Lowest Values for Full Dataset (34 / 17,815 below $50,000):
Lowest Values for Pruned Dataset (1 / 13,060 below $50,000):
Highest Values
Lowest Values for Full Dataset (34 / 17,815 below $50,000):
Lowest Values for Pruned Dataset (1 / 13,060 below $50,000):
Highest Values
Lowest Values for Pruned Dataset:
Highest Values:
Lowest Values for Pruned Dataset:
Highest Values:
Lowest Values for Pruned Dataset:
Highest Values:
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Num. Of Agencies Per Year in Pruned Data
##
## ==================================================================================
## Dependent variable:
## --------------------------------------------------------------
## `Police Officer` `Police Mgr` `Police Other`
## (1) (2) (3)
## ----------------------------------------------------------------------------------
## hpi 35.400*** 27.500* 36.300***
## (10.000) (14.900) (13.900)
##
## ----------------------------------------------------------------------------------
## City Fixed Effects Yes Yes Yes
## Year Fixed Effects Yes Yes Yes
## Observations 811 796 645
## R2 0.943 0.931 0.872
## Adjusted R2 0.918 0.898 0.807
## Residual Std. Error 6,672.000 (df = 564) 9,856.000 (df = 538) 8,830.000 (df = 427)
## ==================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
##
## =====================================================================================
## Dependent variable:
## -----------------------------------------------------------------
## FireFighter `Fire Mgr` `Fire Other`
## (1) (2) (3)
## -------------------------------------------------------------------------------------
## hpi 11.000 -63.400** -44.400
## (29.200) (30.000) (52.700)
##
## -------------------------------------------------------------------------------------
## City Fixed Effects Yes Yes Yes
## Year Fixed Effects Yes Yes Yes
## Observations 429 500 306
## R2 0.904 0.929 0.897
## Adjusted R2 0.854 0.893 0.828
## Residual Std. Error 12,305.000 (df = 282) 13,390.000 (df = 332) 14,477.000 (df = 183)
## =====================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
##
## ====================================================================================
## Dependent variable:
## ----------------------------------------------------------------
## `Police Officer` `Police Mgr` `Police Other`
## (1) (2) (3)
## ------------------------------------------------------------------------------------
## hpi 26.900*** 36.600*** 14.100*
## (7.800) (9.110) (8.510)
##
## ------------------------------------------------------------------------------------
## City Fixed Effects Yes Yes Yes
## Year Fixed Effects Yes Yes Yes
## Observations 1,267 1,322 1,116
## R2 0.894 0.913 0.824
## Adjusted R2 0.864 0.889 0.770
## Residual Std. Error 8,719.000 (df = 984) 10,328.000 (df = 1034) 9,044.000 (df = 854)
## ====================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
##
## =====================================================================================
## Dependent variable:
## -----------------------------------------------------------------
## FireFighter `Fire Mgr` `Fire Other`
## (1) (2) (3)
## -------------------------------------------------------------------------------------
## hpi 40.900** 44.800*** -8.790
## (16.800) (17.000) (26.300)
##
## -------------------------------------------------------------------------------------
## City Fixed Effects Yes Yes Yes
## Year Fixed Effects Yes Yes Yes
## Observations 709 820 586
## R2 0.889 0.917 0.840
## Adjusted R2 0.853 0.892 0.777
## Residual Std. Error 12,059.000 (df = 536) 13,771.000 (df = 627) 15,808.000 (df = 420)
## =====================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01