PURPOSE: This study is a quantitative analysis of the relationship between obesity and food availability in the United States. A vast amount of literature has been produced examining various food and socioeconomic variables for their effect on obesity rates; however, this is the first research project to use the USDA’s Food Environment Atlas in a nationwide quantitative study. METHODS: This study uses multivariate statistical analysis to study the effect of 24 variables identified in the 2015 USDA Food Environment Atlas on county-wide obesity rates. The primary regression of concern looks specifically at the effect five food availability variables (grocery stores, specialty food stores, SNAP stores, supercenters, and convenience stores) have on obesity rates. RESULTS: Grocery store and specialty food store density is negatively correlated to percent countywide obesity, while supercenter and SNAP store density is positively correlated to percent obesity. Convenience store density had virtually no effect on percent obesity. Potentially confounding variables such as density of recreational facilities and farmers’ markets were also negatively correlated with obesity. Additionally, density of fast-food and full-service restaurants were negatively correlated with obesity, while perpetual poverty was positively correlated to percent obesity. CONCLUSION: The analyses presented in this study suggest that greater availability of grocery stores and specialty food stores may help to curb rising obesity rates. Policy recommendation and considerations based on the results are discussed and explored for their potential utility in addressing the obesity epidemic confronting the United States.

<p>This dissertation focuses on exploring U.S. food retailers’ strategic interactions and the impacts on consumers. Specifically, I examine food retailers’ strategies on segmenting consumers, conducting price discrimination, and designing their product portfolio in the context of the U.S. yogurt market. The first essay examines the segmentation strategies employed by food retailers, with a focus on the use of advanced machine learning techniques (i.e., K-means clustering) to group consumers based on various characteristics, including demographics and purchase history. The second essay applies the data-driven market segmentation obtained in the first essay to a second-degree price discrimination model. The third essay relaxes the implicit assumption made in the first two essays that consumers’ choice set is fixed, and studies a non-price strategy, namely, adjusting assortment, that is adopted by food retailers in response to regulations. By analyzing the retailers’ strategies on market segmentation and responses to regulations, this dissertation aims to shed light on the strategic interactions of food retailers and consumers, and the competitive landscape of food market in general.<br> </p> <p>Understanding the strategies employed by food retailers is of utmost importance in agricultural and food economics as it directly influences consumers and their purchasing decisions. The food retail industry in the U.S. is highly competitive, with retailers continuously devising tactics to attract and retain customers. Dimensions of competition such as pricing strategies, product assortment, promotional activities, and customer service can significantly impact consumers’ choices and behaviors. Investigating the strategies employed by food retailers not only provides insights into their business operations but also sheds light on how these strategies affect consumers.<br> </p> <p>The first essay explores the application of machine learning methods in consumer segmentation under different information environments. Machine learning methods become popular in economic and marketing research, partly because of their flexibility in application. Although recent studies apply these advanced methods to various topics including water, housing, health, and food markets, much is less known about using machine learning methods to facilitate firms’ market segmentation decisions. Using Nielsen Consumer Panel data, I show that K-means clustering, one of the unsupervised learning methods, can be applied to conduct market segmentation. From the retailers’ perspective, incorporating more consumer information (i.e., purchase history) leads to the change in segments consumers belong to.<br> </p> <p>The second essay assesses the effectiveness of data-driven market segmentation in enhancing price discrimination models. Price discrimination models are commonly adopted by firms to optimize revenue and profitability by customizing prices to different customer segments. Existing studies often rely on exogenous assumptions for consumer segmentation, which may or may not be applicable in practice. This study advances the existing literature by replacing the consumer segment assumption with data-driven market segmentation obtained through K-means clustering. The results are then applied to the second-degree price discrimination model to analyze how sensitive the firms optimal profits are under different consumer information environments. The findings reveal that adding consumer information to consumer segment leads to a more inelastic demand for the consumer segments and an increase in firm’s profits.<br> </p> <p>The third essay focuses on the non-price strategies retailers adopt to respond to the Unit Pricing Regulation (UPR). UPR requires retailers to display unit prices in addition to product prices and helps consumers make more informed decisions. Despite extensive research on consumers perceptions of unit prices, little is known about retailers price and non-price responses under intensified price competition brought by UPR. Relying on the geographic variation in UPR implementation across U.S. states, we use product-store-level scanner data on the U.S. yogurt market and identify UPR effects on store product offerings and pricing. We find that mass merchandisers reduce product offerings under UPR. Grocery stores that belong to a retail chain entirely under UPR add brands, while other grocery stores make no significant assortment responses. UPR price effects are limited for mass merchandisers as well as grocery stores. Using a structural demand model, we find that the average consumer surplus falls under UPR, highlighting an unintended policy effect.</p>

In these days, pop-economics has become popular. Economics of food has its place in this area. The aim is to confirm the hypothesis that the economic, political, and social development influence people's diet and changes the food culture. The research method is quantitative and qualitative analysis of the data and scientific articles about the Czech Republic and the USA. The thesis describes the current state of knowledge in economic development. Then it describes historical milestones in this area and the current situation. It focuses on events that have had the biggest impact on the quality and quantity of food consumed by individuals. It describes the development from the beginning of the 20th century to the present days with the main focus on the last 50 years. The hypothesis is confirmed and it is demonstrated that the described changes have an impact on the diet and food culture.