Creating Smarter Defaults in Medicaid
Faculty sponsor(s):

Chima Ndumele and Jacob Wallace

Project Description: 

We are seeking a pre-doctoral fellow to work with us on a research project that aims to
improve efficiency in the Medicaid program through the creation of smarter defaults for
the automatic assignment of beneficiaries to health plans. This research exploits quasi-
random assignment of beneficiaries to health plans to generate causal estimates of plan
performance across different domains (e.g. spending and health care quality) as well as
estimates of beneficiary satisfaction. These estimates are then used as inputs into
algorithms that seek to improve efficiency (e.g. minimize spending without harming
satisfaction) by assigning beneficiaries to the correct plan, as opposed to any plan at
random. This project will involve analysis of large claims databases, estimating models,
solving complex constrained optimization problems, creating presentations, and helping
to prepare manuscripts. Fellows may have the opportunity to co-author papers with the
research team and will regularly interact with faculty inside and outside of Yale. We
encourage our fellow to take classes while they are at Yale and participate in various
economics seminars happening across campus. The fellow will also receive dedicated
research training and career development skills with their Tobin pre-doctoral fellow

Requisite Skills and Qualifications:

Candidates should have a long-term interest in pursuing economics-related research
and be completing or have completed a Bachelors or Master’s degree. The ideal
applicant will have strong programming skills and experience working with health care
claims data. Preference will be given to detail-oriented applicants with previous
research and programming experience, particularly in working with large datasets, in
either Python, R, or Stata. We also encourage individuals with previous experience in
economic consulting to apply. 

Special Application Instructions: