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How global inequality changed from 2003 to 2013 and what we can expect for the future

This is part of our entry on global income inequality.
We have just updated our entry on income inequality that mostly looks at the distribution within countries.

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The following visualisation shows global income inequality in 2003 and 2013. The data shown here were carefully prepared by Tomas Hellebrandt and Paolo Mauro from the Bank of England and the IMF.

You see the yearly disposable income for all world citizens in both 2003 and 2013. On the x-axis you see the position of an individual in the global distribution of incomes and on the logarithmic y-axis you see the annual disposable income at that position.

The increase in prosperity – and decrease of poverty – is substantial. The income cut-off of the poorest 10% has increased from 260 international-$ to 480 international-% and the median income has almost doubled from 1,100 international-$ to 2,010. Global mean income in 2013 is 5,375 international-$.

The share of the world population living in extreme poverty fell from 25% (in 2002) to 11% in 2013.

The global income distribution in 2003 and 2013

global-inc-distribution-2003-and-2013

Global income inequality is still very high and will stay very high for a long time

The visualisation below presents the same data in the same way except that the y-axis is now not logarithmic but linear. In this perspective the still very high level of global inequality is shown even more clearly.

The previous and the following visualisation show how very high global income inequality still is: The cut-off to the richest 10% of the world in 2013 was 14,500 int-$; the cut-off for the poorest 10% was 480 int-$. The ratio is 30.2.

While global inequality is still very high we are now living in a period of falling global inequality: In 2003 this ratio was 37.6. The Gini coefficient has also fallen from 68.7 to 64.9 over the ten year period.

Taking the historical experience as a guide for what is possible in the future we have to conclude that global inequality will remain high for a long time. To see this we can ask how long it will take for those with incomes at the poorest 10% cutoff to achieve the current incomes of the richest 10% cutoff which is 14,500 international-$. This income level is roughly the level of GDP per capita above which the poverty headcount gets close to 0% for most countries (see here).

How long does it take for incomes to grow from 480$ to 14,500$?

2% growth 172.1 years
4% growth 86.9 years
6% growth 58.5 years
8% growth 44.3 years
10% growth 35.8 years

Even under a very optimistic scenario it will take several decades for the poor to reach the income level of the global top 10%.

2% is roughly the growth rate that the richest countries of today experienced over the last decades (see here). We have seen that poorer countries can achieve faster growth, but we have not seen growth rates of more than 6% over a time frame as long as necessary to reach the level of the global 10% in such a short time. If the past is a good guide for the future the world will very likely be highly unequal for a long time.

The global income distribution in 2003 and 2013

global-inc-distribution-2003-and-2013-linear-scale

New entries on Our World In Data during summer 2016

We have been a bit quiet on the blog, but that doesn’t mean that we did not keep working on Our World In Data.
We worked on a series of updates and added new entries to our web publication. Here are the links to 7 of our latest additions:

  • We have two completely new meta entries: One for health and for education. They both present the most important aspect on these topics and give a first overview so that you can then go and study and read about the particular aspects in more depth in the specific entries.
  • On the same topics we did quite a lot of work to present the empirical research on how these public goods are financed. You find these in the entry on financing health and on financing education.
  • We have also updated and added a series of specific entries: Three that I think are particularly interesting are the entries on trust, literacy, and international trade.

 

 

 

Trust and GDP

[The visualization below and much more on this topic can be found in our entry on Trust]

 

In a much cited article, Arrow (1972) says that “Virtually every commercial transaction has within itself an element of trust, certainly any transaction conducted over a period of time.”

The extent to which trust is linked to economic development has been the subject of many academic papers in the economics literature on growth (see Guiso et al. 2006, Algan and Cahuc 2010, and the references therein). A common way to get a first-order approximation of this relationship is to estimate the correlations between trust and GDP per capita. The visualization below provides evidence of this correlation, by plotting trust estimates from the World Value Survey against GDP per capita. Each dot on this scatter-plot corresponds to a different country. You can learn more about measures of national income in our entry on GDP data.

As it can be seen, there is a very strong positive relationship. Most academic studies find that this relationship remains after controlling for further characteristics. And similar results can also be obtained by looking at other measures of economic outcomes. Looking at outcomes across individuals, Guiso et al. (2006), for instance, report that trust has a positive and statistically significant correlation with the probability of becoming an entrepreneur, even after controlling for education, age and individual income. Their results also hold if religious affiliation of the respondents’ ancestors is used as a proxy for trust – they thus argue that, since ancestors’ religion correlates with respondents’ trust attitudes, this instrumental variable approach can be taken as evidence that the estimated relationship goes in the suggested direction (i.e. that trust leads to entrepreneurship, rather than the other way around).

Other studies using instrumental variables have also found similarly large effects. Algan and Cahuc (2010) predict that, according to their estimates, African countries would have a five-fold increase in GDP per capita if they had the same level of inherited social attitudes as Sweden, after controlling for lagged GDP per capita, contemporaneous political environment and time-invariant country characteristics.

Algan and Cahuc (2010) show that inherited trust of descendants of US immigrants is significantly influenced by the country of origin and the timing of arrival of their forebears. This is their instrumental variable: the inherited trust of descendants of US immigrants is used as a time-varying measure of inherited trust in the country of origin. This approach allows the authors to control for country fixed effects and interpret the effect of trust on growth causally. You can read a summary of their findings and approach in a voxeu.org article written by the researchers.

Nunn and Wantchekon (2011) provide evidence to explain mistrust in Africa: they show that current differences in trust levels within Africa can be traced back to the transatlantic and Indian Ocean slave trades. More specifically, they show that individuals whose ancestors were heavily raided during the slave trade are less trusting today – and using a variety of different econometric strategies, they claim that this relationship is causal.

Trust vs. GDP per capita, 2014 (or latest available data)

Trust-vs-GDP-per-capita

Link between health spending and life expectancy: US is an outlier

The graph below shows the relationship between what a country spends on health per person and life expectancy in that country between 1970 and 2014 for a number of rich countries.

The US stands out as an outlier: the US spends far more on health than any other country, yet the life expectancy of the American population is not longer but actually shorter than in other countries that spend far less.

If we look at the time trend for each country we first notice that all countries have followed an upward trajectory – the population lives increasingly longer as health expenditure increased. But again the US stands out as the the country is following a much flatter trajectory; gains in life expectancy from additional health spending in the U.S. were much smaller than in the other high-income countries, particularly since the mid-1980s.

This development led to a large inequality between the US and other rich countries: In the US health spending per capita is often more than three-times higher than in other rich countries, yet the populations of countries with much lower health spending than the US enjoy considerably longer lives. In the most extreme case we see that Americans spend 5-times more than Chileans, but the population of Chile actually lives longer than Americans. The table at the end of this post shows the latest data for all countries so that you can study the data directly.

Life expectancy vs. health expenditure over time, 1970-2014

ftothealthexp_pc_usd_long

[This graph and more information can be found in the entry on how healthcare is financed.]

Why is this?

There are several aspects that contribute to the US being such an extreme outlier: Studies find that administrative costs in the health sector are higher in the US than in other countries; The price comparisons between countries rely on adjustment which are not ideally suited for comparisons of health costs and this might make comparisons more difficult. Sometimes it is also pointed out in these comparisons that violence rates in the US are higher than in other rich countries (and this is true). But while this could explain the difference in levels, it is not a likely explanation for the difference in trends. Over the period shown in the chart above violence and homicides have fallen in the US more than in other rich countries and this should have led to a narrowing of the difference to other countries and not to the increase that we see.

One of the reasons for the underachievement of the US is the large inequality in health spending. The chart above showed that average per capita spending on health is exceptionally high, but the average does not tell you about how much each individual in the US receives. The US healthcare system is characterized by little access to care for some and very high expenditure on health by others.

The following graph shows this inequality. The top 5% of spenders accounts for almost half of all health care spending in the US.

This chart is produced by the National Institute for Health Care Management (NIHCM) and it shows the cumulative distribution of healthcare spending per person in the U.S., using data on personal expenditures during the year 2009. The source of the data is the Medical Expenditure Panel Survey – a nationally representative longitudinal survey that collects information on healthcare utilization and expenditure, health insurance, and health status, as well sociodemographic and economic characteristics for civilian non-institutionalized population. According to the source, the data refers to ‘non-institutionalized civilian population’, in the sense that it excludes care provided to residents of institutions, such as long-term care facilities and penitentiaries, as well as care for military and other non-civilian members of the population. The data corresponds to ‘personal healthcare services’, in the sense that they exclude administrative costs, research, capital investments and many other public and private programs such as school health and worksite wellness.

This graph should be read similarly to a Lorenz curve: the fact that the cumulative distribution of spending bends sharply away from the 45% degree line is a measure of high inequality (this is the intuition of the Gini coefficient that we discuss in our income inequality data entry). As it can be seen, the top 5% of spenders account for almost half of spending, and the top 1% account for more than 20%. Some concentration in expenditure is certainly to be expected when looking at the distribution across the entire population – because it is in the nature of healthcare that some individuals, particularly those older and with complicated health conditions, will require large expenditure –, these figures seem remarkably large and suggest important inequality in access. Indeed, the publisher of the graph notes that a report from the Medicare Payment Assessment Commission shows that personal spending for individuals covered by Medicaid is less concentrated than for the population as a whole.

Cumulative distribution of personal healthcare spending in the U.S., 2009 – NIHCM (2012)

nihcm-spending-concentration-titled

Appendix:

Latest available data on life expectancy and spending on health per capita in OECD countries.

CountryLife exectancyHealth Spending per capita
United States78.949,024.21 $
Switzerland82.856,786.57 $
Norway81.756,081.00 $
Netherlands81.305,276.60 $
Germany80.845,119.21 $
Sweden81.965,065.16 $
Ireland81.155,001.32 $
Austria81.344,896.00 $
Denmark80.554,857.03 $
Belgium80.594,522.04 $
Canada81.964,495.69 $
Luxembourg82.214,478.97 $
France82.374,366.99 $
Australia82.254,206.85 $
Japan83.594,152.37 $
United Kingdom81.063,971.39 $
Iceland82.063,896.93 $
Finland81.133,871.39 $
New Zealand81.403,537.26 $
Italy82.693,206.83 $
Spain83.083,053.07 $
Slovenia80.522,598.91 $
Portugal80.722,583.84 $
Israel82.152,547.40 $
Czech Republic78.282,386.34 $
South Korea82.162,361.44 $
Greece81.292,220.11 $
Slovakia76.711,970.52 $
Hungary75.871,796.60 $
Estonia77.241,724.51 $
Lithuania73.971,720.84 $
Chile81.501,688.52 $
Poland77.251,624.87 $
Costa Rica79.401,393.95 $
Russia70.371,368.75 $
Latvia74.191,295.01 $
South Africa57.181,146.47 $
Mexico76.721,052.66 $
Turkey75.16990.19 $
Colombia73.99964.50 $
China75.78730.52 $
Indonesia68.89302.12 $
India68.01267.41 $