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How Our Days Became Numbered:
Risk and the rise of the statistical individual
by Dan Bouk
WHENEVER Frederick Hoffman visited the US South, one of his first concerns was sanitation. He also asked locals how often they washed and if they ever caught colds. Once Hoffman's questions were answered, he would trek to the town cemetery with a typewriter on which he would record deaths.
He was no hypochondriac, but an actuary who worked for the Prudential Insurance Company using mortality rates to set life-insurance premiums – and he took it very seriously. From 1901 to 1921, Hoffman filled a large library with records, including dates of birth and death from 100,000 gravestones.
As Dan Bouk explains in How Our Days Became Numbered, the Prudential funded Hoffman's expeditions because these records hardly existed before actuaries gathered them. Life insurers pondered the lives of ordinary people more than any other institution during the period of America's industrialisation. In this history, Bouk sees the emergence of "numbered" lives, foreshadowing big data.
For life insurers, success has always depended on data about life and death, and as collection improved, companies thrived. Actuaries could predict how long clients were likely to live, and could price insurance policies so that companies paid out less than they took in. The earliest predictions were based simply on age, but over time Hoffman and his colleagues found hundreds of other factors – some troubling, but none more divisive than race.
In 1881, Prudential actuaries determined that black people died earlier than whites. So although black and white workers alike paid a premium of 5 cents a week, families of the former were offered only two-thirds of the payout that whites received. Other companies followed suit, provoking a backlash as black activists "fought life insurers over the relevance of history to their race's future", says Bouk. Optimistic that their civil war victory had fundamentally changed black people's fate, the activists challenged the core belief in the predictive power of the past.
As Bouk documents, blacks won in state legislatures,which banned racial discrimination in payouts, but ended up losing the chance to buy life insurance at all from most companies. Conceding that the civil war was a rupture, Hoffman and his colleagues decreed, unscientifically, that emancipated blacks were uninsurable.
But insurance could also heal society. "Unsatisfied with predicting the future," writes Bouk, life insurance statisticians "set out to change it" – through preventive medicine, especially once they realised that extending lives increased the time clients had to pay into the system.
The data that insurers collected proved invaluable. For example, by 1907, they recognised the risks of obesity, revealing optimal ratios of weight to height. And by 1913, they pioneered the annual check-up.
Yet even the boon to public health had a downside, as most people examined were judged medically defective. The statistical onslaught of insurers, writes Bouk "helped precipitate a broadened... health crisis, or, in another sense, helped invent one". The US became a hypochondriac nation.
Bouk sees resonance both in the contemporary African-American experience and in the mania for diagnosis. Discrimination based on statistics has returned in the form of racial profiling, and personal genomics threatens to overwhelm our lives with new sicknesses and treatments.
Bouk has no advice about the responsible use of an ever-growing data hoard, but this fascinating history should teach us not to be fatalistic. Big data is what we choose to make it.
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