The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Workers, Wages, and Technology
Abstract
This paper documents how plant-level wages, occupational mix, workforce education, and productivity vary with the adoption and use of new factory automation technologies such as programmable controllers, computer-automated design, and numerically controlled machines. Our cross-sectional results show that plants that use a large number of new technologies employ more educated workers, employ relatively more managers, professionals, and precision-craft workers, and pay higher wages. However, our longitudinal analysis shows little correlation between skill upgrading and the adoption of new technologies. It appears that plants that adopt new factory automation technologies have more skilled workforces both pre- and postadoption.
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Citations
Cites
Changes in Relative Wages, 1963-1987: Supply and Demand Factors
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Autor, D. H., & Dorn, D. (2013) · American Economic Review · doi:10.1257/aer.103.5.1553 · Cited by 3,824
Beyond computation: Information technology, organizational transformation and business performance
Brynjolfsson, E., & Hitt, L. M. (2000) · Journal of Economic Perspectives · doi:10.1257/jep.14.4.23 · Cited by 3,335
Galor, O., & Weil, D. N. (2000) · American Economic Review · doi:10.1257/aer.90.4.806 · Cited by 2,319
Computing inequality: have computers changed the labor market?
Autor, D. H., Katz, L. F., & Krueger, A. B. (1998) · The Quarterly Journal of Economics · doi:10.1162/003355398555874 · Cited by 1,830
Inequality and Economic Growth: The Perspective of the New Growth Theories
Aghion, P., Caroli, E., & García-Peñalosa, C. (1999) · Journal of Economic Literature · doi:10.1257/jel.37.4.1615 · Cited by 1,848
Technical change, inequality, and the labor market
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Import Competition and the Great US Employment Sag of the 2000s
Acemoglu, D., Autor, D., Dorn, D., Hanson, G. H., & Price, B. (2016) · Journal of Labor Economics · doi:10.1086/682384 · Cited by 1,113
Investment in Enterprise Resource Planning: Business Impact and Productivity Measures
Hitt, L. M., Wu, D. J., & Zhou, X. (2002) · Journal of Management Information Systems · doi:10.1080/07421222.2002.11045716 · Cited by 877
Trade Adjustment: Worker-Level Evidence *
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What’s Driving the New Economy?: The Benefits of Workplace Innovation
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Violante, G. L. (2008) · The New Palgrave Dictionary of Economics · doi:10.1057/978-1-349-95121-5_2388-1 · Cited by 238
AI, Labor, Productivity and the Need for Firm-Level Data
Seamans, R., & Raj, M. (2018) · doi:10.3386/w24239 · Cited by 117
Technological change and employment: some micro evidence from Italy
Piva, M., & Vivarelli, M. (2004) · Applied Economics Letters · doi:10.1080/1350485042000228222 · Cited by 75
Ribeiro, B., Meckin, R., Balmer, A., & Shapira, P. (2023) · Research Policy · doi:10.1016/j.respol.2022.104607 · Cited by 51
The Policy Challenge of Artificial Intelligence
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Metadata
- Authors
- Doms, M., Dunne, T., & Troske, K. R.
- Venue
- The Quarterly Journal of Economics
- Publisher
- Oxford University Press (OUP)
- Published
- 1997-02-01
- Volume
- 112
- Issue
- 1
- Pages
- 253-290
- ISSN
- 0033-5533 (ISSN-L)
- 1531-4650
- OpenAlex
- W2158985433
- Cited by
- 865
- References
- 2
- FWCI
- 97.28
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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