Consumer surveys indicate that household inflation expectations tend to be much more heterogeneous than those of professional forecasters (Ranyard et al. 2008, Armantier et al. 2013. The literature offers two main explanations for this difference. Some authors attribute it to rational inattention, according to which individuals only partly incorporate information on topics such as inflation, because acquiring that information is costly (relative to the potential gains from using that

Online prices are increasingly being used for a variety of inflation measurement and research applications, yet little is know about their relation to prices collected offline, where most retail transactions take place. This paper presents the results of the first large-scale comparison of online and offline prices simultaneously collected from the websites and physical stores of 56 large multi-channel retailers in 10 countries. I find that price levels are identical about 72% of the time for the products sold in both locations, with significant heterogeneity across countries, sectors, and retailers. The similarity is highest in electronics and clothing and lowest in drugstores and office-supply retailers. There is no evidence of online prices varying with the location of the ip address or persistent browsing habits. Price changes are not synchronized but have similar frequencies and average sizes. These results have implications for National Statistical Offices and researchers using online data, as well as those interested in the effect of the Internet on retail prices.

A large and growing share of retail prices all over the world are posted online on the websites of retailers. This is a massive and (until recently) untapped source of retail price information. Our objective with the Billion Prices Project, created at MIT in 2008, is to experiment with these new sources of information to improve the computation of traditional economic indicators, starting with the Consumer Price Index. We also seek to understand whether online prices have distinct dynamics, their advantages and disadvantages, and whether they can serve as reliable source of information for economic research. The word “billion” in Billion Prices Project was simply meant to express our desire to collect a massive amount of prices, though we in fact reached that number of observations in less than two years. By 2010, we were collecting 5 million prices every day from over 300 retailers in 50 countries. We describe the methodology used to compute online price indexes and show how they co-move with consumer price indexes in most countries. We also use our price data to study price stickiness, and to investigate the “law of one price” in international economics. Finally we describe how the Billion Prices Project data are publicly shared and discuss why data collection is an important endeavor that macro- and international economists should pursue more often.

We use a novel dataset of online prices of identical goods sold by four large global retailers in dozens of countries to study good-level real exchange rates and their aggregated behavior. First, in contrast to the prior literature, we demonstrate that the law of one price holds very well within currency unions for tens of thousands of goods sold by each of the retailers, implying good-level real exchange rates often equal to one. Prices of these same goods exhibit large deviations from the law of one price outside of currency unions, even when the nominal exchange rate is pegged. This clarifies that the common currency per se, and not simply the lack of nominal volatility, is important in reducing cross-country price dispersion. Second, we derive a new decomposition that shows that good-level real exchange rates in our data predominantly reflect differences in prices at the time products are first introduced, as opposed to the component emerging from heterogeneous passthrough or from nominal rigidities during the life of the good. Further, these international relative prices measured at the time of introduction move together with the nominal exchange rate. This stands in sharp contrast to pricing behavior in models where all price rigidity for any given good is due simply to costly price adjustment for that good.

We study the daily behavior of supermarket prices and product availability following two recent natural disasters: the 2010 earthquake in Chile and the 2011 earthquake in Japan. In both cases there was an immediate and persistent effect on product availability. The number of goods available for sale fell 32% in Chile and 17% in Japan from the day of the disaster to its lowest point, which occurred 61 and 18 days after the earthquakes, respectively. Product availability recovered slowly, and a significant share of goods remained out of stock after six months. By contrast, prices were relatively stable and did not increase for months after the earthquakes, even for goods that were experiencing severe stockouts. These trends are present at all levels of aggregation, but appear strongly in non-perishable goods and emergency products. Our findings shed light into the determinants of sticky prices in conditions where traditional adjustment costs are less important. In particular, we look at the frequency and magnitudes of price changes in both countries and find that the results in Chile are consistent with pricing models where retailers have fear of "customer anger". In Japan, by contrast, the evidence suggests a bigger role for supply disruptions that restricted the ability of retailers to re-stock goods after the earthquake.

Online prices are increasingly used for measurement and research applications, yet little is known about their relation to prices collected offline, where most retail transactions take place. I conduct the first large-scale comparison of prices simultaneously collected from the websites and physical stores of 56 large multi-channel retailers in 10 countries. I find that price levels are identical about 72 percent of the time. Price changes are not synchronized but have similar frequencies and average sizes. These results have implications for national statistical offices, researchers using online data, and anyone interested in the effect of the Internet on retail prices. (JEL D22, L11, L81, O14)

The Covid-19 Pandemic has led to changes in consumer expenditure patterns that can introduce significant bias in the measurement of inflation. I use data collected from credit and debit transactions in the US to update the official basket weights and estimate the impact on the Consumer Price Index (CPI). I find that the Covid inflation rate is higher than the official CPI in the US, for both headline and core indices. I also find similar results with Covid baskets in 10 out of 16 additional countries. The difference is significant and growing over time, as socialdistancing rules and behaviors are making consumers spend relatively more on food and other categories with rising inflation, and relatively less on transportation and other categories experiencing significant deflation.

At least one co-author has disclosed a financial relationship of potential relevance for this research. Further information is available online at http://www.nber.org/papers/w22111.ack NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.