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Estimating risk preferences in the presence of bifurcated wealth dynamics: can we identify static risk aversion amidst dynamic risk responses?

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  • Travis J. Lybbert
  • David R. Just
  • Christopher B. Barrett

Abstract

Estimating risk preferences is tricky because controlling for confounding factors is difficult. Omitting or imperfectly controlling for these factors can attribute too much observable behaviour to risk aversion and bias estimated preferences. Agents often modify risky decisions in response to dynamic wealth or asset thresholds, where such thresholds exist. Ignoring this dynamic risk response introduces an attribution bias in static estimates of risk aversion. We demonstrate this pitfall using a simple model and a Monte Carlo simulation to explore the implications of this problem for empirical estimation. While an approach that jointly estimates risk preferences and wealth dynamics may remedy the problem by extracting dynamic risk responses from observed behaviour, it is likely to be challenging to implement in broader empirical settings for reasons we discuss. , Oxford University Press.

Suggested Citation

  • Travis J. Lybbert & David R. Just & Christopher B. Barrett, 2013. "Estimating risk preferences in the presence of bifurcated wealth dynamics: can we identify static risk aversion amidst dynamic risk responses?," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 40(2), pages 361-377, March.
    • Handle: RePEc:oup:erevae:v:40:y:2013:i:2:p:361-377
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    File URL: http://hdl.handle.net/10.1093/erae/jbs027
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    Cited by:

    1. Piraino Patrizio, 2020. "Drivers of mobility," WIDER Working Paper Series wp2020-6, World Institute for Development Economic Research (UNU-WIDER).
    2. Matteo Giuliani & Andrea Castelletti, 2016. "Is robustness really robust? How different definitions of robustness impact decision-making under climate change," Climatic Change, Springer, vol. 135(3), pages 409-424, April.
    3. Linden McBride & Leah Bevis, 2019. "Working Paper 311 - Risk, Returns, and Welfare," Working Paper Series 2437, African Development Bank.
    4. Carpentier, Alain & Gohin, Alexandre & Sckokai, Paolo & Thomas, Alban, 2015. "Economic modelling of agricultural production: past advances and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 131-165, March.
    5. Buchholz, Matthias & Musshoff, Oliver, 2014. "The role of weather derivatives and portfolio effects in agricultural water management," Agricultural Water Management, Elsevier, vol. 146(C), pages 34-44.
    6. Buchholz, Matthias & Holst, Gesa & Musshoff, Oliver, 2015. "Water and irrigation policy impact assessment using business simulation games: Evidence from northern Germany," DARE Discussion Papers 1505, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
    7. Jensen, Nathaniel D. & Mude, Andrew G. & Barrett, Christopher B., 2018. "How basis risk and spatiotemporal adverse selection influence demand for index insurance: Evidence from northern Kenya," Food Policy, Elsevier, vol. 74(C), pages 172-198.
    8. Takahashi, Kazushi & Ikegami, Munenobu & Sheahan, Megan & Barrett, Christopher B., 2016. "Experimental Evidence on the Drivers of Index-Based Livestock Insurance Demand in Southern Ethiopia," World Development, Elsevier, vol. 78(C), pages 324-340.
    9. Patrizio Piraino, 2020. "Drivers of mobility," WIDER Working Paper Series wp-2020-6, World Institute for Development Economic Research (UNU-WIDER).

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