Linking Consumers’ Food Choice Motives to their Preferences for Insect‐based Food Products: An Application of Integrated Choice and Latent Variable Model in an African Context

Consumers’ attitudes, perceptions, personalities and motives play important roles in shaping their food choices. These factors are not fully observed by analysts, so they should be treated as latent variables. A number of economic studies treat such variables as direct measures of consumers’ food choice behaviour, even though this might introduce measurement error and endogeneity bias. We investigate the latent link between consumers’ preferences and food choice motives (FCMs) in an African context. We use an integrated choice and latent variable (ICLV) model specification for data analysis to recognise the latent nature of the FCMs and address the measurement and bias problems. The data originate from an incentivised discrete choice experiment conducted in Kenya to elicit consumers’ preferences for insect‐based foods. Our findings show that consumers’ preferences and choices are influenced by their latent motivational orientation. The results illustrate the benefit of the ICLV approach in accounting for consumers’ latent preference constructs in food choice and valuation research.     

Using Geographically Weighted Choice Models to Account for the Spatial Heterogeneity of Preferences

In this paper, we investigate the use of geographically weighted choice models for modelling spatially clustered preferences. We argue that this is a useful way of generating highly‐detailed spatial maps of willingness to pay for environmental conservation, given the costs of collecting data. The data used in this study come from a discrete choice experiment survey of public preferences for the implementation of a new national forest management and protection programme in Poland. We combine these with high‐resolution spatial data related to local forest characteristics. Using locally estimated discrete choice models we obtain location‐specific estimates of willingness to pay (WTP). Variation in these estimates is explained by characteristics of the forests close to where respondents live. These results are compared with those obtained from a more typical, two stage procedure which uses Bayesian posterior means of the mixed logit model random parameters to calculate location‐specific estimates of WTP. We find that there are indeed strong spatial patterns to the benefits of changes to the management to national forests. People living in areas with more species‐rich forests and those living nearer bigger areas of mixed forests have significantly different WTP values than those living in other locations. This kind of information potentially enables a better distributional analysis of the gains and losses from changes to natural resource management, and better targeting of investments in forest quality.     

Consumers' Willingness to Pay for the Color of Salmon: A Choice Experiment with Real Economic Incentives

In most retail markets, sellers post the price and consumers choose which products to buy. We designed an experimental market with posted prices to investigate consumers' willingness to pay for the color of salmon. Salmon fillets varying in color and price were displayed in twenty choice scenarios. In each scenario, the participants chose which of two salmon fillets they wanted to buy. To induce real economic incentives, each participant drew one binding scenario; the participants then had to buy the salmon fillet they had chosen in their binding scenario. The choice data were analyzed with a mixed logit model.     

Choice of Rice Production Practices in Ghana: A Comparison of Willingness to Pay and Preference Space Estimates

Rice has been identified as an important food security crop in Ghana. However, there is a production deficit and new technologies to reduce the deficit are not widely adopted. Although poor adoption by farmers’ is often linked to constraints such as access to information, farmers’ perceptions of the technologies are also important. We apply an advanced discrete choice experiment to evaluate farmers’ preferences for rice production practices. Specifically, we generate willingness to pay (WTP) estimates using willingness to pay space (WS) and compare these with values from the indirect or preference space (PS) method. Our modelling also accounts for the effects on WTP estimates of farmers’ stated attribute importance (SAI) information. Empirical results from WS and PS models reveal that on average, farmers value higher yields and are negatively affected by higher risk of crop failure and labour requirements. Comparing the performance of the two models, we find the WS model provides a superior fit to our data and reduces the likelihood of producing implausible WTP estimates. Further, SAI inclusion did not produce much variation in our WTP estimates.