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«The Effect of Violent Crime on Economic Mobility1 Patrick Sharkey and Gerard Torrats-Espinosa New York University Abstract Recent evidence has ...»

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The Effect of Violent Crime on Economic Mobility1

Patrick Sharkey and Gerard Torrats-Espinosa

New York University


Recent evidence has demonstrated substantial geographic variation in the level of

upward economic mobility across US states, metropolitan areas, commuting zones, and

counties. However, there has been minimal progress made in identifying the key

mechanisms that help explain why some urban areas have low rates of upward mobility

while others have rates of upward mobility that resemble the most mobile nations in the developed world. In this article we focus attention on one specific dimension of urban areas, the level of violent crime. Using cross-sectional and longitudinal evidence and an array of empirical approaches, we find strong evidence that the level of violent crime in a county has a causal effect on the level of upward economic mobility. We find that a one standard deviation decline in violent crime as experienced during late adolescence increases the expected income rank in adulthood by at least 2 points. Similarly, a one standard deviation decline in the murder rate increases the expected income rank by roughly 1.5 points. These effect sizes are statistically significant and substantively meaningful. Although we are limited in our capacity to provide evidence on the mechanisms explaining the link between crime and mobility, we present suggestive results showing that the decline in the violent crime rate reduced the prevalence of high school dropouts at the county level between 1990 and 2010.

Keywords: economic mobility; violent crime; instrumental variables.

We thank participants in the conference Intergenerational Mobility in the United States: Obtaining New Insights from Population-Based Statistics organized by the Russell Sage Foundation in New York City on May 14, 2015 for helpful comments and feedback. We are particularly grateful to Raj Chetty and Nathaniel Hendren for their comments and suggestions and for making data on income mobility publicly available.

We also thank Emily Owen for generously sharing data from the COPS program. Patrick Sharkey thanks

the Russell Sage Foundation's project on social inequality for funding this research. Corresponding author:

Patrick Sharkey, New York University, Department of Sociology, The Puck Building, 295 Lafayette Street, New York, NY 10012. E-mail: patrick.sharkey@nyu.edu.

The Effect of Violent Crime on Economic Mobility Patrick Sharkey and Gerard Torrats-Espinosa Until the last few years, virtually all research on intergenerational economic mobility in the United States had focused on the transmission of economic advantage and disadvantage in the nation as a whole. Although substantial progress has been made in measuring levels and changes in economic mobility in the US, this research literature has ignored the tremendous heterogeneity in levels of economic mobility across regions of the country, states, cities, and counties (Chetty et al. 2014a, 2014b; Chetty and Hendren 2015, Economic Mobility Project 2012; Graham and Sharkey 2014). Chetty et al. (2014a) find that some commuting zones in the US have levels of mobility equal to the most mobile nations in Western Europe, and others have levels of mobility lower than any of the nations in the developed world. Further, this geographic variation in economic mobility appears to be a function of places themselves, rather than the people within them (Chetty and Hendren 2015).

Although this evidence suggests a causal effect of places on economic mobility, minimal progress has been made in explaining what it is about those places that increases or reduces the chances for residents to move upward in the income distribution. In an initial attempt to shed light on the mechanisms for upward mobility, Chetty et al. (2014a) and Chetty and Hendren (2015) examine several characteristics of counties and commuting zones. The authors find that both commuting zones with the highest levels of absolute upward mobility and counties with the largest positive causal effects on earnings in adulthood have, on average, lower rates of residential segregation by income and race, lower levels of income inequality, better schools, lower rates of violent crime, and a larger share of two-parent households. However, as the authors acknowledge, these associations are a first step and should not be thought of as causal.

In this article we attempt to push this literature forward by focusing our attention on one specific dimension of urban areas, the level of violent crime. Our focus on violent crime is driven by converging evidence from ethnographic, quantitative, and experimental research showing that exposure to violence may be a central mechanism by which growing up in areas of concentrated disadvantage affects the life chances of children (Burdick-Will et al. 2011; Harding 2009; Harding et al. 2011; Sharkey 2010).

This argument is consistent with preliminary evidence reported in Chetty et al. (2014a) and in Chetty and Hendren (2015), who document a strong association between violent crime and upward mobility measured at the level of counties and commuting zones. In this study, we push further to assess the robustness of the relationship between violent crime and upward economic mobility, to assess whether this relationship is causal, and to provide preliminary evidence on possible mechanisms linking violence and economic mobility.

Violence and the life chances of children Violence is unique among major public health problems in that it targets young people. Even after two decades of decades of declining violent crime, homicide remains among the leading causes of death for all 15-34 year-olds and is the leading cause of death among African Americans in this age range (National Vital Statistics System 2015). Unlike other major public health problems, violence is a young person’s disease.

This feature of violence is important because it means that children living within dangerous communities are forced to confront the challenge of violence at an early stage in life, with consequences that can disrupt or impede their academic and developmental trajectories. Ethnographic research focusing on the lives of young people within highly disadvantaged settings demonstrates the ways that youths are forced to navigate strategically through public spaces, shifting their schedules, their networks, and their routines in efforts to minimize the threat of victimization (Anderson 2000; Edin, Rosenblatt and Zhu 2015; Harding 2010; Jones 2010). Parents and their children develop creative ways to manage the threat of violence, but they do so in ways that may limit their children’s capacity to engage in public life within their communities and schools (Jarrett 1999; Furstenberg 1993). Instead of taking advantage of resources and activities that may be available in local schools or community centers, parents and children expend a great deal of energy on the more basic challenge of avoiding victimization.

Research on low-income families participating in housing mobility programs has shown that parents often make choices about important aspects of their children’s lives, such as which school the child will attend, based on concerns about safety rather than concerns about school quality (Clampet-Lundquist et al. 2011; Darrah and DeLuca 2014).

Concerns about violence, drugs, and gangs are consistently found to be the primary reasons why low-income families choose to take part in residential mobility programs designed to offer families the chance to move out of public housing located in areas of concentrated poverty (Mast and Wilson 2013).

The ethnographic literature is supported by a growing base of evidence demonstrating that stress and fear associated with community violence have substantial consequences for children’s cognitive and academic development. Several studies have found that when children are given assessments of cognitive skills or school-based standardized tests in the immediate aftermath of extreme local violence, their performance declines relative to other children assessed at a time when no recent violence has taken place (Gershenson and Tekin 2015; Sharkey 2010; Sharkey et al.

2012; Sharkey et al. 2014). Separate studies find that academic performance declines during periods of time when incidents of violence occur within the school and when students report feeling unsafe within the school setting (Lacoe 2014; Burdick-Will 2013).

The findings from research focusing on the acute effects of violence are consistent with a larger literature linking extended exposure to community violence with the development of cognitive skills, academic performance, attendance, and educational attainment (Bowen and Bowen 1999; Delaney-Black 2002; Grogger 1997; Harding 2009;

Hurt et al. 2001). All of these developmental indicators are highly predictive of later economic outcomes. Despite these connections, the literature on exposure to violence has not been extended to consider effects on economic mobility.

Although the individual-level mechanisms linking violence and upward mobility have received the most empirical attention, the spatial concentration of violence means that the impact of violent crime must be thought of not only at the individual level, but also at the community and city levels. Violent crime is concentrated in pockets of urban areas that frequently are characterized by poverty, joblessness, institutional decay, and racial and ethnic segregation (Massey 1995; Peterson and Krivo 2010; Sampson 2012;

Sampson and Wilson 1995; Wilson 1987). The presence of crime, or the perception that an area is dangerous, can accelerate a process of neighborhood decline, leading to outmigration of families, disinvestment by local businesses, and a deterioration of public life and economic conditions (Cullen and Levitt 1999; Ellen and O’Regan 2010;

Sampson 2012; Skogan 1986). Violence thus has the capacity to undermine the institutions and establishments that are central to the lives of young people, eroding their opportunities to obtain quality schooling, to take advantages of local employment opportunities, and to utilize social networks in order to facilitate entrance in the labor force.

In sum, we hypothesize that violent crime: a) undermines children’s own development and compromises their ability to obtain the education and skills necessary for economic mobility, and b) undermines functioning communities, leading to lower quality institutions like schools, fewer jobs, and lower quality networks that facilitate economic mobility.

Data and Analytic Approach Measures of Intergenerational Economic Mobility Using administrative tax records of more than 40 million children and their parents, researchers from the Equality of Opportunity project (Chetty et. al 2014a, 2014b;

Chetty and Hendren 2015) characterized a child’s expected rank in the national income distribution (at age 26) given her parents’ rank in the income distribution (measured when the child was approximately 16 years old). We use this metric as our measure of absolute intergenerational mobility,2 exploiting temporal variation across seven birth Chetty et al. (2014) provide a detailed discussion of the advantages of this rank-rank measure with respect to other alternative measures such as log income-log income. The key advantage of rank-based measures is that they are less sensitive to the presence of observations with zero or very small incomes.

cohorts (1980-1986) and geographic variation across 1,355 counties.3 Because children’s county of residence at age 16 could be determined, the metric allows us to link children’s expected rank in the income distribution as young adults to the geographic context in which they were raised.

Chetty et al. (2014) demonstrate that the relationship between the mean income rank of children and the mean income rank of parents is almost perfectly linear within counties. Rather than having to resort to non-parametric ways to characterize this rankrank relationship, the linearity of the association allows them to summarize it with two parameters: a slope and an intercept. The slope captures the relative mobility in the county (i.e.: the difference in outcomes between children from families at the top of the income distribution and children from families at the bottom of the distribution). The intercept represents the expected rank in the children’s income distribution for children of families at the bottom of the distribution4.

By combining the slope and the intercept, we can estimate the expected rank in the children income distribution at age 26 for children whose parents were at any given Measures beyond the 1986 birth cohort are not available because children’s income distribution is constructed when the birth cohort was 26 years old (for the youngest cohort, 1986, this means that income was measured in 2012. Chetty and Hendren (2015) recently provided measures of income mobility at age 24 for birth cohorts 1980-1988. We have run all our models using mobility at age 26 and at age 24, and we obtain similar results. We prefer the measure of mobility at age 26 because at that age income is likely to be more stable than at age 24, when some young adults may still be completing their education. In addition, because the crack cocaine index is only available up to year 2000, the 2SLS analyses that exploit the timing when the peak of the crack epidemic occurred are only possible for cohorts 1980-1986.

Formally, the county-specific measure of intergenerational mobility developed by Chetty et al. (2014) is

computed as follows:

හ!" = ෯! + ෯! හ!" + ෰!", where හ!" is the rank in the children income distribution at age 26 for child i growing up in county c, and හ!" is the rank in the parents income distribution for parents of child i in county c (measured when the child was 16 years old). The estimates for parameters ෯! and ෯! provide the measures of absolute and relative mobility, respectively, for county c. These county-level statistics are the two measures that are made publicly available and that we exploit in this analysis. The temporal variation across birth cohorts is obtained by carrying out this estimation separately for birth cohorts 1980 to 1986.

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