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This article reviews how the Panel Study of Income Dynamics (PSID) has contributed to our understanding of the links between childhood economic conditions—in particular, the household incomes with very young children—and the economic attainment and health of those children when they reach adulthood. From its beginning, the PSID has provided data useful for addressing intergenerational questions. In the mid-1990s, PSID data supported a series of studies that link early childhood income to early adult attainments, particularly to completed schooling. At the same time, discoveries in neurobiology and epidemiology were beginning to provide details on the processes producing the observed correlations. These discoveries led to a more recent set of PSID-based studies that focus not only on labor market and behavioral outcomes, but also on links between income in the earliest stages of life (including the prenatal period) and adult health. Links between economic disadvantage in childhood and adult health, and the developmental neuroscience underlying those links, are promising areas for future research.

Policies directed at alleviating poverty rest on a set of assumptions regarding the demographic composition of the poor & the psychological dispositions of poor individuals. Evidence from a representative sample of 5,165 low-income individuals who participated in the 1969-1978 Panel Study of Income Dynamics shows that poverty is widespread but not usually persistent, & that the characteristics of the persistently poor do not conform to the conventional wisdom. The economic status of the poor does not appear to have been caused by psychological dispositions. Intergenerational data from the same study show generally weak links between poverty or welfare status of parents & that of their children. Public policies for dealing with poverty can be properly devised without attempting to resocialize poor people & without undue concern that poverty programs will generate dependency among the majority of those they help. 1 Table. Modified HA

Fixed- and random-effects models are often used to control for unmeasured personspecific effects when examining the influence of measured covariates on an outcome. To date, different estimators are used for fixed- and random-effects models, making it difficult to choose between these two alternatives. In this article, the authors outline a covariance-structure model that captures both fixed and random effects and provides a simple test for comparing them.