The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Robots and humans – complements or substitutes?
Open accessAbstract
The effect of the spread of Artificial Intelligence (AI) on wages depends on both the form of aggregate production relationships and the elasticity of substitution between human and robotic labor. With a conventional production function involving labor, robots, and ordinary capital, an increase in robotic labor can have either a positive or a negative effect on wages. Alternatively, it is possible to estimate the aggregate production relationship without measuring capital or other fixed factors explicitly, using the procedure developed by Houthakker in the 1950s. Houthakker's method is based on the probability distribution of the productivity of the variable factor. Fitting different distributions to cross-sectional data on U.S. productivity, it is shown that if the elasticity of substitution between human and robotic labor is greater than about 1.9, the burgeoning of AI technologies will cause a decline in aggregate wages, other things equal. For the manufacturing sector, an even smaller human-robot elasticity of substitution is likely to result in declining wages of industrial workers as robots proliferate.
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“AI is the broad suite of technologies that can match or surpass human capabilities, particularly those involving cognition such as learning and problem solving (DeCanio, 2016).”
Background
Metadata
- Authors
- DeCanio, S. J.
- Venue
- Journal of Macroeconomics
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 49
- Pages
- 280-291
- ISSN
- 0164-0704 (ISSN-L)
- OpenAlex
- W2514873829
- Cited by
- 304
- References
- 53
- FWCI
- 32.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
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- DeCanio, S. J. (2016). Robots and humans – complements or substitutes? Journal of Macroeconomics. https://doi.org/10.1016/j.jmacro.2016.08.003
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
