Erika Deserranno
Erika Deserranno

Assistant Professor of Managerial Economics & Decision Sciences

Print Overview

Professor Deserranno joined the Kellogg faculty in 2015 after receiving her PhD in Economics from the London School of Economics. Her research interests lie at the intersection between development and personnel economics. She is working on issues related to the selection, recruitment and motivation of workers both in private and public organizations. 

Areas of Expertise
Development Economics
Labor Economics
Economics of Organizations
Experimental Economics

Print Vita
Ph.D., 2015, Economics, London School of Economics
MRes, 2008, Economics, London School of Economics, Distinction
MSc, 2008, Economics and Social Sciences, Bocconi University, Summa Cum Laude
BSc, 2006, Business Engineering, Universite Libre de Bruxelles, Solvay Business School, Distinction

Academic Positions
Teaching Fellow, London School of Economics, 2013-2015
Teaching Assistant, London School of Economics, 2010-2014
Teaching Assistant, Bocconi University, 2008-2009

Honors and Awards
2016 John Hicks Prize for an Outstanding Doctoral Dissertation, 2012-2016
Teaching Excellence Award, LSE
Best Paper Award on Public Organizations, Unicredit, 2014
Teaching Fellowship, London School of Economics, 2013-2015
Department of Economics Scholarship, London School of Economics, 2009-2013
Quota Award, Economic and Social Research Council (ESRC), 2009-2013
IGC Grant, International Growth Centre, 2013
ATAI Grant, Agricultural Technology Adoption Initiative, 2012
Giovanna Crivelli Scholarship, Unicredit, 2010-2011
Marco Fanno Scholarship, Associazione Marco Fanno, 2008-2009
Best Master Thesis Award, Fondazione Franceschi, 2008

Print Research
Research Interests

Development Economics, Personnel Economics, Labor Economics

Print Teaching
Full-Time / Evening & Weekend MBA
Business Analytics II (DECS-431-0)

This core course is equivalent to the course DECS-440 (MMM Business Analytics).

This sequel to DECS-430 extends the statistical techniques learned in that course to allow for the exploration of relationships between variables, primarily through multivariate regression. In addition to learning basic regression skills, including modeling and estimation, students will deepen their understanding of hypothesis testing and how to make inferences and predictions from data. Students will also learn new principles such as identification and robustness. The course has an intense focus on managerial relevant applications, cases and interpretations.