Selling low and buying high: An arbitrage puzzle in Kenyan villages Marshall Burke∗ March 20, 2014 Abstract Large and regular seasonal price fluctuations in local grain markets appear to offer African farmerssubstantialinter-temporalarbitrageopportunities,buttheseopportunitiesremainlargely unexploited: small-scalefarmersarecommonlyobservedto“selllowandbuyhigh”ratherthan thereverse. InafieldexperimentinKenya,weshowthatcreditmarketimperfectionslimitfarm- ers’ abilities to move grain intertemporally, and that providing timely access to credit allows farmers to purchase at lower prices and sell at higher prices, increasing farm profits. To under- stand general equilibrium effects of these changes in behavior, we vary the density of loan offers across locations. We document significant effects of the credit intervention on seasonal price dispersion in local grain markets, and show that these GE effects strongly affect our individual level profitability estimates. In contrast to existing experimental work, our results indicate a setting in which microcredit can improve firm profitability, and suggest that GE effects can substantially shape estimates of microcredit’s effectiveness. JEL codes: D21, D51, G21, O13, O16, Q12 Keywords: storage; arbitrage; microcredit; credit constraints; agriculture ∗DepartmentofAgriculturalandResourceEconomics,UCBerkeley. Email: [email protected]. Ithank Ted Miguel, Lauren Falcao, Kyle Emerick, Jeremy Magruder, and Chris Barrett for useful discussions, and thank seminar participants at Berkeley, Stanford, Kellogg, and the Pacific Development Conference for useful comments. I also thank Peter LeFrancois and Innovations for Poverty Action for excellent research assistance in the field, and One Acre Fund for partnering with us in the intervention. I gratefully acknowledge funding from the Agricultural Technology Adoption Initiative and an anonymous donor. All errors are my own. 1 1 Introduction Imperfections in credit markets are generally considered to play a central role in underdevelopment (Banerjee and Newman, 1993; Galor and Zeira, 1993; Banerjee and Duflo, 2010). These imperfec- tions are thought to be particularly consequential for small and informal firms in the developing world,andforthehundredsofmillionsofpoorpeoplewhoownandoperatethem. Thisthinkinghas motivated a large-scale effort to expand credit access to existing or would-be microentrepreneurs around the world, and it has also motivated a subsequent attempt on the part of academics to rigorously evaluate the effects of this expansion on the productivity of these microenterprises and on the livelihoods of their owners. Findings in this rapidly growing literature have been remarkably heterogenous. Studies that provide cash grants to households and to existing small firms suggest high rates of return to capital in some settings but not in others.1 Further, experimental evaluations of traditional microcredit products(smallloans to poor households) have generally found that individualsrandomly provided accesstotheseproductsaresubsequentlynomoreproductiveonaveragethatthosenotgivenaccess, but that subsets of recipients often appear to benefit.2 In this paper, I study a unique microcredit product designed to improve the profitability of small farms – a setting that has been outside the focus of most of the experimental literature on credit constraints. Farmers in our setting in Western Kenya, as well as throughout much of the rest of the developing world, face large and regular seasonal fluctuations in grain prices, with increases of50-100%betweenpost-harvestlowsandpre-harvestpeakscommoninlocalmarkets(asdescribed in more detail below). Nevertheless, most of these farmers have difficulty using storage to move grain from times of low prices to times of high prices, and this inability appears at least in part due to limited borrowing opportunities: lacking access to credit or savings, farmers report selling their grain at low post-harvest prices to meet urgent cash needs (e.g., to pay school fees). To meet 1Studies finding high returns to cash grants include De Mel, McKenzie, and Woodruff (2008); McKenzie and Woodruff(2008);Fafchampsetal.(2013);Blattman,Fiala,andMartinez(2013). Studiesfindingmuchmorelimited returns include Berge, Bjorvatn, and Tungodden (2011) and Karlan, Knight, and Udry (2012). 2ExperimentalevaluationsofmicrocreditincludeAttanasioetal.(2011);Creponetal.(2011);KarlanandZinman (2011);Banerjeeetal.(2013);Angelucci,Karlan,andZinman(2013). SeeBanerjee(2013)andKarlanandMorduch (2009) for nice recent reviews of these literatures. 2 consumption needs later in the year, many then end up buying back grain from the market a few months after selling it, in effect using the maize market as a high-interest lender of last resort (Stephens and Barrett, 2011). Working with a local agricultural microfinance NGO, I offer randomly selected smallholder maize farmers a loan at harvest, and study whether access to this loan improves their ability to use storage to arbitrage local price fluctuations, relative to a control group. To understand the importance of credit timing in this setting, half of these offers were for a loan immediately after harvest (October), and half for a loan three months later (January). Furthermore, because storage-related changes in behavior could have effects on local prices in a setting of high regional transport costs, I vary the density of treated farmers across locations and track market prices at 50 local market points. Finally, to help bind my hands against data mining (Casey, Glennerster, and Miguel, 2012), I registered a pre-analysis plan prior to the analysis of any follow-up data (see Section 3.1). Despite a seasonal price rise that was in the left tail of both the historical distribution of local price fluctuations and the distribution (across farmers) of the expected price rise for the study year, I find statistically significant and economically meaningful effects of the loan offer on farm profitability, but only for farmers in low-treatment-density areas. On average, farmers offered the loan sold significantly less and purchased significantly more maize in the period immediately following harvest, and this pattern reversed during the period of (typically) high prices 6-9 months later. This change in marketing behavior had discernible effects on prices in local maize markets: prices immediately after harvest were significantly higher in areas with high treatment density, but were lower (although not significantly so) by the end of the study period. Consistent with these differential price effects, I find that while treated farmers in high-density areas stored significantly more than their control counterparts, they were not more profitable; the reduction in seasonal price dispersion in these area reduced the benefits of loan adoption. Conversely, treated farmers in low-density areas have both significantly higher inventories and significantly higher profits relative to control. I find some evidence that the timing of credit matters, with inventories and profits uniformly higher in the treatment group who received the earlier loan, but these results are not 3 always significant. Why do I find positive effects on firm profitability when other experimental studies on micro- credit do not? These studies have offered a number of explanations as to why improved access to capital does not appear beneficial on average. First, many small businesses or potential micro- entrepreneurs simply might not actually face profitable investment opportunities (Banerjee et al., 2013; Fafchamps et al., 2013; Karlan, Knight, and Udry, 2012; Banerjee, 2013).3 Second, profitable investment opportunities could exist but established or potential microentrepreneurs might lack ei- ther the skills or ability to channel capital towards these investments - e.g. if they lack managerial skills (Berge, Bjorvatn, and Tungodden, 2011; Bruhn, Karlan, and Schoar, 2012), or if they face problems of self-control or external pressure that redirect cash away from investment opportunities (Fafchamps et al., 2013). Third, typical microcredit loan terms require that repayment begin im- mediately, and this could limit investment in illiquid but high-return business opportunities (Field et al., 2012). Finally, general equilibrium effects of credit expansion could alter individual-level treatment effect estimates in a number of ways, potentially shaping outcomes for treated individuals (e.g. if microenterprisesaredominatedbyaverysmallnumberofoccupationsandcredit-inducedexpansion of these business bids away profits) as well as for non-recipients (e.g. through increased demand for labor (Buera, Kaboski, and Shin, 2012)). This is a recognized but unresolved problem in the experimental literature on credit, and few experimental studies have been explicitly designed to quantify these effects.4 All of these factors likely help explain why our results diverge from existing estimates. Unlike most of the settings examined in the literature, using credit to “free up” storage for price arbitrage 3For example, many microenterprises might have low efficient scale and thus little immediate use for additional investment capital, withmicroentrepreneursthen preferringtochannelcredittoward consumptioninsteadof invest- ment. Relatedly, marginal returns to investment might be high but total returns low, with the entrepreneur making the similar decision that additional investment is just not worth it. 4Forinstance,Karlan,Knight,andUdry(2012)concludebystating,“Fewifanystudieshavesatisfactorilytackled theimpactofimprovingonesetoffirms’performanceongeneralequilibriumoutcomes.... Thisisagapingholeinthe entrepreneurshipdevelopmentliterature.”Indeed,positivespilloverscouldexplainsomeofthedifferencebetweenthe experimentalfindingsoncredit,whichsuggestlimitedeffects,andtheestimatesfromlarger-scalenaturalexperiments, whichtendtofindpositiveeffectsofcreditexpansiononproductivity–e.g. KaboskiandTownsend(2012). Acemoglu (2010)usestheliteratureoncreditmarketimperfectionstohighlighttheunderstudiedpotentialroleofGEeffectsin broad questions of interest to development economists. 4 does not require starting or growing a business among this population of farmers, is neutral to the scale of farm output, does not appear to depend on entrepreneurial skill (all farmer have stored before, and all are very familiar with local price movements), and does not require investment in a particularly illiquid asset (inventories are kept in the house and can be easily sold). Farmers do not even have to sell grain to benefit from credit in this context: a net-purchasing farm household facing similar seasonal cash constraints could use credit and storage to move purchases from times of high prices to times of low prices. Furthermore,ourresultsalsosuggestthat–atleastinourruralsetting–treatmentdensitymat- ters and market-level spillovers can substantially shape individual-level treatment effect estimates. Whether these GE also influnced estimated treatment effects in more urban settings is unknown, althoughthereissomeevidencethatspilloversdomatterformicroenterpriseswhodirectlycompete foralimitedsupplyofinputstoproduction.5 Inanycase, myresultssuggestthatexplicitattention to GE effects in future evaluations of credit market interventions is likely warranted. Beyond contributing to the experimental literature on microcredit, my paper is closest to a number of recent papers that examine the role of borrowing constraints in households’ storage decisions and seasonal consumption patterns. Using secondary data from Kenya, Stephens and Barrett(2011)alsosuggestthatcreditconstraintssubstantiallyaltersmallholderfarmers’marketing and storage decisions, and Basu and Wong (2012) show that allowing farmers to borrow against future harvests can substantially increase lean-season consumption. As in these papers, my results show that when borrowing and saving are difficult, households turn to increasingly costly ways to move consumption around in time. In my particular setting, credit constraints combined with post-harvest cash needs cause farmers to store less than they would in an unconstrained world, lowering farm profits even in a year when prices don’t rise much. In this setting, even a relatively modest expansion of credit affects local market prices, to the apparent benefit of those with and without access to this credit. Finally, my results speak to an earlier literature showing how credit market imperfections can combine with other features of economies to generate observed broad-scale economic patterns 5See De Mel, McKenzie, and Woodruff (2008) and their discussion of returns to capital for firms in the bamboo sector, all of whom in their setting compete over a limited supply of bamboo. 5 (Banerjee and Newman, 1993; Galor and Zeira, 1993). These earlier papers showed how miss- ing markets for credit, coupled with an unequal underlying wealth distribution, could generate large-scale patterns of occupational choice. I show that missing markets for credit combined with climate-induced seasonality in rural income can help generate widely-observed seasonal price pat- terns in rural grain markets, patterns that appear to further worsen poor households’ abilities to smooth consumption across seasons. That expansion of credit access appears to help reduce this price dispersion suggests an under-appreciated but likely substantial additional benefit of credit expansion in rural areas. The remainder of the paper proceeds as follows. Section 2 describes the setting and the exper- iment. Section 3 describes our data, estimation strategy, and pre-analysis plan. Section 4 presents baseline estimates ignoring the role of general equilibrium effects. Section 5 presents the market level effects of the intervention, and shows how these affect individual-level estimates. Section 6 concludes. 2 Setting and experimental design 2.1 Arbitrage opportunities in rural grain markets Seasonal fluctuations in prices for staple grains appear to offer substantial intertemporal arbitrage opportunities, both in our study region of East Africa as well as in other parts of Africa and elsewhere in the developing world. While long term price data unfortunately do not exist for the small markets in very rural areas where our experiment takes place, price series are available for major markets throughout the region. Average seasonal price fluctuations for maize in available markets are shown in Figure 1. Increases in maize prices in the six to eight months following harvest average roughly 25-50% in these markets, and these increases appear to be a lower bound on seasonal price increases reported elsewhere in Africa.6 These increases also appear to be a lower bound on typical increase observed in the smaller 6For instance, Barrett (2008) reports seasonal rice price variation in Madagascar of 80%, World Bank (2006) reports seasonal maize price variation of about 70% in rural Malawi, and Aker (2012) reports seasonal variation in millet prices in Niger of 40%. 6 markets in our study area, which (relative to these much larger markets) are characterized with much smaller “catchments” and less outside trade. We asked farmers at baseline to estimate average monthly prices for either sales or purchases of maize at their local market point over the last five years, and as shown in the left panel of Figure 3, they reported a typical doubling in price between September (the main harvest month) and the following June. In case farmers were somehow mistaken or overoptimistic, we asked the same question of the local maize traders that can typically be found in these market points. These traders report very similar average price increases: the average reported increase between October and June across traders was 87% (with a 25th percentile of 60% increase and 75th percentile of 118% - results available on request). Farmersdonotappeartobetakingadvantageoftheseapparentarbitrageopportunities. Figure A.1 shows data from two earlier pilot studies conducted either by One Acre Fund (in 2010/11, with 225 farmers) or in conjunction with One Acre Fund (in 2011/12, with a different sample of 700farmers). These studies tracked maize inventories, purchases, and sales for farmers in our study region. In both years, the median farmer exhausted her inventories about 5 months after harvest, and at that point switched from being a net seller of maize to a net purchaser as shown in the right panels of the figure. This was despite the fact that farmer-reported sales prices rose by more than 80% in both of these years in the nine months following harvest. Why are farmers not using storage to sell at higher prices and purchase at lower prices? Our experiment will primarily be designed to test the role of credit constraints in shaping storage and marketing decisions, and here we talk through why credit might matter (these explanations will be formalized in a future draft). First, and most simply, in extensive focus groups with farmers prior toourexperiment,creditconstraintswerethe(unprompted)explanationgivenbythevastmajority of these farmers as to why they were not storing and selling maize at higher prices. In particular, because early all of these farm households have school aged kids, and a large percentage of a child’s school fees are typically due in the few months after harvest (prior to January enrollment), many farmers report selling much of their harvest to pay these fees. Indeed, many schools in the area will accept in-kind payment in maize during this period. Farmers also report having to pay other bills they have accumulated throughout the year during the post-harvest period. 7 Second, as with poor households throughout much of the world, these farmers appear to have verylimitedaccesstoformalcredit. Onlyeightpercentofhouseholdsinoursamplereportedhaving taking a loan from a bank in the year prior to the baseline survey. Informal credit markets also appear relatively thin, with less than 25% of farmers reporting having given or received a loan from a moneylender, family member, or friend in the 3 months before the baseline. Absent other means of borrowing, and given these various sources of “non-discretionary” con- sumption they report facing in the post-harvest period, farmers end up liquidating rather than storing. Furthermore, a significant percentage of these households end up buying back maize from the market later in the season to meet consumption needs, and this pattern of “selling low and buying high” directly suggests a liquidity story: farmers are in effect taking a high-interest quasi- loan from the maize market (Stephens and Barrett, 2011). Baseline data indicate that 35% of our sample both bought and sold maize during the previous crop year (September 2011 to August 2012), and that over half of these sales occurred before January (when prices were low). 40% of our sample reported only purchasing maize over this period, and the median farmer in this group made all of their purchases after January. Stephens and Barrett (2011) report very similar patterns for other households in Western Kenya during an earlier period. Nevertheless, there could be other reasons beyond credit constraints why farmer are not taking advantage of apparent arbitrage opportunities. The simplest explanations are that farmers do not know about the price increases, or that it’s actually not profitable to store – i.e. arbitrage opportunities are actually much smaller than they appear because storage is costly. These costs could come in the form of losses to pests or moisture-related rotting, or they could come in the form of “network losses” to friends and family, since maize is stored in the home and is visible to friends and family, and there is often community pressure to share a surplus. Third, farmers could be highly impatient and thus unwilling to move consumption to future periods in any scenario. Finally, farmers might view storage as too risky an investment. Evidence from pilot and baseline data, and from elsewhere in the literature, argues against a few of these possibilities. We can immediately rule out an information story: as shown in Figure 3 and discussed above, all farmers know exactly what prices are doing, and all expect prices to 8 rise substantially throughout the year.7 Second, pest-related losses appear surprisingly low in our setting, with farmers reporting losses from pests and moisture-related rotting of less than 5% for maize stored for six to nine months. Similarly, the fixed costs associated with storing for these farmers are small and have already been paid: all farmers store at least some grain (note the positive initial inventories in Figure A.1), and grain in simply stored in the household or in small sheds previously built for the purpose. Third, existing literature shows that for households that are both consumers and producers of grain, aversion to price risk should motivate more storage rather than less: the worst state of the world for these households is a huge price spike during the lean season, which should motivate “precautionary” storage (Saha and Stroud, 1994; Park, 2006). Fourth, while we cannot rule out impatience as a driver of low storage rates, extremely high discount rates would be needed to rationalize this behavior in light of the expected nine-month doubling of prices. Furthermore, farm households are observed to make many other investments with payouts far in the future (e.g. school fees), meaning that rates of time preference would also have to differ substantially across investments and goods. Costs associated with network-related losses appear a more likely explanation for an unwilling- ness to store substantial amounts of grain. Existing literature suggests that community pressure is one explanation for limited informal savings (Dupas and Robinson, 2013; Brune et al., 2011), and infocusgroupsfarmersoftentoldussomethingsimilaraboutstoredgrain(itselfaformofsavings). As described below, our main credit intervention might also provide farmers a way to shield stored maize from their network, and we added a small additional treatment arm to determine whether this shielding effect is substantial on its own. 2.2 Experimental design Our study sample is drawn from existing groups of One Acre Fund (OAF) farmers in Webuye district, Western Province, Kenya. OAF is a microfinance NGO that makes in-kind, joint-liability loans of fertilizer and seed to groups of farmers, as well as providing training on improved farming 7Themeanacrossfarmersforallthreereportedprices(thehistoricalpurchaseprice,thehistoricalsalesprice,and the expected sales price) is a 115-134% increase in prices. For the expected sales price over the ensuing nine months after the September 2012 baseline, the 5th, 10th, and 25th percentiles of the distribution are a 33%, 56%, and 85% increase, respectively, suggesting that nearly all farmers in our sample expect substantial price increases. 9 techniques. OAF group sizes typically range from 8-12 farmers, and farmer groups are organized into “sublocations” – effectively clusters of villages that can be served by one OAF field officer. OAF typically serves 20-30% of farmers in a given sublocation. As noted above, extensive focus groups with OAF farmers in the area prior to the experiment suggested that credit constraints likely play a substantial role in smallholder marketing decisions in the region. These interviews also offered three other important pieces of information. First, farmers were split on when exactly credit access would be most useful, with some preferring cash immediatelyatharvest,andsomepreferringitafewmonthslaterandtimedtocoincideexactlywith when some of them had to pay school fees. This in turn suggested that farmers were sophisticated about potential difficulties in holding on to cash between the time it was disbursed and the time it needed to be spent, and indeed many farmers brought these difficulties up directly in interviews. Third, OAF was willing to offer the loan at harvest if it was collateralized with stored maize, and collateralized bags of maize would be tagged with a simple laminated tag and zip tie. When we mentioned in focus groups the possibility of OAF running a harvest loan program, and described the details about the collateral and bag tagging, many farmers (again unprompted) said that the tags alone would prove useful in shielding their maize from network pressure: “branding” the maize as committed to OAF, a well-known lender in the region, would allow them to credibly claim that it could not be given out.8 We allowed this information to inform the experimental design. First, we offer some randomly selected farmers a loan to be made available in October 2012 (immediately after harvest), and some a loan to be available January 2013. Both loan offers were announced in September 2012. To qualify for the loan, farmers had to commit maize as collateral, and the size of the loan they could qualify for was a linear function of the amount they were willing to collateralize (capped at 7 bags). To account for the expected price increase, October bags were valued at 1500Ksh, and January bags at 2000Ksh. Each loan carried with it a “flat” interest rate of 10%, with full repayment due afterninemonths.9 Soafarmerwhocommitted5bagswhenofferedtheOctoberloanwouldreceive 8Such behavior is consistent with evidence from elsewhere in Africa that individuals take out loans or use com- mitment savings accounts mainly as a way to demonstrate that they have little to share (Baland, Guirkinger, and Mali, 2011; Brune et al., 2011). 9Annualized,thisinterestrateisslightlylowerthanthe16-18%APRchargedonloansatEquityBank,themain 10