Coding for Applied Economists

Challenge 5 (Data Analysis)

For this challenge we are going to switch up the data and use a data set of women’s labor force participation and earnings from 1975. The data comes from the paper The Sensitivity of an Empirical Model of Married Women’s Hours of Work to Economic and Statistical Assumptions by Thomas A. Mroz.

Download the Mroz.dta file from the list of data sets on the course website. Load the data using use.

We are going to analysis women’s income conditional a number of different variables. Since we want to understand how women’s earnings varies based on these other variables, start by using keep if to drop from the data set any woman who does not participate in the labor force.

In the data set, women’s earnings have been logged (lwg). We want the unlogged version so create a new variable called earn that is the unlogged lwg. Recall to undo a log we exponentiate the value (exp()) using e. Also, use drop if to drop any values of income (inc) that are less than 0.

1. Draw a scatter plot with earn on the x-axis and inc on the y-axis. Because of the long tail in data like inc we want to “normalize” the data a bit by graphing both variables on a log scale. Use the graphing option yscale(log) and xscale(log).

2. Use tabstat to get mean (stat(mean)) earnings conditional on (by) college attendance (wc). Are average earnings higher if the wife attended college or did not attend college?

3. Now, use egen with cut to create 10 groups (deciles) based on family income inc. Use tabstat to get mean (stat(mean)) earnings conditional on (by) being in one of the 10 deciles (inc_cut). Do women in households that are in the highest decile (group 10) by family income earn the most money?

4. Create a new variable called loginc that is the logged value (log()) of family income (inc).

  • Make a scatter plot with log family income (log_inc) on the y-axis and log women’s earnings (lwg) on the x-axis.
  • Change the marker color to gray and make it 50% transparent (gray%50).
  • To this scatter plot at a fitted line using lfit.
  • Change the color of this line to navy, change the pattern to solid and change the width of the line to thick.
  • Also add a fitted line using lowess. This fits a locally weighted non-linear regression line to the data.
  • Change the color of this line to maroon, change the pattern to solid and change the width of the line to thick.

5. Run two regressions (regress). For the first one, regress the log of women’s earnings on the log of family income. For the second one add college attendance (wc) as a control. Is the unconditional relationship between family income and women’s earnings stronger then that relationship when we condition on college attendance (which regression has the larger co-efficient)?