For the first time in more than a quarter-century, Social Security ran a deficit in 2010: It spent $49 billion dollars more in benefits than it received in revenues, and drew from its trust funds to cover the shortfall. Those funds — a $2.7 trillion buffer built in anticipation of retiring baby boomers — will be exhausted by 2033, the government currently projects.
Those facts are widely known. What’s not is that the Social Security Administration underestimates how long Americans will live and how much the trust funds will need to pay out — to the tune of $800 billion by 2031, more than the current annual defense budget — and that the trust funds will run out, if nothing is done, two years earlier than the government has predicted.
We reached these conclusions, and presented them in an article in the journal Demography, after finding that the government’s methods for forecasting Americans’ longevity were outdated and omitted crucial health and demographic factors. Historic declines in smoking and improvements in the prevention and treatment of cardiovascular disease are adding years of life that the government hasn’t accounted for. (While obesity has rapidly increased, it is not likely, at this point, to offset these public health and medical successes.) More retirees will receive benefits for longer than predicted, supported by the payroll taxes of relatively fewer working adults than projected.
Remarkably, since Social Security was created in 1935, the government’s forecasting methods have barely changed, even as a revolution in big data and statistics has transformed everything from baseball to retailing.
This omission can be explained by the fact that the Office of the Chief Actuary, the branch of the Social Security Administration that is responsible for the forecasts, is almost exclusively composed of, well, actuaries — without any serious representation of statisticians or social science methodologists. While these actuaries are highly responsible and careful and do excellent work curating and describing the data that go into the forecasts, their job is not to make statistical predictions. Yet the agency badly needs such expertise.