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Technological Change Job Susceptibility
Research Guide

What is Technological Change Job Susceptibility?

Technological Change Job Susceptibility measures occupation-level exposure to automation and routine-biased technological change, predicting job displacement risks using task-based frameworks and expert assessments.

Researchers develop metrics like computerization probability from expert surveys and machine learning on occupational tasks (Frey & Osborne, implied in Autor 2015). Studies document routine task decline and job polarization across US and Europe (Goos et al. 2014, 1919 citations). Over 10 key papers since 2001 analyze automation's labor impacts, with Autor's works exceeding 8,000 combined citations.

Curated Papers

Key Challenges

Why It Matters

Occupation-level automation forecasts guide reskilling programs for at-risk workers, as in Autor (2015) explaining why automation complements rather than eliminates most jobs (3252 citations). Policymakers use task frameworks from Acemoglu and Autor (2010) to design labor market interventions amid rising inequality (2192 citations). Goos et al. (2014) quantify routine-biased change driving European job polarization, informing trade-offs between productivity gains and displacement costs (1919 citations).

Key Research Challenges

Measuring Automation Exposure

Quantifying occupation-level susceptibility requires distinguishing routine from non-routine tasks, as in Autor et al. (2001) skill content analysis (2626 citations). Expert surveys like Frey-Osborne face biases in predicting AI feasibility. Recent robot exposure metrics (Acemoglu & Restrepo 2017, 889 citations) highlight data limitations in firm-level adoption.

Distinguishing Tech from Trade Shocks

Isolating technological change from import competition effects challenges models, per Autor, Dorn, Hanson (2013) China shock findings (4077 citations). Routine-biased tech interacts with offshoring (Goos et al. 2014). Frameworks must parse these in employment data.

Predicting Net Job Effects

Automation displaces but reinstates labor via new tasks (Acemoglu & Restrepo 2019, 1852 citations). Forecasting net employment needs dynamic task allocation models. Social skills growth offsets math declines (Deming 2017, 1519 citations), complicating predictions.

Essential Papers

The China Syndrome: Local Labor Market Effects of Import Competition in the United States

David Autor, David Dorn, Gordon Hanson · 2013 · American Economic Review · 4.1K citations

We analyze the effect of rising Chinese import competition between 1990 and 2007 on US local labor markets, exploiting cross-market variation in import exposure stemming from initial differences in...

Why Are There Still So Many Jobs? The History and Future of Workplace Automation

David Autor · 2015 · The Journal of Economic Perspectives · 3.3K citations

In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor—as it is typically...

The Skill Content of Recent Technological Change: An Empirical Exploration

David Autor, Frank Levy, Richard J. Murnane · 2001 · 2.6K citations

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Skills, Tasks and Technologies: Implications for Employment and Earnings

Daron Acemoğlu, David Autor · 2010 · 2.2K citations

A central organizing framework of the voluminous recent literature studying changes in the returns to skills and the evolution of earnings inequality is what we refer to as the canonical model, whi...

Explaining Job Polarization: Routine-Biased Technological Change and Offshoring

Maarten Goos, Alan Manning, Anna Salomons · 2014 · American Economic Review · 1.9K citations

This paper documents the pervasiveness of job polarization in 16 Western European countries over the period 1993–2010. It then develops and estimates a framework to explain job polarization using r...

Automation and New Tasks: How Technology Displaces and Reinstates Labor

Daron Acemoğlu, Pascual Restrepo · 2019 · The Journal of Economic Perspectives · 1.9K citations

We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. A...

The Growing Importance of Social Skills in the Labor Market*

David Deming · 2017 · The Quarterly Journal of Economics · 1.5K citations

Abstract The labor market increasingly rewards social skills. Between 1980 and 2012, jobs requiring high levels of social interaction grew by nearly 12 percentage points as a share of the U.S. labo...

Reading Guide

Foundational Papers

Start with Autor et al. (2001, 2626 citations) for skill content of tech change; Acemoglu & Autor (2010, 2192 citations) for task framework; Autor (2015, 3252 citations) for historical automation context.

Recent Advances

Acemoglu & Restrepo (2019, 1852 citations) on task reinstatement; Deming (2017, 1519 citations) on social skills rise; Acemoglu & Restrepo (2017, 889 citations) on robot employment effects.

Core Methods

Routine-biased technological change (RBTC) models from Autor; task allocation frameworks (Acemoglu-Restrepo); exposure indices via industry robot adoption or import shocks.

How PapersFlow Helps You Research Technological Change Job Susceptibility

Discover & Search

Research Agent uses searchPapers and citationGraph to map Autor's task framework from 'Why Are There Still So Many Jobs?' (2015), revealing 3252 citations linking to Acemoglu-Restrepo automation models. exaSearch uncovers niche expert surveys on computerization risk; findSimilarPapers extends to Goos et al. (2014) polarization studies.

Analyze & Verify

Analysis Agent applies readPaperContent to extract task exposure metrics from Autor et al. (2001), then runPythonAnalysis with pandas to replicate routine task decline regressions. verifyResponse via CoVe cross-checks claims against Deming (2017) social skills data; GRADE scores evidence strength for robot impacts (Acemoglu & Restrepo 2017).

Synthesize & Write

Synthesis Agent detects gaps in US-centric studies versus European polarization (Goos et al. 2014), flagging contradictions between displacement (Autor 2015) and reinstatement (Acemoglu & Restrepo 2019). Writing Agent uses latexEditText and latexSyncCitations for task framework reviews, latexCompile for publication-ready reports, exportMermaid for automation-task flow diagrams.

Use Cases

"Replicate Frey-Osborne automation probabilities with Python on US occupational data."

Research Agent → searchPapers (Frey-Osborne refs) → Analysis Agent → runPythonAnalysis (pandas/ML sandbox on task data) → matplotlib plots of susceptibility scores.

"Draft LaTeX review comparing Autor task model to Acemoglu robot evidence."

Synthesis Agent → gap detection → Writing Agent → latexEditText (intro/methods) → latexSyncCitations (10 Autor papers) → latexCompile (PDF with figures).

"Find GitHub repos implementing routine-biased tech models from Goos et al."

Research Agent → paperExtractUrls (Goos 2014) → Code Discovery → paperFindGithubRepo → githubRepoInspect (replication scripts, data pipelines).

Automated Workflows

Deep Research workflow conducts systematic review of 50+ papers on task-biased change: searchPapers → citationGraph (Autor cluster) → GRADE summaries → structured report on susceptibility trends. DeepScan analyzes China shock interactions (Autor et al. 2013) via 7-step CoVe checkpoints with runPythonAnalysis for exposure regressions. Theorizer generates hypotheses on AI task reinstatement from Acemoglu-Restrepo (2019) literature synthesis.

Try Doxa for Technological Change Job Susceptibility Research

Frequently Asked Questions

What defines Technological Change Job Susceptibility?

It quantifies occupation exposure to automation via task decomposability, as in Autor et al. (2001) routine skill measures (2626 citations).

What methods predict job automation risk?

Expert surveys assign computerization probabilities to 702 occupations (implied in Autor 2015); ML on task data refines forecasts (Acemoglu & Restrepo 2019).

What are key papers?

Autor (2015, 3252 citations) on automation history; Goos et al. (2014, 1919 citations) on polarization; Acemoglu & Restrepo (2017, 889 citations) on robots.