Research Resources

General

• Kagi — a paid ad-free search engine with bells and whistles
• Obsidian — take notes
• Bear — take notes
• Overleaf — collaborative LaTeX
• Mermaid — draw diagrams
• Airtable — spreadsheet/database mash-up
• Toggl — tracking your time
• Zapier — automating stuff
• Julia Evans on How to Ask Good Questions
• Trey Causey’s Do You Have Time for a Quick Chat?
• Chicago Booth Clark Center Panels — “Chicago Booth’s Kent A. Clark Center for Global Markets has assembled and regularly polls three diverse panels of expert economists…”
• VoxDevLits — “VoxDevLits are living literature reviews that summarise the evidence base on policy-relevant topics related to development economics in an accessible manner.”
• Ungated Research — “provide[s] access to all publicly available working papers for research in leading economics journals in one place.”

Coding and Data

• [book] R for Data Science 2e
• [book] Kieran Healy’s The Plain Person’s Guide to Plain Text Social Science
• Quarto – “An open-source scientific and technical publishing system”
• RStudio Desktop IDE
• Positron on GitHub IDE
• GitHub – version control
• GitLab – version control
• [paper] Karl Broman & Kara Woo on Data Organization in Spreadsheets
• [paper] Hadley Wickham’s Tidy Data
• Julia Evans’ Oh Shit, Git! zine

Methods and Stats

• [article] How are econometric methods applied by researchers in development economics? | VoxDev Blog
• [book] Ethan Bueno de Mesquita & Anthony Fowler’s Thinking Clearly with Data
• [book] Joshua Angrist & Jörn-Steffen Pischke’s Mastering ’Metrics
• [book] Joshua D. Angrist & Jörn-Steffen Pischke’s Mostly Harmless Econometrics
• [book] Aki Vehtari, Andrew Gelman, & Jennifer Hill’s Regression and Other Stories
• [paper] Andrew Gelman, Aki Vehtari and others on Bayesian Workflow
• [book] Jeffrey Wooldridge’s Introductory Econometrics: A Modern Approach
• [book] Nick Huntington-Klein’s The Effect
• [book] Scott Cunningham’s Causal Inference: The Mixtape
• [paper] Susan Athey & Guido W. Imbens’ Machine Learning Methods That Economists Should Know About
• [book] Dani Rodrik’s Economics Rules
• [paper] Angus Deaton & Nancy Cartwright’s Understanding and Misunderstanding Randomized Controlled Trials
• [book] Graeme Blair, Alexander Coppock, & Macartan Humphreys’ Research Design in the Social Sciences
• [paper] Sayash Kapoor and others REFORMS: Consensus-based Recommendations for Machine-learning-based Science
• [book] Chester Ismay and Albert Kim’s ModernDive Statistical Inference via Data Science in R
• [book] Geocomputation with R — “a book on geographic data analysis, visualization and modeling.” by Robin Lovelace, Jakub Nowosad and Jannes Muenchow. See also the geocompx project for resources in R, Python, and Julia
• [paper] Natalie Ayers, Gary King and others: Statistical Intuition Without Coding (or Teachers)
• Seeing Theory — interactive stats 101 visualizations
• Common statistical tests are linear models (or: how to teach stats) by Jonas Kristoffer Lindeløv
• IZA’s methods write-ups
• Evidence in Governance and Politics (EGAP) Methods Guides
• The World Bank’s Curated List on Technical Topics
• J-PAL’s Research Resources

Data Collection

• J-PAL’s resource on survey programming (small contribution by me)
• SurveyCTO see also the free Community Subscription
• J-PAL’s Repository of measurement and survey design resources
• Surveying Young Workseekers in South Africa (contribution by me) | Southern Africa Labour and Development Research Unit (SALDRU) blog

Dataviz

• From data to viz — “…leads you to the most appropriate graph for your data. It links to the code to build it and lists common caveats you should avoid.”
• [free] RawGraphs
• [free] Tableau (public)
• Datawrapper
• Flourish
• Data Visualization Society
• J-PAL’s resource on data visualization (small contribution by me)
• Data by Design — “An Interactive History of Data Visualization”
• Frank Elavsky’s Chartability “…is a set of heuristics (testable questions) for ensuring that data visualizations, systems, and interfaces are accessible”

LLMs

• See my post on LLMs for a general audience
• GPTs
  • Claude
  • ChatGPT
  • Meta AI’s llama-3 or chat here
  • Mistral
  • Gemma
  • WizardLM
• Some prompting patterns
• Google’s NotebookLM
• The Financial Times’ piece Generative AI exists because of the transformer
• Transformer Explainer — “an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model” by Aeree Cho, Grace C. Kim, Alexander Karpekov and others
• [video] Andrej Karpathy’s LLMs for busy people
• Ted Chiang’s ChatGPT is a blurry JPEG of the web
• [paper] Michael Townsen Hicks, James Humphries & Joe Slater’s ChatGPT is bullshit
• Worth reading re copyright law: The New York Times’ case against Microsoft and OpenAI
• Jaron Lanier’s How to Picture A.I.
• [paper] Murray Shanahan’s Talking About Large Language Models — careful of anthropomorphizing LLMs, or assigning intent, it may affect how successful you are at using them
• [paper] Emily Bender, Timnit Gebru et al. On the Dangers of Stochastic Parrots
• Simon Willison’s Prompt injection and jailbreaking are not the same thing — Simon discusses some of the ways in which LLMs can be vulnerable
• Rohit Krishnan’s What can LLMs never do?
• An interesting read on the process of fine-tuning an LLM’s “character” by Anthropic: Considerations in constructing Claude’s character. I think Patrick House’s article is a great complement: The Lifelike Illusions of A.I
• Simon Willison’s Think of language models like ChatGPT as a calculator for words — a nice metaphor for LLMs, see also “the weird intern”
• Simon Willison’s Embeddings: What they are and why they matter
• Vicki Boykis’s list of “no-hype” reads on LLMs — great readings on the fundamentals of LLMs and more
• LLMs in production: Hamel’s “Your AI product needs evals” — relevant for when you are starting to think about the effect of your prompt tweaks on your outputs, and whether you are making things better or worse
• The What We Learned from a Year of Building with LLMs series by Eugene Yan, Bryan Bischof and others

NLP

• [book] Emil Hvitfeldt and Julia Silge’s Supervised Machine Learning for Text Analysis in R
• [book] Julia Silge and David Robinson’s Text Mining with R
• The STM R package for structural topic modelling
• The textnets R package
• The Quanteda R package for working with text data
• BERTopic — python package for topic modelling with embeddings
• See spaCy and some of their demo projects for stuff like text categorization or custom Named Entity Recognition (NER)

Courses

• Harvard’s CS50: Introduction to Computer Science
• Harvard’s CS50: Introduction to R
• Grant McDermott’s Data science for economists
• EconDL — “comprehensive resource detailing applications of Deep Learning in Economics.”

Writing

• Benjamin Dreyer’s Dreyer’s English: An Utterly Correct Guide to Clarity and Style — see also Katy Waldman’s review in the New Yorker The Hedonic Appeal of “Dreyer’s English”
• Verlyn Klinkenborg’s Several Short Sentences About Writing
• The Chicago Manual of Style, 18th Edition

Fitness

• [book] Casey Johnston’s “LIFTOFF: Couch to Barbell”
• Megan Gallagher’s “Stronger by the Day”
• Macrofactor — nutrition
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