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

Perceptions of inequality and satisfaction with democracy

Last week, my ECONS101 class covered (among many other things) the faulty causation fallacy. This occurs when we observe two variables that appear to be related to each other (they are correlated), but a change in one of the variables does not actually cause a change in the other variable (there is no causal relationship). We might observe a relationship between two variables (call them A and B), and it might be because a change in A causes a change in B, in which case the relationship is causal. But even if we can tell a really good story explaining why we think a change in A causes a change in B, that in itself doesn't make it true. We might observe that relationship because a change in B causes a change in A (we call this reverse causation). Or, we might observe that relationship because a change in some other variable causes a change in both A and B (we call this confounding). Or, the two variables might be completely unrelated, and the observed relationship happens by chance (we call this spurious correlation).

To illustrate this, I'm going to use the example of the research in this 2024 discussion paper by Nicholas Biddle and Matthew Gray (both Australian National University). They also wrote a non-technical summary of their paper on The Conversation. Biddle and Gray look at the relationship between perceptions of income inequality and faith in democratic institutions. To be fair to them, they do say in the paper that "This does not, however, demonstrate a causal relationship from views on inequality to views on democracy". However, most of their interpretations and their policy recommendations assume that the relationship is causal. For example, they conclude that:

The fundamental issue identified in this paper is that the Australian population has identified the income distribution in Australia as being unfair, and that this appears to be impacting views on democracy.

First though, let's take a step back and look at the research. Biddle and Gray use data from Waves 5 and 6 (from 2018 and 2023 respectively) of the Asian Barometer Survey (with a sample size of over a thousand in each wave for Australia), as well as from the ANUPoll surveys, which is a quarterly survey of public opinion run by the Social Research Centre at ANU. For the ANUPoll, they use the January 2024 data, which includes data from over 4000 respondents.

First, from the Asian Barometer, Biddle and Gray find that there is substantial concern about inequality:

In both waves 5 and 6 of the survey, respondents were asked ‘How fair do you think income distribution is in Australia?’... more Australians think that the income distribution is unfair or very unfair (60.5 per cent) than think it is fair or very fair. This gap has widened slightly since 2018, particularly in terms of those who think the distribution is very unfair as opposed to just unfair.

Second, in the ANUPoll data, they find that:

Combined, 30.3 per cent of Australians were not at all or not very satisfied with democracy in January 2024 (compared to 34.2 per cent in October 2023). This is still well above the January 2023 levels of dissatisfaction (22.9 per cent) and even more so the March 2008 levels (18.6 per cent).

So over time, Australians' perceptions of inequality have gotten worse (they think the income distribution is less fair), and they are less satisfied with democracy. It is reasonable, then, to ask whether those concerns about inequality affect people's faith in democratic institutions. Biddle and Gray next look at that relationship, using to the ANUPoll data, and find that:

There is a very strong relationship between views on income inequality in Australia and views on democracy...

Their model (shown in Table 1 in the paper [*]) shows that the most negative views of the income distribution are associated with negative satisfaction with democracy, while the more positive views of the income distribution are associated with positive views of democracy.

So, there is a strong correlation between perceptions of inequality and satisfaction with democracy. But is that just a correlation, or is there a causal relationship? We can tell a good story here (and Biddle and Gray do that). People who are less satisfied with the income distribution may lay some blame on government, and therefore their satisfaction with democracy falls.

Before we conclude that this relationship is causal though, let me lay out some alternatives. First, perhaps people who are less satisfied with democracy become less satisfied in general with many aspects of society, including the income distribution. In this case, there could be reverse causality. Second, perhaps people who are less satisfied with life in general express less satisfaction with many aspects of life and society, and so they answer more negatively when asked about the satisfaction with democracy, and they answer more negatively when asked about their views of the income distribution. In this case, there would be confounding. Third, perhaps satisfaction with democracy is declining over time for some reason, and views about the income distribution are becoming more negative for some completely different reason. But they look like they are related because they are both trending downwards. In this case, there would be a spurious correlation between perceptions of inequality and satisfaction with democracy.

It isn't straightforward to see two variables that appear to be related, and assume that a change in one of those variables causes a change in the other variable. Economists and other researchers have developed a number of statistical tools and experimental methods to try and tease out when a correlation really is demonstrating a causal relationship. Biddle and Gray haven't done that. It might be that negative perceptions of inequality reduce satisfaction with democracy. By itself, this research doesn't allow us to conclude that.

[HT: The Conversation]

*****

[*] Table 1 in the paper actually has an error. The explanatory variable in the table is labelled as satisfaction with democracy, when that is actually the dependent variable. It is perceptions of inequality that is the explanatory variable.

Try this: The Opportunity Atlas

It's hard to believe that, in over twelve years of blogging, I have never blogged about any of Raj Chetty's research. That's not because I haven't read it. If anything, it's because it is so detailed that it defies a short blog take. For example, we read two related papers published in the journal Nature (here and here, both open access) in the Waikato Economics Discussion Group back in 2022. Ordinarily, I would follow up with a blog post, but they are so in-depth that I couldn't find the time to summarise them effectively [*]. Three years later, they are sitting in a virtual pile of read-but-not-yet-blogged-about papers [**].

Anyway, Chetty and co-authors have suddenly made it much easier for me to summarise their extensive research on social mobility in the US. That's because you can see the data in action for yourself now, at The Opportunity Atlas. This very cool online tool allows you to see social mobility in action. Social mobility is effectively how much a child's socioeconomic position in adulthood depends on their socioeconomic position when they were growing up.

On The Opportunity Atlas, you can choose from a range of outcomes in adulthood, and see where the mean outcome is, if they grew up in a household at different rankings of parental income (1st, 25th, 50th, 75th, or 100th percentile). You can also look separately by gender and by race (Black, White, Hispanic, Asian, Native American). The interface is quite intuitive to use. For example, here's the basic map of expected (mean) income at age 35, for children who grew up in households at the 25th percentile of parental income:

The red areas, such as the South, have lower social mobility, because children who grew up there in households at the 25th percentile have lower incomes as adults. In contrast, the blue areas (in the north and west) have higher social mobility, because children who grew up there in households at the same 25th percentile have higher incomes as adults.

The tool is very flexible. It's very easy to switch to looking at other outcome variables, and for other percentiles of parental income, as well as zooming in on particular areas. For example, here's the teenage birth rate for Black women who grew up in households at the lowest (1st) percentile of parental income in Los Angeles:

The greyed-out Census tracts are those where there are too few Black women who grew up in the lowest income households for the data to be reported. However, the map shows a band of high teenage birth rates for mothers who grew up in the lowest income households, that stretches from South Central to Compton.

Importantly, the underlying data can be downloaded from the Opportunity Insights website. The cool thing about the data underlying the Atlas is that it is based on the census tract where the child grew up, not the census tract where they live as an adult. That means that the Atlas is showing you the adult outcomes for children who grew up in a particular area, not the adult outcomes of adults who live there today. That is explained in this new article by Chetty (Harvard University) and co-authors, published in the journal American Economic Review (ungated earlier version here).

That article outlines the methods underlying the dataset. In short:

...we use de-identified data from the 2000 and 2010 decennial censuses linked to data from federal income tax returns and the 2005–2015 American Community Surveys to obtain information on children’s outcomes in adulthood and their parents’ characteristics. We focus in our baseline analysis on children in the 1978–1983 birth cohorts who were born in the United States or are authorized immigrants who came to the United States in childhood...

We construct tract-level estimates of children’s incomes in adulthood and other outcomes, such as incarceration rates and teenage birth rates by race, gender, and parents’ household income level—the three dimensions on which we find children’s outcomes vary the most. We assign children to locations in proportion to the amount of their childhood they spent growing up in each census tract. In each tract-by-gender-by-race cell, we estimate the conditional expectation of children’s outcomes given their parents’ household income using a univariate regression whose functional form is chosen based on estimates at the national level to capture potential nonlinearities.

Chetty et al. then go on to show why it matters that we look at social mobility based on the place where children grew up, rather than contemporary poverty rates or adult outcomes, and finally give some short use cases for the dataset. I won't go into detail on those (you should read the paper), but one of the things that Chetty et al. do show is that because the effects change slowly over time, looking at outcomes today for children who grew up in a particular census tract in the 1980s still provides meaningful information that can be used for targeting social programmes today.

It's important to note that the Opportunity Atlas by itself doesn't show us causal estimates of adult outcomes. However, Chetty et al. establish how much of the effect is causal using a couple of different methods: (1) using data from the Moving to Opportunity experiment; and (2) a quasi-experiment that looks at how the effects differ depending on how many years a child was 'exposed' to a particular Census tract). Both methods both imply that roughly 62%) of the observed variation across census tracts reflects causal neighbourhood exposure effects, not just higher-opportunity families sorting into better places.

In the conclusion, Chetty et al. highlight a number of applications where the Opportunity Atlas data has already been used:

For researchers, the Opportunity Atlas data provide a new tool to study the determinants of economic opportunity. For example, recent studies have used the Opportunity Atlas data to analyze the effects of lead exposure, pollution, neighborhood redlining, and the Great Migration on children’s long-term outcomes (Manduca and Sampson 2019; Colmer, Voorheis, and Williams 2019; Park and Quercia 2020; Aaronson, Hartley, and Mazumder 2021; Derenoncourt 2022). Other studies use the Atlas statistics as inputs into models of residential sorting (Aliprantis, Carroll, and Young 2024; Davis, Gregory, and Hartley 2019) and to understand perceptions of inequality (Ludwig and Kraus 2019). The ongoing American Voices Project (https://americanvoicesproject.org/) is interviewing families in neighborhoods with particularly low or high levels of upward mobility to uncover new mechanisms from a qualitative lens.

I can see a number of use cases for this as well. For instance, there is probably a lot of value in using the Opportunity Atlas data alongside the data on racial diversity and segregation from the Mixed Metro project (which also offers data down to the Census tract level). Also related is this from a footnote in the Chetty et al. paper:

Understanding how neighborhood effects change with the composition of the neighborhood is an important question that warrants further work...

This also makes me think (again) that we need more detailed work on social mobility in New Zealand, building on the work of my colleagues Niyi Alimi and Dave Maré (see here). One of the amazing things about Chetty's research is that it is now looking at the neighbourhood (Census tract) level, and that sort of spatial disaggregation offers a lot of opportunity for detailed follow-up research and policy action. And with StatsNZ's Integrated Data Infrastructure, we have the basic framework necessary to do this sort of work in New Zealand as well. We could use that to build our own Opportunity Atlas for New Zealand.

*****

[*] So, in lieu of a separate blog post, here's the short summary of those two papers. In the first paper, Chetty et al. use billions of Facebook friendship links to measure local social capital, especially "economic connectedness" (cross-class friendships). They find that places with higher economic connectedness have much higher upward social mobility. In the second paper, the same group of authors show that cross-class friendship gaps come from both who people are exposed to (whether schools, neighbourhoods, or groups) and "friending bias" (less cross-class befriending even when exposed).

[**] In case you're wondering, there are currently 45 papers in that virtual pile, and it seems to be growing. I'm reading research faster than I'm blogging about it. I might have to start blogging about multiple papers in a single post to keep from falling further behind!

The impact of taxes and transfers on inequality in New Zealand

This week, my ECONS102 class covered inequality, and social security. Which is timely, because I have been meaning to blog about this Treasury Analytical Note from 2024, by Tod Wright and Hien Nguyen, for some time. Wright and Nguyen look at the distributional impact of taxes, transfers, and government spending (on healthcare and education).

Importantly, they distinguish between three conceptions of household income: (1) market income, which includes taxable income (including wages, income from self-employment and from investments) and non-taxable income (such as gifts and inheritances) [*]; (2) disposable income, which adjusts market income by subtracting direct taxes (such as income tax) and adding in transfers from government (such as income support payments); and (3) final income, which adjusts disposable income by subtracting indirect taxes (such as GST and excise taxes), and adding estimates of the government spending on health and education services that the household receives in kind. Looking at the difference in the income distribution (and measures on inequality) between market income, disposable income, and final income, gives a sense of how redistributive the tax and transfer system is.

I'm not going to get deep into the weeds on the methods. However, it is worth noting that the analysis is for the 2018/19 tax year, and makes use of Treasury's TAWA (Tax and Welfare Analysis) model, supplemented by data on indirect taxes paid by households from the Household Expenditure Survey (HES) The TAWA model is constructed from administrative data from Stats NZ's Integrated Data Infrastructure. For health and education spending:

We estimate education spending received by children and students based on their reported enrolment in educational institutions in HES. Health spending amounts are distributed over all individuals in HES in proportions determined by the Ministry of Health’s (MoH) Person-Based Funding Formula (PBFF) model... which assigns expected healthcare costs to a person based on their demographic characteristics.

The resulting income distributions are summarised in Figure 2 in the note, which shows the average income (under each of the three conceptions of income) for each income decile:

Notice that, for households in the bottom deciles, market income is low, disposable income is higher, and final income is highest. This reflects that they receive net transfers from the government (they receive more in transfers than they pay in direct taxes), so that disposable income is higher than market income. They also receive more in in-kind government spending (on health and education) than they pay in indirect taxes, so that final income is higher than disposable income. For high-income households, the pattern for market vs. disposable income is reversed (they pay more in direct taxes than they receive in transfers). However, only for the very top decile (the highest income households) does the payment of indirect taxes exceed the benefits received from in-kind transfers, so that final income is less than disposable income.

Overall, the distributions in Figure 2 show that taxes and transfers reduce inequality - there is less inequality in disposable income than market income, and less inequality in final income than disposable income. The effects of the different components of the tax and transfer system in reducing inequality is demonstrated in Figure 9 in the paper:

The coloured parts of the columns show the components that add to income (transfers, or income support, in orange; and in-kind benefits, in blue) and subtract from income (direct taxes, in grey; and indirect taxes, in yellow). The black point estimates in the centre of each column show the combined effect on income within that income decile. Income support declines by income decile, as you would expect, while in-kind benefits are fairly consistent. Direct and indirect taxes both grow with income. Overall, the bottom five quintiles (making up half of all households) receive more in transfers and in-kind benefits than they pay in taxes, while the top four quintiles (and especially the top quintile) pay more in taxes than they receive in transfers and in-kind benefits.

Finally, Wright and Nugyen show the effect on inequality, measured by the Gini Index, where:

...including income support benefits in the calculation results in the lowering of the Gini coefficient from its value of 45.6 ± 1.5 for market incomes to 35.8 ± 1.6 for gross incomes. The inclusion of direct taxes to form disposable incomes further reduces the Gini coefficient to 33.1 ± 1.5. The equalising effects of these contributions are partially offset by the inclusion of indirect taxes, which lead to a post-tax income Gini coefficient of 34.9 ± 1.6 – ie, whereas direct taxes reduce income inequality as quantified by the Gini coefficient, indirect taxes increase it. However, the inclusion of in-kind benefits in the final household income calculation has a significant redistributive impact, resulting in a drop in the Gini coefficient to 28.1 ± 1.4.

As you would expect given the data from the figures, the tax and transfer system substantially reduces measured inequality. That is exactly what it is expected to do, in a country with progressive income tax, a social safety net, and universal access to healthcare and education. There is far more detail in the analytical note, so if you are interested in how taxes and transfers affect the income distribution (or how they affect the distribution for retired and non-retired households separately), I encourage you to dig into it further.

[HT: Inside Government, and Offsetting Behaviour, both last year]

*****

[*] One major caveat for this analysis is that the non-taxable income excludes capital gains. It also includes imputed rent on owner-occupied dwellings, which should be included to better capture the distributional effects of home ownership.

International transmission of inequality through trade

Most people will have a view on the contribution (if any) of globalisation and trade to inequality. There are two main theories that suggest that trade increases inequality. The first theory is the Stolper–Samuelson theorem, which in turn comes from the Heckscher–Ohlin model of international trade, which suggests that trade increases inequality in high-income countries, but decreases inequality in low-income countries.

The explanation works like this. The Heckscher-Ohlin model says that when a country opens to trade, the returns to relatively abundant factors of production in that country will increase, while the returns to relatively scarce factors of production will decrease. Now, in a high-income country, skilled labour is relatively abundant, while unskilled labour is relatively scarce. So, in a high-income country trade increases the wages of skilled workers, but decreases the wages of unskilled workers, increasing inequality. For low-income countries though, unskilled labour is relatively abundant, while skilled labour is relatively scare. So, in a low-income country trade increases the wages of unskilled workers, but decreases the wages of skilled workers, decreasing inequality. At least, that's what the theory says. In practice, trade has increased alongside improvements in technology that have increased the productivity of skilled workers (what economists call skills-biased technological change), which means that trade has been associated with higher inequality within all countries.

The second theory for why trade increases inequality comes from a very influential 2023 paper by March Melitz. In this theory, more productive firms are more likely to export than less productive firms. That means that the wages of workers in exporting firms will increase more than those of workers in non-exporting firms, increasing inequality.

Now, if trade increases inequality (at the least for high-income countries), does it matter what countries they trade with? Does trading more with a high-inequality country have a different effect than trading more with a low-inequality country? In other words, is higher inequality transmitted through trade?

Those are the questions that this recent article by Sergey Nigai (University of Colorado Boulder), published in the American Economic Journal: Economic Policy (sorry, I don't see an ungated version online), sets out to answer. The paper is quite complex and not for the faint-hearted. However, once you realise what Nigai is doing, it is quite elegant. Nigai proposes an interesting mechanism for why inequality in the trade partner country should matter, where firms:

...target specific segments in the distribution of consumers, thereby creating connections between consumer income inequality and the distribution of firm profits. Targeting rich population segments is costly such that there is a positive assortative matching between high-productivity firms and rich consumers. Hence, more unequal income distributions in export markets raise the profits of high-productivity firms relatively more, which, in turn, leads to higher incomes of individuals associated with these firms––ultimately increasing domestic income inequality.

Notice that this is a slightly more nuanced version of the Melitz theory. In this case, the more productive exporting firms sell to the highest income foreign consumers, increasing their profits more than the less productive exporting firms, which sell to lower income foreign consumers. This then leads to higher wages at the more productive exporting firms relative to wages at the less productive exporting firms, which leads to higher inequality. Nigai illustrates the mechanism in Figure 2 in the paper:

On this figure, Nigai explains that:

There are three relevant firm-level relationships:

(R1) Higher income inequality in the importing country generates higher dispersion of profits/export revenues across exporter firms in the exporting country.

(R2) Higher dispersion of export profits/export revenues is associated with higher dispersion of worker incomes across exporter firms such that wage inequality in the exporting country also increases.

(R3) Higher income inequality across workers employed in exporter firms increases overall income inequality in the exporting country.

In terms of the aggregate outcomes, R1–R3 result in R4, indicating a positive effect of higher income inequality in the importing market on inequality at home such that:

(R4) Higher exports to high-inequality countries are associated with increasing inequality in the domestic country.

Next, Nigai notes that R2 and R3 are easily established because:

substantial evidence based on aggregate and micro-level data shows that there is a robust, strong, and positive relationship between exporters’ profit and wages... Given this evidence, the relationship in R2 must hold mechanically. Second, the relationship in R3 also holds mechanically, as higher wage inequality among exporter firms that pay higher wages relative to pure domestic producers must have a positive effect on overall income inequality in the exporter country.

Nigai then goes about showing empirical evidence for R1 and R4. I won't get into the weeds of the methods here, because they are fairly complex, but suffice to say that for R1, firm-level evidence suggests that income inequality in the export destination is associated with higher dispersion of profits in the exporting country. Specifically, income inequality has no effect on profits for most firms, but has a significant effect on the profits of the most-productive firms. On this, Nigai notes that:

...the effects of income inequality on the dispersion of profits operate mainly through large exporters in the right tail.

On R4, Nigai uses country-level data and an OLS panel regression model, and finds that:

...the Gini coefficient in country i would increase by approximately 1 percent if the Gini coefficients in all export markets for country i increased by 10 percent.

The effects for an instrumental variable regression are smaller, but still statistically significant. Overall, the results support both R1 and R4.

Nigai then turns to estimating how important this channel is as an explanation for inequality. He parameterises a general equilibrium economic model based on data from 40 mostly OECD countries, and finds that:

...for an average country, consumer targeting and inequality effects transmitted through international trade explain 4.4 percent of the observed levels of the Gini coefficients and 4.8 percent of the observed levels of income shares of the top 1 percent of population.

That doesn't sound like a lot, but 4.4 percent of the Gini coefficient is the difference in Gini coefficient between Australia (ranked 10th highest inequality in the OECD, with a Gini Index of 34.3), and South Korea (ranked 17th, with a Gini Index of 32.8). So, it's reasonable substantial.

Overall, it appears that not only does trade contribute to inequality, but the specific  trade partners also matter. Is there a policy implication from this? Should countries start imposing export tariffs or export controls on exports to more unequal countries? Answering those questions would require a more careful consideration of the welfare impacts of trade, since it suggests a trade-off between the gains from trade and the inequality effects of trade.

How not to demonstrate that income inequality impacts economic growth

Many studies have estimated the relationship between income inequality and economic growth (see here and here, for example). Fewer studies have attempted to estimate a causal relationship between the two variables. Unfortunately though, that's what most of us are really interested in. Does higher inequality experience inhibit economic growth?

Theoretically, the causal relationship is not straightforward. Rising shares of income among the wealthy may hold back consumer demand, because the rich save a higher proportion of their income. That would mean that more unequal countries have lower GDP. Alternatively, governments may respond to inequality by redistribution such as progressive taxation, which reduces work and profit incentives and reduces growth. Or, high or rising inequality may reduce trust in government and undermine institutions that are critical for economic growth. On the other hand, the rich save a higher proportion of their income, and those savings are then used for investment spending, which increases economic growth. And more inequality means that those who succeed will receive very high incomes, creating incentives for entrepreneurship and innovation. So, even if inequality causes economic growth, it is unclear if inequality should cause economic growth to be higher, or lower.

This recent article by Zixiang Qi, Bicong Wang (both Beijing Wuzi University), and Yaxin Wang (Chinese Academy of Social Sciences), published in the American Journal of Economics and Sociology (sorry, I don't see an ungated version online) attempts to establish the causal relationship between inequality and economic growth in the long run, using data from 99 countries over the period from 1980 to 2018. Qi et al. find that there is an inverted-U shaped relationship between inequality and economic growth. That is, economic growth is lowest when inequality is low, and when inequality is high, but higher when inequality is middling). However, there is a major problem with the analysis.

In order to establish a causal relationship, Qi et al. rely on an instrumental variables analysis. Instrumental variables analysis involves finding some variable that is correlated with the endogenous variable (income inequality), but uncorrelated with the dependent variable (economic growth, measured as the growth rate of per capita gross national income). That means that the only effect of the instrument on the dependent variable must be through its effect on the endogenous variable.

Qi et al.'s proposed instrument is the age-dependency ratio. Here's where the problem lies. Population ageing has a direct effect on economic growth. The reason why is explained in this post. In fact, Qi et al. even acknowledge this themselves, when they write that:

...Japan's economy has been in a period of secular stagnation for several decades because of ageing population.

So, Qi et al. should know that their instrument is not a valid instrument, and yet they press ahead and use it. At that point, I think we can safely disregard the rest of their results. It is interesting that they found an inverted-U shaped correlation between income inequality and economic growth. But that is all it is - a correlation. We need better methods to establish a causal relationship, and this paper simply doesn't live up to its promise.

Read more:

• Latest research seems to support the Kuznets hypothesis
• Income inequality and economic growth in Australia
• Is there an S-shaped relationship between inequality and per capita GDP?

Generative AI may increase global inequality

As I noted in a post earlier this month, the general public appears to be worried about the impact of generative artificial intelligence on jobs and inequality. Some economists are clearly worried as well. Consider this post on the Center for Global Development blog, by Philip Schellekens and David Skilling. They note three reasons why generative AI might increase global inequality, because: (1) richer countries are better equipped to harness AI’s benefits; (2) poorer countries may be less prepared to handle AI’s disruptions; and (3) AI is intensifying pressure on traditional development models.

I have a lot of sympathy for these arguments, but it is worth exploring them in a bit more detail. Here's part of what Schellekens and Skilling said on the first reason:

High-income countries, along with wealthier developing nations, hold a distinct advantage in capturing economic value from AI thanks to superior digital infrastructure, abundant AI development resources, and advanced data systems...

When many people may think about economic growth, we think about catch-up growth. Developing countries often have growth rates that exceed those in developed countries. There are vivid examples of catch-up growth, like the way many developing countries were able to bypass copper telephone lines and move straight to mobile telecommunications. Could AI be like that? It's a hopeful vision. However, the problem with that argument is that AI isn't quite the same as the telecommunications example. There is no outdated technology that is being replaced by AI (unless humans count?). So, developing countries can't leapfrog technology and catch up. If a country doesn't have the technology infrastructure and capital necessary to develop their own AI models, they will be forced to use models developed in other countries. That creates problems for developing countries, and Schellekens and Skilling note two particular concerns:

First, AI could reinforce the dominance of wealthier nations in high-value sectors like finance, pharmaceuticals, advance manufacturing, and defense. As richer countries use AI to enhance productivity and innovation, it becomes harder for poorer countries to penetrate these markets.

Second, while AI is poised to primarily disrupt skill-intensive jobs more prevalent in advanced economies, it can also undermine lower-cost labor in developing countries. Automation in manufacturing, logistics, and quality control would enable wealthier nations to produce goods more efficiently, reducing the need for low-wage foreign workers. This shift, supported by AI-driven predictive analytics and customization capabilities, may allow richer countries to outcompete on cost, speed, and product desirability.

Note that second argument says that in spite of any increase in inequality within developed countries (which is what the general public was most concerned about in my previous post), there would be increases in global inequality because of the differential impact on different labour markets. This is a consequence of past labour market polarisation, where different countries have become reliant on employment in different sectors.

On their second point, Schellekens and Skilling note that, while the social safety net in developed countries may insulate their populations from the negative impacts of AI (a point that I'm not sure that many would agree with), the situation in developing countries is quite different:

Limited resources and underdeveloped social protection systems mean they are less equipped to absorb the economic and social shocks caused by AI-driven disruptions. Many lower-income countries already struggle with high rates of informal employment and fragile labor markets, leaving workers highly vulnerable to sudden economic shifts.

The lack of fiscal space also restricts these countries from investing in crucial areas like reskilling programs, infrastructure upgrades, or targeted welfare schemes to support affected communities. Without such mechanisms, the impact of AI-related job losses could exacerbate unemployment and deepen poverty.

It would be interesting to see some research on the expected impact of generative AI on informal sector employment, but I except that Schellekens and Skilling are largely correct about the impacts on formal sector employment in developing countries.

Finally, on their third point, Schellekens and Skilling note that the model of development that many countries have followed in recent decades, moving first from an agrarian economy, into low-technology manufacturing (like garments), and then into higher-technology manufacturing over time, has become less viable for developing countries over time, and that generative AI may impact the obvious alternative, which is export-oriented service industries:

Countries like the Philippines and India have seen success in business process outsourcing, thanks to booming call center industries and IT services. But AI poses a threat to this model as well. AI has the potential to reduce the labor intensity of these activities, eroding the competitive edge in the international marketplace of lower-cost service providers.

If AI were to undermine labor-intensive service industries, developing countries may find it harder to identify viable pathways for growth, posing a significant challenge to long-term development and dampening the prospects of convergence.

The conclusion here is that generative AI may not only increase within-country inequality, but because of the differential impact on developed and developing countries, it may increase between-country inequality as well. This would potentially reverse decades of declining global inequality (see here and here).

Generative AI and expectations about inequality

In the last week of my ECONS102 class, we covered inequality. In discussing the structural causes of inequality, I go through a whole bunch of causes grouped together under a heading of 'structural changes in the labour market', one of which is skills-biased technological change. The basic idea is that over time, some technology (like computers) has made people in professional, managerial, technical, and creative occupations more productive or allowed them to reach larger audiences at low cost. However, other technology (like robots) has tended to replace routine jobs in sectors like manufacturing. This has increased the premium for skilled labour, increasing the ‘gap’ between skilled and unskilled wages.

In discussing this idea of skills-biased technological change this year, I mused about the potential impact of generative artificial intelligence, and whether skills-biased technological change was about to reverse, leading to job losses in professional, managerial, technical, and creative occupations, while jobs in activities that might broadly be grouped into manual and dexterous labour (like plumbers, electricians, or baristas) would remain. A change like that would likely reduce inequality (but not necessarily in a good way!).

The truth is, I don't think that economists have a good handle on what the impacts of generative AI will be on the labour market. On the one hand, you have some economists like Stanford's Nick Bloom, claiming that a lot of jobs (in particular tasks or occupations or sectors) are at risk. The loss of low-productivity, low-wage jobs that Bloom considers at risk, like call centre workers, will likely increase inequality further. On the other hand, you have other economists like MIT's Daron Acemoglu, claiming that the impact of generative AI on inequality will be small.

Given that economists can't agree on this, it is interesting to know what the general public thinks. That's the question that this post on Liberty Street Economics by Natalia Emanuel and Emma Harrington addresses. Using data from the February 2024 Survey of Consumer Expectations, they report that:

In general, a substantial share of respondents did not anticipate that genAI tools would affect wages: 47 percent expected no wage changes. These beliefs did not differ significantly based on prior exposure to genAI tools.

However, respondents believed that genAI tools would reduce the number of jobs available. Forty-three percent of survey respondents overall thought that the tools would diminish jobs. This expectation was slightly more pronounced among those who had used genAI tools, a statistically significant difference.

And specifically in terms of inequality:

We find that those who have used genAI tools tend to be more pessimistic about future inequality. Specifically, we asked people whether they thought there would be more, less, or about the same amount of inequality as there is today for the next generation... while 33 percent of those who have not used genAI tools think there will be more inequality in the next generation, 53 percent of those who have used genAI tools think there will be more inequality. This gap persists and is statistically significant, even after controlling for other observable traits. 

So, a large minority of the general public seems to be concerned about generative AI's impact on inequality, and that concern is greater among those with experience (where a small majority believe inequality will increase). Now, it could be that those with greater experience are better able to accurately assess the risks to their own (and others') jobs from generative AI. Or maybe people who use generative AI are simply more likely to have read the AI doomers' predictions of an AI apocalypse (or equally, they could be more likely to read the bullish views of AI proponents). The general public may not know that they fear skills-biased technological change, but they may intuitively understand the potential risks. The real question, which we still cannot answer, is whether those risks are real or not.

How income inequality in New Zealand compares with other OECD countries

Colin Campbell-Hunt (University of Otago) wrote an interesting article on The Conversation last week on inequality in New Zealand. It was well-timed, given that I've been covering poverty and inequality with my ECONS102 class this week. Campbell-Hunt compares income inequality in New Zealand with inequality in other OECD countries.

However, what Campbell-Hunt does here is interesting. First, he looks at income inequality (measured using the Gini coefficient) before accounting for taxes and transfers. Then, he looks at income inequality after accounting for taxes and transfers. The difference in ranking gives us a sense of how redistributive the tax and transfer system is, relative to other countries. Campbell is most interested in the difference between those two measures:

The Gini before taxes and transfers is a measure of the inequality produced by the structures of a country’s economy: the way value chains operate, the markets for products and services, the scarcity of certain skills, rates of unionisation, and so on.

This gives us a measure of structural inequalities in a country. Governments, however, use taxes and transfers to shift income between households. They take taxes from some and boost incomes of the more disadvantaged.

Ginis of incomes after taxes and transfers give us a measure of how well members of a society can support similar standards of living... These give us a measure of social inequalities.

So, how does New Zealand compare? Before taxes and transfers, New Zealand is quite middling, ranked 16th-lowest for inequality (Iceland is first, Japan is 37th and last). After taxes and transfers, New Zealand's ranking looks far worse, being ranked 24th (Slovakia is first, Costa Rica is 37th). Campbell-Hunt interprets this as:

As we can see, New Zealand’s structural inequality, shaped by the economic reforms of the mid-1980s, is middling by comparison to other OECD countries.

But New Zealand’s social inequality lies near the bottom third of OECD measures. A halving of top income tax rates in the mid-1980s and the rollback of the welfare state in the 1990s (after then finance minister Ruth Richardson’s 1991 “mother of all budgets”) significantly contributed to this.

Now, I'm sure we can argue about why New Zealand's tax and transfer system does a poor job (compared with other OECD countries) of reducing income inequality. In my view, laying the blame on governments in the 1980s and 1990s (as Campbell-Hunt does) absolves thirty years of subsequent governments from their role in perpetuating the inequality. Regardless of which government/s may be to blame here, we find ourselves with a tax and transfer system that is nowhere near as redistributive as other countries that we might compare ourselves to. Zeroing in just on the effect of taxes and transfers on inequality, Campbell-Hunt's data shows that New Zealand's system is the 11th-least redistributive (Finland's is most redistributive, Mexico's is least), behind the US, the UK, and Canada, but slightly ahead of Australia.

Given that our closest peer countries have more redistributive tax and transfer systems than New Zealand does, that suggests that we can do more to reduce inequality. As Campbell-Hunt notes:

New Zealand can aspire to goals for social equality matching those in the upper half of OECD countries. Beyond revisions to taxation and transfers, inequalities in health and education would also need to come down to reduce the social and economic costs of poverty and disadvantage that should bring shame to us all.

Campbell-Hunt's data doesn't have anything to say about inequality in health and education, but certainly a more generous and less restrictive benefit system, and more progressivity in taxation, would go some way towards ensuring that New Zealand's inequality looked more like the countries that we compare ourselves to.

Read more:

• Is inequality increasing?
• Inequality in New Zealand over the long run

Higher inflation is modestly associated with higher income inequality

There is a fairly large literature looking at the relationship between inflation and income inequality. Some studies find that there is a positive correlation (more inflation is associated with more income inequality). Some studies find the opposite, a negative correlation (more inflation is associated with less income inequality). Still other studies find no relationship at all between (or, at least, no statistically significant relationship). So, what are we to make of this literature?

To the rescue comes this recent article by Andreas Sintos (University of Luxembourg), published in the journal Economic Systems (sorry, I don't see an ungated version online). Sintos presents a meta-analysis of 124 journal articles, containing 1767 estimates of the relationship between inflation and income inequality. Sintos distinguishes between two strands of the literature: (1) looks at how the level of inflation affects the level of income inequality (in other words, the variables are measured in levels); and (2) looks at how changes in inflation affect changes in income inequality (in other words, the variables are measured in differences). The difference is important. In my view, measuring the relationship in levels doesn't make a lot of sense. If you find that the relationship is positive, then that implies that, since inflation is generally positive, income inequality should be ever-increasing. That seems somewhat inconsistent with reality. In contrast, it seems to me that when the level of inflation changes, that might change inequality.

Anyway, Sintos finds that:

...once the correction for publication bias is made, we find that, on average, inflation has a (small-to-moderate) inequality increasing effect for both level and difference estimates...

In other words, inflation increases inequality (to the extent that we can attribute causality to these results - more on that later in this post). The bias-corrected average effect size ranges between 0.051 and 0.120 (which are interpreted as small and moderate effect sizes respectively). Sintos then goes on to investigate the study-level factors that are associated with the estimated relationship. For the result in differences (which I find more theoretically plausible):

...we find that ten regressors matter significantly for the underlying effect of inflation on income inequality in the primary studies... the BMA [Bayesian Model Averaging] results for difference estimates reveal a decisive effect for eight regressors: GDP deflator, Panel data, Time span, Log transformation, GDP growth, Financial development, Publication year, and Citations. Moreover, we find a strong effect for Trade openness and a weak effect for Education.

Specifically, studies that cover a longer time span, use log-transformed variables, and those that control for GDP growth, financial development, and trade openness find a more positive effect of inflation on inequality, as well as those studies that have attracted more citations. Studies that use the GDP deflator (rather than the change in the Consumer Price Index) as a measure of inflation, use panel data, and those that were published most recently, find a more negative effect (or a smaller positive effect) of inflation on inequality. A couple of things jump out from that. First, the fact that more recent studies, which we would expect to use more sophisticated methods and better-quality data, find smaller effects, should lead us to believe that the 'true' effect is somewhat smaller (less positive) than what Sintos finds on average. However, when Sintos goes on to simulate the effect that would be obtained from the theoretical 'best study', they find that:

The associated prediction, which represents the model average across the models estimated using BMA, is 0.275, with a standard error of 0.115 (95% CI 0.051–0.500), for level estimates, and 0.540, with a standard error of 0.236 (95% CI 0.077–1.002), for difference estimates.

This is somewhat larger than the bias-corrected average effects reported in the paper. That makes me wonder whether the assumption that studies are improving in quality over time actually holds. Could it be that more lower-quality studies, or perhaps studies with lower-quality data, are increasingly being published? We don't have a direct answer to that question, but the correlation matrix reported in Figure 2 in the paper suggests that more recent publications are less likely to use OLS regression, and more likely to control for the variables that have important effects (as noted above). So, I remain somewhat at a loss to explain why the 'best study' estimates are larger than the bias-corrected average effects.

Second, the fact that studies that report more positive results have attracted more citations should be a bit of a concern. The literature had a diversity of results, and while the bias-corrected average effect is positive, that in itself shouldn't lead researchers to cite papers with positive effects more than those with the opposite, or with null effects. There is clearly a bit of cherry picking going on in terms of what results are cited in the literature.

Finally, the results don't establish causality definitively. Many of the studies deal with endogeneity problems, but not all of the studies do. So, while we can tell a plausible causal story here, we can't be sure about it. Nevertheless, this paper is another model of reporting meta-analytic results, the second such paper that I've read this year (see here for my post about the other paper). Given the importance of meta-analysis for estimating the average effect across a literature as a whole, the trend towards clearer exposition and interpretation of the results of meta-analyses is very welcome.

What we can take away from this paper is that higher inflation is modestly associated with higher income inequality. Given the sheer number of things that appear to be correlated with inequality, it would be expecting too much for inflation to have a large effect. But nevertheless, when we consider income inequality, inflation (or change in the inflation rate) appears to be an important consideration.

The effect of inequality on crime

A rational choice (economic) model of crime would suggest that higher inequality leads to more property crime. This is because, as the disparity between the rich and poor increases, the poor have more incentive to commit property crime, because there is more to gain from such crime, and the opportunity cost of committing crime is lower for the poor than for the rich. Now, this model is easily criticised as unrealistic, as even the relatively wealthy may commit crimes that have an economic motive (Bernie Madoff being the obvious example). The model also doesn't do a good job of explaining violent and other crimes that do not have an obvious economic motive.

Criminologists have a different view of the relationship between inequality and crime. One criminological theory that may be used to explain the relationship is social disorganisation theory. This theory suggests that higher inequality reduces social cohesion, which in turn increases crime - not just property crime, but crime more generally.

Given how easy it is to criticise the economic model of crime, I was interested to read this new article by Matteo Pazzona (Brunel University London), published in the journal World Development (open access). Pazzona conducts a meta-analysis of studies of the relationship between inequality and crime, limiting the analysis to empirical studies in the economics literature (more on that point later). They identified 43 studies, with 1341 estimates of the relationship between inequality and crime (it is not unusual for a study to report multiple estimates, with different covariates and spread across main results and robustness checks). Meta-analysis provides a method of combining those results to estimate an overall effect. In this case, Pazzona finds that:

Firstly, the true values of the partial correlation coefficients – net of publication bias – are statistically but not economically significant. They are in the range 0.007–0.123, which represents non-existent or small effects, according to the guidelines provided by Doucouliagos (2011). Secondly, I also find some limited evidence of positive publication bias (preference for positive results), but its presence is limited.

So, Pazzona concludes at that point in the paper that there is basically no effect of inequality on crime. However, the Doucouliagos paper that he cites says that effects between 0.070 and 0.173 represent a 'small effect', and three of the six point estimates in Pazzona's preferred model fit within this range. So, perhaps there is a small effect of inequality on crime. Which, to be fair to Pazzona, is what he concludes by the end of the paper:

It is safe to say that, if inequality affects crime, its effect is – at best – small.

However, this is clearly not the last word on this topic. Pazzona limits the analysis to include only studies published in the economics literature. That leaves out many studies within the criminological or sociological literature (and possibly other literatures as well). As he notes, three past meta-analyses conducted by criminologists:

...found correlation coefficients higher than the ones found in this research and no evidence of publication bias.

So, that suggests that leaving the criminological literature out of this meta-analysis probably biases the overall effect downwards. Pazzona gives only a very weak rationale for ignoring the studies outside of economics:

By focusing exclusively on economics, I can also limit the large differences in theoretical and methodological approaches with other sciences.

Yes, but at a cost of probably biasing the estimates. We could try to argue that economics has a larger publication bias problem than many other fields (see here), and so the small effect of inequality on crime from the economics literature overall might even over-estimate the true effect. However, Pazzona has very carefully controlled for publication bias in the meta-analysis, 

Coming back to the choice to limit the analysis to economics studies alone, this was an especially inexplicable choice, given that in subsequent analysis in the paper, Pazzona controls for a variety of features of the studies. That analysis could have dealt with the range of methodological approaches that were applied, and actually been helpful in understanding the differences between the findings in the economics literature and those in criminology. In that heterogeneity analysis, Pazzona found that, when looking at the type of crime that was analysed across the 43 studies:

...the coefficient for Property crime is negative and relatively small... The lack of a positive and statistically significant impact on property crime categories implies that inequality does not primarily influence economically motivated criminal behaviour as predicted by the rational choice model.

Score another one against the economists, since the economic model of crime suggests that the effects of inequality on crime should be largest for property crime. How the variables are measured matters, with studies that use crime victimisation survey data reporting larger estimates than those using police data, and using a measure of inequality that is more sensitive to income differences at the bottom of the distribution also increases the estimated relationship with crime. On the latter point, Pazzona notes that:

This provides some evidence that crime incentives are the highest when criminal payoff increases, rather than when the opportunity cost decreases.

I guess, if you believe the economic model of crime, which the other results might give us reason not to. The other variables that are included in a model matter too. Including unemployment and a measure of police deterrence increases the observed effect, while including measures of income or poverty decrease the observed effect. Cross-sectional studies also seem to inflate the observed effect. These results are important, as they show the consequences of methodological choices in the analysis (and, as per my point above, could have helped us understand the differences with the criminology literature).

Overall, this paper is a good case study of how to conduct and report a meta-analysis (and for that reason I have shared it with one of my PhD students who is doing a meta-analysis in quite a different research area). However, the choice to exclude non-economics literature from the analysis leaves the key research question of the relationship between inequality and crime incompletely answered. Clearly, there is more work to do in this area.

Proud to pay, and yet they don't

This story in The Guardian caught my attention this week:

More than 250 billionaires and millionaires are demanding that the political elite meeting for the World Economic Forum in Davos introduce wealth taxes to help pay for better public services around the world.

“Our request is simple: we ask you to tax us, the very richest in society,” the wealthy people said in an open letter to world leaders. “This will not fundamentally alter our standard of living, nor deprive our children, nor harm our nations’ economic growth. But it will turn extreme and unproductive private wealth into an investment for our common democratic future.”

The rich signatories from 17 countries include Disney heir Abigail Disney; Brian Cox who played fictional billionaire Logan Roy in Succession; actor and screenwriter Simon Pegg; and Valerie Rockefeller, an heir to the US dynasty.

“We are also the people who benefit most from the status quo,” they said in a letter titled Proud to Pay, which they will attempt to deliver to world leaders gathered in Davos in Switzerland on Wednesday. “But inequality has reached a tipping point, and its cost to our economic, societal and ecological stability risk is severe – and growing every day. In short, we need action now.”

A new poll of the super-rich shows that 74% support higher taxes on wealth to help address the cost of living crisis and improve public services.

I'm confused. If these billionaires (let's call them the 'willing wealthy') want to give more of their wealth to the government, there is literally nothing stopping them from doing that right now. Governments don't need to do anything. The willing wealthy who are 'proud to pay' can each cut a cheque right now. It gets even better for the willing wealthy though. Since they are not legally obligated to make this extra payment, they can even choose which government to pay it to. 

So, what is stopping the willing wealthy from making extra payments to the government? I can offer you two cynical explanations for this behaviour.

First, if the willing wealthy give up some of their wealth through taxes, rather than donating it to the government, then they get to maintain their current status relative to other wealthy people. If everyone is made a bit poorer, at the same rate, then the willing wealthy's ranking among wealthy people remains unchanged. Moreover, they will still be much richer than the average person, so they get to keep their wealthy-person lifestyle. [*] Nothing much really changes for them. However, if the willing wealthy give up some of their wealth through donations to the government, they will lose status relative to the wealthy people who don't give up anything. [**]

Second, offering to give up some of your wealth is a great way to virtue signal: Look at all these selfless wealthy people, willing to make a great sacrifice. Why won't governments listen to them?

The willing wealthy know that governments aren't going to call their bluff. Governments are unlikely to implement a wealth tax, or raise the top marginal income tax rate. The willing wealthy are a minority among wealthy taxpayers and political donors. Governments aren't going to piss off their donors. But the willing wealthy can get a lot of good media coverage of their willingness to sacrifice their wealth. What better way to take a target off your back, than to move it from the wealthy (who are willing to be taxed) to the government (who are unwilling to tax them)?

To all this, I say: Put your money where your mouth is, Abigail Disney. Cut a cheque to the IRS today. If you really want the government to have more of your wealth, then give it to them. Put up, or shut up.

*****

[*] Also, if every wealthy person has a bit less wealth, then the demand for luxury goods and services that the wealthy buy will decrease by a little, decreasing the price of those goods and services. it becomes a little bit less expensive at the margin to maintain the wealthy-person lifestyle. I don't think this is much of a motivating factor though.

[**] Note that this is different from donations to other worthy causes. Philanthropy can increase status, but I don't think anyone (wealthy or otherwise) is going to characterise making donations to the government as philanthropy.

Income inequality and life expectancy in Asia and Africa

Many claims have been made that higher inequality causes worse health outcomes. In fact, that is one of the central claims in the Wilkinson and Pickett book The Spirit Level (which I reviewed here). Of course, as I noted in my review, the relationships that Wilkinson and Pickett establish are correlations, not causal relationships. However, if we put that aside and accept that inequality worsens health outcomes, then we should expect there to be a robust association between inequality and life expectancy. In places with higher inequality, life expectancy should be lower ceteris paribus (holding all else equal). And looking at a single place, when inequality is lower, life expectancy should be higher ceteris paribus.

However, the literature is divided on whether such relationships exist. So, I was interested to read this recent article by Lisa Martin (University of Oxford) and Joerg Baten (University of Tübingen), published in the Journal of Economic Behavior and Organization (sorry, I don't see an ungated version online). Establishing a relationship between inequality and life expectancy requires variation in both variables. That is somewhat difficult to establish when income and life expectancy data are only available at the country level for a small number of years for many countries, or for a few countries (all of which are developed countries) over a longer time period.

Martin and Baten avoid this problem by using data on height and height inequality to fill in gaps in the data. These variables are available for a broader range of countries, including developing countries. Their data come from the Clio Infra project, which has data on a range of economic indicators for many countries going back to the early 1800s (and in some cases going back to the 1500s). The data that Martin and Baten use covers the period from 1820 to 2000, and limited to countries in Asia and Africa.

Martin and Baten use data on height to estimate life expectancy, which relies on a fairly simple regression model that includes height and regional dummy variables. They then use data on the coefficient of variation in height to estimate the Gini coefficient measure of income inequality. Both estimates appear to do a reasonable job. Finally, Martin and Baten use the estimated life expectancy and income inequality variables, and look at the relationship between them, controlling for:

...(1) existence of a health insurance system, (2) wars, (3) the pandemic decade of the 1910s–flu, (4) malaria-intensive countries...

In their simplest model, which doesn't include the control variables (only time fixed effects), they find that:

The coefficient of inequality is estimated at approximately -0.23 and statistically significant. It implies that an increase in the Gini coefficient by one index point translates approximately into a two and a half month decrease in estimated life expectancy.

And when the controls are included:

...we again observe a statistically significant negative coefficient for income inequality, though it is smaller than the significant estimates in [the simplest specification]...

Martin and Baten recognise that these results don't establish a causal relationship (which is a problem across this literature), so they then employ an instrumental variables approach. Their instrument of choice is:

...the ratio of the share of the land suitable for the cultivation of the “inequality crop” (sugar) to the share of the land suitable for the cultivation of the “equality crop” (wheat).

They justify this as follows:

A sugar plantation is a clear example of an agricultural production type of large-scale economies... On the other hand, wheat production is already highly productive on much smaller farm units, as has been amply demonstrated in the agricultural economics literature. The specialization of a country on the cultivation of large-scale cash crops is positively associated with inequality, whereas food crops such as wheat are not scale-intensive and were historically planted in smallholdings.

That passes the smell test that this ratio could be a useful instrument for inequality, although whether the instrument affects life expectancy only through its effect on inequality is arguable (this exclusion restriction is a requirement for a valid instrument, but it cannot be tested for). Anyway, in this IV analysis, Martin and Baten find that:

...the significant impact of inequality remains a consistent determinant of life expectancy.

Overall, this study seems to support a causal relationship between income inequality and life expectancy. In other words, lower inequality causes higher life expectancy. Martin and Baten aren't able to test the mechanisms that might explain this causal relationship explicitly, but nevertheless they conclude that:

...all these factors were at work for our sample of Africa and Asia in the last two centuries - a public goods effect that we can separate out with the health insurance system variable, a correlation with income (or poverty) and a psychosocial effect of less healthy behavior in more unequal societies.

However, before we simply accept these results at face value, we need to recognise that all of this is based on estimates of life expectancy and income inequality that are estimated from other regression models. We should be somewhat cautious about results from models that use derived variables, or as in this case (I think), where only some of the data come from derived values (while the rest of the data are 'real'). This sort of approach imposes an additional structure on the data used in the model that does not exist in the real data, and could lead to biased results.

So, the results are interesting, and consistent with the negative correlation between income inequality and life expectancy established in other studies. However, these results are not definitive, and the question of whether the relationship is truly causal remains somewhat open.