Estimating Consumer

Willingness to Pay

for Aflatoxin-free Food in Kenya

Hugo De Groote 1, Charles Bett 2, Simon Kimenju 3,

Clare Narrod 4, Marites Tionco4, Rosemarie Scott4

1 International Maize and Wheat Improvement Centre (CIMMYT)

2 Kenya Agricultural Research Institute (KARI), 3 University of Kiel, Giessen

5 International Food Policy Research Institute (IFPRI)

Nairobi Aflacontrol Project Meeting

Nairobi, November 30, 2011

The Problem

● Aflatoxins are a major health

problem in tropical countries

● New technologies for production,

storage and testing have been

developed,

● These are not cheap: quality costs

money

● How much are consumers willing to

pay for maize of superior quality?

● How do we estimate this WTP?

Estimating Consumer

WTP – Stated preferences

(Contingent valuation)

● ask the consumer directly: cheap, but hypothetical

open question: often hard on respondents

yes/no question: easier, but limited information

usually: with one follow-up question

● But:

hypothetical, no real money (not incentive-compatible)

respondents reply to what we would like to hear

overestimation of WTP

Consumer WTP –

Revealed preferences (experimental auctions)

Real money is exchanged

Group auctions

Individual auctions (BDM)

Bid compared to random number

incentive compatible: respondents

have no reason not to reveal their

real WTP

For aflatoxin: individual auction

● Product: maize grain, in 2 kg bags,

clear plastic

● Type of products

Clean, untested

Clean, tested (with no measurable trace

of aflatoxin)

Moldy poor market quality =

“contaminated”: 5% of moldy, discolored

grain

● Participation fee: twice the estimated

value of the highest quality product

KShs 110/person ($1.5)

Procedure individual auctions

● Participants are offered the

participation fee

● They are asked to bid on different

products

● They draw a number from a random

distribution, from 1 to 80 (40)

● If their bid is higher than the random

number, they purchase the product at

the random price

Consumer survey

● Stratified, 2-stage

● Six maize AEZ

● 120 sublocations

● 10 households/ subloc.

● 1 man or woman per

household (1344)

Kenya – Premium/discount

● Premium for clean maize over poor quality product:

KSHS 20-30 / 2 kg

● Premium for labeled maize: Kshs 10-15/2 kg

Analysis – random effects model

• We estimate the WTP for different product

characteristics through regression

• Dependent variable bij the bid of individual i for product j

• Independent variables: product characteristics, respondent

characteristics, cross effects

• Random effects model (bids of one individual are related)

Where

-i are the different products, j are the different respondents,

- xj is a vector or traits of the product j

- ki is a vector of characteristics of individual I

- C is a vector of cross effects

- i xj s a random error term for the individual

●

Kenya – long regression: effect of consumer

characteristics

Direct effects Cross effects x contaminated Cross effects x tested Variable Coef. Std. Err. P>|z| Coef. Std. Err. P>|z| Coef. Std. Err. P>|z| Constant 33.9 0.8 0.000 Poor market quality -19.5 3.1 0.000 Tested 11.1 2.8 0.000 Aware of aflatoxins -4.6 1.0 0.000 AEZ 2. Dry mid-altitudes -0.7 1.7 0.671 3.3 1.639 0.045

AEZ 3. Dry transitional 4.6 1.8 0.010 5.2 1.582 0.001

AEZ 4. Moist transitional 1.6 1.6 0.334 1.4 1.432 0.337

AEZ 5. High tropics -0.5 1.7 0.778 0.0 0.001 0.566

AEZ 6. Moist mid-altitudes -1.1 2.3 0.625 7.2 2.146 0.001

Age -0.1 0.1 0.315 -0.1 0.035 0.012

Awareness 1.3 1.4 0.359 0.7 1.358 0.631

Cattle 0.0 0.1 0.862 0.1 0.132 0.688

Experience 0.0 0.1 0.536 0.0 0.005 0.924

Female -0.7 1.0 0.512 -0.4 1.014 0.720

Income 0.0 0.0 0.857 0.0 0.001 0.001

Knowledge 0.4 1.4 0.790 -0.7 1.369 0.628

Land owned (ha) -0.3 0.2 0.142 0.0 0.188 0.964

Schooling -0.1 0.1 0.321 0.3 0.143 0.068

Conclusions

● Consumer WTP can conveniently measured with

individual auction

● Consumers are clearly willing to pay a premium

for

visually clean maize

maize tested and labeled aflatoxin-free

● WTP is influenced by age (-) and income (+)

● Needs to be clear differentiation in the market

and needs low cost labelling to have credibility

among consumers

Thank you