Social Rigidity Across and Within Generations: A Predictive Approach

Abstract

How well can individuals’ parental background and previous life experiences predict their mid-life socioeconomic status (SES) attainment? This question is central to stratification research, as a strong power of earlier experiences in predicting later-life outcomes signals substantial intra- or intergenerational status persistence, or put simply, social rigidity. Running machine learning models on panel data to predict outcomes that include hourly wage, total income, family income, and occupational status, we find that a large number (around 4,000) of predictors commonly used in the stratification literature improves the prediction of one’s life chances in middle to late adulthood by about 10 percent to 50 percent, compared with a null model that uses a simple mean of the outcome variable. The level of predictability depends on the specific outcome being analyzed, with labor market indicators like wages and occupational prestige being more predictable than broader socioeconomic measures such as overall personal and family income. Grouping a comprehensive list of predictors into four unique sets that cover family background, childhood and adolescence development, early labor market experiences, and early adulthood family formation, we find that including income, employment status, and occupational characteristics at early career significantly improves models’ prediction accuracy for mid-life SES attainment. We also illustrate the application of the predictive models to examine heterogeneity in predictability by race and gender and identify important variables through this data-driven exercise.

Keywords

social stratification and mobility life course machine learning social rigidity prediction in social sciences

Get full access to this article

View all access options for this article.

References

Athey

Imbens

. 2016. “Recursive Partitioning for Heterogeneous Causal Effects.” Proceedings of the National Academy of Sciences 113(27): 7353–7360.

Badolato

Decter-Frain

Irons

N. J.

Miranda

Walk

Zhalieva

Alexander

Basellini

Zagheni

. 2023. “Predicting Individual-Level Longevity with Statistical and Machine Learning Methods.”

Blau

P. M.

Duncan

O. D.

. 1967. The American Occupational Structure.”

Bloome

Furey

. 2020. “Lifetime Inequality: Income and Occupational Differences and Dynamics in the US.” Research in Social Stratification and Mobility 70: 100470.

Brand

J. E.

Moore

Song

Xie

. 2019. “Why Does Parental Divorce Lower Children’s Educational Attainment? A Causal Mediation Analysis.” Sociological Science 6: 264–292.

Brand

J. E.

Koch

Geraldo

. 2021. “Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning.” Sociological Methodology 51(2): 189–223.

Brand

J. E.

Zhou

Xie

. 2023. “Recent Developments in Causal Inference and Machine Learning.”

Breen

C. F.

Seltzer

. 2023). Sociodemographic Characteristics Alone cannot Predict Individual-Level Longevity.”

Breen

C. F.

Seltzer

. 2024. “Demographic Perspectives on Predicting Individual-Level Mortality.” https://osf.io/fazsj/?view_only=cbefa9a80a684a8b9c17d1e5f8ad967. Draft version, July 31.

10.

Browne

Misra

. 2003. “The Intersection of Gender and Race in the Labor Market.” Annual Review of Sociology 29(1): 487–513.

11.

Budig

M. J.

England

. 2001. “The Wage Penalty for Motherhood.” American Sociological Review 66(2): 204–225.

12.

Bureau of Labor Statistics, 2023. U.S. Department of Labor. National Longitudinal Survey of Youth 1979 cohort, 1979-2020 (rounds 1-29). Produced and distributed by the Center for Human Resource Research (CHRR), The Ohio State University. Columbus, OH .

13.

Chen

Guestrin

. 2016. “Xgboost: A Scalable Tree Boosting System.” Pp. 785–794 in Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining.

14.

Cheng

. 2014. “A Life Course Trajectory Framework for Understanding the Intracohort Pattern of Wage Inequality.” American Journal of Sociology 120(3): 633–700.

15.

Cheng

. 2016. “The Accumulation of (Dis) Advantage: The Intersection of Gender and Race in the Long-Term Wage Effect of Marriage.” American Sociological Review 81(1): 29–56.

16.

Cheng

Adamic

Dow

P. A.

Kleinberg

J. M.

Leskovec

. 2014. “Can Cascades be Predicted?” Pp. 925–936 in Proceedings of the 23rd International Conference on World Wide Web.

17.

Cheng

Chauhan

Chintala

. 2019. “The Rise of Programming and the Stalled Gender Revolution.” Sociological Science 6: 321–351.

18.

Eubanks

. 2018. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: St. Martin’s Press.

19.

Fischer

C. S.

Hout

Jankowski

M. S.

Lucas

S. R.

Swidler

Voss

. 1996. Inequality by Design: Cracking the Bell Curve Myth. Princeton, NJ: Princeton University Press.

20.

Frederick

Hauser

R. M.

. 2010. “A Crosswalk for Using Pre-2000 Occupational Status and Prestige Codes with Post-2000 Occupation Codes.” Center for Demography and Ecology: University of Wisconsin-Madison. PMID25506974. https://cde.wiscweb.wisc.edu/wpcontent/uploads/sites/839/2019/01/cde-working-paper-2010-03.pdf

21.

Garip

. 2020. “What Failure to Predict Life Outcomes Can Teach US.” Proceedings of the National Academy of Sciences 117(15): 8234–8235.

22.

Garip

Macy

M. W.

. 2023. “Machine Learning in Sociology: Current and Future Applications.”

23.

Glauber

. 2007. “Marriage and the Motherhood Wage Penalty Among African Americans, Hispanics, and Whites.” Journal of Marriage and Family 69(4): 951–961.

24.

Goldberger

A. S

. 1972. “Structural Equation Methods in the Social Sciences.” Econometrica: Journal of the Econometric Society 40(6): 979–1001.

25.

Goldin

. 1977. “Female Labor Force Participation: The Origin of Black and White Differences, 1870 and 1880.” The Journal of Economic History 37(1): 87–108.

26.

Haider

Solon

. 2006. Life-Cycle Variation in the Association Between Current and Lifetime Earnings.” American Economic Review 96(4): 1308–1320.

27.

Imai

Yamamoto

. 2013. “Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence From Framing Experiments.” Political Analysis 21(2): 141–171.

28.

Killewald

. 2013. “A Reconsideration of the Fatherhood Premium: Marriage, Coresidence, Biology, and Fathers’ Wages.” American Sociological Review 78(1): 96–116.

29.

Killewald

Gough

. 2013. “Does Specialization Explain Marriage Penalties and Premiums?” American Sociological Review 78(3): 477–502.

30.

Kim

Tamborini

C. R.

Sakamoto

. 2018. “The Sources of Life Chances: Does Education, Class Category, Occupation, Or Short-Term Earnings Predict 20-Year Long-Term Earnings?” Sociological Science 5: 206–233.

31.

Liu

Grusky

D. B.

. 2013. “The Payoff to Skill in the Third Industrial Revolution.” American Journal of Sociology 118(5): 1330–1374.

32.

Lundberg

. 2017. “A Unified Approach to Interpreting Model Predictions.” arXiv preprint arXiv:1705.07874.

33.

Lundberg

Brown-Weinstock

Clampet-Lundquist

Pachman

Nelson

T. J.

Yang

Edin

Salganik

M. J.

. 2024. “The Origins of Unpredictability in Life Outcome Prediction Tasks.” Proceedings of the National Academy of Sciences 121(24): e2322973121.

34.

Lundberg

S. M.

Erion

Chen

DeGrave

Prutkin

J. M.

Nair

Katz

Himmelfarb

Bansal

Lee

S.-I.

. 2020. “From Local Explanations to Global Understanding with Explainable AI for Trees.” Nature Machine Intelligence 2(1): 56–67.

35.

Mazumder

. 2005. “Fortunate Sons: New Estimates of Intergenerational Mobility in the United States Using Social Security Earnings Data.” Review of Economics and Statistics 87(2): 235–255.

36.

McCall

. 2005. “The Complexity of Intersectionality.” Signs: Journal of Women in Culture and Society 30(3): 1771–1800.

37.

Molina

Garip

. 2019. “Machine Learning for Sociology.” Annual Review of Sociology 45: 27–45.

38.

National Center for O*NET Development. 2022. O*NET 26.3 Database. O*NET Resource Center.

39.

Pearl

. 1998. “Graphs, Causality, and Structural Equation Models.” Sociological Methods & Research 27(2): 226–284.

40.

Pearl

. 2012. “The Causal Mediation Formula—A Guide to the Assessment of Pathways and Mechanisms.” Prevention Science 13: 426–436.

41.

Reichman

N. E.

Teitler

J. O.

Garfinkel

McLanahan

S. S.

. 2001. “Fragile Families: Sample and Design.” Children and Youth Services Review 23(4-5): 303–326.

42.

Robins

J. M

. 2003. “Semantics of Causal DAG Models and the Identification of Direct and Indirect Effects.” Pp. 70–81. in Highly Structured Stochastic Systems, edited by P. J. Green, N. L. Hjort, and S. Richardson Oxford, UK: Oxford University Press

43.

Salganik

M. J.

Lundberg

Kindel

A. T.

Ahearn

C. E.

Al-Ghoneim

Almaatouq

Altschul

D. M.

Brand

J. E.

Carnegie

N. B.

Compton

R. J.

, et al. 2020. “Measuring the Predictability of Life Outcomes with a Scientific Mass Collaboration.” Proceedings of the National Academy of Sciences 117(15): 8398–8403.

44.

Salganik

M. J.

Lundberg

Kindel

A. T.

McLanahan

. 2019. “Introduction to the Special Collection on the Fragile Families Challenge.” Socius 5: 2378023119871580.

45.

Savcisens

Eliassi-Rad

Hansen

L. K.

Mortensen

L. H.

Lilleholt

Rogers

Zettler

Lehmann

. 2024. “Using Sequences of Life-events to Predict Human Lives.” Nature Computational Science 4(1): 43–56.

46.

Sewell

W. H.

Haller

A. O.

Portes

. 1969. “The Educational and Early Occupational Attainment Process.” American Sociological Review 34(1): 82–92.

47.

Stevens

Cho

J. H.

. 1985. “Socioeconomic Indexes and the New 1980 Census Occupational Classification Scheme.” Social Science Research 14(2): 142–168.

48.

Sweeney

M. M

. 2002. “Two Decades of Family Change: The Shifting Economic Foundations of Marriage.” American Sociological Review 67(1): 132–147.

49.

Vafa

Palikot

Kanodia

Athey

Blei

D. M.

. 2022. “Career: Transfer Learning for Economic Prediction of Labor Sequence Data.” arXiv preprint arXiv:2202.08370 .

50.

VanderWeele

T. J.

Tchetgen

E. J. T.

. 2017. “Mediation Analysis with Time Varying Exposures and Mediators.” Journal of the Royal Statistical Society. Series B, Statistical Methodology 79(3): 917.

51.

Vansteelandt

Sjolander

. 2016. “Revisiting G-estimation of the Effect of a Time-Varying Exposure Subject to Time-Varying Confounding.” Epidemiologic Methods 5(1): 37–56.

52.

Warren

J. R.

Sheridan

J. T.

Hauser

R. M.

. 1998. “Choosing a Measure of Occupational Standing: How Useful Are Composite Measures in Analyses of Gender Inequality in Occupational Attainment?” Sociological Methods & Research 27(1): 3–76.

53.

Zheng

Haowen

. 2025. Social Rigidity Across and Within Generations: A Predictive Approach. Retrieved (osf.io/7wjg8).

54.

Zhou

. 2022a. “Attendance, Completion, and Heterogeneous Returns to College: A Causal Mediation Approach. Sociological Methods & Research. 53(3): 1136-1166.

55.

Zhou

. 2022b. “Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators.” Journal of the Royal Statistical Society Series B: Statistical Methodology 84(3): 794–821.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.48 MB