0,18,0,10,0,0
3715,0,8,0,30,0,0
3055,0,13,0,12,0,1
4080,0,12,1,12.43,1,0
8852,0,9,0,14,0,0
1162,0,16,0,23.08,0,0
4513,0,12,0,14.65,1,1
1018,0,13,1,26,0,0
278,0,12,1,19.23,0,0
4864,0,13,0,9.45,0,0
4411,0,17,1,11.05,0,0
4506,0,15,1,11,0,1
8911,0,12,0,12.08,0,0
1632,0,16,0,9,0,0
4275,0,12,1,18.5,0,0
2383,0,16,0,39.23,0,0
6247,0,13,0,15,0,0
673,0,16,1,38.46,0,0
5259,0,12,0,20,0,0
8879,0,18,1,16.8,1,1
5330,0,16,0,8,0,0
918,0,16,1,28.85,0,0
1976,0,12,1,26,0,0
6113,0,10,1,14.75,0,0
283,0,15,0,15.12,0,1
4519,0,12,0,13.56,0,0
3142,0,20,1,15.68,0,0
1823,0,11,0,7.25,0,0
2238,0,8,1,29.3,0,1
3090,0,14,1,16.67,0,0
1473,0,16,0,45.67,0,0
2954,0,15,1,14.6,0,0
5238,0,16,0,11,0,0
5231,0,13,1,21.79,0,0
4514,0,14,0,18.01,1,0
3351,0,10,0,4.25,0,0
3466,0,9,0,13,0,0
6283,0,13,0,17.22,0,0
942,0,15,0,9,0,0
1886,0,12,0,31.82,0,1
336,0,20,1,53.49,1,1
2558,0,18,1,34.62,0,0
4396,0,9,0,15,0,0
3450,0,11,0,12.5,0,0
5522,0,12,1,15,0,0
1797,0,16,1,18.23,0,0
2434,0,16,1,26.92,0,0
6064,0,10,0,12.5,0,0
4562,0,12,1,18,0,0
6411,0,14,0,7.5,0,0
3549,0,15,0,15.5,0,0
8760,0,17,1,12.25,0,0
962,0,14,1,70,0,0
3454,0,14,0,19.1,0,0
3773,0,12,0,16.87,0,0
4592,0,12,0,12,0,0
8835,0,16,0,19.47,0,0
2050,0,19,1,16.92,0,0
1435,0,12,0,20,0,0
1068,0,8,0,15,0,0
1503,0,16,0,11.25,0,0
6570,0,11,0,9,0,0
2719,0,13,0,15,0,0
2395,0,18,0,8.65,0,0
4886,0,12,1,9,0,0
4574,0,16,0,9.5,0,0
2833,0,16,0,11.3,0,0
2904,0,8,0,14,0,0
6406,0,14,0,33.17,0,0
6397,0,13,0,12.04,0,0
1049,0,8,0,13.29,0,0
4199,0,14,0,7.5,0,0
1235,0,16,1,45.38,0,0
1851,0,12,0,15.62,1,0
2118,0,12,0,8,0,0
511,0,12,1,17.78,0,0
4139,0,18,1,29.07,0,0
1247,0,17,0,25,0,0
5595,0,16,1,26,0,0
2393,0,16,0,15.77,0,0
8957,0,16,1,38.46,0,0
4591,0,13,0,12,0,0
894,0,17,1,34.88,0,0
4334,0,12,0,15,0,0
3197,0,18,1,43.58,0,0
2483,0,12,0,65,0,0
688,0,13,1,30,0,1
3425,0,12,0,17.5,0,0
957,0,14,0,9.75,0,0
4540,0,17,1,13.29,0,0
2072,0,16,1,34.27,0,0
5546,0,18,0,15,0,0
3643,0,16,0,15.8,0,0
3163,0,12,1,33.33,0,1
8971,0,12,1,12.61,0,0
1586,0,15,0,15,0,0
1090,0,13,1,49.02,0,1
5366,0,12,0,23,0,0
2319,0,12,1,16.75,0,1
3647,0,15,0,20.67,0,0
5983,0,14,0,21.5,1,0
4171,0,14,1,22.05,1,1
89,0,19,1,22.69,1,1
2925,0,12,0,28.7,0,0
294,0,18,1,30,1,1
2800,0,10,0,7.75,0,0
4774,0,13,1,16.69,0,0
4057,0,12,0,12,0,0
5159,0,14,0,10.5,0,0
5715,0,12,0,8,1,0
6504,0,12,0,26.42,0,1
5252,0,10,0,9.35,0,0
1545,0,14,0,11,0,0
6317,0,12,1,16,0,0
3732,0,11,0,8,0,0
3343,0,11,1,13.65,0,1
3146,0,14,0,5.76,0,0
3074,0,18,1,25,0,0
2630,0,12,1,15,0,0
3957,0,12,1,20.98,0,1
3059,0,17,0,28.13,0,0
2608,0,13,0,18.96,0,1
3449,0,17,0,18.5,0,0
870,0,14,0,9,0,0
746,0,16,0,45,0,0
6116,0,12,0,17.35,0,0
3994,0,12,0,21,0,0
6004,0,11,1,11.16,0,0
2778,0,17,0,22.5,0,0
4813,0,16,0,18.36,0,0
564,0,14,1,28.52,0,1
6151,0,12,0,13.29,0,0
2208,0,18,0,26.5,1,0
6495,0,14,0,16.67,0,0
5166,0,13,1,27.51,1,0
4072,0,12,1,17.75,0,0
6383,0,18,0,19.5,1,0
2910,0,13,1,75,0,0
3057,0,10,0,11,0,0
2439,0,16,1,16.83,0,0
122,0,12,0,13.7,0,1
4818,0,16,0,34.19,0,0
4693,0,12,0,14.5,0,0
3281,0,14,0,15,0,0
4615,0,12,0,23,0,0
648,0,12,1,48.72,1,1
5736,0,13,0,15.08,0,0
4712,0,10,0,13.33,0,0
3515,0,14,1,16,0,0
5161,0,13,0,9.33,0,1
1791,0,10,0,9.5,0,0
6252,0,16,0,15.75,0,0
8908,0,13,1,18.55,0,1
5804,0,12,0,15,0,0
6476,0,12,0,18.86,0,0
4600,0,17,1,52,0,0
3896,0,19,0,16,0,0
4776,0,16,0,16,0,0
2907,0,14,0,22,0,0
456,0,20,0,18,0,0
5533,0,12,1,15,0,0
5029,0,12,0,15.96,0,1
6256,0,15,0,12,0,0
1234,0,19,1,10,0,0
5769,0,19,1,29,0,0
2466,0,16,0,17.31,0,0
165,0,16,1,36.66,0,0
3850,0,20,0,21.73,0,0
4475,0,12,1,18.31,0,0
5329,0,17,0,3.74,0,0
2637,0,16,1,21.79,0,0
1130,0,12,0,18.5,0,0
2980,0,14,0,10,0,0
2690,0,12,1,17.75,0,0
3473,0,13,0,10,0,0
3305,0,19,0,10.02,0,0
4663,0,13,1,17.19,0,0
3996,0,9,0,7.65,0,0
1918,0,18,1,27.26,0,0
4347,0,12,0,25,0,0
4668,0,12,0,20.93,0,0
1815,0,16,1,19.8,0,1
222,0,15,0,16,0,0
2350,0,16,1,22.81,0,0
6108,0,18,0,20.73,0,0
2211,0,15,1,21.5,0,0
3145,0,16,0,17.86,0,0
138,0,14,0,12,0,0
555,0,18,0,20.51,0,0
2693,0,15,1,22.44,0,0
653,0,12,0,19,0,0
3938,0,17,0,13.37,0,0
5630,0,14,1,12.15,0,0
5581,0,18,0,11.63,1,0
1226,0,13,0,2.6,0,0
1770,0,13,0,11.45,1,0
2503,0,14,0,12.7,0,0
4295,0,17,1,46.49,0,0
4051,0,12,1,18.5,0,0
640,0,14,0,34.95,0,1
1456,0,16,1,17.71,0,0
5426,0,13,0,18.75,0,0
3464,0,17,1,13.62,0,0
2346,0,16,0,86.54,0,0
6510,0,11,0,16.3,0,0
4296,0,18,1,38.46,1,1
372,0,12,0,14.5,0,0
4942,0,17,0,23.68,1,0
5549,0,11,0,6.25,0,0
3016,0,16,0,17.46,1,1
5765,0,18,0,25,0,1
8910,0,12,0,14,0,0
598,0,16,1,28,1,1
4198,0,16,1,20,0,0
508,0,10,0,14.5,0,0
765,0,13,1,24,0,1
93,0,12,1,14.95,0,0
4158,0,16,0,16.83,0,0
5441,0,15,0,24,0,0
2431,0,14,1,16.8,0,0
5191,0,12,1,5.25,0,0
2658,0,16,1,25.3,0,0
8823,0,12,0,10,0,1
4205,0,14,1,15,0,0
4483,0,9,1,14.1,0,0
1304,0,20,1,23.5,0,0
3004,0,15,0,30,0,0
3328,0,16,0,60,0,0
1556,0,20,0,8.5,0,0
4680,0,12,0,11,0,0
2441,0,17,1,17.31,0,0
2994,0,10,0,20.5,0,0
4025,0,12,0,47.4,0,0
6393,0,14,1,11,0,0
586,0,12,1,8.5,0,0
2678,0,14,1,19.5,0,0
2813,0,12,1,17.26,1,1
717,0,13,1,12,0,0
4710,0,15,1,28.72,0,1
1811,0,18,0,9.5,0,0
6435,0,16,0,22,0,0
4006,0,14,0,24,0,0
6485,0,12,1,35.5,0,1
5950,0,17,1,16.16,0,0
2564,0,17,1,20.98,0,0
4168,0,14,1,10.26,0,0
5895,1,14,1,10,0,0
224,1,12,1,17,0,0
5859,1,14,1,13.36,0,0
1416,1,16,0,7.25,0,0
366,1,18,0,24,1,0
1624,1,16,0,41.96,0,0
4207,1,17,0,17.5,0,0
721,1,17,1,2.67,1,0
2567,1,18,1,24.28,0,1
4024,1,17,0,33.33,0,0
Answered Same Day Apr 29, 2021

Solution

Mohd answered on Apr 29 2021
163 Votes
-
Lena
4/29/2021
knitr::opts_chunk$set(echo = TRUE,cache = TRUE,warning = FALSE,message = FALSE,dpi = 180,fig.width = 8,fig.height = 5)
Loading packages
li
ary(dplyr)
li
ary(ggplot2)
li
ary(magrittr)
li
ary(rmarkdown)
li
ary(skimr)
li
ary(readr)
li
ary(stargazer)
step_2 variable transformation
mydat<- read_csv("datafile.csv")
mydat1<-mydat%>%
mutate(lnearn=log(earnings))%>%
mutate(femar=female*ma
ied)
#View()
step_3 Average_hourly_wage
mean(mydat$earnings)
## [1] 18.45336
Ma
ied_count
mydat%>%
count(ma
ied)%>%
mutate(prop=n/sum(n))
## # A ti
le: 2 x 3
## ma
ied n prop
## ## 1 0 293 0.586
## 2 1 207 0.414
female_proportion
mydat%>%
count(female)%>%
mutate(prop=n/sum(n))
## # A ti
le: 2 x 3
## female n prop
## ## 1 0 250 0.5
## 2 1 250 0.5
catgov-PROPORTION
mydat%>%
count(catgov)%>%
mutate(prop=n/sum(n))
## # A ti
le: 2 x 3
## catgov n prop
## ## 1 0 443 0.886
## 2 1 57 0.114
COLLBARG_PROPORTION
mydat%>%
count(collbarg)%>%
mutate(prop=n/sum(n))
## # A ti
le: 2 x 3
## collbarg n prop
## ## 1 0 438 0.876
## 2 1 62 0.124
Regression models
mod<-lm(lnearn~s,data=mydat1)
mod2<-lm(lnearn~s+female,data=mydat1)
mod3<-lm(lnearn~s+female+ma
ied+femar,data=mydat1)
stargazer(mod2,type = "text",title="Summary")
A. If we increase log of earning by one unit then females have 21.4 percent less earning than men.
Beta coefficient for females is negative and statistically significant.
Earning and female has inversely related to each other.
##
## Summary
## ===============================================
## Dependent variable:
## ---------------------------
## lnearn
## -----------------------------------------------
## s 0.068***
## (0.008)
##
## female -0.214***
## (0.045)
##
## Constant 1.902***
## (0.121)
##
## -----------------------------------------------
## Observations 500
## R2 0.138
## Adjusted R2 0.134
## Residual Std. E
or 0.498 (df = 497)
## F Statistic 39.661*** (df = 2; 497)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
#stargazer(mod,mod2,mod3,type = "text",title="Summary")
stargazer(mod,mod2,type = "text",title="Summary")
B. Yes, omission of females in model 1 appears to cause an omitted variable bias the coefficient estimate of s in model 1.
In model 2, it has increased adjusted r square value, and beta coefficient of s has also increased. It's significantly co
elated with dependent variables and a significant predictor of log of earning.
##
## Summary
##...
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