Gender Bias in Econometric Society Fellowship Elections

Faculty Member: 

Award:
Emily Wang

Proposal Description:

The project goal is to evaluate the impact of gender on Econometric Society (ES) fellowship elections. In collaboration with Professor Petra Todd (U. of Pennsylvania) I will study how gender influences who is nominated and who is elected conditional on nomination. The initial tasks are as follows:

1. Assemble a new database on individuals who have some chance of being nominated. It will include all individuals that meet some specified criteria (such as having some minimum number of publications in top journals). This step will involve processing a large database available through EconLit. It contains entries for every article published in the leading journals in economics. It will also involve processing SCOPUS, a second large database that contains information about the publications and employment histories of many scholars. The work will involve use of Excel and an appropriate programming language. I am not sure which one is best yet.

2. Provide a descriptive analysis of the Econometric Society database. This database pertains to individuals who were nominated, either through the external nominations process or internally by the ES fellows committee, during the years 2005-2015. One goal is to identify common characteristics of nominees that can be used to help define a pool of potential nominees.

3.Use the Econometric Society database to compare the publication records and other characteristics of male and female nominees. Implement “outcomes based” tests of gender bias in nominations and in elections using citations and other publication-based metrics as measures of quality.

Note that my project proposal is currently under review by the Econometric Society. I expect it to be approved, but there is a possibility that it will not be. If it is not approved, then the student will work with me on a study of the value of graduate education. See my other Scarf RA project.

Requisite Skills and Qualifications:

Prior computing experience using a language such as Python and a course or courses in statistical computing will be extremely valuable. My hope is that the student will continue to work with me on the project during the fall 2020 semester.