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An impact evaluation of the Safe Motherhood Program in China

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  • Xing Lin Feng
  • Guang Shi
  • Yan Wang
  • Ling Xu
  • Hao Luo
  • Juan Shen
  • Hui Yin
  • Yan Guo
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    Abstract

    Using 11 years of county-level panel data, fixed effect models are estimated to evaluate the impact of the Safe Motherhood (SM) Program in China. Propensity score matching is used to select comparable factual and counterfactual counties. Out of 2013 counties in China, 283 are selected for the treatment group and 1051 for the control group. The results support the causal relationship between the program and its targeted outcomes and the partial effects increases as years of exposure in the program: 7 years' treatment of the program increases hospital delivery rate by 3.992 per 100 live births and decreases Maternal mortality ratio (MMR) due to hemorrhage by 10.229 per 100 000 live births. Further modeling supports the conclusion that the program reduces MMR by enhancing MCH care. With an average annual incremental unit cost for the program of about 318.0 thousand RMB (39.8 thousand USD) per county, we conclude that the SM Program is effective in reducing MMR through the enhancement of hospital delivery. Copyright (C) 2010 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/hec.1593
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

    Volume (Year): 19 (2010)
    Issue (Month): S1 (September)
    Pages: 69-94

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    Handle: RePEc:wly:hlthec:v:19:y:2010:i:s1:p:69-94

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

    Related research

    Keywords: Safe Motherhood Program ; impact evaluation ; maternal mortality ; Millennium Development Goals ; propensity score matching ; fixed effect matching estimator ;

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    1. Louis S. Jacobson & Robert J. LaLonde & Daniel G. Sullivan, 1992. "Earnings losses of displaced workers," Working Paper Series, Macroeconomic Issues 92-28, Federal Reserve Bank of Chicago.
    2. Jeffrey Smith & Petra Todd, 2003. "Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20035, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
    3. Rajeev H. Dehejia & Sadek Wahba, 1998. "Propensity Score Matching Methods for Non-experimental Causal Studies," NBER Working Papers 6829, National Bureau of Economic Research, Inc.
    4. Smith, Jeffrey & Todd, Petra, 2005. "Rejoinder," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 365-375.
    5. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
    6. Sourafel Girma & David Paton, 2006. "Matching estimates of the impact of over-the-counter emergency birth control on teenage pregnancy," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 1021-1032.
    7. Heckman, James J & Ichimura, Hidehiko & Todd, Petra E, 1997. "Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 605-54, October.
    8. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
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    Cited by:
    1. Squire, Lyn & Jones, Andrew M & Thomas, Ranjeeta, 2010. "Evaluating Innovative Health Programs: Lessons for Health Policy," MPRA Paper 29205, University Library of Munich, Germany.
    2. Thomas, Ranjeeta & Jones, Andrew M & Squire, Lyn, 2010. "Methods for Evaluating Innovative Health Programs (EIHP): A Multi-Country Study," MPRA Paper 29402, University Library of Munich, Germany.

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