Measuring heterogeneous preferences for cattle traits among cattle-keeping households in East Africa.

In spite of the crucial role livestock plays in the economies of many sub-Saharan African countries, livestock productivity remains relatively low in the region. Several studies have shown that factors ranging from technical, institutional, and infrastructural constraints related to feeding, animal health, and genotype are the main causes of the low productivity (Tano et al. 2003). Breed improvement programs serve as natural entry points for productivity increases. However, as argued by Tano et al. (2003), the tendency for genetic improvement programs to concentrate on one aspect, such as meat or milk production, in isolation from broader livelihood system needs often results in the substitution of exotic cattle for indigenous breeds. Although indigenous cattle are often less productive than exotic breeds, when traits such as milk and beef production are considered in isolation, they may be better suited to the conditions of the local environment. This has often led to low adoption rates of exotic or crossbred cows (Abdulai and Huffman 2005). De Haan (1995) points out that livestock technologies have been the source of puzzling outcomes more frequently than crop technologies, partly because new technologies that would improve productivity are simply not adopted.

The low productivity in Africa's livestock has also been partly attributed to the multiple functions that cattle perform in the livelihood system (Moll 2005). It is estimated that approximately 80% of the value of livestock in low-input developing country systems can be attributed to nonincome, sociocultural functions, while only 20% is attributable to physical products such as meat, milk, and wool. In contrast, over 90% of the value of livestock in high-input developed country production systems is attributable to direct production outputs (Gibson and Pullin 2005). Some of the important nonincome and sociocultural functions of cattle in developing countries are embedded in traits that are not traded in the market, therefore lacking price or market values. Hence, the utilization of profit functions for derivation of economic values for cattle traits in such systems would result in exclusion of such traits from the breeding objective, which may result in genotypes not capable of fulfilling the multiple objectives of the cattle enterprise. This calls for the employment of a valuation method that can also permit calculation of economic values of traits without market values to be included in the breeding objective.

Few economic studies have attempted to investigate phenotypic trait preferences for genetic resources using stated preference approaches. Some authors have used conjoint analysis to assess relative importance of traits (Sy et al. 1997; Tano et al. 2003). These studies utilizing conjoint analysis employ ordered probit or multinomial logit models to model preference behavior. A limitation of these models is that they do not explicitly account for heterogeneity of preferences among producers, rendering them less useful for analysis aimed at providing policy recommendations for different environments and production systems.

Recent studies have increasingly focused on the monetary value of traits by employing choice experiments and including a monetary cost or benefit as one of the traits. For example, Scarpa et al. (2003) employ a mixed logit model, a recent advancement in discrete choice analysis, in their empirical estimation of choice data to value the phenotypic traits expressed in indigenous breeds of livestock in a pastoral system in Kenya. Zohrabian et al. (2003) use a maximum entropy search-theoretic framework to estimate the value of precommercial germplasm contained in the U.S. national plant germplasm system. Kontoleon (2003) utilizes a latent class model to account for preference heterogeneity in GM foods. Kline and Wichelns (1998) note the significance of accounting for preference heterogeneity, since preferences often vary among individual decision makers according to their environment, socioeconomic characteristics, and tastes.

The objective of this study is to derive the economic values for cattle traits in Kenya and Ethiopia using a choice modeling approach. Specifically, a mixed logit model is employed to investigate the existence of preference heterogeneity, while a latent class model is used to examine the sources of heterogeneity across segments of cattle keepers. To the best of our knowledge, no previous attempt has been made to investigate the existence of endogenous preference segmentation for cattle traits among cattle keepers in sub-Saharan Africa. Given that trypanosomosis disease is a major challenge to livestock production in the study areas, the study was partly designed to provide a better understanding of farmers' preferences for cattle and to enable a reasonable assessment of programs aiming at encouraging the adoption and use of trypanotolerant breeds.

Choice Modeling

The conceptual framework for choice experiments arises from the consumer theory developed by Lancaster (1966), which postulates that preferences for goods are a function of the traits or characteristics possessed by the good rather than the good per se. An important implication of this theory is that overall utility of a good can be decomposed into separate utilities for its constituent characteristics or traits. In terms of the utility function, this translates into using the characteristics of goods as the arguments of the function. Hence, a good can be described by the characteristics that generate utility or disutility to individuals. For cattle breeding, this permits the analysis of farmers' preferences in terms of the utility they perceive to result from various cattle traits.

Choice experiments are based on the assumption that an individual n receives utility, U, from choosing an alternative A equal to [U.sub.nA] = U([X.sub.nA]) from a finite set j of alternatives in choice set k, if and only if, this alternative generates at least as much utility as any other alternative, with [X.sub.nA] denoting a vector of the attributes of A. Utility is represented as two components, where one portion is deterministic and depends on the attributes of the alternative, and the remainder is stochastic. This can be specified as

(1) [U.sub.nA] = [V.sub.nA] + [[epsilon].sub.nA]

where [V.sub.nA] = g([X.sub.nA]) is the deterministic component and [[epsilon].sub.A] is a random component of the utility function. The probability of individual n choosing alternative A can then be specified as

(2) P(A) = Prob{[V.sub.nA] + [[epsilon].sub.nA] [greater than or equal to] [V.sub.nj] + [E.sub.nj]; A [not equal to] j, [for all] j [member of] k}.

Choice experiments and hedonic price analysis are alternative empirical applications to the Lancaster consumer theory. The strength of both hedonic pricing and choice experiment techniques is the ability to decompose revealed preference data, that is, prices of goods in the case of hedonics and choice of goods' profiles by individuals in the case of choice experiments, into marginal values (Dalton 2004). A number of recent studies, such as Barrett et al. (2003) and Green et al. (2006), have employed hedonic pricing to value livestock traits in eastern Africa. The use of hedonics is nevertheless limited to the valuation of existing traits and not prospective traits that may be of interest to breeders. Choice experiments overcome this limitation since preferences are measured directly, and then related to utility, making it possible to estimate economic values of prospective traits. Choice experiment technique is employed in the present study.

Estimation Techniques and Econometric Models

Discrete choice models are normally used to model the choices made by the decision makers from the choice experiments. Mixed logit and latent class models have recently been developed to relax the limiting assumptions associated with conventional logit and probit models. McFadden and Train (2000) and Train (2003) describe mixed logit as a highly flexible model that can approximate any random utility model. It relaxes the limitations of standard logit by allowing the taste parameters to vary randomly according to a parametric distribution. In addition, it allows for unrestricted substitution patterns and correlation in unobserved factors over time (Train 2003; Hensher and Greene 2002). (1) We refer the interested reader to the above literature for a more in-depth description.

The mixed logit model applies the usual framework of random utility models outlined in the previous section. As indicated earlier, a sampled individual n faces a choice of selecting the preferred alternative amongst a set of j cattle profiles, representing different traits and trait levels in each of the t choice situations. In our case, the number of choice situations is constant per respondent and we assume a linear utility function. An individual, n, is assumed to consider the full set of offered cattle profile alternatives in choice situation t and to choose the alternative with the highest utility. The utility associated with each set of j alternatives as evaluated by each individual n in choice situation t is represented in a discrete choice model by a utility expression of the general form,

(3) [U.sub.njt] = [[beta].sub.n][x.sub.njt] + [[epsilon].sub.njt],