Density-based outliers in labor market text act as leading indicators of new occupational clusters, with an extended Emerging Occupation Score predicting formation 2 quarters ahead at F1=0.74 on 84,988 postings.
and Levy, Frank and Murnane, Richard J
7 Pith papers cite this work, alongside 5,851 external citations. Polarity classification is still indexing.
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Pith papers citing it
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external citations · OpenAlex
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Using optimality conditions from the second-service rule and a structural model on tennis data, the paper shows players value process utility positively and systematically trade off outcome probabilities for it.
The authors propose a retrieval-augmented framework that grounds AI exposure labels for 18,796 O*NET occupation-task pairs in retrieved news and academic abstracts, outperforming zero-shot prompting in 72% of disagreements and aligning better with observed real-world usage.
Difference-in-differences analysis around ChatGPT release shows commoditization of labor in AI-exposed job categories on Upwork, with declining human capital importance and rising price importance.
AI-saturated markets will produce premiums for verified human presence in labor, requiring governance to treat human-provenance verification as infrastructure rather than optional authenticity labels.
Proposes GAGI, a publicly computable index adjusting GDP per capita for inequality and prices to monitor welfare-adjusted prosperity in G7 economies from 2010-2026.
Adopting AI does not guarantee productivity boosts due to five moderating factors (human resource composition, baseline capability, learning curve, incentives for fair use, and objective flexibility), by revising an existing economic model.
citing papers explorer
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Noise is Signal: Density-Based Outliers as Leading Indicators of Occupational Emergence in Labor Market Text
Density-based outliers in labor market text act as leading indicators of new occupational clusters, with an extended Emerging Occupation Score predicting formation 2 quarters ahead at F1=0.74 on 84,988 postings.
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Process Utility in High-Stakes Competition
Using optimality conditions from the second-service rule and a structural model on tennis data, the paper shows players value process utility positively and systematically trade off outcome probabilities for it.
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Jobs' AI Exposure Should Be Measured from Evidence, Not Model Priors
The authors propose a retrieval-augmented framework that grounds AI exposure labels for 18,796 O*NET occupation-task pairs in retrieved news and academic abstracts, outperforming zero-shot prompting in 72% of disagreements and aligning better with observed real-world usage.
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Human Capital, AI, and Labor Commoditization
Difference-in-differences analysis around ChatGPT release shows commoditization of labor in AI-exposed job categories on Upwork, with declining human capital importance and rising price importance.
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Human-Provenance Verification should be Treated as Labor Infrastructure in AI-Saturated Markets
AI-saturated markets will produce premiums for verified human presence in labor, requiring governance to treat human-provenance verification as infrastructure rather than optional authenticity labels.
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GAGI: A Gini-Adjusted GDP-per-Capita Index for Distribution-Aware Macroeconomic Welfare Monitoring
Proposes GAGI, a publicly computable index adjusting GDP per capita for inequality and prices to monitor welfare-adjusted prosperity in G7 economies from 2010-2026.
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Position: Adopting AI in Practice Does Not Guarantee the Productivity Boost
Adopting AI does not guarantee productivity boosts due to five moderating factors (human resource composition, baseline capability, learning curve, incentives for fair use, and objective flexibility), by revising an existing economic model.