Expanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment

Abe Dunn, Eric English, Kyle Hood, Lowell Mason and Brian Quistorff

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: 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.

Date: 2024-07
New Economics Papers: this item is included in nep-big and nep-geo
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www2.census.gov/library/working-papers/2024/adrm/ces/CES-WP-24-37.pdf First version, 2024 (application/pdf)

Related works:
Working Paper: Expanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:24-37

Access Statistics for this paper

More papers in Working Papers from U.S. Census Bureau, Center for Economic Studies Contact information at EDIRC.
Bibliographic data for series maintained by Dawn Anderson ().