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Can a measurement error perspective improve estimation in neighborhood effects research? A hierarchical Bayesian methodology

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  • Duncan J. Mayer
  • Robert L. Fischer

Abstract

Objective Neighborhood effects research often employs aggregate data at small geographic areas to understand neighborhood processes. This article investigates whether empirical applications of neighborhood effects research benefit from a measurement error perspective. Methods The article situates neighborhood effects research in a measurement error framework and then details a Bayesian methodology capable of addressing measurement concerns. We compare the proposed model to conventional linear models on crime data from Detroit, Michigan, as well as two simulated examples that closely mirror the sampling process. Results The Detroit data example shows that the proposed model makes substantial differences to parameters of interest and reduces the mean squared error. The simulations confirm the benefit of the proposed model, regularly recovering parameters and conveying uncertainty where conventional linear models fail. Conclusion A measurement error perspective can improve estimation for data aggregated at small geographic areas.

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  • Duncan J. Mayer & Robert L. Fischer, 2022. "Can a measurement error perspective improve estimation in neighborhood effects research? A hierarchical Bayesian methodology," Social Science Quarterly, Southwestern Social Science Association, vol. 103(5), pages 1260-1272, September.
    • Handle: RePEc:bla:socsci:v:103:y:2022:i:5:p:1260-1272
    DOI: 10.1111/ssqu.13190
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    1. Sylvia Richardson & Laurent Leblond & Isabelle Jaussent & Peter J. Green, 2002. "Mixture models in measurement error problems, with reference to epidemiological studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(3), pages 549-566, October.
    2. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
    3. Kristen S. Slack & Sarah Font & Kathryn Maguire-Jack & Lawrence M. Berger, 2017. "Predicting Child Protective Services (CPS) Involvement among Low-Income U.S. Families with Young Children Receiving Nutritional Assistance," IJERPH, MDPI, vol. 14(10), pages 1-11, October.
    4. Ilir Disha, 2019. "Different Paths: The Role of Immigrant Assimilation on Neighborhood Crime," Social Science Quarterly, Southwestern Social Science Association, vol. 100(4), pages 1129-1153, June.
    5. Eric P. Baumer & Kevin T. Wolff & Ashley N. Arnio, 2012. "A Multicity Neighborhood Analysis of Foreclosure and Crime," Social Science Quarterly, Southwestern Social Science Association, vol. 93(3), pages 577-601, September.
    6. Guido W. Imbens, 2021. "Statistical Significance, p-Values, and the Reporting of Uncertainty," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 157-174, Summer.
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    1. Mayer, Duncan J., 2024. "Lead and delinquency rates; A spatio-temporal perspective," Social Science & Medicine, Elsevier, vol. 341(C).

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