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Microinsurance Demand After a Rare Flood Event: Evidence From a Field Experiment in Pakistan

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Abstract

This paper examines the characteristics that determine demand for microinsurance when individuals have personal or observed experience with a rare weather event: the severe 2010 flooding in Pakistan. Using a sample of 384 individuals (192 in flood-affected and 192 non-affected villages matched using pre-flood propensity data), we combine post-flood survey data with behavioural experiments to test the impact of prior loss experience on willingness to purchase insurance. In the framed experiment setting, we allow participants to choose insurance payments through many rounds of random flood losses, testing whether experiment behaviour is significantly related to real-world experience or observation and whether individuals change insurance demand after experiencing losses. We find that 2010 flood-affected individuals demand significantly more insurance than non-affected individuals, and that both personal losses and observations of others’ losses are significant determinants of demand, when controlling for location-specific flood propensity, pre-flood mitigation, information sources, post-flood assistance, exogenous changes in assets, potential migrant attrition and other household characteristics. Having prior experience with less severe floods before the 2010 event also increases insurance demand, although the effect disappears when controlling for 2010 flood. Contrary to expectation, household beliefs that insurance is non-Islamic are not found to be a significant barrier to take-up.

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Notes

  1. Swiss Re (2013).

  2. Grislain-Letremy (2013); Kousky et al. (2013).

  3. Michel-Kerjan and Volkman-Wise (2011).

  4. Cavallo and Noy (2011).

  5. Arrow (1963).

  6. Mahul and Wright (2007).

  7. See also Rees and Wambach (2008).

  8. Grislain-Letrémy (2013).

  9. Kaplow (1991).

  10. Kunreuther and Pauly (2006).

  11. Deryugina (2013).

  12. Voors et al. (2010).

  13. Guha-Sapir and Santos (2013).

  14. Tversky and Kahneman (1973).

  15. Mullainathan (2002).

  16. Michel-Kerjan and Kousky (2010).

  17. Gallagher (2012).

  18. Aseervatham et al. (2013).

  19. Ehrlich and Becker (1972).

  20. Eckel et al. (2009); Cameron and Shah (2010); Zylberberg (2010); Fleming et al. (2012); Reynaud et al. (2012).

  21. Cameron and Shah (2010).

  22. Eckel et al. (2009).

  23. Jaspersen and Aseervatham (2013).

  24. EM-DAT (2012).

  25. Holt and Laury (2002).

  26. Caballero (2010).

  27. Arun et al. (2012).

  28. Andrabi and Das (2010).

  29. Cole et al. (2013).

  30. Bubeck et al. (2012).

  31. Meyer et al. (2012).

  32. Turner et al. (2014).

  33. www.punjab.gov.pk/?q=punjab_quick_stats

  34. A cluster was designated as flood affected only if all the households in the cluster responded to the question of being affected by the flood in 2010 with a “yes”. This was done to make sure there are no errors due to the migration of households into and out of the cluster since 2010–2011, when the survey was conducted and only clusters where there is minimum likelihood of migration in and out are selected as flood affected.

  35. Note that MICS uses a representative random sample of the total population, not a census of all households, so the percentage of flood-affected clusters calculated is approximate but based on the random sample.

  36. According to the MICS 2011, the districts where any households reported being affected by the floods in 2010 were Rajanpur, Muzaffargarh, Jhang, Layyah, Dera Ghazi Khan, Sargodha, Multan, Rahim Yar Khan, Bhakkar and Bahawalpur.

  37. Note, in using both the 2007–2008 and 2011 rounds of MICS, we have effectively restricted our sample to villages that were common in both rounds. Since the samples in both years were completely random, any villages that have been sampled in both rounds are also random—there is no reason to suspect any bias in the selection of these villages. Note also that resampling the same villages in 2011 that were sampled in 2007–2008 does not mean that the same households were sampled, since the selection of households is random.

  38. The propensity scores of the non-flooded villages do not exceed the propensity scores of the flooded villages by more than 30 per cent of the standard deviation of the scores.

  39. Note that one person did not report their age, so that in the regressions reported in this paper, which include the age variable, the usable sample is 383.

  40. The government distributed some flood relief through “Watan Cards”, which allowed individuals to access payments through automated teller machine withdrawals.

  41. Binswanger (1981).

  42. A practical limitation was the requirement to use only payoffs that could be composed by denominations of Pakistani rupees, so that the possible payoffs could be represented by pictures. Through multiple pilots, we found that participants were much more likely to understand the experiment if they could see a picture of the payoffs.

  43. See, for example, Galarza and Carter (2010).

  44. Gallagher (2012).

  45. Petrolia et al. (2013).

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Acknowledgements

The authors gratefully acknowledge the financial support of the British Academy International Partnerships, The Wharton School Risk Center at the University of Pennsylvania, the Lahore School of Economics and the Travelers Foundation.

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Authors and Affiliations

  1. Swiss Re, New York, NY and Wharton Risk Center, University of Pennsylvania, Philadelphia, PA, U.S.A.

    Ginger Turner

  2. Lahore School of Economics, Lahore, Pakistan

    Farah Said & Uzma Afzal

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  1. Ginger Turner
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Turner, G., Said, F. & Afzal, U. Microinsurance Demand After a Rare Flood Event: Evidence From a Field Experiment in Pakistan. Geneva Pap Risk Insur Issues Pract 39, 201–223 (2014). https://doi.org/10.1057/gpp.2014.8

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