Changing Consumer Food Prices: A User's Guide to ERS Analyses

USDA's Economic Research Service (ERS) uses different economic models to estimate the impact of higher input prices on consumer food prices. The present study compares three ERS models. In the first two models, neither consumers nor food producers respond to market prices. We refer to these two models as short-run models. In the third model, both consumers and food producers respond to changing prices, and we refer to this model as a long-run model. Given published parameter estimates, we simulate the impact of a higher energy price on consumer food prices, and our empirical findings are consistent with our understanding of market responses. In the short run, we find that the full effect of an increase in the price of energy is fully (or nearly fully) passed on to consumers, because neither food producers nor consumers can immediately respond to changing prices. In the long run, however, the price response of food producers and consumers serves to mitigate the increase in consumer food prices.price-spread model, input-output model, variable-proportions model, food prices, energy prices, input prices, Demand and Price Analysis,

RETAIL-FARM PRICE MARGINS AND CONSUMER PRODUCT DIVERSITY

This bulletin provides an alternative approach for computing retail-farm price margins. Current published estimates of retail-farm price margins are calculated assuming that food markets are comprised of identical firms producing, in fixed-factor proportions, a homogeneous set of final food products. The approach presented here relaxes these assumptions by relying on an expenditure-based measure, justified by the Generalized Composite Commodity Theorem, that reflects consumer demand for the many different elementary food products associated with a modern food market. This measure allows a direct link between consumer demand for diverse elementary products and food quality and marketing services where increases in retail-farm price margins, for example, can be traced to increases in consumer purchases of high-value products. Retail-farm price margins based on the alternative approach are estimated here for seven major U.S. food markets for each year from 1980-97. Although the alternative retail-farm price margins and the currently published estimates show similar trends, they also show significant differences, particularly in more recent years, that can be traced to shifts in increased consumer demand for marketing services

RETAIL-FARM PRICE MARGINS AND CONSUMER PRODUCT DIVERSITY

This bulletin provides an alternative approach for computing retail-farm price margins. Current published estimates of retail-farm price margins are calculated assuming that food markets are comprised of identical firms producing, in fixed-factor proportions, a homogeneous set of final food products. The approach presented here relaxes these assumptions by relying on an expenditure-based measure, justified by the Generalized Composite Commodity Theorem, that reflects consumer demand for the many different elementary food products associated with a modern food market. This measure allows a direct link between consumer demand for diverse elementary products and food quality and marketing services where increases in retail-farm price margins, for example, can be traced to increases in consumer purchases of high-value products. Retail-farm price margins based on the alternative approach are estimated here for seven major U.S. food markets for each year from 1980-97. Although the alternative retail-farm price margins and the currently published estimates show similar trends, they also show significant differences, particularly in more recent years, that can be traced to shifts in increased consumer demand for marketing services.Retail-farm price margins, marketing services, food quality, consumer demand, Generalized Composite Commodity Theorem., Demand and Price Analysis, Marketing,

Changing Consumer Food Prices: A User's Guide to ERS Analyses

USDA's Economic Research Service (ERS) uses different economic models to estimate the impact of higher input prices on consumer food prices. The present study compares three ERS models. In the first two models, neither consumers nor food producers respond to market prices. We refer to these two models as short-run models. In the third model, both consumers and food producers respond to changing prices, and we refer to this model as a long-run model. Given published parameter estimates, we simulate the impact of a higher energy price on consumer food prices, and our empirical findings are consistent with our understanding of market responses. In the short run, we find that the full effect of an increase in the price of energy is fully (or nearly fully) passed on to consumers, because neither food producers nor consumers can immediately respond to changing prices. In the long run, however, the price response of food producers and consumers serves to mitigate the increase in consumer food prices

America’s Eating Habits:Food Away From Home

Food away from home (FAFH) has become increasingly integral to the American diet. In 2010, the share of Americans’ food budget for FAFH—reaching 50 percent (up from 41 percent in 1984)—surpassed the share for food at home (FAH) for the first time. Likewise, Americans’ share of energy intake from FAFH rose from 17 percent in 1977-78 to 34 percent in 2011-12, with differences in growth across types of FAFH (e.g., full- and quick-service restaurant foods, school meals, etc.). Along with the demand for FAFH, availability of FAFH has also increased, with much of the growth in recent years attributable to quick-service restaurants. The growing presence of FAFH in Americans’ diets reflects changes in consumer demand and producer behavior and affects the health and nutrition of individuals over time. This report takes a comprehensive look at the role of FAFH in American diets, exploring nutritional composition of FAFH and key Federal programs that may influence FAFH. The report also discusses how FAFH choices and availability relate to diet quality, income, age, and other socioeconomic factors