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 Tionco 4 , Rosemarie Scott 4 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
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Estimating consumer willingness to pay for aflatoxin free food
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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