The data set DRIVING includes state-level panel data (for the 48 continental U.S. states) from 1980 through 2004, for a total of 25 years. Various driving laws are indicated in the data set, including the alcohol level at which drivers are considered legally intoxicated. There are also indicators for “per se” laws—where licenses can be revoked without a trial—and seat belt laws. Some economics and demographic variables are also included.

(i) How is the variable totfatrte defined? What is the average of this variable in the years 1980, 1992, and 2004? Run a regression of totfatrte on dummy variables for the years 1981 through 2004, and describe what you find. Did driving become safer over this period? Explain.

(ii) Add the variables bac08, bac10, perse, sbprim, sbsecon, sl70plus, gdl, perc14_24, unem, and vehicmilespc to the regression from part (i). Interpret the coefficients on bac8 and bac10. Do per se laws have a negative effect on the fatality rate? What about having a primary seat belt law? (Note that if a law was enacted sometime within a year the fraction of the year is recorded in place of the zero-one indicator.)

(iii) Reestimate the model from part (ii) using fixed effects (at the state level). How do the coefficients on bac08, bac10, perse, and sbprim compare with the pooled OLS estimates? Which set of estimates do you think is more reliable?

(iv) Suppose that vehicmilespc, the number of miles driven per capita, increases by 1,000. Using the FE estimates, what is the estimated effect on totfatrte? Be sure to interpret the estimate as if explaining to a layperson.

(v) If there is serial correlation or heteroskedasticity in the idiosyncratic errors of the model then the standard errors in part (iii) are invalid. If possible, use “cluster” robust standard errors for the fixed effects estimates. What happens to the statistical significance of the policy variables in part (iii)?