Comparing consumer preferences for color and nutritional quality in maize: Application of a semi-double-bound logistic model on urban consumers in Kenya

Food Policy 33 (2008) 362–370

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Comparing consumer preferences for color and nutritional quality in maize: Application of a semi-double-bound logistic model on urban consumers in Kenya Hugo De Groote a,*, Simon Chege Kimenju b a b

International Maize and Wheat Improvement Centre (CIMMYT), P.O. Box 1041-00621, Village Market, Nairobi, Kenya Tegemeo Institute, Egerton University, P.O. Box 20498-00200, Nairobi, Kenya

a r t i c l e

i n f o

Article history: Received 4 December 2006 Received in revised form 16 January 2008 Accepted 25 February 2008

Keywords: Maize White maize Kenya Biofortification Contingent valuation

a b s t r a c t Consumer preferences for white maize in East and Southern Africa concerns developers of maize biofortified with provitamin A carotenoids, since carotenoids impart a yellow or orange coloration. Urban consumers’ willingness to pay (WTP) for yellow maize was estimated, using a semi-double-bounded logistic model, based on a survey of 600 maize consumers in Nairobi, Kenya, at posho mills, kiosks and supermarkets. Consumers showed a strong preference for white maize. Only a minority would buy yellow maize at the same price as white maize, and fewer consumers in the posho mills (24%) and kiosks (19%) than in the supermarkets (34%) would do so. On average, consumers need a price discount of 37% to accept yellow maize. This discount was less at the posho mills (35%) and kiosks (37%) than in the supermarkets (48%). Most respondents (76%) were aware of the existence of fortified meal and the generally showed an interest. The average premium for fortified maize was much less than the discount for yellow: 5.9% for those aware and 7.4% for those unaware. Consumer preferences were influenced by socioeconomic factors such as gender, education, income and ethnic background. Women have a stronger preference for both white maize and fortified maize than men, and consumers with more education have a stronger preference for white. Income decreases the WTP for yellow maize as well as the price elasticity, but increases the WTP for fortified maize. Consumers originating from Western Kenya have a lower preference for white, while those from Central Kenya had a stronger preference for fortified maize. Ó 2008 Elsevier Ltd. All rights reserved.

Introduction Maize consumers in East and Southern Africa generally prefer white over colored maize, which poses a particular challenge for the dissemination of maize biofortified with provitamin A carotenoids, which necessarily colors the maize yellow to orange. Micronutrient deficiency, in particular of vitamin A, is a major problem in developing countries (West and Darnton-Hill, 2001; Zimmermann and Qaim, 2004) and predominantly affects low-income groups (Ruel, 2001). In Kenya, 3.8 million children under the age of five years (84.4%) suffer from vitamin A deficiency (VAD) (Ministry of Health, 1999). VAD is common among Kenyan mothers (50.7%) and, to a lesser degree, adult males (42.4%). Several strategies have been developed to reduce VAD, in particular, supplementation, food fortification and public health campaigns (IFPRI, 2002). Vitamin A can be provided directly by administering high-dose capsules every six months, the period for which the human body can store it. Indirectly, people can be made aware of the problem through public health campaigns, promoting dietary improvements and increased consumption of foods rich in vitamin A or * Corresponding author. Tel.: +254 2 7224600; fax: +254 2 7224601. E-mail address: [email protected] (H. De Groote). 0306-9192/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodpol.2008.02.005

its precursors, such as green leafy vegetables or orange fleshed sweet potatoes. Finally, the intake of vitamin A can be increased by obligatory fortification of food staples with retinol, its major form. Unfortunately, all three methods are expensive and have difficulties reaching the poor. Biofortification, or breeding for increased micronutrient content, is a new, alternative approach of tackling micronutrient deficiencies. Given the large numbers of micronutrient deficient people, and the quantities of staple crops they consume, biofortification has high potential (Bouis, 1999). Plants do not contain vitamin A as such, but they do contain carotenoids, some of which can be transformed by the human body into vitamin A. Food crops have, therefore, been bred for increased levels of these provitamin A carotenoids. Examples include orange fleshed sweet potatoes, already being disseminated in Africa, and ‘‘golden rice” (Zimmermann and Qaim, 2004), both containing higher levels of provitamin A carotenoids than their white counterparts. In Kenya, maize is the main staple food, with an average annual consumption of 103 kg (Pingali, 2001). Moreover, a majority of households use maize porridge as weaning food (Ministry of Health, 1999). Most maize consumed in Kenya is white (FAO and CIMMYT, 1997) and, therefore, contains no carotenoids. Screening of maize varieties from other regions, however, found several

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varieties with high levels of provitamin A carotenoids, especially beta-carotene. Carotenoids are always yellow to orange, hence the orange flesh of sweet potatoes and the coloring of golden rice. Similarly, breeding maize for higher levels of provitamin A carotenoids will result in yellow to orange colored grain. The opposite, however, does not hold: yellow or orange maize varieties are not necessarily higher in provitamin A carotenoids, because maize grain also contains many other carotenoids and pigments. The expected coloration of biofortified maize poses a major challenge for East and Southern Africa, where most of the maize produced and consumed is white (FAO and CIMMYT, 1997). Consumers often consider yellow maize to be inferior, more suitable for relief food and animal feed (Rubey et al., 1997) and the price of white maize is often higher than that of yellow maize (FAO and CIMMYT, 1997). A second problem with biofortified crops is that provitamin A carotenoids are quite vulnerable, and can easily be destroyed by exposure to light, oxygen, and prolonged processing (RodriguezAmaya, 1997). Mechanical homogenization and moderate heat treatment, on the other hand, has been shown to enhance the bioavailability of carotenoids of different foods (Van het Hof et al., 2000). Therefore, to analyze the potential impact of fortification and biofortification, it is vital to know how important maize is in the diet, especially for the poor, how maize is purchased and stored, and how it is prepared and consumed. To shed more light on the above questions, a study was undertaken to: (i) describe and analyze maize consumption patterns, in particular which maize products consumers purchased and how they prepared them; and (ii) determine the willingness to accept yellow maize, and compare it to the willingness to pay for maize with enriched provitamin A carotenoid content. For budgetary reasons, the study area for this initial phase was limited to urban consumers in Nairobi, Kenya, and questions were added to a survey on consumers’ perceptions of genetically modified food (Kimenju and De Groote, 2008). In the next section, a short overview of maize and its historic background is presented, followed by the methodology of this particular study. The results are presented in two sections: consumption patterns and willingness to pay for particular traits, and are followed by the conclusion.

Background: white maize in East Africa Maize arrived in East Africa around the 16th century, probably through Portuguese traders (Miracle, 1966). The first maize varieties were mostly of Caribbean origin, flint in texture, and came in a variety of colors. 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 particular for alkaline cooking, as is common in Central America, and dry milling, popular in East Africa (Rooney et al., 2004). Maize was not immediately popular in Africa though, and when British colonists arrived in the late 19th century, the major cereals were still millet and sorghum (Waaijenberg, 1994). In the early years of the 20th century, however, European settler farmers started producing maize using white dent varieties imported from South Africa (Smale and Jayne, 2003). In dent maize, globally the most common type, most of the starch in the endosperm is soft and it contracts during drying, producing a characteristic dent in the top of the kernel (Dowswell et al., 1996, p. 22). Several factors encouraged the development of large-scale maize farming in Kenya at the beginning of the last century, including increased demand for maize during the First World War, the booming starch industry in England, and the need to feed increasing numbers of farm workers (Byerlee and Heisey, 1997). Agronomically, maize was particularly suited to the region, and the placement and cover of its ears protect it from bird attacks, a

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major problem in millet and sorghum production. Another major factor was the introduction of small hammer mills, since maize does not need de-hulling before it is thrown in the grinder, another advantage over millet and sorghum (Smale and Jayne, 2003). The high demand for maize for the war industry and the support of strong lobbying efforts by European settlers, combined with pest problems in millet and sorghum, made maize the major food crop in Kenya by the time of the First World War. A major turning point was when the marketing board started to refuse to buy yellow maize. From then on, only white maize was grown in the central highlands. The maize research program in Kenya, one of the most successful in the region, produced many improved varieties but, given the established market preferences and focus on the highlands, all those were white. It can thus be argued that the dominance of white dent varieties in East Africa was a case of path dependency, caused by a range of favoring factors (Rubey et al., 1997; Smale and Jayne, 2003). Furthermore, once people are used to a particular food with particular characteristics these characteristics easily become preferred traits. Currently, yellow maize is rarely found in the markets of Central Kenya, including Nairobi. Informal discussions with urban and rural consumers indicate a strong preference for white maize, while yellow maize is seen as inferior, often associated with food aid and animal feed. Yellow and other colored varieties are still popular with Kenyan small-scale farmers at the coast and around Lake Victoria. In these areas, they are found in the market and often, but not always, at a lower price. Consumer preferences for white maize have been studied previously in Southern Africa. Prices of yellow maize are generally lower than for white maize, as observed in Zambia (Diskin and Kipola, 1994), Mozambique (Tschirley et al., 1996) and South Africa (FAO and CIMMYT, 1997). During surveys, consumers have frequently stated their preference for white maize, as documented in Mozambique (Tschirley and Santos, 1995) and Zimbabwe (Rubey and Lupi, 1997; Muzhingi et al., 2008). In both countries, consumers were willing to switch to yellow maize given a price discount. Moreover, consumers of low-income groups are more likely to make that switch (Dorosh et al., 1995; Tschirley et al., 1996; Rubey et al., 1997). This characterizing of yellow maize as an inferior good indicates its potential for use in self-targeting, or the subsidizing of commodities less preferred by the better-off (Dorosh et al., 1995; Alderman and Lindert, 1998). This has been documented for Mozambique (Dorosh et al., 1995), as well as Kenya, where this characterizing of yellow maize contributed to a more fair allocation of the free food through self selection (Dreze and Sen, 1990). In East-Africa, however, no empirical studies of consumer preferences for yellow maize have been conducted so far.

Methodology Conceptual framework To estimate a population’s valuation of non-market goods, economists have long used contingent valuation (CV) methods, originally developed in environmental and natural resource economics (Mitchell and Carson, 1989). The method is well suited to soliciting consumers’ willingness to pay (WTP) for a product that is not yet on the market, such as biofortified maize. Contingent valuation methods are now increasingly used in developing countries (Alberini and Cooper, 2000). In this method, the researcher creates a hypothetical market for a non-market good or novel product, invites a group of subjects to operate in that market, and records the results. The values generated through the use of the hypothetical market are treated as estimates of the value of the non-market

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good or service, contingent upon the particular hypothetical market (Mitchell and Carson, 1989). WTP can be estimated using questions that are open-ended, asking the respondents to declare the maximum amount they would be willing to pay, or close-ended, asking the respondents if they would be willing to pay a specific amount or not (dichotomous choice). The open-ended format can be used when the consumer is well informed about the new product and its characteristics. However, it can be problematic when the respondent does not have sufficient information and stimuli to consider thoroughly the values they would attach to such goods if a market were to exist, and might thus not return realistic estimates (Arrow et al., 1993). Close-ended questions, on the other hand, are easier on the respondent and are more realistic since they correspond more to a real market situation. In most markets, consumers are offered a product at a particular price and, perhaps after some bargaining, face a decision to purchase or not. In the single-bounded method, the individual only responds to one bid. This approach is incentive-compatible; it is in the respondent’s strategic interest to say ‘‘yes” if her WTP is greater or equal to the price asked, and ‘‘no” otherwise (Mitchell and Carson, 1989). Utility maximization implies that a person will then only answer ‘‘yes” to the offered bid if his maximum WTP is greater than the bid. However, the single-bounded method requires a large sample size and is statistically inefficient (Hanemann et al., 1991). Efficiency can be improved by offering the respondent a second bid, higher or lower depending on the first response, in an approach generally known as the double-bounded CV method. This method incorporates more information about an individual’s WTP and, therefore, provides more efficient estimates and tighter confidence intervals (Hanemann et al., 1991). The double-bounded method is easy to administer and the model can be estimated with standard econometric software. Consumers in Nairobi generally prefer to eat white maize, but the degree of that preference is not well documented. In this study, consumers were first asked whether they would be willing to pay for yellow maize meal at the current price. When they answered ‘‘yes”, they were not offered a higher bid, as is the practice in the double-bounded method. It was assumed few would be interested anyway, and it was difficult for the enumerators to ask if people would pay more for what is generally regarded as a lower quality product. So, to save time and energy, only when consumers rejected the first bid, were they presented with a second, lower bid. Therefore, instead of the four possible outcomes of the CV in the standard double-bounded method, there are only three. The maximum likelihood function needs to be adjusted to reflect these three options, in what has been called the semi-double-bounded model (McCluskey et al., 2003). The semi-double-bounded logistic model The random utility model is the basic model for analyzing dichotomous choice CV responses, and is based on the assumption that individuals know their preferences with certainty. However, the decisions contain components unobservable to the investigator which are treated as random (Hanemann and Kanninen, 1998). Individuals are then expected to maximize their utility, which may be expressed as U ¼ f ðy; z; eÞ;

ð1Þ

where y is the individual’s income, z is a vector of respondent’s characteristics and other demographic variables and e is the random term. A consumer will agree to buy a product or service at the offered price if the utility with the proposed change is greater than the utility without the change. While U is observable to the

respondent, but not to the interviewer, probabilities can be derived from observed behavior. The probability that the respondent will answer ‘‘yes” and hence be willing to pay is the probability that their utility with the proposed change is greater than that without the change, represented by the equation: Pj ¼ PððU j1 ðyj  b; zj ; ej1 Þ > U j0 ðyj ; zj ; ej0 ÞÞ;

ð2Þ

where Pj is the probability that the jth respondent will answer ‘‘yes” to an offered price of b; Uj1 is the respondent’s utility with the change; Uj0 is the respondent’s total utility without the change; yj is the respondent’s income; zj is a vector of the respondent’s characteristics and other demographic variables; ej1 and ej0 are random components with and without change, respectively. In dichotomous choice CV, the respondents are offered specific amounts, B, and asked whether they are willing to pay that amount or not, to secure some given improvement. The respondent answers with a ‘‘no” or a ‘‘yes”. The yes–no answers are then used along with the required payment to estimate a discrete model from which expected WTP is calculated. The probability of obtaining a ‘‘no” or a ‘‘yes” is a function of the amount of the bid B offered, and the individual’s maximum willingness to pay. It can be represented by Pr ðNo to BÞ ¼ Pr ðB > max WTPÞ;

ð3Þ

and Pr ðYes to BÞ ¼ Pr ðB 6 max WTPÞ:

ð4Þ

Mathematically, we can express the distribution of maximum WTP as a cumulative density function (cdf) of the bid B and a vector of parameters h, G(B; h), where G(.) represents an appropriate statistical distribution function (Hanemann et al., 1991), representing a down-sloping S-shaped curve, from 1 to 0. The probability that a consumer will reject the bid then equals the probability that her maximum WTP is less than B, or pn ðBÞ ¼ Probðmax WTP < BÞ ¼ GðB; hÞ:

ð5Þ

The probability of the consumer accepting the bid is then the reverse py ðBÞ ¼ Prðmax WTP > BÞ ¼ 1  GðB; hÞ:

ð6Þ

Using an appropriate functional form of G(.), the probabilities of the two outcomes can be expressed mathematically, the likelihood function can be constructed, and the parameters estimated. A convenient and conventional function is the logistic, mathematically: Gð:Þ ¼ 1=1 þ expðvÞ;

ð7Þ

where v is an index function, usually linear in the bid B. It follows that ProbðB > max WTPÞ ¼ 1=ð1 þ expðða  qBÞÞ:

ð8Þ

The coefficient q is necessarily positive, to form a down-sloping Scurve, ranging from 1 to 0 (a negative q would result in an upwards sloping WTP, which would contradict economic theory). In the double-bounded approach, consumers are asked a followup question, which presents them with a second bid. The second bid is higher (with a premium) if the consumer accepted the first bid, or lower (with a discount) otherwise. If a new product or characteristic of that product is clearly associated with a negative consumer preference (such as a yellow color for maize in East Africa), it is difficult for the scientist or enumerator to ask consumers if they would want to pay a premium. So consumers are asked if they are willing to buy the new product at the initial bid. If they accept, the process ends, but if they refuse, they are offered the product at a discount. Possible outcomes are therefore (i) ‘‘yes”, acceptance of the first bid; (ii) ‘‘no”, ‘‘yes”: rejection of the first bid and acceptance of the second bid; (iii) ‘‘no”, ‘‘no”: rejection of both the first

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and second bids. Probabilities of these outcomes are denoted as py, pny and pnn, respectively, and are a function of the initial bid B1, and of the lower, discounted bid (after an initial ‘‘no” response), B2. Building on the dichotomous choice model, and using similar notation, the probabilities of the three outcomes are expressed as follows: accepting the first bid would imply that the consumer’s maximum WTP is higher than the bid; rejecting the first bid and accepting the second would imply the WTP falls between the bids, and rejecting both bids would imply it falls below the lowest bid. Mathematically: py ðB1i Þ ¼ PrðB1i 6 max WTPi Þ ¼ 1  GðB1i i; hÞ pny ðB1i ; B2i Þ ¼ PrðB1i P max WTP P B2i Þ ¼ GðB1i ; hÞ  GðB2i ; hÞ

ð9Þ

pnn ðB2i Þ ¼ Prðmax WTP < B2i Þ ¼ GðB2i ; hÞ; where G(B; h) is the cdf, assumed to be logistic, of the individual’s true maximum WTP, with parameter vector h. The log likelihood function becomes ln LðhÞ ¼

N X y ny nn fdi ln py ðB1i Þ þ di ln pny ðB1i B2i Þ þ di ln pnn ðB2i Þg;

ð10Þ

i¼1 y

ny

nn

where di ; di , and di , are binary indicator variables. The maximum likelihood estimator for the semi-double-bounded model, ^ h is the solution to the maximization of the equation, and the mean WTP can be derived by calculating a/q, which is the mean of the logistic distribution function with given specifications. Data collection Consumers were interviewed in Nairobi in November and December 2003, selected using a stratified two-stage approach. To ensure representation of different categories of maize meal consumers, the major types of outlets for maize products were stratified in three groups: supermarkets, kiosks and posho mills (mechanical hammer mills). In each of the strata, the outlets were selected in the first stage, and consumers in the second stage. A list of supermarkets was obtained from Kenya’s Central Bureau of Statistics, and fifteen supermarkets were randomly selected: ten large (with more than three branches in the city), and five small ones. The kiosks were selected in a two-stage procedure. Seven city estates (administrative subdivisions of Nairobi) were first selected proportionate to population size. All kiosks in the estate were enumerated, and three of them were then selected in each estate using simple random sampling, resulting in 21 kiosks. A similar procedure was used for the posho mills. Posho mills are typically found in high-density, low-income neighborhoods with many low-income families, so first a list of 16 estates with posho mills and then the number of mills for each estate was assembled. From each estate, a number of posho mills were selected randomly, proportionate to their total number. In total, 21 posho mills were selected. Five enumerators from the area were hired specifically for this survey, and received appropriate training on prospective respondent approach, general questionnaire administration, and how to ask contingent valuation questions. They were posted at the selected outlets, and interviewed up to 10 consumers per site, approaching every third consumer that came by. Consumers were first asked if they would agree to a short interview of about twenty minutes. The interviews were more difficult to conduct in the supermarkets, as most shoppers indicated their time was limited. About a quarter of the consumers approached in the supermarkets declined to be interviewed, saying they could not spare the 20 min. Of those who initially consented, 27 left during the interview since they felt it took too long, reducing the number of valid, complete interviews to 183 for the supermarkets. On the other hand, consumers in the other outlets had more time, resulting in just a few turn-downs in the kiosks and none in the posho mills. The

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interviews were conducted in either English or Kiswahili, depending on which language the respondent was more comfortable with. These are the official languages of Kenya and the most widely spoken in Nairobi. A structured questionnaire was used to ask consumers about their demographic and socioeconomic characteristics, the maize products they purchase, and their maize consumption behavior. They were asked if they would be willing to buy yellow maize meal at the same price as their current, preferred maize product. If the answer was ‘‘no”, they were offered a lower price for the yellow maize. The discount offered was either 5%, 10%, 20%, 30%, or 50%, systematically distributed over the consumers so they only had to answer one follow-up bid. The resulting bids ranged from KShs 14.5 to KShs 71.5/kg (1US $ = KShs 75 at the time of the survey). Consumers were further interviewed on their interest and preference for fortified maize meal. They were asked if they were aware of some maize meal brands that have been fortified with vitamins and minerals. Consumers aware of these brands were asked if they purchased them and, if so, at what price. Those unaware were read a specific text explaining the added content of these brands (see Appendix), and subsequently asked if they would be willing to purchase them if they were offered at the same price as their most preferred maize product and, if the answer was ‘‘yes”, at a higher price. If the answer was ‘‘yes” again, they were asked in an open-ended question how much more they would be willing to pay for the fortified maize meal. In total, 604 consumers were interviewed, 183 from supermarkets, 210 from kiosks and 211 from posho mills. Analysis Data were analyzed for each of the strata, composed of the consumers from the different outlets. Summary statistics were derived for the whole sample, although these need to be treated with care, because the size of the population in each stratum is not known. Relationships between behavior and socioeconomic group (income, education, age, gender, ethnicity) were analyzed. There are strong correlations between the groups, in particular type of outlet, education and income. Students are technically without income, but their consumption behavior often reflects the higher income groups; therefore, they were separated out. Consumers’ willingness to accept yellow maize meal was first analyzed by estimating the coefficients a and q of the simple semi-double-bounded logistic model from Eq. (8) using the maximum likelihood method, and calculating a/q. Next, factors expected to influence this WTP were analyzed by adjusting the index function v to include the set of explanatory variables z. The probability of the consumer rejecting a bid B becomes ProbðB > max WTPÞ ¼ 1ð1 þ expðða  qB þ c0 zÞÞÞ:

ð11Þ

The log likelihood function is adjusted accordingly and the coefficients estimated by the maximum likelihood method. Several factors can influence the WTP for yellow maize. Consumers with lower income categories are more likely to switch to yellow maize at lower discounts, as discussed above. Since the distribution of yellow maize varieties is strongly geographically determined, the preference for white might be influenced by regional origin. Education can also change consumer preferences, and other demographic variables such as age and gender might have an effect. Given the different socioeconomic profile of consumers at different types of outlets, these were also included. The variables finally included were age (in years), gender (a dummy for female respondents), monthly income (in KShs 1000), education (in years of formal schooling), regional background (based on ethnic group, distinguishing groups originating from Lake Victoria, Rift Valley, the Central region, the Eastern region, the coast, and foreigners). For income, consumers were asked

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in which monthly income category they fell (none, 0-KShs 15,000, KShs 15,000–50,000, and over KShs 50,000). The first category was given the value 0, the second and third categories the central level value of the interval and, for the highest category, a central value of KShs 75,000 was assumed. This variable captures the consumer’s individual cash income, ignoring other sources of income at the household level. Finally, to reflect the grouped nature of the data and the correlation that might arise among customers of the same outlet or from the same area, binary variables were included: one for every supermarket, and one for every estate for the kiosks and posho mills. Previous studies have found that income is an important negative factor on WTP for yellow maize, and that price elasticity is reduced with income (Rubey and Lupi, 1997; Tschirley et al., 1996). To test the last hypothesis, a cross effect q2 of income with the value of the bid was included. The index function becomes v ¼ a  qB þ q2 yB þ c0 z;

ð12Þ

where y is income (in KShs/month). At the same time, income remains included in the vector of socioeconomic factors z. The willingness to pay for fortified maize meal was estimated separately for the consumers aware and unaware of the product. For those aware, the premium was calculated as the difference between the average price of fortified meal paid, and that of the regular maize. For consumers unaware, the premium was calculated as the difference between their declared WTP and the price of regular maize. The effect of consumers’ socioeconomic characteristics on their preference for fortified maize meal was analyzed using a dummy variable indicating if the consumer currently buys fortified maize as a dependent variable, which was regressed on the same independent variables as with the preference for yellow maize, this time using a binary logistic model. Maize consumers’ preferences and willingness to pay for special maize traits Maize consumers’ characteristics and preferences Consumers’ socioeconomic characteristics differed substantially between the three types of outlets: posho mills, kiosks and supermarkets. Relatively more clients of posho mills fall in the low-income category, and more are female (for more details, see Kimenju et al., 2005a). Overall, 28% of consumers reported no income, 48% fell in the low-income category (1–15,000 KShs/month), 22% in the middle-income group (15,001–50,000 KShs/month) and 2% in the high-income group (more than 50,000 KShs). The consumers without income were mostly students, unemployed married women, or unemployed younger men. Although maize is important to almost all consumers interviewed, preferences for different maize products strongly depend on type of outlet and socioeconomic background. The preferred maize product at the posho mills was grain to be milled locally (60% of respondents), while at the kiosks and supermarkets, most consumers prefer the industrial, plain maize meal (66% and 62%, respectively). While half of the consumers without any education usually buy whole maize meal or grain to be milled from the posho mills, this gradually reduces with education down to less than 10% of those with at least some university education. With industrial meal, we see exactly the opposite. A similar link between consumers’ preferences for different types of maize meal and their income had previously been observed after the liberalization of the maize meal markets (Jayne and Argwings-Kodhek, 1997). When consumers were asked why they preferred a particular maize product, they mentioned factors such as price, time saving, nutrient quality and cleanliness. A large majority of consumers who buy whole maize meal or grain mention nutrient quality.

When asked which traits they generally appreciated in maize meal, consumers mentioned freshness, nutritional value, taste and smell. There is a clear difference between the maize consumption at breakfast, as compared to lunch and dinner. Only half the consumers prefer maize for breakfast, largely as porridge, and this proportion decreases with increasing income. For lunch and dinner, however, maize is preferred by the large majority. Preferred dishes are ugali (a stiff, dryer preparation), or githeri (a mixture of boiled maize and beans), in that order, with little difference between socioeconomic groups. Therefore, for biofortification, it is important to analyze the retention of carotenoids during the three major preparations. Given the importance of industrial maize meal, it is necessary to analyze the retention of carotenoids in the storage of industrial meal derived from biofortified maize. This retention should be compared to the retention of retinol currently used in the fortification of industrial maize meal, in the storage of industrial maize meal. WTP for yellow maize in the three types of outlets Only a quarter of respondents said they would be willing to buy yellow maize at the same price as white maize. This acceptance was substantially higher in supermarkets (34%) than in posho mills (24%) or kiosks (19%) (Table 1, first row). Those who rejected the idea were split systematically into five separate groups, and each group was offered a different discount. At the posho mills, 76% of consumers said they would not pay the same price for yellow maize. Of those offered the lowest discount (5% of the current maize price), 23% accepted, while of those offered the highest discount (50% of the going maize price), 38% accepted (Table 1). Because of the small sample size (a fifth of each stratum), the responses were not always consistent: at both the posho mills and the supermarkets more consumers accepted the 30% discount than the 50% discount. To find the total number of consumers who would agree to buy at a particular price, we add up the percentage of consumers who accepted the initial bid, plus the proportion of those who rejected the first bid and who accepted the second bid (Table 1). Posho mill consumers, who generally have lower incomes, were more responsive to the discount, with up to 58% accepting yellow maize at a discount. At the kiosks, consumers had the lowest initial acceptance, but they responded well to the discounts, ending up with a 47% acceptance of the highest discount. At the supermarkets, consumers started with the highest acceptance, but this higher income group was less responsive to price discounts, and the maximum acceptance was 50%.

Table 1 Consumers’ acceptance of yellow maize at the same price as white maize, and at a discount (% of consumers accepting) Discount (% of price)

Type of outlet Posho mill

Kiosk

Super market

Initial bid: accept at same price (% of all respondents) Accept follow-up (% of those who rejected first bid), at discount of:

0

24

19

34

5 10 20 30 50

23 32 20 45 38

9 22 24 25 34

9 4 5 23 13

Accept either initial or follow-up bid (% of all consumers)

5 10 20 30 50

41 48 39 58 53

26 37 38 39 47

41 38 38 50 43

Type

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The relationship between WTP and price was first analyzed by estimating the simple semi-double-bounded logistic model of Eq. (8) (Table 2). This model estimates a cumulative distribution function of consumers who are willing to pay a particular price or, in this case, it fits the curve on the number of consumers accepting the different bids offered. Given the logistic functional form, the average WTP can be calculated from the intercept (a) and the coefficient on the bid (q) as a/q, estimated for all consumers in the survey at KShs 32.3/2 kg. This can be compared to the average price of KShs 51/2 kg, indicating that the Nairobi consumer, on average, requires a price discount of 37% to accept yellow maize. The WTP function of the supermarket consumers is different from those of the other outlets: the constant is lower (reflecting the initial higher acceptance) and so is the slope (reflecting that these consumers are less influenced by price), resulting in a lower average WTP, with an average required price discount of almost half (48%). The estimated parameters of the logistic model are best understood by using them to map the probability of consumers accepting the offered bid, using Eq. (8) (Fig. 1). For the logistic distribution, the mean equals the median, so for each type of outlet the mean WTP is found at that price where the probability of the bid being accepted equals 0.5. The function is quite different for consumers of supermarkets on the one hand, and those of kiosks and posho mills on the other hand. Clearly, the mean WTP of consumers at the supermarkets is substantially lower (at KShs 26.6/ kg) than that of their counterparts at the kiosks (KShs 32.1/kg) and posho mills (KShs 33.3/kg). But the graph also shows a much flatter slope for supermarket consumers, indicating that they are much less responsive to a change in price than the other consumers. Consumers at posho mills and kiosks are much more

Table 2 Parameter estimates for WTP model for yellow maize Variables

Constant (a) Bid (q) Log likelihood function Number of observations Mean WTP (a/q) Average discount (% of average maize price, 51 KShs/2 kg)

Type of outlet

Total

Posho mill

Kiosk

Super market

2.194 (0.335) 0.0658 (0.0075) 229 201 33.343 35

2.252 (0.420) 0.0702 (0.0096) 187 202 32.080 37

0.631489 (0.308) 0.0237 (0.0060) 155 176 26.645 48

responsive to the price, as can be expected given their relatively lower income. Therefore, the WTP for yellow maize at the current average price of maize meal (KShs 51/kg), is substantially higher for consumers in supermarkets (36%) than those in posho mills (24%) and kiosks (21%). Effect of consumer characteristics on their WTP for yellow maize To analyze the factors that might influence WTP for yellow maize, these factors were included in the full model of Eq. (11) and the coefficients estimated (Table 3). For an easier presentation and interpretation of the results, the coefficients for the binary variables to correct for autocorrelation (one for every supermarket, and one for every estate for the kiosks and posho mills) are not included in the table. Five factors were found to have a significant effect on WTP: income, education, gender, type of outlet and ethnic background. Income and education have a clear negative effect on WTP for yellow maize, and women clearly have a lower WTP than men. Consumers at the supermarket, on the other hand, have a higher WTP than those at posho mills (the base) and kiosks. This remains so after adjusting for income and education, which tend to be higher among the supermarket clients but, a bit puzzling, have the opposite effect. Consumers from ethnic groups originating in Central Kenya, where little yellow maize is found, have a significantly lower WTP than the other groups. (The group from the Lake region, where yellow maize is common, was the base). Age, on the other hand, was not a significant factor. Previous studies have found that income is an important negative factor on WTP for yellow maize, and that price elasticity is reduced with income (Rubey and Lupi, 1997; Tschirley et al., 1996). To test the last hypothesis, a cross effect of income with the value of the bid was estimated, and found to be negative and significant (Table 3). This indicates that consumers with higher income not only have lower WTP for yellow maize, but that they are not as responsive to reduced prices as the lower income groups. WTP for fortified maize meal

1.7376 (0.208) 0.0538 (0.0045) 1166 579 32.311 37

, , 

: Statistically significant at the 0.1%, 1%, and 5% confidence levels, respectively.

Fig. 1. Consumers’ willingness to pay for yellow maize, in function of price, at different types of outlets in Nairobi.

Most respondents (76%) were aware of the existence of fortified meal. This proportion was slightly higher at the supermarkets (80%), but lower in the posho mills (73%) (Table 4). Of those aware,

Table 3 Factors influencing WTP for yellow maize Group

Variable

Estimated parameter

Standard error

P-value

Constant Bid

4.061 0.065

0.568 0.006

0.000*** 0.000***

Socioeconomic characteristics

Income x bid Income Education Female Age

0.001 0.028 0.131 0.326 0.001

0.000 0.015 0.032 0.184 0.010

0.034** 0.057* 0.000*** 0.076* 0.890

Type of outlet

Supermarket Kiosk

0.415 0.012

0.223 0.222

0.063* 0.958

Ethnic background

Central group Eastern group Coast group Rift valley Foreign

0.485 0.340 1.053 0.017 0.137

0.204 0.288 2.067 0.371 0.369

0.018** 0.237 0.611 0.964 0.710

Model

Number of observations Log likelihood function Chi squared

565

–

–

553

–

–

1107

–

–

, , 

: Statistically significant at the 1%, 5% and 10% confidence levels, respectively.

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Table 4 Consumer indications of willingness to pay for fortified maize meal (standard deviation in brackets) Group of consumers

Posho mills

Kiosks

Supermarket

Total

All (N = 581)

Consumers aware of the availability of fortified maize meal (% of all consumers)

73

75

80

76

Aware (N = 443)

Consumers who regularly buy fortified maize meal (% of those aware) Consumers who sometimes buy fortified maize meal (% of those aware) Average price difference between preferred meal of those who buy fortified meal regularly and the other consumers (KShs) (std) Estimated mean premium for fortified maize meal (KShs/2 kg) Estimated mean premium for fortified maize meal (% of average price) Consumers willing to buy fortified at same price as favorite brand (% of those unaware) Consumers willing to buy fortified at a higher price than favorite brand (% of those unaware) Mean premium of unaware consumers, willing to pay more for fortified (KShs/2 kg packet) (std) Mean premium over all unaware consumers for fortified (KShs/2 kg packet) Mean premium over all unaware consumers for fortified (% of regular, at 51 KShs/2 kg packet)

26 36 –

44 22 –

54 23 7.0

44 27 7.0

– – – 85 45 6.6 4.1 5.6 10.9

– – – 81 51 6.5 2.9 2.9 5.8

8.2 – – 74 63 8.9 5.5 3.3 6.5

8.2 3.1 5.9 81 52 7.2 4.3 3.8 7.4

Unaware (N = 138)

44% bought it regularly (or 31% of all respondents), and 27% occasionally (20% of all respondents). The proportion of those regularly buying fortified meal was clearly higher among the supermarket clients, while the proportion of those buying it occasionally was higher at the posho mills, likely reflecting the higher income and purchasing power of the first group. Only 11% of consumers, all found at supermarkets, declared that fortified maize meal was their favorite type. Two brands were popular: one offering a distinct high-end product, priced at KShs 65/ 2 kg (24% above average), the other a slight variation of its main maize meal, at KShs 54.4/2 kg (4% more). The average price of the favorite brand of those preferring fortified meal is KShs 57/ 2 kg, or 13%, higher than that of the other consumers. For consumers aware of fortified maize meal, the average market price can be now assumed to reflect the WTP for fortified maize meal for those who buy it regularly (44%), while the premium for those who do not buy it regularly can be assumed to be zero. The mean premium for fortified maize for this group can then be calculated at KShs 3/ 2 kg (6%). Those consumers unaware of fortified maize were read a short explanatory text (see Appendix), and subsequently asked about their interest in buying the product. Most of them (81%) declared they would buy the fortified maize meal, if it were offered at the same price as regular maize meal, and about half (52%) would be willing to pay more. There were more consumers at the posho mills who would be willing to buy it at the same price, while more consumers at the supermarkets would be willing to pay more. Since fortified maize meal is widely available in Nairobi, the products known and the concept easy to explain, WTP was not estimated by discrete choice questions, but people were asked directly how much extra they would be willing to pay. For those consumers who are willing to pay a premium for fortified maize meal (52%), the average is KShs 7.24/2 kg, or 14%. Assuming the premium is zero for those not willing to buy at a premium, the average premium can be calculated as KShs 3.8/2 kg, or a premium of 7.4%. This ranges from 6% at the kiosks and in the supermarkets, to 11% at the posho mills. The factors influencing consumer preference for fortified maize were analyzed using a logistic model, with a binary dependent variable indicating if the consumer buys fortified maize meal regularly (Table 5). Again, to allow for an easier presentation, the coefficients for the binary variables to correct for autocorrelation are not included in the table. Women clearly are more likely to buy fortified maize meal, and interest definitely increases with income. Consumers at the supermarkets and kiosks are more likely to buy fortified meal than those at posho mills (the base), which is understandable since it is not

Table 5 Factors influencing WTP for fortified maize Group

Factor

Estimated coefficient

Standard error

Pvalue

Socioeconomic characteristics

Income Education Female Age (years)

0.016 0.033 0.503 0.012

0.008 0.044 0.240 0.014

0.052* 0.451 0.036** 0.392

Type of outlet

Supermarket Kiosk

2.798 1.356

1.329 0.639

0.035** 0.034**

Ethnic background

Lake region Eastern region Central region Rift valley Foreigner

1.360 1.552 1.165 1.254 2.539

1.208 1.228 1.203 1.288 1.316

0.260 0.206 0.333 0.330 0.054*

Model

Number of observations 2 Log likelihood % Correctly predicted

471

–

–

541.2 74.6

– –

– –

,

,  : Statistically significant at the 0.1%, 1%, and 5% confidence levels, respectively.

usually available at the mills. Finally, there is no difference in preference for fortified maize among ethnic groups, except for foreigners who are more likely to buy it.

Conclusions and discussion Consumers showed a strong preference for white maize, and would, on average, ask for a discount of 37% to buy yellow maize. To market biofortified maize, yellow or orange, will clearly require a substantial effort. This consumer preference for white maize in urban Kenya confirms the results from consumer studies in Southern Africa. Unlike those studies, however, income does not seem to influence the preference for color, but consumers with higher education clearly have a stronger preference for white maize. This effect is, however, confounded by the result that supermarket consumers, who generally have higher income and education, have a higher WTP for yellow maize. Generally, there is a broad interest in fortified maize, and the interest increases with income, but not with education. Unfortunately, average premiums are small: 6% as calculated from the consumers who are aware and actually buy it, and 7.4% as calculated by the contingent valuation method from consumers unaware of the product. These premiums are much lower than the average

H. De Groote, S.C. Kimenju / Food Policy 33 (2008) 362–370

discount needed for consumers to switch to yellow, although slightly higher than the current price difference between regular and fortified maize meal. The literature review suggests that East African consumer preference for white maize is historic in nature, a case of path dependency caused by a range of favoring factors. There is little evidence of difference in taste and processing qualities between yellow and white maize, except that colored varieties are often flint, which is actually often associated with favorable cooking and processing characteristics. Similarly, fortification of food does not change the taste or flavor. It is, therefore, not surprising that the results indicate that consumer preferences for the traits under study are influenced by consumers’ socioeconomic and cultural background, in particular income, education, gender, and ethnic group. More consumers with education and with higher incomes have, unfortunately, a stronger preference for white maize. As in previous studies, higher income reduces the price elasticity for yellow maize. Higher income does increase the WTP for fortified food, not surprisingly, but education does not have an effect on the preference for fortified maize. Ethnic background plays a role: apart from foreigners, people from Western Kenya have the strongest preference for yellow maize. Only consumers from the Central Kenya group have a significant, and negative, difference. In the preference for fortified maize, no differences between the Kenyan ethnic groups was observed, although foreigners clearly had a stronger interest. After taking into account these socioeconomic factors, the type of outlet still plays a role, although the origin of these effects is not clear. Consumers at supermarkets (although more educated) have a significantly higher WTP for yellow maize than the others, while consumers at the posho mills (with a lower income), have a lower WTP for fortified maize. Likely, there are other socioeconomic factors that differ between the customers of the different types of outlet that were not captured in the explanatory variables. This problem might be solved by using a household sampling frame instead of outlets, and by including more explanatory variables. For fortified maize, awareness and knowledge of nutrition could play a role and should be included. For yellow maize, more research is needed as to why people prefer yellow, combined with blind tests to assess taste and flavor. We conclude that substantial efforts will be needed to make biofortified, yellow maize varieties acceptable to the urban consumers. A reduction in price would clearly help, and lower income consumers would be more responsive, but the price difference would have to be substantial. Formal education seems to increase the preference for white maize and has no effect on the preference for fortified. Therefore, special educational and awareness programs seem indicated. Further research is, however, needed to assess what information and awareness is needed for consumers to change their behavior. To develop appropriate awareness campaigns, the sources of this type of information need to be assessed. The study also yielded some methodological lessons. For budgetary reasons, the survey was limited to urban consumers, in a stratified sample with outlets as strata. Unfortunately, the size of the population in each of the strata is not known, so means calculated for the whole sample are not unbiased estimates of the population mean. Sample means for each stratum, on the other hand, are unbiased estimators. A household survey would, therefore, be better suited to study urban consumers. It would avoid the problem of the differences between consumer preferences between the different types of outlet. Further, since biofortified maize intends to reach the poorer consumers, the effect of income on consumer preferences should be explored further. Self-reported individual income, based on the mid-points of the class, was a convenient indicator for point-of-sale surveys, but is not a very precise measure of consumers’ income or socioeconomic status.

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In particular, it does not take into account remittances and income from other household members. Therefore, other measures such as expenditure and family income should be considered, especially with household surveys. This study showed the major ways of maize processing and preparing. To develop appropriate strategies and messages, it is important to measure the retention of vitamin A during storage of the major products (grain, whole flour from posho mills, and refined flower from industrial mills) and cooking (ugali, githeri, and uji). Finally, the urban consumer is not the first target of biofortified maize, so it is important to extend this type of survey to rural consumers. Areas where yellow maize is popular, such as Western Kenya and the coast, need to be surveyed. A preliminary, methodological survey with farmers in Vihiga and Siaya districts of Western Kenya in 2005 showed only little difference in preferences between white and yellow maize (Kimenju et al., 2005b). Therefore, it is important that the survey be extended to include rural areas, in particular those with large numbers of poor, maize consumers. Acknowledgements The authors would like to thank the Syngenta Foundation for Sustainable Development (through the IRMA project) and HarvestPlus for funding this study. We also thank J.V. Meenaksi from IFPRI and two anonymous reviewers for their useful comments; the enumerators for their diligent work; Daisy Ouya, Ivan De Groote and Kathy Sinclair for editing and revising the manuscript; Gregory Poe for advising on the econometrics, and the CIMMYT-Nairobi staff for their support. Finally, we would like to thank the staff and management of the outlets where the survey was conducted, and, most importantly, the Nairobi consumers who participated in the survey. Appendix. Information text read to consumers unaware of fortification Fortified maize meal is meal with additional amounts of minerals and vitamins, higher than those occurring naturally in maize. These are added artificially by the manufacturer to make the maize meal more nutritious. The fortified brands in the market today are Hostess, Jogoo Extra and Annapurna. The maize meal of these brands has been fortified with vitamin A, vitamin B, and vitamin B6. References Alberini, A., Cooper, J., 2000. Applications of the Contingent Valuation Method in Developing Countries, a Survey. FAO, Rome. Alderman, H., Lindert, K., 1998. The Potential and Limitations of Self-Targeted Food Subsidies World Bank Research Observer 13, 213–229. Arrow, K., Solow, R., Leamer, E., Portney, P., Radner, R., Schuman, H., 1993. Report on the NOAA panel on contingent valuation. US Federal Register 58, 4602–4614. Bouis, H.E., 1999. Economics of enhanced micronutrient density in food staples. Field Crops Research 60, 165–173. Byerlee, D., Heisey, P.W., 1997. Evolution of the African maize economy. In: Byerlee, D., Eicher, C.K. (Eds.), Africa’s Emerging Maize Revolution. Lynne Rienner Publishers, Boulder, pp. 9–22. Diskin, P., Kipola, S., 1994. Maize meal preferences and consumption in Lusaka, Zambia: implication for reducing urban food prices. Washington, DC, USA: USAID/AFR/ARTS/FARA. Dorosh, P., Del Ninno, C., Sahn, D.E., 1995. Poverty alleviation in Mozambique: a multi-market analysis of the role of food aid. Journal of Agricultural Economics 13, 89–99. Dowswell, C.R., Paliwal, R.L., Cantrell, R.P., 1996. Maize in the Third World. Westview Press, Inc., Boulder, Colorado. Dreze, J., Sen, A., 1990. Hunger and Public Action (WIDER Studies in Development Economics). Oxford University Press, USA. FAO and CIMMYT, 1997. White Maize: A Traditional Food Grain in Developing Countries. FAO and CIMMYT, Rome.

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