iiinova — where your digital self keeps evolving

David Van Dijcke @packlesshepherd.bsky.social · 01/10/2026

Apply! I just joined and everyone is so friendly. Beautiful town and campus too!

Reposted by David Van Dijcke

UVA Economics @uvaeconomics.bsky.social · 25/09/2026

Forthcoming in the Review of Economics and Statistics, by our colleague David Van Dijcke (@packlesshepherd.bsky.social). After RTO mandates at three large U.S. tech firms, senior employees left at higher rates, shifting the workforce toward junior employees. Paper: direct.mit.edu/rest/article...

direct.mit.edu

Return to Office and the Tenure Distribution

Abstract. Debates over return-to-office mandates have intensified since the COVID-19 pandemic ended, though their economic implications remain poorly understood. Using large-scale resume data, we anal...

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David Van Dijcke @packlesshepherd.bsky.social · 09/06/2026

Alternatively, you can click "Discuss with your AI", which hands off all the context you need to continue chatting with your subscription of choice. Have fun chatting! (3/3)

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David Van Dijcke @packlesshepherd.bsky.social · 09/06/2026

When you click it, a side screen opens up where you can chat with an AI model of choice. Since we don't store your API keys, you'll need to briefly reauthenticate through OpenRouter if you restarted the review session. You pay for the API costs directly, no hidden costs. (2/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/06/2026

You can now talk to your coarse.ink review! Every review comment now has a "Discuss" button that lets you discuss it with an AI model of choice. The model gets all the context from the review, which lets you seamlessly continue the discussion. (1/)

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David Van Dijcke @packlesshepherd.bsky.social · 15/04/2026

Interesting, thanks for the review (pun intended). I was thinking of adding a deep literature search option -- currently it does a simple one through @perplexityai.bsky.social, but at some additional cost that can be upgraded to a deep lit search. Do you think that'd be valuable?

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David Van Dijcke @packlesshepherd.bsky.social · 11/04/2026

The project's spirit is very close to that of @chicagohai.bsky.social @chenhaotan.bsky.social: AI-assisted reviewing should be open and not for profit. Go check out their AI reviewer @ openaireview.org/index.html as well! If it's better, use that one, or submit proposals to create the best of both!

openaireview.org

OpenAIReview — AI-Powered Academic Paper Reviewer

Rigorous AI review to help you publish your best work.

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David Van Dijcke @packlesshepherd.bsky.social · 11/04/2026

Again, the whole system is open-source (github.com/Davidvandijc...) so there are no hidden costs, and you can check what the system does by looking at the source code. My hope is that many people will contribute code and submit issues to make this open system as good as possible!

github.com

GitHub - Davidvandijcke/coarse

Contribute to Davidvandijcke/coarse development by creating an account on GitHub.

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David Van Dijcke @packlesshepherd.bsky.social · 11/04/2026

As the underlying engine, you can choose among any AI model that's on @openrouter.bsky.social (which is nearly all models!). Less budget? Use any open-source model for <$1! For the best results, use SOTA models like Claude Opus 4.6, GPT-5.4, etc., at slightly higher cost.

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David Van Dijcke @packlesshepherd.bsky.social · 11/04/2026

Setup is easy: coarse.ink/setup Create an account at @openrouter.bsky.social , buy a couple $$ worth of credit, and obtain your API key. You can also log in directly with your OpenRouter account.

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David Van Dijcke @packlesshepherd.bsky.social · 11/04/2026

Reviews remain active for 90 days None of your data is stored beyond that, and all calls to whichever AI model you use are done with an explicit denial of data collection, so Claude/Gemini/ChatGPT/... cannot retain your data or use it for training See: coarse.ink/privacy

coarse.ink

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David Van Dijcke @packlesshepherd.bsky.social · 11/04/2026

The review is provided in an interactive panel with major and minor comments that you can trace in the text and tick off when done, see an example here coarse.ink/review/79c43...

coarse.ink

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David Van Dijcke @packlesshepherd.bsky.social · 11/04/2026

Some details on the performance comparisons to other popular systems are here coarse.ink/compare These are all judged by Gemini-3.1-pro, so caveats apply. Anecdotally, some of my colleagues and friendly ppl on X said it rivals reviews from Refine.Ink, when using a SOTA model.

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David Van Dijcke @packlesshepherd.bsky.social · 11/04/2026

I coded up an open-source, not-for-profit AI paper reviewer that rivals the performance of @reviewer3com.bsky.social, Refine.ink, and Stanford Agentic Reviewer (according to Gemini 3.1). Costs <$2! Live @ coarse.ink. Plug in paper, @openrouter.bsky.social key, and email. #econsky

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David Van Dijcke @packlesshepherd.bsky.social · 27/09/2025

Another "good" week on the US academic job market #econsky #econbluesky davidvandijcke.com/joe_tracker/

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David Van Dijcke @packlesshepherd.bsky.social · 20/09/2025

US academic economics market continuing to pull away from the COVID market this week. I created a little tracker for JOE here for those who want to play around with the data. Updates weekly: davidvandijcke.com/joe_tracker/ #econbluesky #econsky

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

The paper integrates causal inference, functional data analysis, and optimal transport, developing (FAST!) new tools for empirical researchers. If you use micro data or focus on inequality effects, I’d love to discuss potential applications! #EconTwitter (11/11)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

The method opens up a large new class of RDDs for distributional policy evaluation. E.g.: local minimum wage impacts on wage distributions, district ed reforms on grade distributions, close elections on constituent outcomes... For more details: (10/) arxiv.org/abs/2504.03992

arxiv.org

Regression Discontinuity Design with Distribution-Valued Outcomes

This article introduces Regression Discontinuity Design (RDD) with Distribution-Valued Outcomes (R3D), extending the standard RDD framework to settings where the outcome is a distribution rather than ...

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

The results suggest a classic "equality-efficiency tradeoff" under Democratic governors: Incomes at the top 10% of the distribution drop significantly, but this effect weakens and becomes statistically imprecise lower down the distribution. (9/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

Finally, I illustrate the method's use in a close-election RD (or rather, R3!) design. I study how Democratic vs Republican governors affect families' income distributions within their states when they barely won/lost their election. (8/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

I validate both methods through extensive simulations, which show rapid convergence to the quantile treatment effects... ...unlike existing quantile RD methods, which do not converge (but remain useful in the classic setting!) (7/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

I develop uniform confidence bands and data-driven bandwidth selection for both approaches, which are fully implemented in an R package (available at davidvandijcke.com/R3D). (6/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

To estimate this unknown beast, I propose two closely related estimators. One extending local polynomial regression to random quantiles, and a functional version of that, based on local Fréchet regression (which has better mathematical and computational properties). (5/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

I propose a new concept of treatment effects for R3D: Local Average Quantile Treatment Effects. Instead of averaging over conditional scalar outcomes, they average over conditional distributions! This captures the average distributional shift across the cutoff. (4/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

Standard RD methods can't handle such settings, as they don't account for the two levels of randomness—within and across distributions. R3D solves this problem by modeling outcomes as random distributions rather than random variables! (3/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

The method is useful when aggregate units receive treatment, but your outcome varies within the unit. E.g., firms receive a subsidy when their revenue (X) drops below a cutoff, and you want to study this subsidy's effect on the employee wage distribution (2/)

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David Van Dijcke @packlesshepherd.bsky.social · 09/04/2025

Hi Bluesky! I'm excited to share my job market paper (for the 2025-26 market)! It introduces a new extension of RDD where outcomes are entire distributions: Regression Discontinuity Design with Distributions (R3D). Thread below 👇 (1/)

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