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Labor Dynamics Institute

Abstract netting in green and red

Our mission is to create and make accessible novel data on the dynamics of the labor markets, we work with research networks and statistical agencies, developing appropriate statistics to inform policy makers, researchers, and simply people seeking knowledge. We emphasize and meet the requirements of stakeholders: users as well as providers, balancing the utility of the data with the confidentiality of the people and businesses whose activities the data describe.

FILLED - LDI Replication project hiring Undergraduate Researchers for Fall 2021

Labor Dynamics Institute

This posting is now closed. Please check back in December for next application window.

We are looking for up to 10 students to work on the LDI-based AEA Data Editor team as undergraduate research interns.

  • Contact: Send your CV to ldi@cornell.edu, mentioning "LDI Replication Lab".
  • Remuneration: An hourly rate commensurate with experience will be offered. $13.00 per hour, up to 10 hours per week while in session; up to 20 hours per week during the summer.
  • Goal: Ensure that supplementary materials for articles in a journal with a replication policy are (a) accessible (b) reproduce the intended results, (c) document results and findings.

Work description

The American Economic Association (AEA) monitors compliance with its Data and Code Availability Policy, under the leadership of the AEA Data Editor. LDI Replcation 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).

Duration

This is ongoing work, and conditional on satisfactory work, continued employment (until graduation) is possible and desirable. Student status with Cornell is required. 

Training will take place in August prior to employment. Training schedule is posted at https://labordynamicsinstitute.github.io/replicability-training/.

Necessary qualifications

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. Applicants must be current Cornell students, residing in the United States.

Requirement

Training is required as a condition of hiring. While employed, attendance (via Zoom) at two weekly meetings is required.

Training will take place on August 20th and 23rd (see schedule at https://labordynamicsinstitute.github.io/replicability-training/). Live attendance should be expected at the posted times, plus some significant self-paced work. Successful trainees will transition to the actual "replicator" activity as soon as adequate skills are demonstrated. Our training success and post-training retention rate is above 90%. Once trained, you will be able to work flexibly, taking into account exams, summer jobs, and other study constraints.

Students are not paid during training. Training is free, but participation may be limited to job candidates.

Location

Day-to-day presence on campus is not required for the actual work (all computer work will be performed on remotely accessible servers), but student workers must be located in the United States due to Cornell policy. Videoconference presence for two weekly meetings is required. Occasional absences are acceptable, but should be an exception.