CONCLUSIONS
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67
68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
102 103 104 105 106 107 108 109 110 111 112 113 114 115
The literature on right-to-carry laws summarized in this chapter has
obtained conflicting estimates of their effects on crime. Estimation results
have proven to be very sensitive to the precise specification used and time
period examined. The initial model specification, when extended to new
data, does not show evidence that passage of right-to-carry laws reduces
crime. The estimated effects are highly sensitive to seemingly minor changes
in the model specification and control variables. No link between right-tocarry
laws and changes in crime is apparent in the raw data, even in the
initial sample; it is only once numerous covariates are included that the
negative results in the early data emerge. While the trend models show a
reduction in the crime growth rate following the adoption of right-to-carry
laws, these trend reductions occur long after law adoption, casting serious
doubt on the proposition that the trend models estimated in the literature
reflect effects of the law change. Finally, some of the point estimates are
imprecise. Thus, the committee concludes that with the current evidence it
is not possible to determine that there is a causal link between the passage
of right-to-carry laws and crime rates.
TABLE 6-7 Trend Model with Varying Postlaw Change Durations
Violent
Years Controlsa Crime Murder Rape
1. Baseline 2000 Yes –0.95 –2.03 –2.81
comm estimateb
from row 1 of
Table 6-6
SE (0.18)** (0.26)** (0.20)**
2. 6 years 2000 Yes –0.97 –1.11 –2.90
SE (0.29)** (0.42)** (0.33)**
3. 5 years 2000 Yes –0.65 0.05 –2.45
SE (0.35) (0.50) (0.40)**
4. 4 years 2000 Yes –0.27 0.48 –0.74
SE (0.44) (0.63) (0.50)
aThe regressions use the covariates and specification from the original Lott and Mustard
(1997) 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), population density in the county, real per capita income variables
(RPCPI RPCUI RPCIM RPCRPO), county population (POPC), and variables for the percentage
of the population that is in each of many race age gender categories (e.g., PBM1019 is
the percentage of the population that is black, male, and between ages 10 and 19).
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. Furthermore,
the usefulness of future crime data for studying the effects of right-tocarry
laws will decrease as the time elapsed since enactment of the laws
increases.
If further headway is to be made on this question, new analytical
approaches and data sets will need to be used. For example, studies that
more carefully analyze changes in actual gun-carrying behavior at the county
or even the local level in response to these laws may have greater power in
identifying the impact of such laws. Surveys of criminals or quantitative
measures of criminal behavior might also shed light on the extent to which
crime is affected by such laws.
bUsing the revised new data set, for the full available time period (1977-2000).
NOTES: All samples start in 1977. All estimates use the trend model. Rows 2 through 4 of
this table restrict the sample to include only years falling fixed numbers of years past the law
change. For example, row 2 includes all the prelaw-change years, the year of the law change
(year 0), plus 5 additional years, for a total of 6 years after the prelaw-change period. SE =
standard error. Standard errors are in parentheses, where * = significant at 5% and ** =
significant at 1%.