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Key facts about Asian Americans living in poverty

Burmese (19%) and Hmong Americans (17%) were among the Asian origin groups with the highest poverty rates in 2022.

1 in 10: Redefining the Asian American Dream (Short Film)

Of the 24 million Asians living in the United States, about 2.3 million live in poverty. This short film explores their diverse stories and experiences.

The Hardships and Dreams of Asian Americans Living in Poverty

About one-in-ten Asian Americans live in poverty. Pew Research Center conducted 18 focus groups in 12 languages to explore their stories and experiences.

Wealth Surged in the Pandemic, but Debt Endures for Poorer Black and Hispanic Families

About one-in-four Black households and one-in-seven Hispanic households had no wealth or were in debt in 2021, compared with about one-in-ten U.S. households overall.

What the data says about food stamps in the U.S.

The food stamp program is one of the larger federal social welfare initiatives, and in its current form has been around for nearly six decades.

Financial Issues Top the List of Reasons U.S. Adults Live in Multigenerational Homes

Nearly four-in-ten men ages 25 to 29 now live with older relatives.

Most Black Americans say they can meet basic needs financially, but many still experience economic insecurity

Fewer than half of Black adults say they have a three-month emergency fund, and some have taken multiple jobs to make ends meet.

One-in-Ten Black People Living in the U.S. Are Immigrants

Immigrants – particularly those from African nations – are a growing share of the U.S. Black population.

Most Americans support a $15 federal minimum wage

About six-in-ten Americans (62%) say they favor raising the federal minimum wage to $15 an hour, including 40% who strongly back the idea.

In the pandemic, India’s middle class shrinks and poverty spreads while China sees smaller changes

The course of the pandemic in India and China will have a substantial effect on changes in the distribution of income at the global level.

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© 2024 Pew Research Center

By: Joe Hasell , Max Roser , Esteban Ortiz-Ospina and Pablo Arriagada

Global poverty is one of the most pressing problems that the world faces today. The poorest in the world are often undernourished , without access to basic services such as electricity and safe drinking water ; they have less access to education , and suffer from much poorer health .

In order to make progress against such poverty in the future, we need to understand poverty around the world today and how it has changed.

On this page you can find all our data, visualizations and writing relating to poverty. This work aims to help you understand the scale of the problem today; where progress has been achieved and where it has not; what can be done to make progress against poverty in the future; and the methods behind the data on which this knowledge is based.

Key Insights on Poverty

Measuring global poverty in an unequal world.

There is no single definition of poverty. Our understanding of the extent of poverty and how it is changing depends on which definition we have in mind.

In particular, richer and poorer countries set very different poverty lines in order to measure poverty in a way that is informative and relevant to the level of incomes of their citizens.

For instance, while in the United States a person is counted as being in poverty if they live on less than roughly $24.55 per day, in Ethiopia the poverty line is set more than 10 times lower – at $2.04 per day. You can read more about how these comparable national poverty lines are calculated in this footnote. 1

To measure poverty globally, however, we need to apply a poverty line that is consistent across countries.

This is the goal of the International Poverty Line of $2.15 per day – shown in red in the chart – which is set by the World Bank and used by the UN to monitor extreme poverty around the world.

We see that, in global terms, this is an extremely low threshold indeed – set to reflect the poverty lines adopted nationally in the world’s poorest countries. It marks an incredibly low standard of living – a level of income much lower than just the cost of a healthy diet .

From $1.90 to $2.15 a day: the updated International Poverty Line

What you should know about this data.

• Global poverty data relies on national household surveys that have differences affecting their comparability across countries or over time. Here the data for the US relates to incomes and the data for other countries relates to consumption expenditure. 2
• The poverty lines here are an approximation of national definitions of poverty, made in order to allow comparisons across the countries. 1
• Non-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account. 3
• Data is measured in 2017 international-$, which means that inflation and differences in the cost of living across countries are taken into account. 4

Global extreme poverty declined substantially over the last generation

Over the past generation extreme poverty declined hugely. This is one of the most important ways our world has changed over this time.

There are more than a billion fewer people living below the International Poverty Line of $2.15 per day today than in 1990. On average, the number declined by 47 million every year, or 130,000 people each day. 5

The scale of global poverty today, however, remains vast. The latest global estimates of extreme poverty are for 2019. In that year the World Bank estimates that around 650 million people – roughly one in twelve – were living on less than $2.15 a day.

Extreme poverty: how far have we come, how far do we still have to go?

• Extreme poverty here is defined according to the UN’s definition of living on less than $2.15 a day – an extremely low threshold needed to monitor and draw attention to the living conditions of the poorest around the world. Read more in our article, From $1.90 to $2.15 a day: the updated International Poverty Line .
• Global poverty data relies on national household surveys that have differences affecting their comparability across countries or over time. 2
• Surveys are less frequently available in poorer countries and for earlier decades. To produce regional and global poverty estimates, the World Bank collates the closest survey for each country and projects the data forward or backwards to the year being estimated. 6
• Data is measured in 2017 international-$, which means that inflation and differences in the cost of living across countries are taken into account . 4

The pandemic pushed millions into extreme poverty

Official estimates for global poverty over the course of the Coronavirus pandemic are not yet available.

But it is clear that the global recession it brought about has had a terrible impact on the world’s poorest.

Preliminary estimates produced by researchers at the World Bank suggest that the number of people in extreme poverty rose by around 70 million in 2020 – the first substantial rise in a generation – and remains around 70-90 million higher than would have been expected in the pandemic’s absence. On these preliminary estimates, the global extreme poverty rate rose to around 9% in 2020. 7

• Figures for 2020-2022 are preliminary estimates and projections by World Bank researchers, based on economic growth forecasts. The pre-pandemic projection is based on growth forecasts prior to the pandemic. You can read more about this data and the methods behind it in the World Bank’s Poverty and Shared Prosperity 2022 report. 8

Hundreds of millions will remain in extreme poverty on current trends

Extreme poverty declined during the last generation because the majority of the poorest people on the planet lived in countries with strong economic growth – primarily in Asia.

The majority of the poorest now live in Sub-Saharan Africa, where weaker economic growth and high population growth in many countries has led to a rising number of people living in extreme poverty.

The chart here shows projections of global extreme poverty produced by World Bank researchers based on economic growth forecasts. 9

A very bleak future is ahead of us should such weak economic growth in the world’s poorest countries continue – a future in which extreme poverty is the reality for hundreds of millions for many years to come.

• The extreme poverty estimates and projections shown here relate to a previous release of the World Bank’s poverty and inequality data in which incomes are expressed in 2011 international-$. The World Bank has since updated its methods, and now measures incomes in 2017 international-$. As part of this change, the International Poverty Line used to measure extreme poverty has also been updated: from $1.90 (in 2011 prices) to $2.15 (in 2017 prices). This has had little effect on our overall understanding of poverty and inequality around the world. You can read more about this change and how it affected the World Bank estimates of poverty in our article From $1.90 to $2.15 a day: the updated International Poverty Line .
• Figures for 2018 and beyond are preliminary estimates and projections by Lakner et al. (2022), based on economic growth forecasts. You can read more about this data and the methods behind it in the related blog post. 10
• Data is measured in 2011 international-$, which means that inflation and differences in the cost of living across countries are taken into account. 4

The rapid progress seen in many countries shows an end to poverty is possible

Each of the countries shown in the chart achieved large declines in extreme poverty over the last generation. 11

The fact that rapid progress against poverty has been achieved in many places is one of the most important lessons we can learn from the available data on extreme poverty.

For those who are not aware of such progress – which is the majority of people – it would be easy to make the mistake of believing that poverty is inevitable and that action to tackle poverty is hence doomed to fail.

The huge progress seen in so many places shows that this view is incorrect.

Interactive visualization requires JavaScript.

After 200 years of progress the fight against global poverty is just beginning

Over the past two centuries the world made good progress against extreme poverty. But only very recently has poverty fallen at higher poverty lines.

Global poverty rates at these higher lines remain very high:

• 25% of the world lives on less than $3.65 per day – a poverty line broadly reflective of the lines adopted in lower-middle income countries.
• 47% of the world lives on less than $6.85 per day – a poverty line broadly reflective of the lines adopted in upper-middle income countries.
• 84% live on less than $30 per day – a poverty line broadly reflective of the lines adopted in high income countries. 12

Economic growth over the past two centuries has allowed the majority of the world to leave extreme poverty behind. But by the standards of today’s rich countries, the world remains very poor. If this should change, the world needs to achieve very substantial economic growth further still.

The history of the end of poverty has just begun

How much economic growth is necessary to reduce global poverty substantially?

• The data from 1981 onwards is based on household surveys collated by the World Bank. Earlier figures are from Moatsos (2021), who extends the series backwards based on historical reconstructions of GDP per capita and inequality data. 13
• All data is measured in international-$ which means that inflation and differences in purchasing power across countries are taken into account. 4
• The World Bank data for the higher poverty lines is measured in 2017 international-$. Recently, the World Bank updated its methodology having previously used 2011 international-$ to measure incomes and set poverty lines. The Moatsos (2021) historical series is based on the previously-used World Bank definition of extreme poverty – living on less than $1.90 a day when measured in 2011 international-$. This is broadly equivalent to the current World Bank definition of extreme poverty – living on less than $2.15 a day when measured in 2017 international-$. You can read more about this update to the World Bank’s methodology and how it has affected its estimates of poverty in our article From $1.90 to $2.15 a day: the updated International Poverty Line .
• The global poverty data shown from 1981 onwards relies on national household surveys that have differences affecting their comparability across countries or over time. 2
• Such surveys are less frequently available in poorer countries and for earlier decades. To produce regional and global poverty estimates, the World Bank collates the closest survey for each country and projects the data forward or backwards to the year being estimated. 6
• Non-market sources of income, including food grown by subsistence farmers for their own consumption, are taken into account. This is also true of the historical data – in producing historical estimates of GDP per capita on which these long-run estimates are based, economic historians take into account such non-market sources of income, as we discuss further in our article How do we know the history of extreme poverty?

Explore Data on Poverty

About this data.

All the data included in this explorer is available to download in GitHub , alongside a range of other poverty and inequality metrics.

Where is this data sourced from?

This data explorer is collated and adapted from the World Bank’s Poverty and Inequality Platform (PIP).

The World Bank’s PIP data is a large collection of household surveys where steps have been taken by the World Bank to harmonize definitions and methods across countries and over time.

About the comparability of household surveys

There is no global survey of incomes. To understand how incomes across the world compare, researchers need to rely on available national surveys.

Such surveys are partly designed with cross-country comparability in mind, but because the surveys reflect the circumstances and priorities of individual countries at the time of the survey, there are some important differences.

Income vs expenditure surveys

One important issue is that the survey data included within the PIP database tends to measure people’s income in high-income countries, and people’s consumption expenditure in poorer countries.

The two concepts are closely related: the income of a household equals their consumption plus any saving, or minus any borrowing or spending out of savings.

One important difference is that, while zero consumption is not a feasible value – people with zero consumption would starve – a zero income is a feasible value. This means that, at the bottom end of the distribution, income and consumption can give quite different pictures about a person’s welfare. For instance, a person dissaving in retirement may have a very low, or even zero, income, but have a high level of consumption nevertheless.

The gap between income and consumption is higher at the top of this distribution too, richer households tend to save more, meaning that the gap between income and consumption is higher at the top of this distribution too. Taken together, one implication is that inequality measured in terms of consumption is generally somewhat lower than the inequality measured in terms of income.

In our Data Explorer of this data there is the option to view only income survey data or only consumption survey data, or instead to pool the data available from both types of survey – which yields greater coverage.

Other comparability issues

There are a number of other ways in which comparability across surveys can be limited. The PIP Methodology Handbook provides a good summary of the comparability and data quality issues affecting this data and how it tries to address them.

In collating this survey data the World Bank takes a range of steps to harmonize it where possible, but comparability issues remain. These affect comparisons both across countries and within individual countries over time.

To help communicate the latter, the World Bank produces a variable that groups surveys within each individual country into more comparable ‘spells’. Our Data Explorer provides the option of viewing the data with these breaks in comparability indicated, and these spells are also indicated in our data download .

Global and regional poverty estimates

Along with data for individual countries, the World Bank also provides global and regional poverty estimates which aggregate over the available country data.

Surveys are not conducted annually in every country however – coverage is generally poorer the further back in time you look, and remains particularly patchy within Sub-Saharan Africa. You can see that visualized in our chart of the number of surveys included in the World Bank data by decade.

In order to produce global and regional aggregate estimates for a given year, the World Bank takes the surveys falling closest to that year for each country and ‘lines-up’ the data to the year being estimated by projecting it forwards or backwards.

This lining-up is generally done on the assumption that household incomes or expenditure grow in line with the growth rates observed in national accounts data. You can read more about the interpolation methods used by the World Bank in Chapter 5 of the Poverty and Inequality Platform Methodology Handbook.

How does the data account for inflation and for differences in the cost of living across countries?

To account for inflation and price differences across countries, the World Bank’s data is measured in international dollars. This is a hypothetical currency that results from price adjustments across time and place. It is defined as having the same purchasing power as one US-$ would in the United States in a given base year. One int.-$ buys the same quantity of goods and services no matter where or when it is spent.

There are many challenges to making such adjustments and they are far from perfect. Angus Deaton ( Deaton, 2010 ) provides a good discussion of the difficulties involved in price adjustments and how this relates to global poverty measurement.

But in a world where price differences across countries and over time are large it is important to attempt to account for these differences as well as possible, and this is what these adjustments do.

In September 2022, the World Bank updated its methodology, and now uses international-$ expressed in 2017 prices – updated from 2011 prices. This has had little effect on our overall understanding of poverty and inequality around the world. But poverty estimates for particular countries vary somewhat between the old and updated methodology. You can read more about this update in our article From $1.90 to $2.15 a day: the updated International Poverty Line .

To allow for comparisons with the official data now expressed in 2017 international-$ data, the World Bank continues to release its poverty and inequality data expressed in 2011 international-$ as well. We have built a Data Explorer to allow you to compare these, and we make all figures available in terms of both sets of prices in our data download .

Absolute vs relative poverty lines

This dataset provides poverty estimates for a range of absolute and relative poverty lines.

An absolute poverty line represents a fixed standard of living; a threshold that is held constant across time. Within the World Bank’s poverty data, absolute poverty lines also aim to represent a standard of living that is fixed across countries (by converting local currencies to international-$). The International Poverty Line of $2.15 per day (in 2017 international-$) is the best known absolute poverty line and is used by the World Bank and the UN to measure extreme poverty around the world.

The value of relative poverty lines instead rises and falls as average incomes change within a given country. In most cases they are set at a certain fraction of the median income. Because of this, relative poverty can be considered a metric of inequality – it measures how spread out the bottom half of the income distribution is.

The idea behind measuring poverty in relative terms is that a person’s well-being depends not on their own absolute standard of living but on how that standard compares with some reference group, or whether it enables them to participate in the norms and customs of their society. For instance, joining a friend’s birthday celebration without shame might require more resources in a rich society if the norm is to go for an expensive meal out, or give costly presents.

Our dataset includes three commonly-used relative poverty lines: 40%, 50%, and 60% of the median.

Such lines are most commonly used in rich countries, and are the main way poverty is measured by the OECD and the European Union .More recently, relative poverty measures have come to be applied in a global context. The share of people living below 50 per cent of median income is, for instance, one of the UN’s Sustainable Development Goal indicators . And the World Bank now produces estimates of global poverty using a Societal Poverty Line that combines absolute and relative components.

When comparing relative poverty rates around the world, however, it is important to keep in mind that – since average incomes are so far apart – such relative poverty lines relate to very different standards of living in rich and poor countries.

Does the data account for non-market income, such as food grown by subsistence farmers?

Many poor people today, as in the past, rely on subsistence farming rather than a monetary income gained from selling goods or their labor on the market. To take this into account and make a fair comparison of their living standards, the statisticians that produce these figures estimate the monetary value of their home production and add it to their income/expenditure.

Research & Writing

Despite making immense progress against extreme poverty, it is still the reality for every tenth person in the world.

$2.15 a day: the updated International Poverty Line

What does the World Bank’s updated methods mean for our understanding of global poverty?

Global poverty over the long-run

How do we know the history of extreme poverty?

Joe Hasell and Max Roser

Breaking out of the Malthusian trap: How pandemics allow us to understand why our ancestors were stuck in poverty

The short history of global living conditions and why it matters that we know it

Poverty & economic growth.

The economies that are home to the poorest billions of people need to grow if we want global poverty to decline substantially

Global poverty in an unequal world: Who is considered poor in a rich country? And what does this mean for our understanding of global poverty?

What do poor people think about poverty?

More articles on poverty.

Three billion people cannot afford a healthy diet

Hannah Ritchie

Homelessness and poverty in rich countries

Esteban Ortiz-Ospina

OWID Data Collection: Inequality and Poverty

Joe Hasell and Pablo Arriagada

Interactive Charts on Poverty

Official definitions of poverty in different countries are often not directly comparable due to the different ways poverty is measured. For example, countries account for the size of households in different ways in their poverty measures.

The poverty lines shown here are an approximation of national definitions, harmonized to allow for comparisons across countries. For all countries apart from the US, we take the harmonized poverty line calculated by Jolliffe et al. (2022). These lines are calculated as the international dollar figure which, in the World Bank’s Poverty and Inequality Platform (PIP) data, yields the same poverty rate as the officially reported rate using national definitions in a particular year (around 2017).

For the US, Jolliffe et al. (2022) use the OECD’s published poverty rate – which is measured against a relative poverty line of 50% of the median income. This yields a poverty line of $34.79 (measured using 2017 survey data). This however is not the official definition of poverty adopted in the US. We calculated an alternative harmonized figure for the US national poverty using the same method as Jolliffe et al. (2022), but based instead on the official 2019 poverty rate – as reported by the U.S. Census Bureau.

You can see in detail how we calculated this poverty line in this Google Colabs notebook .

Jolliffe, Dean Mitchell, Daniel Gerszon Mahler, Christoph Lakner, Aziz Atamanov, and Samuel Kofi Tetteh Baah. 2022. Assessing the Impact of the 2017 PPPs on the International Poverty Line and Global Poverty. The World Bank. Available to read at the World Bank here .

Because there is no global survey of incomes, researchers need to rely on available national surveys. Such surveys are designed with cross-country comparability in mind, but because the surveys reflect the circumstances and priorities of individual countries at the time of the survey, there are some important differences. In collating this survey data the World Bank takes steps to harmonize it where possible, but comparability issues remain.

One important issue is that, whilst in most high-income countries the surveys capture people’s incomes, in poorer countries these surveys tend to capture people’s consumption. The two concepts are closely related: the income of a household equals their consumption plus any saving, or minus any borrowing or spending out of savings.

To help communicate the latter, the World Bank produces a variable that groups surveys within each individual country into more comparable ‘spells’ (which we include in our data download ). Our Data Explorer provides the option of viewing the data with these breaks in comparability indicated.

The international-$ is a hypothetical currency that results from price adjustments across time and place. It is defined as having the same purchasing power as one US-$ in a given base year – in this case 2017. One int.-$ buys the same quantity of goods and services no matter where or when it is spent. There are many challenges to making such adjustments and they are far from perfect. But in a world where price differences across countries and over time are large it is important to attempt to account for these differences as well as possible, and this is what these adjustments do. Read more in our article From $1.90 to $2.15 a day: the updated International Poverty Line .

​​According to World Bank data, in 1990 there were 2.00 billion people living in poverty, and in 2019 that had fallen to 0.648 billion. The average fall over the 29 years in between is: (2.00 billion – 0.648 billion)/29 = 46.6 million. Dividing by the number of days (29 x 365) gives the average daily fall: (2.00 billion – 0.648 billion)/(29 x 365) = 128,000. (All figures rounded to 3 significant figures).

The projections are generally made on the assumption that incomes or expenditure grow in line with the growth rates observed in national accounts data. You can read more about the interpolation methods used by the World Bank in Chapter 5 of the Poverty and Inequality Platform Methodology Handbook.

We use the figures presented in the World Bank’s Poverty and Shared Prosperity 2022 report. Earlier estimates were also published in Lakner, C., Mahler, D.G., Negre, M. et al. How much does reducing inequality matter for global poverty?. J Econ Inequal (2022). https://doi.org/10.1007/s10888-021-09510-w . Available online here .

Earlier estimates were also published in Lakner, C., Mahler, D.G., Negre, M. et al. How much does reducing inequality matter for global poverty?. J Econ Inequal (2022). https://doi.org/10.1007/s10888-021-09510-w . Available online here .

The figures are taken from a World Bank blog post by Nishant Yonzan, Christoph Lakner and Daniel Gerszon Mahler. The post builds on and updates the estimates published by Lakner et al. (2022).In September 2022, the World Bank changed from using 2011 international-$ to 2017 international-$ in the measurement of global poverty. The International Poverty Line used by the World Bank and the UN to define extreme poverty was accordingly updated from $1.90 a day (in 2011 prices) to $2.15 (in 2017 prices).In order to match up to the projected figures, the extreme poverty estimates shown here relate to a previous release of the World Bank’s data using data expressed in 2011 prices, which vary slightly from the latest data in 2017 prices. You can read more about this change and how it affected the World Bank estimates of poverty in our article From $1.90 to $2.15 a day: the updated International Poverty Line .Lakner, C., Mahler, D.G., Negre, M. et al. How much does reducing inequality matter for global poverty?. J Econ Inequal (2022). https://doi.org/10.1007/s10888-021-09510-w . Available online here .

We use the figures provided in the blog post, which extend the methods presented in Lakner et al. (2022).Lakner, C., Mahler, D.G., Negre, M. et al. How much does reducing inequality matter for global poverty?. J Econ Inequal (2022). https://doi.org/10.1007/s10888-021-09510-w . Available online here .

Shown are those countries with a decline of more than 30 percentage points over a period of 15 years or more.There are a number of ways in which comparability across the different household surveys on which this data is based can be limited. These affect comparisons both across countries and within individual countries over time.The World Bank’s Poverty and Inequality Platform Methodology Handbook provides a good summary of the comparability and data quality issues affecting this data and how it tries to address them. In collating this survey data the World Bank takes a range of steps to harmonize it where possible, but comparability issues remain. To help communicate the latter, the World Bank produces a variable that groups surveys within each individual country into more comparable ‘spells’. Our Data Explorer provides the option of viewing the data with these breaks in comparability indicated.

You can read more about how the World Bank sets these higher poverty lines, as well as the International Poverty Line against which it measures extreme poverty, in our article From $1.90 to $2.15 a day: the updated International Poverty Line . To the three poverty lines adopted officially by the World Bank – $2.15, $3.65 and $6.85 – we add a higher line broadly consistent with definitions of poverty in high income countries. See our article Global poverty in an unequal world: Who is considered poor in a rich country? And what does this mean for our understanding of global poverty?

For details of the methods used to produce the long-run poverty data see, Moatsos, M. (2021). Global extreme poverty: Present and past since 1820. In van Zanden, Rijpma, Malinowski and Mira d’Ercole (eds.) How Was Life? Volume II: New Perspectives on Well-Being and Global Inequality since 1820. Available from the OECD here .

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Why we should think about more than cash when seeking to eradicate poverty

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How to Save Humanity in 17 Goals: end poverty in all its forms everywhere (SDG 1)

Poverty is about more than just meeting basic material needs, says Catherine Thomas. Its corrosive effects are also social and psychological, causing people to feel marginalized and helpless.

Thomas’s research into anti-poverty progammes has focused on the effects of one aimed at women in the West African country of Niger, which aims to support subsistence farmers whose livelihoods are impacted by climate change.

One branch of the programme involved providing an unconditional $300 cash transfer alongside business and life skills training. Thomas, who is based at the Unversity of Michigan in Ann Arbor, describes the impact it had, compared to similar schemes. These include microfinance business loans, but these tend not to reach those most in need, she says.

Thomas’s research is very much focused on the first of the United Nations Sustainable Development Goals, which aims to end poverty in all its forms everywhere by 2030. Each episode of How to Save Humanity in 17 Goals, a Working Scientist podcast series, features researchers whose work addresses one or more the targets. The first six episodes are produced in partnership with Nature Food , and introduced by Juliana Gil, its chief editor.

doi: https://doi.org/10.1038/d41586-024-00208-3

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Juliana Gil: 00:25

Hello. Welcome to How to Save Humanity in 17 Goals , a podcast brought to you by Nature Careers, in partnership with Nature Food . I am Juliana Gil, chief editor at Nature Food . In this series, we meet the scientists who have been quietly, incrementally, working towards the globa development targets set by the United Nations.

Back in 2015, world leaders met in New York at a landmark UN conference. The result? A pledge to solve a range of economic, environmental and social issues.

A package of 17 Sustainable Development Goals were born, targets to be reached by 2030. Since then, in a huge effort, thousands of researchers all over the world have been tackling the biggest problems that the planet faces today.

In episode one, we look at Sustainable Development Goal number one: no poverty, and meet a researcher involved in a project in the West African country of Niger, where she’s helping its poorest citizens in an extremely innovative way.

Catherine Thomas 01:42

So I’m Catherine Thomas. I’m an assistant professor of organizational studies and psychology at the University of Michigan. I have my PhD in social psychology. But I'm quite interdisciplinary.

And I’ve been working in conducting research in anti-poverty programming in different capacities over the past decade or so.

The main goal of my research is to address Sustainable Development Goal number one, eradicating extreme poverty everywhere. Eradicating not only financial precarity, but also the indignities that often accompany poverty, the daily challenges that people face, the excruciating decisions that they have to make.

Poverty is financial. It’s about an inability to meet basic material needs, like food and shelter. But it’s also social and psychological. It prevents people from fulfilling basic psychological needs like the need to belong and be accepted by society instead of facing marginalization. The need to feel control and agency over one’s fate rather than feeling helpless and at the whim of others.

Catherine Thomas 02:55

I would say that three of the types of programs that have gotten the most attention for poverty reduction include microfinance, cash transfers, and what’s called the graduation model, which is what we test in our study.

So that’s a multifaceted program. And let me tell you a bit about, kind of, each of these approaches. So microfinance several decades ago was touted as a panacea for poverty.

There’s a lot of enthusiasm for it. Not only could it make entrepreneurs out of people in these informal economies, and help them grow their businesses, but it would also be a sustainable model. You know, programs could get their money back that they put in.

So there’s a lot of excitement about that. But then, we had kind of a randomization revolution in international aid. And so randomistas, as they’re called, conduct randomized control trials of different programs and policies.

And what they were finding was that microfinance was benefiting some people, but it wasn’t benefiting the people it was intended to serve, which includes women living in extreme poverty.

So it turns out that microfinance does a pretty good job for entrepreneurial men who are at or above the poverty line, but not for these people in extreme poverty.

So it just wasn’t working for the poorest populations. Now, there are two schools of thought, two other schools of thought, on reducing extreme poverty.

The first is that it’s just about capital constraints. And the other is that it's about more than capital. It's multi-dimensional. And so let’s, let’s think about the first line, the first school of thought.

And that school of thought was just a capital constraint. Instead of asking people to repay those loans that they received from microfinance programs, we just give them the cash. It’s an unconditional cash transfer and say, you know, you can do whatever you like with this, this money.

So we've seen that really take hold, particularly in Sub Saharan Africa. And in general, what the literature has found is that cash transfers are highly effective in reducing poverty.

This makes sense at many levels. People in poverty lack money. So let’s just give them money.

Now, there’s a second school of thought, which is that poverty is multidimensional. And one reason, you know, microfinance might not work is because you might, for instance, give people a productive asset.

They might start a business, or they might buy a cow. But if they have a health emergency, and they have to take out loans for that health emergency, or maybe they sell off their productive asset. And then they, to pay for that health emergency, to pay for basic needs, then they fall right back into poverty.

So in this multi-dimensional view, that has borne out programs like the graduation model, which is a multifaceted model, and that really took hold in Bangladesh and South Asia.

So for instance, by encouraging savings groups, by giving people a productive asset, a coach, and trainings for their livelihood. Doing that altogether can help lift households out of extreme poverty, not only in the short term, but also in the long term.

Catherine Thomas 06:24

So Niger is a country in Sub Saharan Africa. Specifically, it’s in West Africa in the Sahel region. So it sits just below the Sahara Desert.

It’s a very arid climate, it’s at the frontline of climate change. That’s actually what prompted the government to invest in this anti-poverty program.

What’s clear here is that subsistence farming is no longer reliable due to climate shocks, like increasing drought.

So the target, the aim of the program, was to help households become more resilient in the face of these climate shocks.

So the typical woman who was participating in this program was about 38 years old, had about seven children. She had no formal education, so is not literate, has no personal cellphone, no business of her own, and no means of transport. So no bike, no motorbike.

Generally, women in these villages rarely leave the village except perhaps to walk to the nearest market.

And so this kind of, you know, reflects a situation of extreme poverty and constraint. The reason I share this is not only to explain the financial precarity that exists here, but also to share that in these remote rural villages the vast majority of resources and opportunities, they all come from women’s social networks,from people they know, from word of mouth.

So this might be close family, extended family, friends, leaders in the village. So her access to capital and economic opportunities that dictates her economic mobility, but so does her relationships and her networks.

So what we were trying to do here was to alleviate extreme poverty. The way that we tried doing that was through three different variations of a multifaceted program.

All the, all three of those programmes included a kind of core package of supports, like savings, a coach, business trainings. But in one of the programs, we also gave a large, unconditional cash transfer of about $300.

In the second one, instead of that cash grant, we provided two psychosocial interventions. So one was a community event designed to introduce the program to the village, and another was a one-week life skills training.

And the third program put it all together in the full package so that included that cash grant along with those two psychosocial interventions. The community incentification and the one week life skills training.

Catherine Thomas 09:15

So the first psychosocial intervention was a community incentivisation. So this included a 20-minute film followed by a group discussion on community aspirations, values and norms.

And the second intervention was a week-long consolidated training, teaching life skills like goal setting, effective communication, problem solving, interpersonal communication, and leadership.

Yeah, the idea behind the video and the communitisation was to show women a different future that might be possible for them, to start to get the wheels turning to think about other examples of women like them who are doing different things, to encourage, you know, new aspirations and also ones that they could see their community supporting, see their husbands supporting, seeing people, kind of getting engaged with, alongside them.

So, you know, they could see that there's a way to do this with support from other people, rather than resistance from them.

And the way you could do this is by bringing everyone up together. So a woman could invest in her business, and use the profits from that to help her husband start a business. She could share those new learnings and those new opportunities with other women. So this constellation, this positive, you know, upward spiral that could bring individual women and their communities up together. That was the goal of the community film and discussion.

So with this life skills training, it was about teaching very practical skills. So thinking about, you know, how you might make a plan for growing your business or what you might do when you encounter problems, how you problem-solved, overcome them, how you might seek out different perspectives, and ask people for different types of advice.

So there are those sorts of very practical skills, but it was also about helping women build self-confidence and their ability to engage in this new activity of all-farm businesses. They might have been buying, for instance, a new cow, and selling that milk to other people in their village.

Or they were buying inputs for a garden, maybe growing tomatoes, selling them, taking those to market, or making a condiment out of the tomatoes and different spices.

Cymbala is a kind of common condiment that they sell there. With agriculture, they might have been generating new inputs, buying fertilizers. They might have been taking their products to more profitable markets further away. They might have been better at negotiating, getting higher prices for their products.

So women were doing a variety of things. And within the village, they also might have been going around selling biscuits made out of millet, for instance, or selling grilled meat.

So you often see women holding a bucket and then holding something up, a plate or something on their heads, and getting customers as they walk by people's houses selling them lunch or snacks.

Catherine Thomas 12:35

At a high level, we found that all three program variants, compared with control, led to reduced poverty and reduced food insecurity.

So all program variants were successful in achieving their intended impacts and, and working towards that first sustainable development goal of ending poverty.

So that was really exciting. We did see some different pathways across the different variants, and also some different levels of cost-effectiveness.

And I’ll share more about that in a minute. But first, let me share some of the results on our primary outcomes: extreme poverty and food security.

So extreme poverty was up to last fall defined as living on about $1.90 or less per person per day. Note that the way that that line is calculated, it’s different from our, from the way that our field team calculated it in our trial.

But if we just use that as a very rough benchmark, what we saw was that in our control condition, households were living on about $1.70 per person per day. However in the three variants, that rose to between $1.82 In the capital arm, $1.88 in the psychosocial arm, and $1.95 in the full arm. So bringing the average household just up to or over that line.

In addition, we saw a 20% reduction in experiences of severe hunger. So for instance, having to go a whole day without food. But what likely led to those outcomes were successful improvements and the targets of the program.

So development and diversification of livelihoods, particularly in developing new off-farm business activities, in addition to investing more in livestock and agriculture.

So those new off-farm activities might include things like petty commerce, like selling utensils, or household wares, or processing farm products for sale, so things like pre-prepared condiments or juices or grilled meat.

So what we saw there was annual business revenues of households rose by about $400 in the capital arm, $440 in the psychosocial arm. So those are in the realm of about 30% increases. And then in that full arm they rose $700, so about 50%, by about 50%.

Catherine Thomas: 14:49

The last thing I wanted to note is that the cost-effectiveness varied by arm. So let’s think about the perspective of the government.

All program variants produced similarly positive effects on reduced poverty and food insecurity. However, that psychosocial intervention did so at a fraction of the cost in its implementation. So for calculating benefit to cost ratios one represents the break-even point, for benefits on household consumption to costs incurred for program implementation.

For the psychosocial arm, the benefit to cost ratio was 1.7, to one full for the full arm is 1.3 to one. And those are both significantly greater than that of a capital arm, which is point eight to one.

These are some of the largest benefit-to-cost ratios documented in the literature on multifaceted anti-poverty programs to date.

Catherine Thomas 15:51

For instance, we might take the example of a woman named Hawa, a woman who wasn't actually able to leave her village very often.

So she had relatively little decision-making control in her household over her business. And so it was very difficult for her to expand her business. She, she actually didn’t interact with other women in her village that often.

But when this program came in, she was able to meet other women regularly outside of her household. That was really exciting for her. She didn’t kind of have solidarity with other women. In fact, she expressed some mistrust of other women in the village.

And we actually see that in our results. We see that more women trusted other people, other women and their husbands, as a result of this program.

Hawa, for instance, might have attended the community incentivisation, she might have brought her couple of young children with her. And she also might have brought her husband. She could have seen this film with other other women in her savings groups and probably talk about it later on in the life skills training that they did.

After the film, she might have talked about it with her husband and other people in her household. And kind of what other households were doing to ensure a safer future for their children, the kind of businesses they were starting. Maybe new businesses, new business ideas that they had, for her, for her husband, from or someone else in the household, maybe a co-wife or a cousin.

And, we did see effects of this in our data. So we saw that, for instance, in the psychosocial arm that women trusted their husbands to do things in line with their own interests. They felt closer to their households.

We also saw that other household members started new businesses. So maybe Hawa’s husband started a charging, a business for charging cellphones while, and that she kind of helped seed fund with her own profits and revenues.

Catherine Thomas: 18:14

I’d like to talk about where we’ve gotten and whether we think that this study, for instance, might contribute to that first sustainable development goal.

So I think it does show a path towards it. I think there are a few conditions that will determine whether it will actually contribute to ending poverty, particularly in Sub Saharan Africa, where it’s most needed.

The first question is about generalizability. Will the results of the study hold in different contexts? This was pretty representative in the whole of Niger.

So I think it would hold in this sort of context. But we have other studies going in different areas in the Sahara that will tell us if it’ll work and those other contexts.

The second is whether the program effects are large enough to actually move households out of extreme poverty. And if they would hold over time. We’ve seen some of that in other studies to date.

So positive growth trajectories at this graduation model, even holding over 10 years. So we're really hopeful that we’d see those effects for this program, as well.

But time will tell on that question. We were seeing that we were able to generally move households up to that poverty line and the effects were about in line with what we can expect, based on other studies.

And the final condition that will tell us whether the goal of ending poverty will be achieved, is about political will. So it’s about if governments and large nonprofits will take up evidence-based programs like this.

I think that one of our most compelling and striking findings was that our program that included psychosocial programming was one of the most cost-effective multifaceted anti-poverty programs documented in this literature to date.

So now hopefully governments will be encouraged to scale these programs. Because not only do they have rigorous empirical backing for achieving the goal of ending poverty, but they also are highly cost-effective.

So I’m really hopeful for innovations in this space going forward so that governments might be able to scale evidence-based cost-effective solutions.

At this point, eradicating extreme poverty is a question of political will. At this point in time, there are about 700 million people around the world living in extreme poverty.

The number of people living in extreme poverty is generally expected to fall globally, but it’s still continuing to rise in Sub Saharan Africa.

Economist Jeffrey Sachs has calculated that the total cost per year to eradicate extreme poverty would be about $175 billion. That’s within reach. It represents just less than 1% of the combined incomes of the richest countries in the world. And my goal as a researcher is to figure out the most efficient and effective ways of helping to attain that goal of eradicating extreme poverty

Juliana Gil: 21:11

Thanks for listening to this series How to Save Humanity in 17 Goals. Join us again next week when we look at Sustainable Development Goal number two: how to achieve zero hunger. See you then.

Sponsor message: 21:40

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Mental health effects of poverty, hunger, and homelessness on children and teens

Rising inflation and an uncertain economy are deeply affecting the lives of millions of Americans, particularly those living in low-income communities. It may seem impossible for a family of four to survive on just over $27,000 per year or a single person on just over $15,000, but that’s what millions of people do everyday in the United States. Approximately 37.9 million Americans, or just under 12%, now live in poverty, according to the U.S. Census Bureau .

Additional data from the Bureau show that children are more likely to experience poverty than people over the age of 18. Approximately one in six kids, 16% of all children, live in families with incomes below the official poverty line.

Those who are poor face challenges beyond a lack of resources. They also experience mental and physical issues at a much higher rate than those living above the poverty line. Read on for a summary of the myriad effects of poverty, homelessness, and hunger on children and youth. And for more information on APA’s work on issues surrounding socioeconomic status, please see the Office of Socioeconomic Status .

Who is most affected?

Poverty rates are disproportionately higher among most non-White populations. Compared to 8.2% of White Americans living in poverty, 26.8% of American Indian and Alaska Natives, 19.5% of Blacks, 17% of Hispanics and 8.1% of Asians are currently living in poverty.

Similarly, Black, Hispanic, and Indigenous children are overrepresented among children living below the poverty line. More specifically, 35.5% of Black people living in poverty in the U.S. are below the age of 18. In addition, 40.7% of Hispanic people living below the poverty line in the U.S. are younger than age 18, and 29.1% of American Indian and Native American children lived in poverty in 2018. In contrast, approximately 21% of White people living in poverty in the U.S. are less than 18 years old.

Furthermore, families with a female head of household are more than twice as likely to live in poverty compared to families with a male head of household. Twenty-three percent of female-headed households live in poverty compared to 11.4% of male-headed households, according to the U.S. Census Bureau .

What are the effects of poverty on children and teens?

The impact of poverty on young children is significant and long lasting. Poverty is associated with substandard housing, hunger, homelessness, inadequate childcare, unsafe neighborhoods, and under-resourced schools. In addition, low-income children are at greater risk than higher-income children for a range of cognitive, emotional, and health-related problems, including detrimental effects on executive functioning, below average academic achievement, poor social emotional functioning, developmental delays, behavioral problems, asthma, inadequate nutrition, low birth weight, and higher rates of pneumonia.

Psychological research also shows that living in poverty is associated with differences in structural and functional brain development in children and adolescents in areas related to cognitive processes that are critical for learning, communication, and academic achievement, including social emotional processing, memory, language, and executive functioning.

Children and families living in poverty often attend under-resourced, overcrowded schools that lack educational opportunities, books, supplies, and appropriate technology due to local funding policies. In addition, families living below the poverty line often live in school districts without adequate equal learning experiences for both gifted and special needs students with learning differences and where high school dropout rates are high .

What are the effects of hunger on children and teens?

One in eight U.S. households with children, approximately 12.5%, could not buy enough food for their families in 2021 , considerably higher than the rate for households without children (9.4%). Black (19.8%) and Latinx (16.25%) households are disproportionately impacted by food insecurity, with food insecurity rates in 2021 triple and double the rate of White households (7%), respectively.

Research has found that hunger and undernutrition can have a host of negative effects on child development. For example, maternal undernutrition during pregnancy increases the risk of negative birth outcomes, including premature birth, low birth weight, smaller head size, and lower brain weight. In addition, children experiencing hunger are at least twice as likely to report being in fair or poor health and at least 1.4 times more likely to have asthma, compared to food-secure children.

The first three years of a child’s life are a period of rapid brain development. Too little energy, protein and nutrients during this sensitive period can lead to lasting deficits in cognitive, social and emotional development . School-age children who experience severe hunger are at increased risk for poor mental health and lower academic performance , and often lag behind their peers in social and emotional skills .

What are the effects of homelessness on children and teens?

Approximately 1.2 million public school students experienced homelessness during the 2019-2020 school year, according to the National Center for Homeless Education (PDF, 1.4MB) . The report also found that students of color experienced homelessness at higher proportions than expected based on the overall number of students. Hispanic and Latino students accounted for 28% of the overall student body but 38% of students experiencing homelessness, while Black students accounted for 15% of the overall student body but 27% of students experiencing homelessness. While White students accounted for 46% of all students enrolled in public schools, they represented 26% of students experiencing homelessness.

Homelessness can have a tremendous impact on children, from their education, physical and mental health, sense of safety, and overall development. Children experiencing homelessness frequently need to worry about where they will live, their pets, their belongings, and other family members. In addition, homeless children are less likely to have adequate access to medical and dental care, and may be affected by a variety of health challenges due to inadequate nutrition and access to food, education interruptions, trauma, and disruption in family dynamics.

In terms of academic achievement, students experiencing homelessness are more than twice as likely to be chronically absent than non-homeless students , with greater rates among Black and Native American or Alaska Native students. They are also more likely to change schools multiple times and to be suspended—especially students of color.

Further, research shows that students reporting homelessness have higher rates of victimization, including increased odds of being sexually and physically victimized, and bullied. Student homelessness correlates with other problems, even when controlling for other risks. They experienced significantly greater odds of suicidality, substance abuse, alcohol abuse, risky sexual behavior, and poor grades in school.

What can you do to help children and families experiencing poverty, hunger, and homelessness?

There are many ways that you can help fight poverty in America. You can:

• Volunteer your time with charities and organizations that provide assistance to low-income and homeless children and families.
• Donate money, food, and clothing to homeless shelters and other charities in your community.
• Donate school supplies and books to underresourced schools in your area.
• Improve access to physical, mental, and behavioral health care for low-income Americans by eliminating barriers such as limitations in health care coverage.
• Create a “safety net” for children and families that provides real protection against the harmful effects of economic insecurity.
• Increase the minimum wage, affordable housing and job skills training for low-income and homeless Americans.
• Intervene in early childhood to support the health and educational development of low-income children.
• Provide support for low-income and food insecure children such as Head Start , the National School Lunch Program , and Temporary Assistance for Needy Families (TANF) .
• Increase resources for public education and access to higher education.
• Support research on poverty and its relationship to health, education, and well-being.
• Resolution on Poverty and SES
• Pathways for addressing deep poverty
• APA Deep Poverty Initiative

Poverty describes the condition of being unable to meet the basic needs of life due to inadequate income or material goods. Read the overview below to gain an understanding of the complexities of poverty and explore the previews of additional articles on this global issue.

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The economic and human dimensions of poverty.

"The Economic and Human Dimensions of Poverty." Gale Essential Overviews: Scholarly , Gale, 2016.

There are a number of overlapping forms of human deprivation that cannot be neatly classified as either causes or effects of poverty. For example, chronic hunger and malnutrition are results of poverty, but they also weaken immune systems, undermine maternal health, encourage an unsustainable approach to resource extraction, and reduce school attendance and the ability of students to learn. Saddled with a constellation of problems such as this, an individual or community faces an extremely arduous climb out of poverty. Although national economic development is rightly the focus of many antipoverty programs, these other overlapping forms of deprivation do not necessarily disappear with economic gains at the national level, as measured by growth in gross domestic product (the total market value of final goods and services that are produced within an economy in a given year).

Moreover, addressing the multiple overlapping components of poverty can bring gains in national economic development beyond those achieved through a strict focus on finance and economics. For example, the gender inequities that characterize many developing societies are frequently an affront to the consensus international view of universal human rights, but they also limit economic development by suppressing the productive potential of women. Finally, economic development programs imposed by outside groups, along with economic policies in the developed world, are themselves sometimes blamed for worsening rather than alleviating poverty in developing countries.

The nature of human and economic development challenges varies from region to region. The countries that have seen the biggest gains in human and economic development since the 1980s, such as China, India, and Brazil, have followed diverse paths rooted in their own unique challenges and institutions, rather than enacting a single authorized set of development strategies such as those commonly recommended by the international community after World War II (1939–1945). Increasingly, international leaders recognize the need for pragmatism, flexibility, and knowledge of a particular country's or region's challenges.

Antipoverty efforts in the countries of sub-Saharan Africa, where extremely low average incomes are the norm, must necessarily take different forms from antipoverty efforts in the countries of Latin America and the Caribbean, which have higher average incomes but greater inequality. Indeed, in spite of the fact that Latin American and Caribbean countries are generally more developed and economically dynamic than sub-Saharan African countries, the poorest people in Latin America and the Caribbean are on average poorer than their counterparts in sub-Saharan Africa. In 2014 the average daily income of people living on less than $1.25 per day was just $0.60 in Latin America and the Caribbean, compared with $0.71 for the same category of people in sub-Saharan Africa (“Trends in Poverty Indicators by Region, 1990–2015,” in World Development Indicators 2014, World Bank, Washington, D.C., 2014.

Thus, as the economies of the developing world continue to grow at rates that outpace those in the developed world, the need to understand the complex dynamics of poverty is becoming ever more apparent. This discussion will survey of some of the key overlapping dimensions of poverty across the world, paying particular attention to variations in the challenges faced by different global regions.

Economic Growth and Employment

Economic growth at the national and regional levels has a strong direct correlation with decreasing poverty levels, although it does not solve all the problems associated with poverty. For example, China's extraordinarily robust economic development since the 1980s accounts for much of the decrease in poverty worldwide since that time.

One of the central facts pertaining to economic growth in the contemporary world is the increasing interconnectedness of national economies. Globalization, as this interconnectedness is generally called, has opened up new markets for products, increased companies' ability to obtain financing and affordable labor, and allowed many companies and national economies to operate more efficiently than in the past. With this interconnectedness, however, also comes shared risk.

The effects of the global economic crisis that began with losses in the U.S. housing and financial markets in late 2007 had a profound impact worldwide for many years thereafter because all of the world's major economies are linked in innumerable ways. Increased unemployment rates and decreased rates of economic growth were widespread in the years that followed, even in many regions of the world that did not experience the underlying economic problems that triggered the crisis. Although wealthier countries such as the United States suffer significantly from such economic downturns, the effects of a faltering worldwide economy can be even more catastrophic in regions whose citizens are already extremely vulnerable to poverty and its many related deprivations.

National Economies

The most rapidly expanding economies in the developing world were growing faster than the economies of the developed world prior to the years of the global financial crisis, and five years after the crisis began, the developing world continued to outperform the developed world in terms of growth percentages. According to the International Labour Organization (ILO), in Global Employment Trends 2014: Risk of a Jobless Recovery? (2014), all regions of the developing world saw their economies grow faster than the economies of the developed world between 2011 and 2013, and this trend was expected to continue through 2015.

Even so, both the developed and developing worlds experienced economic slowdowns during this period, in that the global economic growth rate dipped to 2.9% in 2013, its lowest level since 2009, according to the ILO. At a regional level, the economy of the European Union went stagnant, with a growth rate close to zero, while the economies of central and southeastern Europe and the Commonwealth of Independent States (a group of nine former Soviet states), East Asia, Southeast Asia, and Latin America and the Caribbean also lost momentum. Indeed, between 2012 and 2013 the only regions in which economic growth accelerated were South Asia and East Asia, with a percentage point increase of 0.3 and 0.1, respectively.

Employment and Poverty

Economic growth lifts people out of poverty primarily through better-paying job and business opportunities and through higher overall rates of employment. The progress that had been made in combating global poverty prior to 2008 thus slowed during the crisis years, and unemployment climbed above pre-crisis norms. Globally, 201.8 million people were unemployed in 2013, according to the ILO in Global Employment Trends 2014 , 31.8 million more than were unemployed in 2007, prior to the crisis. The global unemployment rate was 6% in 2013, and although the ILO expects this rate to continue to hover around 6% through 2018, it nonetheless projects actual unemployment numbers to continue rising during these years, as the global workforce grows faster than the rate of job creation.

These figures translated into a global employment-to-population ratio (EPR; the percentage of the total working-age population that is employed) of 59.6% in 2013, which was 1.1 percentage point less than in 2007 (“Table A5. Employment-to-Population Ratio, World and Regions (Per Cent),” in Global Employment Trends 2014: Risk of a Jobless Recovery? International Labour Organization, 2014). EPR values varied significantly by global region. The ILO reveals that between 2008 and 2013 developed economies saw EPR drop from 57% to 54.8%, a decline that was matched by South Asia (the region economically dominated by India), where EPR fell from 56.1% to 53.9%. East Asia (the region economically dominated by China) saw its EPR fall from 68% to 67.5%. Other developing regions saw their EPR values remain constant or rise slightly, with the largest increases in Southeast Asia (from 66.6% to 67.4%) and in Latin America and the Caribbean (from 61.4% to 61.9%).

In all regions of the world, men had substantially higher EPRs than women (International Labour Organization, 2014). The largest gender disparities in employment were in the Middle East and North Africa, in which fewer than 20% of women were employed in 2013, compared with nearly 70% of men; and in South Asia, where roughly 30% of women were employed, compared with nearly 80% of men. The highest levels of female employment were in sub-Saharan Africa and East Asia, in which approximately 60% of women were employed. By comparison, the EPR for women in the developed world was more than 13 percentage points lower than for men.

In many parts of the world, employment does not necessarily provide a path out of poverty. Indeed, sub-Saharan Africa had one of the highest rates of employment in the world in 2013, and yet it was the world's poorest region (International Labour Organization, 2014). According to a 2014 analysis by the ILO, 11.9% of the world's employed people made less than $1.25 per day in 2013 (“Table A14a. Working Poor Indicators, World and Regions (US$1.25 a Day),” in Global Employment Trends 2014: Risk of a Jobless Recovery? ). Also in 2013 in Southeast Asia and the Pacific 11.2% of employed people made less than $1.25 per day, and in South Asia 24.6% of employed people made less than $1.25 per day. The proportion of sub-Saharan Africa's employed population that made less than $1.25 per day in 2013 was 39.2%.

When a poverty threshold of $2 per day is used, these percentages climb dramatically. Indeed, in South Asia and sub-Saharan Africa, employment and poverty routinely go together, as nearly two-thirds of employed people in both regions made less than $2 per day in 2013. The number of extremely poor and the poor as a share of total employed people had been declining since 2000 in all regions of the developing world, and the ILO projects the declines to continue. Nevertheless, these statistics underscore the falseness of one of the most persistent stereotypes about the poor: the notion that poverty is the product of an aversion to work ( Global Employment Trends 2014: Risk of a Jobless Recovery? ).

The employed are more likely to escape extreme poverty in the developed world than in much of the developing world, but even in the United States, the country with more high-paying jobs, on average, than any other in the world, 7% of the labor force (the total number of people who had either been working or looking for work for at least 27 weeks) lived below the official poverty line in 2013 (“Table A. Poverty Status of Persons and Primary Families in the Labor Force for 27 Weeks or More, 2007–2013,” in tistics underscore the falseness of one of the most persistent stereotypes about the poor: the notion that poverty is the product of an aversion to work ( A Profile of the Working Poor, 2013, U.S. Department of Labor, Bureau of Labor Statistics, July 2015). This working-poor rate of 7% marked a significant increase since the start of the global economic crisis in 2007, when 5.1% of those in the U.S. labor force for 27 weeks or more lived in poverty. The working-poor rate in the United States was much higher for young people, African Americans and Hispanics, and women. Nearly a quarter (23%) of all African American women aged 20 to 24 years who had been in the labor force for 27 weeks lived below the poverty line in 2013, as did a comparable proportion (23.6%) of African American men in the same age group (“Tab. People in the Labor Force for 27 Weeks or More: Poverty Status by Age, Gender, Race, and Hispanic or Latino Ethnicity, 2013,” in A Profile of the Working Poor, 2013, U.S. Department of Labor, Bureau of Labor Statistics, July 2015).

The Informal Economy

The term informal economy refers to the exchange of goods and services outside of national and international regulatory guidelines. The informal economy includes all unincorporated nonagricultural businesses that produce marketable goods and services, but it does not include informal work that goes toward producing goods for one's own household.

The ILO notes in “Informal Economy” (2016) that in most countries of the developing world between half and three-quarters of all nonagricultural jobs are informal rather than official and that conditions in such jobs tend to be less than ideal. The ILO states, “Although it is hard to generalize concerning the quality of informal employment, it most often means poor employment conditions and is associated with increasing poverty. Some of the characteristic features of informal employment are lack of protection in the event of non-payment of wages, compulsory overtime or extra shifts, lay-offs without notice or compensation, unsafe working conditions and the absence of social benefits such as pensions, sick pay and health insurance. Women, migrants and other vulnerable groups of workers who are excluded from other opportunities have little choice but to take informal low-quality jobs.”

The informal economy occupies a larger share of all economic activity in developing countries than in developed countries, but informal labor does exist in wealthier countries as well, mostly in the form of self-employment and part-time and temporary work (the latter two are known as nonstandard wage employment). In the United States informal workers include casual laborers, as well as some employees with nonstandard pay arrangements, including those who work “under the table” (they are paid in cash and are not reported as official employees).

Although the informal economy resists objective measurement because of its secretive nature, the ILO has since 2003 worked to implement data gathering and reporting efforts aimed at increasing understanding of the informal economy in the developing world, and it has begun issuing reports based on its findings. In Measuring Informality: A Statistical Manual on the Informal Sector and Informal Employment (2013), the ILO distinguishes between “informal employment,” which includes jobs with unregistered employers as well as unregistered or under-the-table jobs in the formal sector; and “employment in the informal sector,” which includes only those jobs undertaken on behalf of unincorporated businesses. Informal work constitutes more than half of all nonagricultural work in much of the developing world, with the informal sector generally accounting for most informal work. In Brazil, for example, 51.1% of all nonagricultural work was informal, 37.4% on behalf of unincorporated businesses and 17.1% on behalf of formal employers or otherwise outside of the informal sector. In six of the 12 countries for which the ILO presents data, informal work constituted more than two-thirds of all employment, and the figure was over 80% in Mali and India (“Table 2.6. Informal Employment, Employment in the Informal Sector and Informal Employment Outside the Informal Sector, as a Percentage of Total Non-Agricultural Employment in Selected Countries by Sex,”).

Although the informal economy acts as a resource for those left out of the formal economy and is in most cases preferable to unemployment, international organizations generally seek to help developing countries transition away from the informal economy. Besides the risks that workers in the informal economy face, informal economic activity imposes costs at the national level, as the World Bank notes in “Workers in the Informal Economy” (2016). Informal work amounts to a loss in tax revenues that countries might have been able to use to improve infrastructure and social services, and it places an extra tax burden on those who are formally employed. In both human rights and development terms, then, formal employment is preferable to informal employment.

Poverty, Education, and Literacy

Lack of education is one of the strongest indicators of the likelihood that an individual or household will live in poverty. People who are illiterate or have low levels of education are more likely to be unemployed than their better-educated counterparts, and among employed people, less education corresponds with a greater likelihood of remaining in poverty even while working. Additionally, countries with high levels of illiteracy often have correspondingly underdeveloped economies. According to the United Nations Educational, Scientific, and Cultural Organization (UNESCO), in Education for All 2000–2015: Achievements and Challenges (2015), although access to quality education has been greatly expanded in recent decades, much work remains to be done to ensure that all people receive the education they need to find decent work, earn a living, contribute to their community and society, and fulfill their potential. UNESCO explains that “there is simply no more powerful or longer-lasting investment in human rights and dignity, in social inclusion and sustainable development.”

In recent decades, the international community has observed some progress in reducing illiteracy worldwide, with the global rate of illiteracy falling from 24% in 1990 to an estimated 14% in 2015. Still, the overall number of illiterate adults remained stubbornly high, at nearly 780.7 million, as of 2015 (“Tab. Key Indicators for Goal 4,” in Education for All 2000–2015: Achievements and Challenges, United Nations Educational, Scientific and Cultural Organization, 2015). Illiteracy is a problem that is overwhelmingly relegated to the developing world, with 99% of the world's illiterate population living outside of North America and western Europe, and more than half of this population living in South and West Asia. Indeed, between 2005 and 2012 the adult literacy rate in South and West Asia stood at just 63%, the lowest in the world, with the exception of sub-Saharan Africa, where the adult literacy rate was only 59% during the same period. Conversely, the regions with the highest adult literacy rates were Central Asia (100%) and East Asia and the Pacific (95%).

Across the developing world, illiteracy disproportionately affects women. Between 1995 and 2004 women accounted for more than 60% of the adult illiterate population in every region of the developing world except for Latin America and the Caribbean, and regional illiteracy remained fairly constant between 2005 and 2012 (“Tab. Key Indicators for Goal 4,” in Education for All 2000–2015: Achievements and Challenges, United Nations Educational, Scientific and Cultural Organization, 2015). Even so, UNESCO reports that all countries where fewer than 90 women per 100 men were literate in 2000 had made progress toward gender parity. For example, Timor-Leste (or East Timor) moved from only 66 literate women per 100 literate men in 2000 to a projected rate of 89 literate women per 100 literate men in 2015 (“Figure 4.2. Many Countries Are Projected to Make Substantial Gains towards Gender Parity in Adult Literacy by 2015,” in Education for All 2000–2015: Achievements and Challenges, United Nations Educational, Scientific and Cultural Organization, 2015). Chad, Yemen, Bangladesh, and Burundi had also made significant strides toward increasing women's literacy since 2000, although none was projected to achieve gender parity by 2015.

Hunger and Health

Undernourishment and malnutrition.

Undernourishment, or the inability to satisfy dietary requirements either because of an insufficient quantity of food or because of problems with food quality and nutritional value, is largely a problem confined to the developing world. Undernourishment in childhood, and especially the first two years of life, is particularly devastating, causing developmental damage that cannot be undone. Hunger's relation to poverty is reciprocal: poverty usually results in hunger, but hunger is a factor that keeps people in poverty. Deficiencies in nutrients such as iodine, vitamin A, iron, and zinc contribute to weakened immune systems, anemia, learning disabilities, complications in pregnancy and childbirth, and many childhood diseases. These conditions result in poverty-causing problems such as absenteeism and poor performance at school and work, unemployment, illiteracy, and the continuing cycle of poverty.

According to the Food and Agriculture Organization (FAO) of the United Nations (UN), in The State of Food Insecurity in the World 2015—Meeting the 2015 International Hunger Targets: Taking Stock of Uneven Progress (2015), 794.6 million people worldwide were chronically undernourished in 2014–16. This was 216 million fewer undernourished people in the world than in 1990–92, a 21% reduction, even while the world population increased by 1.9 billion during the same period.

Of the world's total of chronically hungry people, 779.9 million lived in developing countries, a figure equal to 12.9% of the developing world's total population (“Table 1. Undernourishment around the World, 1990–92 to 2014–16,” in The State of Food Insecurity in the World 2015. Meeting the 2015 International Hunger Targets: Taking Stock of Uneven Progress, Food and Agriculture Organization of the United Nations, 2015, http://www.fao.org/3/a-i4646e.pdf). However, this also represented significant progress in reducing undernourishment since 1990–92, when the prevalence of undernourishment (PoU) in the developing world stood at 23.3%. Even so, the 2014–16 rate of 12.9% fell short of the Millennium Development Goal (MDG) to halve the PoU between 1990 and 2015, as meeting the goal would have meant arriving at a target of 11.6% of the developing world's population.

As with other development and poverty indicators, progress toward eliminating chronic undernourishment varied widely from region to region and country to country in the developing world. Among all regions of the developing world for which the FAO presented data in its 2015 report, only two saw their PoU increase between 1990–92 and 2014–16: in Middle Africa it rose from 33.5% to 41.3%; and in Western Asia it rose from 6.4% in 1990–92 to 8.4% in 2014–16 ( The State of Food Insecurity in the World 2015. Meeting the 2015 International Hunger Targets: Taking Stock of Uneven Progress, Food and Agriculture Organization of the United Nations, 2015). As a whole, sub-Saharan Africa accomplished a significant reduction in hunger levels over the course of those two decades: the percentage of people in the region who were undernourished fell from 33.2% in 1990–92 to 23.2% in 2014–16. Nonetheless, the 2014–16 percentage remained unacceptably high, resembling or exceeding the 1990–92 hunger levels of most other regions in the developing world.

Most of the total progress at combating hunger in this two-decade period came in the high-population regions of Eastern Asia and Southeastern Asia. Eastern Asia, led by declines in China, saw its hunger levels fall by more than 150 million people during the period, from 23.2% to 9.6% of the region's population ( The State of Food Insecurity in the World 2015. Meeting the 2015 International Hunger Targets: Taking Stock of Uneven Progress, Food and Agriculture Organization of the United Nations, 2015). Southeastern Asia, led by declines in the populous countries of Indonesia, Thailand, and Vietnam, saw a slightly smaller decline in terms of raw numbers, as the chronically hungry population fell from 137.5 million to 60.5 million. However, Southeastern Asia's progress was larger than Eastern Asia's in percentage terms, as the proportion of the hungry in the region fell from 30.6% to 9.6%. Both of these regions exceeded the MDG, reducing PoU by more than half. Latin America and the Caribbean made more modest progress, reducing the number of the hungry from 66.1 million to 34.3 million and the percentage of the hungry fell from 14.7% to 5.5%. Thus, although Latin America and the Caribbean did not quite cut its total population of hungry people in half, it still exceeded the MDG by cutting its rate of hunger by significantly more than half.

Malnutrition refers both to the effects of chronic undernourishment and to obesity. Because maternal undernourishment and/or undernourishment in early childhood can have irreversible cognitive and physical effects, much of the international community's attention to malnutrition focuses on children. The personal and societal burdens created by these forms of malnutrition are at present more significant than those created by obesity, although the rapid growth in obesity rates even in the developing world is cause for great concern among public health officials and experts, particularly because of the correlation between obesity and noncommunicable diseases, such as type 2 diabetes and coronary heart disease.

Besides PoU, another critical indicator for monitoring world hunger is the prevalence of underweight children under the age of five years (CU5). The FAO reports that the developing regions as a whole saw CU5 decline from 27.4% in 1991 to 16.6% in 2013, a 39.4% reduction (“Tab. Prevalence of Undernourishment and Prevalence of Underweight in Children under Five Years of Age: Progress during the MDG Monitoring Period,” in TheThe State of Food Insecurity in the World 2015. Meeting the 2015 International Hunger Targets: Taking Stock of Uneven Progress, Food and Agriculture Organization of the United Nations, 2015). Progress in the reduction of CU5 was uneven from one region to the next. Southern Asia, which has the highest prevalence of underweight children in the world, achieved dramatic progress, reducing CU5 from 49.2% in 1991 to 30% in 2013. Southeastern Asia also made considerable strides, cutting CU5 almost in half (from 30.4% to 16.6%) during the same period. By contrast, sub-Saharan Africa made much more modest gains, reducing the prevalence of CU5 from 28.5% in 1991 to 21.1% in 2013.

The consequences of malnutrition are sometimes quantified in terms of “disability-adjusted life years” (DALYs). One DALY is equivalent to the loss of one year of healthy life, so the number of DALYs and the DALY rate in a country or region provide a picture of the gap between current health conditions and optimal conditions in which people enjoy maximum life expectancy and a disease-free old age. According to the World Health Organization's (WHO) Global Health Observatory data (2016), all regions of the world saw reductions in DALYs between 2000 and 2012. Although the largest decline (32%) was seen in the WHO Africa region, this region still experienced the highest DALYs in the world in 2012, with 740 DALYs per 1,000 population, compared with 273 per 1,000 population in the WHO Western Pacific region, where DALYs were lowest. The WHO also notes a significant global reduction—from 40% in 2000 to 30% in 2012—in the proportion of total DALYs borne by children under the age of 15 years, an indication of the substantial improvements in child mortality rates during the same period.

Child Mortality and Life Expectancy

Poverty is directly correlated with increased mortality and shorter life spans. Diseases that are preventable or treatable in the developed world remain life threatening to many in the developing world because of the lack of affordable health care, sanitation, clean water sources, and other necessities. Although such necessities are widely available to ordinary citizens in the developed world, they are frequently obtainable by only the privileged in the developing world. Additionally, chronic hunger claims the lives of many in the developing world either directly, because of starvation, or indirectly, by weakening the body's resistance to disease.

The UN's Inter-Agency Group for Child Mortality Estimation finds in Levels & Trends in Child Mortality: Report 2015 (2015) that the global child mortality rate fell 53% between 1990 and 2015, from 91 deaths per 1,000 live births to 43. Furthermore, of the 195 countries with available data, 62 (including 24 low- and lower-middle income countries) met the MDG target for Goal 4 to reduce the under-five mortality rate by two-thirds between 1990 and 2015. However, in spite of such remarkable progress the global MDG for child mortality was not met, due in large part to persistently high rates in Southern Asia, sub-Saharan Africa, Oceania, and the Caucasus and Central Asia regions. There were an estimated 5.9 million deaths of children under the age of five years in 2015, most of which were from preventable causes or treatable diseases, and 98.7% of which were in the developing world.

More than 80% of global under-the-age-of-five child mortality occurred in sub-Saharan Africa (49.6%) and Southern Asia (31.8%) in 2015 ( Levels & Trends in Child Mortality: Report 2015, United Nations, Inter-Agency Group for Child Mortality Estimation, 2015). All regions of the developing world saw declines of more than 50% in their child mortality rates between 1990 and 2015, with the exception of sub-Saharan Africa, which achieved only a 24% decline in the under-five death rate, and Oceania, which saw only a 6% decline. In Eastern Asia, led by China, child mortality declined by 88% during this period, well above the MDG. The only other region of the developing world that met or exceeded the MDG was Latin America and the Caribbean, which saw child mortality fall by 69%.

Another indicator that demonstrates the severity of the health gap between the developed and the developing world is life expectancy. As research published by the World Bank in 2015 shows, people in developed countries can generally expect to live substantially longer than those in developing countries (“Life Expectancy at Birth, Total (Years),” in World DataBank: World Development Indicators, ). Between 1990 and 2013 the developed region consisting of European Union countries saw life expectancy rise from 74.9 to 80.4, and North American life expectancy rose from 75.4 to 79.1. The developing regions of East Asia and the Pacific (with a 2013 life expectancy of 74), Europe and Central Asia (72.4), and Latin America and the Caribbean (74.6) each made gains comparable to those in the developed world over the same period, but the 2013 life expectancies in these countries remained comparable or below 1990 life expectancies in the developed world. Life expectancies in the Middle East and North Africa, South Asia, and sub-Saharan Africa had likewise increased by substantial amounts between 1990 and 2013, but the 2013 levels in these countries lagged dramatically behind those in the developed world. South Asia's 2013 life expectancy of 66.9 and sub-Saharan Africa's life expectancy of 56.8 were particularly stark reminders of the destructive effects of poverty on health.

Poverty, Governments, and Globalization

The growing economic interdependence of nations has been one of the most consequential developments in the post–World War II world. Proponents of globalization maintain that opening markets across national borders provides opportunities for both large and small economies. They suggest that freer exchange of money and technology can help develop the world's smaller and poorer economies and therefore help alleviate poverty in developing regions while increasing the wealth of developed ones.

Opponents of globalization argue that the system resulting from this interdependence favors those who are already most advantaged and puts the welfare of multinational corporations above the welfare of poor and indigenous people. Multinational corporations that move into developing countries are often seen to be exploiting the populace in the name of providing opportunities for them. Critics suggest that people in traditional cultures are often denied the ability to sustain themselves by growing their own food, making their own clothing, and maintaining economic autonomy. Also, many claim that with globalization has come an increase in unjust labor practices that take advantage of the poor, such as sweatshops and the use of child labor.

A major facet of globalization is the forging of free trade agreements (FTAs), which are arrangements between countries that allow the exchange of goods and labor across borders without governmental tariffs (taxes on imported goods) or other trade barriers. Two of the best-known FTAs are the North American Free Trade Agreement (among Canada, Mexico, and the United States) and the Central American Free Trade Agreement (among Costa Rica, the Dominican Republic, El Salvador, Guatemala, Honduras, Nicaragua, and the United States). Despite the increasing number of FTAs, poor countries are often subject to higher import tariffs and other unfavorable circumstances when they export goods to developed countries. Also, when these FTAs are signed outside of the auspices of the World Trade Organization, they do not always provide poor countries the opportunity to band together to create more favorable rules for themselves. As a result, the rules that favor rich countries often prevail.

Food Aid and Agricultural Subsidies

Administered by the UN's World Food Programme, the U.S. Agency for International Development (USAID), the European Commission's Humanitarian Aid and Civil Protection department, and other agencies, humanitarian food aid plays an important role in the effort to address global hunger and improve food security. Food aid is provided in response to natural disasters and political crises, when people's regular access to food is threatened. In developing countries food aid may also be administered in nonemergency situations, such as in the form of school lunches intended to boost educational outcomes and thereby contribute to a country's long-term economic development.

In U.S. International Food Assistance Report, Fiscal Year 2013 (September 24, 2014), the most recent year for which data were available, USAID reports that in fiscal year 2013 the U.S. government provided $1.7 billion of food assistance (amounting to 1.5 million tons [1.4 million t] of food) to 46.2 million people in 56 countries. The overwhelming majority (78.9%) of U.S. food aid is directed to Africa, in keeping with the disproportionate prevalence of food insecurity in that region.

Although international food aid programs are genuinely intended to alleviate the immediate suffering and long-term detriments caused by hunger, critics nonetheless charge that the United States and other wealthy countries simultaneously use these programs to gain advantages in food export markets and otherwise bolster their own agricultural industries in ways that undermine local agriculture and economic development in the developing countries that receive the aid. Specifically, the governments of wealthy countries routinely pay farmers—mostly the owners of large farms—and agribusinesses billions of dollars each year to produce too much or not enough of certain crops to control the prices of crops for export or import. In Europe, Japan, and the United States, these farm subsidies are designed to work in conjunction with trade barriers such as quotas (limitations of imports) and tariffs. When farmers in developed countries are paid to overproduce certain foods (e.g., rice and corn), those countries export the surplus to poor countries for extremely low prices or sometimes without charge as aid (this is sometimes called food dumping). Meanwhile, trade barriers prevent poor countries from exporting crops and other goods to wealthy countries (this is sometimes called protectionism).

In some emergencies, such as disasters or wars that disrupt supply chains and destroy crops, the importation of food from the United States and Europe represents the most sensible food-aid option. However, in other cases, even in emergency situations, food is available in the country or region affected, and humanitarian funds could be more effectively spent on these locally or regionally sourced foods, with the added benefit of bolstering the earnings of those farmers.

Additionally, supplying foreign-grown crops beyond the early stages of an emergency may hurt local economies by driving prices down to levels that small local farmers cannot match. Once local farmers are prevented from competing in this way, a country can become perennially dependent on food aid, and farmers instead use their land to produce crops such as cut flowers or livestock feed for export to developed countries. These countries thus become more vulnerable to international economic trends ranging from rising prices in world commodity markets (which can make food aid less available), to falling prices for the commodities they produce for export, and to financial meltdowns such as those that touched off the global economic crisis in 2007. A country that is capable of meeting its own food needs without significant amounts of imported food is, by contrast, better positioned to reduce poverty and increase its levels of human development.

International Debt

Lending and debt relief to underdeveloped and developing nations is another controversial issue. Many low-income countries became heavily indebted to wealthy nations during the 1970s, when banks around the world began lending money to developing countries that were rich in resources such as oil. The money, however, was often mismanaged—particularly in the countries of sub-Saharan Africa—and spent on projects to expand the wealth of the upper classes, rather than used to build the infrastructure and make the social investments necessary for economic development. When interest rates on the loans rose and the prices of oil and other resources fell during the 1980s, the indebted countries were unable to repay the loans. Many of these nations turned to the World Bank or the International Monetary Fund (IMF) for help. These organizations underwrote more loans, but required that the poor countries agree to undergo structural adjustment programs (SAPs).

In essence, the World Bank and the IMF demanded that the poor countries restructure their economies by cutting spending and revaluing their currency so that they could begin to repay their loans and emerge from debt. Most low-income countries met the restructuring criteria by limiting their social spending (e.g., on education, health care, and social services), lowering wages, cutting jobs, and taking land from subsistence farmers to grow crops for export. This focus on increasing trade has generated the most severe criticism from opponents of SAPs, who argue that wealthy countries encourage such measures to improve their own trading opportunities, which destroys the ability of poor countries to support themselves by encouraging dependence on imports of food and other basic necessities. However, supporters of SAPs point out that this economic system allows poor countries to participate more fully in the global market and that the benefits of restructuring will eventually “trickle down” to the poor.

In 1996 the World Bank and the IMF created the Heavily Indebted Poor Countries (HIPC) Initiative. The initiative was intended to reduce the debt of the most heavily indebted poor countries to manageable levels. In 2005 the HIPC Initiative was supplemented by the Multilateral Debt Relief Initiative (MDRI) to help countries make progress toward the UN MDGs.

The MDRI cancels the debt of countries that meet the HIPC Initiative criteria, which include implementing agreed-on reforms and developing a Poverty Reduction Strategy Paper (PRSP; the paper describes the policies and programs that a country will pursue over several years to encourage economic growth and reduce poverty). As a country makes progress toward these goals, a decision point is reached, whereby the International Development Association of the World Bank and the IMF determine whether the country should receive debt relief. If the country is granted debt relief, it may begin receiving interim debt relief at the decision point. Once the PRSP has been adopted and implemented for at least one year, and when other criteria have been met, the country is said to have reached its completion point, and full debt relief is given.

Other Factors Associated with Hunger and Poverty

A number of other factors cause or contribute to poverty and the many forms of deprivation with which it is associated. One of the most common contributing factors for poverty and hunger is a country's system of governance. The World Bank, through its ongoing Worldwide Governance Indicators research project, has compiled several hundred variables and developed indicators that measure six dimensions of a country's governance. Four governance indicators in particular—political stability and absence of violence, government effectiveness, rule of law, and control of corruption—are necessary to achieve hunger reduction in a country. Hunger worsens and per capita food production drops, for example, in countries that are experiencing violent conflict and/or political instability.

Natural disasters, such as droughts, excess rainfall, extreme temperatures, and earthquakes, also cause food crises by slowing food production or halting it altogether. These occurrences have far more serious consequences in poor countries, where food production is already low. Displacement is another consequence of natural disasters that increases the incidence of hunger in poor countries. When people are forced to flee after major disasters such as earthquakes or to migrate because of severe weather conditions, pressure to produce enough food to support them is placed on the areas in which they settle.

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Peer-reviewed

Research Article

Poverty and disability in low- and middle-income countries: A systematic review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft

* E-mail: [email protected]

Affiliation International Centre for Evidence in Disability, Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, United Kingdom

Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing

• Lena Morgon Banks, 
• Hannah Kuper, 
• Sarah Polack

• Published: December 21, 2017
• https://doi.org/10.1371/journal.pone.0189996
• Reader Comments

26 Sep 2018: Banks LM, Kuper H, Polack S (2018) Correction: Poverty and disability in low- and middle-income countries: A systematic review. PLOS ONE 13(9): e0204881. https://doi.org/10.1371/journal.pone.0204881 View correction

Introduction

Disability and poverty are believed to operate in a cycle, with each reinforcing the other. While agreement on the existence of a link is strong, robust empirical evidence substantiating and describing this potential association is lacking. Consequently, a systematic review was undertaken to explore the relationship between disability and economic poverty, with a focus on the situation in low and middle income countries (LMICs).

Ten electronic databases were searched to retrieve studies of any epidemiological design, published between 1990-March 2016 with data comparing the level of poverty between people with and without disabilities in LMICs (World Bank classifications). Poverty was defined using economic measures (e.g. assets, income), while disability included both broad assessments (e.g. self-reported functional or activity limitations) and specific impairments/disorders. Data extracted included: measures of association between disability and poverty, population characteristics and study characteristics. Proportions of studies finding positive, negative, null or mixed associations between poverty and disability were then disaggregated by population and study characteristics.

From the 15,500 records retrieved and screened, 150 studies were included in the final sample. Almost half of included studies were conducted in China, India or Brazil (n = 70, 47%). Most studies were cross-sectional in design (n = 124, 83%), focussed on specific impairment types (n = 115, 77%) and used income as the measure for economic poverty (n = 82, 55%). 122 studies (81%) found evidence of a positive association between disability and a poverty marker. This relationship persisted when results were disaggregated by gender, measure of poverty used and impairment types. By country income group at the time of data collection, the proportion of country-level analyses with a positive association increased with the rising income level, with 59% of low-income, 67% of lower-middle and 72% of upper-middle income countries finding a positive relationship. By age group, the proportion of studies reporting a positive association between disability and poverty was lowest for older adults and highest for working-age adults (69% vs. 86%).

Conclusions

There is strong evidence for a link between disability and poverty in LMICs and an urgent need for further research and programmatic/policy action to break the cycle.

Citation: Banks LM, Kuper H, Polack S (2017) Poverty and disability in low- and middle-income countries: A systematic review. PLoS ONE 12(12): e0189996. https://doi.org/10.1371/journal.pone.0189996

Editor: Jacobus P. van Wouwe, TNO, NETHERLANDS

Received: September 19, 2017; Accepted: December 6, 2017; Published: December 21, 2017

Copyright: © 2017 Banks et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files. Specifically, the S2 File contains the citations of included studies, while S2 Table provides a summarized extraction table of the key data used from each included study.

Funding: This work was funded by the Christoffel Blindenmission (CBM) and the Australian Department of Foreign Affairs and Trade (DFAT), agreement number 70400.

Competing interests: The authors have declared that no competing interests exist.

Globally, it is estimated that 15% of the global population–representing 1 billion people–is living with a disability [ 1 ].

Poverty and disability are believed to operate in a cycle, with the one reinforcing the other. In low- and middle-income countries (LMICs) in particular, conditions associated with poverty, such as lack of access to healthcare, inadequate water and sanitation, malnutrition and poor living conditions, increase the risk of disability [ 2 , 3 ]. Even in the absence of absolute poverty, social inequalities and relative poverty can lead to stress and social exclusion, which can worsen both mental and physical health and functioning [ 4 ]. On the other side, disability can lead to exclusion from work, education and healthcare, as well as high healthcare and other expenses, which can cause or exacerbate poverty [ 1 , 5 – 7 ].

While there is broad agreement of a link between disability and poverty, the empirical evidence for this association is less clear. Typically, a small set of statistics are routinely cited–for example, that people with disabilities are twice as likely as people without disabilities to be living in poverty [ 3 , 8 ]. However, despite their widespread citation, upon tracing to the original source, many such figures were found to be based on decidedly weak evidence [ 8 ].

A key focus of the development agenda, including the 2030 Sustainable Development Goals (SDGs), is the alleviation of poverty in all its forms [ 9 ]. The failure to include disability issues in the predecessor Millennium Development Goals has been recognised as leading to the exclusion of people with disabilities from its benefits, potentially widening inequalities between people with and without disabilities [ 10 ]. Consequently, the SDGs have striven to ensure “no one is left behind” by promoting a stronger focus on disability, including in the call to disaggregate data monitoring progress by disability status.

While the interplay of poverty and disability is increasingly identified as a major limitation to growth and development, the lack of robust empirical evidence to inform policy and programmatic decisions may impede effective action. Efforts to provide a more cohesive understanding of the association between disability and poverty have highlighted a need for further research in this field to both substantiate and describe linkages. A critical review on poverty, health and disability in LMICs conducted in 2011, concludes that while some studies do show strong links, the evidence base is relatively limited and the relationship between poverty, disability and health may be more complex than previously assumed. As acknowledged by the authors, however, this was a non-systematic review which identified a relatively small collection of studies [ 8 ]. Similarly, a review on childhood disability and home socio-economic circumstances in LMICs found that quantitative evidence of an association was inconclusive and inconsistent [ 11 ]. Both of these reviews used only general terms for disability in their search strategies (e.g. “disability”, “handicap”) and did not include terms for specific disability types (e.g. vision impairment, intellectual impairments) and thus may have potentially excluded many relevant studies.

While poverty can take many forms, economic measures (e.g. income, assets, consumption) are the most frequently used in international comparisons and provide valuable information about an individual or household’s well-being, relative or absolute deprivation and ability to meet basic needs [ 8 ]. We have thus undertaken a systematic literature review of empirical studies that compare the level of economic poverty between people with and without disabilities in LMICs. By using systematic methods and extensive search strategies, this review aims to provide a more comprehensive analysis which will build on the existing efforts.

This systematic review explores the relationship between disability and poverty, including whether characteristics such as impairment type, gender or study location modify this relationship. The review was conducted in line with PRISMA guidelines ( S1 Table for PRISMA Checklist) [ 12 ].

Search strategy

The following ten electronic databases were searched in March 2016 for studies assessing the relationship between disability and economic poverty: EMBASE, PubMed, MEDLINE, Global Health, Web of Knowledge, Academic Search Complete, FRANCIS, ERIC, Social Policy & Practice and EconLit. Additionally, references of relevant review articles were checked to identify additional potential studies.

Comprehensive search terms for poverty, disability and low and middle income countries (LMICs) were identified through MeSH/Emtree as well as from those used for systematic reviews on similar topics (see S1 File for sample search string) [ 13 , 14 ]. The search was limited to English-language titles and articles published between 1990- March 2016.

Inclusion/exclusion criteria

Since the purpose of this review focused on the published evidence for a relationship between poverty and disability in LMICs, only papers involving all three of these topics were included. Papers exploring both directions of association between poverty and disability, as well as those in which the directionality was not evident, were included in the final sample. We included studies that assessed disability broadly (e.g. through self-reported functional or activity limitations) as well as studies that focussed on specific impairments or disorders (vision, hearing and physical impairments, intellectual disability and mental disorders) measured using standardised tools or clinical measures. Poverty was restricted to economic measures, namely income, expenditures, assets and/or socioeconomic status (SES). SES measures could include a range of indicators as part of their composition (e.g. housing characteristics, access to services, education level); however to be eligible for inclusion, measures of SES had to include at least one economic indicator (income, expenditures, or assets) [ 15 ]. Poverty could be defined as absolute or relative.

Studies with an epidemiological design were eligible for inclusion. Only studies with comparison groups (i.e. to allow comparison of people with disabilities to people without disabilities) were included. Qualitative studies, review articles and case reports were excluded.

Study selection

Articles were screened by one reviewer (LMB) first by titles, then abstract and then finally by full paper to determine eligibility. Ten percent of the abstracts were dually reviewed by a second reviewer (SP or HK) to check for agreement.

The full-text of all eligible studies were assessed against quality criteria [ 14 ] independently by two reviewers (LMB with either HK or SP; see Table 1 for the quality assessment criteria). Differences between the reviewers were discussed and a consensus was reached on all papers. We excluded studies deemed to have a high risk of bias.

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Data extraction and analysis

Data extracted from the final selection of articles included:

• Study Design
• Method of assessment (poverty and disability),
• Setting (country, site of recruitment),
• Population characteristics (disability type, gender and age)
• Primary research outcome (measure of association between disability and poverty: univariate and multivariate).

In addition, although terms for employment were not included in the search strategy, the association between disability and employment status was recorded as a secondary outcome measure for the studies that conducted these analyses. All extracted values were checked by the second reviewer (SP or HK) to ensure accuracy.

In classifying study outcomes, an association was classified as ‘positive’ if either: a) the disability measured was significantly more common among poorer compared to wealthier economic groups or b) people with disabilities were significantly poorer compared to people without disabilities. Reverse associations (e.g. disability was significantly less common among poorer compared to wealthier economic groups) were categorised as ‘negative’. All classifications of association were made based on statistical significance, after adjusting for confounding (for studies employing multivariate analyses). Consequently, if findings did not achieve statistical significance after adjustment for at least one measure of the relationship between disability and poverty, they were labelled as having ‘null association’. If studies presented more than one measurement of association, it was classified as positive or negative if at least one association was statistically significant and the others were null; if both positive and negative statistically significant associations were found, the study was classified as ‘mixed’.

Proportions of studies finding positive, negative, null or mixed associations were then disaggregated by study characteristics, including disability/impairment type, age group of the sample (children, adults, older adults) and poverty indicator used, to explore whether such characteristics modify any relationship between disability and poverty.

The database search generated a total of 15,500 records (9,494 after duplicates removed and years restricted), of which 7,534 and 1,546 records were excluded in the title and abstract screening, respectively. The full-texts of 415 articles were then assessed for inclusion. Of these 265 were deemed ineligible and 3 untraceable. A further 27 articles were excluded during the quality assessment. An additional 8 eligible articles were identified from reference lists of included articles and other reviews, providing a final sample of 150 studies ( Fig 1 )(see S2 File for included study citations).

https://doi.org/10.1371/journal.pone.0189996.g001

Overview of study characteristics

Table 2 shows a breakdown of study characteristics. The majority of the included studies (almost 90%) were published from the mid-2000s onwards ( Fig 2 : Number of included studies by year of publication). Geographically, the largest number of studies were conducted in East Asia & the Pacific (n = 39, 27%; China = 29) followed by Latin America & the Caribbean (n = 31, 19%; Brazil n = 26), South Asia (n = 26, 20%; India n = 17), Sub-Saharan Africa (n = 22, 15%), Middle East/North Africa (n = 11, 8%) and Europe/Central Asia (n = 4, 3%). Of note, almost half of included studies were conducted in China, India or Brazil (n = 70, 48%). In addition, 16 studies were multi-regional. By country income group at time of data collection [ 16 ], study settings were relatively evenly split (low-income, n = 38; lower-middle, n = 42; upper-middle, n = 48). (See S2 Table for summarised extraction table)

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Concerning study design characteristics, the vast majority (n = 124, 83%) were cross-sectional studies. The remainder were comprised of 11 case-control, 13 cohort studies, one pre-post and one ecological study. The majority of studies recruited participants from the general population (n = 133, 89%), while hospitals/clinics (n = 6), and schools (n = 9) were utilized for the rest. In terms of the study population age groups, 48 studies focused on older adults only (33%), 41 included working age adults only (27%), 23 included children/adolescents only (15%) and 37 included participants across age categories (25%).

The majority of studies (n = 114, 77%) focussed on specific impairment types (e.g. vision or hearing impairment) and most used clinical examinations or standardised, objective assessment tools. However, some studies (n = 33, 23%) used indicators such as self-reported activity or functional limitations that are more in line with the World Health Organisation International Classification of Functioning, Disability and Health model of disability [ 17 ]. Mental disorders (n = 73, 49%) were the most frequently assessed disability type, followed by intellectual/cognitive impairments (n = 23, 15%), functional limitations/mixed impairment types (n = 37, 25%), sensory impairments (n = 14, 9%) and physical impairments (n = 12, 8%).

Income was the most frequently measured indicator for economic poverty (n = 82, 55%). Most studies reported total or per capita family/household income, while a small number reported individual or household head income, satisfaction with income and change in income over the life course. SES was the second most common economic measure (n = 36, 24%), followed by asset ownership (n = 30, 20%). The majority of SES indices were based on ownership of assets and household characteristics while some included other more multidimensional facets such as education, occupation, income, sanitation facilitates and use of services. A smaller number of studies collected data on per capita expenditure (n = 10, 7%).

Risk of bias in included studies.

Of the included studies, 54% were deemed to have a low risk of bias and 46% were medium; a further 27 studies were excluded from this review as they were deemed to have a high risk of bias that was likely to alter their findings related to the relationship between disability and economic poverty.

Major sources of bias across studies included the lack of clearly defined, valid economic and/or disability measures. For disability measures, several studies measured disability through self-report of impairments or clinical diagnoses, or through a binary question on whether the respondent identified as disabled; both of these approaches are considered to underestimate the prevalence of disability, skewing estimates to more severe or “visible” forms of disability [ 18 – 20 ]. For economic measures, some metrics were inadequate to detect finer differences between populations that were mostly poor [ 21 ] or lacked sufficient validation.

Lack of adequate adjustment for confounding was also a concern, as, 20 studies (13%) were bivariate analyses only. Finally, low response rates and non-population based samples, were also sources of bias.

Association between disability and poverty

Overall, the vast majority of studies (n = 122, 81%) found evidence for a positive relationship between disability and poverty. The remainder was comprised of 23 studies (16%) that found no significant association, three (2%) that found a negative relationship and two with mixed findings. The study findings are disaggregated by study characteristics in Table 3 .

https://doi.org/10.1371/journal.pone.0189996.t003

Disaggregation by disability/impairment types.

The relationship between disability and poverty was apparent across all types of impairments/disability.

Of the 75 papers that focussed on mental disorder, 87% found evidence of a positive relationship with poverty. Papers in this category could be subdivided into depression/anxiety (n = 31), common mental disorders [ 22 ] (n = 12) and other (n = 32). For depression, 26 papers found a positive association with poverty, a null association and one study found a negative association between lifetime prevalence of depression and assets in older adults in Nigeria, though the analysis was unadjusted by potential confounders. The relationship between common mental disorders and poverty was positive for ten studies, and null for the remaining two studies. For other mental disorders, 29 found a positive association, two found no association and one study found a negative relationship between per capita expenditure and psychiatric disorders.

Eighteen studies included analyses on sensory impairments, with 12 focusing on visual impairment, two on hearing impairment and three on both. Of these, 14 of 18 studies (78%) found a positive association with poverty. Additionally, three studies found no significant association between visual impairment and poverty; two of these studies performed unadjusted analyses only. One study in Vietnam reported mixed findings, with a positive association between hearing impairment and poverty, but a negative association with visual impairment.

Eighteen of the included studies evaluated the link between poverty and physical impairment. Fourteen of these studies (78%) found evidence of a positive association. The remaining four studies found no significant difference in poverty level between people with and without a physical impairment.

Among the 35 studies with more global measures of disability (e.g. mixed impairment types, functional limitations), 28 (80%) found a positive association with poverty and five studies found no significant difference in poverty between people with and without disabilities. Two studies reported mixed findings and one found a negative relationship.

There were 26 studies which reported on the association between poverty and intellectual/cognitive impairments, of which 69% found evidence of a positive relationship. Most studies in this category (n = 16) focused on dementia and cognitive impairment in older adults. Of these, eight (50%) showed a null association. The other ten studies in this category (all but two of which were conducted in children), all found a positive association.

Eighty-nine studies disaggregated data by either levels of poverty or severity of disability. Of these, most (61 of 89, 69%) found the strength of the association between disability and poverty increased with increasing level of poverty/severity of disability. Four studies with negative associations also found a dose response relationship.

Finally, there was little difference between studies that used impairment-based measures of disability (93 of 114, 81%) compared to those that focused on activity or functional limitations (25 of 33, 79%).

Disaggregation by study setting region and country income group.

By region, studies set in the Middle East & North Africa and East Asia & the Pacific countries were most likely to find a positive relationship between disability and poverty, with respectively 87% and 80% of analyses finding significant associations. In contrast, studies from Sub-Saharan Africa and Europe & Central Asia were least likely to report positive associations, with only 51% and 36% of analyses finding positive associations.

By country income group at the time of data collection, the proportion of country-level analyses with a positive association increased with the rising income level, with 59% of low-income, 67% of lower-middle and 72% of upper-middle income countries finding a positive relationship.

Disaggregation by other factors.

By age group, the proportion of studies reporting a positive association between disability and poverty was lowest for older adults and highest for working-age adults (69% vs. 86%). Studies with mixed age groups–which comprised predominantly working-age adults–were mainly positive (92%).

The positive relationship between disability and poverty was consistent by economic indicator, though it was the least consistent for the nine studies using per capita expenditure as the measure (60% positive).

The majority of studies’ settings included both rural and urban areas (n = 83). For studies limited to either or rural or urban settings, there was little difference in their findings on disability and poverty.

By risk of bias, studies with an assessed low risk were slightly more likely to find a positive association between disability and economic poverty (88% vs 74% for studies with a medium risk of bias).

Finally, while the majority of studies did not disaggregate by gender, for the 30 which did provide separate analyses for men and women (22 disaggregated studies, 8 studies among women only), the relationship between disability and poverty did not differ between men and women (86% vs 87% for men and women respectively).

Evidence on directionality of association.

As 83% of the included studies are cross-sectional, it is difficult to ascertain the directionality of the association between disability and poverty in their analyses. The thirteen cohort studies and one pre-post study however, provide some indication. In all these studies, the focus was on how economic poverty impacted the risk of developing disability and all but one found that lower financial status was associated with an increased risk of developing a disability. Nine studies focused on development of mental disorders among different economic groups, with all but one finding a positive association. Additionally, three studies found a positive link between lower household income and developmental delay in children. Two studies on older adults reported individuals from poorer backgrounds were more prone to functional decline and dementia than their wealthier peers.

No longitudinal studies were identified that explored whether disability could lead to poverty.

Association between disability and employment status.

While this review did not systematically explore the relationship between disability and employment, we did extract data from included studies as a scoping exercise to understand potential drivers of the relationship between disability and poverty.

In total, 35 of the studies included in this review assessed the relationship between disability and employment. Of these, 26 (74%) found a positive association (i.e. disability was significantly more common among non-employed versus employed groups or people with disabilities were significantly more likely to be non-employed compared to people without disabilities). The remaining eight studies found no significant association between employment status and disability, with one finding a negative association.

This systematic review finds strong evidence to support the link between disability and economic poverty, with 122 of 150 (81%) included studies reporting a statistically significant, positive relationship between these two variables. This large and comprehensive review therefore provides a robust empirical corroboration to the more theoretical arguments of a link between disability and economic poverty.

In addition to the large proportion of studies reporting a positive association between disability and economic poverty, other factors in line with the Bradford Hill criteria further substantiate the plausibility of a genuine link [ 23 ]. First, the observed relationship remained significant after authors adjusted for a range of confounders, such as age, gender, area of residence and level of education. Second, the trend of association was mostly consistent across regions, impairment types, study designs and age groups. Third, in the studies which disaggregated data by either levels of poverty or severity of disability, most (61 of 89, 69%) found evidence of dose response: namely, the strength of the association between disability and economic poverty increased with increasing level of poverty/severity of disability. Additionally, as explained through the disability-poverty cycle [ 2 ] and social determinants of health inequalities [ 4 , 24 ], there are plausible mechanisms to explain how disability could contribute to economic poverty and vice versa.

Only five studies found a significant negative association (two of which were mixed) between disability and economic poverty [ 25 – 29 ], and these can be at least partially explained by mitigating factors. First, Pham et al found a significant negative relationship between visual impairment in children and household income, even though analyses of other impairment types in the study showed a positive association [ 25 ]. The finding was explained by the authors as likely resulting from additional schooling in wealthier households, with eyestrain from increased engagement in activities such as reading or using a computer heightening the risk of visual impairment. Second, Kuper et al. reported mixed findings on the association between disability and asset ownership in a multi-country study of children who were part of the Plan International Child Sponsorship Programme [ 26 ]. As criteria for entering into the programme is based on poverty and other forms of vulnerability, the comparator group of children without disabilities may have other characteristics (e.g. ethnic/religious/racial minority, orphans), which may be greater drivers of poverty compared to disability in certain contexts.

Third, Nakua et al. found arthritis/joint pain was more common in higher SES groups in Ghana; however this findings is likely explained by the measure of disability, which was self-report of a clinical diagnosis [ 27 ]. As poverty and poorer access to healthcare are linked [ 30 ], the observed association may be more reflective of the relationship between wealth and receiving needed medical attention. Fourth, Islam et al report an increase in psychiatric disorders with rising per capita household expenditures in Bangladesh [ 28 ]; the authors attributed this finding as potentially due to less familiarity and comfort with interview schedules used to ascertain psychiatric disorders among lower individuals from lower SES groups. Finally, Gureje et al. found a negative association between depression and asset ownership [ 29 ]; however, the analysis did not control for any potential confounders.

Twenty-three studies found no significant association between disability and economic poverty. However, eighteen of these studies found evidence of a positive relationship with other broader indicators of poverty (e.g. education, malnutrition, employment) not covered in this review [ 5 , 31 – 47 ], indicating the value of more multi-dimensional approaches to studying poverty.

While overall the relationship between disability and economic poverty was consistent when disaggregated by a range of study characteristics, some variations were observed. For example, studies set in low-income countries or in certain regions (notably sub-Saharan Africa and Europe/Central Asia) were less likely to observe a relationship between disability and poverty. Some of this variation may be due to challenges accurately and appropriately measuring poverty in complex and varying economies. For example, in settings with high absolute poverty, differentiating between households or individuals may be challenging and the studies may have been under-powered to detect these small differences. Furthermore, accurately capturing true economic well-being in economies defined by the dominance of the informal sector, non-cash remunerated work, irregular flows of income and complex community-based resource sharing arrangements requires careful methodological consideration [ 48 ]. An alternative explanation for these trends is that people with disabilities are “left behind” as regions develop economically, so that the gap in poverty between those with and without disabilities will be larger in areas that are less poor.

Similarly, the strength of the relationship between disability and economic poverty differed slightly by age group. Analyses focused on older adults were slightly less likely to be positive (69%), compared to working-age adults (86%) and children (78%). In particular, dementia and cognitive impairment in older adults was not highly correlated with economic poverty (8 of 16 studies finding a positive association). If onset of disability occurs later in life, these individuals may have established more safeguards to protect against sliding into poverty than individuals who develop disabilities earlier life and face exclusion throughout the life course. Additionally, as economic poverty has been linked consistently to lower life-expectancy [ 24 ], poorer individuals who survive into older age may be healthier than their wealthier counterparts.

While these findings provide clear evidence of correlation between disability and economic poverty, it is difficult in most cases to ascertain the direction of association given that 83% of the included studies are cross-sectional. Fourteen longitudinal studies [ 34 , 49 – 59 ]—most of which focused on mental health conditions–assessed the risk of developing disability among different economic groups; all but one [ 34 ] found a positive association, providing evidence supporting the social determinants of health theory [ 4 , 24 ]. The findings for mental health in particular are corroborated by studies in high income countries [ 60 ], which find the daily stresses associated with lower social and economic position, combined with lower access to healthcare and other services, can increase the risk of mental health conditions.

The high proportion of studies showing a positive relationship between disability and economic poverty observed in this review stands in contrast to other reviews [ 8 , 11 ], where findings were more mixed. Several factors may explain this difference. The search strategy for this study which used terms for both general disability as well as specific impairments/conditions and used systematic searching across multiple databases led to the inclusion of substantially more studies than either of the other reviews, thus greatly broadening the pool from which to draw evidence. Additionally, as the others used multidimensional conceptualizations of poverty whereas this review focused solely on the economic component, the divergence in findings may simply underscore the difference in definitions.

Limitations

There are some limitations that should be taken into account when interpreting the findings of this review. First, if studies showing a negative or null association were less likely to be published–resulting in publication bias–the association between economic poverty and disability could be overestimated. However, as most included papers were not focused explicitly on exploring the relationship between economic poverty and disability and instead either investigated this association as a secondary measure or as part of a multivariable analysis, it is unlikely that this source of potential bias was important. Second, we only focussed on economic definitions of poverty and did not include more multidimensional measures such as access to health, education or food security, which presents a limited view of poverty [ 61 ]. Third, as almost half of included studies were conducted in either Brazil, China or India, the findings of this review may be biased towards reflecting the conditions in those countries, which may differ from other LMICs. Similarly, other country-level factors that could affect the strength of the observed association–such as disability prevalence, availability and access to health and rehabilitation services, social protection and other supports–could not be included in the analysis as reliable, comparable data on these indicators are not available in most countries. Fourth, since the majority of included studies (n = 122, 83%) were cross-sectional, it was not possible to comment on the directionality of association in most cases, particularly of disability leading to decreased economic status. Fifth, the wide range of tools used to measure both disability and economic poverty–which varied in their sensitivity and validity–could affect the comparability and reliability of findings.

Finally, this review likely underestimates the full magnitude of the association between disability and economic poverty. Increasingly, experts are pointing to the need for an adjusted poverty line for people with disabilities to account for additional costs associated with disability incurred as a result of the need for assistive devices, personal supports, extra transport or higher medical/rehabilitation expenses [ 7 , 62 , 63 ]. As recognition of and methods for incorporating extra disability-related costs are underdeveloped, little evidence currently exists on relative poverty between people with and without disabilities taking into account this higher economic threshold needed to meet basic needs.

Implications for future research

On the relationship between disability and economic poverty..

While this review did identify a large number of studies exploring the relationship between disability and economic poverty, there is still need for further research in this area to understand how the relationship changes over time, place and between groups. To improve the quality of research in this area, there is a need for more standardised, robust measures of both disability and economic poverty to enable comparisons across contexts and over time. For example, a major source of bias in studies included in this review was the lack of detail on and reliability of economic poverty measures. This reinforces findings in Cooper et al’s review on measuring poverty in psychiatric epidemiology, which highlights the pressing need for more critical and systematic approaches to assessments of poverty in varying contexts [ 21 ].

Longitudinal studies are particularly needed, especially in measuring the economic impacts after the development of disability as no study identified focused on this direction of association. Furthermore, as both disability and economic poverty are dynamic and can fluctuate across the life-course, understanding the impact of these variations over time is also important.

Other forms of poverty.

While economic poverty is a key metric for understanding and comparing well-being, deprivation and ability to meet basic needs, research exploring the relationship between disability and more multi-dimensional forms of poverty is also needed. By using a range of indicators–such as lack of education and engagement in decent work, inadequate living standards and poor health–multidimensional poverty may better capture the complexity of poverty and in turn assist in informing more nuanced strategies for poverty alleviation and disability prevention [ 61 ].

Furthermore, more research is needed on intra-household poverty. Most economic and many multidimensional measures of poverty use the household as the unit of analysis, which may obscure uneven distribution of resources or opportunities within the household [ 6 ]. For example, limited emerging research indicates that people with disabilities may fare worse compared to other household members on indicators such malnutrition and access to education [ 45 , 64 ]–which could be indicative of unequal allocation of resources or additional barriers to meeting basic needs among people with disabilities. Furthermore, additional research is needed on the extra-costs of disability. In particular, gaps in the evidence include: (1) the overall magnitude and sources of these costs, (2) whether individuals are actually able to afford and access all needed goods and services, and (3) the impact of these expense on functioning as well as social and economic well-being [ 63 ].

“Causes of causes” and appropriate interventions.

While this systematic review has provided clear evidence of a link between disability and economic poverty, further research is needed to understand what Marmot calls the “causes of causes” [ 65 ]: the underlying social, political and economic conditions that give rise to the link between disability and economic poverty. Access to health (including rehabilitation), education and employment may explain some of the relationship between disability and economic poverty, potentially in both directions. While this review identified that people with disabilities were more likely to not be working, since work status was a secondary measure without specific search terms, the observed association–as well as other potential drivers such as access to health and education–deserve further attention in separate systematic reviews. Understanding in greater depth how specific drivers impact the relationship between disability and economic poverty can help identify effective and appropriate interventions and strategies to break the cycle. To this end, attention will need to be given to how drivers vary among individuals and contexts, for example by gender, age and rural/urban settings.

Similarly, more research is needed to understand the impact of economic poverty on the lives of people with disabilities, as well as what existing interventions are effective at reducing poverty among people with disabilities. For example, exploring whether current poverty alleviation and social protection programmes are sufficiently disability-inclusive, as well as the impact of participation in both reducing disablement and/or decreasing poverty among people with disabilities is essential for policy and planning [ 66 ]. Similarly, given the finding in this review of a stronger association between disability and poverty as countries grow economically, it is critical to determine if and why people with disabilities are being “left behind” from the promise of economic growth and development.

Failure to address the interaction between disability and poverty will undoubtedly stall economic growth and development, including in meeting the SDGs. With 81% of studies reporting a link between economic poverty and disability, the results of the systematic review provide a robust empirical basis to support the theorized disability-poverty cycle. Furthermore, as people with disabilities often incur additional expenses related to their disability (e.g. assistive devices, extra transportation) and thus may require a higher minimum threshold to meet basic needs [ 7 ], these findings likely underestimate the true extent of economic poverty among people with disabilities. Considering people with disabilities comprise upwards of 15% of the global population [ 1 ], neglecting to make poverty alleviation and development programmes disability-inclusive bars access to a substantial proportion of the population, significantly reducing their potential impact and enhancing inequalities.

Supporting information

S1 table. prisma checklist..

https://doi.org/10.1371/journal.pone.0189996.s001

S2 Table. Summarised extraction table.

https://doi.org/10.1371/journal.pone.0189996.s002

S1 File. Sample search string.

https://doi.org/10.1371/journal.pone.0189996.s003

S2 File. References of included studies.

https://doi.org/10.1371/journal.pone.0189996.s004

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Integrating Environmental Justice into Child-Sensitive Social Protection: The Environmental Roots of Intergenerational Poverty in Amazonia

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• Published: 14 August 2024

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• Thaís de Carvalho   ORCID: orcid.org/0000-0002-7316-7050 1  

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Child-sensitive social protection (CSSP) is heralded as an investment in future human capital, based on the premise that changing poor families’ behaviours can interrupt cycles of poverty reproduction. However, funding for CSSP may come from extractive activities with high environmental costs for the same families that social programmes aim to support. Reflecting on this contradiction in Peruvian Amazonia, the study explores the tensions between State and parental understandings of impoverishment in an Indigenous village impacted by oil extraction. The findings are twofold: (i) although families are sceptical of CSSP’s potential to enhance children’s prospects, they embrace it as a form of compensation for resource dispossession. (ii) CSSP may fail to lift children out of poverty if it overlooks how environmental degradation engenders intergenerational impoverishment. The article makes a case for the adoption of an environmental justice lens into CSPP, emphasising the need for a more holistic understanding of intergenerational poverty.

La protection sociale sensible à l’enfant (en anglais: “Child-sensitive social protection”, CSSP) est saluée comme un investissement dans le capital humain futur, basé sur la prémisse que le changement des comportements des familles pauvres peut interrompre les cycles de reproduction de la pauvreté. Cependant, le financement de la CSSP peut provenir d’activités extractives avec des coûts environnementaux élevés pour les mêmes familles que les programmes sociaux visent à soutenir. Réfléchissant à cette contradiction en Amazonie péruvienne, l’étude explore les tensions entre les compréhensions de l’appauvrissement par l’État et les parents dans un village indigène impacté par l’extraction pétrolière. Les résultats sont doubles: (i) Bien que les familles soient sceptiques quant au potentiel de la CSSP pour améliorer les perspectives des enfants, elles l’embrassent comme une forme de compensation pour la dépossession des ressources. (ii) La CSSP peut échouer à sortir les enfants de la pauvreté si elle néglige comment la dégradation environnementale engendre l’appauvrissement intergénérationnel. L’article plaide pour l’ adoption d’une perspective de justice environnementale dans la CSPP, soulignant le besoin d’une compréhension plus holistique de la pauvreté intergénérationnelle.

La protección social sensible al niño (CSSP, por sus siglas en inglés) es aclamada como una inversión en el futuro capital humano, basada en la premisa de que cambiar los comportamientos de las familias pobres puede interrumpir los ciclos de reproducción de la pobreza. Sin embargo, la financiación para la CSSP puede provenir de actividades extractivas con altos costos ambientales para las mismas familias que los programas sociales buscan apoyar. Reflexionando sobre esta contradicción en la Amazonía peruana, el estudio explora las tensiones entre las comprensiones del Estado y de los padres sobre el empobrecimiento en una aldea indígena afectada por la extracción de petróleo. Los hallazgos son dobles: (i) Aunque las familias son escépticas sobre el potencial de la CSSP para mejorar las perspectivas de los niños, la aceptan como una forma de compensación por la desposesión de recursos. (ii) La CSSP puede fallar en sacar a los niños de la pobreza si pasa por alto cómo la degradación ambiental engendra empobrecimiento intergeneracional. El artículo aboga por la adopción de una perspectiva de justicia ambiental en la CSPP, enfatizando la necesidad de una comprensión más holística de la pobreza intergeneracional.

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Introduction

In the last two decades, child-sensitive social protection (CSSP) became a widely adopted strategy to address poverty, based on the premise that interventions during childhood could interrupt cycles of intergenerational reproduction of poverty (Barrientos and DeJong 2006 ; Minujin et al. 2017 ). The appeal for CSSP was supported by research demonstrating the lasting impact of monetary and food deprivation on child development, presenting childhood as a crucial juncture for investments in future human capital (Heckman 2000 ; Jones and Sumner 2011 ). Since CSSP aims to ‘maximise positive impacts on children as well as minimise any unintended harm’ (Roelen 2021 , p 369), it can encompass any programme that relieves the economic burden of parents to allow poor families to invest in the future of their children. In its most popular form, CSSP incentivises children’s access to educational and health services through programmes such as school meals and conditional cash transfers (CCTs) (Roelen and Sabates-Wheeler 2012 ).

Social protection programmes targeted particularly rural families, who are overrepresented among the global poor (Fotta and Balen 2018 ; Hanlon et al. 2010 ). These programmes’ attention to household dynamics allowed them to reach children at an unprecedented scale, but also led to an oversight of the structural drivers of families’ economic vulnerability (Barrientos and DeJong 2006 ; Devereux and McGregor 2014 ). Many well-known drivers of rural poverty—such as quality of soil and water, exposure to disasters and health hazards—are connected to the overarching political issue of land use and distribution (Devereux 2001 ; Scoones 2021 ). Yet, the transformation of these structures that promote inequality remains far from the objectives of most social protection programmes (Sabates-Wheeler and Devereux 2008 ).

In Latin America, a key driver of rural poverty is the expansion of extractive industries (Bebbington and Bury 2013 ). These activities not only fuel dispossession but tend to worsen the quality of resources available for peasant families (Bebbington and Bury 2013 ; Robins and Fraser 2020 ). Nonetheless, governments across the region have relied heavily on extractive revenues to fund social programmes, often disregarding the adverse impact of these industries on the same communities that social protection programmes aim to assist (Lang 2017 ; Riggirozzi 2020 ). This study examines this contradiction through a case study of a village in Peruvian Amazonia where social protection programmes coexist with oil extraction.

In contrast to the dominant ‘top-down production of evidence’ (Jones and Sumner 2011 , p 6) that informs CSSP, this research centres Indigenous perspectives on and experiences of intergenerational impoverishment and social protection. The methodology involved eight months of participant observation triangulated by interviews and participatory methods, and all findings were validated after data analysis to ensure that they accurately represented the views of the community. Overall, this study underscores the problem of viewing intergenerational impoverishment as an individual behaviour problem when resource scarcity is rooted in environmental degradation. It also shows that dismissing the loss of environmental capital can undermine CSSP’s efforts to address the intergenerational reproduction of poverty.

Although the importance of environmental capital for sustainable rural economies is widely recognised (Devereux 2001 ; Scoones 2021 ), CSSP programmes often fail to recognise how the loss of this capital ‘compromises the ability of future generations to meet their needs’ (World Commission on Environment and Development 1987 , p 41). Governments and development agencies continue to think of social protection as economic safety nets, rather than mechanisms to promote radical social transformation (Sabates-Wheeler and Devereux 2008 , p 65). Integrating an environmental justice lens into these debates is crucial to shifting this mindset. By linking present resource overuse with future scarcity, environmental justice contributes to highlighting the contradictions of a development model focussed solely on economic growth to the detriment of environmental sustainability (Hiskes 2008 ).

Environmental justice also contributes to foregrounding environmental capital in the analysis of intergenerational impoverishment. The literature shows that resource loss due to overuse and contamination disproportionately affects already marginalised populations, such as income-poor families and peasant communities (Bullard et al 2003 ; Martinez-Alier 2014 ). Moreover, Stephens ( 1994 , p 4) aptly noted that children are like ‘canaries in the mines’ who suffer with environmental contamination long before, and at a much greater scale than, adults. Similarly to monetary deprivation, the degradation of food and water sources can have lasting detrimental effects on children’s cognitive development (Satterthwaite 1996 ). Therefore, the lack of attention to environmental sustainability could end up hindering CSSP efforts to break cycles of poverty reproduction. This reflection is essential for questioning the purpose of social protection, and whether the format of existing programmes can foster social justice (Devereux and McGregor 2014 ; Hickey 2014 ; Sabates-Wheeler and Devereux 2008 ).

The article will start by elucidating the intricate ties between CSSP and environmental justice in Amazonia, to establish the value of this case study for broader reconsiderations of the purpose of social protection. This will be followed by a methodology section that includes a contextualisation of the studied village and all aspects of data collection. Finally, the findings will describe families’ perceptions of CSSP in a village that grapples with oil pollution amidst a changing climate. This study will then examine the environmental causes of resource scarcity in rural Amazonia to establish environmental justice as a critical lens of analysis of intergenerational poverty.

Social Protection and Environmental Justice in Latin America

The links between CSSP and environmental justice are deep-rooted and extend beyond their shared interest in younger generations. During the period between 2000 and 2014, the expansion of social protection networks in Latin America was closely tied to the economic growth propelled by the commodities boom (Balakrishnan and Toscani 2018 ). As major exporters, countries in the region experienced a prosperous decade marked by a substantial rise in government income. This economic bonanza, driven by revenues from extractive royalties and taxation of consumption and export, empowered national governments to invest in social programmes (Sánchez-Ancochea 2021 ). At the time, more than half of the region’s social spending was allocated to cash transfers (Holland and Schneider 2017 ).

The results of these investments were overwhelmingly positive, as monetary poverty in the region plummeted from 27 to 12% in this period (Balakrishnan and Toscani 2018 ). This success contributed to building a consensus that extractive development was the route towards more equitable national development, as it allowed governments to fund mass socio-economic inclusion (Svampa 2015 ; Gudynas 2016 ). However, it also entrapped governments in a fiscal and political dependency of extractive revenues (Riggirozzi 2020 , p 516). This economic model accelerated the pace of resource exploitation and, consequently, the dispossession of peasant families (Arsel et al. 2016 ). The negative impacts of extractive industries were disproportionately felt by marginalised rural communities, such as Indigenous peoples (Bebbington and Humphreys Bebbington 2011 ; Svampa et al. 2021 ).

Poverty and Extraction in the Peruvian Amazonia

The complicated relationship between extraction and social protection is especially evident in the Amazon, a region with historically high poverty rates and significant oil reserves. When commodity prices peaked, the region experienced its own resource boom (Orta-Martínez et al. 2018 ). A large portion of the forest was allocated for the extraction of hydrocarbons, iron ore and gold, or the large-scale production of soybeans and cattle (Merino 2024 ). These activities often took place inside or near Indigenous land, threatening the sustainability of these communities (Robins and Fraser 2020 ). In the Peruvian Amazonia, where this research took place, 80% of the land conceded for oil and gas exploration overlapped with a demarcated or proposed Indigenous territory (Orta-Martínez and Finer 2010 ).

Indigenous peoples had limited power over this resource plunder. Although their right to land was legally guaranteed, the law also asserted the State’s prerogative to pursue profitable activities in the remainder of the forest (see, for instance, Peruvian Government 1978 , Art. 30 and 31). In practice, the law effectively dispossessed Indigenous families of vast swaths of land, limiting their ability to maintain themselves as their populations grew (Chirif and García Hierro 2007 ). The impoverishing effects of this kind of development are evident in the autobiography of Agustina Valera, a Shipibo woman:

Before we used to find animals nearby and in abundance … Before we did not know the food from [non-Indigenous] people. Now we barely see things from the forest because the White people have killed all our animals (Valenzuela Bismarck and Rojas 2004 , p 213—author’s translation)

The context of dispossession was aggravated by the resource contamination that ensued from extractive activities. In the period between 2000 and 2019, Peruvian Amazonia had 474 oil spills that exposed 61% of Indigenous communities to toxic substances like lead, mercury, and cadmium (León and Zúñiga 2020 ). These contaminants cannot be easily cleaned up, as they infiltrate food and water sources and cause health issues ranging from skin rashes to permanent cognitive damage, particularly among children (PUINAMUDT and Campanario 2016 ; Whitehead and Buchanan 2019 ). Subsequently, and contrary to the State’s linkage of extractive revenues with social gains, Indigenous communities found themselves grappling with an incongruency: the supposed economic benefits of oil extraction paled in comparison to the high socio-environmental costs they experienced. This led to heightened conflicts between Indigenous peoples and the State (Fraser 2020 ; Svampa et al. 2021 ).

Because Amazonia is a resource-rich territory, different governments across the region have attempted to frame Indigenous opposition to extractive development as a form of ‘backward’ thinking that needed to be corrected. In Ecuador, president Rafael Correa (2007–2017) argued that Amazonian countries ‘cannot be beggars sitting in a sack of gold’ (cited in Lang 2017 , p 80). Similarly, in Peru, president Alan García (2006–2011) likened native communities to the fable of the dog in the manger, accusing Indigenous peoples of impeding national development for lacking the ability to profit from Amazonia’s valuable resources. 1 By conflating extractivism with poverty eradication, these governments obscured the role that resource extraction plays in impoverishing rural communities by degrading vital resources (Gudynas 2019 ). The classification of Indigenous peoples as poor was also instrumental in framing their assimilation into the market economy as a matter of social justice.

Child-Sensitive Social Protection in the Peruvian Amazonia

In Peru, the creation of CCT Juntos, in 2005, and the school-feeding programme Qali Warma, in 2013, happened amid disputes over oil exploration in Amazonia. The assessment of multidimensional poverty conducted by the government, based on families’ access to goods and services, concluded that Indigenous peoples were in a state of severe deprivation (Correa Aste et al. 2018 ). Despite this, the programmes were not immediately welcomed by Indigenous communities, who watched with distrust the arrival of government staff offering them money (see Sarmiento Barletti 2015 ). In some contexts, parents suspected that the sudden government interest in their children could conceal harmful intentions against their communities (Santos-Granero and Barclay 2011 ). Indigenous families also worried that government benefits were an inducement to facilitate the advancement of resource extraction into their territories (Correa Aste et al. 2018 , p 164). These concerns align with Gudynas’ ( 2016 , pp. 107–108) analysis of CCT programmes as a form of compensation to reduce the social tension in communities affected by extractive activities.

However, even after the proliferation of CSSP, the realities of children in Peruvian Amazonia remained challenging. In 2019, only 35.9% of students completed their secondary education and 59% of children still suffered from anaemia (INEI 2019 ). Research suggests a link between environmental degradation and these poor child indicators. For instance, Espinosa and Ruiz ( 2017 , p 32) argue that climate change aggravates school evasion because families who lose crops to extreme floods need their children’s work to recover from this shock. Likewise, studies of malnutrition in Amazonia relate the persistence of child anaemia to declining fish stocks that are causing a shift in local food systems in favour of chicken farming, and exacerbating iron deficiencies (Heilpern et al. 2021 ). Despite this evidence, environmental justice issues remain largely neglected by current social programmes.

Over the past two decades, existing models of social protection have faced criticism for addressing monetary vulnerabilities instead of transforming the unequal structures that cause them (Hickey 2014 ; Sabates-Wheeler and Devereux 2008 ). The case study of Peruvian Amazonia adds to this debate by underscoring the limited understanding of intergenerational poverty dynamics within current forms of CSSP.

Methodology

Overview of the field site.

This research was conducted in a Shipibo village with 600 residents on a tributary of the Ucayali River in Peruvian Amazonia (Map  1 ). The choice of this field site was motivated by the presence of amenities such as a medical unit, a water well, and three school levels (early childhood, primary, and secondary). These features are rare in native communities but were pivotal in ensuring my safety as a solo researcher conducting extended fieldwork. 2 Additionally, they likely provided access to a wider sample, since students could complete their education within the village.

Location of Shipibo native communities in Ucayali, Source: author 3

While this village is not situated over an active oil drilling site, it is exposed to water contaminants stemming from abandoned oil fields in the Ucayali basin (Cépeda and Lossio 2021 ). A government report from 2018 identified that lead levels in the river surpassed ten times the acceptable limits for human consumption, affecting the quantity and quality of available fish and game (National Authority of Water 2018 ). This, in turn, impacts the ability of families to make a living from the forest. The environmental degradation of Ucayali is compounded by other economic activities such as illegal mining, commercial plantations, and timber extraction, which have caused the region to have the worst deforestation rates in the country (Ramírez et al. 2022 ). In addition to worsening climate change at a global level, deforestation has intensified the scale and duration of seasonal floods in Amazonia (Barichivich et al. 2018 ). This has had a profound impact on the Shipibo, as 90% of their villages are annually inundated during the rainy season (INEI 2017 ).

Consequently, the Shipibo community has grown increasingly dependent on the market economy. This reliance has led them to look for alternative sources of income, such as selling handicrafts, offering ethno-tourism services, or seeking waged job opportunities elsewhere through seasonal migration (Sherman et al. 2016 ). Villages have also altered their crop production, with plantain becoming the primary harvest due to their fast growth in wet terrains and easy marketability (Collado Panduro 2021 ). However, this agricultural change has reduced the diversity of local diets, which are now frequently supplemented with Qali Warma food parcels. Given the modest household earnings, Juntos’ bi-monthly payment of s/200 (US$51), though less than 10% of the national minimum wage, can easily become a family’s main source of income.

Data Collection

The main method of data collection for this research was participant observation, which can be explained as ‘a production of knowledge by being and action’ (Shah 2017 , p 45). The main fieldwork for this project was conducted by the author from July 2019 to March 2020, when the project was abruptly interrupted by the first outbreak of the COVID-19 pandemic. During this time, I joined 73 children at the primary school (with ages ranging from 6 to 16) and regularly visited families in the village in the afternoon. The observations of school and family dynamics were supplemented by unstructured interviews that offered a deeper understanding of events (Hockey and Forsey 2012 ). I also conducted semi-structured interviews with representatives at the local Indigenous organisation and a focus group with Shipibo teachers. In such cases, achieving a balanced gender representation was challenging due to the prevalence of men in power positions, but efforts were made to sample participants based on their roles and experiences.

To ensure the credibility of my findings, I later organised a second round of data collection conducted by two Indigenous research assistants in Shipibo language. This multiple triangulation—the variation of informants, investigators, methods and language of data collection—was done to validate and challenge my original fieldnotes (Laws et al. 2013 ). Given the limited time and budget for this research, I developed a toolkit indicating the methods and questions that should be used in each section. We opted for using drawing with children due to the success of this activity as a method of inquiry in my former fieldwork (de Carvalho 2021 ). Table 1 gives an overview of the samples and methods for this second round of data collection, which took place in July 2021:

Given the specific challenges of doing research with children, the research toolkit indicated a specific order of exercises that should be pursued to make children more comfortable. The order was based on the fact that group-based work can empower children to speak up and reduce age-based power imbalances (Boyden and Ennew 1997 ); however, recordings of in-depth interviews in large groups are generally incomprehensible. Individual interviews also facilitate the observation of children’s creative processes, which is essential to the interpretation of drawings, and are more respectful of privacy around sensitive topics. For the collective exercise, we used collaborative mapping to study children’s thoughts about the environment (Alerby 2000 ). This article also draw insights from the prompt ‘draw a child with a good/bad life’, used during individual interviews to discuss children’s perception of well-being (Camfield 2010 ).

Ethical Aspects and Limitations

Before each stage of data collection, we obtained verbal informed consent from all participants in communal assemblies with the support of the village chief. Consent from children was initially obtained at the school and, later, during individual interactions. All researchers received age-sensitive training to note and respect silent signs of distress or withdrawal during research activities (e.g. when a child erased a drawing). To protect the anonymity of my informants, all names have been replaced with pseudonyms in this article. Additionally, I have avoided any disclosure of the village’s location, respecting the safety concerns of the community.

As a non-Indigenous researcher, I am aware that my identity and background may have influenced how families responded to my questions and how I interpreted and presented their data. To ensure methodological rigour, I made changes in the research team and language of inquiry during the second fieldwork, as described above. The data recorded by the two research assistants was transcribed and translated by a third collaborator—a Shipibo linguist—to ensure an unbiased report of parents’ views. Furthermore, in August 2022, we conducted a validation workshop in the village. This not only fostered a sense of transparency about how we were using people’s data but also warranted that research findings were accurately representing the community’s perspective.

This section compiles the results of both rounds of fieldwork by themes. First, it describes participants’ perceptions and experiences of CSSP programmes. Then, it considers how environmental injustice is linked to impoverishment in rural contexts.

Perceptions of CSSP Programmes

From August 2019 to March 2020, 55 out of the 73 primary school children in the Shipibo community where I conducted fieldwork were enrolled in the Juntos programme, and students of all ages received breakfast and lunch from Qali Warma during weekdays. Parents seemed to embrace the pervasive presence of social protection in their children’s lives and complained if a teacher’s absence from school prevented students from receiving meals, or if Juntos’ payments were delayed. However, when asked if there were any projects in their village to help children, 8 out of 11 parents replied negatively. Even when the question was reformulated to inquire directly about the usefulness of Juntos and Qali Warma, their answers did not change. The words of Rosa, a mother of seven, may help explain this paradox: ‘There is nothing wrong with feeding children, but when they finish school, they will have nothing’.

The reaction of parents was distinct from other studies that documented a rise in aspirations among CSSP recipients (Jones 2022 ). But this scepticism was rooted in a discrepancy between programmes’ goals and their children’s needs. Both Juntos and Qali Warma assumed that improving children’s school attendance would lead to improvements in their cognitive capacity and, consequently, in their human capital (Vásquez 2020 ). Nonetheless, parents argued that incentives for school attendance were pointless given ‘the lack of State support to excel in education’ (Nathan). Instead of paving the way for children to achieve new levels of education, CSSP seemed to be focussing on a task that parents felt capable of doing, as argued by Lizzy: ‘I can support my children. If they want to study, I can make them study as it is’. Believing there were more effective ways to improve children’s education, Jacob questioned: ‘If they have all this money, why don’t they pay for at least one of our children to get a good education?’. These parental concerns echo the findings of programme evaluations indicating that CCTs effectively increase children’s school attendance but have minimal impact on their educational achievements (Baird et al. 2013 ; Gaentzsch 2020 ).

The perception that programmes were poorly aimed was exacerbated by misleading messages during Juntos’ monitoring. An incident at the local primary school can illustrate how easily the actual assessment of conditionalities could deviate from programme guidelines. Instead of adhering to Juntos’ rules, the front-line officer who was visiting the school chose to inspect what he perceived as indicative of misuse of CCT funds: the quality and cleanliness of students’ uniforms and school supplies. Students failing to meet his invented conditionalities were warned of the risk of losing the benefit. This punitive form of monitoring, which itself was contrary to programme rules, seemed to convey that adopting new consumption habits, resembling those of children in an urban school, was the utmost goal of the CCT.

Fotta and Balen ( 2018 ) have contended that CCTs reinforce the discrimination against rural lifestyles by suggesting that the inclusion of recipients in the market economy is a positive and desirable change. This stigmatisation is often symbolised by the enforcement of what Cookson ( 2018 ) calls shadow conditions. This phenomenon was not exclusive to this village, as there is substantial evidence of it occurring elsewhere (Correa Aste et al. 2018 , p 172; Cookson 2018 , p 128, Streuli 2010 ). Shadow conditions create confusion about the purposes of programmes, especially since they can be monitored more frequently than official rules (Escobal and Benites 2012 ). Moreover, they can express discrimination against Indigenous recipients (Ramírez 2021 ). As a father once told me, the behaviour of front-line officers during Juntos’ monitoring visits was illustrative of ‘how Peruvians treat forest peoples’ (José).

A similar message seemed to be present in the Qali Warma programme. Its attempt to replace the local diet with a less nutritious alternative suggested that Indigenous eating habits were inadequate for children (see Ricaud Oneto 2019 ). Ricardo, then president of a large Indigenous organisation in the Ucayali, felt offended by the main premise of the programme:

Nobody says that two cups of masato [fermented manioc beverage], two cups of chapo [sweet plantain juice] and three carachama [Amazonian catfish] with plantain is wealth. But the children who eat this every day are not hungry. They are strong and healthy. Why is this not enough? We know that our children were strong. Now that children eat food from stores, we have more sickness. They should change the formula that measures poverty. What generates poverty is the big industries. (Interview on 5 August 2019)

At the heart of Ricardo’s discontent with CSSP lay a fundamental difference in the understanding of the root causes of poverty. From his perspective, the impoverishment of Shipibo families was intricately tied to the depletion of food resources within their territory, caused by the ‘big industries’ operating in Amazonia. Similar perspectives on poverty have been reported by other Indigenous peoples in the region (see Lang 2017 , p 84; Sarmiento Barletti 2015 ). In contrast, CSSP assumed that malnutrition and poverty were related to a cultural deficit that could be addressed with incentives for new consumption patterns.

The same individualistic view of poverty led CSSP programmes to assume that recipients would achieve self-reliance once their children reached the age of 19 or completed secondary school. While this rule may be logical from an administrative perspective, it was out of touch with the realities of Indigenous recipients, whose livelihoods were unlikely to improve with time. After years of relying on the CCT as a source of income, families would find themselves without any support to face their unchanged circumstances. As expressed by Jessica, a mother: ‘after [children] finish school, they have nowhere else to study, and no [Juntos] money either’. This pessimistic view of their children’s future arose from an awareness of the deteriorating conditions in their village, as summarised by Eduardo: ‘the future is bad, the world is barren and not like before’. To unpack this idea of a ‘barren world’ for children, the next section examines the links between environmental degradation and intergenerational impoverishment.

Environmental Degradation and Rural Impoverishment

Indigenous families who rely on hunting, fishing, or subsistence agriculture often define ‘poverty’ as a scarcity of vital resources (Lang 2017 , p 84; Sarmiento Barletti 2015 ). Environmental risks are another well-known contributory cause of rural poverty, as they increase the likelihood of economic shocks (Devereux 2001 ). The expansion of extractive industries in Amazonia is known to have reduced the availability of resources Indigenous families and their resilience against environmental risks, increasing their dependency on cash (Arsel et al. 2019 ; Fraser 2020 ). This cycle of immiseration can be attributed to (i) the pollution of water resources caused by remnants of hydrocarbon extraction and heightened boat traffic; and (ii) the loss of local food sources due to extreme weather events resulting from climate change and deforestation. Both these phenomena affect Shipibo territories, as detailed below.

Water Pollution

Given their proximity to the river, Shipibo families are particularly exposed to water pollution. As mentioned in an earlier section, a government assessment has detected high levels of heavy metals in the river (National Authority of Water 2018 ), which impacts families’ health and livelihood strategies. All participants in this research acknowledged this problem. During a mapping workshop, all groups of children drew pictures of the village’s riverbank and placed post-its over it saying ‘Today we don’t drink water from the river anymore. It makes us sick’ or ‘There is a lot of trash [in the water]. Lots of plastic and motor liquid’.

The economic repercussions of this pollution can be illustrated by an event that took place in the second week of January 2020, when dark mud with a foul smell appeared in the river after a flood. At first, fish became easier to catch as they swam with their heads partially out of the water, and some even floated dead on the river’s surface. Toddlers and small children went playing with bows, arrows, and spurs to practice their skills, catching abundant fish without needing a canoe. Children would throw fish back into the water if they were already dead but would otherwise take their catch home to eat. Nonetheless, over the next few days, the local nurse would caution mothers that the colour and smell of the water were indicative of heavy metals in the river. People then stopped eating that fish, opting instead for preparing chicken, when they had it, or the canned meat provided by Qali Warma.

The toxic mud incident exemplified how environmental degradation drives families into financial hardship. In this scenario, ‘poverty’ did not stem from a lack of financial resources, but rather from losing the village’s primary food source due to heavy metal contamination. The lack of transparent information about abandoned oil and gas sites obstructs efforts to prepare families to cope with resource contamination more appropriately (Cépeda and Lossio 2021 , p 45), even though crude is an important source of lead pollution in soil, water, and wildlife throughout Amazonia (Cartró-Sabaté et al. 2019 ; Guzmán-Gallegos 2019 ). While impacted families can resort to food and cash transfer programmes as reactive interventions (Devereux 2001 , p 515), the absence of supplementary actions to address the root causes of food scarcity suggests that recipients may be left economically vulnerable once they lose access to these programmes.

Extreme Weather Events

Another critical environmental challenge faced by Shipibo families is the frequency of extreme weather events on their land. While this is an expected consequence of climate change, what is often overlooked is how extractive development directly contributed to aggravate this issue (Hughes 2013 ). Apart from oil being a key source of global carbon emissions, the expansion of extractive activities in the Amazon led to a loss of the forest cover that protected local communities from disasters (Cepek 2012 ; Nobre et al. 2016 ).

Although seasonal inundations are common in the Amazonian floodplains, changes in global weather patterns are exacerbating the severity of these events, and posing a threat to livelihood security in affected Indigenous communities (Sherman et al. 2016 ). The repeated occurrence of disasters that destroyed local food sources has increased families’ dependence on cash, particularly during the rainy season. However, families have little means of generating income within their territories (Torres-Vitolas et al. 2019 ). This prompts concern about the long-term sustainability of climate-affected rural communities, as expressed by Billy, an adolescent father: ‘I worry a bit because of the economy. And everything economically, the floods… Our parents do not have work and that’s why we all suffer’.

The increasing unpredictability and scale of floods threat local subsistence agriculture, rendering the cultivation of less water-resistant crops—such as cassava—unviable. It also makes hunting and fishing harder, as animals tend to move further into the forest, while families depend on canoes for basic locomotion. This precarity motivates many parents to migrate in search of waged jobs, to ensure their children can purchase food at the local shop. The extent of this issue is such that Collado Panduro ( 2021 ), who did a comparative study in four Shipibo communities, noted that 75% of households had at least one relative living elsewhere.

This pattern of parental migration has immediate consequences for children (see de Carvalho 2024 ). As described by Freddy (a father): ‘There are many children that were abandoned by their parents, that live with their grandparents, and these children don’t live well in their homes’. Apart from the emotional toll of family separation, children who stayed in the village described assuming a heavier workload to support their household subsistence. Andrea, a 12-year-old girl, explained that when a child lives alone with their grandparents ‘they must take care of the fields all alone with their siblings’. Some adolescents also migrated with their parents to contribute to supporting younger siblings and older kin. These strategies of adaptation can potentially motivate school evasion and children’s engagement in hazardous labour, directly hindering the achievement of two crucial objectives in existing CSSP programmes.

As argued by Espinosa and Ruiz ( 2017 , p 32), policymakers often interpret the persistence of high numbers of school dropouts and working children in Peruvian Amazonia as a result of the ‘economic difficulties’ faced by Indigenous families. This simplified vision of the problem is often used to justify the importance of cash and food transfers as a form of ‘economic protection’ (Sabates-Wheeler and Devereux 2008 , p 65). There is some value in this approach, as CSSP programmes certainly reduced the toll of losing local food sources. However, the programmes did not prevent adolescent engagement in hazardous labour or children’s heavier subsistence workload.

CSSP programmes operate under the assumption that reducing the financial stress experienced by parents can improve the choices they make for children, enabling the next generation to study and thrive (Vásquez 2020 ). Notwithstanding, this premise may inadvertently lead policymakers to overlook how environmental vulnerabilities restrict parental decisions. That is why Gilson, a Shipibo teacher, stressed the importance of observing the broader context of a village before judging the conditions in which children are raised:

The first thing I do [when working in a new village] is see the context: where and how the population lives, and in which conditions the children are living. In many cases parents have left them, and children are living with their grandparents … If the village doesn’t have fish, there will be none for them either. If we don’t buy food, we will sleep with our belly making noises. (Focus group on 27 February 2020).

To break this cycle of impoverishment in Amazonia, governments should aim to transform the contextual factors that render Indigenous livelihoods unsustainable (Sabates-Wheeler and Devereux 2013 ). This raises the critical question of what this transformation would entail. One possible approach is proposed by Nathan, a father, when asked about his aspirations for his children: ‘I would like us to be able to reforest, to still have our fishes and animals, and our medicinal plants to at least teach children that’.

This study highlights the limitations of CSSP as a strategy to reduce intergenerational poverty in contexts where environmental degradation heightens the risk of impoverishment for younger generations. Currently, CSSP is rooted in the notion that poverty persists across generations ‘because parents fail to invest in the human capital of their children, for reasons of incapacity, irresponsibility or ignorance’ (Devereux and Mc Gregor 2014 , p 301). Programmes aim to break cycles of reproduction of poverty by promoting behavioural changes that would, supposedly, secure better lives for younger generations. However, this simplistic view of poverty obscures broader political and environmental causes of impoverishment in rural contexts. Since the root causes of impoverishment remain unaddressed, recipient families are at risk of becoming more economically vulnerable once their children age out of eligibility for assistance.

Environmental injustice is a key driver of intergenerational impoverishment in Indigenous Amazonia. The subsistence strategies of Shipibo families are severely impacted by water pollution and extreme floods, both of which are aggravated by the extractive development of the forest. This finding is not exclusive to this study. Literature on CCTs in Amazonia has argued that monetary solutions may not be the best way to address the economic vulnerabilities experienced by families who rely on subsistence fishing, hunting, and agriculture (Lang 2019 , p 79; Correa Aste and Roopnaraine 2014 ; Verdum 2016 , p 39). This aligns with claims for more transformative social protection that go beyond economic aid to promote social justice in the long term (Hickey 2014 ; Sabates-Wheeler and Devereux 2008 ).

Integrating a notion of environmental justice into social protection could contribute to this, especially considering the predominantly rural nature of global poverty (Dercon 2009 ). Environmental justice debates focus on how environmental degradation disproportionately impacts socially and economically vulnerable families, emphasising the political and economic drivers of resource scarcity (Bullard et al. 2003 ; Martinez-Alier 2014 ). If initiatives were oriented towards promoting environmental justice, programmes would have to transcend the boundaries of monetary poverty and pay attention to the heightened environmental risks and vulnerabilities experienced by rural families (Devereux 2001 ).

Although Hickey ( 2014 , p.335) cautions that social protection may not always be the best approach towards promoting radical structural transformation, environmental justice is key to ensuring the effectiveness of child-sensitive programmes, particularly in a changing climate. Ideally, CSSP programmes should be designed acknowledging the importance of the environment for ensuring reasonable living standards for younger generations (Hiskes 2008 ; Satterthwaite 1996 ). This alone could spark a conversation about the rationale behind and the best means of financing social protection.

This study has shown that CSSP serves as a coping mechanism for rural families pushed into monetary poverty due to environmental vulnerabilities (see also Devereux 2001 ). However, more could be done to ensure that recipients would be better off after leaving these programmes. In addition, complementary strategies could be used to address environmental vulnerabilities directly. To ensure a more transformative and sustainable action against intergenerational impoverishment, policymakers should consider how to combine CSSP with intersectoral measures aimed at enhancing the sustainability of rural livelihoods.

Two main actions could contribute to this goal. Firstly, policymakers need to confront the inherent contradictions within the current model of development in Latin America. As previously discussed, the proliferation of social protection programmes in the region was closely linked to the commodities boom, and this fact is still used to justify the expansion of extractive activities as a means to fund poverty eradication (Svampa 2015 ). However, extractive development has contaminated rivers and reduced the forest cover that protects rural and Indigenous communities from weather extremes. Hence, policymakers must ensure that the provision of social protection does not inadvertently support a consensus favouring extractive development (Arsel et al. 2016 ). Although providing a solution to achieve this objective is beyond the scope of this paper, some economists have suggested that progressive tax reforms could offer a more sustainable strategy for wealth distribution (Holland and Schneider 2017 ; Sánchez-Ancochea 2021 ).

In addition, governments should consider that addressing the loss of environmental capital can complement and improve the effectiveness of traditional social protection in rural contexts. This would imply setting up rigorous laws to protect the lands of families who depend on subsistence agriculture and their surroundings. It would, however, require a radically different relationship between policymakers and programme recipients, in which beneficiaries would be actively consulted and heard about their needs and struggles.

The current framework of CSSP fails to recognise an important cause of rural impoverishment. The existing programmes focus on promoting behavioural change but disregard how environmental risks and vulnerabilities restrict parental decisions about their children. This study shows that environmental degradation is the main cause of economic hardship for families working on subsistence agriculture. Water pollution, alongside the heightened environmental risks prompted by climate change and deforestation, is exacerbating the impoverishment of Shipibo families and hindering the effectiveness of existing CSSP programmes. The insights from this study transcend the boundaries of Amazonia since rural families are globally overrepresented among those in poverty (Dercon 2009 ).

Considering that the rise of social protection in Latin America was linked to extractive revenues, this research examined the contradictions of providing CSSP to rural families while also worsening their environmental vulnerabilities. In such contexts, the pollution and depletion of these resources cannot be dismissed as a side effect of economic progress; rather, they represent a development paradox. To imagine more sustainable CSSP programmes, policymakers should recognise the connection between the prosperity of rural families and the quality and accessibility of vital resources, such as land and water (Devereux 2001 ). Engaging with environmental justice is crucial to acknowledge and address the disproportionate burden of environmental risks borne by socially marginalised families (Bullard et al 2003 ). It can also draw attention to the importance of environmental sustainability in combatting youth impoverishment.

In practice, being attuned to environmental justice issues would require a shift in focus from individual behavioural change to structural transformation (Sabates-Wheeler and Devereux 2008 ). Rather than perceiving rurality as a signifier of poverty (Fotta and Balen 2018 ), CSSP programmes would have to foster the prosperity of rural youth in any context, including their own communities. In practice, this would entail creating additional interventions to strengthen rural youth’s political and environmental resilience while safeguarding the resources they need to thrive in abundant subsistence economies. As a crucial part of this intersectoral approach, governments should re-examine whether the immediate profits of extractive development justify its high and long-term costs for the most economically vulnerable populations.

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Acknowledgements

This study was funded by the Faculty of Social Sciences at the University of East Anglia, the Tim Morris Award (Education Development Trust), and the Society for Latin American Studies. I am grateful to Laura Camfield and Caitlin Scott for their guidance during this research, and to all the people who made my fieldwork possible, especially Danny Chávez (Panshin Jabe), Gésica Pérez (Yantawi), and Douglas Tangoa (Isa Sina). Thanks also to Arabella Fraser, Paul Fenton-Villar, Keetie Roelen and two anonymous reviewers for comments on earlier drafts of this text.

University of East Anglia, Society for Latin American Studies, Education Development Trust (Tim Morris Award).

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The Increasing Risk of Poverty Across the American Life Course

This article extends the emerging body of life course research on poverty by empirically identifying the incidence, chronicity, and age pattern of American poverty and how these dimensions have changed during the period 1968–2000. Using the Panel Study of Income Dynamics, we construct a series of life tables that estimate the risk of poverty for adults during their 20s, 30s, 40s, 50s, 60s, and 70s, and compare these estimates for Americans in the 1970s, 1980s, and 1990s. Our empirical results suggest that the risk of acute poverty increased substantially, particularly in the 1990s. This observed increase was especially pronounced for individuals in their 20s, 30s, and 40s; for all age groups with respect to extreme poverty; and for white males. On the other hand, the risk of chronic poverty declined during the 1990s (as measured by the percentage of the poor who experienced five or more years of poverty within a 10-year interval). The results in this article tell a very different story than the Census Bureau’s yearly cross-sectional rates, which have shown little overall change in the U.S. poverty rate during this 30-year period. In contrast, a life course approach reveals a rising economic risk of acute poverty for individuals, one that is consistent with recent observations and research suggesting that a growing number of Americans will eventually find themselves in an economically precarious position.

Growing concern has been voiced in both public policy and academic circles regarding the increasing number of Americans that appear to be at economic risk. Analysts point to a number of indicators and patterns over the past three decades to support this claim: job security has weakened ( Fligstein and Shin 2004 ; Uchitelle 2006 ), more Americans are without health care ( DeNavas-Walt, Proctor, and Smith 2008 ; Quadagno 2005 ), income volatility and downward mobility has increased ( Gosselin 2008 ; Hacker 2006 ), the social safety net has been seriously eroded ( Hays 2003 ; Zuberi 2006 ), men’s earnings have stagnated ( Blank, Danziger, and Schoeni 2006 ; DeNavas et al. 2008 ), income and wealth inequality have widened ( Smeeding 2005 ), and the level of consumer debt has reached record levels ( Federal Reserve Board 2008 ; Warren and Tyagi 2003 ).

On the other hand, not all indicators or studies find that economic risk and vulnerability have been on the rise. For example, the 1990s saw a significant reduction in the welfare rolls that was partially attributed to economic growth during the mid- to late 1990s ( Iceland 2006 ). Likewise, measuring poverty with respect to levels of consumption rather than levels of income has shown an overall reduction from the 1970s through the 1990s ( Meyer and Sullivan 2004 ; Slesnick 1993 ). Additionally, analyses by Gottschalk and Moffitt (1999) using the Survey of Income and Program Participation (SIPP) indicated that, contrary to other findings, job instability and insecurity did not increase in the 1980s or 1990s. Finally, research by Gottschalk and Danziger (1998) and Hertz (2007) suggested that income mobility changed little during the 1980s and 1990s.

A key indicator of economic risk that is important to this debate is the likelihood of experiencing poverty. Between 1970 and 2007, the official U.S. poverty rate averaged between 11% and 15% ( DeNavas et al. 2008 ). These cross-sectional rates have risen slightly during periods of economic recession (such as in the early 1980s, early 1990s, and early 2000s) and have fallen somewhat during periods of economic expansion (mid- to late 1980s, and mid- to late 1990s). Yet, what is most striking about the patterns of U.S. poverty has been the relative stability of these rates over the past 35 years ( Hoynes, Page, and Stevens 2006 ). For example, the poverty rate of Americans aged 18–64 was 9.0% in 1970, 10.1% in 1980, up slightly to 10.7% by 1990, down to 9.6% in 2000, and 10.9% in 2007 ( DeNavas et al. 2008 ).

If one takes the position that more Americans appear to be economically vulnerable, why then have we not seen more individuals experiencing poverty within the past 20 years or so? We address this question by employing an alternative approach to measuring adulthood poverty. We construct a series of life tables that estimate the likelihood of various age groups experiencing poverty, and in particular, we look at how this life course risk has shifted over the past three decades. The evidence in this article is intended to provide an important piece of information to further inform the debate about whether economic risk in America has been on the rise.

A LIFE COURSE APPROACH TO UNDERSTANDING POVERTY

The concept of the life course has had a long and distinguished history in the social and applied sciences ( Dewilde 2003 ; Elder 1994 ; Moen, Elder, and Luscher 1995 ; Riley 1999 ; Settersten and Mayer 1997 ). It has provided a very useful framework for thinking about how individual lives unfold and how particular events and transitions affect these trajectories ( Elder 1995 ; Voyer 2004 ). The term life course itself generally refers to “social processes extending over the individual life span or over significant portions of it, especially [with regard to] the family cycle, educational and training histories, and employment and occupational careers” ( Mayer and Tuma 1990:3 ). In addition, as Settersten and Mayer have argued, “While these dimensions describe the primary activities across life, a more complete picture of the life course must also include more marginal periods and events—such as brief periods of training, second or part-time jobs, periods of unemployment or sickness” ( 1997:252 ). The event of poverty should also be added to this list.

Interestingly, several of the earliest social scientific studies that examined these more marginal periods incorporated a life course perspective. Rowntree’s (1901) description of 11,560 working-class families in the English city of York was pioneering in developing this approach. Rowntree estimated the likelihood of falling into poverty at various stages of the life course (based on their household economic conditions in 1899). His research indicated that working-class families were more likely to experience poverty at particular stages during the life cycle when they were economically vulnerable (e.g., when starting a new family or during retirement). Likewise, Hunter (1904) in his book, Poverty , attempted to place impoverishment within the context of the life course. As Rowntree did, Hunter viewed poverty as a critical life event tending to occur for working-class families at several points during their life course.

From these early pioneering studies to the present, several key concepts have been instrumental in shaping the approach taken by life course researchers. They overlap to some extent with the classic demographic emphasis placed on the importance of age, period, and cohort effects. They include the dimensions of risk, historical context, and stages of the life course; and they emphasize the importance of particular characteristics, such as race and gender, that influence the trajectory of the life course.

Heightened Risk

One of the key points to emerge from a life course approach to studying poverty is that the risk of experiencing poverty across the life course is much greater than typically found in cross-sectional studies or in longitudinal work of limited duration. For example, Rowntree’s analysis suggested that a life course perspective could more fully reveal the widespread nature of poverty than a point-in-time approach. As he noted, “The proportion of the community who at one period or another of their lives suffer from poverty to the point of physical privation is therefore much greater, and the injurious effects of such a condition are much more widespread than would appear from a consideration of the number who can be shown to be below the poverty line at any given moment” ( 1901:172 ).

Indeed, contemporary research has found that the life course risk of poverty is substantial. For example, the work of Rank and Hirschl has demonstrated that the lifetime risk of experiencing poverty at some point during adulthood is exceedingly high. Between the ages of 20 and 75, 58% of Americans will experience at least one year below the official poverty line, while 75% will encounter a year below 150% of the poverty line ( Rank and Hirschl 1999c ). Furthermore, two-thirds of Americans will rely on a means-tested safety-net program between the ages of 20 and 65, and 40% of Americans will use such a program in five or more separate years ( Rank and Hirschl 2002 ). Yet, most Americans who experience poverty or the welfare system do so for only one or two years at a time.

Similar findings have been observed cross-culturally as well. For example, Leisering and Leibfried wrote with regard to their life course analysis of poverty in Germany,

Poverty is no longer (if ever it was) a fixed condition or a personal or group characteristic, but rather it is an experience or stage in the life course. It is not necessarily associated with a marginal position in society but reaches well into the middle class. Poverty is specifically located in time and individual biographies, and, by implication, has come to transcend traditional social boundaries of class (1999:239).

These life course results compliment and are consistent with longitudinal spell analyses of poverty, which suggest that most spells of poverty are fairly short, often recurring, and can affect a wide segment of the population ( Bane and Ellwood 1986 ; Blank 1997 ; Cellini, McKernan, and Ratcliffe 2008 ; Duncan 1984 ; Stevens 1999 ; Walker 1994 ). This body of work has also shown that events leading households into poverty include the loss of jobs or cutbacks in earnings, family dissolution, and/or medical problems ( Duncan et al. 1995 ; Iceland 2006 ; McKernan and Ratcliffe 2005 ). Over extended periods of time across the life course, these events have a much greater probability of occurring, and hence the likelihood of experiencing a spell of poverty is much greater.

This notion of risk is potentially important in understanding why cross-sectional rates of poverty may have changed very little over several decades while the life course risk of poverty may have increased substantially. A very small change in the yearly risk of poverty from one decade to another could potentially result in a much greater change in the likelihood of encountering poverty across the life course. For example, a small upward change in the risk of poverty from the 1980s to the 1990s could result in a much greater change in the life course risk of poverty if this small increased risk is multiplied over a series of years. 1

Alternatively, the cross-sectional rate of poverty could remain the same but the life course risk could potentially increase if a wider range of individuals in the population experienced poverty. For example, if the poverty rate in the 1980s and 1990s was 12% each year but the turnover from year to year in terms of who was experiencing poverty was much greater during the 1990s, then the life course risk would be substantially higher in the 1990s than in the 1980s.

Historical Context

A second important factor in applying a life course framework to an analysis of poverty is the importance of historical context in influencing the occurrence of events and the trajectory of lives. One of the ongoing concerns of life course researchers has been to analyze how the historical context, and changes in that context, can alter and shape the manner in which individuals’ lives unfold ( Hutchison 2005 ). The classic example of this type of research is Elder’s (1974) examination of the impact of the Great Depression in the 1930s on the lives of children as they have aged.

One of our primary concerns in this article is to examine whether the life course risk of poverty increased across the period 1968–2000. If it has, this would represent a critical and neglected component to the overall argument and debate that there has been a rise in the level of economic vulnerability during this time for American households.

As noted at the beginning of the article, a number of analysts have argued that individual Americans are more economically vulnerable than in the past. For example, Hacker’s research documented the increasing prevalence of income volatility, particularly downward mobility. Using the Panel Study of Income Dynamics (PSID), Hacker (2006) demonstrated that income instability in the mid-1990s was nearly five times higher than in the early 1970s. He noted that such patterns of rising income instability and insecurity mirror an overall trend in the United States: “As both employment-based social benefits and government programs have eroded, social risks have shifted from collective intermediaries—government, employers, large insurance pools—onto individuals and families” ( Hacker 2004:252 ). Hacker argued that although this shift and its accompanying economic insecurity began in the 1970s and continued through the 1980s, the 1990s saw a sharp increase in economic risk as measured by several different indicators. Additional research has also suggested that economic insecurity and income volatility increased significantly in the 1990s ( Bania and Leete 2007 ; Comin, Groshen, and Tracy 2006 ; Dynan, Elmendorf, and Sichel 2008 ; Gosselin 2008 ).

Conversely, as mentioned earlier, other research has called into question whether economic insecurity has in fact risen during this period. For example, in contrast to the findings reported by Hacker with respect to income volatility, an analysis by the Congressional Budget Office (2007) reported that earnings volatility has remained basically constant from the mid-1980s onward. Although it is difficult to precisely determine the extent to which these contradictory studies are the result of differences in methodology, data, or analytical techniques employed, the reported differences may be partly the result of different approaches to measuring income variance (for a discussion of this issue and an overall review of the body of research on rising income instability, see Hacker and Jacobs 2008 ). In addition, contradictory studies may simply reflect different components of economic risk, some of which have risen over time while others have not.

In the present analysis, we look at a particularly important measure of economic risk that has been neglected in this debate: the life course risk of poverty. Our concern is understanding the extent to which this risk has changed over the later decades of the twentieth century. Consequently, incorporating historical time into our analysis would appear to be critical in understanding the changing life course dynamics of American poverty.

Stages of the Life Course

A third key attribute to understanding poverty from a life course framework is the notion that there are certain points and stages during the life course when individuals are more vulnerable to poverty. This, again, was one of the central points of Rowntree’s pioneering research into poverty at the turn of the century in Great Britain. In particular, those in the early and later stages of life have historically been at greater risk of experiencing poverty and near poverty. For example, the 2007 rates of falling below 150% of the poverty line with respect to age were as follows: under 18, 29.3%; 18–24, 27.7%; 25–34, 21.4%; 35–44, 16.6%; 45–54, 14.5%; 55–59, 13.7%; 60–64, 16.4%; 65 and older, 23.1%; and 75 and older, 27.1% ( U.S. Census Bureau 2008 ). 2

Research has shown that during the early and later periods of adulthood, earnings tend to be at their lowest point, while resources and assets to buffer the detrimental events discussed earlier may be in short supply ( Modigliani and Brumberg 1954 ). In contrast, individuals in their 40s and 50s are often at the height of their earning capacity, while their portfolio of assets and resources has grown as well ( Gourinchas and Parker 2002 ; Keister 2000 ; Kennickell and Starr-McCluer 1997 ; Mirowsky and Ross 1999 ; Rigg and Sefton 2004 ). As a result, poverty tends to be at its lowest point during these periods of the life course.

Characteristics That Can Shape the Life Course

A final element to consider in a life course analysis of poverty is the importance of key characteristics that have been demonstrated in prior research to have a profound influence on a variety of life course events—specifically the importance of race and gender. In both cross-sectional and longitudinal analyses, race has been shown to exert a powerful influence on affecting the likelihood of poverty. African Americans and Latinos are much more likely than whites to experience poverty at any point in time and are more likely to encounter lengthier spells in poverty ( Blank 1997 ; Cellini et al. 2008 ; DeNavas et al. 2008 ; Iceland 2006 ). In addition, the likelihood of poverty across the life course has been shown to be much greater for nonwhites than for whites ( Rank 2009 ). Rank and Hirschl demonstrated that whether one examines the years of childhood ( 1999a ; forthcoming ), the working age years ( 2001a ), or the later years of life ( 1999b ), the risk of poverty is substantially greater for blacks than for whites. For example, they found that although 1 out of 2 whites aged 20–75 would experience a year below the poverty line, the corresponding figure for blacks was 9 out of 10 ( 1999c ).

Gender has also been found to be an important attribute associated with poverty. Women in general, and female-headed families in particular, have been shown to be at a much greater risk of poverty than their male counterparts ( DeNavas et al. 2008 ; McLanahan and Sandefur 1994 ). However, when looking across the entire life course, Rank and Hirschl (2001b) showed that the effect of gender is partially mitigated by the fact that women spend a significant amount of time married, which results in poverty rates identical to those of their husbands during those years.

To summarize, four concepts critical to understanding poverty from the perspective of the life course are increased risk, historical change, stages of the life course, and the factors of race and gender. First, the risk of poverty is far more likely when it is examined across the life course than when it is examined in cross-sectional analyses or for limited longitudinal periods of time. Second, the risk of poverty during the life course can be substantially altered as a result of differences across historical periods, with such changes often more readily apparent in a life course framework than in a cross-sectional analysis. Third, the risk of poverty can vary dramatically depending on one’s stage of the life course. Finally, the occurrence of poverty across the life course can also be strongly influenced by race and gender.

We bring each of these four key elements to bear in our analysis. Our intention is to examine the risk of poverty for individuals as they age across their 20s, 30s, 40s, 50s, 60s, and 70s, and to determine how that risk changed during the 1970s, 1980s, and 1990s. In addition, we will explore the impact of race and gender on the life course risk of poverty.

This study adds to the earlier work of Rank and Hirschl, who pooled together the PSID study waves from 1968 to the mid-1990s in order to examine the overall life course risk of poverty. Here we estimate the extent to which the life course risk of poverty has changed across the recent decades and how that change varies by stages of the life course and by race and gender. Furthermore, this work expands upon the current body of poverty research by introducing an alternative way of conceptualizing and measuring the extent of poverty and by demonstrating that this approach can reveal patterns not readily apparent in a cross-sectional or longitudinal spell analyses.

In order to assess the changing life course dynamics of poverty over time, we utilize the Panel Study of Income Dynamics (PSID). The PSID began in 1968 as an annual panel survey (biennial after 1997) and is nationally representative of the nonimmigrant U.S. population. The longest running panel data set in the United States, the PSID oversamples low-income households and contains in-depth information on family demographic and economic behavior, making it uniquely suited for this study. The PSID initially interviewed approximately 4,800 U.S. households in 1968, collecting detailed information on roughly 18,000 individuals within those households. The PSID has since tracked these individuals, including children and adults who eventually broke off from their original households to form new households (e.g., children leaving home, separations, divorce). Thus, the PSID is designed so that in any given year the sample is representative of the entire nonimmigrant U.S. population (for detailed information regarding the PSID sample and its representativeness, see Duncan, Hofferth, and Stafford 2004 ; Fitzgerald, Gottschalk, and Moffitt 1998 ; Kim and Stafford 2000 ; and PSID 2007 ).

Throughout the analysis, we employ the individual sampling weights to ensure that the PSID sample accurately reflects the U.S. population. Specifically, we utilize the weights assigned to individuals for each given wave to take advantage of the PSID practice of periodically adjusting the weights to account for nonresponse bias ( Hill 1992 ). Over time, the PSID has experienced greater attrition among those of lower socioeconomic status. However, as Fitzgerald et al. (1998) pointed out, there is no evidence that this attrition has distorted the representativeness of the PSID; rather the authors reported that there is “considerable evidence that its cross-sectional representativeness has remained roughly intact” ( 1998:251 ).

We utilize both the household and individual levels of information from the initial wave of 1968 through 2000. Consequently, we draw upon 33 years of longitudinal information, which translates into several hundred thousand individual years of information embedded in the analysis. Our analysis does not include data from the supplemental Latino sample that was gathered from 1990 to 1995, but it does include the immigrant refresher sample introduced in 1997. 3

Although the PSID began interviewing households biannually after 1997, income data have been gathered every year. Consequently, each calendar year in our analysis continues to have its own unique poverty and demographic information for PSID households. 4

A second change occurring in 1997 was that the PSID sample size was reduced for cost management reasons ( Duncan et al. 2004 ). The original core sample was reduced from approximately 8,500 in 1996 to 6,168 in 1997 ( Duncan et al. 2004 ). As noted above, the sample weights are used throughout to ensure that the sample continues to represent the overall population and that the reduction in sample size does not bias our estimates ( Gouskova et al. 2008 ).

Measuring Poverty

The measure of poverty used in this analysis is identical to that employed by the U.S. Census Bureau in estimating the overall U.S. poverty rate ( DeNavas et al. 2008 ). Total family income is used to determine whether individuals fall below the poverty line. This encompasses the full range of components that the Census Bureau relies on, including earnings generated by the head, the spouse, and other family members; property income; government cash transfers, such as Social Security or welfare; pensions and annuities; child support; and dividends and royalties. Family income is based on annual income, calculated from pretax dollars, and does not include in-kind program benefits, such as Medicaid or food stamps. 5

Families below specific income levels are considered poor. These levels represent what is considered the least amount of income needed for a family to purchase a minimally adequate basket of goods (e.g., food, clothing, and shelter) throughout the year. The poverty thresholds are adjusted each year in accord with changes to the consumer price index.

The level itself varies depending on family size. For example, in 2007, a family of one was considered poor if its income fell below $10,590; a family of two was counted as poor if its income was less than $13,540; for a family of three, the level was $16,530; and a family of four was considered poor if its income fell below $21,203 ( DeNavas et al. 2008 ).

One major reason for using the official poverty level as our dependent variable is that it represents the measure most used in policy and academic discussions of this topic. Although periodically debated and criticized ( Blank 2008 ; Brady 2003 ; Iceland 2005 ; Meyer and Sullivan 2003 ; National Research Council 1995 ; Ruggles 1990 ), it remains the benchmark in America for judging impoverishment. Furthermore, to facilitate a comparison of our results with the prior trends in poverty, it is essential to use the official measure of poverty. In addition, Danziger (2006) argued that, in fact, it represents a reasonable compromise between those who argue that the poverty line is set too low (as a result of, for example, rising housing and child-care costs) and those who argue it is set too high (as a result of not accounting for in-kind benefits, such as food stamps or for tax credits such as the Earned Income Tax Credit [EITC]). 6

Rates of poverty derived from the PSID tend to be slightly lower than those from the Current Population Survey (CPS), which may reflect a more complete accounting of income that takes place within the PSID than in the CPS ( Duncan 1984 ). This is not surprising because the CPS was primarily designed for measuring unemployment, whereas the PSID was designed to measure income and household well-being. For example, Gouskova and Schoeni (2007) compared estimates of family income for the PSID and CPS between 1968 and 2005 and found a close and consistent pattern throughout these years between the two, with the PSID reporting slightly higher income estimates across the years than the CPS (in addition, see Duncan and Rodgers 1991 ).

Our analysis employs two additional measures of poverty: extreme poverty and near poverty. Extreme poverty consists of households that fall below 50% of the official poverty line, whereas near poverty includes households that fall below 150% of the poverty line. Extreme poverty is therefore a measure of severe income deprivation, while near poverty includes both the poverty stricken and those on the outer edges of poverty. In an analysis that combines case studies of working poor individuals with national survey data, Schwarz (1997) found that 150% of the poverty line is approximately the income level required to attain a frugal, minimal life style.

Life Table Approach

In describing the life course patterns of poverty dynamics over time, we rely on the life table as our major analytical technique. Life tables provide a concise method for describing how the odds of experiencing a specific event change as individuals age. The life table is most closely associated with biological and demographic studies of mortality, but it can be easily applied to estimate the occurrence of other events as well ( Allison 1995 ; Namboodiri and Suchindran 1987 ).

In this analysis, the life table provides a clear advantage over the more common spell analyses of poverty, such as those of Bane and Ellwood (1986) and Stevens (1999) . Rather than focusing on spells and duration, our interest lies in understanding the extent to which Americans fall into poverty across various stages of the life course and the degree to which that risk has changed over time. The life table is ideally suited to this task.

We construct a series of life tables for various age categories across the life course: we look at the risk of poverty for individuals in their 20s, 30s, 40s, 50s, 60s, and 70s. Within each of these age categories, we estimate a series of age-specific probabilities that poverty (or a particular number of years in poverty) will occur for those who have yet to experience impoverishment. From these age-specific probabilities, we are then able to calculate a set of cumulative probabilities that form the core of our analysis. These cumulative probabilities allow us to report the overall percentage of Americans who will experience poverty as they age across their 20s, 30s, 40s, and so on.

Because we are interested in changes in poverty dynamics over time, we split the sample into three equal time periods: 1968 to 1978, 1979 to 1989, and 1990 to 2000. These periods roughly coincide with peak-to-peak variations in the business cycle. We examine the experience of each age cohort over time by selecting all persons who are at the beginning of the age category and following them for as many years as they are at risk within the 10-year period. 7 Cohorts are followed for a period of 10 years. For example, in constructing the life tables for the period 1968 to 1978 for those aged 20–29, we select all individuals who turn age 20 during any year between 1968 and 1978. If a person turns age 20 in 1968 or in 1969, he or she could potentially contribute as many as 10 person-years to the life table. In other words, a person who was aged 20 in 1968 and did not experience poverty through age 29 would contribute 10 person-years to the life table. On the other hand, an individual who turned 20 in 1975 and experienced a year of poverty in 1977 would contribute three person-years to the construction of the life table (at ages 20, 21, and 22). Each person remains in the data set during the period as long as she or he has not experienced poverty or until surpassing the age range of the particular analysis. Using this method of selection ensures that there is no left censoring in the data, yet allows us to use the maximum amount of respondent information from the PSID.

We examine the chronicity of poverty by estimating life tables for three or more years in poverty and for five or more years in poverty within our 10-year age horizons. For example, in our life tables that estimate the likelihood of experiencing three or more years of poverty, after an individual has experienced a third year of poverty (either consecutively or spread out across the interval), the event has occurred, it is recorded, and the individual is then removed from further analysis in such a life table. These estimates allow us to measure the varying degrees of what we call chronic poverty.

Finally, we examine demographic differences in poverty by gender and by race (white versus nonwhite). As discussed earlier, countless studies have shown these variables to be important attributes that influence the risk of poverty. In the PSID, the vast majority of nonwhites are African American.

For all of our estimates, we follow the standard Census Bureau practice of providing 90% confidence intervals around each of our poverty estimates. These confidence intervals were derived from the standard errors calculated using weighted data, but were then normalized to nonweighted metrics. Life table standard errors are a function of the mean and the number of cases ( Klein and Moeschberger 2003 : equation 5.4.4). It is necessary to use PSID weights to compute unbiased estimates of the mean; however, the weights bias the standard errors downward. This bias was corrected by normalizing to the unweighted metrics. A limitation of this approach is that it does not account for potential PSID design effects that are an artifact of the original clustered sampling procedures ( Burkhauser, Weathers, and Schroeder 2006 ). Although these effects exert only a minimal influence on sample variances in PSID waves that are distant from the original 1968 clustered sampling design (because the sample over time has spread out across the country from the original sampling clusters), they may be more influential in the very early waves of the study (F.P. Stafford, personal communication, November 21, 2008). Hence, testing for design effects is important in the present analysis given that we compare period differences calculated from early versus later waves.

To test for design effects, we conducted a series of analyses that incorporated the design effects and then reestimated the significance of the period differences found in our results using survey data estimation procedures in the statistical package program STATA (the “Svy” command; StataCorp 2009 ). These procedures accommodate survey sample weights as well as PSID design effects that can be estimated using the balanced repeated replication (BRR) method of calculating standard errors ( Burkhauser et al. 2006 ; Solon, Page, and Duncan 2000 ). The STATA survey data commands for testing BRR design effects cannot be used in life table procedures but are available in survival regression procedures, specifically Cox regression. As an example of this approach, for each row in the top panel of Table 1 , a Cox model was estimated in which time to first poverty spell was regressed on period dummy variables (one for 1968–1978 and one for 1979–1989, versus the omitted category 1990–2000). This model was estimated both with and without the BRR design effects. These results confirmed the statistical significance of each of the period effects found in the upper panel of Table 1 . Accounting for design effects inflated the variances only slightly, and not nearly enough to suppress statistical significance when alpha was set to .10. Similar analyses were conducted for additional results reported in the article. We found the period effects reported in the results section to be robust and not negated by the presence of any potential design effects. 8

Cumulative Percentage of the U.S. Population Experiencing Poverty Across Age Intervals and Time Periods

Notes: Percentages represent the cumulative percentage of the population experiencing poverty across each 10-year age interval at three different periods: 1968–1978, 1979–1989, and 1990–2000. Three levels of poverty are displayed: falling below the official poverty line (1.00 level poverty), falling below 50% of the poverty line (0.50 level poverty), and falling below 150% of the poverty line (1.50 level poverty). Sample sizes ( N ) represent the total number of unweighted person-years used to construct the life table analysis for each age interval.

Source: Authors’ calculations of the Panel Study of Income Dynamics, 1968–2000.

Cumulative Risk of Poverty

Table 1 displays the cumulative risk of encountering poverty for individuals in their 20s, 30s, 40s, 50s, 60s, and 70s during the 1970s, 1980s, and 1990s (for the exact cumulative percentages for each age within these 10-year age intervals, along with the confidence intervals surrounding these percentages, see the supplemental materials in the online appendix tables: http://www.soc.duke.edu/resources/demography ). Table 1 is divided into three panels. The top panel examines the likelihood of encountering at least one year below the official poverty line (1.00 level poverty); the middle panel looks at the likelihood of experiencing at least one year of extreme poverty (falling below 50% of the poverty line, or 0.50 level poverty); and the bottom panel addresses the risk of falling into poverty or near poverty (below 150% of the poverty line, or 1.50 level poverty).

Several patterns are readily apparent from this table. Perhaps most striking is that the life course risk of poverty increased across all age groups and different levels of poverty for the 1990–2000 period compared with the 1979–1989 and the 1968–1978 periods (the one exception is that poverty at the 1.50 level for those in their 70s was higher in 1968–1978 than it was in 1900–2000). The increase in the life course risk of poverty during the 1990s was particularly strong and significant for those in their 20s, 30s, and 40s, and for all age categories with respect to extreme poverty. For example, as shown in the top panel of Table 1 , our estimates indicate that during the 1970s, 12.8% of the population experienced at least one year below the official poverty line between the ages of 40 and 49. During the 1980s, the percentage increased slightly to 14.9%; and during the 1990s, it rose to 22.0%. 9

A second pattern found in Table 1 is that Americans in the 1990s (and to a somewhat lesser extent in the 1970s and 1980s) faced a significant risk of poverty across all ages of the life course. The estimated percentage of Americans experiencing at least one year below the official poverty line in the 1990s was 37.4% for those in their 20s, 27.1% for those in their 30s, 22.0% for those in their 40s, 19.8% for those in their 50s, 27.9% for those in their 60s, and 38.3% for those in their 70s. These percentages indicate that the risk of poverty is a very real threat across the entire life course. Looking at the percentages of Americans who will experience poverty at the 1.50 level indicates an even higher life course risk.

A third pattern found in Table 1 is the familiar U-shape across the stages of the life course with respect to the risk of poverty. In each of the three-decade analyses, the likelihood of experiencing poverty is highest in the 20s, declines through the 40s and in some cases the 50s, and then increases during the 60s and 70s. For example, from 1990 to 2000, the cumulative likelihood of experiencing poverty or near poverty was 55.3% for those in their 20s; 37.9% for those in their 30s; 33.1% for those in their 40s; 29.2% for those in their 50s; 43.8% for those in their 60s; and 57.0% for those in their 70s. What is also apparent in Table 1 is that this U-shape has effectively been pushed up across the 30 years under examination, while the overall life course shape of the age distribution with respect to poverty has remained the same.

Thus, Table 1 provides strong evidence that the life course risk of poverty increased substantially in the 1990s when compared with the risk in the 1970s and 1980s. However, one important limitation of Table 1 is that it potentially confounds period effects with cohort effects because the three different time periods are composed of three different cohorts. For example, the 20- to 29-year-olds that we examine between 1968 and 1978 obviously compose a different cohort than the 20- to 29-year-olds that we examine from 1979 to 1989 or from 1990 to 2000. Consequently, it is possible that the rise we observe in the risk of poverty in the 1990s is not the result of a period effect, but rather the result of a cohort effect (although the fact that the rise occurs across almost all age groups makes this less likely).

In order to disentangle period effects from cohort effects, we provide in Table 2 the cumulative poverty incidence for four separate cohorts that are followed across all three time periods (found in the diagonal patterns in Table 1 ). If there is a strong period effect in the third period (1990 to 2000), then poverty either should increase during this period for each of the cohorts or should not decline for those entering ages at which poverty is expected to fall (i.e., the 40s and 50s). As in Table 1 , the top panel of Table 2 examines the risk of poverty, the middle panel looks at extreme poverty, and the bottom panel focuses on poverty and near poverty.

Cumulative Percentage of the U.S. Population Experiencing Poverty Across Age Intervals and Time Periods, by Birth Cohorts

Notes: Percentages represent the cumulative percentage of each birth cohort experiencing poverty across 10-year age intervals at three different periods: 1968–1978, 1979–1989, and 1990–2000. Three levels of poverty are displayed: falling below the official poverty line (1.00 level poverty), falling below 50% of the poverty line (0.50 level poverty), and falling below 150% of the poverty line (1.50 level poverty). Numbers in parentheses are 90% confidence intervals.

The top two rows within each of the three panels provide a strong test for the presence of a period effect because, as we have seen, poverty is normally expected to decline markedly across this stage of the life course—that is, from the 20s to the 40s or 50s. Across all three panels, and for each of the four cohorts within those panels, the risk of poverty either increased or remained the same during the 1990s. For example, as shown in the top panel, the 1938–1948 birth cohort experienced a cumulative poverty incidence of 18.2% when they were in their 30s during the years 1968–1978. As they reached their 40s (between 1979 and 1989), their risk of poverty fell to 14.9%. One might expect that given the age dynamics of poverty, their risk would again fall or would remain roughly the same when this cohort reached their 50s (as it did in Table 1 for those in the 1990s). On the contrary, their risk of poverty increased to 19.8%. Each of the other three cohorts also display the same pattern of a rise in the risk of poverty during the 1990s, or a leveling off of the risk of poverty when we should expect a decline. These patterns are repeated in the middle and bottom panels of Table 2 as well. Taken together, they provide strong evidence that the rise in the life course risk of poverty in the 1990s is the result of a period effect rather than a cohort effect.

Number of Years in Poverty

Table 3 displays the cumulative risk of experiencing differing amounts of time below the official poverty line in order to determine whether the increase in poverty we have observed in Table 1 is the result of an acute versus a chronic rise in poverty. Included in Table 3 are individuals who have experienced one or more years of poverty (which is identical to the cumulative estimates in the top panel of Table 1 ), three or more years, and five or more years.

Cumulative Percentage of the U.S. Population Experiencing Various Numbers of Years in Poverty Across Age Intervals and Time Periods

Notes: Percentages represent the cumulative percentage of the population experiencing one or more, three or more, and five or more years below the official poverty line across each 10-year age interval at three different periods: 1968–1978, 1979–1989, and 1990–2000.

Table 3 clearly shows that the earlier observed increase in poverty in the 1990s is primarily confined to individuals who experienced a year or two of poverty, rather than several years of impoverishment. For each of the various age categories, between 1979–1989 and 1990–2000 there were no statistically significant increases in persons experiencing three or more or experiencing five or more years in poverty (the exception being three or more years for those in their 40s). In fact, in a number of age categories, there was a decline in the 1990s of experiencing three or more and five or more years of poverty.

This is further apparent in Table 4 . Here we select individuals who have experienced at least one year of poverty and then calculate the percentage of these individuals who will experience three or more years of poverty and five or more years of poverty. The percentage of the poverty population encountering long-term poverty actually dropped from 1979–1989 to 1990–2000. For example, of those in their 20s experiencing poverty in the 1980s, 27.5% experienced five or more years of poverty, compared with only 17.9% for those experiencing poverty in the 1990s. The corresponding percentage drop for the other ages are 28.1% to 20.3% for those in their 30s; 42.1% to 26.9% for those in their 40s; 28.7% to 26.3% for those in their 50s; and 58.9% to 17.2% for those in their 60s. Only for individuals in their 70s was there an increase in long-term poverty from the 1980s to the 1990s, from 37.7% to 41.7%.

Cumulative Percentage of the U.S. Population Experiencing Additional Years in Poverty Contingent on Having Experienced One Year in Poverty

Notes: Percentages represent the cumulative percentage of the population experiencing three or more and five or more years below the official poverty line across each 10-year age interval for those who have experienced at least one year in poverty. Percentages are shown for three different periods: 1968–1978, 1979–1989, and 1990–2000.

Tables 3 and ​ and4 4 demonstrate that the earlier observed rise in the life course risk of poverty from the 1980s to the 1990s is predominately the result of a rise in the risk of encountering an acute spell of poverty lasting only a year or two. Consequently, the increased risk of poverty in the 1990s seen in Table 1 is distributed across the general population, rather than being concentrated within what has been labeled the “underclass” or “the truly disadvantaged.”

These findings are entirely consistent with the pattern mentioned earlier in the theoretical section that the cross-sectional rates of poverty could remain fairly stable over time (which has been the case over the past 30 years) while the life course risk of poverty increases if there was a rise in the amount of turnover from year to year in terms of who is experiencing poverty. Our results in Tables 3 and ​ and4 4 showing that the increase in poverty is primarily confined to individuals experiencing only a year or two of poverty and that there has actually been a drop in longer term poverty are quite consistent with this scenario.

Race and Gender Differences

Table 5 shows the cumulative life course estimates of poverty by age, race, and gender, and suggests that the increase in poverty in our earlier figures occurred across demographic groups but was particularly noticeable for white males. Due to sample size limitations, we were only able to construct two time periods: 1968–1984 and 1985–2000. Although poverty increased for all race-gender groups (except for nonwhite males in their 60s and nonwhite females in their 20s, 50s, and 60s), the largest increases occurred for white males as a whole and for nonwhite males in their 30s, 40s and 50s. 10 This is consistent with results from Comin, Groshen, and Tracy (2006) showing a significant increase in earnings volatility among white male heads of households from 1984 to 1993 compared with the period of 1970 to 1979, largely as a result of increasing turbulence among U.S. firms.

Cumulative Percentage of the U.S. Population Experiencing Poverty Across Age Intervals and Time Periods, by Race and Gender

Notes: Percentages represent the cumulative percentage of white males, nonwhite males, white females, and nonwhite females experiencing at least one year below the official poverty line during 10-year age intervals at two different periods: 1968–1984 and 1985–2000.

Source: Authors’ calculations of the Panel Study of Income Dynamics data, 1968–2000.

A second finding consistent with virtually all research on poverty is that nonwhites are at a much greater risk of experiencing poverty across all age groups and time periods than their white counterparts. Women are also more likely to experience poverty across the life course than their male counterparts, although these differences are not nearly as wide as the racial differences. As discussed earlier, the reason gender differences are not more pronounced is that women and men spend much of their adulthood in marriage, which results in identical rates of poverty during these years.

In this article, we set out to measure the extent and patterns of poverty across the various stages of the life course and to determine whether that risk has increased during the period 1968–2000. As noted at the beginning of the article, there has been an ongoing debate regarding the extent to which economic risk has been rising. On the one hand, a substantial body of empirical research has documented a number of long-term trends and patterns indicating that Americans have been facing a rising peril of economic vulnerability. On the other hand, several studies have found scant evidence for a rise in economic insecurity.

One key measure of economic risk is that of poverty. Those who argue that economic risk has been increasing over time must reconcile this with the fact that the poverty rate as reported by the Census Bureau has remained fairly stable from the early 1970s onward. We addressed this conundrum by providing an alternative analysis of poverty through the perspective of the life course and the methodology of the life table.

Our findings indicate that the life course risk of poverty increased from the 1970s and 1980s to the 1990s and that the risk itself is substantial. Adult Americans as a whole faced a greater likelihood of experiencing poverty in the 1990s than they did in the 1970s or 1980s. This was particularly the case for those in their 20s, 30s, and 40s, and for all age groups with respect to extreme poverty. The overall increase in the 1990s was predominately the result of a rise in acute rather than chronic poverty. The rise in the likelihood of encountering poverty also occurred across racial and gender lines, but it was particularly noticeable for white males as a whole and for nonwhite males in their 30s, 40s, and 50s.

This research provides an important counterpoint to findings from cross-sectional studies, such as those of the U.S. Census Bureau, showing that overall rates of poverty changed little and actually declined somewhat during the 1990s. By using a different conception of time to measure poverty, our analysis has revealed a pattern that has not been apparent from this earlier work. As Ralf Dahrendorf (1999 :ix) wrote, “ Arguably the most exciting dimension of social analysis is time. Yet it has long been neglected by mainstream sociology. Much of the study of social stratification, even of mobility, is static, based on snapshots which ignore the place of such moments in people’s life histories.”

Our findings also provide an interesting counterpoint to the perception that chronic poverty has increased from the 1970s onward. We have shown that the number of individuals experiencing chronic poverty, sometimes termed the “truly disadvantaged,” has in fact somewhat declined in the 1990s (as measured by the percentage of the poor who experience five or more years of poverty within a 10-year interval). This finding is consistent with research showing that the percentage of the poverty population living in census tracts of extremely high levels of concentrated poverty (40% or more) declined significantly between 1990 to 2000 ( Jargowsky 2003 ).

Taken together, the findings presented in this article would appear to highlight a double-edged sword regarding changes in poverty over time. Although the reach of poverty in the 1990s widened, at the same time, the grasp of poverty became somewhat weaker. More Americans were at risk of poverty in the 1990s than in the 1970s and 1980s, but fewer Americans experienced long bouts of chronic poverty.

The question then arises, to what extent are these countervailing patterns problematic? Although the decline in chronic poverty in the 1990s is certainly a positive development, the substantial increase in the risk of acute poverty is nevertheless troubling. Research has indicated that even short-term spells of poverty can be highly disruptive for individuals and families ( Kaler and Rennert 2008 ). The fact that this risk has become more prevalent across the life course is a cause for concern.

The rise of acute poverty is consistent with the argument that the long-term economic and social policy patterns over the past 15 to 20 years have increased the likelihood of economic vulnerability, potentially leading to poverty. That is, as jobs have become more unstable and less well paying, as the social safety net has become weaker, as quality health insurance has been more scarce, and/or as levels of personal debt have skyrocketed, more Americans are at risk of falling into poverty. Although these periods of impoverishment are generally short lived, they are undoubtedly disruptive and damaging to the individuals and families experiencing such spells of economic turmoil.

We would argue that this increase in poverty strongly suggests that American society is becoming a place where economic hardship is increasingly commonplace, affecting nearly the majority, if not the majority, of Americans at several points throughout the various stages of adulthood. For example, although the risk of poverty is lowest for individuals during the prime earning years of the 40s and 50s, the risk was far from trivial even during these stages of the life course in the 1990s: approximately one-fifth of Americans were likely to experience a year of poverty during both their 40s and 50s, and one-third experienced poverty or near poverty during each of these periods.

The American life course is thus increasingly characterized by periodic spells of economic turmoil. Whether these patterns will continue throughout the first decade of the 2000s and beyond is difficult to say without further longitudinal data, but there is little reason to think that this trend will reverse itself any time soon. If anything, it may have intensified given the continuing patterns of job insecurity, erosion of social protection programs, levels of financial debt, and wage stagnation during the 2000s.

In conclusion, by using a life course approach to measuring poverty over time, we have been able to demonstrate for the first time that the risk of American poverty increased substantially during the 1990s in comparison with the 1970s and 1980s. As we have shown, that risk has become exceedingly high. In fact, it would appear that for most Americans, the question is no longer if, but rather when they will experience poverty. In short, poverty has become a routine and unfortunate part of the American life course.

Acknowledgments

The authors would like to thank Greg Duncan, John Iceland, Jacob Hacker, Benjamin Page, Frank Stafford, and three anonymous reviewers for their helpful comments on earlier versions of this article.

1. In fact, there was a small increase in the overall rate of the poverty across the three periods we study. For those aged 18–64, the average rate of poverty between 1968 and 1978 was 8.8%; between 1979 and 1989, it was 10.9%; and between 1990 and 2000, it was 11.1% ( DeNavas-Walt et al. 2008 ).

2. However, it is also true that the official poverty rate for the elderly has been cut dramatically over the past 50 years in the United States, primarily as a result of an increase in the generosity of Social Security ( Hoynes et al. 2006 ). For example, the poverty rate for those 65 and older in 1970 was 24.6%, but it had fallen to 9.9% by 2000 ( DeNavas et al. 2008 ). Nevertheless, many of the elderly are only a short distance above the official poverty line. As evidenced by the percentages of individuals falling below 150% of the poverty line by age categories, the likelihood of impoverishment increases substantially from the 50s onward.

3. Use of the supplemental Latino sample would make the data from the 1990s less comparable with those of the 1970s and 1980s. We tested for any effects that the immigrant refresher sample might have had on our overall results by computing our poverty life tables for the 1990–2000 final sample (including the immigrant refresher sample) versus a subsample that excluded the immigrant refresher sample. There were no statistically significant differences between these two life table estimations.

4. In order to check for any biases introduced by using the off-year information, we computed life tables for the periods 1980–1986 and 1990–1996, and found identical life course patterns when compared to our final results for 1979–1989 and 1990–2000. Consequently, we detected no evidence of bias introduced into our analysis by the post-1997 changes in PSID data collection (however, for further information regarding PSID off-year income data, see Andreski, Stafford, and Yeung 2008 ).

5. In constructing this variable, we did not use the PSID poverty variable, but rather computed census poverty by simulating the PSID “annual need standard—Census variable” that is provided by the PSID for 1990 and subsequent years. Our simulations accounted for part-year changes in family money income (i.e., PSID variable V18875) and were adjusted for changes in family composition. When our simulation effectively reproduced the 1990 PSID census variable, we applied this method to compute census poverty, backdating to 1968. Consequently, we used this measure across the various waves of the PSID, from 1968 to 2000.

6. One might argue that in an analysis examining whether poverty has risen during a 30-year period, our measure of poverty should include in-kind programs (e.g., the Food Stamp Program) or tax credits (the EITC), which are not included in the official measurement of poverty. However, there are several reasons why we do not incorporate such measures into our analysis in this article. First, the PSID does not have continuous, consistent coverage for some of these programs (e.g., food stamp and Medicaid coverage), as it does for income, which would hinder our construction of consistent life tables across the three decades. Second, it is unclear how several of these programs should be converted into a poverty measure. For example, there are significant problems in attempting to economically factor a program such as Medicaid within a poverty measure ( National Research Council 1995 ). Finally, and perhaps most importantly, our concern in this study is whether poverty, as measured by the Census Bureau definition, could have risen in a life course context in spite of the yearly cross-sectional flattening out of the poverty rate. Using a definition different from the Census Bureau’s would, to some extent, defeat the purpose of the analysis.

7. Consequently, individuals who enter into the sample during the middle of an age category would not be included in the analysis for that particular age category. Including such individuals introduces left-censoring bias into the analysis.

8. These estimations are available from the authors on request.

9. However, two of the increases in Table 1 should be viewed with caution. The increase in poverty at the 0.50 level for 70- to 79-year-olds from 6.5% to 25.4% is particularly large. Likewise, the increase in poverty at the 1.00 level for 60- to 69-year-olds from 15.8% to 27.9% is also large. It is quite likely that these increases may be at least partly due to measurement and/or sampling error within the PSID.

10. In addition to these findings, if all ages categories are pooled for each of the four groups in Table 5 , overall life course poverty increased from 1968–1984 to 1985–2000 for both white and nonwhite males and for white females.

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Reading Lists +

The review +, segregation compounds the effects of poverty.

7 August 2024

Research by

• Kareem Haggag
• Bryan A. Stuart
• Stereotypes
• Wealth Inequality
• Behavioral Economics

In Northern cities, railroad tracks that defined Black neighborhoods remain boundaries against economic mobility

During the Great Migration of Black people from the South to the North, which began in the early 20th century, railroad tracks that created closed-off areas served as  physical barriers to create segregated neighborhoods in many of the landing spots such as Chicago and Detroit. Research published in 2011 in the American Economic Journal made the case that railroads — one of the great economic leaps forward of the 19th century — continue to provide a framework for segregated cities.

Study author Elizabeth Ananat estimated that today’s neighborhoods that are still defined by a high level of such railroad-tracked segregation cause “cities to have Black populations with worse present-day characteristics, both at the top and the bottom of the education/income distribution.”

Segregation Not a Thing of the Past

A work ing paper by University of Texas at Austin’s Eric Chyn, UCLA Anderson’s Kareem Haggag and the Federal Reserve Bank of Philadelphia’s Bryan A. Stuart builds on Ananat’s “railroad division index”  to advance a case for how segregation continues to impede intergenerational economic advancement, especially for Black children from poorer families. They link the level of RDI in 121 northern metro areas identified by Ananat with generational income data on parents and their grown children.

Chyn, Haggag and Stuart compare the average income of parents — based on nationwide percentile rank — with the IRS-reported income levels for their adult children (born between 1978 and 1983) at ages 31 to 37. 

By using the RDI as a proxy for the level of segregation, the researchers are able to tease out the extent to which rearing children in a segregated community impacts the upward mobility of the next generation. They find that a child whose parents are extremely poor — the first percentile in pretax income — and whose family home is in an area that is more segregated leads to income rank being 4.5 percentile points lower than if the child had grown up in a less segregated area.

That translates to those children landing in the 22nd percentile for income as adults ($12,666 annual household income) rather than the 27th percentile ($17,500 household income). 

In a back of the envelope estimate, the researchers suggest that if racial segregation disappeared, the Black child who makes it to the 27th percentile for income as an adult would instead make it all the way to the 50th percentile.

When parents are in the 25th income percentile, growing up in the more segregated area causes a 4 percentile point drop in the adult child’s eventual income rank. This negative impact is still evident when the parents’ income is at the 50th and 75th percentile.

The researchers find that white children growing up with lower-income parents also pay a segregation penalty, but less so, or a 3.3 percentile point penalty for growing up in a segregated area and in a home at the 1st percentile of income.

That is a 9 percentile drop from where the white adult child would otherwise land if they hadn’t grown up in a segregated area. By comparison, the percentile decline for the Black child with the same variables is 17%.

More Segregation Means Less Social Mobility

Chyn, Haggag and Stuart extend their study by applying the same RDI sorting to look at key drivers of social mobility: incarceration, teenage pregnancy and educational test levels.

Black boys whose parents’ income level falls at the 1st percentile and who live in an area with above-average segregation have a 29% greater probability of being incarcerated. For white boys growing up in poverty and extreme segregation, their probability of incarceration rises by 22%.

For both Black and white teenage girls, the probability of becoming pregnant rises 22% when their parents are extremely poor and their home is in a more segregated area. 

Test scores were also lower for these groups. The authors cite this as evidence that a decrease in human capital at a young age suggests that “the segregation-induced decline in upward mobility does not arise simply because of worse labor market discrimination or access to jobs,” as some other research has posited.

That educational loss may be, in part, a function of less public funding for more segregated communities. Chyn, Haggag and Stuart find that per capita government spending is 39% lower in metro areas with higher segregation. Education takes the biggest funding hit, followed by public safety and health care spending. 

Moreover, using national survey data of racial attitudes, the authors build on prior research that finds segregation impacts cross-race perceptions. They find that when there is more segregation, there is a significant increase in negative attitudes of non-Black people toward Black people. And perhaps not surprisingly, less support for government support of Black people. 

“Although Black-White racial segregation in the U.S. has declined since 1970, it remains a defining feature of most cities, which suggests policy efforts to reduce its harmful impacts have significant potential for enhancing economic growth and equality,” the authors conclude.

Featured Faculty

Assistant Professor of Behavioral Decision Making

About the Research

Ananat, E.O., (2011). The wrong side (s) of the tracks: The causal effects of racial segregation on urban poverty and inequality . American Economic Journal: Applied Economics , 3 (2), 34-66.

Chyn, E., Haggag, K., Stuart, B.A. (2023). The Effects of Racial Segregation on Intergenerational Mobility: Evidence from Historical Railroad Placement . 

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