Data and Code for: Field Specializations among Beginning Economists: Are there Gender Differences?

Principal Investigator(s): View help for Principal Investigator(s) Eva Sierminska, LISER; Ronald L. Oaxaca, University of Arizona

Version: View help for Version V1

Name	File Type	Size	Last Modified
Multivariate_data_v3.dta	application/x-stata-dta	2.5 MB	04/29/2021 09:31:AM
README.txt	text/plain	465 bytes	04/29/2021 09:47:AM
program for AEA paper.do	text/plain	6.3 KB	04/29/2021 09:31:AM

Project Citation:

Sierminska, Eva, and Oaxaca, Ronald L. Data and Code for: Field Specializations among Beginning Economists: Are there Gender Differences? Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-05-05. https://doi.org/10.3886/E139282V1

Project Description

Summary: This is data and/or code accompanying the article in which we model the process of field specialization choice among beginning economists within a multivariate logit framework that accommodates single and dual primary field specializations and incorporates correlations among field specialization choices. We investigate whether gender differences in fields are driven by economic or by non-economic factors. We add field specific monetary components (salaries) and expected probabilities of academic employment, which is an additional novel aspect of this research. Conditioning on personal, economic, and institutional variables reveals that women graduate students are less likely to specialize in Labor/Health, Macro/Finance, Industrial Organization, Public Economics, and Development/Growth/International and are more likely to specialize in Agricultural/Resource/Environmental Economics.

Funding Sources: Alfred P. Sloan Foundation (G-2018-11112)

Scope of Project

Subject Terms: economics; gender; specialization

JEL Classification:
      J01 Labor Economics: General
      J16 Economics of Gender; Non-labor Discrimination
      J31 Wage Level and Structure; Wage Differentials

Geographic Coverage: United States

Time Period(s): 2009 – 2018

Universe: individuals that have completed their PhD in Economics

Data Type(s): survey data

Collection Notes: Data and program code for replication

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Published Versions

V1 [2021-05-05]

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