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2019 | nr 35 | 106--123
Tytuł artykułu

Count Data Modelling of Health Insurance and Health Care Utilisation in Nigeria

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Aim/purpose - Estimation of the model of interdependent demand for health insurance and health care utilisation involves issues of stochastic dependence between health insurance and health care utilisation. This study explored a count data estimation technique to determine the most appropriate estimation method for the interdependence of health insurance and health care demand in Nigeria. Design/methodology/approach - The study employed Hidayat and Pokhrel (2010) framework to choose among the six alternatives of two classes of count data model. The data for the study were collected using a purposive sampling survey in the six geopolitical zones in Nigeria. Findings - The results showed that the general method of moments (GMM) estimator is preferable to model the determinants of medical care consumption with health insurance. Price of health care services is positively related to medical care consumption with health insurance and social health insurance. The income-medical care relationship indicated that medical care services are inferior good under private health insurance and a normal good with social health insurance during sick period. Research implications/limitations - The implication of this study is that the estimation method that accommodates endogenous regressors is the appropriate estimation technique for the interdependence of health insurance and health care utilisation. The limitation of this study is that the recall period was just six months prior to the survey. Originality/value/contribution - The study revealed that the estimation techniques for the interdependence of health insurance and health care utilisation must recognised the influence of individual and household characteristics on the decision to purchase health insurance and health care consumption. Hence, diagnostics tests are require to choose the most appropriate estimation technique. (original abstract)
Rocznik
Numer
Strony
106--123
Opis fizyczny
Twórcy
  • Federal University of Technology, Akure, Nigeria
  • Elizade University, Ilara-Mokin, Nigeria
Bibliografia
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  • Baum, C. F., Schaffer, M. E., & Stillman, S. (2003). Instrumental variables and GMM: estimation and testing. The Stata Journal, 3(1), 1-31.
  • Bolhaar, J., Lindeboom, M., & van der Klaauw, B. (2008). A dynamic analysis of the demand for health insurance and health care (Discussion Paper, No. 3698). Bonn: IZA.
  • Cameron, A. C., Trivedi, P. K., Milne F., & Piggott, J. (1988). A microeconometric model of the demand for health care and health insurance in Australia. Review of Economic Studies, 55(1), 85-106.
  • Cutler, D. M., & Zeckhauser, R. J. (2000). The anatomy of health insurance. In J. A. Culyer & J. P. Newhouse (Eds.), Handbook of Health Economics (pp. 563-643). Amsterdam: Elsevier.
  • Hidayat, B., & Pokhrel, S. (2010). The selection of an appropriate count data model for modelling health insurance and health care demand: Case of Indonesia. International Journal of Environmental Research and Public Health, 7, 9-27. doi: 10.3390/ijerph7010009
  • Jones, A. M. (2000). Health econometrics. In J. A. Culyer & J. P. Newhouse (Eds.), Handbook of health economics 1A (pp. 265-344). Amsterdam: Elsevier.
  • Koç, Ç. (2005). Health-specific moral hazard effects. Southern Economic Journal, 72(1), 98-118. doi: 10.2307/20062096
  • Liu, X., Nestic, D., & Vukina, T. (2012). Estimating adverse selection and moral hazard effects with hospital invoices data in a government-controlled health care system. Health Economics, 21(8), 883-901. doi: 10.1002/hec.1756
  • Newhouse, J. P., Sloss, E. M., Manning, W. G., & Keeler, E. B. (1993). Risk adjustment for a children's capitation rate. Health Care Financing Review, 15(1), 39-54.
  • Pagan, A. R., & Hall, D. (1983). Diagnostic tests as residual analysis. Econometric Review, 2, 159-218. doi: 10.1080/07311768308800039
  • Shea, J. (1997). Instrumental relevance in multivariate linear models: A simple measure. The Review of Economics and Statistics, 79(2), 348-352. doi: 10.1162/rest.1997.79.2.348
  • Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65(3), 557-586. doi: 10.2307/2171753
  • Vera-Hernández, A. M. (1999). Duplicate coverage and demand for health care: The case of Catalonia. Health Economics, 8(7), 579-598.
  • Waters, H. R. (1999). Measuring the impact of health insurance with a correction for selection bias: A case study of Ecuador. Health Economics, 8(5), 473-483.
  • Windmeijer, F. A. G., & Santos-Silva, J. M. C. (1997). Endogeneity in count data models: An application to demand for health care. Journal of Applied Econometrics, 12, 281-294. doi:10.1002/(SICI)1099-1255(199705)12:3<281::AID-JAE436>3.0.CO;2-1
  • Zweifel, P., & Manning, W. G. (2000). Moral hazard and consumer incentives in health care. In J. A. Culyer & J. P. Newhouse (Eds.), Handbook of Health Economics (pp. 409-459). Amsterdam: Elsevier.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171545929

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