Category Archives: Measurement

Activation against absenteeism – Evidence from a sickness insurance reform in Norway, published in Journal of Health Economics Volume 62, November 2018, Pages 60-68

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Or why you should take courses that are not for credit and do presentations in class.

In the spring of 2015, I followed a course in microeconometrics at the Department of Economics at the University of Oslo to learn some more econometrics. As part of the course, the students had to present published papers employing the methods from the course. I volunteered to do two such presentations, that both ended up as papers of my own!

For my first presentation, which was on the synthetic control method (SCM), I asked the instructor for that lecture, Tarjei Havnes, if I in stead could present an application of my own. I did not know much about the SCM from before, but in the lecture I noticed that it would fit very well to a recent sickness absence reform in a Norwegian region I had head about from Knut Røed, a colleague at the Frisch Centre. The fact that the seminar schedule was on a few weeks lag from the lectures gave me some time to implement a basic analysis of the reform and put together a presentation. The subsequent positive feedback in the seminar motivated me to develop it into a proper paper, which is now just published in the Journal of Health Economics. The paper if of course much extended since that seminar, but the core remained the same.

The reform in question was a program undertaken by the Norwegian region of Hedmark in 2013. It was aimed at strictly enforcing a requirement that people on long-term sick leave be partly back at work unless explicitly defined as an exception. I found that the reform reduced sickness absenteeism by 12% in the reform region compared to a comparison unit created by a weighted average of similar regions. Thus, making use of the partial work capacity of temporary disabled workers has the potential to reduce long-term absenteeism and bring down social security costs. 

A key graph is below, showing how actual absenteeism in Hedmark (solid line) after the reform diverged from the estimate of absenteeism in the absence of reform (dashed line): 

Fig. 1. Trends in the sickness absence rate in Hedmark and the synthetic control region. Note: The dotted line at the fourth quarter of 2011 indicates the final quarter of the matching period. The dashed line at the second quarter of 2013 indicates the period in which the activation program was introduced.

    The effect is driven by both increased part-time presence of temporary disabled workers and accelerated recovery. Musculoskeletal disorders was the diagnosis group declining the most. I conclude that such an activation strategy represents an alternative to traditional attempts at welfare reform involving stricter screening or reductions in generosity, and may be more compatible with already existing legislation and obligations, as well as easier to find support for across political priorities.

    The paper is freely available for a month here.

    “Is there really an empirical turn in economics?”

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    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.

    Childhood predictors of adult outcomes

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    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.

    Review: Intelligence: A Very Short Introduction

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    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

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    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

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    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?

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    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

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    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.

     

     

    Children and happiness

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    Contemplating whether or the number of children to have? Take a look at “A Global Perspective on Happiness and Fertility” by Margolis and Myrskylä. The authors use data from 25 years of the World Value Survey, totalling 86 countries and over 200 000 respondents. They are interested in what the relation between what people answer on the question “Taking all things together, would you say you are very happy, quite happy, somewhat happy, or not at all happy?” and their number of children.

    Margolis and Myrskylä find that a higher number of children is associated with lower happiness, but stress that looking at this in the aggregate is highly misleading. This is shown by breaking the data down by subcategories and plotting the results. In particular, they “find that the association between happiness and fertility evolves from negative to neutral to positive above age 40,” as shown e.g. here:

    NIHMS369453.html

    FIGURE 3 Happiness and number of children by age and sex, from Margolis, R. and Myrskylä, M. (2011), A Global Perspective on Happiness and Fertility. Population and Development Review, 37: 29–56. doi: 10.1111/j.1728-4457.2011.00389.x

    So more children may pay off in the long run. Though it must be said that this is just descriptive, but valuable and interesting nevertheless. There are more graphs like this one, and the results can be understood simply by looking at the graphs.

    With also the recent report that “economists with two or more kids tend to produce more research, not less, than their one-child or childless colleagues” in hand and just having achieved the second, I am expecting a short-term boost in productivity and long-term in happiness.

    “Oslo is the cradle of rigorous causal inference”

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    Those are the words of James Heckman, from a lecture (slides, paper) at the University of Oslo last week. In particular, it is Trygve Haavelmo’s 1943 paper The statistical implications of a system of simultaneous equations (pdf) that gets the honor of being “the first rigorous treatment of causality”. A summary:

    Heckman 2013 Haavelmos contributions to causality

    According to Heckman, Haavelmo built on Marshall’s general idea of ceteris paribus to define fixing (“an abstract operation that assigns independent variation to the variable being fixed (p. 8)”), that is to be distinguished from classical statistical conditioning (“a statistical operation that accounts for the dependence structure in the data (p. 8)”). This fixing occur hypothetically, thus causality becomes defined in terms of thought experiments, along the earlier thoughts of Ragnar Frisch. In Heckman’s words: “Causal effects are not empirical statements or descriptions of actual worlds, but descriptions of hypothetical worlds obtained by varying – hypothetically – the inputs determining outcomes. (pp. 2-3)”. 

    Much of the lecture and paper is a polemic against Pearl’s do-calculus. Those interested in that debate can read Heckman and Pinto’s paper and Pearl’s comments on it, watch a conference discussion they had last year, or read stuff that more able people than me have blogged about before. Not debatable, though, is that Heckman knows to please his hosts.