a) Run a regression of average hourly earnings (AHE) on age (Age), sex (Female), and education (Bachelor). If Age increases from 25 to 26, how are earnings expected to change? If Age increases from 33 to 34, how are earnings expected to change? (Note: Run the regression using STATA and with robust). b) Run a regression of the logarithm of average hourly earnings, ln(AHE) on Age, Female, and Bachelor. If Age increases from 25 to 26, how are earnings expected to change? If Age increases from 33 to 34, how are earnings expected to change? (Note: Run the regression using STATA and with robust). c) Run a regression of the logarithm of average hourly earnings, ln(AHE) on ln(Age), Female, and Bachelor. If Age increases from 25 to 26, how are earnings expected to change? If Age increases from 33 to 34, how are earnings expected to change? (Note: Run the regression using STATA and with robust). d) Run a regression of the logarithm of average hourly earnings, ln(AHE) on Age, Age2, Female, and Bachelor. If Age increases from 25 to 26, how are earnings expected to change? If Age increases from 33 to 34, how are earnings expected to change? (Note: Run the regression using STATA and with robust). e) Do you prefer the regression in (c) to the regression in (b)? Explain. f) Do you prefer the regression in (d) to the regression in (b)? Explain. g) Do you prefer the regression in (d) to the regression in (c)? Explain. h) Run a regression of ln(AHE) on Age, Age2, Female, Bachelor, and the interaction term Female x Bachelor. What does the coefficient on the interaction term measure? Alexis is a 30-year-old female with a bachelor’s degree. What does the regression predict for her value of ln(AHE)? Jane is a 30-year-old female with a high school diploma. What does the regression predict for her value of ln(AHE)? What is the predicted difference between Alexis’s and Jane’s earnings? Bob is a 30-year-old male with a bachelor’s degree. What does the regression predict for his value of ln(AHE)? Jim is a 30-year-old male with a high school diploma. What does the regression predict for his value of ln(AHE)? What is the predicted difference between Bob’s and Jim’s earnings? (Note: Run the regression using STATA and with robust). i) Is the effect of Age on earnings different for men than for women? Specify and estimate a regression that you can use to answer this question. j) Is the effect of Age on earnings different for high school graduates than for college graduates? Specify and estimate a regression that you can use to answer this question. k) After running all these regressions (and any others that you want to run), summarize the effect of age on earnings for young workers
Empirical Exercise 8.2
On the text website http://www.pearsonhighered.com/stock_watson/, you will find a data file CPS2015, which contains data for full-time, full-year workers, age 25-34, with a high school diploma or B.A./B.S. as their highest degree. A detailed description is given in CPS2015_Description, also available on website. (These are the same data as in CPS96_15, used in Empirical Exercise 3.1, but are limited to the year 2015). In this exercise, you will investigate the relationship between a worker’s age and earnings. (Generally older workers have more job experience, leading to higher productivity and higher earnings).
a) Run a regression of average hourly earnings (AHE) on age (Age), sex (Female), and education (Bachelor). If Age increases from 25 to 26, how are earnings expected to change? If Age increases from 33 to 34, how are earnings expected to change? (Note: Run the regression using STATA and with robust).
b) Run a regression of the logarithm of average hourly earnings, ln(AHE) on Age, Female, and Bachelor. If Age increases from 25 to 26, how are earnings expected to change? If Age increases from 33 to 34, how are earnings expected to change? (Note: Run the regression using STATA and with robust).
c) Run a regression of the logarithm of average hourly earnings, ln(AHE) on ln(Age), Female, and Bachelor. If Age increases from 25 to 26, how are earnings expected to change? If Age increases from 33 to 34, how are earnings expected to change? (Note: Run the regression using STATA and with robust).
d) Run a regression of the logarithm of average hourly earnings, ln(AHE) on Age, Age2, Female, and Bachelor. If Age increases from 25 to 26, how are earnings expected to change? If Age increases from 33 to 34, how are earnings expected to change? (Note: Run the regression using STATA and with robust).
e) Do you prefer the regression in (c) to the regression in (b)? Explain.
f) Do you prefer the regression in (d) to the regression in (b)? Explain.
g) Do you prefer the regression in (d) to the regression in (c)? Explain.
h) Run a regression of ln(AHE) on Age, Age2, Female, Bachelor, and the interaction term Female x Bachelor. What does the coefficient on the interaction term measure? Alexis is a 30-year-old female with a bachelor’s degree. What does the regression predict for her value of ln(AHE)? Jane is a 30-year-old female with a high school diploma. What does the regression predict for her value of ln(AHE)? What is the predicted difference between Alexis’s and Jane’s earnings? Bob is a 30-year-old male with a bachelor’s degree. What does the regression predict for his value of ln(AHE)? Jim is a 30-year-old male with a high school diploma. What does the regression predict for his value of ln(AHE)? What is the predicted difference between Bob’s and Jim’s earnings? (Note: Run the regression using STATA and with robust).
i) Is the effect of Age on earnings different for men than for women? Specify and estimate a regression that you can use to answer this question.
j) Is the effect of Age on earnings different for high school graduates than for college graduates? Specify and estimate a regression that you can use to answer this question.
k) After running all these regressions (and any others that you want to run), summarize the effect of age on earnings for young workers.
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