AGRICULTURALECONOMICS
Agricultural Economics 44 (2013) 399–417

Productivity, credit, risk, and the demand for weather index insurance insmallholder agriculture in Ethiopia
Craig McIntosha,∗ , Alexander Sarrisb , Fotis Papadopoulosb
a Graduate School of International Relations and Pacific Studies, University of California, San Diego, 9500 Gillman Drive, La Jolla, CA, 92093-0519
b Department of Economics, University of Athens, 8 Pesmazoglou St., Athens, 10559, Greece

Received 1 December 2012; received in revised form 25 January 2013; accepted 22 February 2013

Abstract
The article explores the relationship between fertilizer use and the demand for weather index insurance (WII) among smallholder farmers
in Ethiopia. We examine whether fertilizer use is profitable under current smallholder production conditions, whether risk-related factors affectfertilizer use, and we estimate the returns to inputs in the agricultural production function in the absence of insurance. We then study how theseprimitives of agricultural production functions relate to insurance demand. The study compares a survey-based estimate of willingness to pay
with actual uptake for the weather insurance, finding the stated and actual demand to be almost completely uncorrelated. While those with highmarginal returns to inputs say they would purchase insurance, only those with low marginal returns actually do, consistent with the stated purposeof the product as input insurance. Insurance demand proves to be highly responsive to the existence and amount of randomly allocated insurance
vouchers.
JEL classifications: C83, G22, Q12
Keywords: Fertilizer; Weather index insurance; Ethiopia agriculture

1. Introduction and background
The purpose of this article is to explore the context and constraints to fertilizer use among smallholders in Ethiopia, and to
examine how these factors relate to demand for weather index
insurance (WII). We test whether fertilizer demand constraints
pertain to risk, and assess whether WII can contribute toward increasing fertilizer usage. Agriculture remains the main sourceof income for most rural households in sub-Saharan Africa(SSA), and also the main occupation of almost all the rural
smallholders. Hence increasing the productivity of agricultural
production is a key aspect to rural poverty reduction. Given also
the increasing scarcity of productive land in the Ethiopian context, increasing yields is the only way to enhance productivity.
While there are many ways to increase agricultural productivity, fertilizer and other modern input use, along with adoptionof improved varieties, have been identified as the major ways
to do so.
The bulk of agricultural productivity increase in the worldin the past few decades has been attributed to increased use of*Corresponding author. Tel.: 858 822-1125; fax: 858 534-3939. E-mailaddress: ctmcintosh@ucsd.edu (C. McIntosh).

C 2013 International Association of Agricultural Economists

inorganic fertilizers. While SSA fertilizer use has also grown,
the region still lags very much behind the rest of the developingworld. Per hectare fertilizer consumption is less than one fifth ofthat of other developing countries (Heisey and Mwangi, 1996;
Mellor et al., 1987; Morris et al.; 2007), and, while growing,
the fact that it started at low levels (and that growth rates havebeen lower than those of other developing countries) has implied
widening application and yield gaps. Most of the analyses of low
fertilizer use in Africa concentrate on demand factors. Technicalanalyses have shown that the response of yields to fertilizer use
in SSA is very high. For instance Heisey and Mwangi (1996)
report that maize production in most African countries canincrease by more than 10-fold per kilogram of applied fertilizer.
Also profitability of fertilizer use appears to be very high inmost SSA countries. For instance, Duflo et al. (2008) find thatannualized rates of return to fertilizer use in Kenya, withoutcomplementary inputs are around 70%. Nevertheless, demandat farm level is very low. According to the extensive review
by Morris et al. (2007) this is because “ . . . incentives to use
fertilizer are undermined by the low level and high variability ofcrop yields on the one hand and the high level of fertilizer prices
relative to crop prices on the other. The demand depressing
effects of unfavorable price incentives are aggravated by many
DOI: 10.1111/agec.12024

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other factors, including the general lack of market information
about the availability and cost of fertilizer, the inability of many
farmers to raise the resources needed to purchase fertilizer, andthe lack of knowledge on the part of many farmers about how
to use fertilizer efficiently”.
In addition to the driving role of risk, the absence of input
credit is a key reason for the low use of improved inputs among
African rural smallholders. Many studies have found that small
farmers in developing countries are credit constrained, and asa consequence use few modern purchased inputs (for surveysof the extensive literature on rural credit markets and/or theirabsence in developing countries see Besley [1994] and Conningand Udry [2007] among many others). The absence of credit
can come from the supply side as well as the demand side. On
the supply side, banks may find it very risky and expensive toprovide credit to rural smallholders, thus rationing the supply ofcredit or making available contracts that maybe too expensive
or too demanding on collateral. On the demand side, apart fromthe situations where farmers may not have adequate collateral,
even in situations where credit is available farmers may find it
too risky to borrow (Boucher et al., 2008).
Recent years have seen analyses that pay particular attention
to risk factors. Risk aversion is well accepted as a major factor in new technology adoption, such as improved seeds, but
once adoption is made, risk aversion does not appear to reduce fertilizer application by more than 20% (Binswanger andSillers, 1983, Roumasset et al., 1989). In addition to production
risk, Dercon and Christiaensen (2011) recently showed that ex
ante consumption risk could also affect fertilizer use. Similarly
Lamb (2003) shows that risk avoidance in the face of incomplete insurance may be key in understanding limited fertilizer
use.
Contrasting with studies showing high average returns tofertilizer, however, several recent studies emphasize the poor
quality of soils that make adoption unprofitable (Marenya andBarrett, 2009), or the heterogeneity of farmer profitability offertilizer use (Suri, 2011). The question of whether farmers areconstrained from using fertilizer by risk and credit constraints,
or by low returns, thus emerges as a key policy question. The
former constraints can be ameliorated by financial service innovation, while if lack of uptake is driven by low returns thenthe poverty trap of low agricultural fertility may be much more
fundamental.
A promising way to address un-hedged risk among smallholder farmers appears to be the expansion of WII. The interestin this specific financial service arises from the confluence of
two ideas. First, while mutual insurance should make households in low-income countries well able to cope with idiosyncratic shocks, they are expected to be very vulnerable to covariate shocks (Townsend, 1994). For agricultural households this is
likely to be a particularly important issue because agro-climatic
shocks will be a primary driver behind temporal variability inconsumption. This theoretical insight, combined with the lackof moral hazard in weather variation and the relative availability of rainfall and other meteorological data for developing

countries (e.g., satellite-based NDVI), has led many to regard
WII as a particularly promising welfare-enhancing intervention. If protection against risk can additionally unlock demand
for risky productivity-enhancing inputs, WII holds the promiseof first-order improvements in income for poor and risk-prone
agricultural communities.
However, when WII products have been directly marketed
to farming households in developing countries in pilot applications, uptake has typically been quite low (Cole et al., 2012),
and insurance if anything appears to depress credit use (Gineand Yang, 2009). This dissonance between anticipated and actual demand raises a set of interesting questions concerning
both the determinants of modern input use, as well as the willingness to pay (WTP; demand) for insurance. Several candidate
explanations have emerged. First and most direct is the issueof basis risk; while actual farm-level yields may be driven by
farm-level rainfall, the nearest rainfall station may measure avery imperfect correlate of this quantity. Furthermore, crops are
subject to many perils other than rainfall (pests, hail, frost, theft,etc.). A product with high basis risk simply fails to achieve thedesired goal of providing protection against correlated risks to
consumption, and hence is not demanded for perfectly good reasons. More subtle explanations explored in recent years include
the idea of “ambiguity aversion” (Bryan, 2010), under whichhouseholds do not perfectly understand the distribution fromwhich the relevant probabilities are drawn, and because they
have a dislike of taking on contracts with uncertain properties
their demand is limited. Another preference-related explanationis due to Clarke (2011), who suggests that in the presence ofbasis risk it is possible that households end up without payouts
in the worst state of the world and yet still must pay premiums;
hence highly risk-averse agents may dislike the product.
In this study we report first results from a pilot project aiming to utilize WII as a way to expand the supply of credit andconsequently fertilizer demand by smallholders. The issues explored include whether fertilizer use is profitable under current
smallholder production conditions, whether risk-related factors
affect fertilizer use, whether there is ex ante demand for WII
in such a context, and the purchase decisions ultimately madeby farmers when they are offered such insurance. The project
implemented a randomized control trial (RCT) experiment in
the Amhara region of Ethiopia designed to explore whether
the availability of WII interlinked with credit can expand thedemand for fertilizer and thereby increase agricultural productivity. In this article we use the baseline data for the study to
examine the constraints to agricultural productivity, we present
the results of an ex ante stated WTP study, and we examine theactual uptake of insurance from the first year of the pilot.
There would appear to be two straightforward ways to test
the empirical determinants of WTP for index insurance. The
first and most logistically straightforward of these is simply to
run a survey exercise where individuals are asked to provide
a stated value for a variety of types of contracts. In order tocomply with best practice, this type of study should be run asa contingent-valuation survey, and use randomized price levels

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

to elicit WTP for each contract combination. The advantageof this approach is that it is direct and inexpensive, and does
not require the considerable logistical feat of providing an index insurance product in the field. The disadvantage is the useof stated WTP values; quantities are estimated on the basis ofanswers to relatively complex hypothetical questions over unfamiliar financial products. The second possibility, of course, isto provide index insurance in a context of rich baseline data, andobserve the determinants of the actual sales. This latter method
is clearly preferable in terms of the credibility of the results,
but is considerably more complex. In this study we implement
both methods, and compare the results obtained from each.
We first use observational data on input use and plot-level
yields to explore the constraints to fertilizer use, as well as thelikely profitability of increased use. We show that households
in the region of the project are constrained on the credit side,
and also utilize low quantities of improved inputs. We then explore the profitability of additional input use by estimating themarginal products of labor, land, and capital, and comparing
them to market values. We supplement this with an analysis of
the demand for fertilizer. Subsequently we provide some simple
summary statistics on the ex ante stated WTP for index insurance, designed to protect farmers from losing their investment
in modern input use. Next, we validate actual demand against
this stated measure, and find them to be very poorly correlated,indicating that stated WTP would have provided a very poor
proxy for actual demand. Finally, we examine the determinantsof actual uptake for the 460 research subjects who were members of treatment cooperatives in which at least one contract was
sold. We find insurance uptake to be driven very strongly by
price discount vouchers, and that households who were using
the most chemical fertilizer have the highest demand for WII.

2. The Ethiopian context
Agriculture is the main productive sector of the Ethiopian
economy. It accounts for a little under 50% of the gross domestic product, provides employment for 80% of the population,
generates about 90% of the export earnings, and supplies about
70% of the country’s raw material to secondary activities. Crop
production is estimated to contribute on average around 60%,
livestock accounts for around 27%, and forestry and other subsectors around 13% of the total agricultural value. Over 95%
of the cultivated land is under smallholder peasant agriculture. Low input use, and degradation of the natural resources
resulting from the cumulative impact of the actions of thesesmall land users has resulted in the exposure of smallholders tofood insecurity and generally, limited agricultural growth. Any
prospects of growth in Ethiopia, especially of the pro-poor nature, must deal with improving smallholder farm productivity.
The Government of Ethiopia (GOE) has adopted an Agricultural
Development-Led Industrialization (ADLI) strategy, focusing
first on output growth in agriculture through technologies suchas fertilizer, seeds, and infrastructure, and focusing especially in401cereals. Dercon and Hill (2009) have suggested that the technical aspects of this strategy need to be complemented by policies
to make it attractive to farmers to adopt new practices and improved seeds.
Most of Ethiopian agricultural production takes place under rain-fed conditions and is subject to considerable weather
variations. Furthermore, the use of improved inputs, such as
fertilizer and improved seeds is very low. The overwhelming
reason for low use of modern inputs is that they are considered
too expensive or that there is lack of cash. The high cost of
credit adds to the cost of fertilizer. According to the Ethiopian
Rural Household Survey 1994-1999 (ERHS), in 1999 71% of
those purchasing fertilizer used formal seasonal credit provided
via parastatals, and the implicit median interest rate was calculated at 57% (Dercon and Christiaensen, 2011). However, as
Dercon and Christiaensen showed, fertilizer use, while profitable, is risky. They showed that the lack of insurance against
the risks faced leads to low input use and inefficient production choices. These results provide the motivation for the pilot
project reported in this article.
Fertilizer use in Ethiopia has remained low despite efforts
by the government to promote its adoption through improved
extension services and access to credit. Dercon and Hill (2009)
report that fertilizer application in terms of quintals per hectareof fertilized area has not increased between 1997/1998 and2007/2008, despite the apparent doubling of total fertilizer sales
during the same period, which can be partly explained by expansion in cultivated area.
A 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 et al., 2003), irregular rainfall (Alem et al., 2008), limited
profitability of fertilizer use (Dadi et al., 2004; World Bank,
2006), lack of market access (Abrar et al., 2004), incomplete
markets (Zerfu and Larson, 2010), inefficiency of input use (Yuet al., 2011), as well as limited or untimely availability of the
inputs themselves.
The system of fertilizer and other input distribution and sales
in Ethiopia has evolved over time, and has been changing constantly since the mid 1990s. By the mid 1990s, and after thelifting of the state monopoly on the fertilizer distribution under the Derg regime, about 67 wholesalers and around 2,300
retailers handled fertilizer distribution and sales (World Bank,
2006). By 2004 few private companies and a public enterprise,
the Agricultural Input Supply Enterprise (AISE) dominated thewholesale market. In recent years, as it applies in the regionof study (Amhara), the Cooperative Unions (CUs) handle thewholesale function by ordering supplies through the AISE (financed by the publicly owned Commercial Bank of Ethiopia[CBE]), on the basis of potential demands transmitted to them
about 10 months before the actual application time by the primary cooperatives, who collect and aggregate the individual
farmers’ desired purchases considerably before the production
period.

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Timely and adequate supply of fertilizer is one of the major
problems reported by a significant proportion of the households
surveyed in 2004 by the Ethiopian Economic Association according to a World Bank survey mentioned by Zerfu and Larson(2010). More than 70% of the households reported that fertilizer
is often supplied late and around 40% of the households reported
that supplies were inadequate. The survey results also pointed
to high fertilizer price and tight credit repayment schedules asproblems that constrain fertilizer use. Our own fieldwork indicated that credit is provided to eligible farmers largely on thebasis of need and not ability to pay, and is typically guaranteedby the regional governments. Recently the GOE has decided tomove to a system whereby farmers can purchase fertilizer only
on cash. This, of course, is likely to make the demand for fertilizer much more dependent on available cash flow to the farmers
at the time the purchases are made or fertilizer is needed.
The analysis done for this article is part of a larger project
designed to pilot the use of WII as a collateral substitute for
production credit for rural smallholders in Ethiopia. Given theextensive weather risks faced by rural smallholders in Ethiopia,
and the complete absence of private agricultural production
credit, a variety of weather insurance pilots have been implemented in recent years across Ethiopia in order to assess whether
such products can improve the lot of farmers. All previous
projects, however, have tried to pilot index insurance as a safety
net rather than as an incentive to productivity improvements.
The Ethiopian Project on Interlinking Insurance with Credit inAgriculture (EPIICA) works with the largest private bank inEthiopia (Dashen Bank) and the largest private insurance company, Nyala Insurance Company (NISCO), and targets a highpotential production region (Amhara) where it is presumed thatrisk and credit are major constraints to expanding production.
NISCO is the first private insurance company in Ethiopia topilot WII products.
The idea of the project is to test whether providing the private bank with WII on its loans can release credit resources
for production by smallholders, and whether smallholders arewilling to pay for the combined cost of credit and insurance.
EPIICA is piloting the sale of WII tied with short-term production credit. To that end, a baseline survey was conductedin early 2011 in 120 rural Kebeles (villages or farmer associations) in four zones of the Amhara region in Ethiopia.1 The
choice of the Kebeles was nonrandom but instead was designedon the basis of informed opinion of NISCO as to where in the
Amhara region the market for WII has best potential. Households within the selected kebeles were randomly sampled toparticipate in the study; in each village 18 cooperative households and 2 households that are not a member of the primary
cooperative were selected. Because fertilizers are procured exclusively through primary cooperatives and their upper level
zonal CUs in Ethiopia, it is anticipated that cooperative households may display a higher propensity to uptake additional
fertilizer if risk concerns can be ameliorated.

The assumptions on which the project is based are the following: First, from a production possibility perspective, it isassumed that there is considerable unrealized potential. Field
visits to the zones by the project team suggested that fertilizer
usage resulted in large increases in yields on farmer plots (local
extension agents report two- to threefold yield increases in goodyears from the use of improved inputs).
The second assumption relates to the absence of rural productive credit markets. Ethiopia is a country where rural credit
markets for agricultural productive working capital are almost
totally absent save for the inputs provided on credit by the cooperatives and guaranteed by the government through advances by
the government-owned CBE. The assessment from early fieldvisits suggests that there is currently a great unmet need for expanding credit for agricultural production. The stated intent of
the GOE to move to a cash-only basis for fertilizer purchases in
the upcoming years suggests some urgency in the quest to identify alternate sources and modalities for input credit to replace
the previous government-backed system.
The major constraint on the demand side of the credit issue
seems to be the risk that farmers face when unable to repay
from reduced current production. Many farmers in recent years
had to sell assets to pay previous government-guaranteed agricultural debts, and this seems to discourage them from wanting
uninsured credit. In short many farmers seem to be risk rationed in the terminology of Boucher et al. (2008). This riskarises because the incentive constraints against morally hazardous behavior require the lender to leave substantial risk on
the borrower, and if a sufficient component of that risk is outside
the control of the borrower, then profitable loan contracts may
be refused by borrowers. Demand for both credit and insurance may be further depressed by ambiguity over the potential
outcomes (Bryan, 2010).
The link between the provision of insurance and fertilizer
uptake has been examined recently by Hill and Viceisza (2009)
in an experimental setting in Ethiopia. They studied how smallholders’ decisions to purchase fertilizer would be affected by
the availability of insurance. They found some evidence that
fertilizer purchases were positively affected by the availabilityof insurance. Their experimental design, however, did not consider the issue of whether or not the availability of credit would
influence the uptake of fertilizer.
3. Structure of households and production constraints
Table 1 exhibits some basic demographic and agronomic
information from our 2011 rural household survey,2 while
Table 2 exhibits information about the average incomes of thehouseholds surveyed. It can be seen that 47% of household
heads have no education, and only 23% have any formal education (with an average of only 4.7 years); 53% of household
2 The survey was conducted in February–March 2011, and most of the infor-

1 The zones are North Shewa, West Gojam, South Wello, and North Wello.

mation referred to year 2010.

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

403

Table 1
General demographic and agronomic information of the rural households surveyed in Amhara in 2011

Number of households
Average household size
Average age of the head (years)
Sex of household head (%)
Male
FemaleType of household head’s education
No education
Formal education
Informal education
Duration of household head’s formal
education (years)
Average land owned per household (ha)Average land cultivated in the past 12
months (ha)Average number of parcels per household
Percent of area irrigatedAll

North Shewa

West Gojam

South Wello

North Wello

2,399
5.3
49.7

1,199
5.5
51.4

480
5.7
46.3

360
4.6
49.6

360
4.8
48.7

89.4
10.6

89.2
10.8

92.7
7.3

87.2
12.8

88.1
11.9

46.7
22.9
30.5
4.7

37.3
23.6
39.1
5.0

62.1
16.0
21.9
4.6

48.6
26.0
25.4
5.0

56.0
26.5
17.5
3.9

1.28
1.38

1.42
1.54

1.47
1.63

1.00
0.89

0.83
1.01

4.03
11.1

4.18
11.9

4.53
5.3

3.49
6.7

3.42
20.6

West Gojam

South Wello

North Wello

Sources: Authors’ calculations from EPIICA 2011 survey.
Table 2
Level and structure of incomes of surveyed households
All
Total income per capita
Total cash income per capita
Total noncash income per capita
Total cash income per capita
Total noncash income per capita
Nonfarm income
Nonfarm cash income per capita
Nonfarm in kind income per capita
Crop income
Cash crop income per capita
Crop in kind income per capita
Livestock income
Livestock cash income per capita
Livestock in kind income per capita

North Shewa

Birr per year*
1,770
1,836
1,060
1,095
711
742
Percentage of total incomes
59.9
59.6
40.2
40.4

1,873
1,093
781

1,925
1,194
722

1,255
751
505

58.4
41.7

62.0
37.5

59.8
40.2

12.3
1.4

11.9
0.4

6.8
0.1

14.8
1.7

21.8
8.0

29.1
25.2

28.3
21.7

35.4
33.8

32.4
25.5

15.4
24.7

18.5
13.6

19.5
18.2

16.2
7.7

14.9
10.3

22.7
7.5

*In 2010 (the year to which the survey information referred to) 1 US dollar was equal to about 13.3 birr.
Sources: Authors’ calculations from EPIICA 2011 survey.

heads cannot read or write. Land owned and cultivated is verysmall, and split in several parcels. Average income per capita
is around 130 USD of which 40% is noncash income. Of total
income only 13.7% is nonfarm income, 54% is crop income,and 32% is livestock income. Almost 70% of households statethat they have not enough, or just enough, income to cover foodneeds, and only 8.2% state that their income is adequate to cover
all their needs. Clearly most of the households in the sample
are quite poor, despite the intention to pilot the project in areas
thought to be better off and with higher agricultural potential.
Table 3 illustrates use of agricultural inputs. The fraction ofhouseholds that use any chemical fertilizer turns out to be relatively high, with more than 50% of households using at least
some of it. On the other hand only 30% of farmers use improved seeds, with the share dropping to 7.6% in North Wello.

Interestingly the average amounts spent on purchasing chemical fertilizers are quite high given the incomes of households.
It is seen in Table 2 that the average per capita cash income ofthe households of the sample is 1,060 birr, half of which is crop
cash income. Multiplied by the average household size of 5.3,
this translates into an average total household cash income of
5,618 birr per annum, of which half is cash income from crop
sales. Compared to this the average expenditure on chemicalfertilizer per household (both diammonium phosphate [DAP]and Urea) is from Table 3 equal to 1,687 birr or 30% of total
cash income or 60% of total crop cash income. While this isa large share, it does not exhaust the total cash expenditures
for all inputs (which include inputs on livestock and other general inputs), which (not shown in Table 3) amount to 3,468 perhousehold, or 62% of total cash income and more than 100%

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Table 3Use and spending on farm inputs

Percent of households using:
Improved seeds
Organic fertilizers
Chemical fertilizers (Urea)
Chemical fertilizers (DAP)
Chemicals (insecticides herbicides)
Average value of purchased inputs per household (birr)
Improved seeds
Organic fertilizers
Chemical fertilizers (Urea)
Chemical fertilizers (DAP)
Chemicals (insecticides herbicides)
Quantity of inorganic fertilizer used (kg/ha)
Urea inorganic fertilizer
DAP inorganic fertilizer
Total inorganic fertilizer
Percent of households who used credit for:
Improved seeds
Chemical fertilizers (Urea)
Chemical fertilizers (DAP)
Chemicals (insecticides herbicides)

All

North Shewa

West Gojam

South Wello

North Wello

29.9
58.5
55.4
53.0
29.3

21.6
55.7
58.7
56.6
38.4

84.6
53.1
95.2
94.6
43.0

6.0
73.6
13.6
12.8
3.7

7.6
60.2
31.9
24.9
6.2

327
78
704
983
213

731
159
885
1140
122

161
0
629
997
436

114
88
126
143
26

163
3
158
154
51

41.9
47.3
89.2

52.1
53.7
105.8

65.2
91.4
156.6

6.0
6.0
12.0

12.8
8.4
21.2

10.5
28.8
26.4
2.8

14.2
35.4
32.6
3.9

9.2
27.0
23.4
1.5

0.0
6.3
4.4
0.0

10.5
6.2
6.8
13.6

Sources: Authors’ calculations from EPIICA 2011 survey.

of crop cash income. It thus appears that it may not be easy
for households to allocate more cash to any input, including
fertilizer, without access to loans.
According to data in Dercon and Hill (2009), the averagenational fertilizer application rate in 2007/2008 was 116 kg per
fertilized hectare. Alem et al. (2008) report that in the highlands of Ethiopia in 2007 the application rate at farm level is
89.8 kg/ha, while at plot level the rate is much higher at 348.8
kg/ha. This must be compared to the recommended application rates, which are 100 kg of Urea and 100 kg of DAP (or200 kg total inorganic fertilizer per hectare) for the highlandsof Ethiopia. The figures reported in Table 3 suggest inorganic
fertilizer use, which is smaller than what is reported by Derconand Hill (2009), but the same as what is reported by Alem et al.
(2008) for the Ethiopian highlands. The use appears to be higher
in North Shewa and West Gojam, where in fact the average fertilizer application rate appears to be not much lower than therecommended rate. However, use is much lower in South andNorth Wello.
The final rows of the table highlight the fact that few households receive credit for inputs. In North Shewa and West Gojam
about one third of households receive credit for fertilizer, but
this proportion is less than 10% in South and North Wello.
On average only 15% of all households received credit for fertilizer, improved seeds, or pesticides. However, about half ofthe households indicated that they would have wanted to use
more of the relevant modern input, and almost all of them indicated that the reason they could not use more of the input
was unavailability of own funds or credit. These observations
suggest that farmers are not using the amounts of inputs theywant.

We now turn to the uses of finance and to credit constraints. Table 4 exhibits various indicators of financial market
use among the EPIICA smallholders. It seems that the leveland depth of financial services is rather small. Only 20.9%
of households have a member who belongs to a microfinanceinstitution (MFI), 17.4% of households have a member witha bank account, 14.6% have a member who took a nonfarm
loan in the year before the survey, and only 22% applied fora loan to a bank or MFI in the past five years. On the other
hand 16.8% needed an emergency loan in the year before thesurvey. Concerning loans for agricultural inputs, 70% of those
who obtained any loans for inputs obtained them from primary
cooperatives, about 8% from private traders and companies,
15% from MFIs, and the remainder from family, friends, andothers.
4. Profitability and efficiency of input use
In this section we explore the efficiency of input use in crop
production that will be utilized in later estimations. To analyze
this, we first fit a standard Cobb–Douglas production function
for the gross value of crop output, using instrumental variables
for the endogenously determined right-hand variables. We introduce a variety of potential productivity determining variablesin the right-hand side in order to explore the determinants of
total factor productivity (TFP). The details of the analysis andestimations are shown in the Appendix.
To explore allocative efficiency, we use the estimated production function to calculate the value of marginal product offactor k (VMPk ) for each farmer, as in Lerman and Grazhdaninova (2005) and Carter and Wiebe (1990).

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405

Table 4
Finance and credit

Percent of households with at least a member belonging to a
MFI formed group
Percent of households with at least a member having a bankaccount
Percent of households with at least a member having taken
a loan over the past year (for nonagricultural purposes)
Percent of households that applied over the past five years
for a bank or an MFI loan (for nonagricultural purposes)
Percent of households that over the past year needed money
quickly for an emergency that they could not cover fromown resources

All

North Shewa

West Gojam

South Wello

North Wello

20.9

32.1

9.7

23.9

21.9

17.4

13.8

12.3

20.0

16.3

14.6

19.4

6.4

20.6

15.2

22.0

24.6

17.4

44.2

25.2

16.8

21.5

10.3

47.5

21.3

Sources: Authors’ calculations from data in EPIICA 2011 survey.

Allocative efficiency is determined by comparing the value ofmarginal product of factor Xk (VMPk ) with the marginal factor
cost (MFCk ). We assume that farmers are price takers in input
markets, so that the price of factor Xk (Pk ) approximates MFCk .
If VMPk > MFCk , factor k is underused, and farm profits orefficiency can be raised by increasing the use of this factor. If,conversely, VMPk < MFCk , the input is overused and to raise
farm profits its use should be reduced. The point of allocative
efficiency (and maximum profit or minimum cost) is reachedwhen VMPk = MFCk .
For land the marginal product computed from the production
function is compared to the value added per hectare estimatedfrom the current production pattern. It would have been more
appropriate to use land rental values, or land sales prices multiplied by some discount rate, but there are no rentals reported
in the survey, and very few land sales reported. For labor themarginal product is compared to the direct observations fromeach household concerning the wage rates they pay for hired
labor (both in cash and in kind). For intermediate inputs, themarginal products must be compared to 1, as the variables used
for inputs are expressed in ‘000 Birr, and so is output. Concerning capital, the variables for capital and output are expressedin ‘000 Birr. We neither have rental values of capital, nor local
interest rates. Nevertheless, if the discount rate is smaller than
1, the VMP of capital should be compared to a value smaller
than 1. We utilize a value of 20% for the comparisons in thetables.
Table 5 reports the averages of these marginal products andcompares them with the average market values. It seems thatland is utilized at higher than optimum levels, as the averagemarginal product is smaller than the value added per hectare.
For purchased inputs, the average marginal product is 4.5, which
is far above the “market” value of 1. This implies that inputs
are used at levels much below their optimal values. The same
holds for agricultural capital. On the other hand, the averagemarginal product of family labor on their farm is less than halfof the market price of labor, suggesting considerable excess
labor in farms. All of these observations are consistent witha farm structure composed of undercapitalized and labor sur-Table 5
Marginal products of production factors compared to market prices of thefactors (means across surveyed households)

Marginal product of land
Value added crop prod./haMarginal product of purchased inputs(compared to 1)
Marginal product of labor
Market price of labor
Marginal product of capital(Compared to 0.2)

Unit

All

‘000 Birr/Ha’000 Birr/Ha

2.8***
11.9
4.5***

Birr/month/man
Birr/month/man

484***
1,176
1.7******Statistically different from actual or estimated market price (in italics) at
the 1% level.
Sources: Authors’ calculations from data in EPIICA 2011 survey.

plus farms, with land having being allocated inefficiently. From
our perspective the important thing to note is that there seems
to be considerable scope for using additional intermediate
inputs.
5. The demand for fertilizer
In this section we present an analysis of the survey data
concerning the demand for fertilizer. We utilize information
on total expenditures on inorganic fertilizer. There are many
households who exhibit zero fertilizer purchases. Hence theestimation of the various factors determining the demand for
fertilizer must follow a two-step procedure, with the first step
analyzing factors that determine the decision to purchase theinput, while the second stage analyzing the demand factors
given the decision to purchase or use. For the econometricanalysis we utilize the standard Heckman two-step procedure.
Other authors analyzing the same issue, such as Yu et al. (2011)
and Zerfu and Larson (2010) also utilize two-step procedures,
albeit of a different nature.
Table 6 exhibits the results of the linear Heckman corrected
regression. Larger cultivated area and larger household size
imply a higher probability of purchasing fertilizer. Similarly406

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

Table 6
Determinants of purchased inorganic fertilizer
Dependent variable: log value of purchased inorganic fertilizer

Heckman’s two- step consistent estimatorFirst stage estimations

Log acres of land cultivated0.258***(0.093)0.182***(0.051)−0.060(0.110)−0.124(0.140)0.042(0.290)0.437(0.318)−0.036**(0.018)−1.268***(0.172)−0.100*(0.056)−1.581***(0.198)0.394***(0.055)-0.007(0.040)0.249***(0.081)0.204**(0.098)−0.069(0.217)−1.265***(0.179)0.064***(0.015)0.655***(0.121)−0.138***(0.040)Log value of agricultural capitalLog household size in equivalent adults
Dummy = 1 if anyone in the household had operated an income generated
enterprise over the past 12 months
Share of wages to total household income
Share of nonwages – nonfarm income to total household income
No. of big animals (oxen & cows) over the previous year
Average area irrigated
Average rain in past 12 months (meaning 1 better, 3 worse than normal)
Lamda−0.458***(0.110)0.029**(0.014)0.085(0.147)−0.350**(0.142)0.144(0.101)0.010(0.065)−0.251***(0.083)−0.025(0.123)−0.275***(0.083)0.455***(0.067)0.347*(0.211)−0.051

Log age of household head
Education of head of household in years (formal)
Dependency ratio
Risk averse household head
Head or anyone else in the household member of a cooperative
Trust in the kebele cooperative
Quantity credit constrainedPrice credit constrained
Risk credit constrained
Average slope of land (1 meaning all steeply sloped, and 3 all flat land)
Average way farm is cultivated (meaning 1 by hand, 3 by tractor)
Average altitude of land cultivated (meaning 1 much above compared tovillage center and 5 much below compared to village center)
Experienced shock: drought
Constant
Observations
Censored observations
Uncensored observations
Wald Chi2
Prob > Chi2

6.980***(0.374)
2,217
892
1,325
111.930.0000Standard errors shown below point estimates: *significant at 10%; **significant at 5%; ***significant at 1%.
Sources: Computed by authors.

(0.041)−0.369***(0.071)
0.043(0.696)

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

positive influence on the probability is exercised by whether thehousehold operates a nonfarm income generating enterprise, thenumber of large animals in the previous year, and the averagearea irrigated. Negative influence is indicated by the share of
nonfarm, nonwage income of the household, and by the rainfall
shock. The age of the head and the dummy of whether the headof the household is risk averse or not are negatively significant
for the decision to buy fertilizer, while education is positively
significant. At the same time the dummy on whether the household is price constrained on the credit side is insignificant, while
the other two credit contraint dummies (quantity rationing andrisk rationing) are significant and with the proper sign. Estimations using as endogenous variables the value of all purchased
inputs, as well as the value of utilized, rather than purchased fertilizer gave very similar results. The second stage in the quantityof fertilizer purchased indicates positive influence of land cultivated and agricultural capital, as one would expect. However,it indicates a negative influence of the number of big animals,
which is a proxy for wealth, and this seems counterintuitive.
Negative significance is exhibited by the average area irrigated,suggesting that irrigation maybe a substitute for the applicationof fertilizer, and by average rainfall, suggesting that worse rain
implies lower fertilizer demand, as expected.
The results here appear to be compatible with the resultsof recent analyses of fertilizer demand in Ethiopia, such as
those of Yu et al. (2011) and Zerfu and Larson (2010). From
our perspective, the relevant result is that risk-related variables,such as the experience of drought in the previous year and risk
aversion, as well as credit constraint variables appear to affectin the expected way (negatively) the decision to purchase as
well as the demand for fertilizer. This confirms the idea thatrisk-related constraints are important in understanding the useof improved inputs in smallholder agriculture.
6. The ex ante demand for WII
As a part of our baseline data collection exercise during
February–March of 2011, we conducted a Contingent Valuation (CV) study of the stated demand for index insurance.
We first described the product, asking farmers whether theywould generally be interested in such a product, and if not, what
were the reasons. We then framed the product very specificallyaround the closest weather station, and instituted the yes/noquestion that featured prices randomized to 50, 100, 150, 200,
and 250 birr.3 The standalone insurance is framed as covering
the cost of modern inputs (fertilizers and improved seeds), and
3 The wording of the CV exercise was as follows: “Some organizations such as
Nyala Insurance are currently introducing a rainfall based insurance contract.
This type of insurance covers damage to a crop during the period from its
planting until harvesting resulting from drought, excess rainfall, or frost to agiven crop. The spirit of the contract is that if the amount of rain as measured ata rainfall station near the Kebele is below a certain minimum; or above a certain
maximum, or if there is frost you would receive a certain payout The contract
will be written per Timad and you can buy as many contracts as you want.
In other words, if the rainfall falls outside certain range (defined as a fraction407

is priced per-timad (one quarter of a hectare) as would befit a
product covering a specific quantity of inputs. The hypotheticalinsurance contract would pay 1,000 birr per timad insured (theestimated cost of recommended inputs for such a land amount)in one out of every four years, so the actuarially fair price is
250 birr. For those who did want to purchase insurance, we thenasked how many timad they would insure.
For those who did not want to purchase a standalone insurance contract (premiums paid in cash up front), we then asked
the following: “Would you become interested in purchasing insurance now if you were to be able to receive the 1,000 birr worthof inputs on credit rather than having to pay for them in cash up
front?” (for the basic interlinked product), and “Would you become interested in purchasing insurance if both the inputs andthe insurance premium were financed by credit?” (for the fullstate-contingent interlinked loan). Comparison of these threequestions lets us examine the stated WTP for standalone insurance, as well as the additional demand created by interlinking.
The stated WTP demand curves for each type of insurance areplotted in Fig. 1. Clearly, demand for the interlinked products ishigher and less price elastic, and the demand for the interlinked
product with the premium financed is even higher. However,
because the interlinking questions were only asked of those who
had responded that they did not want the standalone product,
the rankings of these responses obtain by definition.
Concerning the reasons given by those who responded thatthey were not interested in purchasing the product, the primary
drivers of low demand based on these purely self-reported results are the lack of money to pay for the insurance product,both generally as well as at the time inputs are needed, as well asa lack of information about the product itself. This is consistent
with the results of the demand for fertilizer, illustrated in Table
6, which indicated credit constraints as major determinants ofthe decision to purchase fertilizer.
A probit regression on the WTP (the WTP question was
asked of all households not only those that expressed interest)
revealed that apart from the hypothetical offer price (which is,
as expected from Fig. 1, negative and very significant) the significant variables and with the correct expected sign that appear
to influence the ex ante WTP are (sign in parentheses) whether
the head of household can read and write (+), the area of farmed
land (+), the frequency of production reductions due to weather
below or above normal rainfall at a rainfall station nearest to your Kebele), or if
there is a frost, you would receive a certain amount at the time of harvest. If you
had bought two contracts, you would receive two times that amount. If you hadbought three contracts, you would receive three times that amount, etc. and you
would receive a multiple of that amount depending on the number of contracts
you have bought. Obviously, such a contract would not come for free and you
would be asked to pay a certain amount or premium for such a contract. The
premium would have to be paid at the time of the purchase of the contract, i.e.
before the planting season. We would like to know if you would be interested insuch contracts and how much you would be willing to pay for such a contract.
Consider the following contract. When rainfall at the weather station near theKebele in the following year is 1/4 or more below or above normal, or if there
is frost then you will be paid an amount equal to 1000 Birr per Timad. Are you
willing to pay (50, 100, 150, 200, 250) Birr/Timad for such a contract?”

408

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

Percent willing to purchase insurance
50
60
70
80
90

Demand for Insurance, by Price

.05

.1

.15
Price

.2

Standalone
Interlinked with premium financed.25
Interlinked

Actuarially fair price is 250 birr

Source: Computed by authors.
Fig. 1. Ex ante demand for rainfall index insurance. Source.

shocks over the previous 10 years (+), whether the household
is credit constrained (−), and whether the household had utilized a variety of coping mechanisms in previous shocks, suchas relying on own savings or other family actions (−). Significant variables but with unexpected signs included a dummy onwhether the head is risk averse (−), and whether the household
experienced a drought shock in the previous year (−).4
From the results of the CV probit regression on the WTP
we were also able to calculate the maximum amount of money
each farming household (among those who said they would beinterested in a WII product and also answered the subsequent
CV question) would be willing to pay for the insurance. Albeit
an estimate of the WTP can be made also for those who answered they were not interested, it would be inappropriate tocompute this, as most of the estimates would be small or negative, and also without any meaning given the answer of “NO” tothe question about general interest. Table 7 summarizes the estimates for the households with an expressed positive demand.
The interesting observation is that the estimated average valueof the WTP (277 birr) is not statistically different from the actuarially fair value of the hypothetical contract (250 birr). The
same holds for the median value of the WTP. Of course if weweigh the average WTP by the share of those who indicatedthat they were interested, the full sample average WTP would
be much lower, namely equal to (1,487/2,399) × 276.7 = 165.9
birr, which is much lower than the actuarial value. This, nevertheless, suggests, ex ante, that the provision of WII could
4 Note that Cole et al. (2012) also find insurance demand to be negatively
correlated with risk aversion; Clarke (2011) provides a theoretical environmentthat motivates this regularity as a result of basis risk. The negative result on theshock in the previous year would be explained if households expect weather
shocks to be negatively serially autocorrelated.

Table 7
Estimates of the ex ante willingness to pay (WTP) for weather index insurance(WII; values for: 1/4 below or above normal rainfall or frost)

WTP (birr)

MeanMedianSt. dev

No. ofhouseholds withpositive WTP*

276.7

284.2

76.5

1,487

No. ofhouseholds withnegative WTP
5*The estimate of the WTP was done only for the households who indicated thatthey were interested in WII and hence answered the hypothetical CV questions.
Source: Authors’ calculations.

be met with adequate commercial demand from a significant
proportion of the intended beneficiaries.
7. The actual demand for WII
Approximately 18 months after the baseline exercise, teams
from Nyala Insurance traveled to the 34 villages that had been
randomly assigned to treatment, among the 49 villages includedin the final study sample.5 Of these 34 villages, cooperative
households in 17 were offered standalone insurance contracts,
while in 17 they were offered interlinked credit and insurance
5 Of the 120 kebeles originally selected for the pilot, the study had to be
first confined to 84, which, after the baseline survey, turned out to be mostly
affected by negative rainfall shocks, while the others were mostly affected by
frost and floods, events for which no adequate index could be designed on
the basis of available information. These kebeles were covered by 17 rainfall
stations. Subsequently, the number of study kebeles had to be further restricted
to 49, as the available historical record of rainfall for 10 of the 17 stations was
not complete enough to allow the construction of rainfall indexes that would
be acceptable by NISCO’s partner reinsurance company. Of these, 34 weretreatment kebeles and 15 were controls for the RCT pilot.

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

contracts as well as standalone insurance contracts. In the end,
Nyala teams were successful in achieving sales only in 23 ofthese 34 cooperatives, with 12 of the 23 being in standalone
villages and the remaining 9 being in interlinked ones. Problems
with implementation on the supply side existed. In particular
the information conveyed to the farmers was not direct, but
through Ministry of Agriculture officials, “model farmers,” andlocal extension agents. It is not clear whether the information
about the nature of the insurance and interlinked credit andinsurance contracts was transmitted clearly to all farmers. These
supply-side problems were particularly severe in the interlinked
arm, where the contract required the local CUs to guarantee
the nonweather-related part of the loan, a risk which the CUs
refused to accept. As a consequence the interlinked product was
initially promoted as permitting access to credit, but in the end
this credit failed to materialize. Consequently all contracts were
ultimately standalone insurance contracts, in both standalone
and interlinked kebeles.
The 20 randomly selected households in the 34 treatment
villages were also included in an additional, individual-level
experiment where insurance purchase vouchers ranging from 0
to 500 birr were randomly allocated to study subjects. Thesevouchers were distributed before the time of the marketing
campaign, and could be subsequently redeemed if they bought
weather insurance. This was done to increase the variability ofprices faced by potential insurance buyers.6
Because the purpose of this analysis is to understand thedemand-side determinants of WII, we take the simple but consequential step of removing from the analysis all cooperatives
in which not a single sale took place. The logic behind thisstep is that we cannot be sure in these cooperatives whether theconstraints to adoption were on the supply or the demand sideof the market, and we may confuse observational correlates of
behavior at the coop level with the supply chain-driven explanations for why contracts could not be offered. Cooperatives inwhich at least one member purchased insurance may have had
uneven promotion of the product across their membership, but
we can at least be assured that there was not a hard supply-side
constraint to adoption. This restricted sample thus consists of
460 observations; 20 individuals per village in the 23 villages
in which at least one member purchased insurance. This restricted sample in which sales occurred proves to be broadly
representative of the entire drought-prone sample of villages aswell as the final study sample close to reinsured rainfall stations(results available from the authors upon request).
The 2012 sales window resulted in 202 insurance policies
sold, of which 183 were sold to individuals who were in the
study (the product was offered to all members of treatment cooperatives while we tracked only a sample of 20 households pertreated village). The take-up rate in the entire final study groupthat we intended to treat is 25%. Within the kebeles that had any
6 Some households also received small vouchers during the baseline periodof 2010. These were added to the vouchers offered during the 2012 campaign.

409

sales, the take-up rate in the study group rises to 37%. Since
this latter sample is the obvious one within which to measure
the demand-side determinants of take-up, we begin this analysis with a healthy take-up rate. Before being too optimistic
about the overall take-up in the pilot, however, some caution isin order. First, the take-up rate in the small studied sample thatreceived no voucher is zero. Critically for the financial successof the program, the take-up rate among the roughly 4,000 cooperative farmers who were offered the insurance but did notparticipate in the study (namely were not sampled in the baseline or given voucher) was roughly half of 1%. So, the field
operations in the first year produced a very useful study sample(clean randomization, relatively high treatment rate, high uptake among study households, and very powerful effects of thevoucher, which can be used as an individual-level instrumentfor demand), but disappointing overall market demand from theperspective of the commercial firms implementing the program.
This could be due to a variety of reasons such as inadequate
information and preparation, lateness in marketing, etc.
The total range of subsidy amounts over the two years ranged
from zero to a subsidy that covers 70% of the intended coverage for the average sized farm. What became clear when we
looked at the uptake figures is that in general rather than using
the voucher amount to cover a fraction of the cost of insuring all of their land, the farmers instead used the voucher tocover all of the cost of covering part of their land. Furthermore,among those who purchased WII only 42 (21% of those buying) paid any of their own cash over and above the amountof the voucher, and of those only a little over one half (57%)
paid anything over 10 birr. The insurance cover provided by ourstudy in the first year is thus largely paid for by the randomized
subsidy vouchers. Nevertheless, even among those who were
given vouchers the uptake rate was around 50%. In other words
even it farmers were offered a “free good,” many chose not totake it.
The variation generated by the randomization of the facevalue of the vouchers provides clear evidence of a downwardsloping demand curve. The most striking feature, however, isthe distinction between demand under any subsidy amount anddemand with no subsidy. Figure 2 illustrates the result (values
further to the right denote smaller subsidies). If we were tolinearize the slope of the demand curve over those study subjects who received some voucher subsidy and extrapolate thislinear demand to those who received no subsidy we would predict a 0-subsidy demand of over 30%. In reality, those offereda zero subsidy within the study sample had exactly zero uptake, indicating that there is an enormous effect of the vouchers (independent of subsidy amount) on realized demand. It
indicates vanishingly small demand for unsubsidized index insurance even in one of the most drought-vulnerable farming
populations in the world. This focuses our attention on the slow
process of building a WII market, and the critical role playedby marketing and outreach activities by the insurance company
when complex new products are introduced.

410

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

0Fraction purchasing insurance
.2
.6
.4
.8

Demand Curve for Index Insurance

.2

.4
.6
.8
Fraction of Premium Price (1000 birr) faced by farmer
All Treatments
All, fitted

1

Kebeles with Any Uptake
Uptake, fitted

Circle size proportional to number of observations at each subsidy amount

Source: Computed by authors.
Fig. 2. Demand for weather index insurance as a function of the fraction of premium price paid by farmer.

We next explore whether the actual uptake of WII is related
to the ex ante WTP from the CV analysis.7 If we can illustratethat the stated WTP is a good proxy for actual demand, thiswould greatly ease future research geared around identifying
promising locations and markets for index insurance products.
Unfortunately, in our study, stated and actual demand are very
poorly correlated, and in fact in several reasonable specifications they display a significant and negative correlation witheach other. Among those who self-reported as being willingto buy the actuarially fair insurance product in the CV stateddemand exercise actual uptake was 37.2%, while among those
who said they would not buy that product, uptake was 44.3%.
Table 8 illustrates this effect via simple probit regressions. Column 1 provides the simple binary/binary correlation correcting
for clustering at the village level, and shows a negative albeit notsignificant relationship. The other two columns, clearly indicatethat the major determinant of ex post uptake is the voucher offer
and amount, while the ex ante WTP is insignificant or negatively
correlated. Different specifications of the regression produced
similar results.
Of the 183 actual purchases in our survey data, 92 had indicated they would be willing to purchase ex ante, and 91 indicatedthey would not. Given that most insurance purchases involved
using the voucher only, it is perhaps particularly surprising that
there were 114 individuals who indicated that they would purchase the ex ante insurance and then did not buy actual insurance
despite having a nonzero voucher and being located in villages
where sales did take place. When we examine how those withvouchers who said they would purchase and then didn’t buy
7 Note that the randomized actual voucher amount and the randomized priceused in the ex ante WTP exercise are properly balanced and orthogonal to eachother.

Table 8Regression of actual uptake on the ex ante desirability of weather index insurance (WII) in baseline
Marginal effects probit with standard errors clustered at the kebele level

Ex ante WTP = yes

Purchasedinsurance

Purchasedinsurance

Purchasedinsurance,kebele-fixed
effects−0.0653(0.039)−0.0777*(0.040)0.344***(0.068)0.421**(0.153)−0.278(0.228)460−0.0176(0.040)0.375***(0.064)0.392**(0.156)−0.193(0.237)
460

Received voucher
Voucher amount
Ex ante WTP study
randomized price
Number of households

460

Standard errors in parentheses below point estimates: *significant at 10%;**significant at 5%; ***significant at 1%.
Source: Authors’ computations.

differ from those in the same state who did ultimately buy, we
see again that the use of chemical fertilizer is the key driverof actual sales, with those who ultimately purchased being lesslikely to use organic fertilizer and more likely to use chemicalfertilizer (results available from authors upon request).
It is worth pointing out a few dimensions in which our stateddemand study is not ideal for comparison with actual demand.
First, the CV WTP exercise was conducted during the baseline
survey, meaning that it preceded the sales window by roughly
16 months. If demand for insurance is time-varying, this willtend to decrease the correlation between any two measurestaken such a long time apart. Second, because the exact contract terms for the final product were not known when we did

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

411

Densities of Sum Insured, by Treatment

0

.0002

Density
.0004

.0006

.0008

Only kebeles with positive sales

0

2000

4000
6000
Sum Insured, Birr
Standalone

8000

Interlinked

Source: Computed by authors.
Fig. 3. Densities of sums insured in the two treatment arms, standalone and interlinked.

the WTP exercise, the hypothetical product does not coincide
exactly with the one that was eventually sold. The CV question
refers to an insurance against excess rainfall, deficit rainfall,or frost, while the actual product is only for deficit rainfall.
Thus the correspondence between the hypothetical and actual
product is not perfect, but the presence of a negative correlation
between stated and actual demand is nonetheless discouraging
for the idea that hypothetical demand surveys can reveal useful
information about uptake in this context.
Albeit no interlinked contracts were sold, the information
conveyed to farmers in the two arms was different, and thismay have induced differences in the uptake rates. Specifically,
farmers in the interlinked arm believed that credit would bemade available to them up to the amount of the sum insured,although in the end the intermediaries were unwilling to takeon the risk of providing this credit during the first year of thepilot. The unadjusted take-up rates of insurance contracts are
44.5% in the standalone arm and 35% in the interlinked arm.
If we adjust for the voucher amounts and cluster the standard
errors at the village level, this difference is not significant.
When we examine the sum insured, and as Fig. 3 shows, thedistributions of the quantity of cover purchased by those who
did take up insurance in the two arms are different, with the
distribution strongly shifted to the right for the interlinked arm.
Among those purchasing insurance, the average sum insured in
the standalone arm was only 1,295 birr, while in the interlinked
arm it was 2,018. This difference is significant at just below
the 90% level. Overall then, the interlinked arm saw somewhat
lower take-up overall, but for those who did choose the productthey demanded a large sum insured. This pattern is consistent
with interlinked farmers purchasing more insurance coverage
so as to gain access to enough credit to cover their entire input
purchase for the year.

Table 9 pursues the connection between constraints to input
use and the demand for insurance by regressing both the exante and the realized demand for insurance on the farm-levelmarginal products of inputs (land, labor, and capital) derived
from the analysis in Section 4. If risk is a driving constraint tothe use of inputs, then we would expect to find those with highmarginal products of land and particularly of capital to desire
insurance. This relationship would suggest a “transformative”
role for WII in relaxing constraints for those who currently underuse inputs. On the other hand, it may be the case that those
who do not use inputs have little demand for financial protection for input risk, and hence those with the highest demand
are those with the highest use of inputs and thus the lowest
marginal products. This would suggest a more “palliative” rolefor insurance; protecting those most exposed but not necessarily
enabling an expansion of input use.
Column 1 in Table 9 shows the relationship between ex antedemand and these marginal products, finding evidence of the“transformative” pattern with respect to the marginal productof inputs. Those with the highest marginal products of laborand inputs have the highest stated WTP for insurance, suggesting the presence of risk as a major constraint in input use.
Those with low marginal product of land (suggesting that theyuse more land than what would be efficient) seem to demand
more WII. Unfortunately, when we turn to actual insurance
demand in column 2 we find no evidence of such relationships. Column 3 focuses on uptake only in the interlinked arm(where the connection between insurance purchases and input
decisions is more concrete) and finds evidence of the “palliative” relationship; those with low marginal products (and hence
closer to efficient use of inputs) are most likely to purchaseinsurance. Columns 4 and 5 examine the sum insured and theamount of own cash put into the contract (above the value of412

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

Table 9Marginal products as determinants of weather index insurance (WII) demand
Whole study sample inkebeles with any sales:
Purchased insurance

0.000713***(0.000)−8.19e−06***(0.000)0.00809**(0.003)0.402***(0.136)
0.339***(0.051)−0.0000564(0.000)−0.00000261(0.000)−0.00547(0.004)440

440

Stated willingness to pay
CV price in WTP survey

Voucher amountMarginal product of landMarginal product of inputs
No. of households

Sum insured

Amount of own cashused in purchase

0.644***(0.195)0.221***(0.071)0.000258(0.000)−0.00000851(0.000)−0.0221***(0.005)

1,981***(492.500)−6.71(9.007)2.137(2.060)−0.0434(0.029)−30.91***(10.540)0.0132(0.029)−0.0000281(0.000)−1.050***(0.383)

194

175

175

−1.542***(0.322)

Received voucher

Marginal product of labor

Among those purchasing insurance:
Interlinked villages only:
Purchased insurance
in interlinked arm

Robust linear probability model, standard errors reported in parentheses
Standard errors in parentheses below point estimates: *significant at 10%; **significant at 5%; ***significant at 1%.
Source: Authors’ computations.

the voucher) among individuals who purchased insurance.
Again, the marginal product of inputs is strongly negatively
significant, suggesting that more insurance is purchased andmore cash is put in by those who were closest to efficient use
of cash inputs to begin with. Overall then, the pattern of realized demand is strongly suggestive of the “palliative” pattern,whereby insurance would be protecting those who were anyways heavily exposed to input risk because of strong demandin the absence of insurance.
Tables 10 and 11 exhibit the results of regressions aimed
at exploring a variety of determinants of the actual uptake ofthe WII contract. Table 10 explores the demographic and economic determinants of demand, showing the pooled demandfor both products together as well as the segregated regressions
showing demand for each type relative to the control separately.
Table 11 shows regressions demonstrating the behavioral factors and basis risk determinants of demand. It can be seen thatapart from the voucher dummy (which simply indicates whether
the household received a voucher) and the voucher amount, very
few other variables explain the uptake of WII. Among economic
variables the per capita income is negatively correlated with uptake, indicating perhaps that wealthier households do not need
WII. On the other hand the use of chemical fertilizer affects
positively the uptake, and this is consistent with the a priori
hypothesis that use of fertilizer and hence need for cash outlays
could be affected by WII. It is also consistent with the analysis
in Table 9 suggesting that those with low marginal products ofcash inputs display the highest demand for WII. Further, since
the insurance is explicitly intended to cover the cost of inputs,
it is most obviously relevant for those who are already usingchemical fertilizer. Behavioral variables and covariates measuring the extent of basis risk appear to have quite a weak effecton realized demand.

8. Conclusions
The results presented in this article highlight the challengesof designing and implementing pilot RCT experiments involving commercial WII products. Introduction of such products
involves the collaboration of many different institutions, whichmust be coordinated appropriately to make the introduction of
WII a success. In the EPIICA case implementation of the pilot
experiment was imperfect. Starting with the design, while thehypothesis was that the main risk faced by households in thenonrandomly selected kebeles was rainfall deficit, it turned outthat in only two thirds of the villages where the study was implemented, the main risk faced was rainfall deficit. Second, itturned out that the availability of rainfall data is something thatcannot be taken for granted. Data gaps can negatively affect thepossibility for estimating appropriate actuarial tables and hence
the proper pricing of WII products. Nevertheless, there was successful implementation in the second year of the project, andresults from this can be assessed.
The first results of this article are that (as hypothesized) there
appear to be significant profitable opportunities for increased
fertilizer use in the fertile highlands of Ethiopia, but farmers
are constrained on both the cash availability side, as well as thecredit side. The marginal products of fertilizer use are significantly above market values, while the marginal products of labor are significantly below market values. These results, whichhave also been found in other African countries, underscorethe importance of means to increase agricultural productivity
via expanded modern input use. Among the factors that condition fertilizer use, it was seen that risk factors as well as credit
constrain factors are significant, thus supporting the underlying
hypotheses of the effort to promote WII as a means to expand
agricultural credit.

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417
Table 10
Demographic determinants of WII uptake (marginal effects probit with standard
errors clustered at the kebele level; dependent variable is a binary variable,
which is equal to 1 if the household bought WII and 0 otherwise)

Voucher amountAny voucher
Insurance promotion atbaseline
Age of household head
Household head literateHousehold size (adultequivalents)
Income per household
memberNumber of large animals
owned
Total area of land
cultivated (hectares)
Area of land irrigated(hectares)
Chemical fertilizer used?
Household is credit
constrained
Household experienced
drought in past
12 months# Of last 10 years
in which shock
experienced
Number of households
R-squared

Purchased
standalone
insurance

Purchasedinterlinked
insurance

0.397**(0.152)0.380***(0.070)0.000526(0.047)0.00118(0.001)
0.026(0.048)0.0122(0.016)−2.08e−05*(0.000)−0.0147(0.011)−0.0411(0.049)−0.0161(0.138)0.177**(0.065)0.00595(0.065)−0.0781(0.093)0.286(0.239)0.437***(0.107)0.0271(0.075)0.000669(0.002)0.0114(0.075)0.026(0.020)−0.0000254(0.000)−0.00974(0.012)−0.084(0.061)
0.154(0.135)0.118(0.093)0.112(0.073)−0.0409(0.127)0.506**(0.209)0.273***(0.079)−0.0429(0.055)0.00274(0.003)0.0255(0.060)−0.00716(0.017)−0.0000167(0.000)−0.0294(0.020)0.0403(0.048)−0.0634(0.094)0.295***(0.062)−0.067(0.091)−0.0429(0.098)−0.00659(0.016)−0.000965(0.024)−0.0168(0.018)

442
0.211

250
0.229

192
0.288

413

Table 11
Behavioral and basis risk determinants of WII uptake (marginal effects probitwith standard errors clustered at the kebele level; dependent variable is a binary
variable, which is equal to 1 if the household bought WII and 0 otherwise)
Behavioral
determinants Basis risk
Voucher amountAny voucher
ImpatientHyperbolic
Risk averse
Numerate
Trusting
Trusts financial institutions
Trusts the cooperative
Trusts district government

0.371**(0.170)0.360***(0.072)−0.119(0.076)−0.0194(0.072)−0.00285(0.064)−0.00325(0.051)
0.0411(0.058)−0.0676*(0.033)0.036(0.033)0.0223(0.050)Distance to nearest rainfall station (km)Elevation difference to nearest station (m)Distance to kebele center (km)Elevation difference to kebele center (m)Average distance from household to plots (minutes)
Household reports different rainfall from kebele

Standard errors in parentheses below point estimates: *significant at 10%;**significant at 5%; ***significant at 1%.
Source: Authors’ computations.

Number of households
R-squared

418
0.185

0.399**(0.156)
0.356***(0.073)0.00232(0.009)0.000264(0.000)−0.00394(0.027)−0.000653*(0.000)0.00101(0.001)0.0179(0.032)450
0.178Standard errors in parentheses below point estimates: *significant at 10%;**significant at 5%; ***significant at 1%.
Source: Authors’ computations.

Is it realistic to expect smallholder farmers to utilize fertilizer at commercially efficient levels? Barrett (2008) discusses
the extensive barriers to market participation in eastern andsouthern Africa that prevent many smallholder producers fromselling in the market at all. However, our context appears to provide some cause for optimism. First, we have selected a sampleof villages with good agricultural prospects and access to markets. Second, because we focus on cooperativized farmers, allof our target farmers are marketing some part of their surplus
and have access to the types of intermediary institutions thatare critical to commercialization in this context. The particularinstitutional setting on Ethiopia, with the government monopolizing the procurement of fertilizer and guaranteeing credit toagriculture seems to create additional risks and constraints tofarmers, especially when the rainfall is inadequate and farmers
have to repay the loans at a time when they are faced with low
production and incomes.

In such a context, one would anticipate that the demand forcommercial WII would be substantial. It turned out that ex ante,demand was indeed high, and in fact on average farmers were
found to be willing to pay the actuarially fair cost for WII.
On the other hand, when offered the actual product, demand
was lower, and significantly influenced by the availability andamounts of subsidy vouchers. Given that most states in Ethiopia
are now moving away from the subsidization of credit for inputs,
finding innovative ways to pull private credit into smallholder
agriculture and to protect farmers from weather-related default
risk appears to be particularly timely.
These results and the lack of correlation between the ex anteand ex post demand for WII could be due to a host of implementation issues. As was mentioned the information transmission mechanism to farmers was inadequate for a new andrather complicated financial product, such as WII. Second,the various delays in implementation implied that when the414

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

product was actually marketed, many farmers might have already bought the fertilizer they intended to use on cash, andhence they might have already incurred the investment, hence
not needing insurance for an amount already spent, and fertilizer
decisions already made. Third, the timing of the marketing andinsurance sales did not pay attention to the availability of cash.
As Duflo et al. (2010) have emphasized, time inconsistencies,
along with cash constraints at critical times may affect considerably the demand for fertilizer, and possibly also the demandfor WII.
The overall conclusion from the insurance purchases in ourstudy is that some factor is prohibitive to purchases in the greater
population, and that promotion and subsidy will be necessary
for a more widespread adoption of index insurance at the farmer
level in Amhara.
The insurance uptake observed among study subjects turned
out to be healthy, but was negligible among those who were
not offered subsidies. This helps to focus attention on the slow
process of building a WII market, and the critical role playedby marketing and outreach activities by the insurance company
when this product is newly introduced.
One of the rather surprising conclusions of the analysis wasthat the ex ante WTP for WII does not seem to be correlated withthe ex post actual demand (in fact, they are negatively correlated!). This may have been the case in our experiment becauseof the institutional implementation issues, or because the timedelay between the period when the ex ante WTP was assessed
and the actual sales was considerable, and circumstances may
have changed in between. Regardless, our results provide noevidence that stated WTP studies provide a useful picture ofactual demand for index insurance products.
Our examination of the determinants of ex post demand for
WII revealed that subsidy vouchers, even at very small cash
amounts, are a very effective way of driving uptake for WII. In
addition, high fertilizer use (indicating a lower marginal productof cash inputs) is a strong determinant of insurance uptake, thesum insured, and the amount of own cash that farmers put intobuying insurance. This suggests that the product is likely toprovide protection primarily to those who were already using
inputs at high levels, rather than enabling a “transformative”
increase in input use among those who had not previously used
them. Variables related to behavioral attributes or to basis risk
appear to have little predictive power on insurance purchasedecisions.
The overall takeaway message from the empirical analysis
is that designing risk management products such as WII indeveloping country context is a challenging proposition. The
empirical results reported here refer to only the first sales yearof a multiyear pilot. They have helped clarify many implementation and institutional issues, and have helped understandsome of the problems and difficulties in implementing commercially viable WII products, but at the same time have raised
several interesting questions. Subsequent envisaged rounds ofthis pilot will hopefully produce much cleaner and robust
conclusions.

Acknowledgments
The authors acknowledge the excellent collaboration of Degnet Abebaw and Assefa Admassie of the Ethiopian Economic
Association, who organized and supervised the collection of
the survey data utilized in this article. They also thank Shukri
Ahmed, Rene Gommes, and Alixa Sharkey, as well as several
staff of Nyala Insurance Company (NISCO) in Ethiopia, especially Eyob Maherete, Demelash Birarra, and Biniam Taddese,for helpful suggestions, comments, and assistance throughout
the research described herein. They also thank three anonymous referees for very careful and thorough reviews of the firstdraft. The work described in this article was financed by theIndex Insurance Innovation Initiative (I4), under a grant from
USAID.
Appendix: Estimation of the production function andmarginal products of inputs
Our estimation of the production function uses the following
general form:
InQ = α + βi log Xi + γj Zj + u,

(A.1)

where Q is a measure of the value of production of a farm,
Xi are factors of production such as land, labor, intermediate
inputs, and capital, β i are the estimated coefficients of eachfactor (the elasticities, if the log specification is chosen), Zj are
TFP determinants such as household characteristics, and u is an
i.i.d. error term.
The dependent variable is equal to the gross value of total
farm crop output. In our setting, explanatory variables such as
inputs of labor, as well as intermediate inputs, may be considered as endogenous variables and jointly determined with Q andthus dependent on the stochastic disturbance. Land in Ethiopia
is allocated by the government and the rental market is almost
nonexistent. In our survey there were no observations of land
rentals. Hence we consider land cultivated as pre-determined.
Similarly, the stock of agricultural capital is pre-determined
as it has been procured in periods before the current one. To
avoid biases in the estimates we used instrumental variables toestimate the two endogenous variables.
For the production function analysis, we use several setsof explanatory variables. First we utilize the standard factorsof production, namely land, labor, capital, and intermediate
inputs (purchased and own produced). We also use a dummy
variable, which is equal to 1 if the household hires labor forcrop production. This variable is supposed to capture whether
the household is facing supervision constraints in hired labor.
If this is the case the sign of this variable should be negative.
Second we utilize household and farm characteristics such as
age and education of the head, number of parcels, share of land
irrigated, and land quality and location characteristics, suchas average rainfall, average slope, and average altitude. We
utilize also several household characteristics, such as whether

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

the head is risk prone or averse, whether he/she is an officer
in the kebele council, and whether the household operates a
nonfarm enterprise. Finally we utilize dummies for drought
and flood shocks in the relevant year.
To control for endogeneity of intermediate inputs, and labor, we have used, as a set of instruments the following: credit
constraints (variables related to credit access, such as credit
constraints have been hypothesized for a long time to affectthe use of inputs and hence production [Eswaran and Kotwal,
1986; Feder, 1995]; the basic assumption used in all studies
is that assets, including land, affect positively the availabilityof credit and through this the availability of inputs and hired
labor, and hence they should affect positively land and agricultural productivity). We also utilize a dummy for whether any
member of the household has a bank account. We use the number of bullocks or oxen 12 months ago as an instrument since
this should correlate well with wealth and access to credit andfinance. Additional instruments include the log of hired labor
prices, and the logs of prices of the two types of inorganic fertilizer (Urea and DAP). All these can be considered as exogenous
to the household. Finally we use the membership in the local
cooperative since this could influence the access to credit for
inputs.
Table A1 indicates the estimation of the agricultural crop
production function for our sample in Amhara, under OLS withkebele-fixed effects, and under IV also with Kebele-fixed effects. All primary factors of production, are significant. The
dummy for whether the household hires labor is not significant, indicating that supervision constraints do not exist. Of theseveral other variables introduced, the significant ones include
the age of the household head (−), the number of parcels in
the cultivated land (+), the share or land that is irrigated (+),
the dummy or whether the household head is risk prone8 (+),
and the dependency ratio (+). Table A2 indicates the first stage
regressions on the endogenous variables. It can be seen thatseveral of the instruments are significant and with the expected
signs, and that the first stage regressions explain a large amountof the endogenous variables variance. The F-test for the hypothesis that the sum of the coefficients on the land, inputs,
labor, and capital variables is equal to 1 is rejected, and thesum of these coefficients is larger than 1, suggesting increasing economies of scale. Furthermore, the Wu–Hausman andthe Durbin–Wu–Hausman tests reject OLS compared to the IV
specification.
For each farmer (we omit an index of the farmer to simplify
notation) the marginal product of factor Xi can be calculated asfollows:Q∂Q∂ ln(Q) Q== βk ,(A.2)
MPX k =∂Xk
∂ ln(Xk ) Xk
Xk
where βk is the estimated Cobb–Douglas regression coefficientfor factor Xk .
8 As defined in the questionnaire, higher values of this variable imply that the

respondent is less prone to assuming risks.

415

Table A1
Estimation of the crop production function
Dependent variable: log of gross valueof crop productiona

OLS estimation withkebele-fixed effects

IV estimation withkebele-fixed effects

Log of value of crop inputs usedb

0.193***(0.026)0.132***(0.041)0.302***(0.052)0.147***(0.021)0.087***(0.035)0.029(0.065)0.001(0.008)0.100***(0.010)0.404***(0.135)−0.035(0.025)0.044(0.037)0.026(0.027)0.003(0.037)−0.251**(0.107)−0.018(0.035)−0.058(0.057)0.070(0.054)0.119(0.087)0.000(0.001)−0.032(0.053)0.082(0.064)−0.068(0.052)4.437***(0.428)2314
0.65350.477***(0.139)0.827***(0.284)0.133**(0.057)0.070**(0.033)0.049(0.054)−0.170**(0.088)0.006(0.008)0.056***(0.0125)0.252*(0.150)−0.034(0.035)0.070(0.046)0.044(0.035)0.089*(0.051)−0.133(0.113)−0.002(0.041)
0.122(0.091)−0.024(0.056)
0.346**(0.151)0.000(0.001)−0.009(0.061)0.060(0.072)−0.069(0.068)0.565(1.232)2232
0.506016.02
0.00016.16
0.0131

Log of total labour (in months) usedb
Log of hectares cultivated
Log of value of agricultural capital
Dummy for hired labor
Log age of household head
Household head’s education in years
No. of parcels cultivatedShare of land irrigated
Average rainfall index
Average slope index
Average altitude index
Risk prone dummy
Risk averse dummy
MFI membership dummy
Female household head dummy“Officer” in the household dummy
Dependency ratio
Average farm distance from household
Dummy = 1 if anyone in the household has
an income generating micro enterprise
Dummy = 1 for drought (shock)Dummy = 1 for flooding (shock)Constant
Observations
R-squared
Test for returns to scale
Test H0 = land+ inputs+ total labor +agricultural capital = 1
F-valueP-value
Test of endogeneity H0 = variables are
exogenous
F-valueP-value

18.48560.0000Note: Instruments used: Credit constrained dummy; Bank account dummy No.
of draft bullocks or oxen 12 months ago; Log hired labor prices; Log price of
inorganic fertilizer—UREA; Log price of inorganic fertilizer—DAP; Head or
anyone else in the household member of a cooperative.
Standard errors shown in parentheses below point estimates. *significant at
10%; **significant at 5%; ***significant at 1%.
a Standard errors clustered at the kebele level.
b Variable instrumented for IV estimation.
Source: Computed by authors.

416

C. McIntosh et al./ Agricultural Economics 44 (2013) 399–417

Table A2
Results of the first stage regressions of the IV estimation of crop production
with kebele-fixed effects

Log of hectares cultivated
Log of value of agricultural capital
Dummy for hired labor
Log age of household head
Household’’s head education inyears
No. of parcels cultivatedShare of land irrigated
Average rainfall index
Average slope index
Average altitude index
Risk prone dummy
Risk averse dummy
MFI membership dummy
Female household head dummy“Officer” in the household dummy
Dependency ratio
Average farm distance fromhousehold
Dummy = 1 if anyone in thehousehold has an income
generating micro enterprise
Dummy = 1 for drought (shock)Dummy = 1 for flooding (shock)Credit constrained dummy
Bank account dummy
No. of draft bullocks or oxen 12
months ago
Log of hired labor prices
Log of price of inorganic
fertilizer—UREA
Log of price of inorganic
fertilizer—DAP
Head or anyone else in thehousehold member of acooperative
Constant
Observations
R-squared

Value of crop
inputs useda

Total laboruseda

0.283***(0.064)0.044*(0.026)
0.196***(0.058)−0.028(0.066)0.006

0.090***(0.025)
0.061***(0.015)−0.042(0.034)0.219***(0.040)−0.006(0.008)0.080***(0.013)0.561***(0.162)0.007(0.034)−0.051(0.042)−0.102***(0.037)−0.047(0.050)−0.111(0.114)−0.063(0.047)−0.044(0.073)0.105*(0.058)0.001(0.098)0.000(0.001)0.069
0.069(0.053)−0.126*(0.068)0.048(0.075)−0.113**(0.049)0.021(0.070)0.089***(0.020)0.097*(0.055)−0.248**(0.113)−0.056(0.046)0.081(0.064)(0.004)0.018***(0.007)0.024(0.060)−0.007(0.025)−0.023(0.026)0.017(0.018)−0.106***(0.030)−0.127*(0.068)−0.017(0.027)−0.173***(0.041)
0.061***(0.024)−0.329***(0.064)0.000(0.001)−0.070**−0.070**(0.032)0.078**(0.039)−0.012(0.034)0.016(0.025)0.075**(0.034)0.070***(0.014)−0.007(0.029)0.017(0.017)−0.013(0.015)0.065**(0.030)10.534***(0.622)2,232
0.72461.720***(0.240)2,232
0.2937

Standard errors shown in parentheses below point estimates.
*Significant at 10%; **significant at 5%; ***significant at 1%.
a Standard errors clustered at the kebele level.
Source: Computed by authors.

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