THE IMPACT OF COMMERCIAL RAINFALL INDEX INSURANCE:EXPERIMENTAL EVIDENCE FROM ETHIOPIA
Shukri Ahmed
Craig McIntosh*Alexandros Sarris
October 2019
Abstract
We present the results of an experiment introducing commercial rainfall index insurance intodrought-prone farming cooperatives in Amhara Region, Ethiopia. We provided a market-priced
rainfall deficit insurance product through producer cooperatives, and tested a number of potential
ways to kick-start private demand. Take-up of the insurance at market prices is very low, between
0.5% and 3% across seasons. When we use a randomized experiment to distribute small free
insurance contracts to farmers, 39% of subsidized individuals enroll but this fails to stimulate input
use, yields, or income, nor does it enhance demand in subsequent seasons. A training andpromotion on the product improves uptake and willingness to pay, but also does not improve
farming outcomes. We conclude with a case study of our efforts to interlink index insurance withcredit for agricultural inputs.
Keywords: Ethiopian Agriculture, Index Insurance, Randomized Experiments
JEL Codes: O13, G22, C93
Suggested running head: The Impact of Commercial Rainfall Index Insurance

*Corresponding Author. Ahmed: Senior Economist, Food and Agriculture Organization of theUnited Nations, Rome, Italy. McIntosh: Professor, University of California, San Diego, USA.
Sarris: Professor, National and Kapodistrian University of Athens, Greece. We are grateful to
BASIS/USAID and to the Agricultural Technology Adoption Initiative for funding this project,
and the Ethiopian Economics Association for their survey work. We thank Rene Gommes for help
on agro-meteorology and index development, Michael Carter, Ethan Ligon, and Elisabeth Sadoulet
for useful comments, and Alex Botsis, Maya Duru, Fotis Papadapoulos, Alixa Sharkey, and Diego
Vera-Cossio for excellent research assistance with the project. This project is covered under
UCSD IRB #101578 and is registered with the AEA Trial Registry as AEARCTR-0000451.

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INTRODUCTION
Smallholder farmers are beset by an interlocking set of market failures, and when credit andinsurance markets are missing, farmers can become trapped in a low-investment equilibrium(Rosenzweig & Binswanger 1993). Production risk likely hampers farmers’ adoption of higher
yielding technologies (Dercon, 2005), particularly when these shocks are covariate (Townsend,
1994, Conning and Udry, 2007). Risk-driven reluctance to invest in inputs such as fertilizer andimproved seeds may be partially responsible for the fact that Africa has not undergone a ‘green
revolution’ (Skees and Collier 2007). This set of insights has raised hopes that weather index
insurance (WII) could succeed as a commercial product and provide substantial welfare
improvements for risk-exposed populations (Barnett and Mahul, 2007). Unfortunately, while
several recent studies have shown dramatic effects of index insurance on producer behavior whenprovided at subsidized prices (e.g., Mobarak and Rosenzweig 2013, Karlan et al., 2014, Elabed
and Carter 2014, Cai et al. 2016), weather index insurance has struggled as a commercial product(JPAL, 2016; de Janvry et al. 2017) and has seen low demand at market prices (Giné & Yang 2009,
Cole et al 2013).
Even if micro-insurance looks unlikely to enjoy the same immediate commercial successas micro-credit, important questions remain as to how risk protection can best be used to improvewelfare.

A standard model of the utility benefits of insurance is based on the smoothing of

consumption, typically ex post to the shock. A special case for insurance can be made if in addition
it has an ex ante effect on the decisions of producers, allowing them to increase productivity as
they are protected from risk. To achieve this potential first-order improvement in welfare farmers
must be directly informed of the protection they will receive, and so marketing insurance directly2

to individuals appears particularly attractive. Absent this response, integrating index insurance
protection into the activities of higher-level entities such as banks (Farrin and Miranda 2015;
Mishra and Miranda 2017) or local governments (de Janvry et al. 2016) is likely a morestraightforward way to achieve ex post risk coverage. This study is designed to examine whether
individually-marketed index insurance can create this supply response, experimenting withsubsidies and training to examine their effect on demand and agricultural decisions.
Two distinct cases can be made for using subsidies to provide individual insurance at submarket prices. The first is a business argument: given the unfamiliarity of WII products as wellas the demonstrated dynamic impacts of subsidies (Hill et al. 2016) and payouts (Cole et al. 2014,
Stein 2016), temporary subsidies can build a path to a sustainable long-term market (Cai et al.
2016). Second, linkages between credit and risk imply that subsidies provided in the form of risk
reduction may have a larger effect than a cost-equivalent lump-sum transfer (Karlan et al., 2014;
Jensen et al. 2014). Numerous recent papers have shown that subsidized insurance can indeed
induce farmers into growing the higher-returning, more weather-exposed crops covered by the
insurance (Mobarak and Rosenzweig 2012; Cai 2016; Cole et al. 2017), suggesting that risk posesa first-order drag on agricultural productivity that can be alleviated with subsidized WII. The
effects of training on insurance are more inconclusive, with recent experimental studies finding
either weak (Cole et al. 2013) or short-term (Hill et al. 2016) effects on demand. In this paper we
present the results of an experiment that attempts to address these questions by cross-randomizing
cost vouchers and an informational promotion of rainfall insurance among cooperativized
Ethiopian farmers. We also present a case study of a product that addresses interlinked credit andinsurance market failures by offering farmers loans explicitly backed by the insurance policy.
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Our study, called EPIICA (Ethiopian Project on Interlinking Insurance with Credit inAgriculture), set out to build a private-sector driven rainfall index insurance product linked withseasonal credit and to field it among drought-exposed farmers in Ethiopia’s Amhara State. We
addressed the credibility problem by working with the largest private insurance company (Nyala
Insurance) and the largest private lender (Dashen Bank) in the country. Our partners identified
market-friendly, drought-exposed villages with potential for increased productivity, and developed
a product that uses Ethiopia’s robust agricultural cooperative system to aggregate demand for
rainfall insurance. The product was designed to cover the cost of purchased inputs to farming,
namely fertilizers and improved seeds. We conducted three consecutive years of standalone
weather index insurance sales (Table A1 in the appendix), during which we experimented withvouchers that provided small amounts of free insurance at the farmer level, re-randomizing eachyear so as to understand the dynamic demand effects of subsidies. A randomized marketing
exercise conducted at the time of baseline tests the importance of farmer comprehension of thedetails of an index insurance product. Four rounds of panel surveys at the household and plot level
allow us to analyze the impact of the insurance vouchers on welfare outcomes such as input use,
productivity, and income.
Our results paint a mostly negative picture of the prospects for standalone index insurance.
Despite well-known commercial partners and the selection of high-potential regions of the country,
uptake in the absence of subsidies was very low, between 0.5% and 2% of cooperative membershipat commercial prices (~130% of actuarially fair). The subsidies for insurance uptake had a verystrong effect on coverage rates (~40% of those offered vouchers registered to be insured); yetsimilar to Takahashi et al. (2016) we find that subsidies have no dynamic effect on private demand.
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Where our results differ from prior studies such as Karlan et al. (2014) is that we then find nodownstream effect of subsidized risk protection on agricultural practices or income. Morepromisingly, we do find that the simple and inexpensive insurance promotion we conducted atbaseline had a lasting effect on demand, and is the only intervention we tested that led tomeaningful willingness to invest one’s own money in premium purchases.
Relative to the literature, the surprising result from our study is the lack of impacts on
producer behavior even when the insurance is provided for free. We explore four dimensions thatmight explain this unresponsiveness. First, we provided only relatively small insurance subsidies
to farmers (average premium voucher value of $20, maximum payout implied of $80). We havean almost perfect ability to predict the sum insured with the voucher amount, and even when we
examine the slope effect or the impact at high voucher amounts, there is no indication of an input
use response. Second, we examine whether we have a strong effect restricted to the sub-populationof individuals risk-rationed at baseline. While these individuals are more likely to take up
insurance when offered a voucher and invest less overall in inputs, there is no differential impactof the treatment on the risk rationed. Third, could the indirect marketing strategy pursued by the
insurer to lower administrative costs and improve commercially viability, be responsible? We use
a survey of comprehension in the fourth round to examine this, and find that while a very highfraction of the treatment sample knew of and had considered the product, detailed understandingof the contract was lacking and very few had direct contact with the insurer. Finally, we discuss
contextual features that may have caused the product to struggle in Ethiopia in particular, but findno heterogeneity in uptake based on cooperative membership or exposure to the main government
safety net program.
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Overall, our study shows that the psychology of protection from risk among smallholder
producers is not easily created. Despite the backing of a well-known national brand, near universal
awareness of the program, and excellent understanding of the subsidies, input investment behavior
did not change in a first-order sense. Our analysis of channels leaves the poor comprehension of
the details of the insurance and the indirect relationship with the insurer as prime suspects. Even
the fact that our training had strongly significant effects on uptake of the insurance reinforces thecritical role of familiarity. In light of these results it appears that serious investment is required tocreate the ex ante response to risk protection, and absent this it may make more sense to focus
attention on meso-level entities who can provide ex post risk protection with smaller transactions
costs.
In the final section of the paper we provide a case study of the one cooperative in which
we were able to successfully field the interlinked insurance-backed credit product. Our intended
study design added to the standalone WII insurance an ‘interlinked’ product that offered
agricultural loans on the condition that the farmer would purchase rainfall insurance to cover theloan. This has the potential to unlock substantial credit demand by reducing risk-related obstacles
to investment. Theoretically, Carter et. al. (2016) showed that when covariate risk is a large shareof total risk faced by farmers, and when there is lack of credit, such a product would be demanded
by small farmers, and would increase input use and output. However, several other recent studies
found that bundling insurance with a credit contract actually decreased demand for fertilizer
relative to a standalone credit product (Giné and Yang, 2009, Banerjee et. al. 2014). The most
likely explanation for these results seems to be that farmers were interested mostly in credit, andanything that increased, albeit in the short-term, its cost (such as insurance premiums) would lower
6

credit demand and the linked insurance. In the end, the interlinked product proved a substantial
challenge to field for reasons that speak to the core issue of the reassignment of agricultural risk
within the credit system. Nonetheless, where we were successful in fielding this product a
substantial improvement in credit access and input use appears to have resulted.
The paper is organized as follows: Section 2 describes the setting, research design and datacollection strategy for the study, as well as the construction of the index and basic statistics on
demand. Section 3 analyzes the impact of insurance vouchers on farmer behavior, Section 4
investigates four reasons for the low impact of our intervention, and Section 5 presents the casestudy of the cooperative where we fielded the interlinked product. Section 6 concludes.
STUDY CONTEXT.
Inputs and Credit in Ethiopian Smallholder Agriculture.
Given the transformative effect of agricultural technology on yields, risk-driven barriers to
adoption present an obvious potential poverty trap (Christiaensen and Demery, 2007), and holdout the possibility that risk-protecting products such as index insurance may not only provide
second-order benefits through variance reduction, but first-order benefits through enhanced
willingness to take profitable risks among producers. Despite large secular increases in fertilizer
use and household income over time, overall input use and productivity remain low.i Per hectare
fertilizer consumption is less than one fifth of that of other developing countries (Morris, et. al.
2007). Dercon and Hill (2009) report that fertilizer intensity per hectare of fertilized area has notincreased between 1997/98 and 2007/08, despite the apparent doubling of total fertilizer sales

7during the same period. This can be largely explained by a heavy reliance on extensification, via
expansion in the cultivated area.
The absence of productive credit may be an important reason for the low use of improved
inputs and hence lower productivity among African rural smallholders. Many studies have foundthat small farmers in developing countries are credit constrained and as a consequence use few
purchased inputs (Besley, 1995; Conning and Udry, 2007).ii The system of input distribution andfinancing in Ethiopia has evolved over time, but consistently features a dominant role for state-led
actors. While thriving private output markets exist, the government is responsible for fertilizer
imports, and the cooperative sector is used as the primary delivery vehicle for most improved
inputs. In Amhara region, the Cooperative Unions handle the wholesale demand aggregation
function, pre-ordering inputs through a public enterprise (AISE), and also serving as a conduit tofinancing through the publicly owned Commercial Bank of Ethiopia. Our fieldwork found thatthe financing chain in recent years has been subject to a game of chicken, whereby the government
announces early in the season that fertilizer would be distributed on a cash-only basis, but once it
becomes clear that this would result in low demand and excess stocks of fertilizer they injectedpublic-sector credit later in the season. Regional governments are required to underwrite the risksof smallholder input financing, and have been eager to find ways of shedding this risk, as any
farmer arrears have to be paid by the state budget. Cooperatives, local government agents, andlocal agricultural extension workers have been pressed into service to assist with the collection of
the debts used to finance inputs, so individual borrowers take on substantial risk when they financeinputs obtained through the cooperatives.

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The Ethiopian context, then, is an ideal one in which to pilot index insurance from theperspective of exposure to risk, and several recent studies have been conducted in the country (e.g.
Hill and Viceisza 2010; Berhane et. al. 2014; Dercon, et. al. 2014; Oxfam’s HARITA projectdescribed in OXFAM 2014; and McIntosh et al. 2016). However, from the institutional perspective
Ethiopia is a challenging environment in which to introduce a novel and complex private-sector
financial service. Rigidity of input supply chains, strong government-led approach to both credit
and fertilizers, and the presence of the massive Productive Safety Net Program (PSNP) iii in
Amhara Region all reinforce the central role that the government plays in agriculture and risk
management. The high climate variability and its central role in driving risks in Ethiopian
agriculture makes reinsurers reluctant to provide coverage, and increases the price of reinsurance.
All of this implies that the public sector plays a driving role in absorbing agricultural risk, andwhile these entities may be eager to spread these risks more broadly it is likely to be a challenging
context in which to engineer private-sector institutional means to do so.
Index Design.
We now describe the insurance product, and Online Appendix B contains further details of index
construction. The starting point was the powerful village-level cooperative system, through whichmost inputs in the Ethiopian smallholder supply chain move. Recognizing the importance of local
networks as means for spreading information (Cai et al. 2015) and sharing the remaining risks inthe face of an imperfect index (Mobarak and Rosenzweig 2013; de Janvry et al. 2014; Dercon etal. 2014), the cooperatives provide a potentially promising avenue through which to offer index
insurance products. Given the potential trust problems with asking cooperatives to smooth risk
(McIntosh et al. 2018) and the importance of making sure that the input decision-maker is directly
9aware of risk protection, we elected to market the product through the cooperative network but todesignate specific individual buyers as the explicit beneficiaries of the insurance policies. The
focus of the project was to improve input use and therefore the insurance product wasconceptualized as insuring the value of purchased inputs rather than trying to cover the variationin the value of output. The input purchases of an average-sized farm in Amhara operating at the
input intensity recommended by the Ministry of Agriculture would be roughly 4,000 birr ($278).
The first sales year (summer of 2012) was characterized by a widespread drought, and theinsurance triggered in 42% of the study villages for at least one crop. The average payout in thefirst year was $27, the minimum $3 and the maximum $121; payout rates ranged from 3% to 75%of the sum insured. In the second sales year (summer 2013) the index triggered for only 13% ofvillages in at least one crop, with smaller payouts averaging $9.

Given an average input

expenditure of $172 on average across all inputs, the observed payouts based on the voucher
experiment cover just under 20% of the value of inputs.
How well does the index correlate with our survey-measured agricultural yields? To
examine this question we take the primary yield outcome (a normalized index of yields across all
crops grown by a farmer) and examine the variation in this variable across villages and acrossyears.iv We present two ways of thinking about the quality of the index. First, in Appendix Table
A2, we show a simple two-by-two table of the village/years in which the index would have paidout versus whether our survey-based measure of yields indicates a shock occurred (as measured
by normalized yield outcomes that are in the bottom 20% of the distribution). This shows that of
the 79 village/years in which the survey indicates no yield shock, we have index payouts (false

10positives) in 21% of them. For the 19 village/years in which the survey does indicate a yield shock,the index pays out just over half of the time.
Second, Appendix Figure A1 plots the Cumulative Density Function of normalized yields
for village/years in which the index did and did not pay out. In the left-hand figure we perform aplacebo test by analysing survey-based yields in the year before insurance sales started as afunction of whether there would be payouts in the future. Here we should see the distributions ofyields very similar, and indeed we do. In the right-hand panel we then plot the same distributions
for the subsequent year in which the index does pay out. In this year, the distribution of yields invillages where the index does not pay out first-order stochastic dominates the distribution where it
does, and there is a particularly pronounced mass of farmers with yields far below average where
payouts occur. A Kolmogorov-Smirnov test shows the CDFs in the right-hand panel to besignificantly different at the 99% level. This pair of graphics illustrates that payouts and yields arestrongly contemporaneously negatively correlated and do not appear to suffer from any spurious
correlation. Overall, while our index was meaningfully correlated with yields a substantial amountof basis risk remains, and given the insights presented in de Nicola (2015) and Clarke (2016) thismay be an important explanation for low overall demand.
Study Design.
The identification of the original 120 study villages (kebeles) in Amhara region was conducted by
Nyala Insurance, who selected regions that were generally drought-exposed, within 20 km of arainfall station, and that they believed to be good markets for weather insurance with wellfunctioning village cooperatives (see Appendix Figures A2A and A2B for maps of the study area).
The selection of the Kebeles was purposive, and designed on the basis of informed opinion of11

Nyala as to where in the Amhara region the market for WII has best potential.v
Within study villages we then randomly sampled 18 households from within themembership of the village agricultural cooperative, and 2 households from the non-cooperative
farming population of the village.vi Appendix Table A3 provides summary statistics by region
from the baseline, underlining the extremely low education levels among household heads
prevalent in our sample.vii We conducted a second, shorter baseline survey in 2012 collecting the
primary agricultural outcomes only so as to provide a more powerful estimate of pre-treatment
outcomes, as advocated in Bruhn and McKenzie (2009). The baseline data were used to conduct
a village-level randomization stratified on average fertilizer use and blocked on region, thatassigned villages into three arms of 40 villages each: standalone villages in which only WII wasoffered, interlinked villages that offered both standalone WII and insured loans, and a control. In
both types of treatment villages we conducted household-randomized experiments giving vouchers
for small amounts of free insurance coverage, and insurance training.
Unfortunately, several implementation problems altered the intended design. First, thereinsurer proved unwilling to underwrite the product in a subset of weather stations that had fewer
than 30 years of valid data. Second, with detailed survey data in hand we realized that some ofthe study villages were more exposed to other weather risks (frost, hail, flooding) than drought.
Taking the entire study sample that reported being drought exposed in the baseline survey as well
as being within twenty kilometers of one of the nine re-insurable weather stations, the reductionof the eligible weather stations left us with a sample of only 49 villages in which we could
reasonably field the insurance product. Third, delays in the implementation of the credit side
meant that it was only possible to offer the interlinked product after the experimental phase of the
12project had ended (see Section 6 for a non-experimental description of our interlinked treatment),and so all insurance studied in the main tables for the paper is standalone. The effective research
design for the village-level experiment therefore compares the 34 villages in which standalone
insurance was offered to the 15 experimental control villages in which it was not. Figure 1 indicatesthe original and eventual study sample.
Fortunately, the experiment also features two dimensions that were randomized at thehousehold level. These individual experiments provide us with a source of variation which is still
experimental (being blocked by village or subsequent to attrition) and relatively high-powered
statistically. First, at the time of the baseline survey we selected a random subset of cooperative
farmers to receive a promotion and training in the way that the index works. Because we wereconcerned that the indirect marketing techniques used by Nyala might generate only an imperfectunderstanding of the product (they trained village level agricultural extension officials to workwith village cooperative leaders to promote the product to farmers) we employed the Ethiopian
Economic Association (EEA) to conduct these randomized promotions as a part of the baseline
activity. EEA was hired using research funds to give an extended promotion of the product,
including conducting a 15-minute presentation of the details of the product for a randomly selected
six households per kebele, discussing and answering any questions the household had about theway the product works, and leaving behind a flyer indicating how Nyala would use rainfall at thenearest station to determine payouts, and which historical years the product would have paid out
in that village.
Secondly, in all years where rainfall insurance was offered, a random subset of households
in treatment villages received vouchers that they could use to obtain some insurance coverage for
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free. The size of the free insurance voucher was designed to be 10, 30, and 50 percent of the amountof insurance premium that the farmer would need to cover inputs for the average sized farm(calculated as a function of average land size and the recommended input usage in Amhara). Each
type of voucher was offered to 5 farmers in each kebele. Given 34 treatment kebeles, a total of 170
farmers received each type of voucher for a total of 510 farmers out of the 680 farmers surveyed
in the treatment kebeles. In sales season 2, vouchers were re-randomized at the household level.
Finally in early 2016 a small group of 120 households in the only kebele which successfully fielded
the interlinked credit-insurance product were surveyed. Table 1 presents the final design of thehousehold-randomized component of the study in the 49 villages that were panel tracked as a partof the survey. Appendix Table A4 shows how core study outcomes evolved across time; despite
impressive overall improvements in input use the study sample has very high levels of foodinsecurity and farm size fragmentation is occurring quickly.
Attrition and Balance.
As described above, the study is subject to two kinds of attrition. The first is at the village level,
where we lost locations to the study due to (a) lack of exposure to drought and (b) rainfall stations
that were not re-insurable. We lost almost 60% of our sample due to these sources of village-level
attrition. Within the panel sample of villages, we then attempted to track all study households inthese villages, and achieved a four-round tracking rate of 92.5%. Tracking this sample of 20
households each in 49 villages, we conducted two post-treatment household and cooperative-level
surveys, one in 2014 and one in 2015. Table A5 analyzes the extent to which the two types of
attrition from the study are correlated with treatment status. First, in columns 1 and 2 we examine
whether the rate of attrition is differential across village-level treatment status, and find that both14

the original standalone and interlinked arms were lost to the study at rates from 7.5 to 12 percentage
points lower than the control. These differences are not significant. Given the high and potentiallydifferential attrition subsequent to randomization, this village-level component (whether insurance
offered) cannot be considered cleanly experimental.
Despite the village level attrition the farmer-level randomization should still be clean
because the training was blocked by village, and vouchers were blocked and randomized
subsequent to village-level attrition. Columns (3) and (4) of Table A5 show that household-level
attrition is low overall (9% in the control) and not significantly different across those who receive
the voucher or training treatments. Appendix Table A6 examines the balance of the un-attrited
sample on baseline covariates (Panel A) and baseline outcomes (Panel B) and finds the panel
experimental sample to be well balanced overall (only 5 comparisons out of 80 significant at 5%
level). Based on this analysis of attrition and balance we place the emphasis of our analysis on theindividually-randomized treatments.
Analysis of uptake of insurance .
As presented in McIntosh et al. (2013), we conducted a stated willingness to pay study at baseline,
which indicated that almost 50% of the study sample would be willing to purchase standalone
index insurance at actuarially fair prices. Table 2 illustrates that this stated WTP radicallyoverestimated the actual demand for the product. The table shows simple summary statistics ofthe rate of take-up and the average sum insured across the various years and arms of the study.
The study featured only one-sided non-compliance, meaning that there were no individuals who
received insurance in the control villages at any point in the study. Distressingly, there were no
surveyed households who ever purchased insurance in the absence of being given a voucher and/or
15

promotion to do so (although voucherless farmers outside the study sample did so). It was typicallythe case that individuals ‘purchased’ mainly the amount provided by the voucher and rarely put
their own money into insurance premium payments. Overall, the fraction of the total membershipof treatment cooperatives (including those not sampled to be surveyed) who purchased insurance
in the absence of any voucher is roughly .5%. This makes it clear that the insurance, as priced toreflect all costs, and as promoted through the indirect marketing strategies used by Nyala, wassimply not viable as a commercial product. The average premium paid towards insurance intreatment villages during the first sales season without the training was $3.77, and the training
added another $3.75 on to this figure (Table 2, third row). However, roughly 95% of this value
came from the vouchers rather than from true willingness to pay in farmers’ own money, and sowhat we study here is almost exclusively the decision to accept free insurance, and the impact ofreceiving it. In the second sales season both willingness to accept the vouchers and the amount
paid from farmers’ own pockets rose, but because the voucher amounts were smaller the overall
sum insured fell. Consequently, as shown in Appendix Figure A3 the average sum insured at theindividual level is an almost linear function of the voucher amount, a feature we exploit in Section
4.2. The sum insured was slightly higher in the first sales season than the second because thevoucher amounts were larger. Non-cooperative members take up standalone insurance at about
2/3rds the rate of coop members.
The more optimistic perspective on Table 2 is that the two individually randomized
components of our insurance promotion had very strong effects on the probability that a farmer isactually covered by the insurance. In both seasons the vouchers have an effect significant well
above the 99% level on insurance uptake. The EEA-administered training and promotion is also16

independently effective; conditional on already having received a voucher, also receiving thetraining has an effect on uptake in the first sales season and roughly doubles the premiums paid byfarmers. In the second sales year the additional effect of the training had faded but trained farmers
were putting $1.46 more of their own money into premiums, significant at the 10% level. Hence,
while our study suffered from multiple forms of attrition at the village level and did not generatemass-scale uptake, we have delivered a statistically relatively well-powered experiment in what
happens to farm outcomes when farmers are covered by WII. We now proceed to analyze theimpact of these individually-randomized components on farm-level outcomes.
IMPACT ANALYSIS.
Intention to Treat Effect of Free Insurance Vouchers.
We can use the experiment to track out the impact of being insured on the primary objective
outcomes of the study: use of fertilizer, use of improved seeds and credit, agricultural yields, andhousehold income. The best powered regression specification is the standard panel fixed effects
difference-in-differences (DID), namely:(1)yict  i   t  1Tct   2Vict   ict

Here i is an individual fixed effect;  t is a fixed effect for each of the post-treatment survey
waves; Tct is a panel treatment dummy that switches on in treatment villages for rounds 3 and 4(pooling together the standalone and interlinked arms since only standalone insurance was sold);and Vict is a panel dummy variable indicating that the individual was treated in that season(received the voucher, the training, or both, in subsequent tables). We cluster standard errors at17

the village level to account the village-level design effect. Given this specification, 1 is theimpact of being in a treated village and not receiving a voucher, and  2 is the ITT impact ofreceiving a voucher. Based on the uptake analysis, we expect 1 to be zero and we are primarily
interested in the sign and magnitudes of  2 .
Table 3 presents the analysis of the insurance vouchers. We begin in Columns 1 and 2with the analysis of uptake, presenting a binary outcome for any insurance coverage (which in thiscase means accepting the free insurance policy) as well as a continuous outcome for the sum
insured in that season. We then examine a core set of agricultural outcomes, including all of the
farming activity of the household and not only the crops covered by the insurance policy. The
primary outcome for the study was fertilizer use, which we study using three variables: a dummy
for whether the household used any chemical fertilizer at all during the meher season covered by
the insurance (column 3), the number of kilograms of fertilizer used (column 4), and the numberof crops on which fertilizer was used (column 5).viii We then examine other inputs, including adummy for any improved seeds (column 6), any credit that was specifically taken for input
purchases (column 7), and the total value of all inputs used (column 8). Finally, we broaden thelens from inputs to study an index of agricultural yields across all five major study crops (teff,wheat, barley, sorghum, and maize) each crop yield is z-scored by subtracting the mean yield off
and dividing by the standard deviation, and we take the average of this z-score across all crops
grown by a given household in column 9), total household income (column 10), and the total areaof land cultivated by the household (column 11).

18

We focus our estimation on the row for ‘Any Voucher’ (  2 ), which generates a very large
first-stage effect on uptake of the insurance. Troublingly, none of the three variables describing
the use of fertilizer are significantly affected by the provision of insurance vouchers, despite thefact that 39% of the individuals in this group are insured. The use of any chemical fertilizer rises
by a mere 3 percentage points off a base of 63%, and this effect has a t-statistic below one. The
number of kilograms of chemical fertilizer used actually falls by 4.6, or 4% of the baseline mean,
and the number of crops on which fertilizer is used sees a similar small decline. Turning to theuse of improved seeds we see an apparently impressive increase of 8 percentage points, but a more
careful examination shows that this is relative to a decline of 18 percentage points in treatment
villages for individuals who do not receive vouchers (both effects significant at 10% level). Credit
use does not increase, and the area cultivated declines slowly over time at the same rate in thetreatment and the control. (Note that given an overall uptake rate of ~40%, our estimate of theTreatment on the Treated for receiving a voucher would simply be the ITT estimates presented
times ~2.5). As we would expect when inputs to the agricultural process remain unchanged, yields
are similar across groups and household income remains unchanged by the receipt of the free
insurance voucher. Additional analysis (not shown) indicates that there was no effect of insurance
coverage on farmers’ decisions to plant or dedicate inputs to teff, the specific crop covered bymore than 70% of the insurance sold.
Against this discouraging picture of the impact of index insurance vouchers, it is importantto highlight the remarkable improvements apparent in the round dummies. The use of chemicalfertilizer surges overall during the course of the 2012 (R3) and 2013 (R4) seasons: the fraction of
farmers using fertilizers rises from a control average of 64% in 2010-2011 to 79% in 2012 and19

75% in 2013. The average number of kilograms used in the control goes from 118 to 130 in 2012
and 2013. So it is important to recognize that lack of insurance impacts is measured during aninterval when the target outcomes of the study were strongly improving overall.
Does the lack of impact arise from a lack of statistical power? Clearly the village-level
insurance experiment in our reduced sample is underpowered. Examination of the standard errors
on the individual-level voucher experiment, however, suggests that power is not a major concern.
Multiplying the standard errors on the impact coefficients in the first row by 1.96, we would be
able to detect an increase of at least 8% in fertilizer use, and 7.5% in improved seeds or credit. For
fertilizer, this represents a standardized effect size of .18, represented as a fraction of the standard
deviation of this variable in the baseline control. This is a relatively small standardized minimum
detectable effect, indicating that the voucher-level experiment is adequately powered. Further, we
see negative coefficients on two of the three variables describing the primary fertilizer outcome,and we clearly have plenty of power to detect the cross-period changes in input use and incomes.
Hence, while the final study sample is less well-powered than the original design intended, we canconclude that at least the individual-level voucher treatment did not result in any substantial
improvement in input use or incomes during the period of the study relative to the absence of avoucher.
The Impact of the Training Exercise.
We next present an analysis that looks at the impact of the training administered by the survey firmat baseline, as well as how the training may interact with receipt of a voucher. Because thisanalysis combines an intervention randomized at baseline (training) with one randomized acrossrounds (vouchers) we examine the first and second sales season separately, using an analysis of20

covariance (ANCOVA) specification to absorb the variation explained by the lagged dependent
variable. This specification is :(2)

yict   0   1Tct   2Vict   3 Pict   4 (Vict * Pict )   yic1   ict .

In this specification Pict is a dummy variable for treatment prior to the outcome observation insurvey year t;  2 gives the impact of vouchers in the absence of the training,  3 gives the effectof training in the absence of a voucher (since there was no adoption of insurance in the study
sample in the absence of a voucher, we expect this term not to be significant), and  4 measures
whether the impact of the voucher experiment is different for those trained directly in how theinsurance works.
Table 4 presents the results of the ANCOVA analysis, interacting vouchers with trainings
for each sales year separately. The first row of this table examines the impact of voucher in thesubsample that did not get trained, and the third row the additional impact of vouchers for those
who were trained. Beginning with take-up, we see that in the first sales season there was a strong
interactive effect of the two treatments (the trained are 11 percentage points more likely to usevouchers) but that this effect of the training had faded by the second sales season. The bottom row
shows the coefficient on the lagged dependent variable; most of our outcomes with the exceptionof ‘total value of inputs used’ have strong positive autocorrelation. In terms of impacts, in neither
sales season and for no combination of treatments do we see any clear pattern of improved
agricultural or welfare outcomes. We might have hoped to find that once the insurance was better
understood as a result of the training exercise, the receipt of free vouchers would induce a
behavioral response among farmers. Unfortunately there is no evidence of this, suggesting that21

while the training exercise had some commercial logic (increased own purchase of insurance aswell as uptake of vouchers), there is no welfare benefit induced by the combination of training andvouchers.
The Dynamic Effect of Subsidies.
The preceding subsection illustrated that demand for the product was vanishingly small in theabsence of subsidies, but that vouchers for insurance created strong variation in the rate at which
individuals were successfully issued insurance policies by Nyala. Given the now well-recognizedproblems of demand in launching a new index insurance product, however, a business case forsubsidies can still be made if they have strong enough effects on subsequent demand, and positive
dynamic effects of subsidies have been suggested in the context of learning about the newprobability distribution of farmer income (Cai et al., 2016). Our study provides a very simple
environment in which to pose this question, because we independently randomized subsidies in
the first and second sales seasons (measured using survey waves 3 and 4, respectively), meaningthat we can examine the impact of first-season subsidies Tic1 and second-season subsidies Tic 2 forindividual i in cluster c on second-season demand Dic 2 , using the cross-sectional regression(3)

Dic 2  0  1Tic1   2Tic 2   ic 2 .

Standard errors in this regression are clustered at the village level to reflect the design effect. This
regression, as all of the main regressions in the paper, is estimated using only the sample ofcooperative members. This first pass at this question is provided in Column 1 of Table 5, whichanalyzes the impact of subsidies in the first and second year on the uptake of insurance in thesecond year. The first clear result from this table is that the vouchers have strong contemporaneous22effects on the probability that an individual is protected by insurance; uptake is elevated by 36
percentage points in the presence of subsidies, and the sum insured is driven strongly by theamount of the voucher in that season. The dynamic subsidy argument requires that subsidies create
more permanent demand, however, and when we look at the effect of season 1 vouchers we findno evidence that the effect has persisted into season 2. The point estimate on this term is verysmall (3.8 pp) and far from significant. Column 2 shows that the amount of the voucher has noeffect on uptake either concurrently or subsequently. The impact of the voucher amount on thesum insured displays a similar pattern; strong contemporaneous effects but no detectable dynamic
effect at all.
One mechanism through which the literature has suggested sales can be built is that
insurance becomes credible once agents see payouts being made (Cole et al. 2014; Cai et al 2016).
To test this, we define a dummy variable Wic1 based on the rainfall realizations at the local rainfall
station and the main crops that a farmer grows, to indicate that they would have received a payout
in the first sales season had they decided to purchase insurance (the variable is constructed in thesame way for the treatment and control). We then interact this variable with the (randomized)
season 1 vouchers.
(4)

Dic 2  0  1Tic1   2Tic 2  1Wic1  2 (Tic1 *Wic1 )   ic 2
The uninteracted term therefore tests whether having payouts made in your village for yourcrop to others around you induces subsequent demand, and the interaction term tests whether thiseffect is bigger for those who received vouchers, roughly 40% of whom actually received
insurance payments. Column 3 shows that neither of these terms is significant, indicating that23

neither the presence of historical payouts nor the receipt of subsidies in combination with payouts
leads to increases in demand. Columns 5-8 show similar results on the sum insured; while thisoutcome responds more strongly to the contemporaneous voucher amount, there is still no impactof lagged voucher receipts. As a final test we calculate the payout amount that an individual would
have received based on location, baseline crop, and voucher amount, and in Columns (4) and (8)examine whether this local payout rate drives future sales; again, it does not. Consequently, thisrelatively well-powered demand experiment provides no evidence that temporary subsidies
resulted in a durable improvement in demand for index insurance.
UNPACKING THE LACK OF IMPACT.
We now seek to understand why our study delivered not only low demand for commercial
insurance, but such small impacts on agricultural outcomes even when insurance was provided forfree. We consider four explanations in turn.
Impacts of the Sum Insured, using Voucher Amounts as an Instrument.
A first possibility is that many of the vouchers we distributed were simply too small to affectbehavior. To investigate this, we pursue two approaches. The first of these is to exploit the factthat the randomly assigned voucher amounts generate very strong experimental variation in thesum insured. Appendix Figure A3 illustrates this relationship, showing the almost perfectly linear
relationship between the voucher amount and the sum insured, and illustrating that at the highest
voucher amounts the sum insured is nearly $80, representing 55% of the total value of inputs
observed in the control group, which is equal to $147. We can use this to estimate a slope effectthat is sensitive to the sum insured, instrumenting for the (endogenous) sum insured with the24randomly assigned voucher amounts. This analysis is presented in appendix Table A7. Panel A
of Table A7 Column 1 shows the first stage of the IV regression, indicating that the instrument
has a t-statistic of 4, and the IV regression should be well powered to detect impacts. Columns 29 present these instrumented impacts on the battery of primary outcomes, illustrating that evenwhen we translate impacts into the marginal effect of the sum insured, we are unable to detect any
significant changes in inputs or agricultural productivity as a result of the insurance.
A second way of tackling this question is to isolate the impact of receiving the largest
voucher amounts. We do this by defining a separate dummy variable that indicates being treatedwith the largest voucher amounts, which we characterize as the vouchers for 300, 400, and 500
Ethiopian birr ($20.83, $27.78, and $34.72). Panel B of appendix Table A7 includes separate
dummies for the large transfer amounts and the smaller transfer amounts, and at the bottom of thetable we present an F-test of the difference between the two impacts. As in Panel A, there is no
indication that the study provided any transfers that generate significant impacts; neither of thetwo voucher dummies is significant and nor is the difference between them, for any of the primary
outcomes in the study. While we cannot, of course, speak to the impacts that would have been
observed had we distributed larger vouchers, we find no indication that even the largest subsidies
in our study had any meaningful impact on investment behavior. This is suggestive that the overalllack of impacts is not arising simply from small average subsidies.
Differential Impacts by Risk Rationing Status.
A second explanation for low uptake and small impacts would be that only a small share of the
farmers in our sample were constrained in their input investment behavior by the presence of risk.
In this case, potentially large impacts in the risk-constrained group could be swamped in the25Intention to Treat analysis by the small numbers of such individuals. This suggests a heterogeneity
analysis where we distinguish the analysis of uptake and impacts by whether individuals were risk
constrained at baseline. This is a sub-group analysis that we anticipated in our earliest project
documents, and we carefully classified the credit rationing status of households at baseline using
the approach outlined in Boucher et al. (2008). Standard agricultural investment models such as
Bardhan and Udry (1999), Boucher et al. (2008), and Carter et al. (2016) would all predict that thefirst-order impacts of insurance on expanding the willingness to borrow and invest in inputs will
be strongest in the risk-constrained group. We use a set of survey questions on the access to anduse of input credit to classify farmers into four constraint categories: unconstrained (currently
using credit), quantity rationed (would not be able to access credit), price rationed (could get loan
but find it too expensive), and risk rationed (could get loan and would be able to cover interest
costs on average, but unwilling to bear the risk of possible default). Overall, we classify 54.6%
of our sample as unconstrained, 18.8% as quantity constrained, 6.8% as price constrained, and19.8% as risk constrained.
Appendix Table A8 shows the results of this interaction analysis. In order to present the
raw effects of the credit rationing status of the household we run this regression as a Difference indifference (DID) without household-level fixed effects. As in previous tables, Columns 1 and 2
show the uptake variables (dummy for purchased insurance and the sum insured), and in neithercase does credit rationing status drive uptake, either in absolute terms or in interaction with thereceipt of a voucher. When we look at the study outcomes in Columns 3-10, we see that those
who were classified as credit rationed at baseline have sharply lower input investment overall:they are 9 percentage points less likely to use any fertilizer than the unconstrained, they use 42
26

Kgs less fertilizer and use it on roughly a third the number of crops, and total input expenditures
among the risk constrained are only about 2/3rds of those of the unconstrained. Despite these large
cross-sectional differences, there are no signs of significant differential impacts of the provisionof vouchers on the risk constrained. Hence, we uncover no evidence that the lack of demand andimpact in our sample is masking large impacts among the subset of individuals whose input
investment decisions are most constrained by the presence of risk.
Comprehension and Trust of the Index Insurance Product.
In the round 4 endline, we conducted a survey in treatment villages that asked household heads todescribe what they knew of the Nyala index insurance product, how they had learned about it, andwhat their experiences with the product had been. In terms of awareness of the product andunderstanding of the voucher distribution, farmers appear to have been properly sensitized. 88.3%
of surveyed cooperative members in treatment villages reported having received information about
the insurance, although Nyala’s strategy of indirect marketing can be clearly seen in their sourcesof information: 81% reported EEAix (the survey entity) as their primary source of information
about the product, 8.3% the cooperative leadership, and only 5.4% reported getting information
directly from a Nyala representative.x A promotional brochure that was developed by Nyala anddistributed by EEA had been seen by 56% of households, and 73% of households reported
understanding the product ‘very well’ (9%) or ‘partially’ (64%). Recall of the voucher amounts
that had been distributed was excellent; of the 370 cooperative members randomly assigned toreceive vouchers in the second sales season, only 6 incorrectly recalled their voucher amounts. So
overall the promotional materials for the insurance and the voucher subsidies were distributed on27

the ground in a manner that closely conformed to the research design, and awareness of the productwas high.
When we dig into the details of the product, however, and the extent of trust that farmers
felt in the index and the company, the picture is less rosy. When asked the factual question as tothe event that would trigger a payout, only 6.4% of respondents correctly indicated deficit rainfall
at the closest rainfall monitoring station. While 23% indicated that they simply did not know, themost common answer (44%) was that payouts would be triggered by crop losses on the
respondent’s farm. This indicates that the plurality of cooperative farmers in fact believed thatthey were being offered indemnity insurance. This certainly points to major potential problems
for the insurer in the event of uncovered losses, but it cannot serve as an explanation for low
demand or meager impacts because it suggests that farmers believed the product to be closer to aform of perfect insurance than was in fact the case, and were not being dissuaded by the presenceof basis risk. Despite this confusion about the index structure, the 22 farmers who reported having
purchased insurance and faced an insurable loss all reported having been contacted by Nyala toarrange the payout, and 17 of them had actually received the money by the time of the R4 survey.
Thus, it does not appear that disagreements over payouts among the insured, or any actual failure
to honor the contact by Nyala, were to blame. Nonetheless, by the time of the endline survey
households that received the promotion are no more knowledgeable about the insurance productthan households in the same village who had not received the promotion.
Overall, it appears that there were meaningful holes in understanding of the product. A
relatively cost-effective promotion of the product led to a substantial increase in both acceptanceof the vouchers and out-of-pocket expenses, but demand among those who received the promotion
28

faded over time, and they did not understand the product better by endline. We conclude that
distrust and misunderstanding were prevalent causes of low demand, but that at least in the shortterm demand can be improved with a more intensive client education and promotion plan.
State-Led Agriculture and Rigidity in the Input Provision Process.
The heavy state role in the agricultural sector could inhibit demand for a market priced product,
particularly given the importance and visibility of the government’s massive safety net program(PSNP) in the study area (Duru 2016). Even when free insurance is provided, as was done with
our vouchers, the rigidities of the Ethiopian input provision process may inhibit the input usage
response at the farmer level. The long planning times involved in the input procurement process,the egalitarian ethos of the cooperatives, and the lack of outside options for purchasing may all
limit the ability of individual farmers to adapt their input usage decisions easily. Preliminary
evidence that this is the case is provided by the fact that the intra-cluster correlation on the use ofchemical fertilizer within the control group across all four rounds is .66, indicating that only one
third of the variation in the key input for this study is found within-village.
A simple way of understanding whether the cooperative supply chain generates aninflexible input response is to incorporate into our analysis the two non-cooperative households
that were included in both the survey and the voucher treatment in each village. In Appendix Table
A9 we conduct analysis of heterogeneity using pooled OLS, including a dummy for the non-coopmembers, the panel voucher treatment dummy, and the interaction between these two terms. This
interaction asks whether a disproportionate response to the treatment is visible in the non-coopmembers, as might be the case if they had differential access to more flexible sources of inputs or
input financing and hence were able to respond more fully to being insured. These results clearly29

indicate that non-members have significantly lower use of chemical fertilizer overall, and despite
relatively low power there is no evidence of a disproportionate response to the treatment among
the non-cooperative members. Indeed, most of the point estimates are negative, suggesting that if
anything those farmers with access to the cooperative supply chain are in fact more responsive tothe treatment.xi
Finally, we can examine whether the baseline presence of PSNP in the village or receipt of
PSNP at the household level has depressed demand for private insurance. By good luck our sample
is relatively well stratified by PSNP access (40% of panel study villages were eligible for PSNP,
and within these villages 31% of study households were direct beneficiaries at baseline). This
analysis (presented in Appendix Table A10, Panel A) shows that among cooperative members
there is no evidence that PSNP depresses uptake of the vouchers; if anything the point estimates
are positive and of moderate magnitude, in the range of 5-7 percentage points.xii In Panel B weshow the differential impacts of having vouchers for those who also are covered by PSNP. In row
3 of this table we see the outstanding household-level poverty targeting of PSNP: eligible
households are less likely to use every kind of measured input at the 99% level and haveagricultural yields that are just under .2 SD lower than the ineligible. Despite the huge disparity
between the eligible and ineligible groups overall, however, there is no solid evidence ofdifferential impacts of insurance vouchers. While the PSNP eligible group do increase ‘any use’of fertilizer when given vouchers relative to the voucher-treated ineligibles (significant at 95%),
the value of fertilizers used is actually somewhat lower, and use of credit is significantly lower.
Total value of inputs used, yields, and household income all see insignificant differential impacts.
Therefore PSNP coverage drives neither uptake nor impacts of formal insurance.
30

So, while it is certainly possible that the marginal effects of free insurance on input use
would have been stronger in a more demand-driven market environment, we do not find evidence
that the cooperative structure, supply chain rigidity, or public safety nets in the Ethiopian
environment are directly responsible for the weak connection between being insured and farmerlevel outcomes. Overall, the most compelling argument we find for the lack of impact of the
product on farmer behavior seems to be the indirect relationship of the insurer to the farmers andthe imperfect informational environment that existed on the ground. In the conclusion we discuss
the implication of these results for the design of programs that seek to provide subsidized risk
protection to farmers.
DESCRIPTIVE ANALYSIS OF THE IMPACT OF THE INTERLINKED PRODUCT.
An additional contribution of the EPIICA project was the emphasis on interlinking WII with acredit product, thereby creating a contingent loan for risk-exposed farmers. We conclude ouranalysis by providing a brief description of our experiences in this endeavor. Under our design,village-level cooperatives would aggregate demand for the interlinked loans, pass it on to theUnions, who would in turn sign a single loan contract with Dashen Bank for all their subcooperatives, collateralized by the productive assets of the Union. This solution represented apotentially fundamental reshaping of the way in which agricultural risk was apportioned in thecredit system, and many parties were eager to see this happen.
In the end, however, fielding the interlinking proved to be a substantial logistical challenge.
The current public credit system makes state governments explicitly liable for the agricultural
lending portfolio, permitting the use of extension agents, police, and other officials in the debt
collection process.xiii Recognizing the exposure it faced as a private sector entity newly entering31

a market previously so dominated by the state, Dashen insisted that the Cooperative Unions (CUs)have a stake in the successful recovery of loans through the interlinked product via collateralizing
the loans with CU assets. Unions are cooperatively managed entities with a somewhat ambiguous
legal status, and this requirement to use their own assets to leverage credit for members was new
and difficult to negotiate. This tension suggests a fundamental issue in the process of interlinking:despite the fact that the index insurance product itself generates little moral hazard, an interlinked
loan may still have substantial non-weather related residual moral hazard. This makes decisions
about where this risk sits very consequential. Given the design of our financial product, thecollateralization of the assets of the quasi-public Unions turned out to be the place where this nonweather driven risk became concentrated and consequently proved one of the most difficult steps
to negotiate.
All five Cooperative Unions in the project implementation area attended workshops
intended to facilitate the understanding and development of the interlinked product, as well as
providing financial documents to Dashen that let the bank begin to assess their creditworthinessand begin the contracting mechanism. Only one Union (Merkeb) was ultimately able to clear thevarious hurdles required to receive loans from Dashen, and they ended up providing interlinked
loans to a single village, Feres Wega (FW). Credit did not reach the famers in this village until
after the fourth and final round of the full household survey. We did nonetheless design a special
survey instrument that was fielded in FW in a fifth round (in early 2016), including all 20 of thepanel individuals that we had previously been tracking and 100 additional randomly sampled
households among those who took up the interlinked product. The product was sold in late 2014
and we conducted this fifth survey in early 2016. In that year (2014) the interlinked product was32

offered to all farmers in the stated village, and not only to those in the panel. In total, 254 farmers
obtained interlinked insurance and credit through the project in that year.
Of the 20 households in the panel from FW, 15 had received at some point a voucher
towards the premium, and 11 bought interlinked insurance for an uptake rate of 55 percent. Of
those in the panel who received a voucher but did not buy any insurance, the majority (4 out of 6)
indicated lack of understanding of the product as the major reason they did not buy. The other 100
surveyed households were outside the original study sample and did not receive any voucher. Of
the 120 farmers surveyed, 107 indicated that they received information about the insurance productoffered by Nyala. And 94 (out of 105 respondents) indicated that they understood the product wellor partially. A very high share (108 out of 120) of farmers among the respondents applied for the
Dashen loan covered by weather index insurance. Of the 108 who applied for the loan 105 received
the full amount they applied, while the other 3 stated that they received less than what they appliedfor. The average amount of loan applied for was 1675 birr, with 75 percent of the applicants asking
1400 birr and only 4 applicants asking for 3000 or more birr. Of the 108 farmers who received theloan, 45 knew that the insurance premium was included in the price of the loan. All 108 applicants
applied via the local cooperatives and 105 out of the 108 felt that the system of getting bank loans
through the cooperatives was appropriate.
We have no counterfactual for the changes measured in Round 5 in Feres Wega, bothbecause this cooperative is clearly endogenously selected in a number of dimensions, and because
we did not conduct panel surveys in other villages in this round. We nonetheless conduct an
exploratory analysis as to the possible effects of interlinked insurance and credit on input use bysimply asking respondents in this village about the changes in their farming behavior that occurred
33

after they received an interlinked loan. Appendix Table A11 shows that among those who applied
for an interlinked loan, the number reporting increases in chemical fertilizer use (72) far outweighthe numbers who indicate no change (27) or a decline (9). In all input categories the number of
those who declared that they increased the use of the relevant input is larger than the number of
those indicating a decrease. So while successfully fielding the interlinked product in only one of
the intended 40 villages underlines the hurdles to implementing these contracts, a surge in input
use in FW is consistent with the idea that where we can manage to relax credit and risk constraints
simultaneously, input use and productivity will rise.
CONCLUSION
Insurance, like credit, is a product that is inherently time inconsistent in the absence of somemechanism to enforce adherence to the contract. The fundamental difference is that credit is time
inconsistent for the client, while with insurance it is the firm who wishes to renege. This meansthat the dynamic credibility of the insurer is a critical precondition for the emergence of market
demand. While Ethiopia is known across the world for the severity of its droughts and the hugetoll they can take on peasant farmers, it is also an environment in which the public sector plays acentral role (particularly in the cooperative sector studied here). Private sector actors in the country
face an ambiguous legal environment without clear property rights or enforcement of contracts.
Thus the context of this study provides a very strong environment in which to test for the impactsof reducing weather risk, but also potentially an uphill climb for private-sector insurance provision.
The effort to shift risk away from smallholder farmers and towards the international
reinsurance system is one that appears to present large welfare benefits, but to achieve it through34

index insurance one must generate wide-scale adoption. Our study illustrates several discouraging
points as to the promise of the private sector to deliver this in the Ethiopian context. First, it isclear that this standalone weather index insurance product as promoted and marketed elicited little
market demand, and even an individually-targeted promotion conducted at the household level by
our survey firm led to little long-term willingness to pay. Improvements in indexes to reduce basis
risk such as the ‘double trigger’ strategy of Stoeffler et al (2015) are likely be an important part ofboosting demand. Nonetheless, if a sustainable private market it is to be achieved, good marketing& promotion will be central.
In our study, subsidies providing small free ‘tester’ insurance policies, generated awillingness to accept the contract but no subsequent improvement in private demand in subsequentseasons even if that insurance policy paid out in the first year. Thus, we find no support for theidea of subsidizing to build the market. Finally, we show that this free insurance did not result inmeaningful increases in input investment, suggesting that the productivity argument for subsidies
is also absent. Hence, marketing and subsidizing micro-insurance policies all the way down to thelevel of individual farmers did not relax risk constraints enough to unlock first-order increases in
productivity. More positively, our results show hope that a properly developed and marketedinterlinked product could meet with demand. In the cooperative where all of the steps in the chain
were forged to provide interlinked insurance to farmers, uptake was strong and more than ahundred farmers were able to access interlinked, private-sector credit.
What is the takeaway message from this study? It is certainly not that risk is unimportantin agriculture; our survey data is full of evidence that this is not the case. A large share of surveyed
farmers report risk as the main impediment to purchasing more inputs. That individually-marketed35index insurance is not a viable commercial product is now relatively well-established. Where ourresults provide particular caution, however is in illustrating that in a context with indirect
marketing and promotion of the insurance to farmers, even free risk protection does not trigger anagricultural response. Many of the studies that have shown production effects from subsidizedrisk protection have done so in the context of large-scale and well-known national programs, suchas those in India (Cole et al. 2017), or China (Cai 2016), or using intensive multi-visit marketingof the insurance at the household level (Karlan et al. 2014). One can therefore interpret our results
as being evidence of the fact that the construction of the infrastructure that credibly delivers an
insurance-driven productivity surge among rural farmers via individually-marketed insurance is aheavy lift. Our product was both new and imperfectly understood, and in this context the hopedfor input response to risk protection did not materialize. This suggests that success in driving
agricultural productivity through ex ante risk protection will need to happen in the context of a
substantial effort to build familiarity and credibility. Otherwise the production-side benefits of ex
ante risk protection are not realized, in which case effective ex post risk protection to smallholders
can likely be more cost effectively provided by meso-level actors. Particular attention should be
paid to innovations that sit at the nexus of credit and risk markets.

36

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43

TABLESTABLE 1. PROMOTION AND VOUCHER EXPERIMENT IN THE PANEL SAMPLE
Control(15 villages)

StandaloneArm(17 villages)

Interlinked Arm(17 villages)

Initial # Cooperative Households:

270

306

306

First Sales Season (outcomes in R3 survey)
Households remaining in panel:
Provided Training & Promotion

257
0

300
102

291

257

46
49
84

Provided Voucher:None
100 birr ($6.94)
200 birr ($13.89)
300 birr ($20.83)
400 birr ($27.78)
500 birr ($34.72)

Second Sales Season (outcomes in R4 survey)
Households remaining in panel:
Provided Voucher:None
100 birr ($6.94)
200 birr ($13.89)
300 birr ($20.83)

78
22
21

249

296

249

82
68
74
72

102
51
48
69
79
24
20

296
133
51
56
56

44

TABLE 2. INSURANCE UPTAKE.

Round 1:
Uptake Rate:

All
N/A

Round 2:
Uptake Rate:All
N/A
Voucher Treatment

Uptake Rate:
Average Premium Payment
Average Own Money Spent:
Average Sum Insured:

0
0
0
0

No
Voucher,
Treatment
Villages:0000Round 4:
Uptake Rate:
Average Premium Payment
Average Own Money Spent:
Average Sum Insured:

0
0
0
0

0
0
0
0

Round 3:Control
Villages

Voucher + Promotion

Voucher
only

Difference
fromcontrol

Voucher + Difference
EEA
from
Promotion voucher only

27.5%
$3.77
$0.20
$25.100.275***
3.772***
0.202
25.10***

36.9%
$7.52
$0.45
$43.150.0947**3.752***
0.251
18.06***

47.4%
$1.19
$0.73
$40.400.474***
1.191
0.727
40.40***

41.0%
$3.04
$2.19
$36.16-0.0638
1.844*
1.464*-4.248TABLE 3. PANEL DID IMPACT OF THE VOUCHER EXPERIMENT

Any Voucher
Treated Village
Round 3 dummy
Round 4 dummy
Constant

Observations
R-squared
Number of households
Baseline mean

Covered by
Sum Insured
Insurance

Any
ChemicalFertilizer

KGs ofChemicalFertilizer(1)0.356***(0.0498)
0.00228(0.0113)-0.000381(0.0179)0.000387(0.0185)
5.58e-05(0.0128)(2)
39.23***(7.761)-1.023(1.596)3.707(2.753)-3.823(2.842)0.0116(1.716)(3)0.0309(0.0419)-0.0448(0.0923)
0.146*(0.0807)
0.115(0.0743)0.640***(0.0272)(4)-4.639(6.218)2.550(7.794)
11.67**(4.363)12.22**(5.497)117.8***(2.542)

Number ofcrops using
ChemicalFertilizer(5)-0.0604(0.0903)-0.0313(0.172)0.350**(0.149)0.237*(0.135)1.383***(0.0476)

3,446
0.297
882
0

3,446
0.190
882
0

2,544
0.042
881
0.631

3,280
0.026
880
114

2,571
0.037
882
1.370

Uses any
Improved
Seeds

Uses any
Input Credit

Total Valueof Inputs
Used

Index of
HH
Area
Agricultural Income
Cultivated
Yieldsper Capita(6)0.0779*(0.0404)-0.185**(0.0743)0.0883(0.0570)0.0362(0.0624)0.475***(0.0196)(7)0.0171(0.0436)0.0646(0.0408)0.0209(0.0238)-0.0875**(0.0345)0.186***(0.0145)(8)-14.33(19.19)-6.399(27.93)-16.95(22.25)-8.733(29.34)174.3***(17.90)(9)-0.0207(0.0481)-0.0713(0.108)-0.0372(0.101)
0.0680(0.108)0.0135(0.0335)

2,544
0.023
881
0.470

3,416
0.022
882
0.185

3,416
0.004
882
172.8

3,191
0.015
875
0.00962

(10)
(11)
78.94
0.418
(66.84)(0.354)-218.8*-0.828(111.0)(0.778)93.86**-0.526(46.05)(0.397)138.9*** -0.529(47.61)(0.329)265.9*** 2.128***(28.10)(0.552)
2,561
0.004
881
265.3

3,423
0.008
882
2.108

Regressions are household fixed-effects analysis weighted to be representative of all cooperative members. The first two columns estimate the effect of the intervention on uptake(acceptance of the free insurance voucher). Remaining columns examine impacts on agricultural and household outcomes. Data includes two pre-treatment rounds for somevariables and one for others; all variables have two post-treatment observations. Voucher treatment re-randomized at the individual level in rounds 3 and 4. Robust standard
errors are reported in parentheses, clustered at the village level to account for the design effect. *** p<0.01, ** p<0.05, * p<0.1

46

TABLE 4. ANCOVA IMPACTS OF VOUCHERS AND TRAINING
First Sales Season
Any
ChemicalFertilizer

KGs ofChemicalFertilizer(1)0.280***(0.0689)-0(1.26e-08)
0.110**(0.0448)(2)-0.0422(0.0536)0.00917(0.0772)-0.0137(0.0833)
0.348***(0.0673)(3)-37.55*(21.20)-34.47(20.64)41.95(26.25)0.794***(0.0342)

Number ofcrops using
ChemicalFertilizer(4)-0.252(0.198)-0.0946(0.211)0.151(0.291)0.643***(0.0522)

848
0.188
0

829
0.175
0.631

787
0.598
114

848
0.400
1.370

Covered by
Insurance

Any
ChemicalFertilizer

KGs ofChemicalFertilizer(1)0.420***(0.0635)-0(5.73e-09)-0.0925(0.0669)(2)
0.0332(0.0793)0.0327(0.0513)0.0427(0.0789)0.404***(0.0659)(3)
15.82(19.76)12.73(24.89)-29.76(25.81)0.789***(0.0619)

Number ofcrops using
ChemicalFertilizer(4)-0.0477(0.158)0.0436(0.142)0.0852(0.184)0.646***(0.0437)

841
0.242
0

834
0.204
0.546

765
0.562
91.11

841
0.437
1.180

Covered by
Insurance

Voucher Provided in Corresponding Season
EEA-provided Training at Baseline
Voucher * Training
Baseline Outcome

Observations
R-squared
Baseline Control Group Mean

Uses any
Improved
Seeds

Uses any
Input Credit

Total Valueof Inputs
Used

Index of
Agricultural
Yields

HH Incomeper Capita

Area
Cultivated(5)0.0423(0.0963)-0.0146(0.122)0.0347(0.119)0.513***(0.0532)(6)0.0459(0.0785)
0.0960(0.107)-0.00244(0.121)0.306***(0.0628)(7)-31.68(28.95)-17.23(30.14)30.60(45.83)0.0254(0.0279)(8)-0.0745(0.0737)
0.0351(0.107)0.0161(0.146)0.177***(0.0598)(9)
18.79(49.99)120.9(106.2)-175.3(122.1)0.324***(0.00658)

(10)
0.127
(0.137)0.0108(0.152)-0.0903(0.200)-0.000210(0.00303)

829
0.262
0.470

820
0.079
0.185

820
0.018
172.8

763
0.037
0.00962

845
0.643
265.3

848
0.005
2.108

Uses any
Improved
Seeds

Uses any
Input Credit

Total Valueof Inputs
Used

Index of
Agricultural
Yields

HH Incomeper Capita

Area
Cultivated

(5)
0.113
(0.0771)0.0506(0.0563)-0.160**(0.0747)0.407***(0.0440)(6)-0.0151(0.0755)-0.0214(0.0649)
0.0110(0.0724)0.196***(0.0494)(7)-50.35*(26.93)-13.28(33.62)29.74(31.22)0.0370(0.0308)(8)-0.0277(0.0719)0.0909(0.0688)0.0129(0.0785)0.111**(0.0466)(9)
7.212(44.21)-51.20(40.49)106.7(69.58)0.446***(0.00467)(10)-0.167(0.184)0.00527(0.126)-0.0251(0.204)0.000986(0.00310)

834
0.186
0.368

831
0.052
0.153

831
0.023
147.6

724
0.034
0.00389

833
0.616
247.6

841
0.011
1.563

Second Sales Season

Voucher Provided in Corresponding Season
EEA-provided Training at Baseline
Voucher * Training
Baseline Outcome

Observations
R-Squared
Baseline Control Group Mean

Regressions are round-by-round ANCOVA estimations weighted to be representative of all cooperative members. The first two columns estimate the effect of the intervention on uptake(acceptance of the free insurance voucher). Remaining columns examine impacts on agricultural and household outcomes. Voucher treatment re-randomized at the individual level in rounds 3
and 4. Robust standard errors are reported in parentheses, clustered at the village level to account for the design effect. *** p<0.01, ** p<0.05, * p<0.1

47

TABLE 5. DYNAMIC EFFECTS OF SUBSIDIES ON DEMAND
Uptake of Insurance in Year 2

Any Voucher Year 1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.0385(0.0446)

0.0433(0.0572)-0.000203(0.00220)0.378***(0.0957)-0.00126(0.00593)-0.0321(0.0357)

0.0439(0.0647)-0.000145(0.00218)
0.371***(0.0979)-0.00111(0.00591)-0.0436(0.0423)
0.0723(0.0879)-0.0280(0.0881)0.0365(0.0567)-0.000385(0.00219)
0.373***(0.0984)-0.00115(0.00592)-0.0378(0.0353)
0.0385(0.0659)55.04(50.95)66.02(83.68)-1.538(3.431)149.7(104.8)19.32**(7.909)-30.57(42.08)44.55(88.93)-1.365(3.381)131.7(102.4)19.69**(7.931)-46.66(47.53)119.4(127.0)10.76(105.3)45.19(83.94)-0.193(3.437)121.2(99.60)19.83**(7.894)-54.33(39.99)183.1(139.7)

Voucher Amount Year 1
Any Voucher Year 2

0.361***(0.0548)

Voucher Amount Year 2
Panel village treatment dummy

Sum Insured Year 2-0.0313(0.0363)

Insurance would have paid out Y1
Voucher Y1 * Insurance would pay Y1
Insurance payout if Y1 voucher was used
Constant

0***
(0)

0***
(0)

-0.00368(0.00500)

5.51e-05(8.59e-05)-0.00196(0.00377)

Observations
R-squared

841
0.237

841
0.237

841
0.240

841
0.241

422.3***(91.09)-44.74(41.91)

0***
(0)

0**
(0)

-6.077(7.450)-0.261(0.161)-9.322(10.86)

841
0.135

841
0.152

841
0.161

841
0.164

Regressions present pooled OLS analysis, weighted to be representative of cooperative members in study villages; dependent variable is the insurance purchase decision observed
using institutional data from the insurer in the second sales season. 'Insurance would have paid out' is based on location and crop grown + actual payout rates per crop andrainfall station. 'Insurance payout if Y1 voucher was used' is the amount the payout would have been given main crop if the sum insured were driven only by the randomized
voucher amount in the previous season. Robust standard errors are reported in parentheses, clustered at the village level to account for the design effect. *** p<0.01, **p<0.05, * p<0.1

48

FIGURESFIGURE 1. CONSORT DIAGRAM OF STUDY RECRUITMENT AND ATTRITION.
Originally assessed as eligible
and surveyed for baselines:
120 villages
2,159 cooperative households
240 non-cooperative households
Randomized to Treatment at Village Level

Allocated to Control
40 villages
720 coop households
80 non-coop households

Allocated to Standalone Arm:
40 villages
720 coop households
80 non-coop households

Allocated to Interlinked Arm
40 villages
719 coop households
80 non-coop households
Lost due to lack of drought exposure, inability toreinsure local weather station
71 villages

Remain in Control
15 villages
270 coop households
30 non-coop households

Remain in Intervention:
17 villages
306 coop households
34 non-coop households

Remain in Intervention:
17 villages
306 coop households
34 non-coop households

Randomized to Vouchers at Individual Level
Lost due to panel attrition in the household
survey47 households
15 villages
249 coop households
30 non-coop households

Surveyed at Round 4 Endline
17 villages
296 coop households
32 non-coop households

17 villages
296 coop households
31 non-coop households

49

iA host of demand and supply side factors have been invoked to explain the limited adoption of fertilizer in Ethiopia including limited knowledge and education(Asfaw and Admassie, 2004, Yu et. al. 2011), risk preferences, credit constraints (Croppenstedt, Demeke and Meschi, 2003), irregular rainfall (Alem et. al.
2008), limited profitability of fertilizer use (Dadi, Burton, and Ozanne, 2004), lack of market access (Abrar, Morrissey, and Rayner, 2004), incomplete markets(Zerfu and Larson, 2010), inefficiency of input use (Yu et. al. 2011), as well as limited or untimely availability of the inputs themselves.
ii

Other recent analyses, however, emphasize the poor quality of soils that make adoption unprofitable (Marenya and Barrett, 2009), or the heterogeneity offarmer profitability of fertilizer use (Suri, 2011).
iii

Productive Safety Net Program (PSNP) is a national program, started in 2005, that helps vulnerable families to ensure minimum nutrition.

iv

The yield index includes the five main crops grown and insured in Amhara: teff, maize, wheat, barley, and sorghum. For each crop we calculate a mean andstandard deviations of yields per hectare based on the control group distributions in the pre-treatment rounds. We then Z-score the yields for each crop by
subtracting the mean and dividing by the standard deviation for each crop, and take the average value of this normalized metric for all farmers who grow the cropin that village/year. This measures how many standard deviations from the average yield per hectare are farmers across all crops in a village/year.
v

The selected zones in Amhara are North Shewa, West Gojam, South Wello, and North Wello

50

vi

Because fertilizers are procured exclusively through primary cooperatives and their upper level zonal Cooperative Unions (CUs) in Ethiopia, it was anticipatedthat cooperative households may display a higher propensity to uptake additional fertilizer if risk concerns can be ameliorated. We focus the analysis in thispaper on the cooperative membership only.
vii

From appendix Table A3 it can be seen that 51 percent of household heads have no education, only 21.5 percent received any formal education, among whom

the average years of schooling is 4.8 years. A full 56.5 percent of household heads cannot read or write.
viii

The outcome ‘any fertilizer’ is measured from an overall question at the household level that asks ““Tell me how much of various inputs did you use and buy

during the past 12 months and how did you finance them?”. The outcomes for the kilograms of fertilizer used and the number of crops on which fertilizer wasused come from plot-level survey questions, as is standard in the World Bank’s LSMS agricultural surveys.
ix

EEA is the Ethiopian Economic Association, which functions as a research organization.

x

The remainder reported other sources such as village officials, friends and relatives, etc.

xi

If the question of rigidity was a driving one, we might also expect that farmers would have a strong preference to receive credit and insurance earlier in theseason, since this would give them more time to adjust input usage. When we look in the data we do not see responses consistent with this; only 3.4% of
respondents say that they would have been more interested in the interlinked product if it could deliver credit in a more timely way. Only 9% of respondents saythat the timeliness of the insurance offer was the major issue.
xiiThis results stands in contrast to Duru (2015), who using the same survey find a depressive effect of PSNP. That analysis includes non-cooperative members

and weights the whole sample to be representative of study villages, meaning that the 2 such individuals sampled in each village have a strong effect on the
results.

51

xiii

The largest parastatal lender in the study credit system at present is the Amhara Credit and Savings Institution (ACSI); some of the means applied by ACSI tocompel repayment has led to discontent amongst the customers.

52