EC 508
Problem Set #3
Last Name:
First Name:
Group members with whom you worked:
Note: The solutions you submit for this problem set should contain:
* Your group?s .do file
* Your own .log file where the results (i.e. output) produced by
ps1.do are displayed
* Your independent writeup for questions 1?2
1
QUESTION 1 – EFFECT OF TVs ON LIFE EXPECTANCY
(a) Below are the results of a regression of life expectancy in years
(lifeex) on number of televisions per hundred people (tv). Interpret
the coefficient on tv.
. reg lifeex tv, robust
Linear regression
Number of obs
F( 1, 117)
Prob > F
R-squared
Root MSE
=
=
=
=
=
119
82.40
0.0000
0.5370
7.1945
—————————————————————————–|
Robust
lifeex |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
————-+—————————————————————tv |
.4155946
.0457829
9.08
0.000
.324924
.5062652
_cons |
57.31097
1.074192
53.35
0.000
55.18359
59.43836
——————————————————————————
(b) Below are the results of a regression of life expectancy (lifeex)
on number of televisions per hundred people (tv) and income per capita
(gdp). Interpret the coefficient on tv.
. reg lifeex tv gdp, robust
Linear regression
Number of obs
F( 1, 117)
Prob > F
R-squared
Root MSE
=
=
=
=
=
119
100.03
0.0000
0.6027
6.6931
—————————————————————————–|
Robust
lifeex |
Coef.
Std. Err.
t
P>|t|
[95% Conf. Interval]
————-+—————————————————————tv |
.1734937
.0592164
2.93
0.004
.0562081
.2907793
gdp |
.0008514
.0001448
5.88
0.000
.0005648
.0011381
_cons |
56.08268
1.015129
55.25
0.000
54.07208
58.09327
——————————————————————————
(c) How and why does your interpretation of the coefficient on tv
change? Make sure you explain why the coefficient on tv changes in the
direction it does both (i) in a technical language, and (ii) in a way
that a policymaker not well versed in statistics can understand.
2
QUESTION 2 – GENDER AND LABOR-MARKET OUTCOMES
Create a .do file named ps3.do to help answer the following questions.
In addition to answering the questions below, submit your Stata output
in the form of a .log file.
This question uses the dataset gender2009.dta which consists of
information on salaries and work hours in a sample of 950 individuals
who identify as male or female. We will use these data to analyze the
gender gap in earnings.
(a) Focus on the sample of individuals are
(inclusive). Write a regression that would
difference in yearly salary across gender,
weekly hours worked. Run the regression in
coefficient estimates.
between 22 and 35 years old
tell you the gender
with a control for usual
Stata, and interpret the
(h) What do you conclude about the gender earnings gap? How does your
answer differ from what you would get if you didn?t control for hours?
3
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