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

Similarly, low watering requirement for the animals should increase utility, since the watering points are located far from the homesteads at an average distance of 3 km. The coefficient values for coat color and traction potential may plausibly take either negative or positive sign depending on an individual's preference. Coat color is an important stimulus in attracting tsetse flies to a target and also has a role in the landing response of the flies. The strongest landing responses have been found to be on black surfaces relative to lighter ones. For this reason, cattle keepers in the Ghibe Valley prefer light-coat-colored cattle in order to minimize the likelihood of trypanosomosis infection through tsetse fly bites. In the Narok district, however, there is preference for blackcoat-colored bulls with white spotted dewlap, features that are important in the selection of bulls for use in ceremonial functions. Finally, the trait coefficients associated with monetary expenditure, that is, purchase price of the animal and the need for purchased feed supplements is expected to have a negative sign due to the positive marginal utility for income generally exhibited by most individuals.

Information on the socioeconomic characteristics of the sample households used in the econometric modeling is presented in table 3. Fifty percent of the households use tsetse fly control methods, which include spraying, applying pour-on insecticides, and clearing bush as a way to control the disease. Generally, illiteracy levels of household heads are high in the sample population. The average number of years of schooling for a household head is 4.9 years. In terms of access to market infrastructure, the households are located relatively far from market centers, at an average distance of 4.8 km. Average cattle herd size per household is thirty animals, but is significantly different between the pastoral, agro-pastoral, and crop-livestock production systems at the 1% level. In the pastoral system, a household owns an average of seventy-three cattle, while in the crop-livestock system in Kenya, households own twelve cattle, mainly comprising the local Zebu breeds. In Ethiopia, the average cattle herd size is six animals, mainly comprising male stock, which is used for traction. In pastoral systems, livestock keeping is a central component of the livelihood system and is closely linked to the cultural and social lives of the communities, where livestock numbers are an important means of demonstrating wealth and a mode of cushioning high livestock losses experienced during major droughts or disease outbreaks.

Empirical Results

The simulated maximum likelihood estimates for the mixed logit model that allows for correlations between taste parameters are reported in table 4. (4) Following Hensher, Rose, and Greene (2005), we used a zero-based, asymptotic t-test for individual parameter standard deviations to determine the set of random parameters. From this, the traits trypanosomosis, traction, fertility, live-weight, purchase price, feeding requirement, and reproduction ability were entered as random parameters in the mixed logit estimations, while watering frequency and coat color were assumed fixed. Further, we assumed a normal distribution for all random parameters, except purchase price, which was assumed to have been drawn from a triangular distribution.

The model was estimated using NLOGIT software version 3.0 (Econometric Software, Inc. 2002) utilizing 100 Halton draws for the simulations. Given that the estimation of mixed logit first involves the estimation of a conditional logit model to derive initial start values for each of the parameters in the mixed logit model, the relative performance of the conditional and mixed logit models can be compared. A likelihood ratio test is performed to test the null hypothesis that the conditional logit fits the data better than the mixed logit. The sample values of the likelihood ratios are 941.8 and 793.6 for bulls and cows traits, respectively, with a critical value of [[chi square].sub.20,0.01] = 37.6, thus rejecting the null hypotheses. This finding indicates that the mixed logit model, which allows random taste variation, fits the data better than the conditional logit model, which assumes fixed taste parameters.

With the notable exception of the purchase price, the mean coefficients of the random parameters for bulls in table 4 are all significant at the 1% level. The model reveals preference for bulls that are trypanotolerant, cheap, highly fertile, have good traction potential, and high live-weight. Associated with each of the mean coefficient estimates of the random parameters are derived standard deviations calculated over the R draws, indicating the amount of spread that exists around the sample population. The standard deviation of each random parameter coefficient is highly significant, indicating that these coefficients are indeed heterogeneous in the population. The nonrandom parameter, watering frequency, is positive and highly significant, implying that there is a preference for bulls that are drought-tolerant (need to water only once in two days). (5)

Mean coefficient estimates for all random parameters for cow traits in table 4 are significantly different from zero at the 5% level, and the standard deviations are also highly significant, implying existence of preference heterogeneity in the population. The results indicate preference for cows that are cheap, trypanotolerant, have high milk yield, and high reproduction ability. Cows that need supplementary purchased feeds are not preferred, as indicated by the negative coefficient on feeding requirement. The constant variable in the model results in table 4 represents the "don't buy" choice option and is the base for the choice model, as it is associated with "zero" utility. It takes a value of one if the option is "don't buy" and zero otherwise. The results indicate a strong negative preference for this option, implying that the respondents preferred to select the other two choice options associated with various trait levels.

The estimated means and standard deviations of the coefficients provide information on the shares of the population that place a positive or negative value on the bull and cow traits. (6) Preference for the trait tolerance to trypanosomosis is skewed to the positive orthant, with 75% and 73% of the cattle keepers preferring trypanotolerant bulls and cows, respectively. A bull with good traction ability is preferred by 88% of the cattle keepers. Similarly, a cow with high reproduction potential is also preferred by 88% of the population, while 72% of the population has a negative preference for paying for feed supplements for cows. The estimated mean and standard deviation coefficients for the purchase price of cows reveal that 39% of the distribution is below zero and 61% is above zero. This indicates that two-thirds of the population display a negative marginal utility for income, implying that for this group, purchasing a cow may result in an increase in utility, which outweighs the decrease in utility from spending monetary resources on the purchase of the animal. This is even more pronounced for bulls where only 23% of the population displays a positive marginal utility for income. A plausible interpretation for these estimates is that cattle keepers may view the cost of purchasing cattle as a worthwhile expense, due to the multiple benefits associated with owning a cow or a bull. (7)

Of significant interest is the derivation of the marginal rate of substitution between the traits and the monetary coefficient (purchase price in our analysis) as it provides an estimation of implicit prices for the traits. (8) Hensher and Greene (2002) suggest that to derive behaviorally meaningful values from mixed logit, the distributions from which the random parameters are drawn need to be constrained. Although little is reported in the literature about the best constraint to implement, Hensher, Rose, and Greene (2005) argue that constraining the standard deviation parameter estimate to that of the mean of the random parameter for a triangular distribution guarantees nonnegative implicit price values. Implicit prices are therefore computed using conditional constrained parameters. (9) The results are reported in table 5. (10)