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Direct and indirect impact of index-based livestock insurance in Southern Ethiopia

Abstract

This study identifies the period-specific impact of index-based livestock insurance sold to pastoral households in southern Ethiopia, based on 4-year panel data. While the impact of insurance payouts is not consistently positive across all sales periods, we find that they increase household income and milk production during drought years. We also find indirect effects for several seasons, whereby insured households receive more informal transfers when they obtain payouts and they tend to reduce cash savings and livestock holdings. These results suggest that formal insurance can crowd in informal insurance and that pastoralists may reduce their precautionary savings in response to an insurance alternative. Further analysis shows that pastoralists with a herd size around the poverty-trap threshold increase their livestock numbers after receiving payouts.

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Notes

  1. 1.

    In a controlled laboratory experiment Harrison and Ng (2016) estimate the expected welfare gain and loss from insurance. Harrison et al. (2016) find that the compound risk in index insurance induced by basis risk negatively affects welfare.

  2. 2.

    In addition to outcomes covered by the present study, the insurance would also have an indirect impact on subjective well-being, as studied by Tafere et al. (2018). Their results show positive subjective well-being effects from the resolution of anxiety over potential adverse shocks, which are higher compared to any ex post buyer’s remorse effects of an insurance policy that lapsed without payouts.

  3. 3.

    Toth et al. (2017) find that households with IBLI insurance reduce herd sizes as buffer savings but increase them as they gain greater knowledge of, and experience with, the insurance.

  4. 4.

    TIHV (ETB) = (Number of camels insured \(\times\) 15,000) + (Number of cows insured \(\times\) 5000) + (Number of goats and sheep insured \(\times\) 700) from the first to third sales period; TIHV (ETB) = (Number of camels insured \(\times\) 10,000) + (Number of cows insured \(\times\) 6000) + (Number of goats and sheep insured \(\times\) 800) from the fourth to sixth sales period.

  5. 5.

    The OIC first began with eight woredas and later extended them to ten. The households in the additional two woredas were not included in our surveys.

  6. 6.

    See Ikegami and Sheahan (2015) for more information on the surveys.

  7. 7.

    1 ETB = 0.03 USD as of April 22, 2018 (https://www.xe.com/).

  8. 8.

    Less than 8% of the sample households indicated that their main source of income was crop production. Others mostly depended on food aid.

  9. 9.

    1 TLU = 0.7 camels, 1 cow, 10 goats or sheep.

  10. 10.

    The average payouts for IBLIs 3 and 4 are ETB 5765.9 and ETB 4989.9, respectively (standard deviation (SD) 10,797.4 and 10,184.0, respectively).

  11. 11.

    The maximum TLU insured is 15 for all 6 IBLIs. This is intuitive because the discount coupons cover only the first 15 TLUs.

  12. 12.

    Similar to Takahashi et al. (2016), we observe differences in administrative records and households’ self-reporting on the number of discounts provided as well as whether they purchased IBLI. We prefer the administrative records due to their accuracy.

  13. 13.

    It must also be noted that our power calculation assumed there would be only one round of the follow-up survey. In our estimation we used three-round follow-up surveys. Therefore, an increased number of observations might mitigate concerns about the low statistical power.

  14. 14.

    As another robustness check, we winsorize all the dependent variables of Table 6 at 1% and 5% and confirm that our estimation results are qualitatively unchanged.

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Acknowledgements

The authors thank the editor and two anonymous referees. We are also indebted to those who contributed to this research and its various subprocesses. Among others, we acknowledge Andrew Mude, Chris Barrett, Russell Toth, Birhanu Taddesse, Wako Gobu, Anne Gesare, Philemon Chelanga, Oscar Naibei, Eddy Chebelyon, Mohamed Shibia, Samuel Mburu, Masresha Taye, Liz Bageant, Nathaniel Jensen, Kibrom Tafere, Amy Kahn, and Miki Kohara, the interviewees, enumerators, and supervisors. This study’s survey and data are the products of a collaborative project of the International Livestock Research Institute, Cornell University, the BASIS Research Program at the University of California at Davis, the University of Sydney, Syracuse University, and the Institute of Developing Economies—JETRO. Data collection was made possible with the support of the U.S. Agency for International Development (USAID) Agreement No. LAG-A-00-96-90016-00 under the Broadening Access and Strengthening Input Market Systems Collaborative Research Support Program (BASIS AMA CRSP), the Department of Foreign Affairs and Trade under the Australian Development Research Awards Scheme through an award titled ‘The Human and Environmental Impacts of Migratory Pastoralism in Arid and Semi-arid East Africa’, a JSPS Grant-in-Aid for Scientific Research (B)-26301021, 14J04327, 18J40059 and CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and Dryland Systems. All views and interpretations expressed in this paper are those of the authors and not necessarily those of the supporting or cooperating institutions.

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Correspondence to Ayako Matsuda.

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Appendix

Appendix

Table 7 Subsample analysis with initial TLUs below the 33rd percentile
Table 8 Subsample analysis with initial TLUs between 33rd and 67th percentiles
Table 9 Subsample analysis with initial TLUs above 67th percentiles
Table 10 Difference in attrited and non-attrited households at the baseline
Table 11 First stage of the IV regression with total insured herd value (TIHV)
Table 12 Second stage of the IV regression with total insured herd value (TIHV)

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Matsuda, A., Takahashi, K. & Ikegami, M. Direct and indirect impact of index-based livestock insurance in Southern Ethiopia. Geneva Pap Risk Insur Issues Pract 44, 481–502 (2019) doi:10.1057/s41288-019-00132-y

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Keywords

  • Insurance
  • Drought
  • Livestock
  • Pastoralism