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<ol> <LI> To estimate demand systems for several different products in the United States to measure price and/or advertising impacts, vertical relations, and the degree of market power held by retailers and/or processors. <LI> To analyze the demand for selected branded food products, including fluid milk, in Europe to explore the cultural and economic context of branding strategies. <LI> Use demand system results for selected products (including carbonated soft drinks) to analyze the impact on obesity. <LI> Develop and test models to analyze competitiveness in food markets, including fluid milk markets, wheat markets, and local food markets. <LI> Analyze supermarket locations in U.S. cities and determine the impact of location choices (entry) by large chains, and crime on food access in urban neighborhoods, especially low income, minority, neighborhoods. <LI> Develop and test models to address key empirical questions regarding the competitive and welfare effects of vertical restraints between manufacturer and downstream firms and the effect of persuasive advertising and informative advertising on market power. <LI> Develop a multi-factorial prioritization framework for use by risk managers to determine areas for immediate and longer run attention and conduct in-depth analysis of specific examples of the market and trade impacts of the adoption of risk management strategies by government.
Non-Technical Summary: Differentiated brands have pricing power and affect competition. Food companies may promote brands that contribute to obesity. Brands in Europe may behave differently. Crime and demographics/race may affect supermarket location. This project measures the sensitivity of brand sales to prices, advertising and corporate strategies in the US and Europe. It measures supply side causes of adult and child obesity. It measures whether crime and race affect supermarket location. <P> Approach: For objectives one to four we will use IRI scanner data to estimate brand level demand (minimum distance, logit, or random coefficient models). Then we will simulate alternative vertical and horizontal pricing games to see which fits the data. Certain brands have more fat and sugar. We will determine if they are marketed differently. We will use GIS mapping, census block level demographics, and crime data to analyze supermarket locations.