This proposal aims to use price data for the universe of goods that have barcodes or are available online to answer three main questions: How do goods prices and product variety vary across space? What are the problems with using online prices as a substitute for offline prices to measure inflation? How do prices and quantities respond to high frequency macroeconomic shocks? In order to answer these questions, this project seeks to measure exact price indexes for goods across cities. This project is the first endeavor to investigate the sources of differences between online, offline, and BLS price indexes and to explore how daily price and consumption data respond to macroeconomic shocks. The project will make use of several datasets. The first is ACNielsen Homescan data for the US. The second is Nikkei-POS data and ACNielsen Scantrak data covering retail sales at the barcode level for Japan and a number of foreign countries. The third database that the project will use has price and click-through information for large number retail products. Jointly, this is vastly more data than has ever been used by any economist or statistical agency. This will enable to construct daily price and demand information for millions of products in each country and compare it to data on offline sales of the same products. The project aims to make breakthroughs in a number of dimensions. First, for academic economists, the proposed research provides the first test of whether Paul R. Krugman?s proposed mechanism that underlies his Nobel Prize winning theory of New Economic Geography is correct. Krugman argues that larger markets have greater product variety and should have lower price indexes for tradable goods. A major problem with existing cross-city measures of prices is that they do not compare identical goods. Hence, it is not possible to know whether, whether observed higher goods prices in cities are due to wealthier urban residents consuming higher quality items or due to prices of identical items. More generally, the project aims to demonstrate how barcode and online real estate data can be used to measure cost of living across locations. Second, the project seeks us to understand the extent to which price indexes based on internet data are good substitutes for conventional price indexes, such as the Consumer Price Index. A major question for economists and statistical agencies is how well online prices track offline prices. Statistical agencies such as the Bureau of Labor Statistics calculate price indexes using data manually collected at offline locations that cannot reflect the high frequency online price changes that are relevant to consumers who are increasingly shopping at online stores. While the increasing availability of internet data has the potential to render this type of data collection obsolete, questions remain regarding the accuracy of price indexes based on internet data. As economists start using online data in place of offline data to investigate many important economic questions, it is important to know how closely online prices track offline prices in general. As a starting point, the project seeks to examine price differences across markets. While this has been done for certain narrowly defined markets -- e.g., books and contact lenses ? this project will be the first to examine whether Internet prices are lower than offline prices in general. The project intends to do this by comparing GPI data with AC Nielsen data that contains price information on 750,000 different UPC's available in mass-merchandising stores like Wal-Mart for a variety of different countries. One of the added benefits of this comparison is that the research will be able to verify the validity of click-through data as a measure of market share. Next goal of the project is to examine how rapidly price differences dissipate across different classes of merchants by examining price movements across online merchants (controlling for their relevance using click-through data). This will be useful for determining how much market power online merchants have. The project seeks to conduct a similar analysis comparing price dynamics of goods available online with those available in brick-and-mortar establishments that sell goods available in AC Nielsen data. The generated information will be useful to answer the question of whether online pricing behavior differs systematically from retail prices. This is a very important question for understanding not only market interactions but also for understanding how closely online prices are linked to offline prices. To the extent that these prices are closely linked, it will provide further validation that one can use online prices as a substitute for offline prices. The information will also enable to obtain estimates for how the development of online stores affects offline marketing. The project?s goal is also to make use of data to understand international market segmentation. In particular, by comparing prices of online goods in different countries, this research will be able to estimate precisely how international cost shocks are transmitted across countries (what economists term ?pass-through?). These types of estimates are extremely important for understanding how exchange rate movements affect prices and propagate international macroeconomic shocks. Finally, the ability to produce daily price indexes and observe daily demand creates a number of exciting possibilities for evaluating pricing and consumption decisions. For example, if one constructs price indexes carefully, it is theoretically possible to produce daily measures of inflation in real time. This potentially could be useful for examining how fast macroeconomic shocks appear in consumer prices. One of the difficulties of evaluating consumption behavior is that consumption and pricing data tend to come out at a quarterly or monthly frequency. As a result, it is often very difficult to determine which event was critical in determining pricing and consumption behavior. The ability to work with price and click-through data at a daily frequency will help to understand how macroeconomic shocks are transmitted to demand as well as real estate and goods prices.
