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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Boundary Discontinuity Methods and Policy Spillovers

Published in Journal of Public Economics (2024), 2024

Estimated spillover impacts from local minimum wage increases on wages and hours are statistically significant and geographically diffuse, creating concern for standard boundary discontinuity estimates even using non-adjacent control regions.

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Paper Title Number 4

Published in GitHub Journal of Bugs, 2024

This paper is about fixing template issue #693.

Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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AI Competence as Human Capital

Published in , 2025

Generative AI can raise productivity, but realized gains depend on how workers use the tool. We study AI competence as a form of human capital defined by the practical ability to organize work with a model, evaluate its output, and retain judgment while using it to extend one’s existing skills. In a preregistered lab-in-the-field experiment, 332 full-time management consultants whose jobs did not routinely require coding were assigned a difficult Python data-analysis task with or without access to a Gen AI tool. AI access increased scores by 34 percentage points, raised completion by 7 percentage points, reduced time on the task by 12%, and improved debugging efficiency. We open the black box of these gains by combining Gen AI transcripts, code-execution logs, task outputs, and surveys. Using an ethnography-inspired agentic coding procedure, we identify nine distinct AI-collaboration practices that capture how workers frame requests, decompose problems, rely on generated code, edit independently, verify outputs, and manage scope. These practices explain substantial heterogeneity in overall performance. Gains concentrate when workers engage proactively with AI while retaining substantive judgment. Finally, we show that productivity gains and workers’ interpretation of those gains are distinct. AI access does not significantly increase average confidence, trust, enjoyment, or behavioral trust in AI, and belief responses vary substantially by gender and prior coding experience. The results suggest that firms should treat AI training as human-capital investment, teaching workers not only how to prompt, but how to divide labor with AI, evaluate its output, and build confidence in using it well.

Generative AI and Labor Market Matching Efficiency

Published in , 2025

Employers randomly offered AI-written job post drafts were 19% more likely to post, but there was no increase in matches — wasting jobseeker time. Per-post loss to jobseeker welfare is six times larger than the gain to employer welfare from time saving.

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Why You Shouldn’t Treat AI Agents Like Employees

Published in Harvard Business Review (2026), 2026

As organizations experiment with placing AI agents on org charts as “employees,” new research shows this framing has unintended consequences. In a large-scale experiment, anthropomorphizing AI reduced individual accountability, increased unnecessary escalation, lowered review quality, and heightened employee uncertainty about their roles—without improving adoption. The findings suggest the core challenge is not whether to deploy agentic AI, but how to redesign workflows, roles, and governance so humans remain clearly accountable while effectively supervising increasingly capable systems.

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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.