Firearms and Violence A Critical Review (2005) / Chapter Skim
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6 Right-to-Carry Laws
6 Right-to-Carry Laws
Pages 120-151
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From page 120... ...
. Lott concludes that the adoption of right-to-carry laws substantially reduces the prevalence of violent crime.
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From page 121... ...
the sensitivity of the empirical results to seemingly minor changes in model specification, (b) a lack of robustness of the results to the inclusion of more recent years of data (during which there are many more law changes than in the earlier period)
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From page 122... ...
. Explanatory variables employed in studies include the arrest rate for the crime category in question, population density in the county, real per capita income variables, county population, and variables for the percent of population that is in each of many race-by-age-by-gender categories.
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From page 123... ...
Any estimate of a policy effect requires an assumption about the "counterfactual," in this case what would have happened to crime rates in the absence of the change in the law. The implicit assumption underlying this simple illustrative dummy variable model is that, in the absence of the change in the law, the crime rate in each county would, on average, have been the county mean plus a time-period adjustment reflecting the common trend in crime rates across all counties.
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From page 124... ...
Trend Model While the dummy variable model measures the effect of the adoption of a right-to-carry law as a one-time shift in crime rates, one can alternatively estimate the effect as the change in time trends. The following trend model, which generated the results in Lott's Table 4.8, allows right-to-carry laws to affect trends in crime: 1992 (6.2)
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From page 125... ...
And so on. The coefficient on each of these variables shows how adopting states' time patterns of crime rates move, relative to the national time pattern, surrounding the respective states' law adoption.
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. In the dummy variable model, the magnitude of the estimated reduction in the rates of violent crime and aggravated assault was reduced, the estimated reduction in the murder rate increased, and the sign of the estimated effects of rightto-carry laws on robbery reversed.
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From page 127... ...
Row 3 displays estimates using the revised new data set restricted to the period 1977-1992. These new results tend to show larger reductions in the violent crime trends than those found using the revised original data.
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From page 128... ...
, for example, raises concerns that county-level control variables may not be precisely measured on an annual basis and that the arrest rate control variable, which includes the crime rate in the denominator, may bias the estimates. In response to these concerns, Duggan estimated a simple dummy variable model that controls only for year and county fixed effects.7 Duggan drops all other covariates from the model.
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From page 129... ...
data, this reduced the magnitude of the estimated reduction in the rates of murder and aggravated assault, and it reversed the signs of the estimated effects of right-to-carry laws on rape, robbery, and all violent crime. That is, according to Duggan's estimates, adoption of right-to-carry laws increases the frequencies of rape, robbery, and violent crime as a whole.
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29* Plassmann and No control for arrest rate 7*
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RIGHT-TO-CARRY LAWS 131 Aggravated Property Auto Assault Robbery Crimes Theft Burglary Larceny 7*
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bAdded covariates for state poverty, unemployment, death penalty execution rates, and regional time trends. TABLE 6-4 Summary of Selected Studies: Trend and Hybrid Variable Model (shaded cells indicate a positive coefficient)
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From page 133... ...
In summary, according to Black and Nagin, adoption of a right-to-carry law may increase, decrease, or have no discernible effect on the crime rate depending on the crime and the state that are involved.8 8To avoid selection problems associated with using counties with positive crime rates, Black and Nagin also restricted their analysis to counties with populations of 100,000 or more. This was done to mitigate a possible bias arising from Lott's use of the arrest rate as an explanatory variable.
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From page 134... ...
However, Lott does not report the details of his analysis or the statistical significance levels of his estimates. Moreover, his response does not explain why Black and Nagin found statistically significant increases in some crime rates for some states following passage of right-to-carry laws.
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The relative trend is the difference between the crime trend in the adopting county and the trend in a nonadopting county with the same values of the explanatory variables X According to the figure, adoption of the law increased the level of violent crime but accelerated a decreasing (relative)
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From page 136... ...
found that rerunning the dummy variable model regressions using the corrected data reduced the magnitude of the estimated reduction in the rates of violent crime, murder, rape, and robbery, and it reversed the sign of the estimated effects of rightto-carry laws on aggravated assault. Moreover, none of the negative estimates is statistically significant, while effects for larceny, auto theft, and property crime overall are positive and significant.
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From page 137... ...
For example, the standard error for the dummy variable model estimate of the effect of right-to-carry laws on violent crime increases from 0.98 when reporting the unadjusted standard error, to 2.31 when estimating clustered sampling standard errors (Duggan, 2001) , to 4.9 when using the methods advocated by Helland and Tabarrok (2004)
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From page 138... ...
Investigators who make clustering corrections usually consider the counties in a state to constitute one of Moulton's clusters and appear to believe that the absence of state-level additive effects in their models causes standard errors to be too low. The models estimated in this literature, including those of Lott and his critics, typically contain countylevel fixed effects (the constants gi in equations 6.1 and 6.2)
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From page 139... ...
The second is the difficulty of estimating the relation among crime rates, a large number of potential explanatory variables, and the adoption of rightto-carry laws. Even if the correct explanatory variables were known, it would be hard to specify a model correctly, especially in high dimensional settings with many explanatory variables.
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models that do not control for state poverty, unemployment, death penalty execution rates, or regional time trends. The controls include the arrest rate for the crime category in question (AOVIOICP)
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, the results have now changed rather substantially. Only the coefficient on murder is negative and significant, while seven coefficients are positive and significant (violent crime overall, aggravated assault, robbery, property crime overall, auto theft, burglary, and larceny)
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, and variables for the percentage of the population that is in each of many race x age x gender categories (e.g., PBM1019 is the percentage of the population that is black, male, earlier sample periods almost completely disappear with the extension of the sample to 2000. The committee views the failure of the original dummy variable model to generate robust predictions outside the original sample as important evidence of fragility of the model's findings.12 These results are also substantially different from those found when using the expanded set of control variables first adopted by Lott (2000: Table 9.1)
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From page 143... ...
To explore why the updated dummy variable and trend models give conflicting results, we do two things. First, we estimate a more flexible year-by-year specification, a variant of Model 6.1, the dummy variable model.
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From page 144... ...
One needs to include at least 6 years following the prelaw-change period to find statistically significant reductions in the violent crime and murder trends. The trend results rely on changes in crime trends occurring long after the law changes, again raising serious questions about whether one can 13That is, we subtract the year 0 coefficient from each year's coefficient.
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, | Top of 95% CI sensibly attribute the estimates from trend models in the literature to the adoption of right-to-carry laws. Are the Results Sensitive to Controls?
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From page 146... ...
, | Top of 95% CI right-to-carry variable, year dummies, and county fixed effects. These estimates tell us how crime has changed in states that have adopted the rightto-carry laws before and after the law change, relative to national time patterns in crime.
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From page 147... ...
For example, the violent crime coefficient with controls is 4.1 percent, while it is 12.9 percent without controls. These results show that states that
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148 FIREARMS AND VIOLENCE Auto Theft 20 10 Change 0 -10 Percentage -20 -30 -10 -5 0 5 10 Years Relative to Law Passage Burglary 10 0 Change -10 -20 Peercentage -30 -40 -10 -5 0 5 10 Years Relative to Law Passage Larceny 30 20 10 Crime 0 -10 Percentage -20 -30 -10 -5 0 5 10 Years Relative to Law Passage FIGURE 6-4 Year-by-year estimates of the percentage change in disaggregate property crimes (normalized to adoption date of right-to-carry law, year 0)
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From page 149... ...
So these estimates indicate that, for the period 1977-1992, states adopting right-to-carry laws saw roughly no change in their violent crime rates and 8.5 percent increases in their property crime rates, relative to national time patterns. Estimating the model using data to 2000 shows that states adopting right-to-carry laws saw 12.9 percent increases in violent crime -- and 21.2 percent increases in property crime -- relative to national time patterns.
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From page 150... ...
models that do not control for state poverty, unemployment, death penalty execution rates, or regional time trends. The controls include the arrest rate for the crime category in question (AOVIOICP)
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From page 151... ...
It is also the committee's view that additional analysis along the lines of the current literature is unlikely to yield results that will persuasively demonstrate a causal link between right-to-carry laws and crime rates (unless substantial numbers of states were to adopt or repeal right-to-carry laws) , because of the sensitivity of the results to model specification.
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