Abstract
This chapter explores innovations in index-based risk transfer products (IBRTPs) as a means to address important insurance market imperfections that have precluded the emergence and sustainability of formal insurance markets in developing countries. Using a combination of disaggregated nationwide weather, remote sensing and household livelihood data commonly available in developing countries, the chapter provides empirical illustration of nationwide and scalable IBRTP contracts to analyse hedging effectiveness and welfare impacts at the micro level, and to explore cost effective risk-financing options. Our analysis uses Thai rice production to extend the methodology and implications to enhance development of national and regional disaster risk management in Asia.
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Notes
- 1.
Much literature has depicted opportunities and challenges of implementing IBRTPs. See IFAD and WFP (2010), Barnett et al. (2008), for example, for review. See Chantarat et al. (2007, 2008, 2011, 2013), Clarke et al. (2012) and Mahul and Skees (2007) for examples related to IBRTP designs in the developing world, and Mahul (2000) for examples related to agriculture in high-income countries.
- 2.
With the exception of some on-going new projects, see for example, Chantarat et al. (2013) and various piloted projects funded by USAID-I4 Index Insurance Innovation Initiative at http://i4.ucdavis.edu/projects/. These ongoing pilot projects undertake rigorous contract design and ex-ante evaluation using high-quality household welfare data in addition to their proposed ex-post evaluation through multi-year household-level impact assessment.
- 3.
Data are obtained from the Office of Agricultural Extension, Ministry of Agriculture and Cooperatives, Thailand. There is no significant trend from the annual areas since 1980.
- 4.
The number of provinces has just recently increased to 77 with one additional province added in the Northeast 2011. Our spatiotemporal data are extracted using the un-updated 76-province GIS information.
- 5.
These results are obtained from PASCO, Co.’s study using a combination of scientific literature reviews, agro-meteorological model (DSSAT) with detailed geographical information and ground checking with the local experts in the key-growing province, Phetchabun, and flood plain modelling.
- 6.
USD 1 = 31.81 baht (Bank of Thailand as of May 29, 2012).
- 7.
Farmers in the risky areas would, in expectation, tend to be the majority of the purchasers of the cheaper contract relative to their risk profiles. And the heavily subsidised insurance contracts for those in the risky zones could further induce excessive risk taking behaviours.
- 8.
This trend estimation has been commonly used in agricultural time series especially when the underlying data are not normally distributed (Ramirez et al. 2003).
- 9.
These also include projected future climate data and are available at http://www.start.or.th/ . The resolutions of these data could be improved. Attempts are current made in gridding weather data at lower resolutions.
- 10.
This results in 9,254 SMUs covering all the 9.1 million hectares of rice growing areas nationwide. The size of constructed SMUs ranges from 0.16 to 35,900Â ha.
- 11.
Data are available worldwide and cost free. See https://lpdaac.usgs.gov/content/view/full/6644.
- 12.
Farm incomes from the last month are also available, but the large variations in cropping patterns as well as survey timing constitute great difficulties in controlling for seasonality effects.
- 13.
These statistics align well with findings in Isvilanonda (2009).
- 14.
Simple local polynomial smoothing is used over all the pixels that fall into provincial boundary over 2000–2011.
- 15.
Current contract piloted by JBIC and Sompo Japan insurance in Khon Kaen relies on simple cumulative rainfall are taken from July to September.
- 16.
For example, drought index insurance for maize piloted in Thailand since 2007.
- 17.
Note that for y(w t ), CR t , MD t , ME t , we standardise at the SMU and pixel first (using SMU and pixel specific moments) before aggregate them into provincial indices. This is different from taking average of index first then dividing by overall long-term average later. The latter case will result in index with lower variations since most the SMU-level variations are already smoothed out in the aggregation process.
- 18.
There are two values for each of the weather indices in the two-crop zones, one for each crop cycle.
- 19.
We reintroduce provincial subscript l here for clarification. The alternative approach of using pseudo provincial panel in estimating (1) controlling for provincial fixed effect would not take full advantage of these rich household data, as it would not yield household-level variations of basis risks.
- 20.
Ideally, we want to estimate provincial-specific β l . The temporal observations per province are simply not enough with 6 years in Thai SES data.
- 21.
Two-day cumulative rainfall exceeding 25Â mm is chosen to trigger the start of each crop cycle, as it provides the best results comparing to others. This chosen trigger might not serve as an appropriate trigger for crop cycle just yet.
- 22.
As the estimated coefficients are specific at provincial level (not household), the simulated households’ optimal coverage scales are specific at provincial level.
- 23.
And so we would expect that specific strike level for each payout frequency to be different across indices and provinces depending on their specific underlying distributions.
- 24.
LIBOR rate as of May 30, 2012 from www.global-rate.com.
- 25.
For example, an investor who purchased a cat bond with required return of 4 %, 50 % cap with 110 % strike at the price = USD0.8823 and received USD1 principle back 1 year later when reinsurance is not triggered, would realise a total compounded return of 12.4 %. The rate can be interpreted as including a risk free LIBOR rate of 1.07 %, 2.93 % premium associated with bond default and other risks not associated with the insured reinsurance risk, and an additional 8.4 % premium associated with this catastrophic risk associated with the reinsurance.
- 26.
This mark up is comparable to other existing index insurance programs in other part of the world. These market rates are comparable to other pilot projects for rice insurance in Thailand. For example, 4.64 % rate changed for recently piloted deficit rainfall index insurance covering only drought peril for only the main rice production in Khon Kaen province during July–September.
- 27.
Because the extreme layers of risk are not so catastrophic, capping at higher level beyond 40 % of sum insured will result in more or less the same effects to market premium rates. The market premium rates for 130 % strike contract do not change with payout caps, as the contracts’ maximum payout rates are already well below the caps.
- 28.
We rescale the simulated representative sample to represent the current 9.2 ha of growing areas nationwide. The current sample represents 63 similar households (9,200,000/(76,000 × 1.92)).
- 29.
Without actual payout statistics, we assume that under the existing program, if household’s actual crop income falls below its 1-in-3 year trigger level, they will be paid 3,787 baht per hectare (606 bath/rai) under disaster relief program and an extra 6,944 baht per hectare (1,111 baht/rai) if they pay for disaster insurance coverage at a subsidised price of 375 baht per hectare (60 baht/rai). We believe this assumption is reasonable, as (1) the program covers larger sets of disasters and (2) it makes payout conditional on government’s declaration of disasters at very local levels with required actual loss verification. Because government disaster insurance is offered at highly subsidised price, our welfare optimisation implies that all representative risk-averse households will purchase full coverage. Hence 100 % insurance penetration rate is used. Note that we abstract from all the incentive problems associated with existing program that could result in larger exposure on government spending.
- 30.
Gridded weather data from WMO stations across Asia are available online at NOAA Global Daily Climatology Network (daily, 1900–present). Various satellite imagery Normalised Difference Vegetation Index (NDVI) available from NASA MODIS at 250 m resolution (15-day; 2000–present) and from NOAA AVHRR at 8 km resolution (10-day; 1982–2000). RADARSAT-1 and RADARDAT-2 with cloud-penetrating SAR sensor at 25 m resolution (every 15 day, 1995–present) have been increasingly used for flood monitoring.
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Chantarat, S., Pannangpetch, K., Puttanapong, N., Rakwatin, P., Tanompongphandh, T. (2015). Index-Based Risk Financing and Development of Natural Disaster Insurance Programs in Developing Asian Countries. In: Aldrich, D., Oum, S., Sawada, Y. (eds) Resilience and Recovery in Asian Disasters. Risk, Governance and Society, vol 18. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55022-8_9
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