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Examining the effects of antidiscrimination laws on children in the foster care and adoption systems

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  • Netta Barak‐Corren
  • Yoav Kan‐Tor
  • Nelson Tebbe

Abstract

How are children affected when states prohibit child welfare agencies from discriminating against same‐sex couples who wish to foster or adopt? This question stands at the heart of a debate between governments that seek to impose such antidiscrimination requirements and child welfare agencies that challenge them on religious freedom grounds. Yet until now there has been no reliable evidence on whether and how antidiscrimination rules for these agencies impact children. We have conducted the first nationwide study of how child outcomes vary when states adopt such antidiscrimination rules for child welfare agencies. Analyzing 20 years of child welfare data (2000–2019), we estimate that state antidiscrimination rules both (1) modestly increase children's success at finding foster and permanent homes, and (2) greatly reduce the average time to place children in such homes. These effects vary among subgroups, such that children who are most likely to find a home are generally not affected by state antidiscrimination requirements, whereas children who are least likely to find a home (primarily older children and children with various disabilities) benefit substantially from antidiscrimination measures. We estimate that the effect of antidiscrimination rules is equivalent to 15,525 additional children finding permanent homes and 360,000 additional children finding foster homes, nationwide, over a period of 20 years. Overall, the project offers two key contributions: First, it provides empirical grounding for some of the most heated constitutional and political battles of the culture wars. Second, it advances empirical legal studies by bringing machine learning causal inference to law.

Suggested Citation

  • Netta Barak‐Corren & Yoav Kan‐Tor & Nelson Tebbe, 2022. "Examining the effects of antidiscrimination laws on children in the foster care and adoption systems," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 1003-1066, December.
  • Handle: RePEc:wly:empleg:v:19:y:2022:i:4:p:1003-1066
    DOI: 10.1111/jels.12333
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    References listed on IDEAS

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    1. Jeffrey Grogger & Sean Gupta & Ria Ivandic & Tom Kirchmaier, 2021. "Comparing Conventional and Machine‐Learning Approaches to Risk Assessment in Domestic Abuse Cases," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 18(1), pages 90-130, March.
    2. Card, David & Krueger, Alan B, 1994. "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review, American Economic Association, vol. 84(4), pages 772-793, September.
    3. David Card, 1990. "The Impact of the Mariel Boatlift on the Miami Labor Market," ILR Review, Cornell University, ILR School, vol. 43(2), pages 245-257, January.
    4. Steven Glazerman & Dan M. Levy & David Myers, 2003. "Nonexperimental Versus Experimental Estimates of Earnings Impacts," The ANNALS of the American Academy of Political and Social Science, , vol. 589(1), pages 63-93, September.
    5. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    6. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    7. Richard A. Berk & Susan B. Sorenson & Geoffrey Barnes, 2016. "Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 13(1), pages 94-115, March.
    8. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    9. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    10. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    11. repec:mpr:mprres:3694 is not listed on IDEAS
    12. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    13. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    14. Lightfoot, Elizabeth & DeZelar, Sharyn, 2016. "The experiences and outcomes of children in foster care who were removed because of a parental disability," Children and Youth Services Review, Elsevier, vol. 62(C), pages 22-28.
    15. Snowden, Jessica & Leon, Scott & Sieracki, Jeffrey, 2008. "Predictors of children in foster care being adopted: A classification tree analysis," Children and Youth Services Review, Elsevier, vol. 30(11), pages 1318-1327, November.
    16. Baker, Andrew C. & Larcker, David F. & Wang, Charles C.Y., 2022. "How much should we trust staggered difference-in-differences estimates?," Journal of Financial Economics, Elsevier, vol. 144(2), pages 370-395.
    17. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    18. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    19. Kaufman, Aaron Russell & Kraft, Peter & Sen, Maya, 2019. "Improving Supreme Court Forecasting Using Boosted Decision Trees," Political Analysis, Cambridge University Press, vol. 27(3), pages 381-387, July.
    20. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
    Full references (including those not matched with items on IDEAS)

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