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Every mainstream science which touches on political or religious ideology attracts more than its fair share of deniers: the anti-vaccine crowd v mainstream medicine, GMO fearmongers v geneticists, creationists v biologists, global warming deniers v climatologists. Economics is no different, but economics cranks differ in that they typically make false claims about the content of economics itself, as opposed, or as a prelude, to false claims about the way the world works. That target sometimes making it hard for non-economists to differentiate crankery from solid criticism.

Here, then, are some symptoms of bad critiques of economics:

1. Treats macroeconomic forecasting as the major or only goal of economic analysis.
2. Frames critique in terms of politics, most commonly the claim that economists are market fundamentalists.
3. Uses “neoclassical” as if it refers to a political philosophy, set of policy prescriptions, or actual economies. Bonus: spells it “neo-classical” or “Neo-classical.”
4. Refers to “the” neoclassical model or otherwise suggests all of economic thought is contained in Walras (1874).
5. Uses “neoclassical economics” and “mainstream economics” interchangeably. Bonus: uses “neoliberal economics” interchangeably with either.
6. Uses the word “neoliberal” for any reason.
7. Refers to “corporate masters” or otherwise implies economists are shills for the wealthy or corporations.
8. Claims economists think people are always rational.
9. Claims financial crisis disproved mainstream economics.
10. Explicitly claims that economics is not empirical, or does so implicitly by ignoring empirical economics.
11. Treats all of economics as if it’s battling schools of macroeconomics.
12. Misconstrues jargon: “rational.”
13. Misconstrues jargon: “efficient” (financial sense) or “efficient” (Pareto sense).
14. Misconstrues jargon: “externality“.
15. Claims economists only care about money.
16. Claims economists ignore the environment. Variant: claims economics falters on point that “infinite growth on a finite planet is impossible.”
17. Goes out of its way to point out that the Economics Nobel is not a real Nobel.
18. Cites Debunking Economics.
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Remarks on Chen and Pearl on causality in econometrics textbooks

Bryant Chen and Judea Pearl have published a interesting piece in which they critically examine the discussions (or lack thereof) of causal interpretations of regression models in six econometrics textbooks. In this post, I provide brief assessments of the discussion of causality in nine additional econometrics texts of various levels and vintages, and close with a few remarks about causality in textbooks from the perspective of someone who does, and teaches, applied econometrics. Like Chen and Pearl, I find some of these textbooks provide weak or misleading discussion of causality, but I also find one very good and one excellent discussion in relatively recent texts. I argue that the discussion of causality in econometrics textbooks appears to be improving over time, and that the oral tradition in economics is not well-reflected in econometrics textbooks.

The Chen and Pearl paper has been around for a while in working paper form and recently came out in the Real World Economics Review, also available here from the authors with much clearer typesetting.

The additional textbooks I discuss below are: Amemiya (1985), Kmenta (1986), Davidson and MacKinnon (1993), Gujarati (1999), Hayashi (2000), Wooldridge (2002), Davidson and MacKinnon (2004), Deilman (2005), and Cameron and Trivedi (2005).

The intuition of robust standard errors

Commonly econometricians conduct inference based on covariance matrix estimates which are consistent in the presence of arbitrary forms of heteroskedasticity; the associated standard errors are referred to as “robust” (also, confusingly, White, or Huber-White, or Eicker-Huber-White) standard errors. These are easily requested in Stata with the “robust” option, as in the ubiquitous

reg y x, robust

Everyone knows that the usual OLS standard errors are generally “wrong,” that robust standard errors are “usually” bigger than OLS standard errors, and it often “doesn’t matter much” whether one uses robust standard errors.  It is whispered that there may be mysterious circumstances in which robust standard errors are smaller than OLS standard errors. Textbook discussions typically present the nasty matrix expressions for the robust covariance matrix estimate, but do not discuss in detail when robust standard errors matter or in what circumstances robust standard errors will be smaller than OLS standard errors. This post attempts a simple explanation of robust standard errors and circumstances in which they will tend to be much bigger or smaller than OLS standard errors.

Behavioral hazard in health insurance

Suppose your health insurance becomes more generous, decreasing the proportion of the cost of care for which you are responsible. At the same time, your premium goes up to cover the extra costs faced by your insurer. In standard theory you are better off because you face less financial uncertainty, but you will also tend to consume too much health care because the price you pay is lower than the cost of your treatment. Standard theory suggests that insurance should be designed to optimally trade-off these benefits and costs. But standard theory assumes rationality: suppose instead people systematically make errors when choosing how much health care to consume. Does it make a difference to how we think about health insurance?

In a recently released NBER working paper, “Behavioral hazard in health insurance,” Katherine Baicker, Sendhil Mullainathan, and Joshua Schwartzstein consider behavioral biases that lead people to (specifically, and with loss of generality) underutilize health care. How should we think about designing health insurance in the presence of such biases?

What do we know about the effect of income inequality on health?

This post briefly surveys some of the methods and results in the literature on health and income inequality, closing with some remarks on problems with the existing literature and where future research may take us. It is not intended as anything resembling a comprehensive survey; Lynch et al (2004) provides a useful review of the empirical literature up to that time.