Introduction to Econometrics Coursework

Introduction to Econometrics Coursework
2020/21
Coursework Questions
Instructions for Introduction to Econometrics Coursework Questions
The deadline for this Coursework is Friday 20th of November 2020, 10am.
Upload your answers in a single file via Turnitin using the link on KEATS.
Include at the end of the file all the Stata commands you used to answer the questions.
Keep your answers short, approx. 30-70 words and no more than 100 words for any
question. Maximum 700 words overall (excluding Stata tables and commands).
The data for Introduction to Econometrics Coursework Questions
In this coursework exercise you will investigate the relationship between a person’s labour
earnings and age, education, gender and a few other characteristics. The file cwdata.dta
contains data for full-time workers in the US. The data are from the US National Health
Interview Survey. The sample is chosen randomly from the whole population of the US.
The variables included in cwdata.dta are :
earnings Annual labour earnings (expressed in USD per year)
age Age in years
educ Years of schooling
woman Gender (0 man, 1 woman)
married Marital status ( 0 unmarried, 1 married)
region Region (1 Northeast, 2 Midwest, 3 South, 4 West)
manager Executive / managerial position (0 no, 1 yes)
height Height without shoes (in inches)
Questionsfor Introduction to Econometrics Coursework Questions
1. Run a regression of annual earnings (earnings) on age (age). How do you interpret
the intercept and slope coefficients? (10 marks)
2. Run a regression of earnings on age, gender (woman), and education (educ). What is
the effect of age on yearly earnings according to this regression? (10 marks)
3. Are the results from the regression in (1) substantively different from the results in
(2) regarding the effects of age on annual earnings? Does the regression in (1) seem
to suffer from omitted variable bias? (20 marks)
[Hint: explain the direction of this bias.]
4. Include now some of the other control variables in the regression: regress earnings
on age, age2, woman, educ, married, and manager. If age increases from 35 to 36
years, how are annual earnings expected to change? (20 marks)
[Hint: create a new variable using: gen age2=age^2. Alternatively, you can do this
automatically in Stata using the operator c.age##c.age.]
1
5. Run the same regression as in (4) but using the logarithm of earnings: regress log of
earnings on age, age2, woman, educ, married, and manager. What is the effect of
an additional year of schooling on earnings according to this regression? (15 marks)
[Hint: create the new variable log earnings using: gen lnearn=ln(earnings)]
6. Run the same regression as in (5) but include also the interaction term woman x educ.
What does the coefficient of this interaction term measure? (15 marks)
[Hint: you can do this automatically in Stata using the operator woman##c.educ.]
7. Include now height in the regression from question (5). Does the height of a person
appear to be a determinant of earnings? Is the effect of height on earnings different
for women than for men? Specify and estimate a regression (or two) that you can
use to answer this question. (10 marks)

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