input subsidies,cash constraints,and …bingu wa mutharika, embarked upon a com-prehensive input...

INPUT SUBSIDIES,CASH CONSTRAINTS, AND TIMING OF

INPUT SUPPLY

STEIN T. HOLDEN AND RODNEY W. LUNDUKA

Is low input use by poor, smallholder farmers caused by time-inconsistent behavior or by limitedability to buy inputs? Are input subsidies the best solution to stimulate input demand or are theresmarter solutions? These issues are investigated by combining survey data, stated preference ques-tions, and randomized experiments in Malawi. The demand for fertilizer at harvest time and atplanting time, farm gate shadow prices for fertilizer, and the gap between the willingness-to-accept(WTA) and willingness-to-pay (WTP) prices for a standard input package were investigated.Significant effects of timing and of cash constraints were found, suggesting the possibility thatsmarter designs exist, such as distribution of smaller packages from harvest time to planting time.

Key words: cash constraints, fertilizer, input delivery timing, input subsidies, Malawi, timeinconsistency.

JEL codes: Q12, Q18.

In recent years, agricultural input subsidieshave regained popularity in several Africancountries after having been condemned bythe World Bank and other internationalinstitutions for several decades. Thesecondemnations came from the perceptionthat subsidies create policy distortions,drain government budgets, and result indebt repayment problems (Morris et al.2007). Under the late President Bingu waMutharika, in 2005 Malawi was the firstcountry to reintroduce high levels of inputsubsidies to improve national food self-sufficiency and reduce its dependence onfood aid. In a short time, Malawi managedto turn a food deficit into a food surplus,and was considered to be a success story(Denning et al. 2009). The new twist tothis subsidy program was that it was tar-geted poor smallholders through a couponsystem. Other countries have looked toMalawi, and similar programs have arisen

Stein Holden is a professor and Rodney Lunduka is aresearcher, both in the School of Economics and Business,Norwegian University of Life Sciences, Ås, Norway. Weacknowledge financial support from Norwegian Agency forDevelopment Cooperation under the NORAD’s Programmefor Master Studies (NOMA) program and a separate projectassessing the impacts of the input subsidy program in Malawi.We are thankful to students in the NOMA program for helpwith data collection and cleaning. The paper has benefitedfrom valuable comments from two anonymous reviewers andthe editor of AJAE, Ed Taylor. The usual disclaimers apply.

and expanded in Ghana, Kenya, Nigeria,Tanzania and Zambia (Dorward 2009; Mindeet al. 2008). However, the cost effective-ness and sustainability of the program havebeen questioned, and donors supporting theMalawian program have asked for a strategyto phase out the program.

An important reason for advocating fer-tilizer subsidies is that rural households arevery poor and typically lack sufficient cashresources to buy productive inputs, whichcan result in suboptimal input use. Indeed,poverty combined with liquidity constraintsmay generate high discount rates that canlead to low investment (Holden, Shiferaw,and Wik 1998). Time-inconsistent behaviormanifested as present bias has been observedin many social experiments and may partiallyexplain low input use and investment levelsin developing countries (Duflo, Kremer, andRobinson 2011). Another reason behind thelow adoption rate of improved technolo-gies may be fixed costs that limit marketaccess (Suri 2011). The seasonality of rain-fedagricultural production, which dominatesproduction, makes food shortages seasonal;thus, food must be stored between harvestseasons. A large share of smallholders may benet buyers of food, and thus at planting timethey face a dilemma between using scarcecash resources to buy food to meet imme-diate needs, or to invest in inputs for next

Amer. J. Agr. Econ. 96(1): 290–307; doi: 10.1093/ajae/aat059Published online September 9, 2013

© The Author (2013). Published by Oxford University Press on behalf of the Agricultural and Applied EconomicsAssociation. All rights reserved. For permissions, please e-mail: [email protected]

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Holden and Lunduka Input Subsidies, Cash Constraints, and Timing of Input Supply 291

year’s production. Time-inconsistent behaviormay cause suboptimal investment in inputs.In such a scenario, subsidies could internal-ize the inter-temporal externality related tosuboptimal input use; such subsidies couldenhance both productivity and equity.

However, one may question whethersuch subsidies are the most appropriateand cost-effective instrument with which toaddress this market failure. Duflo, Kremer,and Robinson (2011) conducted a studyand social experiments in Kenya and foundthat poor households are willing to invest inresponse to small, time-limited discounts inthe form of free fertilizer delivery just afterharvest. Indeed, a 50% subsidy on fertilizerat planting time did not increase fertilizeruse more than this harvest-time free deliverydiscount. These authors’ finding may indicatethat the distribution and sales of fertilizerjust after harvest can be a more effectivesystem than the sale of fertilizers at plantingtime, when households may no longer havesufficient funds remaining from the sale ofthe previous year’s harvest. The purchase ofinputs at harvest time for the next growingseason may serve as a commitment device(DellaVigna 2009) and reduce the need forsubsidies. It could also reduce the pressureon the input delivery system at planting timeand reduce the risk of productivity losses dueto overdue input deliveries.

This study investigates the willingness ofrural households in Malawi to allocate fundsfor input purchase at harvest time. Further, itexamines whether this willingness is as highor higher at harvest time than at plantingtime. Rural households do not face foodshortages at harvest time and may be morewilling and able to spend funds on inputsrelative to planting time. However, net buy-ers of food may still prefer to buy additionalfood at harvest time when food prices are attheir lowest. On the other hand, net sellersmay prefer to store the food and sell it laterat a higher price. Therefore, it is ambiguouswhether the willingness to buy inputs at har-vest time would be high or higher than atplanting time. While our study investigatessome of the same issues that Duflo, Kremer,and Robinson (2011) studied in the Kenyancontext, our study is substantially differentwith respect to the approaches used; it thusprovides novel complementary insights. Wealso apply a different theoretical model,although the basic idea of present bias is thesame. One important contribution of our

study is an assessment of the potential biasthat ignoring seasonal maize price variationcan introduce into the analysis of seasonalinput demand. We show that in the case ofMalawi, maize prices at planting time arealmost twice as much as those observed atharvest time.

Another novel contribution of thispaper is that we use an experimentalapproach to elicit cash-constrained and cash-unconstrained shadow prices for fertilizersin a context in which actual prices paid wereendogenous and dependent on unobservablehousehold characteristics. Stated-preferencequestions were used to explore the propen-sity to spend a given budget on fertilizerversus other goods at harvest time and atplanting time. Stated-preference questionswere also used to assess the gap betweenthe shares of households that were willingto sell versus willing to buy a standard inputpackage at alternating prices varying fromthe full subsidy (90%) to no subsidy. Thefarm gate shadow prices for small amountsof fertilizer were investigated in experimentswhere households could choose between 5 kgfertilizer and a random amount of cash ateither harvest time or at planting time.

The results showed that households werewilling to allocate a significantly larger shareof their budget to fertilizer purchases atplanting time than at harvest time. Previousaccess to input subsidies was not negativelyassociated with fertilizer demand but wasnegatively associated with food demand.A large share of the households had veryhigh shadow prices for small amounts of fer-tilizer when relieved of their cash constraint.However, only 20–25% of the householdswere willing to buy the input package at thecommercial price. Further, only 10–20% ofhouseholds who were hypothetically assignedpossession of an input package were will-ing to resell the package at the commercialprice. The gap between the willingness-to-accept (WTA) and willingness-to-pay (WTP)prices may be explained as a cash constrainteffect rather than an “endowment effect,”as the share of households that were will-ing to buy the package decreased rapidlyas the price increased, while the share will-ing to sell increased much more slowly withincreasing prices (Plott and Zeiler 2005,2007; Horowitz and McConnel 2002). Thesefindings suggest that low use is primarilycaused by limited ability to buy inputs andnot time-inconsistent behavior. The current

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292 January 2014 Amer. J. Agr. Econ.

input subsidy design should be replaced bysmarter and more cost-effective designs thatinvolve smaller packages of fertilizer anddelivery of inputs at harvest time, as well asat planting time.

Background

Malawi is a small landlocked country inSouthern Africa where more than 80% of thehouseholds depend on agriculture for theirlivelihood. Weather risk and inappropriategovernmental policies have contributed toboth severe household and national foodinsecurity (SOAS 2008). The last severefood shortages occurred from 2004–2005;consequently, the newly-elected president,Bingu wa Mutharika, embarked upon a com-prehensive input subsidy program that wascontradictory to the recommendations fromthe International Monetary Fund (IMF)and the World Bank. An argument by thepresident for the subsidy program was thatit is cheaper to import fertilizer than toimport food. Food production in the countryincreased dramatically, and Malawi became anet exporter of food (maize) in the followingyears.

The rural population in Malawi constitutes88% of the total population and the countryhas one of the highest rural population densi-ties in Africa, approximately 2.3 persons perha. The average farm size is approximately1.12 ha. Farm sizes are smaller in the south-ern region of the country, where populationdensity is higher (SOAS 2008). The incidenceof poverty is also higher in the southernregion: 64% of the households are estimatedto fall below the poverty line compared to52% at the national level. Maize is the mainstaple crop grown by 97% of the rural house-holds (SOAS 2008). Some households alsogrow cash crops such as tobacco, sugarcaneand cotton; however, such crops are grown onan average of less than 10% of the farm area,while maize covers 65–70% of the total farmarea (Holden and Lunduka 2010a).

More than 60% of the rural households inthe central and southern regions of the coun-try are net buyers of maize even with theinput subsidy program (Holden and Lunduka2010b). However, access to input subsidieshas reduced the food deficit of these netbuyers of food and more households havebecome self-sufficient or even net sellers(ibid.). Most of the agricultural production

is rain-fed and the rainfall is unimodal, witha rainy season that lasts from Decemberto April. This implies that the planting sea-son for the main staple crop (maize) is inDecember, and harvesting season is in Mayand June.

The Government of Malawi increased itsbudget share for agriculture from 6.1% overthe period 2000–2005 to 15.9% from 2006–2009, and is aiming to further increase it to24% by 2015 through the implementationof the Agricultural Sector Wide Approach(ASWAp) (GoM 2010).

The Ministry of Agriculture and FoodSecurity (MoAFS) in Malawi has developedcriteria for the distribution of input subsi-dies that emphasize targeting land-owningrural resident households, and particularlypoor and vulnerable households (MoAFS2008). The MoAFS issues and distributes freecoupons to identified beneficiary householdsthrough local MoAFS staff in collaborationwith local leaders. Households receivingthe coupons can take them to the nearestdepot where inputs are sold and pay a smallamount (MK500 per 50 kg bag during the2009/10 season) to obtain the inputs. How-ever, the depots can be quite far from thehouseholds, and the members risk having towait in line for a long time before they getthe inputs. This implies that rural householdsin Malawi face higher transaction costs in themarket than the typical household surveyedby Duflo, Kremer, and Robinson (2011) inKenya. There was no easily available marketthat sold small amounts of fertilizers. In the2008/09 season, approximately 2 million inputpackages were distributed to the roughly2.5 million rural households in the country,which implies that the packages coveredclose to 80% of the households. A recentstudy (Holden and Lunduka 2013) has iden-tified substantial leakage of coupons, illegalsecondary markets for coupons and cheapfertilizers, and substantial targeting errors.These authors found that very few house-holds had resold their fertilizers or fertilizercoupons. Therefore, access to inputs andthe actual prices paid depend on householdcharacteristics, including households’ socialcapital in terms of social networks, access toinformation, and ability to negotiate. Wealth-ier households have been found to be moresuccessful in obtaining subsidized inputs,while female-headed households have beenless successful (Dorward et al. 2008; Holdenand Lunduka 2013).

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Table 1. Costs of the Farm Input SubsidyProgram

CostsYear (million US$)

Cost of importingfood in droughtyear

2004/05 110

Cost of fertilizersubsidy program

2005/06 50


2006/07 91


2008/09 360

Total donorassistance toMalawi

2007 500

Sources: Harrigan 2005; Dorward et al. 2008; Denning et al. 2009;Logistic Unit 2009.

The costs of fertilizer subsidies to theMalawian government have risen with theincrease in international fertilizer prices;from 2006–2007, the fertilizers represented40% of the agricultural budget (Dorwardet al. 2008). Given the very high fertilizerprices from 2008–2009, the spending onfertilizer imports and the fertilizer subsidyprogram exceeded the initial budget by morethan 100% (Logistic Unit 2009). Table 1 givesan overview of the costs of the input subsidyprogram compared to some benchmarks, suchas the cost of food import in a drought yearand total donor assistance. Since the 2008/09season, the country has experienced short-ages of foreign exchange, which also have ledto fuel shortages. Tobacco is the main exportcrop, but tobacco exports are also limitedby international agreements. Maize exportshave, to some extent, compensated for thecost of fertilizer imports.

The donor community sees the input sub-sidy program as a temporary solution to thefood insecurity problems of the country, andprovides conditional support to the pro-gram through the general budget or supportsthe funding of particular elements of theprogram such as the seed component.

Malawi’s late president Mutharika arguedthat the subsidies had come to stay whenhe became the chairperson of the AfricanUnion. In addition, other African countrieshave been looking to Malawi’s experienceand considered implementing similar policies;Ghana, Tanzania and Zambia are among thecountries that have scaled up similar inputsubsidy programs. However, international

fertilizer prices are again on the rise, andthere is a need to keep the budgetary costsdown. The main arguments for the programare that rural households cannot afford tobuy inputs if they are not subsidized, andremoving the subsidies would lead to newfood shortages. Credit provision has beenan alternative approach that also has hadmixed results due to high default rates. Untilthe early 1990s, fertilizer use and maizeproduction were stimulated through creditprovision in Malawi. However, maize is adrought-sensitive crop, and droughts in 1992and 1994, combined with political promisesto write off loan debt during an election year(1994), led to widespread loan defaults andthe collapse of the parastatal SmallholderAgricultural Credit Administration (SACA)in 1994 (Zeller et al. 1997). The input subsidyprogram was established as a substitute forthe credit program that had collapsed.

This background provides the frame-work for this study. In particular, the studyexplores the alternative approach of sellinginputs at harvest time to reduce the need forsubsidies and provide a commitment devicethat can reduce the need for credit as well.However, it is an open question whethernet buyers of maize should buy fertilizer ormaize at harvest time when maize prices areat their lowest.

Theoretical Framework

Duflo, Kremer, and Robinson (2011) foundthat small investments in fertilizer gener-ated annualized rates of return between52% and 85% in their study in Kenya. Theseauthors were puzzled that farmers investedso little in fertilizer when profits were solarge and the technology is well known anddivisible. One possible explanation couldbe fixed costs related to buying and learn-ing about the technology, but the authorsfound that these could not be large enoughto provide a full explanation. Therefore,they looked for a behavioral explanation inthe form of present bias; this phenomenonhas also been observed in the United Statesin relation to investments in pension plans(Choi, Laibson, and Madrian 2008) anddraws on models of procrastination in psy-chology and economics. Strong presentbias has been observed among poor ruralhouseholds in developing countries and isrelated to liquidity constraints and poverty

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(Holden, Shiferaw, and Wik 1998). Empiricalevidence shows that the discounted utilitymodel (Samuelson 1937) is a poor fit for thereality of inter-temporal choices (Frederick,Loewenstein, and O’Donoghue 2002).

Laibson (1997) and O’Donoghue andRabin (1999) have formulated the followingalternative (β, δ)preference model:

(1) Ut = ut + βδut+1 + βδ2ut+2 + βδ3ut+3 + . . .

where the difference from the standarddiscounted utility model is the parameterβ ≤ 1, which captures self-control problems.The model has also been expanded on byO’Donoghue and Rabin (2001) to handlenaïve expectations (overconfidence) relatedto future self-control. We argue that the sameparameter may reflect a current period liq-uidity constraint; apparent irrational presentbias may rather reflect a rational responseto short-term constraints and needs in astochastic environment (Holden, Shiferaw,and Wik 1998).

We employ this alternative (β, δ) pref-erence model as a basis for analyzing thebehavior of poor rural producer-consumerhouseholds who make consumption andinvestment decisions facing cash constraintsand imperfect factor markets. Liquidityconstraints among rural households couldinduce a relationship between the timingof investment opportunities and how muchthe households are willing to invest; how-ever, such a relationship could be driven byself-control problems as suggested by Duflo,Kremer, and Robinson (2011). Such controlproblems could cause households to be will-ing to invest more if offered inputs at harvesttime rather than at planting time, when theinputs are to be used but more of the cashresources have already been spent on otheritems. Hence, purchasing inputs at harvesttime could be a self-control device leading tohigher investments.

The model covers three points in time:the first harvest time (t = 1), planting time(t = 2), and the second harvest time (t = 3).Households are offered a fixed budget, Y ,to allocate for food consumption, C, inputinvestments, F , and other goods expendi-ture, X , such that Y = pcC + pf F + pxX .This offer is made either at harvest time(t = 1) or at planting time (t = 2), and thebudget offered at the two points in timeis the same, Y1 = Y2. For a household,i, that receives the offer at harvest time

(t = 1), it will allocate the budget subjectto Y1 = pcC1 + pf F1 + pxX1 such that itmaximizes expected utility. An importantassumption in our model is that producer-consumer households do not have animmediate need to buy food at harvest time.If they prefer to spend extra funds on foodat this point in time it is done to save thefood and consume it later, for example, atplanting time. With (β, δ) preferences, theexpected utility of the food expenditure atfirst harvest time (t=1) may be formulatedas U1(p1

cC1) = βδu2(p1cC1). On the other

hand, if the offer is made at planting time,food expenditure may be for immediateconsumption and the utility is formulatedas U2(p2

cC2) = u2(p2cC2). The expected util-

ity of the budget allocation for inputs whenthe allocation is made at harvest time (t=1),inputs only being available for use at thenext planting time (t = 2), and yield benefitsafter the next harvest time (t = 3) can be for-mulated as U1(p1

f F1) = βδ2u3{p1f (1 + rF )F1},

where rF is the expected return to inputinvestment. When the budget offer is made atplanting time, the expected utility may be for-mulated as U2(p2

f F2) = βδu3{p2f (1 + rF )F2}.

Optimal budget allocations (assuming inte-rior solutions with time-varying prices) atthe two points in time imply the followingconditions:

∂U1(p1cC1)

∂Y1= ∂U1(p1

f F1)

∂Y1and(2)

∂U2(p2cC2)

∂Y2= ∂U2(p2

f F2)

∂Y2.

Substituting in the expected utilities with(β, δ) preferences yields

βδ∂u2(p1cC1)

∂Y1= βδ2∂u3{(1 + rF )p1

f F1}∂Y1

which reduces to

(3)∂u2(p1

cC1)

∂Y1= δ∂u3{(1 + rF )p1

f F1}∂Y1

for harvest time budget allocation decisionsand to

(4)∂u2(p2

cC2)

∂Y2= βδ∂u3{(1 + rF )p2

f F2}∂Y2

for planting time budget allocation decisions.Note that the β coefficient cancelled out

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for the harvest time allocation because it isassumed that food purchased at this point intime is not for immediate consumption.

It follows that, ceteris paribus (assumingno change in prices or the consumption ofother goods), C1 < C2 and F1 > F2 if β < 1.Offering inputs for purchase at harvest timemay then operate as a commitment devicethat increases input expenditure and use.

What if food prices vary systematicallyacross seasons and typically are much lowerat harvest time than at planting time? Interms of the model, suppose that p1

c < p2c

while fertilizer prices are assumed not tochange. It then follows that C1 < C2 andF1 > F2 only if β <

p1c

p2c

and p1f = p2

f and house-holds with rational price expectations andwho are net buyers of food will allocate moreof a given budget for inputs relative to foodat planting time than at harvest time whenp1

cp2

c< β ≤ 1. Likewise, net sellers would prefer

to store the food crop (maize) and sell it atplanting time to buy fertilizer at that pointin time. We note that storage losses couldbe an additional explanation for the maizeprice gap between harvest time and plantingtime; that disparity could move households’optimal decisions in the direction of sellingmaize earlier or buying later.

What if a household expects to accesscheap fertilizers through the subsidy pro-gram at planting time? This would implythat the expected fertilizer price at plantingtime is lower than the fertilizer price offeredat harvest time, that is, E(p2

f ) < p1f . Such an

expectation should reduce the demand forfertilizer at harvest time and may explainhigher demand at planting time. Transactioncosts in commodity markets cause sellingprices to be lower than buying prices; this islikely to be the case for both food and inputs.However, transaction costs are typicallylarger in input markets than in food markets(Binswanger and Rosenzweig 1986). Ruralhouseholds are therefore likely to be hesitantto sell inputs and food that they have bought,but are not likely to make such a sale unlessthey have experienced some form of shockbecause

∂u2(p1cC1)

∂Y1= δ∂u3{(1 + rF )pfbF1}

∂Y1(5)

>∂u2(pfsF1)

∂Y1

where pfs < pfb and represent the selling andbuying prices of inputs. Such a gap betweenselling and buying prices is sufficient forinput expenditures to be “sticky.” This sit-uation could also facilitate greater inputinvestment if inputs are offered at harvest-ing time when food prices are lower thanif inputs are only offered at planting timewhen the available cash budget may havedecreased, Y2 < Y1. However, a severe shockmay destabilize the inter-temporal balanceand cause households to resell the inputsat a lower price. The likelihood that theywould resell inputs depends on how theshock would affect a number of the param-eters that become household-specific in thereformulated model. That is, suppose thatthe discount factor, δi, the expected returnto inputs, rF

i , and the present bias parame-ter, βi, are all household-specific and may beaffected by shocks. Such shocks may causethe discount rate and the present bias param-eter to increase and the expected return toinputs to decrease. A possible consequenceof a shock could then be distressed sales ofassets or inputs that were bought at harvesttime before the shock occurred. These salesmay further affect expected returns.

The discount rate, the present bias parame-ter and the expected return functions cannotbe directly observed, but experiments wereconstructed to assess the existence of presentbias. The results suggest that present biascan be exploited to design a commitmentdevice that also depends on the transactioncosts in the input market. It is commonlyassumed that poor people have high discountrates and stronger present bias and are morevulnerable to shocks. Empirical evidence isconsistent with these assumptions (Holden,Shiferaw, and Wik 1998). Wealth accumu-lation substitutes for missing markets andrelaxes constraint sets (Yesuf and Bluffstone2009), which implies that risk aversion relatedto wealth can also affect input decisions,as poorer households tend to be more riskaverse and less able and willing to make riskyinvestments. Loss aversion may also causehouseholds to be willing to take on less riskwhen they face a downside risk relative to asituation where they do not face downsiderisk (Binswanger 1980; Wik et al. 2004; Yesufand Bluffstone 2009). Risk and risk aversionmay therefore also affect input demand andthe responses in our experiments; however,this relationship was not directly tested inour experiments. Risk aversion, including loss

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aversion, could lead to a positive correlationbetween wealth variables and investment ininputs for net sellers of food (Finkelshtainand Chalfant 1991). However, higher riskaversion and higher levels of risk could alsotheoretically lead to higher input demandby net buyers of food (ibid.) and may implya negative relationship between marketedsurplus and input demand. We are able toassess the latter effect.

Methods and Data

The survey was administered to a randomsample of 450 households across two districtsin Central Malawi (Kasungu and Lilongwe)and four districts in Southern Malawi(Chiradzulu, Machinga, Thyolo, and Zomba)(Lunduka 2010). Approximately 89% of theMalawian population lives in Central andSouthern Malawi, so our survey should befairly representative of a large share of thepopulation. The data were collected in threerounds, in 2006, 2007, and 2009. Only 378 ofthe initial 450 households were located andinterviewed in the third round. The experi-ments were added to the survey instrumentin the 2009 survey round. The two statedpreference (hypothetical) “experiments” andthe real experiment are outlined below.

Experiment1 1: Budget Allocation Experiment

The budget allocation “experiment” took theform of hypothetical stated preference ques-tions. The households were asked how theywould allocate a cash amount of MK 10,0002

among: a) buying fertilizer; b) buying food; c)buying other important/urgent commodities;and d) investing or saving for later use. Someof the sample households took part in thisstated preference budget allocation experi-ment at harvest time (June) and others tookpart at planting time (December). The choice

1 We use a wide definition of “experiment” here. A conventionaldictionary (Macmillian Dictionary 2013) defines an experimentas “a scientific test to find out what happens to someone orsomething in particular conditions”. One could also call our firstand third “experiments” as forms of “thought experiments” asthey are only hypothetical. Collins American English Dictionary(2013) defines a thought experiment as, “a test in which oneimagines the practical outcomes of a hypothesis when physicalproof may not be available”. Stated preference questions maytherefore be seen as thought experiments.

2 The daily wage in unskilled rural employment was about MK300 at the time of the survey. An input package of 2 bags offertilizer and seeds costs about MK 9,000.

of locations for the planting time experimentswas not random, but was determined by low-quality data in the first round survey in somelocations, which required a resurvey in someof the locations. It is possible that the resur-veyed villages are significantly different fromthe other villages. To test for this, the twogroups were compared across key variables.The comparison tests are included in table A1in the appendix. There were significant differ-ences for some of the endowment variables.These have been included in the regressionsto control for these differences; therefore,we do not think that this selection issue hasaffected the key results in this study.

Experiment 2: 5 kg Fertilizer Experiment

A real experiment was conducted wherethe households could choose between 5 kg(1/10th of a bag) basal fertilizer and a vary-ing amount of money determined by thethrow of a die. The amount of money var-ied between MK 200 and MK 1,500. Theseamounts ranged from 50% to 375% of thecommercial price of the fertilizer at the timeof the experiments. Households were uncon-strained in their ability to choose betweenfertilizer and cash; that is, they did not havepay out of pocket for the fertilizer, whichallows us to estimate unconstrained farm gateshadow prices for fertilizer.

Experiment 3: WTA/WTP Input PackageExperiment

This was again a hypothetical (stated pref-erence) experiment in which the WTA andWTP questions were randomly allocatedto households through a coin toss.3 Thethought experiment involved asking ran-domly selected households whether theywould resell an input package that theyhad received for free. The input packageconsisted of one bag of basal fertilizer, onebag of urea, and one bag of hybrid maizeseeds. The households were asked whetherthey would accept or reject a randomizedresale price (WTA price). The other group ofhouseholds was asked whether they would bewilling to buy such a package at a random-ized price (WTP price). The WTA and WTPprices were determined by throwing a die.The price range for the package ranged from

3 See appendix 2 for the formulation of the questions askedto households.

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MK 1,000 (full subsidy) to MK 9,000 (no sub-sidy). This range was based on the price andsubsidy rates decided by the Malawian gov-ernment for the 2009/10 growing season. Theexperiment was to establish whether there isa gap between WTP and WTA prices whenhouseholds’ cash constraints affect the WTP.However, the cash constraint effect may beconfounded with an “endowment effect,”so we must be careful in our interpretationof the result (Plott and Zeiler 2005, 2007;Horowitz and McConnell 2002). However,we do not expect the endowment effect tovary with the randomly determined packageprice. A significant cash constraint shouldimply that as the price increases, the share ofhouseholds that are willing to buy the pack-age falls much more rapidly than the share ofhouseholds that are willing to sell it rises. Theexperiment investigates the incentives to buyor resell an input package when prices areknown and given exogenously.

There is a risk of hypothetical bias in thesestated preference experiments, but we thinkthat this is less of a problem in a private goodsetting with which the respondents are veryfamiliar than for the public and environmen-tal good settings in which such contingentvaluation methods have primarily been used(Bohm 1972; Harrison and List 2004; Levittand List 2009; List and Gallet 2001; Posavac1998).

Analysis of Experimental Data

To assess the factors affecting budget allo-cation priorities in the budget allocationexperiment (experiment 1), Tobit modelswere used because there were many zeroresponses for each commodity (demands forfertilizer and food). The fertilizer and fooddemands were regressed on an aggregatevariable capturing fertilizer subsidy accessin the previous four years (a count of thenumber of years with access), maize pricein the nearest market at the time of the sur-vey experiment, a dummy for planting timeobservations, distance to the nearest largemarket, marketed surplus of maize in theprevious season (2008/09), household wealthvariables, farm size and other householdcharacteristics, and district dummies. For sen-sitivity analysis, a number of control variableswere included that are related to access tofertilizer in the informal markets.

To further investigate factors that are cor-related with households’ choices betweencash and fertilizer, the data from the 5 kg

fertilizer experiment (experiment 2) wereregressed on: the random cash price; thelocation and time-specific maize prices; thetiming dummy for the experiment; the dis-tance to market; access to subsidies andinformal input markets; household charac-teristics, including asset wealth, land andlivestock endowments; and geographicallocation (village). We also included the inter-action between the timing of the experimentand the random cash price in one specifi-cation (logit 3) and predicted its outcomeacross observations in the non-linear logitmodel (figure 2). Furthermore, we also testedtwo variables that were meant to capturehouseholds’ exposure to shocks and a creditmarket participation variable, but thesewere not significant. Credit was not avail-able for the purchase of farm inputs, whichpossibly explains the lack of a significanteffect. Although several of the variables arepotentially endogenous and correlated withunobserved heterogeneity, we do not haveaccess to good instruments. Therefore, weresorted to iteratively running models byadding more of the suspect variables step-wise to assess their effects on the sign andsignificance of the key variables. Randomeffects logit models with village fixed effectswere the model class chosen for this pro-cess. A similar econometric analysis was alsoperformed for the experiments assessingthe WTA and WTP for the input package(experiment 3). For the WTA model intable 6, village fixed effects for 16 villageshad to be replaced by 8 Traditional Authority(TA) fixed effects because of convergenceproblems with the limited sample size. Thebasic findings from the experiments arepresented in the next section.

Experimental Results and Discussion

Experiment 1

Summary statistics by type of expenditureand time of the experiment are presentedin table 2. The allocation for fertilizer wassignificantly higher at planting time than atharvest time, while the opposite was the casefor food. Econometric analyses (Tobit modelswith district dummies) of the factors asso-ciated with household budget allocation forfertilizers and food are presented in table 3.The numbers in the table are the averagepartial effects.

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Table 2. Allocation of a Budget of MK 10,000 by Type of Expenditure and Time ofExperiment

Time ofExperiment Fertilizer Food Other Needs Save-Invest

Harvest time Mean 4,331 2,450 1,642 1,578Standard error 176 128 112 133Sample size 280 280 280 280

Planting time Mean 6,606 1,491 1,280 623Standard error 366 243 231 159Sample size 80 80 80 80

Source: Authors’ own data.

Table 3. Determinants of Preferences for Cash Allocation for Fertilizer versus FoodPurchase

Right-Hand SideVariables

Fert 1 Fert 2 Fert 3 Food 1 Food 2

Planting time dummy 2505.403∗∗ 2270.854∗ 2578.717 1137.928 1614.882Distance to larger

market, km−111.796∗∗ −108.830∗∗ −99.241∗ 94.340∗∗ 103.675∗∗

Aggregate access tosubsidies

149.335 137.097 −652.108∗∗∗∗ −646.005∗∗∗∗

Maize price, MK/kg −73.296 −82.567Marketed surplus of

maize−4.385 −71.495

Offered to buycoupons in

−498.323 711.440

Bought coupons in2008/09

775.716 −1187.688∗

Offered cheapfertilizer

102.459 −27.302

Sex of household head,1 = Male

−485.294 −679.350 228.676 299.991

Age of household head 0.892 −2.492 14.066 14.560Education of

household head−6.255 5.605 75.878∗ 70.288

Male labor force −57.644 −18.843 139.154 149.700Female labor force −280.266 −185.428 346.406 316.530Consumer-worker

ratio−744.885 −503.437 −12.411 −261.252

Quality of house 155.776∗∗ 138.433∗ −92.231 −71.910Value of assets,

1000 MK22.636∗ 23.599∗ −19.847 −21.103

Tropical livestock units 113.374 148.763 −91.006 −202.817Farm size, ha −275.903 −257.106 −72.618 −16.373Village fixed effects Yes Yes Yes Yes YesConstant 5012.491∗∗∗∗ 4603.974∗∗ 7205.193 1163.642 4564.202Sigma constant 3124.069∗∗∗∗ 3030.007∗∗∗∗ 3008.611∗∗∗∗ 2638.271∗∗∗∗ 2646.202∗∗∗∗Prob > chi2 0.000 0.000 0.000 0.000 0.000Number of obs. 346 336 318 336 318Number of left

censored obs.40 38 35 106 103

Notes: Single asterisk (∗), double asterisks (∗∗), triple asterisks (∗∗∗), and quadruple asterisks (∗∗∗∗) denote significance at the 10%, 5%, 1%, and0.1% levels, respectively. The dependent variables are the cash expenditure on fertilizer (first 3 models) or on food (last 2 models). Model resultsare from Tobit models with village fixed effects. The table presents average partial effects.

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Table 4. Average Monthly Maize Prices atHarvest Time and Planting Time, by Year

Month 2007 2008

June (harvest time) 14.55 37.91December (planting time) 30.01 63.35

Source: MoAFS (2009).

The results in table 3 demonstrate that thewillingness to allocate money for fertilizerout of a given budget in December (plantingtime) was significantly higher than at harvesttime in June and July. The difference equaledapproximately 25% of the total budget. Thewillingness to allocate money for food wasnot significantly affected by the timing ofthe experiment, but the allocation for foodwas significantly (at the 0.1% level) lowerfor households with better access to fertilizersubsidies in the previous four years.

Significant wealth effects are also observed.Households with better quality houses andhigher asset values allocated significantlymore cash for fertilizer. This result is in linewith the general theory that poverty canreduce the willingness and ability to investand can result in higher discount rates, asimmediate needs are given higher priority.

We may ask whether the lower willing-ness to buy fertilizer at harvest time thanat planting time could be due to the lowerfood prices at harvest time. We have testedfor this directly in table 3 (models Fert 3and Food 2) by including the maize pricein the nearest location. Monthly prices arecollected for each of our study sites for themonths in which we carried out the surveyand experiments. Price change expectationsmay be based on observed price changes inthe past. An indication of the importanceof these expectations follows from table 4,which presents the average prices at harvesttime and planting time during the previoustwo years. It can be seen that maize priceswere much higher (nearly twice as high) atplanting time compared to harvest time. Asshown in our theoretical model, this shouldprovide a good reason for net buyers of foodto purchase their additional maize require-ment at harvest time rather than later. Inaddition, this price effect may dominate theeffect of present bias, which would otherwisereduce fertilizer demand at planting time.However, the maize price variable was notsignificant in table 3. The other variables thatwere included to capture fertilizer accesswere not significant. When these fertilizer

0

20

40

60

80

100

120

200 400 600 900 1200 1500

Percentage prefering fertilizer to amount of money in MK

Commercial price of fertilizer

Figure 1. Choice experiment betweenreceiving 5 kg basal fertilizer and a vary-ing amount of cash (for color, please seefigure online)

access variables were included, the plantingtime dummy became insignificant, but themagnitude of its coefficient was not reduced.It seems that fertilizer demand has remainedhigh for households that have accessed sub-sidies. This may be due to the rationing ofsubsidized fertilizer among those accessing it.

Experiment 2

The results of experiment 2 are summarizedin figure 1 and table 5. Logit models wereused for the analysis and the figures in thetable are average partial effects. Figure 1demonstrates that the preference for fer-tilizer was reduced from over 90% for thetwo smallest amounts of money, to approx-imately 40% for the two largest amounts ofmoney. The highest price was 375% of thecommercial price of fertilizer at the time ofthe experiment, which implies that there is asubstantial demand for small amounts of fer-tilizer offered at the farm gate among manyhouseholds, to the extent that they are willingto forsake available cash. The input subsidyprogram appears not to have underminedthe valuation of fertilizer. The high pricesalso indicate that few households are willingto resell inputs unless the price offered forfertilizer moves far above the commercialprice. The results may also indicate that inputdemand could be stimulated by offeringfertilizer in bags smaller than the standard50 kg bags. Repacking fertilizer in small bagsand selling it could potentially be a lucrativebusiness.

The logit models (table 5) assessing thefactors correlated with the choices in the5 kg fertilizer experiment confirm the signifi-cance of the planting time dummy variable: itremained positive and significant even when

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Table 5. Real Experiment for Choice between 5 kg Basal Fertilizer and a Random Amountof Cash

Right-Hand Side Variables Logit 1 Logit 2 Logit 3

Cash amount offered −0.460∗∗∗∗ −0.458∗∗∗∗ −0.484∗∗∗∗Planting time dummy 0.349∗∗ 0.341∗∗ 0.227Cash amount∗Planting time dummy 0.123Maize price, MK/kg −0.042∗∗ −0.046∗∗ −0.042∗Offered to buy coupons in 2008/09 −0.001 0.002Bought coupons in 2008/09 −0.172∗ −0.171∗Offered cheap fertilizer in 2008/09 0.108∗ 0.111∗Aggregate access to subsidies −0.001 0.000 −0.002Marketed surplus of maize 2008/09 −0.038 −0.049 −0.052Distance to larger market, km 0.006 0.007 0.006Sex of household head, 1=Male −0.071 −0.073 −0.070Age of household head 0.000 0.001 0.001Education of household head 0.004 0.007 0.007Male labor force 0.039 0.035 0.035Female labor force −0.041 −0.044 −0.049Consumer-worker ratio −0.064 −0.037 −0.035Quality of house 0.002 0.004 0.004Value of assets, 1,000 MK 0.007∗∗∗ 0.006∗∗∗ 0.006∗∗Tropical livestock units 0.018 0.044∗ 0.041∗Farm size, ha −0.043∗∗ −0.045∗∗ −0.042∗Village fixed effects Yes Yes YesProb > chi2 0.000 0.000 0.000Number of observations 336 318 318

Notes: Single asterisk (∗), double asterisks (∗∗), triple asterisks (∗∗∗), and quadruple asterisks (∗∗∗∗) denote significance at the 10%, 5%, 1%, and0.1% levels, respectively. The table shows the average partial effects from the logit models. The dependent variable is a dummy variable = 1 ifhouseholds chose fertilizer, and = 0 if households chose the random cash amount.

we controlled for the maize price and a vari-able capturing market and subsidy access.The maize price variable was also significantand negative, which implies that a highermaize price was associated with a lowerquantity of required fertilizer. While thisseems counterintuitive for a net producer, itmay be a rational response for a net buyer ofmaize whose maize demand is inelastic. Themodels also include village fixed effects (forthe 16 villages in the sample).

The third model in table 5 includes aninteraction variable for the cash amountoffered and the planting time dummy. Thisvariable was insignificant on average. How-ever, this does not necessarily imply that theinteraction effect is unimportant (Ai andNorton 2003). We used a Stata commandprovided by Norton, Wang, and Ai (2004) tograph the predicted interaction effect (seefigure 2). There was a significant positiveinteraction effect for 29% of the observa-tions, which means that many householdswere significantly more likely to choosethe fertilizer at planting time when higherrandom amounts of cash were offered asan alternative to the 5 kg fertilizer. A smallfraction (4.7%) of the sample was predicted

-5

0

5

10

z-st

atis

tic

0 .2 .4 .6 .8 1Predicted Probability that y = 1

z-statistics of Interaction Effects after Logit

Figure 2. The interaction effect betweencash amount and planting time dummy inmodel logit 4 in table 5. (for color, please seefigure online)

to have a significant negative interactioneffect.

Furthermore, table 5 reveals that house-holds with a higher asset endowment weremore likely to prefer fertilizer to cash,showing that more wealthy householdsare able and willing to invest in fertilizer. Onthe other hand, households with larger farmsizes were significantly less likely to prefer

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fertilizer over cash, ceteris paribus. This find-ing indicates that asset poverty reduces theshadow price of fertilizer but land scarcityincreases the shadow price of fertilizer. Per-forming the experiment at planting timerather than at harvest time increased theprobability that households preferred fer-tilizer over cash. This is consistent with thefinding in experiment 1. Decisions in thesetwo experiments did not require the house-holds to pay any cash out of pocket. Weinterpret this design aspect as controlling forvarying cash availability in the household.A liquidity constraint may be more severeat planting time than at harvest time, andmay have resulted in different outcomes ifit was binding in the experiments. Our thirdstated preference experiment provides someadditional insights into this issue.

Experiment 3 involved randomly dividingthe households into two groups (determinedby a coin toss). One group was allocated aninput package for maize production consist-ing of one bag of basal fertilizer, one bag ofurea (top dressing), and one bag of hybridmaize seeds, and then given the opportunityto resell the package at a randomized price.The other group of households was offeredthe opportunity to buy the same package ata randomized price. The fitted values of theresponses for these two groups, along witha 95% confidence interval, are presented asthe upper line in figure 3. The y-axis indicatesthe probability that respondents prefer thepackage to the cash amount offered. The cashamounts varied according to the scale onthe x-axis and ranged from MK 1,000 (fullsubsidy) to MK 9,000 (no subsidy).

Figure 3 demonstrates that very few house-holds were willing to sell the package evenat the highest amount of money offered; thehighest price was equivalent to the commer-cial price of the inputs. This result indicatesthat households highly value the input pack-age. However, figure 3 also demonstratesthat many households face problems buyingthe input package due to cash constraints:only slightly more than 20% were willingto buy the package at the full commercialprice, and approximately 50% were willing tobuy it when offered a 50% subsidy (half ofthe commercial price). Although the differ-ence between the WTA and WTP responsescould partly be due to the “endowmenteffect,” the responses in the real experimentwith fertilizer bags should not create suchan endowment effect, as households were

0.5

1

0 2000 4000 6000 8000 10000

Package price, 100 kg fertilizer

95% CI Fitted valuesFitted values WTBuy/WTSell Package (100kg fertilizer)

Commercial price of fertilizer

Subsidized price of fertilizer

Figure 3. Ratio preferring input packageto cash in the WTA (upper line) and WTP(bottom line) experiments with varying cashamounts (MK) (quadratic prediction plotswith 95% confidence intervals) (for color,please see figure online)

0.5

1

0 100 200 300

95% CI Fitted valuesFitted values Fitted valuesWTBuy 100 kg package prices/kg WTA 5 kg fertilizer prices/kg

Commercial

price of fertilizer

Figure 4. Fertilizer package price for realexperiment versus hypothetical WTSell andWTBuy package: share of households prefer-ring fertilizer package to money at varyingprices (quadratic prediction plots with 95%confidence intervals) (for color, please seefigure online)

offered the choice between cash or fertilizerwithout first being given either one. Theresponse probabilities of these householdswere similar to the response probabilities ofthe households offered prices close to thecommercial price for their fertilizer endow-ment in the WTA experiment. This similaritysuggests that the gap between the lines infigure 3 is primarily driven by a cash con-straint effect. The 95% confidence intervalsin the graph demonstrate the statistical sig-nificance of the difference between the WTAand WTP shares.

Figure 4 combines experiment 2, whichinvolved small amounts (5 kg) of fertilizer,with the hypothetical experiment 3. The

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x-axis shows the price in MK/kg fertilizer toallow comparison of the two experiments onthe same scale (the value of seeds has notbeen included and is relatively small). They-axis shows the share of households thatpreferred the fertilizer package at the dif-ferent prices offered. We used a much widerprice range for experiment 2, while the pricerange was from full subsidy (>90%) to nosubsidy (at 2009 June-December prices) inexperiment 3. The graph provides the meansand 95% confidence intervals for the experi-ments. The graph illustrates the strong effectof a household’s liquidity constraint whengiven the opportunity to buy the full package.It is possible that this effect would have beensmaller if we had offered smaller amounts,but we did not test that particular dimension.We leave that analysis for future research.

Factors associated with the willingness tosell (WTA) and willingness to buy (WTP)the input package were investigated usinglogit models; the results are presented intable 6. We see that the probability of sell-ing was not significantly associated with therandom price offered when this price rangedfrom 90% subsidy to no subsidy (commercialprice). The maize price and the planting timedummy were insignificant. Perhaps surpris-ingly, households that had been offered theopportunity to purchase coupons for fertilizeron the informal market were significantly (atthe 1% level) more willing to sell the pack-age. This may be driven by their expectationthat they would have another opportunity toobtain cheap fertilizers. On the other hand,households that had actually bought fertilizeron the informal market were significantlyless willing to sell their input package (sig-nificant at the 0.1% level). Such householdsapparently still had a high shadow price forfertilizer. Households that had experiencedready access to subsidized fertilizer in thepast were also less likely to be willing tosell the input package (significant at the 5%level). Households located farther away frommarkets were less willing to sell (significant atthe 1% level); male-headed households werealso less willing to sell relative to female-headed households (significant at the 5%level). The marketed surplus of maize waspositively correlated (significant at the 1%level) with willingness to sell the package.The quality levels of house and asset wealthvariables were also positively correlated withwillingness to sell the package. We were onlyable to apply Traditional Authority level fixed

Table 6. Factors Associated with Willingnessto Sell and Willingness to Buy Input Package

Right-Hand SideVariables WTSell WTBuy

Cash amount offered,1,000MK

−0.020 −0.064∗∗∗∗

Planting time dummy −0.183 −0.189Maize price, MK/kg 0.033 0.008Offered to buy

coupons in2008/09

0.762∗∗∗ −0.096

Bought coupons in2008/09

−0.949∗∗∗∗ −0.012

Offered cheapfertilizer in 2008/09

−0.018 0.234∗∗

Aggregate access tosubsidies

−0.064∗∗ −0.026

Marketed surplus ofmaize 2008/09

0.295∗∗∗ 0.020

Distance to largermarket, km

−0.029∗∗∗ 0.007

Sex of householdhead, 1=Male

−0.161∗∗ −0.101

Age of householdhead

−0.002 −0.003

Education ofhousehold head

−0.018∗∗ 0.005

Male labor force 0.058 0.029Female labor force 0.094 0.043Consumer-worker

ratio−0.047 0.363∗∗

Quality of house 0.028∗∗∗ 0.023Value of assets,

1,000MK0.006∗ 0.001

Tropical livestockunits

0.030 −0.010

Farm size, ha 0.031 0.085Traditional Authority

fixed effectsYes No

Village fixed effects No YesProb > chi2 0.000 0.000Number of obs. 95 168

Notes: Single asterisk (∗), double asterisks (∗∗), triple asterisks (∗∗∗),and quadruple asterisks (∗∗∗∗) denote significance at the 10%, 5%,1%, and 0.1% levels, respectively. Logit models with WTSell = 1 andWTBuy = 1 if answer is yes to given random amounts of cash, and zerootherwise. Numbers in the table are average partial effects.

effects (as opposed to village fixed effects)in this model due to the limited sample sizeand perfect correlation with the dependentvariable for some villages.

The second model in table 6 shows whichfactors are associated with willingness tobuy the package: in contrast to the previousmodel, the price offered for the package ishighly significant and negative, while themaize price and planting time dummy wereinsignificant. This is in line with our cash

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constraint hypothesis that a cash constraintmay be more likely to bind as the price ofthe package increases. The cash constraintmay also be more binding at planting timein this experiment than in the previous twoexperiments, which could explain the insignif-icance. Thus, the ability of households topurchase inputs is more limited, even thoughtheir unconstrained demand and the prof-itability of input use are high. This may alsoimply that offering inputs at harvest time, assuggested by Duflo, Kremer, and Robinson(2011), and in small quantities could stimu-late input demand and reduce the need toprovide subsidized inputs at planting time.Those who had been offered cheap fertil-izers in the previous season had a higherprobability of being willing to buy the inputpackage at the given prices (significant at the5% level). It is possible that those exposed tosuch offers had been searching for them andtherefore were less cash constrained. Finally,households with a higher consumer-workerratio were significantly more likely to be will-ing to buy the package. Demand for inputsappears to be driven largely by consumptiondemand of net buyer households, as it maybe cheaper to meet household food needs bybuying fertilizer than by buying maize if thenecessary cash is available. Overall, however,these results seem to indicate the existenceof a poverty trap in a second-best world; inthis setting, interventions such as input sub-sidies create positive welfare effects that maybe partly “paid for” by the efficiency gainsfrom increased input use. The budgetarycosts are high, however, providing a goodreason to experiment further with strate-gies to reduce these costs and enhance theefficiency of subsidies and alternative policyinterventions.

Our theoretical model emphasized season-ality and the tradeoffs between short-termand medium-term needs in a setting whereseasonal price changes for outputs and accessto subsidized inputs affect input demand.The alternative (β, δ) preference model maycapture irrational procrastination as well asrational responses to liquidity constraints;we favor the latter interpretation as beingmore important in our context. Our findingssuggest that households have a limited abilityto buy a lumpy input package. This prob-lem is exacerbated as the package becomesmore expensive. The input subsidy programmay also have contributed to the lumpi-ness of fertilizer inputs (distributed in 50 kg

bags) because it was only available throughofficially accepted depots that imposed addi-tional transaction costs on households. Thisstands in contrast to the study of Duflo,Kremer, and Robinson (2011) in Kenya,where inputs were available in divisibleamounts in nearby markets.

While the study by Duflo, Kremer, andRobinson (2011) assessed a situation in whicha small share of households used fertilizer,even though using it was found to be prof-itable, we assessed a situation where the largemajority of rural households used fertiliz-ers. This difference is partly due to access toinput subsidies. Our study complements thestudy of Duflo, Kremer, and Robinson (2011)and the broader literature on input demandand food production in risky low-incomeenvironments in three important ways. First,we investigated the demand for fertilizerand food at harvest time and planting timewhile accounting for the local variation inmaize prices and access to input subsidies.Second, we investigated the unconstrainedshadow prices for small amounts of fertilizerat harvest and planting time and how it wasaffected by a variety of covariates. Third, weinvestigated the gap between willingness tosell and willingness to buy a standard inputpackage used in the subsidy program andshowed that this gap was sharply increasingin price; this result demonstrates the effectof what we interpret as a severe liquidityconstraint. Our findings do not contradictthose of Duflo, Kremer, and Robinson (2011);rather, they provide complementary evidenceof the great importance of the design of bet-ter agricultural policies for enhancing foodsecurity in low-income environments thatface severe climate risks, food insecurity, andbudget restrictions.

Conclusion

Overall, this study revealed that rural Malaw-ian households highly value fertilizers eventhough they have been exposed to veryhigh fertilizer subsidies over several years.More than 50% of the households preferredsmall amounts of fertilizer to a cash pay-ment that was 50% higher than the currentcommercial price for fertilizer during ourexperiments carried out in 2009 (figure 4).Constraints in accessing both commercialand subsidized fertilizer may explain theseremarkably high shadow prices. The nature

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of these experiments, which were designedto avoid a direct effect of households’ cashconstraint, could also be an explanatoryfactor; the experiments elicited demandthat reflects cash-unconstrained utilitymaximization.

The study tested Duflo, Kremer, andRobinson’s (2011) proposal to stimulateinput demand by supplying inputs at har-vest time rather than at planting time.A hypothetical budget allocation experi-ment revealed that households were willingto allocate more than 40% of a cash budgetof MK 10,000 for fertilizer at harvest time;this budget share increased to approximately65% when elicited at planting time. A realchoice experiment between cash and fer-tilizer revealed that a significantly highershare of households preferred fertilizer to agiven amount of cash at planting time thanat harvest time. This difference could not beexplained by the maize price difference atharvest time relative to the price at plantingtime, or by access to input subsidies; however,the gap may reflect a certain reluctance tobuy inputs earlier than is necessary. How-ever, households were willing to allocate asubstantial budget share to input purchaseat harvest time and were not likely to resellthese inputs later, which suggests a poten-tial positive effect of this approach on inputdemand. This potential effect is especiallylikely to be important when the input subsidyprogram has to be scaled down, as was thecase during the 2011/12 season.

In contrast, when households were offereda full input package consisting of two bagsof fertilizer and a bag of hybrid seeds, theshare of households that responded as willingand able to buy the package declined to 22%when the WTP price increased to MK 9,000(the commercial price of the package). Thisresult demonstrates the significance of thecash constraint that households face, irre-spective of season. When households whohave been offered the same package for freewere asked about their WTA selling price,more than 80% of the households preferredto keep the package even when they wereoffered a WTA price of MK 9,000 (again,equivalent to the commercial price). Thisresult is consistent with the finding of Holdenand Lunduka (2013) that a very small shareof the households that were given subsidizedfertilizers resold these inputs in the informalmarket. This implies that such input expendi-tures are “sticky” and that the sale of inputs

at harvest time may serve as a commitmentdevice (DellaVigna 2009).

Due to high international fertilizer andoil prices, which recently have contributedto fuel and foreign exchange shortages,Malawi and other countries offering inputsubsidies face severe problems sustainingthese programs. An advantage of distributinginputs at harvest time is that the trucks thatcollect the maize can deliver the inputs atthe same time, thus saving transportationcosts. Such a schedule would also reduce thedefault problems that are linked to supplyinginputs on credit at planting time in a riskyenvironment.

Finally, we find that the limited demandfor fertilizer in developing countries (wherepeople are very aware of the positive produc-tivity of fertilizer and food security dependson fertilizer access) is not necessarily drivenby irrationality. Under these conditions,which are also found in Malawi, failure tobuy inputs may be the outcome of a ratio-nal response to binding constraints. That is,poor households have less freedom to act inirrational ways than do wealthy households.

References

Ai, C., and E.C. Norton. 2003. InteractionTerms in Logit and Probit Models.Economic Letters 80 (1): 123–129.

Binswanger, H.P., and M.R. Rosenzweig.1986. Behavioural and Material Deter-minants of Production Relations inAgriculture. Journal of DevelopmentStudies 22: 503–539.

Bohm, P. 1972. Estimating Demand for Pub-lic Goods: An Experiment. EuropeanEconomic Review 3 (2): 111–130.

Choi, J., D. Laibson, and B. Madrian. 2008.$100 Bills on the Sidewalk: Subopti-mal Investment in 401(k) Plans. NBERWorking Paper No. 11554.

DellaVigna, S. 2009. Psychology and Eco-nomics: Evidence from the Field.Journal of Economic Literature 47 (2):315–372.

Denning, G., P. Kabembe, P. Sanchez, A.Malik, R. Flor, R. Harawa, P. Nkhoma,et al. 2009. Input Subsidies to ImproveSmallholder Maize Productivity inMalawi: Toward an African GreenRevolution. PLoS Biology 7 (1).

Dorward, A. 2009. Rethinking Agricul-tural Input Subsidy Programmes in a

at Norw



Dow

nloaded from




Changing World. Centre for Develop-ment, Environment and Policy, School ofOriental and African Studies, Universityof London.

Dorward, A., E. Chirwa, V. Kelly, T. Jayne,R. Slater, and D. Boughton. 2008. Eval-uation of the 2006/07 Agricultural InputSubsidy Programme, Malawi. FinalReport. Study undertaken for the Min-istry of Agriculture and Food Security,Malawi, Department for InternationalDevelopment, United States Agency forInternational Development and FutureAgricultures Consortium.

Duflo, E., M. Kremer, and J. Robinson. 2011.Nudging Farmers to Use Fertilizer: The-ory and Experimental Evidence fromKenya. American Economic Review 101(6): 2350–2390.

Frederick, S., G. Loewenstein, and T.O’Donoghue. 2002. Time Discountingand Time Preference: A Critical Review.Journal of Economic Literature 40 (2):351–401.

Finkelshtain, I., and J.A. Chalfant. 1991. Mar-keted Surplus under Risk: Do PeasantsAgree with Sandmo? American Jour-nal of Agricultural Economics 73 (3):557–567.

Government of Malawi. 2010. Malawi TheComprehensive Africa AgricultureDevelopment Programme Post Com-pact Review. Country Technical ReviewReport. Lilongwe.

Harrigan, J. 2005. Food Security Policies andStarter Pack: A Challenge for Donors? InStarter Packs: A Strategy to Fight Hungerin Developing Countries?, ed. S. Levy.Wallingford: CABI Publishing.

Harrison, G.W., and J.A. List. 2004. FieldExperiments. Journal of EconomicLiterature 42 (4): 1009–1055.

Holden, S.T., and R. Lunduka. 2010a. TooPoor to be Efficient? Impacts of theTargeted Fertilizer Subsidy Program inMalawi on Farm Plot Level Input Use,Crop Choice and Land Productivity.Report to NORAD. Noragric ReportNo. 55, Norwegian University of LifeSciences, Ås.

———. 2010b. The Political Economy ofInput Subsidies in Malawi: Targeting Effi-ciency and Household Perceptions. Paperpresented at the Norwegian Associationfor Development Research-conference,Oslo.

Holden, S.T., and R. Lunduka. 2013. WhoBenefits from Malawi’s Farm Input Sub-sidy Program? Forum For DevelopmentStudies 40 (1): 1–25.

Holden, S.T., B. Shiferaw, and M. Wik. 1998.Poverty, Market Imperfections, and TimePreferences: Of Relevance for Envi-ronmental Policy? Environment andDevelopment Economics 3: 105–130.

Horowitz, J.K., and K.E. McConnell. 2002.A Review of WTA/WTP Studies. Jour-nal of Environmental Economics andManagement 44: 426–447.

Laibson, D. 1997. Golden Eggs and Hyper-bolic Discounting. Quarterly Journal ofEconomics 112 (2): 443–477.

Levitt, S.D., and J.A. List. 2009. Field Exper-iments in Economics: The Past, thePresent, and the Future. EuropeanEconomic Review 53 (1): 1–18.

List, J.A., and C.A. Gallet. 2001. What Exper-imental Protocol Influence Disparitiesbetween Actual and Hypothetical StatedValues? Environmental and ResourceEconomics 20 (3): 241–254.

Logistic Unit. 2009. Final Report. Implemen-tation of Agricultural Input SubsidyProgramme 2008/09. Logistic Unit,Lilongwe, April.

Lunduka, R. 2010. Land Rental Markets,Investment and Productivity under Cus-tomary Land Tenure Systems in Malawi.PhD dissertation, Department of Eco-nomics and Resource Management,Norwegian University of Life Sciences.

Minde, I., T.S. Jayne, J. Ariga, and E. Craw-ford. 2008. Fertilizer Subsidies andSustainable Agricultural Growth inAfrica: Current Issues and EmpiricalEvidence from Malawi, Zambia andKenya. Paper prepared for the RegionalStrategic Agricultural Knowledge Sup-port System (Re-SAKSS) for SouthernAfrica. Food Security Group, MichiganState University.

Ministry of Agriculture and Food Secu-rity. 2008. The 2007/2008 Input SubsidyProgramme Review Report. MoAFS,Lilongwe, Malawi.

Morris, M.,V.A. Kelly, R. Kopicki, and D. Byer-lee. 2007. Fertilizer Use in African Agricul-ture. Washington DC: The World Bank.

Norton, E.C., H. Wang, and C. Ai. 2004.Computing Interaction Effects in Logitand Probit Models. Stata Journal 4 (2):103–116.

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Dow

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O’Donoghue, T., and M. Rabin. 1999. DoingIt Now or Later. American EconomicReview89 (1): 103–124.

O’Donoghue, T., and M. Rabin. 2001. Choiceand Procrastination. Quarterly Journal ofEconomics 116 (1): 121–160.

Plott, C.R., and K. Zeiler. 2005. The Will-ingness to Pay-Willingness to AcceptGap, the Endowment Effect, SubjectMisconceptions, and Experimental Proce-dures for Eliciting Valuations. AmericanEconomic Review 95 (3): 530–545.

Plott, C.R., and K. Zeiler. 2007. ExchangeAsymmetries Incorrectly Inter-preted as Evidence of EndowmentEffect Theory and Prospect Theory?American Economic Review 97 (4): 1449–1466.

Posavac, S.S. 1998. Strategic Overbiddingin Contingent Valuation: Stated Eco-nomic Value of Public Goods VariesAccording to Consumers Expectationsof Funding Source. Journal of EconomicPsychology19 (2): 205–214.

Samuelson, P. 1937. A Note on Measurementof Utility. Review of Economic Studies 4:155–161.

School of Oriental and African Studies(SOAS). 2008. Evaluation of the 2006/7Agricultural Input Supply Programme,Malawi: Final Report. School of Orientaland African Studies, London.

Suri, T. 2011. Selection and ComparativeAdvantage in Technology Adoption.Econometrica 79 (1): 159–209.

Wik, M., T.K. Aragie, O. Bergland, andS.T. Holden. 2004. On the Measurementof Risk Aversion from ExperimentalData. Applied Economics 36: 2443–2451.

Yesuf, M., and R.A. Bluffstone. 2009. Poverty,Risk Aversion, and Path Dependencein Low-income Countries: Experimen-tal Evidence from Ethiopia. AmericanJournal of Agricultural Economics 91 (4):1022–1037.

Zeller, M., A. Diagne, and C. Mataya. 1997.Market Access by Smallholder Farmersin Malawi: Implications for TechnologyAdoption, Agricultural Productivity,and Crop Income. Washington, DC:International Food Policy ResearchInstitute, Food Consumption and Nutri-tion Division, FCND Discussion PaperNo. 35.

Appendix 1

Table A1. Sample Mean Comparisons for the Two Samples for Key HouseholdCharacteristic Variables

Harvest Season Sample Planting Season Sample

Variable Mean St. error N Mean St. error N t-test

Fertilizer expenditure∗ 4331 175.8 280 6606 366.3 80 −5.946∗∗∗Sex of household head 0.750 0.026 284 0.725 0.050 80 0.452Age of household head 46.014 0.929 280 45.864 1.882 76 0.162Education years of head 4.861 0.241 280 6.053 0.451 76 −2.299∗∗Number of children 2.569 0.095 281 3.000 0.184 76 −2.086∗∗House quality index 8.924 0.169 278 9.135 0.271 74 −0.593Value of assets, ML 3813 866 281 6579 1632 76 −1.483Tropical livestock units 0.433 0.063 277 0.886 0.252 76 −2.537∗∗∗Farm size 0.793 0.033 280 1.620 0.188 75 −7.150∗∗∗

Note: Asterisk refers to the hypothetical budget allocation in “Experiment 1.”

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Appendix 2

Format for “Experiment 3”Choice experiment 3 Hypothetical: Assume

that you may be lucky and get a fertilizersubsidy package in the form of one 50 kgbag of 23–21, one 50 kg bag of urea, and onebag of hybrid (HYV) seeds. Whether youbecome the lucky winner is determined byyou tossing a coin:

If the coin lands on HEADS: You win, if thecoin lands on TAILS you do not win, and goto Choice experiment 3b, Outcome of cointoss: 1 = HEADS, 0 =TAILS.

Choice experiment 3a. You got HEADS andhave won the package. A person comes to

you and offers to buy the whole package. Theprice he offers is determined by throwing adie:

Die outcome in MKw: 1 = 1, 000, 2 = 2, 000,3 = 3, 000, 4 = 5, 000, 5 = 7, 000, 6 = 9, 000.

Choice: 1. Choose to keep the package,2. Choose the money.

Choice experiment 3b. You have not receivedan input package. Would you be willingto buy the package at the following pricedetermined by throwing a die?

Die outcome in MKw: 1 = 1, 000, 2 = 2, 000,3 = 3, 000, 4 = 5, 000, 5 = 7, 000, 6 = 9, 000.

Choice: 1. Choose to buy the package, 2. Notwilling/able to buy.

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input subsidies,cash constraints,and …bingu wa mutharika, embarked upon a com-prehensive input...

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