Women in Finance Data
A Monitoring Tool
It is a well-known fact that the proportion of women varies widely between different professions. Women’s careers differ from men’s for many reasons, such as preferences, networks, norms, children, and discrimination. The share of women also varies between different areas within academia and even within the field of economics. Unfortunately, financial economics seems to be one of the subfields that attracts the least women, see e.g. Lundberg and Stearns (2019) and Friebel, Fuchs-Schündeln, and Weinberger (2021).
To increase the knowledge about the representation of female researchers in finance, we have gathered information from leading research institutions by collecting data that are publicly available on the institutions' websites. Our objective is to facilitate fact-finding and induce action by publishing reliable data.
This website draws exclusively on previous work by Guido Friebel, Alisa Weinberger, and Sascha Wilhelm at Goethe University in Frankfurt. They have developed a web-scraping tool that identifies individuals listed on their employers’ websites and records the position description and name titles of these individuals. Gender is identified through first names and a gender identification software analyzing pictures. The technology is explained in detail in Friebel and Wilhelm (2019), and their work is presented on the Women in European Economics website.
Our dataset differs from that on the Women in European Economics website as we only study researchers in financial economics. We collect information from the global top 100 and European top 50 research institutions according to the UT Dallas Business School Rankings based on publications in the JF, JFE, and RFS in 2018–2020. The sample covers 133 universities and business schools from 20 countries. Researchers are included in the sample if they belong to a finance department, finance research area/group/division, or if the institution otherwise provides information from which it can be retrieved whether the researchers are finance researchers.
Based on the scraped position descriptions and name titles, we map all researchers to a hierarchical level. Since every country has its own non-standardized terms for its levels, we map positions into a general hierarchy of positions: (1) Full Professor, (2) Associate Professor, (3) Assistant Professor, (4) Lecturer, (5) Research Fellow, and (6) Research Associate, see Friebel and Wilhelm (2019) for details.
We have done our best to assure the quality of the data, not only by manual checks, but also by asking the people responsible for the respective institution to verify the information we collected. About 70 percent of the institutions responded to our request. Despite this, we cannot completely rule out that there are errors for individual institutions. Corrections or other input are very welcome.
We present our data on the country level, institutional level, and hierarchical level. No personal data are made available on this website.
The data on this website is managed by the Swedish House of Finance at the Stockholm School of Economics. No external parties are liable for the data.
The data may not be used for any purpose other than statistical analysis. Use of this data to learn the identity of any person is prohibited.
Read about compliance with the GDPR here.
We would like to thank all our colleagues for encouraging comments during the data collection process, and we are very grateful for having the opportunity to use the infrastructure developed by our colleagues at Goethe University. Finally, we would like to extend a special thank you to Alisa Weinberger for excellent project management. Without Alisa, this dataset would not have existed.