Does people’s life satisfaction adapt to material improvements? In a recent paper (gated), Galiani, Gertler and Undurraga find that it does, even in a case of very poor people receiving a really basic service (housing). In a large-scale experiment, some poor households in El Salvador, Mexico and Uruguay were randomly selected to receive a ready-made small house. Receiving such housing increased the share of households reporting to be “satsfied” or “very satisfied” with the quality of their life by around around 40 %, from 0.53 to 0.73, thus confirming that it was something these households really needed. What about the effect in the long term? Eight months later, more than half of the gain had disappeared, highly consistent with the hedonic treadmill hypothesis.
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.
Many reported the paper on smart phones and child injuries by Craig Palsson the previous days. The finding is that the construction of 3G networks in US cities may have lead to increased use of smartphones, less supervision of children, and more children going to hostpital with injuries. I believe most parents are too restrictive, so I was happy to see the following sentence regarding the welfare effects:
“Even though child injuries should not be taken lightly, some might argue that parents were oversupplying supervision or that injuries help build character, and therefore the smartphone-induced injures are welfare enhancing.”
Though I would rather call it a sign that the children are more physically active, which is good for both body and brain, as shown by a recent RCT. And as I have written about before, children tend to be very active during self-organized play.
Chetty, Saez and Sandor have experimented on the referees of the Journal of Public Economics. They find that somewhat unsurprisingly that shorter deadlines, cash incentives and social incentives make referees faster. Further, cash does not crowd out intrinsic motivation, report quality is unaffected, and spillovers on other referee activites are small or nonexistent. They do note that “[O]f course, referees must forego or postpone some activity to prioritize submitting referee reports. The social welfare impacts of our treatments depend on what activities get displaced.” To the extent that it is just procrastination that is crowded out, the conclusions could be even more positive.
Conrad Miller from MIT finds in his job market paper that US affirmative action regulation introduced from 1979 onwards had substantial effect on the black share of employees, also after deregulation. The exogenous variation comes from “changes in employers’ status as a federal contractor” and the fact that it was only federal contractors who were subject to these regulations. To get at the full dynamic effect of the regulation, Miller does not stop at comparing employers when they switch contractor status, but exploits also variation in when the firms are contractors for the first or the last time. In this way he can estimate whether there is a (persistent) causal effect also after a firm has lost his status as a federal contractor (has become “deregulated”).
The event study results are striking:
The effect is quite small – becoming a contractor on average increases an establishment’s black share of around 0.15 percentage points per year – but the key point is that it persists, even when the firm is no longer is a contractor. There is much more in the paper, including a proposed explanation in terms of employers being induced to improve their screening procedures for potential employees.
Religious beliefs have been associated with happiness, but psychologists Shariff and Aknin (PLOS ONE) take a more disaggregated look:
They construct life satisfaction and daily affect measures from the Gallup World Poll and put it together with country-level beliefs in Heaven and Hell from the World Values Survey and the European Values Survey. Believing in Heaven is associated with greater well-being, believing in Hell with lower. This cross-national comparison shows the relationship between aggregate measures of daily well-being and “the percentage of population that believes in Heaven minus percentage that believes in Hell”:
There are also some regression results controlling for some things.
What is the causality? Shariff and Aknin also conduct an experiment on Amazon’s Mechanical Turk: People primed to think of Hell by writing a short paragraph about it reported lower happiness and positive emotions and higher sadness, fear and negative emotions afterwards compared to people writing about Heaven or an unrelated topic. The Heaven group or the control group did not differ from each other.
H/t: Kevin Lewis