In our team we are currently trying to find a new design and structure for Our World in Data that makes our work more useful for the readers. Some entries are getting very long (e.g. financing of healthcare or global education) and we think it should be possible to find a format that works better for the reader.
Usually we discuss things like this internally, but this issue I thought I can share here since no one in our team is a designer and since I’m hoping that there is a small chance that someone reads this who understands how to find a good format and gives us some hints.
What do the designers out there think about the look of Our World In Data? What needs to be changed? Fonts? Logos? Colors?
Our entries are dedicated to a specific aspect of global living conditions – poverty, child mortality, etc. They have a standardised format and are structured into 4 sections. What we are looking for is a format that works for all entries and makes sense for our content and 4-section structure.
The new structure should make it easier for the reader to read the text and the visuals. Especially for those readers that are not reading the entire entry (the majority of readers) it should be as easy as possible to find the bit of the entry that they are interested in.
Our current idea is that the “Empirical View” (section 1) section stays as it is. The following “Correlates, determinants and consequences” section we want to change in our current draft. The section should not be shown by default and instead we want to use a mix of (1) accordions that open sections within the document and (2) links to shorter pages that open in a new tab and that have the format of our blog posts.
The first reason for the new structure of the correlates section is to make the entries shorter. The second reason is that linking to outside documents makes sense for our website since a text on the link between AIDS and poverty for example can then be linked to from both the entry on AIDS and the entry on poverty.
I have made an Adobe Illustrator file of an entire entry for our internal discussion, but if you are interested in giving us feedback you can download it here:
Our World in Data presents the empirical evidence on global development in entries dedicated to specific topics.
This blog post draws on data and research discussed in our entry on Economic Growth.
Measuring economic activity in a country is difficult, since ‘the economy’ is a complex system with lots of moving parts. A common way to deal with this is to focus on aggregate indicators, such as total national output: “the monetary value of all goods and services produced within a country (or region) in a specific time period”. That’s what economists call the Gross Domestic Product (GDP).
GDP is measured using prevailing national prices to estimate the value of output. In other words, GDP is calculated using local currency units. This means that in order to make meaningful cross-country comparisons, it is necessary to translate figures into a common currency – i.e. use a consistent ‘unit of measure’.
One option is to simply translate all national figures into one common currency (for instance, US dollars) using exchange rates from currency markets. But economists often opt for a different alternative. They create a hypothetical currency, called ‘international dollars’, and use this as a common unit of measure. The idea is that a given amount of international dollars should buy roughly the same amount – and quality – of goods and services in any country.
The exchange rates used to translate monetary values in local currencies into ‘international dollars’ (int-$) are the ‘purchasing power parity conversion rates’ (also called PPP conversion factors). Below we discuss where PPP rates come from, and why they can often be more useful for comparisons than market exchange rates.
What is purchasing power and why does it matter?
Why do many British pensioners decide to move to Southern Spain? It’s not just about the weather. It also has to do with differences in price levels, which are lower in Spain than Britain. You can buy more things with one sterling pound in Southern Spain than you can in England. In other words, the purchasing power of the British Pound is higher in Spain than in England. This difference in price levels is exactly what PPP conversion rates try to capture.
The following visualization shows cross-country differences in purchasing power, taking the US as the reference country. To be specific, the figures below correspond to the price level ratio of PPP conversion factors to market exchange rates. Hence, numbers below 1 imply that if you exchange 1 dollar at the corresponding market exchange rate, the resulting amount of money in local currency will buy you more in that country than you could have bought with one dollar in the US in the same year.
A price level of 0.5 shown for a country in this map means that for a given sum of US dollars you can buy twice as many goods and services in that country than in the US. In countries with a price level above 1, you can buy fewer goods and services than in the US for a given sum of US dollar.
As we can see, price level differences between developed and developing countries are much larger than those between Spain and England. The amount of goods and services that you can buy with 500 US dollars in the US is very different to what you can buy with 500 US dollars in rural India.
This is important beyond GDP. Price level differences imply that with the same income in US dollars, you could be on the verge of poverty in the US, or fairly well-off in rural India. For this reason, we need to consider purchasing power when comparing variables such as poverty rates between countries.
From the explanation above it should be clear that we need to control for price differentials in order to meaningfully compare GDP between countries. We need a conversion factor that achieves purchasing power parity.
If we take an all-embracing basket of goods and services and we use it as a reference point, we can compute price indices for each country and, using statistical methods, adjust the GDP figures to deal with the problem of different price levels.
This is exactly what purchasing power parity does. It’s an exercise that is done by the International Comparison Programme (ICP). Angus Deaton explains it as follows: “Purchasing power parity exchange rates, or PPPs, are price indexes that summarize prices in each country relative to a numeraire country, typically the United States. These numbers are used to compare living standards across countries, by academics in studies of economic growth, particularly through the Penn World Table, by the World Bank to construct measures of global poverty, by the European Union to redistribute resources, and by the international development community to draw attention to discrepancies between rich and poor countries.”
As the graph below shows, using PPP adjusted international dollars rather than US market dollars as unit of measure can make a huge difference. When price levels in a country are much lower than in the US, using US dollars at market exchange rates will significantly underestimate the standard of living when measured through GDP per capita.
Why are differences in price levels not reflected in currency market exchange rates?
For two countries – A and B – the two different currencies allow for different comparisons. The market exchange rate tells you how many units of currency from country B you can buy with a unit of currency A. The purchasing power parity conversion factor, on the other hand, takes the relative prices between countries into account and allows for comparisons when you want to know how many currency units you have to spend to buy the same amount of goods and services in each of the two countries.
So, why are these two things not the same? This is not a trivial question. There are good reasons why the market exchange rate between two currencies should reflect the relative price levels between the two economies. Imagine that one apple costs 1$ in the US and 1£ in the UK – which also means that the price of 1£ in terms of apples is 1 (i.e. the PPP apple adjusted price). Suppose the market exchange rate is not 1:1, but for example 1$ = 1.5£. Given this situation, a British person with an apple would have an incentive to sell the apple in the US, and then convert dollars into pounds, making a profit. This is what is called arbitrage. People would jump at such opportunities, and before long, market forces would exhaust gains from trade, leading to an equilibrium where currency prices and apple prices adjust and there are no opportunities to engage in this ‘free-money game’.
The above logic, however, assumes that goods and services are tradable internationally. But in reality there are goods and services that cannot be traded internationally. If you have a house in London, you cannot export that house to the US or China. There are many other examples of non-tradable goods, such as public roads, basic services such as schooling, or even more trivial services such as hair-cuts.
The issue is that if you must live in a small village somewhere in Scotland, you do not care about whether you could get better schooling in Northern Italy, or whether rents in Southern Spain are cheaper. And this matters in the context of our discussion because prices of non-tradable goods affect the general price level of a country; but prices of non-tradable goods are determined mainly by domestic dynamics. This is one reason why we observe cross-country differences in price levels that are not mirrored by corresponding differences in currency prices.
Why do rich countries tend to have higher prices?
Empirically, we observe that prices are higher in richer countries: there is a positive cross-country relationship between average incomes and average prices. This can be seen in the visualization below, which plots GDP per capita (in international dollars) against price levels (relative to the US). This observation was formalised by Balassa and Samuelson in the 1960s, and is usually referred to as the ‘Penn effect’.
Pinning down the causes behind the Penn effect is not straightforward; but economic theory provides some hints.
One possible explanation, which has received substantial attention in the academic literature, rests on cross-country productivity differences. Specifically, the fact that labour tends to be more productive in rich countries (e.g. because of the adoption of more advanced technologies) and this tends to affect differently the prices of tradable goods vs. non-tradable goods. This is the essence of the ‘Balassa-Samuelson model’: the greater productivity differentials in the production of tradable goods between countries are, the larger will be differences in wages and in the prices of services and correspondingly the greater will be the gap between purchasing power parity and the equilibrium exchange rate.
The correlation between productivity and the price level can be seen in this scatter plot here.
What are the main limitations of PPP adjustments?
The two last rounds of PPP factors estimated by the International Comparison Programme (ICP) are from 2005 and 2011; and the next one is scheduled for 2017. With every release, estimates improve. But the data limitations have to be kept in mind, particularly if we consider the stakes: international institutions, charities and governments rely on PPP factors in order to design policies and allocate resources internationally.
What are the main limitations?
First, there are issues with the underlying sources used by the ICP. Many low-income countries do not collect sufficiently rich data on price levels, so the ICP often needs to impute missing values by making extrapolations based on regional averages, or by relying on data from price levels in capital cities where prices are often higher than in rural parts of the country.
And second, differences in consumption and production patterns make the identification of a common ‘standard’ basket of goods difficult and arbitrary. Agreeing on broad categories (e.g. ‘food’) is relatively easy; but narrowing down the exact items is much more complicated, since allowances have to be made for differences in factors such as product quality. Hence, the actual items that should be included in the ‘standard basket’ of goods produced and consumed in, say Sweden, are very different to those that should be included in Saudi Arabia.
Our World in Data presents the empirical evidence on global development in entries dedicated to specific topics.
This blog post draws on data and research discussed in our entry on Global Extreme Poverty.
Thanks to Marco Molteni for help with preparing the data for this post.
How much money would we need to transfer to poor households in order to end extreme poverty? Answering this question is difficult because redistributive transfers typically entail inefficiencies. First, transfers are hard to target (it is hard to reach the desired population); and second, transfers have knock-on effects on economic behaviour (transfers change incentives and hence may affect, among other things, baseline income levels).
One way to get a broad sense of the cost of ending poverty is hence to simplify matters, and suppose that we could rely on non-distortionary perfectly-targeted transfers: How much money would we need then to lift the incomes of all poor people up to the global poverty line of $1.90 a day?
This blog post takes this question seriously.
The short answer is that we’d need around 160 billion ‘international dollars’ per year to close the global poverty gap, according to 2013 figures (the latest year for which we have good data). To give you a rough idea of the order of magnitude of this number, the equivalent figure in market dollars is about 90 billion – much less than, for example, the yearly military expenditure of the US ($600 billion as I show below).
The monetary size of the global poverty gap is of course a lower bound to the actual cost of ending poverty, since transfers are likely to entail inefficiencies and administrative costs in practice. But taking these numbers seriously is still worthwhile: they tell us that in recent years we have substantially reduced both the incidence and the intensity of poverty. Ending extreme poverty is very much within our reach.
What’s the poverty gap and why should we care about it?
The most straightforward way to measure poverty is to set a poverty line and to count the number of people living with incomes or consumption levels below that poverty line. This is the so-called poverty headcount ratio.
Measuring poverty through the headcount ratio provides information that is straightforward to interpret; by definition, it tells us the share of the population living with consumption (or incomes) below some minimum level.
Unfortunately, measuring poverty through headcount ratios fails to capture the intensity of poverty – individuals with consumption levels marginally below the poverty line are counted as being poor just as individuals with consumption levels much further below the poverty line.
The most common way to deal with this is to measure the intensity of poverty, by calculating the amount of money required by a poor household in order to reach the poverty line. In other words, the most common approach is to calculate the income or consumption shortfall from the poverty line.
To produce aggregate statistics, the sum of all such shortfalls across the entire population in a country (counting the non-poor as having zero shortfall) is often expressed in per capita terms. This is the mean shortfall from the poverty line.
The ‘poverty gap index’ – a common statistic routinely estimated by the World Bank – takes the mean shortfall from the poverty line, and divides it by the value of the poverty line. It tells us the fraction of the poverty line that people are missing, on average, in order to escape poverty.
The poverty gap index is often used in policy discussions because it has an intuitive unit (per cent mean shortfall) that allows for meaningful comparisons regarding the relative intensity of poverty. But given that the poverty line is very low, and some countries have more poor people than others, it’s often easy to lose perspective on the actual absolute magnitude of the numbers we are dealing with.
Here I want to show the evolution of both the poverty gap index, and the absolute size of the poverty gap (i.e. the absolute value, in international dollars, of closing the poverty gap).
The poverty gap index, country by country
The following map shows the poverty gap index, country by country. As we can see, there is a clear positive correlation between the incidence of poverty and the intensity of poverty: sub-Saharan Africa, where the share of people below the poverty line is higher, is also the region where people tend to be furthest below the poverty line.
Interestingly, the correlation is very strong, but is far from perfect. For example, India and Bolivia have relatively similar poverty gaps (mean shortfall is close to 4% of the poverty line), but they have very different poverty rates (the share of population in poverty in India is 21%, while in Bolivia it is 7.7%). This can be appreciated in a scatter plot of poverty headcount rates vs. poverty gap indices. The fact that the correlation is not perfect justifies the discussion below. The intensity of poverty contains information that is not captured by the poverty headcount rate.
The cost of closing the global poverty gap
The following two visualization show the absolute yearly value of the poverty gap, for the world (top chart) and country by country (bottom chart). Estimates are expressed in international dollars (int.-$) using 2011 PPP conversion rates. This means that figures account for different prices levels in different countries, as well as for inflation.
These estimates were obtained by applying reverse engineering to the poverty gap index figures published by PovcalNet – an interactive computational tool for poverty measurement developed by the Development Research Group of the World Bank. There is a technical note at the end of this blog post explaining the methodology in more detail.
As can be seen, the yearly cost of closing the global poverty gap today is about half of what it was a decade ago. The total amount of resources required to end extreme poverty across the world is getting smaller and smaller.
This very positive development is largely, but not exclusively driven by the remarkable improvements in China. You can read more about the ‘Chinese effect’ on global poverty reductions in our last blog post here.
In the bottom chart, you can also explore trends country by country by clicking on the ‘chart’ tab. While in most cases there is a downward trend, in some countries, such as Nigeria, the size of the poverty gap has been growing. (When adding or removing countries from the chart view bear in mind that, in levels, there are huge cross-country differences; so including countries such as India will stretch the scale).
Some perspective on the cost of ending extreme poverty
The estimates above show that the yearly amount of money required to close the poverty gap is close to 160 billion int.-$. As mentioned before, this is a rough approximation of the cost of ending extreme poverty if we use efficient redistributive mechanisms.
Is this number large? The question is not trivial, since the unit is rather abstract – international dollars are a helpful theoretical construct for cross-country comparisons, but they remain a hypothetical currency. How much is the global poverty gap in market dollars?
Using the same underlying data, they find that the 160 billion int.-$ gap for 2013, amounts to about 90 billion market dollars (in 2015 prices).
So, how much are 90 billion market dollars? It’s less than the global value of foreign aid flows, estimated by Chandy et al. at around 150 billion market dollars for the same year. The visualization below shows the results from this benchmarking exercise in more detail.
90 billion market dollars is also not a huge number when compared to the net worth of billionaires. Chandy et al. compare the value of national poverty gaps to the net worth of billionaires in each country, and conclude that “In each of three countries – Colombia, Georgia, and Swaziland – a single individual’s act of philanthropy could be sufficient to end extreme poverty with immediate effect.”
Annual cost of closing the global poverty gap, and official foreign aid, in constant 2015 market dollars, 1981-2015 – Chandy et al. (2016)
The poverty gap index is defined as the mean shortfall in income (or consumption) from the poverty line, expressed as a percentage of the poverty line. That means that if you multiply a country’s poverty gap index by both the poverty line and the total number of individuals in the country, you get the absolute monetary value of that country’s poverty gap. We constructed all estimates this way, using the International Poverty Line (1.90 int.$) and the poverty gap index estimates from the World Bank’s PovcalNet data.
The poverty gap index estimates from the World Bank’s PovcalNet data cover low and middle income countries, with observations every three years in the period 1981-2013. To achieve this level of granularity, the World Bank relies on interpolation for countries in which survey data are not available in particular years, but are available either before or after (or both). The process of interpolation requires adjusting the mean income or expenditure observed in the survey year by a growth factor to infer the unobserved level in the missing year. You can read more about this process in http://iresearch.worldbank.org/PovcalNet/methodology.aspx.
It should be noted that adding the absolute monetary value of the poverty gaps across all countries with available data in Povcal, yields a global total that is slightly smaller than what Povcal reports as the global total. This is because some countries in the Middle East and North African region do not have data, but are included in the global total. Povcal explains this as follows: “As a compromise between precision and coverage, the regional poverty totals and headcount ratios are not reported for the Middle East and North Africa, but an estimate of the number of the poor is included in the global total (based on regression-based PPPs and 2011 PPPs, depending on the country).”
Finally, it is also important to note that we have found a discrepancy between the global poverty gap index reported in PovcalNet, and the global poverty gap index reported in the World Development Indicators (WDI); and this is despite the fact that WDI lists PovcalNet as its source. The discrepancy seems to stem from the reference population that is used to calculate the mean shortfall in each case.
Our World in Data presents the empirical evidence on global development in entries dedicated to specific topics.
This blog post draws on data and research discussed in our entry on Global Extreme Poverty
Thanks to Marco Molteni for help with preparing the data for this post.
Extreme poverty is defined as living with less than 1.90 international dollars per day. International dollars (int.-$) are inflation-adjusted and corrected for price differences between countries, which is what makes them ‘international’.
The share of the world population living in extreme poverty has fallen very substantially in the last 200 years: from over 80% in 1820 to 10% in the latest estimates. In recent decades, extreme poverty has declined faster than ever before in human history.
Often when I point this out – in conversation or on social media – I hear the response ‘Yes, but this is only because of China.’
This post asks whether this statement is true. Is the substantial decline of global poverty only due to the poverty decline in China?
The historical decline of extreme poverty in China
First, let us look at the historical evolution of extreme poverty in China. The following chart shows the declining share of the Chinese population living below the International Poverty Line (1.90 int.-$), according to World Bank estimates.
In 1981 around 88% of the Chinese population lived in extreme poverty (i.e. below the International Poverty Line). According to the latest estimates, extreme poverty – measured in the same way – has declined to 2% in China.
Comparing a world with and without China
The decline from almost every Chinese person living in extreme poverty to almost no Chinese people living in extreme poverty is of course an exceptional achievement. But is this the entire story of falling global poverty?
To see whether it was China alone that was responsible for this decline in extreme poverty, we recalculated the share of people living in extreme poverty and disregarded China entirely. This allows us to compare a planet with China to a planet without China. (At the end of the post it is explained how poverty for the non-Chinese world population was calculated.)
The chart below shows the results. In blue is the decline of global poverty, in red the decline of poverty excluding China.
We see that the reduction of global poverty was very substantial even when we do not take into account the poverty reduction in China. In 1981 almost one third (29%) of the non-Chinese world population was living in extreme poverty. By 2013 this share had fallen to 12%.
What is also interesting to see in the chart is that until 2005, the inclusion of China increased the share of the world population living in extreme poverty; but since then, this has reversed, and the inclusion of China is now reducing the global poverty headcount ratio. This is because 2005 is the year when China’s poverty fell below the world poverty ratio.
As a side note, it is of course silly anyway to say ‘the decline of global poverty is only because of China’. We care about people – not about countries, and since more than every 5th person in the world is Chinese, it is a really important achievement for the world that extreme poverty has decreased so substantially in China.
Still, the decline of global extreme poverty is even more than that. Extreme poverty declined in China and in the rest of the world.
Explanation of how poverty for the world without China was calculated:
In 1981 there were 4.5 billion people in the world. 42% of these were extremely poor.
So there were 1.9 billion extremely poor people and 2.6 billion people not in extreme poverty.
In the same year – 1981 – the population of China was 1 billion. Of these 1 billion Chinese 88% were living in extreme poverty. This means that out of all the 1.9 billion extreme poor 0.88 billion were Chinese. Almost half. There were 1.02 billion extreme poor non-Chinese in the world
The world population without China in 1981 was 3.5 billion; and of these there were 1.02 billion extreme poor. This is 29%, as shown in the chart.
Our World in Data presents the empirical evidence on global development dedicated to specific topics.
This blog post draws on data and research thoroughly discussed in our entry on Global Extreme Poverty.
This post was updated February 26, 2017
In the World Bank estimates of global extreme poverty, high-income countries are not accounted for. But how well does this simplifying omission capture the reality of people living there?
The short answer is that the real rates of extreme poverty in high-income countries, as measured by the World Bank in low-income countries, are not zero, but are very low. In what follows we discuss the evidence in more detail.
Importantly, the evidence presented here should not lead one to conclude that “there is no poverty in rich countries,” but rather that the standard used to measure extreme poverty is indeed extreme. So extreme, that governments in high-income countries do not use it as a guideline to assess the living standards of their own citizens. Measurement instruments in these countries are simply not designed to capture such levels of extreme deprivation.
To begin, some historical context
What do we know about poverty in the rich world before it became rich?
Martin Ravallion (2015) tries to answer precisely this question. He estimates poverty by relying on National Accounts. This involves calculating changes in the distributions of income by drawing on academic studies that reconstruct historical levels of economic output and inequality. You can read more about how researchers reconstruct poverty estimates from macro data in our related post here.
The chart below plots Ravallion’s estimates, showing how extreme poverty declined in today’s high-income countries. Two points are worth noting.
First, extreme poverty was very common in today’s rich countries until fairly recently; in fact, in most of these countries the majority of the population lived in extreme deprivation only a couple of centuries ago. These huge improvements show that extreme poverty can be reduced very quickly.
And second, we can also see that despite the remarkable improvements mentioned above, in some rich countries – notably the United States – what appears to be a sizable fraction of the population still lives in extreme poverty (visible here for the year 2000, when Ravallion’s estimates end).
Let’s now explore the second point in more detail.
Extreme poverty in the United States
According to official estimates, the poverty rate in the US was 13.5 percent in 2015. This figure, however, is not really informative about extreme poverty relative to the International Poverty Line used by the World Bank. This is because there are important differences between the US and World Bank methodologies, both in the way monetary welfare is measured and in the thresholds used to define absolute deprivation.
To be more precise, the World Bank figures on extreme poverty refer to individuals living in a household with per capita consumption or income below $1.90. The official US poverty estimates, on the other hand, refer to individuals living in households with incomes below a much higher threshold. Allowances are made for the size and composition of households so that, for example, in a household with 2 adults and 2 children, the poverty line is roughly equivalent to $16.5 per person per day.
So, can we make official US poverty estimates comparable to global estimates by simply applying a lower per capita poverty line? As it turns out, the answer is no. Whether poverty is measured via consumption surveys (as the World Bank does for most countries) or income surveys (as the US does), has significant implications. Several issues play a role here.
First, each type of survey has particular weaknesses regarding measurement error – for example, expenditure surveys are known to be particularly sensitive to the choice of reference period (both memory and willingness to maintain a diary impose limits on the period that can be selected to record consumption accurately). Second, survey design affects the particular incentives that respondents have to participate and report accurately – for example, some studies have found that respondents in the US are least likely to respond to income surveys in tax-filing months, when it would make most sense to ask about incomes. And third, there are valid differences between income and consumption that are not ‘errors’, but that stem from conceptual inconsistencies between the two – for example, restrictive definitions of ‘income’ might not account for the consumption that occurs out of ‘non-income’ resources such as savings and assets, borrowing, and some kinds of government welfare benefits.
The following chart from Chandy and Smith (2014) shows a number of different poverty-rate estimates, keeping the poverty line fixed at $2 a day, but changing the underlying definition of monetary welfare. Here is a summary of what characterizes each estimate.
The bars labeled ‘Shaefer-Edin’ correspond to poverty estimates as reported by Shaefer and Edin (2013). These estimates come from measuring welfare via income. The lower-poverty estimate includes tax credits and some forms of in-kind government benefits such as food stamps and housing benefits. The higher-poverty estimate excludes these benefits. Both estimates use the Survey of Income and Program Participation as their source, focusing only on households with children.
The bars labeled ‘SIPP’ also correspond to estimates of poverty based on income using data from the Survey of Income and Program Participation. In this case, the different bars indicate different definitions of income – some more inclusive than others – and take into account the general population, including households without children. You can read the appendix in Chandy and Smith (2014) to see how these definitions compare to those from Shaefer and Edin (2013).
The bars labeled as ‘SPM’ correspond to estimates of poverty using the source used for official poverty estimates (the Census Bureau’s Current Population Survey Annual Social and Economic Supplement) but applying yet another definition of income, called the Supplementary Poverty Measure (SPM). The SPM definition of income seeks to capture the impact of government policies intended to fight poverty and to exclude necessary expenses such as medical contributions and childcare.
The bars labeled as ‘CEX’ correspond to estimates of poverty based on consumption. These estimates use data from the Consumer Expenditure Survey.
As we can see from the chart below, different sources of data give substantially different estimates. And different sources have different limitations. So what can we learn from these numbers?
Different estimates of $2 a day poverty rate – Chandy and Smith (2014)
Chandy and Smith analyze all of the sources, in an attempt to faithfully replicate the the World Bank’s treatment of country data for the compilation of global poverty aggregates. Their approach consists of treating the US “as if it were an anonymous developing country whose source data were submitted to the Bank”. As a result of this exercise, they prioritize the Consumer Expenditure Survey, and obtain an estimate for the extreme poverty rate that is lower than 1%. This is a figure that is statistically indistinguishable from zero.
This means that, although consumption expenditure data is available for the US, the World Bank uses disposable income data to calculate extreme poverty figures that are not included in the global poverty estimates, due to lack of comparability.
That an estimate is ‘statistically indistinguishable from zero’ – rather than ‘exactly zero’ – simply reflects the fact that there is a margin of error involved in the estimation process. But what this exercise is telling us is that the margin is substantial.
Extreme poverty in Europe
In Europe, poverty is not typically measured in absolute terms. Instead, most European countries rely on relative measures of poverty. This means that people are considered poor if they have less income and opportunities than other individuals living in the same society.
In most cases, relative poverty is measured with respect to the median income in the corresponding country (i.e. people are poor if their income is below a certain fraction of the income of the person in the middle of the income distribution). Because of this, relative poverty is actually a metric of inequality – it measures the distance between those in the middle and those at the bottom of the income distribution.
Relative poverty estimates are clearly not comparable to absolute poverty estimates published by the World Bank for low income countries. So we need to look beyond official statistics for evidence on extreme poverty in Europe.
Here, once again, we can rely on the estimates published in PovcalNet. The following visualization shows the available data for high-income countries. The map shows Western Europe, but you can choose the ‘chart’ tab to see all available series.
As mentioned above, these estimates show the share of population living with disposable incomes below the International Poverty Line (1.90 international dollars); and again, the World Bank does not include these figures in the global estimates of extreme poverty.
As we can see, the share of people living in ‘World Bank type’ extreme poverty in high-income European countries is very small.
This is the same result that Bradshaw and Mayhew (2011) find in a study commissioned by the European Commission, using data on per capita household incomes from the EU-SILC survey to measure absolute poverty rates, using a poverty line of $2.15 PPP-dollars per person per day. In this visualization you can see their results; and in this scatter plot you can see how PovcalNet estimates below compare to those by Bradshaw and Mayhew.
Given all the evidence, we can conclude that ‘World Bank type’ extreme poverty is likely to be very low in rich countries, but poverty measurement instruments in these countries are not designed to capture such extreme levels of deprivation; so it is hard to know exactly how low it is.