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Skill-Based Contextual Sorting: How Parental Cognition and Residential Mobility Produce Unequal Environments for Children

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Demography

Abstract

Highly skilled parents deploy distinct strategies to cultivate their children’s development, but little is known about how parental cognitive skills interact with metropolitan opportunity structures and residential mobility to shape a major domain of inequality in children’s lives: the neighborhood. We integrate multiple literatures to develop hypotheses on parental skill-based sorting by neighborhood socioeconomic status and public school test scores, which we test using an original follow-up of the Los Angeles Family and Neighborhood Survey. These data include more than a decade’s worth of residential histories for households with children that are linked to census, geographic information system, and educational administrative data. We construct discrete-choice models of neighborhood selection that account for heterogeneity among household types, incorporate the unique spatial structure of Los Angeles County, and include a wide range of neighborhood factors. The results show that parents’ cognitive skills interact with neighborhood socioeconomic status to predict residential selection after accounting for, and confirming, the expected influences of race, income, education, housing market conditions, and spatial proximity. Among parents in the upper/upper-middle class, cognitive skills predict sorting on average public school test scores rather than neighborhood socioeconomic status. Overall, we reveal skill-based contextual sorting as an overlooked driver of urban stratification.

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Notes

  1. By combining the highly correlated measures (~.8) together into one index, we mitigate multicollinearity concerns that would arise from including both variables in our models. The index is correlated at .96 with each component variable, suggesting it is a strong neighborhood status proxy. The measure’s construction also renders it easily interpreted, with a mean around 0 and a standard deviation of approximately 1.

  2. Even parents of children who do not attend their catchment-assigned school likely consider metrics of quality in the local public schools given their impact on shared perceptions of neighborhood desirability, which influences housing price appreciation and sales potential.

  3. Yearly estimates for all ACS-derived tract-level variables are based on the middle year of each ACS time frame (e.g., ACS 2005–2009 is used for 2007 estimates). We linearly interpolate values from decennial census 2000 and ACS 2005–2009 data for 2001–2006 estimates, given tract-level data availability gaps.

  4. Tract-level variables’ missing data rates are trivial, except for network distance between origin and potential destination tracts (~1%) and K–12 test scores (~7%). Network distance missing values are imputed based on the mean distance between a tract within the respondent’s Los Angeles County region of origin and a tract within the choice set tract’s county region. Missing tract-level measures of K–12 test scores are imputed based on predicted values from a regression including tracts’ housing and sociodemographic characteristics and year fixed effects. Model results are robust to excluding imputed values.

  5. Among L.A.FANS panel respondents who were children at Wave 1 but aged into adulthood by Wave 2 and retook Woodcock-Johnson tests at that time, passage comprehension module percentile rankings correlate at .6–.8 with broad reading, math reasoning, applied problems, and letter-word identification rankings. Ideally, we would replicate our core results using these others skill measures and a composite skill measure that averages scores across modules. However, L.A.FANS fielded only the passage comprehension module to primary caregiver respondents. Nonetheless, we believe this module captures important dimensions of the contemporary housing search, such as the accuracy and perhaps frequency of processing and contextualizing written information.

  6. All individual- and household-level measures contain complete data for the analytic sample except for household income (~15% of the sample is missing data for one or more waves). To estimate missing values, we use the imputed Wave 3 household income values calculated by Sampson et al. (2017), which employ a wide range of covariates.

  7. For recent examples of discrete-choice models of neighborhood sorting, see Bruch and Swait (2019), Gabriel and Spring (2019), Logan and Shin (2016), Quillian (2015), Spring et al. (2017), and van Ham et al. (2018).

  8. We use the term “effect” to remain consistent with the discrete-choice literature’s language, while recognizing the limitations of our data and empirical strategy in identifying causal parameters.

  9. By comparing Model 4 with an identical model that excludes skill interactions with tract status, we find that racial homophily interaction terms are virtually identical in odds ratios and significance (results available upon request), suggesting that skill-based status sorting does not mediate racial residential homophily patterns.

  10. Large relative differences in predicted versus random selection probabilities reflect small absolute differences, given the tendency of residents to remain stationary—another dimension of how inequality is reproduced (Huang et al. 2017; Sampson and Sharkey 2008). Yet simulation models suggest that even small group-based divergences in mobility and location propensities can generate major group-based disparities at the population level (Bruch and Mare 2006; Schelling 1971).

  11. Additional robustness check models include omitting the offset term and incorporating interactions for origin tract with household income, skills, and neighborhood status. Model results are not substantively changed compared with Table 5, Model 4.

  12. Parents plausibly use schools’ sociodemographic properties rather than test scores to infer school quality, especially given the well-established link between the two (Rich 2018). Because our models control for sorting on neighborhood racial and economic status, we partially account for this possibility, although future research probing this concern is necessary.

  13. For more details on how we constructed these measures, see the online appendix section, Educational Expectations and Extracurricular Investments.

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Acknowledgments

We thank Robert D. Mare, Mario L. Small, Benjamin Jarvis, Sasha Killewald, Sandy Jencks, Jennifer Candipan, and the participants in the Urban Data Lab at Harvard University for helpful comments on prior versions of this article. We also thank the Joint Center for Housing Studies at Harvard University; the Project on Race, Class, and Cumulative Adversity funded by the Hutchins Family Foundation and the Ford Foundation; and the National Science Foundation for support.

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  1. Department of Sociology, Harvard University, 33 Kirkland Street, Cambridge, MA, 02138, USA

    Jared N. Schachner & Robert J. Sampson

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  1. Jared N. Schachner
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Correspondence to Jared N. Schachner.

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Schachner, J.N., Sampson, R.J. Skill-Based Contextual Sorting: How Parental Cognition and Residential Mobility Produce Unequal Environments for Children. Demography 57, 675–703 (2020). https://doi.org/10.1007/s13524-020-00866-8

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