Read Online Making EI Work: Research from the Mowat Centre Employment Insurance Task Force (Queen's Policy Studies Series Book 171) MOBI
Since the inception and design of Canada's Employment Insurance (EI) program, the Canadian economy and labour market have undergone dramatic changes. It is clear that EI has not kept pace with those changes, and experts and advocates agree that the program is no longer effective or equitable. Making EI Work is the result of a panel of distinguished scholars gathered by the Mowat Centre Employment Insurance Task Force to analyze the strengths, weaknesses, and future directions of EI. The authors identify the strengths and weaknesses of the system, and consider how it could be improved to better and more fairly support those in need. They make suggestions for facilitating a more efficient Canadian labour market, and meeting the human capital requirements of a dynamic economy for the present and the foreseeable future. The chapters that comprise Making EI Work informed the task force's final recommendations, and form an engaging dialogue that makes the case for, and defines the parameters of, a reformed support system for Canada's unemployed. Contributors include Ken Battle (Caledon Institute of Social Policy), Robin Boadway (Queen's University), Allison Bramwell (University of Toronto), Sujit Choudhry (New York University School of Law), Kathleen M. Day (University of Ottawa), Ross Finnie (University of Ottawa), Jean-Denis Garon (Queen's University), David Gray (University of Ottawa), Morley Gunderson (University of Toronto), Ian Irvine (Concordia University), Stephen Jones (McMaster University), Thomas R. Klassen (York University), Michael Mendelson (Caledon Institute of Social Policy), Alain Noël (Université de Montréal), Michael Pal (University of Toronto Faculty of Law), W. Craig Riddell (University of British Columbia), William Scarth (McMaster University), Luc Turgeon (University of Ottawa), Leah F. Vosko (York University), Stanley L. Winer (Carleton University), Donna E. Wood (University of Victoria), and Yan Zhang (Statistics Canada).
