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Technical Change, Inequality, and the Labor Market
Abstract
This essay discusses the effect of technical change on wage inequality. I argue that the behavior of wages and returns to schooling indicates that technical change has been skill-biased during the past sixty years. Furthermore, the recent increase in inequality is most likely due to an acceleration in skill bias. In contrast to twentieth century developments, most technical change during the nineteenth century appears to be skill-replacing. I suggest that this is because the increased supply of unskilled workers in the English cities made the introduction of these technologies profitable. On the other hand, the twentieth-century has been characterized by skill-biased technical change because the rapid increase in the supply of skilled workers has induced the development of skill-complementary technologies. The recent acceleration in skill bias is in turn likely to have been a response to the acceleration in the supply of skills during the past several decades.
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“The ubiquity and affordability of computers have also spurred the demand for high-skilled workers capable of effectively leveraging these tools, a phenomenon known as capital-skill complementarity (e.g., Krusell et al. 2000, Acemoglu 2002).”
From Skill-Biased Technical Change, Again? Online Gig Platforms and Local Employment (2025) · cites this paper as Acemoglu 2002
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“The A canonical theory in this discourse is SBTC (Acemoglu 2002, Autor et al. 2003), which posits that technological progress complements skilled workers by boosting their relative productivity and substitutes unskilled workers by automating their tasks.”
From Skill-Biased Technical Change, Again? Online Gig Platforms and Local Employment (2025) · cites this paper as Acemoglu 2002
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“Together, the upside for more-educated workers and downside for less-educated workers have magnified the distributional consequences of technological innovation, highlighting what is sometimes called “Skill-Biased Technological Change” (67).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [67]
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Metadata
- Authors
- Acemoglu, D.
- Venue
- Journal of Economic Literature
- Publisher
- American Economic Association
- Published
- 2002-03
- Volume
- 40
- Issue
- 1
- Pages
- 7-72
- ISSN
- 0022-0515 (ISSN-L)
- OpenAlex
- W2106759695
- Cited by
- 1,767
- References
- 132
- Citations by year
- 2026: 124 · 2025: 181 · 2024: 131 · 2023: 123 · 2022: 131 · 2021: 120 · 2020: 86 · 2019: 69 · 2018: 82 · 2017: 77 · 2016: 73 · 2015: 59 · 2014: 57 · 2013: 66 · 2012: 46
- FWCI
- 65.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Economic Literature (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Historical Economic and Social Studies
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Economic Theory and Policy
- Keywords
- skill-biased technological change, wage inequality, returns to education, labor supply, 19th-century industrialization, English cities, technological change
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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