Laurent Picard Distinguished Lecture: Alexandra Kalev
Getting to Diversity
Presented by Alexandra Kalev
Associate Professor of Sociology and Anthropology
Tel Aviv University
Date: Friday, April 14, 2023
Time: 2:30 - 4:00 pm EDT
Location: Room 340, Bronfman Building (1001 Sherbrooke St W, Montreal, Quebec H3A 1G5)
This talk will examine results from evidenced-based research covering almost 1,000 organizations over 45 years about which organizational changes work to increase diversity, which ones don't work, and why. The key argument is that while decision-makers' bias is certainly a culprit, inequality has been institutionalized in the normal way of doing business. The most effective ways for reducing diversity are thus those that reduce the systemic bias rooted in career systems and in work routines that make it hard for women and minorities to succeed in the workplace. The talk will discuss a theory of systemic inequality and present general principles for planning effective diversity programs.
About Alexandra Kalev
Alexandra Kalev is Associate Professor of Sociology and Anthropology at Tel Aviv University. Professor Kalev draws on management, economics, law and psychology to study organizations, work and inequality. Her work on the labor process looks at how the reorganization of work and economic crises affect gender and racial inequality. Her award-winning paper "Cracking the Glass Cages?" shows how reducing segregation helps women and people of color to display their talents and move into management. With Frank Dobbin, she is developing an evidence-based approach to managing diversity in corporations and universities as well as the effects of workforce diversity on firms’ financial performance. Their book "Getting to Diversity: What Works and What Doesn't", was recently published by Harvard University Press. In other projects Kalev studies meaning making in reaction to experiences of discrimination, focusing on the integration of Israeli Arabs in emerging markets, and the effective innovations for reducing managers' ambivalent bias against older workers.
