Agricultural Index Insurance for Sub-Saharan African Development Nathaniel D. Jensen and Christopher B. Barrett November 2015 Abstract: Index insurance is often promoted as a solution to many of the barriers that are thought to limit the supply of formal insurance coverage to small-holder farmers and livestock owners in sub-saharan Africa. This paper summarizes the current state of index insurance in low- and middle-income countries, paying special attention to the arguments of its critics and barriers to further market growth. We then offer a set of recommendations for strategic investments that aim to address those issues by developing public goods and services that work towards improving the quality of the marketed products and addressing key informational gaps. An earlier version of this paper was prepared at the request and with the support of the Bill and Melinda Gates Foundation. We thank Barry Barnett, Sara Boettiger, Michael Carter, Pin Chantarat, Nobu Ikegami, Andrew Mude, Quentin Stoeffler and Josh Woodard for helpful discussions and comments. Any remaining errors are our sole responsibility.
1) Introduction Risk and shocks are an ever-present feature of life for agricultural households in developing countries. Households employ costly strategies to reduce their exposure to risk, sometimes following detrimental mitigation strategies to try to avert shocks and coping strategies to address their consequences after the fact. These risks, shocks and the strategies used to cope with them play a crucial role in the long-term well-being of these households. Excessive risk exposure can trap people in persistent poverty (Barnett et al. 2008; Barrett & Carter 2013; Dercon 2004; Morduch 1995; Rosenzweig & Binswanger 1993; Zimmerman & Carter 2003). In high-income countries, farmers commonly use insurance and other financial products (e.g., futures and options) to protect themselves from shocks. Unfortunately, access to formal insurance and other financial risk management products is essentially non-existent in most rural areas of low- and middle-income countries. Conventional insurance requires a great deal of information about the insured risk as well as for monitoring for moral hazard and for validating claims. The large premium markup required to cover those information costs is so large relative to the size of the market in rural areas of low- and middle-income countries that insurance is effectively priced out of existence. Researchers and policymakers have therefore long sought to address insurance market failures in low- and middle-income countries as part of a strategy to reduce poverty and vulnerability to promote food security and productivity growth among poor rural populations. When insurance reduces agricultural households risk exposure, they are then able to take on more risk, freeing households to increase investments in and uptake of higher-risk/higher-yield production technologies, such as improved seeds and inputs (Deron and Christiaensen 2011). When unavoidable shocks like drought inevitably hit, households that receive or anticipate indemnity payments have more response options, potentially reducing their reliance on detrimental coping strategies such as skipping meals, pulling children from school to work, or distress sale of productive assets like land or livestock (Janzen and Carter 2013). In addition, insurance can in principle crowd-in credit, as households with asset or income insurance pose less risk to creditors (Barrett et al. 2007). Many of those interested in developing formal insurance markets for agricultural households in low- and middle-income countries have endorsed the concept of index insurance as a means for avoiding the high costs of information associated with conventional loss-indemnifying insurance 2
(Alderman & Haque, 2007; Barnett, Barrett, & Skees, 2008; Mahul & Stutley, 2010). If policies are based solely an easily observable and exogenous phenomena, such as weather, there may be no need to collect costly household-level data. In response, many index policies now use indices generated from (often free) remotely sensed data, automating data collection and processing, and thereby reducing the required presence of the underwriters on the ground to sales and indemnity payment delivery. Contrast that with a conventional insurance product, which requires agents to collect historic and often even household-specific data on the insured risk (e.g., crop yields) to set premium rates, then to validate any loss claims all of which often involves travel to remote locations with poor infrastructure. The use of indices may render insurance commercially viable in many settings where it otherwise is not. Since sub-saharan Africa is at once the world s poorest and most agrarian continent as well as the one where rural financial market failures and rainfed agriculture appear most widespread, agricultural index insurance holds special appeal to many people focused on African agricultural and rural development. Although excitement for index insurance has grown rapidly over the past decade, there has also been a substantial amount of criticism and skepticism. Here we group those concerns into four broad categories. The first criticism concerns product relevance. Index insurance is championed as a tool for fighting poverty, yet the poor are the least likely to have the liquidity required to purchase policies. The poor are also least likely to have experience with complex financial instruments such as index insurance. In addition, those facing the greatest risks to their basic needs are typically the least likely to adopt a new (risky) technology such as index insurance. In environments where poverty traps are likely, and thus insurance has the potential to save households from collapse into persistent poverty, simulations find that the poorest are also the least likely to purchase insurance (Janzen, Carter, Ikegami 2013). Furthermore, more wealthy households that can afford the insurance premiums are the most likely to already have access to other forms of insurance and may fall outside of the poverty alleviation goals of implementers (Binswanger-Mkhize 2012). The second main criticism concerns basis risk, the imperfect correlation between the indemnity payments made by an index policy and the actual losses experienced by the index insurance policyholder. Basis risk is a direct cost of the reduced data and monitoring requirements for index insurance and leads to both unindemnified losses and unwarranted indemnity payments. To minimize basis risk (maximize coverage), the index must be highly correlated with the insured 3
risk. But to maintain cost savings, the index must be exogenous and available at low-cost. Often, these two motives high coverage and low cost are at odds. For example, an index developed from historical yield samples of a farmer s field are likely to be highly correlated with that farm s yield, but are expensive to collect and are endogenous to the farmer s actions. 1 Conversely, areaaverage precipitation estimates constructed from remotely sensed data are freely available and exogenous to any one farm s production strategies, but probably do not correlate as well with yields. Moreover, as Miranda (1991) points out, even an area-yield product that perfectly covers all covariate risk leaves households facing idiosyncratic risk. The result is that the value of an index product necessarily varies across heterogeneous populations. Add the very likely errors in the index estimates that is, that they do not track area average losses precisely, a specific component of basis risk sometimes referred to as design risk because it relates to the index s design and commercial loadings to cover the costs and minimum profit margins of the insurer, and an index insurance product could, in theory, be more like a lottery ticket than an insurance policy, offering purchasers negative expected returns with negligible correlation with actual losses suffered. One might reasonably expect that many index insurance contracts may harm specific types of individuals by extracting premium payments while providing little or no actual risk coverage. A third criticism relates to the limited good evaluation evidence available about the on-theground outcomes of index insurance relative to their costs. Consider the scale of investment in index insurance, a 60% to 75% state subsidy for more than 35 million index insured farmers in India, for example. The dearth of rigorous information on product quality and impacts from that product is rather remarkable, especially given longstanding calls for better empirical analysis and documentation of basis risk in piloted index insurance products (Cole et al. 2012; Miranda & Farrin, 2012; Smith & Watts 2009). Even if index insurance does improve the lives of purchasers, the philanthropic or public funds used to facilitate the development, extension/marketing (and often premium subsidies) of index insurance could perhaps be used more effectively elsewhere. Some critics point to limited uptake of some index insurance products as suggestive of poor 1 Insured outcomes that are endogenous to the policyholder s attributes or behaviors give rise to problems of adverse selection and moral hazard, respectively, which also pose problems for the emergence and pricing of commercial insurance. The reader interested in these asymmetric information issues is directed to Hirshleifer & Riley (1992) for an in-depth treatment. We do not dwell on those issues in this selective survey. 4
product quality and low returns on investment, although other credible explanations exist for underwhelming demand observed in some pilots (on which, more below). This last point gives rise to critics fourth concern. In the high-income economies where agricultural insurance is well-established, it typically receives heavy government subsidies (Mahul and Stutley 2010; Glauber 2013). The most recent US Farm Bill, for example, converted many direct payment programs into increased federal government subsidization of crop insurance policies. The resulting political economy pressures can entrench interest groups that benefit from a specific policy, making it difficult to remove subsidies later if they prove ineffective or otherwise hard to justify. Critics understandably worry that subsidies for agricultural insurance might become a hard-to-reverse drain on scarce public and philanthropic resources in low- and middle-income countries. The above concerns are all empirical in nature. Household level data can be used to examine who purchases index insurance, the risk coverage and value offered by index products, and how the impacts of index insurance products compare to those of other interventions aimed at alleviating poverty, improving food security or stimulating agricultural productivity growth. And over time one can establish whether subsidized agricultural insurance develops a powerful entrenched interest group base. Foreshadowing a coming theme in this survey, many indeed, probably the vast majority of index products have no data with which to evaluate any of the above concerns. The very advantage of index insurance that product development, actuarial calculations and indemnity payments are all made according to an index leaves developers and providers with little or no incentive to collect or analyze household-level data. And the agricultural index insurance products are too new, and in most cases too small in scale still, to have generated any political economy track record. So these debates remain very live, turning fundamentally on unsettled empirical questions. The emergent literature does, however, point to some important issues as well as to the promise inherent in high quality index insurance products. We will argue that too little attention has been paid to product quality and the underlying market structure, and stimulating uptake has received too much attention given that index insurance purchase is neither good nor bad intrinsically; its value is completely conditional on its quality and pricing. The rest of this survey is organized as follows. Section 2 provides a brief summary of the current state of agricultural index insurance in the low- and middle-income world. Section 3 5
describes crucial barriers to greater market growth and outlines efforts to address them. The final section discusses areas where larger scale investments could considerably improve access to high quality formal insurance coverage. 2) The current state of agricultural index insurance in the low- and middle-income world Supply Although no reliable enumeration exists of all of the agricultural index insurance products that have been implemented in the low- and middle-income world, the number is likely to be in the hundreds, spanning dozens of countries (Barnett and Mahul 2007, Mahul and Stutley 2010). 2 Most products in low- and middle-income countries focus on weather-related shocks because exogenous rainfall and temperature levels and timing pose a primary risk for low-input cultivators and herders, because the spatially covariate nature of weather shocks is especially well suited for index products, which explicitly insure against covariate shocks, and because satellite platforms and terrestrial meteorological station networks provide abundant, high frequency historical and near-real time weather data, typically at low or no cost to researchers and underwriters. In addition, spatial covariance allows for the use of spatial statistical models to impute data for regions without direct observation, further reducing the burden of data collection. When monitoring for drought, for example, precipitation gauges can be set up at a relatively low density because we expect that precipitation in one location is similar to that of a nearby location. In many cases, weather shocks are extensive enough that remotely sensed data, even of low resolution, are sufficient to monitor for shocks. Index products have been developed and marketed by a variety of parties (i.e., national governments, NGOs, CGIAR Centers, private firms, university researchers, and multilateral organizations) in low- and middle-income countries. These are commonly public-private partnerships where the products are initiated and designed by outside researchers, NGOs, or multilateral agencies in partnership with local underwriters using philanthropic or public funds. The products are then vetted and ultimately priced by actuarial consultants and/or international reinsurers. Those reinsurers typically underwrite much often the vast majority of the risk 2 Many index insurance pilot projects do not produce reports or manuscripts that enter the public domain and there is no central archive or list of index projects. 6
associated with the products, while local underwriters act as primary insurer and handle marketing of the policies. A few larger, donor-funded initiatives (e.g., R4 Rural Resilience Initiative, Basis/I4, International Research Institute-Columbia) have developed a robust research and/or development agenda around index insurance. These entities actively play a role in the development, implementation, and sometimes evaluation of different pilot projects across multiple countries and have been instrumental in building the momentum that index insurance has experienced over recent years. There are also facilities within multilateral organizations such as the Global Index Insurance Facility (World Bank), the Agricultural Insurance Development Program (Netherlands/World Bank Group/USAID), Weather Risk Management Facility (World Food Program/International Fund for Agricultural Development), and the Impact Insurance Facility (International Labor Organization) that explicitly work to facilitate the growth of index insurance markets in low- and middle-income countries. Their work includes capacity building such as education, support for regulatory agencies, and technical support for projects; direct funding support; and help matching underwriters with reinsurers. Many local insurance companies participate as primary underwriters for index insurance products. They commonly serve as an important hub, interfacing with researchers in product design, with national government regulators to get new products approved, with retail brokers to sell policies and distribute indemnity payments, and with reinsurers to price policies and to move a large portion of the (necessarily covariate) risk into offshore capital markets. The pool of reinsurers participating in low- and middle-income country agricultural insurance is far more limited, confined largely to Swiss Re, Munich Re, Axa Re, Africa Re, and Hannover Re, with Swiss Re widely regarded as the dominant player. 3 There is great variety in the index products available. Area-yield index insurance is common in high- and middle-income countries where state funded processes (e.g., crop cutting, large-scale annual surveys) for generate trustworthy area-average estimates. These services are expensive and typically not provided by the governments of low-income countries. Furthermore, there may be an important moral hazard problem for reinsurers if the state (or some other entity that prospectively 3 Anecdotally, these firms provide the vast majority of reinsurance for index insurance but, once again, market information is limited. There is also a limited set of reinsurers that are currently active in only one or two countries, such as (e.g., Microinsurance Catastrophe Risk Organisation (MiCRO), Cica Reinsurance Company, General Insurance Cooperation (GIC)). 7
benefits, directly or indirectly, from indemnity payments) generates the measure(s) that define(s) the index. This survey focuses primarily on products that do not require extensive, costly and manipulable data collection to generate index readings, i.e., on the sorts of products that researchers and donors have been experimenting with in the low-income countries in recent years. These indices are commonly developed from a signal of precipitation, temperature or vegetation conditions (e.g., NDVI, EVI). Sparse weather station coverage in most low- and middle-income countries has led remotely sensed data to become the most popular signal source.4 The signal is then transformed to identify specific types of events, such as flooding, drought, heat days, seasonal precipitation or area-yield/loss. The contract parameters then translate the index into indemnity payments. There have been some recent innovations in index insurance that are worth noting. The ACRE product in Kenya provides a menu of coverage options, each developed to cover a specific growing phase, allowing clients greater flexibility in the risk that they choose to insure. Elabed et al. (2013) describe a product for cotton farmers in Mali that uses multiple strikes at different scales in order to reduce basis risk without increasing moral hazard. The index based livestock insurance (IBLI) product in Kenya used historic household level data and econometric methods to statistically minimize expected basis risk (Chantarat et al. 2013) and has more recently developed an index that makes payments before losses are incurred in order to provide asset protection as opposed to only providing payments that can be used after the fact to replace lost assets. The above description of products is not comprehensive, but it does outline the broad range of experimentation taking place within this field in efforts to improve contract quality, which is a key theme of this survey. Unfortunately, the heterogeneity of contract design the data source(s), index construction, the way the strike (i.e., deductible) works, the indemnity schedule, etc. makes it difficult to generalize fully, which makes it harder to know which product-place-and-timespecific results apply more broadly. The next section examines factors of demand for index insurance, focusing on those that appear robust across multiple studies. Demand 4 It is important to note that both developing a high quality product and actuarial estimate require historic data, so that weather station installation cannot usually be followed immediately by the design and marketing of a high quality product. 8
Worldwide, the number of farmers insured by an index product is at least in the tens of millions. The majority of these farmers are covered under large state-sponsored programs, of which India s is by far the largest. 5 In India, for example, index products are available through both the state-sponsored Agricultural Insurance Company of India Limited (AICI) and private firms. Although subsidies vary by state and product, an Indian farmer typically pays between 25% and 40% of the premium while the government pays the remainder (Aon Benfield 2013). Notably, purchasing index insurance through AICI is compulsory for any farmer using seasonal crop production credit (Mahul & Stutley 2010). In addition, banks operating in India are required to hold a certain amount of credit to agriculture in their loan portfolios (see https://rbi.org.in/scripts/faqview.aspx?id=87). The result is that about 33.4 million Indian farmers purchased index insurance policies in 2013 and AICI insures more farmers than any other agricultural insurance company in the world (Greatrex et al. 2013). Although there has been tremendous growth in the supply of index insurance in low-income countries, voluntary uptake is commonly low, as are coverage rates among those that purchase index insurance. Slow diffusion of innovations is commonplace (Rogers 1962). But there are wellfounded concerns that perhaps demand for such products is far more limited than many proponents imagined, for the reasons outlined above, and that agricultural insurance only attracts buyers in high-income markets because of fiscally costly subsidies. 6 So it is important to understand what, if any, patterns emerge among the various recent studies of the largely disappointing demand for agricultural index insurance in low- and middle-income countries. The empirical evidence from low- and middle-income countries on demand for and impacts of index insurance mostly comes from a number of fairly small (less than 10,000 clients) donorfunded index insurance pilot projects. One notable exception is a set of studies lead by Shawn Cole and Xavier Giné that examine voluntary uptake among farmers in India. 7 Those studies are included in this survey. 5 In China, policies are sold at the state-level and often required of borrowers from state banks; but very little information is available on the scale of these products. In Mexico 2.3 million hectares insured by state-level policies at the in 2009 (World Bank 2010). In Brazil 194,000 farmers in the State of Rio Grande do Sul purchased index insured with a 90% subsidy between 2001 and 2008 (Hazell et al. 2010). 6 See Smith & Watts (2009) for one perspective as to what might be reasonable to expect of agricultural index insurance markets in developing countries, given the history of agricultural index insurance markets in high-income countries. 7 According to the World Bank, India is a low middle income country, as are Kenya and Ghana, two other countries that are associated with important empirical studies of index insurance. 9
Basis risk (explained above) is believed to dampen demand for index products for the intuitive reason that the higher a product s basis risk, the less true insurance coverage it offers a prospective buyer (Clarke 2011; Miranda & Farrin 2012; Binswanger-Mkhize 2012). Indeed, the few recent studies that are able to incorporate estimates of basis risk find that basis risk for index products can be considerable (e.g., Clarke et. al 2012; Jensen, Barrett & Mude 2014a) and that basis risk (or proxies for it) reduces demand for index products (Hill, Robles & Ceballos 2013; Jensen, Mude & Barrett 2014; Karlan et al. 2014; Mobarak & Rosenzweig 2012). Not surprisingly, product quality is important. But there remains very little empirical analysis of basis risk or, more broadly, of index insurance product quality. The absence of empirical evidence on product quality has called into question the fidelity of index products to their insurance label (Jensen, Barrett & Mude 2015). In extreme cases, even subsidized insurance could harm households by reallocating income from poor periods, if they pay a premium and suffer losses but receive no indemnification, to good periods when indemnity payments flow despite a lack of losses. The prospect for harmful, rather than harm-reducing, index insurance is exacerbated by higher premium rates in unsubsidized, commercially loaded products. But it can occur even when policies are heavily subsidized premium subsidies do not necessarily obviate the basis risk problem completely. Demand for index insurance is price sensitive but usually price inelastic, with published estimates ranging from -0.35 to -1.16, and often remains very low even when premiums are heavily subsidized (Bageant & Barrett 2015; Cole et al. 2013; Hill, Robles & Ceballos 2013; Jensen, Mude & Barrett 2014; Mobarak & Rosenzweig 2012). Combined with the market power of the reinsurers who typically set the prices, low price elasticity of demand promotes higher prices and lesser gains to purchasers than if (i) the market were more competitive, or (ii) demand were more price elastic. Spatiotemporal adverse selection is a third product-related characteristic that can degrade the value of index insurance and is commonly overlooked. Demand for insurance responds to information that clients have on expected indemnity payments that is not accounted for in their premium rates. For example, pastoralists in northern Kenya purchase less insurance before seasons when they expect abundant pasture and their expectations are demonstrably good predictors of the coming season s conditions (Jensen, Mude & Barrett 2014). Clients may also have information that allows them to observe (often inadvertent) variation in relative subsidies or loadings between index regions. In addition, variation in basis risk can lead to heterogeneous demand between index 10
regions with different indices but identical product pricing, a phenomenon that may not represent canonical adverse selection, but can easily pose administrative or political complications for underwriters. Researchers have also identified a number of important household or individual characteristics that influence demand for agricultural index insurance. Wealth and liquidity constraints have been shown to be important across a variety of products and populations, which suggests limitations in the use of commercial index insurance as a tool for helping the poorest subpopulations (Cole et al., 2013; Giné, Townsend, & Vickery 2008; Jensen, Mude, & Barrett 2014). The considerable role that product education and trust in providers appear to play in demand points towards the need for complementary investments in extension and marketing, and a prospective role for regulation to ensure product quality (Cole et al. 2013; Dercon et al. 2014; Giné, Townsend, & Vickery 2008; Pratt 2009). There remains very little work to date exploring whether index insurance is gender biased or neutral. One study finds that Ethiopian pastoralist women are more responsive to home based product education and risk aversion than their male counterparts, but purchase as rates similar to their male counterparts so that public investments in index insurance seem to be mostly gender neutral (Bageant & Barrett 2015). In spite of the general absence of formal financial products, it remains an open question whether index insurance fills a real gap in the constellation of risk management tools available to agricultural households in low- and middle-income countries. In particular, demand may be sensitive to access to informal risk sharing arrangements. Informal risk pools, such as those among community members or kin, are often well equipped to redistribute assets or income to households that suffer idiosyncratic (i.e., individual- or household-specific) shocks that are not felt by others in the risk pool. But, because informal risk sharing arrangements are often fairly small and spatially limited, they can be quite vulnerable to covariate shocks that can affect the entire pool. Index insurance covers covariate losses while leaving clients exposed to idiosyncratic risk. So informal risk pools and index insurance are, in principle, complements, providing insurance coverage for idiosyncratic and covariate losses, respectively (Dercon et al. 2014; Mobarak & Rosenzweig 2013). Impacts While the empirical literature on the impacts of index insurance on agricultural households in low- and middle-income countries remains thin, it also underscores the promise that has 11
motivated these initiatives. In Ghana, insurance increased average total farm revenue (net of insurance premiums and indemnity payments) by over 20%, some of which can be attributed to changing production strategies seemingly induced by reduced uninsured risk exposure (Karlan et al. 2014). In Kenya, IBLI reduces dependence on detrimental coping strategies, such as meal skipping and distress livestock sales during severe droughts (Janzen & Carter 2013; Jensen, Barrett & Mude 2014b), and increases both income from milk and child mid-upper arm circumference more generally (Jensen, Barrett & Mude 2014b). Although the channels by which insurance improves policyholders welfare are not yet fully mapped out, there is evidence that households with index insurance increase investments in production and in some cases make riskier production choices, consistent with the mechanisms described by economic theory (Cai et al. 2015; Elabed & Carter 2014; Jensen, Barrett & Mude 2014b; Karlan et al. 2014; Mobarak & Rosenzweig 2013). For example, insured Ghanaian farmers increase the amount of land cultivated, increase expenditures on land preparation, and increase expenditures on chemicals (Karlan et al. 2014). In addition, the peace of mind of having insurance coverage increases the subjective welfare of insured Ethiopian households, even in cases when there have been no indemnity payments (Tafere et al. 2015). There is also theoretical reason to believe that access to insurance could crowd-in credit (Alderman & Haque 2007; Barrett et al. 2007; Carter Galarza & Boucher 2007; Carter et al. 2011) but the two studies that look at the impact of index insurance on credit find either no impact (Karlan et al. 2014) or a negative impact (Giné & Yang 2008), so this theoretical prediction has yet to find strong empirical support. Similarly, some have posited that bundling insurance with productivity increasing investments could positively increase welfare (Lybbert & Carter 2015; Smith & Watts 2009), but the impacts of bundling products on farmer productivity or wellbeing have yet to be rigorously studied. As with basis risk, the lack of empirical evidence on the impacts of index insurance coverage arises partly due to data constraints. Pilot index projects are often short lived and lack funding for rigorous multi-year ex post impact evaluation. Even where the intent and funding exist, it can take years for a shock that triggers indemnity payments to occur. The relative rarity of payments is perhaps a strength of index insurance. While other types of social protection interventions such as conditional or unconditional cash transfers require persistently high public expenditures, the collection of premiums, public-private partnerships, and 12
the irregularity of indemnity payments may make index insurance a cost effective approach to social protection provision. For example, in Kenya the marginal cost to public coffers of an additional cash transfer program client is about 20 times that of an additional IBLI customer, while the average impacts of the two programs are very comparable (Jensen, Barrett & Mude 2014b). These apparently superior marginal benefit/cost returns of IBLI over cash transfers are one major reason why the Government of Kenya is using an index insurance contract as part of a 2015-16 scaling up its social protection program in drylands regions populated mainly by livestock herders and is expanding the IBLI program into a nationwide Kenya Livestock Insurance Program (KLIP). 3) Barriers to market growth In no case of which we are aware has a donor-funded pilot project or government-funded index insurance program led to the development of an unsubsidized private market for index insurance in a low-income country. Critics of index insurance point to the failure of unsubsidized index insurance markets to develop as evidence that index insurance does not meet the critical needs of its intended clients. The emergence of private, unsubsidized markets may not be the appropriate measure of success, however, if, as is the case for agricultural insurance in highincome countries, agricultural index insurance is seen as an alternative to other publicly funded social protection measures for farmers. Furthermore, there exist a host of barriers to market growth that may, at least partially, explain the continued absence of a private agricultural index insurance market in low- and middle-income countries. In this section, we identify key barriers to greater market growth. Local Infrastructure Index insurance schemes need insurance firms with the desire and capacity to sell the product on the ground in rural communities. In most cases, existing domestic firms are recruited from metropolitan areas because they already have regional capacity and regulatory approval. A primary challenge arises, however, because index products are as new to most domestic underwriters and brokers as they are to their new potential clients. And the urban insurers are typically unfamiliar with the rural customer base they are trying to reach with these new products. Before index insurance can be offered, sales agents need to be trained in the new product and mass education schemes need to be developed and implemented for potential clients, who may be illiterate and 13
unfamiliar with insurance concepts. To further complicate matters, local clients may not trust insurance agents from outside their region or that are from a different ethnic group, so an entirely new sales force may need to be recruited and trained. Where index insurance is being used to extend formal insurance markets into new regions, new supply chains need to be developed. Agencies may need to create new methods for documenting sales, collecting premiums, providing customer support, and making indemnity payments. If the insurance is linked to other products (e.g., fertilizer, seed, or credit), additional legal arrangements need to be developed. In remote or insecure locations, security measures may also need to be put in place. For example, in some regions of northern Kenya, security guards and procedures are required any time large amounts of cash are moved or distributed, such as during indemnity payment periods. These large sunk costs can discourage investment by local underwriters. Add to that the relatively small size of policies demanded and general uncertainty around demand, and there are considerable incentives for underwriters to free ride, letting other firms lay the groundwork and develop the new market while they wait to see the outcomes. 8 The Catch-22, however, is that without a market of sufficient scale, the per-policy fixed costs can be prohibitively large, so insurers need an adequate market size in order to invest, but without investment, an adequate market is unlikely to emerge. Thus many rural areas become trapped in a low-level equilibrium with no insurance products available. One common approach for breaking out of that trap and sparking the emergence of index insurance products has been to use public or donor funds to pay for many of the sunk costs of product design and initial consumer education campaigns. In some cases, index insurance product developers may offer the initial local underwriter a limited-time monopoly on the donor-developed product, in conjunction with premium subsidies to purchasers, as a way to ensure an ability to recover the firm s (more limited) sunk costs while still stimulating uptake. Similar to conventional insurance, index insurance needs a regulatory framework to provide standards for consumer protection. This framework should include standard insurance regulations, such as minimum capital-to-liability holdings requirements for underwriters and reinsurers, clear index certification processes, and a process for speedy and accessible disputed settlement 8 Similarly, potential clients for index products also often report the desire to wait and see how the product performs as their reason for not purchasing index insurance coverage. 14
resolutions. Beyond those minimum standards, however, index insurance is similar to other microfinance products with characteristics that require special consideration. In some cases the potential clients of index insurance are illiterate and/or have little understanding of formal financial tools so that complicated contracts may be a barrier to high-quality insurance coverage while adding very little consumer protection. Other considerations include allowing for unconventional insurance agents like NGOs or microfinance institutions, questions about how reserves should be held, and documentation requirements. 9 There may also be a regulatory risk for the underwriter. In some cases, index insurance is not supported by the existing legal definitions of insurance because losses and indemnity payments are not necessarily tied. Under some regulations index contracts are not enforceable or are even illegal. The Global Index Insurance Facility (GIIF) has made considerable investments in improving the regulatory environment for index insurance by helping countries to develop an appropriate regulatory framework for microinsurance and index insurance. For example, GIIF helped draft a revision of the code used by the regional body of the insurance industry for 14 countries in Francophone Africa (Conférence Interafricaine des Marchés d'assurances), so that it explicitly allows for index and micro insurance (GIIF n.d.). Reinsurance markets In many index insurance schemes, as with many other insurance products, local underwriters prefer to have reinsurers take on the majority of the insured risk. The commercial premium rates for policies sold in the field then largely reflect the rates offered by reinsurers to the underwriter. Because access to international reinsurance markets for specialized agricultural insurance products is limited in low-income countries, index insurance product designers and underwriters often have very little bargaining power with their prospective reinsurers. As a result, concerns about imperfect competition in reinsurance markets is widespread. Given prevailing findings of price inelastic demand, imperfect competition can lead to higher loadings (i.e., price mark-ups by the reinsurer) and lower levels of product uptake. The proprietary nature of actuarial calculations further reduces the bargaining power for those seeking reinsurance because there is little accessible information on how rates are calculated. Anecdotally, the rates offered by reinsurers typically include a large markup. One hypothesis is 9 See World Bank (2011) for more details on regulatory considerations for index insurance. 15
that in the absence of adequate high quality data, uncertainty-averse reinsurers place a large premium penalty on uncertainty (Carter 2013). That is, reinsurers base their actuarial estimates on biased (worse than predicted) estimates of losses rather than unbiased (predicted) loss estimates in the presence of Knightian uncertainty. Because the greatest uncertainty is associated with infrequent events, such as catastrophes requiring large payouts, uncertainty aversion could drive the premium rates up considerably. If the level of uncertainty aversion were known, underwriters could develop a value of information estimate to determine if collecting additional information might offer a cost effect approach to reducing the premium rate. Unfortunately, reinsurers are unwilling to provide such information, which makes it more difficult for index projects to take action to reduce the premiums imposed by reinsurers. A specific form of uncertainty aversion and associated insurance mark-ups arises from climate change. Index insurance products are typically priced off of historical data series used to estimate the frequency and magnitude of prospects indemnity payments. A significant number of observations are required, which typically means product designers draw on data from at least a decade or two earlier. Concerns that climate has shifted permanently leads some actuarial consultants to add an ambiguity wedge to estimated payouts. This sort of bias in the estimates used to price new products cannot be overcome with additional historical data, obviously, and downscaling of climate forecasts has thus far proved too coarse to enable out-of-sample validation against established climate change models (Mahowald et al. 2012). This situation poor bargaining power, uncertainty over true loss distributions, and price inelastic demand naturally raises questions among astute donor agencies, as it suggests that a significant portion of the prospective gains from investment in index insurance may be appropriated by multi-billion dollar international reinsurance companies exercising market power. In the absence of credible means of regulating the reinsurance market for agricultural index insurance, the prospect of market capture remains very real. There is some recent, preliminary evidence of increased market competition among reinsurers of agricultural index insurance products, which may begin to drive prices down. The newly created African Risk Capacity, a specialized agency of the African Union, provides an alternative risk pooling and transfer mechanisms that may also directly or indirectly improve the reinsurance market for index insurance. In addition, as reinsurers become more familiar with index 16
products and better understand the risks that they pose, the uncertainty premium that they load into product prices may fall. Informed demand The empirical literature on demand for index insurance has mostly focused on household and contract characteristics that vary within a given project. This approach is useful for learning about how small changes can affect demand for insurance on the margin. But overall demand has been very low for many products. It is important to explore the larger issues that have been revealed across products and populations. At the most basic level, index products must offer coverage appropriate to the shocks that pose relatively large risk to clients economic well-being and must offer coverage for covariate risk that is otherwise unavailable to prospective clients via alternative methods for mitigating risk, such as conventional indemnity insurance, migration, or informal risk pooling. 10 If consumers have grown accustomed to ex post government transfers or other aid, those programs can also compete directly with the insurance products. A major challenge to demand is client education and trust. Index pilots often enter regions where potential clients have very little experience with insurance at all, and no experience with an index product, and commonly no prior experience with the underwriting firm nor its agent(s). Developing these new markets requires educating consumers on the concepts of insurance and the nuances of the index insurance product by a trusted and knowledgeable party. The clients must also trust that the insurer will not renege on its contract. Experimental studies have found that improved understanding of the product and trust in the insurance agents are important factors affecting demand (Cai, de Janvry & Sadoulet 2011; Jensen, Mude, Barrett 2014; Pratt, Suarez & Hess 2010). Consistent with both the educational and trust mechanisms, observed demand often covers only a small share of the assets or income at risk, which is more consistent with a model of consumer experimentation than with one of planned risk reduction. Consumers desire to first learn about product quality and/or agents /underwriters trustworthiness is supported by findings that households are more likely to purchase if they have observed someone in their village receiving an indemnity payment (Cole, Stein, and Tobacman 2014). Considering the large Hawthorne effect 10 Assuming that there are some fixed costs associated with purchasing insurance, a loaded index insurance product with basis risk is more likely to have net benefits if the risk covered by the insurance product reflects a relatively large risk to the household. 17
that has been observed with index insurance, and it may be that the knowledge gap and trust issues are much more severe than is widely appreciated. 11 Because demand is price sensitive, subsidies can be used to increase the number of consumers willing to experiment with index insurance and potentially to increase the speed with which a large portion of the population proceeds through the observation and experimentation phases. The evidence to date is mixed as to whether demand will be sustained if subsidies are dropped quickly. For example, summary statistics from Cole, Stein, and Tobacman (2014) show that even after five years of an index product being available, a small reduction in the substantial subsidies led to a reduction in demand. 12 The same statistics show a slow increase in demand over time when the subsidy is held high and constant. By contrast, a temporary premium reduction arising from receipt of randomly distributed discount coupons has an immediate, positive impact on IBLI uptake in Ethiopia, without dampening subsequent period demand due to referencedependence associated with price anchoring effects (Takahashi 2016). Barriers to demand can also be cultural. For example, the original IBLI product offered in northern Kenya was not compliant with Sharia law (Islamic religious law) and thus was inaccessible for the millions of Muslim pastoralists of the region. To extend formal insurance markets to Muslim pastoralists, Takaful Insurance of Africa, Ltd. launched Index-Based Livestock Takaful, a Sharia-compliant index product that provides coverage against drought. Taboos against betting on bad outcomes is another cultural barrier that index insurance projects have had to overcome. Field experiments have identified key behavioral characteristics that may also be partially responsible for the low demand experienced by many index products. Behind most economists support for index insurance is a model of standard expected utility theory that shows that clients will benefit from and thus demand index insurance. This model shapes how insurance products are developed, how extension and marketing are performed, and how contracts are structured. But a set of experiments by Serfilippi, Carter and Guirkinger (2015) reveals preferences for certainty 11 The Hawthorne effect refers to a change in behavior due to being observed. It is well known that participation in experiments or impact evaluation can have a large Hawthorne effect on demand, sometime greater than that of extension efforts. Either through the building of trust, unintended product education, or because of a feeling of obligation, surveyed households often have much greater demand for the product under study than does the general population as a whole. For example, about 40% of the IBLI survey sample in northern Kenya has purchased IBLI while less than 1% of the general population of that region has purchased (Jensen, Mude and Barrett 2014). Similar outcomes have been reported in India (Cole et al. 2013) and in Ethiopia (Sarris 2014). 12 When the subsidy drops from 72% (2010) to 68% (2011), rate of uptake falls from 56% to 45% (Cole, Stein, and Tobacman, Appendix Table A1: Summary Statistics, 2010). 18
(as opposed to uncertainty) among farmers in western Burkina Faso are much higher than is predicted by expected utility theory. This represents a key issue for demand of index insurance sold using the standard framing with certain premium payments while indemnity payments are only sometimes made (uncertain). A different set of experiments by Elabed and Carter (2015) finds that farmers perceive indexinsured risk as a compound lottery with uncertainty around the insured risk and uncertainty around how well the index will reflect their losses (basis risk). They find that 60% of their sample from southern Mali are compound risk averse. Analysis of willingness-to-pay indicates that under moderate basis risk, the levels of compound risk aversion observed in their sample could cut demand for index insurance in half. Underwriters can do very little to change the relative preferences for certainty or levels of compound risk aversion, but they can adjust their policies and the way that they are sold. Serfilippi, Carter and Guirkinger (2015) find that simply reframing the contract terms can circumvent the uncertainty penalty placed on indemnity payments and increase willingness-to-pay among those that are uncertainty averse. The work by Elabed and Carter (2015) highlights the need for improved products since the already detrimental impact of basis risk on demand is amplified by compound risk aversion. Product quality Developing a high quality index product requires a signal that can be used to develop indemnity schedules that are highly correlated with covariate losses. Unfortunately, actuarial calculations require long time series of historic data, 13 which generally excludes indices that use the newer, more sophisticated, remotely sensed data or the option of building indices from data generated by newly installed weather stations or surveys. 14 Thus, the quality of index products that can be marketed are presently limited by the scarce data sources that have been in operation for a long time. To develop a high quality product, historic signal data must be accompanied by historic loss data. Evaluating and minimizing basis risk requires a comparison of the losses experienced by clients and indemnity payments triggered by a prospective indices. In most cases, indemnity 13 30-50 years of data are an often cited as the minimum requirements for high accuracy estimates. 14 Researchers are working to develop methods for combining data from older sensors with long time series with data from newer sensors to develop long term archives. See Vrieling et al. (2014) for an example of such work. 19
payments simulated or actual are available to the underwriter, but obtaining loss data requires an investment in data collection that underwriters may have little or no incentive to pursue. Instead, premiums are commonly set using actuarial calculations of the index and the relationship between the index and losses is assumed. 15 In some cases, that index-actual loss relationship has turned out to be quite weak, generating what are sometimes called a basis event, in which large covariate shocks cause severe losses while the product s index does not trigger payments. Occasionally, projects have been forced to make ex gratia indemnity payments after basis events rather than risk losing the confidence of their clients, funders, or the local government (e.g., FAO 2014, FSD Kenya 2013, Kerer 2013). The result is an uncertain market environment for underwriters, where there is risk of being obligated to make payments that were not accounted for in establishing the premium rates. Repeated ex gratia payments typically signal an unsustainable (and poorly designed) contract. The nature of most index insurance proprietary actuarial calculations and infrequent payouts triggered by irregular events also makes it very difficult to discern the quality of index products even when data are available (Clarke & Wren-Lewis 2013). This information problem holds for all parties; neither the client nor donor agencies nor insurance providers can easily assess product quality without long time series of data. This creates adverse incentives in product design because it becomes difficult to establish ex ante whether costly investment in more sophisticated design or additional data collection really improves product design, thereby creating strong incentives to underinvest in product design. This discussion on barriers to market growth underscores the key cross-cutting issues that confront index insurance market development throughout the low-income rural world. None of these barriers is insurmountable. But to date relatively little effort has been made to identify cost effective solutions. The first generation of agricultural index insurance development has focused overwhelmingly on product design, on measuring product uptake and, more recently and rarely, estimate the impacts of index insurance coverage on rural households. The next generation of research and action on agricultural index insurance in the developing world might productively focus on identifying solutions to obstacles of market development, especially those in the form of a public good or at least with low marginal costs so that entire index 15 That is not to say that there is not strong evidence for the direction of the relationship between the signal and the outcome, but matching magnitudes requires local data that is not usually collected. 20