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Economic Arbitrage and the Econophysics of Income Inequality

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Economic Arbitrage and the Econophysics of Income Inequality Review of Behavioral Economics, 2020, 7:1–17 Economic Arbitrage and the Econophysics of Income Inequality Anwar Shaikh1 and Juan Esteban Jacobo2 1Department of Economics, New School for Social Research, New York, NY 10003, USA; [email protected] 2Departamento de Economía, Universidad Externado de Colombia, Bogotá, Colombia; [email protected] ABSTRACT Yakovenko and his co-authors have established that the bottom 97– 99% of individual incomes (labor incomes) follow a near-exponential distribution while the top incomes (property incomes) follow a power law. Initial explanations of these patterns relied on various monetary analogues to the physics principle of energy conserva- tion. Subsequent approaches turned to the stochastic dynamics of economic processes, including those of labor and property income modeled as a drift-diffusion processes. Our paper is in the latter tradition, but our specifications of drift-diffusions are derived from the fundamental economic principle of turbulent arbitrage modeled as a mean-reverting process. This approach is well developed in the domain of interest rate arbitrage as in the case of CIR models. Our contribution is to demonstrate that arbitrage can also explain the observed distributions of wages, rates of return on assets, and property income. In the energy conservation approach, stationary distributions are derived from the assumption of entropy maximiza- tion. In both stochastic dynamics approaches, the dynamic paths give rise to stationary distributions that turn out to be entropy maximizing. Keywords: Economics, arbitrage, econophysics, income distribution, entropy maximization, Fokker–Planck equations JEL Codes: B12, C10, C18, D31, D33 ISSN 2326-6198; DOI 10.1561/105.00000129 ©2020 A. Shaikh and J. E. Jacobo 2 Anwar Shaikh and Juan Esteban Jacobo 1 Introduction1 The analysis of income distribution in capitalist societies goes back to Pareto’s (1897) finding that top incomes follow a power law (Silva and Yakovenko, 2005; Yakovenko and Rosser, 2009). But modern evidence indicates that the lower bulk of the income distribution follows different patterns (Dragulescu and Yakovenko, 2001, p. 585). One way to address these issues is to fit the whole range of incomes by means of single distributions such as the lognormal, Levy, Gamma, Champernowne and others (Dragulescu and Yakovenko, 2001, p. 585). A recent econophysics “two-class” theory of income distribution (EPTC) pioneered by Yakovenko and his co-authors has opened up new ground (Dragulescu and Yakovenko, 2000, 2001; Yakovenko and Rosser, 2009; Banerjee and Yakovenko, 2010). This approach provide substantial empirical evidence that the bottom 97–99% of the overall distribution of personal incomes, which is essentially labor income, is well approximated by an exponential distribution, while top 1–3%, which is essentially property income, is well approximated by a power law. Personal income may encompass both labor and property components, but it is plausible that labor income dominates at lower income levels and the property income at the highest ones. The EPTC approach develops an ingenious method of combining the two distributions to create a parsimonious yet powerful approximation to overall Lorenz curve (Silva and Yakovenko, 2005; Yakovenko and Rosser, 2009). Subsequent research showed that even when labor income data is partitioned by gender or race, the empirical distribution within each group is still approximately exponential – despite the fact that Males have persistently higher incomes than Females, and Whites persistently have higher incomes than Non-Whites (Shaikh et al., 2014). In addition, labor incomes by occupation are also approximately exponential (Shaikh, 2016, p. 755). These results make sense if we recognize that the gender and race subsets are samples taken from the overall distribution of individual incomes, while average incomes by occupation are consolidations. It is important to stress that labor and property incomes only approx- imately follow exponential and Pareto laws, respectively. At an empirical level, the pdf of labor income is better approximated by Gamma or Weibull distributions (Shaikh et al., 2014), while that of property incomes can also be well approximated by the log-normal – as we shall see. Nonetheless, we agree with Yakovenko that the first approximations offered by exponential and Pareto distributions offer great advantages: “Universalities are not easy to uncover, but they form the backbone of regularities in the world around us ::: Universalities establish the first-order effect, and deviations represent 1We wish to thank the anonymous referee for insightful and extremely helpful comments, which we have tried to address here. In particular, the interpretation of the kinetic approach in Section3 was most illuminating. Economic Arbitrage and the Econophysics of Income Inequality 3 the second order effect ::: (and) in the first approximation, the distributions are quite similar and universal” (Yakovenko, 2007, p. 2821). For the portion of individual incomes (r) following a pure exponential − r distribution, the probability distribution is P (r) / e ( hri ) where hri represents the mean of the exponential portion. The cumulative probability distribution − r for incomes above r within this same segment is C (r) = e ( hri ). Then r ln C (r) = − hri is parameter free as long as lower incomes in any given year are normalized by their mean. The empirical signature of a normalized r exponential distribution is therefore a straight-line plot in ln hri ;C(r) for all years, and the Gini coefficient (G) is G = 1=2 for individual incomes and G = 3=8 for two-person family incomes (Dragulescu and Yakovenko, 2001, p. 587). On the other hand, top incomes follow a power law in which the cumulative distribution from above is C (r) / r−α. The corresponding empirical signature of a power law is a straight-line plot in (ln r; ln C(r)). The fraction of total income (f) in the power law segment, the Gini G = (1 + f)=2 and the power law coefficients α ranging from 1.4 to 1.8 all vary with the stock market – consistent with the hypothesis that top incomes are largely property income (Silva and Yakovenko, 2005, p. 307; Yakovenko, 2007, p. 2815). We utilize US Internal Revenue Service (IRS) data on adjusted gross income of individuals, pre-binned in varying ranges.2 The main chart in Figure1 displays the overall distribution in 2011 with income r on the horizontal axis and C(r) on the log-scaled vertical axis. The left inset shows that the log-linear plot of the bottom 97% exhibits an exponential signature, while the right inset displays the log-log plot of the top 3% with its power law signature. Table1 will show that these patterns hold for every year (Silva and Yakovenko, 2005, p. 305). In what follows, the lower and upper sections are treated as separate populations, so that C(r) begins from 1 in each. 2 Maximum Entropy Approaches It is a fundamental principle of physics that with a given amount of total energy and a given number of particles interacting in a manner that conserves total energy, the stationary equilibrium distribution of energy corresponding to the maximum-entropy state is Boltzmann-Gibbs. The EPTC approach develops a monetary equivalent of this principle in order to explain labor incomes: a closed economy with a fixed amount of money and a fixed number of agents whose individual monetary transactions conserve total money. Positivity of individual money holding is ensured by assuming that in each interaction individual agents 2IRS data for adjusted gross income (AGI) comes in bins of varying size, so we use bin mid-points for income levels. 4 Anwar Shaikh and Juan Esteban Jacobo 1.00000 Cumulative Probability from Above, Cumulative Probability fromAbove, Bottom 97% : Log-Linear Scale Top 3% : Log-Log Scale 1.00 0.10000 0.10000 0.01000 0.10 0.00100 0.00010 0.01000 0.01 0.00001 $0 $20,000 $40,000 $60,000 $80,000 $100,000$120,000$140,000 $160,000 $100,000 $1,000,000 $10,000,000 Income Income 0.00100 0.00010 0.00001 $0 $1,000,000 $2,000,000 $3,000,000 $4,000,000 $5,000,000 $6,000,000 $7,000,000 $8,000,000 Income Figure 1: Cumulative Probability from Above, 2011 (Log-Linear Scale). spends less money than they possess (Yakovenko, 2007, p. 2805). The resulting entropy maximizing stationary distribution is exponential (Dragulescu and Yakovenko, 2000, pp. 723–724). A proposed extension supposes that one agent out of a randomly selected pair is designated as an “agent-firm” (A) who borrows capital K from agent B and uses this to pay the same amount to workers L. The agent-firm then uses these workers to produce a physical product Q which it in turn sells to agents A, B and L at a unit price p such that total sales pQ > L. The sales proceeds pay off the loan K with interest, leaving some net profits for the agent-firm. “The net result is a many-body exchange of money that still satisfies the conservation law ::: Our computer simulations show that the stationary probability distribution of money in this model always has the universal Boltzmann-Gibbs form independent of the model parameters”.3 (Dragulescu and Yakovenko, 2000, p. 725). Another proposed extension treats debt as form of money so that assets and liabilities cancel out and “the conservation law is restored” (Yakovenko, 2007, p. 2807). At an empirical level, income flows are used as proxies for individual money and wealth stocks due to the lack of data on the latter, even though “there is no fundamental 3Readers will recognize this as a version of money flows in Marx’s Schemes of Simple Reproduction. But there, the critical prior issue is the source of the surplus of output value pQ over input value L (Marx, 1967, Chs. XX–XXI).
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