Empirical Exercises 1
Using the data set Growth described in Empirical Exercise E4.1, but excluding the data for Malta, carry out the following exercises.
Construct a table that shows the sample mean, standard deviation, and minimum and maximum values for the series Growth, TradeShare, YearsSchool, Oil, Rev_Coups, Assassinations, and RGDP60. Include the appropriate units for all entries. [Hint: Some initial R-code is written below. Complete the remaining part.]
Focusing first on women only, run a regression of (1) Earnings on Height and (2) Earnings on Height, including LT_HS, HS, and Some_Col as control variables.
Compare the estimated coefficient on Height in regressions (1) and (2). Is there a large change in the coefficient? Has it changed in a way consistent with the cognitive ability explanation? Explain.
The regression omits the control variable College. Why?
Test the joint null hypothesis that the coefficients on the education variables are equal to 0.
Discuss the values of the estimated coefficients on LT_HS, HS, and Some_Col. (Each of the estimated coefficients is negative, and the coefficient on LT_HS is more negative than the coefficient on HS, which in turn is more negative than the coefficient on Some_Col. Why? What do the coefficients measure?)
Empirical Exercises 3
Use the data set cps12.xlsx to answer the following questions.
Run a regression of average hourly earnings (AHE) on age(Age). What is the estimated intercept? What is the estimated slope?
Run a regression of AHE on Age, gender (Female), and education (Bachelor). What is the estimated effect of Age on earnings? Construct a 95% confidence interval for the coefficient on Age in the regression.
Are the results from the regression in (b) substantively different from the results in (a) regarding the effects of Age and on &AHE*? Does the regression in (a) seem to suffer from omitted variable bias?
Bob is a 26-year-old male worker with a high school diploma. Predict Bobs earnings using the estimated regression in (b). Alexis is a 30-year-old female worker with a college degree. Predict Alexiss earnings using the regression.
Are gender and education determinants of earnings? Test the null hypothesis that Female can be deleted from the regression. Test the null hypothesis that Bachelor can be deleted from the regression. Test the null hypothesis that both Female and Bachelor can be deleted from the regression.
