Tag Archives: data

“Is there really an empirical turn in economics?”

The recent “empirical” turn in economics should be known as an “applied” one and it is just one in a long series of related developments. Moreover, it is a move towards the historical roots of the discipline. Those are some lessons from Beatrice Cherrier‘s essay “Is there really an empirical turn in economics?“. Based on research conducted together with Roger Backhouse, she takes issue with the idea that there has been a revolution in economic research involving empirics. Some points I liked:

  • Empirical work has been live and well, what has changed is its recent larger role in top journals. Besides, the view of theory as dominating in economics is based on looking only at the last 50 years – pre- and immediate post-war economics used to be a lot more empirical.
  • Much theory has become more applied, often involving data. And John Bates Clark medal citations stress “applied,” often taken consisting of a mix of theory and empirics.
  • Increasing availablity of data is a development that has been ongoing since at least the 1960’s. Hype around and criticism of new, large sources of data were the same in the 1970’s as today.
  • Computerization is overrated, much modern empirical work is computationally and numerically very simple.
  • Oscar Morgenstern (of von Neumann and Morgenstern‘s Theory of Games and Economic Behavior fame) proposed that to become a fellow of the Econometric Society, it should be a requirement to “have done some econometric work in the strictest sense” and be “in actual contact with data they have explored and exploited for which purpose they may have even developed new methods.”

H/t: Erwin Dekker.

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Ironic debunking

Who Will Debunk The Debunkers? Daniel Engber asks in a fascinating piece at fivethirtyeight. He tells the story of “meta-skeptic” Dave Sutton, who has made it his specialty to doubt other doubters’ explanations. The first few paragraphs, about iron, prove the point that a good debunking is often too clever. Likewise with Semmelweis – the received version is probably too simple. Finally, Sutton comes off as somewhat of a megalomaniac when it comes to his work about Darwin, providing yet another layer to the story.

Most Norwegians do not ski

silly proposal to spend public money on giving everyone a type of sports equipment that in average can be used around one quarter of the year got me wondering how many Norwegian actually do ski. So I looked up some data from Statistics Norway’s survey on living conditions

skiing1997_2014

Source: Statistics Norway

More than half the population skis at all, skiing (on the extensive margin) is clearly on a downwards sloping trend, but is free skis to everyone the solution? Given that skis can be obtained nearly for free already, perhaps interest is just not that high. Better to build out opportunities for all-year activity close to where people live, restrict time spent watching television, raise sugar taxes, and get more physical activity into school.

“What’s The Point” podcast

I have started listening to a new podcast called What’s The Point, produced by Nate Silver’s fivethirtyeight team. The show “is a short weekly conversation (tag line: “Big Data. Small Interviews.”) that highlights data’s growing influence and brings in the people who are using it in surprising ways.” I have enjoyed the first few episodes and will continue to listen to the show on a regular basis. In the second episode, the guest was astrophysicist Neil deGrasse Tyson, and when talking about his multiple interests and obligations, he said something I liked very much about having too much to do: “When something is out of balance you can get quite innovative in your attempts to resolve that fact.” Anyway, the podcast is recommended.

Review: Intelligence: A Very Short Introduction

Since I am doing some work using intelligence test data, I wanted to read something introductory material on that topic. Enter Intelligence: A Very Short Introduction (2001) by Ian J. Deary. I found this a useful introduction to how psychologists/psychometricians have thought about these things. What I was really after was the foundational stuff in chapter 1, and that is what I will focus on here. (Though chapter 6 on the Flynn effect is also solid, and taught me that American SAT scores have been declining in the same period as IQ scores have been rising.)

Deary takes the test collection called Wechsler Adult Intelligence Scale III as a starting point. WAIS-III consists of 13 different tests, and strikingly,

“every single one of those 13 tests in the WAIS-III  has a positive correlation with every other one. People who are better at any one test tend to be better at all of the others. There are 78 correlations when we look at all the pairings among the 13 tests. Every single correlation is positive – a good score on one of the tests tends to bring with it a good score on the others. There are no tests unrelated to any other one, i.e. there are noe near-to-zero correlations. There are no tests that are negatively related with other ones. Even the lowest correlation between any two tests is still a modest 0.3 (between picture completion and digit span). […]

The first substantial fact, then, is that all of these different tests show positive associations – people good at one tend to be good at all of the others. […]

The second important fact is that some sub-groups of tests in the WAIS-III collection associate higher among themselves than with others. For example, the tests of vocabulary, information, similarities, and comprehension all have especially high associations with each other. So, although they relate quite strongly to every test in the WAIS-III collection, they form a little pool of tests that are especially highly related among themselves. The same thing occurs with digit span, arithmetic, and letter-number sequencing. They relate positively with all of the other tests in the collection, but they relate especially highly with each other (pp. 7-8).”

In the WAIS-II tests, there are four groups of tests that correlate particularly strongly (called “group factors”), labelled: Verbal comprehension, Perceptual organization, Working memory, and Processing speed, ref. the figure below (p. 3). Inline image 2

Positive correlations between the four group factors are high. This has often been taken to imply that the skills required to do well on each have some common source, which has traditionally been called g (“general factor”). Strictly speaking, the fact that the different test scores are positively correlated does not imply that they have something in common or that “g” corresponds to anything real. Deary is at one point fairly clear about this, writing: “The rectangles in Figure 1 are actual mental tests – the 13 sub-tests – that make up the Wechsler collection. The four circles that represent the ‘group factors’ and the circle that contains g are optimal ways of representing the statistical associations among the tests contained in the rectangles. The things in the circles, the specific/group factor abilities and ‘g’, do not equate to things in the human mind – they are not bits of the brain (p. 11).”

Though he muddles it somewhat when continuing with “The names we pencil into the circles are our common-sense guesses about what seems to be common to the sub-groups of tests that associate closely. The circles themselves emerged from the statistical procedures and the data, not from intuition about the tests’ similarities, but the labels we give the circles have to be decided by common sense (p.11),” and later much more by going on to treat ‘g’ as a valid stand-alone explanatory concept, and writing e.g. “We already know from Chapter 1 that there is general ability and there are […] specific types of mental ability (p. 85).”

Nevertheless, the book seems to be a good exposition of intelligence testing and how psychologists have viewed and continue to view the results of these tests.

 

 

Less reading, more television

I have always liked time use surveys and would love to use them more, for example to write posts like this one at Vox. Now I have recently begun working a little with some such Norwegian surveys, so here is a little about recent developments in how young Norwegians spend their leisure time.
(Apologies for the unsatisfying look of some of the graphs, they are simply lifted from an online resource.)

figure_leisure_1970_2010

Percent spending time on various leisure activites an average day, 1970-2010.

In short, since 1970 fewer of us are reading an average day (turquoise), while more area watching television (light blue), and recently using internet (included in “Other” (dark brown)).

A bit more detailed look on average time for 1991-2005 confirms that television time is increasing; figure_media_minutesTVwatched1991_2005

and although there might be somewhat of a Harry Potter effect for the youngest in the beginning of the 2000’s, time spent reading is quite consistently going down, figure_media_percentagebookreaders1991_2005

including time spent on newspapers, figure_media_percentagnewspaperreaders1991_2005

magazines,    figure_media_percentagemagazinereaders1991_2005

and even cartoons. figure_media_percentagecartoonreaders1991_2005

Is that a bad thing? Well, that depends, but if it is passive television entertainment that crowds out reading, I would not be surprised if that had some long term consequences.

How Americans Die

Via Andrew Gelman, a great slideshow about “How Americans Die” from Bloomberg. We see the development of American mortality 1968-2010 broken down in several different ways. It was new to me how important AIDS was as a mortality factor on the population level between the mid-80’s until the mid-90’s and that it affected black men the most. Also, “suicide […] has recently become the number one violent cause of death.” Go have a look.

Bloomberg 2014 How Do Americans Die