You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
Tasks, Automation, and the Rise in U.S. Wage Inequality | The Econometric Society



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Econometrica

Journal Of The Econometric Society

An International Society for the Advancement of Economic

Theory in its Relation to Statistics and Mathematics

Edited by: Marina Halac • Print ISSN: 0012-9682 • Online ISSN: 1468-0262

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Econometrica
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Tasks, Automation, and the Rise in U.S. Wage Inequality
Econometrica: Sep, 2022,
Volume 90
,
Issue 5

Tasks, Automation, and the Rise in U.S. Wage Inequality
https://doi.org/10.3982/ECTA19815
Correction added on 13 October 2022, after first online publication: two sentences inadvertently left out from the paper have been added.
p. 1973-2016
Daron Acemoglu
,
Pascual Restrepo
We document that between 50% and 70% of changes in the U.S. wage structure over the last four decades are accounted for by relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. We develop a conceptual framework where tasks across industries are allocated to different types of labor and capital. Automation technologies expand the set of tasks performed by capital, displacing certain worker groups from jobs for which they have comparative advantage. This framework yields a simple equation linking wage changes of a demographic group to the
task displacement
it experiences. We report robust evidence in favor of this relationship and show that regression models incorporating task displacement explain much of the changes in education wage differentials between 1980 and 2016. The negative relationship between wage changes and task displacement is unaffected when we control for changes in market power, deunionization, and other forms of capital deepening and technology unrelated to automation. We also propose a methodology for evaluating the full general equilibrium effects of automation, which incorporate induced changes in industry composition and ripple effects due to task reallocation across different groups. Our quantitative evaluation explains how major changes in wage inequality can go hand‐in‐hand with modest productivity gains.

Cite This Paper

MLA

Acemoglu, Daron, and Pascual Restrepo. “Tasks, Automation, and the Rise in U.S. Wage Inequality.”
Econometrica
, vol. 90, .no 5, Econometric Society, 2022, pp. 1973-2016, https://doi.org/10.3982/ECTA19815
copy

Chicago

Acemoglu, Daron, and Pascual Restrepo. “Tasks, Automation, and the Rise in U.S. Wage Inequality.”
Econometrica
, 90, .no 5, (Econometric Society: 2022), 1973-2016. https://doi.org/10.3982/ECTA19815
copy

APA

Acemoglu, D., & Restrepo, P. (2022). Tasks, Automation, and the Rise in U.S. Wage Inequality.
Econometrica, 90
(5), 1973-2016. https://doi.org/10.3982/ECTA19815
copy

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Supplemental Material

Supplement to "Tasks, Automation, and the Rise in US Wage Inequality"
Daron Acemoglu and Pascual Restrepo
This zip file contains the replication files for the manuscript. It also contains appendix B as an additional online appendix.

View ZIP

Supplement to "Tasks, Automation, and the Rise in US Wage Inequality"
Daron Acemoglu and Pascual Restrepo
This online appendix contains material not found within the manuscript.

View PDF

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July 5, 2026

New Monograph Editor Announced
Serena Ng stepped down as Coeditor of the Monograph Series on June 30, 2026. On July 1st, Peter Arcidiacono became the new Coeditor responsible for theoretical and applied econometrics.

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</reference>

<statements>
1. Distribution matters as much as aggregates: task displacement explains most recent US wage-inequality growth
2. Task displacement drives inequality: 50–70% of the change in the US wage structure over four decades is attributable to relative wage declines among routine-specialized workers in automating industries
3. Acemoglu and Restrepo (2022), in Econometrica, provided the most comprehensive accounting to date, documenting that "between 50% and 70% of changes in the U.S. wage structure over the last four decades are accounted for by relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation." This locates a large share of rising US wage inequality in task displacement rather than in generic skill-biased demand shifts.
4. The bulk of recent US wage-inequality growth is attributable to task displacement (Acemoglu & Restrepo, 2022)
5. Foundational task-based theory (Autor, Levy & Murnane, 2003; Acemoglu & Autor, 2011; Acemoglu & Restrepo, 2018, 2019) and a rapidly growing body of empirical work—on robots (Acemoglu & Restrepo, 2020), AI vacancies (Acemoglu, Autor, Hazell & Restrepo, 2022), wage inequality (Acemoglu & Restrepo, 2022), and generative AI (Noy & Zhang, 2023; Eloundou et al., 2024; Brynjolfsson, Li & Raymond, 2025)—together support a nuanced conclusion: AI is neither an unambiguous job-killer nor a costless boon.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.