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Determinants of Access to Physician Services in Italy: A Latent Class Seemingly Unrelated Probit Approach

  • Vincenzo Atella

    ()

    (University of Rome II - Faculty of Economics)

  • Francesco Brindisi

    ()

    (Columbia University - Department of Economics
    University of Rome II - Faculty of Economics)

  • Partha Deb

    ()

    (City University of New York - Department of Economics)

  • Furio C. Rosati

    ()

    (UCW Project - University of Rome II - Faculty of Economics)

We examine access to general practitioners, public and private specialists in Italy. We develop a novel model using finite mixtures of probit models that provides a rich and flexible functional form. The mixed distribution is flexible and can accommodate non-normality of response probabilities. The empirical analysis shows that patient behavior can be clustered in two latent classes, and that it changes according to the kind of physician service demanded and the latent class to which the individual belongs. We find that income strongly influences the mix of services. Richer individuals are less likely to seek care from GPs and more likely to seek care from specialists, and especially private specialists. Health status and societal vulnerability are the most important indicators of class membership

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Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 36.

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Length: 23
Date of creation: 26 Sep 2003
Date of revision:
Handle: RePEc:rtv:ceisrp:36
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  1. Gerdtham, Ulf-G. & Johannesson, Magnus, 2000. "Income-related inequality in life-years and quality-adjusted life-years," Journal of Health Economics, Elsevier, vol. 19(6), pages 1007-1026, November.
  2. Wedel, M, et al, 1993. "A Latent Class Poisson Regression Model for Heterogeneous Count Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 397-411, Oct.-Dec..
  3. John F. Geweke & Michael P. Keane, 1997. "Mixture of normals probit models," Staff Report 237, Federal Reserve Bank of Minneapolis.
  4. Gritz, R. Mark, 1993. "The impact of training on the frequency and duration of employment," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 21-51.
  5. Jonathan J. Morduch & Hall S. Stern, 1995. "Using Mixture Models to Detect Sex Bias in Health Outcomes in Bangladesh," Harvard Institute of Economic Research Working Papers 1728, Harvard - Institute of Economic Research.
  6. Wang, Peiming & Cockburn, Iain M & Puterman, Martin L, 1998. "Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 27-41, January.
  7. Shinichi Sakata & Halbert White, 1998. "High Breakdown Point Conditional Dispersion Estimation with Application to S&P 500 Daily Returns Volatility," Econometrica, Econometric Society, vol. 66(3), pages 529-568, May.
  8. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
  9. van Doorslaer, Eddy & Wagstaff, Adam & Bleichrodt, Han & Calonge, Samuel & Gerdtham, Ulf-G. & Gerfin, Michael & Geurts, Jose & Gross, Lorna & Hakkinen, Unto & Leu, Robert E., 1997. "Income-related inequalities in health: some international comparisons," Journal of Health Economics, Elsevier, vol. 16(1), pages 93-112, February.
  10. Propper, Carol, 2000. "The demand for private health care in the UK," Journal of Health Economics, Elsevier, vol. 19(6), pages 855-876, November.
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