Optimal Interventions for Increasing Healthy Food Consumption Among Low-Income Populations
中文摘要
在美国,每年有超过 600 亿美元用于旨在增加低收入家庭水果和蔬菜(FV)消费的政策。这些政策干预中有许多要么是货币性的(例如财务激励),要么与教育相关。本文的目标是通过更具策略性和个性化的资金配置来改善这些干预措施的效果。本文引入了一个关于食品杂货购物决策的消费者行为模型,并将其嵌套于政策制定者的上层优化问题中。政策制定者的目标是,通过使用一小组策略性的不同干预组合——即货币性干预与教育相关干预的组合——确保给定人群中所有消费者的果蔬支出超过某一指定阈值。尽管上层问题的精确解难以求得,我们给出了实现政策制定者目标所需干预组合数量的解析上界,以及一种根据个体特征构建这些干预组合并将其分配给个体的方法。我们利用美国农业部 FoodAPS 数据集中的低收入家庭展示了该模型与方法的实用性。本文由 Jayashankar Swaminathan(运营管理)接受发表。资助:本材料基于由美国国家科学基金会研究生研究奖学金 [资助号 1745302] 支持的工作。补充材料:在线附录与数据文件可从 https://doi.org/10.1287/mnsc.2022.02324 获取。
Abstract
More than $60 billion per year in the United States is spent on policies aimed to increase fruit and vegetable (FV) consumption among low-income households. Many of these policy interventions are either monetary (e.g., financial incentives) or education related. The goal of this paper is to improve the performance of these interventions through a more strategic and personalized allocation of funds. This paper introduces a consumer behavioral model for grocery shopping decisions, which is nested into the policymaker’s upper-level optimization problem. The policymaker’s goal is to ensure that the FV spending of all consumers in a given population exceeds a specified threshold by utilizing a small strategic set of different intervention bundles—combinations of monetary and education-related interventions. Although an exact solution to the upper-level problem is intractable, we provide an analytical upper bound on the number of intervention bundles needed to achieve the policymaker’s goal, as well as a method for constructing these intervention bundles and assigning them to individuals based on their characteristics. We demonstrate the practicality of the model and approach using the low-income households in the U.S. Department of Agriculture’s FoodAPS data set. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under [Grant 1745302]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02324 .