I’m a big fan of transparency and replicability in research. If possible, I will do my best to make available the data and the code I am using in my projects. I typically do statistical analyses in R and everything else (e.g. text analysis, machine learning and web scraping) in Python.

With that said, my coding is mostly self-taught and I am not a trained computer scientist. This means that most of my code, while being (hopefully) serviceable, may be considered inefficient or inelegant by some. The upside of this is that my code is relatively accessible with a modest amount of familiarity with the coding language.

If you end up using some of the data for your own work, please cite the relevant publications as indicated here. If you find errors in the code, please let me know.

Designing Equitable Algorithms. With Alex Chohlas-Wood, Madison Coots and Sharad Goel

  • Replication Data & Code can be found on GitHub

Police agencies on Facebook overreport on Black suspects. 119 Proceedings of the National Academy of Sciences e2203089119 (2022). Ben Grunwald & John Rappaport

Contractual Evolution. 89 The University of Chicago Law Review (2022). With Matthew Jennejohn and Eric Talley

A Computational Analysis of Constitutional Polarization. with David Pozen and Eric Talley

Please note that the code below still requires thorough commenting and cleaning

  • Online Appendix (pdf-file):  Download
  • Word embeddings trained on the Congressional Record of the 43rd-114th Congress (bin-file): Download
  • Creating our data set from the CR (Python3-file): Download
  • Creating our data set on editorials (Python3-file): Download
  • Counting the frequency of terms from the Expansive dictionary (Python3-file): Download
  • Predict the speaker party / ideology for remarks in the CR (Python3-file): Download
  • Predict the speaker party / ideology for remarks in the CR after matching (Python3-file): Download
  • Predict the publisher of editorials (NYT vs WSJ) (Python3-file): Download
  • Create all plots and figures for the analysis of CR remarks (R-file): Download
  • Create all plots and figures for the analysis of editorials (R-file): Download
  • Create word clouds (Python3-file): Download

Giving the Treaty a Purpose: Comparing the Durability of Treaties and Executive Agreements. 113 American Journal of International Law 54 (2019)

  • Panel data of the agreements used in the analysis (csv-file): Download
  • Cross-sectional data of the agreements used in the analysis (csv-file): Download
  • Codebook including the description of the variables (txt-file):  Download
  • R code used to conduct all analyses, produce the tables and figures in the paper (R-file):  Download

Conforming Against Expectations: The Formalism of Non-Lawyers at the WTO. 48 The Journal of Legal Studies 341 (with Jerome Hsiang) (2019)

  • All WTO decisions as .html that were published before July 2015 (zip-file): Download
  • Code to extract precedent citation information from WTO decisions: Python 2, IPython 2 Notebook