A Market for Lemons: Maize in Kenya

Vivian HoffmannAgricultural and Resource Economics

University of Maryland

Samuel Mutiga Michael Milgroom Rebecca Nelson Plant and Microbe-Biology

Cornell University

Jagger HarveyBiosciences East and Central Africa -

International Livestock Research Institute Hub

With thanks to the Atkinson Center for a Sustainable Future

The Market for Lemons

“Business in underdeveloped countries is difficult” - Akerlof (1970)

• When sellers have information about quality of goods not available to buyers:– Quality is not reflected in the price– Low quality goods are traded, high quality retained– Volume of trade is reduced

• Institutions (guarantees, branding) may arise to solve this problem

Previous empirical tests• Used vehicles

Bond, 1982, 1984; Pratt and Hoffer, 1984; Lacko, 1986; Genesove, 1993; Sultan, 2008; Emons & Sheldon, 2009• Workers

Gibbons and Katz, 1991• Slaves

Greenwald & Glasspiegel, 1983; Pritchett & Chamberlain, 1993• Cattle

Anagol, 2011

• We apply the lemons model to a developing country food market

Maize in Kenya

• Main staple food: estimated 400 grams / day per capita consumption (Muriuki and Siboe, 1995)

• Grown by 92% of farm households (KIHBS)• Not well suited to growing conditions

– Vulnerable to drought– Other crops generally more profitable (Tegemeo

Institute, 1996)– Prone to toxic fungal contamination

“It’s not the same as maize from my farm” – respondent who refused to sell sample

Less typical maize shop in Western KenyaMaize for sale in typical roadside market

Maize market, Rift Valley ProvinceAggregators

Maize market structure

Motivating welfare concerns

Human health• Absence of price incentive for quality leads to

underinvestment in good storage practices / over-supply of toxic contamination

Efficiency• Poor quality of food available for purchase could

lead to self-provisioning, inhibit specialization in more profitable activities (among other factors including price risk, transaction costs)

Aflatoxin• Produced by fungus Aspergillus flavus aflatoxin• Can colonize crop in field or post-harvest• Pre-harvest risk factors: drought, heat stress, pest attack• Post-harvest: high moisture content during storageHealth effects (Strosnider et al. 2006):• One of the most potent known carcinogens• Acute exposure: liver failure, death• Chronic exposure: liver cancer, suppressed immune response,

growth faltering in childrenObservability:• A. flavus not always visible; other, non-toxigenic molds may be

visible visible attributes poor proxy• Mold generally tastes bad aflatoxin likely correlated with taste

(also a noisy signal)

This is your chicken

This is your chicken on aflatoxin

Necessary condition for lemons market: asymmetric information

• Farmers have private information about the quality of food in their possession– Knowledge of growing and storage conditions

(own-produced only)– Observe taste (own produced and purchased)– This information is not available to buyers

Necessary condition for over-supply of contaminant:

• Farmers (and / or others in value chain) can influence the dimension of quality on which information is asymmetric

A model of food marketing behavior

(1)

Subject to:

(2)

(3)

(4)

(5)

(6)

(7)

Utility is derived from food quality and consumption of numeraire good

Budget constraint: Income generated by sale of food, cash crop

Exogenous food requirement

Food quality identity

Cash crop production function

Food crop production function

Food crop quality function

Quality of harvested maize

𝑞𝑚 h,𝑖−𝑀 𝑖

Retained

𝑝 (𝑞<𝑞 𝑗)

Sold

𝑞𝑚 h, 𝑖𝑞0 𝐸 [𝑞¿¿𝑝 ]¿

Quality of harvested maize

𝑞𝑚 h,𝑖−𝑀 𝑖

Retained

𝑝 (𝑞<𝑞 𝑗)

Sold

𝑞𝑚 h, 𝑖𝑞0 𝐸 [𝑞¿¿𝑝 ]¿

1. The quality of retained food is increasing in the quantity of maize harvested, controlling for

the quality of harvested food

Testable predictions

2. The quality of retained food is higher than that which is available for purchase.

Where

3. The proportion of food consumed that is self-produced is increasing household wealth.

Data: Posho mill survey

• When need flour, take kernels to small-scale mill (posho mill)

• Interviewed clients & collected maize samples

Data: Posho mill survey

• 176 mills in 138 market centers• Western, Rift Valley, Nyanza Provinces: 2009• Eastern Province: 2010• Diversity of agro-ecological zones• Collected samples and survey data from at least 10

individuals per mill• Maize samples sent to Nairobi for laboratory analysis

as collected• Observations for which both survey & lab data

available: 2124

Respondent characteristics & maize sorting practices

Urban Rural Overall Mean Ndemographic & assetsfemale 0.75 2082age 36.3 2088completed primary 0.83 0.56 0.63 0.70 2077completed secondary 0.61 0.28 0.35 0.19 2077house: electricity 0.58 0.07 0.19 0.13 1441house: permanent roof 0.92 0.78 0.81 0.95 1429house: permanent walls 0.88 0.51 0.60 0.52 1369own cell phone 0.86 0.53 0.62 0.81 1441sorting practicessort at miller? 0.66 2107 sorted for health 0.68 1833 sorted for taste 0.25 1833

Nationally Representative Data (means)

Sample

Maize origin, use, and observable properties

Mean Nmaize originown farm 0.52 1983posho miller 0.04 1983purchased elsewhere 0.38 1983gift 0.06 1983food aid 0.00 1983intended usehousehold food 0.73 2124brewing 0.23 2124livestock feed 0.01 2124sell 0.03 2124maize characteristics1-10% broken kernels 0.28 1019> 10% 0.05 10191-10% discolored 0.32 1019> 10% 0.08 1019price per kg if purchased (KSH) 14.1 402

Analysis of maize samples

• Enzyme-linked immunosorbent assay (ELISA) for aflatoxin contamination

• Test is sensitive up to 20 ppb – above this, sample must be diluted, precision is lost

• Key thresholds: – 10 ppb (Kenyan regulatory standard)– 20 ppb (test limit and FDA standard)

Aflatoxin by region

Vertical lines indicate limit of test accuracy (20 parts per billion) and Kenyan regulatory standard (10 parts per billion).

Empirical Approach

1) Test assumption of asymmetric information2) Test predictions of the model3) Investigate impact of farmer practices on

quality

1) Test assumption of asymmetric information

• Qualities observable to buyers should affect price• Qualities observable to owners should affect use• Are there any attributes that do not affect price,

but do affect use?

Effect of maize characteristics on priceObservable Unobservable Both

1-10% discolored -1.077** -1.102**(0.502) (0.514)

>10% discolored -1.547** -1.592**(0.736) (0.733)

1-10% broken -0.030 -0.119(0.382) (0.395)

>10% broken -1.959* -2.374*(1.112) (1.259)

0 < ppb afla < 10 -0.426 0.042(0.379) (0.394)

10-20 ppb afla -0.630 0.237(0.573) (0.604)

afla ppb > 20 -0.282 0.989(0.588) (0.969)

Constant 14.540*** 14.388*** 14.324***(0.208) (0.303) (0.345)

Observations 294 390 294Communities 94 94 94R-squared 0.044 0.006 0.057

Notes: Linear model with community fixed effects, clustered errors shown in parentheses. Data are from the Eastern sample only. * p<0.10, ** p<0.05, *** p<0.01

To what extent do observables predict unobservables?

ppb = 0 0 > ppb > 10 10 > ppb > 20 ppb > 201-10% broken kernels -0.043 -0.004 0.015 0.032

(0.035) (0.005) (0.012) (0.027)>10% broken kernels -0.139*** -0.041 0.043*** 0.137*

(0.050) (0.033) (0.012) (0.071)1-10% discolored kernels 0.026 0.001 -0.009 -0.018

(0.039) (0.002) (0.014) (0.026)>10% discolored kernels -0.027 -0.003 0.010 0.020

(0.051) (0.007) (0.018) (0.040)

Proportion with no discolored or broken in aflatoxin category

0.331 0.317 0.176 0.176

ObservationsCommunitiesPseudo R-squared

1014

0.067107

Notes: Marginal effects on the likelihood of observing each outcome, derived from an ordered logit regression, with standard errors clustered at the community level. Data are from Eastern Province only. * p<0.10, ** p<0.05, *** p<0.01

Aflatoxin by intended use

mean min25th

percentile median75th

percentile95th

percentile max NFood for HH 28.7 0.0 0.0 1.4 9.3 83.6 4839.3 1492Brewing 41.5 0.0 0.0 7.2 18.9 172.4 1658.1 494Livestock Feed 48.1 0.0 1.1 2.8 17.6 201.3 288.8 19Sale 64.6 0.0 0.0 7.0 33.9 476.3 806.7 69

0.2

.4.6

.81

Pro

babi

lity

<=

ppb

afla

toxi

n

0 20 40 60 80 100aflatoxin

consume brewinglivestock sell

CDF of contamination by use

Effect of aflatoxin level on use of maize

Marginal effects from multinomial logit regressions, with clustered standard errors in parentheses. Base category is no detectable aflatoxin. Eastern data only. * p<0.10, ** p<0.05, *** p<0.01

HH Food BrewingLivestock

FeedSale HH Food Brewing

Livestock Feed

Sale

(5) (6) (7) (8) (9) (10) (11) (12)0 < ppb < 10 -0.114** 0.090** 0.028 -0.004 -0.112** 0.088** 0.027 -0.002

(0.047) (0.041) (0.030) (0.015) (0.046) (0.040) (0.027) (0.015)10 < ppb < 20 -0.223*** 0.192*** 0.019 0.011 -0.232*** 0.207*** 0.015 0.009

(0.048) (0.049) (0.027) (0.021) (0.048) (0.050) (0.023) (0.020)ppb > 20 -0.256*** 0.151** 0.036 0.069** -0.258*** 0.163*** 0.032 0.064**

(0.058) (0.060) (0.036) (0.031) (0.059) (0.062) (0.032) (0.030)1-10% discolored 0.079* -0.085* 0.007 -0.001

(0.046) (0.044) (0.007) (0.012)>10% discolored 0.160*** -0.135*** -0.002 -0.023**

(0.054) (0.051) (0.007) (0.010)1-10% broken -0.071 0.058 -0.002 0.015

(0.048) (0.046) (0.006) (0.014)>10% broken 0.076 -0.171*** 0.017 0.078

(0.082) (0.049) (0.021) (0.065)Proportion used for X at ppb = 0 0.785 0.189 0.003 0.023 0.785 0.189 0.003 0.023

ObservationsCommunitiesPseudo R-squared

984107

0.000107

0.032

984

2) Test predictions of the model

• Impact of quantity grown on quality of retained maize

• Compare quality of purchased vs. retained maize

• Is self-produced maize a normal good?

Effect of harvest quality (yield) & quantity (area planted) on aflatoxin contamination of retained maize

Notes: Marginal effects from an ordered probit regression, with clustered standard errors shown in parentheses. Controls for post-harvest practices not shown. * p<0.10, ** p<0.05, *** p<0.01

ppb = 0 0 <ppb < 10 10 < ppb < 20 ppb > 20(1) (2) (3) (4)

Quality and quantity of harvestHectares under maize 0.028*** 0.001 -0.009*** -0.020***

(0.009) (0.002) (0.003) (0.006)Yield (100 kg / ha) 0.001** 0.000 -0.000** -0.001**

(0.001) (0.000) (0.000) (0.000)ObservationsCommunities(Pseudo) R-squared

1060.228

761

Contamination by source of maize

Contamination by source of maize

Contamination as a function of source

Marginal effects from multinomial probit regressions, with clustered standard errors in parentheses. Eastern data only. * p<0.10, ** p<0.05, *** p<0.01

ppb = 00 > ppb >

1010 > ppb >

20 ppb > 20 ppb = 00 > ppb >

1010 > ppb >

20 ppb > 20

(5) (6) (7) (8) (9) (10) (11) (12)

Miller -0.010 -0.000 0.003 0.008 0.024 -0.000 -0.007 -0.017(0.103) (0.004) (0.029) (0.078) (0.106) (0.003) (0.032) (0.072)

Other purchase -0.052* -0.002 0.014* 0.039* -0.053* -0.002 0.015* 0.040*(0.029) (0.003) (0.008) (0.023) (0.030) (0.003) (0.009) (0.023)

Gift or aid -0.056 -0.004 0.015 0.044 -0.058 -0.004 0.016 0.047(0.051) (0.007) (0.013) (0.044) (0.051) (0.007) (0.013) (0.045)

1-10% broken -0.056* -0.003 0.016* 0.043(0.034) (0.004) (0.009) (0.027)

>10% broken -0.126** -0.018 0.030*** 0.114(0.063) (0.021) (0.012) (0.072)

1-10% discolored 0.030 0.000 -0.009 -0.021(0.038) (0.001) (0.011) (0.027)

>10% discolored -0.034 -0.002 0.009 0.026(0.057) (0.004) (0.015) (0.045)

Share of Own-Grown Maize in Category

0.377 0.280 0.159 0.185 0.377 0.280 0.159 0.185

ObservationsCommunitiesPseudo R-squared

931106

0.018

931106

0.002

Alternative explanation

Perhaps those who purchase are less concerned with bad taste / toxic contamination, purchase even though fully informed

– Compare quality-usage patterns of own vs. purchased maize

Quality-use relationship similar for own-grown and purchased maize

ppb = 0 0 > ppb > 10 10 > ppb > 20 ppb > 20N 296 262 84 99

% 77.9 76.8 60.9 57.6

N 68 66 47 51

% 17.9 19.4 34.1 29.7

N 2 4 3 4

% 0.5 1.2 2.2 2.3

N 14 9 4 18

% 3.7 2.6 2.9 10.5

% total 100 100 100 100

ppb = 0 0 > ppb > 10 10 > ppb > 20 ppb > 20N 241 206 75 46

% 86.1 79.2 66.4 46.5

N 35 45 36 47

% 12.5 17.3 31.9 47.5

N 1 4 1 1

% 0.4 1.5 0.9 1

N 3 5 1 5

% 1.1 1.9 0.9 5.1

% total 100 100 100 100

Food for HH

Brewing

Livestock Feed

Sale

Panel A: Maize grown on own farm

Food for HH

Brewing

Livestock Feed

Sale

Panel B: Purchased maize

Quality-use relationship similar for own-grown and purchased maize

ppb = 0 0 > ppb > 10 10 > ppb > 20 ppb > 20N 296 262 84 99

% 77.9 76.8 60.9 57.6

N 68 66 47 51

% 17.9 19.4 34.1 29.7

N 2 4 3 4

% 0.5 1.2 2.2 2.3

N 14 9 4 18

% 3.7 2.6 2.9 10.5

% total 100 100 100 100

ppb = 0 0 > ppb > 10 10 > ppb > 20 ppb > 20N 241 206 75 46

% 86.1 79.2 66.4 46.5

N 35 45 36 47

% 12.5 17.3 31.9 47.5

N 1 4 1 1

% 0.4 1.5 0.9 1

N 3 5 1 5

% 1.1 1.9 0.9 5.1

% total 100 100 100 100

Food for HH

Brewing

Livestock Feed

Sale

Panel A: Maize grown on own farm

Food for HH

Brewing

Livestock Feed

Sale

Panel B: Purchased maize

Is self-produced maize a normal good? (REPEAT data)

Harvested maize

(100 kg)(1) (2) (3)

Log asset value 2.502*** 0.025** 0.021*(0.479) (0.011) (0.012)

Agricultural land (ha) 1.873** 0.013 0.013(0.892) (0.009) (0.009)

Land squared (ha sqr) -0.034 -0.000 -0.000(0.043) (0.000) (0.000)

Harvested maize (100kg) 0.001(0.001)

Obsevations 679 695 677Communities 86 86 86R squared 0.144 0.017 0.018Notes: Results are from linear regressions with community fixed effects. Standard errors, shown in parentheses, are clustered at the community level . * p<0.10, ** p<0.05, *** p<0.01

Consumed maize produced on own farm past 7 days

3) Impact of farmer practices on quality

Determinants of visible mold & afla

ppb = 0 0 <ppb < 10 10 < ppb < 20 ppb > 20 0 kernels 0-10% kernels >10% kernels

(1) (2) (3) (4) (5) (6) (7)Quality and quantity of harvestHectares under maize 0.056** 0.007 -0.017** -0.046** 0.026 -0.018 -0.007

(0.025) (0.005) (0.008) (0.022) (0.026) (0.019) (0.007)Yield (100 kg / ha) 0.002** 0.000 -0.000** -0.001** 0.001 -0.001 -0.000

(0.001) (0.000) (0.000) (0.001) (0.001) (0.001) (0.000)Post-harvest practicesDry maize in field 0.042 0.007 -0.012 -0.037 -0.295** 0.244** 0.051**

(0.132) (0.005) (0.035) (0.128) (0.126) (0.114) (0.024)Improved drying 0.020 0.000 -0.006 -0.016 -0.236** 0.175** 0.061**

(0.069) (0.000) (0.020) (0.058) (0.092) (0.071) (0.031)Months since harvest -0.048*** 0.067 0.014*** 0.040*** -0.022 0.016 0.006

(0.013) (0.045) (0.005) (0.010) (0.020) (0.014) (0.006)ObservationsCommunities(Pseudo) R-squared 0.225

Discolored kernelsAflatoxin

36174

0.144

36174

Notes: Marginal effects calculated from ordered probit regressions of the categorical proportion of discolored kernels (1 through 3), and aflatoxin contamination (4 through 7). Yellow shading indicates differences between coefficient values across models at p<0.1. Standard errors, shown in parentheses, are clustered at the community level for all models and tests. * p<0.10, ** p<0.05, *** p<0.01

• Visibly moldy maize is less likely to be sold: farmers appear to respond to price incentives

• Impact of farm practices on visible molds and aflatoxin are not well correlated– Inhibits learning about best practices, implies weak

incentives for aflatoxin control

– Technologies to reduce aflatoxin unlikely to be profitable unless asymmetric information problem can be solved

Aflatoxin and farmer incentives

Overcoming the information asymmetry: prospects & implications

• Technologies in development for rapid and affordable screening, e.g. “e-nose”

• Efforts to develop AflaSafe certification– Would better align farmer, trader incentives with

public health goals

– May also encourage more profitable specialization

– Could adversely impact health of poorest consumers

Summary of Key Findings

• Evidence consistent with asymmetric information– Aflatoxin (or correlate) determines how maize is used

observed by maize holders– No impact on market price not observed by buyers

• Evidence consistent with lemons market– Controlling for quality of harvest (yield), quality of retained

maize is increasing in harvested amount (area planted)– Quality of sold and purchased maize is lower than quality of

retained maize– Consumption of self-produced maize is increasing in

household wealth