Econinformatics?

For most business executives, the term “economics” conjures images of either simplistic supply-demand graphs they may have come across in Economics 101, or theoreticians devising arcane macroeconomic models to study the impact of interest rates and money supply on national economies.  Although businesses such as banks and financial institutions have maintained armies of economists on their payrolls, the economist’s stature and standing even in such institutions has been relatively short, limited to providing advice on general market based trends and developments, as opposed to actionable recommendations directly impacting the business bottom-line.  Even the most successful business executive would be stumped when faced with the question of how exactly economics is really applied to improving their day-to-day business.  All this may now be changing, thanks to a more front and center role of economics in new age businesses that now routinely employ economists to sift through all kinds of data to fine tune their product offerings, pricing and other business strategies.  Text book economists of yore are descending down from their ivory towers and taking on a new role, a role that is increasingly being shaped by availability of new analytic tools and raw market data.

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Economists, especially the macro kind, are a dispraised bunch, with a large part of criticism stemming from their inability to predict major economic events (economists missed anticipating the 2008 market crash).  For this and other reasons (not least the Lucas Critique), macroeconomic modeling focused on building large scale econometric models has been losing its allure for some time.  Microeconomic modeling enabled by powerful data-driven micro econometric models focused on individual entities has been transforming and expanding over the past few decades.  The ever expanding use of sophisticating micro-models on large data datasets has caused some to perceive this as paving the foundation for “real time econometrics”.  Econometrics, the interdisciplinary study of empirical economics combining economics, statistics and computer science, has continually evolved over the past several decades thanks to advances in computing and statistics, and is yet again ready for disruption – this time due to availability of massive data sets and easy-to-procure computing power to run econometric analyses.  The likes of Google, Yahoo and Facebook are already applying advanced micro econometric models to understand causal statistics surrounding advertising, displays and impressions and their impact on key business variables such as clicks and searches. Applied econometrics is but one feather in the modern economist’s cap: economists are at the forefront in the sharing economy and “market design”.

A celebrated area of economic modeling and research that has found successful application in business is “market design” and “matching theory”, pioneered by Nobel prize-winning economists Al Roth and Lloyd Shapley.  Market design and matching theory is concerned with optimizing the pairing or matching of providers and suppliers in a market place based on “fit” that is driven by dimensions that go beyond just price, for example. Al Roth successfully applied game theory based market design and matching algorithms to improving a number of market places, including placement of New York City’s high school students, the matching of medical students with residency schools, and kidney donation programs.  The fundamentals of matching theory are being widely applied by economists today: many modern day online markets and sharing platforms such as eBay, Lyft etc. are in the business of matching suppliers/providers and consumers, and economists employed by these outfits have successfully applied those fundamentals to improving their businesses, increasingly with the aid of multi-dimensional data that is available in real time. Other market places, including LinkedIn (workers and employers) and Accretive Heath (doctors and patients) have applied similar learnings to improve their matching quality and effectiveness.  Airbnb economists analyzed data to try to figure out why certain hosts were more successful than others in sharing their space with guests, and successfully applied their learnings to help struggling hosts and also to better balance supply and demand in many of Airbnb markets (their analysis pointed out that successful hosts shared high quality pictures of their homes, which led Airbnb to offer a complementary photography service to its hosts).

Beyond market design, economics research is changing in a number of areas thanks to availability of large data sets and analytic tools in various ways as Liran Einav and Jonathan Levin of Stanford University outline in “The Data Revolution and Economic Analysis“.  One such area is measurement of the state of the economy and economic activity and generation of economic statistics to inform policy making. The issue with macroeconomic measurement is that the raw data produced by the official statistical agencies comes with a lag and is subject to revision.  Gross domestic product (GDP), for example, is a quarterly series that is published with a two-month lag and revised over the next four years.  Contrast this with the ability to collect real time economic data, as is being done by the Billion Prices Project, which collects vast amounts of retail transaction data in near real time to develop a retail price inflation index.  What’s more, new data sets may allow economists to shine the light on places of economic activity that have been dark heretofore.  Small businesses’ contribution to the national economic output, for example, is routinely underestimated because of exclusion of certain businesses. Companies such as Intuit, which does business with many small outfits, now have payroll transactional data that can be potentially analyzed to gauge the economic contribution of such small businesses.  Moody’s Analytics has partnered with ADP, the payroll software and services vendor, to enhance official private sector employment statistics based on ADP’s payroll data.

Conservatives and the old guard may downplay the role of data in applied economics, reveling in their grand macroeconomic models and theories.  To be fair, empirical modeling would be lost without theory.  However, data’s “invisible hand” in shaping today’s online markets and business models is perceptible, if not openly visible.  Economists of all stripes will be well advised to pay attention to the increasing role of data in their field.  Next time you see an economist, ask them to go take a course on Machine Learning in the Computer Science department, to pass on Google Chief Economist Hal Varian’s counsel – it will be time worth spent.