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## 10.2 Panel Data with Two Time Periods: “Before and After” Comparisons

Suppose there are only \(T=2\) time periods \(t=1982,1988\). This allows us to analyze differences in changes of the the fatality rate from year 1982 to 1988. We start by considering the population regression model \[FatalityRate_{it} = \beta_0 + \beta_1 BeerTax_{it} + \beta_2 Z_{i} + u_{it}\] where the \(Z_i\) are state specific characteristics that differ between states but are *constant over time*. For \(t=1982\) and \(t=1988\) we have
\[\begin{align*}
FatalityRate_{i1982} =&\, \beta_0 + \beta_1 BeerTax_{i1982} + \beta_2 Z_i + u_{i1982}, \\
FatalityRate_{i1988} =&\, \beta_0 + \beta_1 BeerTax_{i1988} + \beta_2 Z_i + u_{i1988}.
\end{align*}\]

We can eliminate the \(Z_i\) by regressing the difference in the fatality rate between 1988 and 1982 on the difference in beer tax between those years:
\[FatalityRate_{i1988} - FatalityRate_{i1982} = \beta_1 (BeerTax_{i1988} - BeerTax_{i1982}) + u_{i1988} - u_{i1982}\]
This regression model yields an estimate for \(\beta_1\) robust a possible bias due to omission of the \(Z_i\), since these influences are eliminated from the model. Next we use use `R` to estimate a regression based on the differenced data and plot the estimated regression function.

```
# compute the differences
diff_fatal_rate <- Fatalities1988$fatal_rate - Fatalities1982$fatal_rate
diff_beertax <- Fatalities1988$beertax - Fatalities1982$beertax
# estimate a regression using differenced data
fatal_diff_mod <- lm(diff_fatal_rate ~ diff_beertax)
coeftest(fatal_diff_mod, vcov = vcovHC, type = "HC1")
```

```
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.072037 0.065355 -1.1022 0.276091
## diff_beertax -1.040973 0.355006 -2.9323 0.005229 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

Including the intercept allows for a change in the mean fatality rate in the time between 1982 and 1988 in the absence of a change in the beer tax.

We obtain the OLS estimated regression function \[\widehat{FatalityRate_{i1988} - FatalityRate_{i1982}} = -\underset{(0.065)}{0.072} -\underset{(0.36)}{1.04} \times (BeerTax_{i1988}-BeerTax_{i1982}).\]

```
# plot the differenced data
plot(x = diff_beertax,
y = diff_fatal_rate,
xlab = "Change in beer tax (in 1988 dollars)",
ylab = "Change in fatality rate (fatalities per 10000)",
main = "Changes in Traffic Fatality Rates and Beer Taxes in 1982-1988",
xlim = c(-0.6, 0.6),
ylim = c(-1.5, 1),
pch = 20,
col = "steelblue")
# add the regression line to plot
abline(fatal_diff_mod, lwd = 1.5)
```

The estimated coefficient on beer tax is now negative and significantly different from zero at \(5\%\). Its interpretation is that raising the beer tax by \(\$1\) causes traffic fatalities to decrease by \(1.04\) per \(10000\) people. This is rather large as the average fatality rate is approximately \(2\) persons per \(10000\) people.

```
# compute mean fatality rate over all states for all time periods
mean(Fatalities$fatal_rate)
```

`## [1] 2.040444`

Once more this outcome is likely to be a consequence of omitting factors in the single-year regression that influence the fatality rate and are correlated with the beer tax *and* change over time. The message is that we need to be more careful and control for such factors before drawing conclusions about the effect of a raise in beer taxes.

The approach presented in this section discards information for years \(1983\) to \(1987\). A method that allows to use data for more than \(T=2\) time periods and enables us to add control variables is the fixed effects regression approach.