Polanyi's Paradox and the Shape of Employment Growth
By David H. Autor. 2014
306 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Public Administration, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/6e7e9b485ed386c74eaa04209e2ef02199940797
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Citations in the corpus
- Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (2020, unknown): “While past technologies could only automate tasks that follow explicit, codifiable rules, ML technologies can infer rules automatically from the observation of inputs and corresponding outputs (Autor, 2014).”
- Skill Requirements across Firms and Labor Markets: Evidence from Job Postings for Professionals (2017, unknown): “(3) log(wages)omf = β f + controls+ omf”
- Teacher Effects on Complex Cognitive Skills and Social-Emotional Competencies (2017, unknown): “These findings suggest teacher effectiveness is multidimensional, and high-stakes evaluation decisions are only weakly informed by the degree to which teachers are developing students’ complex cognitive skills and social-emotional competencies.”
- Generative AI at Work (2023, unknown): “…as writing emails, analyzing data, or creating presentations—rely on tacit knowledge, they have so far defied automation (Polanyi, 1966; Autor, 2014).4 Machine learning (ML) algorithms work differently from traditional computer programs: instead of requiring explicit instructions to…”
- What Can Machines Learn, and What Does It Mean for Occupations and the Economy? (2018, unknown): “While not always interpretable or explainable, these ML models open up a new set of possibilities for automation and complementarities to labor ( Autor 2014 . will be The types of tasks affected by ML quite different from those affected in past waves of automation.”
- The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis (2016, unknown): “First of all, experts tend to overestimate the potential of new technologies (Autor 2014, 2015, Pfeiffer and Suphan 2015).”
- Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, unknown): “…and medium-skill workers at the beginning of the 1980s (Goos and Manning, 2007; Goos et al., 2009; Adermon and Gustavsson, 2015), and the more recent hollowing out of the middle class and middle-skilled routinized tasks, which are susceptible to being performed by machines (Autor, 2014; 2015).”
- The impact of automation on employment: just the usual structural change? (2018, unknown): “[27]), if there is a sufficiently large amount of data, cognitive, knowledge-based tasks can also be taken over by AI [7] and are, as such, subject to progressive automation and substitution as well (cf.”
- Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda (2015, background): “…unavoidable loss of considerable numbers of well-established jobs will be outweighed by newly created jobs.4 Neither can we assert any causal relationship between digitization and (un)employment (see also the ‘Jevons’ Paradox’ or ‘Rebound Effect’ in Jevons (1866); cited after Alcott (2005)).”
- Robots at Work (2015, unknown): “AUTOR (2011): “Skills, Tasks and Technologies: Implications for Employment and Earnings,” vol. 4 of Handbook of Labor Economics, chap. 12, pp. 1043– 1171. Elsevier. ACEMOGLU, D., D. AUTOR, D. DORN, G. H. HANSON, AND B. PRICE (2014): “Return of the Solow Paradox? IT, Productivity, and Employment in U”