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The Oxford Handbook of the Social Science of Poverty

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The Oxford Handbook of the Social Science of Poverty

27 Poverty and Crime

Patrick Sharkey, Associate Professor of Sociology, New York University.

Max Besbris, PhD Student in Sociology, New York University.

Michael Friedson, Postdoctoral Fellow, New York University.

  • Published: 05 April 2017
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This article examines theory and evidence on the association between poverty and crime at both the individual and community levels. It begins with a review of the literature on individual- or family-level poverty and crime, followed by a discussion at the level of the neighborhood or community. The research under consideration focuses on criminal activity and violent behavior, using self-reports or official records of violent offenses (homicide, assault, rape), property crime (burglary, theft, vandalism), and in some cases delinquency or victimization. The article concludes by highlighting three shifts of thinking about the relationship between poverty and crime, including a shift away from a focus on individual motivations and toward a focus on situations that make crime more or less likely.

The relationship between poverty and crime is complex. There is substantial evidence indicating that poverty is associated with criminal activity, but it is less clear that this relationship is causal or that higher levels of poverty in a neighborhood, a city, or a nation necessarily translate into higher levels of crime. Perhaps the most powerful illustration of this empirical reality comes from the simple observation made by Lawrence Cohen and Marcus Felson several decades ago in introducing their “routine activities theory” of crime. During the 1960s, when poverty and racial inequality were declining in American cities, the crime rate was rising ( Cohen and Felson 1979 ). The experience during the economic downturn from 2008–2012 provides a more recent example. Despite the rise in poverty and sustained unemployment over these years, crime has not risen in any remarkable way. The implication is that in order to understand the relationship between poverty and crime it is necessary to move beyond the assumption that more poor people translates directly into more crime.

One of the major shifts in criminological thinking, spurred in large part by Cohen and Felson’s ideas, is an expansion of focus beyond the characteristics or the motivations of potential offenders and toward a broader view of what makes an incident of crime more or less likely. This entails a shift from a focus on who is likely to commit a crime toward a focus on when, where, and why a crime is likely to occur ( Birkbeck and LaFree 1993 ; Katz 1988 ; Wikström and Loeber 2000 ). The basic insight of routine activities theory is that the likelihood of a crime occurring depends on the presence of a motivated offender, a vulnerable victim, and the absence of a capable guardian ( Sampson and Wikström 2008 ). Whereas traditional approaches to understanding crime focus primarily on the first element of this equation, the offender, these approaches ignore the two other moving parts: the vulnerable victim, and the presence or absence of capable guardians ( Wikström et al. 2012 ).

The “situational” perspective on crime has important implications for understanding the complexities of the relationship between poverty and crime. It forces one to consider how poverty affects the motivations of offenders, how poverty affects the vulnerability and attractiveness of potential targets, and how poverty affects the presence of capable guardians. We will consider each of these issues throughout the chapter. The research that we review reinforces the point that crime cannot be understood primarily in terms of individual characteristics, incentives, or resources. It has to be understood in terms of situations, attachments, networks, and contexts. This insight is central to a wide range of research in the field, and it frames our approach to considering the relationship between poverty and crime.

In this chapter we review theory and evidence on the relationship between poverty and crime at the level of the individual and at the level of the community. We make no attempt to be comprehensive, but instead we focus on major patterns of findings in the literature and important theoretical and empirical advances and developments. The research that we review considers criminal activity and violent behavior, using self-reports or official records of violent offenses (homicide, assault, rape), property crime (burglary, theft, vandalism), and in some cases delinquency or victimization. This approach, which reflects the dominant focus of research in criminology and sociology, places less emphasis on (or ignores completely) other types of less visible, underreported or understudied criminal activity or deviant behavior, including crime or abuse committed by police or elected officials, domestic violence, crimes committed in prison, and many types of financial or “white-collar” crime. It is important to acknowledge that the disproportionate focus on what might be thought of as “street crime” is likely to lead to biased conclusions about the overall strength of the relationship between poverty and crime. This bias arises due to the dearth of research on crime occurring outside of low-income communities (most notably white-collar crime) and because of the use of official records to measure criminal activity. Official reports of arrests reflect some combination of criminal activity, enforcement, and reporting. These are potential sources of bias that are present in much of the criminological literature and thus are present in this review as well.

The chapter proceeds with a review of the literature on individual- or family-level poverty and crime. Although the literature demonstrates a consistent association between poverty and crime, there are multiple interpretations of this association that have been put forth in the literature. Poverty may lead directly to some types of criminal activity. However, the link between poverty and crime also may be spurious, or it may be mediated by other processes related to labor force attachment, family structure, or connections to institutions like the military or the labor market. We then move to the level of the neighborhood or community. Again, the literature shows a consistent positive association between community-level poverty and crime, although the functional form of this relationship is less settled. A prominent strand of research has argued that community-level social processes play a central role in mediating the association between poverty and crime, generating resurgent interest in the importance of social cohesion, informal social control, and other dimensions of community organization that help explain the link between poverty and crime.

Our review of the literature concludes by highlighting three shifts of thinking about the relationship between poverty and crime: (1) a shift away from the idea that criminal activity is located within the individual, and toward a perspective that locates the potential for criminal activity within networks of potential offenders, victims, and guardians; (2) a shift away from a focus on individual motivations and toward a focus on situations that make crime more or less likely; and (3) a shift away from a focus on aggregated deprivation as an explanation for concentrations of crime and toward a consideration of community social processes that make crime more or less likely.

Individual Poverty and Crime

Evidence for a positive association between individual or family poverty and criminal offending is generally strong. A review of 273 studies assessing the association between different dimensions of social and economic status (SES) and offending concludes that there is consistent evidence from multiple national settings that individuals with low income, occupational status, and education have higher rates of criminal offending ( Ellis and McDonald 2001 ). However, evidence based on self-reported data on delinquent behavior is less consistent ( Tittle and Meier 1990 ; Wright et al. 1999 ). A recent study based on comparable surveys conducted in Greece, Russia, and Ukraine showed no consistent association between social and economic status and various self-reported measures of delinquent or criminal behavior ( Antonaccio et al. 2010 ).

Given this conflicting evidence, it is important to clarify that the claims made in this section are based primarily on research that examines poverty or economic resources and that considers criminal offending as an outcome. Evidence for an association between economic resources and crime is more consistent across settings and is generally quite strong, particularly in the United States ( Bjerk 2007 ). As a whole, however, the studies reviewed do not appear to provide strong evidence that these relationships are causal, nor is the overall association between poverty and crime particularly surprising—this association is consistent with virtually all individual- and family-level theories of criminal behavior. Poverty is associated with self-control and cognitive skills ( Hirschi and Gottfredson 2001 ), with family structure and joblessness ( Matsueda and Heimer 1987 ; Sampson 1987 ), with children’s peer networks ( Haynie 2001 ; Haynie, Silver, and Teasdale 2006 ), and with the type of neighborhoods in which families reside and the types of schools that children attend ( Deming 2011 ; Wilson 1987 ). The association between individual poverty and criminal offending may reflect some combination of all of these pathways of influence.

Alternatively, poverty may have direct effects on crime if the inability to secure steady or sufficient financial resources leads individuals to turn to illicit activity to generate income or if relative poverty in the midst of a wealthy society generates psychological strain ( Merton 1938 ). The “economic model of crime,” put forth formally by economist Gary Becker (1974) and elaborated and refined by an array of criminologists ( Clarke and Felson 1993 ; Cornish and Clarke 1986 ; Piliavin et al. 1986 ), suggests that crime can be explained as the product of a rational decision-making process in which potential offenders weigh the benefits and probable costs/risks of committing a crime or otherwise becoming involved in criminal activities ( Becker 1974 ). Much of the research assessing the economic model of crime has focused on deterrence, or the question of whether raising the costs of criminal behavior reduces crime. However, the theory also has direct implications for the study of poverty and crime, as it suggests that individuals lacking economic resources should have greater incentives to commit crime. Despite the abundance of evidence for an association between economic resources and criminal offending, there is little convincing research demonstrating a direct causal effect. For instance, the experimental programs that are most frequently cited for evidence on the effect of income on various social outcomes—such as the income maintenance experiments of the 1970s or the state-level welfare reform experiments of the 1990s—did not assess impacts on crime ( Blank 2002 ; Munnell 1987 ).

There are, however, a small number of studies that provide persuasive, if not definitive, evidence supporting a direct causal relationship between individual economic resources and crime. One example is a recent study that exploits differences in cities’ public assistance payment schedules in order to assess whether crime rises at periods of the month when public assistance benefits are likely to be depleted. As predicted by the economic model, crimes that lead to economic gain tend to rise as the time since public assistance payments grows, while other types of crime not involving economic gain do not increase ( Foley 2011 ). Another example comes from a set of experimental studies in which returning offenders from Georgia and Texas were randomly assigned to receive different levels of unemployment benefits immediately upon leaving prison, while members of the control group received job-placement counseling but no cash benefits ( Berk, Lenihan, and Rossi 1980 ; see also Rossi, Berk, and Lenihan 1980 ). Although the results are generalizable only to returning offenders, they show that modest supplements of income reduce subsequent recidivism.

A larger base of evidence suggests that unemployment (and underemployment or low wages) is causally related to criminal offending, with a stronger relationship between unemployment and property crime as compared with violent crime ( Chiricos 1987 ; Fagan and Freeman 1999 ; Grogger 1998 ; Levitt 2001 ; Raphael and Winter-Ebmer 2001 ). This finding from the quantitative literature finds support in ethnographic studies arguing that the absence of stable employment and income are important factors leading to participation in informal and illicit profit-seeking activity, ranging from drug distribution and burglary to participating in informal or underground economic markets ( Bourgois 1995 ; Venkatesh 2006 ; Wright and Decker 1994 ).

The evidence linking unemployment with criminal behavior can be interpreted in multiple ways. Economists studying this relationship tend to view criminal activity as a substitute for employment in the formal labor market. From this perspective, individuals who cannot find work or whose wages are low, relative to opportunities in the informal or illicit labor market, are likely to choose criminal activity as an alternative (or supplemental) source of income ( Fagan and Freeman 1999 ; Grogger 1998 ). Criminological and sociological perspectives acknowledge the importance of income as a mechanism underlying the relationship between joblessness and crime but view employment as one of many social bonds that connect individuals to other individuals and to institutions in ways that reduce the likelihood that they will become involved in criminal activity. In their life-course model of deviance and desistance, Robert Sampson and John Laub describe the set of attachments that individuals form at different stages in the life course, including college attendance, military service, and entrance into marriage ( Sampson and Laub 1993 , 1996; Laub and Sampson 2003 ). The formation and maintenance of individuals’ bonds to romantic partners and family, to employers and institutions, and the informal social controls that arise from these social bonds do not only reduce the probability of criminal activity but also help explain patterns of desistance over time. Marriage and employment, for example, can alter the offending trajectory of individuals by serving as turning points from the past to the present, by increasing supervision and responsibilities, and by transforming roles and identities ( Laub and Sampson 2003 ).

The implication is that the relationship between poverty and crime may not be direct and causal; it is plausible that this relationship may be indirect or even spurious. Unemployment is only one characteristic that may confound the relationship between poverty and crime, but there are many others. Growing up in a single-parent household or in a community dominated by single-parent households is strongly related to criminal activity and also is associated with poverty ( Sampson 1987 ; Sampson and Wilson 1995 ). Association with delinquent peers is another potential confounder, as are cognitive skills, work ethic, and exposure to environmental toxins like lead or environmental stressors like violence ( Anderson 1999 ; Matsueda 1982 , 1988 ; Nevin 2007 ; Reyes 2007 ; Stretesky and Lynch 2001 ).

This discussion leaves us with three possible models of the relationship between individual or family poverty and crime. The first model posits that this relationship is direct and causal. In this model, which is reflected in the economic model of crime, poverty and the inability to secure stable and well-paid employment in the formal labor market provide greater incentives for individuals to commit crimes in order to generate income and associated benefits. The second model posits that the relationship between poverty and crime is mediated by other processes, such as the formation of social attachments to romantic partners, jobs, or institutions like the military. The third model posits that the association between poverty and crime is spurious and is the result of bias due to confounding factors. This model would suggest that poverty is linked with crime because it is associated with other criminogenic characteristics of the family or the individual.

The evidence available provides the strongest support for the first two models. Poverty is likely to be linked to crime both because the poor have greater incentives to commit crime and because poverty affects individuals’ environments, their relationships, their developmental trajectories, and their opportunities as they move through different stages of the life course. The strongest evidence in support of this conclusion comes from the literature on unemployment, wages, and crime. However, there is very little convincing evidence that focuses purely on the direct effect of poverty on crime. We consider this to be an important gap in the literature.

Community Poverty and Crime

Despite the myriad ways that individual poverty may be linked with individual criminal activity, the aggregation of individuals who have greater incentives or propensity to commit crime does not necessarily lead to more crime in the aggregate. One simplistic illustration of why this is the case emerges when we return to the situational framework of offenders, victims, and guardians with which we began the chapter. Focusing in particular on the presence of attractive and vulnerable victims, one might conclude that in areas where poverty is concentrated there are likely to be fewer attractive victims vulnerable to potential offenders ( Hannon 2002 ). If one were to consider only the second dimension of the crime equation, one might arrive at the hypothesis that in periods where poverty is rising or in places where poverty is concentrated there should be fewer crimes committed. Just as with theories that focus only on the prevalence of motivated offenders, this hypothesis is simplistic and incomplete.

Theories that focus exclusively on the number of potential offenders or the number of potential victims within a community are equally deficient because they do not consider the ecological context in which criminal activity takes place. Moving beyond the individual-level analysis of crime requires a consideration of social organization within the community; the enforcement of common norms of behavior by community residents, leaders, and police; the structure and strength of social networks within a community; and the relationships between residents, local organizations, and institutions within and outside the community ( Sampson 2012 ; Sampson and Wikström 2008 ).

An illustrative example comes from the Moving to Opportunity (MTO) program, a social experiment that randomly offered vouchers to public housing residents in five cities that allowed them to move to low-poverty neighborhoods. The most common reason that families gave for volunteering for the program was that they wanted their children to be able to avoid the risks from crime, violence, and drugs in their origin neighborhoods ( Kling, Liebman, and Katz 2007 ). However, when data on criminal activity were analyzed years later, the results showed that youth in families who had moved to neighborhoods with lower poverty were no less likely to report having been victimized or “jumped,” seeing someone shot or stabbed, or taking part in violent activities themselves ( Kling, Liebman, and Katz 2007 ; Kling, Ludwig, and Katz 2005 ). A complicated set of findings emerged, with very different patterns for girls and boys. Whereas girls reported feeling more safe in their new communities, boys in families that moved to low-poverty neighborhoods were less likely to have been arrested for violent crimes but more likely to be arrested for property crimes, to have a friend who used drugs, and to engage in risky behaviors themselves ( Clampet-Lundquist et al. 2006 ; see also Sharkey and Sampson 2010 ).

The results from Moving to Opportunity reveal the complex ways in which individuals and aspects of their social environments interact to make crime more or less likely. Boys in families that moved to new environments may have changed their behavior with new opportunities for property crime available to them, but they may also have been subject to greater scrutiny from their new neighbors and from law enforcement. Girls in the same families were likely to be seen in a different light by neighbors and police, leading to different behavioral and social responses ( Clampet-Lundquist et al. 2006 ). The interaction between the characteristics of youths themselves; the types of potential targets that existed in their new communities; and the level of supervision, suspicion, and policing in the new communities created an unexpected pattern of behavior within the new environment.

This example highlights the complexity of community-level models of poverty and crime. In an attempt to synthesize some of the core ideas that have been put forth in the criminological literature on community-level crime, we focus on three stylized facts that guide our discussion of the community-level relationship between poverty and crime. First, crime is clustered in space to a remarkable degree ( Sampson 2012 ). This empirical observation has been made repeatedly by scholars in different settings and in different times, but the study of space and crime has been refined considerably in recent years. Crime is not only spatially clustered at the level of the neighborhood or community, but it is concentrated in a smaller number of “hot spots” within communities ( Block and Block 1995 ; Sherman 1995 ; Sherman, Gartin, and Buerger 1989 ). The spatial dimension of crime leads to questions about the underlying mechanisms that might explain why certain spaces or areas appear to be criminogenic. Over the past few decades, the concentration of poverty has emerged as a primary explanatory mechanism.

This leads to our second stylized fact: the level of poverty in a community is strongly associated with the level of crime in the community ( Patterson 1991 ; Krivo and Peterson 1996 ). This relationship is found not only in the United States but also in nations such as the Netherlands and Sweden, where the levels of concentrated poverty and violent crime are substantially lower (e.g., Sampson and Wikström 2008 ; Weijters, Scheepers, and Gerris 2009 ). Despite the robustness of this relationship across contexts, there is conflicting evidence on the functional form of this relationship. One of the central arguments in William Julius Wilson’s classic book The Truly Disadvantaged (1987) was that urban poverty in the United States transformed in the post–civil rights period, and the new type of concentrated neighborhood poverty that emerged during this period led to the intensification of an array of social problems including a sharp rise in violent crime. Sampson and Wilson (1995) argue that areas of concentrated poverty provide a niche where role models for youth are absent and residents are less fervent in enforcing common norms of behavior, leading to elevated levels of crime. This argument has served as a primary explanation for the rise and concentration of urban crime from the 1960s through the 1990s, but it has been challenged recently by research investigating the form of the relationship between neighborhood poverty and crime. Analyzing data on neighborhood crime from 25 cities in 2000, Hipp and Yates (2011) find no evidence that crime rises sharply in extreme-poverty neighborhoods. All types of crime rise with the level of poverty in the neighborhood, but for most types this relationship levels off as the neighborhood poverty rate reaches 30 percent or higher. As the most rigorous study conducted to date on the form of the relationship between neighborhood poverty and crime, we believe the findings from this article should provoke further theoretical and empirical investigations into this important issue.

Discussions about the functional form of the relationship between community poverty and crime lead directly into a broader discussion about the mechanisms underlying this relationship. An extensive ethnographic literature demonstrates how the threat of violence can come to structure interpersonal interactions and outlooks in areas of extreme poverty, creating the need for individuals to take strategic steps to avoid violence ( Anderson 1999 ; Harding 2010 ). The emergence of patterned responses to community-level poverty and violence involving the adoption of unique frames and repertoires of action becomes visible in this research, with consequences that can affect individual behavior and reinforce the atmosphere of threat, further weakening informal social controls and trust within the community ( Anderson 1999 ; Small, Harding, and Lamont 2010 ).

The importance of community trust, social cohesion, and informal social controls has been theorized and analyzed in a resurgent literature on community social processes and crime and violence. The third stylized fact about communities and crime is that social processes at the level of the community appear to play a central role in mediating the association between poverty and crime. In their classic work on the organization of communities and rates of juvenile delinquency, Shaw and McKay (1942) argued that community organization is lower and crime and delinquency are higher in neighborhoods with low social and economic status, high levels of ethnic heterogeneity, and high levels of residential mobility. In the last few decades these ideas have served as the basis for a resurgent interest in the role that structural characteristics of communities play in facilitating informal social controls, in strengthening or weakening community organization, and in increasing or reducing crime ( Sampson and Groves 1989 ; Sampson, Raudenbush, and Earls 1997 ).

The research of Robert Sampson and several colleagues lies at the heart of this resurgence. With the concept of collective efficacy, Sampson builds on the ideas of Shaw and McKay but puts forth a more refined theory of the role of community-level social processes in influencing patterns of crime and violence. In addition to the three dimensions of communities on which Shaw and McKay focused, this research analyzes the importance of family structure and rates of family disruption as central factors influencing the capacity of a community to supervise and monitor teenage peer groups and to establish intergenerational lines of communication, social cohesion, and informal social controls. The “social process turn” ( Sampson, Morenoff, and Gannon-Rowley 2002 ) in research on neighborhoods and crime leads to a new understanding of the link between neighborhood poverty and crime. According to this perspective, neighborhood poverty is associated with criminal activity not because of the aggregation of motivated offenders, but rather because of community-level dynamics that create an environment in which informal social controls over activity in public space are weakened. Community-level poverty is linked with family structure, residential mobility, the density of housing, labor force detachment, physical disorder, legal cynicism, civic and political participation, and community organization, all of which are associated with crime ( Hagan and Peterson 1995 ; Krivo and Peterson 1996 ; Sampson 2012 ; Sampson and Lauritsen 1994 ).

Comparative research is beginning to assess whether the focus on community social processes is applicable in different national settings. Some research using the same methods developed to study collective efficacy in Chicago suggests that the basic relationships are similar in very different places. For instance, in comparable studies conducted in Chicago and Stockholm, neighborhood collective efficacy was found to have a remarkably similar, inverse association with violent crime ( Sampson and Wikström 2008 ). However, such similarities do not suggest that models of poverty, collective efficacy, and community violence can be blindly transferred across national contexts. In a study of Belo Horizonte, Brazil, Villarreal and Silva (2006) find that neighborhood social cohesion is not predictive of crime rates. In a context in which national policies have led to a retrenchment of public sector employment and the welfare state ( Portes and Hoffman 2003 ), the authors argue that informal networks of reciprocity and exchange are central to community sustainability but also have led to a proliferation of informal labor market activity and have emerged at a time of rising crime and violence. This study provides an example of how the relationships among neighborhood social cohesion, neighborhood poverty, and crime may vary across different local or national contexts.

Summary of the Evidence and Three Shifts of Thinking

The evidence we have reviewed suggests a set of core findings that characterize the relationship between poverty and crime at the level of the individual and the community. First, poverty is strongly associated with crime at both levels of analysis. In the rational choice, or economic model, of crime, the individual-level relationship between poverty and criminal behavior is assumed to be direct and causal, but most theoretical models do not make this assumption. We have uncovered very little empirical research that provides convincing evidence for a direct causal relationship between individual poverty and criminal activity. Some suggestive research is consistent with a causal relationship, but most research does not assess it directly. Instead, most theoretical and empirical evidence suggest that poverty is linked with criminal behavior through individual characteristics and conditions associated with poverty, such as joblessness, family structure, peer networks, psychological strain, or exposure to intensely violent environments.

At the level of the community, there is again a strong relationship between poverty and aggregated rates of crime. However, the most prominent theoretical and empirical work on the topic suggests that this relationship is mediated by community-level social processes that facilitate social cohesion and trust and that act to limit criminal activity in the community. Poverty is thus viewed as one of several characteristics of communities that lead to the breakdown of community organization, in turn leading to higher rates of crime.

These findings lead us to identify three interrelated shifts of thinking that are central to understanding the relationship between poverty and crime. These shifts of thinking reflect the insights of criminologists that have been developed over the past several decades, but they may be less familiar to poverty researchers. The first is a shift away from thinking of the potential for crime as lying within the individual offender and instead thinking of the potential for crime as lying within networks of potential offenders, victims, and guardians situated within a diverse group of contexts and settings ( Papachristos 2011 ; Wikström et al. 2012 ). The field of criminology has a long history of locating the source of criminal activity within the individual. Without denying the importance of individual characteristics in affecting the propensity for criminal activity, and without denying the agency of individual offenders, we argue that the traditionally dominant focus on the offender has stifled progress in understanding variation in crime across places and over time.

This point reflects a second shift of thinking, which involves moving from a focus on individual motivations to a focus on situations ( Wikström et al. 2012 ). Applied to the study of poverty and crime, this shift moves away from the idea that crime is driven primarily by economic calculations. While economic benefit is one important motivation for potential offenders, even the rational choice paradigm has been extended to consider other types of noneconomic rewards arising from criminal offending (e.g., Cornish and Clarke 1986 ). The situational approach to crime, by contrast, expands beyond the motivations of individuals to consider the interactions of offenders, victims, and guardians. The role of poverty as a predictor of criminal offending is much more complex in the situational approach to crime. Poverty may produce more motivated offenders, fewer potential victims (for at least some types of crime), and less effective community guardians. Considered together, one would still expect an association between poverty and crime, but the mechanisms underlying this association are more complex than the economic model suggests.

The third shift of thinking moves beyond a focus on aggregated deprivation as an explanation for concentrations of crime and toward a consideration of community social processes ( Sampson, Morenoff, and Gannon-Rowley 2002 ). Concentrated poverty is one of several characteristics of communities, along with others such as high levels of residential mobility, that tend to disrupt processes of informal social control and social cohesion within communities, or collective efficacy ( Sampson, Raudenbush, and Earls 1997 ). The breakdown of collective efficacy provides the context for the emergence of crime and violence within the community, as informal controls over public space are less effective and violations of collective norms of behavior become common.

These three shifts of thinking reflect the findings from a complex theoretical and empirical literature on the relationship between poverty and crime. In the most simplistic model of this relationship, individual poverty causes individuals to commit more crime and the aggregation of poor individuals in a community, a city, or a nation translates directly into more crime. The experience of the United States over the last 50 years demonstrates that this model is not adequate. When poverty is high, crime does not necessarily rise with it. In critiquing the direct, linear, causal model of poverty and crime, we acknowledge that we do not have an equally simple model to replace it. Instead, we argue that the relationship is complex, that it is driven by a number of different mediating mechanisms, and that these mechanisms vary depending on the level of analysis (e.g., individuals, neighborhoods, nations, etc.). While this may not be a particularly satisfying conclusion, the evidence available suggests that it is the most realistic.

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The Official Journal of the Pan-Pacific Association of Input-Output Studies (PAPAIOS)

  • Open access
  • Published: 05 June 2020

Dynamic linkages between poverty, inequality, crime, and social expenditures in a panel of 16 countries: two-step GMM estimates

  • Muhammad Khalid Anser 1 ,
  • Zahid Yousaf 2 ,
  • Abdelmohsen A. Nassani 3 ,
  • Saad M. Alotaibi 3 ,
  • Ahmad Kabbani 4 &
  • Khalid Zaman 5  

Journal of Economic Structures volume  9 , Article number:  43 ( 2020 ) Cite this article

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The study examines the relationship between growth–inequality–poverty (GIP) triangle and crime rate under the premises of inverted U-shaped Kuznets curve and pro-poor growth scenario in a panel of 16 diversified countries, over a period of 1990–2014. The study employed panel Generalized Method of Moments (GMM) estimator for robust inferences. The results show that there is (i) no/flat relationship between per capita income and crime rate; (ii) U-shaped relationship between poverty headcount and per capita income and (iii) inverted U-shaped relationship between income inequality and economic growth in a panel of selected countries. Income inequality and unemployment rate increases crime rate while trade openness supports to decrease crime rate. Crime rate substantially increases income inequality while health expenditures decrease poverty headcount ratio. Per capita income is influenced by high poverty incidence, whereas health expenditures and trade factor both amplify per capita income across countries. The results of pro-poor growth analysis show that though the crime rate decreases in the years 2000–2004 and 2010–2014, while the growth phase was anti-poor due to unequal distribution of income. Pro-poor education and health trickle down to the lower income strata group for the years 2010–2014, as education and health reforms considerably reduce crime rate during the time period.

1 Introduction

The study evaluated different United Nation sustainable development goals (SDGs), i.e., goals 1 and 2 (poverty reduction and hunger), goals 3 and 4 (promotion of health and education), goal 10 (reduced inequalities), and goal 16 (reduction of violence, peace and justice) to access pro-poor growth and crime reduction in a panel of 16 heterogeneous countries. The discussion of crime rate in pro-poor growth (PPG) agenda remains absent in the economic development literature, though Bourguignon ( 2000 ) stressed to reduce crime and violence by judicious income distribution; however, a very limited literature is available to emphasize the need of social safety nets for vulnerable peoples that should be included in the pro-growth policy agenda for broad-based economic growth. Kelly ( 2000 ) investigated the relationship between income inequality (INC_INEQ) and urban crime, and found that INC_INEQ is the strong predictor to influence violent crime rather than property crime, while poverty (POV) and economic growth (EG) significantly affect on property crime rather than violent crime. The policies should be developed for equitable income and sound EG for reducing POV and crime across the globe. Drèze and Khera ( 2000 ) examined the inter-district variations of intentional homicides rate (IHR) in India for the period of 1981 and found that there is no significant relationship between urbanization/poverty and murder rates, while literacy rate has a strong impact to reduce criminal violence in India. The results further indicate the lower murder rate in those districts where female to male ratio is comparatively high. The study emphasized the need to reduce crime, violence and homicides by significant growth policies for sustained EG in India. Neumayer ( 2003 ) investigated the long-run relationship between political governance, economic policies and IHR using the panel of 117 selected countries for the period of 1980–1997 and concluded that IHR can be reduce by good economic and political policies. The results specified that higher income level, good civic sense, sound EG, and higher level of democracy all are connected with the lower homicides rate in a panel of countries. The study emphasized the need to improve governance indicators in order to lowering the IHR across the globe. Jacobs and Richardson ( 2008 ) examined the interrelationship between INC_INEQ and IHR in a panel of 14 developed democracies nation and found that intentional homicides is the mounting concerns in those nations where the inequitable income distribution exists, while results further provoke the presence of young males associated with the higher murder rates in a region. The policies should be formulated caution with care while devising for judicious income distribution with demographic variables in the pro-growth agenda. Sachsida et al. ( 2010 ) found inertial effect on criminality and confirmed the positive relationship between INC_INEQ, urbanization and IHR. The study emphasized the importance of public security spending to reduce IHR in Brazil. Pridemore ( 2011 ) re-assessed the relationship between POV, INC_INEQ and IHR in a cross-national panel of US states and found POV-homicides’ linkages rather than inequality-homicides’ association. The study argued that there is substantially desire to re-assess the inequality-homicides’ linkages as it might be the misspecification of the model. Ulriksen ( 2012 ) examined the relationship between PPG, POV reduction and social security policies in the context of Botswana and found that broad-based social security policies have a significant impact to reduce POV, thus there is a strong need to include social security protections in the pro-poor growth (PPG) agenda for lowering the POV rates across the globe. Ouimet ( 2012 ) investigated the impact of socio-economic factors on IHR in a panel of 165 countries for the period 2010 and found that GIP triangle are strongly connected with the IHR for all countries, while for sub-samples, the results only support the inequality-homicides association rather than POV and EG induced IHR. The results highlighted the importance of GIP triangle to reduce IHR in a panel of selected countries.

Liu et al. ( 2013 ) investigated the relationship between national scale indicators of socio-economic and demographic factors and crime rates in 32 Mexican states and found that EG, wages and unemployment negatively affect crime rates, while increase federal police force that is helpful to reduce crime rates; however, on the other way around, higher public security expenditures are linked with the higher crime rates in Mexican states. Chu and Tusalem ( 2013 ) investigated the role of state to reduce IHR in a panel of 183 nations and found that political instability increases IHR, while anocracies is the strong predictor to influence IHR in a panel of countries. The study concluded that IHR increases in those countries where there is high level of political instability and death penalty, while the amalgamation of democratic and autocratic features lead to increased IHR. The policies should be drawn to strengthen political governance across the globe. Adeleye ( 2014 ) evaluated the different determinants of INC_INEQ in a large panel of 137 countries using the time series data from 2000 to 2012 and found that per capita income (PCI), secondary education, rule of law index and unemployment rate are the strong predictors for INC_INEQ and IHR, while INC_INEQ considerably affected IHR rate in a region. Dalberis ( 2015 ) investigated the relationship between INC_INEQ, POV and crime rates in Latin American countries and found that INC_INEQ has no significant association with the crime rate in Colombia, Brazil, Uruguay and Salvador, while poverty is the strong predictor to influence crime in Brazil, Uruguay and Salvador. The results highlighted the need for pro-poorness of growth reforms that would be helpful to lowering the crime rates in Latin American countries. Harris and Vermaak ( 2015 ) considered the relationship between expenditures’ inequality and IHRe across 52 districts of South Africa and found that while keeping other district features constant, inequality does appear as a strong dominant player to induce IHR. The rational income distribution along with broad-based EG may play a vital role to reduce IHR in South Africa. Stamatel ( 2016 ) investigated the relationship between democratic cultural values and IHR in a panel of 33 democratic countries for the period 2010 and found that democratic cultural values have a positive and negative impact of IHR in the presence of strong democratic institutions and practices. Ahmed et al. ( 2016 ) identified the different predictors of economic and natural resources in the context of Iran using the time series data from 1965–2011 and found that labor productivity, exports, capital stock and natural resources are the main predictors of EG, which altogether are important for sustained long-term growth of the country. Enamorado et al. ( 2016 ) interlinked crime rates with higher INC_INEQ using a 20-year dataset of more than 2000 Mexican municipalities and confirmed the causal relationships between the two stated factors. The results confined that drug-related crime rates largely increase up to 36% if there is one-point increment in the INC_INEQ during the specified time period. The study concludes with the fact that drug-related violent crime rates are more severe due to high proliferation of large dispersion in the labor market in terms of negative job opportunities in illegal sector. Thus, the sound policies are imperative to seize drug trafficking organizations by force for pro-equality growth. Ling et al. ( 2017 ) analyzed the role of trade openness in Malaysian life expectancy using the data from 1960 to 2014. The results show that continued EG and trade openness substantially increase life expectancy during the study time period. Further, the results established the feedback relationship between income and life expectancy in a country. The study concludes that life expectancy may increase through imported healthcare goods, which improves the quality of life of the people, thus trade liberalization policies are imperative for healthy and wealthy wellbeing.

Zaman ( 2018 ) extensively surveyed the large weighted sample of intellectuals about crime–poverty nexus and explored the number of socio-economic factors that concerned with high crime rate and POV incidence in Pakistan, including INC_INEQ, injustice, unemployment, low spending on education and health, price hikes, etc. There is a high need to increase social spending on education and health infrastructure in order to combat POV and crime rates in a given country. Imran et al. ( 2018 ) considered a time series data of US for a period of 1965–2016 and concluded that incidence of POV increases the intensity of property crime in a given country, while other controlling factors including country’s PCI and unemployment rate are not significantly associated with property crime in a country. The study concludes that property crime should be restricted by strong legislative and regulatory measures, judicious income distribution, and increasing minimum wage rate, which altogether would be helpful for the poor to reap economic benefits from PPG reforms in a country. Zaman et al. ( 2019 ) evaluated the role of education in crime reduction in a panel of 21 countries for a period of 1990–2015 and found a parabola relationship between PCI and crime rates in the presence of quality education and equitable justice across countries. The study further confirmed few other causal conceptions among the variables for making sound policy implications in the context of criminal justice. Piatkowska ( 2020 ) examined the social cost of POV in terms of increasing suicides rates, crime rates, and total violent rates in the United States and across 15 European nations during the period of 1993–2000. The results show that suicides–crime–violent rates are substantially increasing due to increase in relative POV and infant mortality rates across countries. The study argued that relative POV is the strong predictor to increase social cost of nation that needs efficient economic policies to reduce crime rates. Mukherjee ( 2019 ) discussed the role of social sustainability in achieving economic sustainability by reducing different forms of violent/crime rates through state intervention in the context of Indian economy by utilizing the data for a period of 2005–2016. The results further highlighted the need of socio-economic infrastructure development that would be helpful to provide safety nets to the poor in order to reduce crime rates in a country. Duque and McKnight ( 2019 ) presented the channel through which crime rates and legal system provide a pathway to increase INC_INEQ and POV across countries. The study further discussed and highlighted the socio-economic vulnerability that escalates through unequal distribution of income and high POV incidence, which need effective legal system to reduce crime rates. Khan et al. ( 2019a ) surveyed the Bolivian economy to assess pro-poor environmental reforms that could improve the quality of life of the poor through judicious income distribution and sustainable environmental reforms. The results conclude that services’ sector and healthcare infrastructure would be helpful to reduce POV rate and achieve PPG process at country wide. Zaman et al. ( 2020 ) surveyed the large panel of countries (i.e., 124 countries) for a period of 2010–2013 to analyze the role of INC_INEQ and EG on POV incidence across countries. The results generally favor the strong linkages among the three stated factors to support GIP triangle, which forms PPG process. The study emphasized the need to adopt some re-corrective measures in order to provide social safety nets and income distribution in order to make a growth process more pro-poor. Kousar et al. ( 2019 ) confined its finding in favor of POV reduction through managing international remittances’ receipts and financial development that would be helpful to improve the mechanism of income distribution in a country like Pakistan. The study concluded that international remittances may play a vital role to reduce POV via the mediation of financial development in a country.

The real problem is how to make EG more equitable, which is helpful to reduce POV and crime rates, and make a growth more pro-poor. The SDGs largely provoked the need to sustained economic activities, which helpful to make growth policies more poor friendly. The previous studies are widely discussed crime rates and POV reduction (see Zaman 2018 ; Khan et al. 2015 ; Heinemann and Verner 2006 ; etc.); however, a very few studies interlinked POV–crime nexus under PPG and Kuznets curve (KC) hypothesis (see Saasa 2018 ; Berens and Gelepithis 2018 , etc.). Based on the interconnections between crime, POV, and PPG, the study formulated the following research questions, i.e.,

Does crime rate negatively influenced GIP triangle, which sabotages the process of PPG?

The recent study of Khan et al. ( 2019b ) provoked the need of PPG policies to ensure sustainability agenda by including socio-economic and environmental factors in policy formulation, which gives favor to the poor as compared to the non-poor. In the similar lines, the social spending on education and healthcare infrastructure, and reforms needed to reduce labor market uncertainty in the form of lessen unemployment rate is considered the viable option for crime and POV reduction across countries (Khan et al. 2017 ). Thus, the study evaluated the question, i.e.,

To what extent social spending on education, health, and labor market are helpful to reduce crime rate, poverty, and income inequality across countries?

This question would be equally benefited to the developmental economists and policy makers to devise a healthy and wealthy policy by increasing spending on social infrastructure for pro-equality growth (Wang 2017 ). The last question is based upon non-linear formulation of crime–POV nexus where it is evaluated as a second-order coefficient to check the parabola relationship between them, i.e.,

Does crime and poverty exhibit a parabola relationship between them?

The question is all about the second-order condition, which confirmed one out of three conditions, i.e., either it is accepted an inverted U-shaped or U-shaped or flat relationship between them. The second-order condition assessed the probability to reduce crime rates and incidence of POV in policy formulation.

In the light of SDGs, the study explored the impact of GIP triangle and crime rates on pro-growth and PPG policies, which is imperative for sustainable development across countries. The study added social expenditures in PPG dynamics to promote healthy and wealthy economic activities, which improves quality of life of the poor and helpful to reduce crime incidence across countries. The study is first in nature, as authors’ knowledge, which included GIP triangle and crime rate in PPG framework, while controlling different socio-economic factors, including education and health expenditures, unemployment rate, and trade openness. Further, an empirical contribution of the study is to include second-order coefficient of PCI for evaluating crime- and inequality-induced KC, while the study proceed to analyze forecast relationship between the crime and POV incidence over a next 10-year time period. Finally, the study estimated PPG index while including crime rate as a main predictor factor in GIP triangle for robust policy inferences. Thus, these objectives are achieved by different statistical techniques for robust analysis.

2 Data source and methodological framework

The study used number of promising socio-economic variables to determine the dynamic relationship between PPG factors and crime rate under the framework of an inverted U-shaped KC in a panel of 16 diversified countries, using system GMM estimator for the period of 1990–2014. The study used the following variables, i.e., crime rate (proxy by intentional homicides rate per 100,000 population), GINI index measures income inequality, poverty headcount ratio at $1.90 a day (2011 PPP) (% of total population), national estimates of unemployment in % of total labor force, education expenditures as % of GDP, per capita health expenditure in current US$, per capita income in constant 2005 US$, and trade openness as % of GDP. The samples of countries are presented in Table  7 in Appendix for ready reference. The data for the study are obtained from World Development Indicators published by World Bank ( 2015 ).

These countries are selected because of the devastating crime rate during the study time period. The recorded figures for Argentina crime rates about to 245% increase between the period of 1991 and 2007, while 2002 is considered the highest committed crime data recorded when the POV and INC_INEQ reached at their peak levels (Bouzat 2010 ). Brazil economy is working out for reduction of crime by focusing on three-point agenda, i.e., reduction in income disparity, to increase spending on education via an increase in enrollment of school dropout children, and to improve labor market conditionings. These three policies design to deter the crime rates in a given country (World Bank 2013 ). The robbery complaints largely increase since last two decades in Chile, which is being planned by controlling two action strategies, i.e., plan cuadrante and country security plan. Both the plan designed to restructured police force to reduce robbery and violence in a country (Vergara 2012 ). The rural China is suffered by high INC_INEQ that leads to higher crime rate (South China Monitoring Report 2015 ) while POV and INC_INEQ lead to crime and violent factor in Colombia (Gordon 2016 ). The socio-economic factors including low provision of education, health, high POV, and food challenges lead to increase crime in Indonesia (Pane 2017 ), while generating employment opportunities and increasing wage rate in Malaysia may be beneficial to reduce crime–POV nexus in a given country (Mulok et al. 2017 ). Mexican economy is suffered with high rate of homicides that negatively affect labor market outcomes, while country inhibits by increasing strict laws to diminish violence (Kato Vidal 2015 ). The safety situation in Morocco is cumbersome, as one of the country reports shows that an increased rate in crime is about to increase up to 23% in 2016 (OSAC 2017 ). The number of other factors remains visible in selected sample of panel of countries, including rural POV and social exclusion that is considered the main factor of socio-economic crisis in Poland (European Commission 2008 ); POV, unemployment, and INC_INEQ chiefly attributed to crime rate in South Africa (Bhorat et al. 2017 ); politics, democracy, and INC_INEQ arise conflicts in Thailand (Hewison 2014 ); corruption and high unemployment are the major conflicts in Tunisia (Saleh 2011 ); and Uruguay economy needs policy actions to reduce POV by investment in children education, modernizing rural sector, and balancing the gender gap (Thamma 2017 ). Thus, these facts about crime and POV in different countries put a focus to study crime–POV nexus under PPG framework in this study for robust evaluation. Figure  2 in Appendix shows the plots of the studied variables at level.

The study used the following non-linear equations to determine the dynamic relationship between PPG factors and crime rate in a panel of countries, i.e.,

where GDPPC indicates per capita GDP, GDPPC 2 indicates square of per capita GDP, GINI indicates Gini coefficient—income inequality, EDUEXP indicates education expenditures, HEXP indicates health expenditures, POVHCR indicates poverty headcount ratio, TOP indicates trade openness, UNEMP indicates unemployment, and CRIME indicates crime rate.

Equations ( 1 ) to ( 3 ) assessed the possible inverted U-shaped relationships between crime rate and PCI, between POVHCR and PCI, and between GINI and PCI, while Eq. ( 4 ) reviewed the PPG reforms across countries. Arellano and Bond ( 1991 ) developed the differenced GMM estimator, whom argued that the GMM estimator eliminates country effects and controls the possible endogeneity of explanatory variables using the appropriate instrumental list that evaluated by Sargan–Hansen test. The process further involves two-step GMM iterations with the time updated weights and adopted the weighting matrix by White period. The tests for autocorrelations by AR(1) and AR(2) and the Sargan test by Sargan–Hansen of over-identifying restrictions are presented for statistical reliability of the given models. The differenced GMM is superior to the 2SLS and system GMM, i.e., 2SLS regression estimator is used when the known endogeneity exists between the variables, which are handled by including the list of instrumental variables at their first lagged. Thus, the possible endogeneity problem is resolved accordingly. The system GMM further be used instead of 2SLS as if there are more than one endogenous issues exist in the model, which is unable to resolve through 2SLS estimator. Finally, the differenced GMM estimator is used as its estimated AR(1) and AR(2) bound values that would be helpful to encounter the issues of serial correlation and endogeneity problem accordingly.

Using the GMM estimator, the study verified different possibilities of KC, i.e., if the signs and magnitudes of \(\beta_{1} > 0\) and \(\beta_{2} < 0\) , than we may confirm the crime-induced KC, poverty-induced KC, and inequality-induced KC. The inverted U-shaped relationship between crime rate and PCI verified ‘crime-induced KC’, between POVHCR and PCI verified ‘POV-induced KC’, and inverted U-shaped relationship between GINI and PCI verified ‘inequality-induced KC’. On the other way around, if \(\beta_{1} < 0\) and \(\beta_{2} > 0\) , then we consider the U-shaped KC between crime rate and PCI, between POV and PCI, and between GINI and PCI, respectively. There are three other situations we may observe with the sign and magnitude of \(\beta_{1}\) and \(\beta_{2}\) , i.e., (i) \(\beta_{1} < 0\) and \(\beta_{2} = 0\) , (ii) \(\beta_{1} > 0\) and \(\beta_{2} = 0\) , and (iii) \(\beta_{1} = 0\) and \(\beta_{2} = 0\) , referred the monotonically decreasing function, monotonically increasing function, and flat/no relationship with the crime-PCI, poverty-PCI, and inequality-PCI in a panel of cross-sectional countries. The study further employed social accounting matrix by impulse response function (IRF) and variance decomposition analysis (VDA) in an inter-temporal relationship between the studied variables for a next 10-year period starting from 2015 to 2024. As it name implies, VDA explains the proportional variance in one variable caused by the proportional variance by the other variables in a vector autoregressive (VAR) system, while IRF traces the dynamic responses of a variable to innovations in other variables in the system. Both the techniques use the moving average representation of the original VAR system. Figure  1 shows the theoretical framework of the study to clearly outline the possible relationship between the stated variables.

figure 1

Source: authors’ extraction

Research framework of the study.

Figure  1 shows the possible relationship between POV and crime rates in mediation of inequality, unemployment, and EG across countries. It is likelihood that POV increases inequality that leads to decrease in EG. The low-income growth further leads to increased unemployment, which causes high crime rates. This nexus is still rotated through crime rates that increase POV incidence across countries. The PPG process still works under the stated factors that need judicious income distribution to reduce crime rates.

The study further proceeds to evaluate the PPG reforms in a panel of selected countries. Kakwani and Pernia ( 2000 ) proposed an index of PPG called ‘PPG index’, which is evaluated by the growth elasticity and inequality elasticity with respect to POV. The same methodology is adopted in this study to assess the PPG and/or pro-rich growth reforms to assess the changes in the crime rate in a panel of countries. PPG defined as a state in which where the growth trickles down to the poor as compared to the non-poor. Poverty is largely affected by two main factors, i.e., higher growth rate may reduce the POV rates, while higher INC_INEQ reduces the impact of EG to reduce POV; therefore, the PPG index included the following mathematical illustrations, i.e.,

The study further assessed the pro-poorness of social expenditures and evaluates its impact to observe changes in IHR. The study shows the following mathematical illustrations that is extended from the scholarly work of Zaman and Khilji ( 2014 ); Kakwani and Pernia ( 2000 ) and Kakwani and Son ( 2004 ) i.e.,

where \(\alpha =\) 0, 1 and 2 indicate POVHCR, poverty gap and squared poverty gap, respectively, ‘P’ indicates FGT poverty measures, and ‘SOCIALEXP’ indicates social expenditures. Differentiating \(\eta_{\alpha }\) in Eq. ( 9 ) with respect to social expenditures gives more elaborated form of GEP, i.e.,

The elasticity of entire class of poverty measures \(P_{\alpha }\) with respect to Gini index is given by

which will be always positive only when \(S{\text{OCIALEXPE}} > z\) .Equations ( 10 ) and ( 11 ) are combined together to form TPE for all FGT poverty measures, i.e.,

or \(\delta_{\alpha } = \eta_{\alpha } + \xi_{\alpha }\) . Finally, pro-poorness of social expenditures estimated based on the following equation, i.e.,

Kakwani and Son ( 2004 ) presented the following bench mark applications to assess the pro-poor and/or anti-poor policies, i.e., the following value judgments regarding the PPG index ( \(\varphi\) ) are as follows, i.e.,

\(\varphi\)  < 0, growth is pro-rich or anti-poor,

0 <  \(\varphi\) \(\le\) 0.33, the process of PPG is considerable low,

0.33 <  \(\theta\) \(\le\) 0.66, the process of PPG is moderate,

0.66 <  \(\varphi\)  < 1.0, the process of EG considered as pro-poor, and

\(\varphi \ge\) 1.0, the process of EG is highly pro-poor.

The study utilized the PPG model for ready reference in this study.

This section presented the descriptive statistics in Table  1 , correlation matrix in Table  2 , dynamic system GMM estimates in Table  3 , IRF estimates in Table  4 , VDA estimates in Table  5 , while finally Table  6 shows the estimates for PPG in a panel of selected countries. Table  1 shows that GDPPC has a minimum value of US$ 199.350 and the maximum value of US$ 11257.600, with a mean and standard deviation (STD) value of US$ 4340.777 and US$ 2490.554, respectively. GINI has a minimum value of 25% and the maximum value of 64.790%, having an STD value of 8.580% with an average value of 45.095%. The minimum value of EDUEXP is about 0.998% of GDP and the maximum value of 7.657% of GDP, with an average value of 4.051% of GDP. The average value of HEXP per capita is about US$ 321.249 and a maximum value of US$ 1431.154, with an STD value of US$ 292.802. The maximum value of POVHCR is about 69% at US$1.90 a day with an average value of 12.394% at US$1.90 a day. The minimum value of trade is 13.753% of GDP and the maximum value of 220.407% of GDP, with an average value of 62.391% of GDP. The mean value for UNEMP is about 8.890% of total labor force with STD value of 6.010%. Finally, the minimum value of crime rate is about 0.439 per 100,000 inhabitants and the maximum value of 71.786 per 100,000 inhabitants, with an average value of 11.664 per 100,000 peoples. This exercise would be helpful to understand the basic descriptions of the studied variables in a panel of countries.

Figure  3 in Appendix shows the plots of the studied variables and found the stationary movement in the variables at their first difference. Table  2 presents the estimates of correlation matrix and found that GINI (i.e., r  = 0.264), EDUEXP ( r  = 0.243), HEXP ( r  = 0.730), TOP ( r  = 0.061), UNEMP (0.152) and CRIME ( r  = 0.031) have a positive correlation with the GDPPC, while POVHCR ( r  = − 0.599) significantly decreases GDPPC.

The results further reveal that GINI is affected by EDUEXP, HEXP, UNEMP and CRIME, while it considerably decreases by trade liberalization policies. EDUEXP, HEXP, PCI, TOP and UNEMP significantly decrease POVHCR, while crime rate has a positive correlation with the POVHCR. Finally, GINI have a greater magnitude, i.e., r  = 0.671, to influence CRIME, followed by UNEMP ( r  = 0.417), EDUEXP ( r  = 0.188), and POVHCR ( r  = 0.164) while trade liberalization policies support to decrease crime rates in a panel of countries. The study now proceeds to estimate the two-step system GMM for analyzing the functional relationship between socio-economic factors and crime rate. The results are presented in Table  3 .

The results of panel GMM show that GINI and UNEMP both have a significant and direct relationship with the CRIME, while TOP have an indirect relationship with CRIME in a panel of countries. The results imply that GINI and UNEMP are the main factors that increase CRIME, while trade liberalization policies have a supportive role to decrease crime rates across countries. Thorbecke and Charumilind ( 2002 ) evaluated the impact of income inequality on health, education, political conflict, and crime, and surveyed the different casual mechanism in between income inequality and its socio-economic impact across the globe. The policies have devised while reaching the conclusive relationships between them. Kennedy et al. ( 1998 ) concluded that social capital and income inequality are the powerful predictors of intentional homicides rate and violent crime in the US states. Altindag ( 2012 ) explored the long-run relationship between unemployment and crime rates in a country-specific panel dataset of Europe and found that unemployment significantly increases crime rates, while unemployment has a power predictor of exchange rate movements and industrial accident across the Europe. Menezes et al. ( 2013 ) confirmed the positive association between income inequality and criminality, as rational income distribution tends to decrease neighborhood homicides rate while it implies an increase in the intentional homicides rate in the surrounding neighborhoods.

In a second regression panel, the results confirmed the U-shaped relationship between POVHCR and GDPPC, as at initial level of EG, POV significantly declines, while at the later stages, this result is evaporated, as EG subsequently increases POVHCR that shows pro-rich federal policies across countries. The HEXP, however, significantly decreases POVHCR during the study time period. Dercon et al. ( 2012 ) investigated the relationship between chronic POV and rural EG in Ethiopia and argued that chronic POV is associated with the lack of education, physical assets and remoteness, while EG in terms of provide better roads and extension services may trickle down to the poor in a same way that the non-chronically poor benefited. Solinger and Hu ( 2012 ) examined the relationship between health, wealth and POV in urban China and found that wealthier cities prefer to allocate their considerable portion of savings for social assistance funds, while poorer places save the city money and work outside in a hope that the peoples would be better able to support themselves. Fosu ( 2015 ) examined the relationship between GIP triangle in sub-Saharan African countries and found that as a whole, South African countries lag behind the BICR (Brazil, India, China and Russia) group of countries; however, many of them in sub-Saharan African countries have outperformed India. The results further specified that PCI is the main predictor to reduce POV in sub-Saharan African countries; however, rational income distribution is a crucial challenge to reduce POV reduction through substantial growth reforms in a region. Kalichman et al. ( 2015 ) concluded that food poverty is associated with the multifaceted problems of health-related outcomes across the globe.

In a third regression panel, the results confirm an inverted U-shaped relationship between GDPPC and GINI that verified an inequality-induced KC in a panel of countries. The results imply that at initial level of economic development, GINI first increases and then decreases with the increased GDPPC across countries. CRIME, however, it is associated with the higher GINI during the studied time period. Kuznets ( 1955 ), Ahluwalia ( 1976 ), Deininger and Squire ( 1998 ), and others confirmed an inverted U-shaped relationship between INC_INEQ and PCI in different economic settings. Mo ( 2000 ) suggested different channelss to examine the possible impact of INC_INEQ on EG and found that ‘transfer channel’ exert the most important channel, while ‘human capital’ is the least important channel that negatively affects the rate of EG via INC_INEQ. Popa ( 2012 ) argued that health and education both are important predictors for EG, while POV and unemployment negatively correlated with the EG in Romania. Herzer and Vollmer ( 2012 ) confirmed the negative relationship between INC_INEQ and EG within the sample of developing countries, developed countries, democracies, non-democracies, and sample as a whole. In a similar line, Malinen ( 2012 ) confirmed the long-run equilibrium relationship between PCI and INC_INEQ and found that income inequality negatively affected the growth of developed countries.

The final regression shows that HEXP and TOP both significantly increase GDPPC, while POVHCR decreases the pace of EG, which merely be shown pro-rich federal policies in a panel of countries. Ranis et al. ( 2000 ) found that both the health and education expenditures lead to increased EG, while investment improves human development in a cross-country regression. Bloom et al. ( 2004 ) confirmed the positive connection between health and EG across the globe. Gyimah-Brempong and Wilson ( 2004 ) examined the possible effect of healthy human capital on PCI of sub-Saharan African and OECD countries and found the positive association between them in a panel of countries.

The statistical tests of the system GMM estimator confirmed the stability of the model by F-statistics, as empirically model is stable at 1% level of confidence interval. Sargan–Hansen test confirmed the instrumental validity at conventional levels for all cases estimated. Autocorrelations tests imply that except POVHCR model, the remaining three models including CRIME, GINI and GDPPC model confirmed the absence of first- and second-order serial correlation, and as a consequence, we verified our instruments are valid. As far as POVHCR model, we believed the results of Sargan–Hansen test of over identifying restrictions and AR(1) that is insignificant at 5% level, and confirmed the validity of instruments and absence of autocorrelation at first-order serial correlation. Table  4 shows the estimate of IRF for the next 10-year period starting from a year of 2015 to 2024.

The results show that the socio-economic factors have a mix result with the rate of crime, as POVHCR slightly increases with decreasing rate with the crime data, i.e., in the next coming years from 2016, 2018, 2019, and 2022, POVHCR exhibits a negative sign, while in the remaining years in between from 2015 to 2024, POVHCR increases crime rate. GINI will considerably increase crime rate from 2022 to 2024. UNEMP has a mixed result to either increase crime rate in one period while in the very next upcoming periods, it declines crime rate. Similar types of results been found with EDUEXP, HEXP and with the TOP; however, GDPPC will constantly increase the rate of crime in a panel of countries. In an inter-temporal relationship between POVHCR and other predictors, the results show that GDPPC would significantly decrease POVHCR for the next 10-year period; however, UNEMP, HEXP, and crime rate would considerably increase POVHCR. EDUEXP and TOP would support to reduce GINI for the next upcoming years, while remaining variables including crime rate, POV, UNEMP, HEXP, and GDPPC associated with an increased GINI across countries. The GDPPC will be influenced by crime rate, POVHCR, GINI, UNEMP, HEXP, and EDUEXP, while TOP would considerably to support GDPPC for the next 10-year time period. Figure  4 in Appendix shows the IRF estimates for the ready reference.

Table  5 shows the estimates of VDA and found that POVHCR will exert the largest share to influence crime rates, followed by GDPPC, TOP, HEXP, EDUEXP, GINI, and UNEMP. POVHCR would be affected by crime rate (i.e., 4.450%), UNEMP (1.751%), GDPPC (1.120%), GINI (1.043%), HEXP (0.639%), and EDUEXP (0.512%), and TOP (0.299%), respectively.

The results further reveal that GINI will affected by POVHCR, as it is explained by 7.680% variations to influence GINI for the next 10-year period. UNEMP, EDUEXP, and crime rate will subsequently influenced GDPPC about to 1.107%, 0.965%, and 0.312% respectively. The largest variance to explain UNEMP will be TOP, while the lowest variance to influence UNEMP will be GINI for the next 10-year period. Finally, GDPPC would largely influenced by HEXP, followed by UNEMP, CRIME, POVHCR, EDUEXP, TOP, and GINI for the period of 2015 to 2024. Figure  5 in Appendix shows the plots of the VDA for ready reference.

Finally, Table  6 presents the changes in crime rate by five different growth phases, i.e., phase 1: 1990–1994, phase 2: 1995–1999, phase 3: 2000–2004, phase 4: 2005–2009, and phase 5: 2010–2014. The results show that in the years 1990–1994, 1% increase in EG and INC_INEQ decrease POVHCR by − 0.023% and − 0.630%, which reduces TPE by − 0.629 percentage points. The PPG index surpassed the bench mark value of unity and confirmed the trickledown effect that facilitates the poor as compared to the non-poor. However, there is an overwhelming increase in the crime rate beside that the pro-poorness of EG, which indicate the need for substantial safety nets’ protection to the poor that escape out from this acute activities (Wang et al. 2017 ). In a second phase from 1995 to 1999, although EG decreases POVHCR by − 0.187; however, GINI has a greater share to increase POVHCR by 0.517% that ultimately increases TPE by 0.330%. This increase in the TPE turns to decrease PPG as 1.764, which shows anti-poor/pro-rich federal policies and low reforms for the poor that accompanied with the higher rates of crime in a panel of countries. The rest of the growth phases from 2000 to 2014 show anti-poor growth accompanied with the higher INC_INEQ and lower EG; however, crime rate decreases in the year 2000–2004 and 2010–2014 besides that the growth process is anti-poor across countries. The policies should be formulated in a way to aligned crime rate with the PPG reforms across countries (Vellala et al. 2018 ).

The results of PPE index confirmed an anti-poor growth from 1990 to 2004, while at the subsequent years from 2005 to 2014, education growth rate subsequently benefited the poor as compared to the non-poor, i.e., PPE index exceeds the bench mark value of unity. Crime rate is increasing from 1990 to 1999, and from 2005 to 2009, while it decreases the crime rate for the years 2000–2004 and 2010–2014. The good sign of recovery has been visible for the years 2010–2014 where the PPE growth supports to decrease crime rate in a panel of selected countries. Finally, the PPH index confirmed two PPG phases, i.e., from 1990 to 1994, and 2010 to 2014 in which crime rate increases for the former years and decreases in the later years. The remaining health phases from 1995 to 2009 show anti-poor health index, while crime rate is still increasing during the years from 1995 to 1999 and 2005 to 2009, and decreasing for the period 2000–2004. The results emphasized the need to integrate PPG index with the crime rate, as PPG reforms are helpful to reduce humans’ costs by increasing EG and social expenditures, and providing judicious income distribution to escape out from POV and vulnerability across the globe (Musavengane et al. 2019 ).

From the overall results, we come to the conclusion that social spending on education and health is imperative to reduce crime incidence, while it further translated a positive impact on POV and inequality reduction across countries (Hinton 2016 ). EG is a vital factor to reduce POV; however, it is not a sufficient condition under higher INC_INEQ (Dudzevičiūtė and Prakapienė 2018 ). INC_INEQ and unemployment rate both are negatively correlated with crime rates; however, it may be reduced by judicious income distribution and increases social spending across countries (Costantini et al. 2018 ). Trade liberalization policies reduce incidence of crime rates and improve country’s PCI, which enforce the need to capitalize domestic exports by expanding local industries. Thus, the United Nations SDGs would be achieved by its implication in the countries perspectives (Dix-Carneiro et al. 2018 ). The study achieved the research objectives by its theoretical and empirical contribution, which seems challenge for the developmental experts to devise policies toward more pro-growth and PPG.

4 Conclusions and policy recommendations

This study investigated the dynamic relationship between socio-economic factors and crime rate to assess PPG reforms for reducing crime rate in a panel of 16 diversified countries, using a time series data from 1990–2014. The study used PCI and square PCI in relation with crime rate, POVHCR, and GINI to evaluate crime-induced KC, poverty-induced KC and inequality-induced KC, while PPG index assesses the federal growth reforms regarding healthcare provision, education and wealth to escape out from POV and violence. The results show that GINI and UNEMP are the main predictors that have a devastating impact to increase crime rate. Trade liberalization policies are helpful to reduce crime rate and increase PCI. Healthcare expenditures decrease POVHCR and amplify EG. The EG is affected by POVHCR, which requires strong policy framework to devise PPG approach in a panel of selected countries. The study failed to establish crime-induced KC and poverty-induced KC, while the study confirmed an inequality-induced KC. The results of IRF reveal that PCI would considerably increase crime rate, while crime rate influenced GINI and PCI for the next 10-year period. The estimates of VDA show that POVHCR explained the greater share to influence crime rates, while reverse is true in case of POVHCR. The study divided the studied time period into five growth phases 1990–1994, 1995–1999, 2000–2004, 2005–2009, and 2010–2014 to assess PPG, PPH, and PPE reforms and observe the changes in crime rates. The results show that there is an only period from 1990 to 1994 that shows PPG, while crime rate is still increasing in that period; however, in the years 2000–2004, and 2010–2014, crime rate decreases without favoring the growth to the poor. PPE and PPH assessment confirmed the reduction in the crime rates for the years 2010–2014. The overall results confirmed the strong correlation between socio-economic factors and crime rates to purse the pro-poorness of government policies across countries. The overall results emphasized the need of strong policy framework to aligned PPG policies with the reduction in crime rate across the globe. The study proposed the following policy recommendations, i.e.,

Education, health and wealth are the strong predictors of reducing crime rates and achieving PPG, thus it should be aligned with inclusive trade policies to reduce human cost in terms of decreasing chronic poverty and violence/crime.

The policies should be formulated to strengthen the pro-poorness of social expenditures that would be helpful to reduce an overwhelming impact of crime rate in a panel of countries.

GIP triangle is mostly viewed as a pro-poor package to reduce the vicious cycle of poverty; however, there is a strong need to include some other social factors including unemployment, violence, crime, etc., which is mostly charged due to increase in poverty and unequal distribution of income across the globe. The policies should devise to observe the positive change in lessen the crime rate by PPG reforms in a panel of selected countries.

The significant implication of the Kuznets’ work should be extended to the some other unexplored factors especially for crime rate that would be traced out by the pro-poor agenda and pro-growth reforms.

There is a need to align the positivity of judicious income distribution with the broad-based economic growth that would be helpful to reduce poverty and crime rate across countries.

The result although not supported the ‘parabola’ relationship between income and crime rates; however, it confirmed the U-shaped relationship between income and poverty. The economic implication is that income is not the sole contributor to increase crime rates while poverty exacerbates violent crimes across countries. There is a high need to develop a mechanism through which poverty incidence can be reduced, which would ultimately lead to decreased crime rates. The improvement in the labor market structure, judicious income distribution, and providing social safety nets are the desirable strategies to reduce crime rates and poverty incidence across countries, and

The results supported parabola relationship between economic growth and inequality, which gives a clear indication to improve income distribution channel for reducing poverty and crime rates at global scale.

These seven policies would give strong alignment to improve social infrastructure for managing crime through equitable justice and PPG process.

Availability of data and materials

The data are freely available on World Development Indicator, published by World Bank on given URL ID: https://datacatalog.worldbank.org/dataset/world-development-indicators .

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Acknowledgements

The authors are thankful for King Saud university research project number (RSP-2019/87) for funding the study. The authors are indebted to the editor and reviewers for constructive comments that have helped to improve the quality of the manuscript.

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See Table  7 , Figs.  2 , 3 , 4 and 5 .

figure 2

Source: World Bank ( 2015 )

Data trend at level.

figure 3

Source: World Bank ( 2015 ). ‘D’ indicates first difference

Data trend at first differenced

figure 4

Source: authors’ estimation. Note: ‘D’ shows first difference, while ‘LOG’ represents natural logarithm

Plots of IRF.

figure 5

VDA Estimates.

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Anser, M.K., Yousaf, Z., Nassani, A.A. et al. Dynamic linkages between poverty, inequality, crime, and social expenditures in a panel of 16 countries: two-step GMM estimates. Economic Structures 9 , 43 (2020). https://doi.org/10.1186/s40008-020-00220-6

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  • Human behaviour

Humans sometimes cooperate to mutual advantage, and sometimes exploit one another. In industrialised societies, the prevalence of exploitation, in the form of crime, is related to the distribution of economic resources: more unequal societies tend to have higher crime, as well as lower social trust. We created a model of cooperation and exploitation to explore why this should be. Distinctively, our model features a desperation threshold, a level of resources below which it is extremely damaging to fall. Agents do not belong to fixed types, but condition their behaviour on their current resource level and the behaviour in the population around them. We show that the optimal action for individuals who are close to the desperation threshold is to exploit others. This remains true even in the presence of severe and probable punishment for exploitation, since successful exploitation is the quickest route out of desperation, whereas being punished does not make already desperate states much worse. Simulated populations with a sufficiently unequal distribution of resources rapidly evolve an equilibrium of low trust and zero cooperation: desperate individuals try to exploit, and non-desperate individuals avoid interaction altogether. Making the distribution of resources more equal or increasing social mobility is generally effective in producing a high cooperation, high trust equilibrium; increasing punishment severity is not.

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Introduction.

Humans are often described as an unusually cooperative or ‘ultrasocial’ species 1 . The truth is more complex: humans from the same society can cooperate for mutual benefit; or they can simply co-exist; or they can actively exploit one another, as in, for example, crime. A theory of human sociality should ideally predict what mix of these alternatives will emerge under which circumstances. Comparing across industrialised societies, higher inequality—greater dispersion in the distribution of economic resources across individuals—is associated with higher crime and lower social trust 2 , 3 , 4 , 5 , 6 , 7 . These associations appear empirically robust, and meet epidemiological criteria for being considered causal 8 . However, the nature of the causal mechanisms is still debated. The effects of inequality are macroscopic phenomena, seen most clearly by comparing aggregates such as countries or states. It is their micro-foundations in individual psychology and behaviour that still require clarification.

There are, broadly, two classes of explanation for how inequality, a population-level phenomenon, could influence individual-level outcomes like crime or trust. The first class of explanation is compositional: in more unequal societies, the least fortunate individuals are absolutely worse off than in more equal societies of the same average wealth, exactly because the dispersion either side of the average is greater. Some individuals are also better off too, at the other end of the distribution, but if there is any non-linearity in the function relating individual resources to outcomes—if for example the poor becoming absolutely poorer has a larger effect on their propensity to offend than the rich becoming absolutely richer has on theirs—this can still change outcome prevalence in the population 9 , 10 , 11 . In line with compositional explanations, across US counties, the association between inequality and rate of property crime is fully mediated by the prevalence of poverty, which is higher in more unequal counties 2 . Moreover, changes in rates over time track changes in the economic prospects of people at the bottom end of the socioeconomic distribution 12 , 13 . The second class of explanation is psychosocial: individuals perceive the magnitude of social differentials in the society around them, and this affects their state of mind, increasing competitiveness, anxiety and self-serving individualism 8 , 14 . In this paper, we develop an explanatory model for why greater inequality should produce higher crime and lower social trust. Our model provides a bridge between compositional and psychosocial explanations. Its explanation for the inequality-crime association is compositional: individuals offend when their own absolute level of resources is desperately low, and the effect of increasing inequality is to make such desperation more prevalent. On the other hand, the model’s explanation for the inequality-trust association is more psychosocial: all individuals in high-inequality populations end up trusting less, regardless of their personal resource levels.

To provide a micro-foundation in individual behaviour for the macro-level effects of inequality on crime, we must start from explanations for why individuals commit crimes. Economic 15 , 16 and behavioural-ecological 17 approaches see offending as a strategic response to specific patterns of incentive. Economic models predict that offending should be more attractive when the payoffs from legitimate activity are low. This principle successfully explains variation in offending behaviour both within and between societies 12 , 16 . It can also explain the relationship between crime levels and inequality, in compositional manner, because unequal societies produce poorer legitimate opportunities for people at the lower end of the socioeconomic spectrum 2 . However, these models are generally taken to predict that making punishments for crime more severe should reduce the prevalence of offending, because harsher punishment should reduce the expected utility associated with the criminal option. Empirical evidence, though, does not clearly support the hypothesis that increasing punishment severity reduces offending 18 , 19 . There is more evidence for a deterrent effect of increased probability of punishment, though even this effect may be modest 18 , 19 .

Becker 15 pointed out that the puzzle of the weak deterrent effect of punishment severity would be solved if offenders were risk-preferring. The decision to offend is risky in that it has either a large positive payoff (if not caught) or a large negative one (if caught and punished). An individual who prefers risk might thus choose to offend even if the expected utility of offending is negative due to a possible severe punishment. Thus, the question becomes: why would some people—those who commit crime—prefer risk, when people are usually averse to it? To address this question, our model incorporates features of classic risk-sensitive foraging theory from behavioural ecology 20 (for a review in the context of human behaviour, see Ref. 21 ). Risk-sensitive foraging models incorporate a desperation threshold: a level of resources below which it is disastrous to fall, in the foraging case because of starvation. The models show that individuals in sufficient imminent danger of falling below this threshold ought to become risk-preferring. If a risky option is successful, it will allow them to leap back over the threshold; and if not, their prospects will be no more dire than they were anyway. Our model is novel in explicitly incorporating a desperation threshold into decisions about whether to cooperate (analogous in our model to participating in legitimate economic activity) or exploit others (analogous to committing an acquisitive crime).

The desperation threshold is the major theoretical innovation of our model. We justify its inclusion on multiple grounds. First, the ultimate currency in our model is fitness, a quantity with a natural biological interpretation that must necessarily be zero if the individual lacks the minimal resources to subsist and function socially. Thus, it is reasonable that expected fitness should be related to resource levels, but not linearly: there should be a point where, as resources deplete, expected fitness rapidly declines to zero. Our threshold assumption produces exactly this type of function (see Supplementary Sect.  2.1 , Supplementary Fig. S1 ). Second, in experimental games where gaining a payoff is subject to a threshold, people do switch to risk-proneness when in danger of falling below the threshold, as risk-sensitive foraging theory predicts 22 . Although this does not show that such thresholds are widespread or important in real life, it does show that people intuitively understand their implications when they are faced with them, and respond accordingly. Third, there are ethnographic descriptions of ‘disaster levels’, ‘crisis levels’, or ‘edges’ that affect the risk attitudes of people facing poverty 23 , 24 . For example, writing on Southeast Asia, Scott 23 describes the spectre of a “subsistence crisis level—perhaps a ‘danger zone’ rather than a ‘level’ would be more accurate…a threshold below which the qualitative deterioration in subsistence, security and status is massive and painful” (p. 17), as an ever-present factor in people’s decisions. Thus, including a desperation threshold is a simple but potentially powerful innovation into models of cooperation and exploitation, with potential to generate new insights.

In our model, agents repeatedly decide between three actions: foraging alone, foraging cooperatively, or exploiting a cooperative group. Foraging cooperatively is analogous to legitimate economic activity, and exploitation is analgous to acquisitive crime. Agents have variable levels of resources, and their behaviour is state-dependent. That is, rather than having a fixed strategy of always cooperating or always exploiting, each agent, at each interaction, selects a behaviour based on their current level of resources, the behaviour of others in the surrounding population, and background parameters such as the probability and severity of punishment, and the likelihood of resources improving through other means. Agents seek to maximize fitness. We assume that fitness is positively related to resource levels, but that there is a threshold, a critically low level of resources below which there is an immediate fitness penalty for falling. Our investigation of the model has two stages. We first compute the optimal action policy an individual should follow; that is, the optimal action to select for every possible combination of the situational variables. Second, we simulate populations of individuals all following the optimal action policies, to predict population-level outcomes for different initial resource distributions.

To explain the model in more detail, at each time point t in an indefinitely long sequence of time steps (where one time step is one economic interaction), agents have a current level of resources s. They can take one of three actions. Foraging alone costs x units of resources and is also guaranteed to return x. Thus, foraging alone is sufficient to maintain the agent but creates no increase in resources. It is also safe from exploitation, as we conceptualise it as involving minimal interaction with others. Alternatively, agents can team up with n-1 others to cooperate . As long as no other group member exploits, cooperation is mutually beneficial, costing x units but producing a payoff of \(\alpha x \left( {\alpha > 1} \right)\) to each group member. Finally, agents can exploit : join a cooperating group and try to selfishly divert the resources produced therein. If this exploitation is successful, they obtain a large reward β, but if they fail, they receive a punishment π. The probability of being punished is γ. The punishment is not administered by peers: we assume that there is a central punitive institution in place, and both the size and probability of punishment are exogenous. In our default case, the expected payoff for exploitation is zero (i.e. \(\left( {1 - \gamma } \right)\beta = \gamma \pi\) ), making exploitation no better than foraging alone on average, and worse than cooperating. However, the reward for a successful exploitation, β, is the largest payoff available to the agent in any single time step.

At every time step, each agent’s resource level is updated according to the outcomes of their action. In addition, resource levels change by a disturbance term controlled by a parameter r , such that the mean and variance of population resources are unchanged, but the temporal autocorrelation of agents’ resource levels is only 1 −  r . If r is high, individuals whose current resources are low can expect they will be higher in the future and vice versa, because of regression to the mean. If \(r = 0\) , resources will never change other than by the agent’s actions. We consider r a measure of social mobility due to causes other than choice of actions.

In the first stage, we use stochastic dynamic programming 25 , 26 to compute the optimal action policy. Fitness is a positive linear function of expected resource level s in the future. However, in computing the fitness payoffs of each action, we also penalize, by a fixed amount, any action that leaves the agent below a desperation threshold in the next time step (arbitrarily, we set this threshold at s  = 0). The optimal action policy identifies which one of the three actions is favoured for every possible combination of the factors that impinge on the agent. These include both their own current resource state s , and features of their social world, such as the severity of punishment π, the probability of punishment γ, and the level of social mobility r. A critical variable that enters into the computation of the optimal action is the probability that any cooperating group in the population will contain someone who exploits. We denote this probability p . We can think of 1 −  p as an index of the trustworthiness of the surrounding population. Computing the optimal policy effectively allows us to ask: under what circumstances should an individual forage alone, cooperate, or exploit?

In the second stage, we simulate populations of agents all following the optimal policies computed in the first stage. We can vary the starting distributions of resources (their mean and dispersion), as well as other parameters such as social mobility and the probability and severity of punishment. During the simulation stage, each agent forms an estimate of 1 −  p , the trustworthiness of others, through observing the behaviour of a randomly-selected subset of other individuals. We refer to these estimates as the agents’ social trust, since social trust is defined as the generalized expectation that others will behave well 27 . Social trust updates at the end of each time step. Agents’ social trust values are unbiased estimates of the current trustworthiness of the surrounding population, but they are not precise, because they are based on only a finite sample of other population members. The simulation stage, allows us to ask: what are the predicted temporal dynamics of behaviour, and of social trust, in populations with different starting distributions of resources, different levels of social mobility, and different punishments for exploitation?

Each of the three actions is optimal in a different region of the space formed by current resources s and the trustworthiness of others 1 −  p (Fig.  1 a). Below a critical value of s , agents should always exploit, regardless of trustworthiness . In the default case, this critical value is in the vicinity of the desperation threshold, though it can be lower or higher depending on the value of other parameters. With our default values, exploitation will not, on average, make the agent’s resource state any better in subsequent time steps. However, there is a large advantage to getting above the threshold in the next time step, and there is a region of the resource continuum where exploitation is the only action that can achieve this in one go (intuitively, it is the quickest way to ‘get one’s head above water’). Where s is above the critical value, cooperation is optimal as long as the trustworthiness of the surrounding population is sufficiently high. However, if trustworthiness is too low, the likelihood of getting exploited makes cooperation worse than foraging alone. The shape of the frontier between cooperation and foraging alone is complex when resources are close to the desperation threshold. This is because cooperation and foraging alone also differ in riskiness; foraging alone is risk-free, but cooperation carries a risk of being exploited that depends on trustworthiness. Just above the exploitation zone, there is a small region where cooperation is favoured even at low trustworthiness, since one successful cooperation would be enough to hurdle back over the threshold, but foraging alone would not. Just above this is a zone where foraging alone is favoured even at high trustworthiness; here the agent will be above the threshold in the next time period unless they are a victim of exploitation, which makes them averse to taking the risk of cooperating.

figure 1

Optimal actions as a function of the individual’s current resources s and the trustworthiness of the surrounding population, 1 −  p . ( A ) All parameters at their default values. This includes: α = 1.2, r = 0.1, π = 10, and γ = 1/3 (see Table 1 for a full list). ( B ) Effect of altering the efficiency of cooperation α to be either lower (1.05) or higher (1.30) than ( A ). Other parameter values are as for ( A ). ( C ) Effects of varying social mobility, to be either high (r = 0.8), or complete (r = 1.0; i.e. resource levels in this time period have no continuity at all into the next). Other parameter values are as for ( A ). ( D ) Effect of increasing the severity of punishment for exploiters to π = 15 and π = 20. Other parameter values are as for ( A ). ( E ). Effects of altering the probability of punishment for exploiters to γ = 2/3 and γ = 9/10. Other parameter values are as for ( A ).

We explored the sensitivity of the optimal policy to changes in parameter values. Increasing the profitability of cooperation (α) decreases the level of trustworthiness that is required for cooperation to be worthwhile (Fig.  1 B; analytically, the cooperation/foraging alone frontier for \(s \gg 0\) is at \(\left( {1 - p} \right) = 1/\alpha\) ; see Supplementary Sect.  2.2 ). A very high level of social mobility r moves the critical value for exploitation far to the left (i.e. individuals have to be in an even more dire state before they start to exploit; Fig.  1 C). This is because with high social mobility, badly-off individuals can expect that their level of resources will regress towards the mean over time anyway, lessening the need for risky action when faced with a small immediate shortfall.

The optimality of exploitation below the critical level of resources is generally insensitive to increasing the severity of punishment, π (Fig.  1 D), even where the expected value of exploitation is thereby rendered negative. This is because a desperate agent will be below the threshold in the next time step anyway if they forage alone, cooperate, or receive a punishment of any size. They are so badly off that it is relatively unimportant how much worse things get, but important to take any small chance of ‘jumping over’ the threshold. The exploitation boundary is slightly more sensitive to the probability of punishment, γ, though even this sensitivity is modest (Fig.  1 E). When γ is very high, it is optimal for agents very close to the boundary of desperation to take a gamble on cooperating, even where trustworthiness is rather low. Although this is risky, it offers a better chance of getting back above the threshold than exploitation that is almost bound to fail. Nonetheless, it is striking that even where exploitation is almost bound to fail and attracts a heavy penalty, it is still the best option for an individual whose current resource level is desperately low.

We also explored the effect of setting either the probability γ or the severity π of punishment so low that the expected payoff from exploitation is positive. This produces a pattern where exploitation is optimal if an agent’s resources are either desperately low, or comfortably high (see Supplementary Fig. S2 ). Only in the middle—currently above the threshold, but not by far enough that a punishment would not pull them down below it–should agents cooperate or forage alone.

We simulated populations of N  = 500 individuals each following the optimal policy, with the distribution of initial resources s drawn from a distribution with mean μ and standard deviation σ. Populations fall into one of two absorbing equilibria. In the first, the poverty trap (Fig.  2 A), there is no cooperation after the first few time periods. Instead, there is a balance of attempted exploitation and foraging alone, with the proportions of these determined by the initial resource distribution and the values of π and γ. The way this equilibrium develops is as follows: there is a sufficiently high frequency of exploitation in the first round (about 10% of the population or more is required) that subsequent social trust estimates are mostly very low. With trust low, those with the higher resource levels switch to foraging alone, whilst those whose resources are desperately low continue to try to exploit. Since foraging alone produces no surplus, the population mean resources never increases, and both exploiters and lone foragers are stuck where they were.

figure 2

The two equilibria in simulated populations. ( A ) The poverty trap. There is sufficient exploitation in the first time step ( A1 ) that social trust is low ( A2 ). Consequently, potential cooperators switch to lone foraging, resources never increase ( A3 ), and a subgroup of the population is left below the threshold seeking to exploit. Simulation initialised with μ = 5.5, σ = 4 and all other parameters at their default values. ( B ) The virtuous circle. Exploitation is sufficiently rare from the outset ( B1 ) that trust is high ( B2 ) and individuals switch from lone foraging to cooperation. This drives an increase in resources, eventually lifting almost all individuals above the threshold. Simulation initialised with μ = 5.5, σ = 3 and all other parameters at their default values.

In the second equilibrium, the virtuous circle (Fig.  2 B), the frequency of exploitation is lower at the outset. Individuals whose resources are high form high assessments of social trust, and hence choose cooperation over foraging alone. Since cooperation creates a surplus, the mean level of resources in the population increases. This benefits the few exploiters, both through the upward drift of social mobility, and because they sometimes exploit successfully. This resolves the problem of exploitation, since in so doing they move above the critical value to the point where it is no longer in their interests to exploit, and since they are in such a high-trust population, they then start to cooperate. Thus, over time, trust becomes universally high, resources grow, and cooperation becomes almost universal.

Each of the two equilibria has a basin of attraction in the space of initial population characteristics. The poverty trap is reached if the fraction of individuals whose resource levels fall below the level that triggers exploitation is sufficiently large at any point. With the desperation threshold at s = 0, his fraction is affected by both the mean resources μ, and inequality σ. For a given μ, increasing σ (i.e. greater inequality) makes it more likely that the poverty trap will result, because, by broadening the resource distribution, the tail that protrudes into the desperation zone is necessarily made larger.

The boundaries of the basin of attraction of the poverty trap are also affected by severity of punishment, probability of punishment, and the level of social mobility (Fig.  3 ). If the severity of punishment π is close to zero, there is no disincentive to exploit, and the poverty trap always results. As long as a minimum size of punishment is met, further increases in punishment severity have no benefit in preventing the poverty trap (Fig.  3 A). Indeed, there are circumstances where more severe punishment can make things worse. When the population has a degree of initial inequality that puts it close to the boundary between the two equilibria, very severe punishment (π = 20 or π = 25) pushes it into the poverty trap. This is because any individual that once tries exploitation because they are close to threshold (and is unsuccessful) is pushed so far down in resources by the punishment that they must then continue to exploit forever. Increasing the probability of punishment γ does not have this negative effect (Fig.  3 B). Instead, a very high probability of punishment can forestall the poverty trap at levels of inequality where it would otherwise occur, because it causes some of the worst-off individuals to try cooperating instead, as shown in Fig.  1 E. Finally, very high levels of social mobility r can rescue populations from the poverty trap even at high levels of inequality (Fig.  3 C). This is because of its dramatic effect on the critical value at which individuals start to exploit, as shown in Fig.  1 C.

figure 3

Equilibrium population states by starting parameters. ( A ) Varying the initial inequality in resources σ and the severity of punishment π, whilst holding constant the probability of punishment γ at 1/3 and social mobility r at 0.1. ( B ) Varying the initial inequality in resources σ and the probability of punishment γ whilst holding the severity of punishment constant at π = 10 and social mobility r at 0.1. ( C ) Varying the initial inequality in resources σ and the level of social mobility r whilst holding constant the probability of punishment γ at 1/3 and the severity of punishment π at 10.

Though the equilibria are self-perpetuating without exogenous forces, the system is highly responsive to shocks. For example, exogenously changing the level of inequality in the population (via imposing a reduction in σ after 16 time steps) produces a phase transition from the poverty trap to the virtuous circle (Supplementary Fig. S3 ). This change is not instantaneous. First, a few individuals cross the threshold and change from exploitation to foraging alone; this produces a consequent change in social trust; which then leads to a mass switch to cooperation, and growth in mean wealth.

Results so far are all based on cooperation occurring in groups of size n  = 5. Reducing n enlarges the basin of attraction of the virtuous circle (Supplementary Sect.  2.5 , Supplementary Fig. S4 ). This is because, for any given population prevalence of exploitation, there is more likely to be at least one exploiter in a group of five than a group of three. Reducing the interaction group size changes the trustworthiness boundary between the region where it is optimal to cooperate and the region where it is better to forage alone. Thus, there are parameter values in our model where populations would succumb to the poverty trap by attempting to mount large cooperation groups, but avoid it by restricting cooperation groups to a smaller size.

In our model, exploiting others can be an individual’s optimal strategy under certain circumstances, namely when their resource levels are very low, and cannot be expected to spontaneously improve. We extend previous models by showing that it can be optimal to exploit even when the punishment for doing so and being caught is large enough to make the expected utility of exploitation negative. Two conditions combine to make this the case. First, exploitation produces a large variance in payoffs: it is costly to exploit and be caught, but there is a chance of securing a large positive payoff. Second, there is a threshold of desperation below which it is extremely costly to fall. It is precisely when at risk of falling below this threshold that exploitation becomes worthwhile: if it succeeds, one hurdles the threshold, and if it fails, one is scarcely worse off than one would have been anyway. In effect, due to the threshold, there is a point where agents have little left to lose, and this makes them risk-preferring. Thus, our model results connect classic economic models of crime 15 , 16 to risk-sensitive foraging theory from behavioural ecology 20 . In the process, it provides a simple answer to the question that has puzzled a number of authors 18 , 19 : why aren’t increases in the severity of punishments as deterrent as simple expected utility considerations imply they ought to be? Our model suggests that, beyond a minimum required level of punishment, not only might increasing severity be ineffective at reducing exploitation. It could under some circumstances make exploitation worse, by pushing punishees into such a low resource state that they have no reasonable option but to continue exploiting. Our findings also have implications for the literature on the evolution of cooperation. This has shown that punishment can be an effective mechanism for stabilising cooperation 28 , 29 , but have not considered that the deterrent effects of punishment may be different for different individuals, due to variation in their states. Our findings could be relevant to understanding why some level of exploitation persists in practice even when punishment is deterrent overall.

Within criminology, our prediction of risky exploitative behaviour when in danger of falling below a threshold of desperation is reminiscent of Merton’s strain theory of deviance 30 , 31 . Under this theory, deviance results when individuals have a goal (remaining constantly above the threshold of participation in society), but the available legitimate means are insufficient to get them there (neither foraging alone nor cooperation has a large enough one-time payoff). They thus turn to risky alternatives, despite the drawbacks of these (see also Ref. 32 for similar arguments). This explanation is not reducible to desperation making individuals discount the future more steeply, which is often invoked as an explanation for criminality 33 . Agents in our model do not face choices between smaller-sooner and larger-later rewards; the payoff for exploitation is immediate, whether successful or unsuccessful. Also note the philosophical differences between our approach and ‘self-control’ styles of explanation 34 . Those approaches see offending as deficient decision-making: it would be in people’s interests not to offend, but some can’t manage it (see Ref. 35 for a critical review). Like economic 15 , 16 and behavioural-ecological 17 theories of crime more generally, ours assumes instead that there are certain situations or states where offending is the best of a bad set of available options.

As well as a large class of circumstances where only individuals in a poor resource state will choose to exploit, we also identify some—where the expected payoff for exploitation is positive—where individuals with both very low and very high resources exploit, whilst those in the middle avoid doing so. Such cases have been anticipated in theories of human risk-sensitivity 21 . These distinguish risk-preference through need (e.g. to get back above the threshold immediately) from risk-preference through ability (e.g. to absorb a punishment with no ill effects), predicting that both can occur under some circumstances 32 . This dual form of risk-taking is best analogised to a situation where punishments take the form of fines: those who are desperate have to run the risk of incurring them, even though they can ill afford it; whilst those who are extremely well off can simply afford to pay them if caught. When we simulate populations of agents all following the optimal strategies identified by the model, population-level characteristics (inequality of resources, level of social mobility) affect the prevalence of exploitation and the level of trust. Specifically, holding constant the average level of resources, greater inequality makes frequent exploitation and low trust a more likely outcome. Thus, we capture the widely-observed associations between inequality, trust and crime levels that were our starting point 2 , 3 , 4 , 5 , 6 . Note that our explanation for the inequality-crime nexus is basically compositional rather than psychosocial. Decisions to offend are based primarily on agents’ own levels of resources; these are just more likely to be desperately low in more unequal populations. Turning these simulation findings into empirical predictions, we would expect the association between inequality and crime rates to be driven by more unequal societies producing worse prospects for people at the bottom end of the resources distribution, who would be the ones who turn to property crime. Inequality effects at the aggregate level should be largely mediated by individual-level poverty. There is evidence compatible with these claims for property crime 2 , 12 , 13 . This is the type of crime most similar to our modelled situation. Non-acquisitive crimes of violence, though related to inequality, do not appear so strongly mediated by individual-level poverty, and may thus require different but related explanations 2 , 36 .

However, the other major result of our population simulations—that more unequal populations are more likely to produce low trust—is not compositional. In our unequal simulated populations, every agent has low trust, not just the ones at the bottom of the resource distribution. This is compatible with empirical evidence: the association between inequality and social trust survives controlling for individual poverty 6 . Thus, our model generates a genuinely ecological effect of inequality on social relationships that fits the available evidence and links it to the psychosocial tradition of explanation 37 . Indeed, the model suggests a reason why psychosocial effects should arise. For agents above the threshold, the optimal decision between cooperation and foraging alone depends on inferences about whether anyone else in the population will exploit. To know that, you have to attend to the behaviour of everyone else, not just your own state. Thus, the model naturally generates a reason for agents to be sensitive to the distribution of others’ states in the population (or at the very least their behaviour), and to condition their social engagement with others on it.

In as much as our model provides a compositional explanation for the inequality-crime relationship, it might seem to imply that high levels of inequality would not lead to high crime as long as the mean wealth of the population was sufficiently high. This is because, with high mean wealth, even those in the bottom tail of the distribution would have sufficient levels of resources to be above the threshold of desperation. However, this implication would only follow if the location of the desperation threshold is considered exogenous and fixed. If, instead, the location of the desperation threshold moves upwards with mean wealth of the population, then more inequality will always produce more acquisitive crime, regardless of the mean level of population wealth. Assuming that the threshold moves in this way is a reasonable move: definitions of poverty for developed countries are expressed in terms of the resources required to live a life seen as acceptable or normal within that society, not an absolute dollar value (see Ref. 36 , pp. 64–6). Moreover, there is clear evidence that people compare themselves to relevant others in assessing the adequacy of their resources 38 . Thus, we would expect inequality to remain important for crime regardless of overall economic growth.

In addition to the results concerning inequality, we found that social mobility should, other things being equal, reduce the prevalence of exploitation, although social mobility has to be very high for the effect to be substantial. The pattern can again be interpreted as consistent with Merton’s strain theory of deviance 31 : very high levels of social mobility provide legitimate routes for those whose state is poor to improve it, thus reducing the zone where deviance is required. Economists have noted that those places within the USA with higher levels of intergenerational social mobility also have lower crime rates 39 , 40 . Their account of the causality in this association is the reverse of ours: the presence of crime, particularly violent crime, inhibits upward mobility 39 . However, it is possible that social mobility and crime are mutually causative.

Like any model, ours simplifies social situations to very bare elements. Interaction groups are drawn randomly at every time step from the whole population. Thus, there are no ongoing personal relationships, no reputation, no social networks, no kinship, no segregation or assortment of sub-groups. The model best captures social groups with frequent new interactions between strangers, which is appropriate since the phenomena under investigated are documented for commercial and industrial societies. A problem in mapping our findings onto empirical reality is that our population simulations generate two discrete equilibria: zero trust, economic stagnation and zero cooperation, or almost perfect trust, unlimited economic growth and zero exploitation. Although we show that the distribution of resources determines which equilibrium is reached, our model as presented here does generate the continuous relationships between inequality, crime, and trust (or indeed inequality and economic growth 41 ) that have been observed in reality. Even the most unequal real society features some social cooperation, and even the most equal features some property crime; the effects of inequality are graded. We make two points to try to bridge the disconnect between the black and white world of the simulations and the shades of grey seen in reality. First, our model does predict a continuous relationship between the level of inequality and the maximum size of cooperating groups. A highly unequal population, containing many individuals with an incentive to exploit, might only be able to sustain collective actions at the level of a few individuals, whereas a more equal population where almost no-one has an incentive to exploit could sustain far larger ones. Second, we appeal to all the richness of real social processes that our model excludes. In unequal countries, although social trust is relatively low, people can draw more heavily on their established social networks and reputational information; more homogenous sub-groups can segregate themselves; people can use defensive security measures, to keep cooperative relationships ongoing and protected; and so forth. Investment in these kinds of measures may vary proportionately with inequality and trust, thus maintaining outcomes intermediate between the stark equilibria of our simulations. Our key findings also depend entirely on accepting the notion that there is a threshold of desperation, a substantial non-linearity in the value of having resources. As we outlined in the Introduction, we believe there are good grounds for exploring the implications of such an assumption. However, that is very different from claiming that the widespread existence of such thresholds has been demonstrated. We hope our findings might generate empirical investigation into both the objective reality and psychological appraisal of such thresholds for people in poverty.

Limitations and simplifications duly noted, our model does have some clear implications. Large population-scale reductions in crime and exploitation should not be expected to follow from increasing the severity of punishments, and these could conceivably be counterproductive. Addressing basic distributional issues that leave large numbers of people in desperate circumstances and without legitimate means to improve them will have a much greater effect. Natural-experimental evidence supports this. The Eastern Cherokee, a Native American group with a high rate of poverty, distributed casino royalties through an unconditional income scheme. Rates of minor offending amongst young people in recipient households decline markedly, with no changes to the judicial regime 42 . Improving the distribution of resources would also be expected to increase social trust, and with it, the quality of human relationships; and this, for everyone, not just those in desperate circumstances.

The model was written in Python and implemented via a Jupyter notebook. For a fuller description of the model, see Supplementary Sect.  1 and Supplementary Table S1 .

Computing optimal policies

We used a stochastic dynamic programming algorithm 25 , 26 . Agents choose among a set of possible actions, defined by (probabilistic) consequences for the agent’s level of resources s . We seek, for every possible value of s and of p the agent might face, and given the values of other parameters, the action that maximises expected fitness. Maximization is achieved through backward induction: we begin with a ‘last time step’ ( T ) where terminal fitness is defined, as an increasing linear function of resource level s . Then in the period T  − 1 we compute for each combination of state variables and action the expected fitness at T , and thus choose for the optimal action for every combination of states. This allows us define expected fitness for every value of the state variables at T  − 1, repeat the maximization for time step T  − 2, and so on iteratively. The desperation threshold is implemented as a fixed fitness penalty ω that is applied whenever the individual’s resources are below the threshold level s  = 0. As the calculation moves backwards away from T , the resulting mapping of state variables to optimal actions converges to a long term optimal policy.

Actions and payoffs

Agents choose among three actions:

Cooperate The agent invests x units of resource and is rewarded α · x with probability 1 −  p ( p is the probability of cooperation being exploited, and 1 −  p is therefore the trustworthiness of the surrounding population), and 0 with probability p . The net payoff is therefore x · ( α  − 1) if there is no exploitation and −  x if there is. We assume that α  > 1 (by default α  = 1 . 2), which means that cooperation is more efficient than foraging alone. For the computation of optimal policies, we treat p as an exogenous variable. In the population simulations, it becomes endogenous.

Exploit An agent joins a cooperating group, but does not invest x, and instead tries to steal their partners’ investments, leading to a reward of β if the exploitation succeeds and a cost π if it fails. The probability of exploitation failing (i.e. being punished) is γ .

Forage alone The agent forages alone, investing x units of resource, receiving x in return, and suffering no risk of exploitation.

Payoffs are also affected by a random perturbation, so the above-mentioned payoffs are just the expected values. A simple form such as the addition of \(\varepsilon \sim N\left( {0, \sigma^{2} } \right)\) would be unsuitable when used in population simulations. As the variance of independent random variables is additive, it would lead to an ever increasing dispersion of resource levels in the population. To avoid this issue, we adopted a perturbation in the form of a first-order autoregressive process that does not change either the mean or the variance of resources in the population 43 :

Here, µ is the current mean resources in the population and σ 2 the population variance. The term \(\left( {1 - r} \right) \in \left[ {0, 1} \right]\) represents the desired correlation between an agent’s current and subsequent resources, which leads to us describing r as the ‘social mobility’ of the population. The perturbation can be seen as a ‘shuffle’. Each agent’s resource level is attracted to µ with a strength depending on r , but this regression to the mean is exactly offset at the population level by the variance added by the perturbation, so that the overall distribution of resources is roughly unchanged. If r  = 1, current resources are not informative about future resources.

The dynamic programming equation

Let I be the set of actions ( cooperate , exploit and alone ), which we shorten as I  = { C,H,A }. For i   ∈   I , we denote as \(\phi_{t}^{i} \left( {s, .} \right)\) the probability density of resources in in time step t if, in time step t  − 1, the resource level is s and the chosen action i . The expressions of these functions were obtained through the law of total probability, conditioning on the possible outcomes of the actions (e.g. success or failure of exploitation and cooperation), and with the Gaussian density of the random variable.

We can now write the dynamic programming equation, which gives the backward recurrence relation to compute the payoff values (and the decisions) at the period t from the ones at the period t  + 1.

Here, \(E_{i}\) is the conditional expectation if action i is played. The optimal action for the time step t is \({\text{argmax}}_{i \in I} E_{i} (f_{t} )\) . The resource variable s was bounded in the interval [− 50, 50], and discretized with 1001 steps of size 0 . 1.

For any given set of parameters (summarised in Table 1 ), we can therefore compute the optimal decision rule. Note that we can distinguish two types of parameters:

‘Structural parameters’, i.e. those defining the ‘rules’ of the game (the payoffs for the actions and the level of social mobility r , for example). In the subsequent simulation phase, these parameters will be fixed for any run of the simulations.

‘Input parameters’, such as p and s . In the simulation phase, these will evolve endogenously.

Optimal policies rapidly stabilize as the computation moves away from T . We report optimal actions at t  = 1 as the globally optimal actions.

Population simulations

We begin each simulation by initializing a population of N  = 500 individuals, whose resource levels are randomly drawn from a Gaussian distribution with a given mean µ and variance σ 2 . At each time step, interaction groups of n  = 5 individuals are formed at random, and re-formed at each time step to avoid effects of assortment. There is no spatial structure in the populations. Each individual always follows the optimal policy for its resources s and its estimate of p (see below). Varying N has no effect as long as N  >  n and 500 is simply chosen for computational convenience.

To deal with the case where several members of the same interaction group choose to exploit, we choose one at random that exploits, and the others are deemed to forage alone (in effect, there is nothing left for them to take). Also, when there is no cooperator in the group, all exploiters are deemed to forage alone.

Rather than providing each individual with perfect knowledge of the trustworthiness of the rest of the population 1 −  p , we allow individuals to form an estimate (their social trust ) from their experience. Social trust is derived in the following way. Each agent observes the decision of a sample of K individuals in the population, counts the number k of exploiters and infers an (unbiased) estimate of the prevalence of exploiters in the population: \(k^{\prime} = \frac{k}{K}N\) (rounded). The size of the sample can be varied to alter the precision with which agents can estimate trustworthiness. Unless otherwise stated we used K  = 50. Since p is the probability that there will be at least one exploiter in an interaction group, it is one minus the probability that there will be zero exploiters. Each agent computes this from their k’ by combinatorics.

An intentional consequence of social trust being estimated through sampling is that there is some population heterogeneity in social trust, and therefore in decisions about which action to take, even for agents with the same resources s . Note also that agents infer trustworthiness not from observing the particular individuals in their current interaction group, but rather, from a cross-section of the entire population. Thus, the estimate is genuinely social trust (the perception that people in society generally do or do not behave well).

Code availability

The Jupyter notebook for running the model is available at: https://github.com/regicid/Deprivation-antisociality . This repository also contains R code and datafiles used to make the figures in the paper.

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Acknowledgements

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No AdG 666669, COMSTAR). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors thank Melissa Bateson, Juliette Dronne, Ulysse Klatzmann, Daniel Krupp, Kate Pickett, and Rebecca Saxe for their input.

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hypothesis on poverty and crime

Poverty, Inequality, and Area Differences in Crime

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hypothesis on poverty and crime

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American criminological research and theory has predominantly focused on individual offenders. Following the growth and refinement of survey research methods in the 1960s, which had a strict focus on the individual as the unit of analysis, a resurgence of macro-level criminological research has taken place in recent decades. Two of the most commonly assessed ecological correlates of crime rates include poverty (absolute deprivation) and inequality (relative deprivation). This entry takes stock of this body of literature that has examined the effects of poverty and inequality to explain area differences in crime rates.

Introduction

Since its inception, American criminological research and theory has predominantly focused on individual offenders. Criminology grew largely out of the classical school and positivist notions of human behavior in the early 1900s. Both of these perspectives focused on the individual correlates of crime and deviance. While the classical school...

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Pratt, T.C., Eisentraut, B.D. (2014). Poverty, Inequality, and Area Differences in Crime. In: Bruinsma, G., Weisburd, D. (eds) Encyclopedia of Criminology and Criminal Justice. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5690-2_215

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Poverty, Socioeconomic Change, Institutional Anomie, and Homicide *

Objective . This study examined institutional anomie theory in the context of transitional Russia. Methods . We employed an index of negative socioeconomic change and measures of family, education, and polity to test the hypothesis that institutional strength conditions the effects of poverty and socioeconomic change on homicide rates. Results . As expected, the results of models estimated using negative binomial regression show direct positive effects of poverty and socioeconomic change and direct negative effects of family strength and polity on regional homicide rates. There was no support, however, for the hypothesis that stronger social institutions reduce the effects of poverty and socioeconomic change on violence. Conclusions . We interpret these results in the Russia-specific setting, concluding that Russia is a rich laboratory for examining the effects of social change on crime and that empirical research in other nations is important when assessing the generalizability of theories developed to explain crime and violence in the United States.

This study tested institutional anomie theory (IAT) ( Messner and Rosenfeld, 1997a ) in the context of widespread poverty and large-scale socioeconomic change in Russia. Although developed to explain crime in the capitalist culture of the United States, IAT has been tested cross-nationally ( Messner and Rosenfeld, 1997b ; Savolainen, 2000 ) and Bernburg (2002) recently argued that the theory should also apply to the effects of social change on crime. Russia has experienced tremendous social, political, and economic change during the last 15 years as totalitarianism and a command economy are being replaced by a free-market democracy. Since these changes began in the early 1990s, Russians have faced a wide array of social problems, including high levels of poverty and unemployment, increasing inequality, and a mortality crisis ( Walberg et al., 1998 ). It is likely that the anomic environment accompanying the rapid social change has played a role in the increase in and wide cross-sectional variation of Russian homicide rates during the 1990s ( Pridemore, 2003a ).

Durkheim ([1893] 1984 , [1897] 1979) argued that during times of rapid social change norms become unclear and society's control over individual behavior decreases. He believed that as people's aspirations become less limited and as conventional social institutions are weakened, deviance and crime should increase. Large-scale changes have occurred since the Soviet Union collapsed, including fundamental shifts in political and economic philosophies and decreased formal social control, leading to normative uncertainty. Russians' aspirations are now less limited because of newfound individual freedoms and because a free market creates desires, whereas totalitarianism and a planned economy stifles them. Similarly, conventional Soviet institutions are gone and enduring social institutions such as the family and education are weakened by the ongoing changes and the collapse of the Soviet welfare system. The pace and outcome of these changes vary widely throughout the vast nation, however, and deprivation and anomie theories lead us to expect violence to be higher in areas facing greater poverty and change. Institutional anomie theory also leads us to expect that the strength of noneconomic social institutions such as family, education, and polity will moderate the effects of poverty and change on violence ( Bernburg, 2002 ; Chamlin and Cochran, 1995 ; Messner and Rosenfeld, 1997a ).

Background: Transitional Russia

In the early 1990s, Russia launched a program of privatization meant to convert the command economy to a free market. Economic, legal, political, regulatory, and social institutions are a fundamental part of a properly functioning market economy, however, and these institutions were absent or underdeveloped in Russia ( Goldman, 1996 ; Hanson, 1998 ; Intriligator, 1994 ). This vacuum played a role in the ensuing problems, including increased rates of interpersonal violence. 1 There was severe economic instability and uncertainty throughout the decade. According to Goskomstat (2001) , in 2000 nearly 30 percent of the population was living in poverty and the unemployment rate of 10.5 percent was double what it had been in 1992. Regional levels of economic dislocation are not uniform, however, but vary widely throughout the country based on the type of local industry, access to raw materials, and the presence of the requisite legal protections for business transactions ( Gokhberg et al., 2000 ).

The transition also has had a dramatic impact on mortality, which is often an indicator of stressful, anomic, and abnormal conditions. Middle-aged males were the most vulnerable to the increased stress resulting from the rapid social and economic change toward an uncertain future, and male life expectancy declined sharply to less than 60 years ( Leon and Shkolnikov, 1998 ). This group also has the highest homicide offending and victimization rates ( Pridemore, 2003a ) in Russia.

Based largely on the supposed Soviet experience, there was a belief in the past that crime rates were lower under state socialism than in democratic countries with capitalist economies. A higher degree of social justice and social integration in socialist countries were reasons often cited for this assumption. Such low crime rates might also be explained by other more ominous factors, such as “tight social control practiced through a dense network of secret police activities and the considerable power difference between members of the Communist party and nonmembers” ( Savelsberg, 1995 :216).

One of the benefits of Russia's democratic transformation is increasing transparency and thus broader availability of demographic, economic, and social data. Under the totalitarian regime, crime and other data were strictly controlled and often falsified when made public. Pridemore (2001) has used newly available historical data on homicide mortality to dispute the claim that rates of interpersonal violence were low during the Soviet era. These data showed that the Russian homicide victimization rate has been comparable to or even higher than the U.S. rate for at least the past 40 years. More importantly for the present study, the Russian homicide rate rose dramatically following the collapse of the Soviet Union. According to data from the Russian Ministry of Health, the 2001 homicide victimization rate of 29.8 homicides per 100,000 persons was three times what it had been a decade earlier and nearly five times the U.S. rate ( Pridemore, 2003a ). As with the levels of poverty and socioeconomic change mentioned before, however, these rates vary widely throughout Russia, ranging from a low of around six per 100,000 in the Republic of Kabardino-Balkaria to over 130 per 100,000 in the Republic of Tyva.

Institutional Anomie Theory

Institutions are patterned mutually shared ways that people develop for living together ( Bellah et al., 1991 ), providing socially sanctioned rules that define and regulate conduct. According to Bellah et al. (1991 :12), institutions “are the substantial forms through which we understand our own identity and the identity of others as we seek cooperatively to achieve a decent society.” If these institutions remain stable they allow social organization to persist over time despite the constant change of members of society. These institutions are critical for increasing predictability among societal members, which in turn increases trust because it allows “individuals to act based on their perception that others are likely to perform particular actions in expected ways” ( LaFree, 1998 :71).

According to Messner and Rosenfeld (1997a) , culture and structure operate together to create higher crime rates. At the cultural level, capitalist culture “exerts pressures toward crime by encouraging an anomic cultural environment, an environment in which people are encouraged to adopt an ‘anything goes’ mentality in the pursuit of personal goals … [and] the anomic pressures inherent in the American dream are nourished and sustained by an institutional balance of power dominated by the economy” (1997a:61). Messner and Rosenfeld argue that capitalist culture promotes intense pressures for economic success at the expense of pro-social noneconomic institutions such as family, education, polity, and religion. Social structure comes to be dominated by the economic structure, thereby weakening institutional controls. As former communist countries move toward a free market it is likely their citizens are beginning to adopt capitalist ideologies, including an emphasis on individual economic success at the expense of noneconomic social institutions ( Merton, 1938 ; Polanyi, 2001 ), making institutional anomie theory appear applicable to the Russian situation.

While the negative socioeconomic changes in Russia are expected to create higher crime rates, this association may be conditioned by the strength of noneconomic social institutions such as family, education, and polity ( Bernburg, 2002 ). First, even in the face of difficult structural conditions, strong families and the accompanying social cohesion can inhibit crime ( Sampson, Raudenbush, and Earls, 1997 ). According to institutional anomie theory, families can mitigate anomic pressures by providing emotional support and social bonds for their members ( Messner and Rosenfeld, 1997a ). Pridemore (2002) has shown family structure to be associated with regional homicide rates in Russia and Pridemore and Shkolnikov (2004) found marriage to be an individual-level protective factor against homicide victimization in the country. Second, the educational system can reduce crime by effectively monitoring and supervising the behavior of children and by creating environments in which children are strongly committed to their education and aspirations ( LaFree, 1998 ). Since education is directly connected to socialization, the system's capacity to exercise social control may lessen the impact of social change on crime. An educated population is also more likely to possess networks and social skills that allows it to cope better with social change. Third, trust in political institutions reflects the legitimacy of these institutions among the populace, which may be closely related to social control efforts ( LaFree, 1998 ). Given the wide variation in poverty, negative socioeconomic change, and the strength of social institutions throughout the country, post-Soviet Russia provides a unique opportunity to test IAT.

Only a handful of studies have specifically tested institutional anomie theory. According to Chamlin and Cochran (1995) , Messner and Rosenfeld's (1997a) model implies that economic stress will be less salient as a predictor of serious crime in the presence of strong noneconomic institutions. They hypothesize that the impact of poverty on property crime is thus moderated by the strength of religious, political, and family institutions. Results from their state-level analysis are consistent with this hypothesis, since they show that high church membership, low divorce rate, and high voter turnout significantly reduced the effect of poverty on property crime.

Piquero and Piquero (1998) tested institutional anomie theory with cross-sectional data from the United States, employing several different operationalizations of the main social-institutions variables. Their findings provided some support for the institutional anomie hypotheses, but they also concluded that the inferences drawn about IAT may depend on how the institutional variables are operationalized.

A study by Messner and Rosenfeld (1997b) draws on Esping-Anderson's (1990) decommodification index as the indicator of economic dominance in the institutional balance of power. According to Esping-Anderson (1990) , decommodification is the degree to which the state's policies protect the individual standard of living of its citizens from the forces of the market. Messner and Rosenfeld argue that decommodification influences crime independently of economic stratification. Using cross-national data, the authors found support for this hypothesis since the index of decommodification had a relatively strong negative effect on national homicide rates, controlling for economic discrimination, income inequality, and the level of socioeconomic development.

Savolainen (2000) pointed out the differences between Chamlin and Cochran's and Messner and Rosenfeld's studies. The main difference was that Chamlin and Cochran emphasized that IAT implies an interaction effect between economic conditions and the strength of noneconomic institutions, while Messner and Rosenfeld were concerned with the main effect of the institutional balance of power on homicide rates. Savolainen hypothesized that the positive effect of economic inequality on lethal violence is strongest in nations where the economy dominates the institutional balance of power. This implies a negative interaction effect between economic stratification and the relative strength of noneconomic institutions, which is what he finds in his analyses. Savolainen concluded that nations that protect their citizens from market forces appear to be immune to the effects of economic inequality on homicide.

Since contemporary Russia is moving toward capitalism, it is likely that citizens of the country have begun to adopt capitalist ideologies such as an emphasis on individual economic success. Thus the “American dream” may now be the Russian dream (and that of other nations in an era of globalization), and as in other capitalist nations, Russia's institutional balance of power may be tilting toward the economy and away from social welfare. Pridemore (2002) and Pridemore and Kim (2004) have shown elsewhere that poverty and negative socioeconomic change, respectively, are positively related to the cross-sectional variation of homicide in Russia. Further, the main focus of the empirical literature on IAT has become testing for moderating effects of noneconomic social institutions, and Bernburg (2002) argues that we should expect similar conditioning effects of these institutions on any association between social change and crime. Our study is the first of its kind to test Bernburg's hypothesis and to test IAT in a single country other than the United States, and thus provides a bridge between studies of the United States and the cross-national studies that use nations as the unit of analysis.

Summary of Hypotheses

This review of literature led us to test the following hypotheses.

  • The level of poverty is positively related to the cross-sectional variation of homicide rates in Russian regions.
  • Negative socioeconomic change is positively related to the cross-sectional variation of homicide rates in Russian regions.
  • The strength of social institutions is negatively related to the cross-sectional variation of homicide rates in Russian regions.
  • The strength of social institutions conditions the effects of poverty and negative socioeconomic change on homicide rates in Russian regions.

Data and Method

This was a cross-sectional study of Russian regions. With the exception of the measures used to create the change index, all data were for 2000 unless otherwise noted. Of the 89 regions, nine are autonomous districts embedded within a larger region and their data are covered by the larger unit. Data from the neighboring Ingush and Chechen Republics are unreliable and were not used. This left 78 cases for analysis. In Russia, local data are aggregated to the regional level and only the aggregate data forwarded to Moscow and published. Thus, while a lower level of aggregation might be preferable, the nature of data collection makes this untenable. Versions of institutional anomie theory have been tested at even higher levels of analysis, such as the nation, however, so we are confident with our use of regions ( Messner and Rosenfeld, 1997b ; Savolainen, 2000 ).

Dependent Variable

Regional homicide estimates are available from both police (MVD) and vital statistics data, though the former are highly suspect. For example, annual estimates from the vital statistics reporting system have reported nearly 40 percent more homicides than the MVD data over the last 15 years, and there is a relatively low correlation between the two reporting systems among the regions ( Pridemore, 2003b ). We thus used the regional homicide victimization rate per 100,000 persons as our dependent variable. Russia used the abridged Soviet cause of death coding system until 1999, when it began to use the International Classifications of Diseases Codes—10th revision ( Pridemore, 2003b ). Regional mortality rates, including homicide, are published annually by the Russian Ministry of Health (2001) . Table 1 provides descriptive statistics and brief descriptions of all variables.

Descriptive Statistics ( N = 78)

Independent Variables: Poverty and Socioeconomic Change

Poverty was measured as the proportion of the regional population living below the poverty line. Data were unavailable for 2000, so 1999 data were used. These data are available from Goskomstat (2001) . We used the natural logarithm of these values because of the pronounced positive skew in their distribution.

We created a composite index to account for regional variation in socioeconomic change. The variables used to measure the index represent multiple dimensions of change (e.g., population, economic, and legal) and thus should not be considered different attempts to capture a single underlying concept. As described below, the measures were coded in a way that highlighted those regions that have experienced the worst effects of change relative to other regions. The measures of these different dimensions were population change, unemployment change, poverty change, privatization, and foreign capital investment. 2 Data for these measures were obtained from Goskomstat. Population change and the proportion of the active labor force unemployed were measured as residual change scores when 2000 values were regressed on 1992 values. The poverty variable was measured as the residual change score when 1999 poverty rates (2000 data unavailable) were regressed on poverty rates from 1994 (earlier data unavailable). For poverty, for example, the equation was ΔPoverty = Poverty2000 − (α+β* Poverty1992). Residual change scores are superior to raw change scores since they are independent of initial values ( Bohrnstedt, 1969 ). Since all the regions were used to estimate the regression, the residual scores also take into account changes in the entire ecological system under study ( Morenoff and Sampson, 1997 ).

Since the Soviet economic system was characterized by state ownership, two further important indicators of legal, political, and economic change are privatization and foreign investment. The former was measured as the percentage of the labor force employed in private companies and the latter as foreign capital investment per capita in U.S. dollars. In essence, these are change scores since both were virtually zero until the adoption in 1992 of the “Basic Provision for the Privatization of State and Municipal Enterprises in the Russian Federation” ( Chubais and Vishnevskaya, 1993 ). Foreign capital investment is an especially interesting measure since it is an indicator not simply of worthwhile investment potential but of political and economic stability and of the presence of the relatively strong legal framework required for a free market.

In the context of this study, privatization and foreign investment were “positive” since they represent economic revitalization in economically depressed areas by providing jobs, income, and other advantages ( Firebaugh and Beck, 1994 ; Frey and Field, 2000 ). An increasing population is also considered positive, since a decreasing population usually represents a concentration of poverty as people with greater resources move out ( Centerwall, 1992 ; Wilson, 1996 ) and leave behind residents with fewer resources and thus a higher proportion of people who are economically dependent. Recent research has shown this to be the case for regional mobility in Russia ( Andrienko and Guriev, 2004 ; see Heleniak (1997) for a discussion of Russian migration patterns in the early transition years). Therefore, in order to create our index of negative change, we coded privatization, foreign investment, and population change as 1 if they were more than 0.5 standard deviations below the mean (i.e., they were substantially worse off than other regions on these measures), 0 otherwise, and coded unemployment and poverty as 1 if they were more than 0.5 standard deviations above the mean (i.e., they had substantially higher levels of poverty and unemployment relative to other regions), 0 otherwise. These scores were summed, providing a value of 0–5 (with 5 being the worst) for each region. In one respect, this approach means we lose information since we turn interval variables into dummies and thus restrict their variance. Creating a factor or constructing an index by summing z scores, however, might not allow us to capture the different components of socioeconomic change in the manner we wish.

Institutional Anomie Variables

Our measure of family stability was the proportion of households with a single parent and at least one child under the age of 18. This was reverse coded to interpret it in terms of institutional anomie (i.e., family strength). Although new data on this variable will soon be available from the 2002 Russian census, at the moment we must use data from the 1994 Russian micro census, which are available from several Goskomstat publications. Educational strength was measured as the number of people enrolled in college per 1,000 residents ( Goskomstat, 2001 ). Voter turnout or proportion voting for a specific party/candidate is often used as a measure of trust, apathy, or anomie in macro-level studies ( Putnam, 1995 ; Villarreal, 2002 ), including in studies of institutional anomie ( Chamlin and Cochran, 1995 ). We thus measured polity as the proportion of registered voters who voted in the 2000 Russian presidential election ( Orttung, 2000 ).

Control Variables

Two further economic measures common to macro-level studies were included as controls. Inequality was measured as the ratio of the income of the top 20 percent of wage earners to that of the bottom 20 percent of wage earners. Unemployment was measured as the proportion of the active labor force that was unemployed. Data for these measures were obtained from Goskomstat (2001) and both were logged due to heavy positive skews.

Recent research on Russia by Andrienko (2001) and Pridemore (2002) has found alcohol consumption to be positively and significantly associated with regional homicide rates after controlling for other structural factors. We thus controlled for this by using the latter's proxy for consumption (i.e., deaths per 100,000 persons due to alcohol poisoning; examples of and reasons for using this proxy in Russia are explained elsewhere: Chenet et al. (2001) and Shkolnikov, McKee, and Leon (2001) ). These data are from the Russian Ministry of Health (2001) .

Research has shown that the age distribution of Russian homicide offenders and victims is very different from that in the United States. The mean age of Russian homicide arrestees is 10–11 years older than in the United States and victimization is highest among males in their mid-20s to late 40s ( Pridemore, 2003a ). Since factors such as the labor market and migration have led to variation in the size of this group by region, we included the proportion of the population male 25–44 as a control. Values were logged due to the heavy positive skew in the distribution.

Finally, homicide victimization rates in Russia are geographically patterned. Controlling for other factors, rates have been shown to be significantly lower in the northern Caucasus and higher east of the Ural Mountains ( Pridemore, 2002 , 2003a ). We thus included two regional dummy variables to control for these differences.

Missing Data

Northern Osetia and the Chukot Autonomous Okrug had missing data on foreign capital investment and the latter also on education, and the missing observations were replaced in order to retain these cases for analysis. Mean substitution can be problematic because it may produce biased estimates of variances and covariances ( Allison, 2001 ), so we replaced the missing values by using information from other variables in the model, which can be used as instruments to predict the missing observations if we assume they are uncorrelated with the error term ( Pindyck and Rubinfeld, 1998 ). We regressed the variable with the missing observation on all the other independent variables that had complete data and used the predicted value to replace these three missing observations.

Homicide is a rare event and its distribution is usually positively skewed, which can lead to several methodological problems. The skew statistic for the distribution of regional homicide victimization rates in Russia is several times its standard error. Further, regional populations vary widely, which may result in violation of the OLS assumption of homogeneity of error variance since prediction errors likely vary by population size. One way to account for the skewed distribution is to logarithmically transform the homicide rate to help normalize its distribution. Recent work has shown, however, that misleading findings can result from logging the dependent variable ( Hannon and Knapp, 2003 ; Osgood, 2000 ). A more appropriate alternative is to use negative binomial regression since it does not assume homogeneity of error variance. The negative binomial model is being increasingly used in macro-level criminological studies ( Osgood and Chambers, 2000 ) and we employed this method with our data. Negative binomial regression is normally used for count data, so a small change was necessary since we are interested in crime rates relative to population size. This was accomplished by adding to the model the log of the population at risk and assigning this variable a fixed coefficient of 1 (Gardner, Mulvey, and Shaw, 1995; Osgood, 2000 ). Common exploratory data analysis techniques and regression diagnostics were carried out and are discussed below where appropriate.

Table 1 shows descriptive statistics. The mean regional homicide victimization rate of 30 per 100,000 persons in 2000 was about five times higher than the rate of six per 100,000 in the United States that year ( Miniño et al., 2002 ). On average, over 40 percent of the regional populations were living in poverty. As for institutional strength, both the mean for single-parent households of 16 percent and mean voter turnout in the presidential election of 69 percent were very similar to comparable measures in the United States ( Federal Election Commission, 2003 ; Fields and Casper, 2001 ).

Table 2 shows the correlation matrix. As expected, poverty ( r = 0.30) and negative socioeconomic change were positively correlated with homicide rates ( r = −0.40), and the strength of family ( r = −0.44), education ( r = −0.20), and polity ( r = 0.37) were all negatively correlated with homicide rates. Other results show that alcohol consumption had the strongest correlation with homicide ( r = 0.50) and confirmed that homicide rates were lower in the northern Caucasus ( r = −0.26) and higher in the regions east of the Urals ( r = 0.56).

Correlation Matrix

Table 3 presents the results of model estimation testing the direct effects of poverty and institutional variables on homicide, as well as the interaction effects for poverty with each of the three institutional measures. The inferences for each variable are the same and effect sizes are very similar across all four models. Model 1 shows that poverty is positively and significantly associated with homicide rates ( b = 0.32, p = 0.013) net of all other variables in the model. The results from this model also show that regions with stronger family ( b = −4.36, p = 0.016) and polity ( b = −1.98, p = 0.001) have lower homicide rates. The results for the education variable are in the expected direction, but its negative association with homicide is not significantly different from zero. Homicide rates in the northern Caucasus did not remain significantly lower when controlling for the other structural factors, but rates in the east remained significantly higher.

Results for Homicide Rates Regressed on Poverty, Social Institutions, and Interaction Terms ( N = 78)

Note: All models were estimated with negative binomial regression. In the negative binomial model, α represents the overdispersion parameter. In each case, the likelihood ratio test for the value of α (not shown here) showed it to be significantly different from zero. This means that there is overdispersion beyond that expected by a simple Poisson process and thus signifying the negative binomial model is more appropriate. The R 2 statistic is not part of the maximum-likelihood estimation of the negative binomial model and thus it is not reported here. When these models were reestimated using OLS regression, adjusted R 2 values were over 0.60.

Models 2–4 of Table 3 include the interaction terms of poverty with family, education, and polity. In each of these models, the respective variables were mean standardized before creating the interaction term in order to purge them of nonessential collinearity and thus avoid any problems associated with multicollinearity ( Jaccard and Turrisi, 2003 ). The results show that none of the interaction terms conditioned the effect of poverty on homicide. The slope coefficients for the family and polity interaction terms are in the expected negative direction but are not significant.

The models in Table 4 are similar to those in Table 3 except the negative socioeconomic change index has been included (and thus the poverty and unemployment variables excluded). Again, the inferences are the same across all four models and are essentially the same for all variables as in Table 3 . The direct effects on homicide of negative socioeconomic change and institutional strength are shown in Model 1. As expected, those regions that have faced more negative effects of socioeconomic change have higher homicide rates. The results for the direct effects of the institutional variables are the same as above, though the p value for family strength is around 0.06 in each model. Models 2–4 present the interaction terms, which show that the strength of institutions such as family, education, and polity do not condition the effects of negative socioeconomic change on homicide rates in Russia.

Results for Homicide Rates Regressed on Socioeconomic Change Index, Social Institutions, and Interaction Terms ( N = 78)

Note: All notes in Table 3 are relevant here. Poverty and unemployment are not included in these models because their change scores were components of the socioeconomic change index.

Overall, the results from Tables ​ Tables3 3 and ​ and4 4 provide (1) support for the first hypothesis that poverty is positively associated with regional homicide rates, (2) support for the second hypothesis that negative socioeconomic change is associated with homicide rates, (3) partial support for the hypothesis that institutional strength is negatively associated with homicide rates (i.e., family and polity were negatively associated with homicide, but educational strength was not), and (4) no support for the hypothesis that institutional strength conditions the effects of poverty and of negative socioeconomic change on homicide rates.

Model Sensitivity

Regression diagnostics and several other strategies were employed to test the stability and sensitivity of the results presented here. First, since highly aggregated data are often highly collinear, models were reestimated with OLS regression in order to generate variance inflation factors (VIF). The VIFs showed that multicollinearity did not appear to be a problem (all VIFs were below 3.0 in all models). Second, several methods were employed to search for outliers on the X- and Y-axis and for undue influence on the regression line of individual cases ( Pindyck and Rubinfeld, 1998 ). Both Moscow and Tyva had high values on the influence statistics. Reestimating models that excluded these cases individually and together, however, had no affect on the inferences drawn. Third, in order to test how our decisions about how to construct the negative socioeconomic change index affected its association with homicide rates and its interaction with measures of institutional strength, we created a second index that simply summed the z scores of the variables' original values. The results for the models shown in Table 4 were very similar when this index was included. The only meaningful difference was that p values for the new index were around 0.07–0.10 in the four models. Finally, the negative socioeconomic change index represents dynamic effects, whereas the dependent variable in these models is static (i.e., the homicide victimization rate in 2000). Since data were not available to create change scores for all variables, we partially accounted for this by including the 1992 homicide rate as a control in the models in Table 4 . There were no meaningful changes from the inferences drawn from these models when this control was included. As a further check, change models were reestimated using residual change scores for homicide, which were created in the same manner as the residual change scores for the index. Again, the results are similar to those here and do not result in any changes to inferences drawn for the main issues under study.

Russia has experienced widespread poverty since the collapse of the Soviet Union. The level of poverty, however, varies widely among the Russian regions as a result of many factors, including type of industry, level of development, and the quality of social services provided by the state. Our results show that poverty is positively and significantly related to regional homicide rates in Russia, which provides support for the first hypothesis and is consistent with research in the country using data from the mid-1990s ( Pridemore, 2002 ) and with the U.S. literature on social structure and homicide.

Aside from poverty, Russia experienced other forms of change following the collapse of communism that likely disrupted the social equilibrium and produced anomic conditions that in turn were partially responsible for the increase in and wide variation of crime and violence in the country. Our results show that regions experiencing the worst effects of socioeconomic change had higher homicide rates. This result provides support for the second hypothesis, based on Durkheimian anomie theory, and is consistent with recent research on socioeconomic change and crime in Russia ( Pridemore and Kim, 2004 ).

The third hypothesis was drawn from institutional anomie theory and concerned the direct effects of social institutions on homicide rates. Our results provide partial support for this hypothesis. First, according to institutional anomie theory, families function to mitigate anomic pressures by providing emotional support and social bonds ( Messner and Rsoenfeld, 1997a ). We found that regional family strength was negatively and significantly associated with regional homicide rates, which provides support for this hypothesis. Further, this association is consistent with Pridemore's (2002) findings using Russian homicide data from the mid-1990s and with Pridemore and Shkolnikov (2004) , who found that marriage is an individual-level protective factor against homicide victimization in Russia. Second, education appears to have no relationship to homicide rates. This is somewhat surprising given the disruption of the Russian educational system resulting from underfunding and changing curricula. Third, our results show a negative and significant association between polity and homicide rates. One interpretation of this is that faith in political institutions decreases crime rates since it represents a level of trust and social cohesion. Distrust in political institutions threatens their legitimacy, which can reduce the effectiveness of the social-control system, and our result is consistent with research in the United States that has shown that distrust in political institutions is positively associated with crime ( LaFree, 1998 ; see also Stucky, 2003 ). Although these findings are largely consistent with one aspect of institutional anomie theory, they are also consistent with other structural-level theories that may claim these same variables or measures. The real heart of IAT lies in the claim that these institutions moderate the negative effects of other structural factors on crime.

Institutional Anomie

Institutional controls are expected to condition the effects of culture and structure on crime rates. Research has shown that crime rates are lower where social institutions and informal control are stronger ( Friedman, 1998 ; LaFree, 1998 ; Sampson and Groves, 1989 ; Sampson, Raudenbush, and Earls, 1997 ), and studies using cross-national and U.S. data have shown support for this aspect of institutional anomie theory. Further, Bernburg (2002) argued that the strength of social institutions should also act to reduce the effects of social change on crime. We followed a strategy common to previous studies of IAT by testing the hypothesis that the effects of poverty and of negative socioeconomic change on homicide rates are dependent on the strength of social institutions such as family, education, and polity. The results show that none of the interaction terms was significant, indicating that the strength of noneconomic social institutions does not appear to condition the effects of poverty and socioeconomic change on homicide in Russia.

There are a few possible substantive reasons for these results. First, any potential conditioning effects of social institutions simply may be over-whelmed because the changes were so strong and so swift in Russia. Thus our results may represent a period effect, an artifact of the current transitional conditions. Perhaps in the context of slower-paced societal development, social institutions retain their ability to temper the effects of change on crime. Second, social institutions may be weakened by socioeconomic change, thereby reducing their ability to condition its impact. Several studies have shown, for example, that institutional characteristics are shaped by economic and social changes ( Fligstein, 1987 ; Fligstein and Brantley, 1992 ; Thornton and Ocasio, 1999 ), and many theories of crime posit that anomie weakens social institutions and thus leads to crime and deviance ( Passas, 1990 ; Passas and Agnew, 1997 ; Thorlindsson and Bjarnson, 1998 ).

Finally, institutional anomie theory was not developed to explain the role of rapid socioeconomic change on crime. It focuses instead on the specific cultural pressure for monetary success that gives rise to anomie because of the (1) imbalance between the economic institution and other noneconomic institutions and (2) interplay between cultural pressure for material desire and the structural imbalance of social institutions. However, Bernburg (2002) argued that institutional anomie theory links crime, anomie, and contemporary social change by bringing in the notion of the disembedded market economy, a central notion in the institutionalism of Durkheim ([1897] 1979) and Polanyi (2001) . Thus, while institutional anomie theory was not developed to explain the relationship between social change and crime, it appears a logical extension to test it in this context.

Summary and Conclusion

Despite important gains in individual freedoms and the move toward democracy, the Russian transformation has not been smooth. The transition led to a collapse of Soviet state paternalism such as social guarantees of health, housing, and education, and price controls on many staples such as food products ( Shkolnikov and Meslé, 1996 ). Russia and Russians are also experiencing uncertainty and instability as many former social values and institutions are being replaced by a completely new political economy. These rapid structural and cultural changes have likely created anomic conditions that may contribute to various social problems in the country, including increases in and a widening variation of homicide rates.

Our findings suggest that poverty and socioeconomic change are positively and significantly related to the variation of regional homicide rates in Russia. This is consistent with our first and second hypotheses and provides support for deprivation theories and for Durkheim's anomie theory. Stronger families and polity appear to reduce regional homicide rates, providing partial support for one part of institutional anomie theory, though again these variables are also claimed by other macro-level theories. The main hypothesis tested here, a version of the key aspect of institutional anomie theory, finds no support. That is, our results show that none of the social institutions moderate the positive effects of poverty and socioeconomic change on homicide. One interpretation of these results is that change was so swift and powerful in Russia that social institutions were unable to buffer the effect of the anomic conditions. Similarly, social institutions may have been weakened by these changes, and in their weakened state do not have the power to condition the effects of change on violent crime.

In building on the present study, future research might more fully develop and extend the construct for socioeconomic and political change. It will also be useful to use alternative research designs and model specifications. For example, time-series analysis should be employed to examine whether socioeconomic change influences crime over time in Russia, and one could also test the alternative model specifications that we suggest in the discussion section of this article, such as the hypothesis that socioeconomic change negatively influences the strength of social institutions, thereby reducing or negating their ability to reduce crime rates. Further research also must test specific pathways through which socioeconomic change affects crime. For example, among the control variables included here, alcohol consumption is consistently significantly and positively related to homicide rates. Many researchers suggest that negative socioeconomic change, repeated crises, and continued uncertainty in Russia likely played a part in increased levels of alcohol consumption during the 1990s ( Gavrilova et al., 2000 ; Leon and Skolnikov, 1998 ; Pridemore, 2002 ; Shkolnikov et al., 1998 ). This presents yet another alternative model to explore and thus further research should test the hypothesis that socioeconomic change influences rates of crime and violence indirectly via alcohol consumption.

Other potential alternative explanations to anomie theory should also be examined in the context of transitional Russia. For example, less authoritarian law enforcement, together with the overall disarray and corruption of the police force, may have resulted in less fear of the state in general and less fear of being caught and punished for violent acts. Finally, we should note that the idea here is not simply that Russia shifted from a low-violence country to a high-violence country with the move toward capitalism. Pridemore (2001) has already shown that Russia has had homicide rates comparable to or higher than the those of the United States for several decades. The results of our study instead suggest that the poverty and anomic conditions associated with the transitional period between communism and capitalism and between totalitarianism and democracy are associated with the cross-sectional variation of homicide rates in Russia. Those regions that felt the more negative consequences of these changes are those regions with higher rates of violence.

In conclusion, our study is the first of its kind to test institutional anomie theory in a nation besides the United States and it provides a link between the single-country studies of IAT in the United States and cross-national studies of IAT that use nations as units of analysis. The study also provides the first empirical test of Bernburg's (2002) hypothesis that IAT should help explain the association between social change and crime rates. Russia offers an excellent locus in quo for researchers to test this hypothesis and to examine more general aspects of the impact of large-scale social change on society. Rigorous research on social change, institutions, and crime in the country should not only provide knowledge about Russia but important theoretical and empirical findings that are more broadly applicable to other societies and to our criminological knowledge.

Acknowledgments

The authors will happily share all data and coding materials with those wishing to replicate the study. This research was supported by Grant 5 R21 AA 013958-02 awarded to the second author by the National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism. Points of view do not necessarily represent the official position of NIH/NIAAA. The authors thank Kelly Damphousse, Harold Grasmick, Wil Scott, and Brian Taylor for their helpful critiques of earlier drafts. The second author thanks the Davis Center at Harvard University, where he was a Research Fellow when this article was written.

* Direct correspondence to William Alex Pridemore, Indiana University, Department of Criminal Justice, Sycamore Hall 302, Bloomington, IN 47405 〈 ude.anaidni@omedirpw 〉; Sang Weon-Kim, Dong-Eui University, Department of Police Science, 995 Eomgwangno, Busanjin-gu, Busan 614-774, Korea 〈 rk.ca.ued.liam@mikgnas 〉.

1 Russian organized crime has received considerable media attention. Compared to Western nations, the number of mafia-related murders is high. This type of violence is detrimental to a free market and a democracy since it targets businesspeople and politicians, and the widespread attention it receives may foster a climate that recognizes violence as an acceptable form of conflict resolution. When estimating models to test macro-level theories of crime, however, it is necessary to make it clear that this type of violence plays little to no role since the number of mafia-related killings is a tiny fraction of the nearly 40,000 homicides in Russia annually (see also Andrienko, 2001 ; Gavrilova et al., 2005 ).

2 The poverty and unemployment variables were used in the “poverty” models below. Since the change scores for these measures are part of the socioeconomic change index, these variables were not included in the “socioeconomic change” models.

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hypothesis on poverty and crime

Potential tough-on-crime ballot measure promises less homelessness in California. Experts aren’t convinced

Two tents set up across from Roeding Park in a small homeless encampment in Fresno on Feb. 10, 2022.

Backers of a tough-on-crime California ballot measure put homelessness at the forefront of their campaign to roll back Prop. 47. But would the measure actually help get people housed?

Homelessness gets top billing in a measure likely to make it onto your November ballot. Whether the measure has anything to do with homelessness is debatable.

The initiative proponents are calling the “ Homelessness, Drug Addiction, and Theft Reduction Act ” would increase penalties for some drug and theft crimes, by rolling back Proposition 47 — the criminal justice changes California voters passed a decade ago. It also would force some people arrested three or more times for drug crimes into treatment.

But where does homelessness factor into this tough-on-crime measure? The initiative includes no money for housing, shelter or treatment beds — leading some experts to question how it would help get California’s more than 181,000 unhoused residents off the street in a state where recent research shows loss of income is the leading cause of homelessness. Nor does the measure allocate or create new funding sources to pay cities or counties to enforce it.

For Yolo County District Attorney Jeff Reisig, who helped author the proposed ballot measure, the philosophy is simple: The measure would slash the homeless population by pushing those struggling with drug addiction into treatment.

“The big part of this, which is the key to the program, is it’s going to be compelled,” Reisig said. “People are going to have to go through the program or accept the consequences.”

But according to Elliott Currie, a professor of criminology, law and society at the University of California Irvine, the measure is based on a false assumption.

“The theory is that people are homeless because we’ve been too lenient with drug addiction,” Currie said. “I think I can safely say that I don’t see one shred of serious evidence that that’s what’s going on.”

Did Prop. 47 increase homelessness in California?

The proposed ballot measure targets Prop. 47, which, when passed by voters in 2014, reduced certain theft and drug crimes from felonies to misdemeanors. In some circles, Prop. 47 now is being blamed for a perceived increase in crime – and a fierce debate is raging over whether, and how, to change it .

Backers of the measure, which is likely to qualify for the ballot after it recently submitted more than 900,000 signatures (about 547,000 valid ones are required), also blame Prop. 47 for California’s dire homelessness crisis.

In the decade that Prop. 47 has been in effect, homelessness in California has grown by more than half — and backers of the proposed ballot measure say the two are “directly connected.” They argue by watering down the legal consequences for drug use, Prop. 47 removed the incentives for homeless Californians to participate in mental health and drug treatment, and as a result, fewer are. Because of that, they argue, more people are living on the streets.

“One of the primary root causes of homelessness is serious addiction, which is debilitating and results in people not being able to function or even hold a job,” Reisig said in an interview with CalMatters.

It’s true that participation in drug courts dropped throughout the state in the wake of Prop. 47. In San Diego County, for example, more than 650 people went through drug court in the year before Prop. 47 passed. By 2021, it was down to just 255 .

As evidence Prop. 47 is tied to homelessness, backers of the measure point to states with stronger drug laws and smaller homeless populations. Illinois, for example, has a homeless rate about five times less than California’s.

But there are a lot of other factors — especially housing costs — contributing to the state’s homelessness crisis. Fair market rent for a two-bedroom in Chicago is just $1,714 – nearly half the going rate in San Francisco. The San Francisco area rate increased 72% since Prop. 47 passed, hitting $3,359 this year, according to the U.S. Department of Housing and Urban Development.

The number one reason Californians end up homeless  is a loss of income — not drug use, according to a  UC San Francisco study

For some experts who study crime and homelessness, the ballot measure is baffling.

“I’m not aware of any data that shows a connection between Prop. 47 and homelessness,” said Charis Kubrin, a professor of criminology at UC Irvine. “So it’s a bit of a puzzle to me why they’re together like that.”

Blaming the state’s spike in homelessness on Prop. 47 is “preposterous,” said Sharon Rapport, director of California state policy for the Corporation for Supportive Housing. “All of the changes that the (ballot measure) is proposing have nothing, nothing whatsoever, to do with homelessness.”

The organization hasn’t even taken an official position on the measure, because, Rapport said, it’s not related to homelessness.

The number one reason Californians end up homeless is a loss of income — not drug use, according to a UC San Francisco study that provides the most comprehensive look yet at the state’s homelessness crisis. And in the six months before becoming homeless, the people surveyed were making a median income of just $960 a month.

That doesn’t mean drug use has nothing to do with homelessness. Nearly a third of people surveyed reported using methamphetamines three times a week, while 11% used non-prescribed opioids. Other studies have had varying results: a 2022 Stanford Institute for Economic Policy Research study, which cited research from multiple surveys across several states, showed 43% to 88% of the homeless population struggled with drug abuse.

Drug and alcohol overdoses are also the leading cause of death for homeless people nationwide, according to a February study examining mortality rates among unhoused people between 2011 and 2020.

But it’s clear not everyone on the streets has an addiction. Therefore, the proposed ballot measure would leave out a large chunk of the state’s homeless population.

If the measure helps even a third of California’s 181,000 unhoused residents — that’s a huge number, Reisig said.

“I’ll take that,” he said. “I’ll take that number to try and get those people well, and to get them reintegrated, and to keep them out of jail and prison, and keep them from dying on the street of overdose or murder.”

This measure might help some people get sober, said Benjamin Henwood, director of the USC Center for Homelessness, Housing and Health Equity Research. But for many people, that won’t be enough to end their homelessness, he said. While being sober might make someone more likely to get a job, it won’t make housing any less expensive.

“The question is: Once treatment is up, where do they go?” he said.

Under this measure, the answer to that question will depend on each individual county and how much, if any, housing they make available for people coming out of treatment.

How would the Homelessness, Drug Addiction, and Theft Reduction Act work?

If the proposed ballot measure is approved by voters, certain repeat drug offenses could be prosecuted as a “treatment-mandated felony.” That means the third time someone is arrested for a drug offense, they could be given a choice between jail or mandatory addiction and mental health treatment.

The measure says people participating in mandatory treatment also would be offered “shelter, job training, and other services designed to break the cycle of addiction and homelessness.” But it doesn’t say how any of that would be paid for. It would be up to counties to decide whether to offer shelter and other services, and how to fund them, Reisig said.

“That will have to be deployed in each county to the extent they can do it,” he said.

The measure also doesn’t specify how the mandatory drug and mental health treatment would be funded.

But without guaranteeing those housing services, the measure could actually worsen homelessness, Currie said. There’s already a robust jail-to-homelessness pipeline in California: 43% of those surveyed in the UCSF study were in jail or prison, or on probation or parole, in the six months before they became homeless.

“Anybody who says you’ve got to solve the problem by putting more people behind bars, but you then don’t say anything about how you’re going to help them re-enter when they come out — I think that’s pretty bogus,” Currie said.

Resources for treatment already are stretched thin in California. In a 2022 survey by the state’s Department of Health Care Services, 70% of California counties reported “urgently” needing more residential addiction treatment, while nearly 40% didn’t have any residential facilities at all.

In addition to providing no new funding, the proposed ballot measure could actually end up reducing  funds for the very programs it’s trying to bolster, according to a report from the independent Legislative Analyst’s Office . That’s because Prop. 47 saved the state money in criminal justice costs by diverting people away from prison and jail. Those savings are earmarked for projects that provide mental health and substance use treatment ( nearly $104 million was awarded between 2017 and 2020, and another $96 million between 2019 and 2023).

By gutting Prop. 47 and funneling more people into the state’s jails and prisons, the Legislative Analyst estimates the proposed ballot measure would eat away at those savings and increase criminal justice costs by as much as tens of millions of dollars per year. That could mean less money for mental health services and addiction treatment.

Reisig dismissed that worry, saying, at least in Yolo County, where he is district attorney, Prop. 47 savings haven’t made much difference. “There’s literally nothing that I fear losing through this program,” he said.

There is some new money available from other pots. In March, California voters approved a $6.4 billion bond to pay for 6,800 beds in facilities treating mental illness and addiction, and as many as 4,350 housing units for people who need those services. The state is set to start awarding that money in the spring and summer of 2025, Newsom said this month .

But at the same time, to plug a yawning budget deficit, Newsom has proposed cutting funds from the Behavioral Health Bridge Housing Program, which provides beds for people who need mental health and addiction services.

Currie said he is “skeptical” of the lack of funding mechanisms for treatment programs and other services to ensure homeless people stay off the streets post-treatment. That, he said, could burden counties that already struggle with insufficient funding for such services — one in five homeless people surveyed by UCSF researchers said they sought substance abuse treatment but failed to get it.

“You can’t just say, ‘Ok, you counties. Since you are swimming in so much money after all … we are going to mandate drug treatment for some people on top of the existing number of clients,’” Currie said.

The politics of homelessness

Some political strategists say the measure’s tie to homelessness represents the campaign’s attempt to capitalize on public concern about the problem. Homelessness is a top issue on California voters’ minds, according to a February 2024 statewide survey by the Public Policy Institute of California.

“This notion somehow that it addresses homelessness is deceptive and downright farcical,” said Garry South, a longtime Democratic consultant who has worked on ballot measures for more than 20 years.

Homelessness is ultimately due to a lack of housing, he argued, and measures aiming to address the problem without providing housing are “disingenuous.”

“You’ve heard the old saying ‘Putting lipstick on a pig,’” South said. “I’m not saying that this measure is a pig, but what I’m saying is it’s a standard procedure … to try to gussy it up with some reference or some provision that really strikes a responsive chord with voters when that’s not really what the initiative is about.”

If the measure appears before voters in November, “homelessness” won’t be in the title they see on their ballots. The official title of the measure, chosen by the state attorney general, is: “Allows felony charges and increases sentences for certain drug and theft crimes.”

A lot of thought, politics, and sometimes even litigation goes into drafting the title and summary of a ballot measure. While proponents of a proposition want to entice voters with their description, it’s ultimately the state attorney general’s job to make sure the language is fair.

Even without mentioning homelessness, South said the ballot measure could still “pass on its own merits.” He, for one, would likely vote for it as a way to decrease crime.

Drugs and homelessness

Tom Wolf, who has experienced both homelessness and addiction first hand in San Francisco, said the proposed ballot measure has great potential to help people who were like him.

An opioid addiction cost Wolf his job and his home, and landed him on the streets of San Francisco’s Tenderloin neighborhood in 2018. He said he worked as a “holder” for nearby drug dealers, safeguarding their stash of narcotics in case they were busted by the police. Sometimes he stole razor blades from a nearby Target and sold them for money to buy heroin.

Wolf says he was arrested on drug charges five times within three months, and was released back to the street each time. The sixth time he was arrested, the judge let him sit in jail for three months, where he got sober. Wolf finally called his brother, who said he would bail him out if Wolf went to drug treatment. Wolf agreed. He says that if he had been given the choice between jail and treatment the last time he was picked up, he would have chosen treatment.

In June, Wolf will have six years sober. He’s now an advocate for drug policy reform, and works as director of West Coast initiatives for the Foundation for Drug Policy Solutions.

“That accountability piece was the key to me getting off the street,” he said, “getting sober, becoming willing to accept an opportunity to go to treatment and give recovery an honest try.”

CalMatters  is a nonprofit, nonpartisan media venture explaining California policies and politics. 

hypothesis on poverty and crime

New York prosecutors failing to show the ‘other crime’ in Trump trial: Legal experts

T he New York hush money trial against Donald Trump is entering day 18 of the historic first criminal proceedings against a former president, but legal experts say prosecutors have yet to identify the "other crime" needed to upgrade misdemeanor charges to a felony conviction.

Trump's former attorney and fixer Michael Cohen is set to take the witness stand again Thursday for his second day of cross-examination by the former president's defense team. The trial has pushed forward at a steady pace and is ahead of schedule, yet prosecutors have left a glaring hole in their argument that Trump is guilty of 34 felony counts of business record falsifications in the first degree.

The problem prosecutors face is that they must first prove the falsification of business records, which is a misdemeanor, and then the felony "escalator," or the claim that the records were falsified with the intent to conceal or aid in the commission of another offense, former federal prosecutor Katie Cherkasky told the Washington Examiner.

By the time the prosecution finished questioning Cohen on Tuesday morning, they had not clearly laid out what additional crime Trump was aiming to facilitate by allegedly falsifying records, though they appeared to rely heavily on Cohen's decision to stop being loyal to Trump when he pleaded guilty to federal charges in 2018. Cohen's plea was in relation to a $130,000 payment to porn star Stormy Daniels to hide her story of an alleged sexual encounter with Trump.

One potential object offense prosecutors say Trump could be culpable of is a violation of the Federal Election Campaign Act, the same federal campaign finance law Cohen pleaded guilty to violating.

But Cherkasky said she and many lawyers "don't think that you can incorporate a federal offense that isn't within the jurisdiction of the New York court as the escalating offense," adding that doing so would turn into a bigger problem for an appellate court if the jury voted to convict Trump.

Manhattan District Attorney Alvin Bragg, an elected Democrat, brought the indictment against Trump in April 2023 with the help of a COVID-19-era law that allowed the state to extend its statute of limitations by a year. Without that change, prosecutors could not have brought the case against Trump because falsifying business records is a misdemeanor with a two-year statute of limitations.

Simply put, Bragg's team is seeking to prove two underlying allegations against Trump: that the 11 checks he paid to Cohen in 2017 were misclassified as "legal expenses" in order to cover up hush money payments and that it was done for electoral reasons rather than merely to save Trump from personal embarrassment. The former president has denied Daniels's allegations about the alleged affair and contends the lump $420,000 amount he paid Cohen was for legal work.

Prosecutor Joshua Steinglass has said the "primary" crime his office is seeking to use to prove a conspiracy is  Section 17-152  under New York law, which is conspiracy to promote or prevent an election. However, Bragg did not charge Trump with any election-related offenses and has hinted there are three other "potential object offenses" that Trump may have committed, adding more confusion to the mix.

Another "potential problem" for the 12-member jury is their task to find "beyond a reasonable doubt" that the alleged falsification of business records was done to cover up another crime, according to former federal prosecutor David Sklansky.

"If I had to bet, I would say that the jury ultimately will be convinced that this was done to cover up another crime, but the theory that they have to follow in order to find that is a little convoluted, and I think that even for most lawyers who have been following the trial, it’s been a little difficult to figure out exactly what the DA’s theory is," Sklansky said in a recent interview.

And if prosecutors want to rely on Cohen's 2018 guilty plea to the campaign finance violation, then they must confront the fact that the Federal Election Commission dropped its investigation into whether Trump violated election law with the Daniels payment in May 2021. Additionally, the Justice Department under the Biden administration declined to charge Trump with any crimes related to the payment after investigating.

Cherkasky said the true description of the "other crime" prosecutors are trying to prove that Trump committed may not be revealed until presiding Judge Juan Merchan hands the jury instructions when the time comes for a verdict in the case, which could come as soon as the first week of June.

Moreover, Cherkasky said Bragg's team will likely argue for "very specific instructions that make it easy to prove their case ... and then the defense is going to have their chance to argue against it."

CLICK HERE TO READ MORE FROM THE WASHINGTON EXAMINER

"The judge's instructions are going to be very critical. That's going to tell us all of the answers to these things in terms of how the judge is interpreting this," Cherkasky said.

Trump attorney Todd Blanche said Tuesday he expects cross-examination of Cohen will continue from Thursday morning into the afternoon. The trial is taking a break on Friday, and the defense will begin presenting their case and witnesses on Monday.

New York prosecutors failing to show the ‘other crime’ in Trump trial: Legal experts

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Former South Africa leader Zuma promises jobs and free education as he launches party manifesto

Former South African President Jacob Zuma arrives at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party's manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in a bitter battle in the general elections later in May. (AP Photo/Jerome Delay)

Former South African President Jacob Zuma arrives at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in a bitter battle in the general elections later in May. (AP Photo/Jerome Delay)

Former South African President Jacob Zuma, centre, arrives at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in a bitter battle in the general elections later in May. (AP Photo/Jerome Delay)

Supporters wait for former South African President Jacob Zuma to arrive at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in the general elections later in May. (AP Photo/Jerome Delay)

Supporters cheer former South African President Jacob Zuma as he arrives at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in the general elections later in May. (AP Photo/Jerome Delay)

Former South African President Jacob Zuma arrives at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in the general elections later in May. (AP Photo/Jerome Delay)

Supporters wait for former South African President Jacob Zuma to arrive at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto, Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in the general elections later in May. (AP Photo/Jerome Delay)

Veteran fighters parade as they wait for former South African President Jacob Zuma to arrive at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in the general elections later in May. (AP Photo/Jerome Delay)

Former South African President Jacob Zuma greets supporters at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in the general elections later in May. (AP Photo/Jerome Delay)

A young Zulu warrior makes his way through the crowd , waiting for former South African President Jacob Zuma to arrive at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in the general elections later in May. (AP Photo/Jerome Delay)

Supporters of former South African President Jacob Zuma hold a pro-Putin sign at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party’s manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in the general elections later in May. (AP Photo/Jerome Delay)

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JOHANNESBURG (AP) — Former South African President Jacob Zuma Saturday lamented the high levels of poverty among black South Africans and promised to create jobs and tackle crime as he launched his new political party’s manifesto ahead of the country’s much anticipated elections.

He told thousands of supporters who gathered at Orlando Stadium in Johannesburg that his party would build factories where many people would be employed and provide free education to the country’s youth.

“We want our children to study for free, especially those from poor households because the poverty we have was not created by us. It was created by settlers who took everything, including our land. We’ll take all those things back, make money and educate our children,” he said.

He has also pledged to change the country’s Constitution to restore more powers to traditional leaders, saying their role in society has been reduced by giving more powers to magistrates and judges.

Zuma’s uMkhonto weSizwe party, known as the MK Party, has emerged as a significant player in South Africa’s upcoming elections after it was launched in December last year.

Former South African President Jacob Zuma arrives at Orlando stadium in the township of Soweto, Johannesburg, South Africa, for the launch of his newly formed uMkhonto weSizwe (MK) party's manifesto Saturday, May 18, 2024. Zuma, who has turned his back on the African National Congress (ANC) he once led, will face South African President Cyril Ramaphosa, who replaced him as leader of the ANC in a bitter battle in the general elections later in May. (AP Photo/Jerome Delay)

He is currently involved in a legal battle with the country’s electoral authority , the Independent Electoral Commission. He has appealed against a court judgment which barred him from standing in the election because of his criminal record.

Zuma was sentenced to 15 months in prison for defying a court order to appear before a judicial commission of inquiry which was probing corruption allegation in government and state-owned companies during his presidential term from 2009 to 2018.

In 2018, he was forced to resign as the country’s president following wide-ranging corruption allegations, but he has made a political return and is now seeking to become the country’s president again.

“When they talk about unemployment, they are talking about us, there is nobody else. When they talk about people who leave in shacks, that is us, there is nobody else who lives in shacks except us,” Zuma told his supporters, many of whom had travelled from other provinces like Mpumalanga and KwaZulu-Natal, where he still enjoys significant support.

Poverty among black people is the reason behind South Africa’s high levels of crime, according to the former president.

“Our hunger and poverty is what creates a perception that we are criminals, we don’t have a brain, we have nothing. That time is over, because we are good people who are giving, but some people are pushing us towards criminality,” he said.

Zuma said his party was aiming to get more than 65% of the national vote in the upcoming elections as it would allow them to change many laws in the country’s constitution.

Recent polls and analysts have suggested that the ruling African National Congress might get less than 50% of the vote and would need to form a coalition with smaller parties to remain in power.

South Africans will go to the polls on May 29.

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COMMENTS

  1. Poverty and Crime

    Abstract. This article examines theory and evidence on the association between poverty and crime at both the individual and community levels. It begins with a review of the literature on individual- or family-level poverty and crime, followed by a discussion at the level of the neighborhood or community. The research under consideration focuses ...

  2. The Relationship Between Poverty and Crime:

    Crime is a complicated issue, and other variables like education, healthcare, and housing have to be taken into consideration. The results indicate that there is a relationship between certain types of crime and poverty, and that income inequality is significant to all types of crime. JEL Classification: I32, A13.

  3. Dynamic linkages between poverty, inequality, crime, and social

    The study examines the relationship between growth-inequality-poverty (GIP) triangle and crime rate under the premises of inverted U-shaped Kuznets curve and pro-poor growth scenario in a panel of 16 diversified countries, over a period of 1990-2014. The study employed panel Generalized Method of Moments (GMM) estimator for robust inferences. The results show that there is (i) no/flat ...

  4. Why do inequality and deprivation produce high crime and low trust

    Specifically, holding constant the average level of resources, greater inequality makes frequent exploitation and low trust a more likely outcome. Thus, we capture the widely-observed associations ...

  5. Urban Poverty and Neighborhood Effects on Crime: Incorporating Spatial

    A later extension of this theory proposes that independent of social ties, collective efficacy—a combination of social cohesion, ... We argue that for a more complete understanding of the impact of neighborhoods and poverty on crime, sociological research would benefit from expanding the analytical focus from the residential neighborhoods to ...

  6. Is poverty the mother of crime? Evidence from homicide rates in China

    Abstract. Income inequality is blamed for being the main driver of violent crime by the majority of the literature. However, earlier work on the topic largely neglects the role of poverty and income levels as opposed to income inequality. The current paper uses all court verdicts for homicide cases in China between 2014 and 2016, as well as ...

  7. Poverty as a Harbinger of Crime

    North America. In one of the earliest study on the poverty and crime nexus, McKeown's investigation across several United States of America's cities discovers a significant relationship between poverty and crime.Shubert's inquiry into the lifestyle of poor Alberta women in Canada shows that the deeper they fall into poverty, the more they engage in criminal activities.

  8. Poverty, Income Inequality, and Violent Crime: A Meta-Analysis of

    In the late 1970s and early 1980s, several important reviews of the literature failed to establish a clear consensus on the relationship between economic conditions and violent crime. The research presented here applies the procedures of meta-analysis to 34 aggregate data studies reporting on violent crime, poverty, and income inequality.

  9. Income inequality, poverty and crime across nations

    We examine the relationship between income inequality, poverty, and different types of crime. Our results are consistent with recent research in showing that inequality is unrelated to homicide rates when poverty is controlled. In our multi-level analyses of the International Crime Victimization Survey we find that inequality is unrelated to ...

  10. (PDF) The dynamics of poverty and crime

    There is a direct correlation between poverty and criminality (Kelly, 2000; Block and Heineke, 1975). Becker's economic theory of crime (1968) assumes that people resort to crime only if the costs ...

  11. Poverty, Inequality, and Area Differences in Crime

    For example, much of this work does not treat "crime rates" as the dependent variable. Rather, a number of studies grounded in the poverty-crime literature have examined the "threat hypothesis" of crime control (Liska et al. 1981). In this body of work, researchers have drawn on the proposition that impoverished members of racial and ...

  12. Poverty, Socioeconomic Change, Institutional Anomie, and Homicide

    This study tested institutional anomie theory (IAT) (Messner and Rosenfeld, 1997a) in the context of widespread poverty and large-scale socioeconomic change in Russia.Although developed to explain crime in the capitalist culture of the United States, IAT has been tested cross-nationally (Messner and Rosenfeld, 1997b; Savolainen, 2000) and Bernburg (2002) recently argued that the theory should ...

  13. Theories of the Causes of Poverty

    This article proposes that most theories of poverty can be productively categorized into three broader families of theories: behavioral, structural, and political. Behavioral theories concentrate on individual behaviors as driven by incentives and culture. Structural theories emphasize the demographic and labor market context, which causes both ...

  14. Inequality and Crime

    Social disorganization theory argues that crime occurs when the mechanisms of social control are weakened. Factors that weaken a community's ability to regulate its members are poverty, racial heterogeneity, residential mobility, and fam-ily instability. In this case, inequality is associated with crime because it is linked to poverty: areas ...

  15. PDF Poverty and Crime Review

    poverty and crime (P‐C) link in the United States, United Kingdom and Europe. The start date 1980 reflects a growing interest in the impact of poverty on crime, coinciding with steep rises of poverty and unemployment at a time that began to see steep rises in the crime rate too.

  16. Social Disadvantage and Crime

    crime, social disadvantage, social mechanisms, situational action theory 'Everybody believes that "poverty causes crime" it seems; in fact, I have heard many a senior sociologist express frustration as to why criminologists would waste time with theories outside the poverty paradigm. The reason we do… is that the facts demand it'.

  17. The stark relationship between income inequality and crime

    A new survey by Gallup, a polling organisation, appears to go some way to verifying Becker's theory. It asked 148,000 people in 142 countries about their perceptions of crime and how safe they ...

  18. Crime and Poverty: Some Experimental Evidence From Ex-Offenders

    In this article we will present evidence on the relationship between poverty and crime gathered from two randomized ex- periments, each conducted with samples of close to 2,000 persons who were re- leased over a 6-month period in 1976 from the state prisons of Georgia and Texas.

  19. Is poverty the mother of crime? Empirical

    The positive coefficient of poverty variable confirms the economic theory of crime that poverty leads to more criminal activities. This result is also in line with Fafchamps and Minten's (2002) econometric result that poverty is associated with rise in property related crime. They took into account the number of people below poverty line in ...

  20. Poverty, Income Inequality, and Violent Crime: A Meta-Analysis of

    In the late 1970s and early 1980s, several important reviews of the literature failed to establish a clear consensus on the relationship between economic conditions and violent crime. The research presented here applies the procedures of meta-analysis to 34 aggregate data studies reporting on violent crime, poverty, and income inequality. These studies reported a total of 76 zero-order ...

  21. (PDF) THE RELATIONSHIP BETWEEN POVERTY AND CRIME

    Abstract. One of the major problems facing many countries is poverty. The factor of poverty among the causes of crime. increases its importance from day to day. Crime can be defined as a violat ...

  22. Criminal Opportunity Theory and The Relationship Between Poverty and

    This hypothesis suggests that the relationship between levels of deprivation and property crime is curvilinear where the positive effect of deprivation on property crime is stronger at low levels of neighborhood poverty than it is at high levels. Research and policy implications are discussed.

  23. Violence & Socioeconomic Status

    Violence & Socioeconomic Status. Socioeconomic status (SES) encompasses not just income but also educational attainment, financial security and subjective perceptions of social status and social class. Socioeconomic status can encompass quality of life attributes as well as the opportunities and privileges afforded to people within society.

  24. Potential tough-on-crime ballot measure promises less homelessness

    Backers of a tough-on-crime California ballot measure put homelessness at the forefront of their campaign to roll back Prop. 47. But would the measure actually help get people housed?

  25. New York prosecutors failing to show the 'other crime' in ...

    The New York hush money trial against Donald Trump is entering day 18 of the historic first criminal proceedings against a former president, but legal experts say prosecutors have yet to identify ...

  26. Former South Africa leader Zuma promises jobs and free education as he

    Former South African President Jacob Zuma has lamented the high levels of poverty among black South Africans and promised to create jobs and tackle crime as he launched his new political party's manifesto ahead of the country's much anticipated elections.

  27. The Sunday Read: 'Why Did This Guy Put a Song About Me on Spotify?'

    Listen and follow The Daily Apple Podcasts | Spotify. Have you heard the song "Brett Martin, You a Nice Man, Yes"? Probably not. On Spotify, "Brett Martin, You a Nice Man, Yes" has not yet ...