A lack of infrastructure — from roads, bridges, and wells, to cables for light, cell phones, and internet — can isolate communities living in rural areas. Living off the grid often means living without the ability to go to school, work, or the market to buy and sell goods. Traveling further distances to access basic services not only takes time, it costs money, keeping families in poverty. Isolation limits opportunity. Without opportunity, many find it difficult, if not impossible, to escape extreme poverty.
Many people living in the United States are familiar with social welfare programs that people can access if they need healthcare or food assistance. Ineffective governments also contribute to several of the other causes of extreme poverty mentioned above, as they are unable to provide necessary infrastructure or healthcare, or ensure the safety and security of their citizens in the event of conflict.
This might seem like a no-brainer: Without a job or a livelihood, people will face poverty. Dwindling access to productive land often due to conflict, overpopulation, or climate change and overexploitation of resources like fish or minerals puts increasing pressure on many traditional livelihoods. In the Democratic Republic of Congo DRC for example, most of the population lives in rural communities where natural resources have been plundered over centuries of colonial rule — while conflict over land has forced people away from their source of income and food.
Now, more than half of the country lives in extreme poverty. All of the above risk factors — from conflict to climate change or even a family illness — can be weathered if a family or community has reserves in place. Cash savings and loans can offset unemployment due to conflict or illness. Proper food storage systems can help if a drought or natural disaster ruins a harvest. This means that, when a risk turns into a disaster, they turn to negative coping mechanisms, including pulling children out of school to work or even marry , and selling off assets to buy food.
That can help a family make it through one bad season, but not another. For communities constantly facing climate extremes or prolonged conflict, the repeated shocks can send a family reeling into extreme poverty and prevent them from ever recovering.
This is why we make an effort to study a wide range of aspects, including education, health, human rights, etc. If you are interested in understanding poverty through these other lenses, you are welcome to explore our website—the content menu at the top of the page links to all of our entries on these topics.
Tracking various indicators of well-being independently can make comparisons difficult, since different indicators move in different directions across time and space. Because of this, researchers and policymakers often construct synthetic indicators that aggregate various dimensions of deprivation, by attaching welfare weights to a set of key underlying metrics of well-being.
Different from other indexes like the Human Development Index , the MPI is not aggregated at the country level, but instead at the individual level—it measures how one and the same individual is deprived in different dimensions. The MPI is constructed from ten indicators across three core dimensions: health, education and living standards.
This table specifies how the different indicators are defined and aggregated. And you can find a more technical discussion of the MPI and its properties in Alkire and Foster In the following map, we show the share of MPI poor people country by country i. As we can see, this alternative metric shows that poverty is also particularly acute in sub-Saharan Africa.
As we mentioned above, poverty is multidimensional in nature, and it is therefore useful to try to measure poverty through alternative instruments that capture deprivation beyond income and consumption. The former is the same metric we have discussed extensively throughout this entry. As we can see, there is a positive correlation between these two measures of deprivation, but they are clearly not identical.
This highlights the usefulness of tracking deprivation across multiple dimensions of well-being, including both standard and non-standard economic indicators.
National prosperity is a strong predictor of extreme poverty at the individual level. The following graph shows this relationship between average incomes GDP per capita and the share of the population living in extreme poverty.
The chart shows that today there is no country with a GDP per capita higher than 15, int. And in most countries with GDP per capita below 4, int.
The scatter plot is interactive—by moving the time slider under the plot, you can see the change over time. How poverty changes is not only a consequence of economic growth, it also depends on the distribution of incomes and how this inequality changes during the growth process.
If growth only lifts the incomes at the top, poverty levels will remain unchanged. On the other hand, if growth is inclusive and lifts all boats, the economy is able to reduce absolute poverty over time.
As discussed in our entry on income inequality , income inequality has developed quite differently in different countries. In India, for example, inequality has increased , while in most Latin American countries, inequality has fallen. Researchers have compared how much changes in inequality matter for poverty reduction relative to economic growth. David Dollar and Aart Kraay studied this link between growth, inequality and poverty reduction in a widely cited paper in Twelve years later the same two authors and Tatjana Kleineberg revisited the question on the consequences of growth and changes in inequality.
In their newer paper, they broadened the scope of the research question to study social welfare. This approach—using the concept of social welfare—takes into account not just poverty, but also the change in living standards of individuals above the poverty line. As in their earlier research, Dollar, Kleineberg, and Kraay 34 studied a large number of countries over the past 40 years. In contrast, the contribution of changes in relative incomes to social welfare growth is on average much smaller than growth in average incomes, and moreover is on average uncorrelated with average income growth.
The following chart focuses on the population living in extreme poverty. It plots the change of national average income against the change in extreme poverty levels over time. Each country is shown here over a succession of points, one for each yearly observation of GDP and poverty. As countries like India, Brazil, Indonesia, and China got richer, the share of their population living in extreme poverty has fallen. One way to think about this is to consider how low prosperity is before an economy achieves sustained economic growth that lifts the majority of the population out of poverty.
India in had a GDP per capita of 1, int. At the end of the period in the connected scatter plot, average income was more than 4-times higher at 4, int. Persistent economic growth really is a very powerful force, and the findings of Dollar, Kleineberg, and Kraay and the chart make this very clear. What is true for the recent decades is also true for the long-run perspective on a global scale.
Without the increase in productivity that brought economic growth , it would not have been possible to lift hundreds of millions of people out of extreme poverty. Seen from the long historical perspective, it is clear that countries have to be extraordinarily rich to have the possibility to end extreme poverty for the majority of their population. The idea is simple: poverty today causes poverty in the future, so households that start poor, remain poor. Insufficient nutrition, for example, can lead to a poverty trap.
More precisely, if physical capacity to work increases nonlinearly with food intake at low levels i. For example, low-income countries might lack the good growth fundamentals e. Such policies are meant to trigger a virtuous cycle of more savings, more investment, and economic growth. As we discuss below, although unidimensional poverty traps such as those caused by single factors are conceptually appealing e.
The following chart provides some evidence regarding the cross-country evolution of incomes over time. It plots, for each country, the national income in against the corresponding national income in The latter are the countries which experienced income growth over these 54 years.
And a couple of countries such as Niger and the Democratic Republic of Congo have even experienced negative growth over the reference period. But the large majority of countries, all those above the blue line, have experienced growth. Those countries that are far above the blue line had the strongest growth. Botswana fold increase , South Korea fold , Romania fold , China fold , and Thailand fold are some of the countries with the strongest growth over these 54 years.
A closer look at the data suggests that the typical poor country grew at least as fast as the global average over this period. Of course, what we see in this chart is only part of the story, since the micro and macro dynamics of incomes can be very different. It is possible, for example, that country-level average incomes are not stagnant, but household-level incomes lag for particular segments of the population within those countries.
Indeed, in the US there is evidence of stagnating incomes for those at the bottom of the distribution. Thus, a proper test for the existence of poverty traps requires a more sophisticated econometric analysis. Kraay and McKenzie 37 provide such an analysis in an interesting and detailed review of the available studies testing for the existence of mechanisms leading to poverty traps.
They argue that there is limited evidence for most of the mechanisms when operating in isolation; except perhaps for spatial poverty traps individuals being trapped in low-productivity locations , and behavioral poverty traps individuals being stuck in situations where they devote the most mental effort to meeting daily needs, leaving little attentional resources for solving other problems that could raise their incomes.
Other, less traditional policies might work better. 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.
This involves targeting the most vulnerable, increasing basic resources and services, and supporting communities affected by conflict and climate-related disasters.
View goal targets. A Yemeni woman improves lives and changes minds. Goats bring stability and income to female farmers in Zambia. UNDP photos of the year Building new lives after decades of conflict. Growing farming businesses in Sudan. Improving the health of Southeast Asia's largest lake. Tonle Sap in Cambodia is a key habitat for freshwater fish and many endangered species.
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