Countries which suffer poverty

Wracked by military coups since its independence from France in , in Niger declared veteran opposition leader Mahamadou Issoufou winner of the presidential polls. Since then, the adoption of a new investment code, improved access to credit and somewhat faster access to water have contributed to a sharp increase in foreign direct investment. When Issoufou stepped down this year after two five-year terms in office, despite an attempted and thwarted military coup, Niger inaugurated a new president—the teacher and former interior minister Mohamed Bazoum—in its first democratic power transfer.

And while the country has reported a relatively low number of cases, last year its economy grew by just 1. Since gaining independence from Belgium in , the Congo has suffered decades of rapacious dictatorship, political instability and constant violence. The tasks he faces are daunting. With 80 million hectares of arable land, over a thousand minerals and valuable metals under its surface and a citizen median age of just 17, the Democratic Republic of the Congo—the World Bank says—has the potential to become one of the richest African nations and a driver of growth for the entire continent.

Political instability, endemic corruption and now the coronavirus pandemic continue to frustrate that potential. Adding to these factors, new cases of Ebola resurfaced in the northeast of the country last February, less than a year after another outbreak claimed the lives of more than two thousand people. As a result, while living standards in urban areas are broadly improving, food insecurity in rural parts is extremely high.

Malawi is a generally peaceful country that has had stable governments since gaining independence from Britain in However, disputed poll results are far from being an anomaly. Theologian and politician Lazarus Chakwera, who was sworn in his place, declared that he wanted to provide the kind of leadership "that makes everybody prosper.

Rich in gold, oil, uranium and diamonds, the Central African Republic is a very wealthy country inhabited by very poor people. However, after claiming the title of the poorest in the world for the best part of the decade, this nation of just 4. Yet, while his successful election has been seen as an important step towards national reconstruction, large swaths of the country remain controlled by anti-government and militia groups. Despite these problems and incidents, in recent years growth has somewhat picked up, driven by the timber industry and a revival of both agricultural and mining sectors.

The economy is also benefitting from the partially resumed sale of diamonds, which were found to be funding inter-religious armed groups and placed under international embargo in To make matters worse, lockdowns and other measures taken by the government to limit the spread of the coronavirus forced many families to stay home, leaving them unable to earn an income. Three decades of internal violence and conflict, frequent droughts and floods followed by food insecurity and displacement of people, lack of access to health services coupled with the rapid spread of communicable diseases, massive levels of unemployment among especially young people —Somalis are growing hopeless.

This country of 16 million on the Horn of Africa never seems to catch a break. The power grab triggered a political crisis and rival factions clashed in the capital Mogadishu. Ultimately, Mohamed bowed to the growing opposition and called for a new presidential election.

Yet, experts say, there is still room for trouble. South Sudan is the newest nation in the world. It was born on July 9, , six years after the agreement that ended the conflict with Sudan, Africa's longest-running civil war. However, violence has continued to ravage this land-locked state of roughly 11 million. Formed by the 10 southern-most territories of Sudan and home to around 60 indigenous ethnic groups, a new conflict broke out in when president Salva Kiir accused his former deputy, rebel leader Riek Machar, of staging a coup.

As a result, it is estimated that as many , people were killed in clashes and nearly 4 million have been internally displaced or fled to neighboring countries. South Sudan could be a very rich nation, but with oil accounting for almost all of its exports, falling commodity prices and rising security-related costs hammered the country's economy. Outside the oil sector, the majority of the population is employed in traditional agriculture, although violence often prevents farmers from planting or harvesting crops.

Will the people of South Sudan ever have a real shot at living more prosperous lives? After signing a ceasefire and a power-sharing agreement in , last year government and opposition parties formed a unity cabinet led by president Kiir and Machar as first vice president. The accord has proved fragile—yet, to a certain extent, it helped in reducing politically motivated conflicts. It has been less effective in countering the pandemic-related oil price shock and the effects of the most severe series of floods the country has experienced in 60 years—since last summer, they have killed livestock, destroyed crops and displaced thousands of families.

The small landlocked country of Burundi, scarred by Hutu-Tutsi ethnic conflict and civil war, has the rather unenviable distinction of topping the world's poverty ranking. All these problems, needless to say, have been exacerbated by the pandemic.

How have things come to this, despite the civil war formally ending 15 years ago? Lack of infrastructure, endemic corruption, security concerns: the ingredients leading to extreme poverty are often the usual suspects.

Pierre Nkurunziza, the charismatic former Hutu rebel turned president in , had initially managed to unite the country behind him and to start rebuilding the economy. In , however, the announcement that he would run for a third term—which according to the opposition was in violation of the constitution—reignited old disputes. A failed coup attempt followed, hundreds of people died in clashes and tens of thousands were displaced internally or abroad. Much of this progress is due to the China Belt and Road Initiative and investment in several African countries.

Another proof of Africa's potential is the extremely large share of young people on the continent. This could translate into a sizeable future workforce, a growing internal market and potential for innovation and economic progress.

This article looks at the bottom 25 countries by this metric. Unsurprisingly, all but four of these countries are located on the African continent. LDCs are classified as low-income countries confronting severe structural impediments to sustainable development.

They are highly vulnerable to economic and environmental shocks and have low levels of human assets. Thanks for signing up for our daily insight on the African economy. We bring you daily editor picks from the best Business Insider news content so you can stay updated on the latest topics and conversations on the African market, leaders, careers and lifestyle. Below we discuss some examples, such as encouraging migration, and implementing multifaceted programs that relieve joint constraints at the household level.

Around the world, most government programs hope to reduce poverty through short-term interventions that have lasting effects. While this is not an easy task, there is concrete evidence suggesting that it is possible. In six different countries, a multifaceted program offering short-term support along various household dimensions has been shown to cause lasting progress for the very poor.

The intervention in question consists of six elements: 1 a productive asset grant, 2 temporary cash consumption support, 3 technical skills training, 4 high frequency home visits, 5 a savings program, and 6 health education and services. The light blue bars show the impact of this intervention, measured by the yearly average increase in household consumption, three years after the productive asset transfer and one year after the end of the program intervention.

Although the costs of this intervention are substantial, we can see that the net benefits are still positive and large—precisely because impacts are sustained into the future. This is also the idea behind medical trials, and has become increasingly popular in development research.

The full study and results are explained in Banerjee et al. They find statistically significant impacts on all of these outcomes. The evidence most consistent with poverty traps comes from poor households in remote rural regions—these are households that are trapped in low-productivity locations, but which could enjoy a rising standard of living if they were somehow able to leave see Kraay and McKenzie 39 for a review of the evidence. There are many possible mechanisms—one is the lack of financial resources.

Bryan, Chowdhury, and Mobarak 40 argue that households close to subsistence are often unwilling to take the risk of migration; but they become more willing to do so if insured against this risk. This relaxes the liquidity constraint and opens a window of possibility for policies aiming to promote migration, both within and across countries.

How large are the potential gains from migration to a high productivity country such as the United States? Clemens, Montenegro, and Pritchett 41 offer a tentative answer. Specifically, they provide a lower bound estimate on the annual wage gain of low-skilled male workers migrating to the United States from various low-income countries.

The following visualization plots their results, and compares them to the benefits from the successful multifaceted anti-poverty intervention we discussed above. As we can see, the effect of migration for the poor is remarkably high. These figures suggest that the total lifetime value of the most successful anti-poverty program is less than a quarter of the gain every year from letting a worker work in a high productivity environment, in this case the United States.

Targeted transfer programs have become an increasingly popular policy instrument for reducing poverty in low-income countries. Gentilini et al. Cash transfer programs have been shown to reduce poverty across a variety of contexts.

Fiszbein and Schady 44 provide a comprehensive analysis of the evidence. As a result, they have resulted in sometimes substantial reductions in poverty among beneficiaries—especially when the transfer has been generous, well targeted, and structured in a way that does not discourage recipients from taking other actions to escape poverty. As the last part of the conclusion above notes, a common concern among policymakers is that welfare programs can potentially discourage work—in fact, this is a concern that is shared by policymakers in both low- and high-income countries.

Banerjee et al. The chart provides a graphical summary of their main findings. In the top panel, the authors graph the employment rate for all eligible adults in both the control and treatment arms for each evaluation.

The bottom panel replicates the one above, but for hours of work. As we can see, the overall figures for both employment and hours of work are similar across treatment and control in all of the evaluated programs and do not statistically differ.

Growing international trade has changed our world drastically over the last couple of centuries. One particular effect has been a substantial increase in the demand for industrial manufacturing workers in low income countries, mainly due to the rise in offshoring of low-skilled jobs. A common argument put forward is that these industrial manufacturing jobs are a powerful instrument for reducing poverty, even if salaries tend to be very low by the standards of rich countries.

A more careful analysis of the argument reveals a complex reality. On the one hand, low skilled industrial jobs do provide a formal, steady source of income, so it is possible that they raise incomes and reduce poverty.

Yet, on the other hand, these jobs tend to be unpleasant and very poorly paid opportunities even by the standards of low income countries. To answer this question, Blattman and Dercon 46 ran a policy experiment in Ethiopia. They were able to convince five factories to hire people at random from a group of consenting participants, and then tracked the effects on their incomes and health.

They find that these low-skill industrial jobs paid more than the alternatives available to a substantial fraction of workers; but at the same time, they had adverse health effects and did not offer a long-term solution—most applicants quit the formal sector quickly, finding industrial jobs unpleasant and risky.

But it does suggest that while low-skilled industrial jobs may improve consumption opportunities, providing a short-term safety net, they may do so at important costs in other dimensions of well-being.

This reaffirms the importance of measuring poverty beyond just income and consumption, and of maintaining a nuanced understanding of how global living conditions change. Countries where more people live in extreme poverty tend to have particularly bad health outcomes.

The following visualization provides evidence of this relationship. It shows life expectancy at birth on the vertical axis, against poverty rates for a poverty line equivalent to 3. The button at the bottom allows you to change the reference years, so that you can see how these two variables covary across time. As we can see, there is a clear negative relationship: people tend to live longer in countries where poverty is less common.

Yet the correlation is far from perfect—some countries such as South Africa have a relatively low life expectancy in comparison to other countries with similar poverty rates. This reinforces the importance of thinking about deprivation beyond income and consumption. Above we showed that poverty correlates with health. Here, we provide evidence of another important correlate: education. The following visualization plots mean years of schooling against poverty rates again using a poverty line equivalent to 3.

As before, the button at the bottom allows you to change the reference years, so that you can see how these two variables covary across time. As we can see, there is once again a clear negative relationship: poverty tends to be more frequent in countries where education is less developed.

As we discussed above, there is also household-level evidence of this correlation—schooling is one of the strongest predictors of economic well-being, even after controlling for other household characteristics. The most straightforward way to measure poverty is to set a poverty line and count the number of people living with incomes or consumption levels below that poverty line and divide the number of poor people by the entire population.

This is the poverty headcount ratio. Measuring poverty through the headcount ratio provides information that is straightforward to interpret; it tells us the share of the population living with consumption or incomes below the poverty line are. But 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 poverty gap index is an alternative way of measuring poverty that considers the intensity of deprivation. The most common way to measure the intensity of poverty is to calculate the amount of money required by a poor person to just 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 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.

Absolute poverty is measured relative to a fixed standard of living; that is, an income threshold that is constant across time. Absolute poverty measures are often used to compare poverty between countries and then they are not just held constant over time, but also across countries. The International Poverty Line is the best known poverty line for measuring absolute poverty globally.

Some countries also use absolute poverty measures on a national level. These measures are anchored so that comparisons relative to a minimum consumption or income level over time are possible.

Relative Poverty , on the other hand, is measured relative to living standards in a particular society, and varies both across time and between societies. The idea behind measuring poverty in relative terms is that the degree of deprivation depends on the relevant reference group; hence, people are typically considered poor by this standard 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 a poverty line that is defined relative to the median income in the corresponding country. This poverty line defines people as 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 can be considered a metric of inequality —it measures the distance between those in the middle and those at the bottom of the income distribution. Relative poverty can be measured using the poverty headcount ratio and the poverty gap index.

Indeed, these indicators are common in Europe. Historical estimates of poverty come from academic studies that reconstruct past income and consumption levels by estimating economic output and inequality for the time before household surveys became available.

A seminal paper following this approach and estimating global poverty figures from onward is Bourguignon and Morrison The change in extreme poverty is then calculated via changes in the share of the world population with incomes below the poverty line, according to the corresponding estimated distribution of incomes.

Bourguignon and Morrison rely on three types of data in order to estimate the distributions of income: economic output real GDP per capita , population, and inequality. The approach outlined above leads to a natural question: How can researchers construct economic output for the distant past? Fouquet and Broadberry 49 provide a detailed account of how economic historians construct these estimates.

It is painstaking work with which researchers occupy themselves for years. The generally preferred approach to estimating national income is the output approach, which relies on historical records by economic sector.

For example, for agricultural production, researchers use church records for the estates of farmers, as well as accounting documents produced by farmers and kept in local record offices. Agricultural outputs are then calculated by multiplying the acreage for each crop by the yield per acre. Outputs related to other sectors, such as leather and food processing, are estimated using a similar approach applied to the specifics of each sector.

Finally, when the output of all sectors is reconstructed, these various series are brought together and—using a set of sectoral weights that capture the changing structure of the economy—an estimate of the total historic output of the productive work of the population is reached. The World Bank is the most important institution measuring the extent of global poverty for the time since The World Bank estimates are produced from three key ingredients: household surveys providing evidence about household consumption per head or, in some cases as we will see, income per head ; domestic price indexes and purchasing power parity rates; and an International Poverty Line based on national lines in the poorest countries for which such lines are available.

Below we provide an overview of each of these ingredients. Ferreira et al. In principle, one could use household surveys to estimate i resource outflows monetary expenditures, home production and transfers ; ii resource inflows earnings and other non-market sources of income such as, again, home production and transfers ; and iii change in assets between the beginning and end of the relevant period including savings, owned durable goods, etc. Given all this information, consumption, as per the definition above, could be estimated directly from i , or as the difference between ii and iii.

In theory, both approaches should give the same result. In practice, however, surveys on expenditures are different from surveys on incomes more on this below. For the majority of countries, the World Bank estimates consumption directly from household surveys on expenditures.

For a significant minority of countries, however, World Bank estimates are based on income surveys. Notably, in both cases, the estimation methodology does include home production and transfers, by attaching monetary values to such non-market transactions.

How are monetary values placed on things like food grown at home and gifts from relatives? One common approach is to ask survey-respondents about the amount of such resources consumed over a given reference period.

The aim is to then ascribe a monetary value to the reported consumption. This is done by multiplying the consumed amounts by extrapolated market prices.

A second approach asks households directly about their own valuation of the amount of money they would expect to pay if they had bought such items themselves, or, the amount of money they would expect to receive if they had sold these items. The second approach is commonly used to establish a rental equivalent for housing and durable goods owned by the household.

How are income and expenditure surveys actually conducted? Different countries use different surveying instruments, and while there is much scope for harmonization see Beegle et al 52 , there are some basic common features that allow for cross-country comparisons. In the case of expenditures, different reference periods are used to record responses across different categories of goods, with longer periods for goods or services that tend to be acquired less frequently.

Income and consumption measures available from national household surveys are denominated in local currency units. This means that in order to make meaningful cross-country poverty comparisons, it is necessary to translate figures into a common currency—i. One possibility would be to simply use the exchange rates from currency markets to translate all national figures into one common currency—such as, for example, the US-dollar. This approach, however, would fail to account for differences in price levels: one US dollar allows you to achieve higher consumption in India than it does in the US.

If we are interested in material deprivation, any monetary income should be considered in relation to the amount of goods and services that it can buy locally. 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.

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. As the graph shows for GDP per capita, assessing living standards using PPP adjusted international dollars rather than US market dollars 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 value of incomes. The two last rounds of PPP factors estimated by the ICP are from and and the next one is scheduled for You can read more about PPP adjustments in our dedicated blog entry on this topic.

Today, the International Poverty Line is 1. Where does this number come from? The pioneering work that set out to count the number of people in poverty using a common global standard was published by Montek Ahluwalia, Nicholas Carter, and Hollis Chenery in To rely on the national poverty line of a low-income country is still the basic idea on which the International Poverty Line is based.

But today it is not just the poverty line of India that is taken into account rather, as we can see in the following table, it is based on the national poverty lines of 15 different low-income countries. There were several major revisions between the first formulation of a global poverty line in and today.

The table shown here, taken from Ferreira et al. The International Poverty Line is intended to be a global poverty line for absolute measurement of deprivation—so it is not recurrently adjusted as low-income countries grow richer. However, it is important to bear in mind that the International Poverty Line is sometimes updated; in , for example, the line was updated from 1. This last update was made in order to incorporate new evidence on relative price levels, rather than to change the underlying real welfare standard used to define deprivation.

The current methodology for choosing the set of countries used to define the International Poverty Line was first proposed by Chen and Ravallion In other words: they found that the poorest fifteen countries in their sample used a roughly similar absolute poverty line, independent of differences in their per capita consumption levels. These are the fifteen countries that were chosen as reference. The current methodology has been criticized because of lack of comparability in the underlying set of national poverty lines that were used to choose the fifteen reference countries.

Jolliffe and Prydz 56 address this issue of comparability by estimating the national poverty lines that are implied by poverty rates. The set of national poverty lines estimated by Jolliffe and Prydz suggests, in contrast to earlier findings by Chen and Ravallion, that there is substantial variation in poverty lines even among the poorest countries.

However, this variation does not seem to contradict the choice of the International Poverty Line: if we order the poverty lines of the poorest 25 percent of countries, the value in the middle is close to 1.

This is in line with a broader point made by Ferreira et al alternative approaches that were proposed for updating the International Poverty Line to PPPs end up generating lines that are either exactly or very close to 1. The following visualization shows how national poverty lines in different countries compare to the International Poverty Line. The figures come from Jolliffe and Prydz , 59 and correspond to the poverty lines that are implied by national poverty head-counts.

As can be seen, there is a clear gradient: poorer countries tend to use lower poverty lines. Importantly, this chart also shows us that although the International Poverty Line is very low, it is still higher than the official poverty lines used by many low-income countries.

In Malawi, for example, the national poverty line is 1. For reference, in this chart we have included also OECD relative poverty lines. It should be noted that, by definition, these poverty lines change over time since they are defined relative to the median income.

They are however included to give an idea of the degree of variation in standards used by countries to measure poverty. While in Malawi the national poverty line is equivalent to 1. A person defined as poor in Norway can be times richer than a person defined as poor in Malawi, a country in which GDP per capita is times lower than in Norway.

This approach first stipulates a consumption bundle that is deemed adequate for basic consumption needs in the local context, and then estimates the cost of this specific bundle. What is an adequate consumption bundle? One common starting point is to rely on a generic food requirement, such as 2, calories per person per day, and then include a nonfood component that is added to reflect costs for housing, clothing, electricity, and so on. Another approach—less common but also employed in practice—is to set absolute lines based on asking people what minimum consumption or income level they need just to make ends meet.

Above , we show that there is indeed a close relationship between the self-assessment of living conditions and the mean income in that society, both between and within countries. Above, we discussed the methodology used by the World Bank to measure extreme poverty. Here, we focus on the various limitations of this methodology. We follow the points discussed by Ferreira et al.

For all countries shown in grey in this map, there is not a single survey available to the World Bank in the last three decades. Many of these countries are rich countries in which extreme poverty is very low. But there is also missing data for some poorer countries, in which surely a considerable share of the population is living in extreme poverty. As we can also see from this map, there are some countries with very few observations.

This is the case for many African countries, where there is only one survey available in the last decade. This is extremely infrequent, even in comparison to Latin America and Central Asia, where many countries have almost annual surveys. By moving the time slider below the map, you can see how many surveys are available in each decade.

For individual countries, the World Bank publishes poverty estimates only for years in which household survey data is available. But for regional and global estimates, the World Bank publishes estimates every three years. Clearly, since not all countries have survey data for all years in which regional and global estimates are produced, the World Bank must rely on approximations. The process of lining up estimates relies on interpolation for countries in which survey data are not available in particular years, but are available either before or after or both.

You can read more about this process in PovcalNet. The bottom line is that the accuracy of these approximations relies heavily on the availability of survey data—the more survey years are available for a country, the more accurate the approximation. For low and middle income countries without reliable survey data in any year mainly countries in the Middle East and North Africa , the World Bank chooses not to publish country-specific estimates, but still includes an approximated number in the regional and global totals, by relying on alternative statistical techniques.

But not all national statistical agencies report consistent estimates of consumption based on expenditure surveys to the World Bank. The issue is that national statistical agencies design and execute surveys to serve the specific needs and interests of each particular country; which means that different countries use different concepts, methods, and questionnaire designs.

Income and consumption, as measured by household surveys, are not usually perfectly comparable. The implication is that, by definition, zero income is a feasible value, while zero consumption is not a feasible value—people with zero consumption would starve. As one would then expect, this is reflected in the data. Indeed, in rich countries such as the US, the problem of comparability is so substantial that the World Bank decides not to include estimates of its extreme poverty in the global totals.

This is a point we discuss below in more detail. The following charts from Chandy and Smith 66 show how income and consumption estimates differ for the US and for Malawi.

In these charts, each dot represents a household. More specifically, for each household, the chart compares income per day horizontal axis against expenditures per day vertical axis.

Both measures for each household come from the same survey. If incomes and expenditures are identical, then we should see all households lined up along the degree blue line where expenditure and income are equal.

The orange and red lines plot the trend that best fits the data i. As we can seen, in the US the best-fit line is significantly different to the blue line: at very low levels of income, expenditure is significantly higher than income; and at high levels of income, expenditure is lower than income. In contrast, in Malawi the best-fit line is close to the blue line: households with very low income have comparably low expenditure.

This is because unlike in the US, households in Malawi can rely less on savings, borrowing, and government welfare. Above, we pointed out that World Bank poverty estimates for some countries come from income data, while in other countries they come from consumption data.

As it turns out, comparability issues arise even among countries that rely on consumption data from expenditures, since survey questionnaires are not always standardized. Beegle et al. They conduct an experiment in Tanzania, in which they randomly choose households and test eight alternative methods of measuring household consumption.

They find significant differences between consumption reported by the benchmark personal diary and other diary and recall formats. The table summarizes the differences in measured poverty that arise from the various survey designs you can see an overview of the differences between questionnaires here , with more details in the paper.

As we can see, the differences are substantial for both the headcount ratio and the poverty gap index. Researchers have found that the recall period for food consumption matters for the assessment of food consumption in a population for an example on India see Deaton and Kozel ; 69 and it has also been observed that questionnaires with more food items listed report higher food consumption when compared with a questionnaire with fewer items for an example on El Salvador see Jolliffe Studies also suggest that survey design matters for sampling purposes.

There is theoretical and empirical evidence presented by Korinek et al. When richer individuals or households are less likely to answer surveys than poor people, survey-based estimates of consumption and income will understate the mean level of prosperity and overstate the share of people in poverty. 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? A simple look at the reality of homelessness in high-income countries suggests that we need to take this question seriously. The first point that we need to consider here is that the standards used by rich countries to measure poverty nationally are substantially different to the standards used by the World Bank to measure extreme poverty in low- and middle-income countries.

Consider the case of the US. According to official estimates, the poverty rate in the US was This figure is not really informative about extreme poverty relative to the International Poverty Line used by the World Bank: the official US poverty estimates refer to individuals living in households with incomes below a much higher threshold than the International Poverty Line. This is more than 8-fold higher than the International Poverty Line. The second point to consider is that even if we try to apply the same standards used by the World Bank, the survey instruments in rich countries are typically not suitable to produce estimates that are comparable to those published by the World Bank.

Keeping these comparability issues in mind, the World Bank does estimate poverty rates in high income countries, but chooses not to include them in the global figures. This can be confusing for researchers—including yours truly! The World Bank uses disposable income data to calculate extreme poverty figures that are published in PovcalNet, but chooses not to include them in the global poverty estimates and in many other reports such as those relying on the World Development Indicators , due to lack of comparability.

Countries of this type cannot be used in aggregation. You can read more about extreme poverty in rich countries in our blog post here ; and you can read about the link between homelessness and poverty in rich countries here.

The above discussion of data limitations in the context of World Bank poverty estimates highlights an important fact: any estimate of poverty—of either its level or change over time—is surrounded by a margin of error. Keeping in mind that the World Bank poverty estimates are only approximations is important when making policy decisions, such as the allocation of international aid.

The fact that the World Bank poverty estimates are only imperfect approximations does not mean that these are meaningless or useless numbers—it means that they should be used as one more source of information to assess living standards. As we point out above, there are many other complementary ways of measuring deprivation. By virtue of being approximations, the World Bank poverty estimates can underestimate, as well as overestimate the size of the underlying problem.

As a matter of fact, there is some evidence suggesting that World Bank figures might be over-estimates. The green series plots all countries in the PovcalNet dataset—this is the benchmark.

The other lines exclude different countries, depending on whether they have comparable and good-quality data. At Our World In Data we are particularly interested in how living conditions change over the long run. The available data suggests that the decline of poverty has been so large over the long run, that it cannot be the result of measurement error. Even if we consider realistic confidence intervals, the trends hold.