Police, Crime and the Problem of Weak Instruments:Revisiting the “More Police, Less Crime” Thesis

A key question in the general deterrence literature has been the extent to which the police reduce crime. Definitive answers to this statement, however, are difficult to come by because while more police may reduce crime, higher crime rates may also increase police levels, by triggering the hiring of more police. One way to help overcome this problem is through the use of instrumental variables (IV). Levitt, for example, has employed instrumental variables regression procedures, using mayoral and gubernatorial election cycles and firefighter hiring as instruments for police strength, to address the potential endogeneity of police levels in structural equations of crime due to simultaneity bias.We assess the validity and reliability of the instruments used by Levitt for police hiring using recently-developed specification tests for instruments. We apply these tests to both Levitt’s original panel dataset of 59 US cities covering the period 1970–1992 and an extended version of the panel with data through 2008.Results indicate that election cycles and firefighter hiring are “weak instruments”—weak predictors of police growth that, if used as instruments in an IV estimation, are prone to result in an unreliable estimate of the impact of police levels on crime.Levitt’s preferred instruments for police levels—mayoral and gubernatorial election cycles and firefighter hiring—are weak instruments by current econometric standards and thus cannot be used to address the potential endogeneity of police in crime equations

Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides

<div><p>Executions in Texas from 1994–2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models—based on pre-tests for unit roots that correct for earlier model misspecifications—one cannot reject the null hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention.</p></div

Gun Prevalence, Homicide Rates and Causality: A GMM Approach to Endogeneity Bias

The positive correlation between gun prevalence and homicide rates has been widely documented. But does this correlation reflect a causal relationship? This study seeks to answer the question of whether more guns cause more crime, and unlike nearly all previous such studies, we properly account for the endogeneity of gun ownership levels. We discuss the three main sources of endogeneity bias - reverse causality (higher crime rates lead people to acquire guns for self-protection), mismeasurement of gun levels, and omitted/confounding variables - and show how the Generalized Method of Moments (GMM) can provide an empirical researcher with both a clear modeling framework and a set of estimation and specification testing procedures that can address these problems. A county level cross-sectional analysis was performed using data on every US county with a population of at least 25,000 in 1990; the sample covers over 90% of the US population in that year. Gun ownership levels were measured using the percent of suicides committed with guns, which recent research indicates is the best measure of gun levels for cross-sectional research. We apply our procedures to these data, and find strong evidence of the existence of endogeneity problems. When the problem is ignored, gun levels are associated with higher rates of gun homicide; when the problem is addressed, this association disappears or reverses. Our results indicate that gun prevalence has no significant net positive effect on homicide rates: ceteris paribus, more guns do not mean more crime.counties; crime; endogeneity; GMM; gun levels; homicide

Women\u27s Status and Risk of Homicide Victimization: An Analysis With Data Disaggregated by Victim-Offender Relationship

Several feminist theories predict that women\u27s socioeconomic status, both absolute status and their status relative to men, influences the prevalence of violence against women, with some suggesting a positive correlation and others a negative one. Although each theory provides insight into the possible causal connection between women\u27s status, gender inequality, and violence, empirical tests of these relationships are inconclusive. The present study addresses this issue by using a cross-sectional design with 2000 census and crime data to assess the impact of women\u27s absolute status and gender inequality along educational, employment, income, and occupational dimensions and their risk of homicide victimization by intimate partners and nonintimates. The findings indicate that women\u27s absolute status is significantly correlated with female homicide victimization rates by intimate partners. However, tests for equality of regression coefficients between the intimate and nonintimate partner models suggest that these differences may be attributed to random chance

ARIMA transfer function models of Monthly Texas Homicides with additional covariates, 1994–2005.

<p>Standard errors in parentheses.</p