Skip to main content


Log in

Human capital in the inner city

  • Published:
Empirical Economics Aims and scope Submit manuscript


Twenty-six percent of black males in the USA report seeing someone shot at before turning 12. This paper investigates how black young males alter their behavior when living in violent neighborhoods, using the nationally representative National Longitudinal Survey of Youth 1997 to quantitatively characterize the “code of the street” from the sociology literature. Black and white young males are equally likely to engage in violent behavior, conditional on reported exposure to violence. Education and labor market outcomes are worse when reporting exposure, unconditionally and controlling for observables. Mediators documented in the ethnography are quantitatively important in the estimated structural model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others


  1. The analogous percentages for white and black females are, respectively, 5 and 15 %.

  2. The county-level variables are homicide and assault rates, and the individual-level variables are having seen someone shot at and the frequency of hearing gunshots in one’s neighborhood. The county-level variables are created by combining the NLSY97 geocode file with the FBI’s UCR data. Details are provided in Sect. 3.

  3. This dynamic selection control model (Hotz et al. 2002) employs panel data methods and does have an updating, endogenous state variable, but is not dynamic in the broader case where agents’ choices also depend on their beliefs about the future evolution of their state variable (Kydland and Prescott 1977).

  4. Here credibility is defined as in Manski (2007) as the strength of assumptions necessary for inference. Although no researcher would subjectively rank the estimated models as having the same credibility one would achieve by randomly exposing individuals to violence, many researchers will find the weaknesses of their identification strategies to be preferable to avoiding the question at hand (Imbens 2010).

  5. Related studies also find evidence of effects from trauma (Gerson and Rappaport 2013; Becker and Kerig 2011; Kilpatrick et al. 2003; Breslau et al. 1991; Abram et al. 2004), gun violence (Cook and Ludwig 2002; Hemenway 2006), and maltreatment (Currie and Tekin 2012) in the psychology, public health, and economics literatures.

  6. Silverman (2004) discusses stylized facts motivating a focus on non-pecuniary returns.

  7. “Street” and “decent” are the labels used by inner city residents themselves; for discussions of these labels, see page 35 of Anderson (1999, (2002).

  8. This security arrangement may in fact be viewed as a personalized version of realpolitik as defined in Kissinger (1995). To be clear, “street” is not a synonym for “black,” as similar security arrangements can be found around the world and throughout history. For example, two contemporary groups operating under versions of the code of the street are the Taliban in Afghanistan (Gaviria and Smith 2009) and Golden Dawn in Greece (Konstandaras 2013; Stangos 2013).

  9. The social isolation mechanism results from the conjunction of statistical discrimination and segregation. A black person could face worse outcomes than a similar white person even in the absence of statistical discrimination, due to lower-quality schools, less resources for other things like public safety, inferior labor market networks or opportunities within commuting range, or even the types of social interactions they tend to have. All of these mechanisms affect the relative returns to choices made in response to statistical discrimination, including psychological returns.

  10. Although street capital “is not always useful or valued in the wider society, \(\dots \) it is capital nonetheless. It is recognized and valued on the streets, and to lack it is to be vulnerable there” (Anderson 1999, p. 105).

  11. “Appendix 3” reports the Introduction to the self-administered section of the NLSY97 questionnaire.

  12. For example, total hours worked in 1997 is the total hours worked between the 40th week of 1997 and the 39th week of 1998.

  13. Respondents have attacked someone if they report they have “attacked someone with the idea of seriously hurting them or have a situation end up in a serious fight or assault of some kind,” or police have charged them with “an attack \(\dots \) such as battery, rape, aggravated assault, or manslaughter.” By carrying a handgun, an individual is signaling to themselves and possibly to others that they expect some chance of encountering situations in which they would be willing to use violence.

  14. Arrests for non-violent offenses exclude arrests for an attack such as battery, rape, aggravated assault, or manslaughter.

  15. Variables collected related to carrying a gun and having been in a gang are exceptions. The last time a respondent carried a gun is not recorded in the first round, so the incidents in which one has carried a gun in the past 30 days are assumed to be uniformly distributed between the age when a respondent first carried a gun and their current age. A respondent is assumed to have been in a gang at all times between the first and last times they report belonging to a gang.

  16. An individual’s choice is considered missing if there are observations for 5 or less months during any year.

  17. “Appendix 2” compares \(\underline{D}\) and \(\overline{D}\) with county-level measures of violent crime as measures of exposure to violence.

  18. Additional assumptions are imposed in the potential outcome framework. For example, there cannot be general equilibrium effects such as a feedback mechanism in which individuals adopting the code of the street weaken institutions (Heckman and Vytlacil 2007). Similarly, there cannot be social interaction effects, so that another individual j’s exposure to violence cannot influence individual i’s treatment response (Manski 2010). These assumptions are also imposed in the dynamic models specified in Sect. 5.

  19. A two-parent indicator (relative to the reference group other household type) is not balanced in this block, although a one-parent indicator is balanced.

  20. The correct standard errors for this estimator have yet to be established (Imbens 2004, p. 14), with the asymptotic behavior of this and related estimators being an active area of research (Abadie and Imbens 2012).

  21. These models do not play a more prominent role in the analysis for three reasons. First, the measures of exposure to violence are each over several years. As a result, we do not know precisely when the exposure to violence occurred. Second, since we only have measures of exposure over two time periods, \(T=2\) if we are thinking about changes in exposure. Third, the most appropriate outcome variable given the data is street behavior, which is binary.

  22. I adopt Definition 5.4.1 of structural equation from Pearl (2009), so that a structural equation communicates all exclusion restrictions at a given level of measurement. Further discussion can be found in Aliprantis (2015b).

  23. Alternatively, one could interpret the model as a dynamic programming model in which agents are assumed to entirely discount next period’s value function, or as a dynamic programming model without \(\beta =0\) under stochastic concavity (Cunha et al. 2007).

  24. I did not include imprisonment explicitly in the analysis for two reasons: (1) The event history data on imprisonment spells are entirely missing for the 2003 (7th round of the NLSY97 due to a survey error (McClaskie 2009). And (2) imprisonment is relatively rare—at least in a direct/incapacitation sense. At age 16, <2 % of black males spent more than 6 months imprisoned. See Holzer et al. (2006) and Western et al. (2001) for related evidence.

  25. See Cameron and Heckman (1998) and Keane and Wolpin (1997) for discussions about unobserved heterogeneity modeled in this way.

  26. D-1 could be relaxed by allowing \(\varvec{\xi }_i\) to be a random variable with some covariance structure across individuals. Such an assumption could also allow for the special case in which each component is independent, which is analogous to A-1.

  27. Note: Simulated employment data from the model are generated for Fig. 12a as follows: (1) 100 observations simulated for each individual in the data assuming type \(\tau \). (2) Individuals sampled as type \(\tau \) using the estimated distribution. (3) Each individual contributes their 100 type \(\tau \) simulated observations to the data.

  28. Because counterfactuals are complicated functions of both parameters and data, standard errors of counterfactual changes are typically not reported when based on dynamic programming models due to the computational intensity of estimation (See Keane and Wolpin 2009 for some examples.).

  29. Using the mediation formula stated as equation 16 in Pearl (2014), the natural direct effect (NDE) of violent street capital on violent street behavior at ages 15 and 21 is 72 and 82 % of the nearest neighbor total effect of exposure to violence (the ATTs in Table 4), and the NDE of non-violent street capital on non-violent street behavior is 43 and 54 % of the total effect of exposure. As well, the NDE of non-violent street capital on graduation is 72 % of the total effect of exposure, and the NDE of violent street capital on age-23 h worked is 95 % of the total effect of exposure.

  30. See 21.6 of Greene (2002) for a derivation.

  31. It should be noted that the NACJD data are not official FBI UCR data, as the NACJD has made imputations.


  • Abadie A, Imbens GW (2006) Large sample properties of matching estimators for average treatment effects. Econometrica 74(1):235–267

    Article  Google Scholar 

  • Abadie A, Imbens GW (2012) Matching on the estimated propensity score. Harvard University, Cambridge

    Google Scholar 

  • Abram K, Teplin L, Charles D, Longworth S, McClelland G, Dulcan M (2004) Posttraumatic stress disorder and trauma in youth in juvenile detention. Arch Gen Psychiatry 61(4):403–410

    Article  Google Scholar 

  • Akerlof GA, Kranton RE (2002) Identity and schooling: some lessons for the economics of education. J Econ Lit 40(4):1167–1201

    Article  Google Scholar 

  • Aliprantis D (2015a) Assessing the evidence on neighborhood effects from Moving to Opportunity. Empirical Economics. Forthcoming

  • Aliprantis D (2015b) A distinction between causal effects in Structural and Rubin Causal Models. Federal Reserve Bank of Cleveland working paper 15-05

  • Aliprantis D, Carroll D (2013) Neighborhood dynamics and the distribution of opportunity. Federal Reserve Bank of Cleveland working paper 12-12r

  • Aliprantis D, Richter FGC (2014) Evidence of neighborhood effects from MTO: LATEs of neighborhood quality. Federal Reserve Bank of Cleveland working paper 12-08r

  • Aliprantis D, Hartley D (2015) Blowing it up and knocking it down: the local and city-wide effects of demolishing high concentration public housing on crime. J Urban Econ 88:67–81

    Article  Google Scholar 

  • Anderson E (1990) Streetwise: race, class, and change in an urban community. University of Chicago Press, Chicago

    Google Scholar 

  • Anderson E (1994) The code of the streets. The Atlantic, Boston

    Google Scholar 

  • Anderson E (1999) Code of the street: decency, violence, and the moral life of the inner city. W. W. Norton and Company, New York

    Google Scholar 

  • Anderson E (2002) The ideologically driven critique. Am J Sociol 107(6):1533–1550

    Article  Google Scholar 

  • Anderson E (2008) Against the wall: poor, young, black, and male. In: Anderson E (ed) Against the wall: poor, young, black, and male. University of Pennsylvania Press, Philadelphia, pp 3–27

    Google Scholar 

  • Anderson E (2012) The iconic ghetto. Ann Am Acad Polit Soc Sci 642(1):8–24

    Article  Google Scholar 

  • Asante MK Jr (2008) It’s bigger than hip hop: the rise of the post-hip-hop generation. St. Martin’s, New York

    Google Scholar 

  • Au C-C (2008) Labor market opportunities and education choice of male black and white youths. Brown University, Providence

    Google Scholar 

  • Austen-Smith D, Fryer RG Jr (2005) An economic analysis of acting white. Q J Econ 120(2):551–583

    Google Scholar 

  • Baum-Snow N, Lutz B (2011) School desegregation, school choice, and changes in residential location patterns by race. Am Econ Rev 101(7):3019–3046

    Article  Google Scholar 

  • Bayer P, Hjalmarsson R, Pozen D (2009) Building criminal capital behind bars: peer effects in juvenile corrections. Q J Econ 124(1):105–147

    Article  Google Scholar 

  • Becker GS (1968) Crime and punishment: an economic approach. J Polit Econ 76(2):169–217

    Article  Google Scholar 

  • Becker SP, Kerig PK (2011) Posttraumatic stress symptoms are associated with the frequency and severity of delinquency among detained boys. J Clin Child Adolesc Psychol 40(5):765–771

    Article  Google Scholar 

  • Billings S, Deming DJ, Rockoff J (2012) School segregation, educational attainment and crime evidence from the end of busing in Charlotte-Mecklenburg. Mimeo, New York

    Book  Google Scholar 

  • Bjerk D (2007) Measuring the relationship between youth criminal participation and household economic resources. J Quant Criminol 23(1):23–39

    Article  Google Scholar 

  • Bjerk D (2010) Thieves, thugs, and neighborhood poverty. J Urban Econ 68(3):231–246

    Article  Google Scholar 

  • Borghans L, Duckworth AL, Heckman JJ, Ter Weel B (2008) The economics and psychology of personality traits. J Hum Resour 43(4):972–1059

    Google Scholar 

  • Breslau N, Davis G, Andreski P, Peterson E (1991) Traumatic events and posttraumatic stress disorder in an urban population of young adults. Arch Gen Psychiatry 48(3):216–222

    Article  Google Scholar 

  • Cameron SV, Heckman JJ (1998) Life cycle schooling and dynamic selection bias: models and evidence for five cohorts of American males. J Polit Econ 106(2):262–332

    Article  Google Scholar 

  • Cameron SV, Heckman JJ (2001) The dynamics of educational attainment for black, hispanic, and white males. J Polit Econ 109(3):455–499

    Article  Google Scholar 

  • Canada G (1996) Fist stick knife gun: a personal history of violence in America. Beacon Press, Boston

    Google Scholar 

  • Card D, Rothstein J (2007) Racial segregation and the black–white test score gap. J Public Econ 91(11–12):2158–2184

    Article  Google Scholar 

  • Chalak K, White H (2012) Causality, conditional independence, and graphical separation in settable systems. Neural Comput 24:1611–1668

    Article  Google Scholar 

  • Clampet-Lundquist S, Massey DS (2008) Neighborhood effects on economic self-sufficiency: a reconsideration of the moving to opportunity experiment. Am J Sociol 114(1):107–143

    Article  Google Scholar 

  • Collins WJ, Margo RA (2000) Residential segregation and socioeconomic outcomes: when did ghettos go bad? Econ Lett 69(2):239–243

    Article  Google Scholar 

  • Cook P, Ludwig J (2002) The costs of gun violence against children. Future Child 12(2):87–99

    Article  Google Scholar 

  • Cunha F, Heckman JJ, Navarro S (2007) The identification and economic content of ordered choice models with stochastic thresholds. Int Econ Rev 48(4):1273–1309

    Article  Google Scholar 

  • Currie J, Tekin E (2012) Understanding the cycle: childhood maltreatment and future crime. J Hum Resour 47(2):509–549

    Google Scholar 

  • Cutler DM, Glaeser EL (1997) Are ghettos good or bad? Q J Econ 112:827–872

    Article  Google Scholar 

  • Damm AP, Dustmann C (2014) Does growing up in a high crime neighborhood affect youth criminal behavior? Am Econ Rev 104(6):1806–1832

    Article  Google Scholar 

  • Durlauf SN (2004) Neighborhood effects. In: Henderson JV, Thisse JE (eds) Handbook of regional and urban economics, vol 4. Elsevier, Amsterdam

    Google Scholar 

  • Durlauf SN, Fafchamps M (2004) Social capital. In: Aghion P, Durlauf S (eds) Handbook of economic growth, vol 1B. Elsevier, Amsterdam, pp 1639–1700

    Google Scholar 

  • Elliott DS, Ageton SS (1980) Reconciling race and class differences in self-reported and official estimates of delinquency. Am Sociol Rev 45:95–110

    Article  Google Scholar 

  • Fang H, Loury GC (2005) Toward an economic theory of dysfunctional identity. In: Barrett CB (ed) Social economics of poverty: on identities. Routledge, Abingdon, pp 12–55

    Google Scholar 

  • Fang H, Moro A (2011) Theories of statistical discrimination and affirmative action: a survey. In: Benhabib J, Jackson MO, Bisin A (eds) Handbook of social economics, vol 1A. North-Holland, Amsterdam, pp 133–200

    Chapter  Google Scholar 

  • Freedman D (1999) From association to causation: some remarks on the history of statistics. Stat Sci 14(3):243–258

    Article  Google Scholar 

  • Gaviria M, Smith M (2009) Frontline: Obama’s War.

  • Gerson R, Rappaport N (2013) Traumatic stress and posttraumatic stress disorder in youth: recent research findings on clinical impact, assessment, and treatment. J Adolesc Health 52(2):137–143

  • Gould E, Weinberg B, Mustard D (2002) Crime rates and local labor market opportunities in the United States: 1979–1997. Rev Econ Stat 84(1):45–61

    Article  Google Scholar 

  • Greene WH (2002) Econometric analysis, 5th edn. Pearson, London

    Google Scholar 

  • Guryan J (2004) Desegregation and black dropout rates. Am Econ Rev 94(4):919–943

    Article  Google Scholar 

  • Heckman JJ, Singer B (1984) A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 52:271–320

    Article  Google Scholar 

  • Heckman JJ, Vytlacil E (2005) Structural equations, treatment effects, and econometric policy evaluation. Econometrica 73(3):669–738

    Article  Google Scholar 

  • Heckman JJ, Vytlacil E (2007) Econometric evaluation of social programs, part I: causal models, structural models and econometric policy evaluation. In: Heckman JJ, Leamer EE (eds) Handbook of econometrics, vol 6B. Elsevier, Neew York, pp 4779–4874

    Google Scholar 

  • Heckman J, Ichimura H, Smith J, Todd P (1998) Characterizing selection bias using experimental data. Econometrica 66(5):1017–1098

    Article  Google Scholar 

  • Heckman JJ, Urzúa S, Vytlacil E (2006) Understanding instrumental variables in models with essential heterogeneity. Rev Econ Stat 88(3):389–432

    Article  Google Scholar 

  • Heller SB, Pollack HA, Ander R, Ludwig J (2012) Improving social-cognitive skills among disadvantaged youth: a randomized field experiment. University of Chicago, Chicago

    Google Scholar 

  • Hemenway D (2006) Private guns, public health. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Holzer HJ, Raphael S, Stoll MA (2006) Perceived criminality, criminal background checks, and the racial hiring practices of employers. J Law Econ 49(2):451–480

    Article  Google Scholar 

  • Hotz VJ, Xu LC, Tienda M, Ahituv A (2002) Are there returns to the wages of young men from working while in school? Rev Econ Stat 84(2):22–236

    Article  Google Scholar 

  • Imai S, Krishna K (2004) Employment, deterrence, and crime in a dynamic model. Int Econ Rev 45(3):845–872

    Article  Google Scholar 

  • Imbens GW (2004) Nonparametric estimation of average treatment effects under exogeneity: a review. Rev Econ Stat 86(1):4–29

    Article  Google Scholar 

  • Imbens GW (2010) Better LATE than nothing: some comments on Deaton (2009) and Heckman and Urzua (2009). J Econ Lit 48(2):399–423

    Article  Google Scholar 

  • Imbens G (2014) Matching methods in practice: three examples. NBER working paper 19959

  • Imbens GW, Wooldridge JM (2009) Recent developments in the econometrics of program evaluation. J Econ Lit 47(1):5–86

    Article  Google Scholar 

  • Jacob BA (2004) Public housing, housing vouchers, and student achievement: evidence from public housing demolitions in Chicago. Am Econ Rev 94(1):233–258

    Article  Google Scholar 

  • Keane MP, Wolpin KI (1997) The career decisions of young men. J Polit Econ 105(3):473–522

    Article  Google Scholar 

  • Keane MP, Wolpin KI (2000) Eliminating race differences in school attainment and labor market success. J Labor Econ 18(4):614–652

    Article  Google Scholar 

  • Keane MP, Wolpin KI (2009) Empirical applications of discrete choice dynamic programming models. Rev Econ Dyn 12(1):1–22

    Article  Google Scholar 

  • Kilpatrick D, Ruggiero K, Acierno R, Saunders B, Resnick H, Best C (2003) Violence and risk of PTSD, major depression, substance abuse/dependence, and comorbidity: results from the national survey of adolescents. J Consul Clin Psychol 71(4):692

    Article  Google Scholar 

  • Kissinger H (1995) Diplomacy. Simon & Schuster, New York

    Google Scholar 

  • Kling JR, Liebman JB, Katz LF (2005) Bullets don’t got no name: consequences of fear in the ghetto. In: Weisner TS (ed) Discovering successful pathways in children’s development: mixed methods in the study of childhood and family life. University of Chicago Press, Chicago, pp 243–281

    Google Scholar 

  • Kling JR, Liebman JB, Katz LF (2007) Experimental analysis of neighborhood effects. Econometrica 75(1):83–119

    Article  Google Scholar 

  • Konstandaras N (2013) Golden Dawn, national trauma, and division.

  • Kydland FE, Prescott EC (1977) Rules rather than discretion: the inconsistency of optimal plans. J Polit Econ 85(3):473–492

    Article  Google Scholar 

  • León G (2012) Civil conflict and human capital accumulation. J Hum Resour 47(4):991–1022

    Google Scholar 

  • Leventhal JM, Gaither JR, Sege R (2014) Hospitalizations due to firearm injuries in children and adolescents. Pediatrics 133(2):219–225

    Article  Google Scholar 

  • Lochner L (2004) Education, work, and crime: a human capital approach. Int Econ Rev 45(3):811–843

    Article  Google Scholar 

  • Ludwig J, Kling JR (2007) Is crime contagious? J Law Econ 50(3):491–518

    Article  Google Scholar 

  • Manski CF (2007) Identification for prediction and decision. Harvard University Press, Cambridge

    Google Scholar 

  • Manski CF (2010) Genes, eyeglasses, and social policy. Northwestern University, Evanston

    Google Scholar 

  • Massey D, Denton N (1993) American apartheid: segregation and the making of the underclass. Harvard University Press, Cambridge

    Google Scholar 

  • McClaskie S (2009) Personal communication: email.NLS user services

  • Mocan HN, Billups SC, Overland J (2005) A dynamic model of differential human capital and criminal activity. Economica 72:655–681

    Article  Google Scholar 

  • NCHS (2009) Health, United States, 2008. National Center for Health Statistics, Hyattsville

  • NLSY97 (2000) NLSY97 questionnaire and interviewer’s reference manual, 2nd edn. Center for Human Resource Research, The Ohio State University, Ohio

  • Neal DA, Johnson WR (1996) The role of premarket factors in black-white wage differences. J Polit Econ 104(5):869–895

    Article  Google Scholar 

  • O’Flaherty B, Sethi R (2007) Crime and segregation. J Econ Behav Organ 64(3):391–405

    Article  Google Scholar 

  • O’Flaherty B, Sethi R (2010a) Homicide in black and white. J Urban Econ 68(3):215–230

    Article  Google Scholar 

  • O’Flaherty B, Sethi R (2010b) The racial geography of street vice. J Urban Econ 67(3):270–286

    Article  Google Scholar 

  • Pearl J (2009) Causality: models, reasoning and inference, 2nd edn. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Pearl J (2014) Interpretation and identification of causal mediation. Psychol Methods 19(4):459

    Article  Google Scholar 

  • Pearl J, Imbens G, Chen B, Bareinboim E (2014) Are economists smarter than epidemiologists? (comments on Imbens’ recent paper). UCLA causality blog.

  • Perry I (2004) Prophets of the hood: politics and poetics in hip hop. Duke University Press, Durham

    Book  Google Scholar 

  • Rose T (2008) The hip-hop wars: what we talk about when we talk about hip hop—and why it matters. Basic Books, New York, NY

  • Sampson R (1987) Urban black violence: the effect of male joblessness and family disruption. Am J Sociol 93(2):348–382

    Article  Google Scholar 

  • Sampson RJ, Raudenbush SW, Earls F (1997) Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277(15):918–924

    Article  Google Scholar 

  • Sampson RJ (2008) Moving to inequality: neighborhood effects and experiments meet social structure. Am J Sociol 114(1):189–231

    Article  Google Scholar 

  • Sampson RJ (2012) Great American city: Chicago and the enduring neighborhood effect. The University of Chicago Press, Chicago

    Book  Google Scholar 

  • Schultz TW (1961) Investment in human capital. Am Econ Rev 51(1):1–17

    Google Scholar 

  • Sharkey PT (2006) Navigating dangerous streets: the sources and consequences of street efficacy. Am Sociol Rev 71(5):826–846

    Article  Google Scholar 

  • Sharkey P (2010) The acute effect of local homicides on children’s cognitive performance. Proc Natl Acad Sci 107(26):11733–11738

    Article  Google Scholar 

  • Silverman D (2004) Street crime and street culture. Int Econ Rev 45(3):761–786

    Article  Google Scholar 

  • Stangos A (2013) The threat of golden dawn.

  • Thornberrry TP, Krohn MD (2002) Measurement problems in criminal justice research: workshop summary, chapter comparison of self-report and official data for measuring crime. National Academies Press, Washington, pp 43–94

    Google Scholar 

  • Turner C, Kim L, Rogers S, Lindberg L, Pleck J, Sonenstein F (1998) Adolescent sexual behavior, drug use, and violence: increased reporting with computer survey technology. Science 280(5365):867–873

    Article  Google Scholar 

  • Urzúa S (2008) Racial labor market gaps: the role of abilities and schooling choices. J Hum Resour 43(4):919–971

    Google Scholar 

  • US DoJ (1997). Uniform crime reporting program data [United States]: county-level detailed arrest and offense data [computer file]. Washington, DC: US Department of Justice, Federal Bureau of Investigation. Produced and distributed by inter-university consortium for political and social research, Ann Arbor, MI

  • Verdier T, Zenou Y (2004) Racial beliefs, location, and the causes of crime. Int Econ Rev 45(3):731–760

    Article  Google Scholar 

  • Ward S, Williams J, van Ours JC (2015) Bad behavior: delinquency, arrest and early school leaving. CEPR working paper 10755

  • Weiner DA, Lutz BF, Ludwig J (2009) The effects of school desegregation on crime. NBER working paper no. 15380

  • Western B, Kling JR, Weiman DF (2001) The labor market consequences of incarceration. Crime Delinq 47(3):410–427

    Article  Google Scholar 

  • Wilson WJ (1987) The truly disadvantaged: the inner city, the underclass, and public policy. University of Chicago, Chicago

    Google Scholar 

  • Woodward J (2003) Making things happen: a theory of causal explanation. Oxford University Press, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Dionissi Aliprantis.

Additional information

I thank Ken Wolpin, Petra Todd, Elijah Anderson, Becka Maynard, Michela Tincani, Francisca Richter, Charlie Branas, Mark Schweitzer, three anonymous referees, and numerous seminar participants for helpful comments. I also thank Steve McClaskie for his help with the NLSY data. The research reported here was conducted with restricted access to Bureau of Labor Statistics data and was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305C050041-05 to the University of Pennsylvania. The opinions expressed are those of the author and do not represent views of the Federal Reserve Bank of Cleveland, the Board of Governors of the Federal Reserve System, the U.S. Department of Education, or the Bureau of Labor Statistics.


Appendix 1: Derivation of the likelihood function

Denote the standard normal CDF and pdf, respectively, by \(\Phi \) and \(\phi \). To construct the likelihood function of the dynamic selection model, begin by considering the conditional likelihood. Conditional on type, we can express

$$\begin{aligned} Pr(\overline{D}_i=1 | \tau _i = \tau )&= \,\Phi \left( \beta X_i + \xi ^D_{\tau } \right) . \end{aligned}$$

We can also write the probability of graduation at age 23, \(Pr(G_i | \tau _i = \tau )\), as

$$\begin{aligned} Pr\left( G_i =1 | \tau _i = \tau \right) =&\Phi \Big ( \beta ^G X_i + \gamma ^G_{v,1} K_v(18) + \gamma ^G_{v,2} K^2_v(18) + \gamma ^G_{nv,1} K_{nv}(18) \nonumber \\&+ \gamma ^G_{nv,2} K^2_{nv}(18) + \overline{\gamma }^G \, \overline{D_i} \, \mathbf {1} \{ a > 18 \} + \xi ^{G}_i \Big ). \end{aligned}$$

Hours worked contribute either

$$\begin{aligned} Pr(W_i = 0 | \tau _i = \tau ) =\,&1-\Phi \Big ( \big [\beta ^W X_i + \gamma ^W_{v,1} K_v(18) + \gamma ^W_{v,2} K^2_v(18) + \gamma ^W_{nv,1} K_{nv}(18)\\ \nonumber&+ \gamma ^W_{nv,2} K^2_{nv}(18)+ \overline{\gamma }^W \, \overline{D_i} \, \mathbf {1} \{ a> 18 \} + \gamma ^W \, G_i + \xi ^{W}_i \big ]/\sigma _W \Big ), \text {or} \\ Pr(W_i = w | \tau _i = \tau ) =\,&\frac{1}{\sigma _W} \phi \Big ( \big \{w - \big [\beta ^W X_i + \gamma ^W_{v,1} K_v(a) + \gamma ^W_{v,2} K^2_v(a) + \gamma ^W_{nv,1} K_{nv}(a) \\ \nonumber&+\gamma ^W_{nv,2} K^2_{nv}(a) + \overline{\gamma }^W \, \overline{D_i} \, \mathbf {1} \{ a > 18 \} + \gamma ^W \, G_i + \xi ^{W}_i \big ] \big \}/\sigma _W \Big ), \end{aligned}$$

so that the contribution to the likelihood of age 23 weekly hours worked is

$$\begin{aligned} Pr(W_i | \tau _i = \tau ) = \mathbf {1}\{W_i = 0\} Pr(W_i = 0 | \tau _i = \tau ) + \mathbf {1}\{W_i > 0\} Pr(W_i = w | \tau _i = \tau ). \end{aligned}$$

Define \(\mathbf {S}_{i}(a)=(S_{v, i}(a), S_{nv, i}(a))\), \(\mathbf {K}_{i}(a)=(K_{v, i}(a), K_{nv, i}(a))\), and

$$\begin{aligned} \mu ^{v}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) =\,&\beta ^{v} X_i + \gamma ^{v}_{v} K_v(a) + \gamma ^{v}_{v,2} K_v^2(a) + \gamma ^{v}_{nv,1} K_{nv}(a)\\ \nonumber&+ \gamma ^{v}_{nv,2} K^2_{nv}(a) + \overline{\gamma }^{v} \, \overline{D_i} \, \mathbf {1} \{ a> 18 \} + \xi ^{S_{v}}_i + \lambda ^{v}(a) \\ \mu ^{nv}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) =\,&\beta ^{nv} X_i + \gamma ^{nv}_{v} K_v(a) + \gamma ^{nv}_{v,2} K_v^2(a) + \gamma ^{nv}_{nv,1} K_{nv}(a) \\ \nonumber&+ \gamma ^{nv}_{nv,2} K^2_{nv}(a) + \overline{\gamma }^{nv} \, \overline{D_i} \, \mathbf {1} \{ a > 18 \} + \xi ^{S_{nv}}_i + \lambda ^{nv}(a). \end{aligned}$$

Then where \(\Phi _2(x ; m, \Sigma )\) is the cumulative distribution function of a bivariate normal distribution at x with mean m and covariance matrix \(\Sigma \),Footnote 30

$$\begin{aligned} Pr(\mathbf {S}_{i}(a) =(1,1) | \tau _i = \tau )&=\, \Phi _2 \left( \left[ \begin{array}{c} \mu ^{v}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) \\ \mu ^{nv}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) \\ \end{array} \right] ; \left[ \begin{array}{c} 0\\ 0 \\ \end{array} \right] , \left[ \begin{array}{cc} 1 &{} \rho ^{S} \\ \rho ^{S} &{} 1 \\ \end{array} \right] \right) \\ Pr(\mathbf {S}_{i}(a) =(0,1) | \tau _i = \tau )&= \Phi _2 \left( \left[ \begin{array}{c} -\mu ^{v}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) \\ \mu ^{nv}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) \\ \end{array} \right] ; \left[ \begin{array}{c} 0\\ 0 \\ \end{array} \right] , \left[ \begin{array}{cc} 1 &{} -\rho ^{S} \\ -\rho ^{S} &{} 1 \\ \end{array} \right] \right) \\ Pr(\mathbf {S}_{i}(a) =(1,0) | \tau _i = \tau )&= \Phi _2 \left( \left[ \begin{array}{c} \mu ^{v}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) \\ -\mu ^{nv}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) \\ \end{array} \right] ; \left[ \begin{array}{c} 0\\ 0 \\ \end{array} \right] , \left[ \begin{array}{cc} 1 &{} -\rho ^{S} \\ -\rho ^{S} &{} 1 \\ \end{array} \right] \right) \\ Pr(\mathbf {S}_{i}(a) =(0,0) | \tau _i = \tau )&= \Phi _2 \left( \left[ \begin{array}{c} -\mu ^{v}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) \\ -\mu ^{nv}(X_i, \mathbf {K}_i(a), \tau _i = \tau , a) \\ \end{array} \right] ; \left[ \begin{array}{c} 0\\ 0 \\ \end{array} \right] , \left[ \begin{array}{cc} 1 &{} \rho ^{S} \\ \rho ^{S} &{} 1 \\ \end{array} \right] \right) . \end{aligned}$$

Note that the estimated model constrains time trends in street behavior to cross at a particular age, so that:

$$\begin{aligned} \lambda ^{v}_{4}&= \frac{\lambda ^{v}_{1} - \lambda ^{v}_{3}}{16} + \lambda ^{v}_{2} \\ \lambda ^{nv}_{4}&= \frac{\lambda ^{nv}_{1} - \lambda ^{nv}_{3}}{14} + \lambda ^{nv}_{2}. \end{aligned}$$

Writing an individual’s outcome as \(\mathcal {O}_i = (D_i, \mathbf {S}_{i}(12), \dots , \mathbf {S}_{i}(23), G_i, W_i)\) and defining \(\pi _{\tau } \equiv Pr(\tau _i = \tau )\), we can write

$$\begin{aligned}&Pr(\mathcal {O}_i | \tau _i=\tau ) = Pr(D_i | \tau _i = \tau ) Pr(\mathbf {S}_{i}(12) | \tau _i = \tau ) \cdots \\&Pr(\mathbf {S}_{i}(23) | \tau _i = \tau ) Pr(G_i | \tau _i = \tau ) Pr(W_i | \tau _i = \tau ), \end{aligned}$$


$$\begin{aligned} Pr(\mathcal {O}_i) = Pr(\mathcal {O}_i | \tau _i=1) \pi _1 + \cdots + Pr(\mathcal {O}_i | \tau _i=T) \pi _T. \end{aligned}$$

The estimated model conditions type probabilities on initial exposure to violence (\(Pr(\tau | \underline{D}=0)\) and \(Pr(\tau | \underline{D}=1)\)). The log-likelihood function is \(\mathcal {LL} = \sum _i ln\left( Pr(\mathcal {O}_i) \right) .\)

Appendix 2: Measuring exposure to violence and ecometrics

The self-reported data on exposure to violence used in the analysis are consistent with related administrative data, such as the Centers for Disease Control and Prevention (CDC)/National Center for Health Statistics (NCHS) individual-level measures of homicide death rates displayed in the text. County-level administrative crime data from the Federal Bureau of Investigation’s Uniform Crime Reporting (UCR) Program also relate large racial gaps in exposure to violence. At age 16 the average homicide rate per 100,000 residents in NLSY97 black males’ county of residence was 11.2, again more than twice the average for NLSY97 white males, 5.2. On average, black males lived in counties at age 16 with 193 additional assaults per 100,000 residents compared to their white counterparts (517 vs 324).

Prominent researchers in the neighborhood effects literature have proposed directing attention to “ecometrics” or using tools from the psychometric literature to improve the quality of neighborhood-level measures (Sampson 2012, p. 60). Relatedly, an important topic that has received little analysis in the empirical neighborhood effects literature is the appropriate definition of neighborhood (Durlauf 2004). In this Appendix, I provide preliminary ecometric evidence on the importance of the definition of “neighborhood” in measuring exposure to violence, which is broadly consistent with the results in Damm and Dustmann (2014) that different measures can provide very different information.

We might define neighborhoods as counties: This is the finest geographic partition available for the NLSY97, and there is ample variation in homicide or violent assault rates across counties even in the same metro area. If we were to define neighborhoods in this way, we would interpret the empirical evidence as falsifying Anderson’s theory applying to an empirically large share of black young males, as there is no correlation between this measure of exposure to violence and street behavior. In contrast, if we measure exposure to violence using the self-reported variable in the NLSY97 asking whether respondents have seen someone shot, we find the strong correlations displayed in the text.

1.1 UCR county-level measures of exposure to violence

An alternative measure of exposure to violence to the one used in the analysis in the paper would be constructed using homicide and assault rates in combination with data on county of residence from the NLSY97 Geocode File. I construct such a variable using crime data that come from the Federal Bureau of Investigation’s Uniform Crime Reporting (UCR) Program by way of the National Archive of Criminal Justice Data (NACJD), a project of the Inter-university Consortium for Political and Social Research (ICPSR).Footnote 31

I use the county-level detailed arrest and offense files for the years 1997 until 2007, such as US DoJ (1997), to create county-level homicide and assault rates. These variables are considered missing if they have been imputed based on less than 6 months of data for the year. The homicide (assault) rate is calculated as the number of homicides (assaults) reported in a county divided by the county population of agencies reporting crimes. Following convention, this rate is then multiplied by 100,000 to be expressed as the annual homicide (assault) rate per 100,000 individuals. Using the Federal Information Processing Standards (FIPS) state and county codes in which NLSY97 respondents report residing, these county-level homicide and assault rates are then assigned to individuals for the period beginning in the previous year.

1.2 Descriptive statistics

Surprisingly, street behaviors appear independent of the violence in a respondent’s county of residence, regardless of whether it is measured by the homicide or assault rate. Figure 13a, c shows rates of street behavior after dividing black males in the NLSY97 into county homicide rate quartiles, and Fig. 13b, d does the same by assault rate quartiles. We can see that respondents are no more likely to engage in street behaviors when residing in more violent counties than when living in less violent counties. Street behaviors of white males follow similar patterns that are also uncorrelated with violence in a respondent’s county of residence.

Fig. 13
figure 13

Street behavior of black males by violence in county of residence. a By county homicide rate quartile. b By county assault rate quartile. c By county homicide rate quartile. d By county assault rate quartile

One fact making these data especially surprising is that there is substantial variation in homicide rates between counties, even within the same metropolitan statistical areas (MSAs). Figure 14 shows such variation between 1997 and 2007 between counties in the same MSA. For example, the homicide rate in St. Charles, Missouri, was 1.15 per 100,000 in 2007, compared with 39.92 in St. Louis City. The homicide rate in Montgomery County, Maryland, was 2.80 per 100,000 in 1997, compared with 56.90 in the District of Columbia. Even among less extreme examples, there is considerable variation in homicide rates between counties.

There is also significant variation in the homicide rate between the counties of residence of African-American males in the NLSY97. Figure 15a shows that while the homicide rate of counties in which NLSY97 males lived decreased between 1997 and 2007, it is clear that African-American males still lived in much more violent counties in 2007 than white males did in 1997. The 75th percentile county-level homicide rate for white males was 8.5 in 1997 and 7.5 in 2007. For black males in the NLSY97, the 75th percentiles for 1997 and 2007 were, respectively, 15.5 and 14.3. Figure 15b shows similar patterns when we look by age of respondents in the NLSY97.

Fig. 14
figure 14

Homicide rates in select MSAs (by county). a Washington, DC. b New Orleans, LA. c Cleveland, OH. d Detroit, MI. e Philadelphia, PA. f St. Louis, MO. g Atlanta, GA. h Kansas City, MO/KS. i Indianapolis, IN. j Columbus, OH. k San Francisco, CA. l Newark, NJ

Fig. 15
figure 15

Homicide rates in NLSY97 males’ county of residence. a Males in the NLSY97 by year. b Males in the NLSY97 by age

Fig. 16
figure 16

Distribution of homicide rate by census tract in Cleveland City, Ohio

1.3 Interpretation and ecometrics

If we believed that county-level crime variables accurately measure the neighborhood violence to which individuals were exposed, these data would be interpreted as empirical evidence falsifying Anderson’s theory applying to an empirically large share of black young males. This highlights the importance of ecometrics, because another explanation for the surprising results in Fig. 13 is that county-level homicide or assault rates do not accurately measure the violence to which youth are exposed. This analysis adopts the second interpretation, using two key justifications.

First, consider the following evidence from Cleveland, Ohio, on the variation in homicide rates between census tracts within the same county. Figure 16 shows data from the Northeast Ohio Community and Neighborhood Data for Organizing (NEO CANDO) illustrating that homicide rates exhibit tremendous variation between census tracts in Cleveland City, only one municipality of the 58 located within Cuyahoga County, Ohio. In 1990 the 90th percentile homicide rate per 100,000 residents for census tracts in Cleveland City was 116, and in 2000 it was 43. Since county of residence is the finest geographic partition available for the NLSY97 data, this evidence from the NEO CANDO data set suggests this partition could be too coarse to accurately measure the violence to which youth are exposed in their neighborhoods.

Second, the individual-level measures of exposure to violence self-reported in the NSLY97 exhibit patterns consistent with Anderson’s theory. These variables are then used to measure exposure to violence in the analysis, since it is plausible that these variables are more accurate measures and since they are consistent with the qualitative evidence documented by urban ethnographers (Table 12).

Table 12 NLSY97 males’ neighborhood and school characteristics, by race (%)

Appendix 3: The self-administered section of the NLSY97 questionnaire

The Introduction to the self-administered section of the NLSY97 questionnaire is as follows (Taken from page 210 of NLSY97 2000):


This part of the interview is different from the previous parts. In the previous parts I read you the questions and recorded your answers. For this section you will hear the question through the headphones while you read the question on the computer screen. Let me show you how this works. We have several practice questions. The first practice question asks you if you like chocolate ice cream.


Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aliprantis, D. Human capital in the inner city. Empir Econ 53, 1125–1169 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


JEL Classification