Estimating consumer willingness to pay for food quality with experimental auctions: the case of...

AGRICULTURALECONOMICS

Agricultural Economics 42 (2011) 1–16

Estimating consumer willingness to pay for food quality with experimentalauctions: the case of yellow versus fortified maize meal in Kenya

Hugo De Grootea, Simon Chege Kimenjua, Ulrich B. Morawetzb,∗aInternational Maize and Wheat Improvement Center (CIMMYT), P.O. Box 1041-00621, Nairobi, Kenya

bUniversity of Natural Resources and Applied Life Sciences (BOKU), Vienna, Austria, Feistmantelstr. 4, 1180 Vienna, Austria

Received 28 April 2008; received in revised form 6 December 2009; accepted 3 April 2010

Abstract

Biofortification of maize with provitamin A carotenoids is a new approach to the alleviation of vitamin A deficiencies in Africa. Unfortunatelythese varieties are yellow or orange, while consumers generally prefer white. Consumer willingness to pay for yellow and fortified maize wascompared in experimental auctions in three regions in Kenya. The premium that consumers are willing pay for fortified maize (24%) was higherthan the discount they require to buy yellow maize (11%), and in one zone consumers prefer yellow. Yellow color is, therefore, not an impossibleobstacle for biofortified maize, although it would clearly be easier to introduce this maize first in regions where yellow maize is currently grown.

JEL classifications: C93, D12, I12, Q18

Keywords: Africa; Kenya; Maize; Consumer; Preferences; Biofortification

1. Introduction

Deficiencies in vitamins and minerals are among the world’smost widespread health problems, affecting more than two bil-lion people worldwide (MI and UNICEF, 2004, p. 90). VitaminA deficiency (VAD) is one of the most important such micronu-trient deficiencies: lack of vitamin A can hinder the normalfunctioning of the visual system, growth and development, themaintenance of epithelial cellular integrity, as well as immunefunction and reproduction (UN SCN, 2004). Each year, about 1million children under five die from VAD worldwide (MI andUNICEF, 2004). In Kenya, 70% of preschool children sufferfrom VAD (MI and UNICEF, 2004) and 2% of the popula-tion as a whole suffers from night blindness (UN SCN, 2004),which is one of its major symptoms. VAD usually results fromlimited diversity in the food intake, caused by poverty and alimited purchasing power for buying products high in vitamin

∗Corresponding author. Tel.: +43-1-47654-3672; fax: +43-1-47654-3692.E-mail address: [email protected] (U. B. Morawetz).

Data Appendix Available Online

A data appendix to replicate main results is available in the online version ofthis article. Please note: Wiley-Blackwell, Inc. is not responsible for the contentor functionality of any supporting information supplied by the authors. Anyqueries (other than missing material) should be directed to the correspondingauthor for the article.

A precursors such as vegetables, fruits, dairy products, meat,and fish.

Several approaches have been developed to reduce VAD. Inmany developing countries, supplements are provided to chil-dren through vitamin A capsules, typically every six months.These capsules contain retinol, which is stored in the liver fromwhere it is slowly released, in sufficient quantity to sustainvitamin A requirements for four to six months (dela Cuadra,2000). Supplementation is generally considered cost effective(IVACG, 2003), although the costs and the number of childrenreached are influenced by the transport infrastructure. In Kenya,90% of 6-59-month-old children are reported to have receivedat least one dose of vitamin A in 2001 (UN SCN, 2004). Duringinformal discussions with this article’s authors, however, offi-cers at some health care centers indicated that they might havereached only half of the target population. Moreover, childrenonly receive one dose at the vaccination time, while they mayneed one every six months. Children older than nine monthswho have already received immunization can only receive theVA supplement either when they are sick and the mother takesthem to a health clinic, or if the mother takes them to the clinicfor the recommended monthly check-up.

An alternative to supplementation is industrial fortification,through which micronutrients are added to other products dur-ing processing or packaging. In Latin America, fortification of

c© 2010 International Association of Agricultural Economists DOI: 10.1111/j.1574-0862.2010.00466.x

2 H. De Groote et al. / Agricultural Economics 42 (2011) 1–16

sugar contributed substantially to the control of VAD (IVACG,2003). Unfortunately, fortification is only possible with indus-trially processed products that are widely consumed, and inAfrica most people still live in rural areas and process most oftheir food within the homestead. Moreover, in Africa’s urbanareas the poorest often consume foods that are processed intraditional ways, either from their own production or purchased(Jayne and Argwings-Kodhek, 1997).

The third method of reducing VAD is through dietary di-versity, by creating awareness about the problem in affectedcommunities and increasing use of foods with high levels ofprovitamin A from home gardens and other sources. The pro-motion of such crops has been shown to have an impact onreducing VAD among children in South Africa, for example(Faber et al., 2002). Unfortunately, availability of appropriatefood crops is often seasonal: vitamin A intake of preschoolchildren from low-income rural households in Kenya has beenshown to differ significantly between the lean and the post-harvest months (Kigutha et al., 1995).

The latest approach is biofortification, or breeding food cropswith increased micronutrient content. This approach is used byHarvestPlus, a challenge program of the Consultative Groupof International Agricultural Research, to increase the levels ofprovitamin A, zinc, or iron in major food crops such as cassava,maize, rice, and sweet potato. Given the high number of peoplewith micronutrient deficiencies, and the large amounts of sta-ples the poor eat, biofortification has the potential to help manypeople at limited cost (Bouis, 1999; Meenakshi et al., 2010).Varieties of sweet potatoes with high provitamin A content arealready on the market, and have been shown to increase vita-min A intake and serum retinol concentrations in young chil-dren (Low et al., 2007). High vitamin A rice varieties, dubbed“golden rice,” have also been developed and preliminary esti-mates indicate a high potential impact (Zimmermann and Qaim,2004).

The biofortification of maize is being undertaken by the In-ternational Maize and Wheat Improvement Center (CIMMYT).Yellow maize has a wide spectrum of provitamin A carotenoids,which the human body converts into vitamin A (West andDarnton-Hill, 2001). Several varieties with high provitaminA levels were identified from the collections of CIMMYT’sgene bank and the University of Illinois. These are now be-ing crossed with promising high-yielding maize varieties fordifferent agroecological zones (Ortiz-Monasterio et al., 2007).Benefit-cost analysis of vitamin A biofortified maize indicatesgood returns for Kenya and Ethiopia, although not as high aswith golden rice in Asia (Meenakshi et al., 2010). This is dueto the fact that the expected increase of provitamin A in bio-fortified maize is substantially less than that in golden rice, andthe number of rice consumers in Asia far outnumbers the maizeconsumers in Africa.

An important concern for the development and disseminationof biofortified maize in East Africa, however, is that biofortifi-cation is likely to result from an increased content of provitaminA carotenoids, which, because of their chemical structure, will

give it a yellow to orange color (Rodriguez-Amaya and Kimura,2004). However, most of the maize for human consumption inEast and Southern Africa is white (FAO and CIMMYT, 1997),largely for historical reasons (Smale and Jayne, 2003), andconsumers generally prefer white maize over yellow (Rubeyet al., 1997). The extent and the level of that preference is,however, largely unknown. Some studies analyzed differencesin prices, for example in Zambia (Diskin and Kipola, 1994) andMozambique (Tschirley et al., 1996), but the available evidenceis limited. Most studies use contingent valuation (CV) meth-ods in which consumers state their preferences, usually as thewillingness to pay (WTP) at a particular price, as documentedfor Mozambique (Tschirley et al., 1996), Zimbabwe (Rubeyet al., 1997), and Kenya (De Groote and Kimenju, 2008). Apreliminary survey in Kenya, however, showed a strong biasin the CV method, and its results differed substantially fromthose obtained from revealed preference methods (Kimenju etal., 2005).

In this article, therefore, maize consumers’ preference forwhite maize is quantified using revealed preferences. ConsumerWTP for white, yellow, and white fortified maize was assessedusing experimental auctions, involving real monetary transac-tions. The auctions were organized in urban as well as ruralareas, and in different agroecological zones of Kenya, corre-sponding to the organization of research and targeting of newmaize varieties such as those biofortified with provitamin A.

2. Background

2.1. Historic overview

Maize is a relatively recent introduction in Africa. In EastAfrica, indigenous cereals such as sorghum and millet have beenthe dominant food crops for thousands of years (National Re-search Council, 1996), but are being replaced by maize in mostareas with favorable conditions. Major factors are its higheryield, low labor requirements, ease of handling and storage,and good pest resistance, especially against birds (McCann,2005). The first maize varieties were likely brought to EastAfrica by Portuguese traders at the end of the fifteenth century,and were Caribbean flints of different colors (Miracle, 1966).In flint maize, most of the kernel is composed of hard starch,which gives the kernel a shiny surface (Dowswell et al., 1996,p. 21). Hard kernels are preferred for human food, in particu-lar for alkaline cooking, common in Central America, and drymilling, popular in Africa (Rooney et al., 2004).

Maize only became a dominant food crop in East Africa atthe beginning of the twentieth century, starting with the in-troduction of new, white dent varieties imported from SouthAfrica by European settlers (Smale and Jayne, 2003). In dentmaize, globally the most common type, most of the starch inthe endosperm is soft and it contracts during drying, producinga characteristic dent in the top of the kernel (Dowswell et al.,1996, p. 22). African workers on the settler farms took the

H. De Groote et al. / Agricultural Economics 42 (2011) 1–16 3

varieties home (Harrison, 1970), while demand from the Britishstarch industry for white maize stimulated its production.Marketing boards soon refused any colored maize (Smale andJayne, 2003). The adoption of maize as the new staple food ofSouthern and East Africa was also influenced by the large-scaleremoval of high-potential farmland from African farmers, whowould form a large pool of cheap labor for mines and commer-cial farms. Maize, requiring less labor and a shorter growingseason than millet and sorghum, and being easier to processin hammer mills since it does not need dehulling, became thecheapest way to feed the new workers (Smale and Jayne, 2003).

After independence, agricultural policies in East Africa gen-erally aimed for food self-sufficiency, based on white maize,and low food prices. Through new technologies, policies, andinstitutions, maize production increased rapidly in Kenya from1965 to 1980 (De Groote et al., 2005), and in other Africancountries (Byerlee and Eicher, 1997). A major factor in thisproductivity increase was the development of improved maizevarieties (Harrison, 1970; Smale and Jayne, 2003), such as thewhite hybrids released in Kenya in the 1960s that spread quicklyamong small- and large-scale farmers (Hassan, Njoroge, Njore,et al., 1998).

Unfortunately, progress stalled in the late 1980s, influencedby poor rainfall, reduction of state intervention in marketing andresearch, and uneven progress in the liberalization of the maizeseed industry (Smale and Jayne, 2003). The expected effects ofmarket liberalization on agricultural productivity were limited,and maize yields did not increase (Kherallah et al., 2002).

2.2. Maize meal preferences

Consumer preferences can change over time, as demonstratedby the switch from colored to white maize in colonial times.Preferences are also influenced by policy, as shown by the shiftfrom sifted maize meal to whole maize during market liberal-ization (Jayne et al., 2002). Public awareness campaigns andcommercial advertising also matter. Initial preferences are im-portant, however, and in most of East and Southern Africa con-sumers now generally prefer white maize over yellow maize.Yellow grains are often associated with food aid and with ani-mal feed (FAO and CIMMYT, 1997), or is simply unfamiliar.Where available, the price of yellow maize is generally lower,as observed in Zambia (Diskin and Kipola, 1994), Mozambique(Tschirley et al., 1996), and South Africa (FAO and CIMMYT,1997). Consumer surveys in southern Africa, using CV meth-ods, have observed a preference for white maize in Mozambique(Tschirley et al., 1996) and Zimbabwe (Muzhingi et al., 2008;Rubey and Lupi, 1997). Consumers in both countries are will-ing to switch to yellow maize only when given a price discount,but consumers have shown a willingness to switch especiallythose from low-income groups (Dorosh et al., 1995; Rubey etal., 1997; Tschirley et al., 1996).

In Mozambique, where only white maize is grown but yel-low maize is imported as food aid, consumers who buy yellow

maize generally have a lower income than those buying white(Tschirley et al., 1996). In South Africa, where white maize isproduced for human consumption and yellow maize for feed,consumers are willing to pay a 40–50% price premium for whitemaize (MOA and MSU Research Team, 1993, p. 90). In Swazi-land, white maize has been imported at a 70% price premiumbecause of consumers’ rejection of yellow maize (Tschirleyet al., 1993). In Zambia, the price of yellow maize importedduring the drought of 1992 was 10% to 35% lower than that ofwhite maize (Diskin and Kipola, 1994).

Except for the price analyses from Mozambique (Tschirleyet al., 1993) and Zambia (Diskin and Kipola, 1994), studiesof consumer preferences are based on stated preferences, inwhich consumers have little incentive to reveal their true pref-erences. So far, only one maize consumer study used exper-imental methods, combining sensory evaluation with a fieldexperiment: urban consumers in Mozambique were asked totrade local white maize for orange biofortified maize (Stevensand Winter-Nelson, 2008). The study concluded that existingpreferences for white maize did not preclude the acceptanceof orange, biofortified varieties, although without quantifyingthese differences. It is possible to quantify the preferences ofAfrican consumers, however, as was demonstrated by a study ofmothers’ WTP for certification of baby food in Mali (Mastersand Sanogo, 2002).

In East Africa, so far, only one study of consumer preferencesfor white maize has been conducted, in Nairobi (De Groote andKimenju, 2008). This survey confirmed the preference for whitemaize meal, although it was limited to urban consumers, andused stated preferences. To build and expand on these previousexperiences, this study was undertaken in Kenya, now usingrevealed preferences, and including both urban and rural con-sumers. The method applied allows for quantification of thedifferences in WTP for yellow, white, and fortified maize mealtypes. Direct conclusions on the WTP for biofortified maizemeal cannot be drawn, however, since the WTP for combinedcharacteristics is not necessarily the sum of what was the WTPfor individual characteristics. Theoretically it would have beenpossible to mix yellow maize with vitamin A powder. However,this mix would still not be identical to a flour of biofortifiedmaize, while the technical difficulties in mixing the maize flourwith the powder would have lead to food safety issues and,therefore, ethical problems.

3. Methodology

3.1. Experimental auctions

In experimental auctions, real products are offered for sale,and participants are endowed with some cash to purchase them.In the classic open ascending price or English auction, the priceis increased until no participant is willing to bid further, and thehighest bidder buys the product at a price equal to their bid. Insealed auctions, all bidders simultaneously submit sealed bids,


and the highest bidder wins. The winner or winners can eitherpay the price they submitted (first-price auction), the secondhighest bid (Vickrey or second-price auction), or a randomlyassigned bid out of all bids (nth price auction). There is onewinner in the first-price and second-price auction and (n − 1)winners in the nth price auction.

An especially useful type of auction is due to Beckeret al. (1964) in which the bids of individual participants arenot compared to one another but to a randomly generated num-ber. This Becker-DeGroot-Marschak (BDM) approach is not areal auction, but rather a simulated auction to solicit individ-uals’ WTP, and it is commonly called the BDM mechanism(Shogren, 2005). In the standard BDM procedure, if the bidoffered is higher than the random number, the bidder purchasesthe good at a price equal to the random number drawn (therandom price), as long as it is equal to or lower than their ownoffer (the bid price).

All four of the auction types discussed above—the Englishauction, the second-price auction, the random nth price auc-tion, and the BDM mechanism—are incentive compatible, inthe sense that participants have an incentive to set their bidsaccording to the same underlying true preferences that theywould reveal in a commercial marketplace (Lusk et al., 2004;Shogren, 2005). A key difference is that, although BDM auc-tions in groups are possible and have been reported (Monchuket al., 2007), the BDM approach can be executed individually,which may be more convenient for researchers. The usefulnessof the BDM approach has been demonstrated in various fieldstudies, such as Wertenbroch and Skiera (2002).

For this study, both Vickrey and BDM auctions were testedin Western Kenya (Kimenju et al., 2005). The second-price auc-tions are a group exercise, which saves time in explaining theprocedure once the group is assembled but may require a heavyinvestment to assemble the group at a given time and place.In contrast BDM auctions are conducted individually, so enu-merators can meet subjects at their homes or other convenientlocations. Experience in Kenya showed that the BDM proce-dure was much faster and more efficient, especially for ruralhouseholds. We also tested the procedure in an urban setting,meeting consumers at their points of purchase, where it wasalso found to be very convenient.

3.2. Analyzing auction results—a general linear model

Consumer WTP for particular products is driven by theirappreciation of particular characteristics of that product. WhenI consumers are asked to bid on J products, and these productshave K characteristics of interest, the WTP yij, of consumer ifor product j, can be regressed on the vector xj, representing thevalues of the K characteristics for product j

yij = α + β ′xj + vij , (1)

where ν ij is the error term for consumer i’s WTP for product j.In this model, the regressors are characteristics such as color

and fortification, which are embodied in each product in various

combinations. The data come from consumers’ WTP for theproducts, introducing correlation in the error terms and theneed to include a disturbance term ui for each consumer

yij = α + β ′xj + ui + vij . (2)

The consumers were selected randomly from a larger popu-lation, so the individual disturbance term ui is also assumed tohave a random distribution. By imposing a particular structureon the covariance matrix of the disturbance term, the two errorterms are considered in the GLS estimation, a model generallyreferred to as the random effects model. One of the key assump-tions for unbiased estimates is that the ui are uncorrelated withthe explaining variables, which can be tested with the Hausmantest (Greene, 2003, p. 301).

WTP may vary among consumers in systematic ways, whichcan be analyzed by including a vector zj of L consumer char-acteristics (Lusk et al., 2004). Since the particular interest ofthis study is to analyze the effect of consumer characteristics ontheir preference for particular product characteristics, a matrixA (dimension L × K) of cross effects is also included. Eachcell alk of the matrix represents the effect of consumer charac-teristic l on the WTP for product characteristic k. The generalmodel with product characteristics, consumer characteristics,cross effects, and individual disturbance term becomes

yij = α + β ′xj + γ ′zi + x′j Azi + ui + vij . (3)

In this study, consumers were asked to bid on three products:plain white maize meal, fortified white maize meal, and plainyellow maize meal. The characteristics of interest are there-fore nutritional quality (plain or fortified) and color (white oryellow). Consumer characteristics considered to affect the bid-ding behavior included demographic, economic, and cognitivevariables.

3.3. Sampling design

To better understand people’s knowledge and perception ofyellow maize, nutrient quality, and fortification, group discus-sions were held in two villages of Vihiga district in WesternKenya. This led to the development of a questionnaire, includ-ing a protocol for the experimental auctions. The questionnaireand protocols were tested in the same villages and adjustedaccordingly. The actual surveys were then conducted in bothWestern and Eastern Kenya.

Western Kenya was selected for this research because of theprevalence of yellow maize. Two districts, Siaya and Vihiga,were purposely selected because they are easily accessible andrepresentative of two agroecological zones: Siaya for the mi-daltitude zone, and Vihiga for the moist transitional zone (Fig.1). Since these zones are specially designed for maize research(Hassan, Njoroge, Corbett, et al., 1998), and new maize vari-eties are specifically developed for each zone, it is important tostudy consumer acceptance of these new varieties in the differ-ent zones. Consumers were selected using a stratified, two-stage


Fig. 1. Sublocations and districts in the survey, with their respective agroecological zone.

sampling design, with the districts as the strata, the subloca-tions as the first-stage sampling units, and the households asthe second-stage sampling units. From each district, five sublo-cations (smallest administrative unit) were randomly selectedproportionate to size, with the number of households in eachsublocation obtained from the 1999 population census (CentralBureau of Statistics, 2001), as an indicator of size. From eachsublocation, 10 households were then randomly selected, result-ing in a sample size of 100 households. From each household,either the head of the household or the spouse was selected,based on responsibility for food purchases in the household.If neither of these was available, another person in the house-hold who also participated in food purchases was selected. Thesurvey took place in June 2005.

To provide contrast, Eastern Kenya, where little or no yellowmaize is found, was selected. To add more differentiation, bothurban and rural areas were chosen. For rural areas, a subsetwas selected of the households interviewed in a previous study,which used a stratified two-stage sampling design, with agro-ecological zones as strata and sublocations as first-stage survey(De Groote et al., 2005). For this survey, only the dry transitionalzone was maintained because of easy access and low cost.The original sample included 10 sublocations, seven falling

in Machakos district and three in Makueni district, with 20households each, and all these 200 households were visited inJanuary 2006.

The town of Machakos was purposely selected for the urbansegment, since it is the largest urban center of the dry tran-sitional zone and is centrally located. Following a samplingdesign developed previously for Nairobi in 2003 (Kimenju andDe Groote, 2008), consumers were interviewed at three typesof outlets for maize meal: posho mills (small hammer mills),shops, and supermarkets. Key informant interviews estimatedthat about half the population buys maize at posho mills, a quar-ter at shops, and another quarter in supermarkets, therefore thesample was organized accordingly. Posho mills are mechanicalhammer mills where consumers have their maize grain milled,a cheaper alternative to the sifted and pre-packaged industrialmaize meal. Ten posho mills were systematically selected, andat each mill, 10 consumers were interviewed, 100 in total. Thereare many small shops in the town, but each with relatively fewcustomers. Therefore, two shops were systematically selectedin each of the five residential estates, and a smaller sample offive consumers each was selected. There are five supermarketsin town and all are quite busy. Therefore all five were included,and 10 consumers were interviewed in each one. In all selected


outlets, each third consumer was approached and asked for aninterview. When they agreed (almost always), the interview wasconducted on the spot. All interviews took place in December2005 and January 2006.

The surveys were conducted by enumerators specially hiredand trained, and supervised by CIMMYT staff, in collaborationwith KARI officers and with the support of representatives fromthe communities. In total, 500 consumers were interviewed.

3.4. Survey tools

To elicit the consumers’ WTP for white, yellow, and whitebiofortified maize meal, the individual BDM experimental auc-tion was used. Unlike the standard procedure, the bid offeredby the participant (the bid price) was used as transaction price,because we knew participants would be familiar with traditionalEnglish auctions and we were concerned that having them paythe randomly generated price in a true BDM procedure mightbe difficult to understand and might be considered unfair.

The procedure was first explained in detail to the respondent,emphasizing that the transaction had to be executed if the re-spondent’s bid was higher than the random number. To helprespondents understand the procedure, a test round with cupcakes was first organized. The respondent was provided with15 Kenyan Shillings (KShs) (US$1 = KShs75 in December2005), and asked to bid for three types of cup cakes, one at atime, with information about each cake being given before bid-ding. Respondents were asked to make a bid for the first typeof cup cake, which was written down, and the procedure wasrepeated for the other two types. The bid for one of the roundswas compared to a number, randomly drawn from a normal dis-tribution and when the bid was higher, the respondent boughtthe cup cake at the bid price offered. Time was provided forclarifying questions.

For the actual maize meal auction, respondents were firstprovided with KShs70 cash, and then presented with three typesof maize meal: plain white, plain yellow, and fortified white.The fortified white maize meal is an industrial meal nutritionallyenhanced with vitamin A, B, zinc, and iron. This meal wasbought from the market but repackaged in plain brown paperbags, to look the same as the other maize meals. A 2-kg packetof either plain white, plain yellow, or fortified white maize mealwas then presented to the respondent, who was informed aboutthe content and was also allowed to see it to check the color.Respondents were informed that white fortified contained morevitamin A, but were not informed about the increased contentof the other micronutrients.

To avoid selection bias, the different maize meal types wereoffered in alternate order, but white fortified was never presentedbefore plain white. Respondents were asked to make a bid forthe first product, which was recorded, and the procedure wasrepeated for the other two products. To reduce the auction costsand to avoid the effects of reduced marginal utility of maizemeal, only one of the auctions, randomly selected at the end, wasmade binding and executed. The bid of that round was compared

to a number, randomly drawn from a normal distribution withmean KShs50 (the price of maize meal was about KShs40/2kg)and a standard deviation of KShs10. If the respondent’s bid washigher than the random number, the purchase took place at thebid price and money was exchanged for product.

Before and after the auctions, the participants were also askedquestions about their maize preferences and about their socio-economic background. These data were used, after cleaning, todescribe the characteristics of the sampled population.

3.5. Additional survey

When a participant drew a number higher than her bid and“won” the auction, she was asked to pay the amount of thebid. However, in such an auction a fully rational, optimizingrespondent would have some incentive to understate her valu-ation (Cox et al., 1982). The standard BDM procedure avoidsthat by using the random number as the exchange price, thusdecoupling the price paid from the bidding and eliminating anyincentive to understate the valuation (Becker et al., 1964). Thisproblem was pointed out by the reviewers of the first draft ofthis article. An additional survey was conducted to compare thetwo methods, as suggested by the editor of this journal.

To test if the original experiments might have resulted inan underestimation of WTP due to strategic behavior, the ex-periment was repeated with half of the participants randomlyassigned to the modified BDM auction procedure, in which theypaid their own bid when it was higher than the randomly drawnnumber, while the other half of the participants paid the ran-dom bid as an exchange price, as in the standard BDM auction(Becker et al., 1964). This experiment was conducted with 151individuals during the last week of August 2009 in the town ofMachakos, one of the sites of the original experiment, selectedfor best accessibility and lowest cost. The experiments weredesigned to repeat those from 2005/2006 as closely as possi-ble. Of the six enumerators who conducted the new round ofexperiments, four had already participated in the 2005/2006 ex-periments. Since maize prices had increased substantially sincethe first survey, each participant now received KShs 105 forthe maize meal auctions and KShs 25 for the trial round. Therandom distribution for the BDM mechanism was also changedproportionally to the price changes. Due to the combination ofmaize shortage and rationed electricity supply in Kenya, manyposho mills in the suburbs of Machakos were closed. Whilein 2005/2006 half of the interviews were done at posho mills,a quarter at shops, and a quarter at supermarkets, in 2009 theinterviews were allocated equally between posho mills, shops,and supermarkets. The posho mills that were dropped from thesample of 2009 were predominantly those from the more ruralsuburbs of Machakos Town, which had the most difficulties inaccessing electricity.

3.6. Data analysis—estimating WTP

The WTP data were analyzed by comparing average pricesfor different products, and by regression using the random


effects model. First, the bids collected for different maize mealtypes using the BDM auctions represent the revealed preferenceof the respondent. The bids’ sample mean, therefore, representsan unbiased estimator of consumers’ mean WTP for the partic-ular product, in this case plain yellow, plain white, or fortifiedwhite maize meal. The difference in WTP for the first two prod-ucts can be interpreted as the WTP for the trait “yellow maize.”This difference should not, however, be taken as an appreci-ation of the isolated trait “color,” but rather of the bundle oftraits associated in the consumer’s mind with the color in maizemeal. The difference between WTP for the last two productscan be interpreted as the WTP for the trait “fortified.” Thesedifferences are called premiums when positive, and discountswhen negative, and are commonly expressed as a percentage ofthe WTP or of the price of the product without the trait, in ourcase plain white maize meal.

Next, the WTP for the traits “fortified” and “yellow maize”were estimated by regressing the bids on these traits using bi-nary variables in the GLS random effects model of Eq. (2), usingStata and R software. The effect of other factors was estimatedby including cross effects in the model of Eq. (3), first withonly the geographical variables, and then with demographic,economic, and cognitive variables. The geographical variablesincluded the three agroecological zones, and urban versus ruralconsumers. Demographic characteristics that might influenceWTP are age, gender, and years of formal education; economicvariables included wealth indicators such as the number of cat-tle owned; and cognitive variables included familiarity withyellow maize, awareness of night blindness, and awareness ofvitamins.

The hypothesis of a bias due to strategic behavior was tested,using the data from the additional survey, by analyzing if themean bid of respondents, where the exchange price was therandomly drawn number (random price, standard BDM), isgreater than the mean bid of those where it was the respondent’sbid (bid price, modified BDM). For each of the maize mealtypes (yellow, white, and white fortified) a one-sided t-test thatallows for the variances to be different (Welch Test) was appliedusing the software R. Subsequently, the bids were pooled andused as dependent variables in a random effects model withindependent variables the type of meal xj (two binary variables,one for yellow and one for fortified) and the type of auction di

(one binary variable for second-price auction).

yij = α + δdi + β ′xj + ui + vij . (4)

4. Results

4.1. The study area

The survey took place in three agroecological zones. In west-ern Kenya, Siaya falls mostly in the moist midaltitude zone,which starts at the shores of Lake Victoria at 1,100 m, andreaches an altitude of about 1,500 m (Fig. 1). Vihiga falls in the

moist transitional zone, transitional between the midaltitudesand the highlands, reaching about 2,000 m. These districts ben-efit from a favorable climate, but suffer from high populationdensity and poor soils. The proportion of individuals below thenational poverty line is estimated at 64% in Siaya and 58% inVihiga (Central Bureau of Statistics, 2003). Western Kenya isof particular interest for the development of biofortified maize,because farmers grow several local yellow maize varieties hereand yellow maize is regularly found in the market. The yellowmaize for the experiments was purchased here, and the observedmarket price for yellow maize was the same as for white duringboth experiments (KShs 42 and 70, respectively, for 2 kg ofgrain). To compare the price of maize grain with that of maizemeal the milling cost needs to be added (KShs 5/2 kg in 2005,and KShs 8/2 kg in 2009).

In eastern Kenya, the price of maize is generally higher thanin western Kenya. In the town of Machakos, maize meal wasselling for KShs 54/2 kg in 2005. In the same place, maizemeal biofortified with vitamins and minerals was also found inthe shops, selling at a price 11% higher than the regular whitemaize (KShs 60/2 kg).

The districts of Machakos and Makueni in eastern Kenya arelocated in the dry transitional zone (transitional again betweenthe midaltitude zone, dry here, and the highlands) and facelow average rainfall with high fluctuations (De Groote et al.,2004). Poverty levels are high: 62% for Makueni and 60% forMachakos (Central Bureau of Statistics, 2003). Machakos Townis the center of the district and has about 150,000 inhabitants. Itis an important administrative center and market for agriculturalproducts, and is situated about 70 km south east of Nairobi,connected with a paved road. The poverty level of the centraldivision of Machakos, in which the town is situated, is estimatedto be 50%.

4.2. Characteristics of respondents, their households, andtheir farms

A large majority of respondents in the rural areas were farm-ers (90% in western and 84% in rural eastern Kenya), as wereabout a fifth (21%) of those in the urban area in 2005/2006(Table 1). Average age was 46 years in the rural areas, but only34 years in the urban area. This most likely reflects the differentsampling strategies: sampling from a sampling frame for therural households, compared to systematically approaching con-sumers in the urban outlets. The high percentage of women inthe sample (70%) is due to the fact that individuals in the ruralareas were selected based on availability and responsibility forfood purchase in the household. The average years of formaleducation is six years in the western districts, seven years inthe rural east and 10 years in town. The average household sizeof respondents was between 4.9 (in town) and 6.7 (in the west)members.

Farm sizes are much smaller in the west, with averages ofbarely 1 ha in Siaya and even less in Vihiga. On the other hand,


Tabl

e1

Cha

ract

eris

tics

ofre

spon

dent

s,th

eir

hous

ehol

ds,a

ndth

eir

farm

s,by

regi

onan

dar

ea

Cat

egor

yY

ear

2005

/06

2009

Var

iabl

eW

este

rnK

enya

Eas

tern

Ken

yaE

aste

rnK

enya

Moi

stm

idal

titud

es(S

iaya

)M

oist

tran

sitio

nal

(Vih

iga)

Dry

tran

sitio

nal,

rura

l(M

acha

kos

and

Mak

ueni

)

Dry

tran

sitio

nal,

urba

n(M

acha

kos)

Dry

tran

sitio

nal,

urba

n(M

acha

kos)

(N=

50)

(N=

50)

(N=

200)

(N=

200)

Ove

rall

(N=

151)

Mea

nSt

d.de

v.M

ean

Std.

dev.

Mea

nSt

d.de

v.M

ean

Std.

dev.

Mea

nSt

d.de

v.M

ean

Std.

dev.

Pers

onal

Prof

essi

on“f

arm

er”

(%)

90.0

30.3

90.0

30.3

83.5

37.2

21.1

40.9

59.9

49.1

9.0

32.5

Age

(yea

rs)

45.5

16.0

46.6

14.3

46.0

14.0

34.2

10.9

41.3

14.3

35.5

12.7

Fem

ale

(%)

72.0

45.4

62.0

49.0

80.5

39.7

62.0

48.7

70.4

45.7

48.5

47.8

Form

aled

ucat

ion

(yea

rs)

5.6

4.1

6.5

4.2

7.2

3.9

10.1

4.0

8.2

4.3

11.1

3.5

Hou

seho

ldH

ouse

hold

size

(mem

bers

)5 .

92.

76.

72.

46.

03.

05.

02.

45.

72.

85.

22.

8L

and

size

(ha)

1.1

1.2

0.7

0.6

2.2

3.1

2.0

3.6

1.9

3.1

2.9

10.9

Cat

tleow

ners

hip

(%)

66.7

47.7

94.0

24.0

74.5

43.7

58.5

49.4

69.3

46.2

49.0

47.8

Cat

tleow

ned

(num

ber)

2.5

3.3

2.2

1.5

2.9

3.5

2.4

3.4

2.6

3.3

2.8

5.3

Lan

dar

eain

mai

ze(h

a)0.

50.

30.

40.

41.

01.

21.

01.

60.

91.

30.

81.

3L

and

area

inm

aize

(%of

tota

l)53

.422

.157

.624

.256

.823

.861

.325

.058

.324

.251

.137

.1

Mai

zeM

aize

prod

uctio

n(k

g/ye

ar)

384.

638

9.2

436.

062

2.5

1,20

2.2

2144

.397

5.9

1,64

0.3

953.

31,

744.

769

5.8

793.

2M

aize

sold

(%of

prod

uctio

n)9.

219

.67.

017

.121

.327

.423

.829

.319

.527

.26.

615

.4A

nypr

oduc

tion

ofye

llow

mai

ze(%

)92

.027

.442

.950

.09.

529

.49.

529

.421

.040

.84.

523

.8


in the dry transitional zone of the east, average farm size is morethan 2 ha, even for the consumers interviewed in town, althoughthis is influenced by eight outliers with more than 10 ha. Morethan half of the people interviewed own cattle, varying from58% for the eastern consumers interviewed in town, to 94% inVihiga. The average number of cattle was fairly constant, from2.18 in Vihiga to 2.86 in the dry transitional zone.

Across zones, the area under maize production is about halfof total farm area: 0.5 ha in Siaya and 0.4 in Vihiga, compared toabout 1 ha in the east. Therefore, household maize productionis much lower in the west (60 kg per person) than in the east(200 kg per person). Even though people from the west producesubstantially less than the average national maize consumptionof 103 kg/capita (Pingali, 2001), they still sell some: 9% inVihiga and 7% in Siaya. Slightly more is sold in the east, abouta fifth of their production.

In 2009 the data were only collected in urban Machakos. Theresults show that the participants from 2009 had similar char-acteristics as those from 2005/2006. Where differences wererecorded, this is likely to be connected to the drought of 2009as mentioned earlier. The reduced number of interviews in therural suburbs of Machakos Town is likely to have caused the re-duction in the share of farmers (from a fifth down to a tenth) andwomen (from 62% to 49%). Drought had reduced the harvest inthis area dramatically and consequently, the maize productionand the percentage of the maize sold are lower. Additionally,some variables such as land size are influenced by single highvalues as is indicated by the high standard deviation.

4.3. Maize consumption and consumers’ knowledge

Maize consumed mostly originates from home production,although the annual consumption per person per month fromtheir own production is much lower in the west (4.3 kg/person/month in Siaya and 3.9 kg/person/month in Vihiga),than in the east (7.7 kg/person/month in the rural area and7.0 kg/person/month in the urban area) (Table 2). Almost allrespondents have to supplement their maize production withpurchases. These amounts are higher in the west (about 13.2kg/person/month in Siaya and 10.9 kg/person/month in Vihiga)than in the east (9.5 kg/person/month in the rural area and 7.9kg/person/month in the urban area), although this is probablyaffected by seasonality and the time of the survey. Maize isbought at the local market (55% of consumers), at local shops(44%), from neighbors (34%), and from supermarkets (6%).

In the year 2009, the percentage of maize produced that wasconsumed in the household increased by more than 20% in ur-ban Machakos. Also, the quantity of maize bought, as expressedin kg per person per month, increased. Both are probably relatedto the 2009 drought.

Kenyans are clearly familiar with yellow maize: almost allrural consumers have eaten it, and only 13% of the consumersin town had not tried it. However, there is a big differencebetween the regions. Almost all consumers in the west eat it at

least occasionally, and in Siaya, 80% of respondents reportedregular consumption. In the east, in contrast, only 30% of ruraland 10% of urban consumers eat it occasionally, although 10%still eat it regularly (at least once a year). In the 2009 sampleof urban consumers, there were more occasional yellow maizeconsumers (21%) and less regular yellow maize consumers(4%).

In 2005/2006, more than half (57%) of consumers in the eastwere aware of fortified maize while the number was smaller inthe west (36% in Siaya). When people were asked if they boughtfortified maize, 20% of consumers in Siaya and 14% in Vihigareported doing so. In the east, substantially more consumerspurchase fortified maize: 38% in the rural areas and 42% intown. Most consumers are aware of night blindness (84%) andthe existence of vitamins (96%). When asked to identify somevitamins, 70% identified vitamin A, although this was sub-stantially lower in Vihiga (44%). Understanding of the role ofvitamin A is, however, limited. When asked about the effects ofvitamin A deficiency, only 18% mentioned that it affects vision,and 21% mentioned its effect on the body’s immunity. Interest-ingly, the 2009 sample showed a lower awareness among urbanconsumers on night blindness (from 81% to 65%) and a lowerawareness of vitamins (from 98% to 72%).

Maize constitutes the major ingredient of the most commondishes consumers listed in this survey: ugali (stiff maize por-ridge), githeri (boiled maize kernels and beans), and uji (liq-uid porridge). In eastern Kenya, muthokoi (same ingredientsas githeri, but the maize kernels are processed differently) isvery popular. For most people (63%), uji is the most commonbreakfast food, followed by ugali (16%) and githeri (6.2%). Forlunch, the most common dish is ugali, although this is moreprominent in the west (91%) than in the east, where some con-sumers eat more githeri (16%) and muthokoi (11%). Half theconsumers list githeri as the second most common dish forlunch. Preferences for dinner are similar to those for lunch: themost common dish is ugali, again more in the west (95%) thanin the east (64%) where githeri (14%) and muthokoi (15%) arealso popular, and half of the consumers list githeri as the secondmost common dish for dinner.

4.4. WTP for maize meal products

Experimental auctions were used to obtain the WTP of con-sumers for different maize products: standard white maize flour,yellow maize flour, and white fortified maize flour. Averagingthe bids for standard white maize meal results in a WTP of42.6 KShs/2kg (see Table 3, last column). There are clear dif-ferences between zones, with the lowest WTP for plain whitemaize found in Siaya, and the highest in the town of Machakos.The WTP for yellow maize, on the other hand, follows exactlythe opposite trend. In Siaya, where yellow maize is most com-mon, consumers actually prefer it to white, and are willing topay a premium of 4.9%. Consumers in other zones prefer whitemaize, with only a small discount of 3.5% for the other western


Tabl

e2

Mai

zeco

nsum

ptio

nan

dco

nsum

erkn

owle

dge

Cat

egor

yY

ear

2005

/200

620

09V

aria

ble

Wes

tern

Ken

yaE

aste

rnK

enya

Eas

tern

Ken

ya

Moi

stm

idal

titud

es(S

iaya

)M

oist

tran

sitio

nal

(Vih

iga)

Dry

tran

sitio

nal,

rura

l(M

acha

kos

and

Mak

ueni

)

Dry

tran

sitio

nal,

urba

n(M

acha

kos)

Dry

tran

sitio

nal,

urba

n(M

acha

kos)

(N=

50)

(N=

50)

(N=

200)

(N=

200)

Ove

rall

(N=

151)

Mea

nSt

d.de

v.M

ean

Std.

dev.

Mea

nSt

d.de

v.M

ean

Std.

dev.

Mea

nSt

d.de

v.M

ean

Std.

dev.

Mai

zeco

nsum

ptio

nM

aize

cons

umpt

ion

from

prod

uctio

n(k

g/pe

rson

/mon

th)

4.3

3.5

3.9

3.7

7.7

5.1

7.0

6.2

6.6

5.4

7.5

6.8

Mai

zeco

nsum

ptio

n(%

mai

zepr

oduc

edth

atis

cons

umed

)90

.819

.693

.017

.178

.727

.476

.229

.380

.527

.293

.415

.4

Mai

zepu

rcha

sed

(kg/

pers

on/m

onth

)13

.15.

410

.94.

99.

66.

57.

97.

49.

46.

89.

46.

7

Yel

low

mai

zeH

asea

ten

yello

wm

aize

befo

re(%

)10

0.0

0.0

100.

00.

098

.512

.287

.033

.794

.223

.470

.026

.1E

ats

yello

wm

aize

atle

ast

occa

sion

ally

(%)

100.

00.

092

.027

.430

.546

.29.

529

.435

.247

.821

.045

.0

Eat

sye

llow

mai

zere

gula

rly

(%)

81.2

40.3

18.2

40.5

8.5

28.0

10.5

30.7

12.4

33.0

4.0

23.5

Fort

ified

mai

zeA

war

eof

fort

ified

mai

ze(%

)36

.048

.544

.050

.157

.549

.656

.049

.853

.449

.949

.047

.9B

uys

fort

ified

mai

ze(%

)20

.440

.714

.936

.038

.548

.842

.049

.535

.948

.036

.050

.2

Kno

wle

dge

Aw

are

ofni

ghtb

lindn

ess

(%)

88.0

32.8

62.0

49.0

91.0

28.7

81.5

38.9

84.0

36.7

65.0

34.7

Aw

are

ofvi

tam

ins

(%)

90.0

30.3

94.0

24.0

96.5

18.4

98.0

14.0

96.2

19.1

72.0

21.1


Table 3Willingness to pay (WTP) for maize meal products (KShs/2kg) and their traits in experimental auctions in 2005/2006

Western Kenya Eastern Kenya, dry transitional Overall

Moist Moist Rural Urbanmidaltitudes transitional (Machakos and (Machakos)(Siaya) (Vihiga) Makueni)

Maize product Statistic (N = 50) (N = 50) (N = 200) (N = 200) (N = 500)

Mean WTP White maize meal (a) Mean 38.7 40.5 43.0 43.6 42.6St.Deviation 10.7 11.0 10.9 9.7 10.5

Yellow maize meal (b) Mean 40.6 39.1 38.1 37.2 38.1St.Deviation 13.3 11.7 12.3 11.8 12.1

White fortified Mean 50.4 53.2 53.3 53.4 53.0maize meal (c) St.Deviation 12.6 14.3 12.4 11.0 12.1

WTP for traits Yellow (b-a) Kshs/2kg 1.9 −1.4 −4.8 −6.4 −4.5St.Deviation 11.5 11.1 11.4 12.4 12.0% 4.9 −3.5 −11.3 −14.8 −10.5

Fortification (c-a) Kshs/2kg 11.7 12.7 10.3 9.8 10.5St.Deviation 12.0 11.4 9.7 9.7 10.1% 30.2 31.2 24.0 22.4 24.6

consumers (in Vihiga), but high discounts in the east: 11.3%in the rural areas and 14.8% in town. Consumers are willingto pay substantially more for fortified maize, with an averagepremium of 24.6%. The premium is higher in western Kenya.

The consumers’ average WTP for different products is gen-erally below the market price, where this could be observed. InMachakos Town, for example, the average WTP for 2 kg whitemaize meal was KShs 46.3, or 19% less than the local priceof KShs 54, while the average WTP for 2 kg fortified maizemeal was 52.5%, or 11% less than the local price. In Siaya, theaverage WTP for 2 kg of maize meal was KShs 38.7 for whiteand KShs 40.6 for yellow, respectively 18% and 17% less thanthe estimated market price (KShs 43/2 kg grain plus millingcost of KShs 5/2 kg).

The results of the basic model, estimating Eq. (2), are similar(Table 4). The product characteristics entered in the model wereyellow (1 = yellow, 0 = white) and fortified (1 = fortified,0=plain). The constant then represents the WTP for plain whitemaize meal and is estimated at 42.6 KShs/2kg. The coefficienton the trait “fortified” is estimated at 10.5 KShs/2 kg, meaningconsumers are willing to pay 25% more for fortified maize. Thecoefficient on the trait “yellow,” on the other hand, is estimatedat −4.5 KShs/2 kg; consumers put its value at a discount of10%.

The model was then expanded by including cross-effects forthe traits with the geographical origin of the consumers, fol-lowing Eq. (4). Geographically, the consumers are divided intofour categories, combinations of the binary variable urban (1 =urban, 0 = rural) and two binary variables for the zones “moistmidaltitude” and “moist transitional” (“dry transitional” beingthe omitted category). Those four groups have different WTPfor the maize products offered. The constant, KShs 42.97/2 kgcan be interpreted as the mean WTP of the base group, ruralconsumers in the dry transitional zone, for the base product,plain white maize meal. Similarly, the effects of the traits “for-

tified” (+24%) and “yellow” (−11%), are to be interpreted asthe WTP for this base group for plain white maize meal, andthey are very similar to the results of the basic model. Further,the direct effects of the geographic categories are to be inter-preted as the difference in WTP with the base group, hencethe rural consumers of Siaya (moist midaltitude zone), have asignificantly lower WTP for white plain maize, a difference of4.25 KShs/2 kg, or 10% less.

Estimating cross effects of zones and traits show no dif-ferences in appreciation of fortified maize between consumersfrom the different zones, or between urban and rural consumers.Similarly, there is no difference in preference for white versusyellow maize between rural and urban consumers, but thereis a big difference between zones. Consumers in the moisttransitional zone (Vihiga) are willing to pay 3.4 KShs/2 kgmore, and those in the moist midaltitudes (Siaya) will pay even6.7 KShs/2 kg more for yellow maize than the base group.These premiums need to be compared to the WTP for plainwhite maize in these zones, 40.6 and 38.7 KShs/2 kg, respec-tively, leading to a premium for yellow maize of 8% in Vihigaand 17% in Siaya.

4.5. Factors influencing the WTP

The factors that contribute to consumers’ preferences canalso be analyzed with the random effects regression model, byadding cross effects of the WTP for the traits “fortified” and“yellow” with socioeconomic characteristics to the model ofEq. (4) (Table 5). While the direct effects and the effects ofthe geographical variables were similar to those of the shortermodels (with exception of the coefficient for yellow likely be-ing due to the fact that some socioeconomic variables thatexplain the preferences for yellow are now explicitly in themodel), few of the other factors were found to affect consumer


Table 4WTP for yellow and fortified maize from experimental auctions in 2005/2006 and 2009 (random effects model with geographic interaction terms)

Effects Year 2005/2006 2009

Short model Long model Short model

Est. coeff. Std. error Est. coeff. Std. error Est. coeff. Std. error

Direct effects Constant 42.58 0.52∗∗∗ 42.97 0.82∗∗∗ 76.32 1.94∗∗∗Fortified 10.48 0.53∗∗∗ 10.32 0.82∗∗∗ 13.08 1.47∗∗∗Yellow −4.46 0.53∗∗∗ −4.84 0.82∗∗∗ −8.13 1.47∗∗∗Urban 0.68 1.16Moist midaltitudes (Siaya) −4.25 1.83∗∗Moist transitional (Vihiga) −2.43 1.83

Cross-effect on fortified Urban −0.540 1.17Moist midaltitudes (Siaya) 1.360 1.84Moist transitional (Vihiga) 2.34 1.84

Cross-effect on yellow Urban −1.600 1.17Moist midaltitudes (Siaya) 6.720 1.84∗∗∗Moist transitional (Vihiga) 3.440 1.84∗

Strategic behavior Random price group (binary) 0.55 2.47Model R2, within 0.45 0.47 0.41

R2, overall 0.23 0.23 0.19Number of observations 1,497 1,497 453Number of respondents 499 499 151σ u 8.08 8.12 13.26σ v 8.32 8.25 12.76ρ (share variation due to ui) 0.49 0.49 0.51

Signif. codes: ∗∗∗ = 0.01; ∗∗ = 0.05; ∗ = 0.1.

Table 5WTP for yellow and fortified maize meal from experimental auctions in 2005/2006 (random effects model with consumer characteristics)

Characteristics Variable Direct effects Cross effects with fortified Cross effects with yellow

Est. coeff. Std. error Est. coeff. Std. error Est. coeff. Std. error

Product Constant 42.20 5.00∗∗∗Fortified 10.58 5.05∗∗Yellow −16.21 5.05∗∗∗

Geographic Urban 1.54 1.34 −0.71 1.36 −1.14 1.36Moist midaltitudes −4.90 1.99∗∗ 1.54 2.01 7.36 2.01∗∗∗Moist transitional zone −2.85 1.96 2.41 1.98 4.01 1.98∗∗

Socioeconomic Age 0.04 0.05 0.02 0.05 0.01 0.05Female 1.29 1.24 −0.56 1.25 0.17 1.25Education (years) −0.04 0.16 −0.05 0.16 0.08 0.16Cattle owned (number) 0.25 0.16 0.16 0.16 0.08 0.16

Cognitive Has tried eating yellow maize 1.84 2.33 −3.77 2.36 4.37 2.36∗Aware of night blindness −2.01 1.51 1.21 1.53 −0.70 1.53Aware of vitamins −2.67 2.97 2.05 3.00 6.59 3.00∗∗

Model R2, within 0.47R2, overall 0.25Number of observations 1438Number of respondents 1476σ u 8.08σ v 8.26ρ (share variation due to ui) 0.49

Signif. codes: ∗∗∗ = 0.01; ∗∗ = 0.05; ∗ = 0.1.

preferences. In fact, none of the factors were found to signifi-cantly affect consumers’ WTP for fortified maize. Two factors,other than the positive effect of the western zones, were foundto increase consumers’ WTP for yellow maize: familiarity with

yellow maize (a premium of 6%) and awareness of vitamins(7%).

The Hausman tests for all three model estimations sup-ported, highly significant, the hypothesis of the individ-


ual specific effects being uncorrelated to the explainingvariables.

4.6. Comparison of random price (standard) and bid price(modified) BDM auctions

No bias of underreporting WTP due to strategic behavior,which could theoretically be expected, was observed in the trial.The differences between the mean bids in auctions where theexchange price was the randomly drawn number (second price)and those where it was the respondent’s bid (first price) wereall minor: −1.6% for white, +2.5% for yellow, and +1.3% forwhite fortified maize meal, and none was significantly differentfrom zero (P > 0.3). Estimation of the random effects model(Table 4, last columns), with type of maize and type of auction asexplaining variables resulted in a coefficient of 0.55 KShs/2kgfor the random-price auction dummy, again not significantlydifferent from zero (P = 0.80).

The question of bidding in different experimental auctiontypes has been discussed widely in the literature (see Lusk andShogren, 2007, for a recent overview). Differences in biddinghave been reported even across incentive-compatible auctions,in which fully optimizing participants would be expected tomake identical bids. Rutstrom (1998), for example, finds lowerbids in English home-grown value auctions than a Vickrey auc-tion. These differences are likely to arise due to differences inframing and context, as the English and Vickrey auctions in-volve quite different procedures. In contrast, the procedures forour modified and standard BDM auctions are nearly identical.Only the actual price paid differs, so any difference in bidding islikely to be due only to strategic behavior, and among our sub-jects in these experiments we observe no significant differenceat all. Furthermore, no difference in participants’ understand-ing of the two procedures was observed, as had been initiallyfeared. The random price BDM auction is equally acceptable,and provides additional benefits to the participants in case theywin the auction, since they do not have to pay their original bidbut the lower random number.

5. Discussion and conclusion

This study shows how experimental auctions are a convenientway to capture preferences of rural and urban consumers inAfrica. The results indicate that maize consumers in Kenya arewilling to pay a premium for fortified maize, and confirm theirpreference for white maize. This preference for white is clearlyless pronounced in western Kenya, and consumers in SiayaDistrict actually prefer yellow maize over white.

The experience with BDM experimental auctions shows thatthey require substantial time and effort, both in learning thetechnique and in conducting the exercise. For the enumera-tors, proper training and sufficient practice is essential. For theconsumers, a test round with a popular consumer item should

be included, with time for questions and clarification, beforeconducting the auction with the product of interest.

Contrary to group auctions such as second or nth-price auc-tions, English or Dutch auctions, consumers in the individualone-shot BDM format do not learn through feedback from themarket clearing and consecutive rounds of bidding. Group auc-tions, however, are time-consuming and expensive, and canlead to competitive behavior when consumers pay a premiumfor the satisfaction of being the winner. Moreover, they need tobe conducted in laboratory settings, while BDM auctions canbe conducted in the field at points of sale or homesteads. BDMauctions are conducted one-on-one, and are therefore easierto organize and to execute. BDM auctions take considerablylonger and cost more than CV methods, but auctions with realproducts and a real exchange of money are more accurate andyield results much closer to market behavior.

The use of a modified BDM procedure for our main exper-iment in which subjects paid their bid price is problematic,since strategic behavior provided an incentive to underreportWTP. Our second experiment, however, found that in practicethere was no significant difference between the results of thisapproach and a standard random-price BDM mechanism. Anyeffect of strategic behavior was sufficiently small that the resultsof our main experiment can be considered robust to this con-cern. However, since no particular difference in participants’understanding of the two procedures was observed, future stud-ies could use the random-price approach so as to rule out thisconcern entirely.

The results of the auctions clearly indicate that Kenyan maizeconsumers are highly interested in nutritionally enhanced maizemeal. Consumers are willing to pay a premium of 24% for maizefortified with minerals and vitamins. We found no evidence thatknowledge of nutritional quality or any of the socioeconomicfactors measured had an impact on WTP for fortified maize.Consumers’ knowledge might have been too limited to be mea-sured, or maybe the questionnaire did not capture it properly.Most consumers were found to be aware of fortified maize, andof night blindness and vitamin A. However, their understandingis limited, and most consumers do not know much about thebeneficial effects of vitamin A.

As expected, Kenyan consumers generally prefer whitemaize, and would buy yellow maize only at a discount of11%. Still, and comforting for the developers of biofortifiedmaize, this discount is substantially less than the premium theyare willing to pay for fortification. Moreover, there are clearregional differences, with yellow maize being much more ac-cepted in western Kenya. In the Vihiga district of the moisttransitional zone, consumers do not have a clear preference forwhite, while in the Siaya district of the moist midaltitude zone,consumers actually have a clear preference for yellow maize.In this zone, colored landraces are common and their taste isoften preferred to the newer, white maize varieties. Apart fromthis regional difference, two more factors were found to affectWTP for yellow maize—familiarity with it, and knowledgeof vitamin A. When people are familiar with yellow maize,


and have eaten and tasted it, their aversion decreases. Thissuggests that the aversion to yellow maize stems from prej-udice and negative associations, such as food aid and animalfeed, rather than from its consumer characteristics such as taste.Clearly, the aversion is for a bundle of traits associated with thecolor.

Knowledge of vitamin A was also found to have a positiveeffect on WTP for yellow maize. This was a bit surprising, sincean effect on WTP for fortified maize was expected. In futuresurveys, the questionnaire could be expanded to explore thisrelationship.

This study was the first to use experimental auctions on maizeproducts in Africa, and the experience and literature review pro-vide insights for further research. It would be most interestingto compare different auction methods, in particular to contrastindividual BDM auctions with the group auctions popular inconsumer research elsewhere. To guide future research, it is im-portant to measure the effect of repeated cycles on consumers’WTP and the difference between laboratory and field settings.Laboratory settings might be indicated to study certain effectsin more detail, such as isolating the color from the taste usingcolor filters or blindfolds in a taste panel. The importance ofinformation also needs to be studied further, especially whichtype of information and knowledge affects consumer prefer-ence. Moreover, consumers’ sources of information should beidentified and analyzed. Finally, studies are needed to measurethe efficiency of those different sources in reaching the targetpopulation with the required information.

The results from Western Kenya indicate that maize, bioforti-fied with provitamin A carotenoids, would be easier to introducein areas where yellow maize is grown. In Kenya, markets forwhite maize have been well integrated since the liberalizationin the mid 1990s. Markets for yellow maize, however, are ratherisolated and are largely limited to those regions where they areproduced, with restricted, if any, trading of yellow maize be-tween regions. In the markets where we observed trading inyellow maize, or were informed of such trade by the traders orother key informants, no price difference between yellow andwhite maize was observed. Therefore, if provitamin A bioforti-fied, yellow maize is targeted for markets dominated by whitemaize, a price discount would be needed to interest consumers.In the smaller regional centers where yellow maize is traded, nosuch discount would be needed. However, biofortified crops arefirst and mostly targeted for consumption by the farm house-holds who produce them (Meenakshi et al., 2010). Therefore, abiofortified yellow maize variety targeted to the regions whereyellow maize is currently grown, and with equal or superioragronomic, culinary, and sensory characteristics, is likely to besuccessful there. The maize should, however, first be tested forthese characteristics.

Unfortunately, only scant information is available on whereyellow maize is currently grown, to which agroecological zonesit is adapted, how it is consumed, how consumers perceive it,and what is the price difference with white. Further research istherefore indicated, focusing on mapping the production areas

of yellow maize and the price differences. A survey of maizeexperts and other resource people would likely provide a goodfirst estimate. More information is already available for EastAfrica, in particular Kenya and Ethiopia where yellow maizeis produced and where high levels of VAD are prevalent. Bothcountries also have well-established maize research programs.It is, therefore, indicated that while appropriate biofortified va-rieties are being developed, the production and use of yellowmaize in these countries should be mapped in more detail. Fur-ther, the consumers’ interest in fortified maize and acceptanceof yellow maize needs to be estimated in the areas not yet cov-ered. As shown here, the BDM auctions offer a convenient toolfor this research.

Acknowledgments

Authors are in alphabetical order; senior authorship is notassigned. This research was possible through the support of thegovernment of Austria, through the HarvestPlus project “Pref-erences of Maize Consumers in Eastern Africa” (project no.8012). The additional survey was possible through the financialsupport of the Theodor Korner Fonds. The technical support andscientific advice from Howarth Bouis, J.V. Meenakshi, BonnieMcClafferty from HarvestPlus, and Nilupa Gunaratna of the In-ternational Nutrition Foundation was highly appreciated, as wasthe logistic support of the CIMMYT—Nairobi staff, the help ofEsther Rutto with the surveys, and the editing of Kathy Sinclairand Judie-Lynn Rabar. The local knowledge of the enumera-tors was indispensable for the data collection and analysis. Thesupport of the Kenyan Agricultural Research Institute (KARI),in particular Charles Bett, was essential for the field work inthe Machakos region. We received valuable comments fromtwo anonymous reviewers and the editor. Finally, we thank allrespondents who kindly agreed to be interviewed.

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