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Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement, with much debate about whether some covariate is or is not a "valid instrument" in an application of interest. There is therefore good reason to consider weaker but more credible assumptions. assumptions. To this end, we introduce monotone instrumental variable (MIV) A particularly interesting special case of an MIV assumption is monotone treatment selection (MTS). IV and MIV assumptions may be imposed alone or in combination with other assumptions. We study the identifying power of MIV assumptions in three informational settings: MIV alone; MIV combined with the classical linear response assumption; MIV combined with the monotone treatment response (MTR) assumption. We apply the results to the problem of inference on the returns to schooling. We analyze wage data reported by white male respondents to the National Longitudinal Survey of Youth (NLSY) and use the respondent's AFQT score as an MIV. We find that this MIV assumption has little identifying power when imposed alone. However combining the MIV assumption with the MTR and MTS assumptions yields fairly tight bounds on two distinct measures of the returns to schooling.
Empirical measurement of impacts of active labour market programmes has started to become a central task of economic researchers. New improved econometric methods have been developed that will probably influence future empirical work in various other fields of economics as well. This volume contains a selection of original papers from leading experts, among them James J. Heckman, Noble Prize Winner 2000 in economics, addressing these econometric issues at the theoretical and empirical level. The theoretical part contains papers on tight bounds of average treatment effects, instrumental variables estimators, impact measurement with multiple programme options and statistical profiling. The empirical part provides the reader with econometric evaluations of active labour market programmes in Canada, Germany, France, Italy, Slovak Republic and Sweden.
The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice. - Answers both 'why' and 'how' questions in network analysis, bridging the gap between practice and theory allowing for the easier entry of novices into complex technical literature and computation - Fully describes multiple worked examples from the literature and beyond, allowing empirical researchers and data scientists to quickly access the 'state of the art' versioned for their domain environment, saving them time and money - Disciplined structure provides latitude for multiple sources of expertise while retaining an integrated and pedagogically focused authorial voice, ensuring smooth transition and easy progression for readers - Fully supported by companion site code repository - 40+ diagrams of 'networks in the wild' help visually summarize key points
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.
In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.
The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics.
Understanding why so many people across the world are so poor is one of the central intellectual challenges of our time. This book provides the tools and data that will enable students, researchers and professionals to address that issue. Empirical Development Economics has been designed as a hands-on teaching tool to investigate the causes of poverty. The book begins by introducing the quantitative approach to development economics. Each section uses data to illustrate key policy issues. Part One focuses on the basics of understanding the role of education, technology and institutions in determining why incomes differ so much across individuals and countries. In Part Two, the focus is on techniques to address a number of topics in development, including how firms invest, how households decide how much to spend on their children’s education, whether microcredit helps the poor, whether food aid works, who gets private schooling and whether property rights enhance investment. A distinctive feature of the book is its presentation of a range of approaches to studying development questions. Development economics has undergone a major change in focus over the last decade with the rise of experimental methods to address development issues; this book shows how these methods relate to more traditional ones. Please visit the book's website at www.empiricalde.com for online supplements including Stata files and solutions to the exercises.
How does education affect economic and social outcomes, and how can it inform public policy?Volume 3 of the Handbooks in the Economics of Education uses newly available high quality data from around the world to address these and other core questions. With the help of new methodological approaches, contributors cover econometric methods and international test score data. They examine the determinants of educational outcomes and issues surrounding teacher salaries and licensure. And reflecting government demands for more evidence-based policies, they take new looks at institutional feaures of school systems. Volume editors Eric A. Hanushek (Stanford), Stephen Machin (University College London) and Ludger Woessmann (Ifo Institute for Economic Research, Munich) draw clear lines between newly emerging research on the economics of education and prior work. In conjunction with Volume 4, they measure our current understanding of educational acquisition and its economic and social effects. - Uses rich data to study issues of high contemporary policy relevance - Demonstrates how education serves as an important determinant of economic and social outcomes - Benefits from the globalization of research in the economics of education