Abstract
To measure the change in U.S. women and children's health insurance coverage as a result of welfare reform (i.e. the creation of Temporary Assistance for Needy Families or TANF) in 1996. 1992-1999 longitudinal data from the Survey of Income and Program Participation (SIPP) merged with data on the timing of state implementation of welfare reform after 1996. Two key advantages of the SIPP data are that they permit matching type of insurance coverage to the welfare policy environment in each state in each month, and permit controlling for individual-level fixed effects. We measure how much insurance coverage changed after welfare reform using a difference in differences method that eliminates the influence of time-invariant unobserved individual heterogeneity and of statewide trends in insurance coverage. Models also control for individual, state, and year fixed effects, individual-level characteristics such as education, age, and number of children, plus state-level variables such as real per capita income, real minimum wage, and Medicaid eligibility. We limit our analysis to the SIPP data specific to the month just completed prior to the interview; as a result, we have up to twelve observations for each individual in the SIPP. This paper uses pooled data from the 1992-1996 panels of the SIPP covering the period 1992-1999. Publicly available state identifiers permit the merger of state policies and macroeconomic variables with the SIPP. TANF implementation is associated with an 8.1 percent increase in the probability that a welfare-eligible woman was uninsured. Welfare reform had less of an impact on the health insurance coverage of children. For example, TANF implementation was associated with a 3.0 percent increase in the probability that a welfare-eligible child lacked health insurance. An unintended consequence of welfare reform was to adversely impact the health insurance coverage of economically vulnerable women and children, and that this impact was several times larger than the previous literature implies.
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