- Papke (1994) studied the effect of the Indiana enterprise zone (EZ) program on unemployment claims.
She analyzed 22 cities in Indiana over the period from 1980 to 1988. A simple policy evaluation model
is
log(uclmsit) = β0 + β1ezit + ai + uit, (1)
where uclmsit is the number of unemployment claims filed during year t in city i. The binary variable
ezit is equal to one if city i at time t was an enterprise zone; we are interested in β1. The unobserved effect ai represents fixed factors that affect the economic climate in city i. Because enterprise
zone designation was not determined randomly—enterprise zones are usually economically depressed
areas—it is likely that ezit and ai are positively correlated (high ai means higher unemployment
claims, which lead to a higher chance of being given an EZ). Use data from ezunem.dta to answer
the following questions: - Estimate (1) using fixed effects model. The presence of an EZ is associated with how much fall in
unemployment claims? Is the effect statistically significant? - An alternative model is
log(uclmsit) = θt + β1ezit + ai + uit. (2)
The parameter θt just denotes a different intercept for each time period. Generally, unemployment
claims were falling statewide over this period, and this should be reflected in the different year intercepts. Estimate (2) using fixed effects model with year dummies. The presence of an EZ is associated
with how much fall in unemployment claims? Is the effect statistically significant? - Use data from wage.dta to answer the following questions:
- Estimate the following model by OLS and report robust standard errors:
log(wage) = β0 + β1educ + β2exper + β3exper2 + β4married + u. (3)
Interpret the coefficient on years of education. Why might this be a biased estimate of the effect of
education on earnings? - The variable brthord is birth order (brthord is one for a first-born child, two for a second-born
child, and so on). Explain why educ and brthord might be negatively correlated. Regress educ on
brthord to determine whether there is a statistically significant negative correlation. - Use brthord as an IV for educ in equation (3). Report the coefficient and robust standard error
on years of education. Interpret the coefficient on years of education. Compare the results to your
earlier findings in (1) without using 2SLS estimation.
For this exercise, the TAs will provide support on coding in Stata (free on Apporto -
see the link to VCL on Canvas), but you can use any software you are comfortable with.
Please submit your computer codes and a log file that stores all your estimation results.
Please submit answers for questions using comments in your codes.
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