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Dossier Data in Advance of Theory

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    Dossier | N. 6 articoliEconomics on trial

    Data in Advance of Theory

    Advanced training in economics, its critics lament, is out of touch with the times. In the first year of graduate programs – all graduate programs – students receive detailed tutoring in methods. Instruction focuses on technique, abstracting from real-world problems.

    In the second year, students are then shown how to apply those methods to various fields of economics. After that they are supposed to pursue research projects of their own.

    But prioritizing methods rather than questions encourages aspiring economists to value technical elegance over practical solutions to pressing economic problems. Students are told how to analyze data rather than dirtying their hands with data themselves. Because training starts with two years of classroom instruction, where students are told what to do instead of doing it, young economists are rudderless when the time comes to decide on what to work. They consequently end up aping their instructor, working on a minor aspect of their professor's established research program. There is little intellectual innovation. There is little incentive or, for that matter, ability to respond to new economic problems.

    This would be a devastating critique were it accurate. In truth, however, this familiar critique, and not economics training itself, is what is out of touch with the times.

    As we speak, training in economics, like the world around it, is being radically reshaped by Big Data. Theory is being dethroned, starting from the very beginning of graduate training, in favor of empirical analysis of big data sets documenting the actual behavior of households, firms and markets. Economists are seeking to understand household behavior not by assuming “rational consumers” but analyzing supermarket scanner data on actual purchases. They are parsing investment decisions not by assuming “efficient financial markets” but by analyzing individual flows in and out of banks and mutual funds. They are seeking to characterize migration decisions not by assuming “optimizing migrants” but by tracing actual movements across generations using Ancestry.com.

    The current generation of students, who grew up with the Internet and are familiar with “web crawlers” and “search bots,” are best able to harvest these kinds of data. The best students – the same ones who will train future generations of economists at top universities and head up central banks and treasuries – are inverting the traditional sequence, starting their graduate studies with empirical analyses of Big Data and then learning just as much theory and technique as are useful for analyzing it. Their findings, which are at variance with the predictions of mainstream models, are in turn reshaping economic theory itself. Witness the award of this year's Nobel Prize to Richard Thaler for his work on behavioral economics.

    The University of California-Berkeley, like in many other Ph.D. programs, has long required a second-year econometrics paper. This requirement was a make-shift way of forcing students to get their hands dirty with actual data analysis, and of easing the transition to research by requiring students to complete a modest research project. We are now contemplating abolishing this requirement, since students are already taking this step, on their own and often before the second year.

    A related critique is that economics education is ahistorical. Aspiring economists have little opportunity to develop a historical perspective on modern economic problems or to understand the historical origins of modern economic institutions. Whereas we at Berkeley require a course in economic history, in most other programs this has been crowded out by instruction in theory and statistics.

    But here too the ground is shifting. Greater ease of harvesting data from the web and digitizing manuscript sources means improved ability to digitize historical data. Economists interested in the origins of modern economic growth, rather than re-litigating the theory behind Max Weber's thesis about the connections between Protestantism and the Industrial Revolution, are able to analyze data on the expropriation of individual monasteries and on wealth transfers from the Catholic Church to secular lords. They are able to assemble new disaggregated data for 2,000 German towns on the degrees and occupations of graduates of Protestant universities. They are able to explore detailed data on construction activity to detect a shift from the construction of churches and monasteries to the construction of civil administrative buildings in Protestant and Catholic lands. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3053735

    Historical sources are attractive because they provide the kind of quasi-natural experiments of which economists are fond. Because the border between the Ottoman and Austro-Hungarian Empires was arbitrarily set by the Treaty of Karlowitz in 1699, they can study neighboring villages in Romania today to see whether Ottoman rule has long-term economic consequences. http://behl.berkeley.edu/working-papers/ Because Brazil was divided between Portugal and Spain by the Treaty of Tordesillas in 1494, they can study towns on the two sides of the treaty line to see whether the legacy of slavery, tolerated for longer by the Portuguese, continues to hinder economic and financial development. https://laudares.com/academic/ Because villages, towns, monasteries and universities can be precisely geo-coded, such detailed comparisons are now possible.

    The danger is that history will become a hobby for economists, rather than a subject of serious study. It would be unfortunate if economists only looked to it for natural experiments rather than seriously attempting to understand how history shapes economic processes. They would then be engaging in the 21st century equivalent of “looking under the lamp-post for the $20 bill because that's where the light is.”

    Thus, there is the risk that economists will use historical evidence and Big Data mechanically, just like their predecessors used theory and statistics mechanically. Progress is never without risks. But progress there is.

    Barry Eichengreen is Professor of Economics and Political Science at the University of California, Berkeley.

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