GU-Q Faculty Fellowship

In order to enhance research opportunities for members of the university, CIRS ‎launched an annual fellowship to be awarded to a member of the GU-Q faculty. The ‎fellowship supports original research, and is awarded to ‎faculty members who propose to use the time granted to pursue high quality projects ‎with promising prospects for publication in leading journals or university presses. The ‎fellowship entails a one-course release from teaching per academic year, in either the ‎fall or spring semester, to a deserving member of the SFS-Q faculty to enable ‎concentration on a research project. ‎The selected fellow is appointed for one academic year, and is expected to be in ‎residence in Doha during the period of the fellowship. Fellows will be asked to give a ‎presentation of their research at a CIRS-sponsored talk, and, where possible, to ‎contribute to CIRS’s research agenda. ‎

CIRS GU-Q Faculty Fellow 2017-2018

Mongoljin Batsaikhan is an assistant professor of Economics at Georgetown University's School of Foreign Service in Qatar. He received his Masters and Doctorate in Economics from Brown University after his BA in Economics from the University of Tokyo. His research fields are Development and Experimental Economics, with a special focus on industrial organization, entrepreneurship, small and medium size enterprises, and social capital. His publications include several scholarly articles in international scientific journals such as Management ScienceEconomic Inquiry and Journal of Public Economics.

In the age of globalization, as our societies become more diverse, we face a fundamental and challenging problem that affects every part of our society: racial discrimination. Accordingly, discrimination has been a central question for many social sciences and humanities, and economics is not an exception. Economists split discrimination into two categories: statistical and taste-based. Statistical discrimination happens when the subject in question makes a decision based on the general information associated with an observable variable such as race or gender. Read More.