Productivity and Inequality

Presents cutting-edge and rigorously developed papers covering productivity, inequality, and efficiency analysis from NAPW IX
Includes worldwide perspectives and challenges that local economies and institutions may face when changes in productivity are observed
Editors are respected names in the field

Auteur

William Greene is a professor in the department of economics at New York University Stern School of Business. In his current positions, Professor Greene teaches courses in econometrics, statistics, and economics of the entertainment and media industries. Professor Greene has been with NYU Stern for more than 30 years. His primary research areas of interest include econometrics and applied microeconomics; productivity and production economics, health econometrics, technical change and the entertainment industry. He has published numerous articles in publications including Econometrica, Economics Letters, American Economic Review, Journal of Econometrics, Journal of Economic Education, Economics Letters, Journal of Economic Perspectives, Review of Economics and Statistics and Journal of Political Economy . Before joining NYU Stern, Professor Greene served as a consultant for the Civil Aeronautics Board in Washington, D.C. Recent consultancies also include the World Health Organization, Ortho Biotech, National Economic Research Associates, American Express, the Federal Reserve Bank, FDIC, the United States Postal Service, and regulatory authorities in the UK and Brazil. He has also held a professorial position at Cornell University and served as a visiting lecturer at the University of Oxford, University of Sydney, Curtin University, University of Lugano, University of Putra, Universities of Southern Denmark and Aarhus in Denmark, Monash University, American University, University of Hull, Pennsylvania State University, the University of Umea, Sweden and the University of London and numerous others. Professor Greene received his Bachelor of Science in business administration from Ohio State University and his Master of Arts and Doctor of Philosophy degrees in econometrics from the University of Wisconsin, Madison.

Robin C. Sickles is the Reginald Henry Hargrove Professor of Economics, a Joint Professor at the Department of Statistics at Rice, andan Adjunct Professor at the Baylor College of Medicine. He is also a Research Associate at the Ovideo (Spain) Efficiency Group and a Member of Executive Committee for the International Finance and Banking Society. Professor Sickles is also the Editor-in-Chief of the Journal of Productivity Analysis, and has served as associate editor of Journal of Applied Econometrics, Communications in Statistics, Theory and Methods, Southern Economic Journal, Journal of Econometrics, and Empirical Economics. His research interests center around applied economics and empirical measurement of productivity.

Paul Makdissi is a Professor of Economics at the University of Ottawa since 2007. Previously he has held positions at the Université de Sherbrooke and at the Vrije Universiteit Amsterdam. His main areas of research are socioeconomic health inequality, income distribution and fiscal policy.

Lynda Khalaf is a Professor of Economics at Carleton University. She received her Ph.D in Economic Science at the University of Montréal. Her research fields include econometrics, energy econometrics, and financial econometrics.

Michael Veall obtained his doctorate in Economics from MIT in 1981. He taught at the University of Western Ontario, the University of Mannheim and Queen's University and was a Visiting Fellow at Australian National University and the University of Western Australia, Honorary Visiting Professor at York University and an Alexander von Humboldt Research Fellow at both the University of Mannheim and at SELAPO, University of Munich. He is currently professor at McMaster University and co-investigator in the research program on the Socioeconomic Dimensions of an Aging Population. He was a Research Fellow at IZA Bonn from April 1998 to September. His research interests include computationally-intensive methods such as bootstrapping and their application in econometrics and the microeconometric analysis of saving for retirement.

Marcel-Cristian Voia is an Associate Professor and Co-Director of the Centre for Monetary and Financial Economics at Carleton University. He received his Ph.D. in Economics at the University of Western Ontario. His research fields include micro-econometrics, applied econometrics, industrial organization, and labour economics.

Contenu
Chapter 1. Estimating Efficiency in the Presence of Extreme Outliers: A Logistic-Half Normal Stochastic Frontier Model with Application to Highway Maintenance Costs in England.- Chapter 2. Alternative User Costs, Productivity and Inequality in US Business Sectors.- Chapter 3. On the Allocation of Productivity Growth and the Determinants of US Income Inequality.- Chapter 4. Frontier Estimation of a Cost Function System Model with Local Least Squares: An Application to Dutch Secondary Education.- Chapter 5. Aggregate Productivity and Productivity of the Aggregate: Connecting the Bottom-Up and Top-Down Approaches.- Chapter 6. Confidence Sets for Inequality Measures: Fieller-type Methods.- Chapter 7. Poverty-Dominant Marginal Transfer Reforms in Socially Risky Situations.- Chapter 8. Exploring the Covariance Term in the Olley-Pakes Productivity Decomposition.- Chapter 9. The Decline of Manufacturing in Canada: Resource Curse, Productivity Malaise or Natural Evolution?.- Chapter 10. Flexible Functional Forms and Curvature Conditions: Parametric Productivity Estimation in Canadian and US Manufacturing Industries.- Chapter 11. Productivity Growth, Poverty Reduction and Income inequality: New Empirical Evidence.- Chapter 12. Contribution of Productivity and Price Change to Farm-level Profitability: A Dual Approach Analysis of Crop Production in Norway.- Chapter 13. Estimation of Health Care Demand and its Implication on Income Effects of Individuals.- Chapter 14. Quantile DEA: Estimating qDEA-alpha Efficiency Estimates with Conventional Linear Programming.