Tag Archives: statistics

Good Government vs. Less Government

Or: Why the Heritage Freedom Index is a Damned Statistical Lie

This guest post was contributed by StatsGuy, a frequent commenter and occasional guest on this blog. It shows how quickly the headline interpretation of statistical measures breaks down once you start peeking under the covers.

Recently, a controversy raged in the blogosphere about whether neo-liberalism has been a bane or a boon for the world economy. The argument is rather coarse, in that it fails to distinguish between the various elements of neo-liberalism, or moderate deregulation vs. extreme deregulation. But if we take the argument at face value, one of the major claims of neoliberals is that countries in the world which are more neoliberal are more successful (because they are more neoliberal). I disagree.

My disagreement is not with the raw correlation between the Heritage Index and Per Capita GDP. A number is a number. My disagreement is with the composition of the index itself, and interpreting this correlation as causation between neo-liberalism and ‘good things.’

My primary contention below is that many of these measures used in the composite Heritage Index have nothing to do with less government, and a lot more to do with good government. It is these measures of good government that correlate to economic growth and drive the overall correlation between the “Freedom Index” and positive outcomes. Secondarily, I will argue that many of the other items in the index (like investment freedom) are not causes of growth, but rather outcomes of growth.

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Rewarding Teacher Performance? Resist the Temptation to “Race to Nowhere”

This guest post is contributed by Kathryn McDermott and Lisa Keller. McDermott is Associate Professor of Education and Public Policy and Keller is Assistant Professor in the Research and Evaluation Methods Program, both at the University of Massachusetts, Amherst.

On March 29, the U.S. Department of Education announced that Delaware and Tennessee were the first two states to win funding in the “Race to the Top” grant competition.  A key part of the reason why these two states won was their experience with “growth modeling” of student progress measured by standardized test scores, and their plans for incorporating the growth data into evaluation of teachers.  The Department of Education has $3.4 billion remaining in the Race to the Top fund, and other states are now scrutinizing reviewer feedback on their applications and trying to learn from Delaware’s and Tennessee’s successful applications as they strive to win funds in the next round.

One of the Department’s priorities is to link teachers’ pay to their students’ performance; indeed, states with laws that forbid using student test scores in this way lost points in the Race to the Top competition.  A few months ago, James pointed out some of the general flaws in the pay-for-performance logic; here, our goal is to raise general awareness of some statistical issues that are specific to using test scores to evaluate teachers’ performance.

Using students’ test scores to evaluate their teachers’ performance is a core component of both Delaware’s and Tennessee’s Race to the Top applications.  The logic seems unassailable: everybody knows that some teachers are more effective than others, and there should be some way of rewarding this effectiveness.  Because students take many more state-mandated tests now than they used to, it seems logical that there should be some way of using those test scores to make the kind of effectiveness judgments that currently get made informally, on less scientific grounds.

The problem is that even if you accept the assumption that standardized tests convey useful information about what students have learned (which we both do, in general), measuring the performance gains (or losses) of students in a particular classroom is far more complicated than subtracting the students’ September test scores from their June test scores and averaging out the gains.  We’re concentrating on the statistical issues here; there are other obvious challenges in test-based evaluation, such as what to do for teachers who teach grade levels where students do not take tests and/or subjects without standardized tests.

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Same-Sex Marriage and Time

Yesterday Maine voted to restrict marriage to opposite-sex couples. It’s a sad day for people who believe that all couples who love each other should be allowed to marry, full stop.

But the chart below may cushion the blow a tiny bit. It’s from a paper by Jeffrey Lax and Justin Phillips, “Gay Rights in the States: Public Opinion and Policy Responsiveness,” recently published in the American Political Science Review (via The Monkey Cage). What you are seeing is support for same-sex marriage in 1994-96, 2003-04, and 2008-09; solid dots indicate that same-sex marriage is equal, hollow dots that it is not.

marriage

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How to Waste 45 Minutes (Like I Just Did)

Start here. Then keep reading. It has nothing to do with economics, but a lot to do with statistics.

Or don’t, if you need to get something done this morning.

Update: One reader writes in:

“I beg to differ that this link has nothing to do with economics:  The same technique it uses to detect possible making-up of poll numbers was used in a recent paper to detect possible making-up of National Accounts statistics in some developing countries in a widely used economic dataset:

http://www.bepress.com/bejm/vol7/iss1/art17/.”

By James Kwak

Modeling Everything, Public Plan Edition

Ezra Klein and Paul Krugman are both highlighting Nate Silver’s analysis of campaign contributions and the public health plan option. The quick summary? Campaign contributions matter – in this case, by about nine senators. Mainly I’m impressed and encouraged that people can use publicly-available data to quickly whip together plausible models answering questions that otherwise we would all just pontificate about.

Coincidentally, I was getting my car inspected this morning and picked up an October 2008 copy of New York Magazine in the waiting room, which had an article about . . . Nate Silver. The article includes a picture of the presidential electoral map as Silver predicted on October 8, in which he called every state correctly except Missouri (which, remember, took a few weeks to figure out whom it had voted for). Most of the article is about how the empirical approach to baseball turns out to be useful in other areas, like politics and public policy.

Update: Mark Thoma points out this counterargument by Brendan Nyhan (who long ago wrote a blog with the brother of one of the best developers at my company). Nyhan says “studies have typically found minimal effects of campaign contributions on roll call votes in Congress,” and cites a Journal of Economic Perspectives paper as backup.

OK, Nyhan may be right. But he may not be.

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