nov_2023_regressions
Olivia Martin
2023-11-13
Transparent California Regressions
Basic OLS with data ~ 2017
## # A tibble: 8 × 2
## Percentile `Average Value`
## <chr> <dbl>
## 1 p_salary_25 61549.
## 2 p_salary_50 96629.
## 3 p_salary_75 119351.
## 4 p_salary_90 139992.
## 5 ff_salary_25 82163.
## 6 ff_salary_50 112217.
## 7 ff_salary_75 137389.
## 8 ff_salary_90 158748.##
## =======================================================================================
## Dependent variable:
## --------------------------------------------------------
## p_salary_25 p_salary_50 p_salary_75 p_salary_90
## (1) (2) (3) (4)
## ---------------------------------------------------------------------------------------
## total_population 0.012* 0.011* 0.012 0.014
## (0.006) (0.006) (0.008) (0.009)
##
## median_hh_income 0.149** 0.248*** 0.304*** 0.323***
## (0.057) (0.059) (0.070) (0.085)
##
## pct_non_white -13,654.000 7,738.000 17,095.000 27,433.000
## (12,128.000) (12,577.000) (14,729.000) (17,966.000)
##
## pct_65_plus -112,923.000** 13,410.000 42,047.000 74,411.000
## (48,378.000) (50,171.000) (58,754.000) (71,666.000)
##
## demshare_pres_2016 28,958.000** 43,444.000*** 59,960.000*** 65,856.000***
## (14,143.000) (14,667.000) (17,176.000) (20,951.000)
##
## Constant 54,079.000*** 40,943.000*** 39,086.000** 43,626.000**
## (13,425.000) (13,923.000) (16,305.000) (19,888.000)
##
## ---------------------------------------------------------------------------------------
## Observations 193 193 193 193
## R2 0.108 0.195 0.229 0.200
## Adjusted R2 0.085 0.173 0.209 0.179
## Residual Std. Error (df = 187) 26,874.000 27,870.000 32,638.000 39,811.000
## F Statistic (df = 5; 187) 4.550*** 9.050*** 11.100*** 9.360***
## =======================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01##
## ======================================================================================
## Dependent variable:
## -------------------------------------------------------
## ff_salary_25 ff_salary_50 ff_salary_75 ff_salary_90
## (1) (2) (3) (4)
## --------------------------------------------------------------------------------------
## total_population 0.014 0.018 0.021 0.027*
## (0.011) (0.012) (0.013) (0.014)
##
## median_hh_income 0.629*** 0.721*** 0.746*** 0.754***
## (0.156) (0.180) (0.191) (0.207)
##
## pct_non_white 35,324.000 27,293.000 23,053.000 20,928.000
## (26,683.000) (30,760.000) (32,695.000) (35,407.000)
##
## pct_65_plus 31,227.000 93,750.000 131,052.000 130,733.000
## (107,980.000) (124,476.000) (132,305.000) (143,281.000)
##
## demshare_pres_2016 30,058.000 47,589.000 76,295.000* 92,097.000**
## (33,514.000) (38,634.000) (41,065.000) (44,471.000)
##
## Constant -12,476.000 -4,775.000 -2,785.000 9,169.000
## (30,465.000) (35,120.000) (37,329.000) (40,426.000)
##
## --------------------------------------------------------------------------------------
## Observations 111 111 111 111
## R2 0.211 0.229 0.261 0.261
## Adjusted R2 0.173 0.192 0.226 0.226
## Residual Std. Error (df = 105) 43,854.000 50,554.000 53,734.000 58,191.000
## F Statistic (df = 5; 105) 5.610*** 6.240*** 7.430*** 7.410***
## ======================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01##
## ======================================================================================
## Dependent variable:
## -------------------------------------------------------
## p_salary_25 p_salary_50 p_salary_75 p_salary_90
## (1) (2) (3) (4)
## --------------------------------------------------------------------------------------
## total_population 0.007 0.062* 0.085** 0.120**
## (0.035) (0.034) (0.039) (0.046)
##
## median_hh_income 0.193 0.275** 0.250* 0.144
## (0.120) (0.116) (0.133) (0.158)
##
## pct_non_white -10,596.000 4,239.000 11,374.000 19,913.000
## (14,200.000) (13,685.000) (15,678.000) (18,650.000)
##
## pct_65_plus -117,318.000* -77,908.000 -59,555.000 -41,857.000
## (63,128.000) (60,838.000) (69,697.000) (82,911.000)
##
## demshare_pres_2016.x 16,856.000 20,601.000 24,942.000 21,229.000
## (15,493.000) (14,931.000) (17,105.000) (20,348.000)
##
## hpi 4.030 35.000** 49.900*** 77.000***
## (15.100) (14.600) (16.700) (19.900)
##
## Constant 55,349.000*** 50,626.000*** 55,691.000*** 64,469.000***
## (16,155.000) (15,569.000) (17,837.000) (21,218.000)
##
## --------------------------------------------------------------------------------------
## Observations 138 138 138 138
## R2 0.080 0.297 0.322 0.331
## Adjusted R2 0.038 0.264 0.291 0.300
## Residual Std. Error (df = 131) 25,244.000 24,328.000 27,871.000 33,155.000
## F Statistic (df = 6; 131) 1.890* 9.210*** 10.400*** 10.800***
## ======================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01Add in CZ and Year Fixed Effects, Crime, City-level HPI (rolled up from tracts)
##
## ==========================================================================================
## Dependent variable:
## ----------------------------------------------------------
## p_salary_25 p_salary_50 p_salary_75 p_salary_90
## (1) (2) (3) (4)
## ------------------------------------------------------------------------------------------
## total_population 0.147 0.156* 0.152* 0.202**
## (0.108) (0.091) (0.092) (0.099)
##
## median_hh_income 0.029 0.171** 0.206*** 0.274***
## (0.093) (0.078) (0.079) (0.085)
##
## pct_non_white -49,039.000 -49,873.000* -52,215.000** -22,124.000
## (30,415.000) (25,513.000) (25,814.000) (27,804.000)
##
## pct_65_plus -78,289.000 -97,508.000** -167,373.000*** -195,719.000***
## (52,869.000) (44,348.000) (44,871.000) (48,330.000)
##
## ------------------------------------------------------------------------------------------
## Observations 1,590 1,590 1,590 1,590
## R2 0.718 0.803 0.845 0.869
## Adjusted R2 0.648 0.754 0.806 0.836
## Residual Std. Error (df = 1272) 17,817.000 14,946.000 15,122.000 16,288.000
## ==========================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01##
## =====================================================================================
## Dependent variable:
## -------------------------------------------------------
## p_salary_25 p_salary_50 p_salary_75 p_salary_90
## (1) (2) (3) (4)
## -------------------------------------------------------------------------------------
## total_population 3.390*** 1.580 2.380* 1.260
## (1.250) (1.140) (1.430) (1.810)
##
## pct_non_white -183,361.000 -122,893.000 -100,512.000 -101,408.000
## (152,157.000) (138,979.000) (173,236.000) (219,810.000)
##
## pct_65_plus 548,476.000 14,236.000 -226,393.000 -159,684.000
## (368,871.000) (336,926.000) (419,974.000) (532,882.000)
##
## violent_crime 6.200 0.812 16.200 8.430
## (10.200) (9.360) (11.700) (14.800)
##
## hpi -18.400 -5.010 93.500 104.000
## (56.300) (51.400) (64.000) (81.300)
##
## -------------------------------------------------------------------------------------
## Observations 104 104 104 104
## R2 0.744 0.794 0.827 0.825
## Adjusted R2 0.600 0.678 0.730 0.727
## Residual Std. Error (df = 66) 11,801.000 10,779.000 13,436.000 17,048.000
## =====================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
Census of Governments Regressions
Log Variables, CZ and Year FE (new city-level HPI)
Just HPI*Union
##
## ==================================================================
## Dependent variable:
## ---------------------------------
## log_police_salaries log_police_pc
## (1) (2)
## ------------------------------------------------------------------
## log_housing_index 0.148*** -0.149
## (0.026) (0.102)
##
## union_localpol 0.114* -0.789***
## (0.061) (0.241)
##
## log_housing_index:union_localpol -0.026 0.306***
## (0.027) (0.105)
##
## ------------------------------------------------------------------
## Observations 3,279 3,279
## R2 0.868 0.741
## Adjusted R2 0.856 0.718
## Residual Std. Error (df = 3008) 0.044 0.173
## ==================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01Add in other covariates
##
## ==================================================================
## Dependent variable:
## ---------------------------------
## log_police_salaries log_police_pc
## (1) (2)
## ------------------------------------------------------------------
## log_housing_index 0.094*** 0.209**
## (0.026) (0.103)
##
## union_localpol 0.119** -0.635***
## (0.059) (0.235)
##
## log_hh_income 0.147*** -0.588***
## (0.014) (0.054)
##
## log_tot_crime 0.015*** 0.019***
## (0.001) (0.005)
##
## log_housing_index:union_localpol -0.024 0.226**
## (0.026) (0.102)
##
## ------------------------------------------------------------------
## Observations 3,279 3,279
## R2 0.877 0.756
## Adjusted R2 0.866 0.734
## Residual Std. Error (df = 3006) 0.043 0.169
## ==================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01CZ clustered SE
##
## ==================================================================
## Dependent variable:
## ---------------------------------
## log_police_salaries log_police_pc
## (1) (2)
## ------------------------------------------------------------------
## log_housing_index 0.094** 0.209
## (0.045) (0.188)
##
## union_localpol 0.119 -0.635
## (0.113) (0.442)
##
## log_hh_income 0.147*** -0.588***
## (0.031) (0.220)
##
## log_tot_crime 0.015*** 0.019
## (0.003) (0.015)
##
## log_housing_index:union_localpol -0.024 0.226
## (0.046) (0.161)
##
## ------------------------------------------------------------------
## Observations 3,279 3,279
## R2 0.877 0.756
## Adjusted R2 0.866 0.734
## Residual Std. Error (df = 3006) 0.043 0.169
## ==================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01