What’s the Big Deal about Big Data? Analysis Rids Social Security Forecasts of Bias

img_1345For decades since its origins in Depression-era America, Social Security – the superannuation insurance program for the United States — has relied on having enough money in the bank to cover the checks its pays out to retirees. And so, explains Gary King, the director of Harvard’s Institute for Quantitative Social Science, “In order for the program to succeed, to survive, the Social Security Administration must forecast how much money is in the Social Security trust fund.”

In this fifth video drawn from a talk King made this summer on Capitol Hill in Washington, D.C. to an audience filled with policymakers and fellow academics, he explains how this single largest government program faced an existential crisis which computational social science helped to forestall.

The mostly qualitative methods used to draft these forecasts came from a small army of actuaries who insulated themselves from political whim – and from scientific advancement – for 85 years. “These,” King explains, “are the 85 years in which we’ve learned more about forecasting than any other time in human history, and this was the time the United States chose not to update the methods by which they ensured the solvency of our retirement.”

Nonetheless, the forecasts they made, while not spot on, were roughly (and sufficiently) accurate – until the year 2000. That was when, as a result of growing life expectancies and the unwillingness of the actuaries to account for this, the forecasts became systematically biased toward making the system look healthier than it actually was.

To its rescue came … well, watch the video and learn who and how!

King’s talk, “The big deal about big data,” was hosted by SAGE Publishing with co-sponsors the American Political Science Association and the American Statistical Association.

Videos in the series

  1. Ziyad Marar On The Opportunities That Big Data Provides Social Scientists
  2. What Makes Big Data Valuable?
  3. Examples: Exciting Data That Is Useless Without Analytics
  4. Example: Social Scientists Determine Cause Of Death At A Distance
  5. Example: Analysis Rids Social Security Forecasts Of Bias
  6. Example: New Data Methods Combat Gerrymandering
  7. Example: Big Data Much Better Than People At Determining Keywords
  8. Example: Watching Chinese Citizens Get Around Censorship
  9. Example: Learning The Real Reason For Chinese Censorship
  10. The Spectacular Success Of Quantitative Social Science
     
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