Table 10.
Robustness checks: Alternative DDD estimates for total crime incidents occurring in the low-SES neighborhoods
| Total Crime | For the entire stores in the low-SES neighborhoods (65 stores, N=213,363) | For the reduced stores in the low-SES neighborhoods (19 stores, N=97,147) | ||||
|---|---|---|---|---|---|---|
| Within a 1/8-mile radius | Between 1/8- and 1/4-mile radii | Between 1/4- and 1/2-mile radii | Within a 1/8-mile radius | Between 1/8- and 1/4-mile radii | Between 1/4- and 1/2-mile radii | |
| (1) Original model : Estimate by Poisson regressions (from Table 4 and 5) | 0.045* (0.018) |
0.043* (0.033) |
0.041 (0.031) |
0.061** (0.021) |
0.035* (0.015) |
−0.013 (0.026) |
| (2) “Tuesday” as the false affected day (falsification test) | −0.003 (0.012) |
−0.024 (0.013) |
−0.028 (0.033) |
−0.018* (0.008) |
−0.024 (0.017) |
−0.038 (0.050) |
| (3) Estimate when comparing Sundays vs. Friday & Saturday | 0.037* (0.018) |
0.034 (0.021) |
0.049 (0.025) |
0.045*** (0.015) |
−0.001 (0.020) |
−0.009 (0.017) |
| (4) Estimate when removing days adjacent to Sunday (removing Saturday and Monday) | 0.049* (0.020) |
0.049* (0.022) |
0.046 (0.035) |
0.071** (0.025) |
0.038* (0.018) |
0.002 (0.036) |
| (5) Estimate by negative binomial regressions | 0.042* (0.019) |
0.040* (0.020) |
0.043 (0.031) |
0.059** (0.021) |
0.033* (0.014) |
−0.012 (0.027) |
Note: See the note in Table 4. All the other coefficients ((2)~(5)) are fro separate regressions and stand for estimates of changes in crime incident numbers a day in terms of average marginal effects. For the statistical significance,
*
p<0.05;
**
p<0.01;
***
p<0.001.