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Special Topics in Forensic Economics

Paper Session

Saturday, Jan. 5, 2019 2:30 PM - 4:30 PM

Atlanta Marriott Marquis, International 4
Hosted By: National Association of Forensic Economics
  • Chair: Lane Hudgins, Lane Hudgins Analysis

Business Strategy and Firm Location Decisions: Testing Traditional and Modern Methods

Patrick L. Anderson
Anderson Economic Group


For nearly a century, economists have relied upon the neoclassical principle of a profit-maximizing firm. Challenges to this principle have recently arisen: the theory of the value-maximizing firm, and machine learning methods. We make use of an unusual natural experiment, and extensive data, to empirically compare the predictive power of both traditional and modern methods.
We proceed with:
1. Outline competing models of business decisions from both traditional, and modern, approaches: expert judgment; 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 test these approaches against each other, to determine which best explains the selection of 20 cities by Amazon Inc. for its HQ2.
We observe that expert judgment, of the type traditionally performed by business economists, outperformed a suite of machine learning models--even though these supervised learning models benefited from data unavailable to the experts. Indeed, some machine learning models performed worse than a coin flip. Second, we found that the novel model of a value-maximizing firm slightly outperformed an income model using exactly the same underlying data, and captured valuable insights that the traditional model missed. Based on these results, we recommend that business economists consider value methods for business strategy decisions. We also warn against the naive reliance on machine learning methods, particularly when the potential costs of errors are high. Finally, for forensic economists, we note frontier areas where the growth of machine learning methods will produce challenges in future practice.

Neutralizing the Adverse Effect of State and Federal Income Taxes (due to the Tax Cuts and Jobs Act of 2017) on Lump Sum Awards in Employment Cases

Michael Nieswiadomy
University of North Texas
Thomas Loudat
Economic Consultant


This paper provides a methodology to “gross-up” calculations to determine an award amount when estimated losses are taxable income such as in employment (e.g., wrongful termination, discrimination) and non-physical injury cases. This calculation presents a simultaneity problem wherein the award amount is a function of the income taxes paid and the income taxes paid are a function of the award (plus other income in the year the award is received). This paper makes two contributions. First, it extends existing literature by providing “gross-up” calculations in the context of several real world factors impacting income tax calculations: spousal income, investment income, Social Security and Medicare taxes, and attorney fees in the context of the new tax changes starting in 2018 stemming from the Tax Cuts & Jobs Act of 2017. Second, it provides an estimation process using available spreadsheet functions to iterate a user-friendly solution. We give an example to calculate state and federal income taxes, Social Security and Medicare taxes and the deductibility of attorneys’ fees to determine an award amount.

JEL Codes: K13 Forensic Economics; H24 Personal Income and Other Nonbusiness Taxes and Subsidies

Mitigating Future Economic Damages in Disputes Involving Credit Damages

Roman Garagulagian
Forensic Economic Services


Mitigation of economic damages involving credit damage has not been discussed thoroughly among economists. Often, the defense side argues that damages involving loss of interest in the future could be mitigated but the truth is mitigation can happen only under certain circumstances. In this paper we outline the detailed analysis of a hypothetical case involving loss of interest on a loan due to credit damages. We provide a simple calculation on the optimal time for a refinance that is beneficial.

JEL Code: K13 Forensic Economics
A. Frank Adams III
Adams Economic Consulting
Thomas Roney
Economic Consultant
Lane Hudgins
Lane Hudgins Analysis
JEL Classifications
  • K1 - Basic Areas of Law