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Identification of Time-Inconsistent Models: The Case of Insecticide Treated Nets

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  • Mahajan, Aprajit
  • Michel, Christian
  • Tarozzi, Alessandro

Abstract

Time-inconsistency may play a central role in explaining inter-temporal behavior, particularly among poor households. However, little is known about the distribution of time-inconsistent agents, and time-preference parameters are typically not identified in standard dynamic choice models. We formulate a dynamic discrete choice model in an unobservedly heterogeneous population of possibly time-inconsistent agents. We provide conditions under which all population type probabilities and preferences for both time-consistent and sophisticated agents are point-identified and sharp set-identification results for \naive and partially sophisticated agents. Estimating the model using data from a health intervention providing insecticide treated nets (ITNs) in rural Orissa, India, we find that a little over two-thirds of our sample comprises time-inconsistent agents and that both sophisticated and naiive agents are considerably present-biased. Counterfactuals show that the under-investment in ITNs attributable to present-bias leads to substantial costs that are about five times the price of an ITN.

Suggested Citation

  • Mahajan, Aprajit & Michel, Christian & Tarozzi, Alessandro, 2023. "Identification of Time-Inconsistent Models: The Case of Insecticide Treated Nets," CEPR Discussion Papers 17888, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:17888
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    1. Esther Duflo & Michael Kremer & Jonathan Robinson, 2011. "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 101(6), pages 2350-2390, October.
    2. A. Norets & X. Tang, 2014. "Semiparametric Inference in Dynamic Binary Choice Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 1229-1262.
    3. van der Klaauw, Wilbert & Wolpin, Kenneth I., 2008. "Social security and the retirement and savings behavior of low-income households," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 21-42, July.
    4. Kremer, Michael & Duflo, Esther & Robinson, Jonathan, 2009. "Nudging Farmers to Utilize Fertilizer: Theory and Experimental Evidence from Kenya," CEPR Discussion Papers 7402, C.E.P.R. Discussion Papers.
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    Cited by:

    1. Gizem Koşar & Cormac O'Dea, 2022. "Expectations Data in Structural Microeconomic Models," NBER Working Papers 30094, National Bureau of Economic Research, Inc.
    2. Michel, Christian & Stenzel, André, 2021. "Model-based evaluation of cooling-off policies," Games and Economic Behavior, Elsevier, vol. 129(C), pages 270-293.
    3. Chao Wang & Stefan Weiergraeber & Ruli Xiao, 2022. "Identification of Dynamic Discrete Choice Models with Hyperbolic Discounting Using a Terminating Action," CAEPR Working Papers 2022-010 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    4. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    5. Friedman, Willa & Wilson, Nicholas, 2022. "Can nudging overcome procrastinating on preventive health investments?," Economics & Human Biology, Elsevier, vol. 45(C).

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    More about this item

    Keywords

    Time inconsistency; Partial identification; Mixture model; Expectations; Bednets; Dynamic discrete choice;
    All these keywords.

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • D9 - Microeconomics - - Micro-Based Behavioral Economics

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