IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v54y2025i4p1463-1504.html

Social Mobility as Causal Intervention

Author

Listed:
  • Lai Wei
  • Yu Xie

Abstract

The study of mobility effects is an important subject of study in sociology. Empirical investigations of individual mobility effects, however, have been hindered by one fundamental limitation, the unidentifiability of mobility effects when origin and destination are held constant. Given this fundamental limitation, we propose to reconceptualize mobility effects from the micro- to macro-level. Instead of micro-level mobility effects, the primary focus of the past literature, we ask alternative research questions about macro-level mobility effects: What happens to the population distribution of an outcome if we manipulate the mobility regime, that is, if we alter the observed association between social origin and social destination? We relate individual-level mobility experience to macro-level mobility effects under special interventions. The proposed method bridges the macro and micro agendas in social stratification research, and has wider applications in social stratification beyond the study of mobility effects. We illustrate the method with two analyses that evaluate the impact of social mobility on average fertility and income inequality in the United States. We provide an open-source software, the R package socmob , that implements the method.

Suggested Citation

  • Lai Wei & Yu Xie, 2025. "Social Mobility as Causal Intervention," Sociological Methods & Research, , vol. 54(4), pages 1463-1504, November.
  • Handle: RePEc:sae:somere:v:54:y:2025:i:4:p:1463-1504
    DOI: 10.1177/00491241251320963
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00491241251320963
    Download Restriction: no

    File URL: https://libkey.io/10.1177/00491241251320963?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. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College," NBER Working Papers 9546, National Bureau of Economic Research, Inc.
    2. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    3. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," IZA Discussion Papers 767, IZA Network @ LISER.
    4. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    5. Stacy Berg Dale & Alan B. Krueger, 2002. "Estimating the Payoff to Attending a More Selective College: An Application of Selection on Observables and Unobservables," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1491-1527.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Geoffrey Wodtke & Weiqi Wang & Kristina Butaeva & Steven N. Durlauf, 2026. "Class Mobility in the Era of Rising Inequality: A Synthetic Dynasty Analysis," NBER Working Papers 34800, National Bureau of Economic Research, Inc.

    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. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    2. James J. Heckman, 2010. "Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 356-398, June.
    3. Jorge Rodríguez & Fernando Saltiel & Sergio Urzúa, 2022. "Dynamic treatment effects of job training," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 242-269, March.
    4. Gautier, Eric & Hoderlein, Stefan, 2011. "A triangular treatment effect model with random coefficients in the selection equation," TSE Working Papers 15-598, Toulouse School of Economics (TSE), revised 25 Aug 2015.
    5. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    6. James J. Heckman, 2008. "Econometric Causality," International Statistical Review, International Statistical Institute, vol. 76(1), pages 1-27, April.
    7. Christian Belzil, 2008. "Testing the Specification of the Mincer Wage Equation," Annals of Economics and Statistics, GENES, issue 91-92, pages 427-451.
    8. Pablo Lavado & Nelson Oviedo & Hernán Ruffo, 2016. "Destruction of Cognitive and Noncognitive Skills in Adulthood," Working Papers 16-07, Centro de Investigación, Universidad del Pacífico.
    9. Cunha, Flavio & Heckman, James, 2008. "A New Framework For The Analysis Of Inequality," Macroeconomic Dynamics, Cambridge University Press, vol. 12(S2), pages 315-354, September.
    10. Lars Kirkebøen & Edwin Leuven & Magne Mogstad, 2014. "Field of Study, Earnings, and Self-Selection," NBER Working Papers 20816, National Bureau of Economic Research, Inc.
    11. Belzil, Christian & Hansen, Jorgen, 2007. "A structural analysis of the correlated random coefficient wage regression model," Journal of Econometrics, Elsevier, vol. 140(2), pages 827-848, October.
    12. Belzil, Christian, 2007. "The return to schooling in structural dynamic models: a survey," European Economic Review, Elsevier, vol. 51(5), pages 1059-1105, July.
    13. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory & Urzua, Sergio, 2014. "Education, Health and Wages," IZA Discussion Papers 8027, IZA Network @ LISER.
    14. Peter A. Savelyev & Kegon T. K. Tan, 2019. "Socioemotional Skills, Education, and Health-Related Outcomes of High-Ability Individuals," American Journal of Health Economics, MIT Press, vol. 5(2), pages 250-280, Spring.
    15. Robert J. Gary‐Bobo & Marion Goussé & Jean‐Marc Robin, 2016. "Grade retention and unobserved heterogeneity," Quantitative Economics, Econometric Society, vol. 7(3), pages 781-820, November.
    16. Rodney J. Andrews & Jing Li & Michael F. Lovenheim, 2016. "Quantile Treatment Effects of College Quality on Earnings," Journal of Human Resources, University of Wisconsin Press, vol. 51(1), pages 200-238.
    17. Anirban Basu & James J. Heckman & Salvador Navarro-Lozano & Sergio Urzua, 2007. "Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patients," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1133-1157.
    18. James J. Heckman & John Eric Humphries & Gregory Veramendi, 2018. "Returns to Education: The Causal Effects of Education on Earnings, Health, and Smoking," Journal of Political Economy, University of Chicago Press, vol. 126(S1), pages 197-246.
    19. Flavio Cunha & James Heckman & Salvador Navarro, 2005. "Separating uncertainty from heterogeneity in life cycle earnings," Oxford Economic Papers, Oxford University Press, vol. 57(2), pages 191-261, April.
    20. Oliver Cassagneau-Francis, 2022. "Revisiting the Returns to Higher Education: Heterogeneity by Cognitive and Non-Cognitive Abilities," Sciences Po Economics Publications (main) hal-04067399, HAL.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:sae:somere:v:54:y:2025:i:4:p:1463-1504. 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: SAGE Publications (email available below). General contact details of provider: .

    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.