Abstract
In any fee-for-service system, doctors may be encouraged to increase the number of services (private activity) they provide to receive a higher income. Studying private activity determinants helps to predict doctors’ provision of care. In the context of strong feminization and heterogeneity in general practitioners’ (GP) behavior, we first aim to measure the effects of the determinants of private activity. Second, we study the evolution of these effects along the private activity distribution. Third, we examine the differences between male and female GPs. From an exhaustive database of French GPs working in private practice in 2008, we performed an ordinary least squares (OLS) regression and quantile regressions (QR) on the GPs’ private activity. Among other determinants, we examined the trade-offs within the GPs’ household considering his/her marital status, spousal income, and children. While the OLS results showed that female GPs had less private activity than male GPs (−13 %), the QR results emphasized a private activity gender gap that increased significantly in the upper tail of the distribution. We also find gender differences in the private activity determinants, including family structure, practice characteristics, and case-mix variables. For instance, having a youngest child under 12 years old had a positive effect on the level of private activity for male GPs and a negative effect for female GPs. The results allow us to understand to what extent the supply of care differs between male and female GPs. In the context of strong feminization, this is essential to consider for organizing and forecasting the GPs’ supply of care.
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Notes
67 %, according to Eco-Santé, Adeli. The remaining 33 % are salaried GPs practicing at a hospital or nursing home.
The median percentage of income earned from salaried activities for self-employed GPs who work part time as salaried workers is 10 %. These GPs are more likely to be women, to perform fewer acts, to conduct fewer home visits, and are more likely to be located in rural areas.
According to a list of illnesses and to coordinate for a referral doctor (médecin traitant).
Since the implementation of the ‘care pathway’ (2004), patients can consult with the GP they want, but they are required to report to the public insurer through a médecin traitant) who is in charge of coordinating the treatments and allowing access to secondary care (specialists).
MEP GPs are older, more likely to be women, practice less in rural areas, perform fewer services, and conduct fewer home visits, but they perform more specialized procedures than other GPs.
Any service provided by such a doctor is not compensated by the public health insurance and also by most complementary health insurance. This group includes 1.15 % of GPs.
We did not use the logarithm of the total number of services because the total number of services was better adjusted with a normal distribution.
We measure age in deviations from the age of 30 years.
Civil partnership corresponds to the civil solidarity pact, commonly known as a PACS, which is a form of civil union between two adults (same sex or opposite sex). The PACS was approved by the French Parliament in November 1999.
The elasticity calculation was \(- 0.027 = - 0.007\,*\,\frac{20811.94}{5329}\) for men and \(- 0.031 = - 0.002*\frac{61300.84}{3908}\) for women.
For female GPs, the difference between the coefficient for having a youngest child under 12 years of age and having a youngest child over 12 years old was significant (p < 0.05), whereas for male GPs, the difference was not significant.
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Acknowledgments
We are grateful to the two anonymous referees for helpful comments. We also thank the participants at XXX “Journées de Microéconomie appliquée”, at the 62nd Annual Meeting of the French Economic Association and at Journées des economistes de la santé. This research received financial support from the DREES (Direction of Research, Studies, Evaluation and Statistics) of the French Ministry of Health.
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Dumontet, M., Franc, C. Gender differences in French GPs’ activity: the contribution of quantile regressions. Eur J Health Econ 16, 421–435 (2015). https://doi.org/10.1007/s10198-014-0582-8
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DOI: https://doi.org/10.1007/s10198-014-0582-8