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A viable and cost-effective weather index insurance for rice in Indonesia

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Abstract

The potentially adverse effects of droughts on agricultural output are obvious. Currently, Indonesian rice farmers have little financial protection from climate risk via catastrophic weather risk transfer tools. Done well, a weather index insurance (WII) program can not only provide resources that enable recovery, but can also facilitate the adoption of prevention and adaptation measures and incentivize risk reduction. However, implementations of WII programs have faced difficulties because of basis risk—among several other obstacles. Here, we quantify the applicability, viability, and likely cost of introducing a WII for droughts for rice production in Indonesia. To reduce basis risk, we construct district-specific indices that are based on the estimation of Panel Geographically Weighted Regressions models. With these spatial models, and detailed district level data on past agricultural productivity and weather conditions, we identify an algorithm that can generate an effective and actuarially sound WII in some districts but not in others. We then measure its effectiveness in reducing income volatility for farmers by reducing this basis risk, at the district level. We end by calculating an actuarially robust and welfare-enhancing price for this scheme and prioritize the districts in which it can be implemented.

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Notes

  1. By ‘predictable a-rationality’ we mean the well-documented ways in which people deviate from the rational homo economicus predictions that are used in traditional modelling in economics. The term, as used here, is a modified version of Dan Ariely’s bestseller, Predictably Irrational. A comprehensive description of these predictable a-rationalities as they pertain the risk management is available in Kunreuther and Meyer’s book The Ostrich Paradox.

  2. Type I error refers to circumstances where payouts are given when no crop failure occurred and type II refers to situations where farmers endure crop losses without receiving insurance payouts.

  3. Compared to flood or storm surges, drought is the most dominant climatic hazard that results in considerable losses to the agriculture sector in Indonesia in the last 4 decades (Lassa 2012).

  4. http://www.esrl.noaa.gov/psd/data/gridded/data.pdsi.html.

  5. http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_change.shtml.

  6. Previous work has shown that de-trending agricultural yield time-series data are useful for isolating the impact of technological changes on crop yield, particularly for actuarial purposes, see for example, Skees et al. (1997).

  7. The greatest difference is found in Kalimantan. The trigger level for Kalimantan is almost one and a half points PDSI lower than the cluster analysis trigger (see Annex 12 for the detail results). This resulted in less coverage and a lower insurance premium.

  8. In many cases, and for many reasons, private sector risk transfer mechanisms are not available, and this justifies public sector investments in weather-related agricultural insurance (Mahul 2001; Miranda and Vedenov 2001; Skees et al. 2004; Owen and Noy 2017).

  9. According to Statistics Indonesia, for wetland paddy in 2015, that amount is IDR 2 million. The full value of the average wetland paddy crop per hectare is IDR 17 million; https://www.bps.go.id/statictable/2015/09/25/1855/nilai-produksi-dan-biaya-produksi-per-musim-tanam-per-hektar-budidaya-tanaman-padi-sawah-padi-ladang-jagung-dan-kedelai-2014.html.

  10. Historical loss cost data may not be adequate for estimating future indemnities if the insurance product covers losses from extreme but infrequent events which may or may not have occurred over the observed historical period.

  11. The assumption that insurance is the only risk transfer tool available is further discussed in the conclusion.

  12. The MSV model in this paper was used also by Shi and Jiang (2016) to evaluate the efficiency of an index insurance in hedging revenue risk against extreme weather conditions in paddy production in China. See their Appendix B for details. Note that the MSV calculation uses the count of all observations, to account for the share of downside risk observations in the full sample of realized crop production.

  13. Pseudo-significance for the GWR refers to the t-statistic for the coefficient associated with a (local) regression point.

  14. These El Niño results are consistent with previous findings (Naylor et al. 2001; D'Arrigo and Wilson 2008).

  15. See Choudhury et al. (2016).

  16. (http://www.bps.go.id/linkTabelStatis/view/id/1855).

  17. For an overview of these cash transfer programs, see Kwon and Kim (2015).

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Authors and Affiliations

  1. School of Economics and Finance, Victoria University of Wellington, POB 600, Wellington, New Zealand

    Aditya Kusuma & Ilan Noy

  2. School of Geography, Environment, and Earth Science, Victoria University of Wellington, POB 600, Wellington, New Zealand

    Bethanna Jackson

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  1. Aditya Kusuma
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  2. Bethanna Jackson
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  3. Ilan Noy
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Correspondence to Ilan Noy.

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Kusuma, A., Jackson, B. & Noy, I. A viable and cost-effective weather index insurance for rice in Indonesia. Geneva Risk Insur Rev 43, 186–218 (2018). https://doi.org/10.1057/s10713-018-0033-z

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