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What Is Poverty? Key Takeaways Poverty is a state or condition in which a person or community lacks the financial resources and essentials for a minimum standard of living. As of January , the U. COVID response Developing countries are most at risk during — and in the aftermath — of the pandemic, not only as a health crisis but as a devastating social and economic crisis over the months and years to come.
Rise for All. Why it matters: No Poverty. Infographic: No Poverty. Facts and Figures Goal 1 Targets Links. Facts and Figures. Southern Asia and sub-Saharan Africa are expected to see the largest increases in extreme poverty , with an additional 32 million and 26 million people, respectively, living below the international poverty line as a result of the pandemic. Even before COVID, baseline projections suggested that 6 per cent of the global population would still be living in extreme poverty in , missing the target of ending poverty.
The fallout from the pandemic threatens to push over 70 million people into extreme poverty. One out of five children live in extreme poverty, and the negative effects of poverty and deprivation in the early years have ramifications that can last a lifetime. These figures are the result of important changes across time. As we mentioned above in our discussion of regional trends, in Asia was the world region with the largest number of poor people million in South Asia, plus million in East Asia and the Pacific.
However, with rapid economic growth in Asia over the past two decades, poverty in Asia fell more rapidly than in Africa. The World Bank Group recently published a new set of poverty estimates, as part of their report Poverty and Shared Prosperity These estimates, explained in detail in two related background papers Newhouse et al. In order to produce disaggregated estimates, the World Bank relied on new data from the Global Micro Database that augments survey data in 89 countries, by providing a set of harmonized household characteristics, enriching the other survey instruments used by the World Bank to measure poverty.
According to the World Bank, the sample of 89 countries included in the Global Micro Database contains an estimated As the authors point out, while not every country is covered, this new set of estimates is the most updated and comprehensive source currently available to researchers and policymakers trying to understand the demographics of poverty.
The following visualization uses this source to provide a characterization of those who live in extreme poverty. As we can see, across all world regions the poor tend to be young and live in rural areas. In the background paper accompanying the data, Castaneda et al.
Interestingly, and perhaps also surprisingly, we can see from this visualization that those with no education are now a distinct minority of the population. Global estimates of child poverty are unfortunately not available. However, as we mentioned above, we can have a reasonable picture of this issue by looking at the estimates recently published by the World Bank using the Global Micro Database.
For measurement purposes, children are considered to be poor if they live in a poor household i. A household is considered poor, in turn, if the per capita consumption of its members or per capita income, depending on the country , falls below 1. This is the standard definition of absolute extreme poverty used by the World Bank. The following chart summarizes the available data. The height of each bar in this plot shows the share of people living in extreme poverty by age group, while the width of the bars reflects the total size of each age group in the overall population.
The area of each bar height times width gives the number of individuals living in extreme poverty within each age bracket—these are the numbers written inside each bar. By looking at the total number of people in extreme poverty area of the bars we can also see another important fact: virtually half of the people living in extreme poverty are under 18 years of age. This is a large share if we consider that those under 18 account for only around a third of the general population as shown by the width of the bars.
The above-mentioned data from the Global Micro Database allows us to study poverty across age groups for various poverty lines—not just the International Poverty Line.
The following chart shows the cumulative distribution of welfare for different age groups. Each of the lines in this plot shows, for each age group, the share of the population living below a given level of per capita daily income or consumption after accounting for differences in prices across countries. Following this logic, we can read the poverty rates at any poverty line.
As we can see, the distribution of consumption for adults is always to the right of the distribution for children. In economics lingo, what we observe is that the distribution for adults stochastically dominates that of children. This means that poverty rates for children are higher at any poverty line. Bear in mind that these are estimates of household per capita income. That means that children living in households with rich adults are also assumed to be rich.
The methodology used by the World Bank to measure poverty relies on income and consumption. While informative, this methodology certainly leaves out many important aspects of welfare. At Our World in Data, we believe that it is important to track progress in dimensions of well-being spanning beyond standard economic indicators. 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.
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