ECON 120B University of California Confidence Interval Stata Assignment 2 Hi, I need help with a Stata assignment, please follow all the instructions i att

ECON 120B University of California Confidence Interval Stata Assignment 2 Hi, I need help with a Stata assignment, please follow all the instructions i attached and make sure it runs Econ 120B Spring 2020 · Xinwei Ma
Department of Economics, UCSD
Stata Assignment 2
• You should submit your answers on Canvas, including your do-file.
• The deadline of submitting this Stata Assignment is May 31 (Sunday), 11:59pm. No late submission will be
accepted.
• This Stata Assignment will be graded on five scales: 0%, 25%, 50%, 75%, and 100%. If your do-file does not run, we
will subtract 25%.
• Start your do-file with the following (include your name and PID in your do-file)
/*******************************************************************************
ECON 120B, Spring 2020
Stata Assignment 2
Name:
PID:
*******************************************************************************/
clear all // clear the environment/memory
set more off
sysuse nlsw88 // load the built-in dataset nlsw88
Please make sure your do-file is clearly documented to help us understand your code.
• nlsw88 is a built-in dataset that comes with Stata. It is an extract from the 1988 round of the National Longitudinal
Survey of Mature and Young Women. Following is a summary of the variables in this dataset.
idcode
age
race
married
never married
grade
collgrad
south
smsa
c city
industry
occupation
union
wage
hours
ttl exp
tenure
survey id
age
race, can take three values, white, black or other
= 1 if is currently marries, = 0 otherwise
= 1 if never married, = 0 otherwise
current grade completed
= 1 if graduated from college, = 0 otherwise
= 1 if lives in southern states, = 0 otherwise
= 1 if lives in standard metropolitan statistical area, = 0 otherwise
= 1 if lives in central city, = 0 otherwise
industry, use tab industry to see the categories
occupation, use tab occupation to see the categories
= 1 if is in a union, , = 0 otherwise
hourly wage, measured in $
hours worked per week
total work experience, measured in years
current job tenure, measured in years
More information on the original data can be found here:
https://www.bls.gov/nls/orginal-cohorts/mature-and-young-women.htm
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1. In this exercise you will run multiple regressions to study how education, experience, and job tenure affects women’s
wage. First, consider the following regression model:
lnwagei = β0 + β1 gradei + β2 ttl expi + β3 tenurei + ui ,
To estimate this model, generate a new variable called lnwage which is the natural logarithm of wage times 100. Note
that one unit change in lnwage corresponds to 1% change in wage.
(a) Use the regress command to estimate the OLS coefficients. What is the percentage change in wage when education
increases by one year? How about job tenure?
(b) To test the null hypothesis that β2 = 3, what is the t-statistic? What is the p-value? Will you reject the null
hypothesis at the 10% significance level?
Next, to study if education has a quadratic effect on lnwage, consider the following regression model:
lnwagei = β0 + β1 gradei + β2 ttl expi + β3 tenurei + β4 grade2 i + ui ,
To estimate this model, generate a new variable which equals grade2 . Use the regress command to estimate the OLS
coefficients.
(c) What is the value of β̂1 ? What is the 95% confidence interval?
(d) What is the value of β̂4 ? Is it statistically significant at the 10% level?
(e) For someone with 12 years of education, what is the percentage change in wage if she receives an additional year
of education?
(f) To test the null hypothesis that β1 = β4 = 0, what is the Bonferroni statistic? How many restrictions are in this
hypothesis? What is the p-value? Will you reject the null hypothesis at the 5% level?
2. In this exercise, you will run multiple regressions with binary and categorical variables.
(a) Use the command tabulate to show the categories of the variable occupation and their frequencies. What is the
relative frequency of the category Sales? Please report a number between 0 and 1.
(b) Use the same command, this time specifying the option nolabel, to visualize the numeric values corresponding
to the different categories of occupation. Which numeric value corresponds to the label Sales?
(c) Use the command summarize with the option if to compute the sample mean of wage for workers with Sales
occupation. What is the average wage for workers with Sales occupation?
(d) Use the command regress wage i.occupation to run a regression with binary variables for every occupation
category. (Adding i. to a categorical variable will automatically generate a binary variable for each category.)
The occupation with numeric value 1 is used as the base group. Given the regression results, what is the average
wage for workers with Sales occupation? How does your answer compare to part 2(c)?
(e) Which occupation has the highest average wage? How much is it?
(f) Use a similar command as step 2(d), this time to study the average hours for each occupation. Which occupation
works the longest hours per week? How many hours on average?
Next, we follow a similar procedure as in steps 2(a)–(d) to study the wage gap among different races.
(g) Use the command regress wage i.race to run a regression with binary variables for every race category. What
is the average wage for white women?
(h) What is the wage gap between white and black? What is the 95% confidence interval for this wage gap?
(i) Generate three binary variables for categories in race to run a saturated regression instead of 2(g). What is the
average wage for white women? How does your result compare to 2(g)?
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3. In this exercise, you will run multiple regressions with interaction terms. First, consider the following regression with
an interaction between the two binary regressors, collgrad and union:
wagei = β0 + β1 unioni + β2 collgradi + β3 union×collgradi + ui ,
To estimate this model, generate an interaction term between collgrad and union. Use the regress command to
estimate the OLS coefficients.
(a) What is the base category in this model? What is the average wage for workers in this base category?
(b) What is the difference in average wage for non-college graduates in a union and non-college graduates not in a
union? Please report a positive number.
(c) What is the difference in average wage for college graduates in a union and college graduates not in a union?
Please report a positive number.
Next, consider the following regression with an interaction term between ttl exp and union:
lnwagei = β0 + β1 unioni + β2 ttl expi + β3 union×ttl expi + ui ,
To estimate this model, generate an interaction term between ttl exp and union. Use the regress command to
estimate the OLS coefficients.
(d) For non-unionized workers, what is the average percentage change in wage when experience increases by one year?
(e) For unionized workers, what is the average percentage change in wage when experience increases by one year?
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