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Abstract
We analyze data from a natural experiment involving Denver public housing that quasirandomly assigns low-income Latino and African American youth to neighborhoods. Intent-to-treat and treatment-on-treated models reveal substantial effects of neighborhood socioeconomic status, ethnicity, and safety domains on youth and young adult educational, employment, and fertility outcomes. Effects are contingent on when a youth was first assigned to public housing and the neighborhood characteristic in question. Benefits from neighbors of higher occupational prestige are stronger if a child begins experiencing them at a younger age, whereas negative consequences of neighborhood crime are only manifested for teens. Neighborhood effect sizes apparently depend on the interaction among exposure duration, disruption effects of mobility, and developmental stage-specific differences in vulnerability to the given neighborhood effect mechanism operative. Our results hold powerful and provocative implications for where assisted housing should be developed and how applicants should be assigned to neighborhoods.
Notes
1. The term was introduced in published form in a special issue of Housing Policy Debate in 1995 (Galster & Hornberg, Citation1995). In that issue, Galster and Killen (Citation1995) first formally conceptualized this concept, which was recently modified and extended by Galster and Sharkey (Citationin press). Besides the articles in that special issue, other seminal analyses related to this theme were published in Briggs (Citation1995). (For a recent discussion of the normative foundations of this concept, see Dawkins, Citation2016).
2. The authors provide no theoretical justification for the age 13 bifurcation. We employ the same cutoff in our study so our findings can be compared with theirs, but also note that this age has conventionally been used by developmental psychologists as the onset of early adolescence (Booth & Crouter, Citation2001).
3. Although we adopt the conventional terminology about treatment, we would not wish our human subjects to be seen as ciphers in a dosage-treatment regimen instead of efficacious agents with complex lives woven into neighborhood networks and conditions of varying density and salience.
4. DHA dwellings are located in nearly 60% of the 137 tracts in the City and County of Denver.
5. Over the period from which we drew our sample, housing authorities like DHA were given considerable latitude in specifying local preferences for which households received priority on the wait list, such as those who have been recently rendered homeless or displaced by fires or natural disasters, the elderly or disabled, or victims of domestic violence.
6. Of the post-1986 vintage tenants residing in conventional public housing developments at the time of the Denver Child Study interviews, 99% were originally placed in such; only 1% moved in from dispersed housing. Of the post-1986 vintage tenants residing in dispersed housing at that time, 94% were originally placed in such; 6% moved in from the conventional developments. Moreover, an unknown number of these transfers were involuntary, required by regulations after changes in family size or composition.
7. Slightly more than one third of all caregivers interviewed in our study were former DHA residents. The primary reason for leaving DHA was improving economic circumstances that rendered the family ineligible for public housing and/or allowed it to secure preferable accommodations in the private market. A sizable share of households exited DHA involuntarily because of lease violations.
8. We employ the more general term caregiver because some sample youths were living with a relative other than a biological parent.
9. The time when covariates are measured varies according to outcome; those in Table correspond to the onset of dropping out of school (or age 18 if never). For our youth outcomes (last grade point average [GPA], dropping out, witnessing violence), all covariates are measured at the same time as the onset of the outcome, or age 18 if the outcome never occurred. For all our young-adult outcomes (school, work, out-of-wedlock child bearing) all covariates are measured as averages during youth ages 15–17.
10. Previous research has indicated that acute economic shock to a household can have seriously disruptive effects on adolescents’ mental and physical health than can impair a variety of outcomes over the longer term (Shonkoff et al., Citation2012).
11. In preliminary models we experimented with a more covariates, including measures of fertility and employment history, family size, and whether the household had health insurance. We also assessed caregiver gender, but since virtually all were female this was not included as a covariate. Given our relatively modest sample sizes we omitted from our final models covariates that never proved statistically significant in preliminary trials.
12. Although out-of-wedlock births were observed in our sample beginning at age 15, 90% occurred between ages 18 and 24.
13. For a review of theory and evidence related to this relationship, see Santiago and Galster (Citation2014).
14. For those who dropped out in middle school, we employed grades reported by caregiver for middle school.
15. We recognize that GPA is not a true ratio-level of measurement. In a supplementary test, we retained the original ordinal categories of responses and employed ordered probit models. The results were not substantially different from those produced by the simpler ordinary least squares (OLS) models reported here.
16. Dropping out and grades were correlated at −.35.
18. We acknowledge that nonlinear changes between census years can create random measurement error for the within-decade neighborhood indicator values, although we note that such errors are likely to bias the statistical significance of measured neighborhood effects toward zero.
19. This criterion led us to eliminate the percentage of African American residents as a neighborhood indicator in preliminary analyses.
20. Residential location information was cross verified using an array of automated search engines (Anchor, Intellius, and Lexis-Nexis) as well as several Internet-based people search and telephone directories (e.g., Anywho). These additional search engines identified deceased residents (N = 80, or 1.9%) as well as all known addresses for former DHA residents. In an attempt to reach former DHA residents, we sent letters soliciting study participation to all known addresses.
21. As a test of our exclusion restriction we included calendar year of assignment in our second-stage regressions, but they never proved statistically significant.
22. The particular statistics shown in Table A1 apply to the high school dropout outcome but are representative of all such first stage results. Recognize that such first stages were used to generate a distinct set of IV for each of our six outcomes, given that the dependent variables in each were measured at a different point of onset.
23. In a supplementary test, we retained the original ordinal grade categories of caregiver responses and employed ordered probit models. The results (available from the authors) were not substantially different from those produced by the simpler OLS models reported here.
24. We do not need to worry about clustering at the neighborhood level here because sampled youths who lived in the same neighborhood during their respective secondary-school years typically experienced different values for all the neighborhood indicators because they were experiencing such for different calendar years (ranging from the late 1980s to the late 2000s). There thus is no commonly experienced higher spatial scale as is typically the case in hierarchical data structures when all values of the variable(s) at the higher scale are identical for all observations at the lower scale. As a check, however, we estimated our models with robust standard errors based on clustering at the neighborhood level and found the standard errors to be much smaller than those generated by clustering at the family level. We chose the more conservative approach to report here.
25. Our sample sizes were too small to permit stratification by age less than 13 and 13–18 years.
26. For parsimony we do not present results for interactions with dummy variables denoting assignment to DHA under age 13. They support they main conclusions reported using the continuous age interactions and are available upon request.
27. Although we note this interaction is only significant at p < .10.
28. Although we note this interaction is only significant at p < .10.
29. Robustness tests using interactions with dummies indicating assignment at ages below 13 confirmed that the relationship between violent crime and these outcomes at young ages was positive.
30. For deeper studies on the social capital built within Latino communities, see, for example, Sanchez (Citation1993), Klinenberg (Citation2002), Ricourt and Danta (Citation2003) and Dávila (Citation2004).
31. Indeed, models employing interactions with dummy variables indicating assignment before age 13 (not shown) indicate no negative impacts of property or violent crime on youth or young-adult outcomes if assignment occurs before age 13.
32. The importance of neighborhood and school peers in encouraging youth criminality has been convincingly demonstrated by Billings, Deming, and Ross (Citation2016) with a natural experiment.
33. In 2000, the rough midpoint of our sample youths’ lives, African Americans represented only 11% of the overall Denver population, whereas Latinos comprised 32%. Ethnic residential segregation during the period of our study was lower than national averages for both Latinos and African Americans (Iceland, Weinberg, & Steinmetz, Citation2002). Denver has a unified city–county government, and thus has much less geographic variation in local fiscal capacity and public services than in the other cities. All of these distinctions imply that Denver offers quite different opportunity structures, local cultural norms, public expectations, and institutional supports than the typical U.S. metropolitan area.
34. For example, we find that neighborhood occupational prestige, and Bifulco, Fletcher, and Ross (Citation2011) find that the share of students in a school cohort with college-educated mothers, is inversely related to the odds of dropping out of high school.