Brian Quistorff

Big data offers potentially enormous benefits for improving economic measurement, but it also presents challenges (e.g., lack of representativeness and instability), implying that their value is not always clear. We propose a framework for quantifying the usefulness of these data sources for specific applications, relative to existing official sources. We specifically weigh the potential benefits of additional granularity and timeliness, while examining the accuracy associated with any new or improved estimates, relative to comparable accuracy produced in existing official statistics. We apply the methodology to employment estimates using data from a payroll processor, considering both the improvement of existing state-level estimates, but also the production of new, more timely, county-level estimates. We find that incorporating payroll data can improve existing state-level estimates by 11\% based on out-of-sample mean absolute error, although the improvement is considerably higher for smaller state-industry cells. We also produce new county-level estimates that could provide more timely granular estimates than previously available. We develop a novel test to determine if these new county-level estimates have errors consistent with official series. Given the level of granularity, we cannot reject the hypothesis that the new county estimates have an accuracy in line with official measures, implying an expansion of the existing frontier. We demonstrate the practical importance of these experimental estimates by investigating a hypothetical application during the COVID-19 pandemic, a period in which more timely and granular information could have assisted in implementing effective policies. Relative to existing estimates, we find that the alternative payroll data series could help identify areas of the country where employment was lagging. Moreover, we also demonstrate the value of a more timely series.

The U.S. Bureau of Economic Analysis (BEA) produces economic statistics through its system of satellite accounts that highlight specialized areas of the economy that are not directly apparent in BEA’s official economic statistics published under the North American Industry Classification System (NAICS), such as outdoor recreation and arts and culture. BEA recently developed a Digital Economy Satellite Account (DESA) to better understand this area of the economy as it involves production that spans multiple NAICS industries, ranging from computer manufacturing to internet-based retail trade (e-commerce) to software production. Currently, BEA’s digital economy statistics do not fully capture production of digital intermediary services earned from operating a digital platform that facilitates the direct interaction between multiple buyers and multiple sellers for a fee (such as rideshare), resulting in an incomplete picture of the digital economy. In this paper, we discuss options for measuring digital intermediary services across selected industries of interest to other international statistical agencies as well as BEA: rideshare, travel services, and food/grocery delivery services. We also provide experimental estimates of gross output for these services that cover 2018–2021 using two approaches. We find that digital intermediation services for rideshare, travel services, and food/grocery delivery services represented at least $31 billion in 2021 gross output, or close to 1 percent of the overall value of the digital economy based on the latest DESA statistics.