Summary: skip to the pictures after the
<big> text under heading 2.
Since 2009, pundits have concerned themselves with economic inequality. Robert Reich’s infographic about the US I’ll treat as a summary.
Let me dummyise the opinionscape into three camps:
- John Galt. The etymology of aristo-cracy is “rule by the best people”. The market rewards output fairly. Tax the best people and you will drive them out of France and into perfect stateless seasteads. Lose them and you’ll be sorry.
- Maximilien de Robespierre. F—k the rich. They inherited their way to the top. Connections, luck, brown-nosing, and false confidence determine incomes more than "merit". The middle manager is no better than his underling. The applicant who got the job is no better than another applicant who was ignored. Guillotine the superfluous gentleman, the role will still be filled; the new girl may even do it better.
- Vilfredo Pareto. Hey—if the rich aren’t actively making the poor worse off, what does it matter?
The third view is the one I want to challenge just now.
When I see a manual farmer being destroyed by Nature, I feel:
- sorry for the farmer
- the longer I spent thinking about their suffering, the sorrier I feel
- Why doesn’t somebody do something? They don’t need much. They just need a little help.
- This is so unfair.
And somehow, gut reactions are part of real morality and ethics.
So here’s my challenge to the Paretians. Which image galls you more:
- a farmer suffering from drought, with the whole community destroyed—families crying into each other in solidarity as they all lost pretty much everything
- next to the damned farmers weeping on their knees, stands the Monopoly Man, laughing, swirling a flute of champagne and recounting the fable of the grasshopper and the ant.
To the extent that these gut reactions translate into legitimate morals, the Robespierreans win over the Galtists and over the Paretians.
Envy exists. From this one infers that when the rich get richer but the poor don’t, that their individual utilities can still drop. But let’s go beyond society-as-a-collection-of-independent-individuals.
The image of the Monopoly Man merrily dancing next to the poor (or even indifferently ignoring their plight) curdles the blood. Gucci little piggies go first against the wall for a reason.
- Schoolboy #1: I heard Janae is D.T.F.
- Boy #2: What's DTF?
- Boy #1: Down To French
- (Janae overhears): Nut uh! I never said that!
- Boy #1: Yes you did!
- Janae: Noooo, I said I was down to FRANCH, not down to French.
- Janae: As in I like Franch stuff from Fran-suh. Dummy.
- Boy #1: I'm going to go tell Teacher that you are a liar! (Runs off)
- Janae (to boy #2): Actually I was lying, Paolo. I WOULD be down to French…with youuuuuuu.
- Paolo: Euw! I'm not going to France with you! You. are. a. GIRL.
Statisticians are crystal clear on human variation. They know that not everyone is the same. When they speak about groups in general terms, they know that they are reducing N-dimensional reality to a 1-dimensional single parameter.
Nevertheless, statisticians permit, in their regression models, variables that only take on one value, such as
No one doing this believes that all such people are the same. And anyone who’s done the least bit of data cleaning knows that there will be
NA's, wrongly coded cases, mistaken observations, ill-defined measures, and aberrances of other kinds. It can still be convenient to use binary or n-ary dummies to speak simply. Maybe the marriages of some people coded as
currently married are on the rocks, and therefore they are more like
divorced—or like a new category of people in the midst of watching their lives fall apart. Yes, we know. But what are you going to do—ask respondents to rate their marriage on a scale of one to ten? That would introduce false precision and model error, and might put respondents in such a strange mood that they answer other questions strangely. Better to just live with being wrong. Any statistician who uses the
cut function in R knows that the variable didn’t become basketed←continuous in reality. But a
facet_wrap plot is easier to interpret than a 3D wireframe or cloud-points plot.
To the precise mind, there’s a world of difference between saying
- "the mean height of men > the mean height of women", and saying
- "men are taller than women".
Of course one can interpret the second statement to be just a vaguer, simpler inflection of the first. But some people understand statements like the second to mean “each man is taller than each woman”. Or, perniciously, they take “Blacks have lower IQ than Whites” to mean “every Black is mentally inferior to every White.”
I want to live somewhere between pedantry and ignorance. We can give each other a break on the precision as long as the precise idea behind the words is mutually understood.
Dummyisation is different to stereotyping because:
- stereotypes deny variability in the group being discussed
- dummyisation acknowledges that it’s incorrect, before even starting
- stereotyping relies on familiar categories or groupings like skin colour
- dummyisation can be applied to any partitioning of a set, like based on height or even grouped at random
It’s the world of difference between taking on a hypotheticals for the purpose of reaching a valid conclusion, and bludgeoning someone who doesn’t accept your version of the facts.
So this is a word I want to coin (unless a better one already exists—does it?):
- dummyisation is assigning one value to a group or region
- for convenience of the present discussion,
- recognising fully that other groupings are possible
- and that, in reality, not everyone from the group is alike.
- Instead, we apply some ∞→1 function or operator on the truly variable, unknown, and variform distribution or manifold of reality, and talk about the results of that function.
- We do this knowing it’s technically wrong, as a (hopefully productive) way of mulling over the facts from different viewpoints.
- In other words, dummyisation is purposely doing something wrong for the sake of discussion.
That is assigning numbers to price-points and time-points; contingencies form a “surface”.
I tend to forget that for farmers, the actual landscape—the actual (sur)face of the Earth is itself the risk landscape.
- Hillocks get more sun (could be good or bad depending on the cooling-degree days
, the chance of frost, and the abundance of rain)
- Dells and ravines get more water—which could be good if it’s dry,
or catastrophic in case of flood.
- Of course that depends on the crop type. Rice wants to be flooded. Even I know that.
- And just like derivatives, agriculture has its term contingencies. Water in autumn is too late to grow the baby saplings but, too, a flood might not be as bad for the granary as it was for spring's seedlings.
- Symbiosis between “funded” (planted) neighbours could result in a “value-added merger” if, for example, the bugs which are attracted to one plant fend off another plant’s predators.
- A monogenetic crop could all be wiped out by the same disease.
- Diversification, then, would seem to mirror finance as one wants to invest fully in the “cash crop” (let’s say a junk bond), but risks increase as eggs are concentrated in one basket.
- Or say you wish that lucrative bridge loan’s IRR were applied to your entire portfolio—perhaps this is like a plant with rare seeds, or a plant that only takes in exactly perfect parts of your land.
- If a farmer could get “negative correlated assets” (half the plants do better in dry; half do better in wet), that would reduce the “portfolio variation”.
- Is there anything in finance that, like alfalfa, regenerates the “soil” for the next year’s crop?
- We speak of “exposure” in finance—well, furrows in la terre literally change the exposure to the sun over the course of its chariot ride across the sky!
Is it possible, then, to apply the lessons of modern portfolio theory to crop selection?
Advertising rises and falls with the economy, though how much is a matter of debate. Randall Rothenberg … points to the remarkable stability of advertising at about 2% of GDP since 1919, when the data began to be collected.
Flash Boys is deliberately set up to suggest a “perfect world gone bad” scenario: As if, prior to the advent of HFT, … nobody ever got bad fills and liquidity was provided by a fairy godmother who never skimmed. It is … irresponsible, … dumb and deceptive, … to … talk about HFT without talking about what HFT replaced.
… Why … did floor traders and market makers play a key role in the function of markets for multiple centuries? Because floor traders provide liquidity. Liquidity provision is a service, and it has a cost. A discussion of what HFT replaced—with examination of new systems, old systems, and continuity between the two, with attendant pluses and minuses [would have been better]. Yet for Lewis it barely [merits] a paragraph.
… Liquidity has always been an issue. The more size you want to move, the more of an issue it becomes. There has always been a need for middlemen to provide it, and friction / incentive issues in doing so, ever since the fabled meeting under the Buttonwood tree.
In the late 1980s, the Justice department busted 46 traders and brokers in the Chicago trading pits. The stealing had gotten so bad, the FBI came onto the trading floor.
Flash Boys reveals itself as a tempest in a teapot on pages 52 and 64. (I speak here of the hardcover edition from Amazon.) When Lewis … uses real numbers, the frivolity of his case is revealed.
On page 52 … an HFT “tax” that amounts to $160 million per day on $225 billion worth of volume. That is significantly less than one-tenth of one percent.
The auction for capital has happened in such diverse places as under banyan trees, in coffee shops, in whore houses, near the sandal market (Egypt, Jerusalem), in a cave, at farm crossroads, near lake edges and river deltas, at magic springs, over a levee, in private country club gardens, etc.
The folks who made it more comfortable thrived (Mr. Lloyd, for example). The folks who made this process more uncomfortable eventually killed the golden goose (not many trades in Florence anymore, and it is all Savanarola and the Bishop’s fault. Bonfire of Vanities indeed).