Roman rule, and personality traits and subjective wellbeing in modern Germany

History has a long tail. Events in the distant past can have surprising effects today. For instance, past research I have blogged on has shown that autocratic rule in Qing dynasty China affects social capital today (see here), the Spanish Inquisition affects GDP in Spanish municipalities (see here), and Roman roads affect the modern location and density of roads in Europe (see here). In that vein, this recent article by Martin Obschonka (University of Amsterdam) and co-authors, published in the journal Current Research in Ecological and Social Psychology (open access), looks at the effect of Roman rule on modern incidence of personality traits and subjective wellbeing in Germany. To do this, Obschonka et al. compare people on either side of the Limes Wall, noting that:

To protect their territory with its cultural and economic advancements, the Romans built the Limes wall around 150 AD and it served as a border of the empire for more than a century. The Limes consists of three major rivers, namely the Rhine, the Danube, and the Main ("Main Limes"), as well as a physical wall ... It is well-documented that the Limes constituted a physical, economic, and cultural border between the Roman and Germanic cultures...

By comparing people on either side of the Limes Wall, Obschonka et al. try to reveal the enduring impact of Roman rule. They expect this effect on personality traits and subjective wellbeing because:

...the Roman society was much wealthier and considerably more structured and organized than the “barbaric” Germanic tribes, with an effective public administration and a relatively well-elaborated legal system... When the Romans occupied parts of the territories inhabited by Germanic tribes, they imported superior scientific knowledge and a civic structure.

To measure personality traits, Obschonka et al. turn to the German dataset from the Gosling-Potter Internet Personality Project, the largest dataset on the 'Big Five' personality traits. The German sample they use includes over 73,000 observations between 2003 and 2015, which they aggregate to regional-level averages. For subjective wellbeing (life satisfaction), they use data from the German Socioeconomic Panel between 1984 and 2016, again aggregated to regional-level averages. They also look at life expectancy. Using a simple OLS regression model, with a 'treatment variable' indicating that a region was in the Roman occupied area, Obschonka et al. find that:

...the populations in those regions that were occupied by the Romans nearly 2000 years ago show significantly higher levels of extraversion, agreeableness, and openness, and significantly lower levels of neuroticism (which points to more adaptive personality patterns in the former Roman regions of present-day Germany) than do the populations living in the non-occupied regions... Moreover, populations living in the formerly Roman areas today report greater satisfaction with life and health, and also have longer life expectancies...

After including a range of control variables into their models, the effects on agreeableness and openness became statistically insignificant. However, that leaves significant effects of Roman rule on extraversion and neuroticism, as well as life satisfaction and life expectancy. The results are similar when they use a spatial regression discontinuity design (RDD) instead of OLS. The spatial RDD takes account of how far away an observation is from the Limes Wall, which separates the 'treated' and 'control' regions (and regions closer to the line provide more information about the distinctive effect of the treatment, in this case Roman rule). The method assumes that places on either side of the border are similar except for the Roman occupation. This seems plausible, so the spatial RDD results in particular make the results more believable.

Obschonka et al. then turn to looking at the mechanisms that might explain the enduring effect of Roman rule. They show that:

Density of road infrastructure built by the Romans shows a statistically significant, positive effect on life and health satisfaction, as well as on life expectancy. There is a negative, statistically significant relationship with neuroticism, a positive one with extraversion, and a non-significant one with agreeableness, conscientiousness, and openness...

Running the models with the number of Roman markets and mines as the independent variable reveals a negative effect on neuroticism and a positive effect on extraversion. In addition, there is also a positive effect on conscientiousness (and openness). None of the effects on psychological well-being or health were statistically significant. Including Roman road density and the number of Roman markets and mines in the same model... clearly indicates that markets and mines are more strongly related to the personality traits, whereas Roman road density is more closely related to the health and well-being outcomes.

These results should be seen as more exploratory, but Obschonka et al. interpret them as showing:

...support for the notion that the tangible and lasting economic infrastructure built and established by the Romans left a long-term macro-psychological legacy...

Perhaps. I find it less plausible that Roman physical infrastructure had a lasting effect on modern personality traits and subjective wellbeing, and more likely that Roman worldviews and 'social infrastructure' (things like institutions or social norms, for example) was passed down from one generation to the next, showing up as a lasting effect on personality and wellbeing. Unfortunately, Obschonka et al. aren't able to tease out those sorts of mechanisms. Either way, it’s another reminder that borders drawn 2000 years ago can still show up in the data, even in places we might not think to look.

[HT: Marginal Revolution, early last year]

The misery of diversity?

I just finished reading this 2024 NBER Working Paper by Resul Cesur (University of Connecticut) and Sadullah Yıldırım (Marmara University), provocatively titled "The Misery of Diversity". They look at whether greater genetic diversity is associated with subjective wellbeing (SWB, measured as happiness, or life satisfaction, or affect balance), and find that:

...diversity lowers human SWB, measured by cognitive life evaluations and hedonic assessments of emotional states.

Cesur and Yıldırım demonstrate these results using data on genetic diversity that comes from this 2013 article by Ashraf and Galor (ungated version here). As Cesur and Yıldırım explain:

Population geneticists demonstrate that the dispersal of anatomically modern humans via migratory routes determined within-ethnic genetic heterogeneity. As one moves away from Ethiopia via migratory tracts, genetic diversity, defined as the likelihood of two randomly picked individuals having dissimilar genetic material, decreases...

Our diversity measure impacts the outcomes of interest through social ecology, which, over many generations, likely has influenced cultural evolution. In particular, interpersonal diversity determines the endowment of genetic variation, a measure of social diversity, capturing within-group interpersonal differences across the globe...

This measure of social diversity performs better than conventional diversity indicators, such as the indices of fractionalization and polarization, in capturing the true extent of diversity... In particular, these authors show that while interpersonal population diversity has a substantial and precisely estimated impact on intrastate conflict, fractionalization, and polarization indices fail to explain it.

Underlying data for this index is the expected heterozygosity measures of 53 indigenous human populations genotyped at 780 microsatellite loci as a part of the Human Genome Diversity Project (HGDP–CEPH). It captures the probability that two randomly selected individuals within an ethnic group differ in genetic makeup. In light of the Out of Africa hypothesis, Ashraf and Galor (2013a) constructed predicted genetic diversity for each country by using the coefficient estimate of the impact of migratory distance to Addis Ababa on genetic diversity in the sample of indigenous ethnic groups across the world. Although

Using this measure, with an instrumental variables analysis, Cesur and Yıldırım show that genetic diversity causally decreases subjective wellbeing at both the country level and the individual level (using data from the World Values Survey and the World Happiness Report). Their results are robust to excluding countries that experienced large migrations after 1500 (such as countries in North America and Oceania), and to various other modelling choices. Cesur and Yıldırım dig into the mechanisms for lower subjective wellbeing, and conclude that:

...the misery of diversity is an evolutionary trap caused by the mismatch it creates between the ancestral and current social environments via reduced social cohesion, retarded state capacity, elevated mistrust, and increased inequality of economic opportunities.

So, it seems like this is good evidence that genetic diversity decreases subjective wellbeing. However, there are a couple of problems. First, when most people think about diversity, they are thinking about between-group diversity, not within-group diversity. Between-group diversity is what you get when people from different ethnic groups are together. Within-group diversity is what you get when people from the same ethnic group differ genetically from each other. Cesur and Yıldırım's measure is heavily weighted towards within-group diversity. And indeed, in one of their analyses they find that it is within-group diversity that matters the most in their analysis. When they split their measure into within-group and between-group diversity, within-group diversity has a statistically significant (and negative) effect on subjective wellbeing measures, while between-group diversity is statistically significant.

So, Cesur and Yıldırım's analysis might be correct, but at the same time kind of misses the point. Between-group diversity is something that has potential policy levers (migration policy), whereas within-group genetic diversity is not something that is amenable to policy change. At least, not without eugenics (and, to be clear, I am not advocating for that). 

The second problem comes from the analysis of first-generation and second-generation immigrants in Europe and the US, where Cesur and Yıldırım find that:

...while home country diversity continues to hurt the SWB of first-generation immigrants, such effects weaken among the second-generation, suggesting that long-run improvements in the social environment can mitigate the misery of diversity over generations.

These results are not well-explained. If a person is born in one country, and then moves to a new country, shouldn't it matter how long they are exposed to the genetic diversity in the country of birth, and how long they are exposed to the genetic diversity in the destination country, in terms of the impact on subjective wellbeing? Cesur and Yıldırım don't show any dose-response relationship here. And there should be no effects at all on the second generation (which is what they find), because for the second-generation immigrants, the genetic diversity they have been exposed to is the country of their own birth, not the country of birth of their parents. However, that is only a small problem in an otherwise interesting paper.

Overall, I think Cesur and Yıldırım need to engage a bit more with why anyone should care about genetic diversity, given that it is not amenable to policy change. Until they can do that, this paper can be filed under the interesting, but unhelpful category.

[HT: Marginal Revolution, last year]

Book review: Measuring Happiness

The field of happiness economics lost two giants of the field last December. First, Ruut Veenhoven passed away on 9 December. Veenhoven is well known among those working on happiness data, as the founding director of the World Database of Happiness, which has collated thousands of studies and datasets measuring happiness across many countries and over time. Second, barely a week later, Richard Easterlin passed away on 16 December. Easterlin is most famous for the Easterlin Paradox, the idea that while life satisfaction is higher among people with more income within a country as well as between countries, as average income increases, average life satisfaction doesn't change. The Easterlin Paradox gave rise to the idea of a hedonic treadmill, that people adapt to higher incomes and so their higher income doesn't make them any more satisfied with life.

So, it was timely for me to read the book Measuring Happiness, by Joachim Weimann, Andreas Knabe, and Ronnie Schöb. The book was published in English in 2015, with an earlier edition in German in 2011, so it had been sitting on my bookshelf for about nine years. The main focus of the book is the Easterlin Paradox, and a survey of the literature on happiness economics (as it stood in 2011).

The book largely concludes that the evidence doesn't support the Easterlin Paradox, and the reasons are primarily methodological, as well as shortcomings in the data, particularly in the sampling (of countries where life satisfaction data are collected, and of the populations within countries that make up the life satisfaction datasets). The World Values Survey comes in for particular criticism by Weimann et al, who point to the Gallup World Poll as a less biased data source. Using Gallup World Poll data, as Weimann et al. do (and as others have done as well, including this very thorough 2008 paper by Betsey Stevenson and Justin Wolfers), the key features of the Easterlin Paradox are absent.

The critiques that Weimann et al present are a precursor to many other more recent (and more technical) critiques (see this post, and the links at the end of that post). Clearly, as you may expect, the literature has moved on, but the conclusion remains that the Easterlin Paradox doesn't fully stand up to scrutiny (although many proponents of the Easterlin Paradox have not been convinced by the critiques).

I say that the Easterlin Paradox doesn't fully stand up, because one of the key implications of the Paradox does appear to hold, that people's life satisfaction depends on their position in the income distribution relative to other people in their community, and that people's life satisfaction depends on their current income relative to their income earlier in their life. Weimann et al are careful in ensuring that this point comes across in their review.

Given that it is now quite dated, this book is probably not worth reading for the review of the literature that it provides. However, there is a hidden gem in the form of the Appendix. The Appendix looks at the place of life satisfaction (and happiness economics) within the broad scope of the history of economic thought. This starts with the idea that the ultimate foal of human activity is the pursuit of happiness, as expressed by Jeremy Bentham and John Stuart Mill in the 19th Century, and concludes with (relatively) recent developments in the measurement of brain activity in neuroeconomics. Along the way, Weimann et al discuss the scientification of economics, with neoclassical economics in particular eschewing subjective measures such as life satisfaction. I very much enjoyed that section of the book, and it is that material that remains relevant, even if the literature review itself is somewhat dated.

If you are interested in happiness economics, and in particular if you are interested in the place of happiness within the broader scope of the history of economic thought, this book is well worth reading.

What does the Cantril Ladder really measure?

Imagine a ladder with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?

Now, consider the question you probably just answered. What factors played into your answer? What sorts of things contribute to the best possible life for you, compared with the worst possible life for you? If we used your answer to that question as a measure of life satisfaction, what is it really measuring?

That's not an unimportant question. The first paragraph of this post is a commonly used way of measuring life satisfaction, known as the Cantril ladder (see here). It is used in the Gallup World Poll, and is recommended by the OECD as a way of measuring subjective wellbeing. When researchers (or governments, or others) measure life satisfaction or happiness, it is often the Cantril ladder that is being used.

The question of what the Cantril ladder measures was explored in this recent article by August Nilsson (Lund University), Johannes Eichstaedt (Stanford University), Tim Lomas (Harvard University), Andrew Schwartz (Stony Brook University), and Oscar Kjell (Lund University), published in the journal Scientific Reports (open access, with non-technical summary on The Conversation). Nilsson et al. looked at the framing of the Cantril ladder, and investigated how nearly 1600 people responded to different framings of the question, and the words that they used to describe the top and the bottom of the scale in those different framings, and where they would 'prefer to be' on the scale. The first framing was the traditional Cantril ladder. The second framing essentially replaced the ladder metaphor with the word "scale" (but left the rest intact). The third framing removed references to the "bottom" and "top" (as well as the ladder metaphor). The fourth framing did all of that plus changed "best possible life" to "happiest possible life" (and "worst possible life" to "unhappiest possible life"). And the fifth and final framing instead replaced "best possible life" to "most harmonious life" (and "worst possible life" to "least harmonious life").

Nilsson et al. found that:

The ladder and bottom-to-top scale anchor descriptions influenced respondents to use significantly more words from the LIWC dictionaries Power and Money when interpreting the Cantril Ladder... compared to when these anchors were removed. Of all the words respondents used to describe the top of the Cantril Ladder, 17.3% fell into the Power and Money dictionaries. This language was reduced by more than a third when the ladder was removed in the no-ladder condition (absolute difference of 6.0%, d = 0.35, p < 0.001), and more than halved when the bottom-to-top scale descriptions were removed too (absolute difference of 10.3%, d = 0.64, p < 0.001). Further, for the Cantril Ladder, words in the Power and Money dictionaries occurred 3.3 times as frequently compared to the alternative Harmony anchor condition (absolute difference of 12%, d = 0.77, p < 0.001).

They interpret those results as meaning that:

...the original Cantril Ladder influenced respondents to focus more on money in terms of wealth (whereas when the ladder framing was excluded, they focused more on financial security) than the other conditions.

Were you thinking about the financial aspects of life when you answered the question above? The results seem to suggest that is more common than thinking about social relationships or the various other contributors to our subjective wellbeing. Nilsson et al. don't explore the use of words other than in the 'Power' and 'Money' domains, but it would have been interesting to see some others to compare with.

It's not surprising that financial security, income, or wealth are important contributors to subjective wellbeing or life satisfaction. We should expect people to be better able to satisfy their needs when they have greater financial resources available to them. However, the results on research participants' preferred level on the ladder are genuinely surprising, because:

...over 50% did not prefer the highest level (of 10) in any of the study conditions, and less than a third preferred the top of the Cantril Ladder, which had a significantly lower average preferred level than all the other study conditions.

In other words, even though the top of the Cantril ladder is framed as the 'best possible life', around two-thirds of research participants said that they would prefer not to be at the top of the ladder. This proportion was lower (but still not zero) for other framings, as shown in Figure 4 from the article (where the dark blue part of the bar shows the proportion of research participants who responded that 10 was their preference):

What was your preferred level on the ladder? Did you want to have the best possible life (that is, 10 on the scale)? Or would you prefer to be somewhere just below the best possible life? What do you think about in answering the question on your preferred level? Maybe research participants want 'room to grow' and become even happier or more satisfied with their lives? I have no idea. Nilsson et al. have given us something to really think about here, but unfortunately the article doesn't go far enough in exploring why people don't prefer the top of the ladder. There is definitely scope for further follow-up research on this point.

In addition to being surprising, that last result may call into question how the Cantril ladder is interpreted (on top of the arguments about the validity of happiness data generally - see here, and here, and here). If the top of the scale is not the top of the scale, or if it is different for different research participants, then how do we interpret an average across all people responding to the question? That should make researchers worry, and makes follow-up research even more important.

[HT: New Zealand Herald, back in April]

Read more:

• Happiness is dead?
• More evidence that life satisfaction may be dead
• The reports of the death of life satisfaction may have been greatly exaggerated

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.

The reports of the death of life satisfaction may have been greatly exaggerated

The measurement of subjective wellbeing (or life satisfaction, or happiness) has attracted a lot of criticism over the last few years (for example, see here and here). The problems arise mostly because we cannot observe people's true happiness, and so instead we use a survey proxy that is typically measured using ordinal categories (for example, very happy, somewhat happy, somewhat unhappy, very unhappy, etc.). Because the way that the proxy measure of happiness maps to 'true happiness' is unknown, researchers who make different distributional assumptions can conclude almost anything. At least, that's the short version of one of the arguments against the current measurement of subjective wellbeing.

However, we may now have a solution of sorts to this problem. As Shuo Liu (Peking University) and Nick Netzer (University of Zurich) explain in this recent article published in the journal American Economic Review (ungated earlier version here), it may be possible to use the length of time a respondent takes to answer the life satisfaction question, to get a measure of the intensity of their happiness (or otherwise). As they explain:

In this paper, we argue that the use of survey response time data can help to solve the problem. Response time is the duration that a survey participant needs to answer a given question. To understand the logic of our argument, consider a happiness survey with just two response categories, “unhappy” and “happy.” Suppose you answer this survey at a moment when you feel very happy. Most likely, you will find it easy to respond “happy” and you will do so quickly. Now suppose you answer the survey at a moment when you feel only moderately satisfied. You may still end up responding “happy” but most likely it will take you longer to decide. The observable distribution of response times among the survey participants who respond to be happy then contains information about the unobservable distribution of happiness within that response category, and analogously for the “unhappy” category. Response time data can provide precisely the evidence that was missing for identification.

Liu and Netzer note that this 'chronometric effect' has been observed in many previous studies, but hasn't previously been applied to the measurement of happiness. They then demonstrate how the use of response times can improve measurement using data from a survey of 8000 MTurk research participants. Specifically, they:

...implemented two versions of the survey, one with two answer categories and one with three answer categories. In both versions of the survey, each substantive question was accompanied by a follow-up question in which participants were asked to refine their previous answer. For example, a subject giving the highest possible response “rather happy” in the initial question about overall life happiness subsequently had the choice between “very happy” and “moderately happy” in the follow-up question.

Conducting the survey online makes it easy to record response times, which we define as the time between the display of the question and the moment when the participant clicked on her answer. To account for individual heterogeneity in response speed, we follow our theoretical analysis and normalize the raw response times by subtracting (in logs) each subject’s response time in the sociodemographic question about marital status, where there are arguably no uncertainties or varying intensities about the correct answer, and which was also answered quickest on average.

Essentially, research participants who were happier should be more certain about being happy, and answer the first happiness question in less time than those who were less certain about being happy. Those happier participants should also be more likely to answer in the follow-up question that they are very happy. And indeed, that is what Liu and Netzer find:

We find that, among subjects who initially gave an identical answer, those who reveal a more extreme position in the follow-up question responded faster on average in the initial question. More specifically, we consider all subjects who responded in the same extreme category in an initial question (e.g., “rather happy”) and partition them into two subgroups based on their response in the follow-up question. Those who give a more extreme response in the follow-up (e.g., “very happy”) should have larger values of the latent variable than those who give a more moderate response (e.g., “moderately happy”). The chronometric effect then predicts that the former should have responded more quickly in the initial question than the latter. We find this prediction confirmed in our data, for both extreme response categories in all seven substantive questions and both versions of the survey.

Liu and Netzer then go on to show similar results when the first question has three levels rather than two levels, although they note that the statistical power is lower in that case.

Overall, these results should provide some comfort for users of subjective wellbeing data, as Liu and Netzer show that the previous concerns about distributional assumptions may be overstated. And, they have provided a way forward, although it is fair to note that this requires that the data be collected digitally (so that response times can be easily captured). Fortunately, it does not require that the data be collected online (which we should be wary of now, as I noted here). So, collection by surveys completed on a tablet or similar should be fit for purpose. Then, either the response time can be used as Liu and Netzer do, or researchers can at least test whether such an adjustment to the underlying subjective wellbeing assessment is necessary.

So, it appears that life satisfaction is not dead. At least, not yet.

Read more:

• Happiness is dead?
• More evidence that life satisfaction may be dead

Who benefits the most from free speech?

Earlier this year, I read a most interesting article by Diana Voerman-Tam, Arthur Grimes (both Victoria University of Wellington), and Nicholas Watson (Motu), published in the Journal of Economic Behavior and Organization (open access, with less technical summaries here and here). We also discussed it in the Waikato Economics Discussion Group earlier this year. The article looks at the economics of free speech. Specifically:

Can we measure the impact of free speech on people’s wellbeing (rather than its impact on economic growth per se), and can we determine which groups value free speech the most? This paper takes an empirical approach to answer these questions. We test whether free speech is valued differently by different groups in society, according to their level of re- sources as proxied by income or education. These relative valuations are analysed both using surveyed stated preferences and using estimates of the realized relationships between individuals’ subjective wellbeing (SWB), their country’s degree of freedom of speech, and individuals’ income or education levels, controlling also for other influences.

Voerman-Tam et al. used individual subjective wellbeing data from the World Values Survey and the Latino Barometer, and looked at the correlation with data on free speech and other human rights drawn from the CIRIGHTS database and the Varieties of Democracy database. They test two hypotheses:

The first reflects a view that free speech is a ‘luxury good’... The second reflects a view that free speech has an ‘empowerment effect’ for people with lower socio-economic status and who are therefore more likely to be marginalized in society...

If free speech is a luxury good, then people with higher income (or education) would have a more positive relationship between free speech and subjective wellbeing. That is, higher income people would have the greatest wellbeing gains from more free speech. On the other hand, if free speech has an empowerment effect, then people with lower income (or education) would have a more positive relationship between free speech and subjective wellbeing. That is, lower income people would have the greatest wellbeing gains from more free speech.

Interestingly, the paper starts with an analysis of stated preferences over the importance of free speech, based on responses to a World Values Survey question that reads:

If you had to choose, which one of the things on this card would you say is most important?: 1. Maintaining order in the nation. 2. Giving people more say in important government decisions. 3. Fighting rising prices. 4. Protecting freedom of speech.

Respondents were then asked which of the four choices was the next most important. Voerman-Tam et al. create two measures of the priority attached to free speech. The first is equal to one only if the survey respondent ranked freedom of speech as the most important (and zero otherwise). The second is equal to one if the survey respondent ranked freedom of speech either first or second in importance (and zero otherwise). Then, looking at the relationship between these variables and personal characteristics (including income and education), they find that:

A positive gradient is observed across both income and education for each of the free speech prioritization variables.

In other words:

...people with higher incomes or education place higher priority on free speech (relative to other alternatives that they are asked to rank).

I don't think too many people would find it surprising that people with higher income (or education) are more likely to state that free speech is more important to them than people with lower income (or education. This bit was also interesting, and mostly unsurprising:

Several other associations stand out: free speech is prioritized more by people who are young, students, and/or have no children, and by people to the left of the political spectrum. These characteristics are more in keeping with the hypothesis that people who are more marginalized favour free speech.

So, people with higher income (and education) say that free speech is more important. That is suggestive that free speech is a luxury good. However, it is based on stated preferences for free speech. People with low income (or education) likely have much more important things to worry about in their daily lives than whether they are able to exercise free speech.

A better question to ask, then, is who gains the most (in terms of wellbeing) from free speech? In the second part of their analysis, Voerman-Tam et al. find that overall, there is no correlation between free speech and subjective wellbeing (after controlling for other variables). However, that isn't what they were really interested in. When they interact free speech with income (or education), they find that:

...people with lower income (relative to others within their own country) benefit more with free speech than those with higher incomes, especially in countries with full free speech.

The results are similar for education. Overall, these results support the second hypothesis. So, despite the stated preference results suggesting that free speech is a luxury good, it turns out that people with lower income (or education) benefit the most from free speech (in terms of its contribution to subjective wellbeing).

In my view, the combination of these two results is important. People with higher incomes (or education) make up the elites in most societies, often holding positions of political (or if not political, then at least bureaucratic) power. If those groups believe that free speech is important (which according to these results, they do), then they are more likely to argue for more free speech. That, in turn, creates the greatest benefits (in terms of wellbeing) for people with lower incomes (or education), who are likely to be more disenfranchised. These results suggest to me that we might be optimistic for increases in free speech and somewhat of an equalising of wellbeing between the richer and poorer segments of society as a result.

However, before we get carried away, there are some important caveats. This research is based on correlations. It doesn't demonstrate that greater free speech causes increases in subjective wellbeing. We'd want to establish that more definitively. And, as with all research that involves subjective wellbeing data, we must be aware of its limitations and the criticisms it faces (for example, see here and here, but also see this post by Arthur Grimes as well).

Nevertheless, in the meantime high-income people should feel good about continuing to believe that free speech is of high importance.

No, you don't need an income of $193,000 in order to be happy

You may have seen a news story in the last few weeks, saying that in order to be happy in New Zealand, you need a household income of $193,000 (see Stuff here, or the New Zealand Herald here, or here). The problem is, that framing is a misrepresentation of what the original research actually found.

The $193,000 estimate comes from this blog post by S Money, which updated figures from this 2018 research article by Andrew Jebb, Louis Tay (both Purdue University), Ed Diener (university of Illinois, Urbana-Champaign), and Shigehiro Oishi (University of Virginia), published in the journal Nature (sorry, I don't see an ungated version online). However, that article isn't about the amount of income required to be happy, it is an estimate of the amount of income, beyond which additional income isn't associated with higher levels of happiness (or life satisfaction) - what the authors refer to as 'income satiation'.

So, if we take the S Money number at face value, at household incomes below $193,000, higher income is associated with more happiness, but at household incomes above $193,000, higher income is not associated with more happiness. That doesn't at all mean that you need a household income of $193,000 in order to be happy. S Money has completely misrepresented what the research is finding (their key findings mention the "cost of happiness" several times, even though that's not what this is about.

Let's take New Zealand as an example. S Money lists New Zealand in the "Countries with the highest cost of happiness", and writes that:

Meanwhile, New Zealand and Israel suffer a high cost of happiness despite not being among the top earners. 

In fact, you could easily interpret this 'high cost of happiness' as positive for New Zealand, rather than negative. If you think of the relationship between income and happiness as causal (which is not established in the Jebb et al. paper - it is just a correlation), then in New Zealand, earning a higher income increases your happiness, and continues to increase your happiness all the way up to an income of US$114,597 (or about NZ$193,000). So, there is a good reason to earn more money, since it will make you happier. For other countries with a lower 'cost of happiness', happiness peaks much earlier, and the incentives to earn more disappear at a much lower income. It is little wonder that Sierra Leone remains a poor country, if earning any more than US$8,658 doesn't make you any happier. What a desperately awful place to live (sorry, Sierra Leone).

The S Money figures tell us the level of income at which income satiation occurs, but not the level of happiness (or life satisfaction) at which income satiation occurs. If happiness peaks at a fairly low level, then a low 'cost of happiness' is simply representing a very unhappy place to live. The level of happiness at income satiation isn't reported by S Money, and it isn't reported separately for New Zealand in the Jebb et al. paper. However, for Australia and New Zealand combined, life satisfaction peaks at about 8 (on a 1-10 scale), which is much higher than for any other world region. In Africa (including Sierra Leone), life satisfaction peaks at about 6 on the same 1-10 scale. The difference between 6 and 8 doesn't seem like much, but is actually very substantial (about 1.2 standard deviations). So, while the 'cost of happiness' is much lower in Sierra Leone than in New Zealand, the happiness that you would be 'buying' is much less.

Many of the news stories also breathlessly referred to the saying that 'money can't buy you happiness'. However, the original research actually finds evidence that supports exactly the opposite conclusion. Up to the point of 'income satiation', income is associated with greater happiness (again, that doesn't mean that higher income causes greater happiness, only that they are correlated with each other).

This news story is complete rubbish, and I didn't even have to get into the problems with life satisfaction measures (see here or here). It does no credit to the journalists who have simply repeated this story and the reported figures without considering what they really mean. Sadly, on the New Zealand Herald Front Page podcast, Robert MacCulloch (University of Auckland) looked at the general question of the relationship between income and happiness, and missed the real point, which is that this particular news story is trash.

Read more:

• Money can't buy happiness, but it can buy life satisfaction
• More evidence that money can't buy happiness, but it can buy life satisfaction

More evidence that money can't buy happiness, but it can buy life satisfaction

Back in 2019, I wrote this post entitled "Money can't buy happiness, but it can buy life satisfaction". That was based on research from Swedish lottery winners, which showed that:

...the increase in wealth as a result of an unexpected lottery win was associated with a persistent increase in life satisfaction. Hedonic adaptation didn't occur. However, the lottery win didn't increase 'happiness' or an index of mental health.

This week, I read this 2010 article by 2002 Nobel Prize winner Daniel Kahneman and 2015 Nobel Prize winner Sir Angus Deaton, published in the Proceedings of the National Academy of Sciences (open access), which comes to a similar conclusion. Kahneman and Deaton used data from about 440,000 responses to the Gallup-Healthways Well-Being Index (now called the Gallup-Sharecare Well-Being Index) in 2008 and 2009. The survey collects detailed data on a range of measures of subjective wellbeing, including the Cantril Ladder ("Rate your current life on a ladder scale in which 0 is “the worst possible life for you” and 10 is “the best possible life for you.”), and whether the participant felt each of a range of emotions "a lot of the day yesterday", including enjoyment, happiness, worry, sadness, and whether the participant "smiled or laughed a lot yesterday" (all of the emotional questions have yes or no answers). Kahneman and Deaton combined the positive emotions (enjoyment, happiness, and smiling) into a single score for 'positive affect', and the negative emotions (worry and sadness) into a single score for 'blue affect' (they keep stress separate from the other negative emotions). They then look at how those measures vary by income level.

The results are neatly summarised in Figure 1 from the article:

Notice that the Cantril ladder measure of life satisfaction increases with income across the whole range. In contrast, the measures of emotional wellbeing all increase for low levels of income, but quickly level off, with no further improvements beyond an annual income of between US$75,000 and $100,000. Kahneman and Deaton note that:

The data for positive and blue affect provide an unexpectedly sharp answer to our original question. More money does not necessarily buy more happiness, but less money is associated with emotional pain. Perhaps $75,000 is a threshold beyond which further increases in income no longer improve individuals’ ability to do what matters most to their emotional well-being, such as spending time with people they like, avoiding pain and disease, and enjoying leisure.

So, there you go. Money may be able to buy life satisfaction, but it can't buy happiness. Of course, these results are subject to the convincing critiques of happiness and life satisfaction as measures (for example, see here and here).

Read more:

• Money can't buy happiness, but it can buy life satisfaction

More evidence that life satisfaction may be dead

Back in 2020, I wrote a post that questioned the foundations of subjective wellbeing, based on this article by Bond and Lang. They essentially showed that the conclusions that are drawn from studies of subjective wellbeing (or happiness) depend on how the ordinal variable (subjective wellbeing, being measured on a 0-10 scale) is converted to a cardinal variable. They concluded that:

It is essentially impossible to rank two groups on the basis of their mean happiness using the types of survey questions prevalent in the literature.

It seems that Bond and Lang's result is not an aberration. I recently read this 2017 article by Carsten Schröder (Free University Berlin) and Shlomo Yitzhaki (Hebrew University), published in the journal European Economic Review (ungated earlier version here), which came to a very similar conclusion. Schröder and Yitzhaki first rightly note that:

Well-being (life satisfaction or happiness) is a latent variable that is impossible to observe directly and that has no natural quantitative measurement unit. Data are collected in scientific surveys that ask questions like, “All in all, how satisfied are you with your life at the moment?” Respondents answer these questions by ranking their satisfaction levels on a pre-defined scale, usually ranging from 4 to 11 points (and sometimes more), with the individual points assigned terms such as “very bad,” “bad,” or “good.”... In a scale like this, we know that “very bad” is lower than “bad” and that “bad” is lower than “good”, but it is not clear whether the distance between “very bad” and “bad” is greater or smaller than the difference between “bad” and “good”. The ordinal nature of this data means that any monotonic increasing transformation of the scale is allowed.

They then apply some simple monotonic transformations to subjective wellbeing data from the German Socio-Economic Panel (SOEP), and the results are not good (at least, for those who hope to use subjective wellbeing and want it to be meaningful). IN a simple comparison of the gender gap in life satisfaction, Schröder and Yitzhaki find that:

...in only one case is a simple comparison of average satisfaction robust to monotonic increasing transformations. In the other 47 cases, an admissible transformation that can reverse the sign of the gender gap in satisfaction exists...

In other words, they can reverse the gender gap by simply transforming the data in a way that shouldn't affect the results. So much for simple comparisons of mean differences. Looking at regression models, they also find that:

...using random effects does not prevent the reversal of coefficients. Using fixed effects avoids reversals but the significance of the coefficients hinges on the transformation. Note, however, that the robustness of the sign of the fixed-effects regression coefficients to monotonic increasing transformations, in general, is not guaranteed. Reversals of signs of coefficients can be shown for this type of model as well...

So, pretty standard linear regression models aren't robust. What about ordinal models? After all, they are designed specifically to deal with ordinal data. Schröder and Yitzhaki first note that:

...they result, by definition, in non-intersecting cumulative distributions. That is, these models assume a dominance relationship of the cumulative distributions that the raw data do not necessarily support...

Then they find that, comparing life satisfaction between German and non-German respondents:

In three of the five significant cases, the empirical distributions intersect. Of course, the importance and frequency of violations of dominance depend on the empirical context.

So, the distributions intersect, and so the ordinal models are finding statistically significant differences when they should not. Overall, the results are not flattering for users of subjective wellbeing data, and should especially be of concern to those who are trying to look beyond GDP as a measure of wellbeing (as noted in my recent book reviews here and here). No doubt there is more to come on this topic.

Read more:

• Happiness is dead?

Māori alienation from land and intergenerational wellbeing

The most important, or certainly most used, model of Māori health is Te Whare Tapa Whā, which was developed by Sir Mason Durie in the 1980s (see here). The model says that Māori health is underpinned by four pillars: taha wairua (spiritual health), taha hinengaro (mental or emotional health), taha tinana (physical health) and te taha whānau (family health). Durie outlined that a key component of taha wairua is Māori connections with the land, and that alienation from land would lead to worse health across all four pillars.

So, I was interested to read this new article by Rowan Thom and Arthur Grimes (both Victoria University of Wellington), published in the journal Social Science and Medicine (open access). Thom and Grimes look at the impact on wellbeing (variously measured) in more modern times of land confiscations as a result of the New Zealand Settlements Act 1863 and the Suppression of Rebellion Act 1863, during the New Zealand Wars - these confiscations are referred to by Māori as the Raupatu. They also look at the impact of land alienation more broadly (not limited to confiscations). The theory is that, if they can identify a statistical relationship between land alienation or land confiscation and modern Māori health, then that would provide evidence of a long-lasting intergenerational trauma affecting Māori.

Thom and Grimes start with a database of Māori land from 1840 (assuming all land at the time of signing of the Treaty of Waitangi was Māori-owned), 1864, 1880, 1890, 1910, 1939, and 2017. They then compute the proportion of land loss for each iwi (or iwi grouping) in the North Island for each year as one measure. By 2017:

Three-quarters of these iwi groupings today hold less than 12.5% of their rohe [region] as Māori land, and a quarter hold less than 2.7%.

Thom and Grimes then look at the relationship between the proportion of land retained by Māori for each year, and several measures of health and wellbeing, including: (1) the proportion of each iwi that can speak te reo Māori well or very well; (2) the proportion who report that it is hard or very hard to get support with Māori cultural practices; (3) the proportion who report that involvement in Māori culture is very important or quite important; (4) the proportion who had visited their ancestral marae in last year; and (5) smoking prevalence. These data were drawn from the 2018 wave of Te Kupenga - the Māori social survey run by Statistics New Zealand, which had a sample size of 5548. They also look at the impact of an iwi having experienced the Raupatu (as a binary variable - yes or no). They find that:

In each case, landholdings are a significant predictor of current cultural wellbeing outcomes, and it is landholdings around the end of the nineteenth century (1890 or 1910) that have the greatest explanatory power. Iwi that retained a greater proportion of their land at that time now have higher rates of te reo proficiency, place greater importance on involvement in Māori culture and are more likely to have visited an ancestral marae over the previous year; they are less likely to find it hard getting support with Māori cultural practices. Thus, greater retention of land has – over a century later – assisted those iwi in the retention of their cultural roots.

In 1910, the lower and upper quartiles of landholdings for our estimation sample (covering North Island iwi groupings) were 6.0% and 30.7% respectively. The effect of moving from the lower to the upper quartile of land retention in 1910 is estimated to be an extra 1.6 percentage points (p.p.) of that iwi grouping being able to speak te reo proficiently (well/very well) today. The same change in landholdings is estimated to have led to an increase in the current proportion of iwi members who find it important to be involved in Māori culture of 1.8 p.p., and an increase in the proportion who have visited a marae of 2.8 p.p.; the proportion who finds it difficult to find support for Māori cultural practices is estimated to decline by 0.6 p. p. 

Land retention is estimated to have had no significant effect on smoking rates across iwi, but experience of confiscation does. An iwi that was subject to land confiscation during the Raupatu is estimated to have a smoking rate that is 2.6 p. p. higher than in an iwi for whom confiscation did not occur.

So overall, the proportion of land retained (as opposed to the proportion alienated) was associated with better cultural outcomes. However, there was no additional effect of their iwi having experienced land confiscation (Raupatu). For smoking, the opposite was the case - land confiscation was associated with higher smoking prevalence, but the proportion of land retained was not. In all cases, the point estimates were pretty consistent across measures at different points in time, although the level of statistical significance was not. I would have been interested to see to what extent statistical significance survived an adjustment for multiple hypothesis testing.

That minor gripe about multiple testing aside, this research is potentially important. However, it did disappoint me a little, as the direct correlation between land alienation and health will now prevent me from using land alienation as an instrument for Māori social capital in another research project, as I had intended.

Thom and Grimes conclude that:

The research indicates the importance of reconnecting people with their whenua and rohe, and the central role that they have in improving the wellbeing outcomes of iwi. The process of individuals reconnecting with their rohe is a form of active healing, in which they are strengthening and expressing culture, rebuilding relationships and addressing trauma and grief...

That raises an interesting possibility for follow-up research. To what extent has the return of land through the Treaty settlement process contributed to improved Māori health and wellbeing? I wonder if anyone has considered that idea?

[HT: Matt Roskruge]

The state of the art in happiness economics, and future directions

I've written a number of posts about happiness economics, which essentially involves the study of subjective wellbeing or life satisfaction. Not everyone is happy about the measurement of life satisfaction, but Andrew Clark (Paris School of Economics is). Back in 2018, he wrote this article, published in The Review of Income and Wealth (open access), which summarises the state of research in happiness economics, and proposes some future directions for this research.

Clark's summary of the last forty years of happiness economics research is difficult to succinctly summarise (since, as a summary itself, it extends to around 16 pages), but in short it covers: (1) What makes people happy, or what are the factors that are correlated with subjective wellbeing?; (2) What do happy people do, or what are the impacts of higher (or lower) subjective wellbeing?; and (3) What else can we do with subjective wellbeing data? If you're looking at understanding the current state of the research literature (as of 2018) on happiness economics, then this would be an excellent starting point.

However, Clark then looks forward, anticipating future directions for happiness economics research. Clark first laments the lack of diversity in the datasets that are used, with most research based on data from Australia (HILDA), Germany (SOEP), or the UK (BHPS). I think he over-states the issue here, as there is significant cross-country research using the Gallup World Poll, the World Values Survey, as well as research based on the US General Social Survey and other similar surveys in other countries.

Second, Clark highlights that:

It is undoubtedly true that we care about average wellbeing in a society, but we probably care about its distribution too: for given average satisfaction, we would prefer the variance of well-being to be lower, as this would imply fewer people with low well-being (and our social-welfare function may put more weight on those in misery than on those with high subjective wellbeing). There are very few contributions in this sense.

That definitely remains true, and it would be interesting, as one example, to know whether there is a happiness Kuznets Curve (see also this post). It would also be interesting to better understand the relationship (if any) between happiness inequality and income inequality (that is my interest, rather than anything Clark noted in his paper). Clark also raises better understanding quantile effects - that is, different relationships between subjective wellbeing and other variables at different points in the happiness distribution.

Third, Clark notes that:

Research has also been concentrated on the adult determinants of adult subjective well-being. There are at least two possible extensions here. One is to consider the distal (childhood and family) correlates of adult well-being... The other is to consider childhood well-being as an outcome in its own right.

These are interesting questions, with very real potential for real-world impact. Given the rise of a focus on wellbeing, particularly by governments across the more developed countries (more on that in a future post), the fact that we know little about subjective wellbeing in childhood, and how childhood circumstances (including subjective wellbeing) affect subjective wellbeing in adulthood, seems like an important research gap to fill. However, such research ideally would rely on long-term data, and careful research design to establish causal relationships. These are the next two points that Clark makes (although in the case of causality, he focuses on exogenous changes, but that is not the only research design that can extract causal estimates).

Clark then highlights research on brain activity and its links with subjective wellbeing, and the role of genetics. Neuroeconomics is growing in influence, but the role of genetics is broadly underexplored. Finally, Clark notes that we still don't really know the 'best' way to measure subjective wellbeing. Unfortunately, despite decades of research, we haven't really nailed the measurement problem (and that has been the source of many of the arguments against this field of research).

There is a lot of exciting potential in happiness economics, and I look forward to seeing what continues to come out of this field.

Drug use and life satisfaction

One of the late Gary Becker's many contributions to economics is a theory of rational addiction (RA; which he developed along with Kevin Murphy (see here, or here for an ungated version). That theory suggests that drug users (and other addicts) are rational utility maximisers, and that drug use today contributes to higher utility from future drug use. For drug users to be rational though, they need to be able to accurate predict their future utility from drug consumption. In contrast with the rational addiction model, the utility misprediction (UM) theory in psychology suggests that people make errors in predicting their utility from consumption. In the context of drug use, they may overestimate the short-term benefits and/or underestimate the long-term costs of their drug use.

In the context of these competing theories, this 2018 article by Julie Moschion (University of Melbourne) and Nattavudh Powdthavee (Warwick Business School), published in the Journal of Economic Behavior and Organization (ungated earlier version here), attempts to determine which theory is more consistent with empirical data. They first note that:

The key distinction between the RA and the UM models is the prediction on the effect of current substance use on life satisfaction. If current use is associated with lower life satisfaction, then the coefficient on Uit [current use] should be negative – and even more negative than the coefficient on Uit+1 [future use]. This would be consistent with the psychology model of UM, which predicts that consuming addictive substances that have harmful properties should lead to a significant deterioration of life satisfaction. However, if the coefficient on Uit is positive – or significantly less negative than the coefficient Uit+1 - then we have evidence that current consumption may have improved users’ life satisfaction compared to the previous period, which would be consistent with the prediction made by the RA model.

Moschion and Powdthavee use data from 1174 respondents who answered all six waves of the Journeys Home survey in Australia over the period from September 2011 to May 2014. The survey included in each wave measures of respondents':

...usage and the frequency of usage in the past 6 months of: tobacco, alcohol, cannabis, and any other type of illegal/street drugs (which might include amphetamines, such as speed and ice, heroin, cocaine, ecstasy, and so on). We focus on the following substance use variables: smoking tobacco daily (versus not smoking or smoking less than daily), drinking 21 or more standard drinks a week (versus not drinking or drinking less than 21 drinks a week)... using cannabis daily (versus not using or less than daily), using illegal/street drugs weekly (versus not using or less than weekly).

The dataset also includes a measure of life satisfaction (measured on a 0-10 scale). Essentially, Moschion and Powdthavee explore the relationship between respondents' current life satisfaction and their past, current, and future drug use. They find that:

• Average life satisfaction drops significantly prior to the consumption of illegal/street drugs, but not prior to the consumption of other types of substances. The evidence on illegal/street drugs is consistent with the idea that less satisfied individuals are more likely to consume addictive substances because of the expectation that by doing so, their experienced utility could improve.
• An excessive consumption of alcohol, the use of cannabis daily and illegal/street drugs weekly is observed together with a significant drop in life satisfaction. This finding is more consistent with the UM framework in which experienced utility is predicted to decrease in periods following individuals’ consumption of addictive substances.
• We find evidence that the consumption of illegal/street drugs 6 months to a year prior to the interview is associated with lower current life satisfaction, independently of current consumption. This suggests that of the substances studied, only illegal/street drugs may have an effect on life satisfaction for more than 6 months.
• Our results thus suggest a vicious circle of illegal/street drug use by which lower life satisfaction increases the propensity to consume illegal/street drugs that in turn further lowers life satisfaction in the future.

They also find that:

...the estimated effects of past, present, and future consumption of illegal/street drugs on current life satisfaction to be noticeably more negative and statistically more pronounced for women and those from a lower educational background.

These results seem more consistent with the UM model than the RA model. On that point, Moschion and Powdthavee note that:

Our estimates could therefore suggest that respondents’ substance use patterns don’t follow a pattern consistent with the RA model although in reality they do if mental health issues explain non-rational patterns of substance use. To test the implications of controlling for mental health issues, we add 5 dummy variables to capture whether the respondent was diagnosed with any of the following mental health conditions between interviews: bipolar affective disorder, schizophrenia, depression, post-traumatic stress disorder and anxiety disorder. Our results are largely unchanged.

However, in spite of those results Moschion and Powdthavee are still unwilling to declare the rational addiction model dead:

Indeed, this drop in average life satisfaction of substance users over the six months following the use of substances is less consistent with the RA model, but does not rule it out completely, provided that the use of substances increases individual’s life satisfaction in the very short term and that individuals have an extremely high discount rate.

So, I guess until we have a dataset that can not only include past, current, and future drug use, along with a measure of utility (like life satisfaction, and probably measured at high frequency in order to pick up short-term effects) and a measure of discount rate, we're not going to be able to definitively satisfy ourselves that the rational addiction model cannot adequately capture drug users' behaviour. In the meantime, the utility misprediction model has the edge.

A few papers related to evaluating the optimal coronavirus lockdown

Earlier this week, I posted about the optimal length of the COVID-19 lockdown, and noted that:

...if the marginal benefit of lockdown is highly uncertain, and the marginal cost is also uncertain, then we really have no way of knowing for sure whether the lockdown has been too long, or too short.

Of course, I'm not the only one thinking about the issue of how long an optimal lockdown should last, although as I also noted in that post, the quality of work is highly variable. In the main, it is because the researchers aren't systematically thinking through both the benefits and the costs of lockdown (most are focused on one or the other). However, I have taken note of a number of papers that seem to address the question of the optimal lockdown length using a suitable framework of both costs and benefits.

For instance, this NBER Working Paper by Fernando Alvarez (University of Chicago), David Argente (Pennsylvania State University), and Francesco Lippi (Einaudi Institute for Economics and Finance), investigates:

...the problem of a planner who has access to a single instrument to deal with the epidemic: the lockdown of the citizens... The planner's problem features a tradeoff between the output cost of lockdown, which are increasing in the number of susceptible and infected agents, and the fatality cost of the epidemic.

Notice that this is pretty much the same trade-off I outlined in my post (although I didn't frame it in exactly their terms). Using a combination of a simple epidemiological model and a simple economic model, Alvarez et al. study (emphasis is theirs):

...how the optimal intensity and duration of the lockdown depend on the cost of fatalities, as measured by the value of a statistical life, on the effectiveness of the lockdown (the reduction in the number of contacts once the citizens are asked to stay home), and on the possibility of testing, i.e. to identify those who acquired immunity to the disease. We show that if the fatality rate (probability of dying conditional on being infected) is increasing in the number of infected people, as is likely the case once the hospital capacity is reached, the policy maker motive for lockdown is strengthened.

Their findings are not at all surprising, and depend on their parameter assumptions:

In our baseline parameterization, conditional on a 1% fraction of infected agents at the outbreak, the possibility of testing and no cure for the disease, the optimal policy prescribes a lockdown starting two weeks after the outbreak, covering 60% of the population after 1 month. The lockdown is kept tight for about a full month, and is subsequently gradually withdrawn, covering 20% of the population 3 months after the initial outbreak. The output cost of the lockdown is high, equivalent to losing 8% of one year's GDP (or, equivalently, a permanent reduction of 0.4% of output). The total welfare costs is almost three times bigger due to the cost of deaths... 

One other interesting point is this:

...the elasticity of the fatality rate to the number of infected is a key determinant of the optimal policy - we found that when the fatality rate is flat the optimal policy is to have no lockdown.

In other words, lockdowns make the most sense when the fatality rate is high and increasing in the number of infected - that is, when the fatality rate has a non-linear relationship with the number of infected.

Alvarez et al. used the value of a statistical life in their calculations, as a measure of the health benefits of a lockdown. Weighing up costs and benefits requires a common metric be used, and economists often use dollars (which is why the value of a statistical life is important - it provides a dollar value that can be used as a measure of the value of lives saved or deaths averted).

An interesting alternative is proposed in this working paper by Richard Layard (London School of Economics) and co-authors. They use a measure of 'WELLBYs' - a wellbeing equivalent of the QALY (Quality-Adjusted Life Year), which is often used as an evaluation tool in health policy. Essentially, one year of perfect life satisfaction is worth one 'WELLBY', while one year with life satisfaction of 5/10 is half a 'WELLBY'.

Having converted the benefits and costs of the lockdown into the WELLBY metric, Layard et al. show that the UK lockdown should be lifted around 1 June. Of course, their analysis has a huge number of assumptions that are pretty heroic - I wouldn't take their headline results as a strong endorsement of a date for lifting the UK lockdown. And of course, any analysis based on life satisfaction is ignoring the strong theoretical problems of life satisfaction measurement (as noted in this post). However, the overall framework of considering costs and benefits of the lockdown is important, even if you don't believe the measurement using WELLBYs.

Finally, both the Alvarez et al. and Layard et al. papers outline trade-offs between public health benefits and economic costs of the lockdown. However, not going into lockdown can have economic costs as well, as this working paper by Martin Bodenstein (Federal Reserve Board), Giancarlo Corsetti (University of Cambridge), and Luca Guerrieri (Federal Reserve Board) notes. They use a more sophisticated economic model and epidemiological model than the Alvarez et al. paper. In particular, their economic model distinguishes between a core economic sector and another sector, while their epidemiological model distinguishes three groups (one associated with each economic sector, and one non-working group). They are able to show that:

...by affecting workers in this core sector, the high peak of an infection not mitigated by social distancing may cause very large upfront economic costs in terms of output, consumption and investment.

So, the simple trade-off between economic output and lives saved may not be quite so simple. Of course, we are still early on in properly understanding the trade-offs associated with the coronavirus lockdown, and clearly we're not going to be able to evaluate the optimal lockdown length until well after the lockdowns have been lifted. However, the methods and measurement necessary to better understand this problem for future pandemic outbreaks are developing quickly.

[HT: Marginal Revolution for all three papers: here, here, and here]

Read more:

• How long should the coronavirus lockdown last?

Online social networks, social capital, and wellbeing

In economics, capital is essentially defined as a collection of resources that an individual uses to produce goods or services (even if those goods or services are not traded in markets). Some types of capital are obvious, such as machinery or tools (physical capital), or financial wealth (financial capital). Others are a little less obvious, like the stock of human capital (our knowledge, training, and experience, etc.) and natural capital (land, air, water, biodiversity, etc.). Then there is social capital - the social relationships that we have with other people. Social capital is the most difficult to measure (even more difficult than natural capital), but typically we measure it in terms of the number of relationships, and the quality of those relationships, and in terms of quality, we often use the degree of social trust as one measure.

All of that is a long-winded way of talking about the value of online social networks (which is a topic I have discussed before - see the links at the end of this post). In theory, social networks could increase social capital, because they allow us to increase the number of social relationships. However, social networks could also decrease social capital if, in spite of a larger number of relationships, the quality of those relationships is lower. If a social network has a particularly bad culture, the quality might even be negative (if belonging to the network makes us worse off, holding the number of relationships constant).

How can we understand whether social networks have a net benefit (the increase in relationships outweighs any decrease in their quality) or a net cost (the decrease in quality outweighs the increase in relationships)? One way is to look at subjective wellbeing (or life satisfaction, or happiness), and that's what a lot of studies have done (see the links at the end of the post for a few examples). However, fewer studies have also looked at social capital.

One notable exception is this 2017 article by Fabio Sabatini (Sapienza University of Rome) and Francesco Sarracino (STATEC, Luxembourg), published in the journal Kyklos (appears to be open access, but just in case there is an ungated earlier version here). They use data on around 50,000 people from the 2010-2012 waves of the Italian Multipurpose Household Survey. Subjective wellbeing was measured on a 0-10 scale (which is quite common), but online social network use was measured by the yes/no response to the question: "Did you make use of social networking sites such as Facebook and Twitter in the last 12 months?". Social capital was measured by the number of interactions with friends (quantity), and the dichotomous response to the question: "Do you think that most people can be trusted, or that you can’t be too careful in dealing with people?" (quality).

The problem with most studies of online social networks is that they can't show that using the social network causes a change in subjective wellbeing, because perhaps happier (or less happy) people are more likely to use the online social network, in which case the causality runs in the wrong direction. Sabatini and Sarracino try to get around this by using instrumental variables analysis - essentially they predict people's social network access by looking at whether the area they lived in had access to high-speed broadband (DSL or fibre) or not in 2008 (i.e. two years before the survey), then see if the predicted social network access is related to subjective wellbeing. There are issues with these instruments, which I will come back to shortly. Sabatini and Sarracinofind that there is:

...a significant and negative correlation between the use of SNS [social networking sites] and subjective well-being which is independent from the controls for social capital.

In their instrumental variables analysis, they find that:

...the proxies of social capital are positively and significantly associated with life satisfaction, while the use of SNS has negative and significant coefficients.

Finally, they use structural equation modelling (SEM) to look at the inter-relationships between online social network use, social capital, and subjective wellbeing. They find that:

The SEM analysis suggests that the significantly negative correlation between SNS use and subjective well-being obtained in OLS estimates is not only the result of a direct negative effect, but it also results from the combination of two indirect channels:

1. the negative correlation between the use of SNS and social trust that negatively affects well-being.

2. the positive correlation between the use of SNS and face-to-face interactions that positively affects well-being.

Taken all together, their results show that online social networks reduce subjective wellbeing, but that is because online social network use is associated with lower quality of social capital (lower trust), even though online social network use is associated with greater number of social relationships.

This is a nice paper, because of the combination of several methods of analysis. However, the results aren't as strong as the authors claim. The instruments (DSL and fibre broadband access) are not good instruments, because high-speed internet is not necessary in order to access social networks, and because high-speed internet is also associated with increases in the use of many other internet tools (online video, for example). However, despite that, the results are at least consistent with our theoretical predictions from the start of this post. Of most interest may be that the use of online social networks was associated with more face-to-face interactions, rather than just more online interactions.

More papers in this research area should take account of social capital though, if we really want to understand how online social networks affect subjective wellbeing.

Read more:

• The latest NZ research on social media and mental health is nothing special
• What happens when you disconnect from Facebook?
• Does the Internet make people happier?
• The more you use Facebook, the worse you feel
• Quitting Facebook might make you happier

The 2015 refugee crisis, attitudes towards immigrants, and the effect of immigrants on subjective wellbeing

I've been catching up on a bit of reading related to immigration, refugees, and their effects on the native-born population (see also this earlier post of mine on a related topic). The 2015 refugee crisis in Europe provides an interesting natural experiment to test a number of theories about immigrant assimilation, the impacts of migrants on natives, and attitudes towards migrants. A 2019 article by Dominik Hangartner (ETH Zurich) and co-authors, published in the journal American Political Science Review (open access), looks at the last of those three.

Using survey data from 2,070 residents of the Greek islands, Hangartner et al. look at how exposure to the refugee crisis has affected attitudes. They compare residents of islands that received any refugees during the crisis to residents of islands that did not, and use an instrumental variables approach. Their instrument is the distance of the island to the Turkish coast, which would be expected to affect the likelihood that an island receives refugees, but shouldn't affect attitudes directly (especially after controlling for a bunch of other variables in their analysis). They find that:

...direct exposure to the refugee crisis has statistically and politically meaningful effects on natives’ exclusionary attitudes, preferences over asylum and immigration policies, and political engagement. Exploiting the exogenous variation in refugee arrivals caused by distance to the Turkish coast — our instrument — we find that respondents directly exposed to the refugee crisis experience a 1/4 standard deviation (SD) increase in their anti-asylum seeker and anti-immigrant attitudes as well as a 1/6 SD increase in their anti-Muslim attitudes. Compared to respondents on unexposed islands, they are more likely to oppose hosting additional asylum seekers and to support the ban from school for asylum seekers’ children and are less likely to donate to UNHCR and to sign a petition that lobbies the government to provide better housing for refugees.

In other words, it's all bad news. Looking into the reasons for their results, they can exclude economic concerns:

...because refugees quickly left the islands for other European countries, the usual materialist concerns that immigrants compete with natives over scarce resources such as jobs or welfare benefits... do also not apply in this context.

So, it was the mere exposure to the crisis itself that led to these changes in attitudes. Most worryingly, it appears that these attitudinal changes had some persistence over time, because the survey was conducted in early 2017, nearly a year after the refugee crisis had abated.

Hangartner et al. put their results down to:

The inability of the local and European authorities to effectively manage the refugee flows and provide medical support and sanitary services caused chaotic scenes at the hotspots and sparked concerns about the spread of diseases.

That at least suggests that better handling of the situation could have avoided the worst effects. However, it is speculative, since we don't know what would have happened had the crisis been more effectively dealt with. There are two other conclusions from the research that are also worrying:

Our findings of a uniform effect of exposure to the refugee crisis across the sample suggest that this threat triggered exclusionary reactions not only among those already predisposed against immigration, but also among respondents who otherwise would exhibit inclusionary attitudes and have not voted for (extreme) right-wing parties in the past...

...we find that exposure to large numbers of asylum seekers causes natives to become more hostile not only toward refugees, but also toward economic migrants and Muslims, including native Muslims who have been residing in Greece for centuries.

The effects were generalised in the population, and had negative spillovers on attitudes to other out-groups. It may take some time for these effects to dissipate.

However, not all studies show bad news. This 2014 article by Alpaslan Akay (University of Gothenburg), Amelie Constant (George Washington University), and Corrado Giulietti (Institute for the Study of Labor, Germany), published in the Journal of Economic Behavior & Organization (ungated earlier version here), looks at the impact of immigration on subjective wellbeing (life satisfaction, measured on a 1-10 scale) of native-born Germans. Using 170,000 observations from the German Socio-Economic Panel survey over the period from 1998 to 2009, they find that:

...an increase of one standard deviation in the immigrant share [in the local labour market area] is associated with an increase of 0.142 standard deviations in natives’ [subjective wellbeing]. This is rather a large effect if one considers that the standardized coefficient for being unemployed is −0.112 and for wage is 0.017.

Local unemployment and GDP don't seem to affect the results, so again there isn't an economic (or labour market) explanation for these results. When they dig a bit further, it is satisfaction with housing (and not satisfaction with job, health, or income) that seems to be driving the overall result.

Despite some attempts by the authors to argue otherwise, it isn't clear to me that these results are necessarily causal - perhaps immigrants are simply more likely to move to areas where people are happier. However, the results are at least suggestive, because the natives are happier where there are more immigrants, but the immigrants are not.

It would be interesting to see some further results on subjective wellbeing after the refugee crisis. The Hangartner et al. results suggest a dramatic change in attitudes following the crisis in an area that the refugees are simply transiting through. It would be interesting (and important for policy purposes) to know whether that effect spills over to their ultimate destinations.

[HT for the Hangartner et al. article: Marginal Revolution, back in January 2019]

Read more:

• Which asylum seekers do Europeans want?