Loss aversion and the demand for index insurance

Immanuel Lampe and Daniel Würtenberger

Journal of Economic Behavior & Organization, 2020, vol. 180, issue C, 678-693

Abstract: This work analyzes if reference dependence and loss aversion can explain the puzzling low adoption rates of rainfall index insurance. We present a model that predicts the impact of loss aversion on index insurance demand to vary with different levels of insurance understanding. Index insurance demand of farmers who are unaware of the loss-hedging benefit that insurance provides decreases with loss aversion. In contrast, insurance demand of farmers who are aware of the loss-hedging benefit increases with loss aversion. The model further predicts that farmers who are unaware of the loss-hedging benefit will not demand an even highly subsidized index insurance. Using data from a randomized controlled trial involving a sample of Indian farmers we provide empirical support for our core conjecture that insurance understanding mitigates the negative impact of loss aversion on index insurance adoption.

Keywords: Prospect theory; Reference dependence; Microinsurance; Farm household (search for similar items in EconPapers)
JEL-codes: D91 G22 Q12 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167268119303269
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:180:y:2020:i:c:p:678-693

DOI: 10.1016/j.jebo.2019.10.019

Access Statistics for this article

Journal of Economic Behavior & Organization is currently edited by Houser, D. and Puzzello, D.

More articles in Journal of Economic Behavior & Organization from Elsevier
Bibliographic data for series maintained by Catherine Liu ().