One and a half years ago, I blogged about a working paper by Simen Markussen, Knut Røed and myself showing that access to commercial television channels during childhood and adolescence reduced cognitive ability scores and high school graduation rates of Norwegian men. Now, a substantially revised version is forthcoming in The Journal of Human Resources. (Preprint here.) The effects appear to be driven by consumption of light television entertainment crowding out more cognitively stimulating activities.
Two years ago, I blogged about a working paper on conditions for welfare and high school completion by Simen Markussen, Knut Røed and myself. The paper is now finally accepted for publication and is forthcoming in Labour Economics. The final version is here, freely downloadable until the beginning of October. An updated working paper version can be found here.
Robert Frank writes in the Upshot that “Chance events play a much larger role in life than many people once imagined.” Maybe so, but this piece is poorly argued. Frank is first quoting some small marginal effects, like time of year birh effects and author order effects. These factors probably play a role, however, in absolute size I am pretty sure they are dominated by other non-random factors. Being born in the right country is a good example, though.
This reminds me of Johannes Haushofer‘s “CV of Failures,” which made the rounds in the blogosphere and several newspapers earlier this year. (He got the idea from a piece by Melanie Stefan.) He writes in his CV of Failures:
Most of what I try fails, but these failures are often invisible, while the successes are visible. I have noticed that this sometimes gives others the impression that most things work out for me. As a result, they are more likely to attribute their own failures to themselves, rather than the fact that the world is stochastic, applications are crapshoots, and selection committees and referees have bad days. This CV of Failures is an attempt to balance the record and provide some perspective.
I like this a lot, and kudos to Haushofer (and others he references as having done the same thing), but of course he is doing this as a hugely succesful guy. His actual CV lists PhDs from Harvard and Zurich, academic positions at Princeton, Harvard, and MIT, along with publications in top journals. What about the “failures”? Several other top schools and papers rejected at AER, QJE, Science, …
This point is that although there is some randomness along the way, it is not random or due to luck that Haushofer have accomplished a great deal.
That is the title of a just released working paper by Simen Markussen, Knut Røed, and myself. We show that access to commercial television channels during childhood and adolescence from the 1980’s onwards in Norway reduced cognitive ability scores and high school graduation rates of young men.
Two papers in The Economic Journal November 2014 deal with how childhood information may predict adult outcomes.
Frijters, Johnston and Shields consider the question Does Childhood Predict Adult Life Satisfaction? Using repeated surveys of people born in the UK in 1958, they are able to explain only 7 % of people’s adult life satisfaction with a very wide range of family and childhood variables. Interestingly, exploiting the panel dimension, they estimate that around 40 % of adult life satisfaction is a trait (i.e. fixed), so it is surprising that their first number is so low. It is as if type of childhood almost does not matter. Education and wages are predicted much better.
I do not know if information on time preferences would have helped, but Golsteyn, Grönqvist and Lindahl at least claim that Adolescent Time Preferences Predict Lifetime Outcomes in their article in the same issue. They find that Swedes who were future-oriented (had low discount rates) as children went on to obtain more education, better grades, higher incomes, and better health (obesity and mortality) as adults than their more impatient peers. The authors are admirably clear that they are not estimating causal effects.
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).
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.
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.)
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)).
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.