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.

Ageing and inequality in China

Two major ongoing trends for China over the last decade or more have been increasing income inequality, and an ageing population. Could they be related? That is the research question addressed in this 2018 article by Xudong Chen (Baldwin Wallace University), Bihong Huang (Asian Development Bank Institute), and Shaoshuai Li (University of Macau), published in the journal The World Economy (ungated earlier version here). They use data from the China Health and Nutrition Survey (CHNS), which includes longitudinal data on 4400 households from 36 suburban neighbourhoods and 108 towns, collected over nine waves between 1989 and 2011. They look at how within-cohort inequality varies over the life cycle within their data, and find that:

An increasing age effect on income inequality is observed for most cohorts, although not linear...

The coefficients on age, our main variable of interest, are significantly positive, indicating that ageing population enlarges inequality in both income and durable consumption.

The implication is that, as the population ages in aggregate, overall inequality will increase. That is because as birth cohorts age, the within-cohort component of inequality increases. It is also because more of the population will be in older age groups, where within-cohort inequality is higher.

However, there is an important piece of the puzzle missing in the Chen et al. paper. That is the between-cohort component of inequality. Chen et al. include cohort fixed effects in their models, but they don't tell us anything about whether the inequality between birth-cohorts is increasing, decreasing, or remaining steady over time. If the income gap between successive cohorts is narrowing, that could offset the increasing within-cohort inequality. On the other hand, if the income gap between successive cohorts is increasing, that will make inequality even worse. We just don't know, and yet there is evidence that inequality in China may have started to decrease (see here).

The Chen et al. paper therefore gives us some insight into only part of the question about how an ageing population may overall contribute to increasing inequality over time. Interestingly, that is one potential contributor to global inequality that could have used more thorough exposition in Branko Milanovic's book Global Inequality (which I reviewed yesterday). After all, China is a large contributor to global inequality (see here).

Now, there are good theoretical reasons to believe that ageing populations increase inequality (and those reasons, starting with Modigliani's lifecycle theory, are briefly explained in the Chen et al. paper). How much extra inequality we may have as a result of population ageing, and the consequences (if any) of increasing inequality that arises from population ageing, are interesting questions that thoughtful researchers are hopefully considering. I just hope that they are considering both the within-birth-cohort and between-birth-cohort components of inequality.

Book review: Global Inequality (Branko Milanovic)

Inequality has become a major topic of conversation in recent years, both in the media and in casual conversation. When most people think about inequality, they are thinking about inequality within their country - the difference between the lives of the rich, and the lives of the poor, but within the same country context. However, if we broaden the scope beyond simply considering a single country, and consider global inequality, it quickly becomes clear that the within-country inequality is dwarfed by the inequality between countries. To see this, consider the difference in living standards between a poor family in a rich country like New Zealand, and a poor family in a poor country like Chad or Congo. Those families really are worlds apart.

Both components (within-country and between-countries) are covered in Branko Milanovic's 2016 book Global Inequality, which I just finished reading. Milanovic is one of the world's greatest authorities on global inequality, having written many seminal research articles on the topic (many of which I have discussed on this blog, including here and here and here and here). The book has essentially four parts. In the first part, Milanovic looks at how global inequality has changed over the last 25 years. This sets the scene for what follows, because the last 25 years has seen a decline in global inequality, driven in large part by the rapid growth of China. This idea is well established in Milanovic's research, which I have discussed earlier. Chinese growth, which is lowering global inequality, sits aside rising inequality within China, as well as within the US and other developed countries. The juxtaposition of these trends sets up the potential for an interesting exploration of global inequality overall.

The second part of the book looks at the deficiencies of the Kuznets Curve. The classical Kuznets Curve suggests that from low levels of income per capita, inequality initially rises, but then at higher levels of income per capita, inequality begins to fall. However, this explanation is unable to explain the recent rise in inequality in western developed countries. Milanovic offers an alternative explanation, which he terms Kuznets Waves. He suggests that western countries have been through a first wave (increasing inequality up to the World Wars, and then decreasing inequality from then until the 1970s), and have begun a second wave (with inequality increasing in recent years). In contrast, developing countries remain in their first wave.

The third part looks at the evolution of inequality over a much longer timeframe of the past two centuries. For those like me who are familiar with Milanovic's other work, this section has little new to offer. However, the fourth part of the book looks to the future, using the concept of Kuznets Waves, as well as increasing income convergence between countries, to consider general trends in future global inequality. This last section was the highlight of the book for me, even though the fraught nature of prediction will render the specific details already out of date in some places. In particular, Milanovic's views on inequality within the US are interesting, and include that:

• Higher elasticity of substitution between capital and labor, in the face of increased capital intensity of production, will keep the share of national income that accrues to capital owners high.
• Capital incomes will remain highly concentrated, thus leading to high interpersonal inequality of incomes.
• High labor and capital income earners may increasingly be the same people, thus further exacerbating overall income inequality.
• Highly skilled individuals who are both labor- and capital-rich will tend to marry each other.
• Concentration of income will reinforce the political power of the rich and make pro-poor policy changes in taxation, funding for public education, and infrastructure spending even less likely than before.

Some of these trends and predictions are already unfolding, and were underway at the time of Milanovic writing the book, so this is not extensive futurism. Nevertheless, the underlying explanations for these trends is helpful to understanding Milanovic's highlighting of them as particularly important for future global inequality. Along the way, Milanovic refers to a 'new capitalism', where:

...rich capitalists and rich workers are the same people. The social acceptability of the arrangement is enhanced by the fact that rich people work. It is moreover difficult or impossible for the outsider to tell what part of their income comes from ownership and what part from labor. While in the past, rentiers were commonly ridiculed and disliked for doing work that involved nothing more demanding than coupon-clipping, under the new capitalism, criticism of the top 1 percent is blunted by the fact that many of them are highly educated, hardworking, and successful in their careers. Inequality thus appears in a meritocratic garb...

The book isn't all good, however. Regular readers of this blog will know that I truly dislike people who compare stocks with flows (see here, for example), and Milanovic at one point does compare wealth (a stock) to GDP (a flow). However, that is a minor gripe in a book that is filled with excellent explanations that avoid unnecessary technicality. Some readers will be looking for a prescription of how to tackle inequality in the future. On that point, Milanovic is surprisingly unclear. He does seem to strongly favour a move towards the equalisation of endowments (which many economists now refer to as pre-distribution), rather than using the tax-and-transfer system that most governments currently rely on (re-distribution). However, Milanovic carefully leaves specifics on how such equalisation could be achieved alone in this book.

If you are looking for an overall primer on global inequality, then I recommend this book as a good place to start. It's not a page-turner, but Milanovic has done a good job of making this topic come alive. Inequality is an important topic for us to understand, and we need to broaden our understanding of it beyond considering the inequality only our own country.

Bounded rationality, and international egg smuggling from Mexico

New Zealand has an egg shortage, but we are not alone. The US is also suffering an egg shortage, but for a different reason. In New Zealand, the shortage arose because of a ban on battery caged eggs, as well as supermarkets choosing to no longer sell colony caged eggs as well (see here). In the US, it's because of avian influenza killing egg-laying chickens.

The egg shortage has led to an interesting side effect in the US, as NPR reported last week:

As the price of eggs continues to rise, U.S. Customs and Border Protection officials are reporting a spike in people attempting to bring eggs into the country illegally from Mexico, where prices are lower...

A 30-count carton of eggs in Juárez, Mexico, according to Border Report, sells for $3.40. In some parts of the U.S., such as California, just a dozen eggs are now priced as high as $7.37.

Shoppers from El Paso, Texas, are buying eggs in Juárez because they are "significantly less expensive," CPB spokesperson Gerrelaine Alcordo told NPR in a statement.

In New Zealand, rational egg consumers have switched to buying their own chickens. In the US, they are becoming international egg smugglers. To see why, consider how a rational egg consumer would respond to the egg shortage.

Mexican eggs and US eggs are substitutes. When the price of one substitute increases, or one substitute becomes unavailable, some consumers will switch to the other. In this case, some rational consumers want to switch from US eggs to Mexican eggs. However, because Mexican eggs are banned from import to the US (more on that in a moment), the only way for egg consumers to get Mexican eggs is to smuggle them into the country. That comes with costs in the form of the risk of fines, if the smuggler is caught. However, if consumers aren't aware of the fines (and it appears from the story that many are not), then these boundedly rational consumers would not take those costs into account (we call these smugglers boundedly rational, because they lack full information so their rationality is bounded). These boundedly rational consumers would engage in more egg smuggling than they would if they took the costs of getting caught into account. That explains the huge increase in egg smuggling.

Now, the ironic thing about this whole situation, from the same NPR story:

Eggs from Mexico have been prohibited by USDA since 2012, "based on the diagnosis of highly pathogenic avian influenza in commercial poultry."

So, an egg shortage, caused by avian influenza in the US, cannot be alleviated by importing eggs from Mexico, because of a 2012 rule designed to keep avian influenza out of the US. Sometimes, the craziness of international trade rules is just too much to bear.

[HT: Marginal Revolution]