In Part I, I discussed power laws and how they describe the distribution of wealth and income. I employed the clearest example of the Pareto principle, or 80–20 rule or Zipf’s Law, where 20% of the population controls 80% of the resources. More generally, the Pareto Principle is the observation that most things in life are not distributed evenly because most inputs (effort, reward) and outputs are not distributed evenly — some contribute and receive more than others.
Why is this so and how does that explain the power law’s occurrence in nature? Why do 20% of the pea pods produce 80% of the peas? I think the best way to grapple with these questions is intuitively. Success in nature is not evenly distributed: some of us are stronger, some are smarter, some are more beautiful, some are just plain lucky. Furthermore, in a competitive world of scarce resources, success breeds more success by the laws of natural selection. This is fairly logical: In a dynamic world of change, nature has dictated that successful organisms must adapt to survive. The successful adaptations reproduce and soon the species is transformed. This demonstrates a positive feedback loop: the rich get richer and the poor, well, hopefully, get by.
In the natural world, unsuccessful mutations and unsuccessful species die off, like dinosaurs, and evolution moves on. However, with economic exchange markets — the markets that determine our skewed distributions of wealth and income — power laws become problematic. Why? Because in an exchange market, the successful rely on the less successful in a symbiotic relationship. A game of Monopoly or poker is a good analogy: the game always ends when one player ends up with the whole pot. A market where the most successful player ends up with everything is a market that will break down and cease to exist. Game over.
Macroeconomic theory assumes this will not happen because markets “adjust” through price changes and advantages are competed away. In principle, this is true, to an extent. If it wasn’t, civilization would have ceased to exist a long time ago. But the “price adjustment” process is not a pretty one — it involves bankruptcies, unemployment, asset crashes, hunger, etc. These are outcomes our collective governments do everything to prevent. But if markets don’t adjust in a natural cycle of creative destruction and renewal, what do we get? Ever bigger and inevitable crashes with economic and financial crises. In a milder context we might call it the business cycle.
Certainly the economics profession has been aware of the phenomenon of business cycles for as long as we’ve had market breakdowns, but the prescriptions for managing the business cycle are rooted in the constraints of modern mathematical modeling techniques. General equilibrium models using simultaneous equations to solve for optima cannot analyze the dynamics of distributional outcomes like inequality. (I try to explain why here. This is an appendix from my economics primer, Common Cent$.) If you think about it, the economics profession has done very well prescribing how to create wealth, and abysmally in trying to solve poverty, hunger, environmental degradation, etc. (Hint: these are ALL distributional failures.)
In an attempt to overcome these limitations, I designed, with a former academic colleague, an agent-based computer simulation model I call CasinoWorld. (The published version can be downloaded here.) With CasinoWorld, we hoped to demonstrate how a simple population of heterogenous agents making gambles in a risky environment using two simple survival rules will result in a severely skewed wealth distribution after a relatively small finite number of gambles.
The best analogy, which gave the model it’s name, is a Casino, where all players enter the casino with equal stakes and choose different games to play based on their different risk preferences. Their choices adhere to two decision criteria: how much to gamble and at what odds. After each gamble, their preferences change based on whether they won or lost. If a player (agent) wins, she makes safer bets with higher stakes. If a player loses, he makes riskier bets with smaller stakes. The winning strategy is akin to capital preservation in the investment world and the second is akin to lottery playing in order to win a big score and get back in the game. The wealth distributions that result after many plays of the game are consistent with power laws, gini coefficients of inequality, and wealth distributions across many different countries and societies. This type of study has profound implications for how we can and should deal with markets that generate skewed wealth and income distributions that may cause those markets to continually fail. In short, it’s the dynamic process, as opposed to unequal initial endowments (existing wealth, education, intelligence, etc.), that matters most.
In Part III I will address the relevant policy ramifications of inequality for market stability and long-term economic growth and sustainability.