Business strategy and firm location decisions: testing traditional and modern methods

  • Patrick L. AndersonEmail author


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.


Machine learning Recursive Neoclassical Managerial decisions Artificial intelligence 

JEL Classification

B21 C61 D21 J23 K20 L21 



  1. Alphabet, Inc. Form 10-K, 2017. 2018. Alphabet investor relations website.Google Scholar
  2. Amazon. 2018. “Amazon Announces Candidate Cities for HQ2.”  January 18, retrieved from
  3. Amazon, Inc. 2017. Amazon HQ2 RFP. (September 7)
  4. Amazon, Inc. 2016-2017. Form 10-K (annual results). Securities and Exchange Commission.Google Scholar
  5. Amazon, Inc. 2016-2017. Form 10-Q (quarterly results). Securities and Exchange Commission.Google Scholar
  6. Anderson Economic Group. 2017. 2017. The HQ2 Index (October); and Updated: The Anderson Economic Group HQ2 Index  (February 2018). Both retrieved from
  7. Anderson Economic Group. 2017. 2017 State Business Tax Burden Rankings Report.
  8. Anderson Economic Group. 2018. 2018 State Business Tax Burden Rankings Report. Retrieved from
  9. Anderson Economic Group. 2018. Supplemental Information on Professional Sports Teams Acquired from Websites of NFL, NBA, MLB, and NHL Professional Sports Teams; Supplemental Information on Female Mayors and Female City Council Membership Acquired from Websites of Relevant Cities; County Government Election Official Websites of the Central Cities for HQ2 Analysis Regions; Latitude and Longitude for Cities.Google Scholar
  10. Anderson, Patrick. 2012. Economics of Business Valuation. Stanford: Stanford University Press.Google Scholar
  11. Anderson, Patrick. 2014. Policy Uncertainty and Persistent Unemployment: Numerical Evidence from a New Approach. Business Economics 49 (1): 2–20.CrossRefGoogle Scholar
  12. Burwell v. Hobby Lobby, et al. 2014. No. 13-354, 573 U.S. 723 F. 3d 1114.Google Scholar
  13. Corporation for National and Community Service. 2015. Volunteering and Civic Life in America.
  14. Cover, T.M., and P.E. Hart. 1967. Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13 (1): 21–27.CrossRefGoogle Scholar
  15. Dixit, Avanish, and Robert S. Pindyck. 1994. Investment Under Uncertainty. Princeton: Princeton University Press.Google Scholar
  16. Dodge v. Ford Motor Company. 1919. 204 Mich. 459, 170 N.W. 668.Google Scholar
  17. Federal Transit Administration. 2017. National Transit Database Monthly Module, August 2016-July 2017.
  18. Flightview. 2018. Airport information for HQ2 analysis regions, acquired from Flight-, 2018.Google Scholar
  19. Fraser Institute. 2017.  Economic Freedom of North America Index.
  20. Friedman, Milton. 1953. The Methodology of Positive Economics, in Essays in Positive Economics, 3–43. Chicago: Chicago University Press.Google Scholar
  21. Friedman, Thomas. 2005. The World is Flat. New York: Farar, Straus & Giroux.Google Scholar
  22. Fujita, Masahisa, Paul Krugman, and Anthony Venables. 1999. The Spatial Economy: Cities, Regions and International Trade. Cambridge: MIT Press.CrossRefGoogle Scholar
  23. Gallup Organization. 2017 Gallup-Sharecare Well-Being Index.
  24. Galton, Francis. 1886. Anthropological Miscellanea: Regression towards Mediocrity in Hereditary Stature. The Journal of the Anthropological Institute of Great Britain and Ireland 15: 246–263.CrossRefGoogle Scholar
  25. Ghemawat, Pakaj. 2007. Why the World Isn’t Flat. Foreign Affairs.Google Scholar
  26. Harvard Business School Institute for Strategy and Competitiveness. 2015. Business Services Cluster Employment. U.S. Cluster Mapping Project 2015.
  27. Hotelling, Harold. 1929. Stability in Competition. Economic Journal 39 (153): 41–57.CrossRefGoogle Scholar
  28. Jones Lang LaSalle. 2017. Office Outlook Q4 2016.
  29. Jones Lang LaSalle.  New Jersey Office Statistics Q4 2016.
  30. Jones Lang LaSalle. 2017. Washington, D.C. Office Statistics Q4 2016”
  31. Kaldor, Nicholas C. 1966. Marginal Productivity and the Macro-Economic Theories of Distribution: Comment on Samuelson and Modigliani. The Review of Economic Studies 33 (4): 309–319.CrossRefGoogle Scholar
  32. Kauffman Foundation. 2016.  Index of Startup Activity Metropolitan Area Rankings.
  33. Krugman, Paul. 1991. Increasing Returns and Economic Geography. Journal of Political Economy 99 (3): 483–499.CrossRefGoogle Scholar
  34. Ljungvist, Lars, and Thomas J. Sargent. 2012. Recursive Macroeconomic Theory, 3rd ed. Cambridge: MIT Press.Google Scholar
  35. Lucas, Roberts. 1976. Econometric Policy Evaluation: A Critique. In The Phillips Curve and Labor Markets (ed. K. Brunner and A. Meltzer), 19–46. New York: American Elsevier.Google Scholar
  36. Modigliani, Franco, and M. Miller. 1958. The Cost of Capital, Corporation Finance and the Theory of Investment. American Economic Review 48 (3): 261–297.Google Scholar
  37. Marshall, Alfred. 1890. Principles of Economics. London: MacMillan.Google Scholar
  38. National Center for Education Statistics. Integrated Postsecondary Education Data System: Degrees Granted 2015–2016.
  39. Parilla, Joseph. 2017. Who is best positioned to land Amazon’s HQ2? Brookings Metropolitan Policy Council. Accessed July 2018.
  40. Ricardo, David. 1817. Principles of Political Economy and Taxation. London: J.M. Dent & Sons.Google Scholar
  41. Rubenstein, Mark. 2006. A History of the Theory of Investments: My Annotated Bibliography. New Jersey: Wiley.Google Scholar
  42. Rutgers University Center for American Women and Politics. 2018. Women Mayors in U.S. Cities, 2018.
  43. Samuelson, Paul A. 1983. Thünen at Two Hundred. Journal of Economic Literature 21: 1468–1488.Google Scholar
  44. Stansel, Dean. 2013. An Economic Freedom Index for U.S. Metropolitan Areas. Journal of Regional Analysis and Policy 43 (1): 3–20.Google Scholar
  45. Stokey, Nancy, Robert Lucas, and Edward Prescott. 1989. Recursive Methods in Economic Dynamics. Cambridge: Harvard University Press.Google Scholar
  46. Texas Transportation Institute. 2015. 2015 Urban Mobility Scorecard.
  47. Tobin, James, and W.C. Brainard. 1977. Asset Markets and the Cost of Capital. Cowles Foundation  Discussion paper no. 427.
  48. Thünen, Johan Heinrich. 1826. Von Thünen’s Isolated State. Oxford: Pergamon Press.Google Scholar
  49. Trust for Public Land. 2018. Park
  50. United Nations World Meteorological Organization. 2010. Standard Normals.
  51. U.S. Bureau of Labor Statistics. 2016. Occupational Employment
  52. U.S. Bureau of Economic Analysis. 2015.  GDP & Personal Income data by State, Retrieved from
  53. U.S. Census Bureau. 2010-2014 Business Dynamics Statistics.
  54. U.S. Census Bureau. 2016.  American Community Survey 1-Year Table B01003: Total Population.
  55. U.S. Census Bureau. 2016.  American Community Survey 5-Year Table B01003: Total Population, 2012–2016.
  56. U.S. Census Bureau. 2016. American Community Survey 1-Year Table S0701: Geographic Mobility by Selected Characteristics.
  57. U.S. Census Bureau. 2016. American Community Survey 5-Year Table B25105: Median Monthly Housing Costs.
  58. U.S. Census Bureau. 2016. American Community Survey. 5-Year Table S0501: Selected Characteristics of the Native and Foreign-Born Populations.
  59. U.S. Census Bureau. 2016.  American Community Survey 5-Year Table S1601: Language Spoken at Home.
  60. U.S. Census Bureau. 2016. American Community Survey 5-Year Table S2403: Industry by Sex for the Civilian Employed Population 16 Years and Over.
  61. U.S. Census Bureau.  Table PEPANNRES: Population Estimates, 2010–2017.
  62. U.S. Center for Disease Control and Prevention. 2017. Local Data for Better Health.
  63. U.S. Department of Justice. 2014. Uniform Crime Reporting Statistics.
  64. U.S. Environmental Protection Agency. 2017. Air Quality Index Report.
  65. U.S. National Oceanic and Atmospheric Administration. 2018. Temperature Data for HQ2 Analysis Regions.
  66. Urban Institute. 2015. Economic Inclusion Index.
  67. Urban Institute. 2015. Racial Inclusion Index.
  68. Walkscore. 2018. Walk Score, Transit Score, and Bike Score for Various Cities and Regions.
  69. Zandi, Mark. 2017. Where Amazon’s Next Headquarters Should Go, and Metro Analysts on Amazon’s Top Cities. Accessed 12 Oct 2017.

Copyright information

© National Association for Business Economics 2019

Authors and Affiliations

  1. 1.Anderson Economic Group LLCEast LansingUSA

Personalised recommendations