arXiv 3 Nov 2021 · Econometrics · 2 citations (OpenAlex)
arXiv:2111.02528 · PDF · DOI · OpenAlex · Extracted main text
We propose occ2vec, a principal approach to representing occupations, which can be used in matching, predictive and causal modeling, and other economic areas. In particular, we use it to score occupations on any definable characteristic of interest, say the degree of \textquote{greenness}. Using more than 17,000 occupation-specific text descriptors, we transform each occupation into a high-dimensional vector using natural language processing. Similar, we assign a vector to the target characteristic and estimate the occupational degree of this characteristic as the cosine similarity between the vectors. The main advantages of this approach are its universal applicability and verifiability contrary to existing ad-hoc approaches. We extensively validate our approach on several exercises and then use it to estimate the occupational degree of charisma and emotional intelligence (EQ). We find that occupations that score high on these tend to have higher educational requirements. Turning to wages, highly charismatic occupations are either found in the lower or upper tail in the wage distribution. This is not found for EQ, where higher levels of EQ are generally correlated with higher wages.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Webb, M (2019) The Impact of Artificial Intelligence on the Labor Market | 0.965 | 10 | 4 | 90% |
| 2 | Felten, E. W., M. Raj, and R. Seamans (2018) A Method to Link Advances in Artificial Intelligence to Occupational Abilities | 0.956 | 8 | 3 | 88% |
| 3 | Acemoglu, D. and D. Autor (2011) Skills, Tasks and Technologies: Implications for Employment and Earnings | 0.950 | 7 | 3 | 86% |
| 4 | Brynjolfsson, E., T. Mitchell, and D. Rock (2018) What Can Machines Learn, and What Does It Mean for the Occupations and Industries | 0.950 | 7 | 3 | 86% |
| 5 | Autor, D. H., F. Levy, and R. J. Murnane (2003) The Skill Content of Recent Technological Change: An Empirical Exploration* | 0.811 | 4 | 2 | 100% |
| 6 | Autor, D. H., L. F. Katz, and M. S. Kearney (2006) The polarization of the U.S. labor market | 0.737 | 3 | 2 | 100% |
| 7 | Autor, D. H. and M. J. Handel (2013) Putting Tasks to the Test: Human Capital, Job Tasks, and Wages | 0.737 | 3 | 2 | 100% |
| 8 | Autor, D. H. and D. Dorn (2013) The growth of low-skill service jobs and the polarization of the U.S. labor market | 0.737 | 3 | 2 | 100% |
| 9 | Acemoglu, D., D. Autor, J. Hazell, and P. Restrepo (2022) Artificial Intelligence and Jobs: Evidence from Online Vacancies | 0.644 | 2 | 2 | 100% |
| 10 | van der Maaten, L. and G. Hinton (2008) Visualizing Data using t-SNE | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 172 scored citations.