research on economic crimes

Vulnerability of individuals to economic crime and the role of financial literacy in its prevention: Evidence from India

• Published: 23 January 2024

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• Naveen Sirohi 1 &
• Gaurav Misra   ORCID: orcid.org/0000-0001-9371-4173 2  

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Economic crime has been an area of concern for regulators and governments across countries, and billions of hard-earned money is siphoned off by fraudsters yearly. Financial service consumers' lives are made easier with the new technological developments in fintech. However, with the growing participation of individuals in formalised channels of banking and finance, through greater emphasis on financial inclusion, there is also a massive surge in the victimisation of individuals through investment-related scams and digital financial frauds. Using nationally representative data, the present study looks into the significant demographic and socioeconomic factors influencing the likelihood of individual victimisation through investment scams and digital frauds. It further explores how financial literacy helps reduce the odds of victimisation in both cases. The study finds that in case of fraudulent investment scheme victimisation, males belonging to the 40–59 age group who have studied graduation or post-graduation (including professionals) and belong to middle-income groups are more vulnerable to victimisation than other groups. Similarly, in the case of digital financial fraud victimisation, males below the age group of 60 years who have studied at least high school or more, belonging to middle-income groups and lower socioeconomic classes, have higher odds of victimisation than other groups. The study also finds that the odds of victimisation of individuals through digital fraud are higher than investment-related fraud. It further suggests that financial education significantly reduces the odds of victimisation in both types of economic crime. The study concludes with policy recommendations to governments and regulators based on the findings.

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This study is based on a pan-India survey conducted by the Indian Institute of Corporate Affairs (IICA), a leading think tank of the Government of India. The survey was conducted in 2018 and covered 11,234 individuals across 20 tier-II cities in 20 states across India. The survey was titled "India Assessment of Financial Capability 2018" under the aegis of the Investor Education and Protection Fund Authority of India (IEPFA), a statutory body of the Government of India with the primary mandate of investor education and protection. The detailed report of the survey and the methodology/findings may be accessed at https://iica.nic.in/sof_research.aspx under the tab “India’s First Financial Capability Survey of Investors (2018)”.

Data Availability

The detailed report of the survey and the methodology/findings may be accessed at https://iica.nic.in/sof_research.aspx under the tab “ India’s First Financial Capability Survey of Investors (2018)”.

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Sirohi, N., Misra, G. Vulnerability of individuals to economic crime and the role of financial literacy in its prevention: Evidence from India. Crime Law Soc Change (2024). https://doi.org/10.1007/s10611-024-10138-w

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Data Science perspectives on Economic Crime

Economic crimes including corruption, fraud, collusion, and tax evasion impose significant costs to societies all around the world. Beyond their direct economic costs, these behaviors reduce mutual trust and cohesion in society. The erosion of these fundamental elements of a healthy society is hypothesized to contribute to growing inequality and the strengthening of political populism. Altogether there are significant incentives to study economic crimes. However, until recently, its investigation remained largely the preserve of law enforcement, which has resources to investigate only a tiny minority of cases.

Researchers now have more data than ever to investigate these phenomena, but face several unique challenges. The lack of unbiased ground-truth data hinders the straightforward application of machine learning. Publicly available data often contains only suggestive traces of illegal activity. Though economic crimes are increasingly international, data availability and quality varies highly across borders. Despite these difficulties, recent years have witnessed a remarkable increase in scientific activity in this area. Studying economic crime from a data science perspective offers unique insights and can inform the design of novel solutions. The results of such research are of eminent interest to governments, law enforcement, organizations, companies and civil society watchdogs. In light of this recent activity, there is a need to survey the field, to reflect on progress, shortcomings, and open problems, and to highlight promising new methods.

In this special issue, we gather research that highlights novel applications of data science to the problems and challenges of economic crime. We also welcome data-critical studies and mixed-methods papers, recognizing that data-driven methods complement rather than substitute for other approaches.

Topics of interest include, but are not limited to: - Estimating levels and trends of economic crimes using open source data - Corruption in public procurement - Collusion and cartels - Network science perspectives on economic crime - Agent-based models of economic crime - Detecting fraud in transaction data - Critical perspectives on the application of data science to economic crime (i.e. pitfalls and biases of predictive policing and profiling) - Data-driven analyses of organized crime and mafia-type groups - Tax evasion and money laundering - Terrorist financing - The political organization of economic crimes - Lobbying networks and political favoritism - Social and communication networks of criminal conspiracies - Transactions on the darknet and the role of crypto-currency in economic crime - Data-driven journalism and OSINT perspectives on economic crime - Novel datasets for measuring and tracking economic crime - Mixed-methods approaches to studying economic crime

Lead Guest Editor

Johannes Wachs Vienna University of Economics and Business, & Complexity Science Hub Vienna, Austria

Guest Editors

Janos Kertesz Department of Network and Data Science, Central European University, Vienna, Austria

Mihaly Fazekas School of Public Policy, Central European University, Vienna, Austria

Elizabeth David-Barrett Department of Politics/Centre for the Study of Corruption, University of Sussex, UK

Uncovering the size of the illegal corporate service provider industry in the Netherlands: a network approach

Economic crimes such as money laundering, terrorism financing, tax evasion or corruption almost invariably involve the use of a corporate entity. Such entities are regularly incorporated and managed by corpora...

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Practices of public procurement and the risk of corrupt behavior before and after the government transition in México

Corruption has a significant impact on economic growth, democracy, and inequality. It has sever consequences at the human level. Public procurement, where public resources are used to purchase goods or service...

Corruption red flags in public procurement: new evidence from Italian calls for tenders

This paper contributes to the analysis of quantitative indicators (i.e., red flags or screens ) to detect corruption in public procurement. It presents an approach to evaluate corruption risk in public tenders thr...

The effect of anti-money laundering policies: an empirical network analysis

There is a growing literature analyzing money laundering and the policies to fight it, but the overall effectiveness of anti-money laundering policies is still unclear. This paper investigates whether anti-mon...

Fair automated assessment of noncompliance in cargo ship networks

Cargo ships navigating global waters are required to be sufficiently safe and compliant with international treaties. Governmental inspectorates currently assess in a rule-based manner whether a ship is potenti...

Effective strategies for targeted attacks to the network of Cosa Nostra affiliates

Network dismantling has recently gained interest in the fields of intelligence agencies, anti-corruption analysts and criminal investigators due to its efficiency in disrupting the activity of malicious agents...

Tainted ties: the structure and dynamics of corruption networks extracted from deferred prosecution agreements

Corruption, bribery, and white-collar crime are inherently relational phenomena as actors involved in them exchange information, resources, and favours. These exchanges give rise to a network in which the acto...

The geometry of suspicious money laundering activities in financial networks

Corruption and organized crime are social problems that affect different communities around the world, involving public and private organizations in diverse sectors and activities. However, these problems are ...

What Do Recent Studies Say About Crime and Policing? Part 1

Crime plays a major role in the well-being of a community. It can influence where people live, where businesses locate and how local economies perform. Identifying causal effects, however, is difficult for a number of reasons. Still, a considerable amount of recent economic literature examines many aspects of crime. Part one of our Economic Brief series focuses on the impact of crime on communities, the relationship between crime and policing and how policing affects deterrence.

One of our goals as a Research Department in a Regional Fed is to better understand and offer constructive solutions to issues that our District's residents face. This is especially important for the economic vitality of both individuals and our communities.

A force that looms large is crime. At a high level, crime and policing matter for where people live, how local economies perform, how healthy cities are and what differences exist between rural and urban areas in our District and beyond. In this two-part Economic Brief , we review recent literature that examines crime, policing and community well-being as well as connections among the three. In this first part, we focus on four areas:

• Measuring crime
• Crime control policies
• The relationship between crime and policing
• The deterrence effects of policing

Measuring Crime

Before we begin our review of the literature, it's important to discuss how crime is measured, as this can impact studies and their results. When gauging crime, most studies rely primarily on crime measurements provided by local police departments as part of the FBI's Uniform Crime Reporting (UCR) Program . This reliance is understandable. These data have been compiled and categorized for decades (the UCR program started in 1930), and this information is readily available at the local level. 1 The data, however, are subject to well-known limitations. 2

One such limitation is that crime data reliably measure crime activity only as long as resident and police reporting behaviors remain consistent over time. If reporting changes over time, crime statistics may not consistently reflect crime levels and would require supplemental information to provide more accurate measures of criminal activity and of policing effectiveness.

Data on crimes reported by victims — separate from police reports — are one supplemental source. The National Crime Victimization Survey (NCVS) is an annual, nationally representative survey that provides information on:

• Nonfatal personal crimes, such as rape/sexual assault, robbery, aggravated/simple assault and personal larceny
• Household property crimes, such as burglary/trespassing for the purpose of shoplifting, and motor vehicle and other theft

The NCVS also asks if these crimes were reported to the police. However, a shortcoming of these data is that they are not representative at the local level.

Recently, several law enforcement agencies have made their crime statistics openly available through online portals as part of the Police Data Initiative (PDI) . While the UCR not only gathers data from various agencies, but also attempts to make them uniform and comparable, data reporting through the PDI follows the submitting agency's own criteria and includes more data on a wider variety of crimes than the UCR. Currently, 130 U.S. police agencies participate.

These data may be used along with officially reported crime statistics to identify changes in reporting behavior as well as police behavior: The number of crimes that the police themselves notice and report may serve as a proxy of police engagement (or "effort") and may not closely align with police numbers.

Crime Control Policies and Community Well-Being

Crime limits the ability of cities and neighborhoods to prosper by negatively affecting the quality of life of residents, causing people to leave the city and generating social and economic decline. Understanding the effectiveness of crime control policies is, therefore, key for communities.

The impact of crime on neighborhoods has been heavily researched. The literature shows that crime (especially violent crime) negatively affects neighborhood growth, increases racial segregation and lowers housing prices. 3

Furthermore, affluent residents are very sensitive to crime: They are willing to pay (relatively) more for housing at locations where this problem is less severe. Due to generally high crime rates in central cities, higher-income households tend to locate in the suburbs, as seen in a 1979 paper by William Frey and a 1999 paper by Julie Berry Cullen and Steven Levitt . And a 2005 paper by Arthur O'Sullivan noted that income segregation occurs to the extent that high-income residents are willing to pay more to reside at places with lower crime rates.

At the same time, an unsafe and more dangerous local environment can have significant impact on residents' incomes and opportunities as well. A substantial amount of research suggests that neighborhoods and the social interactions within them influence individuals' economic opportunities later in life. Growing up in neighborhoods with high levels of violent crime affects individuals' criminal behavior and reduces upward mobility, as noted in a 2018 paper by Raj Chetty and Nathaniel Hendren and a 2017 paper by Patrick Sharkey and Gerard Torrats-Espinosa . Peer effects may also reinforce income and racial segregation, leading to further deterioration of the local environment.

Crime also has a significant impact on where businesses operate, which can directly affect neighborhood opportunities and well-being. For example, a 2010 paper by Stuart Rosenthal and Amanda Ross showed that retailers and entrepreneurs choose nonviolent environments when deciding where to establish their operations.

Relationship Between Crime and Policing

With crime having so many impacts on community well-being, a natural question may be: To what extent can police reduce crime? Several papers attempt to quantitatively assess how changes in police force size affect crime rates. Identifying such causal effect is, however, extremely challenging for several reasons:

Measurement Error

In general, the number of police and crime are not measured accurately, as noted in a 2018 paper by Aaron Chalfin and Justin McCrary . Moreover, reporting and recording different types of crime are likely affected by the number of active police officers. For example, changes in the size of the police force may affect the police department's propensity to record victim crime. Examples include not having enough police officer staffing to take statements from victims and fully document crime for reporting purposes. 4

Police Intensity

Changes in the intensity of policing are generally not random: Places with more crime tend to have more police, almost certainly in part as a response aimed at mitigating crime.

Deterrence Versus Incapacitation

Criminal justice policies reduce crime through both deterrence and incapacitation:

• Deterrence implies that a policy change stops individuals from committing crimes they would have otherwise.
• Incapacitation takes place when offenders are removed from broader society (pretrial detention or incarceration).

More police could act as a deterrent, and since police make arrests during their regular activities, more police could also lead to higher levels of incarceration. Distinguishing between these two effects associated with policing is challenging.

Probability of Arrest

Actual and perceived risks of committing a crime matter, especially when using the number of police to measure deterrence. If the number of police in a community aligns with the number that people think are in the community, then using the number of police to measure the effect of size on deterrence is straightforward. However, if an increase in police numbers doesn't dissuade potential offenders, this approach would not offer sensible conclusions.

Reporting Bias

Studies that use reported crime data to evaluate the effectiveness of policing policies may provide biased results if the reporting and recording of crime is also affected by the policies. A larger police force may encourage residents to report more crimes if residents believe crimes are more likely to be solved. If such bias is present, reported crime rates may increase with the size of the police force, even if the true victimization rate is falling.

Displacing Crime to Other Areas

More police in one area may simply displace crime to other areas. The impact of an increase in police size would therefore depend on the size of the area considered in the analysis and whether it accounts for potential displacement.

Deterrence Effects of Policing

After controlling for most of these issues, the economics literature supports the view that a larger police force generally reduces the index level of crime. The effect seems to be larger for violent crime (especially murder) than for property crime. A 1998 paper by Levitt suggests that the deterrent effect of policing — as opposed to incarceration — appears to be the relatively more important factor, mostly for property crime. Other work that relies on exogenous shocks to police presence finds similar results. 5

Some studies suggest that investments in police may both deter crime and reduce incarceration. Investment in police could therefore be a relatively more efficient means of crime control. A 2019 paper by Chalfin and Jacob Kaplan , for instance, finds that investments in law enforcement are unlikely to markedly increase state prison populations and may even lead to a modest decrease in the number of state prisoners. In other words, the deterrent effects of policing are strong enough to reduce crime, and a lower number of criminals in turn imply less people going to prison.

Certain types of crime — such as drug dealing and shootings — can be highly concentrated in very small areas of cities. "Hot-spot" police tactics — which include a substantial increase in police visibility in those zones — have been shown to reduce crime in those areas, and there is little evidence of a spatial "relocation" or displacement of crime.

Upcoming: Race, Policies and Policing

While these studies generally show that an increase in police results in favorable outcomes, many other factors come into play that could affect these results. One that looms especially large in light of recent events is race. In the second part of this series, we'll examine what the literature says about race in terms of policing and crime reporting. We'll also review studies that have examined various efforts and policies aimed at addressing issues in policing.

Ray Owens and Santiago Pinto are senior economists and policy advisors in the Research Department at the Federal Reserve Bank of Richmond.

Law enforcement agencies representing roughly 95 percent of U.S. population participate in the program.

The annual crime indexes reported by UCR include crimes reported and known to the police, but does not include, for instance, crimes at jails or prisons. Also, these crimes have been recorded and tabulated by local police agencies, allowing them considerable discretion at this stage.

Several papers shows these findings, including a 2001 paper by Edward Glaeser, Jed Kolko and Albert Saiz , a 2010 paper by Keith Ihlanfeldt and Tom Mayock , a 2011 paper by Kelly Bishop and Alvin Murphy , a chapter by Vania Ceccato and Mats Wilhelmsson (PDF) in a 2011 book, a 2012 paper by Devin Pope and Jaren Pope , a 2013 paper by Paolo Buonanno, Daniel Montolio and Josep Maria Raya-Vílchez , a 2013 paper by Joshua Congdon-Hohman and a 2016 paper by Patrick Bayer, Robert McMillan, Alvin Murphy and Christopher Timmins .

A few studies (for instance, a 2012 paper by Ben Vollaard and Joseph Hamed ) conclude that the impact of measurement error in police statistics is large and cannot be ignored when assessing the impact of police size on crime.

This work includes a 2004 paper by Rafael Di Tella and Ernesto Schargrodsky (which uses a 1994 terrorist attack on the main Jewish center in Buenos Aires, Argentina, as the exogenous shock), a 2005 paper by Jonathan Klick and Alexander Tabarrok (which measures the effects of changes in the terror alert levels on police presence in Washington, D.C.) and a 2019 paper by Steven Mello (which uses the increased funding for the Community Oriented Policing Services hiring grant program from the American Recovery and Reinvestment Act).

This article may be photocopied or reprinted in its entirety. Please credit the authors, source, and the Federal Reserve Bank of Richmond and include the italicized statement below.

V iews expressed in this article are those of the authors and not necessarily those of the Federal Reserve Bank of Richmond or the Federal Reserve System.

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The Impact of Economic Activity on Criminal Behavior: Evidence from the Fracking Boom

Executive Summary

According to economic theory, crime should decrease as economic growth and opportunity improve. That’s because the incentive to engage in illegal activity decreases as legal avenues of earning income become more fruitful. However, there are documented cases where economic growth has led to higher crime rates. By looking at administrative level data in North Dakota, before, during, and after an economic expansion we are able to understand the nature and characteristics of higher crime rates in newly prosperous areas.

Instead of focusing on overall crime rates, author Brittany Street focuses on crimes committed by residents who lived in the area before the economic expansion began. This separates local crime impacts from those created by migrant workers when measuring the effect of economic opportunity on individual criminal behavior.

Crime initially decreases during the leasing period

This study finds that there is an initial twenty-two percent reduction in the number of criminal cases filed in areas affected by the fracking boom. The effects were most significant and most consistent for drug-related crimes. This reduction happens during the leasing period when mineral leases are signed and large-scale drilling activities have yet to begin.

The study’s author suggests that there are two different potential explanations for why crime rates would decrease during this initial phase:

• As their economic prospects improve, residents may become more engaged with legal activities rather than illegal activities.
• An improved economic outlook may reduce the desire to engage in illegal activities, as the cost (either real or perceived) of apprehension increases compared to legal activity.

Some crime returns during the production period

The author also finds that the initial reduction of crime among residents diminishes once drilling activities begin and production ramps up. Aggregate crime also increases during this period, which is consistent with the findings of other studies examining overall crime rate changes in relation to fracking booms. The author gives two potential reasons for this increase in crime: demographic changes or an increase in the number of bars and illegal markets. As the job market improves in an area, new people move in seeking better job opportunities. The in-migration of young men in particular—a historically crime-prone group—may contribute to an overall increase in crime.

The key policy implication of this work is that the fracking boom and accompanying economic growth in North Dakota led to a significant reduction in the amount of crime being committed by long-term local residents. Increases in crime rates in fracking areas are a potential symptom of migratory patterns that accompany economic booms, not the direct result of fracking itself. As a result, policymakers concerned about increasing crime related to economic growth should seek to understand not just aggregate impacts, but also impacts across various demographics. Understanding those differences may help in crafting more effective public policies.

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• North Dakota

Introduction

Since Becker (1968), crime has been viewed as the outcome of rational individuals weighing costs and benefits of legal and illegal forms of employment. Thus, if individuals face improved labor markets, the returns to legal activity increase and individuals should substitute away from illegal activities. Yet, local economic booms are often associated with increases in crime (Grinols and Mustard, 2006; Freedman and Owens, 2016; James and Smith, 2017). Several theories can rationalize this phenomenon including increases in criminal opportunities, access to disposable income for activities that complement crime, and population changes. However, the extent to which each of these theories explains this puzzle is unclear, especially since changes in crime are typically observed at an aggregate level.

The purpose of this paper is to address this puzzle by estimating the effect of local economic opportunity on the criminal behavior of residents who already lived in the area prior to the economic boom. By focusing on the criminal behavior of existing residents, I disentangle the effect of economic opportunity from the effect of the compositional changes in the population caused by in-migration during the boom. This is important, because people tend to leave as labor market conditions worsen and migrate to areas during economic expansions. I use the recent boom in hydraulic fracturing in North Dakota as a large, exogenous shock to an individual’s relative returns to legal versus illegal behavior. This approach, combined with the focus on the behavior of residents already living there prior to the start of hydraulic fracturing, enables me to identify the effect of economic opportunity on individual criminal behavior.

I identify effects using a difference-in-differences framework, comparing counties located in the shale play, a geological area with oil and natural gas, to counties not located in the shale play over time. Importantly, I measure the impact on residents, separating out migration effects, by using information on local residents prior to the economic shock. The sharp increase in hydraulic fracturing activity in the United States is an ideal economic shock for several reasons. First, areas were affected based on the formation of the shale play beneath the Earth’s surface. Second, the shock was largely unforeseen, as fracking suddenly became a viable method due to a combination of technological innovations (Wang and Krupnick, 2015; Crooks, 2015). Together, these support the assumption that fracking affected local labor markets for reasons unrelated to prior local conditions and household behaviors, overcoming common critiques of the difference-in-differences research design. 1 For example, see Besley and Case (2000) for discussion about policy endogeneity in difference-in-differences frameworks. Third, hydraulic fracturing was large enough to affect the entire local economy in many areas. Finally, the shock affected predominately low-skill jobs, a population of policy interest.

Studying the effects at the individual level requires detailed data on hydraulic fracturing activities, criminal behavior, and local residents in North Dakota. 2 North Dakota is well suited for this analysis as it was the third-slowest-growing state in 2000, and increased its real gross domestic product 115% by the end of the fracking boom in 2016 (U.S. Bureau of Economic Analysis, 2018). Also, it is the second largest crude oil producing state in the United States. I obtained detailed administrative data on the universe of criminal cases filed in the state from 2000 to 2017. I identify residents in each county from printed directories in the early 2000s before the in-migration associated with production activities. I also observe which residents signed a mineral lease and received royalty payments during this period. This enables me to not only identify the effect of improved labor market opportunities, but also isolate differential effects on residents who received large, non-labor income shocks and those who did not. Matching these datasets makes it possible to study the effect of local economic shocks on the criminal behavior of local residents. This is an important advantage given the large migration effects that have been documented in response to economic conditions in general, and to fracking in particular (Wilson, 2017).

Results indicate that the start of the economic expansion — defined as the period when companies began leasing mineral rights and investing in the area — led to a statistically significant 0.44 percentage point (22%) reduction in criminal behavior by local residents. The effects are largest for drug-related crimes, though I also see some less precisely estimated declines in other crimes. The effect after production began is smaller and not consistently statistically significant, with a 0.27 percentage point decrease in the likelihood of committing a crime. This suggests that changes during the production period, such as increased income or changes in peer composition, offset some of the effect of improved job opportunities. These results do not appear to be driven by changes in the police force, addressing concerns about detection and deterrence.

In addition, I exploit variation in mineral rights ownership and royalty income to assess the extent to which the effects are driven by labor market opportunities versus non-labor income shocks. Results indicate that the reduction in crime seems to be driven by non-leaseholders. This is consistent with the hypothesis that those not receiving income through alternative means are more responsive to increased job opportunities. However, I note that the effect sizes are not statistically distinguishable.

To my knowledge, this is the first paper to identify effects of economic shocks on individuals’ criminal behavior separate from the effect of migration. In doing so, it contributes to two bodies of literature. First, it contributes to the literature showing how aggregate crime changes in response to plausibly exogenous shocks to economic conditions (e.g., Dix-Carneiro, Soares and Ulyssea, forthcoming; Axbard, 2016; Grinols and Mustard, 2006; Gould, Weinberg and Mustard, 2002; Raphael and Winter-Ebmer, 2001; Evans and Topoleski, 2002; Montolio, 2018; Grieco, 2017). These studies generally show aggregate crime is inversely related with economic conditions, with some exceptions.

In particular, this paper complements a subset of this literature that has documented the role of criminal opportunity and income inequality in explaining the observed increases in aggregate crime that arise during economic expansions (e.g., Mejia and Restrepo, 2016; Cook, 1986). For example, Freedman and Owens (2016) study the effect of BRAC funding in San Antonio on crime using individual-level data. They find an increase in property-related crime in neighborhoods with a high composition of construction workers, those most likely to benefit from the economic shock. They also find that crime is more likely to be committed by individuals with a prior criminal record, who are unable to be employed by the project. In a similar way, this paper documents that once one accounts for population changes that accompany economic expansions, one observes the expected relationship between improved job opportunity and individual crime. Together, the findings of those papers and this paper suggest that both criminal opportunity and shifts in population can explain the puzzling finding that aggregate crime often rises during economic expansions.

Second, this study contributes to the growing literature on the effects of fracking, which has transformed many regions in the United States. Specifically, crime has generally been shown to increase in areas with fracking activities (James and Smith, 2017; Andrews and Deza, 2018; Komarek, 2017; Bartik, Currie, Greenstone and Knittel, 2016). 3 Alternatively, Feyrer, Mansur and Sacerdote (2017) do not find statistically significant evidence of an increase in crime across all counties with fracking. However, the increase could be driven by changes in the population of workers moving to the area or an individual’s response to the changing economic conditions. I measure a similar increase in aggregate cases filed in fracking counties, but find that local residents in the county are actually less likely to commit crime when exposed to relatively stronger labor market conditions. This is consistent with predictions of the economic theory of crime when the returns to legal employment increase, and indicates that fracking has reduced individuals’ propensity to commit crime. 4 This is also consistent with empirical evidence documenting a similar inverse relationship between recidivism and economic conditions (e.g., Agan and Makowsky, 2018; Yang, 2017; Galbiati, Ouss and Philippe, 2018; Schnepel, 2017) and in the fracking context specifically (Eren and Owens, 2019), as well as increased lifetime criminal behavior for cohorts graduating high school in harsher economic conditions (Bell, Bindler and Machin, forthcoming)

Finally, while the primary purpose of this study is to examine the impact of economic expansions on the criminal behavior of local residents, this study’s findings on aggregate crime also speak to the literature on (im)migration and crime. Immigration to the United States and Western Europe typically increases in response to improved relative economic opportunity in those countries. Many worry that the immigration to high-income countries could increase crime rates, though some recent empirical evidence suggests this fear may be misplaced (Bell, Fasani and Machin, 2013; Chalfin, 2015; Spenkuch, 2013; Miles and Cox, 2014; Butcher and Piehl, 2007). 5 While the overall evidence on this question is mixed, Bell, Fasani and Machin (2013) finds no effect on violent crime and mixed effects on property crime, Chalfin (2015) shows an increase in aggravated assaults, but decreases in other crimes, and Spenkuch (2013) reports small increases in crime, particularly financial crime. Relatedly, Miles and Cox (2014) finds no effect of a deportation policy on local crime. Moreover, Butcher and Piehl (2007) shows that immigrants typically have lower crime rates than do native-born residents potentially due to a combination of heavy screening of would-be migrants, and self-selection of those migrants. Results on aggregate crime presented here indicate that the migration of mostly young, American men does lead to increased crime overall. Thus, changing the composition of a local population can be an important driver of criminal activity, although the effects may depend heavily on who the migrants are. Since young men are a particularly crime-prone population, economic booms that attract this group may be more likely to lead to higher crime rates.

Advances in hydraulic fracturing contributed greatly to the recent oil boom in the United States. From 2000 to 2015, oil produced from fractured wells increased from 2% to over 50% of domestic production, increasing total oil production faster than at any other point in time (Energy Information Administration, 2016). Fracking suddenly became more profitable due to a breakthrough in directional drilling, hydraulic fracturing technologies, and seismic imaging (Wang and Krupnick, 2015; Crooks, 2015). Hydraulic fracturing involves injecting fluids at a high pressure into a shale play in order to crack the rock formation and extract tight oil and shale gas. 6 The Energy Information Administration defines a shale play as a “fine-grained sedimentary rock that forms when silt and clay-size mineral particles are compacted, and it is easily broken into thin, parallel layers. Black shale contains organic material that can generate oil and natural gas, which is trapped within the rock’s pores” (2018). I focus on oil production as North Dakota’s production is typically only 10-20% gas, with the rest being oil. This process allowed mineral resources to be extracted from shale plays that were previously not economically viable.

One such area is the Bakken formation in North Dakota. It is smaller only than the Permian and Eagle Ford formations in Texas in crude oil production. Figure 1 shows the 17 counties that produce oil and gas in North Dakota, each classified by production levels as either a core (major) or balance (minor) county. Four counties make up the major fracking counties producing 80% of North Dakota’s oil from 2000–2017, with the remaining 13 producing 20%.

Figure 1. Fracking counties in North Dakota

Source: Labor Market Information Center, Job Service North Dakota

Companies leased the mineral rights required for production from individuals or agencies in exchange for a portion of total revenue. Figure 2a plots the number of leases signed by households in North Dakota each year from 2000 to 2017. It is clear that lease signing first spiked in 2004 signaling when companies first began investing in hydraulic fracturing in North Dakota. Similarly, Figure 2b graphs total oil production in North Dakota showing that production lagged leasing by a few years, starting to increase in 2008. From 2008 to 2017, North Dakota produced oil valued at an estimated $2,904,191 million. 7 This estimate is calculated based on total monthly oil production in North Dakota (Department of Mineral Resources, 2018) and the monthly North Dakota oil first purchase price (Energy Information Administration, 2018a).

Figure 2. Leasing and production

Notes: All leases in North Dakota are collected from Drilling Info for 2000–2017. Only leases matched to rural residents in the early 2000s are depicted in the figure above, as this is the sample of leases used in the analysis. Monthly county production data are from North Dakota Department of Mineral Resources.

Perhaps unsurprisingly, the presence of hydraulic fracturing activities has had a substantial impact on local labor markets. Feyrer, Mansur and Sacerdote (2017) estimate that every one million dollars of new production generates 0.85 jobs and $80,000 in wages in counties with a shale play across the United States. 8 Other papers estimating increases in wages and employment from fracking activities include Bartik, Currie, Greenstone and Knittel (2016); Allcott and Keniston (2017); Fetzer (2014); Maniloff and Mastromonaco (2014); Weber (2014) and Gittings and Roach (2018) to name a few. Similarly, in response to the stronger labor markets, Wilson (2017) estimates that the in-migration of workers increased the baseline population in fracking counties by 12% on average in North Dakota. Additionally, individuals who also owned mineral rights received 10–20% of production revenues through royalty payments. As I show in the next section, I estimate that the average leaseholder earned a royalty of $7,500 per month, which is a substantial non-labor income shock.

Figure 3 shows that prior to the fracking boom, counties in North Dakota were relatively similar in terms of per capita income, total jobs, population, and total police officers. 9 County-level data on income and total jobs are obtained from the Bureau of Economic Analysis. Population is calculated using the number of migrant and non-migrant tax exemptions from the Internal Revenue Service. The number of personal exemptions provides a year to year estimate of the population for counties based on the address listed on an individual’s income tax return. Police employment data are from the Uniform Crime Reporting Program: Police Employee (LEOKA) data. The leasing period, when residents first knew of the fracking boom, was characterized by slight increases in per capita income, total jobs, and population (2004 to 2008). Oil production began ramping up in 2008 and is the more labor-intensive period. This is when companies began offering high paying jobs and moving in a large number of workers, often into camps due to housing shortages. It is also the period when the majority of households that had signed a lease received royalty payments and increases in overall crime were reported. This is reflected in the data, as Figure 3 shows fracking counties experienced large increases in income, jobs, population, and police officers during the post-2008 production period. While the economic opportunities continued through this period, counties changed in several other ways as well. As a result, in my analysis I will estimate the effect of expected economic opportunity that occurs after signing but before drilling, as well as the effect of drilling. I expect the former will pick up the effect of job opportunities both expected and realized, while the latter will measure the effect of job opportunities along with large population and income changes.

Figure 3. County demographics by fracking region

Notes: Data on income and jobs are from Bureau of Economic Analysis. Population is calculated using the number of migrant and non-migrant tax exemptions from the Internal Revenue Service. Police employment data are from the Uniform Crime Reporting Program: Police Employee (LEOKA) Data.

Economic theory predicts that the labor market changes from fracking activities may affect crime in several ways. First, the additional jobs and higher wages should induce individuals to substitute away from illegal activities now that the returns to legal activities are higher. Alternatively, the large cash transfers—via royalty payments—to some households may lead to more crime through increased income inequality and opportunity of crime. Additionally, the increased income through either royalties or higher wages could affect crime by easing financial constraints or providing more disposable income to consume goods that may complement crime (e.g. alcohol). Finally, the large migration effects observed in the production period are likely to affect crime both through population increases and compositional changes.

There are three main advantages of studying the effect of positive economic shocks on crime in this context. First, the sudden increase in hydraulic fracturing activities creates plausibly exogenous variation in exposure to improved labor market conditions. Second, I am able to distinguish the effect on crime by the existing population from aggregate effects which include individual changes in behavior as well as compositional changes. Specifically, I am able to focus my analysis on households already living in the area using directory files in each county to identify residents. Finally, I can study how these residents respond to changes in economic opportunity as well as the economic opportunity plus the subsequent influx of people and income by examining both the earlier leasing period and the more labor-intensive production period.

For this analysis, it is necessary to identify residents in years prior to the fracking boom to account for migration. To do this, I collected a list of all rural residents for each county in North Dakota prior to 2008 from the Great Plains Directory Service. 10 The Great Plains Directory Service obtains their information from property tax records reported by each county and reports who resides at the property for areas outside the city. Residents living within city limits are not included in the directories and thus are not considered in this analysis. 11 Notably, the directories are created for a county every few years and directories for all counties were not created in the same year. All counties are included except Cass, Grand Forks, Pembina, and Traill, which were not covered by the Great Plains Directory Service. Households listed in these directories represent roughly 20% of all households in North Dakota during this time. The directory information includes the name, address, and city of all rural residents. In total, there are 31,168 households defined by resident last name, street number, city, and zip code. I consider this to be the universe of households, which I match to lease and crime data using a Levenshtein Index. 12 I allow matches with a string distance of two or less. In practice, this means two strings are matched across datasets if there are only two changes that need to be made to the concatenated string of last name, street number, city, and zip code in order for them to be exact matches. In Table A.2, I show that main results are robust to this index.

One potential concern with identifying residents is that some people may have moved into fracking counties prior to the large in-migration associated with the production period. For example, strategic households may move in advance to have first access to housing or jobs. However, to be recorded in the resident directories, any movers would have to move into the rural areas. If this were the case, we would expect to see an increase in property sales prior to the production period. I show in Figure A.1 that property sales in fracking counties remain similar to sales in non-fracking counties throughout the leasing period. Thus, the residents in directory files are likely all long-time residents of the county.

A second concern is that household composition could be changing over time. Even if households are not moving into fracking counties, it is possible that some members of the household move in response to the local economic shock. Specifically, younger men may move either to or from a resident household address as jobs enter the county. In this case, if they officially change their address, their criminal behavior may be assigned to a household differentially during treatment periods. Thus, the results could be picking up a change in household composition, rather than a change in criminal behavior. If this were the case, I should not see the same effects when limiting the analysis to the criminal behavior of older, more stable household members. However, in Section 5.1, I show results are robust to this sample restriction, suggesting that it is changes in criminal behavior that are driving my results.

I identify which households also have a mineral lease by collecting all leases signed from 2000 to 2017 in North Dakota from Drilling Info, a private company designed to aid companies participating in all steps of mineral production. Data include name and address of the grantor, company listed as grantee, number of acres leased, royalty rate, and date of record. Production data at the county- and well-level are collected from the North Dakota Department of Mineral Resources. I use these datasets to approximate the amount of monthly oil production from a given well that is attributed to an individual leaseholder. This amount is dollarized using the North Dakota Crude Oil First Purchase Price to estimate the amount leaseholders receive in the form of royalty payments. 13 Each well in North Dakota is assigned a spacing unit which defines the area of land surrounding the well with rights to production. These boundaries are determined in court hearings at the request of the proposed well operator and based on the recommendations of geologists. By matching leaseholders to spacing units, I define the proportional interest in monthly production for each leaseholder based on acres leased. The dollar value is calculated using the monthly North Dakota Crude Oil First Purchase Price. I subtract $10/barrel to account for post production costs, namely, transportation. I deduct 10% for severance tax, since North Dakota collects 5% for gross production in lieu of property tax on mineral rights and 5% for oil extraction. Leaseholders then get a fraction depending on their negotiated royalty rate, typically 12–18%.

The State of North Dakota Judicial Branch provided restricted administrative data on all criminal cases filed in North Dakota from 2000 to 2017. Importantly, data contain identifying information including the name, date of birth, and address of individuals charged with a crime. This allows me to link to residential files and identify crime committed by local residents. I also observe the file date, specific charges filed, disposition of each charge, sentence received, and county of filing for every case.

There are two main advantages to using cases filed as a measure of criminal behavior. First, cases filed are considerably more serious than 911 calls or arrests, as an individual has officially been charged with a crime. As a result, charges filed are arguably a less noisy measure of criminality than the other possible alternatives. This is reflected by the fact that only 61% of all arrest charges in North Dakota were filed by the prosecutor’s office over the last five years. 14 This estimate is based on numbers produced by the North Dakota Attorney General’s Office, received September 2018 Additionally, the State of North Dakota specifically advises employers not to ask about prior arrests as “an arrest does not mean that someone actually committed a crime” North Dakota Department of Labor and Human Rights (2018). Second, since cases filed are recorded in an administrative database, they do not suffer from voluntary reporting practices or a lack of coverage, particularly in areas that are sparsely populated. Additionally, these data report information on all charges, including offenses which are often not tracked in other commonly used datasets, such as drug charges or driving while under the influence.

Summary statistics are shown in Table 1. Close to 20% of households are ever charged with a crime from 2000 to 2017 (Table 1, Panel A). The types of charges filed for this population, namely rural residents, are summarized in Panel C. The majority of crimes are misdemeanors (∼90%), with driving-, drug-, and property-related charges making up roughly 44%, 17%, and 17% of all charges, respectively. Smaller crime categories representing less than 10% of all charges, such as assault (4%), are grouped together in other charges. Of the households in my sample, roughly 20% sign a lease and may receive royalty payments during this period (Table 1, Panel A). Close to 40% of leaseholders in my sample actually receive payments during this period, with the average leaseholder receiving $7,500 per month. These royalty payments can be thought of as an additional treatment over the local economic shock, as some residents in fracking counties receive large, additional lump sums of money while others do not.

Table 1. Summary statistics

Methodology

Main analysis.

The unexpected rise in fracturing activities coupled with spatial variation in the shale play provide a plausibly exogenous shock to local economic conditions. Using a generalized difference-in-differences framework, I compare the criminal behavior of residents in counties within the shale play to residents in counties outside the shale play, before and after the fracking boom. 15 I also report aggregate county-level estimates of equation 1 in Figure A.4 for comparison. Given the timing of fracking activities and subsequent changes in affected counties, I consider the effects separately in each period: leasing (2004 to 2008) and production (2008 to 2017). Formally, I estimate the effects of local economic shocks on criminal behavior using the following linear probability model:

The assumption behind this approach is that, absent hydraulic fracturing activities, residents’ criminal behavior in fracking counties would have changed similarly over time with residents’ criminal behavior in non-fracking counties. I check this assumption in several ways. First, I provide visual evidence that treated and control counties are tracking prior to any treatment. Relatedly, I formally test for pre-divergence using the above regression model with an indicator for the treated group one year before treatment. Additionally, I allow counties to trend differently over time by including county-specific linear time trends. I also include interactions between pre-treatment controls and year effects. In doing this, I allow for counties with different levels of observable characteristics, such as per capita income, to respond differentially to year-to-year shocks.

In all models, robust standard errors are clustered at the county level, allowing errors to be correlated within a county over time. I also report permutation-based inference for the primary specification when considering all crime, similar in spirit to Abadie, Diamond and Hainmueller (2010) for inference when using the synthetic control method. To do this, I randomly assign treatment to 17 counties and compare the estimated coefficient to 1000 placebo estimates to compute two-sided p-values. In addition, I report Adjusted False Discovery Rate (FDR) Q-values when estimating effects separately by crime type (property, driving, drug, and other) following Anderson (2008). Adjusted FDR Q-values correct for the increased likelihood of rejecting the null hypothesis when making multiple comparisons, and are interpreted similar to p-values.

Given that some counties experience larger shocks than others, detected effects could be driven solely by counties with more extreme local shocks. However, it is beneficial to know if smaller economic shocks also affect criminal behavior. Therefore, I also consider heterogeneous effects by the amount of fracking activity experienced by a county. Specifically, I estimate the treatment effect for the four major oil and gas producing counties as defined by the Labor Market Information Center, namely Dunn, McKenzie, Mountrail, and Williams, separate from the effect in the thirteen minor fracking counties.

Effects by leaseholder status

Finally, I examine the potentially differential effects of fracking on leaseholders and non-leaseholders. As previously discussed, some households receive large sums of money in the form of royalty payments while others do not. This creates the potential for increased crime due to changes in both income inequality and criminal opportunities. I consider leaseholders and non-leaseholders within fracking counties as separate treated groups, comparing each of them to residents in non-fracking counties. To the extent that signing or not signing a lease and receiving royalty payments is also a form of treatment, this strategy separates the effect on the two groups living in fracking areas. Formally, I estimate the following regression model:

Main Results

I begin by estimating the overall effect of local economic shocks on crimes committed by residents. As noted above, I consider only the population of residents prior to the fracking boom in North Dakota. In doing so, I am able to exclude all crimes committed in the county by new workers who migrated to the relatively stronger labor markets. In this way, I can distinguish the effect of the economic shock from the impact of the changing demographics on overall crime rates.

First, I graph the estimated divergence over time in crimes committed by residents in fracking and non-fracking counties, relative to the difference between the two sets of counties in 2000 and 2001. Figure 4 plots the dynamic difference-in-differences estimates for all crimes, controlling for household and year fixed effects. Importantly, there is no evidence of divergence prior to the start of the fracking boom in 2004. This supports the identifying assumption that absent hydraulic fracturing activities, residents in fracking counties would have experienced similar changes in criminal behavior as residents not in fracking counties. Additionally, the figure indicates that the probability of being charged with a crime falls in fracking counties when leasing starts, then rises some during the production process. This suggests economic opportunity is reducing crime, but the effect seems to be offset at least somewhat by the indirect effects that accompany oil production. For example, the production period also includes interactions with new workers and increases in disposable income from royalty payments or high-paying drilling jobs. I report the average treatment effects for each period in Table 2.

Starting with the leasing period, Column 1 indicates an initial drop of 0.42 percentage points in overall crime by residents in fracking counties relative to residents in non-fracking counties. This translates to a 22% drop in cases filed and is statistically significant at the 1% level. Moreover, the permutation-based p-value is less that 1%, with 4 out of 1,000 placebo estimates less than -0.0042, shown graphically in Figure A.5. In Column 2, I formally test for pre-divergence and find no evidence of it, with the coefficient on the lead indicator being close to zero, -0.0008, and statistically insignificant. In Column 3, I allow for county-specific linear trends. This allows for both observable and unobservable county characteristics to change linearly over time. If results are driven by fracking counties being on a different path than non-fracking areas, then adding a county-specific linear trend should absorb the treatment effect. However, results indicate the coefficient increases slightly to -0.48 percentage points. Finally, counties with different baseline populations, total jobs, police officers, per capita income, and production may respond differentially to year-to-year shocks. For example, if fracking counties also tend to be smaller in population then detected effects could be a result of small counties differentially responding to yearly shocks. In Column 4, I allow these baseline characteristics, observed in 2000, to differentially affect criminal behavior each year. The magnitude remains stable at -0.50 percentage points. Notably, all coefficients are statistically significant at the 1% level, and the estimated effect is robust to the inclusion of various controls and a lead term.

Overall, estimates in Table 2 are consistent with Figure 4 in showing that while there is a significant drop in criminality initially, the drop is somewhat diminished in the production period. Column 1 indicates a 0.26 percentage point reduction in cases filed for residents in fracking counties compared to residents in non-fracking counties, although not statistically significant at conventional levels. The permutation-based p-value is marginally significant at 10.2%, with 102 of the 1000 placebo estimates less than or equal to the estimated coefficient. Moving across Columns 2 through 4, coefficients remain negative ranging from -0.18 to -0.43 percentage points. It appears as though the reduction in criminal behavior from the boost in economic activity may be at least somewhat offset by additional effects on criminal behavior during the production period. This could be due to the effects of in-migration, such as peer effects and increased social interaction (Glaeser, Sacerdote and Scheinkman, 1996; Ludwig and Kling, 2007; Bernasco, de Graaff, Rouwendal and Steenbeek, 2017), or to an increase in the number of bars and illegal markets. 17 This is graphically depicted in Figure A.3 with a large increase in the average total number of liquor licenses per county in counties with major fracking activity

Given that estimates are at the household level, a potential concern is that effects are being driven by changes in household composition rather than changes in criminal behavior. For example, the results could be driven by composition if young men, a more crime-prone demographic, were more likely to move out during the leasing period and then move back during the height of the production period in fracking counties. 18 Importantly, even if some household members move, they would have to officially change their address for their crime to no longer be attributed to the household. I examine this concern by restricting the sample to crime committed by household members that are older than 25 years at the start of my period in January 2000. The results for the older, more stable sample are shown in Figure A.2 and Table A.2. They are the same as the results in Figure 4 and Table 2, respectively, suggesting that it is changes in criminal behavior rather than household composition that are driving this pattern of results.

To better understand the type of crime affected by local economic shocks, I present treatment effects separately for financial-related crimes (e.g. theft, criminal mischief, fraudulent checks), driving-related crimes (e.g. DUIs, reckless driving), drug-related crimes (e.g. possession), and other crimes (e.g. assault, resisting arrest, criminal conspiracy). The dynamic difference-in-differences estimates, controlling for household and year fixed effects, are plotted for each crime type in Figure 5. Notably, the figures show that residents in fracking and non-fracking counties do not diverge prior to the fracking boom in these types of crime. However, residents exposed to fracking activities change their criminal behavior relative to residents in non-fracking counties in response to the economic shock. Results show relatively large reductions in driving, drug, and other offenses in the leasing period. However, this reduction is diminished once production starts.

Similar to Table 2, Table 3 reports average treatment effects for each period, with panels for each crime type and adjusted FDR Q-values for statistical inference. Panel A indicates a -0.06 to -0.16 percentage point decrease in property cases filed during the leasing period, and a -0.12 to -0.21 percentage point decrease during the production period. Similarly, estimates are negative for driving-related cases during the leasing (-0.19 to -0.22 percentage points) and production period (-0.02 to -0.08 percentage points). Panel C shows a decrease in drug cases filed of -0.20 to -0.28 percentage points during the leasing period, and a reduction of 0.04 to -0.15 percentage points during the production period. Finally, all other crimes have a similar negative effect during the leasing period ranging from -0.15 to -0.25, with a smaller effect once production begins ranging from -0.04 to -0.22 percentage points. All coefficients are fairly robust to the inclusion of controls and a lead term.

Because I consider four types of crime, I also report statistical significance of these estimates using the Adjusted False Discovery Rate Q-values proposed in Anderson (2008). These values correct for the increased chance of rejecting the null hypothesis when making multiple comparisons for two treatments across four groups (eight categories). The negative effects on driving and property cases are generally not statistically significant once corrected for multiple comparisons. However, the effect on drug cases filed during the leasing period is sufficiently large across all specifications in Column 1 through 4 as to not have occurred by chance with Q-values of 0.076, 0.049, 0.063, and 0.052, respectively. There is no statistical effect on drug cases during the production period. The effect on all other cases is less robust with two of the four Adjusted FDR Q-values less than 0.10 during the leasing period, again with no estimated effect once production began.

For comparison, I also report the effect of hydraulic fracturing activities on aggregate changes in cases filed per 1000 persons. Figure A.4 plots the dynamic coefficients from the county level model of equation 1, with county and year fixed effects, for all cases and by case type. Again, counties do not diverge prior to fracking activities. However, estimates indicate increases in total cases filed, as well as drug, driving, assault, and all other cases during the fracking periods, specifically during production, which is consistent with prior literature.

Finally, I test whether the migrants entering the fracking counties were committing crimes at higher rates than the native population. This enables me to speak directly to a question of interest in the immigration literature of whether those moving into an area are more criminogenic in general. I measure the propensity to commit crime for a subset of those moving into the county. Specifically, I calculate the crime rate using the number of cases filed with an out-of-state address over the number of migrant tax exemptions filed in the county. I do the same for all crime committed by those with an address in North Dakota and the number of non-migrant tax exemptions in the county, fixing the total as of 2000. I find that the crime rate from 2004–2015 is higher for those moving into the county at 17%, as measured by crime committed by out-of-state individuals, compared to a rate of 7% for in-state individuals. 19 This can be thought of as a conservative estimate. First, the crime rate for people moving into the county only considers crime from out-of-state individuals, even though there is some in-migration to fracking counties from other areas in North Dakota. This also means that any additional crimes committed by those that move into the county from within the state are being considered as crimes committed by non-migrants for this exercise. Second, migrant and non-migrant tax exemptions are based on whether there is a change in filing county and state. To be conservative, the denominator for the out-of-state crime rate is the total of all inflows from 2000 through 2015. Similarly, I fix the total number of non-migrants in each county at the total in 2000 for each year as migrants that move into the area will be counted as non-migrants in their second year residing in the county

Taken together, findings provide strong evidence of a reduction in residents’ criminal behavior during the leasing period; these effects seem to be partially reduced by other effects during the production period. While I observe reductions in all crime types, results are primarily driven by drug-related crimes. This is in contrast to the county-level results, suggesting that compositional changes play an important role in the criminal response to economic conditions. Put differently, this suggests that the aggregate increases seen are due largely to additional crimes committed by those who move into the area. In contrast, the effect of the economic opportunity itself seems to have a negative effect on crime.

Results by intensity

Results thus far have treated all counties on the shale play as receiving the same economic shock. However, some counties, particularly the four major oil and gas producing counties, experience much larger economic shocks than others. The oil production in each of these four counties was greater than the amount produced in the other 13 counties combined over this time period. To estimate the differential effect by treatment intensity, I report estimates from equation 1 separately for major and minor fracking counties in Table 4. For the leasing period, 2004 to 2008, estimates in Column 1 indicate a 0.38 percentage point decrease in cases filed by residents in counties with minor fracking activity and a 0.53 percentage point decrease in the major fracking counties, significant at the 5% and 10% level respectively. This represents a 19.5% reduction in cases filed in counties with minor fracking activity and a 27.5% reduction in the major fracking counties. The estimated effect is stable to the inclusion of a lead indicator, county specific trends, and allowing for time-shocks that vary with levels of pre-period observables. Estimates in Columns 2 to 4 range from a 0.38 to 0.43 percentage point decline in minor fracking counties and 0.57 to 0.73 in major fracking counties. All estimates are significant at conventional levels.

During the production period, estimates for minor fracking counties are similar in magnitude to the leasing period, ranging from a 0.18 to 0.39 percentage point reduction in cases filed, although the effect is only marginally significant. Estimates for major fracking counties are smaller in magnitude during the production period relative to the leasing period (-0.07 to -0.62 percentage points), and not consistently significant at conventional levels.

As expected, the effect is larger in magnitude for the major fracking counties than the minor fracking counties initially, although coefficients are not statistically different. Importantly, this demonstrates that the effect is not driven solely by the four large fracking counties, as counties experiencing more modest economic shocks also see a significant reduction in crime. Additionally, the effect seems to fade more dramatically in the major fracking counties which also experience larger population and income changes during the production period. This is consistent with the interpretation that it is the other consequences of the in-migration, such as peer effects, and income that lead to a diminished reduction in crime for residents.

Results by lease-holder status

In addition to the local economic shock, some residents in fracking counties also receive a large positive income shock in the form of oil royalties during the production period. Recall that the average household that signs a lease receives over $7,500 per month from royalty payments. These payments may affect the decision to commit crime both for the leaseholder and the non-leaseholder as payments increase disposable income for illegal activities by leaseholders while increasing the income inequality and criminal opportunities for non-leaseholders. In Table 5, I estimate the extent to which the fracking activities may differentially affect residents using equation 2.

Estimates for lease-holders are mostly negative during leasing (-0.20 to -0.27 percentage points) and production (0.06 to -0.27 percentage points), although none are significant at conventional levels. Estimates for non-lease-holders range from -0.64 to -0.69 percentage points during the leasing period and are all significant at the 1% level. During the production period, estimates range from -0.33 to -0.57 for non-lease holders, with three of the four estimates significant at the 5% level. Estimates by lease-holder status are statistically different from each other at the 5% level, indicating that the overall reductions in crime shown in Table 2 are primarily driven by those who do not receive royalty payments. This suggests that it is the increase in job opportunities that reduces crime, rather than income per se. Moreover, the effect of job opportunities seems to be stronger than the effect of increased criminal opportunities.

In summary, I find that crime decreases during the leasing period in response to improved job opportunities (0.42 percentage point reduction), and that the effect is partially reduced once drilling activities escalate throughout the production period (0.26 percentage point reduction). Effects are largest and most consistent for drug-related crimes with a decrease of 0.21 percentage points in drug-related cases filed, although estimates are negative for all other crime categories as well. Importantly, the effect is not solely driven by the four largest oil producing counties. During the leasing period, there is a 0.53 percentage point crime reduction in the four major fracking counties, as compared to a 0.38 percentage point reduction in the minor fracking counties. The effects diminish more in the major fracking counties, which suggests that other factors related to production contribute to offsetting the effect of improved labor market conditions. Additionally, I find that effects are strongest for non-leaseholders (0.64 percentage point reduction during the leasing period), and persist into the production period (0.44 percentage point reduction). This is consistent with those not receiving alternative income streams being most sensitive to the job opportunities.

One concern in interpreting the results described above is that the differences over time may be due to changes in the number of police officers. Becker (1968) and others highlight that the probability of detection factors into an individual’s decision to commit crime, which is also echoed in the lab (Harbaugh, Mocan and Visser, 2013). Moreover, empirical evidence has shown that crime decreases in response to increased police presence (di Tella, 2004; Machin and Marie, 2011). To test for changes in the police force, I estimate the main model at the county level with total police officers as the outcome of interest. Figure 6a, indicates that the change in the number of police officers was negligible during the leasing period. As a result, changes in police are unlikely to be driving the significant reduction in crime observed during the leasing period. However, changes in police are potentially part of the treatment during the production period, although this is difficult to disentangle from other factors that changed during that period. Similarly, reductions in police resources from population increases may lead to fewer reported cases filed (Vollaard and Hamed, 2012). Figure 6b shows little evidence of changes in the population from 2004 to 2008, with large increases during the more labor-intensive production period. Again, population changes are less of a concern during the leasing period, but are likely to be a part of the treatment effects after 2008 as previously discussed.

Relatedly, a concern may be that individuals identified as residents may have moved out of the county or, more importantly, the State of North Dakota during the fracking periods. This could be an issue if changes in crime are simply from not observing the criminal behavior of an individual that moved out of the state. Anecdotally, it seems improbable that residents would disproportionately move out of fracking counties as economic conditions improved. I empirically check for evidence of out-migration using the number of tax exemptions that move out of a county each year. I find no evidence of differential out-migration during the initial leasing period. I find signs of out-migration only toward the end of the production period when those who had moved into the county begin leaving as shown in Figure 7

While I am not able to directly test for the mechanism underlying the decrease in crime from improved economic opportunities, I suggest two potential pathways. First, it is possible that decreases in crime are the result of an incapacitation effect, as individuals become occupied with legal work and thus have less time for criminal activities. This is similar to the incapacitation effect of school on juvenile crime (Jacob and Lefgren, 2003). A second explanation is that residents may no longer feel the need to engage in activities related to crime, such as drug use, given their improved economic outlook. This is consistent with work by Case and Deaton (2015; 2017) and Autor, Dorn and Gordon (forthcoming), who document an increasing number of deaths from drugs, alcohol and suicide associated with deteriorating economic conditions. This is also consistent with Becker (1968) which predicts individuals are less likely to engage in criminal activity if they have more to lose if apprehended. As a result, a more positive outlook on economic conditions, whether expected or realized, may also lower crime.

This paper studies the effect of local economic shocks on individuals’ decisions to commit crime. Specifically, I exploit the recent boom in hydraulic fracturing activities as a plausibly exogenous shock to local economic conditions. Using detailed administrative data on all criminal cases filed in North Dakota from 2000 to 2017, I estimate the effect of increased job opportunities on criminal behavior. An important strength of this study is that by focusing the analysis on all rural residents already living in the area prior to fracking, I can distinguish the effect of improved economic opportunity from the effect of population inflows on aggregate crime.

Results indicate that, consistent with the existing literature, aggregate crime increased in fracking counties relative to non-fracking counties. This was particularly true during the more labor-intensive fracking activities. However, local residents engage in less criminal activity at the start of the boom with a smaller effect in later years. Effects are largest and most robust for drug offenses, and are observed across all counties with fracking activity. Additionally, I show that effects are most pronounced for residents that do not also receive royalty payments. Taken together, results suggest that residents reduce their criminal activity in response to improved job opportunities, but that other changes from local economic shocks, such as peer composition, seems to offset this effect. This is consistent with economic opportunities reducing crime and highlights the role of compositional changes on the aggregate effects on crime.

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Ten Economic Facts about Crime and Incarceration in the United States

• Ten Economic Facts about Crime and Incarceration in the United States - Full Text

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Melissa s. kearney and melissa s. kearney nonresident senior fellow - economic studies , center for economic security and opportunity , the hamilton project @kearney_melissa ben harris ben harris vice president and director - economic studies , director - retirement security project @econ_harris.

May 1, 2014

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This Hamilton Project policy memo provides ten economic facts highlighting recent trends in crime and incarceration in the United States. Specifically, it explores the characteristics of criminal offenders and victims; the historically unprecedented level of incarceration in the United States; and evidence on both the fiscal and social implications of current policy on taxpayers and those imprisoned.

Read the full introduction »

Crime and high rates of incarceration impose tremendous costs on society, with lasting negative effects on individuals, families, and communities. Rates of crime in the United States have been falling steadily, but still constitute a serious economic and social challenge. At the same time, the incarceration rate in the United States is so high—more than 700 out of every 100,000 people are incarcerated—that both crime scholars and policymakers alike question whether, for nonviolent criminals in particular, the social costs of incarceration exceed the social benefits.

While there is significant focus on America’s incarceration policies, it is important to consider that crime continues to be a concern for policymakers, particularly at the state and local levels. Public spending on fighting crime—including the costs of incarceration, policing, and judicial and legal services—as well as private spending by households and businesses is substantial. There are also tremendous costs to the victims of crime, such as medical costs, lost earnings, and an overall loss in quality of life. Crime also stymies economic growth. For example, exposure to violence can inhibit effective schooling and other developmental outcomes (Burdick-Will 2013; Sharkey et al. 2012). Crime can induce citizens to migrate; economists estimate that each nonfatal violent crime reduces a city’s population by approximately one person, and each homicide reduces a city’s population by seventy persons (Cullen and Levitt 1999; Ludwig and Cook 2000). To the extent that migration diminishes a locality’s tax and consumer base, departures threaten a city’s ability to effectively educate children, provide social services, and maintain a vibrant economy.

The good news is that crime rates in the United States have been falling steadily since the 1990s, reversing an upward trend from the 1960s through the 1980s. There does not appear to be a consensus among scholars about how to account for the overall sharp decline, but contributing factors may include increased policing, rising incarceration rates, and the waning of the crack epidemic that was prevalent in the 1980s and early 1990s.

Despite the ongoing decline in crime, the incarceration rate in the United States remains at a historically unprecedented level. This high incarceration rate can have profound effects on society; research has shown that incarceration may impede employment and marriage prospects among former inmates, increase poverty depth and behavioral problems among their children, and amplify the spread of communicable diseases among disproportionately impacted communities (Raphael 2007). These effects are especially prevalent within disadvantaged communities and among those demographic groups that are more likely to face incarceration, namely young minority males. In addition, this high rate of incarceration is expensive for both federal and state governments. On average, in 2012, it cost more than $29,000 to house an inmate in federal prison (Congressional Research Service 2013). In total, the United States spent over $80 billion on corrections expenditures in 2010, with more than 90 percent of these expenditures occurring at the state and local levels (Kyckelhahn and Martin 2013).

A founding principle of The Hamilton Project’s economic strategy is that long-term prosperity is best achieved by fostering economic growth and broad participation in that growth. Elevated rates of crime and incarceration directly work against these principles, marginalizing individuals, devastating affected communities, and perpetuating inequality. In this spirit, we offer “Ten Economic Facts about Crime and Incarceration in the United States” to bring attention to recent trends in crime and incarceration, the characteristics of those who commit crimes and those who are incarcerated, and the social and economic costs of current policy.

Chapter 1 describes recent crime trends in the United States and the characteristics of criminal offenders and victims. Chapter 2 focuses on the growth of mass incarceration in America. Chapter 3 presents evidence on the economic and social costs of current crime and incarceration policy.

Chapter 1. The Landscape of Crime in the United States

Crime rates in the United States have been on a steady decline since the 1990s. Despite this improvement, particular demographic groups still exhibit high rates of criminal activity while others remain especially likely to be victims of crime.

Fact 1. Crime rates have steadily declined over the past twenty-five years.

After a significant explosion in crime rates between the 1960s and the 1980s, the United States has experienced a steady decline in crime rates over the past twenty-five years. As illustrated in figure 1, crime rates fell nearly 30 percent between 1991 and 2001, and subsequently fell an additional 22 percent between 2001 and 2012. This measure, calculated by the FBI, incorporates both violent crimes (e.g., murder and aggravated assault) and property crimes (e.g., burglary and larceny-theft). Individually, rates of property and violent crime have followed similar trends, falling 29 percent and 33 percent, respectively, between 1991 and 2001 (U.S. Department of Justice [DOJ] 2010b).

Social scientists have struggled to provide adequate explanations for the sharp and persistent decline in crime rates. Economists have focused on a few potential factors—including an increased number of police on the streets, rising rates of incarceration, and the waning of the crack epidemic—to explain the drop in crime (Levitt 2004). In the 1990s, police officers per capita increased by approximately 14 percent. During this same decade, sentencing policies grew stricter and the U.S. prison population swelled, which had both deterrence (i.e., prevention of further crime by increasing the threat of punishment) and incapacitation (i.e., the inability to commit a crime because of being imprisoned) effects on criminals (Abrams 2011; Johnson and Raphael 2012; Levitt 2004). The waning of the crack epidemic reduced crime primarily through a decline in the homicide rates associated with crack markets in the late 1980s.

Though crime rates have fallen, they remain an important policy issue. In particular, some communities, often those with lowincome residents, still experience elevated rates of certain types of crime despite the national decline.

Figure 1. Crime Rate in the United States, 1960–2012

Source: DOJ 2010b; authors’ calculations. Note: The crime rate includes all violent crimes (i.e., aggravated assault, forcible rape, murder, and robbery) and property crimes (i.e., burglary, larceny-theft, and motor vehicle theft).

Fact 2. Low-income individuals are more likely than higher-income individuals to be victims of crime.

Across all types of personal crimes, victimization rates are significantly higher for individuals living in low-income households, as shown in figure 2. In 2008, the latest year for which data are available, the victimization rate for all personal crimes among individuals with family incomes of less than $15,000 was over three times the rate of those with family incomes of $75,000 or more (DOJ 2010a). The most prevalent crime for low-income victims was assault, followed closely by acts of attempted violence, at 33 victims and 28 victims per 1,000 residents, respectively. For those in the higher-income bracket, these rates were significantly lower at only 11 victims and 9 victims per 1,000 residents, respectively.

Because crime tends to concentrate in disadvantaged areas, low-income individuals living in these communities are even more likely to be victims. Notably, evidence from the Moving to Opportunity program—a multiyear federal research demonstration project that combined rental assistance with housing counseling to help families with very low incomes move from areas with a high concentration of poverty—suggests that moving into a less-poor neighborhood significantly reduces child criminal victimization rates. In particular, children of families that moved as a result of receiving both a housing voucher to move to a new location and counseling assistance experienced personal crime victimization rates that were 13 percentage points lower than those who did not receive any voucher or assistance (Katz, Kling, and Liebman 2000).

Victims of personal crimes face both tangible costs, including medical costs, lost earnings, and costs related to victim assistance programs, and intangible costs, such as pain, suffering, and lost quality of life (Miller, Cohen, and Wiersama 1996). There are also public health consequences to crime victimization. Since homicide rates are so high for young African American men, men in this demographic group lose more years of life before age sixtyfive to homicide than they do to heart disease, which is the nation’s overall leading killer (Heller et al. 2013).

Figure 2. Victimization Rates for Persons Age 12 or Older, by Type of Crime and Annual Family Income, 2008

Source: DOJ 2010a; authors’ calculations. Note: The victimization rate is defined as the number of individuals who were victims of crime over a six-month period for every 100,000 U.S. residents.

Fact 3. The majority of criminal offenders are younger than age thirty.

Juveniles make up a significant portion of offenders each year. More than one quarter (27 percent) of known offenders—defined as individuals with at least one identifiable characteristic that were involved in a crime incident, whether or not an arrest was made— were individuals ages eleven to twenty, and an additional 34 percent were ages twenty-one to thirty; all other individuals composed fewer than 40 percent of offenders. As seen in figure 3, this trend holds for all types of crimes. More specifically, 55 percent of offenders committing crimes against persons (such as assault and sex offenses) were ages eleven to thirty. For crimes against property (such as larceny-theft and vandalism) and crimes against society (including drug offenses and weapon law violations), 63 percent and 66 percent of offenders, respectively, were individuals in the eleven-to-thirty age group.

A stark difference in the number of offenders by gender is also evident. Most crimes—whether against persons, property, or society—are committed by men; of criminal offenders with known gender, 72 percent are male. This trend for gender follows for crimes against persons (73 percent), crimes against property (70 percent), and crimes against society (77 percent) (DOJ 2012). Combined, these facts indicate that most offenders in the United States are young men.

Some social scientists explain this age profile of crime by appealing to a biological perspective on criminal behavior, focusing on the impaired decision-making capabilities of the adolescent brain in particular. There are also numerous social theories that emphasize youth susceptibility to societal pressures, namely their concern with identity formation, peer reactions, and establishing their independence (O’Donoghue and Rabin 2001).

Figure 3. Number of Offenders in the United States, by Age and Offense Category, 2012

Source: DOJ 2012; authors’ calculations. Note: The FBI defines crimes against persons as crimes whose victims are always individuals. Crimes against property are those with the goal of obtaining money, property, or some other benefit. Crimes against society are those that represent society’s prohibition against engaging in certain types of activity, and are typically victimless crimes (DOJ 2011). Offender data include characteristics of each offender involved in a crime incident whether or not an arrest has been made; offenders with unknown ages are excluded from the analysis. Additionally, incidents with unknown offenders—1,741,162 incidents in 2012—are excluded. For more details, see the technical appendix.

Fact 4. Disadvantaged youths engage in riskier criminal behavior.

Youths from low-income families (those with incomes at or below 200 percent of the federal poverty level) are equally likely to commit drug-related offenses than are their higher-income counterparts. As seen in figure 4, low-income youths are just as likely to use marijuana by age sixteen, and to use other drugs or sell drugs by age eighteen. In contrast, low-income youths are more likely to engage in violent and property crimes than are youths from middle- and highincome families. In particular, low-income youths are significantly more likely to attack someone or get into a fight, join a gang, or steal something worth more than $50. In other words, youths from lowincome families are more likely to engage in crimes that involve or affect other people than are youths from higher-income families.

A standard economics explanation for the socioeconomic profile of property crime is that for poor youths the attractiveness of alternatives to crime is low: if employment opportunities are limited for teens living in poor neighborhoods, then property crime becomes relatively more attractive. The heightened likelihood of violent crime among poor youths raises the issue of automatic behaviors—in other words, youths intuitively responding to perceived threats—which has become the focus of recent research in this field. However, the similar rates of drug use across teens from different income groups is consistent with a more general model of risky teenage activity associated with the so-called impaired decision-making capabilities of the adolescent brain.

Some intriguing recent academic work has proposed that adverse youth outcomes are often the result of quick errors in judgment and decision-making. In particular, hostile attribution bias— hypervigilance to threat cues and the tendency to overattribute malevolent intent to others—appears to be more common among disadvantaged youths, partly because these youths grow up with a heightened risk of having experienced abuse (Dodge, Bates, and Pettit 1990; Heller et al. 2013). Some experts have consequently begun promoting cognitive behavioral therapy for these youths to help them recognize and rewire the automatic behaviors and biased beliefs that often result in judgment and decision-making errors. Promising results from several experiments in Chicago— in particular, improved schooling outcomes and fewer arrests for violent crimes—suggest that it is possible to change the outcomes of disadvantaged youths simply by helping them recognize when their automatic responses may trigger negative outcomes (Heller et al. 2013).

Figure 4. Adolescent Risk Behaviors by Family Income Level

Source: Kent 2009. Note: Original data are derived from the 1997 National Longitudinal Survey of Youth. Adolescent risk behaviors are measured up to age eighteen, except for marijuana usage, which is measured up to age sixteen. Low-income families are those whose incomes are at or below 200 percent of the FPL. Middle-income families have incomes between 201 and 400 percent of the FPL. High-income families have incomes at or above 401 percent of the FPL.

Chapter 2. The Growth of Mass Incarceration in America

The incarceration rate in the United States is now at a historically unprecedented level and is far above the typical rate in other developed countries. As a result, imprisonment has become an inevitable reality for subsets of the American population.

Fact 5. Federal and state policies have driven up the incarceration rate over the past thirty years.

The incarceration rate in the United States—defined as the number of inmates in local jails, state prisons, federal prisons, and privately operated facilities per every 100,000 U.S. residents—increased during the past three decades, from 220 in 1980 to 756 in 2008, before retreating slightly to 710 in 2012 (as seen in figure 5).

The incarceration rate is driven by three factors: crime rates, the number of prison sentences per number of crimes committed, and expected time served in prison among those sentenced (Raphael 2011). Academic evidence suggests that increases in crime cannot explain the growth in the incarceration rate since the 1980s (Raphael and Stoll 2013). However, the likelihood that an arrested offender will be sent to prison, as well as the time prisoners can expect to serve, has increased for all types of crime (Raphael and Stoll 2009, 2013). Given that both the likelihood of going to prison and sentence lengths are heavily influenced by adjudication outcomes and the types of punishment levied, most of the growth in the incarceration rate can be attributed to changes in policy (Raphael and Stoll 2013).

Policymakers at the federal and state levels have created a stricter criminal justice system in the past three decades. For example, state laws and federal laws—such as the Sentencing Reform Act of 1984—established greater structure in sentencing through specified guidelines for each offense. Additionally, between 1975 and 2002,all fifty states adopted some form of mandatory-sentencing law specifying minimum prison sentences for specific offenses. In fact, nearly three quarters of states and the federal government—through laws like the Anti-Drug Abuse Act of 1986—enacted mandatory sentencing laws for possession or trafficking of illegal drugs. Many states also adopted repeat offender laws, known as “three strikes” laws, which strengthened the sentences of those with prior felony convictions. These policies, among others, are believed to have made the United States tougher on those who commit crime, raising the incarceration rate through increased admissions and longer sentences (Raphael and Stoll 2013).

The continued growth in the federal prison population stands in contrast to recent trends in state prison populations. Between 2008 and 2012, the number of inmates in state correctional facilities decreased by approximately 4 percent (from roughly 1.41 million to 1.35 million), while the number of inmates in federal prisons increased by more than 8 percent (from approximately 201,000 to nearly 218,000) (Carson and Golinelli 2013). This increase in federal imprisonment rates has been driven by increases in immigrationrelated admissions. Between 2003 and 2011, admissions to federal prisons for immigration-related offenses increased by 83 percent, rising from 13,100 to 23,939 (DOJ n.d.).

Figure 5. Incarceration Rate in the United States, 1960–2012

Fact 6. The U.S. incarceration rate is more than six times that of the typical OECD nation.

The United States is an international outlier when it comes to incarceration rates. In 2012, the incarceration rate in the United States—which includes inmates in the custody of local jails, state or federal prisons, and privately operated facilities—was 710 per 100,000 U.S. residents (Glaze and Herberman 2013). This puts the U.S. incarceration rate at more than five times the typical global rate of 130, and more than twice the incarceration rate of 90 percent of the world’s countries (Walmsley 2013).

The U.S. incarceration rate in 2012 was significantly higher than those of its neighbors: Canada’s and Mexico’s incarceration rates were 118 and 210, respectively. Moreover, the U.S. incarceration rate is more than six times higher than the typical rate of 115 for a nation in the Organisation for Economic Co-Operation and Development (OECD) (Walmsley 2013). As seen in figure 6, in recent years incarceration rates in OECD nations have ranged from 47 to 266; these rates are relatively comparable to the rates seen in the United States prior to the 1980s. Indeed, mass incarceration appears to be a relatively unique and recent American phenomenon.

A variety of factors can explain the discrepancy in incarceration rates. One important factor is higher crime rates, especially rates of violent crimes: the homicide rate in the United States is approximately four times the typical rate among the nations in figure 6 (United Nations Office on Drugs and Crime 2014). Additionally, drug control policies in the United States—which have largely not been replicated in other Western countries—have prominently contributed to the rising incarcerated population over the past several decades (Donahue, Ewing, and Peloquin 2011). Another important factor is sentencing policy; in particular, the United States imposes much longer prison sentences for drug-related offenses than do many economically similar nations. For example, the average expected time served for drug offenses is twenty-three months in the United States, in contrast to twelve months in England and Wales and seven months in France (Lynch and Pridemore 2011).

Figure 6. Incarceration Rates in OECD Countries

Fact 7. There is nearly a 70 percent chance that an African American man without a high school diploma will be imprisoned by his mid-thirties.

For certain demographic groups, incarceration has become a fact of life. Figure 7 illustrates the cumulative risk of imprisonment for men by race, education, and birth cohort. As described by Pettit and Western (2004), the cumulative risk of imprisonment is the projected lifetime likelihood of serving time for a person born in a specific year. Specifically, each point reflects the percent chance that a man born within a given range of years will have spent time in prison by age thirty to thirty-four. Notably, most men who are ever incarcerated enter prison for the first time before age thirty-five, and so these cumulative risks by age thirty to thirty-four are reflective of lifetime risks.

Men in the first birth cohort, 1945–49, reached their mid-thirties by 1980 just as the incarceration rate began a steady incline. For all education levels within this age group, only an 8-percentage point differential separated white and African American men in terms of imprisonment risk (depicted by the difference between the two solid lines on the far left of figure 7). As the incarceration rate rose, however,discrepancies between races became more apparent. Men born in the latest birth cohort, 1975–79, reached their mid-thirties around 2010; for this cohort, the difference in cumulative risk of imprisonment between white and African American men is more than double the difference for the first birth cohort (as seen on the far right of figure 7).

These racial disparities become particularly striking when considering men with low educational attainment. Over 53 percentage points distance white and African American male high school dropouts in the latest birth cohort (depicted by the difference between the two dashed lines on the far right of figure 7), with male African American high school dropouts facing a nearly 70 percent cumulative risk of imprisonment. This high risk of imprisonment translates into a higher chance of being in prison than of being employed. For African American men in general, it translates into a higher chance of spending time in prison than of graduating with a four-year college degree (Pettit 2012; Pettit and Western 2004).

Figure 7. Cumulative Risk of Imprisonment by Age 30–34 for Men Born Between 1945–49 and 1975–79, by Race and Education

Source: Western and Wildeman 2009. Note: Cumulative risk of imprisonment is the projected lifetime likelihood of imprisonment for a person born in a specific range of years. For more details, see the technical appendix.

Chapter 3. The Economic and Social Costs of Crime and Incarceration

Today’s high rate of incarceration is considerably costly to American taxpayers, with state governments bearing the bulk of the fiscal burden. In addition to these budgetary costs, current incarceration policy generates economic and social costs for both those imprisoned and their families.

Fact 8. Per capita expenditures on corrections more than tripled over the past thirty years.

In 2010, the United States spent more than $80 billion on corrections expenditures at the federal, state, and local levels. Corrections expenditures fund the supervision, confinement, and rehabilitation of adults and juveniles convicted of offenses against the law, and the confinement of persons awaiting trial and adjudication (Kyckelhahn 2013). As figure 8 illustrates, total corrections expenditures more than quadrupled over the past twenty years in real terms, from approximately $17 billion in 1980 to more than $80 billion in 2010. When including expenditures for police protection and judicial and legal services, the direct costs of crime rise to $261 billion (Kyckelhahn and Martin 2013).

Most corrections expenditures have historically occurred at the state level and continue to do so. As shown in figure 8, in 2010, more than 57 percent of direct cash outlays for corrections came from state governments, compared to 10 percent from the federal government and nearly 33 percent from local governments. Increased expenditures at every level of government are not surprising given the growth in incarceration, which has far outstripped population growth, leading to a higher rate of incarceration and higher corrections spending per capita (Census Bureau 2001, 2013; Raphael and Stoll 2013). Per capita expenditures on corrections (denoted by the dashed line in figure 8) more than tripled between 1980 and 2010. In real terms, each U.S. resident on average contributed $260 to corrections expenditures in 2010, which stands in stark contrast to the $77 each resident contributed in 1980.

Crime-related expenditures generate a significant strain on state and federal budgets, leading some to question whether public funds are best spent incarcerating nonviolent criminals. Preliminary evidence from the recent policy experience in California—in which a substantial number of nonviolent criminals were released from state and federal prisons—suggests that alternatives to incarceration for nonviolent offenders (e.g., electronic monitoring and house arrest) can lead to slightly higher rates of property crime, but have no statistically significant impact on violent crime (Lofstrom and Raphael 2013). These conclusions have led some experts to suggest that public safety priorities could better be achieved by incarcerating fewer nonviolent criminals, combined with spending more on education and policing (ibid.).

Figure 8. Total Corrections Expenditures by Level of Government and Per Capita Expenditures, 1980–2010

Source: Bauer 2003a, 2003b; Census Bureau 2001, 2011, 2013; Gifford 2001; Hughes 2006, 2007; Hughes and Perry 2005; Perry 2005, 2008; Kyckelhahn 2012a, 2012b, 2012c; Kyckelhahn and Martin 2013; authors’ calculations. Note: The dollar figures were adjusted to 2010 dollars using the CPI-U-RS (Consumer Price Index Research Series Using Current Methods). Population estimates for each year are taken from the Census Bureau’s estimates for July 1 of that year. The figure includes only direct expenditures so as not to double count the value of intergovernmental grants. For more details, see the technical appendix.

Fact 9. By their fourteenth birthday, African American children whose fathers do not have a high school diploma are more likely than not to see their fathers incarcerated.

In 2010, approximately 2.7 million children, or over 3 percent of all children in the United States, had a parent in prison (The Pew Charitable Trusts 2010). As of 2007, an estimated 53 percent of prisoners in the United States were parents of children under age eighteen, a majority being fathers (Glaze and Maruschak 2010). Furthermore, it is not the case that these parents were already disengaged from their children’s lives. For example, in 2007, approximately half of parents in state prisons were the primary provider of financial support for their children—and nearly half had lived with their children—prior to incarceration (ibid.). Furthermore, fathers often are required to pay child support during their incarceration, and since they make little to no money during their incarceration, they often accumulate child support debt.

Figure 9 illustrates the cumulative risk of imprisonment for parents— or the projected lifetime likelihood of serving time for a person born in a specific year—by the time their child turns fourteen, by child’s race and their own educational attainment (Wildeman 2009). Regardless of race, fathers are much more likely to be imprisoned than are mothers. These risks of imprisonment are magnified when parental educational attainment is taken into account; high school dropouts are much more likely to be imprisoned than are individuals with higher levels of education. Fathers who are high school dropouts face a cumulative risk of imprisonment that is approximately four times higher than that of fathers with some college education. An African American child with a father who dropped out of high school has more than a 50 percent chance of seeing that father incarcerated by the time the child reaches age fourteen.

Young children (ages two to six) and school-aged children of incarcerated parents have been shown to have emotional problems and to demonstrate weak academic performance and behavioral problems, respectively. It is unclear, however, the extent to which these problems result from having an incarcerated parent as opposed to stemming from the other risk factors faced by families of incarcerated individuals; incarcerated parents tend to have low levels of education and high rates of poverty, in addition to frequently having issues with drugs, alcohol, and mental illness (Center for Research on Child Wellbeing 2008).

Figure 9. Cumulative Risk of Parent’s Imprisonment for Children by Age 14, by Race and Parent’s Education

Source: Wildeman 2009. Note: Cumulative risk of imprisonment is the projected lifetime likelihood of a parent’s imprisonment by the time his or her child turns fourteen. Children included in the analysis were born in 1990. For more details, see the technical appendix.

Fact 10. Juvenile incarceration can have lasting impacts on a young person’s future.

After increasing steadily between 1975 and 1999, the rate of youth confinement began declining in 2000, with the decline accelerating in recent years (Annie E. Casey Foundation 2013). In 2011, there were 64,423 detained youths, a rate of roughly 2 out of every 1,000 juveniles ages ten and older (Sickmund et al. 2013). Detained juveniles include those placed in a facility as part of a court-ordered disposition (68 percent); juveniles awaiting a court hearing, adjudication, disposition, or placement elsewhere (31 percent); and juveniles who were voluntarily admitted to a facility in lieu of adjudication as part of a diversion agreement (1 percent) (ibid.).

Youths are incarcerated for a variety of crimes. In 2011, 22,964 juveniles (37 percent of juvenile detainees) were detained for a violent offense, and 14,705 (24 percent) were detained for a property offense. More than 70 percent of youth offenders are detained in public facilities, for which the cost is estimated to be approximately $240 per person each day, or around $88,000 per person each year (Petteruti, Walsh, and Velazquez 2009).

In addition to these direct costs, juvenile detention is believed to have significant effects on a youth’s future since it jeopardizes his or her accumulation of human and social capital during an important developmental stage. Studies have found it difficult to estimate this effect, given that incarcerated juveniles differ across many dimensions from those who are not incarcerated. Aizer and Doyle (2013) overcome this difficulty by using randomly assigned judges to estimate the difference in adult outcomes between youths sent to juvenile detention and youths who were charged with a similar crime, but who were not sent to juvenile detention. The authors find that sending a youth to juvenile detention has a significant negative impact on that youth’s adult outcomes. As illustrated in figure 10, juvenile incarceration is estimated to decrease the likelihood of high school graduation by 13 percentage points and increase the likelihood of incarceration as an adult by 22 percentage points. In particular, those who are incarcerated as juveniles are 15 percentage points more likely to be incarcerated as adults for violent crimes or 14 percentage points more likely to be incarcerated as adults for property crimes.

Figure 10. Effect of Juvenile Incarceration on Likelihood of High School Graduation and Adult Imprisonment

Source: Aizer and Doyle 2013. Note: Bars show statistically significant regression estimates of the causal effect of juvenile incarceration on high school completion and on adult recidivism. For more details, see the technical appendix.

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BRIEF RESEARCH REPORT article

How does media contribute to the rise of hate crimes against foreign domestic helpers in hong kong an unfair problem frame and agenda setting provisionally accepted.

• 1 The University of Hong Kong, Hong Kong, SAR China

The final, formatted version of the article will be published soon.

Hate crimes are widespread in Hong Kong society. Foreign domestic helpers working in Hong Kong also experience unfair agenda-setting by the media due to their dual economic and social disadvantages, and the media tries to portray them in a hostile social role. At the same time, the media creates negative social images of minority groups through news coverage, which leads to an increase in social hate crimes against them. This study used WiseSearch, a Chinese newspaper collection and analysis platform, to explore how Hong Kong news media use news themes and content to create a negative image of Hong Kong foreign domestic helpers in order to understand the media origins of hate crimes against Hong Kong foreign domestic helpers. Ultimately, the study found that local news media in Hong Kong are more inclined to cover the legal disputes of foreign domestic helpers in the agenda-setting process. In addition, they are more likely to associate foreign domestic helpers with "fear" rather than "rest assured." The study also found that because of the news value orientation, Hong Kong media tended to treat foreign domestic helpers as outsiders and less sympathetically when writing news stories.

Keywords: hate crimes, Agenda setting, Foreign domestic helpers, Hong Kong, Problem frame approach

Received: 21 Jan 2024; Accepted: 15 May 2024.

Copyright: © 2024 CHEN. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. ZHUOLI CHEN, The University of Hong Kong, Pokfulam, Hong Kong, SAR China

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