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Crime and Inequality in Academic Achievement Across School Districts in the United States

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.

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Notes

  1. 1.

    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.

  2. 2.

    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.

  3. 3.

    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.

  4. 4.

    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).

  5. 5.

    For additional details on the construction of aggregate measures from student test score data, see Ho and Reardon (2012) and Reardon and Ho (2015).

  6. 6.

    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.

  7. 7.

    The COPS data were collected by William Evans and Emily Owens, who generously shared them for this project.

  8. 8.

    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.

  9. 9.

    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.

  10. 10.

    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.

  11. 11.

    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.

  12. 12.

    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).

  13. 13.

    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.

  14. 14.

    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.

  15. 15.

    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.

  16. 16.

    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.

  17. 17.

    This figure corresponds to the average enrollment per grade in years 1994–2008 in the set of 813 school districts included in the sample.

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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).

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Correspondence to Gerard Torrats-Espinosa.

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Torrats-Espinosa, G. Crime and Inequality in Academic Achievement Across School Districts in the United States. Demography (2020). https://doi.org/10.1007/s13524-019-00850-x

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Keywords

  • Crime
  • Education
  • Inequality
  • Causal inference