Paul Clist

@paulclist.bsky.social

Development Economist @ UEA, UK Experiments, Language, Risk, Lying, Development Aid, Tax

I've been down on and critical of development economics in recent years (see marcfbellemare.com/wordpress/14... and marcfbellemare.com/wordpress/14...), but idea.devecon.org, by Dean Yang et al., looks awesome *and so much more inclusive than anything that came before.* Kudos to a great initiative!

International Development Economics Association

IDEA is a global professional association advancing inclusive, rigorous research in development economics.

idea.devecon.org

New working paper with Amol Singh Raswan and Chris Udry: "The Sisyphean Pursuit of Evidence for Poverty Traps." A central idea in development economics is that poverty can trap people. We went looking for the cleanest evidence. Here's what we found – and didn't.

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Rats are virtually everywhere humans live. Except for Alberta, Canada. Most residents there have never even seen one. How did they eliminate a pest we take as a fact of life? By waging war.

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Noah’s subhead for this piece of his post: “Borjas again”. Failed replications are embarrassing, but they happen. However, they need to be not too common, and they very much need to not always lean the same way. Then they are not inadvertent errors or “mistakes”: they are violations of the... 1/

I spent a few months trying to get proficient in DAGs. I learned a few things about myself: -Thinking in terms of DAGs is not intuitive to me, even though it is visual -I rarely found that DAGs of complex phenomena helped me understand them. -I see the potential, but am on the outside looking in.

In the Fall I'll be teaching a new MA-level methods course entitled "Applied Statistical Evaluation of Development Projects". It will be 12 weeks, in R, and aimed around RCT evaluations. This is a draft outline. What am I missing? What seems redundant?

1. Course Introduction and Setup
 Course overview; installing RStudio; introduction to causal inference; ModernDive Chapters 1–2 for newcomers.
2. Data, Tidy Data, Wrangling, and Visualization
 Core R skills for importing, cleaning, reshaping, summarizing, and visualizing evaluation data.
3. Sampling, Uncertainty, and Inference 
Sampling variation, confidence intervals, hypothesis testing, and the logic of statistical uncertainty.
4. Difference in Means as Regression 
Equivalence between difference-in-means estimates and lm(y ~ treat); ATE as the treatment coefficient; control mean as the intercept; covariates for precision gains; simulations and re-analysis of Karlan–List charity data.
5. Interactions and Treatment Effect Heterogeneity 
Interaction terms, subgroup analysis, heterogeneous effects; simulations, Karlan–List charity data, and Thornton HIV data.
6. Standard Errors, Power, and Research Design 
Bias, variance, RMSE, clustering, power analysis, and how underpowered studies contribute to selection on significance and inflated estimates.
7. Noncompliance, Take-Up, and Instrumental Variables 
ITT, TOT, LATE, compliers etc, and randomized encouragement designs; Thornton HIV testing incentives; reading from The Effect Chapter 19 or Causal Inference: The Mixtape IV chapter.
8. Spillovers, Externalities, and Peer Effects
 How spillovers can bias experimental estimates; identifying, measuring, and interpreting spillover effects in development evaluations.
9. Pre-Analysis Plans, Measurement, and Cost-Effectiveness
 PAPs, outcome measurement, measurement error, index construction, and basic cost-effectiveness analysis.
10. Meta-Analysis and Evidence Aggregation 
Fixed-effect and random-effects meta-analysis; Bayesian meta-analysis using baggr; interpreting accumulated evidence across studies.
11. Case Study: Deworming Evidence I
 Critical re-analysis of the main deworming results; statistical interpretation; cost-effectiveness implications.

🚨Replication alert🚨 I'm pleased to announce that my replication of Moretti (2021) is now accepted as a comment at AER. I find ten issues in the paper. My comment focuses on two major problems; in the appendix, I document eight (relatively) minor problems. 1/

It's ironic to see a discipline care **so much** about unbiasedness (causal inference!) at the level of a single test but then have a research production system and culture that is basically a ferocious bias generation machine. This is not good.

‘Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition… We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average.’ arxiv.org/pdf/2601.20245

Abstract
Al assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of Al.
We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that Al-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation - particularly in safety-critical domains.

Who leaked this Number 10 discussion to Jeffrey Epstein? And are there consequences for the leaker? It’s an internal discussion re. getting markets moving in the aftermath of the financial crisis. No doubt of great interest to Epstein and his financial market clients.

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🆕 There's a growing body of evidence on the common misperceptions people have about the world. And it turns out that, across a bunch of different settings, correcting those misperceptions seems to be a very cheap way of improving society. Here are some examples: voxdev.org/topic/common...

Common misperceptions: What people get wrong about the world and why it matters

What do people get wrong, i.e. misperceive, about the world? Why do misperceptions matter for economic development? How can fixing misperceptions benefit society?

voxdev.org

Check out our new VoxDevLit on International Migration! Thanks to co-editors @catiabatista.bsky.social, @econgaurav.bsky.social, @dmckenzie.bsky.social, @mushfiq-econ.bsky.social, & Caroline Theoharides! We look to working together on this "living literature review" in the years to come...

VoxDev@voxdev.bsky.social · 7mo ago

📢 Our new VoxDevLit on International Migration is out now! Senior Editor Dean Yang & Co-Editors Catia Batista, Gaurav Khanna, David McKenzie, Ahmed Mushfiq Mobarak & Caroline Theoharides review research on international migration. Read & download here: https://ow.ly/qXx950XWAar

These economists are unsurpassed in research on migration & development. Global authorities. Their new resource at @voxdev.bsky.social is a gift that will keep on giving —>

Dean Yang@deanyang.bsky.social · 7mo ago

Check out our new VoxDevLit on International Migration! Thanks to co-editors @catiabatista.bsky.social, @econgaurav.bsky.social, @dmckenzie.bsky.social, @mushfiq-econ.bsky.social, & Caroline Theoharides! We look to working together on this "living literature review" in the years to come...