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Mostly Harmless Econometrics: An Empiricist's Companion
Joshua D. Angrist,Jörn-Steffen Pischke +1 more
- 01 Jan 2009
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TL;DR: The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes.
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Abstract: The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications
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Citations
Race as a Bundle of Sticks: Designs that Estimate Effects of Seemingly Immutable Characteristics
Maya Sen,Omar Wasow +1 more
TL;DR: This article argued that race should be disaggregated into elements and that causal claims may be made using two designs: (a) studies that measure the effect of exposure to a racial cue and (b) studies exploiting within-group variation.
373
Two-Sample Instrumental Variables Estimators
Atsushi Inoue,Gary Solon +1 more
TL;DR: In this paper, the authors compare the asymptotic distributions of the two estimators and find that the commonly used TS2SLS estimator is more asypptotically efficient than the TSIV estimator.
Demand Effects in Survey Experiments: An Empirical Assessment
Jonathan Mummolo,Erik Peterson +1 more
TL;DR: This paper found that participants exhibit a limited ability to adjust their behavior to align with researcher expectations, a finding with important implications for the design and interpretation of survey experiments, and showed that providing participants information about experimenter intent does not alter the treatment effects in these experiments.
370
Institutions, Human Capital, and Development
TL;DR: In this article, the authors revisited the relationship among institutions, human capital, and development, and showed that the impact of institutions on long-run development is robust, whereas the estimates of the effect of human capital are much diminished and become consistent with micro estimates.
Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis.
TL;DR: The Neyman-Rubin causal model is reviewed, which is used to prove analytically that linear regression yields unbiased estimates of treatment effects on binary outcomes and, when interaction terms or fixed effects are included, linear regression is safer.