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The Basket-Based Choice Experiment: A Method for Food Demand Policy Analysis

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  • Caputo, Vincenzina
  • Lusk, Jayson L.

Abstract

Although economic research on food consumer demand has exploded in recent years, most survey demand elicitation approaches have substantial limitations for food policy evaluations as they involve consumers choosing only one item out of a bundle. There is a need to design a more flexible approach capturing more realistic consumption patterns. This study introduces such an approach – a basket-based choice experiment – where consumers select their preferred food item or combination thereof. Our basket-based choice experiment includes 21 food items that can be freely combined to construct over 2 million possible baskets. Our results show that when given the opportunity, consumers select multiple items for their basket, most commonly three or four items. A composite conditional likelihood function approach is used to reduce the computational burden associated with modeling the choice of over 2 million possible baskets, and estimates are utilized in a multivariate logit model to calculate the probability of bundle selection and individual food price elasticities. Unlike typical choice experiments utilizing multinomial logit model variants, which forces products to be demand substitutes, our basket-based approach is able to capture a rich set of substitution and complementary patterns, and we find that most of the 21 food items studied are demand complements. The BBCE is used to explore policy questions related to the impacts of changing prices on the healthfulness of consumer dietary choices and the welfare effects of product bans, such as Meatless Monday.

Suggested Citation

  • Caputo, Vincenzina & Lusk, Jayson L., 2022. "The Basket-Based Choice Experiment: A Method for Food Demand Policy Analysis," Food Policy, Elsevier, vol. 109(C).
  • Handle: RePEc:eee:jfpoli:v:109:y:2022:i:c:s0306919222000343
    DOI: 10.1016/j.foodpol.2022.102252
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    3. Hernandez, Jose Ignacio & van Cranenburgh, Sander & Chorus, Caspar & Mouter, Niek, 2023. "Data-driven assisted model specification for complex choice experiments data: Association rules learning and random forests for Participatory Value Evaluation experiments," Journal of choice modelling, Elsevier, vol. 46(C).

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