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Short and long-term relationship between physician density on infant mortality: a longitudinal econometric analysis

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  • Mansour Farahani

    ()
    (Harvard School of Public Health)

  • S. V. Subramanian

    (Harvard School of Public Health)

  • David Canning

    ()
    (Harvard School of Public Health)

Abstract

While countries with higher levels of human resources for health typically have better population health, the evidence that increases in the level of human resources for health leads to improvements in population health is limited. We provide estimates of short-run and long-term effects of physician density on infant mortality. We use a dynamic regression model that allows an estimation of both short- and long-run effects of physician density on infant mortality. We also used instrumental variables analysis to identify the causal effect of physician density on health. We estimate that increasing the number of physicians by one per 1,000 population decreases the infant mortality rate by 15% within five years and by 45% in the long-run. We find all countries are moving towards their own steady state at around 3% a year and are only half way there after 15 years. We conclude that the long-run effects of human resources for health are substantially larger than previously estimated. Our results suggest that health sector inputs can play a role in reducing infant mortality. However, meeting the Millennium Development Goal of reducing child mortality rate by two thirds from 1990 to 2015 would have required much earlier action.

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Bibliographic Info

Paper provided by Program on the Global Demography of Aging in its series PGDA Working Papers with number 4909.

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Date of creation: Jun 2009
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Handle: RePEc:gdm:wpaper:4909

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Web page: http://www.hsph.harvard.edu/pgda
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Keywords: Physician density; infant mortality; longitudinal; eocnometric;

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  1. David Cutler & Angus Deaton & Adriana Lleras-Muney, 2005. "The Determinants of Mortality," Working Papers 235, Princeton University, Woodrow Wilson School of Public and International Affairs, Center for Health and Wellbeing..
  2. R Blundell & Steven Bond, . "Initial conditions and moment restrictions in dynamic panel data model," Economics Papers W14&104., Economics Group, Nuffield College, University of Oxford.
  3. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  4. Mizon, Grayham E & Richard, Jean-Francois, 1986. "The Encompassing Principle and Its Application to Testing Non-nested Hypotheses," Econometrica, Econometric Society, vol. 54(3), pages 657-78, May.
  5. Or, Zeynep & Wang, Jia & Jamison, Dean, 2005. "International differences in the impact of doctors on health: a multilevel analysis of OECD countries," Journal of Health Economics, Elsevier, vol. 24(3), pages 531-560, May.
  6. Judson, Ruth A. & Owen, Ann L., 1999. "Estimating dynamic panel data models: a guide for macroeconomists," Economics Letters, Elsevier, vol. 65(1), pages 9-15, October.
  7. Richard Blundell & Steve Bond & Frank Windmeijer, 2000. "Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator," IFS Working Papers W00/12, Institute for Fiscal Studies.
  8. Aakvik, Arild & Holmas, Tor Helge, 2006. "Access to primary health care and health outcomes: The relationships between GP characteristics and mortality rates," Journal of Health Economics, Elsevier, vol. 25(6), pages 1139-1153, November.
  9. Hosung Jung, 2005. "A Test for Autocorrelation in Dynamic Panel Data Models," Hi-Stat Discussion Paper Series d04-77, Institute of Economic Research, Hitotsubashi University.
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