Monday 18 March 2024

What happiness data tells us about whether life is getting better or worse over time

If you believed everything you read online, or in the media, you might get the impression that that state of the world is not only bad, but getting worse over time. It's gotten so bad, that everything seems to be in crisis. If it was the case that life is getting worse over time, we would expect to be able to see this reflected in people's subjective evaluations of their wellbeing - that is, their reported happiness. If life is worse now, surely people are reporting being less happy?

That is the research question at the heart of this new working paper by Ruut Veenhoven (Erasmus University Rotterdam) and Silke Kegel (University of Konstanz). Veenhoven and Kegel look at the happiness data from the World Database of Happiness, Report on Average Happiness in Nations, tracking changes in happiness measures over time for countries where the data:

...cover at least 20 years and involve at least 10 data-points... This left us with 80 timeseries in 50 nations over ranges of 71 to 20 years in the period 1945-2021.

They then apply some fairly simple comparisons (average happiness at the end of the time-series compared with average happiness at the start of the time-series), and simple linear regressions, to identify time trends in average happiness. If life is getting worse over time, the time trend should be negative. Instead, they find that:

...average happiness changed significantly only in 37 nations, of which 26 changed to greater happiness and 11 to less, the average size of the chances being similar. So again, more rise than decline.

In their linear time trends analysis, there was very little evidence of decreasing happiness. As they note, 11 nations (and 19 time trends) were statistically significant and negative, compared with 26 countries (and 62 times trends) that were statistically significant and positive, while 35 countries (and 119 time trends) were not statistically significant at all.

And when you look at which countries and time trends are positive or negative, they results seem to make some intuitive sense. For example, Japan since the 1960s shows a significant positive increase in happiness, but Japan since the 1990s shows no significant change, consistent with improvements in wellbeing that occurred mainly from the 1960s to the 1980s. Venezuela since the 1990s shows a large negative change, consistent with the basket case that country has become over that time. Ireland since the 1980s shows a positive change. And so on.

What we can take away from this (provided we suspend disbelief of all happiness data, which should be a real concern - see here, and here, but for a counterargument see here), is that life may not be getting worse after all.

Saturday 16 March 2024

Causation vs. correlation in the relationship between ultra-processed food and mental health

The New Zealand Herald reported earlier this week:

According to an annual global report, if you’re after mental wellbeing and a flourishing life, you should pay attention to those who live in the Dominican Republic, Sri Lanka, Tanzania and Panama.

The report, by Sapiens Labs, is available here. However, it was this bit of the article that caught my eye:

According to Sapien Labs, adults’ risk of mental health challenges is four times lower if you have close family relationships - but wealthier countries were least likely to say they were close with many adult family members, at just 23 per cent...

Similarly, there is a strong body of research on the impact of processed food and a growing number of studies around technology use.

“We found that over half of those who eat ultra-processed food daily are distressed or struggling with their mental wellbeing, compared to just 18 per cent of those who rarely or never consume ultra-processed food,” the report stated. This is almost a three-fold increase.

I just talked about the difference between causation and correlation with my ECONS101 class a couple of weeks ago. Everything that Sapiens Labs has found is correlation. Sure, you can tell a plausible story about how ultra-processed foods lower mood and lead to worse mental wellbeing. However, there is also a plausible story going in the other direction (reverse causation) - people with worse mental health might comfort eat, thereby consuming more ultra-processed foods. Just because we observe a correlation between higher ultra-processed food consumption and lower mental wellbeing (a negative correlation), it doesn't mean that ultra-processed food consumption causes decreases in mental wellbeing.

Even worse than that, the report itself (but not the New Zealand Herald article) tries to suggest a link between higher consumption of single-use plastics and lower mental wellbeing. I'm not even sure that you can tell a plausible story linking those two variables in that direction - how would plastic straws, forks, and grocery bags make our mental health worse? This could well be spurious correlation. However, I'd be surprised if there is even a correlation there at all. Many countries (including New Zealand) have recently banned single-use plastics. Have we seen an immediate improvement in mental health in those countries? I thought not.

Just because two variables are moving together (either in the same direction, or opposite directions) that doesn't mean that changes in one variable are causing changes in the other one. No matter how much you might want them to, or how much you are looking for a simple explanation. Correlation is not the same as causation.

Friday 15 March 2024

This week in research #14

Here's what caught my eye in research over the past week:

  • Badunenko and Popova (open access) find that in Germany from 1985 to 2015, while income inequality has increased significantly, migration did not contribute to that increase
  • Adabor (open access) finds that the COVID-19 support payment in Australia is positively associated with gambling, with larger effects on male gamblers and online gamblers (I guess gambling is a normal good?)
  • Hadavand, Hamermesh, and Wilson show that economics publishing proceeds much more slowly than in the natural sciences, and more slowly than in the other social sciences and finance, and that much of the lag is the result of authors taking a long time to complete revisions (time for economists to stop complaining, and start revising-and-resubmitting their papers!)
  • Eugster and Uhl (open access) show that sentiment data, based on 730.000 news articles between Q1 2003 and Q4 2021, is able to forecast inflation more accurately than a naive random walk
  • Tomlin (open access) finds in a field experiment that tenant applicants that reveal their pronouns are less likely to receive a response from a landlord, regardless of whether the pronouns signalled that the applicant was cisgender or transgender
  • Brander et al. (open access) estimate from a survey that households would be willing to pay US$79 per year to conserve marine turtles, implying that taking policy action to conserve, manage and protect marine turtles would generate US$55 billion in value

Finally, I am giving a Professorial Lecture at the University of Waikato on Tuesday 26 March. This public lecture is titled Beyond the Buzz: The Sobering Economics of Alcohol, and it builds on my last 15-plus years of research on the social impacts of alcohol. All new professors must give one of these lectures, and I've been dodging it for over a year! The event is free (and they'll even feed you beforehand), but to go you need to register here. I'm not just adding a sales pitch when I say that tickets have been selling fast, so if you want to come along you need to get your ticket sooner rather than later.

Wednesday 13 March 2024

One way that dynamic pricing in retail or fast food is different from Uber's surge pricing

I had an interesting discussion after yesterday's post about the difficulty of regulation to prevent dynamic pricing. It highlighted a key difference between Uber surge pricing and otherwise similar dynamic pricing in retail or fast food contexts.

As I noted in this 2015 post:

...surge pricing is used to manage excess demand - when the quantity of Uber rides demanded by users exceeds the quantity of rides available from drivers at that time. In other words, there is a shortage of available Uber drivers.

Surge pricing solves a market problem - a shortage of Uber drivers. Increasing the price of Uber rides induces more drivers to make themselves available, increasing the quantity of Uber rides supplied. This reduces the shortage of Uber drivers, and makes it easier for Uber customers to find a ride (albeit, a more expensive ride).

The dynamic pricing I've been blogging about this week doesn't work that way. It doesn't solve a market problem. There is no shortage of Wendy's burgers, where raising the price would induce Wendy's to offer more burgers for sale, reducing the shortage.

So, while you could argue that Uber's surge pricing may make some consumers better off (since they don't have to wait as long for a ride), it's more difficult to make that case for Wendy's dynamic burger pricing. As I noted on Monday, maybe the burgers will be cheaper in periods of low or elastic demand, making consumers who are buying at those times better off. But the burgers will be more expensive in periods of high or inelastic demand, making those consumers worse off.

Now, none of this means that the appropriate response is for government to regulate dynamic pricing. As I noted yesterday, that regulation would likely break a bunch of things that we wouldn't want broken, and may simply end up with consumers all worse off as a result. However, that just means that there isn't anything easy that government can do to intervene. Consumers themselves have some power here too. And indeed, that's exactly what happened with Wendy's. After consumer backlash, they walked back any plans to roll out dynamic burger pricing.

I still think it's coming eventually. Obviously, just not yet.

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