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Bednets, Information and Malaria in Orissa

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  • Aprajit Mahajan
  • Alessandro Tarozzi
  • Joanne Yoong
  • Brian Blackburn

Abstract

We study the identification and estimation of key parameters in a basic model of technology adoption when specifically collected information on subjective beliefs and expectations about the technology’s impact is available. We discuss identification with both non-parametrically and parametrically specified utility as well as parametric and semi-parametric specifications for unobserved heterogeneity. We propose parametric and semi-parametric estimation methods to recover underlying preferences and use the model to study the adoption of bednets among poor households in rural Orissa (India). We carry out counterfactual exercises to examine the effects of price and belief changes on net ownership decisions. The results suggest that net purchase decisions are relatively insensitive to changes from current prices and beliefs. The methods proposes here should have applicability to other discrete choice settings with non-linear indices.

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Bibliographic Info

Paper provided by Duke University, Department of Economics in its series Working Papers with number 10-78.

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Length: 43
Date of creation: 2009
Date of revision:
Handle: RePEc:duk:dukeec:10-78

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Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/

Related research

Keywords: Malaria; Expectations; Bednets; Identification; Median Restrictions;

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References

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Cited by:
  1. Eddie Dekel & Barton L. Lipman, 2009. "How (Not) to Do Decision Theory," Levine's Working Paper Archive 814577000000000339, David K. Levine.
  2. Delavande, Adeline & Giné, Xavier & McKenzie, David, 2011. "Measuring subjective expectations in developing countries: A critical review and new evidence," Journal of Development Economics, Elsevier, vol. 94(2), pages 151-163, March.
  3. Bhattacharya, Debopam & Dupas, Pascaline, 2012. "Inferring welfare maximizing treatment assignment under budget constraints," Journal of Econometrics, Elsevier, vol. 167(1), pages 168-196.

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