The Labor Dynamics Institute has four goals: [1] study and improve labor market outcomes of workers through digital tools; more generally [2] improve access to and understanding of data sources for the study of the labor market; and improve the [3] transparency and accessibility of economics research to enhance its credibility. We have added an additional dimension in 2025, [4] connecting researchers in economics to each other in more effective ways.

The American Economic Association (AEA) monitors compliance with its Data and Code Availability Policy, under the leadership of the AEA Data Editor. LDI Replication Lab members will access pre-publication materials provided by authors, and assess how well these materials reproduce the results published in the manuscript or article. The provided materials and instructions will be assessed using a checklist. Authors’ instructions will be followed (if possible), and success or failure to (i) perform the analysis (ii) replicate the authors’ results will be documented. Other related activities, such as literature search or tabulation of results, may also be assigned. Team work is encouraged, and activity will be supervised by graduate student or faculty member. Team members must be at ease working in various computer environments (Windows Remote Desktop, local laptops) and software tools (statistical software, Git).

Interns will learn and observe parts of the scientific publication process. They will learn and practice the details of the process of reproducibility checking, and will experience the challenges of ensuring that data and code are available and functional. At the end of their internship, they will have run and learned to debug code for multiple papers (typically around 5-6), reviewed output, prepared reports which will be read by senior economists throughout the world (after review by the Data Editor). They may encounter and learn about novel software and data sources, as well as how to run code on multiple platforms, including powerful Windows and Linux servers.
 

Some experience with empirical social science data analysis using statistical software is required. Knowledge of at least one of Stata, Matlab, R or SAS is required, as is familiarity with the Windows Desktop environment. Experience with Git and the command line (Linux, Mac, or Powershell) are assets.