Human capital in the inner city


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.

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  1. 1.

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

  2. 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. 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. 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. 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. 6.

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

  7. 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. 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. 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. 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. 11.

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

  12. 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. 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. 14.

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

  15. 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. 16.

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

  17. 17.

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

  18. 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. 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. 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. 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. 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. 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. 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. 25.

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

  26. 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. 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. 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. 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. 30.

    See 21.6 of Greene (2002) for a derivation.

  31. 31.

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


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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.

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.

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

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

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

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

Fig. 16

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

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.


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

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  • Code of the street
  • Interpersonal violence
  • Human capital
  • Race
  • Propensity score matching
  • Dynamic selection control

JEL Classification

  • I21
  • J15
  • J24
  • O15
  • O18
  • Z13