ECON 303, EXAM 1
FALL 2008
DUE: FRIDAY, NOVEMBER 14, 2008 at 12:40 PM
Please show your work.
There are 3 sections.
Each section carries equal weight.
If you have ANY questions, please ASK ME.
Yes, the LC HONOR CODE APPLIES
Interpreting Regressions
1.
Earnings functions attempt to find the determinants of earnings, using both continuous and
binary variables. One of the central questions analyzed in this relationship is the returns to
education.
Collecting data from 253 individuals, you estimate the following relationship
0.54 + 0.083
×
Educ
,
2
R
= 0.20,
SER
= 0.445
(0.14)
(0.011)
(S.E.)
where
Earn
is average hourly earnings and
Educ
is years of education.
a.
What is the effect of an additional year of schooling? If you had a strong belief that
years of high school education were different from college education, how would you
modify the equation? What if your theory suggested that there was a “diploma
effect”?
You read in the literature that there should also be returns to onthejob training. To
approximate onthejob training, researchers often use the so called Mincer
or potential
experience variable, which is defined as
Exper
=
Age
–
Educ
– 6.
b.
Explain the reasoning behind this approximation. Is it likely to resemble years of
employment for various subgroups of the labor force?
You incorporate the experience variable into your original regression
0.01 + 0.101
×
Educ
+ 0.033
×
Exper
– 0.0005
×
Exper
2
,
(0.16)
(0.012)
(0.006)
(0.0001)
(S.E.)
2
R
= 0.34,
SER
= 0.405
c.
What is the effect of an additional year of experience for a person who is 40 years old
and had 12 years of education? What about for a person who is 60 years old with the
same education background?
d.
Test for the significance of each of the coefficients of the added variables. Why has
the coefficient on education changed so little? Sketch the age(log)earnings profile for
workers with 8 years of education and 16 years of education.
1
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View Full DocumentYou want to find the effect of introducing two variables, gender and marital status. Accordingly
you specify a binary variable that takes on the value of one for females and is zero otherwise
(
Female
), and another binary variable that is one if the worker is married but is zero otherwise
(
Married
). Adding these variables to the regressors results in:
0.21 + 0.093
×
Educ
+ 0.032
×
Exper
– 0.0005
×
Exper
2
(0.16)
(0.012)
(0.006)
(0.0001)
 0.289
×
Female
+ 0.062
Married
,
(0.049)
(0.056)
2
R
= 0.43,
SER
= 0.378
e.
Are the coefficients of the two added binary variables individually statistically
significant? Are they economically important? In percentage terms, how much less do
females earn per hour, controlling for education and experience? How much more do
married people make? What is the percentage difference in earnings between a single
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 Spring '10
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 Econometrics, Regression Analysis, National Income, Null hypothesis, per capita, gross national income

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