Social Rigidity across and within Generations: A Predictive Approach
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| Titel: | Social Rigidity across and within Generations: A Predictive Approach |
|---|---|
| Sprache: | English |
| Autoren: | Haowen Zheng (ORCID |
| Quelle: | Sociological Methods & Research. 2025 54(4):1683-1725. |
| Verfügbarkeit: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 43 |
| Publikationsdatum: | 2025 |
| Publikationsart: | Journal Articles Reports - Research |
| Descriptors: | Socioeconomic Status, Adults, Parent Background, Social Stratification, Experience, Prediction, Predictor Variables, Models, Social Mobility, Artificial Intelligence |
| Assessment and Survey Identifiers: | National Longitudinal Survey of Youth |
| DOI: | 10.1177/00491241251347984 |
| ISSN: | 0049-1241 1552-8294 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Dokumentencode: | EJ1485818 |
| Datenbank: | ERIC |
Nájsť tento článok vo Web of Science | 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. |
|---|---|
| ISSN: | 0049-1241 1552-8294 |
| DOI: | 10.1177/00491241251347984 |