Wage Analysis Assignment: Education, Wages, and Gender Differences

This assignment requires students to analyze the relationship between education and wages, specifically focusing on potential gender differences. The task involves using the CPS ASEC data from IPUMS to construct a measure of hourly wage, considering variables like incwage, uhrsworkly, and wkswork1, and accounting for missing data. Students are instructed to create a data frame for years of schooling from the EDUC variable and merge it with their main data frame. The assignment requires students to write out the model, state null and alternative hypotheses, describe the data used, create a regression results table, perform a hypothesis test, and interpret the results. Students must also plot predicted wages for men and women against years of schooling and discuss the economic significance of the findings and the limitations of the analysis. The goal is to provide a thorough answer to the question of whether a year of education increases wages more for women than for men. The assignment encourages students to reflect on the causal effect of sex on returns to education and the variables that might have been left out.

For your assignment this week, I want you to answer the question "Does a year of
education increase wages more for women than for men?"
Data
 Use the CPS ASEC data from ipums.org. It is fine to pick just one year (2016 is a
good year).
 You should use the logarithm of wage income. You will need to construct a measure
of hourly wage using a combination of variables like incwage, uhrsworkly, and
wkswork1.
 Read the descriptions of the variables.
 On IPUMS, before you "submit extract", change the "data format" to csv.
 Pay special attention to codes for "missing" or "not in universe" (NIU). Convert these
to "NA" before doing anything with those variables or you will see a bunch of people
with $9999999 in income. This information is usually under "codes" for each variable
on IPUMS.
 Try to focus on full-time workers (this depends on both hours worked per week and
number of weeks worked per year) that are adults of working age.
I put together the following variables for you. They are the same length, and you can
use them to convert the categories in EDUC to years of schooling. You will need to put
them in a data frame (as I did in the third line of code below) and merge that data
frame with your main data frame (again as I did in the fourth line of code below). You
will need to change the name of the "cps" data frame to whatever you named yours.
EDUC = c(999,0,1,2,10:14,20:22,30:32,40,50,60,70:73,80,81,90:92,100,110,111,120:125)
school =
c(NA,NA,NA,0,2.5,1:4,5.5,5:6,7.5,7:11,rep(12,4),13,rep(14,4),15,16,17,17,rep(18,4),20)
edyears = data.frame(EDUC,school)
cps2 = merge(cps,edyears,by="EDUC")

Write up your results
This is a little like practice for your paper. Write out the model you will use. Clearly state
the null and alternative hypotheses. Clearly describe the data you used (including
exactly which observations you included--only people aged 15-64 who worked more
than 35 weeks last year?) Make a regression results table. Perform the hypothesis test
and clearly state the results. Explain if the coefficients are economically significant (not
just statistically). Plot the predicted wages for women and men against hours of
schooling (this is just two lines on the same graph). Are there any variables that you left
out that should be included?
Added:
Comment on the graph. What is important? Does it help answer the question? Are there
portions of the graph that are not useful to us because they are outside of your sample?
Does your regression recover the causal effect of sex on returns to education?
The goal here is to be thorough in answering the question.