This book reviews recent approaches for
partial identification of average treatment effects with instrumental variables in the program evaluation literature, including Manski’s bounds, bounds based on threshold crossing models, and bounds based on the Local Average Treatment Effect (LATE) framework. It compares these bounds across different sets of assumptions, surveys relevant methods to assess the validity of these assumptions, and discusses estimation and inference methods for the bounds. The book also reviews some empirical applications employing bounds in the program evaluation literature. It aims to bridge the gap between the econometric theory on which the different bounds are based and their empirical application to program evaluation.
Inhaltsverzeichnis
Chapter 1. Introduction.- Chapter 2. Econometric Framework.- Chapter 3. Bounds under Different Identification Assumptions.- Chapter 4. Comparison of Bounds across Different Assumptions etc.
Über den Autor
Carlos A. Flores is Professor of Economics at the Orfalea College of Business, California Polytechnic State University at San Luis Obispo. He received his Ph.D. in Economics and M.A. in Statistics from the University of California at Berkeley. His main fields of interest are econometrics and labor economics. Prof. Flores’ research focuses on the development and application of new econometric methods for program evaluation and causal inference to assess the effects of policies, programs, and interventions.
Xuan Chen is Assistant Professor at the School of Labor and Human Resources, Renmin University of China. She received her Ph.D. in Economics from the University of Miami. Dr. Chen’s main research areas are program evaluation and labor economics. Her current research focuses on the development of partial identification approaches in the instrumental variable framework. She is also interested in the econometric evaluation of public policies regarding the Chinese labor market.
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