Category Archives: Data

Weekly hours of television and internet consumption in Norway 1991-2018

A couple of days ago, I blogged about time spent watching television and video by different age groups in Norway. Of course the issue of internet immediately popped up, so I made this graph showing both time spent watching television and time spent on the internet:

With regards to total screen time, note that these graphs leave out time spent watching video tapes and dvds and time spent on computers, electronic games and mobile phones without using the internet.

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Time spent watching TV and video media in Norway 1991-2018

Inspired by a tweet by Gray Kimbrough graphing changes in television and video watching in the US between the mid-2000s and the mid-2010s for various age groups, I decided to follow up with a similar figure with Norwegian data. Gray showed that in the US, people aged 45 and older increased their watching substantially, while younger people decreased it at least to some extent. In Norway the picture was somewhat different – there was very little change for the oldest groups, but the youngest ones reduced their watching by much more than in the US.

Part of the reason for making the graph was to learn how to use the pcarrow option in Stata, which I accomplished, however, I found that in this case with only five groups, a simple line chart may actually provide more information and be preferable:

“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.

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.

“Can welfare conditionality combat high school dropout?”

I have a new working paper out, joint work with Simen Markussen and Knut Røed. Simen has written provocatively about the paper in the today’s Dagens Næringsliv, which is also running a companion piece. These are only in Norwegian (and behind a paywall), however, so here is a brief summary in English:

We investigate what happens when Norwegian social insurance offices increase their use of conditions would-be welfare recipients need to satisfy in order to receive welfare. Using the staggered introduction of this program and based on double and triple difference models, we find that such conditionality reduces the number of young people that receive welfare, and more importantly, increases the high school graduation rate. For young people from disadvantaged backgrounds, we find substantial and precise effects, whereas we find no effects on youth from more resourceful backgrounds, as expected. A few years later, we find that those who were exposed to new regime have more education, earn more, and are more likely to be employed. Thus even though activating these people may cost something upfront, it pays off in the long run.

The newspaper has an interview with a guy who got on track and gets some work experience through this system. Here is the abstract of the research paper:

Based on administrative data, we analyze empirically the effects of stricter conditionality for social assistance receipt on welfare dependency and high school completion rates among Norwegian youths. Our evaluation strategy exploits a geographically differentiated implementation of conditionality. The causal effects are identified on the basis of larger-thanexpected within-municipality changes in outcomes that not only coincide with the local timing of conditionality implementation, but do so in a way that correlates with individual ex ante predicted probabilities of becoming a social assistance claimant. We find that stricter conditionality significantly reduces welfare claims and increases high school completion rates.

“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.