## # A tibble: 5 × 2
## year n
## <dbl> <int>
## 1 2002 25375
## 2 2007 520
## 3 2012 28963
## 4 2017 27546
## 5 2022 28530
## .
## Dependent Var.: violent_crime_cleared
##
## Constant 0.4236*** (0.0062)
## excess_log_q -0.1275*** (0.0212)
## _______________ _____________________
## S.E. type IID
## Observations 677
## R2 0.05081
## Adj. R2 0.04941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## .
## Dependent Var.: violent_crime_cleared
##
## Constant 0.4343*** (0.0063)
## excess_log_q_nocrime -0.1683*** (0.0199)
## ____________________ _____________________
## S.E. type IID
## Observations 677
## R2 0.09552
## Adj. R2 0.09418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## .
## Dependent Var.: violent_crime_cleared
##
## Constant 0.4083*** (0.0066)
## pct_change_fte 0.0103 (0.0087)
## _______________ _____________________
## S.E. type IID
## Observations 533
## R2 0.00262
## Adj. R2 0.00074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Police Killings
## # A tibble: 24 × 9
## city st fte_Police pop_2020 log_q_pol log_pop log_med_income log_crime
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ATLANTA GA 2095 498715 7.65 13.1 11.3 NA
## 2 BAKERSF… CA 617 403455 6.42 12.9 11.2 NA
## 3 CHARLOT… NC 2588 874579 7.86 13.7 11.2 NA
## 4 CHESAPE… VA 473 249422 6.16 12.4 11.4 NA
## 5 CHICAGO IL 12453 2746388 9.43 14.8 11.2 NA
## 6 CINCINN… OH 1054 309317 6.96 12.6 10.8 NA
## 7 DURHAM NC 548 283506 6.31 12.6 11.2 NA
## 8 FREMONT CA 260 230504 5.56 12.3 12.0 NA
## 9 FRESNO CA 1101 542107 7.00 13.2 11.1 NA
## 10 IRVINE CA 321 307670 5.77 12.6 11.7 NA
## # ℹ 14 more rows
## # ℹ 1 more variable: log_outlays <dbl>
## .
## Dependent Var.: police_killings
##
## Constant -37.18*** (9.821)
## excess_log_q -0.4069 (1.836)
## log_pop 1.918 (1.215)
## log_crime 1.566. (0.8387)
## _______________ _________________
## S.E. type IID
## Observations 37
## R2 0.55385
## Adj. R2 0.51329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## .
## Dependent Var.: police_killings
##
## Constant -35.96*** (9.613)
## excess_log_q_nocrime 0.3973 (1.928)
## log_pop 1.800 (1.173)
## log_crime 1.580. (0.8324)
## ____________________ _________________
## S.E. type IID
## Observations 37
## R2 0.55376
## Adj. R2 0.51319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## .
## Dependent Var.: avg_police_killings
##
## Constant 2.910*** (0.4399)
## pct_change_fte 3.425 (3.567)
## _______________ ___________________
## S.E. type IID
## Observations 53
## R2 0.01776
## Adj. R2 -0.00150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Load up LEOKA data and compare to Census
Redo with LEOKA
## # A tibble: 974 × 12
## city st year violent_crime violent_cleared property_crime
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 HOUSTON TX 2022 26402 6640 105536
## 2 PHILADELPHIA PA 2022 16388 4882 68197
## 3 MEMPHIS TN 2022 15080 3224 45296
## 4 SAN ANTONIO TX 2022 13091 2383 74778
## 5 MILWAUKEE WI 2022 8605 3014 18707
## 6 ALBUQUERQUE NM 2022 7770 1542 27094
## 7 DENVER CO 2022 7588 3677 45592
## 8 SEATTLE WA 2022 6163 1965 42067
## 9 SAN DIEGO CA 2022 5978 1757 25354
## 10 CLEVELAND OH 2022 5928 779 16002
## # ℹ 964 more rows
## # ℹ 6 more variables: property_cleared <dbl>, n_months_V <dbl>,
## # n_months_P <dbl>, ori <chr>, agency_name <chr>, crime <dbl>
## .
## Dependent Var.: violent_crime_cleared
##
## Constant 0.4101*** (0.0073)
## excess_log_q_leoka -0.0897** (0.0291)
## __________________ _____________________
## S.E. type IID
## Observations 437
## R2 0.02134
## Adj. R2 0.01909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## .
## Dependent Var.: police_killings
##
## Constant -36.96*** (9.455)
## excess_log_q_leoka -0.5362 (1.980)
## log_pop 1.906 (1.187)
## log_crime 1.557. (0.8400)
## __________________ _________________
## S.E. type IID
## Observations 37
## R2 0.55418
## Adj. R2 0.51365
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
