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Precise Ignorance

Posted: Apr 23 2014 by Nathan Grawe

Last week the Census Bureau made a very sad announcement. Starting in the fall of 2014 they will be using revised questions in the Current Population Survey concerning insurance coverage. The old questions, used for three decades, had been widely regarded by health experts as flawed because the wording created potential ambiguity that likely led to over-stating the number of uninsured. The new questions were tested alongside the old last year and it looks like the hypothesis was right–estimates of the uninsured were a couple percentage points lower with the new questions than with the old (10.6% vs. 12.5%).

The problem is that the wording of the new questions is so different than the old that the Census Bureau does not believe the two to be comparable. And so their data on insurance coverage will include an irreconcilable break between 2013 and 2014.

This is a real blow to evidence-based policy. The Affordable Care Act is arguably the biggest federal policy reform of the last generation. But because we have chosen to change methodologies right at the point of implementation, it will be difficult to assess its effectiveness. The fact that the old questions were biased is, by comparison, a second-order concern. So long as the bias remains more or less the same over time, the resulting data would still have been useful in estimating the ACA's effect. Instead, a highly contentious empirical debate is likely to continue needlessly for want of empirical data.

The underlying issue here is of broader QR application. Often we are offered fancy methods which promise to eliminate a bias (at least in "large" samples) at the cost of larger standard errors. One example is the use of instrumental variables as a consistent alternative to ordinary least squares. I'm often left thinking I'd rather have my more-precise-but-biased estimate. If I understand the source of the bias I can often back it out of the answer. By contrast, the imprecise-yet-accurate estimator often tells me very little. I'd rather be precisely wrong (in a predictable way) than vaguely right.

And this is exactly where we are with the new Census questions. I'll trust the survey experts that these are better questions, but the sad result will be that we can say precisely nothing.

The QR of Water

Posted: Apr 15 2014 by Nathan Grawe

The New York Times reports that China is planning a large desalination plant to provide 1 million tons of fresh water to Beijing. The paper cites planners' beliefs that the plant "could account for one-third of the water consumption of Beijing, a city of more than 22 million people." Interestingly, the cost of desalinated water isn't as great as you might expect. The Times estimates a cost of $1.29 per ton or about twice the cost of tap water. (Nice job by the journalist in providing that useful context!)

This is potentially important because we consume a lot of water. The USGS estimates that we in the US use about 400 billion gallons per day. Given that a ton is about 240 gallons, that's over 1.7 billion tons of water. At an added cost of 65 cents per ton that means we save about $1 billion every day we are able to avoid desalination to meet our water needs. But with over 96% of the earth's water accounted for by sea water, it is comforting to know that for a bit less than half the cost of Social Security we could find alternative water sources. Here's hoping it doesn't come to that!

QR in Bracketology

Posted: Mar 21 2014 by Nathan Grawe

It's March Madness time. This year, a few more people may beplaying brackets. With financial backing from Warren Buffet, Quicken Loans is offering billion to anyone who filled out a perfect NCAA bracket. As the USAToday article I've linked to notes, that's not as big a risk for Buffet as it sounds like because there are 9,223,372,036,854,775,808 (9 quintillion) different possible brackets. Of course, not all of those are equally likely. Nate Silver and the folks at have put a lot of effort into picking the games. Silver figures that all of his number crunching dramatically improved his odds of a perfect 1 in "7.4 billion". (Actually it is 1 in 7,419,071,319–don't forget those last 19+ million outcomes!)

So, how's it all working out? Not surprisingly, Quicken reports at least 50 people have perfect brackets after the first 16 games. Unfortunately for Nate Silver, he didn't see Dayton beating Ohio State.

Growth of the Internet

Posted: Mar 15 2014 by Nathan Grawe

This news story announces US plans to give up its central role in administering the internet. That got me wondering who in the world (literally!) uses the internet. UNData provides the answer, with a graph designed by Drastic Data. [Note: The Drastic Data site includes a link to the UN data and also allows you to re-cast the data in percentage terms. In case you are wondering, the UN internet data are reported by national statistical offices, so these statistics are likely of widely varying quality.]

The image makes two things clear:

1) Growth has been dramatic–though not literally exponential. We are apparently around 1 billion users, increasing at a pace of about 100 million users per year. At this rate we won't have the entire world online until the end of the 21st century (assuming the world population grows to approximately 9 billion and then stabilizes). Of course, because the last adopters will likely be harder to draw online due to poverty, we probably won't get anywhere near that many users.

2) Until 1997, the US made up more than half of internet users. Since then we have fallen to only 20% of the internet market.

3) Despite the US's decreasing role, we remain the dominant internet user. It looks like European users have only just recently risen equal US users. So, while "the rest of the world" has long overtaken the number of US users, no other homogeneous political group has matched the US use until just recently. Perhaps this explains why the pressure to shift internet control out of the US has taken so long to build considerable pressure.

QR of Equal Pay

Posted: Mar 12 2014 by Nathan Grawe

Around this time of year, news stories often turn to the "gender pay gap." In part, this is a response to gender-equality activists such as the American Association of University Women who celebrate Equal Pay Day to mark the day at which women make up the gender gap from the previous year. This year's State of the Union address gave us a head start on the conversation.

Given the attention, it seemed useful to apply a little QR to the topic to gain a deeper understanding. If you click the link above, you will quickly learn that the gender gap is 23%. But who is in the sample? It takes a little digging, but you eventually find the answer: "full-time, year-round workers." That's a start, but a student armed with 10 Foundational Quantitative Reasoning Questions would know to press further by wondering how the concept of "full-time" is defined. The answer turns out to be anyone working over 35 hours is employed full-time. That well-prepared student might then wonder what is controlled for in this analysis. The answer is nothing other than full time status–not even hours worked.

A recent Labor Department study reports that the wage gap shrinks by 71% (to around 5%) after controlling for well-documented income-altering factors other than sex like hours worked, age, number of children, marital status, union representation, race, education, and fraction of women working in the person's industry and occupation. In other words, the gender pay gap is largely explained by choices.

So, does that mean that we can largely ignore complaints of discrimination? Clearly not. That women and men on the same career path earn the same income does not rule out discrimination that limits work choices. Are there barriers in the education system or in hiring that make it harder for women to end up in higher-paying positions? Does society push women into lower paying life paths? These are hypotheses that cannot be ruled out by the data.

But the tools of QR can help us focus in on the real explanations for an important observation.

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