Mostly Harmless Econometrics: An Empiricist's Companion

Mostly Harmless Econometrics: An Empiricist's Companion

Mostly Harmless Econometrics: An Empiricist's Companion

Mostly Harmless Econometrics: An Empiricist's Companion


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 Jörn-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


[I checked it very thoroughly,] said the computer, [and that
quite definitely is the answer. I think the problem, to be quite
honest with you, is that you've never actually known what
the question is.]

Douglas Adams, The Hitchhiker's Guide to the Galaxy

This chapter briefly discusses the basis for a successful research project. Like the biblical story of Exodus, a research agenda can be organized around four questions. We call these frequently asked questions (FAQs), because they should be. The FAQs ask about the relationship of interest, the ideal experiment, the identification strategy, and the mode of inference.

In the beginning, we should ask, What is the causal relationship of interest? Although purely descriptive research has an important role to play, we believe that the most interesting research in social science is about questions of cause and effect, such as the effect of class size on children's test scores, discussed in chapters 2 and 6. A causal relationship is useful for making predictions about the consequences of changing circumstances or policies; it tells us what would happen in alternative (or [counterfactual]) worlds. For example, as part of a research agenda investigating human productive capacity— what labor economists call human capital—we have both investigated the causal effect of schooling on wages (Card, 1999, surveys research in this area). The causal effect of schooling on wages is the increment to wages an individual would receive if he or she got more schooling. A range of studies suggest the causal effect of a college degree is about 40 percent higher wages on average, quite a payoff. The causal . . .

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