IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/4znu5.html
   My bibliography  Save this paper

Should We Trust Regression to Measure Social Disparities?

Author

Listed:
  • Wu, Christopher

Abstract

When investigating socioeconomic disparities in exposure to social ills, it is common to use (semi-)parametric regression, and interpret the parameter estimates as measures of inequality. However, disparity researchers often know little about the data generating process; therefore, not only are models misspecified, but it becomes difficult to even know which model is `better'. Under misspecification, common interpretations of regression coefficients can break down. Without a `right' model, model selection can be arbitrary, leading to credibility concerns as estimates are often sensitive to model selection. Predictive model selection can attenuate the measure of inequality if irrelevant predictors explain away the inequality. Examples are presented to illustrate key pitfalls.

Suggested Citation

  • Wu, Christopher, 2022. "Should We Trust Regression to Measure Social Disparities?," SocArXiv 4znu5, Center for Open Science.
    • Handle: RePEc:osf:socarx:4znu5
    DOI: 10.31219/osf.io/4znu5
    as

    Download full text from publisher

    File URL: https://osf.io/download/62d7940427b746063a0ac0bf/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/4znu5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Brett Baden & Douglas Noonan & Rama Mohana Turaga, 2007. "Scales of justice: Is there a geographic bias in environmental equity analysis?," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 50(2), pages 163-185.
    2. Charles F. Manski & John V. Pepper, 2018. "How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 232-244, May.
    3. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ashesh Rambachan & Jonathan Roth, 2020. "Design-Based Uncertainty for Quasi-Experiments," Papers 2008.00602, arXiv.org, revised Feb 2024.
    2. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Mar 2024.
    3. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    4. Martinez, Isabel Z., 2016. "Beggar-Thy-Neighbour Tax Cuts: Mobility after a Local Income and Wealth Tax Reform in Switzerland," VfS Annual Conference 2016 (Augsburg): Demographic Change 145643, Verein für Socialpolitik / German Economic Association.
    5. Martínez, Isabel Z., 2022. "Mobility responses to the establishment of a residential tax haven: Evidence from Switzerland," Journal of Urban Economics, Elsevier, vol. 129(C).
    6. Jonathan Roth & Pedro H. C. Sant’Anna, 2023. "Efficient Estimation for Staggered Rollout Designs," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 1(4), pages 669-709.
    7. Manski, Charles F., 2023. "Probabilistic prediction for binary treatment choice: With focus on personalized medicine," Journal of Econometrics, Elsevier, vol. 234(2), pages 647-663.
    8. Derksen, Laura & Kerwin, Jason Theodore & Reynoso, Natalia Ordaz & Sterck, Olivier, 2021. "Appointments: A More Effective Commitment Device for Health Behaviors," SocArXiv y8gh7, Center for Open Science.
    9. Crouse, Dan L. & Ross, Nancy A. & Goldberg, Mark S., 2009. "Double burden of deprivation and high concentrations of ambient air pollution at the neighbourhood scale in Montreal, Canada," Social Science & Medicine, Elsevier, vol. 69(6), pages 971-981, September.
    10. Aibo Gong, 2021. "Bounds for Treatment Effects in the Presence of Anticipatory Behavior," Papers 2111.06573, arXiv.org, revised Dec 2022.
    11. Özler, Berk & Çelik, Çiğdem & Cunningham, Scott & Cuevas, P. Facundo & Parisotto, Luca, 2021. "Children on the move: Progressive redistribution of humanitarian cash transfers among refugees," Journal of Development Economics, Elsevier, vol. 153(C).
    12. Dimitris Christelis & Dimitris Georgarakos & Tullio Jappelli & Geoff Kenny, 2020. "The Covid-19 Crisis and Consumption: Survey Evidence from Six EU Countries," Working Papers 2020_31, Business School - Economics, University of Glasgow.
    13. Marco Stenborg Petterson & David Seim & Jesse M. Shapiro, 2023. "Bounds on a Slope from Size Restrictions on Economic Shocks," American Economic Journal: Microeconomics, American Economic Association, vol. 15(3), pages 552-572, August.
    14. Shan Zhou & Douglas S. Noonan, 2019. "Justice Implications of Clean Energy Policies and Programs in the United States: A Theoretical and Empirical Exploration," Sustainability, MDPI, vol. 11(3), pages 1-20, February.
    15. Gabriella Conti & Stavros Poupakis & Peter Ekamper & Govert Bijwaard & L. H. Lumey, 2021. "Severe Prenatal Shocks and Adolescent Health: Evidence from the Dutch Hunger Winter," Working Papers 2021-056, Human Capital and Economic Opportunity Working Group.
    16. Brunello, Giorgio & Christelis, Dimitris & Sanz-de-Galdeano, Anna & Terskaya, Anastasia, 2022. "Does College Selectivity Reduce Obesity? A Partial Identification Approach," IZA Discussion Papers 15612, Institute of Labor Economics (IZA).
    17. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    18. Vikram Dayal & Anand Murugesan, 2020. "Demystifying causal inference: ingredients of a recipe," IEG Working Papers 393, Institute of Economic Growth.
    19. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    20. Schmidpeter, Bernhard, 2023. "Labor market news and expectations about jobs & earnings," Ruhr Economic Papers 1030, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:4znu5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.