What causes violent crime?

Abstract

This study uses panel data of intentional homicide and robbery rates for a sample of developed and developing countries for the period 1970–1994, based on information from the United Nations World Crime Surveys, to analyze the determinants of national crime rates both across countries and over time. A simple model of the incentives to commit crimes is proposed, which explicitly considers possible causes of the persistence of crime over time (criminal inertia). A panel-data based GMM methodology is used to estimate a dynamic model of national crime rates. This estimator controls for unobserved country-specific effects, the joint endogeneity of some of the explanatory variables, and the existence of some types of measurement errors afflicting the crime data. The results show that increases in income inequality raise crime rates, crime tends to be counter-cyclical, and criminal inertia is significant.

Introduction

The heightened incidence of criminal and violent behavior in recent years has become a major concern across the world. From Eastern Europe to the developing countries of Latin America, violence and crime threaten social stability and are becoming major obstacles to development. Between the early 1980s and the mid 1990s, the rate of intentional homicides increased by 50% in Latin America and by more than 100% in Eastern Europe and Central Asia. In countries such as Colombia, Russia, and Thailand, the homicide rate more than tripled in about the same period. The concern with crime is well justified given its pernicious effects on economic activity and, more generally, on the quality of life of people who must cope with the reduced sense of personal and proprietary security. Despite the fact that violent crime is emerging as a priority in policy agendas worldwide, we know little regarding the economic, social, and institutional factors that make some countries have higher crime rates than others or make a country experience a change in its crime rate. The objective of this paper is to help understand the social and economic causes of violent crime rates in a worldwide sample of countries.

The economics literature on crime has followed Becker's (1968) paradigm, according to which criminal acts result from a rational decision based on a cost-benefit analysis.¹ The expected benefits are given by the difference between the loot and the opportunity cost of crime; and the costs are given by the penalties imposed to apprehended criminals. Thus, research on crime has focused on either deterrence issues or economic factors that affect the costs and benefits related to criminal actions. The literature on the efficacy of punishment to prevent crime began in the 1970s. Ehrlich 1973, Ehrlich 1975a found that crime rates were sensitive to the expected size of punishment. Using variation across U.S. states, Ehrlich (1975a) concluded that capital punishment had a significant impact on major crime rates. Working with sub-city data, Mathieson and Passell (1976) also find large deterrence elasticities of crime. On the other hand, Archer and Gartner (1984) find no impact of capital punishment on murders in their cross-national study. The endogeneity of punishment with respect to crime makes difficult the interpretation of simple deterrence elasticities. Taylor (1978) and, more recently, Grogger (1991) and Levitt 1996, Levitt 1997 have taken into account endogeneity issues to study and quantify the effectiveness of punishment to prevent crime. Using micro-level data in the U.S., these authors find a significant effect of policing and punishment on crime reduction.

The literature focusing on the benefits and opportunity costs of crime has also been rich, particularly in the U.S. In their work on U.S. cities, Fleisher (1966) and Ehrlich (1973) examined the effect of unemployment rates, income levels, and income disparities on the incidence of crime. Though their findings on the effects of average income levels are contradictory, both authors find a significant crime-inducing impact of unemployment and income inequality. Using the National Longitudinal Survey of Youth in the U.S., Freeman (1992) finds that youth in poverty are more likely to be arrested and go to jail. Tauchen and Witte (1994) find that in a sample of young men, going to work or school tends to reduce the probability of being involved in criminal activities. On the other hand, the effect of education on crime reduction is controversial in most studies. For example, Ehrlich (1975b) finds a positive relationship between the average number of school years completed by the adult population and property crimes committed across the U.S. in 1960.

Recently, in somewhat of a departure from Becker's paradigm, some research has turned to sociological aspects that affect the incidence of crime. DiIulio (1996) links the lack of ‘social capital’ to the rise of crime rates in U.S. cities. Similarly, Freeman (1986) finds a strong association between church attendance and lower crime participation rates for needy youths. Demographic factors and social interactions has also been the subject of recent research. Using a survey of disadvantaged youths in Boston, Case and Katz (1991) find that an individual's propensity to commit a crime rises when his peers are also engaged in criminal activities. In a related paper, Glaeser et al. (1996) emphasize the role of social interactions in explaining the continuous prevalence of high crime rates in certain places and the significant variance of crime rates across space.

The literature surveyed above is a point of departure for this paper. In choosing the variables to explain the incidence of crime, we follow Becker's paradigm and its recent extensions emphasizing sociological and demographic aspects. We consider the variables that the literature favors as determinants of crime rates. However, rather than using micro-level data or concentrating on a single city or country, we use cross-country data to explain national crime rates with social and economic variables at the same level of aggregation.

Our basic regression considers economic variables that may affect the incidence of crime. Then, we extend the basic model along four dimensions. The basic (or core) model includes as explanatory variables the lagged crime rate, the output growth rate, the average income of the population, the level of income inequality, and the average educational attainment of the adult population. The four extensions are the following. First, we consider deterrence factors by estimating, alternatively, the effects of police presence in the country and the existence of capital punishment. Given the importance of deterrence in the crime literature, we would have wanted to include these variables in the core model. We decided against it because we only have limited cross-sectional data for these variables. The second extension deals with the effects of illegal drugs in two aspects, namely, the production of drugs in the country and the rate of drug possession. The third extension considers demographic issues. In particular, we study whether the degree of urbanization and the age composition of the population, respectively, have an effect on the incidence of violent crime. Finally, we begin to explore cultural issues by considering the effect of geographic region and religion dummies.

One of the reasons cross-country studies are uncommon is that it is difficult to compare crime rates across countries. The issues of mismeasurement associated with aggregate variables are quite severe for most types of crime data. Underreporting is widespread in countries with low-quality police and judicial systems and with poorly educated populations. In fact, Soares (1999) finds that the extent of underreporting is negatively correlated with the level of development. Underreporting is most pronounced for low-value property crime (e.g., common theft) and for crimes carrying a social stigma for the victim (e.g., rape.) We attempt to reduce the biases caused by measurement errors by, first, choosing the types of crime that are least likely to be affected by underreporting and, second, employing an econometric methodology that deals with systematic measurement error. The types of crime we work with are intentional homicides and robberies. Intentional homicide statistics suffer the least from underreporting because corpses are more difficult to ignore than losses of property or assaults. Robberies are crimes against property that include a violent component, which means that the victim has two reasons to report the crime. To the extent that intentional homicide and robbery are good proxies for overall crime, our conclusions apply to criminal activities broadly understood. However, if these types of crime proxy mostly for violent crime, our results apply more narrowly. We assembled a new data set on intentional homicide and robbery rates based on information from the United Nations World Crime Surveys. The data set consists of an unbalanced panel of 45 countries for homicides and 34 countries for robberies, covering the period 1970–94.

Panel data permits a rich model specification. First, we can consider both variables that vary mostly across countries (e.g., income inequality) and those that vary in the time and country dimensions (e.g., output growth rates and measures of development). Second, by considering the patterns of crime rates for a given country over time, we can test whether there is inertia in the incidence of crime. Third, we can control for the joint endogeneity of some of the explanatory variables, through the use of their lagged values as instruments. Controlling for joint endogeneity is essential to obtain consistent estimates of the effect of various economic and social variables on crime rates. For instance, in assessing the impact of output growth on crime rates, we must control for the possibility that higher crime rates scare away domestic investment and hurt economic growth. Finally, the use of panel data allows us to control for the effect of unobserved variables that vary little over time and can, thus, be considered as country-specific effects. In the context of crime regressions, possibly the most important unobserved country-specific effect is the systematic error involved in the measurement of crime rates. By controlling for these specific effects, we are reducing the estimation bias due to the underreporting of crime. Our econometric methodology follows the generalized method of moments (GMM) estimator applied to dynamic models of panel data (see Holtz-Eakin et al., 1988, particularly, Arellano and Bond, 1991; Arellano and Bover, 1995).

The rest of the paper is organized as follows. Section 2 presents a simple economic model of criminal behavior. It begins with a cost-benefit analysis for the individual and ends with a framework to study the determinants of national crime rates. Section 3 presents the data and the econometric methodology. Section 4 discusses the results for, respectively, intentional homicide rates and robbery rates. Section 5 summarizes the main conclusions of the paper and suggests directions for future research.