Abstract
This study investigates the effect of violent crime on school district–level achievement in English language arts (ELA) and mathematics. The research design exploits variation in achievement and violent crime across 813 school districts in the United States and seven birth cohorts of children born between 1996 and 2002. The identification strategy leverages exogenous shocks to crime rates arising from the availability of federal funds to hire police officers in the local police departments where the school districts operate. Results show that children who entered the school system when the violent crime rate in their school districts was lower score higher in ELA by the end of eighth grade, relative to children attending schools in the same district but who entered the school system when the violent crime rate was higher. A 10% decline in the violent crime rate experienced at ages 0–6 raises eighth-grade ELA achievement in the district by 0.03 standard deviations. Models that estimate effects by race and gender show larger impacts among Black children and boys. The district-wide effect on mathematics achievement is smaller and statistically nonsignificant. These findings extend our understanding of the geography of educational opportunity in the United States and reinforce the idea that understanding inequalities in academic achievement requires evidence on what happens inside as well as outside schools.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Work by Lauritsen et al. (2016) showed a discrepancy between crime trends in the FBI’s Uniform Crime Report (UCR) data and in the National Crime Victimization Survey (NCVS). Their findings suggest that NCVS data are more reliable indicators of the trends in violent crime from 1973 to the mid-1980s. Given the period being studied here, 1996 to 2008, the UCR data provide an accurate account of how crime rates changed over time and across space.
Baltimore and Milwaukee cannot join Chicago and Detroit on that list because of the spike in crime experienced in 2015, which brought the murder rate above the level in 1991. If changes are measured between 1991 and 2014, Baltimore and Milwaukee had reductions in their murder rates of 18% and 44%, respectively.
Among the 813 school districts, 59 have data on eighth-grade achievement for two birth cohorts; 85, for three birth cohorts; 100, for four birth cohorts; 148, for five birth cohorts; 205, for six birth cohorts; and 216, for seven birth cohorts. All findings remain qualitatively the same if the analyses are restricted to the 216 school districts for which data for the seven birth cohorts are available.
School districts are defined according the geographic catchment areas that include students in traditional public schools and local charter schools. Test scores from charter schools are included in the public school district in which they are chartered. For charter schools that are not chartered by a district, their test scores are included in the district in which they are physically located (Reardon 2018).
The choice of focusing on exposure to crime at ages 0–6 is motivated by the research design, which exploits the availability of funds to hire police officers through the COPS program. The first phase of the COPS program—the one considered in this study—ended in 2008, which is the year when the 2002 birth cohort was 6 years old. After 2008, the COPS program changed its rules for adjudicating grants, making the use of post-2008 data inadequate for the estimation strategy proposed here (for an analysis of the COPS program under the new grant allocation rule, see Mello 2019). Another reason to average crime rates over ages 0–6 is to obtain more stable crime rates. These multiyear averages also help in the 2SLS estimation by yielding a stronger first stage. Figure A8 in the online appendix shows OLS results when the violent crime rate is measured in one-year windows from age 0 to age 13.
The COPS data were collected by William Evans and Emily Owens, who generously shared them for this project.
The minimum, median, and maximum number of police officers per 100,000 residents at ages 0–6 for the 1996 birth cohort were, respectively, 0, 15.15, and 145.2. For the 2002 birth cohort, these figures were, respectively, 0, 24.75, and 431.84.
In a set of robustness tests, all OLS and 2SLS models are estimated including a vector of interpolated demographic controls, \( {\mathbf{X}}_{sc}^{\prime } \), measured for school district s when birth cohort c was 0–6 years old. These controls include percentage non-Hispanic White, percentage non-Hispanic Black, percentage Hispanic, percentage foreign-born, percentage unemployed, percentage of families with income below the poverty line, and median household income (in 2000 USD). All these demographics are computed by linearly interpolating between census years. All results remain the same when these controls are included.
Prior studies of the COPS program have shown that most police officers hired through the grants remained in the police force over the long run (Evans and Owens 2007). Given that this study is focused on long-term impacts on achievement, I use the cumulative number of police officers who had been hired and retained up to the time when a birth cohort was 0–6 years old.
One could be concerned about a potential violation of the exclusion restriction in light of prior work that has documented a correlation between crime and the residential choices of families of different groups (Dugan 1999; Ellen et al. 2017; Xie and McDowall 2014). These studies relied on correlational data, and it is difficult to extract any benchmarks from them. It is also important to keep in mind that my sensitivity analyses in Fig. A6 in the online appendix do not focus on changes in crime rates; rather, they focus on changes in the COPS grants and the extent to which they changed student composition of the school district. I find no clear evidence of that being the case.
These controls are obtained from the 2000 census and include percentage non-Hispanic White, percentage non-Hispanic Black, percentage Hispanic, percentage foreign-born, percentage unemployed, percentage families with income below the poverty line, and median household income (in 2000 USD).
In the SEDA data, the measure of achievement for all students combined is constructed from the test scores of students of all racial and ethnic groups in the school district, which include more groups than the three being considered here. I report disaggregated results for Black, Hispanic, and White children (but not for others) because in some districts, the number of students of other racial/ethnic minorities is too low to yield reliable estimates of their achievement.
The SEDA data include estimates of racial/ethnic gaps for each school district and birth cohort. In Table A2 in the online appendix, I use these racial/ethnic gaps as outcomes in the 2SLS regressions. Results from these models suggest a positive impact of changes in crime rates (i.e., gaps narrowed as crime rates declined), but these effects are statistically nonsignificant.
In models that use SEDA estimates of gender gaps as outcomes (Table A2 in the online appendix), the female-male gap in mathematics is statistically significant.
The range of 0.3–0.5 standard deviations in growth per school year captures most of the estimates that the literature in education has generated up to this point. School years are assumed to have nine months of instruction.
This figure corresponds to the average enrollment per grade in years 1994–2008 in the set of 813 school districts included in the sample.
References
Aizer, A. (2007). Neighborhood violence and urban youth. In J. Gruber (Ed.), The problems of disadvantaged youth: An economic perspective (pp. 275–307). Chicago, IL: University of Chicago Press.
Anderson, E. (2000). Code of the street: Decency, violence, and the moral life of the inner city. New York, NY: WW Norton & Company.
Braga, A. A., Hureau, D. M., & Papachristos, A. V. (2011). The relevance of micro places to citywide robbery trends: A longitudinal analysis of robbery incidents at street corners and block faces in Boston. Journal of Research in Crime and Delinquency, 48, 7–32.
Braga, A. A., Papachristos, A. V., & Hureau, D. M. (2010). The concentration and stability of gun violence at micro places in Boston, 1980–2008. Journal of Quantitative Criminology, 26, 33–53.
Burdick-Will, J. (2013). School violent crime and academic achievement in Chicago. Sociology of Education, 86, 343–361.
Burdick-Will, J., Ludwig, J., Raudenbush, S. W., Sampson, R. J., Sanbonmatsu, L., & Sharkey, P. (2011). Converging evidence for neighborhood effects on children’s test scores: An experimental, quasi-experimental, and observational comparison. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity: Rising inequality, schools, and children’s life chances (pp. 255–276). New York, NY: Russell Sage Foundation.
Chetty, R., & Hendren, N. (2018). The impacts of neighborhoods on intergenerational mobility I: Childhood exposure effects. Quarterly Journal of Economics, 133, 1107–1162.
Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the land of opportunity? The geography of intergenerational mobility in the United States. Quarterly Journal of Economics, 129, 1553–1623.
Devine, J. (1996). Maximum security: The culture of violence in inner-city schools. Chicago, IL: University of Chicago Press.
Dugan, L. (1999). The effect of criminal victimization on a household’s moving decision. Criminology, 37, 903–930.
Ellen, I. G., Mertens Horn, K., & Reed, D. (2017). Has falling crime invited gentrification? (Furman Center working paper). New York: NYU Furman Center.
Evans, W. N., & Owens, E. G. (2007). COPS and crime. Journal of Public Economics, 91, 181–201.
Federal Bureau of Investigation. (2015). Uniform crime reporting statistics. Available at https://www.ucrdatatool.gov/
Friedson, M., & Sharkey, P. (2015). Violence and neighborhood disadvantage after the crime decline. Annals of the American Academy of Political and Social Science, 660, 341–358.
Gershenson, S., & Tekin, E. (2017). The effect of community traumatic events on student achievement: Evidence from the beltway sniper attacks. Education Finance and Policy, 13, 513–544.
Griffin, E. A., & Morrison, F. J. (1997). The unique contribution of home literacy environment to differences in early literacy skills. Early Child Development and Care, 127, 233–243.
Harding, D. J. (2009). Violence, older peers, and the socialization of adolescent boys in disadvantaged neighborhoods. American Sociological Review, 74, 445–464.
Harding, D. J. (2010). Living the drama: Community, conflict, and culture among inner-city boys. Chicago, IL: University of Chicago Press.
Harding, D. J., Gennetian, L., Winship, C., Sanbonmatsu, L., & Kling, J. R. (2011). Unpacking neighborhood influences on education outcomes: Setting the stage for future research. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity: Rising inequality, schools, and children’s life chances (pp. 277–296). New York, NY: Russell Sage Foundation.
Heissel, J. A., Sharkey, P. T., Torrats-Espinosa, G., Grant, K., & Adam, E. K. (2018). Violence and vigilance: The acute effects of community violent crime on sleep and cortisol. Child Development, 89, e323–e331. https://doi.org/10.1111/cdev.12889
Ho, A. D., & Reardon, S. F. (2012). Estimating achievement gaps from test scores reported in ordinal “proficiency” categories. Journal of Educational and Behavioral Statistics, 37, 489–517.
Jones, N. (2009). Between good and ghetto: African American girls and inner-city violence. New Brunswick, NJ: Rutgers University Press.
Lafortune, J., Rothstein, J., & Schanzenbach, D. W. (2018). School finance reform and the distribution of student achievement. American Economic Journal: Applied Economics, 10(2), 1–26.
Lauritsen, J. L., Rezey, M. L., & Heimer, K. (2016). When choice of data matters: Analyses of us crime trends, 1973–2012. Journal of Quantitative Criminology, 32, 335–355.
Legewie, J., & Fagan, J. (2019). Aggressive policing and the educational performance of minority youth. American Sociological Review, 84, 220–247.
Levitt, S. D. (2004). Understanding why crime fell in the 1990s: Four factors that explain the decline and six that do not. Journal of Economic Perspectives, 18(1), 163–190.
Liew, J., McTigue, E. M., Barrois, L., & Hughes, J. N. (2008). Adaptive and effortful control and academic self-efficacy beliefs on achievement: A longitudinal study of 1st through 3rd graders. Early Childhood Research Quarterly, 23, 515–526.
McCoy, D. C., Raver, C. C., & Sharkey, P. (2015). Children’s cognitive performance and selective attention following recent community violence. Journal of Health and Social Behavior, 56, 19–36.
Mello, S. (2019). More cops, less crime. Journal of Public Economics, 172, 145–200.
Morenoff, J. D., & Sampson, R. J. (1997). Violent crime and the spatial dynamics of neighborhood transition: Chicago, 1970–1990. Social Forces, 76, 31–64.
Office of Community Oriented Policing Services. (2015). FY 2016 performance budget. Washington, DC: U.S. Department of Justice. Retrieved from https://www.justice.gov/sites/default/files/jmd/pages/attachments/2015/02/02/28._office_of_justice_programs_ojp.pdf
Peterson, R. D., & Krivo, L. J. (2010). Divergent social worlds: Neighborhood crime and the racial-spatial divide. New York, NY: Russell Sage Foundation.
Reardon, S. F. (2016). School district socioeconomic status, race, and academic achievement (Working paper). Stanford, CA: Stanford Center for Educational Policy Analysis.
Reardon, S. F. (2018). Educational opportunity in early and middle childhood: Variation by place and age (CEPA Working Paper No. 17-12). Stanford, CA: Stanford Center for Education Policy Analysis.
Reardon, S. F., & Ho, A. D. (2015). Practical issues in estimating achievement gaps from coarsened data. Journal of Educational and Behavioral Statistics, 40, 158–189.
Reardon, S. F., Kalogrides, D., Ho, A., Shear, B., Shores, K., & Fahle, E. (2016a). Stanford education data archive [Data set]. Retrieved from https://edopportunity.org/get-the-data/
Reardon, S. F., Kalogrides, D., & Shores, K. (2016b). The geography of racial/ethnic test score gaps (CEPA Working Paper No, 16-10). Stanford, CA: Stanford Center for Education Policy Analysis. Retrieved from http://cepa.stanford.edu/wp16-10
Rios, V. M. (2011). Punished: Policing the lives of Black and Latino boys. New York: NYU Press.
Sampson, R. J. (2012). Great American city: Chicago and the enduring neighborhood effect. Chicago, IL: University of Chicago Press.
Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924.
Sampson, R. J., & Sharkey, P. (2008). Neighborhood selection and the social reproduction of concentrated racial inequality. Demography, 45, 1–29.
Sharkey, P. (2010). The acute effect of local homicides on children’s cognitive performance. Proceedings of the National Academy of Sciences, 107, 11733–11738.
Sharkey, P. (2018a). The long reach of violence: A broader perspective on data, theory, and evidence on the prevalence and consequences of exposure to violence. Annual Review of Criminology, 1, 85–102.
Sharkey, P. (2018b). Uneasy peace: The great crime decline, the renewal of city life, and the next war on violence. New York, NY: WW Norton & Company.
Sharkey, P., Schwartz, A. E., Ellen, I. G., & Lacoe, J. (2014). High stakes in the classroom, high stakes on the street: The effects of community violence on student’s standardized test performance. Sociological Science, 1(14), 199–220. https://doi.org/10.15195/v1.a14
Sharkey, P. T., Tirado-Strayer, N., Papachristos, A. V., & Raver, C. C. (2012). The effect of local violence on children’s attention and impulse control. American Journal of Public Health, 102, 2287–2293.
Sharkey, P., & Torrats-Espinosa, G. (2017). The effect of violent crime on economic mobility. Journal of Urban Economics, 102, 22–33.
Shedd, C. (2015). Unequal city: Race, schools, and perceptions of injustice. New York, NY: Russell Sage Foundation.
Skogan, W. (1986). Fear of crime and neighborhood change. Crime and Justice, 8, 203–229.
Stock, J., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. Andrews & J. Stock (Eds.), Identification and inference for econometric models: Essays in honor of Thomas Rothenberg (pp. 80–108). New York, NY: Cambridge University Press.
United Nations Office on Drugs and Crime (UNODC). (2019) Global Study on Homicide: Homicide trends, patterns and criminal justice response. Vienna, Austria: UNODC.
U.S. Bureau of Justice Statistics. (2018). Law enforcement agency identifiers crosswalk, United States, 2012 (ICPSR Report No. 35158). Ann Arbor, MI: ICPSR. https://doi.org/10.3886/ICPSR35158.v2
Xie, M., & McDowall, D. (2014). Impact of victimization on residential mobility: Explaining racial and ethnic patterns using the national crime victimization survey. Criminology, 52, 553–587.
Zimring, F. E. (2006). The great American crime decline. New York, NY: Oxford University Press.
Acknowledgments
The author thanks Ingrid Ellen, Jennifer Hill, Mike Hout, Sean Reardon, Patrick Sharkey, Florencia Torche, and participants at the Furman Center Fellows Meetings and the Russell Sage Foundation conference “Improving Education and Reducing Inequality in the United States: Obtaining New Insights from Population-Based Academic Performance Data” for helpful comments. This research has been supported by a grant from the Russell Sage Foundation and the William T. Grant Foundation (RSF Award: 83-17-07).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic Supplementary Material
ESM 1
(PDF 459 kb)
Rights and permissions
About this article
Cite this article
Torrats-Espinosa, G. Crime and Inequality in Academic Achievement Across School Districts in the United States. Demography 57, 123–145 (2020). https://doi.org/10.1007/s13524-019-00850-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13524-019-00850-x