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Mapping Labor Demand for Data‐Intensive Work in the United Kingdom, Canada, and the United States

Julia Schmidt et al.

Review of Income and Wealth2026https://doi.org/10.1111/roiw.70047article
AJG 3
Weight
0.50

What the paper says

The paper makes three main contributions. First, it develops an NLP‐based methodology to identify data‐intensive skills in job advertisements at scale. Second, it operationalizes a transparent indicator of data intensity for occupations and industries. Third, by applying the method to job advertisements from the United Kingdom, Canada, and the United States, it provides harmonized cross‐country estimates of data‐expert hiring, offering a key input to derive a proxy for investment in data assets. Empirical results show that although the ranking of data‐intensive occupations is broadly similar across countries, the industrial distribution of data‐intensive jobs differs, reflecting distinctive labor‐demand structures.

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https://doi.org/https://doi.org/10.1111/roiw.70047

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@article{julia2026,
  title        = {{Mapping Labor Demand for Data‐Intensive Work in the United Kingdom, Canada, and the United States}},
  author       = {Julia Schmidt et al.},
  journal      = {Review of Income and Wealth},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/roiw.70047},
}

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Mapping Labor Demand for Data‐Intensive Work in the United Kingdom, Canada, and the United States

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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.