Perhaps the most important economic statistic calculated by the government is the Consumer Price Index (CPI). The CPI is used as a guide for fiscal and monetary decision-making, as a deflator to compare times series data in constant dollars, and as an inflation-measure to adjust the purchasing power of federal benefits like Social Security. The basic methodology for calculating the CPI, however, has hardly changed since the statistic was first introduced over a century ago. First, using a series of voluntary surveys of businesses and consumers, the Bureau of Labor Statistics attempts to measure average monthly prices in 38 geographical areas for 211 broad consumption categories like men’s shirts. Second, the BLS estimates the proportion that each of these individual categories represents of a typical household’s spending on all 211 categories. The goal is to calculate how much a typical basket of goods and services would cost today, then compare that to the cost of the same basket at the end of some fixed period. Although this may sound straightforward, worrisome and worsening challenges arise from this methodology. First, the time required to collect responses to these surveys and aggregate data means that the CPI is not calculated for months after data is collected. Second, falling response rates to polls and surveys of all kinds means that the CPI calculation is increasingly reliant on an ever-smaller set of data, with consequent worries about accuracy. Third, the metadata collected by surveys about goods and services sold is often insufficient to gauge how much the price increase in, say, a mobile phone, reflects improvements in the phone’s quality or feature-set. Funds from this grant support a project called RESET, Re-Engineering Statistics Using Economic Transactions, led by economist John Haltiwanger (University of Maryland College Park), Matthew Shapiro (University of Michigan) and Ron Jarmin (U.S. Census Bureau). The RESET team has forged data partnerships with major online retailers and market data analysis firms to allow them access to real-time, highly granular information about the prices and features of retail products for sale online. The team proposes to use advanced machine learning techniques to solve the problems with current data collection methods. The RESET partnerships promise to be able to calculate the CPI more quickly, using more comprehensive data, and in a way that promises to give both regulators and researchers more ability to calculate how much of a price variation is due to systemic inflation and how much actually reflects changes in product quality.