Regional Fresh Fruit and Vegetable Price Indices Using Supermarket Scanner Data
Creators: name: Beatty, Timothy
name: Snyder, Samantha
name: Beatty, Timothy
name: Snyder, Samantha
Publisher: name: Unknown
Year: 2015
Subjects: subject: Agricultural and Food Policy
subject: Consumer/Household Economics
subject: Marketing
subject: FOS: Economics and business

description: The Consumer Price Index (CPI) as constructed by the Bureau of Labor Statistics is one of the most widely-used measures of both aggregate and category specific price indices in the US. Within the broad CPI, price indices for several food categories and individual items are also calculated. For example, there is an index for aggregated fresh fruits and vegetables, while there are also separate indices for apples, bananas, tomatoes and onions, among others. In general, there has been little innovation in terms of CPI methodology over the past several decades. This research combines leading-edge techniques in index number theory with a strong practical application to the food CPI, a highly cited economic indicator with real implications on policy. Specifically, this study will help to detail and quantify the range in prices of fresh fruits and vegetables across major metropolitan areas in the US. As focus is placed on American diet quality, with special attention paid to low-income households, food assistance programs feature heavily in policy recommendations aimed at improving household purchase and consumption decisions. This research sheds light on how regional fresh fruit and vegetable price differences might impact household purchasing power given nationally fixed benefit levels for food assistance program participants. While the construction of the US’ CPI has remained largely unchanged, statistical agencies in a few European countries have recently augmented traditional CPI construction approaches to take advantage of improvements in data collection capabilities, namely the proliferation of store scanner data and the adoption of chained indices. The use of store scanner data presents a host of challenges, however, most notably contributing to chain drift in resulting indices (Ivancic et al. 2011). Chain drift, common in indices built with high-frequency data, is characterized by indices that do not return to a value of one even when prices return to those in the base period. To address this issue, Ivancic et al. (2011) propose a rolling window GEKS method that serves as a sort of hybrid between traditional fixed base indices and the more flexible chained indices. A second challenge arises when trying to perform multilateral price comparisons. Using a panel of price indices (multiple regions, measured monthly, over several years) adds an additional dimension to the typical index construction focused on temporal variation only. Spatial comparisons are especially confounding because, unlike time, there is no natural ordering amongst regions. Hill (2004) proposes a number of potential approaches to multilateral index construction and comparison and the Minimum Spanning Tree and the standard Geary Khamis methods are most amenable to the challenges presented by high frequency price measurements like those contained in store scanner data. Adopting both of these important innovations in index number theory potentially represents an important contribution to the field. From a more applied standpoint, the price indices constructed in this paper fill important holes left by the CPI and apply the use of store scanner data and the rolling window GEKS method to the US market for fruits and vegetables. First, we have extended the measurement of the fresh fruit and vegetable subcategories to include monthly observations at the Metropolitan Statistical Area (MSA) level. The CPI currently only reports a price level for ‘Food’ at the MSA level. Second, we have adopted the methods recommended in Hill(2004) to enable price level comparisons across regions. As constructed, multilateral, cross-MSA comparisons are not possible with the CPI as each MSA’s index is constructed independently of all the others. To perform our price analysis, we use store scanner data collected by IRI. The data includes food at home purchases recorded at the store-UPC level made at a variety of retailer types including grocery store, supercenter and convenience store formats. Purchase quantity and total expenditure are reported weekly at the store level over the five-year period between 2008 and 2012. Along with the quantity and expenditure level for each UPC, store location is also recorded. Using purchase data on fresh fruits and vegetables as well as store location, we are able to construct price indices for the 26 MSAs covered at least quarterly by the CPI. Because we are able to perform both inter- and intraregional price level comparisons across the five years studied, our results may be particularly useful in informing policy. The Supplemental Nutrition Assistance Program (SNAP) provides low-income households with monthly benefits earmarked for purchases of food. In general, benefit levels are determined based on the size of the household and are uniform nationwide. As a targeted response to concerns about diet quality, especially among low-income and low-education households, there are some efforts to link SNAP program participation with nutrition education and/or limit the types of foods that are eligible to be purchased with SNAP dollars (Shenkin and Jacobson 2010). While there is some evidence of significant regional food price variation, especially among fruits and vegetables (Sturm and Datar, 2011), more work needs to be done. This research enables a closer look at regional price differences in fresh fruits and vegetables and could shed light on whether uniform benefit levels are making purchases of fresh fruits and vegetables relatively more difficult for low-income households in some regions of the country.