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

Abe Dunn et al.

Review of Income and Wealth2026https://doi.org/10.1111/roiw.70042article
AJG 3
Weight
0.50

What the paper says

Designing effective public policies depends on accurate and timely economic measurement. Big data promises substantial benefits for improving economic measurement but also presents significant challenges. This paper introduces a framework for quantifying the usefulness of big data for specific applications, relative to official statistics. We 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 improving timely regional employment estimates. We find that using data from a payroll processor reduces out‐of‐sample error in state employment estimates by 11%. Additionally, we produce new county‐level estimates offering more timely, granular insights than previously available. Applying a novel test, we cannot reject the hypothesis that the new county estimates have an accuracy in line with official measures. An application to COVID highlights the practical benefits of these estimates.

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https://doi.org/https://doi.org/10.1111/roiw.70042

Or copy a formatted citation

@article{abe2026,
  title        = {{Expanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment}},
  author       = {Abe Dunn et al.},
  journal      = {Review of Income and Wealth},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/roiw.70042},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.