Friendly Society

If given the choice to live in a world dominated by risk or uncertainty, I would choose risk every time. Risk is manageable. Risk can be hedged. Risk can even bring people together.

Take the Friendly Societies of the 18th and 19th century. These predecessors of the modern insurance cooperative helped to distribute financial risks among their members. But in addition to providing access to doctor care or income in tough times, members could literally count on a shoulder to cry on. The prerogative of a decentralized social safety net had the by-product of strengthening the all around community, in the form civic engagement, social events and close knit relationships.

Friendly and mutual aid societies flourished for over 300 years in places like England thanks to the challenge of measuring risk accurately, especially on an individual level. For the most part, insurance schemes, both formal and friendly, brush over the immense heterogeneity of risk types to come up with a flat rate or membership premium — an average cost — which in our fundamental ignorance we agree to pay.

In fact, when individual risk is well known (usually by the individual him or herself) our ability to manage risk with insurance or mutual aid tends to break down. For instance, a person seeking health insurance may choose to conceal their heightened risk of cancer by not sharing their family history. In their famous 1976 paper, Stiglitz and Rothschild showed how this kind of asymmetry of information makes insurance hard, if not impossible. In contrast, consider that a young man cannot easily conceal the salient fact that he is young and male. Since this correlates with worse driving, auto insurance is able to separate into several pools, or to charge several prices, without worry of members misrepresenting their risk type.

In the jargon of game theory, this is the difference between a pooling and separating equilibrium, and it’s not limited to insurance. In any scenario where the type of person or good is not directly observed, you instead observe a signal — a piece of communication — which may or may not be informative. But when different types put off different signals, even if they’re not wholly accurate, types can be discerned, separated and priced accordingly.

In the case of England, Friendly Societies tended to be grouped around industry, skill level, and other imperfect “types”. As a whole, then, the Friendlies weren’t totally unsophisticated. But relative to modern insurance, the mechanisms available for making members reveal their riskiness were first order approximations at best.

History’s Card Sharks

This wasn’t necessarily a bad thing. In the limit, if every person has an idiosyncratic and public risk profile, insurance would be like trying to bet a round of poker with the cards face up. Rather than spreading the cost of car accidents, or health care, or unemployment across large groups, we would be much closer to paying our own way in full. While this could be considered efficient in a narrow sense (each consensually pays his or her marginal cost), in practice it could also be disastrous. Rather than having the congenitally lucky occasionally support the unlucky, the unlucky would lose by predestination. There’s no point in bluffing — you’re simply dealt the hand you’re dealt.

Now imagine Friendly Societies as represented by a group of casual poker players that meet regularly. The play is sloppy and heuristic based, and no one really knows how to calculate pot odds. Sometimes you’re up, sometimes you’re down, but in long run everyone tends to break even. Then one day a new player is invited, a player who happens to be a poker tournament champion and retired statistician. Sometimes he’s up, sometimes he’s down, but in the long run the rest of the table ends up consistently going bust. A friendly game among friendly society suddenly isn’t that friendly anymore, and the group disbands. This is more less the story of how Friendly Societies went from flourishing to sudden decline around the turn of the 20th century.

Innovations in the science of actuarial analysis (the statistical study of risk) had been diffusing through society since at least 1693, when Edmond Halley constructed the first “life table” allowing him to calculate annuities based on age. Not long after in 1738, Abraham de Moivre published “The Doctrine of Chances,” credited as discovering the normal distribution that was greatly expounded on by Gauss in the 1800s. Then in 1762, The Equitable Life Assurance Society was founded, with the first modern usage of the term “actuary” (the company exists to this day as Equitable Life, the world’s old mutual insurer). However, as a profession, insurance was truly born much later in 1848, with the founding of the Institute of Actuaries in London, thanks to breakthroughs in measurement and accounting techniques (such as commutation functions) that brought the doctrine of chances from theory to practice.

Scientific actuaries were history’s card sharks. In order to compete, Friendly Societies were forced to adapt — to learn to better calculate the odds — and ultimately they converged on many of the same administrative, procedural, and “scientific” insurance-like structures. The growing (and widely misused) economic surplus this generated fueled an insurance boom peaking in the later part of the 19th century. For efficiency advantages, societies began deepening national networks well beyond the scope of brotherly love, and strove to expand risk classifications and reduce exposure to high risk types.

By better classifying risk, the “flat rate” pooling equilibrium of the 18th century and earlier rapidly became untenable. Across Europe, the market became increasingly separated, with many differentiated premia and some high-risk types pushed out altogether. This fueled a growing industrial unrest that culminated with the consolidation of private social insurance schemes into nationally run systems.

Commercial insurance, by generating a burst of competition and transitory political instability, was in a sense a victim of its own success. But as many economists have noted, while decidedly non-voluntarist, national schemes (like the one instituted in the UK by the National Insurance Act of 1911) were able to discover large efficiencies of scale through less administrative intensity, tax-based collections, and a comprehensive risk pool. This transaction-cost advantage — and the centuries of social capital it crowded out — guarantees that the days of the close knit mutualists are gone for good, save for some religious congregations. In their stead stands L’Etat Providence — The Welfare State — via a historical process that (as I’ve described) was most rigorously identified by French legal scholar Francois Ewald in a book by the same name.

Classification

The point of this essay (if you’ve made it this far) is to suggest that we are in the midst of a measurement and statistical revolution of equal or greater scale as the 19th century diffusion of actuarial science, with potentially many of the same social and political implications.

With a $99 genotype and sub-$1000 whole genome sequence, in the near future the idea of an insurer asking for your family history of cancer will seem quaint. The immense and inevitable promise of genomics and personalized medicine also portends the inevitable collapse of large, relatively heterogeneous insurance pools, in favour of equally “personalized” healthcare costs schedules.

As I hinted at earlier, this phenomena of moving from pooling to separating equilibria following advances in measurement technology is by no means limited to risk or health care. Any qualitative distribution can be theoretically mapped to a price distribution, but wind up collapsing into a single price given practical measurement constraints. For example, in the past mediocre restaurants were partially supported by the churn of ignorant consumers, since reliable ratings and reviews were hard to come by. Today, rating platforms like Yelp.com mean that restaurants of different quality have more room to raise or reduce prices accordingly, to separate based on credible signals. It’s the end of asymmetric information.

In the corporate setting, pooling equilibria are represented by relatively flat salary structures given a particular seniority, department or education level. Sometimes there is a commission or performance bonus, but day to day productivity is rarely if ever tracked. This opacity is what permits the possibility of zero marginal product (ZMP) workers — workers who literally contribute nothing to a firm’s output.

For any given kitchen, at some point an additional cook does not actually produce more food. While it can be misleading to say that any particular cook is non-productive (maybe there are simply too many cooks in the kitchen), in deciding on which cook to dismiss it matters a great deal that the cooks aren’t all equally productive. On the contrary, the individual contribution of every kind of worker to a firm’s output is often extremely heterogeneous, with the top 20% of workers contributing as much as the lower 80%.

With automation and artificial intelligence reducing the demand for human inputs, the kitchen, so to speak, is shrinking. It has therefore become paramount for firms to identify the 20 and eject the 80. The contemporary increase in country level inequality is widely recognized as technology driven, but few have put their finger on the micro-foundations that explain why. Part of the story is surely “human-machine substitutability,” but in addition firms have simply started monitoring and classifying worker productivity better than in the past. This leads to a separating equilibrium that shows up in the data as job market polarization, rising premia on the central “signals” like college degrees, and (to the extent that signals are sticky) reduced social mobility. Unsurprisingly, a class based society is first and foremost a society which classifies. The silver lining in this case is that, rather than classes based on pedigree, nobility or race, the future promises to be highly — if not brutally — meritocratic.

In one future scenario, just as actuaries identified groups that were uninsurable, perhaps large sections of society will discover they are unhirable. Supposing they have too many “one-star” ratings, as it were, on their HR record, their only hope will be in working for fellow one-stars, and to build a matching-economy around their mediocrity. This is essentially the “Average is Over” scenario imagined by Tyler Cowen, who foresees the return of shanty-towns across the United States. But I wouldn’t bet on it. In many ways, the recent calls for a “universal basic income” exactly parallel the early 20th century’s push towards nationalized social insurance. Only here it is labour-income itself that would be nationalized, as part of the inescapable political economy of separation anxiety.

My own anxiety stems from that fundamental uncertainty of the future, as if the social order is dancing along a knife edge dividing two radically different steady states. In either state — from hyper-meritocracy to a New New Deal — the case of the Friendly Societies demonstrates that the only thing for certain will be the loss of our sacred intangibles: the unmeasured qualities that united distinct types under one roof, from the fraternal lodge to the corporate office.