Ephraim S. Leibtag

Geographic Differences in the Relative Price of Healthy Foods

Although healthy foods can be affordable, if less healthy foods are cheaper, individuals may have an economic incentive to consume a less healthful diet. Using the Quarterly Food-at-Home Price Database, we explore whether a select set of healthy foods (whole grains, dark green vegetables, orange vegetables, whole fruit, skim and 1% milk, fruit juice, and bottled water) are more expensive than less healthy alternatives. We find that not all healthy foods are more expensive than less healthy alternatives; skim and 1% milk are less expensive than whole and 2% milk and bottled water is generally less expensive than carbonated nonalcoholic drinks. We also find considerable geographic variation in the relative price of healthy foods. This price variation may contribute to geographic variation in diet and health outcomes.

A Spoonful of Sugar Helps the Medicine Go Down: the Relationship Between Food Prices and Medical Expenditures on Diabetes

We investigate the impact of changes in the relative price of low- and high-carbohydrate foods on medical expenditures for diabetes care using Nielsen Homescan price data merged to the 2000--2005 Medical Expenditure Panel Survey. We find that an increase in low-(high-)carbohydrate food price increases (decreases) both the likelihood of a diabetes diagnosis and the level of medical expenditures among those with diabetes. We also find small impacts of food prices on body mass index that differ by gender. Policy simulations suggest that subsidizing the low-carbohydrate food purchases of people with diabetes could result in significant reductions in health care costs. Copyright 2010, Oxford University Press.

Cost Pass-Through in the U.S. Coffee Industry

A rich data set of coffee prices and costs was used to determine to what extent changes in commodity costs affect manufacturer and retail prices. On average, a 10-cent increase in the cost of a pound of green coffee beans in a given quarter results in a 2-cent increase in manufacturer and retail prices in that quarter. If a cost change persists for several quarters, it will be incorporated into manufacturer prices approximately cent-forcent with the commodity-cost change. Given the substantial fixed costs and markups involved in coffee manufacturing, this translates into about a 3-percent change in retail prices for a 10-percent change in commodity prices. We do not find robust evidence that coffee prices respond more to increases than to decreases in costs.

How much lower are prices at discount stores? An examination of retail food prices.

Nontraditional stores, including mass merchandisers, supercenters, club warehouse stores, and dollar stores, have increased their food offerings over the past 15 years and often promote themselves as lower priced alternatives to traditional supermarkets. How much lower are food prices at these stores? In order to better understand nontraditional stores’ impact on the cost of food, ERS analysts evaluate food price differences between nontraditional and traditional stores at the national and market level using 2004-06 Nielsen Homescan data. Findings show that nontraditional retailers offer lower prices than traditional stores even after controlling for brand and package size. Comparisons of identical items, at the Universal Product Code (UPC) level, show an expenditure-weighted average price discount of 7.5 percent, with differences ranging from 3 to 28 percent lower in nontraditional stores than in traditional stores. Nontraditional stores in metro areas where such stores have a higher-than-average market share have smaller and less frequent price discounts than those in areas where such stores have a lower market share.

How Retail Beef and Bread Prices Respond to Changes in Ingredient and Input and Costs

The extent to which cost changes pass through a vertically organized production process depends on the value added by each producer in the chain as well as a number of other organizational and marketing factors at each stage of production. Using 36 years of monthly Bureau of Labor Statistics price indices data (1972-2008), we model pass-through behavior for beef and bread, two retail food items with different levels of processing. Both the farm-to-wholesale and wholesale-to-retail price responses are modeled to allow for the presence of structural breaks in the underlying long-term relationships between price series. Broad differences in price behavior are found not only between food categories (retail beef prices respond more to farm-price changes than do retail bread prices) but also across stages in the supply chain. While farm-to-wholesale relationships generally appear to be symmetric, retail prices have a more complicated response behavior. For both bread and beef, the pass through from wholesale to retail is weaker than that from farm to wholesale.

Scanner Data-Based Panel Price Indexes

We construct panel price indexes using retail scanner data that allow comparisons of consumption cost across space and time. Two types of panel indexes are examined: the rolling-window panel extensions of the multilateral Cave-Christensen-Diewert (CCD) index with the Törnqvist index as its elements, and of the multilateral Gini-Eltetö-Köves-Szulc (GEKS) index using the Fisher ideal index as its elements. The rolling window method maintains the nonrevisability of published index numbers while it allows index numbers for new periods and locations be calculated and the basket of items be updated. Meanwhile, the multilateral structure of price comparison eliminates significant downward drift in standard chained indexes. Using county-level bilateral and panel indexes based on retail beverage scanner data, we experimentally adjust for purchasing parity the portion of Supplemental Nutrition Assistance Program (SNAP) benefits that participants spend on beverages. Accounting for temporal and spatial cost differences causes over 2% of SNAP allotment spent on beverages be reallocated, or approximately a 5% change in allotment on average for a county. About 90% of the relocated SNAP fund is to adjust for spatial differences in food cost. We also compare SNAP allotments implied by the retail scanner data indexes with those implied by indexes based on the USDA Quarterly Food-at-Home Price Database (QFAHPD). The treatment of unit values and product quality may have contributed to the significant differences observed between the retail scanner data indexes and the QFAHPD indexes.