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.
[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)
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
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)
Latest available data on life expectancy and spending on health per capita in OECD countries.
The brutal reality of child mortality becomes clearer when one remembers what it means for each woman who loses her child.
The following visualization uses the data on fertility – the number of children born per woman – over time and combines it with information on child mortality over time. Taking these two time series together makes it possible to estimate how many children a woman on average lost before the children were five years old.
Below you see the data for Sweden which is one of the country for which we have the best historical data, the Swedish office for population statistics Tabellverket was founded as early as 1749. The visualization shows that throughout most of the 19th century Swedish women gave birth to more than 4 children. As we have shown in the Our World In Data-entry on child mortality, in 19th century Sweden 1 out of 4 children died before its 5th birthday: This means that fertility and child mortality were so high that on average every woman lost 1 of her children before they could celebrate their 5th birthday.
With the decline of child mortality and fertility this became very rare. In 2015 Swedish women lost on average 0.006 children before they were 5 years old.
In this visualization you can change the country for which this time-series is visualized to explore the trends in countries around the world. To compare the number of children lost per woman for several countries – and see the data on a world map – you can use this visualisation.
The data shown in this visualization is calculated based on the historical estimates of fertility and child mortality presented at Gapminder.org (see the sources tab in the chart for more information); particularly for the early period data coverage is not as good as one wishes and it should be understood that the presented estimates are coming with a considerable uncertainty.
Education systems around the world have expanded significantly over the last fifty years. This is true both in terms of funding and outcomes: in most countries education expenditure and mean years of schooling have increased. What has this implied in terms of education inequality? The short answer is that in most countries education is much more equally distributed today than fifty years ago. Here is some evidence, also discussed in our entry on Financing Education.
The following figure, from Crespo Cuaresma et al. (2013), shows a series of graphs plotting changes in the Gini coefficient of the distribution of years of schooling, by age groups, across different world regions. The Gini coefficient is a measure of inequality, with higher values denoting higher inequality. As it can be seen, in the period 1960-2010 education inequality went down every year, for all age groups and in all world regions. In fact, notice that inequality is lower among today’s younger generations – this implies that further reductions in education inequality are still to be expected, particularly within developing countries where the generational gap is largest; and if the expansion of global education can be continued, we can get closer to global convergence.
Education Gini coefficients by world region for selected age groups, 1960- 2010 – Figure 4 in Crespo Cuaresma et al. (2013)
How quickly can education inequality be reduced? The experience of South Korea shows that it is possible to reduce education inequality very rapidly, across all levels of education. The following visualization shows two graphs comparing the concentration of years of education in South Korean between the years 1960 and 1990. To be precise, each of these graphs shows an education Lorenz curve: a plot showing the cumulative percentage of the schooling years across all levels of education on the vertical axis, and the cumulative percentage of the population on the horizontal axis. As it can be seen, in 1990 education was much less concentrated than in 1960, not only because there was a smaller share of individuals without schooling at the bottom (in 1990 only 10% of the population had no schooling, compared to 40% in 1960), but also because there was a smaller share of individuals concentrating large proportions of school-years at higher levels of education. Indeed, in only 30 years South Korea was able to double the mean years of schooling and get remarkably close to the 45-degree line marking the hypothetical scenario of complete equality of schooling. In this same period South Korea increased drastically its GDP per capita while significantly improving health outcomes, such as child mortality.
Education Lorenz Curves, Korea 1960-1990 – Figure 5 in Thomas et al. (2000)