Seasonal liquidity constraints and agricultural productivity:
Evidence from Zambia⇤
Günther Fink
Harvard School of Public Health
B. Kelsey Jack
Tufts University
Felix Masiye
University of Zambia
preliminary and incomplete:
do not circulate, do not cite
Abstract
Small-scale farming remains the primary source of income for a majority of the population in
developing countries. In rainfed agricultural settings, income typically arrives only once a year.
To meet consumption and investment needs over the subsequent months in the absence of formal
financial markets, households adopt a range of coping strategies. Two common strategies are
reducing food consumption and selling labor to other farms, both of which improve short term
liquidity, but may harm subsequent harvest outcomes. To investigate whether farm productivity
is increased by access to short-term credit, we conducted a randomized controlled trial across
175 villages rural Zambia, which provided selected households with access to approximately
US$ 40 of loans during the lean season, with repayment due after harvest. We find that access
to credit during the lean season increases harvest output and revenue by around 10 percent
relative to the control group, an increase similar in magnitude to the amount owed on the loan.
This impact is driven mostly by increases in food consumption and labor hiring, as well as
decreases in the frequency of selling labor to other farms. We observe no statistical impact on
other agricultural input expenditures, and no effect on other consumption smoothing strategies
that are less seasonal in nature, such as asset sales. Our results suggest that both the seasonal
consumption and labor allocation of small-scale farmers are affected by frictions in the capital
market. The high take up and repayment rates for the loans as well as reduced prevalence
of hunger and improvements in self-reported well-being suggest that the overall welfare gains
associated with increased access to credit may be substantial.
⇤
We thank audience members at IGC Growth Week for comments and suggestions. We are grateful to the
Growth and Labor Markets in Low Income Countries (GLM-LIC), the International Growth Centre, the Agricultural
Technology Adoption Initiative (JPAL/CEGA) and an anonymous donor for financial support, and to Innovations
for Poverty Action for logistical support. Many thanks to Rachel Levenson for management of the field work and to
Chantelle Boudreaux and Carlos Riumallo Herl for assistance with the data.
1
1
Introduction
In Zambia, like in much of Sub-Saharan Africa, agriculture employs the vast majority of the rural
population, with generally low levels of productivity and farming income.1 A lack of irrigation
infrastructure combined with a long dry season means that harvest income arrives only once per
year, and must cover household needs for the subsequent 10-12 months. Distributing resources
across seasons is difficult if access to capital markets is limited.2 In the absence of functional
capital markets, households may turn to alternative strategies for smoothing consumption, including
livestock and asset sales (Rosenzweig and Wolpin 1993, Janzen and Carter 2014), off-farm labor
(Kochar 1995, 1997; Jayachandran 2006; Ito and Kurosaki 2009) migration (Halliday 2012; Bryan
et al. 2013), or lowering food intake (Kazianga and Udry 2006; Kaminski et al. 2014).3
All of these mechanisms tend to be costly. In the case of livestock or asset sales, high transaction costs along with seasonal price fluctuations lead to financial losses to the household; in the
case of hunger or off-farm labor, farmers may suffer from physical and emotional hardship and
lower subsequent harvest outcomes. The high cost of these smoothing mechanisms implies that
both the marginal cost of consumption and investment varies substantially across the agricultural
season. Anticipating this, utility-maximizing farms will alter both the total quantity of land used
and the crop mix chosen and deviate from the optimal production plan in an unconstrained environment (Fafchamps 1993; Rosenzweig and Binswanger 1993). These extensive margin or ex ante
inefficiencies can be further compounded by intensive margin inefficiencies if households experience
unanticipated income or expenditure shocks during the farming season. To cover liquidity needs
associated with these shocks, farming households may further deviate from their original (adjusted)
production plan by reallocating inputs, such as household labor, to meet consumption needs (Kochar
1995, 1999; Rose 2001; Ito and Kurosaki 2009).4
To investigate the degree to which agricultural productivity can be improved through short-term
credit during the lean season, we conducted a cluster-randomized experiment with 3140 small-scale
farmers from 175 villages in rural Zambia. Zambia’s agricultural cycle is centered around the rainy
season from November to April. Harvest takes place in May and June, and generates income that
would ideally cover all consumption and investment needs in the subsequent year. As illustrated
1
A recent study in the region of proposed research shows average gross production value of USD 500 for a family
of six (Fink and Masiye 2012). Once capital input, land and labor costs are considered, net profits for many of these
families may be negative.
2
The general relationship between income seasonality and consumption smoothing is not well established in the
literature. While some studies suggest that precautionary savings are sufficient to smooth consumption even if income
is highly seasonal (Paxson 1993; Chaudhuri and Paxson 2002; Jacoby and Skoufias 1998), others have highlighted
the pronounced consumption differences over the year (Dercon and Krishnan 2000; Khandker 2012). Kaminski et al.
(2014) point to recent evidence from Africa that seasonal consumption patterns are closely linked to seasonality in
staple crop prices, which suggests that households are not able to adequately smooth consumption.
3
For summaries of the consumption smoothing literature, see Morduch (1995) and Besley (1995).
4
Typical income shocks in the study area include the loss of stored food reserves due to pests or theft; expenditure
shocks include funerals, school uniforms and medical costs.
2
in Figure 1, household (food) reserves gradually decline between July and December, and are most
scarce from January to March before early crops become available for consumption. The January
to March period is generally referred to as the “lean season” or the “hungry season” by farmers, and
is the period we directly targeted with the intervention. For the experiment, villages were randomly
divided into three groups. In treatment group 1 (59 villages), households could borrow 200 Kwacha
(approximately USD 40) of cash in January. In the second group (58 villages), farmers could borrow
three bags of maize. The three bags of maize were roughly equivalent in terms of financial value to
the cash loan, and theoretically provide a sufficient amount of calories to feed a family of five for
the at least two months.5 In both loan groups, repayment was due after the harvest in late June to
early July. All borrowing households were given the option to either repay in cash (260 Kwacha) or
in kind (4 bags of maize).6 The remaining third of the villages were assigned to a control group.7
Both the demand for and the willingness to repay loans was high, with around 98 percent take
up among eligible households and close to 95 percent repayment in both treatment arms. Though
households in both treatment arms were told that they were free to repay in whichever modality
(cash or maize) they preferred, households were more likely to repay cash loans with cash and maize
loans with maize. To assess the impact of loans on agricultural productivity, we develop a series of
predictions through a simple multi-period agricultural production model, and test them empirically
using our experimental data. Consistent with the model, we find that agricultural output increases
in villages where loans were available, with an estimated intention-to-treat effect of KR 271 or 8.7
percent, marginally (but not statistically significantly) higher than the loan repayment amount of
KR 260. To investigate the causal mechanisms underlying these results, we examine impacts on
food intake and nutrition, as well as asset and livestock retention during the hungry season. We
also examine program impact on household labor allocation and short-term labor hiring, as well
as household investment in productive inputs such as fertilizer and pesticides. Overall, we find
no evidence of loan programs affecting inputs (seeds, pesticides, fertilizer), which may partially
be a result of the delivery of loans relatively late in the cropping season in January, when the
need for fertilizers and pesticides is limited. We do, however, find relatively large impacts on food
consumption and labor: on average, farms in the loan treatment arms were on average 15 percentage
points less likely to experience hunger during the peak “hungry season” (January to March) and
consumed on average 0.2 more meals per day. In terms of labor allocation, farms eligible for a loan
were 4 percentage points less likely to sell labor to other farms, and 6.5 percentage points more
likely to hire additonal labor for their own farms.
We also test whether the nature of the loan (cash or in kind) matters in this setting. Our results
5
One kilogram of maize provides approximately 3600 kcal. Three bags would thus provide 5 household members
about 1800 calories per day over a 60 day period
6
Official rates set one 50 kg bag of maize at 65 Kwacha. However, local seasonal fluctuations in maize prices affect
the relative value of the two loan offers. In Section 4, we calibrate the interest rate and value of each loan.
7
In a small sub-sample of the control villages, households were given a gift of 60 Kwacha, which serves as a control
for any income-effects that the loan may generate.
3
suggest that the overall utilization of the additional resources provided differed across the two arms:
while the maize loan induced larger increases in food consumption than the cash loan, cash loans
seem to have induced more labor hiring, and overall higher labor inputs on farms. The effects of
the maize loan program on agricultural output are smaller than in the cash treatment arm, and
are generally not statistically significantly different from either the control group or the cash loan
villages.
Recent evidence on the impacts of capital access interventions on agricultural productivity is
mixed.8 In Ghana, Karlan et al. (2014) find no evidence that liquidity constraints impede agricultural investments. Beaman et al. (2014) find that relaxing credit constraints through grants
increases agricultural investment and yields among rice farmers in Mali, but that the same is not
true for loan programs. Both studies focus on farming inputs (farm expenditure on seeds, fertilizer
or pesticides) as the primary mechanism through which credit impacts yields. The results presented
in this study suggest that loans may have an impact on farm productivity via their effect on smoothing strategies including labor allocation and nutrition. In settings where access to capital markets
are limited, farms engage in a range of costly smoothing strategies both to finance consumption
in the lean part of the season and to deal with unanticipated liquidity needs (Kochar 1995, 1999;
Rose 2001; Ito and Kurosaki 2009). The results presented in this paper suggest that reducing food
intake and selling labor to other farms are the most common strategies chosen in the setting studied.
A large literature on nutrition and productivity debates whether farmers’ constrained nutritional
intake leads to suboptimal production (Pitt and Rosenzweig 1986; Strauss 1986; Behrman et al.
1997), an idea also supported by recent evidence from India (Schofield 2013). Similarly, a growing
literature suggests that selling off-farm labor is not consistent with income maximization, and likely
to lower overall farm productivity (Kerr 2005; Bryceson 2006; Orr et al. 2009; Michaelowa et al.
2010; Cole and Hoon 2013). Our results suggest that loans are effective in increasing staple crop
consumption, an effect which appears to be particularly large for maize loans. Cash loan programs
appear to be more effective in moving farms towards the income-maximizing level of labor input.
This second adjustment seems to be more relevant in terms of the overall impact on output; however,
the benefits of improved nutrition may clearly go beyond total agricultural productivity.
The results in this study are also linked to a growing literature investigating the impact of
seasonal transfer or loan programs. Burke (2014) offered farmers in Kenya a loan product that
allowed them to exploit seasonal variation in maize prices and finds significant effects on total maize
revenues and household expenditures. Bryan, Chowdhury and Mobarak (2013) provide credit and
grants for short run seasonal labor migration in Bangladesh and argue that credit market failures
and highly uncertain returns likely keep long-distance labor supply below welfare-maximizing levels.
Most similar to our study, Basu and Wong (2014) study a seasonal food credit and improved storage
8
An earlier literature uses observables to define whether households are credit constrained, and compares productivity and consumption across constrained and unconstrained households (e.g. Feder et al. 1990).
4
program in Indonesia and find that food loans increase non-staple food consumption during the lean
season and income from crop sales at harvest, but do not analyze impacts on yields. Our findings
contribute to that literature by providing the first direct evidence that capital market interventions
timed to coincide with the hungry season can increase agricultural productivity.
The paper proceeds as follows. In the next section, we present a simple model of agricultural
production in the presence of credit constraints. Section 4 describes the study context, experimental
design and implementation. Section 5 sets up the identification strategy and presents the results,
and Section 6 concludes.
2
Conceptual framework
Consider a simple model of agricultural production, where rational farming households maximize
their utility over consumption and leisure. Households start off with an endowment consisting of
previous assets and their most recent harvest, and maximize their utility by optimally allocating
resources to investment and consumption. The agricultural season is divided into three periods:
period 1, the post-harvest season (July to September in the Zambian context), where farm activities
are limited due to lack of rainfall; period 2, when fields need to be prepared and crops need to
be planted (October - December); and period 3, where fields need to be weeded and maintained
(January - April). Forward-looking farmers maximize the following utility function:
u(c1 ) + ⇢u(c2 ) + ⇢2 u(c3 ) + ⇢3 V (Y, B)
(1)
where u(.) is a generic concave utility function, ⇢ < 1 is the subjective discount rate, and V (.) is the
indirect utility derived from the final net harvest value Y , net of borrowed resources. We measure
all inputs and outputs in monetary units and normalize all prices to one. Farms have an initial
capital endowment A0 , which comprises previous assets and savings as well as net harvest outcomes
from the most recent season. Farms can earn a return rs on savings, and can borrow locally at a
rate rb > 1.9 In period 1, farms can consume or save. In periods 2 and 3, farms can consume, save
or invest into their field. The investment in both periods 2 and 3 (I2 , I3 ) can be financed by loans,
which need to be fully repaid at the end of the season. Given this, the consumption constraints in
periods t = 1, 2, 3 are given by
c t + I t + S t  St
1 rs
+ Bt
(2)
The total debt payments due at the end of the season are given by B = B1 rb 3 + B2 rb 2 + B3 rb , and
net harvest income is given by Y (I2 , I3 )
9
B.
Note that r is therefore equal to the interest rate on borrowing or savings plus one.
5
Perfect Capital Market Equilibrium
In the absence of credit market frictions, farmers can save and borrow at a constant return r = rs =
rb . With unrestricted capital access, the optimal amount of investment in periods one and two is
such that the marginal product of on-farm inputs, including labor, equals the market rate of return,
i.e.
Y 0 (It⇤ ) = r(1+t) ,
(3)
0
where Y is the marginal product (harvest) generated by the period-specific investment, t = 2, 3,
and r is the market interest rate. We shall denote the output achieved under this optimal investment
plan by Y ⇤ = Y (I2⇤ , I3⇤ ). Consumption will be such that the marginal rate of substitution across the
three period equals r, which implies
u0 (c1 ) = r⇢u0 (c2 ) = r2 ⇢2 u0 (c3 ) = r3 ⇢3 V 0 (Y, B).
(4)
Capital Market Frictions and Interventions
Similar to most other developing country settings, capital market access is limited in Zambia, with
low (or no) interest earned on savings, and generally very high interest rates on borrowing, so that
rs < r < rb . The model is set up to allow for a range of borrowing mechanisms: in principle, farms
can borrow from informal and formal money lenders; they can sell livestock or household assets and
repurchase it after the harvest at future dates; they can also take on work on other farms (ganyu),
giving up some of their own future production. All borrowing mechanisms are likely to be very costly
in practice either due to high interest rates, high transaction cost, high risk or a combination of all
factors. This friction in the credit market leads to non-separability of investment and consumption
decisions, with two important production adjustments relative to the optimum with perfect capital
markets. First, rs < r implies that farms shift resources towards consumption in the first period,
lowering subsequent investment and final harvest output. Second, higher cost of borrowing imply
- by optimality condition (3) - that farms will invest less in both periods 2 and 3, which results in
lower harvest outcomes, and lower net incomes compared to the unrestricted capital market model.
This stylized model generates several testable hypotheses for the roll-out of credit programs like
the ones described in the study:
H1: Access to credit markets increases average small scale farmer output and welfare. The
smaller the starting endowment of the farmers, the larger the impact on output and welfare.
Hypothesis 1 is relatively straightforward: access to credit markets implies that small scale
farmers are able to save at a higher return or to borrow at lower cost, which in turn allows them
6
to invest more into their plots and achieve higher yields. If access to credit is in fact limited, we
expect willingness to participate in loan programs to be high among small scale farmers. We expect
smaller impacts for farmers with larger initial endowments who should – for a constant plot size –
be better able to self-finance consumption and investment needs.
H2: Loans announced and made available during period 3 will increase period 3 consumption,
period 3 investment and final harvest output.
H3: Loans announced during period 2 and made available period 3 will increase consumption
and investment in both periods, and lead to larger output increases than loans announced in period 3.
Hypotheses 2 and 3 highlight the importance of anticipating credit availability. Given that a
substantial fraction of farming decisions are taken early on in the agricultural cycle, if loans are
not announced until late in the agricultural cycle, they can only be used to adjust one margin of
the production process, with accordingly smaller productivity impact than loans announced at the
beginning of the agricultural cycle, which allow for changes in both early and late decisions. Earlier
knowledge of loan availability allows farmers to increase consumption and investment in period 2 as
well as period 3. Given the adjustments on both margins with early announcement, we expect consumption increases in period 3 to be smaller with early announcement than with late announcement.
H4: The long run productivity impact of single-period loan programs increases with the the
marginal return to investment and decreases with the farmer’s discount rate.
Hypothesis 4 is more complex, highlighting the importance of the loan allocation chosen by
farmer. While loans should unambiguously increase output and overall utility (as described in
hypothesis 1), the long-run benefits for farmers are less clear, since increases in output may be more
than offset by increases in total cost. With high degrees of myopia (small ⇢) it may be optimal for the
farm to use the full loan amount for consumption, and invest very little in additional inputs; farmers
would then be better off overall in terms of the discounted net present value of their utility, but
worse off in terms of future availability of resources. Similarly, lower marginal return to investment
(for example in case of small farms, or in case of loans arriving late in the season) will mean a
relatively larger share of the additional resources allocated to consumption, with an accordingly
smaller amount of net resources available for period 3.
The model resembles in many ways a classic poverty trap setup: low initial endowments (high
poverty rates) combined with capital market frictions lead to a suboptimal production plan including
suboptimal nutrition and suboptimal labor inputs on fields. Improved access to capital could thus
in theory not only improve production in the short run, but also raise longer run output by reducing
farm’s dependency on external capital in subsequent years.
7
3
Background and context
The study was implemented over the course of a year in Chipata District in Eastern Zambia. Chipata
District is located at the southeastern border of Zambia, with an estimated population of 456,000 in
2010 (CSO 2010). Approximately 100,000 people live in Chipata town, the district and provincial
capital; the remaining population lives in rural areas, with small-scale farming as primary source
of income. According to the 2010 Living Conditions and Measurement Survey, rural households
in Chipata are on average poorer than in the rest of the country, with 47 percent of household
classified as “very poor” in the district overall, and 63 percent of households classified as very poor
in the rural parts of Chipata. Average monthly expenditure of rural households is about one third
of the national average, and access to electricity and piped water close to zero in rural areas (see
Appendix table A.1 for a summary of differences between Chipata and the rest of Zambia).
3.1
Local credit and labor markets
The conceptual framework builds on several contextual features, namely local capital and labor
markets. We provide additional qualitative background on these features of the study setting. As
described in greater detail below, the study sample was limited to small farmers – those with land
holdings of 5 hectares (12 acres) or less. The attribute of “small-scale” is somewhat misleading
since it suggests that these farmers are unusually small; in fact, small-scale farmers represent the
overwhelming majority of households in rural villages in Zambia. In our study villages, over 90
percent of listed households fall into this category.
Capital markets In terms of borrowing opportunities, the study setting is also fairly representative of rural areas in developing countries, where credit markets are absent or very costly to
access. In the baseline survey, 2 percent of household respondents report accessing formal loans for
something other than inputs.10 Input loans are more common: around forty percent of baseline
respondents accessed an in-kind input loan, typically provided by companies purchasing cash crops
like cotton and tobacco from small scale farmers. For accessing cash, informal borrowing channels
are slightly more common: around 7 percent report taking high interest loans, locally referred to
as kaloba, with interest rates over 100 percent. Loans between friends and family are reported by
around 8.5 percent of baseline respondents. Rotating savings and credit associations (ROSCAs) are
very rare in the study setting, reported by around 1 percent of baseline respondents, as are village
savings and loan associations (VSLAs), also reported by around 1 percent of baseline respondents.
Rates are similarly low for savings. Only 5.6 percent report saving in a bank; slightly more
(9.1 percent) report saving with friends, family or employers. By far the most common savings
strategy, reported by 76.7 percent of households, is saving money at home, while only 8 percent of
10
Formal lenders include banks, credit unions, government sources, NGOs, and agricultural companies
8
baseline respondents report zero savings over the past year. The median self reported cash savings
(a measure likely to be reported with substantial error) at baseline, at the start of the planting
season, is 80 Kwacha or around 16 USD. Savings also occurs through grain storage, which typically
occurs in a thatch (28 percent of respondents) or bamboo (62 percent of respondents) granary. Sixty
percent of households report storage losses and the median grain in storage at baseline is only four
bags, or enough to last a family of four until February or March at most. Thus, both cash savings
and grain storage are insufficient to last most households until the next harvest.
Ganyu labor Local wage earning opportunities for study households are defined largely by casual
or piecewise labor locally referred to as “ganyu”. In focus groups, a majority of small-scale farmers in
Chipata described ganyu labor both as the most common strategy to cope with temporary liquidity
shortages, as well as an activity most farmers would rather avoid if possible. In the baseline survey,
the most common response to why an individual in the household worked ganyu during the previous
agricultural season was to obtain food. The second most common reason was to access cash for a
personal purchase, and the third was to deal with an emergency. When asked what the household
will do in the coming year if they run out of food, 56 percent report that they will do ganyu.
The next most common answers include borrow from friends or family (28 percent), using savings
(22 percent) and sell assets or livestock (17 percent). Households appear reasonably accurate in
their forecast of whether they will have to engage in ganyu in a given year. Among control group
households that predicted at baseline that they would have to do ganyu in the coming year, around
76 percent did; among those that predicted not doing ganyu, around 41 percent ended up working
off-farm. Households that sell ganyu one year are not necessarily sellers in all years. Among control
group households that did not engage in ganyu the year before the study, 40 percent sold ganyu the
following year.
The model that we present in Section 2 simplifies a complex rural labor market. In the study
setting, road infrastructure is extremely bad, there is no motorized public transport and distances
between villages are substantial. Most casual labor takes place in or near the worker’s own village.
In the labor survey, over 60 percent of reported ganyu incidents occurred in the respondents own
village, and almost 90 percent were for another small farmer (i.e. fewer than 5 hectares of land).
This highlights that fact that the boundaries between ganyu buyers and sellers are fluid, and the
same farm may sell ganyu at one point in the year, and purchase labor at another when more
resources are available.
3.2
Study sample
The study sample was constructed to be representative of Chipata District. The district is divided
into 8 administrative blocks, each of which contains a number of camps. We randomly sampled 5
villages from 50 of the 53 camps in the district, omitting the camps that contain Chipata town.
9
The village list was assembled from the Ministry of Agriculture’s farm registry, which includes all
registered farms in the district. To facilitate sampling, villages with less than 20 or more than 100
farms listed were excluded.
IPA enumerators visited the sampled villages in order, recording the number of households, farm
sizes and screening for eligibility. Villages were ineligible if: (1) IPA had worked there before, (2)
the village bordered a village that was in the study pilot, (3) the village bordered a village already
listed, (4) the village had fewer than 17 households, or (5) it was impossible to get a 4x4 vehicle
within a 5km radius of the village during rainy season. These eligibility criteria eliminated more
villages than expected, and an additional 150 villages were sampled randomly across all camps to
supplement the list. Enumerator screening visits stopped once 201 villages met all eligibility criteria.
During the baseline survey, 25 additional villages were eliminated for a failure to meet one or
more of the eligibility criteria that had been overlooked during the screening process. In addition,
one village refused to participate in the baseline survey. This left us with a sample of 175 villages
for the study.
Within each eligible village, households were sampled from the village rosters collected during
the screening visits. Only small farmers – less than 5 hectares according to the Zambian Ministry
of Agriculture – were eligible for the program.11 Eligible households were randomly sorted and the
first 22 selected for the baseline survey. A total of 3,701 households were sampled for the baseline
and 3,141 were surveyed (84.9 percent).12 We describe attrition, conditional on being in the baseline
sample, in Section 4.4 below.
4
Experimental design
Study implementation began in October 2013 and will last for two years. We describe the experimental design for both years but show results only for year 1.
4.1
Loan treatments
The main objective of the project was to estimate the productivity impact of short run loans offered
during the hungry season on household-level outcomes. In January 2014,two types of loans were
offered to randomly selected subsets of households: a maize loan and a cash loan. During year
1 of the program, 58 villages (1033 farms) were assigned to a control group, which received no
intervention, 58 villages (1092 farms) were assigned to a cash loan program, and 59 villages (1095
11
We restricted our sample to households with at least 2 acres of land to distinguish households with very small
scale home gardens from households engaged in crop production, and also to increase the likelihood of sufficient
harvest to repay the loan.
12
The most common reasons that listed households were not surveyed were that they were temporarily or permanently away from the village (N=219) or that they were ineligible when land size was verified with the household
head (N=146).
10
farms) were assigned to a maize loan program. In the second year of the program, the treatment
groups will be rotated to identify persistent effects of the loan treatments. The timing of the loan
announcement was also varied, with half of the treated villages receiving notification before the
start of the planting season, in September. The design details for the intervention are described in
Appendix table A.1.
The loan treatments are summarized in Table 1. In both treatment arms, the loan offer was
announced in early January 2014, at the start of the hungry season. In the maize loan treatment
arm households were offered three 50-kilogram mags of unpounded maize, enough to feed a family
of five for at least two months. In the cash loan treatment arm households were offered 200 Kwacha
(~ USD 40), an amount equivalent to three bags of unpounded maize at government prices. In both
treatment arms, repayment was due in July, toward the end of the harvest period, and households
could repay either 4 bags of maize of 260 Kwacha (or a mix at K65 = 1 bag). While both treatment
arms were designed to reflect an interest rate of about 30 percent, actual interest rates are hard
to compute due to substantial seasonal price fluctuations in major crop prices. The calculation is
further complicated by the transaction costs associated with buying and selling maize, which is often
unavailable in the village during the lean season. As shown in Table 1, the interest rate in the maize
arm is between -11 and 33 percent (excluding transaction costs), depending on the calculation,
and also depending on the repayment modality chosen by farmers.Some further discussion of the
comparability of the maize and cash loan sizes is warranted. While the value of the maize loan
may appear higher than the value of the cash loan in January based on locally reported seasonal
prices, few maize transactions take place in January, because most households are severely liquidity
constrained during this period. To make the two loan programs as comparable as possible, we
conducted a series of hypothetical choice experiments in non-target villages within the district in
November 2013. In these choice experiments, respondents (N=72) were asked a series of dichotomous
choice questions on whether they would prefer a loan for three bags of maize over a cash loan of
x Kwacha, with x varied between 50 and 600 Kwacha.13 84.7 percent of respondents preferred a
maize loan over a cash loan of 175 Kwacha, while 36 percent preferred the maize loan over the next
choice value – a cash loan of 250 Kwacha. In a second set of questions, respondents were asked if
they would take up a maize (cash) loan that paid 3 bags (200 Kwacha) in January with a repayment
of 4 bags (265 Kwacha) due in x month, with x varied between February and December. For both
hypothetical choice sets, acceptance rates jumped from 27.8 and 20.8 to 81.9 and 83.3 in June the
maize and cash loan questions, respectively. The hypothetical choice experiments therefore provide
support for indifference between the loan options around the values chosen for implementation.
Further detail on the implementation of the choice experiments is provided in Appendix A.2.
Treatments were assigned at the village level using min-max T randomization (Bruhn and
13
Hypothetical loan dates were consistent with program offered (pay out in January and repayment in June), but
the hypothetical loans involved no interest.
11
McKenzie 2009), checking balance on both household and village characteristics. The approach
relies on repeated village-level assignment to treatment and selects the draw that results in the
smallest maximum t-statistic for any pairwise comparison across treatment arms. Balance was
tested for 14 household level variables, village size and geographic block dummies, with results described in Section 4.4. The smallest p-value for the pairwise comparisons observed in the final draw
was p=0.213.
In addition to the main loan treatments, a small number (N=6) of villages were assigned to an
income effect control, which provided a cash gift of 60 Kwacha, to capture potential income effects
of the loan program.14 These were selected by random draw within geographic block from among
villages assigned to the control group.
4.2
Implementation
The loan was administered under the project name Chipata Loan Project (CLP) to distinguish it
from the surveys, which were being run by IPA. This distinction was intended to minimize strategic
responses to the survey questions, but the relationship between CLP and IPA was not denied if a
participant asked.
The loan intervention was announced to households during a village meeting to which eligible
households were invited.15 At the meeting, project staff began by describing eligibility for the
program to clarify why only some households were invited to the meeting. The terms of the loan
were then described, followed by details on how the loan distribution would be organized. Loan
enrollment and consent forms were provided to eligible households. If a household wished to join
the program, they were required to present both forms with a signature of the household head when
picking up the loan.
Loans were distributed between 3 days and one week after the village meeting at a location
convenient for transportation, selected in cooperation with the village headman. Project staff
registered attendees, confirmed their identity using the national registration card,16 and collected
their signed enrollment and consent forms. Before finalizing the transaction, project staff confirmed
that the participant understood the terms of the loan. The loans (cash or maize) were handed over
and a receipt was provided to the household and kept for project records.
Repayment was due six months later, in late June to early July. Villages were notified in advance
about the location and date of repayment. Households were provided with a repayment receipt upon
14
The size of the cash gift in the income effect control was calibrated using choice experiments described in greater
detail in Appendix A.2.
15
Ineligible households were not barred from listening in. Eligible households could send an adult representative
if the household head was not available to attend. All village headmen were eligible for the loan, even if they were
not sampled for the baseline survey (and are therefore not in our study sample). In addition, the baseline data for 3
households who were surveyed was lost. They are dropped from the sample.
16
In select cases, a household representative picked up the loan. In these cases, the representative needed to carry
the loan-holder’s NRC card with him or her.
12
full repayment, and a second visit was made to selected villages to follow up on loans that were
not fully repaid during the first visit. Throughout, households were told that the program might
or might not continue for a second year. Further summary statistics on repayment patterns are
described in Section 5.
4.3
Data
We rely on both survey and administrative data in our analysis. Administrative records include
loan take up and repayment, two key outcomes in our data. Survey data come from the following
sources:
1. Baseline survey (November-December 2013): Survey of up to 22 households per village, conducted with household heads. The baseline survey includes sections on household demographics (including individual roster, employment roster of working household members, general
household information about assets owned and food insecurity faced, farming information for
2012-2013 season, expected farming activity for 2013-2014 season, risk and time preferences),
2. Labor survey (January 2014-ongoing): Rolling survey of ~70 households per week (7 of the
baseline households in 2 villages per day). The list of baseline households for each village
were randomized and the first ~7 households interviewed, in cases where a household can’t
be interviewed (temporarily busy, moved, etc.), the household is skipped and the next household on list visited. Survey asks one week and one day recall questions on household labor
allocation, ganyu earnings, and consumption (including consumption of green maize).
3. Employer survey (January 2014-ongoing): Rolling survey of ~10 ganyu employers per week.
Sampling is based on Labor survey records of where households in a village report doing
ganyu. Additional sampling is done in a snowball method where employers interviewed then
provide names of other employers of ganyu that they know. The employer survey tracks the
labor survey by geographic block and rotates through villages rather than targeting an explicit
sample.
4. Midline maize assessment (February-March 2014): On-field assessments of maize height (measurement) and visual records (photographs) for a sample of 380 households in 64 villages. Only
households with their nearest field within a 30 minute walk were eligible.
5. Midline survey (February-March 2014): Hungry season survey of 1200 randomly selected
households, stratified on treatment. One week and one month recall questions on labor supply,
ganyu earnings, consumption, basic strength and anthropometric measurement.
6. Harvest survey, year 1 (July-September 2014): Survey of all baseline households. Includes sections on changes to household composition, shocks experienced by the household, agricultural
13
productivity. Includes anthropometric measures for adults and children.
7. Year 2 data: Data collection will be repeated in Year 2, including the labor and employment
surveys, and an abbreviated harvest survey. The midline survey will be collected for a reduced
sample size.
4.4
Identification
We estimate intention-to-treat regressions, including all households regardless of whether they selected into the loan. Our primary specification for evaluating the overall effect of relaxing credit
constraints is:
yivt = ↵ + ✓loanvt + Xiv +
t
+ uivt
(5)
where yivt is an outcome of interest for household or individual i located in village v and month or
season t. loanvt indicates that the village was assigned to either the cash or the maize loan treatment,
Xiv is a vector of controls measured at baseline and
t
are month-year or season-year dummies to
capture seasonal effects. Treatment assignment varies over time according to the treatment rotation
between years, as described in Section4.1. Errors are clustered at the level of the randomization
unit, the village v. We can also break out the treatment effect by treatment arm, and estimate
separate coefficients for the cash and maize loans. In much of the analysis, we analyze self-reported
outcomes from the midline or harvest surveys by collapsing equation (5) into a cross section and
controlling for lagged outcomes measured at baseline.
We also estimate time-specific treatment effects by interacting treatment indicators with time
dummies and including village fixed-effects,
:
yivt = ↵ + ✓loanvt ⇥
t
+ Xiv +
v
+ uivt ,
(6)
which delivers a vector of coefficients for each treatment arm by month or season. Given the large
number of causal mechanisms and pathways explored, we show both unadjusted p-values and pvalues correcting for multiple testing. Specifically, we show significance level with the very restrictive
family-wise error rate (FWER) correction as well as under the less restrictive false discovery rate
(FDR) adjustments originally developed by Benjamini and Hochberg (1995) and Benjamini and
Yekutieli (2001). See Fink et al. (2014) for further details on these methods.
Balance and summary statistics
The coefficients presented in the subsequent analysis identify the causal effect of the loan under the
identifying assumption that treatment assignment is orthogonal to uiv . Table 2 presents the means
and standard deviations of baseline survey characteristics among study households, by treatment
14
arm (columns 1-3). Column 4 shows the largest pairwise t-statistic and column 5 the largest pairwise normalized difference. While the t-statistic on household assets suggests a significant difference
between the maize loan treatment arm and the control group, the normalized difference is nevertheless small. Overall, the randomization successfully balanced households across treatment arms.
The variables shown in Table 2 are our household-level controls, used throughout the analysis. We
also test for balance in other surveys that rely on a sub-sample of the study households. Appendix
tables A.2 and A.3 show the covariate balance for the midline sample and the rotating labor sample,
respectively.17 While a couple of the pairwise t-statistics suggest significant differences, none of the
pairwise normalized differences exceed the 0.25 rule of thumb threshold for balance.
Attrition and selection
The main identifying assumption of our empirical analysis could be violated if households select
into eligibility status or drop out of the loan program differentially across treatments. Households
could exit the study during year 1 both during the midline survey and the endline survey. Overall
attrition rates are low as shown in Table 3. The table reports means and standard deviations at
baseline in column 1 and the coefficients from a regression of a binary interview indicator for each
survey round on our main set of controls, with standard errors clustered at the village level. While
the individual coefficients suggest some selection into the midline and endline, particularly among
households with children, the overall attrition rate was low across both interview rounds. In the
midline, of the 1223 households sampled for interviews, 97.6 percent were surveyed. In the year 1
endline, 96.5 percent of the full sample was surveyed.
5
Results
Following our pre-analysis plan, we organize our analysis and results into five groups. The main
objective of the study is to assess whether relaxing frictions in the capital market can improve
agricultural productivity. Given this objective, the primary outcome of interest is agricultural
yields. Based on the existing literature as well as the evidence compiled as part of the pilot study
concluded in 2013, we hypothesize that credit constraints affect productivity through several causal
pathways. We will begin with this core set of outcomes, and report adjusted p-values for multiple
hypothesis testing. The second main question of interest addressed by the study – and the main
rationale underlying its 3-arm design – is whether in-kind maize loans are more effective than cash
loans for improving productivity. Last, we will analyze spillovers of the intervention on market
prices in order to be able to (at least partially) address the general welfare implications of the
program, and calculate preliminary cost effectiveness comparisons.
17
Results are similar if we break the labor sample into the hungry season (January - March) and harvest season
(April - June).
15
Before turning to these hypotheses, we provide results on administrative outcomes: loan take
up and repayment.
5.1
Take up and repayment
We begin with an analysis of take up by treatment group. Take up is 98.6 percent among eligible
households in the cash loan treatment arm and is statistically indistinguishable from take up in the
maize treatment arm, as shown in columns 1 and 2 of Table 4. The remaining columns of Table 4
show repayment rates. 93 percent of the 2008 households that took out a cash loan repaid it in full
(columns 3 and 4). Full repayment does not differ between the cash and maize loan treatment arms.
Some households partially repaid, bringing overall average repayment to 94.3 percent (columns 5
and 6). Finally, in the cash loan treatment arm, 35 percent of households with loans made at least
some of their repayment in cash. In the maize treatment arm, the probability of repaying any cash
was 22 percentage points lower (columns 7 and 8). That said, in both treatment groups, some
households repaid in cash, some in kind and some with a combination of the two. Consistent with
our conceptual framework, demand for the loans is high.
5.2
Yield impacts
To the extent that credit and savings constraints affect consumption and input decisions, as described in the conceptual framework (Section 2), both types of loans (cash and maize) are likely to
affect agricultural yields. We first establish the impact on yields before turning to causal pathways
and secondary outcomes of interest.
Agricultural yields are measured through self-reported quantity estimates collected as part of the
harvest survey and endline for all crops. Under the strong assumption that crop mix is unaffected by
the loan treatment, which is plausible if the loan announcement occurs after all cropping decisions
are made, we can use the raw output measure, number of kilograms, as an outcome. However, given
that the crop mix varies considerably across farmers, and typical yields and values per kilogram
vary substantially across crops, we compute a combined production value across all crops. For
this purpose, farmers are asked to report the total quantity of all crops on their field, including
the quantities used for own (early) consumption, quantities sold or used for payment, quantities
stored and quantities still on the field. To convert total harvests into monetary terms, all reported
quantities are multiplied by the self reported price to capture heterogeneity in value based on
location. We also use median prices in the treatment group to reduce measurement error. We do
not attempt to calculate profits, nor do we calculate revenue net of the loans, since the loans offered
through the program may have substituted for other higher (or lower) interest borrowing. We instead
examine these other responses, including impacts on borrowing and agricultural inputs (fertilizer,
labor, pesticides), below. Revenue net of the program loans can, of course, be approximated by
16
subtracting around 260 Kwacha from the harvest revenues.
We estimate equation (5) for these three outcome measures, and report results in Table 5. Panel
A shows treatment effects for any loan type, which we discuss first. We return to the effects by
treatment arm (Panel B) in Section 4.1. We control for household-level baseline variables as shown in
Table 2 and geographic block dummies in even numbered columns. By controlling for previous year
outcomes, baseline controls substantially improve precision, therefore we focus on the specifications
with controls in our discussion of the results (even numbered columns).
Panel A indicates that the loans had a positive effect on the total kilograms of output across
all crops (column 2) and harvest value at median prices (column 6). At own prices (column 4),
the coefficient on the loan intervention is slightly smaller than at median prices and less precisely
estimated. Effects at median prices are consistent with a 8.7 percent increase in harvest value,
while the effect on total output in kilograms is around an 8 percent increase over the control group.
Consistent with the conceptual framework, the gross impact of relaxing credit constraints on the
value of agricultural output is positive. Table 5 also reports the effect, measured as part of the
midline survey, of treatment on maize height. Maize height is self-reported and measured according
to a standard meter stick in centimeters. While the measures are noisy, they show statistically
insignificant increases in self-reported maize height overall in the treatment groups.
5.3
Causal mechanisms
A number of potential causal pathways underlie the effect of the loans on agricultural productivity.
In addition, many of these pathways represent outcomes in their own right. For example, even if
yields do not increase as a result of the loan, better consumption smoothing may make households
better off. We break our analysis down into five groups, as described in our pre-analysis plan: (i)
food intake and nutrition, (ii) labor supply, (iii) other productive inputs, (iv) cognition and decision
making and (v) the ex ante selection of the crop mix. We investigate (i) through (iii) in the current
draft and leave (iv) and (v) until year 2 data are available.
5.3.1
Food intake and nutrition
As stated in Hypothesis 2, the availability of loans during the hungry season will increase hungry
season consumption.18 In our data, we expect to see an increase in food consumption (a reduction in
food shortages and missed meals), improved strength and endurance and potentially also an increase
in body weight and musculature. We also expect to see less engagement in costly consumptionsmoothing strategies, such as livestock and asset sales and consumption of green maize before
harvest. We focus on the consumption measures in the current analysis.
18
Hypothesis 2 predicts that if the availability of loans is announced in advance, then consumption will adjust even
before loans become available. We will test this hypothesis with the variation in the timing of loan announcement in
year 2 of the intervention.
17
We begin with the harvest survey data, which asks recall questions about months when the
household did not have adequate food to feed the family for the entire agricultural season. We
estimate equation (6) by month, and plot month-specific treatment effects in Figure 2. Panel
A shows the effects for the pooled loan treatments. Relative to control group food shortages,
treatment effects are pronounced, particularly in February and March, at the peak of the hungry
season. Because the loans were rolled out over the course of January, not all treated households
had received their loans until the end of the month. The peak of food shortages observed at the
height of the hungry season in February is substantially smoothed for households in either treatment
group, and significantly more so for households in the food loan treatment arm. This pattern is
perhaps not surprising given that the food loan gave households maize in January, which would have
been sufficient to last most households in the sample through at least March. The corresponding
regression coefficients for months short of food between January and June are shown in column 6
of Table 6.
The harvest survey has a long recall window, asking respondents to report food shortages by
month for the past year. The midline and rotating labor surveys have much lower recall burdens,
asking questions about the past two days to two weeks. The trade off, of course, is the smaller
number of respondents. We turn next to two main consumption measures in the midline data: the
number of times nshima (the staple starch) and protein were consumed in the past two days. Panel
A of Table 6 shows the results for the pooled loan intervention. Nshima consumption increases
significantly, with about 0.16 more meals or about a 4.5 percent increase in the number of meals
over the control group. Protein consumption, on the other hand, is unaffected by the intervention.
Consistent with the longer run recall questions from the harvest survey and with the predictions in
the conceptual framework, consumption increases during the hungry season. However, even when
credit constraints are relaxed, consumption appears low with an average of fewer than two meals of
nshima a day over the past two days.
An outcome closely related to food intake is health within the household. As part of the harvest
survey, respondents were asked a series of questions about their own health. As Table 7 shows,
treatment had a consistently positive effect on health: on average, subjects report to feel healthier
and stronger overall, and also (self-report) to be better able on average to complete physically
challenging tasks, such as carrying a 50kg bag of maize for 100 meters, or carrying a 20L jerrycan
for 2 kilometers. While subjective measures are likely a reflection of subjects’ well-being and thus
important, it is clearly possible that subjects in the treatment arms over-report their own well-being
in response to treatment. We also measured weight, waist and biceps of a subsample of 1200 farmers
and find no statistically significant differences in these measures.
To the extent that the loans allow households to substitute away from costly consumption
smoothing strategies, we expect to see a decrease in other strategies such as livestock and asset
sales and consumption of unripe maize during the hungry season. We estimate equation (5) for
18
the following consumption smoothing measures: asset and livestock sales over the course of the
agricultural season (measured during the harvest survey) and consumption of green maize up to the
time of the midline survey (measured during the midline survey). We report the regression results
in Table 8. None of these consumption smoothing strategies responds to the loan interventions.
5.3.2
Labor supply
Household labor allocation plays two roles in our conceptual framework. First, labor acts as an
input to production during periods 2 and 3. Credit and liquidity constrained farms may therefore
invest less than optimally, particularly if optimal investment requires supplementing family labor
with labor hired from the market. Second, the sale of family labor off-farm during periods 2
and 3 increases liquidity and consumption, via the wage rate earned in the casual labor or ganyu
market. Depending on whether the household labor endowment binds at different points during the
agricultural season, off-farm labor shifts income from future harvest income to current consumption
at a rate equal to the marginal product of labor. Of course, off farm labor may also be an incomemaximizing response to wages that exceed marginal on-farm labor productivity.
To examine how labor allocation and hiring decisions respond to access to credit, we once again
begin with the harvest survey data, which asks questions about on- and off-farm labor by household
members and labor hiring over the previous agricultural season. We focus on relatively coarse
outcomes that are less subject to recall bias when using the harvest survey measures. Table 9 shows
treatment effects for any treatment (Panel A) and by loan treatment arm (Panel B) from estimating
equation (5) for four different labor allocation outcomes. We focus here on the results from Panel A
which test whether relaxing credit constraints impacts labor allocation. In response to treatment,
the likelihood of doing any ganyu falls by around 4 percentage points while the probability of
hiring any ganyu increases by 6.5 percentage points. The means for hiring and selling ganyu are
62.8 percent and 29.9 percent, respectively. To examine the timing and intensive margins of the
treatment effects, we turn to the rotating labor survey.
Recall that the rotating labor survey asks one week recall questions and runs from January-June
on a rotating cross section of households, balanced by treatment group. Table 10 reports the pooled
loan treatment effect estimated off of the labor survey sample for three different periods: (a) the
full year, (b) the hungry season (January - March) and (c) the transition to the harvest season
(April - June). Regressions include month dummies as well as the full set of household controls.
Effects by treatment arm are discussed in Section 5.5. We observe that the days of ganyu sold by
the household falls by 0.36 days overall (column 1), which is driven by a decrease in ganyu during
the hungry season (Panel B). The overall decrease represents a 44 percent decline in ganyu relative
to the control group mean ganyu in the past week. At the same time, both family days on farm and
days of hiring increase, though the effects are imprecisely estimated. When pooled into a measure
of total days on the farm (column 4), the impact is an approximately 1.3 additional person days
19
over the previous week, or an 11 percent increase over the control group. Aside from the impact
on ganyu shown in Column 1, standard errors are large when the effects are broken out by season,
however, it appears that the bulk of additional hiring occurs in the hungry season, soon after loans
are delivered. Both the results from the harvest survey and the midline survey suggest increases in
labor investment on the farm, both in form of family and hired labor, and a decrease in off-farm
labor consistent with a reduced need to smooth consumption via outside wages.
5.3.3
Other productive inputs
The previous sub-section suggests that labor inputs increase as a result of the loans, and most
dramatically during the hungry season. While harvest value increased in the treatment groups, we
have not directly calculated impacts on profits. We next investigate impacts on expenditures on
agricultural inputs over the agricultural season (some of which would have occurred before the loans
arrived). The three largest categories of spending, as reported in our harvest data, are fertilizer,
seeds and hired ganyu. We analyze the impact of treatment on these input categories, as well as
a total expenditures category, which also includes pesticides, herbicides, oxen rental and any other
input expenditures reported as part of the harvest survey. Table 11 shows the results. None of
the input categories increases significantly in response to the loan treatments. While most point
estimates are positive, particularly after controlling for household level variables (even numbered
columns), none are statistically different from zero, but nor can we rule out increases in overall
expenditures of up to around 190 Kwacha or approximately the value of the cash loan. In spite
of the imprecision of these results, the timing of the loan also means that households would have
been less able to the increased liquidity toward inputs other than labor. January, when the loan
started, is too late for new seeds, for most fertilizer or for other planting investments. Labor, and
potentially herbicides, are the primary inputs used during the lean season when loan impacts were
felt.
5.4
Results summary and adjustment for multiple testing
Table 13 summarizes the main results and corresponding p-values from the analysis reported above.
We focus on the outcome measures obtained for the full sample through the harvest survey. Given
the large number of causal pathways explored, the likelihood of finding significant results for one of
the variables analyzed by chance is relatively high. To address these multiple testing concerns, we
apply two multiple testing corrections suggested in the literature: the family-wise error rate (FWER)
correction, which keeps the risk of false discovery constant under the very restrictive assumption
that all tested variables are independent, and the slightly less restrictive false discovery rate (FDR)
proposed by Benjamini and Hochberg, which keeps the proportion of false discoveries below the
chosen threshold (Benjamini and Hochberg 1995; Fink et al. 2014). The overall picture changes
20
relatively little: as expected, the variables which are only marginally significant in the unadjusted
models (yields and ganyu selling) lose significance with a targeted error rate of ↵ = 0.05 and the
FWER correction; with the FDR correction, the null of zero yield impact (kgs. and median value)
is rejected, while ganyu selling is not significant. Ganyu hiring, food intake and self-reported health
remain significant with both corrections.
5.5
Cash loans versus maize loans
So far, treatment effects have been estimated by pooling the two loan treatment arms. We now turn
to an analysis of effects by treatment arm (cash and maize loans), to gain further insight into the
mechanisms through which the loans effect yields, consumption and labor allocation. If the maize
inputs provided through the loan are either inframarginal or can be converted to cash at minimal
transaction costs, then we should see impacts as large or larger in the maize treatment arm (for a
summary of the theoretical differences between cash and in-kind transfers, see Currie and Gahvari
2008). In addition, if cash is more likely to be diverted for temptation goods (e.g., alcohol, tobacco),
then the maize loan may have an additional advantage. If, on the other hand, there are substantial
transaction costs in converting maize into cash, then households in the maize treatment arm may
be well equipped to smooth maize consumption but not other types of consumption or investments.
Many of the tables presented so far include the results by treatment arm, so we refer to these earlier
tables where relevant.
Harvest yield and income The main results indicated that short run loans increase agricultural
output. Results in Panel B of Table 5 show harvest quantity and value by treatment arm. The
results show that results are slightly stronger for the cash loan treatment than for the maize loan
treatment, though the coefficients are significantly different only for harvest value at self reported
prices (column 4) and self reported maize height (column 8). Total kilograms of output increase by
179.2 (s.e. 77.4) in the cash loan treatment and by 150.1 (s.e. 87.3) in the maize loan treatment.
The cash loan treatment thus increases kilograms of output by around 8 percent over the control
group mean of 2039 kilograms of output. Harvest value at own prices also increases significantly in
the cash treatment arm (320.6 s.e. 141.8) but not in the maize treatment arm (81.9, s.e. 133.3).
Comparing results with own prices (column 4, self-reported during the harvest survey) and median
prices for the control group (column 6) suggests that some of the lower harvest value in the maize
treatment arm is driven by the worse prices that maize treatment farmers appear to get for their
crops. At median prices, the treatment effects are more similar across treatment arms: 316.9, s.e.
129.7 in the cash treatment arm and 220.5, s.e. 142.5. Differences between the own price and
median price specifications are not statistically significant for any treatment. The treatment effect
on harvest value in the cash loan arm corresponds to an approximately 10 percent increase over
the control group mean, suggesting that the assumption that crop mix is unaffected by treatment
21
is plausible.
Consumption In the main analysis, staple food consumption increased at midline and the number
of months that the household was short of food decreased. To analyze effects by treatment arm,
we first revisit these outcomes. Panel B of Figure 2 shows the month-specific estimated treatment
effects for whether the household reports being short of food. The treatment arms demonstrate
a very clear pattern, in which the greatest reductions in food shortages occur during the hungry
season (peaking in February) for the maize treatment arm. The cash treatment arm shows a similar
patterns, but with treatment effects that fall between those of the maize arm and the patterns shown
in the control group. Column 6 of Table 6 (Panel B) reports the corresponding regression coefficient
for the number of months a household reports being short of food between January and June. The
drop in the maize treatment arm is significantly larger than the drop in the cash treatment arm,
and corresponds to a 25 percent decline relative to the control group. The remainder of Panel B of
Table 6 shows effects on nshima and maize consumption in the previous two days, as measured at
midline. The effects are very similar across treatment arms, and correspond to about a 4.5 percent
increase in nshima consumption relative to the control group.
Next, we turn to a series of questions about consumption patterns collected during the rotating
labor survey, grouping responses into the month for which we have data before the loan was fully
rolled out (January), the main months of the hungry season (February-March), and the months
transitioning toward harvest as food becomes available (April-June). Figure 3 reports coefficients
from estimating (6) with the three seasonal intervals as the time variable. The outcome is the average
number of times nshima (the staple food) was consumed by different individuals or groups within
the household within a day. Sub-figure a reports the respondent’s own average daily consumption
over the previous week. While the number of meals falls in the control group during the hungry
season, the drop is smoothed considerably in the cash and food treatment arms, with no noticeable
seasonal decline in the maize treatment arm. Sub-figure b reports similar patterns for the average
daily consumption for all adults in the household over the previous week. Sub-figure c shows the
same estimates but for all children in the household. The number of times nshima was consumed
starts slightly higher for children, and falls during the hungry season, though not as much as for
adults. The treatment effects on children’s consumption are not as pronounced as for adults, though
both types of loans do appear to smooth seasonal consumption, and more so for the food treatment
arm. All three panels demonstrate a common patterns: all three treatment arms start from a similar
point in January, diverge in February, March and April and reconverge in May and June, as the
hungry season comes to an end. The standard errors are also widest during the hungry season,
suggesting that different households experience different consumption outcomes during this period.
Overall, the consumption impacts of the maize treatment arm appear slightly larger than the
consumption impacts of the cash treatment arm, particularly for maize-based consumption out22
comes.
Labor allocation Following the analysis of labor allocation for the pooled loan treatments, we
start by investigating the extensive margin of household engagement in the labor market using the
household survey. As shown in Panel B of Table 9, the likelihood that a household sold any ganyu
during the year falls by 5.3 percentage points or around 8 percent in the cash loan arm, and by 2.9
percentage points in the maize loan arm (column 2). The latter is not statistically different from
both the control group and the cash treatment arm. Cash loan households were 8.9 percentage
points (column 4) and maize households were 4.2 percent points more likely to hire ganyu, 30 and
14 percent increases relative to the control group, respectively.
Turning to the shorter recall questions from the labor survey, which allow us to investigate
intensive margin adjustments, we break the results down into the hungry season and the months
leading up to the harvest season. Table 14 presents the results. The overall effects of days of
ganyu worked by the family in the preceding week are similar across the treatment arms (Panel
A, column 1) and driven by decreases in the hungry season (Panel B, column 1). Family days onfarm increase in the cash treatment arm (Panel A, column 2). The change in the maize treatment
arm is not significantly different from either the control group or the cash loan treatment. For the
cash treatment, the magnitudes are similar in the hungry season and the harvest season, while the
magnitudes are larger in the harvest season for the maize arm, though none of the results for family
on-farm labor are statistically significant when they are broken out by treatment. The days of ganyu
hired increase in both treatments, and more so in the cash treatment arm, though the results are
statistically insignificant. Finally, the total days of labor on farm increase by almost 2 days over
the past week for the cash treatment arm, a 16 percent increase over the control group. The total
labor inputs in the maize treatment are less than half the magnitude though the difference between
the treatments is not statistically significant.
The results by treatment arm indicate that the labor allocation adjustments observed when the
treatments were pooled are driven substantially by the cash treatment arm, though both treatments
show evidence of household reoptimization of labor allocation after constraints are relaxed.
Other outcomes The other outcomes investigated for the pooled loan treatments showed no
effect on other consumption smoothing strategies (livestock or asset sales) or input expenditures on
inputs. When broken out by treatment arm, the statistical insignificance of these results remains,
as shown in Panel B of Tables 8 and 11.
Interpretation of treatment differences Results on agricultural output, consumption and
labor allocation suggest meaningful, if not always statistically significant, differences across the
treatment arms. The results for both agricultural output and labor allocation are typically larger
23
and more precisely estimated for the cash loan treatment. The only set of outcomes where the
maize arm outperforms the cash arm is in maize consumption. Given potential transactions costs
to converting the maize loan into cash, we explore evidence for distortions in household expenditures
due to the in-kind nature of the maize loan. In future work, we will analyze whether the lending
modality affected wasteful consumption, other household expenditures (such as schooling), and
intrahousehold outcomes including spousal disagreements and bargaining power.
While we do not have direct measures of distortions to the household budget, we hypothesize
frictions in the maize market that prevent households from converting maize to cash. At harvest,
we asked households in the maize loan treatment arm directly whether they sold any of their maize.
Only 50 (5 percent) households reported selling any maize. We analyze purchases across three major
categories (household assets, farming tools, and livestock) in Table 12. Overall, we see no significant
increases, though effects are consistently larger in the cash treatment arm with p-values between
0.10 and 0.20, which is suggestive of greater spending flexibility. Finally, we ask households during
the labor survey about the reasons for doing ganyu over the previous week, shown in Table 15. The
number of ganyu incidents are small, and disproportionately so in the treatment arms, where ganyu
rates are lower. However, we see a clear pattern of distortion in the reasons for doing ganyu in the
maize loan group. While the distribution of reasons is fairly similar between the control and cash
loan treatment (though the numbers are smaller in the latter), the share of ganyu incidents driven
by hunger falls substantially in the maize treatment arm and the share driven by household needs
increases. This suggests that maize treatment households may be less able to smooth consumption
of non-maize goods.
These patterns are consistent with frictions associated with turning maize into cash, which would
lower the value of the maize loan relative to the cash loan to the extent that households have needs
during the hungry season that extend beyond maize consumption. The patterns of reasons for ganyu
shown in Table 15 are consistent with a variety of cash needs that cannot necessarily be satisfied by
the maize loan. At the same time, food consumption needs are substantial, and the higher maize
prices described during the hungry season may have made it hard for households in the cash loan
treatment to have obtained enough food. While that may be the case, there is evidence for frictions
in the market that are stronger for the conversion of maize to cash than vice versa. Namely, at
midline, around 92 percent of households report a positive quantity of maize in stock while only
40 percent report a positive amount of cash savings, only half of whom report more than US$ 10
in savings. Thus, the availability of food in the villages, while scarce, is much greater than the
availability of cash.
5.6
Heterogeneous effects
As noted in the conceptual framework, we expect response to the treatments to depend on the
household’s initial endowment. Heterogeneity in the response to treatment can also help shed light
24
on the mechanisms underlying the overall treatment effects. We repeat our main specification,
interacting the treatment variables with key heterogeneity variables Hiv :
yiv = ↵ + ✓loanv + Hiv + loanv ⇥ Hiv + Xiv + uiv .
(7)
The coefficient of interest is , which tells us whether treatment effects vary with Hiv . We estimate
the impact of the loan treatment arms separately and show the results in Table16. We analyze
four main outcomes measured as part of the harvest survey: harvest value (at median prices), the
likelihood of selling and of hiring ganyu and the months of food shortage (January - June). We
look for evidence of heterogeneous treatment effects on four variables. First, the household’s labor
endowment, relative to the land endowment, is likely to matter if labor allocation is a primary
mechanism through which the loans relax household constraints. We expect the treatment effects
to therefore decrease as the household’s labor endowment (labor/land ratio) increases. Second, the
stored maize at baseline is an important determinant of how constrained the household is likely
to be in the coming season. We expect the treatment effects to be decreasing in stored maize at
baseline. Third, the distance to the paved road offers a measure of the household’s isolation from
outside markets, which may play a role in the differences between the treatments. Fourth, the size
of the village determines the share of households that were eligible for a loan, as well as the strength
of local markets. We therefore expect treatment effects to be decreasing in the size of the village.
Each panel of Table 16 shows results for one of the heterogeneity variables. Overall, the statistical
power for detecting heterogeneous treatment effects is weak. Beginning with measures of household
endowments, in Panel A, we see that households with more family workers (adults) per hectare of
land cultivated at baseline have smaller increases in harvest value. The coefficient is marginally
significant in the maize loan arm only. The effects on selling ganyu go in the opposite direction as
expected, while the effects on ganyu hiring are smaller for households with larger land endowments.
All ganyu effects have large standard errors, as do the impacts on months of food shortage. Panel B
shows another initial endowment at the start of the study: baseline maize storage in 50 kg bags. As
expected treatment effects on harvest value is smaller if the household has more stored maize, though
coefficients are insignificantly different from the control group. The treatment effects on both selling
and hiring ganyu depends on baseline maize endowments. The likelihood of selling ganyu falls less
among households with greater baseline maize reserves, across both treatment groups. Similarly,
the likelihood of buying ganyu increases less among households with greater baseline maize reserves.
Thus, the treatment effects on labor allocation are greater for households with fewer resources at
baseline, consistent with the predictions of the model. Finally, the months of food shortage decrease
less if maize is more available in the maize treatment arm only. The interaction terms is half as
large in the cash treatment arm and statistically insignificant. This suggests that consumption in
the maize arm is more sensitive to available maize than it is in the cash arm.
Turning next to the two measures of isolation and surrounding markets, Panel C shows the
25
heterogeneity in response to distance from the road in kilometers. Households in the cash loan
treatment show smaller increases in harvest value if they are closer to the road. The pattern is
the same in the maize arm, though the coefficient is smaller and less precisely estimated. This
suggests that treatment effects are smaller in more isolated villages, perhaps because of the greater
difficulty accessing markets for maize or for other inputs. Ganyu selling and hiring do not vary with
distance to the road. Months of food shortage, however, is decreasing in distance to the road in the
cash loan treatment, with smaller and imprecise estimates in the maize loan treatment. This may
indicate that households in the cash treatment arm were more likely to invest their loan in food
if they were further from markets where other items were available. Finally the size of the village
does not appear to impact any of the treatment effects, with all interaction terms close to zero and
imprecisely estimated.
5.7
Spillovers and cost effectiveness
We perform a preliminary assessment of the impacts of the loans on prices. In future work using
results from year 2 of the study, we will analyze these effects and the persistence of the program
more closely. We conclude this sub-section with a discussion of cost effectiveness.
We record maize prices at both midline and endline and report how they respond to treatment
in Table 17. At midline, we see a decrease across treatment arms, which is driven by the maize loan
treatment, where prices fall by approximately 5 percent. This is perhaps not surprising, given the
influx of additional maize into these villages. Coefficients on the cash treatment are also negative
but statistically imprecise and small in magnitude. At harvest, maize prices across both treatment
arms and the control are statistically indistinguishable, suggesting that the maize repayment was
insufficient to affect local prices.
In terms of cost effectiveness, the maize loan was far more expensive to administer than the cash
loan for several reasons. First, the transportation of maize to the villages and the collection of maize
from villages during loan repayment was a substantial part of the overall project budget. Second,
because of the price fluctuations in the maize market, the price of the maize purchased in January far
exceeded the price obtained for the maize repaid by farmers in June and July. In theory, these price
fluctuations could be arbitraged, though storage costs would still contribute costs to the logistics of
maize provision. Finally, the maize loans required manual labor for loading and unloading the maize
during loan delivery and repayment. Cash loans, on the other hand, posed minor security concerns
but were otherwise cheap to administer. Just focusing on impacts on agricultural output, the cash
loans did at least as well or better than the maize loans on most outcomes measured, making them
by far the more cost effective intervention. In future work, these cost effectiveness estimates will be
quantified and other outcomes will be examined.
26
6
Summary and Conclusion
In this paper we present results from a cluster-randomized controlled trial that was designed to assess
the degree to which frictions in formal savings and credit markets affect agricultural productivity
via distortions to consumption, labor and investment decisions among among small-scale farmers
in rural Zambia. Our results suggest that the demand for short-term credit as well as farmers’
willingness and ability to repay short-term loans are high, with over 95 percent of farmers taking
up the loan, and around 95 percent of loans fully repaid at an estimated interest rate of up to 30
percent over a six months period. On average, loans increase the value of agricultural output, raise
food consumption, reduce the frequency of selling labor to the casual labor market and increase the
probability of hiring other labor when labor needs are highest. These shifts in labor allocation appear
to be larger for cash loans, while maize loans appear to have a larger impact on food consumption.
The overall impact on farming output is positive in both loan groups, but significant only in the
case of cash loans. Our estimates suggest the average production value of farms in the cash loan
group increased by about KR 320 (~US$ 60), which is only slightly higher than the amount farmers
had to repay after the harvest. In terms of net income, this means that the impact of both programs
was likely limited, though we do not observe other savings and debts that may have been affected
by the program. The relatively small effects on harvest outcomes could be partially explained by
the arrival of the loans in January, which is relatively late in the season and after the need for other
inputs like seeds and fertilizer is passed. In addition, the loan size was small: the total “credit line”
would have been insufficient to cover high value inputs such as fertilizer. Yield analysis conducted
as part of this experiment suggests that successful farmers can earn a net profit of about US$ 200
per acre of land in this area, while the average farmer earns less than half this amount and uses
only a fraction of the chemical inputs ideally needed. While large improvements in yield may be
possible, they are likely to require much more substantial shifts in resources than the one generated
in this experiment, which arrived late in the season, by design, and were small in magnitude.
The overall remarkably high repayment rates as well as the observed reoptimization of consumption and family labor allocation suggests that welfare gains generated by the loan programs may be
substantial. Large reductions in the exposure to hunger and a reduced need to take on stigmatized
short-term labor opportunities imply a substantially increased quality of life in the short run for
many farmers; the second year of the program will show whether these improvements are large
enough for farmers to find these programs attractive in hindsight. It will also show whether these
temporary improvements in welfare can have positive or negative longer run impacts on farming
output and poverty levels.
27
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30
(a) Share of baseline sample reporting insufficient food
Figure 1: Baseline seasonality (2012-13)
31
June
May
April
March
February
January
December
November
October
September
Share of households reporting food shortages
0
.2
.4
.6
.8
Household short of food
.2
.4
.6
ne
Ju
ay
M
ril
Ap
ch
ar
ar
M
ru
Fe
b
Ja
nu
ar
y
y
r
be
r
D
ec
be
em
er
em
N
ov
r
be
O
ct
ob
gu
Au
Se
pt
em
ly
st
0
Ju
Control
Treatment
Control
ne
Ju
ay
M
ril
Ap
ch
ar
ar
Cash
M
ru
Fe
b
nu
ar
y
y
r
Ja
be
r
be
D
ec
em
er
em
N
ov
r
be
O
ct
ob
pt
em
gu
Se
Au
Ju
ly
st
0
Household short of food
.2
.4
.6
.8
(a) Any loan treatment
Maize
(b) By treatment arm
Figure 2: Months household reports being short of food
Notes: Harvest survey recall measures of months in which household was short of food.
Treatment effects estimated off of OLS regressions of treatment interacted with month indicator variables. All regressions include the full set of household level control variables (see
Table 2) and cluster standard errors at the village level.
32
2.2
Times respondent consumed nshima
1.8
2
1.6
Pre-loan
Hungry season
Control
Harvest season
Cash
Maize
1.6
Times adults in hh consumed nshima
1.8
2
2.2
(a) Respondent’s consumption
Pre-loan
Hungry season
Control
Harvest season
Cash
Maize
1.6
Times children in hh consumed nshima
1.8
2
2.2
(b) Average adult consumption
Pre-loan
Hungry season
Control
Harvest season
Cash
Maize
(c) Average child consumption
Figure 3: Consumption outcomes by month
Notes: Labor survey one-week recall measures of average consumption among different
groups within the household. Season specific coefficients estimated off of OLS regressions
of treatment interacted with season indicator variables. All regressions include the full set
of household level control variables (see Table 2) and cluster standard errors at the village
level.
33
Table 1: Loan offer details
Loan (January)
Offer
Value (official)
Value (reported)
Offer
Repayment (July)
Maize Loan
4 bags (50 kg ea)
K 260
K 234
Cash Loan
K 260
3 bags (50 kg ea)
K 195
K 261
K 200
Implied interest
30%
33%
-10%
30%
Notes: Columns describe the loan and repayment terms, and the implied interest rate for the
maize and cash loan treatment arms. The official value is the government-set maize price. The
reported value is the average reported in the harvest survey for buying and selling maize.
34
Table 2: Covariate balance
Control
mean
(SD)
Cash mean
(SD)
Maize mean
(SD)
Largest
pairwise tstat
Largest
pairwise
normalized
difference
(5)
0.0377
(1)
(2)
(3)
(4)
0.614
0.636
0.610
1.229
(0.487)
(0.481)
(0.488)
Household will do ganyu this season
0.622
0.640
0.634
0.827
0.0257
(0.485)
(0.480)
(0.482)
Acres of cotton
0.817
0.857
0.937
2.520
0.0781
(1.018)
(1.141)
(1.160)
Acres of local maize
1.241
1.170
1.230
1.314
0.0409
(1.281)
(1.157)
(1.213)
Acres of hybrid maize
1.153
1.149
1.104
0.782
0.0243
(1.424)
(1.343)
(1.438)
Household members under 5
0.925
0.975
0.927
1.227
0.0381
(0.907)
(0.940)
(0.939)
Household member 5-14
1.775
1.720
1.755
0.826
0.0257
(1.530)
(1.545)
(1.492)
Household members 15-64
2.474
2.423
2.409
1.175
0.0365
(1.263)
(1.320)
(1.264)
Household members 65 and older
0.172
0.187
0.172
0.753
0.0234
(0.441)
(0.473)
(0.457)
Female headed household
0.236
0.262
0.246
1.372
0.0426
(0.425)
(0.440)
(0.431)
Age of household head
42.91
43.07
42.54
0.817
0.0252
(14.95)
(15.16)
(14.84)
Value of last season's harvest (KR)
571.4
547.8
556.0
1.189
0.0370
(496.0)
(398.3)
(434.6)
Number of different crops
2.978
3.042
3.012
1.361
0.0423
(1.120)
(1.018)
(1.083)
Household asset quintile
3.057
3.013
2.931
2.052
0.0637
(1.392)
(1.426)
(1.423)
Total value of livestock (KR)
3586.0
3304.4
3421.0
1.047
0.0325
(6220.1)
(6023.1)
(6584.8)
Notes: N=3141. All variables measured at baseline. Columns (1)-(3) report means and standard deviations
for each treatment arm. Column (4) shows the largest pairwise t-statistic and column (5) shows the largest
pairwise normalized difference.
Household did ganyu last season
35
Table 3: Attrition
Baseline
mean (SD)
(1)
3141
1.00
Number eligible
Share interviewed
Household did ganyu last season
In midline
In endline
(2)
1223
0.976
(3)
3141
0.965
0.620
-0.017
-0.007
(0.485)
(0.014)
(0.010)
Household will do ganyu this season
0.632
0.018
0.010
(0.482)
(0.016)
(0.009)
Acres of cotton
0.871
-0.007
0.002
(1.111)
(0.004)
(0.003)
Acres of local maize
1.213
0.007*
0.001
(1.217)
(0.004)
(0.003)
Acres of hybrid maize
1.135
-0.003
-0.002
(1.402)
(0.004)
(0.003)
Household members under 5
0.942
0.007
0.009**
(0.929)
(0.005)
(0.004)
Household member 5-14
1.750
0.004*
0.005**
(1.522)
(0.003)
(0.002)
Household members 15-64
2.435
0.002
0.003
(1.283)
(0.004)
(0.003)
Household members 65 and older
0.177
-0.003
0.004
(0.457)
(0.014)
(0.008)
Female headed household
0.249
0.008
-0.009
(0.432)
(0.012)
(0.008)
Age of household head
42.84
0.000
0.000
(14.98)
(0.001)
(0.000)
Value of last season's harvest (KR)
558.2
0.000
0.000
(443.9)
(0.000)
(0.000)
Number of different crops
3.011
0.001
0.005
(1.074)
(0.004)
(0.003)
Household asset quintile
2.999
0.008*
0.001
(1.415)
(0.004)
(0.003)
Total value of livestock (KR)
3434.7
-0.000
-0.000
(6280.8)
(0.000)
(0.000)
Notes: Column (1) shows means and standard deviations for the baseline sample
of 3141 respondents. Columns (2) and (3) show the number of households
eligible, the share of households interviewed, and the coefficients from OLS
regressions of an indicator for interviewed on control variables with standard
errors clustered at the village level.
36
37
Full repayment
Fraction repaid
Repaid any cash
Cash loan mean
0.933
0.942
0.350
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Maize loan
-0.003
-0.002
0.013
0.012
0.016
0.015
-0.225***
-0.224***
(0.007)
(0.007)
(0.020)
(0.020)
(0.018)
(0.017)
(0.035)
(0.035)
Controls
yes
yes
yes
yes
N
2039
2008
2007
2008
Notes: Regression of loan administrative outcomes on treatment arm, with standard errors clustered at the village level. The cash loan
is the omitted category. Even numbered columns include household controls. * p<0.10 ** p<0.05 *** p<0.01.
Take up
0.986
Table 4: Take up and repayment
Table 5: Treatment impacts on agricultural output
Kilograms
(1)
Loan treatment
Constant
R-squared
129.752
(100.601)
2038.851***
(86.397)
0.002
(2)
165.118**
(71.619)
98.832
(136.804)
0.328
Harvest value
(own price)
(3)
(4)
121.280
(194.646)
3290.119***
(168.346)
0.000
Harvest value
(median price)
(5)
(6)
Panel A: Any treatment
202.110
224.495
(122.317)
(174.588)
-139.313 3112.151***
(291.301)
(149.240)
0.436
0.002
270.748**
(118.093)
90.486
(237.096)
0.346
Self reported maize height
(midline)
(7)
(8)
1.207
(5.191)
191.663***
(4.104)
0.000
5.300
(4.648)
213.611***
(10.258)
0.095
38
Panel B: By loan type
136.082
179.191**
242.007
320.657**
252.535
316.929**
4.589
9.743
(111.570)
(77.373)
(229.566)
(141.781)
(196.471)
(129.675)
(6.581)
(6.004)
Maize loan treatment
115.063
150.127*
-7.748
81.895
188.087
220.514
-3.740
-0.407
(123.544)
(87.267)
(219.052)
(133.296)
(210.361)
(142.533)
(5.313)
(4.926)
Income effect control
-44.314
-3.589
-40.444
0.352
-43.683
-17.033
-7.990
-6.412
(246.025)
(204.551)
(555.019)
(388.308)
(474.351)
(335.548)
(19.921)
(11.345)
Constant
2043.000***
98.930
3293.905*** -141.293 3116.241***
91.291
192.510*** 214.573***
(92.377)
(138.730)
(177.634)
(286.561)
(158.103)
(238.977)
(3.947)
(10.203)
R-squared
0.002
0.328
0.001
0.437
0.002
0.346
0.004
0.100
Cash = Maize (p-val)
0.839
0.673
0.199
0.028
0.723
0.405
0.192
0.062
Control group mean
2038.851
3290.119
3112.151
88.100
Controls
yes
yes
yes
yes
Notes: N=3030 (N=1177 in columns 7 and 8). Intention to treat regressions of agricultural output variables, measured during the year 1
harvest survey (midline in columns 7 and 8), on treatment dummies. Even numbered columns control for household baseline variables
(see Table 2) and geographic block dummies. All specifications cluster standard errors at the village level. * p<0.10 ** p<0.05 ***
p<0.01.
Cash loan treatment
Table 6: Treatment impacts on consumption (midline survey)
Times nshima
(1)
(2)
Loan treatment
Constant
R-squared
Cash loan
Maize loan
Constant
R-squared
Cash = Maize (p-val)
Times protein
(3)
(4)
Months short of food
(5)
(6)
0.177***
(0.067)
3.474***
(0.058)
0.010
0.163**
(0.064)
3.185***
(0.193)
0.049
Panel A. Any treatment
0.043
0.064
(0.098)
(0.092)
0.896***
0.895***
(0.078)
(0.250)
0.000
0.086
-0.353***
(0.083)
2.064***
(0.068)
0.013
-0.354***
(0.074)
2.309***
(0.195)
0.066
0.182**
(0.075)
0.172**
(0.073)
3.474***
(0.058)
0.010
0.873
0.166**
(0.073)
0.160**
(0.069)
3.185***
(0.193)
0.049
0.916
Panel B. By loan treatment
0.093
0.097
(0.118)
(0.107)
-0.005
0.031
(0.111)
(0.107)
0.896***
0.896***
(0.078)
(0.249)
0.001
0.086
0.408
0.540
-0.159*
(0.093)
-0.549***
(0.092)
2.064***
(0.068)
0.025
0.000
-0.166**
(0.084)
-0.546***
(0.083)
2.306***
(0.183)
0.077
0.000
Control group mean
3.474
0.896
2.064
N
1192
1192
3030
Controls
yes
yes
yes
Notes: Columns 1-4 show OLS regression coefficients for consumption outcomes from midline survey,
with a two week recall period. Outcomes are the meals in the last two days where household members have
eaten nshima or protein. Columns 5 and 6 report regression coefficients for the number of months the
household was short of food between January and June 2014. Columns 2, 4 and 6 include the full set of
household level controls, and cluster standard errors at the village level. * p<0.10 ** p<0.05 *** p<0.01.
39
Table 7: Treatment impacts on self reported health (harvest survey)
Loan treatment
Constant
R-squared
Walk 5km
Carry 50kg
100m
Carry 20l
2km
(2)
(3)
(4)
(5)
PCA Selfreported
Health
(6)
0.0935**
(0.0411)
4.225***
(0.122)
0.168
0.0407**
(0.0167)
1.049***
(0.0531)
0.155
0.0266
(0.0195)
1.127***
(0.0572)
0.321
0.0320*
(0.0170)
1.100***
(0.0514)
0.211
0.192***
(0.0574)
1.743***
(0.182)
0.352
Overall
health
Overall
strength
(1)
0.143***
(0.0426)
3.903***
(0.125)
0.113
Control group mean
3.139
3.617
0.706
0.587
0.649
-0.119
Observations
3027
3017
3024
3020
3019
2989
Notes: Intention to treat regressions of self-reported health variables from the year 1 harvest survey on
treatment dummies. All columns control for household baseline variables (see Table 2) and geographic block
dummies. All specifications cluster standard errors at the village level. * p<0.10 ** p<0.05 *** p<0.01.
40
Table 8: Treatment impacts on non-labor consumption smoothing strategies
Sold any asset
(1)
(2)
Loan treatment
Constant
R-squared
Cash loan treatment
Maize loan treatment
Constant
R-squared
Cash = Maize (p-value)
Sold any livestock
(3)
(4)
Consumed green maize
(5)
(6)
-0.009
(0.019)
0.100***
(0.016)
0.000
-0.018
(0.018)
0.057
(0.038)
0.034
Panel A: Any loan
0.026
0.025
(0.037)
(0.032)
0.389***
-0.041
(0.030)
(0.093)
0.000
0.098
-0.058
(0.057)
0.375***
(0.048)
0.009
-0.006
(0.048)
0.664***
(0.119)
0.201
-0.015
(0.020)
-0.003
(0.024)
0.100***
(0.016)
0.000
0.598
-0.023
(0.020)
-0.012
(0.022)
0.057
(0.038)
0.035
0.581
Panel B: By loan type
0.028
0.031
(0.043)
(0.037)
0.024
0.018
(0.042)
(0.037)
0.389***
-0.041
(0.030)
(0.093)
0.000
0.098
0.918
0.717
-0.063
(0.066)
-0.054
(0.065)
0.375***
(0.048)
0.009
0.881
-0.006
(0.055)
-0.005
(0.054)
0.664***
(0.119)
0.201
0.983
Control group mean
0.100
0.393
0.392
Controls
yes
yes
yes
N
3030
3030
3030
3030
1193
1177
Notes: Intention to treat regressions of coping strategy measures on treatment dummies. Sales (columns 1
- 4) are measured at the harvest survey and cover the full agricultural season. Green maize consumption
(columns 5 and 6) is measured at midline. Columns (2), (4) and (6) control for household baseline
variables (see Table 2) and geographic block dummies. All specifications cluster standard errors at the
village level. * p<0.10 ** p<0.05 *** p<0.01.
41
Table 9: Treatment impacts on labor allocation (harvest survey)
Did any ganyu
(1)
(2)
Loan treatment
Constant
R-squared
Cash loan treatment
Food loan treatment
Constant
R-squared
Cash = Food (p-value)
-0.030
(0.028)
0.628***
(0.024)
0.001
-0.046
(0.031)
-0.014
(0.031)
0.628***
(0.024)
0.002
Hired any ganyu
(3)
(4)
Panel A. Any treatment
-0.041*
0.058**
(0.022)
(0.023)
0.496***
0.299***
(0.048)
(0.019)
0.175
0.003
0.065***
(0.019)
0.263***
(0.061)
0.117
Panel B. By loan type
-0.053**
0.085***
(0.026)
(0.026)
-0.029
0.031
(0.025)
(0.026)
0.496***
0.299***
(0.048)
(0.019)
0.175
0.005
0.296
0.089***
(0.022)
0.042*
(0.023)
0.263***
(0.062)
0.119
0.039
Control group mean
0.628
0.299
Controls
yes
yes
Notes: N=3030. Labor allocation outcomes from harvest survey, with full
agricultural season recall period (Sept - June). Regressions include the full set of
household level controls, and cluster standard errors at the village level. * p<0.10
** p<0.05 *** p<0.01.
42
Table 10: Treatment impacts on labor allocation (rotating labor survey)
Ganyu days
(1)
Loan treatment
R-squared
Control group mean
N
Loan treatment
R-squared
Control group mean
N
-0.358**
(0.171)
0.133
0.816
1248
Family days
Total days onHired days
on-farm
farm
(2)
(3)
(4)
Panel A. Full year (Jan-Jun)
0.979
0.344
1.323*
(0.625)
(0.343)
(0.782)
0.265
0.086
0.249
10.750
0.835
11.585
1248
1248
1248
Panel B. Hungry season (Jan-Mar)
-0.609**
0.805
0.484
1.289
(0.266)
(0.860)
(0.509)
(1.101)
0.131
0.258
0.121
0.260
1.272
11.952
1.020
12.972
778
778
778
778
Panel C. Harvest season (Apr-Jun)
0.060
1.406
0.230
1.636
(0.078)
(0.846)
(0.483)
(1.041)
R-squared
0.063
0.286
0.043
0.243
Control group mean
0.161
9.023
0.569
9.592
N
470
470
470
470
Notes: Labor allocation outcomes from rotating labor survey, with one week
recall period. Regressions include month dummies, the full set of household
level controls, and cluster standard errors at the village level. * p<0.10 **
p<0.05 *** p<0.01.
Loan treatment
43
Table 11: Treatment impacts on other agricultural inputs (harvest survey)
Fertilizer
(1)
Loan treatment
Constant
R-squared
44
Cash loan treatment
Maize loan treatment
Constant
R-squared
Cash=Maize (p-value)
Seeds
(2)
(3)
-18.366
(65.315)
719.865***
(58.231)
0.000
22.839
(44.010)
33.747
(97.808)
0.378
-0.214
(6.696)
61.910***
(5.830)
0.000
-9.556
(71.762)
-27.227
(71.673)
719.865***
(58.241)
0.000
30.477
(51.180)
15.111
(47.089)
33.664
(97.802)
0.378
0.727
-1.410
(7.385)
0.990
(7.539)
61.910***
(5.831)
0.000
(4)
Hired ganyu
(5)
(6)
Total expenditure
(7)
(8)
Panel A: Any loan
3.774
5.803
(5.133)
(16.154)
25.277
115.910***
(17.840)
(13.670)
0.204
0.000
16.930
(12.909)
45.998
(40.465)
0.137
1.193
(80.714)
958.478***
(71.874)
0.000
58.834
(52.764)
123.216
(125.660)
0.384
Panel B: By loan type
2.489
13.373
(5.920)
(18.687)
5.075
-1.811
(5.646)
(17.875)
25.291
115.910***
(17.769)
(13.672)
0.204
0.000
0.630
23.010
(15.064)
10.779
(14.545)
45.932
(40.307)
0.137
0.402
18.496
(87.867)
-16.211
(89.512)
958.478***
(71.885)
0.000
74.640
(60.780)
42.842
(56.896)
123.046
(125.622)
0.384
0.545
Control group mean
719.865
61.910
115.910
958.478
Controls
yes
yes
yes
yes
Notes: N=3030. OLS regressions of expenditures on agricultural inputs, measured in Zambian Kwacha, during the 2013-14 agricultural
season on treatment dummies. Even numbered columns include a full set of household controls and all specifications cluster standard
errors at the village level. * p<0.10 ** p<0.05 *** p<0.01.
Table 12: Treatment impacts on household purchases (harvest survey)
Bought hh asset
Loan treatment
Constant
R-squared
Cash loan treatment
Maize loan treatment
Constant
R-squared
Cash = Maize (p-value)
Bought farm asset
(1)
(2)
(3)
(4)
0.026
(0.022)
0.618***
(0.017)
0.001
0.026
(0.020)
0.519***
(0.060)
0.098
Panel A: Any treatment
0.016
0.011
(0.022)
(0.022)
0.524***
0.426***
(0.017)
(0.059)
0.000
0.070
0.035
(0.027)
0.017
(0.025)
0.618***
(0.017)
0.001
0.040
(0.024)
0.012
(0.023)
0.519***
(0.060)
0.099
0.273
Panel B: By loan type
0.034
0.036
(0.026)
(0.024)
-0.002
-0.013
(0.028)
(0.026)
0.524***
0.426***
(0.017)
(0.059)
0.001
0.072
0.057
Bought livestock
(5)
(6)
0.031
(0.021)
0.329***
(0.017)
0.001
0.030
(0.021)
0.299***
(0.053)
0.042
0.029
(0.025)
0.032
(0.024)
0.329***
(0.017)
0.001
0.032
(0.025)
0.029
(0.023)
0.299***
(0.054)
0.042
0.899
Control group mean
0.618
0.524
0.329
N
3030
3019
3030
3019
3030
3019
Controls
yes
yes
yes
Notes: Intention to treat regressions of household purchases, measured during the year 1 harvest survey, on
treatment dummies. Even numbered columns control for household baseline variables (see Table 2) and
geographic block dummies. All specifications cluster standard errors at the village level. * p<0.10 ** p<0.05
*** p<0.01.
45
46
0.028
Yes
No
0.029
Yes
No
(2)
270.748**
(118.093)
(1)
165.118**
(71.619)
0.000
Yes
Yes
(3)
-0.354***
(0.0741)
Food
shortage
0.001
Yes
Yes
(4)
0.206***
(0.0634)
Self-reported
health
Hired
ganyu
0.072
No
No
0.001
Yes
Yes
(5)
(6)
-0.0407* 0.0655***
(0.0224) (0.0192)
Sold
ganyu
0.272
No
No
(7)
55.64
(50.46)
Total input
expenditure
R-squared
0.021
0.013
0.095
0.374
0.119
0.079
0.080
Notes: N=3030. All models control for household baseline variables (see Table 2) and geographic block dummies. All
specifications cluster standard errors at the village level. * p<0.10 ** p<0.05 *** p<0.01. False Discovery Rate (Benjamini &
Hochberg, 1995) and FWER (Bonferrroni) cutoffs are based on α=0.05.
Uncorrected p-value
Significant with FDR correction
Significant with FWER correction
Loan Program
Harvest value
(median
price)
Harvest kg
Table 13: P-value adjustments for multiple inference
Table 14: Treatment impacts on labor allocation, by treatment arm (rotating labor survey)
Ganyu days
(1)
Cash loan treatment
Food loan treatment
R-squared
Cash = Food
Control group mean
N
Cash loan treatment
Food loan treatment
R-squared
Cash = Food
Control group mean
N
-0.389**
(0.185)
-0.327*
(0.184)
0.133
0.649
0.816
1248
-0.624**
(0.274)
-0.589*
(0.299)
0.131
0.865
1.272
778
Family days
Total days onHired days
on-farm
farm
(2)
(3)
(4)
Panel A. Full year (Jan-Jun)
1.358**
0.539
1.897**
(0.677)
(0.451)
(0.881)
0.609
0.154
0.763
(0.821)
(0.387)
(0.970)
0.266
0.087
0.250
0.377
0.427
0.258
10.750
0.835
11.585
1248
1248
1248
Panel B. Hungry season (Jan-Mar)
1.409
0.639
(0.902)
(0.564)
0.032
0.286
(1.145)
(0.661)
0.260
0.122
0.217
0.597
11.952
1.020
778
778
2.049*
(1.174)
0.318
(1.408)
0.262
0.197
12.972
778
Panel C. Harvest season (Apr-Jun)
0.088
1.446
0.503
1.950
(0.090)
(0.969)
(0.939)
(1.369)
Food loan treatment
0.044
1.382
0.066
1.447
(0.089)
(1.101)
(0.304)
(1.229)
R-squared
0.063
0.286
0.044
0.243
Cash = Food
0.628
0.960
0.594
0.748
Control group mean
0.161
9.023
0.569
9.592
N
470
470
470
470
Notes: Labor allocation outcomes from rotating labor survey, with one week recall
period. Regressions include month dummies, the full set of household level
controls, and cluster standard errors at the village level. * p<0.10 ** p<0.05 ***
p<0.01.
Cash loan treatment
47
Table 15: Reasons for doing ganyu (labor survey)
Reason for ganyu
Hunger
Control
Cash
Maize
0.49
0.43
0.2
(0.50)
(0.50)
(0.40)
Expected household needs
0.37
0.43
0.65
(0.48)
(0.50)
(0.48)
Unexpected household needs
0.20
0.08
0.13
(0.30)
(0.27)
(0.34)
Personal expenses
0.04
0.05
0.03
(0.21)
(0.23)
(0.18)
N
90
75
60
Notes: Means with standard deviations in parentheses. Responses are for
a one-week recall period in the labor survey, conditional on doing any
ganyu.
48
Table 16: Heterogeneous treatment effects (harvest survey)
Harvest value
(median price)
(1)
Cash loan
Maize loan
Workers per acre
Cash loan × Workers per acre
Maize loan × Workers per acre
Cash loan
Maize loan
50 Kg bags of storage at baseline
Cash loan × Baseline maize storage
Maize loan × Baseline maize storage
Cash loan
Maize loan
Distance to nearest paved road in km
Cash loan × Distance to road
Maize loan × Distance to road
Any ganyu
Any hiring
Months short
of food
(4)
522.025**
(230.199)
523.346*
(270.966)
-188.176
(256.000)
-302.532
(228.666)
-461.947*
(260.757)
(2)
(3)
Panel A: Labor / Land ratio
-0.037
0.103**
(0.047)
(0.041)
-0.007
0.048
(0.048)
(0.041)
0.067
0.009
(0.046)
(0.044)
-0.023
-0.023
(0.052)
(0.049)
-0.032
-0.008
(0.058)
(0.050)
445.304***
(143.143)
275.991*
(157.376)
56.034***
(15.512)
-21.227
(21.539)
-10.327
(19.533)
Panel B: Baseline maize storage
-0.090***
0.120***
(0.030)
(0.027)
-0.056**
0.080***
(0.028)
(0.026)
-0.009***
0.006***
(0.002)
(0.002)
0.006**
-0.006*
(0.003)
(0.003)
0.005**
-0.007**
(0.002)
(0.003)
-0.221**
(0.095)
-0.648***
(0.093)
-0.032***
(0.008)
0.009
(0.010)
0.018**
(0.009)
628.631***
(181.249)
396.169*
(229.375)
82.595*
(45.640)
-93.103*
(50.296)
-61.541
(57.176)
Panel C: Distance to paved road
-0.051
0.088**
(0.036)
(0.040)
-0.025
0.046
(0.040)
(0.035)
-0.003
-0.001
(0.007)
(0.006)
0.000
-0.000
(0.009)
(0.009)
0.000
-0.000
(0.009)
(0.007)
0.060
(0.137)
-0.441***
(0.136)
0.036
(0.023)
-0.063**
(0.031)
-0.033
(0.027)
-0.255**
(0.129)
-0.468***
(0.131)
-0.121
(0.150)
0.131
(0.151)
-0.125
(0.166)
Panel D. Village size
221.020
-0.038
0.077
-0.244
(285.610)
(0.064)
(0.048)
(0.182)
Maize loan
179.701
-0.086
0.078
-0.704***
(334.110)
(0.058)
(0.050)
(0.237)
Number of farms in village
0.187
-0.001
0.000
-0.004*
(5.307)
(0.001)
(0.001)
(0.002)
Cash loan × Number of farms
2.314
-0.000
0.000
0.002
(6.082)
(0.002)
(0.001)
(0.004)
Maize loan × Number of farms
1.067
0.001
-0.001
0.004
(8.310)
(0.001)
(0.001)
(0.005)
Notes: N=3030. Heterogeneous treatment effects from regressions of key outcomes on treatment
interacted with continous measures of the heterogeneity variables. Each panel shows results for a
heterogeneity variable. All columns include the full set of household controls, geographic block
dummies, and standard errors clustered at the village level. * p<0.10 ** p<0.05 *** p<0.01.
Cash loan
49
Table 17: Treatment impacts on maize prices (midline and harvest surveys)
Midline maize price
(1)
(2)
Loan treatment
Constant
R-squared
Cash
Maize
Constant
R-squared
Cash = Maize (p-value)
-2.819
(1.829)
88.100***
(1.461)
0.005
-1.387
(2.150)
-4.244**
(2.107)
88.100***
(1.461)
0.009
Harvest maize price
(3)
(4)
Panel A: Any loan
-2.989**
0.098
(1.402)
(0.624)
92.938***
59.176***
(3.979)
(0.532)
0.179
0.000
0.216
(0.482)
60.146***
(1.474)
0.058
Panel B: By loan type
-1.676
0.626
(1.567)
(0.728)
-4.300**
-0.434
(1.692)
(0.675)
92.955***
59.176***
(4.028)
(0.532)
0.182
0.002
0.118
0.816
(0.576)
-0.400
(0.530)
60.133***
(1.479)
0.060
0.028
Control group mean
88.100
59.176
N
1165
1161
3010
2999
Controls
yes
yes
Notes: Intention to treat regression so self reported local maize prices at Midline
(February-March) and Harvest (June-July) on treatment dummies. Even numbered
columns control for household baseline variables (see Table 2) and geographic
block dummies. All specifications cluster standard errors at the village level. *
p<0.10 ** p<0.05 *** p<0.01.
50
Appendix
A.1
Tables and figures
51
175$Villages$$
3135$hh$
Year%1%
Control$Group$
58$Villages.$1009$hh$
Cash$Loan$Group$$
58$Villages,$1061$hh$
Maize$Loan$Group$
59$Villages.$1065$hh$
Cash$Gi?:$6$villages.$96$
farms$(100%)$
100%$Cash$Loan$Offer$
100%$Maize$Loan$Offer$
Year%2%
38$villages$control$
10$villages$maize$loans$
10$villages$cash$loan$
29$villages$control$
30$villages$cash$loan$
28$villages$control$
30$$Villages$maize$loan$
Early$noMficaMon:$50%$of$treated$(28$villages)$informed$about$program$at$start$of$planMng$season$
Figure A.1: Study design
52
Table A.1: Comparison of Chipata District with the rest of Zambia
Number of household members
Number of rooms
Electricity access
Private water access
Private toilet
Grows crops
Monthly expenditure in USD
Classified as very poor
All households
Rural households only
Zambia
H0: Equal Zambia
H0: Equal
excluding Chipata
means
excluding Chipata
means
Chipata
(p-value)
Chipata
(p-value)
5.31
5.11
0.21
5.41
5.37
0.88
3.29
3.02
0.11
2.93
2.60
0.02
0.33
0.22
0.64
0.07
0.02
0.00
0.26
0.23
0.01
0.03
0.00
0.00
0.71
0.59
0.14
0.65
0.52
0.00
0.50
0.60
0.01
0.80
0.89
0.00
333.97
219.21
0.00
219.16
121.94
0.00
0.32
0.47
0.00
0.42
0.63
0.00
Observations
18948
449
19397
8243
225
8468
Notes: Tables summarizes average household characteristics as reported in the Zambian Living Conditions
Measurement Survey (LCMS) 2010. P-values reported are based on a two-sample mean comparison; standard
errors adjusted for 940 survey clusters in the LCMS.
53
Table A.2: Midline sample balance
Control
mean
(SD)
Cash mean
(SD)
Maize mean
(SD)
Largest
pairwise tstat
Largest
pairwise
normalized
difference
(5)
0.0451
(1)
(2)
(3)
(4)
0.626
0.635
0.603
0.900
(0.485)
(0.482)
(0.490)
Household will do ganyu this season
0.646
0.642
0.623
0.653
0.0327
(0.479)
(0.480)
(0.485)
Acres of cotton
0.810
0.824
0.903
1.225
0.0613
(1.004)
(1.126)
(1.127)
Acres of local maize
1.230
1.108
1.319
2.483
0.124
(1.283)
(1.145)
(1.255)
Acres of hybrid maize
1.157
1.194
1.056
1.399
0.0701
(1.409)
(1.324)
(1.451)
Household members under 5
0.902
0.970
0.858
1.734
0.0870
(0.899)
(0.957)
(0.856)
Household member 5-14
1.781
1.617
1.711
1.526
0.0767
(1.539)
(1.497)
(1.507)
Household members 15-64
2.523
2.373
2.434
1.605
0.0807
(1.331)
(1.290)
(1.267)
Household members 65 and older
0.173
0.173
0.162
0.356
0.0178
(0.441)
(0.458)
(0.454)
Female headed household
0.249
0.269
0.269
0.663
0.0332
(0.433)
(0.444)
(0.444)
Value of last season's harvest (KR)
569.3
545.5
550.3
0.771
0.0387
(461.7)
(405.0)
(419.3)
Number of different crops
3.005
3
3.012
0.170
0.00853
(1.109)
(1.016)
(1.050)
Household asset quintile
3.090
3.058
2.918
1.739
0.0870
(1.395)
(1.423)
(1.413)
Total value of livestock (KR)
4118.9
3357.3
3267.9
1.834
0.0918
(7359.3)
(5827.0)
(5633.0)
Notes: N=1193. All variables measured on randomly selected households in February-March 2014. Columns
(1)-(3) report means and standard deviations for each treatment arm. Column (4) shows the largest pairwise tstatistic and column (5) shows the largest pairwise normalized difference.
Household did ganyu last season
54
Table A.3: Labor survey sample balance
Control mean Cash mean
(SD)
(SD)
Maize mean
(SD)
Largest
pairwise tstat
Largest
pairwise
normalized
difference
(5)
0.0111
(1)
(2)
(3)
(4)
Household did ganyu last season
0.630
0.635
0.627
0.223
(0.483)
(0.482)
(0.484)
Household will do ganyu this season
0.606
0.630
0.662
1.648
0.0814
(0.489)
(0.483)
(0.474)
Acres of cotton
0.790
0.860
0.906
1.547
0.0765
(1.044)
(1.184)
(1.106)
Acres of local maize
1.282
1.164
1.196
1.299
0.0644
(1.380)
(1.211)
(1.171)
Acres of hybrid maize
1.103
1.103
1.088
0.162
0.00809
(1.411)
(1.328)
(1.401)
Household members under 5
0.925
0.962
0.916
0.678
0.0338
(0.911)
(0.930)
(0.999)
Household member 5-14
1.787
1.720
1.728
0.614
0.0305
(1.537)
(1.573)
(1.476)
Household members 15-64
2.517
2.436
2.491
0.869
0.0432
(1.318)
(1.341)
(1.212)
Household members 65 and older
0.191
0.199
0.143
1.726
0.0863
(0.477)
(0.491)
(0.421)
Female headed household
0.227
0.254
0.240
0.910
0.0452
(0.419)
(0.436)
(0.427)
Age of household head
43.60
43.82
42.23
1.503
0.0754
(15.10)
(15.53)
(14.34)
Value of last season's harvest (KR)
604.5
525.5
535.3
2.281
0.113
(587.3)
(379.7)
(416.2)
Number of different crops
2.944
2.997
3.044
1.283
0.0634
(1.133)
(0.989)
(1.098)
Household asset quintile
3.104
2.975
2.911
1.974
0.0975
(1.396)
(1.412)
(1.399)
Total value of livestock (KR)
3625.2
2824.6
3008.2
2.102
0.104
(6080.5)
(4704.2)
(6015.9)
Notes: N=1216. All variables measured between January and June 2014 for a rotating sample of ~14
households/week. Columns (1)-(3) report means and standard deviations for each treatment arm. Column (4)
shows the largest pairwise t-statistic and column (5) shows the largest pairwise normalized difference.
55
A.2
Choice experiments
Hypothetical choice experiments were conducted on a convenience sample of participants in November and December 2013. In the initial wave of questions, 72 respondents were interviewed, one-third
of which were female. The surveys took place in villages in and around the study area, but not
eligible for the study either because they were too large (>100 households) or they had participated
in the pilot program. Respondents were approached by an enumerator who explained the exercise, emphasizing that the offers were hypothetical and that responses would not affect any future
programs they might be offered. In spite of these disclaimers, which were intended to minimize
strategic responses and avoid building expectations, respondents took the decision tasks seriously.
Six scenarios were presented to respondents, involving different dichotomous choices that varied
a relevant parameter of the loan offer. The ordering of the parameter set were varied across respondents.
Scenario 1: Maize loan versus cash loan
Script: Suppose that we had two loans available that would start in January. The first would
offer three (3) bags of [50 kg maize] in January that you have to repay in June. The second would
offer cash that you would have to repay in June. Please take your time to make your choice, as I
will be going through different categories. Would you prefer a cash loan that paid ____ KR that
you would pay back in June or would you prefer the [maize] loan that you would pay back in June?
Parameters: 50, 110, 150, 175, 250, 275, 350, 375, 425, 450, 600 Kwacha
Scenario 2: Cash repayment
Script: Now, supposed the loan changed so that you could still receive three (3) bags of [mealie
meal / maize] in January. But instead of repaying in maize in June, you had to repay in cash.
I’m going to go through some different repayment amounts. You should tell me whether you would
choose to take up a loan that gave you [maize] in January and had to repay that amount of cash
in June. Would you be willing to take up a loan that gave you 3 bags of [maize] in January and
required that you repay ___ KR in June?
Parameters: 600, 450, 400, 325, 275, 250, 200, 175, 125, 100, 75, 50 Kwacha
Scenario 3: Cash gift vs. maize loan
Script: Again, suppose, we were to offer a loan that offered three (3) bags of [maize] in January
that you had to repay in June. Would you prefer to take that loan or would you prefer to receive
____ Kwacha in January, which you would not require to pay back?
Parameters: 10, 30, 60, 80, 100, 110, 130, 150, 175, 200, 250 Kwacha
56
Scenario 4: Cash gift vs. cash loan
Script: Suppose now that the loan was cash instead and we were to offer a loan that provided
200 KR in January that you had to repay in June without any interest (repay 200 KR in June).
Would you prefer to take that loan or would you prefer to receive ____ Kwacha in January which
you would not require to pay back.
Parameters: 10, 30, 60, 80, 100, 110, 130, 150, 175, 200, 250 Kwacha
Scenario 5: Maize loan repayment month
Script: Suppose, we were to offer a loan that offered three (3) bags of [maize] in January that
required you to repay four (4) bags. I’d like you to think about whether you would choose to take that
loan. I will list different months when the repayment would be due. Would you be willing to take a
loan of three bags of [mealie meal / maize] in June that required you repay 4 bags if the repayment
were due in ______?
Parameters: February, March, April, May, June, July, August, September, October, November,
December
Scenario 6: Cash loan repayment month
Script: Again, let’s look at this activity but considering a loan in cash instead of maize: Suppose,
we were to offer a loan that offered 200 KR in cash in January that required you to repay 330 KR in
cash. Would you be willing to take that loan for 200 KR in cash that repaid 265 KR if the repayment
were due in ______?
Parameters: February, March, April, May, June, July, August, September, October, November,
December
57