There was an interesting series of articles in the January 10, 2015 The Economist magazine. Boldly declaring that the field of economics is "A long way from dismal," the magazine posits that "A new breed of high-tech economist is helping firms crack new markets." Given the sullied reputation of economics since the onset of financial crises, this perspective, if true, would be a welcome development for the discipline.

As I read the articles, three perspectives on economics in the real world came to mind.

First Perspective

The first is that the branch of econ that's suffered most with the crises is macro, “The field of economics that studies the behavior of the aggregate economy. Macroeconomics examines economy-wide phenomena such as changes in unemployment, national income, rate of growth, gross domestic product, inflation and price levels.”  Macroeconomists are still reeling from their failure to both predict and treat the deep recession.

Just as bad for the field is the current political divide between neoliberal and Keynesian camps. The neoliberal model, with a laissez-faire perspective that promotes the market as king and seeks to minimize government, is dogma for the politically “red”. The Keynesian side championed by the “blue”, in contrast, sees a significant role for government fiscal (tax and spend) intervention in nudging the economy. To say there've been disagreements between the camps over the causes and remedies for the economy's ills is to understate the obvious. Indeed, those watching the President's State of the Union Address and the Republican response are challenged to think that both sides were talking about the same country.

Which gets down to my problems with macroeconomics: It's too grand in theory and analytics; too politically infused; and, because it's not an experimental discipline, too difficult to methodologically discern cause and effect.

Second Perspective

And that leads to my second perspective, shared with The Economist, that “For anyone starting out in economics, the future is micro ….Microeconomists are a humble bunch. Rather than seeking a unified theory of everything, they hone in on a particular area, often a single type of market or firm, and try to find out how it works. But technology is lending them clout. Armed with vast data sets produced by tech firms, microeconomists can produce startlingly good forecasts of human behaviour. Silicon Valley firms have grown to love them: by bringing a cutting-edge economist in house, they are able to predict what customers or employees are likely to do next.”

Both the domain and tools of microeconomics are less grandiose than those of macro. I recall being uncomfortable with the macroeconometric methods I learned in grad school many years back, even then thinking how would I ever apply them in the real world. Alas, the contents of today's macroeconometrics curricula seem remarkably similar to what I experienced  35 years ago. Too much top-down  math ; too little bottom-up data.

In a 2009 interview, eminent Stanford statistician Brad Efron, cited the “conservative” leanings of econometrics in contrast to statistics and machine learning: “If data analysis were political, biometrics/econometrics/psychometrics would be “right wing” conservatives, traditional statistics would be “centrist,” and machine learning would be “left-leaning.” The conservative-liberal scale reflects how orthodox the disciplines are with respect to inference, ranging from very to not at all.”

For my money, micro has a big advantage in statistics/econometrics as well as theory. Microeconometrics Methods and Applications by Cameron and Trivedi, is my trusty companion, often a first reference for new modeling challenges. Its emphases on practical techniques like treatment effects, hierarchical models, survival models, panel data, non-parametric regression, Bayesian analysis and simulation/resampling are highly pertinent to today's analytics professionals.

Microeconomics thus has much to offer the data science world: a solid but grounded theoretical foundation on scarcity, supply-demand, risk, optimization and incentives, along with a statistics/analytics framework useful for divining answers to real world questions. Though I don't agree with The Economist that “The new breed ignore the whiteboard, chucking numbers together and letting computers spot the patterns,” I do think microeconomists balance a  data-driven urgency with a social science theoretical/modeling perspective that many big data geeks don't offer.

Third Observation

My third observation is that despite this solid and highly pertinent theoretical/analytical background, what is sometimes missing with micro experts are the data integration and computation skills needed to be successful data scientists. Though certainly addressable, it'd be nice if graduate economics training included assurances that students possess those skills when they enter the work world. Perhaps an “Applied Economics” degree tract could provide some focus on computation.

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