The Conversation

The Obama Administration's Serial Abuse of Numerical Talking Points

From jobs created-or-saved to Obamacare sign-ups, one of the constants in the Obama administration has been its touting of inflated or otherwise misleading numbers to make it's case for a particular policy.

It was a pattern that got started early. In May 2009 the President announced that his massively expensive stimulus package had "saved or created nearly 150,000 jobs." Even Politifact agreed that was "a guess presented as a fact." In December 2009 the administration quietly abandoned it's made-up metric (albeit for another one nearly as misleading).

During the passage of Obamacare Democrats routinely talked about 46 million uninsured Americans. It was a big number but only accurate if you included roughly 10 million who were not Americans, 12 million who were already eligible for a program like Medicaid but hadn't applied and 9 million who could afford healthcare but chose not to buy it. But forget about the details and just focus on the big number.

Remember the President's assurance that Obamacare would save the average family $2,500 in premiums? That misleading talking point was based on some back-of-the-envelope calculations, guesstimates really, by a couple of his advisers. Candidate Obama then further abused the public's trust by claiming that total, which was meant to be a savings to the health care system as a whole, would be a direct reduction in family premiums. It was utterly misleading, to the point that the NY Times called Obama out, but the President certainly got a lot of people's attention promising that $2,500 savings.

Of course we've seen the same thing with Obamacare enrollment. When the exchanges first opened for business we heard how many millions of people tried to log on to the website but only later found out the total enrollment on day one was six people. We also heard that the surge of visitors caused the site--which we were told was built to handle 60,000 concurrent users--to crash. Weeks later we learned the site could only handle 1,100 people at a time as of the day before launch. The unstated rule seems to be: when in doubt, throw out a big number. It'll make you sound like less of a failure.

Back in December the administration started focusing attention on millions of new Medicaid enrollments. They forgot to mention that these millions included people who were renewing their enrollment. You could find that information in the footnotes of CMS documents but it wasn't obvious from the way administration figures talked about it. Glenn Kessler at the Post gave the big Medicaid numbers claim 3 Pinocchios back in January.

Just as it did with jobs created-or-saved, the administration used the politically helpful metric, "sign ups," which is significantly higher than actual enrollments. They've been reporting every major milestone as if it were a triumph, always assuring reporters they don't know what the real (read: significantly lower) enrollment figures are. Better to announce 7.1 million signed up than admit only 5.5-6 million enrolled of which maybe two million were not previously insured.

All of this came to mind this afternoon when I read part of an interview MSNBC's Irin Carmon did with Betsy Stevenson who sits on the president’s Council of Economic Advisers. Carmon asked why the administration continually used a statistic claiming women make 77 cents for every dollar a man makes even though it has been repeatedly shown to be more misleading than enlightening. Here's a bit of Stevenson's reply:

I agree that the 77 cents on the dollar is not all due to discrimination. No one is trying to say that it is. But you have to point to some number in order for people to understand the facts.

That final bit about pointing to "some number" might as well be the White House motto. It's a perfect summary of what they actually do, i.e. throw out a catchy number--big or small depending on what is politically helpful-- and assume most people will never read the fine print. This is the administration of big numbers and bogus data.


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