Inequality is not robustly associated with lower subjective wellbeing or worse mental health

When I cover inequality with my ECONS102 class, we talk about some of the many problems that have been associated with inequality, such as unequal access to political representation, lower social cohesion, and less interaction between increasingly segregated social groups. I also talk about the theory that links higher inequality with lower economic growth, though noting that the research is far from settled on that issue (see here and here, for example).

In that class, I don't discuss the research linking inequality with lower physical or mental health. That goes back to my objections to the Wilkinson and Pickett book The Spirit Level (which I discussed here). It turns out that might have been a good choice, at least in terms of mental health.

This recent article by Nicolas Sommet (Université de Lausanne) and co-authors, published in the high-profile journal Nature (ungated earlier version here), presents the results of meta-analyses of the relationship between inequality and subjective wellbeing, and inequality and mental health. Meta-analysis is a way of combining the results of many studies to generate an estimate of the overall effect size.

Their meta-analysis of inequality and subjective wellbeing included 152 estimates from 100 different studies, and they found that inequality was not significantly associated with subjective wellbeing. Their results were qualitatively the same whether subjective wellbeing was measured as life satisfaction or happiness, and did not depend on study quality. That is worth noting, given that about 80 percent of the studies included in the two meta-analyses were judged to be at high or very high risk of bias.

Sommet et al.'s meta-analysis of inequality and mental health included 99 estimates from 75 different studies, and they found a small negative association between inequality and mental health. Specifically:

A 0.05-point increase in the Gini coefficient predicted a 4% (1/0.961) increase in the odds of experiencing a mental-health issue.

The Gini coefficient measures inequality on a scale of 0 to 1, and a 0.05-point increase is similar to the difference between New Zealand (0.326, above the OECD average) and Denmark (0.276, close to the lowest inequality among OECD countries - see Figure 6.1 in this report). Similar to subjective wellbeing, the results were qualitatively the same whether mental health was measured as depression or 'other mental disorders', and did not depend on study quality. The effect was larger when inequality was measured at the 'macro level', meaning the country level (or large subnational areas with populations over one million), although the effect was also statistically significant when inequality was measured at the 'meso level' (small subnational areas, such as counties).

Not so fast, though. After correcting for publication bias (the tendency for statistically significant results to be more likely to be published), Sommet et al. find that, for mental health:

The median bias-corrected estimate was OR+0.05 = 1.018 (0.986–1.051), P = 0.266, thereby confirming the lack of a significant association after correcting for publication bias.

Moreover, Sommet et al. conduct a specification-curve analysis, which tests how sensitive the results are to different modelling choices. They find that their conclusions are robust across approximately 95 percent of 768 alternative model specifications.

The overall result is that inequality is not statistically significantly associated with either subjective wellbeing or mental health, at least not robustly so. However, Sommet et al. then dig a little further, finding some interesting differences in the estimated effects across countries with different characteristics. First, for mental health, they find that the negative association is significant in lower-income samples, but not in higher-income samples. They replicate this result using data from the Gallup World Poll, using average national income to distinguish between lower- and higher-income countries. They explain this as suggesting that:

...inequality may be particularly harmful to low-income populations, possibly by undermining community cohesion... fostering adverse social comparison... or fuelling perceptions of unfairness...

Second, for subjective wellbeing they find that there is a negative association with inequality in high-inflation countries, but a positive association with inequality in low-inflation countries. They also replicate this finding using data from the Gallup World Poll. They explain this in the following way:

Rising costs of living drive distress... and inequality may exacerbate the burden of inflation by increasing social discontent and resentment. Conversely, when prices are stable, inequality might be interpreted as a signal of future economic mobility and foster hope... That is, inequality may only harm well-being under degraded economic conditions (here, high inflation) and have positive effects under others (low inflation).

Their explanations are speculative, and I would be cautious about over-interpreting the mechanisms underlying these latter results. Nevertheless, the fact that both findings were replicated using the Gallup World Poll should provide some comfort about the results. Overall, the results of this meta-analysis can be taken to mean that, while there may be a relationship among some subgroups or some countries at some points in time, there is not a robust overall association between inequality and lower subjective wellbeing or worse mental health.

[HT: Marginal Revolution, last year]

Two papers on inequality in pre-industrial times, that extend our view over two millennia

Back in 2015, I wrote a post about some research by Branko Milanovic (City University of New York) on inequality in ancient times. Milanovic is a prolific inequality researcher, but I only recently got time to read this 2019 article of his, published in the journal Review of Income and Wealth (ungated version here). The article is something of a sequel to the earlier article, tracking changes in income inequality from 14 to 700 C.E. in Europe and the Mediterranean.

This involves combining data from a variety of other studies, first to estimate changes in mean income, then overall and regional inequality, before turning to individual inequality. The resulting estimates are summarised in Figure 2 from the paper, which shows upper and lower bounds on the estimated Gini index:

The 'social table' referred to in Figure 2 comes from this earlier paper by Scheidel and Friesen, with an estimated Gini index of 41.3. The decline from there to a Gini index of between 3 and 13 represents both an unprecedented decline, and an extraordinarily low level of inequality by 700 C.E. Milanovic notes the apparent disconnect between the low inequality he estimates and the impressions of contemporary accounts of poverty, and explains that:

We believe that it is possible to reconcile the conjectured low measured inequality with the impression of a high post-300 inequality because the latter may, in reality, refer to the speculation (or a fact) that the elite was appropriating proportionally much more of the surplus above subsistence than before... Those who observed poverty of most contrasted to the wealth of the very few tended to conclude that inequality must have unambiguously risen. But they might have failed to account for the fact that the numerous poor were now all equally impoverished, and that measured inequality (calculated across all individuals) could not, on that account alone, have been very high...

It is worth noting that these low estimates depend on an assumption that average incomes were barely above subsistence. When almost everyone is extremely poor, even a rich elite that appropriates most of the surplus above subsistence might not produce a particularly high overall Gini index. So, we should be careful that we don't mistake low inequality for prosperity, because it certainly isn't implying that. Nevertheless, the results remain somewhat extraordinary, and it will be interesting to see other estimates of inequality during that time, taking advantage of different data sources or methods.

Milanovic's paper finishes with 700 C.E. What happened to inequality after that? This 2021 article by Guido Alfani (Bocconi University), published in the Journal of Economic Literature (ungated earlier version here), picks up the story in 1300 C.E. and takes it through until the end of the 18th Century, synthesising estimates from a number of other studies of inequality in various parts of Europe. Alfani makes note of Milanovic's results (from the paper I noted above), but doesn't really critically appraise them, which is fine because Alfani is more interested in the period from the late Middle Ages to the early modern period. The studies he synthesises are summarised in Figure 2 Panel A from his paper, which again focuses on the measures of the Gini index, although note that this is for wealth inequality (rather than income inequality):

Alfani uses this to suggest that the evidence supports two stylised facts about inequality in this period: (1) that inequality in Europe increased from 1450 or 1500 until 1800; and (2) before 1450, there was a period of declining inequality that coincides with the Black Death. The decreasing inequality during the Black Death is consistent with an explanation that my ECONS102 students will be familiar with - agricultural wages increased dramatically during that period, alongside a decrease in land rents (as discussed in the book The Rise of the Western World by North and Thomas, which I reviewed here). Alfani then goes on to explore reasons that might explain the overall increase in inequality. He dismisses economic growth as the main explanation, noting that inequality appeared to increase even during periods of economic decline (aside from during the Black Death). Instead, Alfani points to the importance of institutions, and in particular the rise of the fiscal-military state, alongside generally regressive taxation, as well as inheritance systems that protected wealth within families. He concludes that:

The different, and sometimes partly contrasting, explanatory factors for long-term inequality change discussed above lead to a clear conclusion, which can be summarized as follows:

(i) In preindustrial times, there was no necessary cause of inequality growth (like for example, economic growth).

(ii) There were instead a number of sufficient causes of inequality growth, among which we could name economic growth, demographic factors (including urbanization growth), institutional change and politics, “proletarianization” (i.e., the crisis of small ownership), and so on. When one or more of these potential causes became active, inequality grew.

(iii) The bottom line being, in the long preindustrial period it was seemingly much easier for inequality to grow than to decline—and indeed, we have very few examples of long-lasting inequality decline, and almost all of them can be connected to large-scale catastrophes.

Finally, Alfani takes a longer view of wealth inequality through to modern times, by comparing his estimates (based on an average of the various studies included in his synthesis) to those of Thomas Piketty, which begin in 1800. The results are shown in Figure 8 in the paper, which shows the share of wealth held by the richest 10 percent, rather than the Gini index:

So, now we are getting a longer-term picture of nearly two millennia of changes in inequality in Europe, albeit with some important gaps. Milanovic's results suggest that income inequality declined dramatically after the fall of Rome, while Alfani's results suggest that wealth inequality declined around the time of the Black Death, before increasing through to the early 20th Century. In the 20th Century, wealth inequality saw another period of rapid decline (but nothing like the decline in income inequality after the fall of Rome), before increasing again in the second half of the century. Of course, income inequality and wealth inequality are not directly comparable, even if they tend to be correlated. So we should be cautious about drawing conclusions about the overall trajectories of both types of inequality. More studies are needed to confirm these long-run patterns, as well as to fill in the gaps and to extend these results to consider other regions outside of Europe.

Read more:

• Inequality was similar, or higher, in ancient times than it is today
• The last two centuries of global inequality
• Changing global inequality, migration and open borders
• More evidence of declining global inequality
• Chinese growth and global inequality
• Several research articles on inequality
• The global inequality boomerang

This week in research #148

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

• Greenaway‐McGrevy and So (open access) find that upzoning reform in Auckland decreased rents by 23 percent eight years after the reform
• Hess and Thomas (open access) find that state-level recreational marijuana legalisation in the US was associated with near‐zero initial college enrolment growth in STEM majors as out-of-state enrolments increased but were offset by a decrease in in-state enrolment, but that total enrolment increased in later periods as out‐of‐state gains persisted and in‐state enrolment returned to baseline
• Stephenson finds that the 2015-2018 NFL drafts had negligible effects on room rentals and hotel revenue in the host city, while the 2019-2024 drafts (excluding 2020 and 2021) had statistically significant but small effects
• Hillman (with ungated earlier version here) finds no lasting or statistically significant effects of unilateral‐no‐fault divorce laws on divorce rates, using state-level data in the US

Non-market valuation, and the economic value of birdsong

When economists want to know the value of something, we typically look at the price it is traded for in a market. That works well when you want to know the value of an apple, a haircut, or a rare Pokémon card. It doesn't work at all for goods and services that are not traded in markets (like clean air) or those that are not traded because they don't yet exist (like vaccines for HIV). To value those goods and services, we need to apply non-market valuation.

I've previously written several posts about the contingent valuation method and its problems (most recently here), but it is one of several methods that are used for non-market valuation. A similar method is to run a discrete choice experiment, where survey participants are presented with a series of choices between different alternatives that have different characteristics, and are asked to choose the alternative they prefer. By getting participants to make a number of choices between different alternatives, researchers can establish how important each of the characteristics are to the participants. And if one of the characteristics is a price, then researchers can estimate how much participants are, on average, willing to pay for each characteristic.

One of the issues with the contingent valuation method is that it is vulnerable to context. For example, in this 2015 post I describe research that shows that research participants are less rational in their choices if they complete the task late at night. However, researchers may also be able to use context to improve their estimates of what people are willing to pay.

Consider this new article by Anthony Higney (University of Glasgow) and co-authors, published in the journal Ecological Economics (open access). They use a discrete choice experiment, conducted in an online sample of 1094 people from the UK, to measure willingness to pay for ecological restoration. Specifically, they presented each research participant with a series of choices about habitat restoration for birds, where the characteristics of each alternative included the habitat type (woodland, grassland, or arable farmland), the size of bird habitat (compared with baseline), and the quality of the habitat (in terms of the number of bird species), as well as a one-time donation amount. The inclusion of the donation amount means that Higney et al. can use the results to estimate the willingness-to-pay for each of the other characteristics.

Research participants were randomised so that half of the participants had access to a sound recording of birdsong for each of the alternatives (the treatment group), while the other half had no birdsong (the control group). Higney et al. essentially argue that listening to birdsong increases the salience of the habitat quality in the participants' decisions about which alternative to choose. They found that:

...the treatment of playing birdsong causes respondents to be less sensitive to costs in making their choices and rate quality changes from the status quo more highly.

Higney et al. can't tell whether their respondents actually listened to the birdsong or not. So, what their results really say is that respondents who had access to birdsong were willing to pay more for habitat restoration than respondents who did not have access. How much more? The willingness-to-pay for medium-quality habitat (compared with low-quality) was £276.5 higher for those with access to birdsong than those without access, while the willingness-to-pay for high-quality habitat (compared with low-quality) was £274.6 higher. That compares with a baseline willingness-to-pay in the control group of £99.5 for medium quality, and £86.0 for high quality. Interestingly, much of the difference arose because respondents who had access to birdsong were less sensitive to the donation amount, rather than simply placing greater value on improvements in habitat quality.

One reason for caution about the size of these estimates is that the highest donation amount participants were offered in the experiment was £200, which is smaller than the estimated willingness-to-pay for medium-quality and high-quality habitat among the treatment group. So, participants were never actually asked to consider donation amounts as high as their estimated willingness-to-pay. This is not an unusual outcome in this sort of hypothetical exercise, but some follow-up research using a wider range of donation amounts would be useful.

Overall, there was no significant difference in willingness-to-pay between the medium-quality and high-quality habitats (either for the treatment or the control group), and Higney et al. don't have a good explanation for why people weren't sensitive to the quality of habitat restoration beyond medium quality. That would also be a good avenue for future research.

Why would listening to birdsong change what people are willing to pay for habitat restoration? Higney et al. suggest that this could be due to framing:

Listening to the sound clips might have reduced the use of cognitive shortcuts in the treatment group. This could include negativity bias, i.e. giving disproportionate weight to negative information such as the payment vehicle...

Alternatively, respondents in the treatment group may consciously place less weight on other aspects of their choice because the sounds make them feel more connected to nature.

Framing does seem like an obvious explanation, and Higney et al. show in a follow-up regression analysis that participants who had access to birdsong reported significantly greater feelings of connection to nature. That is consistent with the addition of the sound clip meaningfully changing how the alternatives are interpreted. Higney et al. intentionally created sound clips to match the quality of the habitat they were asking about. It would have been interesting to run the same exercise, but with every sound clip being the same (or very similar) generic level of birdsong. Or to have two treatments: (1) generic birdsong; and (2) birdsong that conveys the quality of habitat. That may have helped tease out whether the participants were receiving better information about the alternatives by having access to the sound clip, or whether they were simply feeling more connected to nature after hearing the birdsong.

When valuing the benefits of ecological restoration, we can't rely on market prices and so non-market valuation techniques are really important. The way that we undertake those non-market valuation exercises is important, and understanding how framing affects people's decisions in discrete choice experiments is important for designing research that will give us the most accurate assessment of value. This research can't tell us whether playing birdsong to research participants gives us estimated willingness-to-pay that is closer to the 'true' value. But it does tell us that the information that research participants are presented with, and how that information is presented, matters.