Selling low and buying high: An arbitrage puzzle in Kenyan villages Marshall Burke,1,2,3, Lauren Falcao Bergquist,4 Edward Miguel3,5 ⇤ 1Department of Earth System Science, Stanford University 2Center on Food Security and the Environment, Stanford University 3National Bureau of Economic Research 4Becker Friedman Institute, University of Chicago 5Department of Economics, University of California, Berkeley October 12, 2017 Abstract Large and regular seasonal price fluctuations in local grain markets appear to o↵er African farmerssubstantialinter-temporalarbitrageopportunities,buttheseopportunitiesremainlargely unexploited: small-scale farmers are commonly observed to“sell low and buy high”rather than the reverse. In a field experiment in Kenya, we show that credit market imperfections limit farmers’ abilities to move grain inter-temporally. Providing timely access to credit allows farm- ers to purchase at lower prices and sell at higher prices, increasing farm profits and generating a return on investment of 28%. To understand general equilibrium e↵ects of these changes in behavior, we vary the density of loan o↵ers across locations. We document significant e↵ects of the credit intervention on seasonal price fluctuations in local grain markets, and show that these GE e↵ects greatly a↵ect our individual level profitability estimates. We also find sugges- tive evidence that these GE e↵ects generate benefits for program non-recipients, benefits which are unlikely to be recouped by a financial institution and suggest a potential role for public intervention. In contrast to existing experimental work, our results thus indicate a setting in which microcredit can improve firm profitability, and suggest that GE e↵ects can substantially shape estimates ofmicrocredit’s e↵ectiveness. Failure toconsider these GE e↵ects could leadto substantial misestimates of the social welfare benefits of microcredit interventions. JEL codes: D21, D51, G21, O13, O16, Q12 Keywords: storage; arbitrage; microcredit; credit constraints; agriculture ⇤We thank Kyle Emerick, Jeremy Magruder, and Chris Barrett for useful discussions, and thank seminar par- ticipants at Berkeley, Stanford, Kellogg, ASSA, and PacDev for useful comments. We also thank Peter LeFrancois, Ben Wekesa, and Innovations for Poverty Action for excellent research assistance in the field, and One Acre Fund for partnering with us in the intervention. We gratefully acknowledge funding from the Agricultural Technology Adoption Initiative and an anonymous donor. All errors are our own. 1 1 Introduction Imperfections in credit markets have long been considered to play a central role in underdevel- opment (Banerjee and Newman, 1993; Galor and Zeira, 1993; Banerjee and Duflo, 2010), with these imperfections thought to have particularly large consequences for small and informal firms in the developing world and for the hundreds of millions of poor people who own and operate them. This thinking has motivated a large-scale e↵ort 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 e↵ects 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 access to these products are subsequently no more productive on average than those not given access, but that subsets of recipients often appear to benefit.2 Here we study a unique microcredit product designed to improve the profitability of small farms – a setting that has been largely outside the focus 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 of 50-100% between post-harvest lows and pre-harvest peaks common in local markets. Nevertheless, most of these farmers have di�culty 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 consumption needs later in the year, 1Studies finding high returns to cash grants include De Mel et al. (2008); McKenzie and Woodru↵ (2008); Fafchamps et al. (2013); Blattman et al. (2013). Studies finding much more limited returns include Berge et al. (2011) and Karlan et al. (2012). 2ExperimentalevaluationsofmicrocreditincludeAttanasioetal.(2011);Creponetal.(2011);KarlanandZinman (2011); Banerjee et al. (2013); Angelucci et al. (2013) among others. See Banerjee (2013) and Karlan and Morduch (2009) for nice recent reviews of these literatures. 2 many then end up buying back grain from the market a few months after selling it, in e↵ect using the maize market as a high-interest lender of last resort (Stephens and Barrett, 2011). Working with a local agricultural microfinance NGO, we study the role that credit constraints play in farmers’ inability to store grain and arbitrage these seasonal price fluctuations. We o↵er randomly selected smallholder maize farmers a loan at harvest,3 and study whether access to this loan improves their ability to use storage to arbitrage local price fluctuations relative to a control group. We find that farmers o↵ered this harvest-time loan sell significantly less and purchase significantly more maize in the period immediately following harvest, and this pattern reverses during the period of higher prices 6-9 months later. This change in the marketing behavior results in a statistically significant increase in revenues (net of loan interest) of 545Ksh, suggesting that the loan produces a return on investment of 28%. We replicate the experiment in two back-to-back yearstotesttherobustnessoftheseresultsandfindremarkablysimilarresultsonprimaryoutcomes in both years. We then run a long-run follow-up survey with respondents 1-2 years after harvest-time credit intervention had been discontinued by the NGO, to test whether farmers are able to use the additional revenues earned from this loan product to “save their way out” of credit constraints in future years. We find no evidence of sustained shifts in the timing of farm sales in subsequent seasons,nordoweseelong-rune↵ectsonsalesorrevenuesinfutureyears(thoughthelaterestimate is measured with considerable noise). We also find no evidence of increased input use or harvest levels in years after the credit had ended. GiventhehightransportcostsinourruralAfricansetting,wealsostudywhetherstorage-related changes in marketing behavior a↵ected local market prices. Did this individual-level intervention have market-level e↵ects? To answer this, we experimentally varied the density of treated farmers across locations and tracked market prices at 52 local market points. We find that the greater storage of grain at the market level (induced by the credit intervention) led to smoother prices over theseason: inareaswithhightreatmentdensity, pricesimmediatelyafterharvestweresignificantly 3Thisisunusual-andseeminglycounter-intuitive-timingforaloantoagriculturalhouseholds;ourmicrofinance NGOpartnerandmanyothergroupso↵erloansatplantingtimeinordertofacilitatefarmeradoptionofhighquality inputs such as fertilizer. 3 higher, while prices during the lean season were lower (although the latter not significantly so). Discernible price e↵ects from such a localized shift in supply imply that agricultural markets in the region are highly fragmented. We find that these general equilibrium e↵ects greatly alter the profitability of the loan. By dampening the arbitrage opportunity posed by season price fluctuations, treated individuals in high saturated areas show diminished revenue impacts relative to farmers in lower saturation areas. We find that while treated farmers in high-saturation areas store significantly more than their control counterparts, doing so is not significantly more profitable; the reduction in seasonal price dispersion in these area reduces the benefits of loan adoption. Conversely, treated farmers in low- density areas have both significantly higher inventories and significantly higher profits relative to control. These general equilibrium e↵ects — and their impact on loan profitability at the individual level — have lessons for both policy and evaluation. In terms of policy, the general equilibrium e↵ects shape the distribution of the welfare gains of the harvest-time loan: while recipients gain relatively less than they would in the absence of such e↵ects, we find suggestive evidence that non- recipients benefit from smoother prices, even though their storage behavior remains unchanged. Though estimated e↵ects on non-treated individuals are measured with substantial noise, a welfare calculation taking the point estimates at face-value suggests that 70% of overall gains in high- treatment-intensity areas accrued to program non-recipients. These gains to non-recipients, which cannot be readily recouped by private sector lending institutions, may provide some incentive for public provision of such products. Theerodingprofitabilityofarbitragethatweobservealsohasimplicationsforimpactevaluation in contexts of highly fragmented markets. In these settings in which general equilibrium e↵ects are likely to be more pronounced and the SUTVA assumption (Rubin, 1986) more likely to be violated, an evaluation of a simple individually-randomized loan product could have di�culty discerning null e↵ects from large positive e↵ects on social welfare. While this issue may be particularly salient in our context of a loan explicitly designed to enable arbitrage, it is by no means unique to our setting. Any enterprise operating in a small, localized market or in a concentrated industry may 4 face price responses to shifts in own supply, and credit-induced expansion may therefore be less profitable than it would be in more integrated market or in a less concentrated industry. Proper measurement of these impacts requires a study design with exogenous variation in these general equilibrium e↵ects. Why do we find positive e↵ects on firm profitability when many other experimental studies on microcredit do not? Existing studies have o↵ered a number of explanations for why improved access to capital does not appear beneficial on average. First, many small businesses or potential micro-entrepreneurs simply might not face profitable investment opportunities (Banerjee et al., 2013; Fafchamps et al., 2013; Karlan et al., 2012; Banerjee, 2013).4 Second, profitable investment opportunities could exist but microentrepreneurs might lack either the skills or ability to channel capital towards these investments - e.g. if they lack managerial skills (Berge et al., 2011; Bruhn et al., 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 immediately, and this could limit investment in illiquid but high-return business opportunities (Field et al., 2012). Finally, as described above, general equilibrium e↵ects of credit expansion could alter individual-level treatment e↵ect estimates in a number of ways, potentially shaping outcomes for both treated and untreated individuals. This is a recognized but unresolved problem in the experimental literature on credit, and few experimental studies have been explicitly designed to quantify the magnitude of these general equilibrium e↵ects (Acemoglu, 2010; Karlan et al., 2012).5 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 4For example, many microenterprises might have low e�cient scale and thus little immediate use for additional investment capital, with microentrepreneursthen preferring to channelcredit toward consumption instead of 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. 5For instance, Karlan et al. (2012) conclude by stating, “Few if any studies have satisfactorily tackled the im- pact of improving one set of firms’ performance on general equilibrium outcomes.... This is a gaping hole in the entrepreneurship development literature.” Indeed, positive spillovers could explain some of the di↵erence between the experimental findings on credit, which suggest limited e↵ects, and the estimates from larger-scale natural exper- iments, which tend to find positive e↵ects of credit expansion on productivity – e.g. Kaboski and Townsend (2012). Acemoglu (2010) uses the literature on credit market imperfections to highlight the understudied potential role of GE e↵ects in broad questions of interest to development economists. 5 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 farmers 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 its purchases from times of high prices to times of lower prices. Furthermore, our results also suggest that – at least in our rural setting – treatment density matters and market-level spillovers can substantially shape individual-level treatment e↵ect esti- mates. Whether these GE also influenced estimated treatment e↵ects in the more urban settings examined in many previous studies is unknown, although there is some evidence that spillovers do matter for microenterprises who directly compete for a limited supply of inputs to production.6 In any case, our results suggest that explicit attention to GE e↵ects in future evaluations of credit market interventions is likely warranted. Beyond contributing to the experimental literature on microcredit, our 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. Similarly, Dillion (2017) finds that an administrative change in the school calendar that moved the timing of school fee payments to earlier in the year in Malawi forced credit constrained households with school-aged children to sell their crops earlier and at a lower price, and Fink et al. (2014) find that agricultural loans aimed at alleviated seasonal labor shortages can improve household welfare in Zambia. As in these related papers, our results show that when borrowing and saving are di�cult, households turn to increasingly costly ways to move consumption around in time. In our particular setting, credit constraints combined with post-harvest cash needs cause farmers to store less than 6SeeDeMeletal.(2008)andtheirdiscussionofreturnstocapitalforfirmsinthebamboosector,allofwhomin their setting compete over a limited supply of bamboo. 6 they would in an unconstrained world. In this setting, even a relatively modest expansion of credit a↵ects local market prices, to the apparent benefit of both those with and without access to this credit. Finally, our 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 (Banerjee and Newman, 1993; Galor and Zeira, 1993). These earlier papers showed how missing markets for credit, coupled with an unequal underlying wealth distribution, could shape large-scale patterns of occupational choice. We show that missing markets for credit combined with climate- induced seasonality in rural income can help generate widely-observed seasonal price patterns in rural grain markets, patterns that appear to further worsen poor households’ abilities to smooth consumptionacrossseasons. Evidencethattheexpansionofharvest-timecreditaccesshelpsreduce thispricedispersionsuggestsanunder-appreciatedbutlikelysubstantialadditionalbenefitofcredit 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 e↵ects. Section 5 presents the market level e↵ects of the intervention, and shows how these a↵ect 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 o↵er 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 7 harvest average roughly 25-40% in these markets, and these increases appear to be a lower bound on seasonal price increases reported elsewhere in Africa.7 These increases also appear to be a lower bound on typical increase observed in the smaller markets in our study area, which (relative to these much larger markets) are characterized with muchsmaller“catchments”andlessoutsidetrade. Weaskedfarmersatbaselinetoestimateaverage monthly prices of maize at their local market point over the five years prior to our experiment. As shown in Figure 2, they reported a typical doubling in price between September (the main harvest month) and the following June.8 We also collected monthly price data from local market points in our sample area during the two years of this study’s intervention, as well as for a year after the intervention ended (more on this data collection below).9 Figure 3 presents the price fluctuations observed during this period. Unfortunately, because data collection began in November 2012 (two months after the typical trough in September), we cannot calculate the full price fluctuation for the 2012-2013 season. However, in the 2013-2014 and 2014-2015 seasons we observe prices increasing by 42% and 45% respectively. These are smaller fluctuations than those seen in prior years (as reported by farmers in our sample) and smaller than those seen in subsequent years, which saw increases of 53% and 125% respectively.10 There is therefore some variability in the precise size of the price fluctuation from season to season. Nevertheless, we see price consistently rise by more than 40% and, in some years, by substantially more. Farmersdonotappeartobetakingadvantageoftheseapparentarbitrageopportunities. Figure A.1 shows data from two earlier pilot studies conducted either by our NGO Partner (in 2010/11, with 225 farmers) or in conjunction with our partner (in 2011/12, with a di↵erent sample of 700 7For 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%. 8Incasefarmersweresomehowmistakenoroveroptimistic,weaskedthesamequestionofthelocalmaizetraders 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%. Results available on request. 9The study period covers the 2012-2013 and 2013-2014 season. We also collect data for one year after the study period, covering the 2014-2015 season, in order to align with the long-run follow-up data collection on the farmer side. 10For the 2015-2016 season, we combine our data with that collected by Bergquist (2017) in the same county in Kenyaandestimatethatmaizepricesincreasedby53%fromNovembertoJune. Forthe2016-2017season,wethank Pascaline Dupas for her generosity in sharing maize price data collected in the same county in November 2016 and June 2017, from which we estimate an increase of 125%. 8 farmers). 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 grain at higher prices and purchase at lower prices? Our experiment is designed to test the role of credit constraints in shaping storage and marketing decisions. In extensive focus groups with farmers prior to our experiment, credit constraints were the (unprompted) explanation given by the vast majority of these farmers as to why they were not storingandsellingmaizeathigherprices. Inparticular, becausenearlyallofthesefarmhouseholds have school aged kids, and a large percentage of a child’s school fees are typically due in the few months after harvest in January, given the calendar-year school year schedule, 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. Further, 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.11 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 grain 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 e↵ect taking a high-interest quasi-loanfromthemaizemarket(StephensandBarrett,2011). Baselinedataindicatethat35%of 11Notethatevenatthehighinterestrateschargedbyformalbankinginstitutions(typicallyaround20%annually), storagewouldremainprofitable,giventhe40%plus(oftenmuchlarger)increasesinpricesthatareregularlyobserved overthe9-monthpost-harvestperiodandrelativelysmallstoragelosses(e.g.,duetospoilage),whichweestimateto be less than 5%. 9 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 is 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 several of these possibilities. We can immediately rule out an information story: farmers are well- aware that prices rise substantially throughout the year. When asked in our baseline survey about expectations for the subsequent season’s price trajectory, the average farmer expected prices to increase by 107% in the nine months following the September 2012 harvest (which was actually an over-estimate of the realized price fluctuation that year).12 Second, pest-related losses appear surprisinglylowinoursetting,withfarmersreportinglossesfrompestsandmoisture-relatedrotting of2.5%formaizestoredforsixtoninemonths. Similarly,themarginalcostsassociatedwithstoring forthesefarmersaresmall(estimatessuggestthatthecostperbagisabout3.5%oftheharvest-time price)andthefixedcostshavetypicallyalreadybeenpaid(allfarmersstoreatleastsomegrain;note the positive initial inventories in Figure A.1), as grain in simply stored in the household or in small sheds previously built for the purpose.13 Third, while we cannot rule out impatience as a driver 12The5th,10th,and25thpercentilesofthedistributionarea33%,56%,and85%increase,respectively,suggesting that nearly all farmers in our sample expect substantial price increases. 13Though note that Aggarwal et al. (2017) find that o↵ering group-based grain storage can encourage greater storage. 10