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In the second stage, the dependent variable was whether an individual did or did not receive each of the following services within the past two years: mammograms, pap smears, blood pressure checkups, cholesterol level checkups, and flu shots. The probability of receiving a service within the past two years was the Y in the equation shown in the empirical model section. The explanatory variables were predisposing, enabling, need, and the endogeneity correction factors. A probit model was used in the second stage.Because the MEPS used a complex sampling design, person weight, primary sampling unit, and sample stratification were incorporated in the two-stage estimation to obtain nationally representative estimates and correct standard errors. The standard errors for the use of predicted probabilities were also adjusted by the weighting scheme of the MEPS. Computer software STATA[R] version 7 (Stata Corp: College Station, TX) was used to perform the regressions controlling for the complex sampling design. The most noticeable patterns in Table 4 are that, after controlling for endogeneity, (1) having a regular doctor has a greater impact on only two services than having a regular site and is as effective as having a regular site for the other three services, and (2) the magnitude of the impact of having a usual source of care differs by service. In descending order of the impact magnitude, having a regular doctor affected: cholesterol level checkups, pap smears, blood pressure checkups, flu shots, and mammograms. The ordered sequence for the impact magnitude of having a regular site was: pap smears, cholesterol vero level checkups, mammograms, blood pressure checkups, and flu shots. A regular doctor outperformed a regular site in ensuring that individuals have blood pressure and cholesterol level checkups. No significant differences were found in the effects of having a regular doctor and having a regular site on an individual''s receiving flu shots, mammograms, and pap smears. The control function model takes the form of a two-stage estimation. The first stage, the selection, predicts the probabilities of having no usual source of care, having a regular doctor, and having a regular site, respectively, given predisposing, enabling, and need factors. The second-stage estimation incorporates the transformations of the beach predicted probabilities from the first stage. Let [I.sub.0], [I.sub.d], and [I.sub.s], be binary dummy variables indicating having no usual source of care, having a regular doctor, and having a regular site. Without the control for selection, the prediction of service use can be written as
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