A Market for Lemons in Kenyan Maize

Transcription

1 A Market for Lemons in Kenyan Maize Vivian Hoffmann University of Maryland Agricultural and Resource Economics Samuel Mutiga Rebecca Nelson Michael Milgroom Cornell University Department of Plant Microbe-Biology Jagger Harvey Biosciences East and Central Africa Informal food markets in developing countries are characterized by asymmetric information and an absence of regulation. While consumers are able to observe certain quality attributes, information on food safety is often unavailable. In particular, certain fungal contaminants, increasingly recognized as a serious public health problem in much of the developing world, are both highly toxic and invisible. In this paper, we provide evidence that the informal market for maize in Kenya conforms to the predictions of Akerlof s model of a market characterized by information asymmetries. Using data from over 2000 maize samples collected from consumers in four Kenyan provinces, we show that observable characteristics affect pricing, but that the presence of aflatoxin, a dangerous fungal contaminant, is not reflected in the price. The level of contamination with aflatoxin, a dangerous fungal contaminant, is significantly higher in grain that is sold, compared to that which is retained by farmers for own consumption. Similarly, maize that has been purchased on the market is of lower quality than that grown on the consumer s own farm. Reputation effects appear to mitigate this problem in the case of maize sold by millers, who operate from a fixed location, but the share of millers selling maize is small.

2 1. Introduction Informal food markets in developing countries are characterized by asymmetric information and an absence of regulation. While consumers are able to observe certain quality attributes, information on food safety is often unavailable. In particular, fungal grain contaminants, increasingly recognized as a serious public health problem in much of the developing world, are both highly toxic and invisible. In this paper, we provide evidence that the informal market for maize in Kenya conforms to the predictions of Akerlof s (1970) model of a market characterized by information asymmetries. The level of contamination with aflatoxin, a dangerous fungal toxin, is significantly higher in grain that is sold, compared to that which is retained by farmers for own consumption, but does not affect pricing. Consumption of high levels of the fungal byproduct aflatoxin can be fatal, and chronic exposure at lower levels increases the risk of liver cancer, depresses immune function, and causes growth faltering in children (Strosnider et al. 2006). The fungus that produces aflatoxin, Aspergillus flavus (A. flavus), is endemic in soils throughout Kenya, particularly in the eastern part of the country. Plant stress due to climatic conditions and pest attacks allow the colonization of maize grains with the fungus, which may then spread during storage. Growth of A. flavus, as well as other, less harmful fungi, is exacerbated by inadequate drying and poor storage conditions. While aflatoxin contamination is correlated with observable properties, such as discoloration, grain moisture content, and likely taste, the toxin itself is invisible and tasteless, and may be present in maize without visible signs of damage. While those selling maize may be aware of pre-harvest damage, the conditions under which maize was stored, the timing and extent of drying, and the extent to which discolored kernels were present and subsequently removed, the information available to buyers about the quality and possible contamination of maize is limited. Maize is the primary staple grain for Kenyans, who consume an average of 400 grams of the cereal daily (Muriuki et al. 1995). The majority of maize consumed in Kenya is either grown by the consumer or procured at informal open air markets.

3 Cultivation of maize is almost ubiquitous among small farmers, with 96 percent of farmers growing the crop (Yamano et al., 2005). Whether purchased or grown on one s one farm, maize is typically stored as kernels. When desired for consumption, it is taken to a neighborhood hammer mill, where it is ground into flour from which porridge is made. In addition to providing grinding services, some neighborhood maize millers also sell maize from their own stock. Since millers operate at a fixed location, they are likely to have more regular customers, and may be more subject to reputation effects than the mobile traders who frequent a number of intermittent markets. We collected survey data and maize samples from over 2000 clients at 176 smallscale hammer mills across four Kenyan provinces. As would be expected, observable qualities of maize, such as the proportion of broken and rotten kernels, are reflected in its price, but aflatoxin contamination, which is unobservable, is not. We find, however, that the use to which grain is put depends upon the level of aflatoxin contamination. Consumers are more likely to sell maize with the highest levels of contamination, to use maize with intermediate levels for brewing and livestock feed, and to consume the cleanest maize as food. Similarly, aflatoxin contamination is significantly higher for purchased maize compared to that which was grown on the consumer s own farm. This paper represents the only investigation known to the authors of adverse selection in developing country grain markets. A number of papers, notably Bond (1982) investigate the existence of adverse selection into vehicle markets in developed country settings. Recent work also finds evidence of adverse selection into the market for cattle in India (Agarwal, 2011). The paper proceeds as follows. We begin by outlining a simple conceptual framework in Section 2. We then describe the survey and maize quality data in the following section. Section 4 describes the empirical strategy and results, and Section 5 concludes with a summary of findings and suggestions for future research.

4 2. Conceptual Framework The quality of maize is determined during each stage of production: cultivation, harvest, processing, and storage. Information about quality is gained during each of these stages, through knowledge of both the practices employed, such as harvest and drying methods, and problems encountered, such as drought, excessive rains, and pest attack. Once the maize is ready for consumption, perhaps the most important quality attribute taste can be observed. Farmers in particular, but also maize consumers more generally thus have access to a wealth of information about the quality of the maize in their possession. We hypothesize that the informal maize market suffers from adverse selection due to the information asymmetries described above. Farmers, knowing something about the quality of maize in their possession may selectively sell maize of lower quality, or which they fear may have been spoiled during storage. A simple model of the farmer s decision of whether to sell or consume the staple grain is as follows: Max EU = U(x, m, q) (1) Subject to: p pc c p pm s p ps p < y m h m s + m p = m q h ~f(h) q p ~f(p) Where x is the numeraire and q is a weighted average of the quality of harvested and purchased grain. The quantity of grain consumed, m is the sum of m h, the amount harvested, and m p the amount purchased, less that which is sold, m s. The farmer derives income from sales of both maize and of a quality-invariant cash crop, c. There is a single market price for maize, its quality being unobservable, and the for the cash crop. The entire distribution of the quality of harvested grain, f(h), is known, ex post, to the producer, who can choose which portion of the harvest to sell, and which to retain for household consumption. The quality of maize available for

5 purchase, q p, is not known, but is known to be distributed according to the distribution f(p). Given that unobservable quality is not rewarded in the market, a farmer will retain maize that is above her expectation of the average quality available in the market, and sell that which is below this quality threshold, 1 resulting in an average quality of own-produced grain of q hmax q h = f(h)dq q h, (2) ht Where q hmax is the highest quality of maize produced by the household, and q ht is the threshold below which maize is sold. To the extent that sellers are subject to reputation effects, information about f(p) will be available, presumably creating variation in p pm, and solving the problem of adverse selection into the market. 3. Methods and Data Subjects were recruited at 176 hammer mills across 138 villages in the Kenyan provinces of Western, Rift Valley, Nyanza, and Eastern over two years. While Eastern Province is known to be a hot spot for aflatoxin, contamination is also present, albeit at lower levels, in other provinces as well (Mutiga et al., in progress). Each province was stratified by agroecological zone to ensure a broad representation of growing conditions. Districts, and then towns, were subsequently selected in each of these zones from a database of market centers using two-stage random sampling. The largest mill in a selected town was generally used as the sampling point, though in areas where market centers were sparse, samples were collected from up to four mills in order to better represent the geographical diversity of growing conditions. 1 If the agent is risk averse, the quality threshold below which maize is sold will be below the expected quality of maize found in the market.

6 Survey staff spent between one and three days at each hammer mill interviewing customers and procuring samples of maize brought by customers for milling. Visual inspection of maize grains for rot and broken kernels was performed by enumerators at the time of sample collection. Maize samples were then sent to the Biosciences East and Central Africa (BECA) laboratory in Nairobi to be tested for aflatoxin contamination. The laboratory test employed to detect aflatoxin contamination is sensitive up to 20 parts per billion. For higher levels of contamination, samples were diluted and reanalyzed. Precision is lost with each dilution, so aflatoxin values greater than 20 ppb should be considered approximate. Respondent characteristics Table 1 provides summary statistics for individuals interviewed and the maize samples collected. 2 The majority of respondents were female, reflecting the traditional female responsibility of milling grain. The average age in our sample is just over 35, and just over half of the sample had completed eight years of primary school. Over 80 percent of respondents lived in households that owned a cell phone but only 13 percent had electric connections to their households. Almost all respondents lived in a dwelling with a tile or metal roof, and approximately half in a house with walls made of concrete or baked brick. TABLE 1 ABOUT HERE Maize sample characteristics Half of maize samples collected were grown on the respondent s own farm, just over four percent from the maize mill, and 38 percent was purchased elsewhere. Gifts and food aid account for the remaining 6 percent of maize. The mean price paid for purchased maize was 14 Kenyan Shillings per kilogram, approximately US Different survey instruments were used in Eastern and Western Kenya. Data on visible maize quality and maize price are only available for the Eastern sample, and data on assets are only available for a subset of the Western sample.

7 Seventy-five percent of maize was intended for own consumption. Brewing was the next most common use at 21 percent, and less than one percent of grain was intended for use as livestock feed. Just over 3 percent of respondents reported an intention to sell the grain they were milling. Since most farmers sell maize as kernels rather than flour, it is not surprising that we observe such a low proportion of sales. Similarly, maize is normally fed to livestock as kernels or cobs. Approximately two thirds of clients sorted discolored or broken grains and debris from their maize at the mill. Of these, two thirds reported doing so for health reasons, and a quarter said the main reason for sorting was to improve the taste of the maize. Other responses to this question included to clean it and to remove insects. Eighty percent of the maize observed was grown from hybrid seed. Among those who brought maize they grew themselves, the average total harvest was almost 1000 kg, with a very large spread. Most samples collected were free of visible rot or broken kernels, but approximately one third contained between one and ten percent discolored kernels, and a similar share contained the same proportion of broken kernels. Samples with greater than ten percent discolored or broken kernels were relatively rare, constituting just 7.6 percent and 5.1 percent of samples respectively. Twenty ppb is the allowable limit of aflatoxin contamination for human consumption according to both the US Food and Drug Association (FDA) and the World Food Program (WFP). The limit in Kenya was recently decreased from 20 to 10 parts per billion (ppb), owing to the dietary importance of maize. The distribution of aflatoxin contamination is highly skewed, with 23 percent of observations overall containing no detectable aflatoxin, 48 percent between 0 and the Kenyan standard of 10 ppb, 14 percent between 10 and the FDA/WFP standard of 20 ppb, and 14 percent of samples containing over 20 ppb. The data confirm that contamination in Eastern Province is much higher than in the western part of the country (Figure 1).

8 FIGURE 1 ABOUT HERE 4. Empirical approach and results Effect of maize quality on price We begin our analysis by investigating which quality characteristics affect maize prices, by regressing prices paid by those who obtained maize through the market on the proportion of rotten and broken kernels, as well as the level of aflatoxin contamination using an OLS model with village fixed effects and standard errors clustered at the village level. As shown in columns 1 through 3 of Table 2, both the proportion of rotten and broken grains affect price, though correlation between these two dimensions of quality prevents their detection in a single model. TABLE 2 ABOUT HERE Columns 4 through 8 of Table 2 show the results of various specifications relating maize price to aflatoxin contamination. None of these show an association between contamination and price, suggesting that contamination is unobservable to buyers. Aflatoxin contamination and the use of maize There are four uses to which maize can be put: it can be consumed by the household, used for brewing, fed to livestock, given away, or sold. Feeding aflatoxin contaminated maize to livestock inhibits livestock growth and reduces milk production, but farmers may not be aware of this. Aflatoxin is not affected by the fermentation process if contaminated maize is used to produce spirits, though again farmers awareness of this fact is not known. TABLE 4 ABOUT HERE Aflatoxin contamination levels at various points in the distribution of maize destined for household consumption, brewing, livestock feed, and sale, are shown in Table 3. Maize destined for consumption as food by the household has the lowest level of aflatoxin for most of the distribution, but also the greatest dispersion. Maize

9 destined for sale has the highest average level of contamination, with over twice the mean contamination level as that intended as food. Figure 2 plots the cumulative distribution function of aflatoxin content for maize intended to be used for each purpose. For ease of visualization, we omit observations above 100 ppb. This omits 5.5% of the data, which is disproportionately used for livestock feed (2.4%), sale (5.7%), and brewing (35.8%). FIGURE 2 ABOUT HERE Results from a multinomial logit regression, presented in Table 4, confirm what the graphical analysis suggests. We employ a specification that focuses attention on allowable limits of aflatoxin by the two commonly used standards discussed above. A categorical variable indicating the intended use of the maize is regressed on three binary variables, each representing a different level of contamination. 3 The excluded category is no detectable aflatoxin contamination. The share of observations with no detectable aflatoxin that is put to each use is reported below the coefficients. In addition to its ease of interpretation, this specification has the advantage of not being affected by measurement error in samples for which aflatoxin content is greater than 20 ppb. TABLE 4 ABOUT HERE There is no significant difference in the use of grain that is free of aflatoxin versus that which contains aflatoxin below the current allowable limit of 10 ppb. Grain at an intermediate level of contamination, between 10 ppb and the previous limit of 20 ppb is ten percentage points (14 percent on the base level) less likely to be consumed as food relative to aflatoxin-free grain, and eight percentage points (33 percent) more likely to be turned into a local brew. Maize containing more than 20 ppb aflatoxin is even less likely, by 13 percentage points or 19 percent on the base probability, to be consumed as food than aflatoxin-free maize. Maize in this highest 3 Alternative approaches, including a quadratic specification, a tobit with an upper limit of 20, and one using dummies for each quintile of aflatoxin contamination, all yield similar results. Errors are clustered at the village level.

10 category of contamination is similarly likely as that in the previous range to be used to brew alcohol. While the small number of observations of maize destined for sale 4 leads to an estimated effect that is significant only at the ten percent level, the size of this effect is large, with maize in the highest category of contamination over twice as likely to be sold as that which contains an undetectable level of the toxin. Using data on the source of maize, rather than its intended use, give results consistent with the hypothesis of adverse selection into the market. Figure 3 illustrates the likelihood of contamination by source. Results from an ordered probit model in which the category of contamination (0 ppb, 0-10 ppb, ppb, and >20 ppb), is regressed on variables indicating how maize was obtained are shown in Table 5. Grain received as a gift or as food aid has similar levels of contamination as that grown on one s own farm. Maize that has been purchased somewhere other than the mill is 3.3 percentage points more likely to contain a detectable level of aflatoxin than maize grown on one s own farm. This represents a twelve percent increase in the probability of contamination. The likelihood of contamination above each of the two regulatory thresholds is also higher for purchased maize, though the difference at these levels are smaller and magnitude and significant only at the 10 percent level. Maize purchased from maize mills, which, unlike most maize traders, operate from a fixed location, is of similar quality to that grown by consumers, suggesting that reputation effects may serve to mitigate the adverse selection problem. However, given that only four percent of samples were obtained from the miller, the absence of an effect is imprecisely measured. With only 22 observations of price for maize procured from a miller, the existence of a reputation premium is difficult to ascertain. In this small sample, though, we see no evidence of any difference in prices by type of vendor: maize purchased at the mill cost shillings per kg, versus shillings per kg from other sources. We note as well that the share of millers who sell maize appears to be low: at 73 4 Most maize is sold in the form of kernels, and thus milled after sale. We compare the quality of maize that has been purchased with that that was home grown below.

11 percent of the 176 mills in the sample, no purchases from the mill were recorded. If we allow that for those mills where only one mill-purchased sample was recorded, this observation could have been a coding error, the proportion of mills selling maize may be lower still: at least two observations of mill purchases were recorded at 10 percent of mills. 5. Conclusion We have shown in this paper that contamination with the toxic fungal byproduct aflatoxin is not reflected prices in the informal Kenyan market for maize. Owners of maize, however, appear to possess information about the level of contamination or correlates thereof, and to use this information to decide how maize is used. This process results in adverse selection of low-quality grain into the market, with maize that is sold more likely to exceed regulatory thresholds for aflatoxin contamination. Likewise, maize that has been purchased is more likely to be contaminated than maize which has been grown on the consumer s own farms. However maize purchased from millers, whose location is fixed, is of higher average quality than that purchased from other, more mobile traders. While the number of price observations is small, the apparent absence of a premium for maize from this source may explain the small number of millers who sell maize. At the same time, this raises questions about consumers willingness to pay for maize that is likely, but not certain, to be of higher quality. These findings suggest that asymmetric underlies a market failure in the informal maize market in Kenya. While suggesting an opportunity for credible safety and quality certification or branding, the results raise questions about how reputation effects function in characterized by experience attributes. Future research focused on the strategies of different types of maize vendors could shed light on the role played by reputation effects in this market, and how policy could serve to strengthen these and lessen the adverse selection problem.

12 6. References Akerlof, G. A The Market for Lemons : Quality Uncertainty and the Market Mechanism, Quarterly Journal of Economics, 84(3): Anagol, S Adverse Selection in Asset Markets: Theory and Evidence from the Indian Market for Cows. Working paper. Bond, Eric (1982) A Direct Test of the Lemons Model: The Market for Used Pickup Trucks. American Economic Review, 72, pp Muriuki GK, Siboe GM Maize flour contaminated with toxigenic fungi and mycotoxins in Kenya. African Journal of Health Sciences. 2(1): Mutiga, S.K. V. Hoffmann, J. Harvey, M.G. Milgroom, and R.J. Nelson (in progress). Sampling of maize at Kenyan neighborhood mills provides evidence of high grain contamination with aflatoxin in eastern region and a widespread occurrence of fumonisin. Strosnider, H., E. Azziz-Baumgartner, M. Banziger, R. V. Bhat, et al. (2006) Workgroup report: Public health strategies for reducing aflatoxin exposure in developing countries. Environmental Health Perspectives. 114(12): Yamano, T, K. Otsuka, F. Place, Y. Kijima, and J. Nyoro The 2004 REPEAT Survey in Kenya (First Wave): Results. National Graduate Institute for Policy Studies.

13 Table 1: Summary statistics Overall Western / Nyanza / Rift Eastern Mean Std. Dev. N Mean Std. Dev. N Mean Std. Dev. N demographic age male completed primary assets own cell phone house: permanent walls house: permanent roof house: electricity maize origin own farm posho miller purchased elsewhere gift food aid price per kg (KSH) intended use and practices household food brewing livestock feed sell sort at miller? sorted for health sorted for taste maize characteristics hybrid seed (own-grown) harvest (KG) (own-grown) discolored kernels: 1-10% > 10% broken kernels: 1-10% > 10% aflatoxin (ppb) Notes: Due to variations in questionnaires across regions time, data on grain pricea and observable train quality are only available in the Eastern dataset. Data on assets are available for only a small sub-set of the Western dataset.

14 Table 2: Effect of observable and unobservable quality characteristics on maize price. 1-10% rotten * (1) (2) (3) (4) (5) (6) (7) (8) (0.610) (0.652) >10% rotten * *** (0.891) (0.761) 1-10% broken (0.465) (0.509) >10% broken ** (1.364) (1.269) Aflatoxin(ppb) (0.001) 0 < ppb afla < (0.496) ppb afla (0.747) afla ppb > (0.431) afla ppb > (0.747) (0.551) afla ppb > (0.465) Constant *** *** *** *** *** *** *** *** (0.253) (0.213) (0.153) (0.044) (0.418) (0.186) (0.123) (0.043) Observations R-squared Clusters Notes: Coefficients are from linear regressions of price per on quality attributes with village fixed effects and standard errors clustered at the village level in parentheses. Data are from the Eastern sample only. * p<0.10, ** p<0.05, *** p<0.01

15 Table 3: Distribution of aflatoxin contamination by intended use Minimum 25th Percentile 50th Percentile 75th Percentile 90th Percentile Maximum Mean Standard Deviation N Food for HH Brewing Livestock Feed Sale Notes : Pooled data for both regions. Table 4: Use of maize as a function of aflatoxin content HH Food Brewing Livestock Feed Sale 0 > ppb > (0.026) (0.024) (0.010) (0.008) 10 > ppb > ** 0.083** (0.042) (0.038) (0.017) (0.011) ppb > *** 0.085** * (0.047) (0.041) (0.019) (0.016) Eastern *** 0.301*** *** (0.032) (0.030) (0.008) (0.008) Proportion used for X at ppb = 0 Observations Clusters LLR Notes: Marginal effects from a multinomial logit regression, with clustered standard errors shown in parentheses. Base category is zero aflatoxin content. * p<0.10, ** p<0.05, *** p<0.01

16 Table 5: Aflatoxin level as a function of maize source ppb=0 0 > ppb > > ppb > 20 ppb > 20 Purchased from Miller (0.034) (0.003) (0.013) (0.025) Purchased Elsewhere ** * 0.025* (0.016) (0.004) (0.006) (0.013) Received as Gift or Aid (0.028) (0.004) (0.011) (0.022) Eastern Province * * 0.035* (0.026) (0.004) (0.010) (0.019) Share of Own-Grown Maize in Category Observations Clusters LLR Notes: Marginal effects on the likelihood of observing each outcome, derived from an ordered probit regression, with standard errors clustered at the village level shown in parentheses. * p<0.10, ** p<0.05, *** p<0.01

17 Western / Nyanza / Rift Eastern Figure 1: Histogram of aflatoxin contamination by region, excluding 5.5% of the sample with contamination of greater than 100 ppb. Black vertical lines indicated the 10 ppb and 20 ppb regulatory limits Probability <= ppb aflatoxin Percent Aflatoxin(ppb) Graphs by region aflatoxin consume livestock brewing sell Figure 2: Aflatoxin contamination by stated use of grain. Excludes 5.5% of the sample with greater than 100 ppb aflatoxin contamination. Black vertical lines indicated the 10 ppb and 20 ppb regulatory limits.

18 Eastern Province Probability <= ppb aflatoxin aflatoxin own farm purchased Figure 3: Cumulative distribution of aflatoxin contamination by origin of grain, Eastern Province. Black vertical lines indicated the 10 ppb and 20 ppb regulatory limits. Western, Nyanza, and Rift Valley Provinces Probability <= ppb aflatoxin aflatoxin own farm purchased Figure 4: Cumulative distribution of aflatoxin contamination by origin of grain, Western, Nyanza and Rift Valley Provinces. Black vertical lines indicated the 10 ppb and 20 ppb regulatory limits.