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Proactive Policing and Robbery Rates across Large U.S. Cities: Assessing Robustness

Proactive Policing and Robbery Rates across Large U.S. Cities: Assessing Robustness. Charis E. Kubrin George Washington University Steven F. Messner Glenn Deane Kelly McGeever State University of New York, Albany Thomas D. Stucky Indiana University-Purdue University Indianapolis.

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Proactive Policing and Robbery Rates across Large U.S. Cities: Assessing Robustness

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  1. Proactive Policing and Robbery Rates across Large U.S. Cities: Assessing Robustness Charis E. Kubrin George Washington University Steven F. Messner Glenn Deane Kelly McGeever State University of New York, Albany Thomas D. Stucky Indiana University-Purdue University Indianapolis

  2. Aims of Current Study • To replicate Sampson and Cohen (1988) • To expand their model specification • To explore the possible implications of endogeneity

  3. Explanations for Discrepant Findings on Policing and Deterrence • Police work is not devoted to crime reduction • Police practices do not affect arrest certainty • Displacement of offenders • Methodological issues: • Limitations with arrest certainty measures • Nature of causal relationship between police strength and crime rates

  4. Proactive Policing and Crime • Indirect effect of proactive policing on crime through arrest risk • Increasing arrest/offense ratio • Proactive policing may directly affect crime rate by influencing community perceptions regarding the probabilities of apprehension for illegal behavior • Public disorder

  5. Specifying a More Complete Model • Index of concentrated disadvantage • Poverty, family disruption, joblessness • Role of local politics • Wilson (1968) Varieties of Police Behavior • Policing styles: watchman, legalistic, service • Elected mayors, partisan elections, district based council representation

  6. Data and Methods • Sample: U.S. cities with pop. of 100,000+ with at least 1,000 blacks in 2000 (n=181) • 5 data sources: (1) counts of robberies known to police and city pop. totals; (2) yearly arrest counts for DUI and disorderly conduct; (3) police employee data; (4) demographic data from 2000 census; (5) two databases on political system characteristics of city governments

  7. Data and Methods Contd. • Dep. vble= robbery offenses known for all cities that were available in UCR for 4-yr. period: 2000-03 • Smoothed data • Key Indep. vble= proactive policing • Sum of # arrests for DUI and disorderly conduct / # sworn police officers • Lagged measure of proactive policing using data for 4-year period (1996-99) immediately preceding period of interest • Indep. vble= robbery arrest/offense ratio • Lagged measure

  8. Data and Methods Contd. • Controls: city pop size (logged), median family income, % divorced, % non-Hisp. Black, racial income inequality, dummy vble. for West location • Model extension: • Resource deprivation: % poverty, % non-Hisp. Black, % unemployed, % high school grad, % female-headed households, median family income • Residential instability, % young males • City political system characteristics • 3 elements: (1) mayor-council forms of government, (2) council members represent specific geographic areas, and (3) city elections are partisan

  9. Table 1. Regressions of Certainty of Arrest and Robbery Rates. *Statistically Significant for a Two-Tailed Test at the .05 Level a Incorporated in the "Resource Deprivation Index" for Model II of Robbery Rates

  10. Table 2. Non-Recursive Models of the Police Measures and Robbery Rates. * Statistically Significant for a Two-Tailed Test at the .05 Level Model 1 = 2SLS with lagged Proactive Policing as instrument Model 2 = 2SLS with lagged Proactive Policing and lagged Certainty of Arrest as Instruments

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