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10.6 Drunk Driving Laws and Traffic Deaths 417

3. The coefficient on the first offense punishment variable is also estimated to
be small and is not significantly different from zero at the 10% significance
level.

4. The economic variables have considerable explanatory power for traffic fa-
talities. High unemployment rates are associated with fewer fatalities: An
increase in the unemployment rate by one percentage point is estimated to
reduce traffic fatalities by 0.063 death per 10,000. Similarly, high values of
real per capita income are associated with high fatalities: The coefficient is
1.82, so a 1% increase in real per capita income is associated with an increase
in traffic fatalities of 0.0182 death per 10,000 (see Case I in Key Concept
8.2 for interpretation of this coefficient). According to these estimates, good
economic conditions are associated with higher fatalities, perhaps because
of increased traffic density when the unemployment rate is low or greater
alcohol consumption when income is high. The two economic variables are
jointly significant at the 0.1% significance level (the F-statistic is 29.62).

Columns (5) through (7) of Table 10.1 report regressions that check the sen-
sitivity of these conclusions to changes in the base specification. The regression in
column (5) drops the variables that control for economic conditions. The result is
an increase in the estimated effect of the real beer tax, which becomes significant
at the 5% level, but no appreciable change in the other coefficients. The sensitivity
of the estimated beer tax coefficient to including the economic variables, com-
bined with the statistical significance of the coefficients on those variables in col-
umn (4), indicates that the economic variables should remain in the base
specification. The regression in column (6) shows that the results in column (4)
are not sensitive to changing the functional form when the three drinking age
indicator variables are replaced by the drinking age itself. When the coefficients
are estimated using the changes of the variables from 1982 to 1988 [column (7)],
as in Section 10.2, the findings from column (4) are largely unchanged except that
the coefficient on the beer tax is larger and is significant at the 1% level.

The strength of this analysis is that including state and time fixed effects mit-
igates the threat of omitted variable bias arising from unobserved variables that
either do not change over time (like cultural attitudes toward drinking and driv-
ing) or do not vary across states (like safety innovations). As always, however, it
is important to think about possible threats to validity. One potential source of
omitted variable bias is that the measure of alcohol taxes used here, the real tax
on beer, could move with other alcohol taxes, which suggests interpreting the
results as pertaining more broadly than just to beer. A subtler possibility is that
hikes in the real beer tax could be associated with public education campaigns. If