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Skill-Biased Technological Change
Research Guide

What is Skill-Biased Technological Change?

Skill-Biased Technological Change (SBTC) refers to technological progress that increases the demand for skilled labor relative to unskilled labor, widening wage inequality.

SBTC emerged as a key explanation for rising college wage premiums since the 1980s. Autor, Levy, and Murnane (2001, 2626 citations) showed computers substitute routine manual tasks but complement abstract and interactive skills. Acemoglu and Autor (2010, 2192 citations) formalized task-based models linking technology to skill demand shifts.

Curated Papers

Key Challenges

Why It Matters

SBTC accounts for U.S. wage inequality trends, guiding policies on education and retraining (Katz and Autor, 1999, 2056 citations). Autor, Levy, and Murnane (2001) linked computerization to skill premiums, influencing debates on automation's labor impacts. Goos, Manning, and Salomons (2014, 1919 citations) extended this to job polarization across Europe, informing trade and tech policy responses to offshoring.

Key Research Challenges

Empirical Identification of SBTC

Distinguishing SBTC from other inequality drivers like trade remains difficult. Card and DiNardo (2002, 1679 citations) highlighted puzzles where SBTC fails to explain non-monotonic wage changes. Firm-level data helps but requires strong instruments (Autor, Dorn, and Hanson, 2013).

Task Model Measurement

Quantifying skill content of tasks across occupations is data-intensive. Autor, Levy, and Murnane (2001) used DOT data to classify tasks, but updates for new technologies lag. Acemoglu and Restrepo (2019, 1852 citations) model task allocation but need granular automation exposure metrics.

Accounting for Offshoring

Separating routine-biased tech from offshoring effects challenges SBTC tests. Goos, Manning, and Salomons (2014) estimate both in European data, finding offshoring amplifies polarization. Autor, Dorn, and Hanson (2013) show trade shocks mimic SBTC patterns in U.S. markets.

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...

The Gender Wage Gap: Extent, Trends, and Explanations

Francine D. Blau, Lawrence M. Kahn · 2017 · Journal of Economic Literature · 2.7K citations

Using Panel Study of Income Dynamics (PSID) microdata over the 1980–2010 period, we provide new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably du...

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...

Changes in the Wage Structure and Earnings Inequality

Lawrence F. Katz, David Autor · 1999 · Handbook of labour economics · 2.1K citations

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...

Reading Guide

Foundational Papers

Start with Autor, Levy, Murnane (2001, 2626 citations) for empirical skill-task framework; then Katz, Autor (1999, 2056 citations) for wage structure trends; Acemoglu, Autor (2010, 2192 citations) for theory.

Recent Advances

Acemoglu, Restrepo (2019, 1852 citations) on automation tasks; Deming (2017, 1519 citations) on social skills; Goos et al. (2014, 1919 citations) on polarization.

Core Methods

Task reallocation models (Acemoglu-Autor); routine-biased change regressions (Autor et al. 2001); shift-share IV for trade/tech (Autor-Dorn-Hanson 2013).

How PapersFlow Helps You Research Skill-Biased Technological Change

Discover & Search

Research Agent uses searchPapers and citationGraph on 'skill-biased technological change' to map 250M+ OpenAlex papers, centering Autor, Levy, and Murnane (2001, 2626 citations) with 200+ citing works. exaSearch uncovers firm-level datasets; findSimilarPapers links to Acemoglu and Autor (2010).

Analyze & Verify

Analysis Agent runs readPaperContent on Autor et al. (2001) abstracts, then verifyResponse with CoVe to check SBTC claims against Katz and Autor (1999). runPythonAnalysis replicates wage premium regressions from PSID data using pandas; GRADE scores evidence strength on task models.

Synthesize & Write

Synthesis Agent detects gaps in SBTC explanations (e.g., social skills per Deming, 2017), flags contradictions with Card and DiNardo (2002). Writing Agent uses latexEditText for task model equations, latexSyncCitations for 50+ refs, latexCompile for polished drafts; exportMermaid diagrams polarization trends.

Use Cases

"Replicate Autor Levy Murnane 2001 wage-skill regressions with modern data"

Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas on PSID-like CSV) → matplotlib plots of routine task decline → GRADE verification → output: Verified replication with 95% R² match.

"Draft SBTC review paper with task model LaTeX equations"

Research Agent → citationGraph → Synthesis → gap detection → Writing Agent → latexEditText (Acemoglu-Autor framework) → latexSyncCitations (10 key papers) → latexCompile → output: Compiled PDF with cited equations and bibliography.

"Find GitHub repos analyzing China shock labor data like Autor 2013"

Research Agent → paperExtractUrls (Autor Dorn Hanson 2013) → Code Discovery → paperFindGithubRepo → githubRepoInspect → output: 5 repos with replication code, do-files for import exposure regressions.

Automated Workflows

Deep Research workflow scans 50+ SBTC papers via searchPapers → citationGraph → structured report ranking by GRADE scores (e.g., Autor 2001 tops). DeepScan's 7-steps verify task models: readPaperContent → runPythonAnalysis on wage data → CoVe checkpoints. Theorizer generates extensions to Acemoglu-Restrepo (2019) automation framework from literature contradictions.

Try Doxa for Skill-Biased Technological Change Research

Frequently Asked Questions

What defines Skill-Biased Technological Change?

SBTC is technological progress raising relative demand for skilled workers, increasing wage gaps (Autor, Levy, Murnane, 2001).

What are main methods in SBTC research?

Task-based models classify occupations by routine vs. non-routine skills; regressions link tech exposure to wage premiums (Acemoglu, Autor, 2010). Firm-level panels instrument trade shocks (Autor, Dorn, Hanson, 2013).

What are key SBTC papers?

Foundational: Autor et al. (2001, 2626 cites) on skill content; Acemoglu-Autor (2010, 2192 cites) on tasks. Critiques: Card-DiNardo (2002, 1679 cites).