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Attrition in Models of Intergenerational Links Using the PSID with Extensions to Health and to Sibling Models
The B.E. Journal of Economic Analysis & Policy
|February 28, 2012
Summary
Selective attrition in surveys can bias results. This study found that while some estimates are affected, the Panel Study of Income Dynamics (PSID) generally remains representative, with sibling correlations largely unaffected by attrition.
Area of Science:
- Sociology
- Economics
- Public Health
Background:
- Selective attrition in longitudinal surveys can introduce bias into estimates of intergenerational links.
- The Panel Study of Income Dynamics (PSID) is a key resource for studying intergenerational mobility.
- Understanding attrition bias is crucial for accurate analysis of health and economic status transmission.
Purpose of the Study:
- To document attrition in the PSID for a cohort of children through 2007.
- To investigate attrition bias in intergenerational models predicting adult health, education, and earnings.
- To assess the impact of attrition on sibling difference models.
Main Methods:
- Analysis of attrition in the PSID cohort data up to 2007.
- Comparison of weighted PSID representativeness with the National Health Interview Survey (NHIS).
- Estimation of intergenerational models (individual and sibling-based) with and without covariates.
Main Results:
- The weighted PSID generally maintained representativeness compared to NHIS, despite attrition affecting unconditional means.
- Sibling correlations in outcomes and father-son earnings correlations were not significantly affected by attrition.
- Models with covariates showed mixed results: females had few robust impacts of attrition, while males showed potential bias in education and earnings.
Conclusions:
- Attrition in the PSID has limited impact on sibling correlations and father-son earnings correlations.
- Attrition bias is more pronounced in male education and earnings models with covariates.
- Sibling models did not consistently show higher attrition impacts than individual models.
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