The Long-Run Impact of Early Childhood Education
Faculty Supervisor(s):

John Eric Humphries and Seth Zimmerman 

Project Description:

Over the past few decades, many states have increased funding for early childhood education (ECE) and expanded access to subsidized ECE programs. Advocates of further expansion call for free, universally available pre-kindergarten at the state or federal level. The argument in favor of ECE expansion is that it can improve later educational outcomes, address growing inequality, and reduce the burden childcare places on families, and that these benefits exceed the costs of program provision.

This project addresses key gaps in the evidence on ECE through lottery-based evaluation of the effects of free pre-kindergarten on long run educational, labor market, and benefits receipt outcomes for children and their parents.

The pre-doc hired for this position would work closely with Professors John Eric Humphries, Seth Zimmerman, and their coauthors. As part of this project, the pre-doc will have the opportunity to develop a broad array of research-related skills. They will work with administrative records, work on conducting causal analysis, and contribute to the drafting of manuscripts. The researcher will be involved in all parts of the research process.

Requisite Skills and Qualifications:

We are looking for candidates that are excited about research, working with big data sets, causal methods, and working with a research team. Methodological interests in labor economics, public economics, the economics of education, econometrics, machine learning, or statistics are also a big plus.

Applicants should be completing or have completed a bachelor’s or master’s degree. A major in economics is not required but an interest in economics as well as coursework in economics and econometrics is. The ideal candidate will have a strong interest in economics, will be detail-orientated, will enjoy working with data and will be enthusiastic about problem-solving. Prior coding experience in a statistical language (especially R and Stata) is essential. Prior experience with Latex, Julia, python, and other programming languages is also a plus.

Special Application Instructions: