Table 2.
Bivariate probit model | Naïve probit model | |||||
---|---|---|---|---|---|---|
B | SE | p | B | SE | p | |
Health insurance (Ref: uninsured) | ||||||
Medicaid | .200 | .638 | .755 | .246 | .121 | .046 |
Medicare | .044 | .106 | .680 | .044 | .106 | .680 |
Private insurance | −.201 | .061 | .002 | −.201 | .061 | .002 |
Other insurance | .163 | .133 | .227 | .163 | .133 | .228 |
Female sex | −.180 | .052 | .001 | −.180 | .052 | .001 |
Age, years (Ref:18–29) | ||||||
30–39 | −.023 | .075 | .761 | −.023 | .075 | .761 |
40–54 | .020 | .069 | .776 | .020 | .069 | .776 |
55+ | −.555 | .108 | <.001 | −.555 | .108 | <.001 |
Race/ethnicity (Ref.: Non-Hispanic white) | ||||||
Non-Hispanic black | −.050 | .089 | .575 | −.050 | .089 | .575 |
Hispanic | −.038 | .085 | .654 | −.038 | .085 | .654 |
Other | −.126 | .157 | .423 | −.126 | .157 | .423 |
Personal income in $1,000 (Ref: <20) | ||||||
20-<35 | −.113 | .071 | .120 | −.113 | .071 | .120 |
35-<60 | −.290 | .087 | .001 | −.290 | .087 | .001 |
60+ | −.361 | .101 | .001 | −.361 | .101 | .001 |
SUD in past year (at T1) | .256 | .052 | <.001 | .256 | .052 | <.001 |
Constant | −1.266 | −1.268 | ||||
Model predicting individual Medicaid enrollment | ||||||
Instrumental variables | ||||||
% of low-income covered by Medicaid | .027 | .010 | .009 | |||
% of low-income covered by Medicaid among those with Medicaid or uninsured | .004 | .007 | .586 | |||
Constant | −2.672 | |||||
rho | .021 | .282 | .941 |
Abbreviations: T1 represents baseline interview and T2, the follow-up interview. B stands for the regression coefficient, SE, for standard error, and SSI for supplemental security income.
The models also adjusted for the state fixed effects. The full analyses results are presented in Appendix Table B.