Details

Chapter 1. Introduction.- Chapter 2. Econometric Framework.- Chapter 3. Bounds under Different Identification Assumptions.- Chapter 4. Comparison of Bounds across Different Assumptions etc.

<p>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.</p><p> 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.</p>

This book reviews recent approaches for<b> </b>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.

Provides a comprehensive survey of recently developed approaches for partial identification of average treatment effects in program evaluation Compares the identification power of different sets of assumptions employed by various partial identification approaches in program evaluation Discusses implementation of the bounds in practice, including interpretation and assessment of the assumptions, as well as estimation and inference Illustrates the use of bounds on treatment effects with instrumental variables in program evaluation by surveying recent applications Bridges the gap between the theory of the econometric derivation of bounds on treatment effects with instrumental variables in the program evaluation literature and their empirical application