Real-World Economics Review

from Lars Syll The point is that a superficial analysis, which only looks at the numbers, without attempting to assess the underlying causal structures, cannot lead to a satisfactory data analysis … We must go out into the real world and look at the structural details of how events occur … The idea that the numbers by themselves can provide us with causal information is false. It is also false that a meaningful analysis of data can be done without taking any stand on the real-world causal...

from Peter Radford “American economics harbors fierce political debates over theory, methodology, and policy.  In practice and in comparative perspective, however, the main trend over the course of the twentieth century has been the standardization of training as well as a homogenization of evaluation criteria that has marginalized nonorthodox approaches.  After institutionalism was dethroned by the rise of mathematical economics and more politically challenging forms of intellectual...

from Lars Syll How, then, can social scientists best make inferences about causal effects? One option is true experimentation … Random assignment ensures that any differences in outcomes between the groups are due either to chance error or to the causal effect … If the experiment were to be repeated over and over, the groups would not differ, on average, in the values of potential confounders. Thus, the average of the average difference of group outcomes, across these many experiments,...

from Thomas Palley The US is currently enjoying another stock market boom which, if history is any guide, also stands to end in a bust. In the meantime, the boom is having a politically toxic effect by lending support to Donald Trump and obscuring the case for reversing the neoliberal economic paradigm. For four decades the US economy has been trapped in a “Groundhog Day” cycle in which policy engineered new stock market booms cover the tracks of previous busts. But though each new boom...

from Lars Syll Randomised experiments require much more than just randomising an experiment to identify a treatment’s effectiveness. They involve many decisions and complex steps that bring their own assumptions and degree of bias before, during and after randomisation … Some researchers may respond, “are RCTs not still more credible than these other methods even if they may have biases?” For most questions we are interested in, RCTs cannot be more credible because they cannot be applied...

from Asad Zaman I was professionally trained as an economist, and learned how to build models with the best. As described in detail in a previous post on “The Education of An Economist“, it was only by accident that, a long time after graduate school, I learned of glaring conflicts between the theory I had been taught, and the historical evidence about effects of free trade and trade barriers. Further exploration along this direction dramatically widened the chasm between the economic...

from Jared Bernstein and Dean Baker One of most enduring, economically and socially damaging, downright frustrating facts about life in the United States is how expensive health care is here. Not only does U.S. health care cost far more than in other advanced economies, but compared with the nations that spend less, we have worse or equivalent health outcomes. In fact, U.S. life expectancy now lags behind that of all the advanced economies. An MRI scan that cost $1,400 here went for...

from Lars Syll If I ask myself what I could legitimately assume a person to have rational expectations about, the technical answer would be, I think, about the realization of a stationary stochastic process, such as the outcome of the toss of a coin or anything that can be modeled as the outcome of a random process that is stationary. I don’t think that the economic implications of the outbreak of World war II were regarded by most people as the realization of a stationary stochastic...

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from Asad Zaman Statistics and Econometrics today are done without any essential reference to causality – this is much like try to figure out how birds fly without taking into account their wings. Judea Pearl “The Book of Why” Chapter 2 tells the bizarre story of how the discipline of statistics inflicted causal blindness on itself, with far-reaching effects for all sciences that depend on data. These notes are planned as an accompaniment and detailed explanation of the Pearl, Glymour,...