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Access to primary care and workers’ opportunity costs. Evidence from Italy

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Author Info
De Luca, Giuliana
Ponzo, Michela
Abstract

This paper explores whether and to which extent employment condition and working hours influence individuals’ decision process in consuming primary care. The hypothesis is that the higher the workers’ opportunity cost in terms of earning forgone, the less the demand for General Practitioner (GP) visits. Data used in the analysis come from the 2004/2005 “Health conditions and recourse to health services” survey provided by the Italian National Institute of Statistics (ISTAT). We apply a negative binomial regression to model the relationship between the number of GP visits and employment related variables, controlling for a rich set of individual demographic characteristics, socio-economic variables, health status, supply and geographical factors. We show that self-employed workers, managers and cadres use significantly less primary care services notwithstanding the access is free. We interpret these findings as being due to the fact that these type of workers have higher opportunity costs than white and blue collars, since they suffer more from the loss of earnings related to the absence from work

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 15479.

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Date of creation: May 2009
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Handle: RePEc:pra:mprapa:15479

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Related research
Keywords: Opportunity cost; hours of work; utilisation of GP; labour market.;

Find related papers by JEL classification:
I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
I10 - Health, Education, and Welfare - - Health - - - General

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  1. Duan, Naihua, et al, 1983. "A Comparison of Alternative Models for the Demand for Medical Care," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 115-26, April.
  2. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December. [Downloadable!] (restricted)
  3. Hugh Gravelle & Matthew Sutton & Stephen Morris & Frank Windmeijer & Alastair Leyland & Chris Dibben & Mike Muirhead, 2003. "Modelling supply and demand influences on the use of health care: implications for deriving a needs-based capitation formula," Health Economics, John Wiley & Sons, Ltd., vol. 12(12), pages 985-1004. [Downloadable!]
  4. Fernandez-Olano, C. & Hidalgo, J.D. Lopez-Torres & Cerda-Diaz, R. & Requena-Gallego, M. & Sanchez-Castano, C. & Urbistondo-Cascales, L. & Otero-Puime, A., 2006. "Factors associated with health care utilization by the elderly in a public health care system," Health Policy, Elsevier, vol. 75(2), pages 131-139, January. [Downloadable!] (restricted)
  5. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July. [Downloadable!] (restricted)
  6. Cameron, A C & P. K. Trivedi & Frank Milne & J. Piggott, 1988. "A Microeconometric Model of the Demand for Health Care and Health Insurance in Australia," Review of Economic Studies, Blackwell Publishing, vol. 55(1), pages 85-106, January. [Downloadable!] (restricted)
  7. Eddy van Doorslaer & Xander Koolman & Andrew M. Jones, 2004. "Explaining income-related inequalities in doctor utilisation in Europe," Health Economics, John Wiley & Sons, Ltd., vol. 13(7), pages 629-647. [Downloadable!]
  8. Grossman, Michael, 1982. "The demand for health after a decade," Journal of Health Economics, Elsevier, vol. 1(1), pages 1-3, May. [Downloadable!] (restricted)
  9. Greene, William, 2008. "Functional forms for the negative binomial model for count data," Economics Letters, Elsevier, vol. 99(3), pages 585-590, June. [Downloadable!] (restricted)
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