Version: Spring 2018
EC200 Econometrics and Applications

Problem Set 7\

1. In 1985, neither Florida nor Georgia had laws banning open alcohol containers in vehicle passenger compartments. By 1990, Florida had passed such a law, but Georgia had not.

   1. Suppose you collect random samples of the driving-age population in both states, for 1985 and 1990. Let $arrest$ be a binary variable equal to one if a person was arrested for drunk driving during the year. Without controlling for any other factors, write down a linear probability model that allows you to test whether the open container law reduced the probability of being arrested for drunk driving. Which coefficient measures the effect of the law?

   2. Why might you want to control for other factors in the model? What might some of these factors be?

   3. Now, suppose that you can only collect data for 1985 and for 1990 at the county level for the two states. The dependent variable would be the fraction of licensed drivers arrested for drunk driving during the year. How does this data structure differ from the individual-level data described in part (a)? What econometric method would you use?

2. For this exercise, use JTRAIN.dta to determine the effect of a job training grant on hours of job training per employee. The basic model for the three years is the following: $$\begin{array}{rlr}hrsem{p}_{it}& ={\beta }_0+{\delta }_{1}d{88}_t+{\delta }_{2}d{89}_t+& {\beta }_{1}gran{t}_{it}+{\beta }_{2}gran{t}_{i,t-1}+{\beta }_{3}log(emplo{y}_{it})+a_i+u_{it}\end{array}$$

   1. Estimate the equation using first differencing. How many firms are used in the estimation? How many total observations would be used if each firm had data on all variables (in particular, $hrsemp$) for all three time periods?

   2. Interpret the coefficient on $grant$, and comment on its significance.

   3. Is it surprising that $gran{t}_{-1}$ is insignificant? Explain.

   4. Do larger firms train their employees more or less, on average? How big are the differences in training?

3. Use CRIME4.dta for this exercise, and see scanned upload for example 13.9.

   1. Replicate the results in Example 13.9.

   2. Re-estimate the unobserved effects model for crime in Example 13.9, but use fixed effects rather than differencing. Are there any notable sign or magnitude changes in the coefficients? What about statistical significance?

   3. Add the logs of each wage variable in the data set and estimate the model by fixed effects. How does including these variables affect the coefficient on the criminal justic variables in part (b)?

   4. Do the wage variables in part (c) have the expected sign? Are they jointly significant?

4. Finish and submit Lab 6.