Skip to main content
SpringerLink
Log in

Intergenerational Profiles of Socioeconomic (Dis)advantage and Obesity During the Transition to Adulthood

  • Published:
Demography

Abstract

Investigations of socioeconomic status (SES) and health during the transition to adulthood in the United States are complicated by the later and more varied transitions in residence, employment, schooling, and social roles compared with previous generations. Parental SES is an important influence during adolescence but cannot sufficiently capture the SES of the independent young adult. Typical, single SES indicators based on income or education likely misclassify the SES of young adults who have not yet completed their education or other training, or who have entered the labor force early with ultimately lower status attainment. We use a latent class analysis (LCA) framework to characterize five intergenerational SES groups, combining multidimensional SES information from two time points—that is, adolescent (parental) and young adult (self) SES data. Associations of these groups with obesity, a high-risk health outcome in young adults, revealed nuanced relationships not seen using traditional intergenerational SES measures. In males, for example, a middle-class upbringing in adolescence and continued material advantage into adulthood was associated with nearly as high obesity as a working poor upbringing and early, detrimental transitions. This intergenerational typology of early SES exposure facilitates understanding of SES and health during young adulthood.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Arbuckle, J. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 243–278). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Baltrus, P. T., Lynch, J. W., Everson-Rose, S., Raghunathan, T. E., & Kaplan, G. A. (2005). Race/ethnicity, life-course socioeconomic position, and body weight trajectories over 34 years: The Alameda County Study. American Journal of Public Health, 95, 1595–1601.

    Article  Google Scholar 

  • Benoit-Smullyan, E. (1944). Status, status types, and interrelations. American Sociological Review, 9, 151–161.

    Article  Google Scholar 

  • Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: Conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31, 285–293.

    Article  Google Scholar 

  • Blau, P. M., & Duncan, O. D. (1967). The American occupational structure. New York: John Wiley and Sons.

    Google Scholar 

  • Bumpass, L., & McLanahan, S. (1989). Unmarried motherhood: Recent trends, composition, and black-white differences. Demography, 26, 279–286.

    Article  Google Scholar 

  • Burholt, V. (1999). Testing a behavioural and a developmental model of migration: A reevaluation of migration patterns among the elderly and why older people move. Environment and Planning A, 31, 2071–2088.

    Article  Google Scholar 

  • Carson, A. P., Rose, K. M., Catellier, D. J., Kaufman, J. S., Wyatt, S. B., Diez-Roux, A. V., & Heiss, G. (2007). Cumulative socioeconomic status across the life course and subclinical atherosclerosis. Annals of Epidemiology, 17, 296–303.

    Article  Google Scholar 

  • Clogg, C. C. (1980). Characterizing the class organization of labor market opportunity: A modified latent structure approach. Sociological Methods & Research, 8, 243–272.

    Article  Google Scholar 

  • Clogg, C. C. (1995). Latent class models. In G. Arminger, C. C. Clogg, & M. E. Sobel (Eds.), Handbook of statistical modeling for the social and behavioral sciences (pp. 311–359). New York: Plenum.

    Google Scholar 

  • Collins, L. M., Hyatt, S. L., & Graham, J. W. (2000). LTA as a way of testing models of stage-sequential change in longitudinal data. In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multiple-group data: Practical issues, applied approaches, and specific examples (pp. 147–161). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Crissey, S. R. (2005). Race/ethnic differences in the marital expectations of adolescents: The role of romantic relationships. Journal of Marriage and Family, 67, 697–709.

    Article  Google Scholar 

  • De Jong, G. F., & Madamba, A. B. (2001). A double disadvantage? Minority group, immigrant status, and underemployment in the United States. Social Science Quarterly, 82, 117–130.

    Article  Google Scholar 

  • Desantis, S. M., Houseman, E. A., Coull, B. A., Stemmer-Rachamimov, A., & Betensky, R. A. (2007). A penalized latent class model for ordinal data. Biostatistics, 9, 249–262.

    Article  Google Scholar 

  • Everitt, B. S. (1988). A finite mixture model for the clustering of mixed-mode data. Statistics & Probability Letters, 6, 305–309.

    Article  Google Scholar 

  • Everitt, B. S. (1993). Cluster analysis. London: Edward Arnold.

    Google Scholar 

  • Featherman, D. L., & Hauser, R. M. (1978). Opportunity and change. New York: Academic Press.

    Google Scholar 

  • Goldscheider, F. K., Thornton, A., & Yang, L.-S. (2001). Helping out the kids: Expectations about parental support in young adulthood. Journal of Marriage and Family, 63, 727–740.

    Article  Google Scholar 

  • Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215–231.

    Article  Google Scholar 

  • Goodman, E., Hinden, B. R., & Khandelwal, S. (2000). Accuracy of teen and parental reports of obesity and body mass index. Pediatrics, 106(1,Pt 1), 52–58.

    Article  Google Scholar 

  • Gordon-Larsen, P., Adair, L. S., Nelson, M. C., & Popkin, B. M. (2004). Five-year obesity incidence in the transition period between adolescence and adulthood: The national longitudinal study of adolescent health. The American Journal of Clinical Nutrition, 80, 569–575.

    Google Scholar 

  • Greenland, S. (2004). Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies. American Journal of Epidemiology, 160, 301–305.

    Article  Google Scholar 

  • Hagenaars, J., & McCutcheon, A. C. (2002). Applied latent class analysis models. New York: Cambridge University Press.

    Book  Google Scholar 

  • Hallqvist, J., Lynch, J., Bartley, M., Lang, T., & Blane, D. (2004). Can we disentangle life course processes of accumulation, critical period and social mobility? An analysis of disadvantaged socio-economic positions and myocardial infarction in the Stockholm Heart Epidemiology Program. Social Science & Medicine, 58, 1555–1562.

    Article  Google Scholar 

  • Harris, K. M., Gordon-Larsen, P., Chantala, K., & Udry, J. R. (2006). Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood. Archives of Pediatrics & Adolescent Medicine, 160, 74–81.

    Article  Google Scholar 

  • Harris, K. M., Halpern, C. T., Whitsel, E., Hussey, J., Tabor, J., Entzel, P., & Udry, J. R. (2009). The national longitudinal study of adolescent health: Research design [WWW document]. Retrieved from http://www.cpc.unc.edu/projects/addhealth/design

  • Hart, C. L., Smith, G. D., & Blane, D. (1998a). Inequalities in mortality by social class measured at 3 stages of the lifecourse. American Journal of Public Health, 88, 471–474.

    Article  Google Scholar 

  • Hart, C. L., Smith, G. D., & Blane, D. (1998b). Social mobility and 21 year mortality in a cohort of Scottish men. Social Science & Medicine, 47, 1121–1130.

    Article  Google Scholar 

  • Heard, H. E. (2007). The family structure trajectory and adolescent school performance: Differential effects by race and ethnicity. Journal of Family Issues, 28, 319–354.

    Article  Google Scholar 

  • Holtgrave, D. R., & Crosby, R. (2006). Is social capital a protective factor against obesity and diabetes? Findings from an exploratory study. Annals of Epidemiology, 16, 406–408.

    Article  Google Scholar 

  • House, J. S., & Harkins, E. B. (1975). Why and when is status inconsistency stressful? The American Journal of Sociology, 81, 395–412.

    Article  Google Scholar 

  • Iacovou, M. (2002). Regional differences in the transition to adulthood. The Annals of the American Academy of Political and Social Science, 580, 40–69.

    Article  Google Scholar 

  • James, S. A., Fowler-Brown, A., Raghunathan, T. E., & Van Hoewyk, J. (2006). Life-course socioeconomic position and obesity in African American Women: The Pitt County study. American Journal of Public Health, 96, 554–560.

    Article  Google Scholar 

  • Jensen, L., & Slack, T. (2003). Underemployment in America: Measurement and evidence. American Journal of Community Psychology, 32, 21–31.

    Article  Google Scholar 

  • Jorgensen, M., & Hunt, L. (1996). Mixture model clustering of data sets with categorical and continuous variables. In D. L. Dowe, K. B. Korb, & J. J. Oliver (Eds.), Proceedings of the conference, information, statistics and induction in science (pp. 375–384). Melbourne, Australia: World Scientific.

    Google Scholar 

  • Kalleberg, A. (1988). Comparative perspectives on work structures and inequality. Annual Review of Sociology, 14, 203–225.

    Article  Google Scholar 

  • Kaplan, D. (2004). The Sage handbook of quantitative methodology in the social sciences. Newbury Park, CA: Sage Publications.

    Google Scholar 

  • Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: John Wiley and Sons.

    Google Scholar 

  • Kawachi, I., & Berkman, L. F. (2001). Social ties and mental health. Journal of Urban Health, 78, 458–467.

    Google Scholar 

  • Kawachi, I., Kim, D., Coutts, A., & Subramanian, S. (2004). Commentary: Reconciling the three accounts of social capital. International Journal of Epidemiology, 33, 682–690.

    Article  Google Scholar 

  • Kim, D., Subramanian, S. V., Gortmaker, S. L., & Kawachi, I. (2006). US state- and county-level social capital in relation to obesity and physical inactivity: A multilevel, multivariable analysis. Social Science & Medicine, 63, 1045–1059.

    Article  Google Scholar 

  • Kleinbaum, D. G., Kupper, L. L., Muller, K. E., & Nizam, A. (1998). Applied regression analysis and other multivariate methods (3rd ed., pp. 250–251). Pacific Grove: Duxbury Thomson Learning.

    Google Scholar 

  • Kreider, R. M., & Fields, J. (2005). Living arrangements of children: 2001 (Current population reports P70-104). Washington, DC: U.S. Census Bureau.

    Google Scholar 

  • Krieger, N., Williams, D. R., & Moss, N. E. (1997). Measuring social class in US public health research: Concepts, methodologies, and guidelines. Annual Review of Public Health, 18, 341–378.

    Article  Google Scholar 

  • Kuh, D., Ben-Shlomo, Y., Lynch, J., Hallqvist, J., & Power, C. (2003). Life course epidemiology. Journal of Epidemiology and Community Health, 57, 778–783.

    Article  Google Scholar 

  • Lanza, S. T., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis. In J. A. Schinka & W. F. Velicer (Eds.), Handbook of psychology: Vol. 2. Research methods in psychology (pp. 663–685). Hoboken, NJ: Wiley.

    Google Scholar 

  • Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 14, 671–694.

    Google Scholar 

  • Lazarsfeld, P., & Henry, N. (1968). Latent structure analysis. New York: Houghton-Mifflin.

    Google Scholar 

  • Lin, T. H., & Dayton, C. M. (1997). Model selection information criteria for non-nested latent class models. Journal of Educational and Behavioral Statistics, 22, 249–264.

    Google Scholar 

  • Lumley, T., Kronmal, R., & Ma, S. (2006). Relative risk regression in medical research: Models, contrasts, estimators, and algorithms (UW Biostatistics Working Paper No. 293). Berkeley, CA: The Berkeley Electronic Press (bepress).

  • Lynch, J. W., Kaplan, G. A., & Shema, S. J. (1997). Cumulative impact of sustained economic hardship on physical, cognitive, psychological, and social functioning. The New England Journal of Medicine, 337, 1889–1895.

    Article  Google Scholar 

  • McCutcheon, A. C. (1987). Latent class analysis. Beverly Hills, CA: Sage Publications.

    Google Scholar 

  • McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: John Wiley.

    Book  Google Scholar 

  • McLanahan, S. (1985). Family structure and the reproduction of poverty. The American Journal of Sociology, 90, 873–901.

    Article  Google Scholar 

  • McLanahan, S. (2004). Diverging destinies: How children are faring under the second demographic transition. Demography, 41, 607–627.

    Article  Google Scholar 

  • McLoyd, V. C., Cauce, A. M., Takeuchi, D., & Wilson, L. (2000). Marital processes and parental socialization in families of color: A decade review of research. Journal of Marriage and the Family, 62, 1070–1093.

    Article  Google Scholar 

  • McNutt, L. A., Wu, C., Xue, X., & Hafner, J. P. (2003). Estimating the relative risk in cohort studies and clinical trials of common outcomes. American Journal of Epidemiology, 157, 940–943.

    Article  Google Scholar 

  • McTigue, K. M., Garrett, J. M., & Popkin, B. M. (2002). The natural history of the development of obesity in a cohort of young U.S. adults between 1981 and 1998. Annals of Internal Medicine, 136, 857–864.

    Google Scholar 

  • Messineo, M. (2005). Influence of expectations for parental support on intergenerational coresidence behavior. Journal of Intergenerational Relationships, 3(3), 47–64.

    Article  Google Scholar 

  • Moore, S., Daniel, M., Paquet, C., Dube, L., & Gauvin, L. (2009). Association of individual network social capital with abdominal adiposity, overweight and obesity. Journal of Public Health, 31, 175–183.

    Article  Google Scholar 

  • Moors, G. (2008). The valued child. In search of a latent attitude profile that influences the transition to motherhood. European Journal of Population, 24, 33–57.

    Article  Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (1998–2006). Mplus user’s guide (4th ed.). Los Angeles, CA: Muthen & Muthen.

    Google Scholar 

  • NHLBI. (1998). Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: The evidence report. Obesity Research, 6(Suppl), 51S–209S.

    Google Scholar 

  • Nylund, K. L. (2007a). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569.

    Google Scholar 

  • Nylund, K. L. (2007b). Latent transition analysis: Modeling extensions and an application to peer victimization (Unpublished doctoral dissertation). University of California, Los Angeles.

    Google Scholar 

  • Oakes, J. M., & Rossi, P. H. (2003). The measurement of SES in health research: Current practice and steps toward a new approach. Social Science & Medicine, 56, 769–784.

    Article  Google Scholar 

  • Ogden, C. L., Carroll, M. D., Curtin, L. R., McDowell, M. A., Tabak, C. J., & Flegal, K. M. (2006). Prevalence of overweight and obesity in the United States, 1999–2004. JAMA, 295, 1549–1555.

    Article  Google Scholar 

  • Osgood, D. W., Ruth, G., Eccles, J. S., Jacobs, J. E., & Barber, B. L. (2004). Six paths to adulthood: Fast starters, parents without careers, educated partners, educated single, working singles, and slow starters. In R. A. Settersten, F. F. Furstenberg, & R. G. Rumbaut (Eds.), On the frontier of adulthood: Theory, research, and public policy (pp. 320–355). Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Pastor, D. A., Barron, K. E., Miller, B. J., & Davis, S. L. (2006). A latent profile analysis of college students' achievement goal orientation. Contemporary Educational Psychology, 32, 8–47.

    Article  Google Scholar 

  • Pollitt, R. A., Rose, K. M., & Kaufman, J. S. (2005). Evaluating the evidence for models of life course socioeconomic factors and cardiovascular outcomes: A systematic review. BMC Public Health, 5, 7.

    Article  Google Scholar 

  • Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. New York: Simon and Schuster.

    Google Scholar 

  • Rindfuss, R. R. (1991). The young adult years: Diversity, structural change, and fertility. Demography, 28, 493–512.

    Article  Google Scholar 

  • Rosner, B. (2000). Fundamentals of biostatistics (5th ed.). Pacific Grove, CA: Duxbury Thomson Learning.

    Google Scholar 

  • Rosvall, M., Chaix, B., Lynch, J., Lindstrom, M., & Merlo, J. (2006). Similar support for three different life course socioeconomic models on predicting premature cardiovascular mortality and all-cause mortality. BMC Public Health, 6, 203.

    Article  Google Scholar 

  • Sandefur, G. D., Mclanahan, S., & Wojtkiewicz, R. A. (1992). The effects of parental marital status during adolescence on high school graduation. Social Forces, 71, 103–121.

    Article  Google Scholar 

  • Scharoun-Lee, M., Adair, L. S., Kaufman, J. S., & Gordon-Larsen, P. (2009). Obesity, race/ethnicity and the multiple dimensions of socioeconomic status during the transition to adulthood: A factor analysis approach. Social Science & Medicine, 68, 708–716.

    Article  Google Scholar 

  • Schoeni, R., & Ross, K. (2004). Material Assistance from Families during the Transition to Adulthood. In R. A. Settersten, F. F. Furstenberg, & R. G. Rumbaut (Eds.), On the frontier of adulthood: Theory, research, and public policy (pp. 396–416). Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Schoon, I. (2008). A transgenerational model of status attainment: The potential mediating role of school motivation and education. National Institute Economic Review, 205(1), 72–82.

    Article  Google Scholar 

  • Schwartz, G. (1978). Estimating the dimensions of a model. Annals of Statistics, 6, 461–464.

    Article  Google Scholar 

  • Sewell, W. H., & Hauser, R. M. (1975). Education, occupation & earnings: Achievement in the early career. New York: Academic Press.

    Google Scholar 

  • Shanahan, M. J., Porfeli, E. J., Mortimer, J. T., & Erickson, L. D. (2004). Subjective age identity and the transition to adulthood: When do adolescents become adults? In R. A. Settersten, F. F. Furstenberg, & R. G. Rumbaut (Eds.), On the frontier of adulthood: Theory, research, and public policy (pp. 225–255). Chicago, IL: University of Chicago Press.

    Google Scholar 

  • Singh-Manoux, A., Ferrie, J. E., Chandola, T., & Marmot, M. (2004). Socioeconomic trajectories across the life course and health outcomes in midlife: Evidence for the accumulation hypothesis? International Journal of Epidemiology, 33, 1072–1079.

    Article  Google Scholar 

  • Slack, T., & Jensen, L. (2002). Race, ethnicity and underemployment in nonmetropolitan America: A 30-year profile. Rural Sociology, 67, 208–233.

    Article  Google Scholar 

  • Smith, G. D., & Hart, C. (2002). Life-course socioeconomic and behavioral influences on cardiovascular disease mortality: The collaborative study. American Journal of Public Health, 92, 1295–1298.

    Article  Google Scholar 

  • Snyder, A. R., & McLaughlin, D. K. (2004). Female-headed families and poverty in rural America. Rural Sociology, 69, 127–149.

    Article  Google Scholar 

  • Sobal, J. (1991). Obesity and socioeconomic status: A framework for examining relationships between physical and social variables. Medical Anthropology, 13, 231–247.

    Article  Google Scholar 

  • Sobal, J., & Stunkard, A. J. (1989). Socioeconomic status and obesity: A review of the literature. Psychological Bulletin, 105, 260–275.

    Article  Google Scholar 

  • StataCorp. (2007). Stata statistical software: Release 9.2. College Station, TX: Stata Corporation.

    Google Scholar 

  • Teachman, J. D., Tedrow, L. M., & Crowder, K. D. (2000). The changing demography of America’s families. Journal of Marriage and the Family, 62, 1234–1246.

    Article  Google Scholar 

  • Thomson, E., Hanson, T. L., & McLanahan, S. S. (1994). Family structure and child well-being: Economic resources vs. parental behaviors. Social Forces, 73, 221–242.

    Article  Google Scholar 

  • Uebersax, J. (2001–2003). Latent class analysis. Retrieved from http://www.john-uebersax.com/stat

  • Usdansky, M., & McLanahan, S. (2003). Looking for Murphy Brown: Are college-educated, single mothers unique? (Working Paper No. 03-05-FF). Princeton, NJ: Center for Research on Child Wellbeing.

    Google Scholar 

  • Vermunt, J. K., & Magidson, J. (2000). Latent class cluster analysis. In J. Hagenaars & A. C. McCutcheon (Eds.), Applied latent class analysis (pp. 89–106). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Wothke, W. (2000). Longitudinal and multi-group modeling with missing data. In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multiple group data: Practical issues, applied approaches and specific examples (pp. 219–240). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Wright, E. O. (1979). Class structure and income determination. New York: Academic Press.

    Google Scholar 

  • Zhan, M., & Pandey, S. (2004). Postsecondary education and economic well-being of single mothers and single fathers. Journal of Marriage and Family, 66, 661–673.

    Article  Google Scholar 

  • Zou, G. (2004). A modified poisson regression approach to prospective studies with binary data. American Journal of Epidemiology, 159, 702–706.

    Article  Google Scholar 

Download references

Acknowledgements

This analysis was supported by the National Institutes of Health, NICHD, Ruth L. Kirshstein (NRSA) F31-HD049334 and R01HD057194. This work was done while Dr. Scharoun-Lee was a graduate student at the University of North Carolina. The authors would like to thank Dr. Glen Elder for his valuable comments on previous drafts of this manuscript. We also thank Mr. Tom Swasey for assistance with graphic analysis. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by Grant P01-HD31921, from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from Grant P01-HD31921 for this analysis. There are no potential or real conflicts of financial or personal interest with the financial sponsors of the scientific project.

Author information

Authors and Affiliations

  1. Carolina Population Center and Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Melissa Scharoun-Lee, Penny Gordon-Larsen, Linda S. Adair & Barry M. Popkin

  2. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada

    Jay S. Kaufman

  3. Carolina Population Center and Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Chirayath M. Suchindran

Authors
  1. Melissa Scharoun-Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Penny Gordon-Larsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linda S. Adair
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Barry M. Popkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jay S. Kaufman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chirayath M. Suchindran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Penny Gordon-Larsen.

Appendix

Appendix

Table 7 Description of parental and young adult SES variables used to define intergenerational SES using social mobility framework and latent class analysis in longitudinal sample with weights (N = 14,322) from the National Longitudinal Study of Adolescent Health
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Scharoun-Lee, M., Gordon-Larsen, P., Adair, L.S. et al. Intergenerational Profiles of Socioeconomic (Dis)advantage and Obesity During the Transition to Adulthood. Demography 48, 625–651 (2011). https://doi.org/10.1007/s13524-011-0024-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13524-011-0024-5

Keywords

Search

Navigation