facilitatingsavingsforagriculture:fieldexperimental evidence from...

Facilitating Savings for Agriculture: Field ExperimentalEvidence from Malawi

lasse brune

University of Michigan

xavier giné

World Bank and BREAD

jessica goldberg

University of Maryland

dean yang

University of Michigan, BREAD, and NBER

I. IntroductionAgriculture in sub-Saharan Africa employs two-thirds of the labor force andgenerates about one-third of gross domestic product ðGDPÞ growth. Accord-ing to the World Development Report ðWorld Bank 2008Þ, GDP growth orig-inating in agriculture is about four times more effective in reducing povertythan GDP growth originating outside agriculture. For this reason, policies thatfoster agricultural productivity can have a substantial impact on food securityand poverty reduction.In recent decades, there has been substantial interest among policy makers,

donors, and international development institutions in microfinance ðfinancialThis article was previously titled “Commitments to Save: A Field Experiment in Rural Malawi.” Wethank Niall Keleher, Lutamyo Mwamlima, and the Innovations for Poverty Action staff in Malawi;Steve Mgwadira, Mathews Kapelemera, andWebster Mbekeani of Opportunity Bank of Malawi; andthe Opportunity Bank of Malawi management and staff of the Kasungu, Mponela, and Lilongwebranches. Matt Basilico and Britni Must provided excellent research assistance. We are grateful toBeatriz Armendariz, Orazio Attanasio, Oriana Bandiera, Abhijit Banerjee, Luc Behagel, Marcel Faf-champs, Maitreesh Ghatak, Marc Gurgand, Sylvie Lambert, Kim Lehrer, Rocco Macchiavello, LouMaccini, SharonMaccini,MarcoManacorda, CostasMeghir, Rohini Pande, Albert Park, Imran Rasul,Chris Woodruff, Bilal Zia, Andrew Zeitlin, and seminar participants at the Financial Access InitiativeMicrofinance Impact and Innovation Conference, Ohio State, London School of Economics, War-wick, Institute for Fiscal Studies, Paris School of Economics, and Oxford for helpful comments. Weappreciate the support of David Rohrbach ðWorld BankÞ and Jake Kendall ðBill and Melinda GatesFoundationÞ. We are grateful for research funding from the World Bank Research Committee andthe Bill and Melinda Gates Foundation. The views expressed in this article are those of the authorsand should not be attributed to theWorld Bank, its executive directors, or the countries they represent.

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services for the poorÞ as an antipoverty intervention. Provision of microcredithas perhaps attracted the most attention. In 2009, the Microcredit Summitestimated that there were more than 3,500 microfinance institutions aroundthe world with 150 million clients ðDaley-Harris 2009Þ. While these outreachnumbers are impressive, microcredit today is largely devoted to nonagricul-tural activities ðMorduch 1999; Armendariz de Aghion and Morduch 2005Þdue to the substantial challenges inherent in agricultural lending.1 Given thelimited supply of credit for agriculture, many donors and academics ðe.g.,Deaton 1990; Robinson 2001; and more recently the Bill and Melinda GatesFoundationÞ have emphasized the potential for increasing access to formalsavings.2

The motivating question of this study is whether facilitating formal savingscan promote agricultural development. To this end, we collaborated with abank and private sector firms to implement a randomized controlled trial of aprogram facilitating formal savings for Malawian cash crop ðtobaccoÞ farmers.To our knowledge, this is the first randomized study of the agricultural im-pacts of an intervention facilitating savings in a formal banking institution.In advance of the May–July 2009 harvest season, farmers were randomized

into a control group or one of several treatment groups. Formal savings werefacilitated for farmers in the treatment group by offering them the opportu-nity to have their cash-crop proceeds from the upcoming harvest channeledinto bank accounts that would be opened for them in their own names. Twomain variants of this treatment were implemented: ð1Þ an “ordinary” savingstreatment, where the bank accounts offered had no special features, and ð2Þ a“commitment” savings treatment, in which farmers had the option of savingin special accounts that disallowed withdrawals until a set date ðchosen by theaccount ownerÞ. In addition, these treatments were cross-randomized withanother treatment intended to create variation in the public observability ofsavings balances ðdetails are explained in Sec. IIÞ.Treated farmers were encouraged to use these accounts to save for future

agricultural input purchases. Farmers in the control group, however, also re-ceived the generic encouragement to save for future agricultural input pur-chases but did not receive any facilitation of formal savings accounts and were

1 Giné, Goldberg, and Yang ð2012Þ find that imperfect personal identification leads to asymmet-ric information problems ðboth adverse selection and moral hazardÞ in the rural Malawian creditmarket.2 Aportela ð1999Þ finds that a post office savings expansion in Mexico raised savings by 3–5 per-centage points. Burgess and Pande ð2005Þ find that a policy-driven expansion of rural banking re-duced poverty in India, and they provide suggestive evidence that deposit mobilization and creditaccess were intermediating channels. Bruhn and Love ð2014Þ find that bank branch openings byconsumer durable stores in Mexico leads to increases in the number of informal business owners, intotal employment, and in average income.

000 E C O N O M I C D E V E L O P M E N T A N D C U L T U R A L C H A N G E



simply paid their crop sale proceeds in cash ðwhich was the status quoÞ. Weexamine treatment impacts on savings at the partner bank ðobserved in ad-ministrative dataÞ as well as on agricultural and other household outcomes ðviaa household surveyÞ.The first key finding is that there are positive and statistically significant

treatment effects on a range of outcomes. Facilitating formal savings leads tohigher deposits into formal savings accounts at the partner bank, higher savingsat the partner bank immediately before the next planting season ðNovember–December 2009Þ, higher agricultural input expenditures in that season, higheroutput in the subsequent harvest ðMay–July 2010Þ, and higher per capita con-sumption in the household after that harvest. Impacts on agricultural inputexpenditures and on output are substantial, amounting to increases over thecontrol group mean of 13.3% and 21.4%, respectively.The second key finding is somewhat unexpected and has to do with the

mechanism through which treatment translates into agricultural outcomes. Exante, the leading candidate mechanism was the alleviation of savings con-straints. In the status quo, farmers have imperfect means of preserving fundsbetween harvest and the subsequent planting season. Depletion of funds notheld in bank accounts over this period could be due to self-control problems,demands for sharing with one’s social network, and losses due to other fac-tors ðe.g., theft, fireÞ. Improving access to formal savings would therefore givefarmers a better means of preserving funds between harvest and the subsequentplanting, leading to increases in agricultural input expenditures ðand then toimprovements on other subsequent related outcomesÞ.Our results indicate, however, that only a fraction of the treatment effect on

agricultural input expenditures is likely to be attributable to alleviating formalsavings constraints. While amounts initially deposited into the accounts wouldhave been sufficient to pay for the increase in agricultural input expendituresthat we observe, administrative data from the bank reveal that the majority ofthese funds were withdrawn almost immediately after being deposited. Threemonths later, just before the end of the 2009 planting season, treated farmersstill had 1,863 Malawi kwacha ðUS$12.85Þ higher savings than did control-group farmers, but the treatment effect on agricultural input expenditures ishigher by a factor of four: MK 8,023 ðUS$55.33Þ.3 Therefore, only about aquarter of the effect of the treatment on agricultural input expenditures canbe attributed to alleviation of savings constraints per se.4

3 The exchange rate at the time of the study was MK 145/US$1.4 The low balances in the accounts result in low power to detect effects of the raffle treatments.Therefore, while in total there were six different randomly assigned treatment types, differences inimpacts across treatments are typically not statistically significantly different from one another, so weplace little emphasis on differentiating impacts across treatment types in this article.

Brune et al. 000



We discuss a variety of mechanisms for which we are able to provide in-complete evidence as well other mechanisms that can be ruled out. In the end,with the design implemented and data available we are not able to identifythe precise mechanisms through which our treatment effects operated. Forexample, the funds held in accounts may have served as a buffer stock, allow-ing farmers to self-insure and take on more risk ðby investing more in agri-cultural inputsÞ. Alternately, the existence of the accounts could have helpedstudy participants resist demands to share resources with their social network.Behavioral phenomena such as mental accounting or reference dependencealso provide possible explanations. We must leave exploration of these andother possible mechanisms to future work.This article contributes to the burgeoning literature on the effects of for-

mal savings accounts, and in particular of making offers of commitment sav-ings. Dupas and Robinson ð2013aÞ offer ordinary savings accounts to Kenyanurban entrepreneurs, finding positive impacts on investment and income forwomen. In this article, by contrast, we test the effect of direct deposit of ag-ricultural proceeds into ordinary and commitment savings accounts. Prinað2015Þ finds that random assignment of basic savings account access tohouseholds in Nepal leads to increases in financial assets and in human capitalinvestments. Atkinson et al. ð2013Þ offer microcredit borrowers in Guatemalasavings accounts with different features, including reminders about a monthlycommitment to save and a default of 10% of loan repayment as a suggestedmonthly savings target. They find that both features increase savings balancessubstantially. Dupas and Robinson ð2013bÞ test the impact of commitmentfeatures for health savings in western Kenyan rotating savings and credit asso-ciations; their qualitative findings from a postintervention survey are sugges-tive of a mental accounting channel.The remainder of this article is organized as follows. The next section de-

scribes the experimental design and data sources. Section III describes ourempirical specification. Section IV presents the treatment effect estimates. Sec-tion V then considers evidence on the mechanisms through which the treat-ment effects may have operated. Section VI concludes.

II. Experimental Design and Survey DataThe experiment was a collaborative effort between Opportunity Bank of Ma-lawi,5 Alliance One, Limbe Leaf, the University of Michigan, and the WorldBank. Opportunity International is a private microfinance institution oper-ating in 24 countries that offers savings and credit products; in Malawi, it has

5 At the time of the study, our bank partner went by the company name Opportunity InternationalBank of Malawi ðOIBMÞ but has since changed its name to Opportunity Bank of Malawi ðOBMÞ.




a full banking license that allows it to collect deposits and on-lend funds.Alliance One and Limbe Leaf are two large private agribusiness companiesthat offer extension services and high-quality inputs to smallholder farmers viaan out-grower tobacco scheme.6 These two companies work with smallholderout-growers by organizing them geographically into clubs of 10–20 mem-bers who obtain tobacco production loans under group liability from OBM.7

Tobacco clubs meet regularly and sell their crop output collectively on thetobacco auction floor. In the central Malawi region we study, tobacco farmershave similar poverty and income levels to those of non-tobacco-producinghouseholds.8

While all farmers in the study were loan customers of OBM at the start ofthe project, the loans provided a fixed input package that for the majority offarmers fell short of optimal levels of fertilizer use on their tobacco plots.9 Thisis important because it suggests that there is room for savings to increase inpututilization. In addition, while a minority of farmers were using optimal levelsof fertilizer for the amount of land they were cultivating at baseline, even thosefarmers could use savings generated by the intervention to obtain additionalinputs and expand land under tobacco cultivation or shift land devoted toother crops toward tobacco. Finally, the savings intervention could also affectuse of fertilizer and other inputs on maize ðthe main staple crop in MalawiÞand other crops.10

6 Tobacco is central to the Malawian economy, as it is the country’s main cash crop. About 70% ofthe country’s foreign exchange earnings come from tobacco sales, and a large share of the labor forceworks in tobacco and related industries.7 The cost of an input loan includes an interest rate of 28% per year and a onetime 2.5% processingfee.8 According to authors’ calculations from the 2004 Malawi Integrated Household Survey ðIHSÞ,individuals in tobacco-farming rural households in central Malawi live on purchasing power parityðPPPÞ $1.46/day on average, while the corresponding average for nontobacco farmers is PPP $1.51/day. That said, the two groups are different in other ways. Tobacco farmers have somewhat largerhouseholds ð6.68 compared to 4.94 persons for households not farming tobaccoÞ, higher levels ofeducation of the household head ð5.61 compared to 4.63 yearsÞ, and a higher share of school-agekids ð6–17 yearsÞ currently in school conditional on having school-age children ð88.1% comparedto 77.9%Þ.9 The input package was designed for a smaller cultivated area. As a result, 60.4% of farmers wereapplying less than the recommended amount of nitrogen on their tobacco plots at baseline. Thefigures for the two other key nutrients for tobacco are even more striking: 83.2% and 84.7% offarmers used less than the recommended amount of phosphorus and potassium, respectively. Foreach of the three nutrients, among farmers using less than recommended levels, the mean ratio ofactual use to optimal use was about 0.7. Optimal use levels were determined by Alliance One andLimbe Leaf in collaboration with Malawi’s Agricultural Research and Extension Trust and are simi-lar to nutrient level recommendations in the United States ðPearce, Denton, and Schwab 2011Þ.10 At baseline, 89.5% and 99.9% of farmers were applying less than the recommended amount ofnitrogen and phosphorus, respectively, on their maize plots, and 44.1% and 98.6% of farmersapplied less than half the recommended amounts for the two nutrients. Among farmers applying less

Brune et al. 000



The experiment was designed to test the impact of facilitating savings informal bank accounts. In addition, we sought to test whether offering accountswith “commitment” features would have a greater impact than offering “or-dinary” bank accounts without such features.11 Farmer clubs were randomlyassigned to either a control group offered no savings facilitation, an “ordinarysavings” treatment group that was offered assistance setting up direct depositinto individual liquid savings accounts, and a “commitment savings” treat-ment group that was offered assistance setting up direct deposit into individualordinary savings accounts and additional accounts with commitment features.The design of the experiment also aimed to explore the role of savings ac-

counts in helping farmers resist pressure to share resources with others in theirsocial network. Farmer clubs in the ordinary and commitment savings treat-ment groups were further cross-randomized into subgroups that were or werenot entered into a raffle wherein they could win prizes based on their accountbalances ðdescribed further belowÞ.In sum, the two crosscutting interventions result in seven treatment con-

ditions: a pure control condition without savings account offers or raffles; or-dinary savings accounts with no raffles, with private distribution of raffle tick-ets, and with public distribution of raffle tickets; and commitment savingsaccounts with no raffles, with private distribution of raffle tickets, and withpublic distribution of raffle tickets ðsee table 1Þ.Figure 1 presents the timing of the experiment with reference to the Ma-

lawian agricultural season. The baseline survey and interventions were ad-ministered in April and May 2009, immediately before the 2009 harvest. As aresult, farmers in the commitment treatment group made allocation decisionsinto the commitment and ordinary accounts in the “cold state” before receiv-ing the net proceeds from tobacco sales.12 Planting starts between November

11 Research on savings accounts with features that self-aware individuals can use to limit theiroptions in anticipation of future self-control problems includes that by Ashraf, Karlan, and Yinð2006Þ, who investigate demand for and impacts of a commitment savings device in the Philippinesand find that demand for such commitment devices is concentrated among women exhibitingpresent-biased time preferences. Duflo, Kremer, and Robinson ð2011Þ find that offering a small,time-limited discount on fertilizer immediately after harvest has an effect on fertilizer use that iscomparable to that of much larger discounts offered later, around planting time. Giné et al. ð2013Þfind that Malawian farmers with present-biased preferences are more likely to revise a plan abouthow to use future income, a result that supports the potential of commitment accounts to improvewelfare for those with self-control problems.12 If decisions had been made the day that tobacco sales were transferred to OBM, then theallocations into the commitment accounts by present-biased individuals would have been lower.

than the recommended amount of nitrogen ðphosphorusÞ on maize, the ratio of actual use tooptimal use was 0.48 ð0.14Þ. Potassium is not recommended for maize cultivated in central Malawi.Nutrient recommendations are from Benson ð1999Þ.




and December, depending on the arrival of the rains. We therefore refer tothe time from harvest until end of October as the preplanting period.Randomization of the savings and raffle treatments was conducted at the

club level in order to minimize cross-treatment contamination.13 The sam-ple consists of 299 clubs with 3,150 farmers surveyed at baseline ðFebruary–April 2009Þ, for whom we can track savings deposits, withdrawals, and bal-ances in our partner bank’s administrative data. In addition, we have data froman endline survey administered in July–September 2010, after the 2010harvest, for 2,835 farmers from 298 clubs. Attrition from the baseline to theendline survey was 10.0% and is not statistically significantly different acrossdifferent treatment groups ðas shown in table C1, available online onlyÞ. Theendline survey will be used to examine impacts on outcomes such as farminputs, production, and household per capita expenditures.

Financial EducationMembers of all clubs attended a financial education session immediately afterthe baseline survey was administered. The session reviewed basic elementsof budgeting and explained the benefits of formal savings accounts, with anemphasis on how such accounts could be used to set aside funds for futureconsumption and investment. The full script of the financial education sessioncan be found in appendix A, available online only.The same financial education session was deliberately provided to all clubs—

including those subsequently assigned to the control group—so that treatmenteffects could be attributed solely to the provision of the financial products,abstracting from the effects of financial education that are implicitly pro-vided during the product offer ðe.g., strategies for improved budgetingÞ. Forthis reason, we can estimate neither the impact of the ordinary and commit-

13 Before randomization, treatment clubs were stratified by location, tobacco type ðburley, fluecured, or dark fireÞ, and week of scheduled interview. The stratification of treatment assignmentresulted in 19 distinct location/tobacco-type/week stratification cells.

TABLE 1ASSIGNMENT OF CLUBS TO TREATMENT CONDITIONS

Savings Intervention

No SavingsIntervention

Ordinary AccountsOffered

Ordinary and CommitmentAccounts Offered

No raffle Group 0: 42 clubs Group 1: 43 clubs Group 4: 42 clubsPublic distribution of raffle tickets NA Group 2: 44 clubs Group 5: 43 clubsPrivate distribution of raffle tickets NA Group 3: 43 clubs Group 6: 42 clubs

Brune et al. 000



ment treatments without such financial education nor the impact of the fi-nancial education alone.

Savings TreatmentsImplementation of the savings treatments took advantage of the existing sys-tem of depositing crop sale proceeds into OBM bank accounts. At harvest,farmers sold their tobacco to the company at the price prevailing on the near-est tobacco auction floor.14 For farmers in the control group, the proceedsfrom the sale were then electronically transferred to OBM, which deductedthe loan repayment ðplus fees and surchargesÞ of all borrowers in the club andthen credited the remaining balance to a club account at OBM. Club mem-bers authorized to access the club account ðusually the chairman or the trea-surerÞ came to OBM branches and withdrew the funds in cash.Farmers in the ordinary savings treatment were offered account-opening as-

sistance and the opportunity to have their harvest proceeds ðnet of loan re-paymentÞ directly deposited into individual accounts in their own individual

14 The tobacco-growing regions are divided among the two tobacco buyer companies. In their cov-erage area, each buyer company organizes farmers into clubs and provides them with basic extensionservices.

Figure 1. Project timing




names ðsee fig. 2 for a schematic of the money flowsÞ. These ordinary savingsaccounts are regular OBM savings accounts with an annual interest rate of2.5%. After their crop was sold, farmers traveled to the closest OBM branchto confirm that funds were available at the club level, that is, that club pro-ceeds exceeded the club’s loan obligation. Authorized members of the clubsðoften accompanied by other club membersÞ then filled out a sheet specifyingthe division of the balance of the club account between farmers. Funds weretransferred into the individual accounts of club members who had opted toopen them. Other club members received their share of the money in cash.Farmers in clubs assigned to the ordinary savings treatment were offered

only one ðordinaryÞ savings account. Farmers assigned to the commitmenttreatment had the option of opening an additional account with commitmentfeatures. The commitment savings account had the same interest rate as theordinary account but allowed farmers to specify an amount to be transferredto this illiquid account and a “release date” when the bank would allow ac-cess to the funds.15 During the account-opening process, farmers stated howmuch they wanted deposited in the ordinary and commitment savings ac-counts after the sale of their tobacco crops. For example, if a farmer stated thathe wanted MK 40,000 in an ordinary account and MK 25,000 in a com-mitment savings account, funds would first be deposited into the ordinaryaccount until MK 40,000 had been deposited, then into the commitmentsavings account for up to MK 25,000, with any remainder being depositedback into the ordinary account. The choice of a “trigger amount” that had toflow into the ordinary account before any money would be deposited into thecommitment account turns out to be important because many farmers chosetriggers higher than their eventual crop sale revenue and therefore ended upwithout deposits into their commitment accounts. Opening the commit-ment account or ordinary account only was not an option, although farm-ers could have set the “trigger amount” to zero or a very large amount if theyonly wanted to use the ordinary or commitment account, respectively. Nofees were charged for the initial post-crop-sale deposits into the ordinary orcommitment accounts. Further details on account features and fees can befound in appendix A.Farmers who were not offered a particular account type due to their treat-

ment status ðe.g., control group farmers who were not offered either type ofaccount or ordinary treatment group farmers who were not offered the com-

15 By design, funds in the commitment account could not be accessed before the release date. In asmall number of cases, OBM staff allowed early withdrawals of funds when clients presented ev-idence of emergency needs, e.g., health or funeral expenditures.

Brune et al. 000



mitment accountÞ but learned about and requested themwere not denied thoseaccounts, but they were not given information about or assistance in open-ing them.16 In other words, the savings treatments were implemented as anencouragement design.

Raffle TreatmentsTo study the impact of public information on savings and investment be-havior, we implemented a crosscutting randomization of a savings-linked raffle.Participants in each of the two savings treatments were randomly assigned toone of three raffle conditions ðmembers of the control group were not eligiblefor raffle tickets because the tickets were based on savings account balancesÞ.We distributed tickets for a raffle to win a bicycle or a bag of fertilizer ðone

of each per participating branchÞ, where the number of tickets each partici-pant received was determined by his or her savings balance as of preannounceddates that fell before large expenditures ðlike fertilizer purchasesÞ were likely todeplete savings balances. Every MK 1,000 in an OBM account ðin total acrossordinary and commitment savings accountsÞ entitled a participant to one raf-

16 During the baseline interaction with study participants, no farmers in the control group expressedto our survey staff a desire for either ordinary or commitment accounts, and none in the ordinarytreatment group requested commitment accounts. According to OBM administrative records, sevenindividuals in the control group ð1.7%Þ and 52 farmers in the ordinary treatment group ð3.7%Þ hadcommitment accounts by the end of October 2009 ðthese were opened without our assistance orencouragementÞ. None of these farmers had any transactions in the accounts.

Figure 2. Tobacco sales and bank transactions




fle ticket. Ticket allocations would be on the basis of average balances fromJuly 1 to August 1 ðfirst distributionÞ and from September 1 to October 1ðsecond distributionÞ. By varying the way in which tickets were distributed,we sought to exogenously vary the information that club members had abouteach other’s savings balances.Because the raffle itself could provide an incentive to save or could serve as

a reminder to save ðKast, Meier, and Pomeranz 2012; Karlan et al., forth-comingÞ, one-third of clubs assigned to either ordinary or commitment sav-ings accounts were randomly determined to be ineligible to receive raffle ticketsðand were not told about the raffleÞ. Another one-third of clubs with savingsaccounts were randomly selected to have raffle tickets distributed privately.Study participants were called to a meeting for raffle ticket distribution butwere handed their tickets out of view of other study participants. The finalthird of clubs with savings accounts were randomly selected for public distri-bution of raffle tickets. In these clubs, each participant’s name and the numberof tickets received was announced to everyone that attended the raffle meeting.A feature of the simple formula for determining the number of tickets was

that farmers in clubs where tickets were distributed publicly could easily esti-mate other members’ savings balances. Private distribution of tickets, though,did not reveal information about individuals’ account balances. The rafflescheme was explained to participants during the account-opening visit ðbutbefore accounts were openedÞ with a participatory demonstration. Memberswere first given hypothetical balances and then given raffle tickets in a mannerthat corresponded to the distribution mechanism for the treatment condi-tion to which the club was assigned. In clubs assigned to private distribution,members were called up one by one and given tickets in private ðout of sightof other club membersÞ. In clubs assigned to public distribution, memberswere called up and their number of tickets was announced to the group. Sincereal tickets based on actual account balances were distributed twice during theexperiment, the first distribution also functioned as an additional demonstra-tion. As reported in Section IV below, however, substantial withdrawals fromboth the ordinary and commitment accounts occurred soon after funds weredeposited, and as a result, this public revelation treatment was likely to have hadlittle effect.

SampleTable 2 presents summary statistics of baseline household and farmer clubcharacteristics. All variables expressed in monetary terms are inMalawi kwachaðMK 145/US$1 during the study periodÞ. Baseline survey respondents ownan average of 4.7 acres of land and are mostly male ðonly 6% were femaleÞ.

Brune et al. 000



TABLE

2SU

MMARYST

ATIST

ICS

Mea

nSD

10th

Percen

tile

Med

ian

90th

Percen

tile

Observations

Trea

tmen

tco

ndition

:Con

trol

group

.135

.341

3,15

0Pa

nelA

:Any

trea

tmen

t.865

.341

3,15

0Pa

nelB

:Com

mitm

enttrea

tmen

t.417

.493

3,15

0Ordinarytrea

tmen

t.448

.497

3,15

0Pa

nelC

:Com

mitm

ent,no

raffle

.136

.342

3,15

0Com

mitm

ent,priv

ateraffle

.142

.349

3,15

0Com

mitm

ent,pub

licraffle

.139

.346

3,15

0Ordinary,no

raffle

.146

.354

3,15

0Ordinary,priv

ateraffle

.149

.356

3,15

0Ordinary,pub

licraffle

.153

.360

3,15

0Baselinech

aracteristic

:Num

ber

ofmem

bersper

club

13.88

6.44

9.00

11.00

23.00

299

Female

.063

.243

3,15

0Marrie

d.955

.208

3,15

0Ageðye

arsÞ

45.02

13.61

28.00

44.00

64.00

3,15

0Ye

arsof

educ

ation

5.45

3.53

.00

6.00

10.00

3,15

0Hou

seho

ldsize

5.79

1.99

3.00

6.00

9.00

3,15

0Asset

index

−.02

1.86

−1.59

−.67

2.46

3,15

0Live

stoc

kindex

−.03

1.15

−1.00

−.36

1.37

3,15

0La

ndun

der

cultivatio

nðacresÞ

4.67

2.14

2.50

4.03

7.50

3,15

0Cashsp

enton

inputsðM

KÞ

25,169

41,228

010

,000

64,500

3,15

0Proc

eedsfrom

crop

salesðM

KÞ

125,65

717

4,97

77,00

067

,000

300,00

03,15

0Has

ban

kacco

unt

.634

.482

3,15

0Sa

ving

sin

cash

atho

meðM

KÞ

1,24

43,89

50

03,00

03,15

0Sa

ving

sin

ban

kacco

unts

ðMKÞ

2,08

38,26

50

03,00

02,94

9Hyp

erbolic

.102

.303

3,11

7

000



Patie

ntno

w,im

patient

later

.304

.460

3,11

7Net

tran

sfersmad

ein

past12

mon

thsðM

KÞ

1,75

37,64

5−2,99

050

08,10

03,15

0Missing

valueforform

alsaving

san

dcash

.064

.244

3,15

0Missing

valuefortim

epreferenc

es.010

.102

3,15

0Tran

sactions

with

partner

institu

tion:

Any

tran

sfer

viadire

ctdep

osit

.154

.361

3,15

0Dep

osits

into

ordinaryacco

unts,preplantingðM

KÞ

18,472

82,396

00

38,907

3,15

0Dep

osits

into

commitm

entacco

unts,preplantingðM

KÞ

615

5,36

70

00

3,15

0Dep

osits

into

othe

racco

unts,preplantingðM

KÞ

296

3,80

40

00

3,15

0To

tald

eposits

into

acco

unts,preplantingðM

KÞ

19,383

84,483

00

40,694

3,15

0To

talw

ithdrawalsfrom

acco

unts,preplantingðM

KÞ

18,600

82,744

38,600

00

3,15

0Net

ofalltransactio

ns,preplantingðM

KÞ

762

13,857

00

649

3,15

0Net

ofalltransactio

ns,Nov

ember–Dec

ember

ðMKÞ

−84

86,87

00

02

3,15

0Net

ofalltransactio

ns,Janu

ary–April

ðMKÞ

−26

94,03

20

04

3,15

0Any

activ

eacco

untwith

Opportunity

Ban

kof

Malaw

i.322

.467

3,15

0En

dlin

esurvey

outcom

es:

Land

under

cultivatio

nðacresÞ

4.52

2.66

2.00

4.00

8.00

2,83

5Cashsp

enton

inputsðM

KÞ

21,632

32,853

500

11,000

51,500

2,83

5To

talv

alue

ofinputsðM

KÞ

68,046

84,014

1,50

043

,750

157,27

22,83

5Proc

eedsfrom

crop

salesðM

KÞ

109,60

416

2,58

00

56,000

270,00

02,83

5Va

lueof

crop

output

ðsold

andno

tsoldÞð

MKÞ

177,74

720

1,13

127

,480

115,58

238

7,20

32,83

5Fa

rmprofit

ðoutput

−inputÞð

MKÞ

110,70

315

6,74

70

70,372

264,95

32,83

5To

tale

xpen

ditu

rein

last

30daysðM

KÞ

11,905

13,219

2,25

07,50

026

,000

2,83

5Hou

seho

ldsize

5.80

2.15

3.00

6.00

9.00

2,83

5To

taltransfers

mad

eðM

KÞ

3,15

25,09

90

1,30

08,00

02,83

5To

taltransfers

rece

ived

ðMKÞ

2,20

44,37

70

500

6,05

02,83

5To

taln

ettran

sfersmad

eðM

KÞ

939

5,89

6−3,00

035

05,75

02,83

5To

bacco

loan

amou

ntðM

KÞ

40,787

77,962

00

130,00

02,83

5Has

fixed

dep

ositacco

unt

.067

.250

2,83

5Not

interviewed

infollo

w-up

.100

.300

3,15

0

Note.Databased

ontw

osurvey

sco

nduc

tedin

February–April

2009

ðbaselineÞ

andJu

ly–Se

ptember

2010

ðend

lineÞ

andon

administrativereco

rdsof

ourpartner

institu

tion.

MK14

55

US$

1duringstud

yperiod.W

ithdrawalspresented

asne

gativenu

mbers.Se

eap

p.B

,available

onlin

eon

ly,for

varia

ble

defi

nitio

ns.

000



Respondents are on average 45 years old. They have an average of 5.5 yearsof formal education and have low levels of financial literacy.17 Sixty-three per-cent of farmers at baseline had an account with a formal bank ðmostly withOBMÞ.18 The average reported savings balance in bank accounts at the timeof the baseline was MK 2,083 ðUS$14Þ, with an additional MK 1,244ðUS$9Þ saved in the form of cash at home.

Balance of Baseline Characteristics across Treatment ConditionsTo examine whether randomization across treatments achieved balance in pre-treatment characteristics, table 3 presents the differences in means of 17 base-line variables in the same format as used for the subsequent analysis. Panel Achecks for balance between the control group and the treatment group, thelatter pooled across all of the savings and raffle treatments. Panel B looks fordifferences between the control group, the ordinary savings group, and thecommitment savings group, with each of the savings treatments pooled acrosstheir respective raffle subtreatments.With a few exceptions, the sample is well balanced. We test balance for 17

baseline variables. In panel A, respondents assigned to the savings treatmentare 4 percentage points more likely to be female and 2 percentage points lesslikely to be married than those assigned to the control group. At baseline, theyreport spending nearly MK 4,000 more in cash on agricultural inputs, adifference that is statistically significant at the 90% confidence level.Panel B reveals that respondents in both the commitment and ordinary

treatment groups are more likely to be female and less likely to be married.The treatment-related imbalance with respect to cash spent on inputs foundin panel A appears to be driven by imbalance in the ordinary treatment group,which is different from the control group at the 5% level ðthe difference be-tween the commitment treatment group and the control group for that var-iable is not statistically significant at conventional levelsÞ. This pattern of im-balance contrasts with the pattern of treatment effects ðin results belowÞ inwhich statistically significant effects ðand larger point estimatesÞ are concen-trated in the commitment treatment ðrather than the ordinary treatmentÞ andtherefore may assuage concerns that the baseline imbalance is driving the

17 In particular, 42% of respondents were able to compute 10% of 10,000, 63% were able to divideMK 20,000 by five, and only 27% could apply a yearly interest rate of 10% to an initial balance tocompute the total savings balance after a year.18 This includes a number of “payroll” accounts opened in a previous season by OBM and one ofthe tobacco buyer companies as a payment system for crop proceeds and which do not actuallyallow for savings accumulation. Our baseline survey unfortunately did not properly distinguish be-tween these two types of accounts.




TABLE

3TE

STOFBALA

NCEIN

BASE

LINECHARACTE

RISTICS:

ORDIN

ARYLE

AST

SQUARESREGRESS

IONS

ð1Þ

ð2Þ

ð3Þ

ð4Þ

ð5Þ

ð6Þ

ð7Þ

ð8Þ

ð9Þ

ð10Þ

ð11Þ

ð12Þ

ð13Þ

ð14Þ

ð15Þ

ð16Þ

ð17Þ

Pane

lA:

Any

trea

tmen

t.044

***

−.018

**−1.42

.14

−.03

.08

−.07

−.01

6,99

73,91

8*−.021

371

.012

−.054

72−.002

.001

ð.012

Þð.0

09Þ

ð.93Þ

ð.20Þ

ð.13Þ

ð.11Þ

ð.09Þ

ð.14Þ

ð8,891

Þð2,027

Þð.0

29Þ

ð550

Þð.0

17Þ

ð.034

Þð452

Þð.0

13Þ

ð.005

ÞP-values

ofF-test

for

jointsignific

ance

of

baselinevaria

blesa

.148

1

Pane

lB:

Com

mitm

enttrea

tmen

t.045

***

−.019

*−1.39

.09

−.04

.07

−.06

−.05

5,60

43,33

7−.039

376

.024

−.076

**−19

5−.004

.003

ð.013

Þð.0

10Þ

ð.97Þ

ð.22Þ

ð.13Þ

ð.12Þ

ð.09Þ

ð.15Þ

ð9,779

Þð2,357

Þð.0

32Þ

ð612

Þð.0

19Þ

ð.036

Þð476

Þð.0

14Þ

ð.005

ÞOrdinarytrea

tmen

t.042

***

−.018

*−1.45

.19

−.02

.09

−.07

.02

8,29

44,45

9**

−.005

367

.000

−.034

320

.000

.000

ð.013

Þð.0

10Þ

ð.98Þ

ð.22Þ

ð.13Þ

ð.12Þ

ð.09Þ

ð.15Þ

ð9,639

Þð2,209

Þð.0

31Þ

ð588

Þð.0

18Þ

ð.037

Þð475

Þð.0

15Þ

ð.005

ÞP-values

ofF-test:

Coe

fficien

tson

commitm

entan

d

ordina

rytrea

tmen

ts

areeq

ualb

.790

.912

.924

.557

.857

.825

.936

.549

.731

.592

.219

.985

.083

.110

.094

.730

.661

P-values

ofF-test

for

jointsignific

ance

of

baselinevaria

bles:a

Com

mitm

entsaving

s.616

8

Ordinarysaving

s.885

1

Mea

ndep

enden

tvaria

ble

inco

ntrolg

roup

.024

.972

46.23

5.31

5.81

−.11

.03

4.67

117,49

521

,798

.658

3,23

5.095

.352

1,65

5.066

.009

Num

ber

ofob

servations

3,15

03,15

03,15

03,15

03,15

03,15

03,15

03,15

03,15

03,15

03,15

02,94

93,11

73,11

73,15

03,15

03,15

0

Note.Stan

darderrorsðin

paren

thesesÞa

reclusteredat

theclub

leve

l.US$

1isap

proximatelyMK14

5.Allregressions

includ

estratifi

catio

nce

llfixe

deffects.Fo

rvariable

defi

nitio

ns,see

app.B

,available

onlin

eon

ly.Colum

nhe

adingsareas

follo

ws:

(1)Fe

male,

(2)marrie

d,(3)ag

e(yea

rs),(4)ye

arsof

educ

ation,

(5)ho

useh

oldsize,(6)assetindex

,(7)livestock

index

,(8)land

under

cultivatio

n(acres),

(9)p

roce

edsfrom

crop

sales(M

K),(10)

cash

spen

toninputs(M

K),(11)

hasban

kacco

unt,(12)

saving

sin

acco

untsan

dcash

(MK),(13)

hyperbolic,(14

)patient

now,impatient

later,(15)

nettransfersmad

ein

past12

mon

ths(M

K),(16)

missing

value:

form

alsaving

san

dcash,(17

)missing

value:

timepreferenc

es.

aTe

stof

jointsignificanc

ein

regressionof

resp

ectiv

etrea

tmen

tdum

mieson

all1

7baselinevaria

bles.

bTe

ststheeq

ualityof

mea

nsin

commitm

entan

dordinarytrea

tmen

tgroup

s.*Significant

at10

%leve

l.**

Significant

at5%

leve

l.***Significant

at1%

leve

l.



estimated treatment effects. Those in the commitment treatment group arealso less likely to be patient now and impatient later, compared to the controlgroup ðsignificant at the 5% levelÞ.The baseline characteristics in table 3, plus stratification cell fixed effects,

are included as controls in the main regressions. This concords with the rec-ommendations in Bruhn and McKenzie ð2009Þ to include stratification cellfixed effects in stratified randomization designs and also to control for baselinevariables that are highly correlated with the posttreatment outcomes of in-terest ðwhich, in our case, include baseline savings and key agricultural de-cisions such as land and input utilizationÞ.

III. Empirical SpecificationWe study the effects of our experimental interventions on several sets of out-comes: deposits into and withdrawals from savings accounts, savings balances,agricultural outcomes from the next year’s growing season and household ex-penditure after that season, households’ financial interactions with others intheir network, and future use of financial products. These data come fromthe endline survey administered after the 2010 harvest and from administra-tive data on bank transactions and account balances collected throughout theproject.We present two regression specifications reported as separate panels in the

main results tables. The first tests the effect of being randomly assigned to anyof the savings facilitation treatments, relative to being assigned to the controlgroup. In panel A of the subsequent tables, we run regressions of the form

Yij 5 d1 aSavingsj 1 b0X ij 1 εij; ð1Þ

where Yij is the dependent variable of interest for farmer i in club j and Savingsjis an indicator variable for club-level assignment to either of the two savingstreatment groups. The coefficient ameasures the effect of being offered directdeposit into an individual savings account ðeither ordinary savings accountsonly or ordinary plus commitment accountsÞ, and X ij is a vector that includesstratification cell dummies and the 17 household characteristics measured inthe baseline survey before treatment ðsummarized in table 3Þ, and εij is a mean0 error term. Because the unit of randomization is the club, standard errors areclustered at this level ðMoulton 1986Þ.In panel B, we compare the impact of assignment to the ordinary savings

treatment to the impact of assignment to the commitment savings treatment.Regressions are of the form




Yij 5 d1 g1Ordinaryj 1 g2Commitmentj 1 b0X ij 1 εij; ð2Þ

where Yij and Xij are defined as above, Ordinaryj is an indicator for club-levelassignment to the ordinary savings treatment, and Commitmentj is an indi-cator for assignment to the commitment savings treatment. The coefficientg1 represents the effect of eligibility for direct deposit into ordinary accountsonly, relative to the control group, and g2 captures the analogous effect foreligibility for direct deposit into ordinary accounts and automatic transfersinto commitment savings accounts. The difference between those two coef-ficients, then, captures the marginal effect of the commitment savings ac-count relative to direct deposit into the ordinary account. The p-value forthe test of the null hypothesis that g1 5 g2 is reported at the bottom of eachpanel B.Both regression equations ð1Þ and ð2Þ measure treatment effects that pool

the raffle subtreatments. Results with full detail on the raffle subtreatments ðsixtreatments in allÞ are presented in tables C3–C6, available online only.Throughout the analysis, we focus on intent-to-treat estimates because not

every club member offered account-opening assistance decided to open an ac-count. We do not report average treatment on the treated estimates because itis plausible that members without accounts are influenced by the training scriptitself or by members who do open accounts in the same club, either of whichwould violate the stable unit treatment value assumption ðAngrist, Imbens, andRubin 1996Þ.

IV. Empirical ResultsWe first examine the effects of our experimental interventions on formal sav-ings: the flow of funds into and out of accounts and savings account bal-ances. We then turn to the impacts on agricultural input use, farm output,household expenditures, and other household behaviors.

Take-Up and Impacts on Savings TransactionsThe first question of interest is whether the experimental treatments changeduse of individual savings accounts. Table 4 presents estimates of equations ð1Þand ð2Þ ðin panels A and B, respectivelyÞ for outcomes from administrativedata on account transactions.Column 1 presents treatment effects on “take-up” of the offered financial

services: opening of individual bank accounts coupled with direct deposit of

Brune et al. 000



tobacco crop proceeds.19 Panel A indicates that take-up was 19.4% amongrespondents offered any treatment ðthis dependent variable is zero by designin the control groupÞ. Take-up is very similar across the commitment andordinary treatments ðpanel BÞ and statistically indistinguishable across themðthe p-value of the difference in take-up across the two groups is 0.432Þ. Inorder to understand the drivers of take-up, table C7, available online only, re-ports the results of a probit regression of two measures of take-up againsthousehold and individual characteristics. The dependent variables are a broaderdefinition than that in column 1 of table 4 of opening an account withperhaps no direct deposit ðtable C7, col. 1Þ and the more restrictive definitionused in column 1 of table 4, that is, opening an account and a positive directdeposit into the account ðtable C7, col. 2Þ. The sample in panel A includes allindividuals in the ordinary and commitment treatment groups, while in panel Bonly individuals in the commitment group are included. The results sug-gest that education, already having a formal account ðperhaps opened to de-posit the proceeds of a loanÞ, and notably net transfers ðgiven minus receivedÞin panel A are all positively correlated with both having one account openedas well as having an account opened with a positive direct deposit. In contrast,whether the individual is hyperbolic does not seem to predict take-up.Owing to the study’s aim to promote agricultural input investments in the

November–December 2009 planting season, for the remaining dependentvariables in table 4, we examine transactions over the months preceding thatperiod, March–October 2009. In column 2, the dependent variable is totaldeposits into all accounts at the partner bank ðthese are direct deposits fromthe tobacco companies as well as other deposits made by account holdersÞ.The mean of this variable in the control group is MK 3,281 ðUS$21.72Þ.Compared to this amount, the impact of being assigned to any treatmentgroup shown in panel A is large ðMK 17,609, or US$121.44Þ and statisticallysignificantly different from zero at the 1% level. Given that take-up was verysimilar across the two treatment groups, and that take-up by design meantthat all crop proceeds were deposited with the partner bank, it should notbe surprising that the treatment effect is very similar across commitment andordinary treatment groups ðpanel BÞ. Each separate treatment effect is sta-tistically significantly different from zero at the 1% level, but the treatment

19 The time period over which this dependent variable is calculated is intentionally very broadðMarch 2009–April 2010Þ, so as to capture any direct deposit from the tobacco purchase companiesinto the study respondent accounts. In practice, the vast majority of direct deposits took place in theMay–July 2009 harvest season.




TABLE

4IM

PACTOFTR

EATM

ENTS

ON

DEPOSITS

AND

WITHDRAWALS

:ORDIN

ARYLE

AST

SQUARESREGRESS

IONS

March

2009

–April

2010

March

–Octob

er20

09

Any

Tran

sfer

Via

Dire

ctDep

ositðTa

ke-UpÞ

TotalD

eposits

into

Accou

ntsðM

KÞ

Dep

osits

into

Ordinary

Accou

ntsðM

KÞ

Dep

osits

into

Com

mitm

ent

Accou

ntsðM

KÞ

Dep

osits

into

Other

Accou

ntsðM

KÞ

TotalW

ithdrawals

from

Accou

ntsðM

KÞ

ð1Þ

ð2Þ

ð3Þ

ð4Þ

ð5Þ

ð6Þ

Pane

lA:

Any

trea

tmen

t.194

***

17,609

***

16,807

***

668***

134

−16

,761

***

ð.036

Þð3,910

Þð3,773

Þð224

Þð163

Þð3,819

ÞPa

nelB

:Com

mitm

enttrea

tmen

t.207

***

18,801

***

17,021

***

1,49

0***

290

−17

,511

***

ð.039

Þð4,360

Þð4,137

Þð358

Þð202

Þð4,235

ÞOrdinarytrea

tmen

t.181

***

16,513

***

16,611

***

−88

−9

−16

,071

***

ð.040

Þð4,840

Þð4,743

Þð181

Þð163

Þð4,745

ÞP-valueof

F-test:co

effic

ientson

commitm

entan

dordina

rytrea

tmen

tsareeq

uala

.432

.642

.931

.000

.074

.764

Mea

ndep

enden

tvaria

ble

inco

ntrolg

roup

.000

3,28

13,10

70

174

−3,25

6

Note.Stan

darderrorsðin

paren

thesesÞa

reclusteredat

theclub

leve

l.US$

1isap

proximatelyMK14

5.Allregressions

includ

estratifi

catio

nce

llfixe

deffectsan

dthefollo

wingbaseline

varia

bles:

dum

myformaleresp

onden

t;dum

myformarrie

d;ag

ein

years;

yearsof

completeded

ucation;

number

ofho

useh

oldmem

bers;

assetindex

;livestock

index

;land

under

cultivatio

n;proce

edsfrom

tobacco

andmaize

salesduringthe20

08season

;cashsp

enton

inputsforthe20

09season

;dum

myforow

nershipof

anyform

alban

kacco

unt;am

ount

ofsaving

sin

ban

kor

cash

ðwith

missing

values

replace

dwith

zerosÞ;

dum

myforh

yperbolicðm

issing

values

replace

dwith

zerosÞ;

dum

myfor“

patient

now,impatient

later”

ðmissing

values

replace

dwith

zerosÞ;

nettran

sfersmad

eto

social

netw

orkov

er12

mon

ths;

dum

myformissing

valuein

saving

sam

ount;dum

myformissing

valuein

hyperbolic

and“patient

now,

impatient

later.”

Forc

omplete

varia

ble

defi

nitio

ns,see

app.B

,available

onlin

eon

ly.P

lantingseason

isNov

ember–April.F

ertilizer

applicationoc

cursin

Nov

ember–Dec

ember.F

ertilizer

purch

ases

occu

rin

bothpre-plantingperiodðO

ctob

eran

dbeforeÞ

andstartof

plantingseason

ðNov

ember–Dec

emberÞ.Net

dep

osits

aredep

osits

minus

with

drawals.N

53,15

0.aTe

ststheeq

ualityof

mea

nsin

commitm

entan

dordinarytrea

tmen

tgroup

s.***Significant

at1%

leve

l.



effects are not statistically significantly different from one another ðp-value 5.642Þ.The next three columns provide more detail on the types of account into

which deposits were destined, examining treatment effects on deposits intoordinary accounts, commitment accounts, and “other” accounts that studyparticipants might have held at the partner bank ðwhich we did not assist inopeningÞ. The vast majority of deposits were into ordinary savings accounts.Treatment effects on that outcome ðpanels A and B of col. 3Þ are very similarin magnitude and statistical significance levels to those for total deposits incolumn 2.In contrast, treatment effects on deposits into commitment accounts were

much smaller ðtable 4, col. 4Þ. Panel A reveals that respondents assigned toany treatment group deposited less than MK 700 into a commitment accountðsignificant at the 1% levelÞ, but that figure pools across individuals offeredthe commitment savings accounts and those offered ordinary accounts only.In panel B, as we might expect, the impact of the ordinary treatment is veryclose to zero ðand not statistically significantÞ, while the impact of the com-mitment treatment is MK 1,490 ðUS$10.28Þ and statistically significant atthe 1% level. Results in column 4 reveal that the encouragement design had theintended effect of increasing use of illiquid savings instruments in the com-mitment treatment group. While impacts on commitment savings balancesare positive and statistically significant, it is clear commitment savings de-posits are substantially lower than deposits into ordinary accounts, even amongthose offered the commitment treatment.Table 4 column 5 indicates that there were no large or statistically signif-

icant treatment effects on deposits into other partner bank accounts that werenot offered by the project. Treatment effects on withdrawals in the preplantingperiod ðcol. 6Þ are nearly as large in magnitude as effects on deposits. The “anytreatment” coefficient in panel A as well as the separate commitment and or-dinary treatment coefficients in panel B are all statistically significantly differ-ent from zero at the 1% level.

Time Patterns of Deposits and WithdrawalsA key aim of this project was to promote savings for agricultural input in-vestments, by facilitating individual bank account opening and channelingsubstantial resources ðrespondents’ own crop proceedsÞ into those accounts.The results in table 4 are therefore sobering, in that both deposits into andwithdrawals from OBM accounts in the 2009 preplanting period were sub-stantial for both the commitment and the ordinary treatments.




A question of interest is whether funds remained deposited in the accountsuntil the following planting period ðNovember–December 2009Þ, when ag-ricultural inputs are typically applied. As it turns out, in many cases fundsin ordinary accounts were withdrawn relatively quickly after the initial depositof crop proceeds was made. About 22% of the initial deposits into ordi-nary accounts were followed by withdrawals on the same day of nearly equalamounts.20 On average, only 26% of the original balance remained in anordinary savings account 2 weeks after it was initially deposited.Figure 3 presents average deposits into and withdrawals from ordinary and

other ðnoncommitmentÞ accounts, by month, from March 2009 to April2010.21 The sample in figure 3a is individuals in the commitment treat-ment, while the sample for figure 3b is individuals in the ordinary treat-ment. For comparison, the sample used in figure 3c is individuals in the con-trol group.The figures indicate that peak deposits occurred in June, July, and August

2009, coinciding with the peak tobacco sales months. Average deposits in ev-ery month for individuals in both the commitment and ordinary treatmentsare quite similar in magnitude to average withdrawals, indicating that the ma-jority of deposited funds were withdrawn soon thereafter. As a result, sav-ings balances during the preplanting period were much lower than depositedamounts, explaining why most farmers did not participate in the raffle.22

One likely reason funds in the ordinary accounts were withdrawn soon afterthey had been deposited has to do with transaction costs. Farmers lived, onaverage, 20 kilometers away from the bank branch and would typically travelthere by foot, bus, or bicycle.23 In addition to travel time, farmers report amedian waiting time at the branch to withdraw money of 1 hour.In contrast to the time pattern of the ordinary accounts, funds into com-

mitment accounts do stay in accounts for longer periods. Figure 3d displaysaverage deposits into and withdrawals from commitment accounts, by month,for individuals in the commitment treatment. For deposits, the peak monthsare June, July, and August, coinciding with the peak deposit months for theordinary accounts. But withdrawals from the commitment accounts are de-layed substantially, occurring in October, November, and December, coin-

20 See app. B, available online only, for details about the construction of deposit spells underlyingthese calculations.21 The data presented are the sum of the dependent variables in cols. 3 and 5 of table 4.22 The pattern is similar for individuals in the control group, but levels are much lower, owing tothe fact that direct deposit from the tobacco auction floor into farmer accounts was not enabled forthat group.23 The median round-trip bus fare is MK 400 and takes 2 hours each way.

Brune et al. 000



ciding with the key months when agricultural inputs must be purchased andapplied on fields. Of course, as revealed in table 4, the amount of money in-volved in these transactions is much lower than that in ordinary accounts.

Impacts on Savings BalancesNotwithstanding the fact that substantial amounts were withdrawn from ac-counts very soon after the direct deposits occurred, it is still possible that enoughfunds remained in total across both types of accounts to be able to detect sta-tistically significant effects on savings balances. Because of our interest in fa-

Figure 3. Deposits into and withdrawals from accounts ðin Malawi kwachaÞ: a, commitment treatmentgroup deposits into ordinary accounts; b, ordinary treatment group deposits into ordinary accounts;c, control group deposits into ordinary accounts; d, commitment treatment group deposits into com-mitment accounts. a, b, and c include transactions in ordinary accounts opened as part of the interventionas well as other noncommitment accounts owned by study participants ðsum of dependent variables incols. 3 and 5 of table 4Þ.




cilitating savings for agricultural input utilization in the November–December2009 planting season, we now examine treatment effects on savings balancesimmediately before that period.Table 5 reports coefficients from estimation of equations ð1Þ and ð2Þ for sav-

ings balances in the different types of OBM accounts, on October 22, 2009.In panel A, which presents the impact of “any treatment,” we find that thetreatment effect is positive and statistically significantly different from zeroat the 1% level for total savings balances ðcol. 1Þ, ordinary savings balancesðcol. 2Þ, and commitment savings balances ðcol. 3Þ. In addition, the coefficient

Figure 3 (Continued )

Brune et al. 000



in the regression for savings balances in other accounts ðcol. 4Þ is also positiveand statistically significantly different from zero at the 5% level.In panel B, which estimates separate effects for the commitment and or-

dinary treatments, we find that the effects of each treatment on total savingsbalances ðtable 5, col. 1Þ are positive and statistically significantly differentfrom zero at the 1% level. That said, the effect of the commitment treatmentis larger than that of the ordinary treatment, and this difference is statisticallysignificant at the 5% level. Effects of the treatments are very similar on sav-ings in ordinary accounts and on savings in other accounts ðcols. 2 and 4Þ;we cannot reject equality of the ordinary and commitment treatment effectsfor these outcomes at conventional significance levels. By contrast, the twotreatments ðunsurprisinglyÞ differ in their impact on savings balances in com-mitment savings accounts: the commitment treatment effect is positive andstatistically significantly different from zero at the 1% level, while the ordinarytreatment effect is very close to zero and is not statistically significant. Equality

TABLE 5IMPACT OF TREATMENTS ON SAVINGS BALANCES: ORDINARY LEAST SQUARES REGRESSIONS

All Accounts,in Total Ordinary Only

CommitmentOnly Other

ð1Þ ð2Þ ð3Þ ð4ÞPanel A:

Any treatment 1,863*** 1,167*** 435*** 262**ð412Þ ð302Þ ð154Þ ð124Þ

Panel B:Commitment treatment 2,475*** 1,167*** 935*** 372**

ð524Þ ð364Þ ð238Þ ð187ÞOrdinary treatment 1,301*** 1,167*** −26 160

ð442Þ ð349Þ ð129Þ ð129ÞP-value of F-test: coefficientson commitment and ordinarytreatments are equala .019 .999 .000 .290

Mean dependent variable incontrol group 364 302 0 62

Note. Dependent variable is savings balance on October 22, 2009, just before the November–December2009 planting season. Standard errors ðin parenthesesÞ are clustered at the club level. US$1 is approxi-mately MK 145. All regressions include stratification cell fixed effects and the following baseline variables:dummy for male respondent; dummy for married; age in years; years of completed education; number ofhousehold members; asset index; livestock index; land under cultivation; proceeds from tobacco andmaize sales during the 2008 season; cash spent on inputs for the 2009 season; dummy for ownership of anyformal bank account; amount of savings in bank or cash ðwith missing values replaced with zerosÞ; dummyfor hyperbolic ðmissing values replaced with zerosÞ; dummy for “patient now, impatient later” ðmissingvalues replaced with zerosÞ; net transfers made to social network over 12 months; dummy for missing valuein savings amount; dummy for missing value in hyperbolic and “patient now, impatient later.” For com-plete variable definitions, see app. B, available online only. N 5 3,150.a Tests the equality of means in commitment and ordinary treatment groups.** Significant at 5% level.*** Significant at 1% level.




of these two coefficients is rejected at the 1% level. It is therefore clear that thedifference in the impacts of the commitment and ordinary treatments on totalsavings ðshown in col. 1Þ is being driven by the differing impacts on savings incommitment accounts ðcol. 3Þ.These results reveal that both types of savings accounts have a positive

impact on savings preservation between the May–July 2009 harvest and theNovember–December 2009 planting season, with the commitment treatmentproviding an additional boost to savings on top of the impact of the ordinaryaccount. The magnitudes of these effects are not negligible in absolute termsfor rural Malawian households as well as in comparison to control group sav-ings of MK 364 ðUS$2.36Þ. The impact of “any treatment” on savings frompanel A is MK 1,863 ðUS$12.85Þ. From panel B, the impact of the com-mitment savings treatment is MK 2,475 ðUS$17.07Þ, and the impact of theordinary treatment is MK 1,301 ðUS$8.97Þ.

Impacts on Agricultural Outcomes and Household ExpenditureIn table 6, we turn to the impacts of the treatments on agricultural out-comes in the 2009–10 season ðland cultivation, input use, crop outputÞ and onhousehold expenditures after the 2010 harvest.24 Column 1 presents treatmenteffects on land under cultivation in acres. Panel A indicates that land cultivatedwas higher by 0.30 acres among respondents offered any treatment ðstatisti-cally significant at the 5% levelÞ, compared to 4.28 acres in the control group.Treatment effects are very similar when estimated for the commitment andordinary treatments separately ðpanel BÞ, and the difference between the twois not statistically significantly different from zero.25

Results in table 6, column 2, panel A show that the treatment had a positiveimpact on the total monetary value of agricultural inputs used in the 2009–10planting season, which is statistically significant at the 10% level. Estimatingthe effects separately for the commitment and ordinary treatments reveals thatboth effects are positively signed, and the effect of the commitment treat-ment is statistically significantly different from zero at the 5% level. While thecommitment treatment coefficient is larger in magnitude than the ordinarytreatment coefficient, we cannot reject at conventional statistical significancelevels that the two treatment coefficients are equal to one another.

24 All outcomes in table 6 are for the total household, not per capita. We show in table 7, col. 1,that the treatments have no effect on household size, so interpretation of impacts in table 6 is notclouded by concurrent changes in household size.25 We investigated whether the treatment effects on land are due to increased land rentals and foundno large or statistically significant effect ðfor “any treatment” and for the commitment and ordinarytreatments separatelyÞ. Results available from authors on request.

Brune et al. 000



TABLE

6IM

PACTOFTR

EATM

ENTS

ON

AGRICULT

URALOUTC

OMESIN

2009

–10

SEASO

NAND

HOUSE

HOLD

EXPENDITUREAFT

ER20

10HARVEST

Land

under

Cultiv

ation

ðAcresÞ

TotalV

alue

ofInputsðM

KÞ

Proc

eedsfrom

CropSa

lesðM

KÞ

Valueof

Crop

Output

ðSoldan

dNot

SoldÞð

MKÞ

Farm

Profit

ðOutput

−InputÞ

ðMKÞ

TotalE

xpen

ditu

rein

30Daysbefore

Survey

ðMKÞ

ð1Þ

ð2Þ

ð3Þ

ð4Þ

ð5Þ

ð6Þ

Pane

lA:

Any

trea

tmen

t.30**

8,02

3*19

,595

**23

,921

**16

,927

*1,15

1*ð.1

5Þð4,131

Þð8,996

Þð11,52

9Þð9,117

Þð601

ÞPa

nelB

:Com

mitm

enttrea

tmen

t.33**

10,297

**26

,427

***

31,259

**21

,369

**1,44

2**

ð.16Þ

ð4,563

Þð9,979

Þð12,51

0Þð10,06

4Þð656

ÞOrdinarytrea

tmen

t.27*

5,94

613

,358

17,223

12,872

885

ð.16Þ

ð4,504

Þð9,518

Þð12,20

4Þð9,577

Þð650

ÞP-valueof

F-test:co

effic

ientson

commitm

ent

andordinarytrea

tmen

tsareeq

uala

.614

.246

.086

.117

.246

.283

Mea

ndep

enden

tvaria

ble

inco

ntrolg

roup

4.28

60,372

91,747

155,68

595

,210

10,678

Note.Stan

darderrors

ðinparen

thesesÞa

reclusteredat

theclub

leve

l.US$

1isap

proximatelyMK14

5.Allregressions

includ

estratifi

catio

nce

llfixe

deffectsan

dthefollo

wing

baselinevaria

bles:dum

myform

aleresp

onden

t;dum

myform

arrie

d;ageinye

ars;ye

arsof

completeded

ucation;

number

ofho

useh

oldmem

bers;assetind

ex;livestock

index

;lan

dun

der

cultivatio

n;proce

edsfrom

tobacco

andmaize

salesduringthe20

08season

;cashsp

ento

ninputsforthe20

09season

;dum

myforow

nershipof

anyform

alban

kacco

unt;

amou

ntof

saving

sin

ban

kor

cash

ðwith

missing

values

replace

dwith

zerosÞ;

dum

myforh

yperbolicðm

issing

values

replace

dwith

zerosÞ;

dum

myfor“

patient

now,impatient

later”

ðmissing

values

replace

dwith

zerosÞ;

nettransfersmad

eto

socialne

tworkov

er12

mon

ths;dum

myform

issing

valuein

saving

sam

ount;d

ummyform

issing

valuein

hyperbolican

d“patient

now,impatient

later.”Fo

rco

mplete

varia

ble

defi

nitio

ns,see

app.B

,available

onlin

eon

ly.N

52,83

5.aTe

ststheeq

ualityof

mea

nsin

commitm

entan

dordinarytrea

tmen

tgroup

s.*Significant

at10

%leve

l.**

Significant

at5%

leve

l.***Significant

at1%

leve

l.



The increase in agricultural input utilization caused by the treatment ap-pears to have, in turn, caused increases in agricultural output. Table 6 col-umns 3–5 show treatment effects on, respectively, crop sale proceeds, value ofcrop output ðboth sold and unsoldÞ, and farm profit ðvalue of output minusvalue of inputsÞ. For each of these outcomes, the “any treatment” coefficientin panel A is positive and statistically significant at the 5% or 10% level. Inpanel B, the commitment treatment coefficient is positive and statisticallysignificant in each of the regressions at the 1% or 5% level and is larger inmagnitude in each case than the corresponding ordinary treatment coeffi-cient. Only in column 3 ðproceeds from crop salesÞ can we reject at conven-tional levels ð10% in this caseÞ the hypothesis that the commitment and or-dinary treatment coefficients are equal.26

Given the positive treatment effects on agricultural production, it is of in-terest to examine effects on household expenditures, in table 6 column 6.The effect of any treatment is positive and statistically significant at the 10%level ðpanel AÞ. Results in panel B show that both commitment and ordinarytreatment effects are positive in magnitude, and the commitment treatmenteffect is statistically significantly different from zero at the 5% level. We can-not reject at conventional significance levels that the commitment and ordi-nary treatment effects are equal.The treatment effects identified in table 6 are economically significant. In

panel A, the treatment effect on total value of inputs isMK 8,023 ðUS$55.33Þ,amounting to an increase of 13.3% over the control group mean, while thetreatment effect on value of crop ðsold andunsoldÞ isMK23,921 ðUS$164.97Þ,an increment of 15.4% over the control group mean. The increase in house-hold expenditure is 10.8% vis-à-vis the mean in the control group. These re-sults show large, consistent effects of “any treatment” on outcomes that arelikely connected to household well-being.Consistent with these findings, column 6 of table 7 shows that being as-

signed to a savings treatment group increased the probability of owning a fixed-

26 The increase in farm profit in table 6 col. 5 and in the value of the inputs in col. 2 suggests a highrate of return to inputs. Most of the increases in expenditures were on firewood to cure tobacco andon fertilizer. Among the different varieties of tobacco grown, the highest-value variety needs morecuring, so the increased profits could be due to a shift in the crop mix toward higher-value tobaccoas well as the increased inputs. In addition, historical production and weather data suggest that 2010was a good production year with average crop prices. In results available on request, we find thatincreases in production caused by the treatments are relatively concentrated in tobacco production.In the control group, tobacco accounts for 66.5% of the kwacha value of production, but increasesin tobacco production account for 81.4% of the treatment effect ðMK 19,477 of the MK 23,921increase in the value of crop outputÞ.

Brune et al. 000



deposit account over a year later by 3 percentage points, a statistically signifi-cant increase of 75% relative to the control group mean of 0.039.27 In addition,study participants continue to use the offered ordinary accounts. Using thebank’s administrative data, we find that treatment effects on deposits, with-drawals, and net deposits persist during May–July 2010, more than a year af-ter the initial intervention, particularly in the ordinary treatment group. Thecontinued usage of ordinary accounts and the increased take-up of fixed de-posit accounts 1 year after the intervention suggest that farmers in the treat-ment group found something of value in the savings products offered.

27 In response to the positive results of this study, OBM decided to continue offering fixed depositaccounts as well as the commitment accounts ðwhich they call SavePlan accountsÞ that were de-signed for the project. As of the beginning of 2015, they remain part of OBM’s deposit productofferings.

TABLE 7IMPACT OF TREATMENTS ON HOUSEHOLD SIZE, TRANSFERS, AND FIXED DEPOSIT DEMAND

HouseholdSize

TobaccoLoan

AmountðMKÞ

TotalTransfersMadeðMKÞ

TotalTransfersReceivedðMKÞ

Total NetTransfersMadeðMKÞ

HasFixed

DepositAccount

ð1Þ ð2Þ ð3Þ ð4Þ ð5Þ ð6ÞPanel A:

Any treatment .14 3,158 215 −301 477 .032***ð.09Þ ð4,583Þ ð249Þ ð248Þ ð322Þ ð.012Þ

Panel B:Commitment treatment −.004 3,418 304 −316 568 .050***

ð.019Þ ð4,897Þ ð275Þ ð258Þ ð347Þ ð.014ÞOrdinary treatment −.010 2,920 134 −288 394 .016

ð.019Þ ð5,068Þ ð267Þ ð262Þ ð342Þ ð.012ÞP-value of F-test: coefficientson commitment andordinary treatmentsare equala .748 .899 .431 .856 .483 .008

Mean dependent variablein control group 5.72 40,147 2,872 2,492 418 .039

Note. Standard errors ðin parenthesesÞ are clustered at the club level. US$1 is approximately MK 145. Allregressions include stratification cell fixed effects and the following baseline variables: dummy for malerespondent; dummy for married; age in years; years of completed education; number of householdmembers; asset index; livestock index; land under cultivation; proceeds from tobacco and maize salesduring the 2008 season; cash spent on inputs for the 2009 season; dummy for ownership of any formal bankaccount; amount of savings in bank or cash ðwith missing values replaced with zerosÞ; dummy for hyper-bolic ðmissing values replaced with zerosÞ; dummy for “patient now, impatient later” ðmissing valuesreplaced with zerosÞ; net transfers made to social network over 12 months; dummy for missing value insavings amount; dummy for missing value in hyperbolic and “patient now, impatient later.” For completevariable definitions, see app. B, available online only. N 5 2,835.a Tests the equality of means in commitment and ordinary treatment groups.*** Significant at 1% level.




V. MechanismsWe now turn to considering the mechanisms through which our treatmenteffects may have operated. Studies of the impact of savings account accesstypically posit ðimplicitly or explicitlyÞ that effects would operate via allevi-ation of savings constraints ðe.g., Dupas and Robinson 2013a; Prina 2015Þ. Astudy population is typically thought to have imperfect methods for pre-serving funds, which can be depleted for a variety of reasons such as self-control problems, demands for sharing with one’s social network, or theft. Inour study population, alleviation of savings constraints via provision of formalsavings accounts could help farmers preserve funds between harvest and thesubsequent planting season, leading to positive impacts on agricultural inputexpenditures ðand then on other subsequent related outcomesÞ.While we do find positive treatment effects on both savings balances and

subsequent agricultural input utilization, the relative magnitudes of the effectsare inconsistent with alleviation of savings constraints being the only mech-anism at work. Consider the impact of “any treatment” on the value of ag-ricultural inputs used ðtable 6, col. 2Þ, MK 8,023. While the treatment didcause an increase in deposits exceeding that amount ðMK 17,609, from ta-ble 4, col. 2Þ, withdrawals happened quite soon after deposits, so that verylittle remained in the accounts some months later when the time came for theNovember–December input purchases: the treatment effect on savings bal-ances at the end of October is just MK 1,863 ðtable 5, col. 1Þ, which is just23% of the increase in the value of inputs.28 Therefore, no more than about aquarter of the effect of the treatment on agricultural input expenditures can beattributed to alleviation of savings constraints per se.In table 7, we estimate treatment effects on other outcomes, to test for other

operative mechanisms behind our main results. One possible explanation forthe increase in total expenditure on inputs for the savings treatment groupcould be that increased savings at the bank led to increased eligibility for loans,and it is these loans that funded the increased purchases of inputs.29 Column 2examines the size of loans provided by a lender in the subsequent season.Whilecoefficients in panels A and B are positive, none are statistically significantly

28 A one-sided test that the “any treatment” effect on the value of agricultural inputs ð8,023Þ islarger than the treatment effect on end-of-October savings balances ð1,863Þ is statistically significantat the 10% level ðp-value5 .061Þ. Corresponding tests for the ordinary treatment and commitmenttreatment have p-values of .143 and .038 respectively.29 Loans from informal lenders and friends and family account for a small fraction of total bor-rowing. At any rate, conducting this analysis for total credit instead of just tobacco credit yields verysimilar results.

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different from zero.30 It should be said, however, that the point estimates arerelatively imprecise, and 95% confidence intervals do include the estimatedtreatment effects on the value of agricultural inputs.Other alternate explanations have in common the hypothesis that while

most funds deposited in the accounts at harvest time were withdrawn fairlysoon thereafter, they may have nonetheless been spent on agricultural inputs.They could have been spent on inputs sometime between harvest and theNovember–December planting ðmaking immaterial our finding of low sav-ings balances in late OctoberÞ. Or they could have been preserved outside thebank ðsay in cash held at home or with “money guards”Þ and used for inputpurchases during the planting season. In either case alleviation of savings con-straints via provision of formal accounts per se cannot be the operative mech-anism, so we search for other mechanisms.One hypothesis is that the existence of the accounts allowed households

to resist social network demands for resources ðwhat one might call “othercontrol” problemsÞ in the period between the harvest and planting seasons.While the data from our partner bank show relatively low savings overall, withonly a minority in the restricted-access commitment accounts, neither totalbalances nor the share in commitment accounts were public knowledge tothe community. The existence of formal accounts may have provided an ex-cuse to turn down requests for assistance from the social network by claimingthat savings were inaccessible.31 Table C7 shows that individuals with higherprior net transfers ðmeasured at baselineÞ are more likely to take up the or-dinary and commitment accounts when offered. In table 7 we regress threedirect measures of transfers between households ðtransfers made, transfers re-ceived, and net transfersÞ after the intervention on the treatment variables.We find no effect of either intervention in any of these outcomes, however. Allcoefficients ðin both panels A and BÞ are relatively small in magnitude, andnone are statistically significantly different from zero at conventional levels.That said, these measures span the preplanting to postharvest period and arethus consistent with lower transfers during the preplanting season, when com-mitment accounts were active and therefore could serve as a valid excuse for

30 Similarly, we find no difference across treatment and control groups in the probability of ac-cessing a loan ðresults not shownÞ.31 To be sure, one of the “raffle” arms involved public distribution of raffle tickets based on savingsbalances. We do not find that these effects are distinguishable from the effects of treatments with nodistribution of tickets. Also, the distribution of funds across ordinary and commitment accounts wasnot public knowledge because the cross-randomized raffle treatments awarded raffle tickets on thebasis of total funds across all accounts, so even the public raffle did not reveal how little was saved incommitment accounts.




reducing transfers, followed by higher transfers after the harvest, when farm-ers with commitment accounts realized larger revenues. Unfortunately, welack the data needed to examine the timing of transfers. In addition, it isstill possible that the commitment treatment allowed study participants tokeep funds from others within the household or to refrain from consumingresources early in anticipation of future requests from others ðas in Goldberg2011Þ.Another possibility is that the ability to hold a buffer stock in formal savings

accounts made farmers willing to take on the risk of making higher inputinvestments ðAngeletos and Calvet 2006; Kazianga and Udry 2006Þ. Alter-natively, treatment may have affected agricultural production decisions viaone or more of several mechanisms suggested by research in psychology andbehavioral economics. Because the savings accounts were framed by the ex-periment as vehicles for accumulating funds for agricultural inputs, the veryact of signing up for deposits into savings accounts could have been viewedby farmers as a commitment to raise expenditures of this type. This mereelicitation of farmers’ intentions may have influenced their later behaviorðFeldman and Lynch 1988; Webb and Sheeran 2006; Zwane et al. 2011Þ.Relatedly, the act of signing up for direct deposit into savings accounts mayhave created an “agricultural input” mental account for the deposited fundsðThaler 1990Þ, even if most funds were withdrawn soon after being depositedand relatively small amounts remained in the accounts. Finally, signing up fordirect deposit into accounts could have altered study participants’ referencepoints about future input use, farm output, and consumption. In this context,prospect theory ðKahneman and Tversky 1979Þ would predict that farmersoffered savings accounts could have become more willing to invest in agri-cultural inputs, so as to avoid losses in the form of failing to achieve theirðexperimentally inducedÞ higher reference points for input use, output, andconsumption. Unfortunately, we can offer no direct evidence to support orcontradict that such psychological channels may have been at work.

VI. ConclusionViewed as a policy intervention for increasing the use of agricultural inputs byhouseholds in developing countries, savings accounts have appealing features.Unlike subsidies, they do not require major government budget commit-ments. While the supply of credit for agricultural inputs is often constrained,banks are eager to attract new savings customers. The results of our field ex-periment among cash crop farm households in Malawi show that offeringaccess to individual savings accounts not only increases banking transactionsbut also has statistically significant and economically meaningful effects on

Brune et al. 000



measures of household well-being, such as investments in inputs and subse-quent agricultural yields, profits, and household expenditure. Ours is one ofthe first randomized studies of the economic impact of savings accounts andthe first ðto our knowledgeÞ to measure impacts on important agriculturaloutcomes ðinput use and farm outputÞ and household consumption levels.An important direction for future research would be to provide evidence on

the mechanisms underlying the effects we found, since our treatment effectson input utilization are larger than can be explained by alleviation of savingsconstraints alone. Other mechanisms that might be explored include the roleof savings as a buffer stock for self-insurance, increases in credit access, re-ductions in demands from others in the social network ðother-control prob-lemsÞ, as well as mechanisms suggested by behavioral economics ðe.g., mentalaccounting and reference dependenceÞ.

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