Income variables and the measures of gains from crime (original) (raw)
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Crime and income inequality: An economic approach
Atlantic Economic Journal, 1992
The criminology and sociology literature are rich with scholarly attempts to identify the variables affecting criminal activity, giving particular emphasis to crimes against property. Naturally, special attention has been devoted to the influence of inequality in the distribution of income, since its importance is so intuitively appealing. An excellent study of this type is that of Carroll and Jackson [1983], who used a sample from 93 non-southern cities with a population of over 50,000. Their regression results demonstrated [p. 186] "that inequality has strong causal effects on crime rates." In their introductory survey of the literature, Caroll and Jackson [p. 179] also point out that "recent research tends to the conclusion that it is inequality rather than poverty that is the crucial variable." As they correctly point out, this argument was very strongly made by Braithwaite [1979, p. 211], who after an exhaustive review of the literature concluded that while the evidence regarding the effect of poverty on crime rate is inconsistent, "the literature shows fairly uniform support for a positive association between inequality and crime .... " This relationship, first noted by Eberts and Schwirian [1968], is generally accepted today. Surprisingly, this positive association seems to hold true only for the U.S. In a cross-national study of 62 countries, Stack [1984, p. 247] concludes that, "the best estimates in the analysis offer no support for the hypotheses on property crime and inequality. The results of research based predominantly on American samples are not replicated for a large set of nations with varied cultural and institutional frameworks." The most significant variable affecting crime rates according to Stack was the total level of wealth of the society or what he termed the degree of economic development [p. 246]. "Again, however, a control variable does most of the explaining of the variance in crime: the level of economic development." The importance of the total level of wealth in explaining property crime within the U.S. itself is also pointed out by Jacobs [1981]. He finds [p. 23] that, "if we look at the effects of particular variables we find that the indicator with the most consistent effects was the measure of economic development." And he shows once again that (in contrast to cross-national data) for the U.
Does Inequality really increase Crime? Theory and Evidence
2018
The standard model of the economics of crime predicts that inequality and (property) crime are positively associated. We show that, allowing for simple modification to the standard framework, such relationship becomes ambiguous. In order to give robustness to this intuition, we conduct a meta-analysis based on 37 empirical papers and 1,130 effect sizes. We find evidence of the presence of (positive) publication bias. When the bias is taken care of, the true effect of inequality on crime becomes almost zero. Finally, we also provide evidence of the source of the estimates’ heterogeneity.
Crime and Inequality: Reverse Causality
2007
Crime and Inequality are often associated. Although, scholars and the regular press have been mostly looking at income inequality as a determinant of crime, in this study we make the case for reverse causality. We explore the theoretical mechanism through which crime -a toll on property rights-leads to lower levels of disposable income, since individuals resort to costly forms of protection. Given concave preferences, the poor will be much more reluctant to save the remainder income and to invest in future consumption via any sort of high yielding asset. The rich su¤er less from this toll since concavity has a much lower e¤ect for the wealthy . Empirically, we show that violent crime leads poorer people to use more expensive means of transportations to work. We also show that property crime increases the percentage of population that moves to another home. This increase is of the same amount for all income levels above $25,0000. To conclude, we show, using overcrowding litigation of prisions in the US as an instrument for crime, that a doubling in property crime has a 10%-15% positive and signi…cant e¤ect on inequality, measured by the Gini coe¢ cient, after 6 or more years.
Income Inequality and Crime: A Review and Explanation of the Time?series Evidence
Sociology and Criminology-Open Access, 2013
This review analyses the time-series evidence of the effects of changing income inequality on crime for a number of countries and types of crime. 17 papers analysing this relationship using time-series evidence were found via a systematic search. The papers' findings on the relationship between inequality and crime were classified as providing evidence of Significant Positive Associations, No Significant Associations, or Significant Negative Associations. The analysis indicated that property crime increases with rising income inequality and specific measures of violent crime, such as homicide and robbery, also display sensitivity to income inequality over time. Aggregated non-specific measures of violent crime, however, do not display such sensitivity, which is most likely to be due to differences in crime reporting. The majority of the differences in the findings can be explained by the choice of covariates, and the estimators and measures used in the paper. The paper concludes with a unified interpretation of the time-series evidence.
Income inequality and pecuniary crimes
2009
This paper verifies the relationship between income inequality and pecuniary crimes. The elasticity of pecuniary crimes relative to inequality is 1.46, corroborating previous literature. Other factors important to decrease criminality are expanding job opportunities and a more efficient legal system.
The Journal of Law and Economics, 2002
In this article we take an empirical cross-country perspective to investigate the robustness and causality of the link between income inequality and crime rates. First, we study the correlation between the Gini index and, respectively, homicide and robbery rates along different dimensions of the data (within and between countries). Second, we examine the inequality-crime link when other potential crime determinants are controlled for. Third, we control for the likely joint endogeneity of income inequality in order to isolate its exogenous impact on homicide and robbery rates. Fourth, we control for the measurement error in crime rates by modelling it as both unobserved country-specific effects and random noise. Lastly, we examine the robustness of the inequality-crime link to alternative measures of inequality. The sample for estimation consists of panels of non-overlapping 5-year averages for 39 countries over 1965-95 in the case of homicides, and 37 countries over 1970-1994 in the case of robberies. We use a variety of statistical techniques, from simple correlations to regression analysis and from static OLS to dynamic GMM estimation. We find that crime rates and inequality are positively correlated (within each country and, particularly, between countries), and it appears that this correlation reflects causation from inequality to crime rates, even controlling for other crime determinants.
Income Inequality: Impact of Inequality Measures on Crimes An Analysis of the State of New Jersey
International journal of business and social research, 2016
This research used time-series data for the 50 year period of 1964 to 2014 to investigate the relationship between income inequalities and crimes in the state of New Jersey, United States of America. It found that income inequality had a significant relationship to all four types of crime measured – murder, forcible rape, aggravated assault and property crimes. Statistical significance would seem to depend on the model and inequality measure used. A log-log relationship existed between inequalities and all the crimes. Different inequality measures enabled different measures of significance. It also found that it was possible to come to different conclusions with respect to the relationships by using different inequality measures- the Gini and the 20/20 measures in our case.
Wealth Redistribution and the Social Costs of Crime and Law Enforcement
SSRN Electronic Journal, 2008
This paper explores how the distribution of wealth affects the social costs of crime and law enforcement and whether more or less equality, in this regard, is socially desirable. Generally, the optimal distribution of wealth should balance the social costs of enforcing the law upon wealthy individuals and those costs vis-à-vis poor individuals. The paper shows that, in a broad set of circumstances, greater or even perfect equality in the distribution of wealth is socially desirable. This is the case even though, as is assumed, the distribution of the benefits and harms resulting from harmful acts are the same for all individuals, all of whom also have identical and linear utility functions. However, there are certain circumstances under which inequality is socially preferable, circumstances that, all other things equal, are more likely to arise in poorer societies.
The Determinants of Income Inequality and the Relationship to Crime
University of Sussex (UK), MSc Thesis, 2014
The goal of this thesis is to study the determinants of income inequality and to establish the relationship between income inequality and crime (which in this dissertation is limited to homicide rates per 100,000 population). Theoretical expectations on the relationship between income inequality and homicide rates are very strong and so are the empirical findings. A panel dataset covering 137 countries from 2000 – 2012 and four econometric methodologies – OLS, fixed effects, 2-stage fixed effects and 2-step difference GMM – are adopted to test for the determinants of income inequality, the link between inequality and homicide rate and to test for criminal inertia in the data. On the determinants of income inequality, I find evidence that income inequality is strongly associated with GDP per capita, the rule of law index, secondary education and unemployment rate. Although, this was not the focus of this research, it is important to state here that amongst other determinants of income inequality, the significance of the rule of law index was constant and consistent in all the different methodologies used. On the relationship between income inequality and homicide rate, I find Gini index to be a strong determinant of homicide rate. Empirical results find that the death penalty is not a crime deterring factor but rather that with good governance, the rate of homicide can be greatly reduced both in the short-run and long-run. Also, not surprising is the fact that factors determining income inequality also determine homicide rates. The results of my dynamic model support the hypothesis that criminal inertia is important in the study of crime rates as the lagged homicide rate is positively associated with the homicide rate. On the basis of my work, it seems clear that other factors determining income inequality and homicide rates should, whenever possible, be studied separately.