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 The sample composition of the 15,501 individuals is shown in Table 1. Predisposing variables were age, gender, race, ethnicity, education, marital status, region of residence, and MSA (Metropolitan Statistical Area) status. For need variables, individuals'' general health status was used. Family poverty level and insurance status were included as enabling factors. Having insurance was defined as having coverage for physician and hospital services. Insurance status was grouped into: no insurance for 12 months, insured for 1-11 months, insured for 12 months by different types of plans, and insured for 12 months by the same plan. The probabilities of having no usual source of care, having a regular doctor, and having a regular site in the first stage were estimated by a multinomial logit model. In the selection stage, a reduced-form estimation was employed. The probabilities of having no usual source of care, a regular doctor, and a regular site were predicted for each individual. These probabilities were then transformed into the endogeneity correction terms. Whether an individual had children and the total number of conditions the children had were included as two additional exogenous variables appearing only in the selection stage. Y = X[beta] + [y.sub.1][I.sub.d] + [y.sub.2][I.sub.s] + [mu]where X represents the vector of predisposing, needs, and other enabling factors. Incorporating the selection using the control function model, we haveY = X[beta] + [y.sub.1][I.sub.d] + [y.sub.2][I.sub.s] + [[alpha].sub.0](1 - [I.sub.d] - [I.sub.s])[[lambda].sub.0] + [[alpha].sub.d][I.sub.d][[lambda].sub.d] + [[alpha].sub.s][I.sub.s][[lambda].sub.s] + vThe [lambda]''s are the transformations of the probabilities predicted from the first stage (Heckman 1978; 1979; 1990; Lee 1982; 1983). They are the correction factors similar to the correction factor (inverse Mill''s ratio) in a Heckman correction model where the response variable in the first stage is binary rather than multicategorical (Heckman 1979; 1990). Vella and Verbeek (1999) showed that testing for the presence of endogeneity is equivalent to testing whether [alpha]''s are zero. With the incorporation of the endogeneity control, the effects of having a regular doctor ([I.sub.d]) and having a regular site ([I.sub.s]), conditional on the choice of a usual source of care, are [y.sub.1]+([[alpha].sub.d][[lambda].sub.d]- [[alpha].sub.0],[[lambda].sub.0]) and [y.sub.2]+([[alpha].sub.s][[lambda].sub.s] - [[alpha].sub.0][[lambda].sub.0]), respectively.* A single-equation estimation that regresses utilization over having a usual source of care would provide biased results of the effect of having a usual source of care because of the omitted correction terms. With Doctor-Finder.biz you can find and compare docotor's in your area! Check it out!
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