Econ Project Templates: Modern, Reproducible Research in Economics - Templates for Reproducible Research Projects in Economics

Why Reproducibility Matters¶

We are in the midst of a reproducibility crisis in the social sciences. Journals like the AER now mandate rigorous data and code availability policies, and the days of “trust me, I ran the regression” are over. But beyond compliance, reproducibility is about your peace of mind. It means knowing that if you find a mistake in your raw data three days before submission, you can re-run your entire project—tables, figures, and paper—with a single command. It means your future self (and your co-authors) can understand and run your code on a new machine without spending a week fighting dependency hell.

The Solution¶

This template is a “batteries-included” starting point for professional economic research. It replaces fragile, manual workflows with a robust, automated pipeline. By combining Pixi for hermetic environment management, Pytask for workflow automation, and MyST for integrated writing, we provide a structure where code and text live in harmony. You don’t build an environment; you define it. You don’t “paste” results into LaTeX; you generate a dynamic document. This project gives you the tooling of a senior software engineer with an interface designed for economists.

Navigating this Documentation¶

We have structured our documentation to get you working immediately, regardless of your experience level.

Getting Started (Start Here): This is the “magic” section. We show you how to install one tool (pixi) and run one command to build the entire paper and presentation. Read this first to verify your system is ready.
Background (Under the Hood): For those who want to understand the design choices and architecture. Here we explain why we use Directed Acyclic Graphs (DAGs), how the workflow is structured, and the philosophy behind our folder structure.
Guides and Explanations (How-to Guides): For the researcher in the middle of a project. When you need to know “How do I add a new Python package?” or “How do I start from scratch?”, look here. These are short, recipe-style answers.
Programming Languages: While the example focuses on Python, we explain how to integrate other languages like R, Julia, or Stata.
Development: Information on how to contribute to this project.