TVHE

I agree with Dim Post that the choice of countries to add to the choice of countries made in the Spirit Level is a bit arbitrary (although I think Not PC and Kiwiblog also have a point regarding how sensitive the regression results are to the choice of countries that aren’t strictly the largest outliers in the sample) – but I still think that this particular “regression” is a steaming pile of unmentionables.

Lets ignore the fact that the slope of the  “regression line” appears very sensitive to the addition of a few countries.  Lets instead focus on the fact that it is a poor regression and that there isn’t a clear “theoretical background for causation”.

Read more…

Via Scott Sumner we saw the following article that mentions economic data and economic predictions.  The statements that stood out to me were:

(Economic) predictions are, of course, the bread and butter of economic institutions. But can we believe them?

In recent years, some economists have begun to express doubts over predictions made from huge volumes of data, but they are in the minority. Most embrace the idea that more measurements mean better predictive abilities.

Hold up.

For one, as we have mentioned prediction is not the central element of what economists do – and even when they do predict the goal of such prediction is to give some view regarding risks and movements, not direct figures (it is more ordinal than cardinal in some sense).

Secondly, ever since the Lucas critique economists have been very nervous about predictions from large amounts of data without theory – I would say that the majority of economists doubt the usefulness of econometric models relying solely on huge amounts of data.

Economists would like data with less measurement error, that is closer to representing the true economic variables we discuss in theory – we aren’t looking for an infinite number of measures we can stick together to find a result.  An economist that doesn’t use theory to inform their discussions of the economic outlook, but uses lots of data, isn’t an economist – that is all.

I would suggest that anyone who is interested in economic models reads this post, namely because I agree with it wholeheartedly

While it might seem cool to run some regressions and get a result that you believe will tell you the future it is important to realise what your implicit assumptions are.  If you add a variable to you model and it is significant but you don’t know what it has to do with anything you should be careful.  This is how using a lag functions.

You need to ask yourself why is this lag significant? what is the process behind it? Data can only really be used when you can frame it with a model of how the world works.

Personally, I hate using too many lags unless I have an understanding of why the lag matters.  When you do an empirical model it isn’t just the “significance of the variables” that matters – it is the believability of the implicit model that it represents.

After the sadness of California passing Proposition 8 banning gay marriage last year, there have been a couple of recent victories for civil rights campaigners in Vermont and Iowa. That motivated Nate Silver to work the numbers and ask when we might expect the rest of the US to reject a ban on gay marriage. The outcome is shown in the following diagram from The Map Scroll:

Rejection of ban on gay marriage

I’m lucky enough to be flicking through the latest Review of Economics and Statistics so I thought I might sum up a couple of interesting tidbits while I’m reading: Read more…

I couldn’t resist posting this chart linked byMegan McArdle. It is just SO appropriate given Matt’s recent post. If using graphs like this counted as proving a scientific fact then the world would be a whole lot easier to explain

For all the nerds and geeks who cursed the popular people at high school but comforted themselves with the thought that future success would be theirs… Steven Levitt has bad news:

…each extra close friend in high school is associated with earnings that are 2 percent higher later in life after controlling for other factors. While not a huge effect, it does suggest that either that a) the same factors that make you popular in high school help you in a job setting, or b) that high-school friends can do you favors later in life that will earn you higher wages.

Read more…

According to a recent book by Christian Broda and David E. Weinstein (Prices, Poverty, and Inequality: Why Americans are Better Off Than You Think) (ht Marginal Revolution) growth in income inequality was less pronounced in the US because of changes to the quality and cost of goods that “poor” people purchased.

This indicates to me that a tiered consumer price index could be a useful thing.  Currently the household economic survey (HES) provides an annual tiered income measure (where we see the average income of different income deciles).  However, this nominal measure is not particularly useful if the change in prices experienced by different groups are very diverse.

As a result, a similarly tiered CPI measure (so a CPI for each income decile) would actually give us a much better way to figure out change in “real income” and thereby a fairer measure of the distribution of real income – which is something we care about.

Surely the HES has a measure of purchases by different income groups.  As the CPI is broken down into different products it should be possible to take these weights and come up with a loose set of indicies that represent the price inflation faced by different income declines shouldn’t it?

Vox report a recent study on adaptation to life-changing events. I don’t want to venture into a discussison of happiness research — a topic fraught with controversy — but the results are always fun to read!

Apparently marriage really isn’t all that exciting and most people get progressively less happy as it drags on. Unsurprisingly, ending an unhappy marriage by divorcing one’s partner is a fantastic thing for most people; however, I’m surprised to see a strong increase in happiness in the lead up to the divorce with no drop immediately around it. Providing more evidence that marriage is a happiness wrecker is the plot for widowhood: while the death of a spouse is devastating, people are happier 3-4 years later than they were during the marriage!

Economists like to claim that their discipline is about providing tools for analysis, not answers to ready-made problems. OK, so they’re hardly alone in that but Dani Rodrik links an interesting paper by Michael Greenstone that walks the walk. Greenstone

…shows how data from world financial markets can be used to shed light on the central question of whether the Surge has increased or diminished the prospect of today’s Iraq surviving into the future. In particular, I examine the price of Iraqi state bonds, which the Iraqi government is currently servicing, on world financial markets. After the Surge, there was a sharp decline in the price of those bonds, relative to alternative bonds. This decline signals a 40% increase in the market’s expectation that Iraq will default. This finding suggests that, to date, the Surge is failing to pave the way toward a stable Iraq and may in fact be undermining it.

I really like how he uses modern economic and econometric techniques to tackle important public policy issues. However, we shouldn’t rely too heavily on the wisdom of the market in evaluating the effectiveness of the surge. While the aggregation of information that occurs in market pricing might be a better indicator than anecdotal evidence, ‘The Market’ doesn’t have superpowers that grant it access to information nobody else has. Many of the decision makers in that market likely have little more access to information about the surge than you or I. Aggregation tends to dampen the influence of extreme views but it doesn’t guarantee accuracy.