The most interesting scientific projects are those that surprise, when the mathematics, or the code, tells us something we didn’t expect. In our study of US wealth dynamics that’s what happened. We wrote it up in a paper, but that’s only the end product not the curious route by which we got there. Hence this post.

The equation of life

We started thinking about wealth dynamics some time in 2010 or 2011. We had been studying ensembles of growth processes, and that naturally led to thinking about ensembles of people and their growing wealths. Here’s what we did (“we” being Yonatan Berman, Alex Adamou, and I): we started with a ridiculously simple model for personal wealth, namely geometric Brownian motion (GBM).

(1) dx=x(\mu dt + \sigma dW)

I like to call this the equation of life. Why? Because life can be (and has been) defined as the thing that self-reproduces, and that’s what the equation describes. A quantity x that produces more of itself in a noisy way. It describes what happens to the biomass of an embryo in its early stages of development, or to the population of some species growing in a rich environment.

Morowitz
Figure 1: From the book “Beginnings of cellular life” by Harold Morowitz. Although I have met parents of teenagers who disagree with the precise statement encircled in red, life is defined as that which produces more of itself.
Once we’ve got self-reproduction in an environment with some fluctuations, evolution gets going, and beautiful structures like the ones we see around us follow sooner or later.

Equation (1) doesn’t just model biomass or populations but is also quite good at describing stock price dynamics. So we thought that it may be good at describing personal wealth too. After all, in one way or another both the stock market and our monetary fortunes reflect something that is happening in the economy. Let’s actually name the thing: we’re talking about capitalism. The genius of capitalism is precisely its multiplicative nature. Unused resources — capital — can be deployed to produce more of themselves. In this way a capitalist economy resembles the basic dynamic of evolution.

Our model does resemble wealth in a capitalist structure, but we were aware of its simplifying assumptions. It pretends that any changes in wealth are proportional to current wealth, whereas I could be poor and nonetheless boost my wealth through earned income. We treat everyone the same and pretend that differences in skill or earnings potential are random and not persistent etc. Nonetheless, we were curious about what would happen in a world where people’s wealth simply followed GBM.

Re-allocation: stability, Pareto tail, middle class

The first observation is this: under GBM the distribution of wealth never stabilizes, not even relative wealth stabilizes (that’s personal wealth divided by total population wealth). If we wait for long enough, essentially one person ends up with all the wealth. That struck us as unrealistic: we don’t live under feudalism. But we used to live under feudalism, so the real dynamic must be less extreme than GBM. That makes some sense — after all, the government collects taxes, and there are institutions that fund all sorts of social programs. We decided to make the model a little more realistic and included re-allocation of wealth. Surely the poor are helped by the rich in some way. So we changed the equation to

(2) dx=x([\mu-\tau] dt + \sigma dW) + \tau \langle x \rangle_N dt.

The new terms say this: every year everyone in the economy contributes a proportion \tau of his wealth to a central pot, and then the pot is split evenly across the population (\langle x \rangle_N is per-capita wealth). Again, this is very simplistic — \tau represents a lot of different effects: collective investment in infrastructure, education, social programs, taxation, rents paid, private profits made… The equation can be re-written, which is very neat.

(3) dx=x(\mu dt + \sigma dW) - \tau (x- \langle x \rangle_N) dt.

This shows that it’s just like GBM (the first term) plus a mean-reversion process that attracts wealth to the population average. If I’m richer than the average, I’m likely to become a little poorer (relative to the average — my wealth can still grow); if I’m poorer I’m likely to become a little richer. The strength of the reversion is \tau, which can be thought of as a social cohesion parameter.

This equation is great! Whereas GBM leads to a diverging (unstable) log-normal distribution of relative wealth, equation (3) leads to a stationary inverse-gamma distribution. I mean if you let the equation run for a while, the number of people with a given wealth will follow an inverse gamma distribution. That distribution has a power-law tail, similar to what has been observed many times since Pareto‘s first studies. So it’s already pretty good, on a coarse-grained level.

What else did we know? Under GBM, wealth cannot become negative. Since the poor are always better off under equation (2), this is also true here.

Enter the computer

Thanks to tremendous efforts by many authors, including Tony Atkinson, Thomas Piketty, Emmanuel Saez, Gabriel Zucman, Wojciech Kopcuk, Jesse Bricker, Alice Henriques, Jacob Krimmel, and John Sabelhaus, we have a fairly good idea of the US wealth distribution over the past 100 years. So we took those observed distributions, created 100,000,000 individuals on a computer, fixed \mu directly from the wealth data and set \sigma roughly to the values observed in the stock market, and let the computer tune \tau each year so as to reproduce the real distributions.

Just for fun, we then looked at the individual wealths that had been produced by this procedure, and we noticed something strange. Many of them were negative. So back to the code, what did we do wrong? An error in the discretization scheme? Some other bug? No, the effect was real.

Negative re-allocation

Here’s what happened: in order to reproduce the data, towards the end of the analyzed period the algorithm had to make \tau negative, see figure 2 below. But what happens under those conditions to equation (3)?

tau_1
Figure 2: The red line shows the effective rate of wealth re-allocation in the US (ten-year moving average of black one-year values). It was positive over most of the 20th century but turned negative some time around 1980. Since then wealth has trickled up from poor to rich.
Well, it describes negative re-allocation. Everyone pays the same dollar amount into a central pot, and then everyone receives from the pot an amount in proportion to how much he already has. That means if I have nothing, then I receive nothing but I still have to pay. That can make my wealth negative.

Look at equation (3) again, imagining \tau to be negative. The second term now describes mean repulsion. Whereas before wealth was attracted to the population mean, which generates a middle class, now wealth is repelled from it. If I’m a bit richer than the average, I’ll be boosted up even further; if I’m a little poorer, I’ll be pushed down even further. Run this equation for a little while and a large class of negative-wealth individuals arises.

At is turns out, something like that exists in reality. The cumulative wealth of the poorer half of the American population is roughly zero, meaning there must be a large class of negative-wealth individuals.

graph_dl
Figure 3: The share of the total wealth in the US held by the less wealthy half of the population. This has recently become negative. The cumulative wealth of half the population is zero, meaning a large class of negative-wealth individuals exists. Data from the World Wealth and Income Data Base

Falling interest rates

This is a blog post, so let me be speculative and push the story a little further than in the paper. How do those who have less than nothing keep giving to the rich? Simple: they go deeper into debt, deeper into negative wealth. But how can that be sustained over a long time? Debts don’t need to be paid off, but they do need to be serviced. To service growing debt with stagnant income (the situation in the US roughly since 1980), we need to lower interest rates.

Interest rates have been falling since about 1980, see Figure 4, precisely the time when the re-allocation rate became negative (c.f. Figure 1). What if there’s a causal link?

fredgraph.png
Figure 4: US interest rates have been falling (with a few bumps) since about 1980. Data from the Federal Reserve Bank of St. Louis. Falling interest rates allow servicing more debt with constant income. Can we afford to raise interest rates?
Now it gets interesting: interest rates have hit zero. What do we do? How can the poor keep paying the rich? Sure, let’s have some quantitative easing, but can that go on forever? Or will it break at some point? Is redistribution from poor to rich a threat to our monetary system? Is it a threat to our democracy? Where does the system go from here?

Let’s be clear about what we’ve done. We built a simple model and fitted its one main parameter. This wraps everything that’s actually happening into this one parameter. There are loose ends — the model may be fooling us, but we’re certainly not in a regime where we can comfortably rely on stabilization. We don’t claim that the world really works like equation 2, but that’s not the point of the exercise. Instead we say “pretend that equation 2 describes the dynamics of wealth; what parameter values would then best resemble what really happens?” The model is no more than a model and as such brushes over many details. For example, we don’t explicitly treat inheritance or income tax or some specific welfare program. Rather, this is all treated implicitly: our \tau summarizes everything that affects the wealth distribution beyond the null model of GBM. It reflects the overall trend in the complete economic system.

Ergodicity

That the model produced behavior beyond our (initial) imagination is encouraging. It means we didn’t accidentally constrain our study to confirm our beliefs. We wanted to know by how much we need to slow down the increase in wealth inequality implied by GBM to get to a realistic model. The model said: no, you’re asking the wrong question. GBM actually understates the increase in wealth inequality, and you need to correct the other way. Under GBM relative wealth is non-ergodic. The ergodic hypothesis as it is made in studies of wealth inequality thus excludes GBM as too extreme. Now it turns out that real wealth dynamics are better described by correcting GBM to make it even more strongly non-ergodic. None of us had expected that.

We should have written down our guesses for \tau before we started the study. We didn’t do this, but we certainly thought we would find a positive value. In a private correspondence, from the time before we looked at the data, Jean-Philippe Bouchaud set \tau =5\% p.a. in an example calculation, and we all felt that was the right order of magnitude. It could be 2% but obviously not as small as 0% (which would be GBM, equation 1).

Time scales

The connection to interest rates is speculative, but here’s one rock solid message about time scales that may hint at how we got here. A change in the effective re-allocation rate, \tau, takes decades to feed through. These processes operate on time scales of generations, not election cycles. That means it’s easy to oversteer because the consequences of policy changes only become visible after 30 or 50 years, long after whoever made the policy changes has left office, and at a time when the reasons for making the changes may no longer be valid. We certainly mustn’t assume rapid equilibration. However, rapid equilibration — the ergodic hypothesis — is a standard assumption in studies of wealth distributions.

The basic dynamic of a multiplicative-wealth economy — capitalism — seems underappreciated to me. If we “do nothing” (\tau =0), inequality increases indefinitely. If we re-distribute fast enough (\tau>0), inequality will stabilize at some level. If we actively destabilize (\tau<0) as we seem to have done in recent decades, the middle class vanishes and we create a division between rich and poor — a poor person behaving reasonably is as unlikely to become middle class as a rich person behaving reasonably.

 

——

p.s. we can make the model arbitrarily complex. One aspect we later singled out is the effect of earnings, by including observed earnings in equation (2). Usually earnings have a stabilizing effect (meaning the process that describes only wealth must be less stable when earnings are treated explicitly). In the last 10 years or so, that stabilizing effect has been absent because of earnings inequality. Consequently, the values we find for \tau with this version of the model are smaller (more negative) up until about 2000 and then unchanged, see figure 5 below.

earnings.png
Figure 5: Wealth reallocation rates, treating earnings explicitly. Red line: for reference, same as in figure 1 (no earnings treated explicitly). Blue line: assuming the wealthiest earn the most. Yellow line: assuming the least wealthy earn the most.

10 thoughts on “Wealth: redistribution and interest rates

    1. Thank you for your comment, Scott. It’s not easy to say in words what an equation does, and you’re right that this sentence is not strictly correct. The following is correct: all individuals are modelled with the same parameter values \mu and \sigma. There are no differences at this level.

      However, it’s sloppy to turn this into English by saying that “we treat everyone the same.” Let me try to make a few correct statements that also explain a little bit why this model is so strangely powerful.
      1: individuals are fully characterised by their wealth — that’s the only idiosyncratic property we keep track of. Although wealth generally changes with time in the model, it is also persistent: if an individual is rich this year it will not suddenly be poor next year, maybe a bit less rich, but wealths don’t jump around.
      2: that means the distribution of individual wealth changes also doesn’t jump around, but it also changes gradually, so there’s persistence in that too, which you could interpret as persistence in earnings potential or skill. Mathematically speaking, there are strong temporal correlations in the changes of wealth of any one individual. This is a feature of GBM that the model maintains (and that’s different from just Brownian motion).
      3: wealth in the model does not just set the scale of future wealth changes. Rather, because there’s that re-allocation mechanism — being rich can mean paying systematically more or paying systematically less into the common pot. Individuals are therefore treated differently in this respect.
      4: everyone experiences a different realization of the noise, so we don’t treat everyone the same in that sense either — randomly some win, some lose.
      5: at the beginning of the numerical analysis we set wealths so as to resemble the wealth distribution of 1918, meaning from the beginning there are differences in wealth and not everyone is the same, with the consequences described above.

      The sentence you quote had two aims: it highlights the simplicity of the model (a good thing), and it warns against using the model outside its domain of applicability. It’s well suited to the purpose of our study, but that doesn’t mean it’s well suited to all studies of wealth distribution. For example, it doesn’t distinguish between male and female, so I can’t study gender differences with it.
      Why people have more or less is a very important question, but we’re setting that aside. If you’re rich, you’ll probably stay rich for a while. That’s all the model knows. We just ask where the net flows are going. To assess _what_ happens, in this case, we don’t need to know precisely _why_ it happens, and therefore we can make simplifying assumptions that ignore the “why” but don’t affect the “what.”

      By throwing out details, we make the model simple, and we can begin to understand systemic stability, from a bird’s eye perspective. Whether it’s because of talent, hard work, ability, luck or anything else — if flows go systematically from poor to rich, then the middle class disappears. Many will find themselves in debt they cannot repay, and some will find themselves in wealth they can’t consume.

      I should also say that this is not a value judgement, it just lays out the naked mechanics. We may want to concentrate wealth and power in the hands of a small elite, corresponding to \tau<0 (that's seems to be what we're doing at the moment). Royalists have argued that that’s a good thing, and feudal societies are organized that way. At the other extreme communists would argue that \tau \to \infty is a good thing — like in feudalism, private property doesn’t strictly exist in communism. Capitalist democrats tend to be somewhere in the middle, they might want \tau \approx 2\% p.a., something small but positive. What value of \tau a society aims for is a political choice. In our work we’re just helping to measure it so that people can have an informed debate about the the choices we want to make as a society.

      Liked by 2 people

    1. Thank you, Richard. I had a quick look at the paper. They study GBM, meaning re-allocation rate \tau =0. Even GBM itself is a fabulously powerful model, but one thing it cannot explain is falling or stable inequality. There have been periods in history when wealth inequality dropped, and that cannot be reproduced with GBM. With RGBM (re-allocating geometric Brownian motion), such periods can be represented with a positive reallocation rate, \tau>0.

      Under GBM, as the authors show, a vanishing proportion of the population ends up with all the wealth. That means the system does not properly equilibrate and does not satisfy the ergodicity conditions that for some reason many researchers require of their models. It’s a strange belief in the field: the model has to be ergodic, otherwise it’s not economic science. But that excludes GBM from the set of models that are allowed. Because of this unnecessary restriction not even these very simple models (GBM or RGBM) have been properly explored in this context.

      Liked by 1 person

  1. Thanks for the fascinating post. I especially like your “equation of life” interpretation. I’ll remember that one.

    Something that stands out when I look at your plots is that the noise of unaveraged tau seems large relative to its moving average. Do you agree? I wonder if it can be reduced… perhaps with more trials? Or with a change to the model? Or is it possible that tau is one of your weak ergodicity breaking variables?

    On the other hand, perhaps the unaveraged (black) tau line is even more informative than the average, and is telling us something about the contemporaneous state of the economy. To my naked eye, it appears that tau is more positive in the “garden variety” recessions of the early 80’s, early 90’s, and early 2000’s. (And in the wild crash of 1929-30.) But not during the credit crisis of 2008. Some factors to investigate might be unemployment insurance, declines in stock market wealth (which disproportionately affect the wealthier among us), and personal bankruptcies (which help the least wealthy get a fresh start). (But the dynamics of these factors were very different in 1930 compared to 2000.)

    To explain the recent negativity of (averaged) tau, you have mentioned interest rates as a possible contributor. On a related note, perhaps home-ownership is a contributor too… The less wealthy typically don’t own homes. In many markets, homes have greatly increased in value, hand-in-hand with the decline in interest rates, which enables the the wealthier to afford increasingly larger mortgages. It might be interesting to fit the model to different metro areas, to compare those with more home price appreciation to those with less.

    Thanks again for the great work. Your ideas are among the most exciting ones to hit financial economics in a long time.

    Liked by 2 people

    1. Hi Patrick, thanks for your comment. I am one of the paper’s co-authors. Regarding the noise in \tau, we cannot have more trials, because we only have one – the reality. That being said, we show in the paper that the averaging process does not introduce artificial biases and essentially captures the information in the non-averaged \tau (see figure 4 in http://papers.ssrn.com/abstract=2794830).

      Like

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