Tag Archives: measurement

“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.

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

Did Manchester United’s decline start under Ferguson?

Carl Bialik (previously known as The Numbers Guy) at 538 argues that Manchester United was exceptionally lucky in Alex Ferguson’s last season, and that David Moyes just kept steady a downward trend. Bialik’s point is that looking at finer measures of performance than simply points, such as the share of shots taken and the goal differential, United should not have done as well as ghey did last year, so Moyes should not be blamed for the (apparent) decline this year. There is a nice graph of the development of United’s share of shots taken the last 12 years in the post showing this clearly.

Ragnar Frisch on economic planning

Ragnar Frisch in the early 1960’s had high hopes for future Soviet economic development:

The blinkers will fall once and for all at the end of the 1960s (perhaps before). At this time the Soviets will have surpassed the US in industrial production. But then it will be too late for the West to see the truth. (Frisch 1961a)

That is from an article by Sæther and Eriksen in the new Econ Journal Watch. The paper contains much more than this angle.

It must be said that it was quite common for economists at the time to believe that the Soviet Union had a sustainable system. For instance Paul Samuelson, who repeatedly pushed his predictions for when the American GNP would be overtaken by the Soviet GNP further into the future. If anyone knows about any modern Norwegian debate about this, I would be interested to learn about it.

H/t: MR, Arnold Kling.

 

 

How much would you demand to be paid if I took out your appendix without anesthetizing you?

Discussing economists’ reliance on measures of growth and GDP, Joel Mokyr asks:

So you go to somebody who is about to have surgery and you ask him, How much would you demand to be paid if I took out your appendix without anesthetizing you, without putting you to sleep? Nobody would agree. The sum would be infinite. What can anesthesia contribute to GDP when it was introduced in the 1850s and 1860s? Russ:Could not be very much. Guest: Nothing. It’s very small. But that is exactly the kind of thing we fail to account for in our calculations. So that’s why I gave that whole list of things; and we could make this list infinitely large. It is the small things that actually don’t amount to an awful large part of our income and product that actually have improved life a great deal and that we really wouldn’t want to do without any more.

From Russ Roberts’ interview with Mokyr at Econtalk. The quote was first brought to my attention by Arnold Kling.

Open government data for learning

How to encourage release of data?

“Open data” and “open government” have become buzzwords in recent years, but are often conflated. Mark Headd at the Civic Innovations blog refers to the “Open Data vs. Open Government” debate, which he considers an issue of data for transparency vs for “operational” needs. He emphasizes the need to keep in mind that the original objective of the open data movement was transparency, and warns about getting lost in bus schedules, etc.

As an example of data for transparency he gives the City of Philadelphia’s release of complaints against Philadelphia police officers. Most probably agree that police complaints are not a good thing and might provide clues into bad behavior. However even in this case one should be careful about stressing the transparency/accountability angle, since this easily creates the conception that the goal is to find wrongdoers, whereas what should be the goal is to learn. If there are systematic factors affecting complaints, it would be valuable to learn about them. And even in the absence of these, a complaint does not imply a presumption of guilt, that is for other investigations to determine.

If someone fears being subjected to unfair criticism, that is a legitimate motive for non-cooperation.

Two collections of interesting urban government datasets can be found here.

Measurement is about learning and improvement, not control

Chris Blattman rants against the resistance to trying to estimate cost-effectiveness that he has encountered in the aid world. One thing he writes about is the “we do not experiment on people” argument (counter: there are always some who gets the stuff and some who do not).

Another expression of reluctance to measurement that I have encountered is: “We understand that we should be held accountable to donors, but why the need for such tight control? Don’t they trust us?” But this gets wrong the rationale for measuring, which primarily is to learn about the effects of what we do in order to do it better. Even if you are not accountable to anyone, measurement may help you learn what you do best and to improve.