This paper, written by Patrick L. Anderson for the January issue of the NABE’s Business Economics journal, is the recipient of a 2018 Edmund A. Mennis Contributed Paper Award. The work earned Mr. Anderson his third Mennis award for the nationally-recognized accuracy of its analysis in handicapping location bids for Amazon’s “HQ2.”
Abstract
For nearly a century, economists have relied upon the neoclassical principle of a “profit-maximizing firm.” Two modern challenges to this principle have arisen: the theory of the value-maximizing firm, and machine learning. In this article, we empirically compare the predictive power of both traditional and modern approaches to business decisions. To do so, we make use of an unusual natural experiment, and extensive data, as follows: (1) Outline competing models of business decision making from both traditional and modern approaches: Expert judgement; an income model of a profit-maximizing firm; a suite of machine learning models; and a recursive model of a value-maximizing firm. (2) Assemble data on costs, productivity, workforce, transit, and other factors for over 50 large North American cities. (3) Empirically compare these models to determine which best explains the selection of 20 cities by Amazon Inc. for its “HQ2.” We observe first that expert judgement, of the type traditionally performed by business economists, outperformed all other approaches. Second, we observe that “supervised learning” machine learning models performed poorly, with results that were often worse than a coin flip. Third, we found that the model of a value-maximizing firm slightly outperformed an income model using the same underlying data, and handily outperformed machine learning. Based on these results, we conclude that expert human judgement remains superior over machine learning methods, and warns against naive reliance on such models when the penalty for an incorrect decision is high. We also recommend that businesses economists consider value methods for business strategy decisions.
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