Browsing by Subject "Crime"

A moral dilemma confronts criminal justice in unjust states. If the state punishes marginalized citizens whose crimes are connected to conditions of systemic injustice the state has failed to alleviate, it perpetuates a further injustice to those citizens. If the state does not punish, it perpetuates an injustice to victims of crime whose protection is the duty of the criminal justice system. Thus, no reaction to crime by the unjust state appears to avoid perpetuating further injustice. Tommie Shelby proposes a new solution to this old dilemma, suggesting that certain theoretical and practical qualifications can save the unjust state from perpetuating injustice. He argues that punishment can be just even as society remains unjust if it is: (a) administered through a fair criminal justice apparatus; (b) only directed at mala in se crimes; and (c) not expressive of moral judgment. In the first part of this thesis, I explore Shelby’s solution to show that certain aspects of his framework are superior to alternative ones, but that it nonetheless fails to resolve the dilemma. In Part 2, I use a novel technological reform that promises to make criminal justice fairer, the AI risk assessment, as a case study to show why even punishment that meets Shelby’s criteria will continue to perpetuate injustice as long as it operates under systemic social injustice. Punishment can only be just if society is.

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How does violence affect political participation and voting behavior? Why does a violent context push some to be politically active, while others decide to stay at home? Our current understanding of political behavior is mostly derived from analyses conducted in a peaceful and democratic context, or in post-conflict periods. However, citizens in many developing countries make their political decisions in the midst of violence.

The dissertation's central argument is that political participation and voting behavior largely depend on the context surrounding the individual. In particular, the level of criminal violence greatly impacts 1) citizens' decision to participate politically, 2) their forms of participation, and 3) the logic of their vote choice. Faced with violence, voters are generally pushed away from electoral politics. I argue that those that do decide to take part of the electoral process will consider their evaluations of security when deciding to punish or reward the incumbent government. While some may be inclined to take further action and demand peace through non-electoral participation, such a decision carries certain risks that are not easily overcome. I contend that social networks can encourage participation by shaping the understanding of crime, as well as the perception of costs and benefits associated with participation amid violence.

To evaluate this argument, I draw on a rich array of sources. I designed an original post-electoral survey that took place in Mexico a few days after the 2012 presidential election. I also created a novel newspaper databank of protests against crime in Mexico during the 2006-2012 period. In addition, together with Guillermo Trejo, I developed a unique dataset on criminal violence in Mexico. My statistical evidence is complemented with participant observation in marches for peace and qualitative in-depth interviews with victims and non-victims of crime in four Mexican cities.

Statistical evidence shows that violent criminal activity depresses electoral turnout. Voters living in violent contexts are less likely to participate in elections. Victims of crime are significantly less likely to participate in elections. However, faced with rising violence, active voters are able to consider both economic and insecurity evaluations in their assessments of government performance and voting decisions. Overall, as a voter's evaluation of national security worsens, her likelihood of supporting the incumbent national party and government diminishes. At the same time, while institutional channels are not attractive to victims of crime, societal accountability mechanisms are also available to citizens affected by insecurity. Victims of crime and those connected to mobilizing networks are more likely to participate in protests against insecurity than non-victims and "socially disconnected" individuals.

This dissertation consists of three essays in economics of crime. The first chapter examines the relationship between economic inequality and crime, and provides a new theoretical explanation and empirical evidence. Economists traditionally explained the empirical relationship between inequality and crime in terms of differentials in potential criminal gains. As inequality increases, low-income individuals are likely to be left with little increase in their legitimate earnings potential but much larger increases in potential criminal gains, because there are now more wealthy potential victims who possess goods worth taking. This increase in potential criminal gains for the disadvantaged population then results in higher crime rates. While simple and intuitive, this explanation is inconsistent with the high concentration of crime victimization among the poor who offer the least potential gains to offenders. After a careful empirical analysis using recent panel data on large U.S. counties, I find evidence that the previously-reported empirical link between inequality and crime at the county-level masks two opposing effects of economic inequality underneath; crime is positively linked with economic segregation across neighborhoods, but negatively correlated with local inequality. One immediate implication of this finding is that introducing greater economic inequality, or income variability, in highly disadvantaged neighborhoods may reduce local crime, instead of increasing crime.

In the second chapter, I examine the impact of the North Carolina sex offender residency restriction on recidivism and residential outcomes of sex offenders. Sex offender residency restriction aims to reduce the danger of repeat sex offense against children by prohibiting sex offenders from living near child-related facilities. But existing research finds little evidence that the restriction lowers the risk of sex offense recidivism, and predicts that the restriction may have adverse impacts on sex offenders' community reintegration, residential stability and employment prospects, which may increase the likelihood of recidivism among sex offenders. Taking advantage of the individual-level information on North Carolina offenders' criminal and residential histories and potentially exogenous variations in sex offenders' timing of release and proximity of former residences to nearest child-related facilities, I test the hypothesis that the residency restriction causes sex offenders to be more likely to be involved in general recidivism. Estimation results indicate that the North Carolina sex offender residency restriction indeed adversely influences sex offenders' likelihoods of overall recidivism, as well as causing them to be more likely to live in high-poverty neighborhoods.

The last chapter, a joint work with Professor Philip Cook, explores the relationship between school entry age, education and crime. It has been well-documented that students who are older than their classroom peers tend to outperform their younger peers in standardized tests. On the other hand, older students are also associated with a higher rate of high school dropout, likely due to the presence of the age threshold after which students are allowed to drop out from school. Given the strong relationship between education and crime, it is of great interest the extent to which the relative educational advantages and disadvantages associated with students' relative age influences their criminal outcomes. Our analyses exploit a plausibly exogenous variation in the age in which students are enrolled in third grade, caused by the minimum age requirement for school entry in North Carolina. Based on the sample of North Carolina students, we find strong evidence that students who are relatively older than their classroom peers tend to do better in standardized reading and math tests and are more likely to be enrolled in advanced courework during the periods of primary and secondary education. Consistent with the gap in academic achievement while at school, children who are relatively older are less likely to be involved in criminal offense as juveniles. On the other hand, we also document that relatively older students are less likely to graduate from high school, and have a higher likelihood of offending as adults, likely reflecting the difference in the level of educational attainment. Our finding that the timing of school entry are associated with important costs and benefits in children's educational and criminal outcomes may have important policy implications on the optimal school entry and exit ages.

This dissertation encompasses three essays on policing and criminal justice, algorithms and inequality. The first two essays examine the efficacy and equity implications of data-driven algorithms that are increasingly used in important life-altering decision-making contexts. The third essay investigates when crime responds to punishment.

The first essay studies the impacts of neighborhood targeting of police presence brought about by predictive policing algorithms on crime and arrests. While predictive policing is widely used, the impacts of neighborhood targeting brought about by predictive policing on crime, and whether there are disproportionate racial impacts are open questions. Using a novel dataset, I isolate quasi-experimental variation in police presence induced by predictive-policing algorithms to estimate the causal impacts of algorithm-induced police presence. I find that algorithm-induced police presence decreases serious violent and property crime, and evidence that algorithm-induced neighborhood targeting of police presence has disproportionate racial impacts on traffic incident arrests and serious violent crime incident arrests.

The second essay investigates how data-driven algorithms can maximize overall predictive power at the cost of racial and economic justice. Examining a tool that is already widely used in pretrial decision-making, I build a framework to evaluate how input variables trades off overall predictive power, and racial and economic disparities in the scores that defendants receive. I find that using information on neighborhoods where defendants live only marginally contributes to overall predictive power. However, the use of defendant neighborhood data substantially increases racial and economic disparities, suggesting that machine learning objectives tuned to maximize overall predictive power risk being in conflict with racial and economic justice.

Finally, in the third essay, joint with Sarah Komisarow and Robert Gonzalez, we examine when crime responds to punishment severity increases. While economic theory suggests that crime should respond to punishment severity, empirical evidence on this link is ambiguous. We propose an explanation for this empirical evidence -- the effect of punishment severity increases depends on the probability of detection; punishments deter crime when the probability of detection is moderate. We test and validate this explanation using increases in punishment severity in drug-free school zones along with changes in the probability of detection resulting from a community crime-monitoring program.

Urban problems have several features that make them inherently dynamic. Large transaction costs all but guarantee that homeowners will do their best to consider how a neighborhood might change before buying a house. Similarly, stores face large sunk costs when opening, and want to be sure that their investment will pay off in the long run. In line with those concerns, different areas of Economics have made recent advances in modeling those questions within a dynamic framework. This dissertation contributes to those efforts.

Chapter 2 discusses how to model an agent’s location decision when the agent must learn about an exogenous amenity that may be changing over time. The model is applied to estimating the marginal willingness to pay to avoid crime, in which agents are learning about the crime rate in a neighborhood, and the crime rate can change in predictable (Markovian) ways.

Chapters 3 and 4 concentrate on location decision problems when there are externalities between decision makers. Chapter 3 focuses on the decision of business owners to open a store, when its demand is a function of other nearby stores, either through competition, or through spillovers on foot traffic. It uses a dynamic model in continuous time to model agents’ decisions. A particular challenge is isolating the contribution of spillovers from the contribution of other unobserved neighborhood attributes that could also lead to agglomeration. A key contribution of this chapter is showing how we can use information on storefront ownership to help separately identify spillovers.

Finally, chapter 4 focuses on a class of models in which families prefer to live

close to similar neighbors. This chapter provides the first simulation of such a model in which agents are forward looking, and shows that this leads to more segregation than it would have been observed with myopic agents, which is the standard in this literature. The chapter also discusses several extensions of the model that can be used to investigate relevant questions such as the arrival of a large contingent high skilled tech workers in San Francisco, the immigration of hispanic families to several southern American cities, large changes in local amenities, such as the construction of magnet schools or metro stations, and the flight of wealthy residents from cities in the Rust belt, such as Detroit.

Few studies have historically assessed the surges and troughs of public perception regarding juvenile offenders across over a century of legislative and social change. Furthermore, a minority of juvenile crime investigations have holistically examined the interplay between changing demographic conditions (notably, economic stability, racial composition and crime rates) with its accompanying ideological shifts. Through a theoretical emphasis on social constructionism and moral panic theory, this dissertation illuminates the cyclical nature of juvenile justice reform and illustrates that panics regarding juvenile offenders are more closely related to fears regarding the maintenance of power and the insecurity that comes with historical change than with an authentic threat of juvenile crime. Over 9,000 records in The New York Times, Congressional record, and Supreme Court decisions were coded and analyzed to reveal three chronological partitions of the social construction of youthful offenders: (1) the 1890s-1930s during which the most destabilizing force to those in positions of power revolved squarely around urbanization, industrialization, and the waves of immigration from Eastern and Southern Europe; (2) the 1930s-1970s during which faith in juvenile offender rehabilitation was replaced with punitive policies stressing deterrence and an increased focus on the "problem" of racial minorities; (3) the 1970s-present which demonstrates the declining discussion of race in print media and legislative debates even as its effects in sentencing and prosecution grow in strength. This dissertation illuminates the ways in which insecurity and panic breed violence and expounds upon that notion to specify that how the violence manifests itself, whether through punitive policies or interpersonal crime, depends on the resources available and the historically-situated social norms. Over time, however, the explicit racial hostility in rhetoric and policy has been replaced with an evasion the recognition that race undoubtedly affects both juvenile justice policies as well as their implementation. In order to combat the inevitable instability that accompanies historical change, a resurgence of dialogue acknowledging the connection between race and juvenile justice is urged.

Introduction: Police brutality is a form of violence that negatively impacts health globally. Illegal drug use increases the likelihood that substance users will meet police globally. Methamphetamine use trends in South Africa have been increasing since the early 2000s. In this paper, secondary data analysis was done to examine predictors of methamphetamine users experiencing police brutality. Methods: Respondent driven sampling was used to recruit a sample of 360 active methamphetamine users in Delft, Cape Town, South Africa. Clinical interviews and computerized interviews were used to capture historical information about demographics, drug use, experiences of police violence, attitudes towards police, gang involvement, and arrest records. Logistic regression models were used to determine predictors of experiences of police brutality by sex. Results: The sample had a total of 202 males and 160 females, experiences of police violence were 53% and 14%, respectively by sex. 94% of participants reported prior arrests. Of those arrests, 48% and 39% were gang and drug related, respectively. Both male and female participants were more likely to experience police violence when reported weapon-related (OR 2.988, 95% CI=1.543-5.787) and drug-related (OR 1.796; 95% CI=1.140-2.829). Discussion: The analysis presented here adds to the current data about predictors of experiencing police violence. Further research should be conducted to determine if these results are generalizable to populations of substance users outside of the Delft township. The creation of policing policies based on research driven interventions need to be drafted and implemented to decrease the negative effects that abuses of police power have on public health.

*Designated as an exemplary master's project for 2015-16*

Longitudinal surveys have revolutionized empirical research and our understanding of the dynamic processes that affect the economic prosperity, health and well-being of the population. This dissertation explores and provides evidence, through three empirical applications, on the costs and benefits of designing, implementing and using data from a new, innovative longitudinal survey, the Mexican Family Life Survey (MxFLS). The survey, which is representative of the Mexican population living in Mexico in 2002, is designed to follow movers within Mexico and also those who move to the United States. This design lies at the center of the contributions of my research to the scientific literature.

Attrition is the Achilles heel of longitudinal surveys. The first essay of the dissertation focuses on the cost of attrition for scientific knowledge. Following the same individual through time allows a researcher to trace the evolution of a respondent's behaviors and outcomes in a dynamic framework; however, if attrition is selected on unobserved characteristics, the advantage of using panel data could be severely hindered. Exploring different methods to adjust for attrition, this essay provides evidence of limitations of standard post-survey adjustments strategies that are the standard in the literature. These approaches, exploit only baseline characteristics of the respondents and, conditional on those characteristics, treat attriters as missing at random. I provide evidence that this assumption is substantively important and rejected in the MxFLS in spite of the fact that attrition in that survey is low relative to other nationally-representative surveys conducted in the United States and abroad.

The second essay in this dissertation exploits the fact that MxFLS follows movers within Mexico and those who move across the Mexico-US border to provide new insights into the mechanisms that underlie the selectivity of migrants within Mexico, how they differ from migrants who move from Mexico to the U.S. and how those who return contrast with the migrants who remain in the U.S. more permanently. The results provide evidence that human capital is predictive of migration within Mexico and to the United States, but that there is little indication that the decision to stay in the United States is highly correlated with education. In contrast, having relatives in the United States is not only a powerful predictor of migration to the United States, but it is also predictive of successful economic assimilation.

The third essay exploits a different dimension of the longitudinal survey in order to address an important question regarding the impact of unanticipated crime and violence on population well-being. To wit, the essay rigorously examines the impact of the recent surge in violent crime in Mexico on the labor market outcomes, migration, and wealth of the Mexican population. The timing of the last two waves of the MxFLS paired with the panel nature of the survey, allows the comparison of outcomes of the same individual in periods of low and high violence, which removes the potentially endogenous time-invariant unobserved heterogeneity between respondents. Moreover, due to the fact that the MxFLS was designed to follow migrant respondents, this study is able to directly test whether there is a systematic migratory response to crime. The results from this analysis find that crime predicts migration and it negatively affects the labor outcomes of self-employed individuals. In addition, the negative effects on the labor outcomes have translated into reductions in per capita expenditure at the household level, which suggests that the recent wave of violence in Mexico may have long-term consequences on the wealth and well-being of Mexican households.

Firearms regulation rarely passes in the United States due to the strength of the gun rights lobby. However, in the past several years, policymakers in traditionally pro-gun states have passed laws that restrict domestic abusers’ access to firearms. The success of these policies suggests that domestic violence and firearms regulations may represent a rare opportunity for bipartisan agreement in this contentious policy area. This dissertation examines domestic violence related firearms regulations from three angles. The first chapter presents a qualitative comparative case study analysis of domestic violence and firearms legislation to understand how these policies overcame robust barriers to passage. The results demonstrate that the prominence of domestic violence prevention advocates and “strategic absence” of larger gun control groups in the policy process increased the probability of the legislation’s passage. The second chapter leverages an original survey experiment involving 1,000 participants in the Cooperative Congressional Election Survey (CCES) to test whether framing gun regulation as domestic violence prevention instead of gun control can increase support for the legislation. While I find that framing does not impact respondents’ support for a new law, women are more likely to support the policy than men. Importantly, this result remains significant after controlling for political party, suggesting that women’s collective action could bring about bipartisan cooperation on some gun regulations. The third and final chapter demonstrates the importance of preemptive gun regulations for reducing intimate partner homicide through an analysis of homicide data from the North Carolina Violent Death Reporting System. Together, this collection of studies sheds light on the politics and design of domestic violence and firearms regulation and creates a foundation for future research in this important policy area.