poverty trap essay

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What Is a Poverty Trap?

Understanding poverty traps.

• Addressing Poverty Traps
• Potential Solutions

The Bottom Line

Poverty trap: definition, causes, and proposed solutions.

James Chen, CMT is an expert trader, investment adviser, and global market strategist.

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A poverty trap is a mechanism that makes it very difficult for people to escape poverty . A poverty trap is created when an economic system requires a significant amount of capital to escape poverty. When individuals lack this capital, they may also find it difficult to acquire it, creating a self-reinforcing cycle of poverty.

Key Takeaways

• A poverty trap refers to an economic system in which it is difficult to escape poverty.
• A poverty trap is not merely the absence of economic means. It is created due to a mix of factors, such as access to education and healthcare, working together to keep an individual or family in poverty.
• Noted economist Jeffrey Sachs has made the case that public and private investments need to work in concert to eradicate the poverty trap.

Many factors contribute to creating a poverty trap, including limited access to credit and  capital markets , extreme environmental degradation (which depletes agricultural production potential), corrupt governance,  capital flight , poor education systems, disease ecology, lack of public healthcare, war, and poor infrastructure.

In order to escape the poverty trap, it is argued that individuals in poverty must be given sufficient aid so that they can acquire the critical mass of capital necessary to raise themselves out of poverty. This theory helps to explain why certain aid programs that do not provide a high-enough level of support may be ineffective at raising individuals from poverty. If those in poverty do not acquire the critical mass of capital , they will simply remain dependent on aid indefinitely and regress if aid is ended.

Recent research has increasingly focused on the role of other factors, such as healthcare, in sustaining the poverty trap for a society. Researchers at the National Bureau of Economic Research (NBER) found that countries with poorer health conditions tend to be mired in a cycle of poverty as compared to others with similar educational attainments.

Researchers at the University of Florida in Gainesville collected economic and disease data from 83 of the world's least and most developed countries. They found that people living in areas with limited human, animal, and crop disease were able to lift themselves out of the poverty trap as compared to people who lived in areas with rampant disease.

Types of Poverty Traps

Poverty traps can vary in their underlying causes and characteristics, but they all share the common feature of perpetuating poverty or making it difficult for individuals or communities to escape poverty. Here is an overview of the various types of poverty traps.

Economic Poverty Traps

Economic poverty traps are characterized by low income and limited economic opportunities. People in these traps may face challenges such as unemployment or underemployment, low wages, and lack of access to credit or financial services . This makes it difficult for them to save, invest, or escape poverty because they are often living hand-to-mouth, struggling to meet their basic needs.

Geographic Poverty Traps

Geographic poverty traps occur in areas that are geographically isolated or marginalized. These regions may lack essential infrastructure like roads, electricity, and clean water, making it hard for residents to access education, healthcare, and job opportunities. Limited connectivity to markets and resources can further perpetuate poverty in these areas.

Health Poverty Traps

Health-related poverty traps are linked to poor health and inadequate healthcare access. Individuals in these traps often face chronic illnesses, lack of preventive care, or insufficient treatment options. Medical expenses can drain their limited resources, and ill health can limit their ability to work and earn a stable income.

Educational Poverty Traps

Educational poverty traps stem from inadequate access to quality education. High dropout rates, lack of schools, and limited educational opportunities hinder individuals' ability to acquire skills and knowledge necessary for better employment prospects. Without education, they may remain trapped in low-paying, low-skilled jobs .

Social Poverty Traps

Social factors such as discrimination, social exclusion, or limited social support networks can create social poverty traps. These factors may restrict individuals' access to resources, opportunities, and social mobility. Discrimination based on factors like race, gender, or ethnicity can perpetuate inequality and poverty, and social factors can perpetuate other types of poverty traps listed within this section.

Generational Poverty Traps

Generational poverty traps occur when poverty is passed down from one generation to the next within families. Children born into impoverished households may face limited access to quality education, healthcare, and proper nutrition. These individuals may also face barriers or limitations based on debts or financial limitations passed down from generation to generation.

Institutional Poverty Traps

Institutional poverty traps are related to weak governance, corruption, and ineffective institutions. Inadequate rule of law, lack of property rights protection, and rampant corruption can stifle economic growth, discourage entrepreneurship , and hinder access to essential services like healthcare and education. These institutional weaknesses can keep individuals and communities in poverty.

Poverty traps are often the subject of political debate; some believe the government has a responsibility to alleviate poverty traps, while others believe markets should evolve on their own.

The Public and Private Role in Addressing the Poverty Trap

In his book  The End of Poverty: Economic Possibilities for Our Time, Jeffrey Sachs recommends that, as a way of combating the poverty trap, aid agencies should function as  venture capitalists  that fund startup companies.

Sachs proposes that, just like any other startup , developing nations should receive the full amount of aid necessary for them to begin to reverse the poverty trap. He points out that the extremely poor lack six major kinds of capital: human capital , business capital, infrastructure, natural capital, public institutional capital, and knowledge capital .

Sachs added in his book:

"The poor start with a very low level of capital per person, and then find themselves trapped in poverty because the ratio of capital per person actually falls from generation to generation. The amount of capital per person declines when the population is growing faster than capital is being accumulated... The question for growth in per capita income is whether the net capital accumulation is large enough to keep up with population growth."

Sachs postulates that the public sector should concentrate its efforts on investing in:

• Human capital—health, education, nutrition
• Infrastructure—roads, power, water and sanitation, environmental conservation
• Natural capital—conservation of biodiversity and ecosystems
• Public institutional capital—a well-run public administration, judicial system, police force
• Parts of knowledge capital—scientific research for health, energy, agriculture, climate, ecology

Business capital investments , Sachs says, should be the domain of the private sector , which he claims would more efficiently use the funding to develop the profitable enterprises necessary to sustain growth enough to lift an entire population and culture out of poverty.

Solutions to Overcome Poverty Traps

With Sachs context in mind, let's dig deeper into potential solutions that could overtake poverty traps. Bear in mind that this list is not meant to be exhaustive; in addition, this list is meant to be a high-level overview of potential strategies whose success may ebb and flow over time.

Invest in Education

Investing in education is often cited as being crucial for breaking the poverty cycle. Quality education, with well-trained teachers, updated curriculum, and modern facilities, ensures that children have the skills and knowledge needed for better job opportunities. Equitable distribution of educational opportunities, particularly for marginalized groups, is vital for addressing inequality. Additionally, vocational and technical training programs prepare individuals for skilled employment, which can be a pathway out of poverty via better job opportunities.

Improve Healthcare Access

Access to affordable healthcare services is an essential poverty reduction strategy. Establishing and maintaining healthcare facilities, especially in underserved areas, guarantees access to medical services. Preventive care measures such as immunizations and health education reduce the prevalence of diseases and lower healthcare costs in the long run. Expanding health insurance coverage is crucial to protect low-income individuals and families from the financial burden of healthcare expenses.

Develop Infrastructure

Investing in basic infrastructure like roads, electricity, and water supply improves living conditions and stimulates economic activity. This is especially true in remote or marginalized areas that may have a lack of physical accessibility for resources and access. This development facilitates growth to markets, education, healthcare, and professional opportunities.

Promote Credit Accessibility

Poverty traps may be alleviated by increasing financing and credit accessibility. Microfinance institutions can provide small loans to aspiring entrepreneurs and small business owners who lack access to traditional banking services. These loans enable individuals to invest in income-generating activities and improve their economic prospects. In addition, expanding access to formal banking services, savings accounts , and insurance products for marginalized populations promotes financial inclusion.

Promote Social Inclusion

Promoting gender equality and women's empowerment is not only a human rights issue but also a key driver of poverty reduction. When women have equal access to education, economic opportunities, and leadership roles, entire communities can benefit. Effective anti-discrimination laws and policies can also protect the rights of minority groups and promote social inclusion.

Improve Governance and Anti-Corruption

Transparent governance, the rule of law, and accountability are crucial for reducing corruption and ensuring equitable resource allocation. Independent anti-corruption agencies can investigate and prosecute corruption cases, discouraging corrupt practices. In addition, advocacy efforts push for policy changes and reforms at various levels to address the root causes of poverty and promote equity and inclusion.

According to World Bank Group, the number of individuals in extreme poverty rose to over 700 million people during the pandemic and may be around 685 million at the end of 2022. During the pandemic, the global extreme poverty rate reached 9.3%.

Examples of a Poverty Trap

One of the most important considerations in studying the poverty trap is the amount of government aid necessary to lift a family out of their present conditions.

Consider the case of a family of four, made up of parents and two children who are below legal working age. The family has an annual income of $24,000, with the parents working in jobs that pay $10 per hour. According to the latest federal poverty guidelines , a family of four is considered to be poor if its income is less than $30,000.

In a simple case, let us assume that the government begins handing out aid amounting to $1,000 per month. This raises the family's annual income to $36,000. While it is capped at $1,000, the government aid decreases in proportion to increases in the family's income. For example, if the family's earnings increase by $500 to $2500 per month, government aid reduces by $500. The parents would have to work an extra 50 hours in order to make up for the shortfall.

The increase in working hours comes at an opportunity and leisure cost to the parents. For example, they might end up spending less time with their children or may have to hire babysitters for the time that they are out of the home. The extra hours also mean that the parents will not have the leisure to upgrade their skill-sets for a better paying job.

The aid amount also does not take into account living conditions for the family. Because they are poor, the family lives in one of the most dangerous neighborhoods in the city and does not have access to proper healthcare facilities. In turn, crime or susceptibility to disease could drive up their average monthly spending, making an increase in the family's income effectively useless.

Real-World Example

In the real world, the case of Rwanda, a country wracked by genocide and civil war until recently, is often held up as an example of a nation that tackled the poverty trap by identifying factors beyond income. The African country focused on healthcare and insurance to increase the average daily calorie intake.

However, certain researchers charge the country's government with reducing the measurement threshold in order to make Rwanda rate better on poverty statistics.

What Causes Poverty Traps?

There are several factors that make it difficult for people to escape poverty. A lack of access to capital is a major contributor to poverty traps as is poor education, infrastructure, and healthcare. 

Why Is It So Hard to Get Out of Poverty?

Many of the things that can help pull people out of poverty require the one thing poor people don't have: money. For example, without money, it’s difficult to get a decent education and acquire new skills to boost job prospects and earnings potential. Spare time to address issues and boost wellbeing is also in short supply, as every hour spent not sleeping is dedicated to earning money and surviving.

How Many People in the U.S. Live in Poverty?

According to the United States Census Bureau, 37.9 million people in the U.S. lived in poverty in 2022, which represents 11.5% of the population.

Poverty traps are self-perpetuating cycles of poverty, where individuals or communities struggle to escape. The reasons for poverty cycles may include low income, limited access to education, healthcare, and limited economic opportunities. These factors reinforce one another, trapping people in a cycle of deprivation and hindering their ability to break free from poverty without external interventions and support. However, there are numerous ways to break poverty traps and solutions to promote economic and financial equity for all.

Jeffrey Sachs. "The End of Poverty: Economic Possibilities for Our Time. " Penguin Publishing Group, 2006.

Scott W. Allard. " Poverty Traps ."

National Bureau of Economic Research. " Barriers to Health and the Poverty Trap ."

Science. " Why So Much of the World is Stuck in a ‘Poverty Trap.' "

Jeffrey Sachs. "The End of Poverty: Economic Possibilities for Our Time," Page 245. Penguin Publishing Group, 2006.

The World Bank. " Poverty ."

Office of the Assistant Secretary for Planning and Evaluation. " HHS Poverty Guidelines for 2023 ."

Stanford University. " Stanford Scholars Examine Ecological Underpinnings of Rural Poverty ."

Quartz Africa. " Rwanda Is in a Dispute Over How It Measures Poverty ."

U.S. Census Bureau. " Poverty in the United States: 2022 ."

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Why Do People Get Trapped In Poverty?

Savings, investments and growth, why do entire economies become trapped in poverty.

Poverty traps are caused by a number of factors, including a lack of access to education, poor health, and a lack of assets. These factors lead to low levels of income, which make it difficult to save and invest. This self-reinforcing cycle makes it difficult for people to break out of poverty.

A poverty trap is a situation in which poverty forces people to remain poor. It is a vicious cycle that causes individuals, communities, regions or entire economies to get stuck in extreme poverty, where they are unable to break out of it for significantly long periods of time. The worst case of a poverty trap is where all of the above, from individuals up to national governments, become trapped in this cycle of poverty.

It is a self-reinforcing cycle in which people and economies that start poor will also end poor. For them, poverty itself becomes the cause of poverty.

Poverty traps are different from temporary poverty, which may stem from bad market outcomes, such as recessions or other financial crises, which are usually transitory. Economies experiencing such crises can recover from these adverse shocks relatively easily, but those trapped in persistent poverty traps remain poor for a very long time.

To better understand how poverty inhibits growth, we’ll explain the details, alone with the help of some ants and a grasshopper!

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You are likely familiar with Aesop’s fable about ants and the grasshopper. The ants, who worked hard during the summer, were able to relax and stay safe during the harsh winter months. On the other hand, the grasshopper, who played fiddle all day and feasted on the bountiful provisions of summer, struggled to survive through the winter. This story has the moral of working hard and saving for the future.

Let’s slightly change the story. In this version of the story, both the ants and the grasshopper work hard to save for the winter, but just before winter, there was a calamity in the grasshopper’s neighborhood, causing all of his savings to be destroyed. Come winter, the leaves fell and the weather changed. The ants were relaxed, since they had enough savings, but the grasshopper was starving. He even had to sell his fiddle to the ants in exchange for food!

The ants had high earnings and low consumption during the summer, and saved for later consumption during the winter. On the other hand, the grasshopper, who had neither savings nor assets, would spiral into poverty without external support.

Savings provide a cushion in the face of adverse situations that may arise in the future. But why else do we need to save? We need to save so that we can invest and grow!

Imagine that the ants, instead of simply storing the seeds for the winter, cultivated them in a greenhouse they constructed in their anthill. Since the seeds will have grown into an even larger bounty, in winter, they will have both income and savings. Thus, the saved seeds are turned into capital that can yield future income. This means that the saved seeds have been invested.

Because of savings and investment, the ants have enough not only to survive, but also to grow. On the other hand, since the grasshopper does not have any savings, he will struggle to survive. The small amount of food, if any, which he would earn during winter, would all be consumed. To grow, he needs to earn enough to not only consume, but also to save and invest. The situation of the grasshopper will not improve without an opportunity to earn more. Unless there is a higher level of income, there will be no savings, no investment and thus no growth.

People in poor countries face a similar situation. Without enough income, they find themselves unable to build capital that they can invest and grow. They cannot increase their income because they are facing a perpetual “winter”, namely, an inability to improve their situation.

Let’s look at some of these destructive, cyclical factors.

Why can’t people who have low income earn enough to save? Many factors affect the ability of a person to earn, particularly in the face of poverty. Poverty puts people at a higher risk for health problems due to a lack of nourishment and sanitation. This leads them to take days off work, which further reduces their income. On the other hand, constant health problems increase their medical expenses, which makes it very difficult for them to earn capital.

People who are below the poverty line struggle to make ends meet. In such situations, education becomes a luxury. A lack of adequate education leaves them incapable of getting better jobs, further cementing their situation and extending poverty across generations.

They lack the funds to purchase any assets that would serve as an initial productivity factor. The rich ants, who cultivated, for example, a hundred seeds in a day, would be in a position to cultivate a thousand seeds a day with the help of a machine. However, the grasshopper, who didn’t have any funds to buy a machine, would be unable to increase his income.

This is the self-reinforcing aspect of this circle. Low levels of income stop people from getting a good education and protecting their health, which leads back to the problem of low income. It is difficult for them to invest in things that could help them increase their income, since they have no surplus income to begin with.

They would require external support to escape from this situation. However, since the poor lack collateral, banks won’t lend them any money. Without being able to borrow funds, they have no economic opportunities, leading them back to low income and the inability to provide collateral. Thus, they get trapped in the poverty circle.

Now, let’s look at the bigger picture.

Also Read: Why Do Rich People Always End Up Saving More Money Than Poor People?

The main income for governments is the tax that they collect from their population. They collect tax from people and utilize these funds to provide various facilities in the form of infrastructure, security, health care and so on. A good national infrastructure contributes to raising the quality of life of the people by creating amenities and providing transport and communication services. It reduces the cost of production for industries and provides access to modern technology.

In poor economies, the government has a limited tax-collecting capacity due to its poor population. Hence, it finds itself unable to afford expensive investments in infrastructure. This leads to the limited availability of specialized inputs and forces downstream industries to rely on less advanced (or labor-intensive) technologies, thereby leading to low productivity. Their profits hardly cover the owner’s consumption, thus leaving very little or no surplus at all to be invested.

Without funds, the government cannot provide employment opportunities to its people, which in turn contributes to higher levels of unemployment and poverty. In addition to unemployment, poor countries usually have a large population, which makes things worse. The resources available per person gradually become less and less.

Due to poor health and sanitation, diseases and epidemics are frequent and become unmanageable in the face of poverty and a lack of adequate facilities. This further erodes the standard of living of the economy. Adverse natural and stochastic shocks perturb the capital and drop the economy below a certain threshold point. Some countries also have geographical disadvantages due to their topography and climate. Harsh agricultural conditions with temperatures that are too hot or too cold result in low outputs.

Clearly, there are many factors that cause poverty traps to both emerge and remain in place. These factors have different aspects that make the issue multidimensional and complex. This makes it very difficult to suggest policy proposals, since there are hundreds of traps that an economy may fall into. Any policy intervention attempting to pull it out of one trap may end up pushing it into another, even bigger, crisis. As a result, economies and entire countries can stagnate in their impoverished state.

Also Read: Why Do Only The Developed Parts Of Developing Nations Keep Getting Investments?

• 1 Poverty Trap. Northwestern University
• Hassan B. Izhar - Why Poverty Traps Emerge? - CiteSeerX
• (2017) Growth and Poverty Traps: Examples from Literature - CORE. The University of Connecticut
• Dr Carolyn O’Fallon - Linkages Between Infrastructure And Economic Growth - CiteSeerX

Sushmitha Hegde is a Commerce graduate from University of Pune. She can say “hello” in 61 different languages, but she is learning Spanish so she can say more. She loves to talk about topics ranging from taxation and finance to history and literature. She is just a regular earthling who laughs at her own jokes, cries while watching movies and is proud of her collection of books!

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• Published: 25 June 2024

The poverty trap: a grounded theory on the price of survival for the urban poor in Mexico

• Bernardo Turnbull 1 ,
• Sarah Frances Gordon   ORCID: orcid.org/0000-0001-5131-8519 1 ,
• Angélica Ojeda-García 1 ,
• Jaime Fuentes-Balderrama 2 &
• Cinthia Cruz del Castillo 1  

Humanities and Social Sciences Communications volume  11 , Article number:  826 ( 2024 ) Cite this article

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People living in poverty inhabit the outskirts of Mexican cities. They struggle and survive but cannot overcome poverty. The present article seeks to understand how the survival tactics of the urban poor keep them in poverty and, with this understanding, inform future interventions. A study based on qualitative grounded theory was conducted in 10 impoverished neighbourhoods in three main regions and cities in Mexico. Observational accounts and in-depth individual and group interviews were conducted with diverse social actors for 115 participants (75 women and 40 men) aged between 12 and 76 years. The data were analysed using an interpretative thematic analysis. Research findings revealed that the urban poor tackle adversities, such as a lack of basic services, health, education, environment, and nutrition. The strength, patience, and hard work employed by these individuals to survive were evident, but their actions to tackle these threats were costly. Ultimately, the urban poor remain in a cycle of poverty, or poverty traps. Interventions to fight poverty, improve life conditions, and general development must include integrated and participatory plans that consider the knowledge and strengths of the urban poor.

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Introduction.

Between the homeless and middle classes, the urban poor in Mexico are constantly increasing in size and expanding in the unplanned settlements on the outskirts of nearly all major cities in the country. These neighbourhoods are considered unlawful by local governments, and these families usually live on land they aspire to own one day (Tellman, 2019 ). Although they retained certain rural practices, such as raising chicken in their yard or cooking with firewood, the majority did not come from the country, as they did a few decades ago, but from a place in the city where they could no longer afford rent. These places fit the current definitions of slums, but they are neither old nor densely populated nor surrounded by other parts of the cities. They are constantly sprouting out in the outskirts and, in the stages that this research visited, not yet crowded. However, their inhabitants occupy underserviced and irregular land but still expect their current situation to improve (Iracheta Cenecorta and Smolka, 2000 ). This new urban migration has yielded an estimated six million irregular plots that exist on the outskirts of Mexican cities with nearly one hundred thousand new ones popping up every year (Avalos, 2020 ). The present study shows that although people living in poverty do not have a choice now, they often move into these irregular spaces in the hope of a better life that may not come. Eventually, local governments try to regulate the unregulated, yet do not try to solve the problem of poverty (Iracheta Cenecorta and Smolka, 2000 ).

In 2020, 50.8% of the total Mexican population had income below the poverty line and 17.2% below the extreme poverty line (CONEVAL, 2021 ). Furthermore, 50.2% had no access to social security and 22.5% had low nutrition (CONEVAL, 2021 ).

In Mexico, a neighbourhood or a land development is called a ‘colonia’ Footnote 1 in Spanish. There is no documented historical explanation for this term, but it is the legal name of urban subdivisions (Real Academia Española, 2023 ) and is distinguished by a name and not by a number or organised classification. The term ‘colonia’ is used to refer to irregular and poor settlements on the American side of the US border (Chahin, 2005 ), but in Mexican cities, it has been used for as long as people remember. The places where poor urban life may be, officially, a colonia now, but they were not when people first moved in and began building shelters and homes. The people who live there have been calling it a ‘colonia’ long before the authorities did. The main reason these neighbourhoods do not have running water, electricity, sewerage, and other services is that they are not legally part of the cities they surround, and no government entity wants to accept responsibility for them. The process of becoming a colonia is a daily struggle for these inhabitants, and their interactions with powerful external actors partly motivate the present study.

The varied and brave strategies of the urban poor to survive under these conditions have been documented for some time, both by academic research and by the media (Hernández et al., 2022 ). However, our data show that the survival actions of the urban poor resemble tactics rather than strategies because they cannot afford the luxury of a planned course or the information on obstacles and availability of resources that a strategy would demand. Instead, they improvise and develop a varied set of skills and actions they may not be aware of (Berardi, 2021 ). They learn and practice new skills to navigate through changing conditions, but their circumstances do not allow for strategic planning, which is one of the many luxuries they cannot afford.

In this article, we examine our data in terms of of the concept of ´relational poverty, which does not consider poverty a category people fall in and out of, but the result of social, economic, and political influences between people in poverty and others (Feldman, 2019 ). As Feldman ( 2019 , p. 1710) states, “Under the relational microscope, then, poverty is defined as a problem of power, as privileged state and non-state actors exert power to the disadvantage of the poor”. We will consider this approach to poverty, and our data suggest that it is not a self-contained phenomenon as a concept of the culture of poverty hints at (Gorski, 2008 ). When the urban poor are kept in poverty by their actions to survive, they are caught in what theorists call a poverty trap (Haushofer and Fehr, 2014 ; Frankenhuis and Nettle, 2020 ). This refers to self-reinforcing mechanisms that act at different levels of a social-ecological system. Adaptive behaviour incurs a variety of resources from people in poverty and robs them of the opportunity to improve their future or escape poverty (Dercon, 2005 ; Barrett and Swallow, 2006 ).

Given the increasing number of people living in urban poverty and unplanned settlements or colonias in Mexico, our research sought to study this multidimensional poverty, including not only the lack of income or satisfaction of basic needs but also disadvantages in education, health, safety, opportunities, and power (Sánchez et al., 2020 ). We want to see how the survival actions of the urban poor in Mexico keep them stuck in poverty traps (Auyero and Benzecry, 2017 ; Hernández et al., 2022 ). By analysing over 120 interviews conducted on the outskirts of 10 cities in three regions in Mexico, we describe the tactics that the urban poor use to survive. We highlight the trade-offs that the urban poor experience to reveal the poverty traps concealed at the source of the problem. We argue that these strategies do not help the urban poor rise above the poverty line and only deplete their resources, so their progress falls below expectations. We show how their best efforts contribute to keeping them in a cycle of poverty because they cannot invest in generic capacities that could break them out of the trap (Eakin et al., 2016 ). We seek answers to the question: How do the actions that the urban poor take to drive themselves out of poverty contribute to keeping them poor? Ultimately, we believe that understanding urban poverty from the perspective of the urban poor will inform future research and interventions aimed at poverty reduction.

The view of the urban poor

The authors chose Grounded Theory to base our understanding of the situation on the interactions and relations of the urban poor with each other and other social actors, as described by themselves. We also considered it necessary to examine the change over time in these families and finally to place them within the surrounding social structures that contribute to shaping their lives (Corbin and Strauss, 2008 ). We intended to understand as much as possible from the perspective of the urban poor and how they deal with the poverty trap , their surroundings, and the many social actors they interact with. We hope that an emic perspective will draw on the knowledge of the urban poor, and thus avoid the failure of many interventions aimed at reducing poverty. Most of our participants were individuals, but our unit of analysis was the household, normally composed of more than two generations and often recomposed after a change in spouse.

People participating in observation and interviews

We recruited people from 11 marginalised neighbourhoods on the outskirts of 10 cities in three regions (North, Centre, and South) of Mexico, which included mostly heads of 36 households and key informants for each of the dimensions of poverty: education, health, nutrition, income, environment, and safety (see Table 1 for participants included in the analysis). Household heads were mainly mothers, grandmothers, and in four cases fathers. The health personnel and health providers consisted of nine doctors and nurses. Households include more than two generations and are seldom nuclear. These households did not pay rent and were in an unpredictable, informal process of owning a piece of land where they lived. They also had been gradually building their housing using labour and materials. The key participants in the education sector were teachers, school staff, and students. The key participants in the health sector were doctors and nurses from nearby government clinics. Key participants included employers, police officers, people on the streets, and heads of households.

Seven participants were school teachers and were in a better financial situation than the families of their students; however, teachers also struggled with low-income and substandard living conditions. Eight students (four girls and four boys) were interviewed about their school, family life, and teachers, but any information they provided about other themes was recorded and coded. Seven of the participants were mployers and included people with small businesses functioning inside the colonias and, in general, their houses were bigger and better, or outside the settlement. Three participants were police officers and security personnel and lived in a slightly better neighbourhood because of their steady income; however, they consider themselves relatively poor. The nurses and doctors we interviewed did not live in the settlements on the outskirts of the cities, and their clinics or hospitals were outside these areas. However, their patients came from impoverished settlements. The most well-educated participants were those working in the health sector.

The data collection process

Cities and colonias were selected based on their degree of underdevelopment and socioeconomic status from the official maps. After the initial inspection, two cities were excluded to guarantee the safety of the team. Our field researchers observed and interviewed key people in these colonias or neighbourhoods. To represent the dimensions of urban poverty, our semi-structured interview guides included themes of income, nutrition, education, health, safety, and environment.

After one day of field observation and familiarisation in the colonia , individuals were approached face-to-face at the institutions they worked for, on the streets, while doing something outside their homes or selling something through their windows. The initial approach consisted of introducing the project, the interview process, and the informed consent process. If a person agreed to participate, they were invited to participate in one of the two group interviews in the neighbourhood. The participants and guardians of the children read and signed informed consent forms, agreeing to be interviewed and photographed. Before starting the interviews, the participants provided additional verbal consent to be interviewed and recorded. Since participants constituted a vulnerable population, as highlighted by Liamputtong ( 2007 ), the field team did not knock on doors to get interviews but approached participants in public spaces. Since the research sites had poor or no streetlights and there were normally no police personnel around them, the interviewers always left the colonia before dark for their safety.

People who did not accept the interview did so because of lack of time or suspicion. In one place, some inhabitants were uncomfortable speaking Spanish. University groups are usually welcome, even in these places, and the interviewers were trained not to instil false hope in the participants; no compensation or incentive was offered or given. The field team did not try to gain access to the caciques or other social gatekeepers involved in the settlement process because approaching them could have discouraged or influenced the responses of our main participants (Long and Villarreal, 1994 ). Families or household members who managed to leave these colonias to find a better place elsewhere were also not included in our sample.

Interviews started with general questions, like “How long have you lived here?” or “How many people live with you?”. The interviewers did not read the written questions but, along with the conversation, introduced the topics in the semi-structured interview guide. When possible, the interviewers followed the lead of the participants and asked questions about what seemed to be their interests, such as ‘How did you manage to get electricity in your house?’. As the interviews developed, the interviewers would sometimes ask the participants to elaborate on a specific topic, such as “Is your child’s education important to you?” There was no fixed format for the questions, but during the transcription process, the interviewers worked to improve their technique through self-observation and critique.

Two semi-structured group interviews were conducted with a group of trained 14 participants: six women and eight men. Conversations were audio-recorded and transcribed by trained researchers. All interviews began with the explicit permission of the participants to perform and record the interviews. All interviews and observations were conducted between February 11 th and 11 April 2019. The interviews were, on average, 38 min long, but as the respondents were free to finish when they wanted or to continue if they wished, a few lasted only 10 min and some lasted longer than one hour.

The total number of interviews and observations was determined by the resources of the project, mainly time and money. In the analysis, we reached theoretical saturation in time to show that the number of interviews was sufficient to answer our research questions. The analysis process was guided by the themes and categories found in the sources (Morse and Richards, 2002 ).

Analysis of grounded theory

The interviews were transcribed by interviewers and other trained personnel. The coding sampling process was intended to balance the interview topics, types of interviewees, and places where the data were collected. Therefore, the coding order was determined to maximise the variety of these characteristics as well as their combinations. Coding was stopped when theoretical saturation was reached, as no new themes were constructed from the data. The total number and distribution of the coded interviews are presented in Table 1 . The general population and heads of households were the most frequent respondents across topics. Other participants appeared only on their respective topics of interest.

Initial coding was carried out by the first author of this article, and later, it was discussed, modified, or confirmed by all authors. As it was not possible to validate our interpretations with the participants themselves, triangulation was performed between different participants and observations and interview data. We also triangulated the information provided by the participants with interacting roles, such as students and teachers.

Although the interview guide only included questions about the specific topics of education, health, nutrition, etc., the interviewers were trained to let the participants speak about other issues if they chose to do so. Therefore, we coded and analysed all transcriptions under both expected and emergent themes, regardless of the initial topic of the interview. We coded the transcripts under such labels as housing conditions and the surrounding areas, adversities experienced by the participants, their actions, and those of other members of their households.

Additionally, interviewers registered their direct observations of the field in a pre-designed form along with their diaries. These observations were included in the analysis and compared with the verification interviews. Both the interview transcriptions and observation sheets were classified and coded using QSR NVivo 11 (QSR International Pty Ltd. 2015 ). We used thematic analysis (Vaismoradi and Snelgrove, 2019 ; Braun and Clarke, 2021 ) to classify and reflect on the data to theorise and integrate interview content and observations. The relationships between themes and how they relate to the overall meaning of the text were also explored.

By enlisting grounded theory in a contemporary and reflexive way, researchers were able to continue interacting with their data and emerging ideas. This process meant that the analysis led to the adoption of multiple methods of data collection as the data led the research. Furthermore, the thematic analysis process was guided by how researchers interacted with and interpreted their comparisons and emerging analyses rather than by external prescriptions of the data (Charmaz, 2006 ).

The data revealed the range of individual forms that our concepts could adopt, as well as their characteristics and dimensions. All of these are integrated into our results and discussion. The possibility of dialectical concepts was always considered, so if, for example, one participant reported acceptance or approval of health services, the respective category was created to include the rejection of such services by potential participants. If such data were found, they were incorporated into the analysis model as opposite points on a continuum, and not as separate concepts. All these concepts were examined by the authors to produce the final integrated model.

To verify the accuracy of our data and analysis, we developed a series of preliminary models using different core categories, until we achieved a model that integrated all partial models and showed the best possible fit between the concepts constructed from the data. This model is presented in the Results and Discussion section.

Our interview participants covered many different topics. These findings do not cover all of them, but only those that contribute to answering our research questions.

General life conditions in an irregular settlement

Our participants and informants lived on underserviced and unregulated land (Iracheta Cenecorta and Smolka, 2000 ) on the outskirts of the city. There are also regular developments that share similar problems in urban areas in Mexico (Reyes, 2020 , 2021 ), but the plots where our participants have lived have not been formally zoned for housing by the government.

The absence of an authority with formal responsibility for land creates a patchwork of structures and services. In these colonias , there may be one or two paved roads, but there are mostly dirt streets and no formal drainage system. If there is a sewer, wastewater is normally dumped in the nearest already polluted stream. The lack of paved roads also means no transport, waste management services, gas distribution, and formal security services because the drivers of the vehicles that carry these functions do not want to risk their trucks in potholes, dirt, and rocks, thus avoiding these areas. At night, if there are any streetlights at all, they are insufficient or unreliable. Most streets may be completely dark at night, except for houses that have lamps outside, if the power has not failed.

Houses are usually small, with few windows, incompletely constructed with cheap materials such as cinder blocks and tin roofs, and usually have no structural foundations. Besides the discomfort of improvised shelters, there is a threat of losing it all. Some houses may have running water, but this water is often not clean, cheap, or accessible. Circumstances are often very similar to electricity, with the added risk of electrocution owing to improvised connections.

There are no schools or hospitals in these colonias . The nearest is usually beyond a reasonable walking distance and, although situated in regulated areas, these facilities are usually understaffed and underfunded.

Agency and survival without regular services

The residents in these circumstances face a constant struggle involving a variety of strategies, some individual, most familial, a few in the community, and many involving interactions with not very visible actors from higher social and power scales. However, throughout the process, the urban poor develop new skills and strengthen themselves and their families in several ways.

The families that move to these places do not come directly from the countryside but from another urban neighbourhood in the hope that owning a piece of land and a house will help them improve their standard of living. They have faced cycles of unemployment and under-employment, but always with a low income. Through a variety of processes, most of them move to these areas outside of the law (Tellman, 2019 ) because they are motivated by the opportunity to own a piece of land, even if they are denied formal services. They are usually unable to pay rent and do not seek a regular piece of land because they know that payments will be much higher than the rent that they already cannot afford.

The circumstances are not the same for all, and the way a particular family approaches these problems differs. However, there are some similarities and trends. There is no unique or culturally competent way of doing things (Bernard et al., 2016 ), and not all households take the same steps with a specific problem. However, our participants shared some continuities, as they tell us in the following paragraphs.

Hard work and entrepreneurship are common in these colonias . Employees will work extra hours, those who clean houses for a living will try to find more work, one mother sells goods door-to-door, and another runs a food business in their yard. However, their income will not improve substantially, and they still face the same adversities equipped with the meagre resources that under-employment and out-of-the-window shop work bring.

To access water, residents buy and set up large drums in the yard, plastic jerrycans, basins, and buckets, and wait for a water lorry to show up and fill their containers at a reasonable price. Eventually, someone may offer to connect them to a source if they can buy several hundred metres of hose and find a way to lay it on the backstreets. Neither the connection to the water source nor the intermittent service will be free (Kumar et al., 2022 ), and they may still have to keep using the drums and the truck, which incurs costs of time and money. Pedro, a male household head (P) in his early forties, said to the interviewer (I):

I: And… how did you arrange things to have water?

P: Oh, the same local leader was here… we were all involved in that… I kept telling her… well… I would say… we just took the water, just drilling and tapping [into] the pipes anyway we could… by force. They didn’t want to… the [water] company didn’t want to, and they never did. (05.D18)

A similar story has emerged regarding informal access to electricity. This strategy included a long wire and a chance to hook it up to a cable a few hundred metres from home. Maritza, a 50-year-old mother (M), said,

M: Then… well, later on, bit by bit, it got better. But, for example, some twenty years ago, for example, we had no electricity… we arrived… three of us arrived and, right after, others came where we lived and, back then, whoever could afford it would buy their power cable, [hook it up] and pull it all the way from down there up to here. (01.G02.)

This informal and often unreliable service will cost them money and put them at risk of both electrocution and possible legal action. However, individuals living in urban poverty in these contexts do not have a choice but to resort to these informal or ‘pirate’ practices, which can be costly and pose risks to their health. These individuals are in no position to appeal to the law if they are extorted by clandestine providers.

There is no “do-it-yourself” substitute for paved roads, as there is for water and electricity. The urban poor may try to smooth their dirt streets with a shovel, and some try to fix potholes; however, communities must wait for asphalt if they want an adequate solution. No paved roads mean doing without cooking gas because the gas delivery trucks cannot access their homes. For the same reason, there will be no regular rubbish collection services, and because of these practices, burning rubbish, which pollutes the air and soil, is common. Police cars will not drive through these streets because of a lack of paving. Jaime, a male police officer (J) serving a colonia with some recently paved streets, told our researcher in an interview:

J: Now… as they have finally paved the streets… they [the thieves] would see us from afar and make a run for it, while the patrol car got there; it would go very slow. Now we get there faster and they don’t get away; we can be more dissuasive. We can reach places where we could not go before. (03.E14.)

The lack of paved roads also increases the cost of education and the chances of students dropping out because transportation is scarce. As a result, going to work and accessing public transportation is also more expensive. Public health services are also located far from these settlements, and access to food in these colonias is limited. All of this takes a daily toll on participants’ money and time, ultimately draining their resources.

Paved streets may come, one street at a time and not everyone. Sometimes, local governments pave the streets; however, due to political motivation and self-interest, the quality will be low and the paved road will not last long. In a few years or even months, potholes, mud ditches, and flooding will occur on these roads.

Paved roads may include sewage. Again, the low quality of paved roads often means that they are not long-lasting and collapse due to pollution and flooding. Damage caused by floods can range from mild discomfort to health risks to a significant threat to life. Belinda, a 50-year-old female head of the household, said to the interviewer:

B: … it was very hard for us here, very very hard. For example, later, more people came by and got to work to fix things, but when we put the drainage in, even someone died. (01.G02)

The urban poor in these communities still count on each other for their help and support. As has been documented in previous research (Lomnitz, 1973 , 1993 ), local support based on family networks and proximity helps the urban poor survive adverse conditions. However, these networks have been fragmented by migration and have gradually weakened through resource scarcity among their members. Although they are still at work, solidarity, and kinship are just enough to survive.

Education and health

As mentioned before, schools and health services will be harder to reach from these colonias . The quality of school and health services near these communities is also not of the same standard as in wealthier neighbourhoods in Mexico. Families may need to contribute cash, materials, and labour to make their school operational, while clinics and hospitals are likely to have less, older, and worse equipment and fewer personnel; queues are longer, drugs and materials are scarce, and the urban poor will have to pay for their drugs or even opt for private care to solve a health crisis. Norma, a 39-year-old head of household (M) said:

I: In terms of money, did you have a problem with the medical attention that you required?

M: Mhh, in expenses?

M: Yes; there were expenses… a lot .

M: Many expenses, because we had to make trips, be there, buy food and all that. Yeah sure… a lot of expenses .

I: I mean, the medical treatment, as such, had no cost, I mean, did you get your meds for free and the… you could see the doctors?

M: Mmh… from the institution, yeah. But the fuel, the food, the housing, all that, came from our pockets. (10. F30)

During our time in the field, we observed that participants reported some improvement in their living conditions, but these improvements took too much time and were neither high-quality nor for everyone in the neighbourhood. Things improve over time and with the development of the family. When children grow and can work, families tend to improve their standard of living, but never reach the level they expect or are promised. Our participants never mentioned the need to leave the colonia , but they did talk of their children or grandchildren having a better life elsewhere in the present or near future. This means that retribution for escaping rent and struggling for years may only come to the next generation.

Summary of findings: the cost of surviving

In conclusion, the promise of a better life without rent brings them into the colonias , and they survive through a variety of strategies operating at different levels and involving other social actors. The common theme is undoubtedly the unexpected cost of their achievements, be they in cash, time, work, space, and/or by a trade-off with another area of the family’s well-being, such as education, health, and nutrition. These costs are often overlooked when the focus of research is on the coping strategies of the poor.

When one or more of these actions is also ‘illegal’ (such as ‘hanging yourself’ Footnote 2 from an overhead power line), it weakens the household by barring them from resorting to legal services.

People in charge of services are not responsible, because these places are not officially part of their cities. When they promise to provide utility, they do so for political gain and self-interest. The people who brought the urban poor to the neighbourhood did not keep their word and seldom signed documents. They are under no pressure because the urban poor are powerless. Those who have power over them may extort them by pocketing their payments and not providing goods, taking advantage of the weakness their illegality implies. The urban poor managed to stay in these neighbourhoods, but just barely.

The nature of the Poverty Trap

As we have shown, surviving without basic services takes a toll on the resources of the urban poor and spoils their chances of saving money, increasing their income, and otherwise achieving social mobility. The place they moved to fails to become a comfortable living space yet costs them much more than they expected, and they are unable to improve their living spaces or move elsewhere. The more powerful social actors in their life worlds appear to offer no opportunities for better jobs, proper education, or training that could improve their income in the future. Health services and education were neither truly free nor good enough to serve as rungs to climb poverty. Troublesome logistics, distance, time, and expensive transportation narrowed down the opportunities of participants, so that their entrepreneurship, agency, and hard work only perpetuated their hand-to-mouth situation. This study has highlighted the hidden costs of poverty, be they emotional, financial, or costs to bodily health; they exist, and this trend is often overlooked in research on the urban poor in Latin America.

Integrating Mendenhall and Carney’s ( 2020 ) strength model into our understanding of urban poverty reveals that endurance is not a simple, linear variable, but a complex and growing network of forces, skills, relations, and characteristics. These survival strengths are very admirable and may make the urban poor stronger, more creative, resourceful, and wiser (Scott, 1985 ); however, this does not achieve social mobility (González de la Rocha, 2020a ). All their actions have a cost that is often permanent and maintained by their relations with people who have power over them outside their colonias . Their strengths allow them to survive, but doing so perpetuates poverty. When poverty leads to behaviour that further perpetuates poverty, this is called a “ poverty trap ” (Frankenhuis and Nettle, 2020 , p. 17).

As our findings show, the adaptive strategies participants engage in to combat poverty often contribute to their downfall. The moment they decide to live in an informal settlement, they step out of the government’s framework (and the government is temporarily comfortable getting rid of it). They maintain their human rights but forfeit many of their legal rights when often unaware of it, they trespass the law just to access public services and survive (Tellman, 2019 ).

The price of resisting poverty

Almost every one of their strategies of resisting poverty costs them dearly, may it be in cash, in kind, in time or energy, in legal power, in their scarce space, and frequently in their future. This happens when they must spend their savings or give up a family member’s job or educational opportunity to take care of younger children, go out to work, or just stay home for the water truck to drive (Eakin et al., 2016 ). These costs continue to accumulate until they further perpetuate the poverty that they are already experiencing. In this sense, as people try to avoid paying unaffordable rent, they enter into a poverty trap they cannot escape.

Family networks and futile support

One of the themes was the presence of family support networks as a strategy to resist poverty. However, these networks are limited by poverty and appear to possibly fade. Being able to fall back on kin also implies eventual reciprocation, which can manifest as resentment among family members (González de la Rocha, 2020b ). While these networks of mutual help have positive effects on self-confidence, sense of kin, and humanisation of daily life, they also contribute to the depletion of household resources.

Aid comes in many forms, including the government, private sector, and religious and political institutions. However, the services and goods delivered to the urban poor are of low quality, unreliable, costly, politically motivated, stigmatised, poorly targeted, and insufficient (Eiró, 2019 ). Governments feel no commitment towards the impoverished and powerless and may be in urbanisation, education, or health; the forces confabulate to apply what Tudor Hart ( 1971 ) called “the inverse care law” that dictates that the less power you have, the lower the quality of the services you receive in life.

The bait and the trap

Therefore, despite their agency, resilience, and strategies to resist poverty, community support, and external help, they remain in precarity (González de la Rocha, 2020b ) because poverty traps remain pervasive across all levels (Radosavljevic et al., 2021 ). In this sense, these poverty traps are socially constructed partly because the urban poor do not see their situation as a trap. They believe they are taking steps toward a better future and are hopeful (Pettit, 2019 ). This is why the places they move to have been called “slums of hope’ (United Nations Human Settlement Programme, 2003 , p. 9). As they saw, they did what they had to do and had no other choice. Outside, the social construction of these poverty traps operates through the definition of these informal settlements as irregular, informal, and illegal. The way we name things and our knowledge of them determine what we do about them, and, with that, what happens to the people who inhabit these places (Berger and Luckmann, 2016 ; Eiró, 2019 ; Matus Pérez, 2019 ).

In this sense, their needs drive them into a ‘Faustian bargain’ in which they accept the immediate relief from rent and, in the long run, end up paying the high price of accumulated negative consequences that contribute to keeping them poor (Wood, 2003 ). In some cases that were not included in our sample, people were attracted to the place by the promise of a steady income that would never have been enough to sustain a living (Noibi et al., 2021 ). What could be interpreted as a situation of exclusion from society is, in fact, an adverse incorporation (Hickey and du Toit, 2012 ; Lawson, 2012 ; Feldman, 2019 ) and people experiencing poverty are seen as a “pool of low-cost labour” (United Nations Human Settlement Programme, 2003 , p. 67). The urban poor in these underserviced spaces are in vulnerable positions in society and are unable to achieve social mobility (Iracheta Cenecorta and Smolka, 2000 ).

Ineffective help

The curative process of regularising the properties of the urban poor that the government has attempted has not been helpful and has contributed to the process of producing irregularity (Iracheta Cenecorta and Smolka, 2000 ). External interventions that import solutions into the colonias interact with the urban poor but do not consider their knowledge. The urban poor cannot be expected to get out of the city by just working hard and staying united. If their knowledge and strengths are not incorporated in the design, planning, and implementation of the interventions, the interventions will not work. The objectives of these interventions must be modified and adapted to people’s needs through human agency (Clark, 2009 ; Turnbull et al., 2009 ) and coordinated with other interventions. An integrated and participatory plan is necessary to address these complex situations. This approach has been described by the United Nations as “Current best practice” (United Nations Human Settlement Programme, 2003 p. 132).

Recommendations for research

The scope of this study did not allow for a deeper description of cases; rather, we concentrated on a general picture of the patterns between the urban poor in different regions. It would be useful to contrast these conditions with those of other Latin American cities and their surroundings. Also, there is a need for a microeconomic model that reveals a vicious circle in which the urban poor live. In the interviews, we noticed that they were seldom accounted for. They do not know if their informal shops are making a profit, how much they are spending, and how much they are reinvesting. However, they could have hidden this information from the interviewers because we were strangers, (Scott, 1985 ) but it would be useful to know how they conceptualised saving and day-to-day finances.

Limitations of the study

This project was not without its limitations. By excluding these two cities for safety reasons, the results on this theme could not represent the reality for other Mexican colonias . Similarly, we could not approach or gather information about families that had managed to move from these colonias to further understand slum mobility. Another limitation of the present study was its transversality. We reconstructed the life cycle of a family by observing different households at different developmental stages; however, further research could research families in a longitudinal study. Our sample was large, but analysis of the data generated questions that warranted futher questioning and more research on the topic. It would be important to learn if any of these families have managed to leave the colonia for a better place, or if any of these neighbourhoods managed to bring up their life status to the expected level. At the opposite end of the continuum, it is also necessary to ascertain what happened to families that could not stay in the colonia . Finally, we need to understand what complementary social actors have to say. These would be the political leaders that ‘gave’ them the land or the self-appointed brokers that sold it to them, the persons with contacts that let them hook up running water or electricity, the ‘aboneros Footnote 3 ’ that sell them appliances in payments and so on. Although we know that this is a population that is difficult to reach, it would be valuable.

The poverty trap has been set and continues to attract the urban poor. It will absorb their efforts and not take them out of poverty. The task was to make these evident and generate alternatives.

Data availability

The data from which this Grounded Theory was constructed have been uploaded to the Dataverse repository and are available to readers through the following Drop Box link: https://www.dropbox.com/sh/hllr5wp9251ttwz/AAAXHMPN_LoghemmxC5C8FU4a?dl=0 .

“Colonia” which obviously means ‘colony’ is the official and colloquial word for a neighbourhood in Mexico. Old and new, poor and rich are all called ‘colonias’ and their names are included in the postal address of any household.

“Colgarse” literally means ‘to hang’ as a reflexive verb. It has nothing to do with hanging from the neck or any self-harm. It means to use a long pole or bamboo stick to hang wires directly from the electric air lines to bring electric power to your home. They do not connect them permanently because they unhang them during the day to avoid being caught by the electric company personnel.

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Special thanks to Dr. Muriel Robinson OBE DL for her thorough review of this manuscript. The authors honour the memory of the late Joaquina Palomar, PhD who was the initial creator of the Urban Poverty Project.

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Turnbull, B., Gordon, S.F., Ojeda-García, A. et al. The poverty trap: a grounded theory on the price of survival for the urban poor in Mexico. Humanit Soc Sci Commun 11 , 826 (2024). https://doi.org/10.1057/s41599-024-03328-3

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Poverty traps across levels of aggregation

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Poverty trap studies help explain the simultaneous escape from poverty by some households and regions alongside deep and persistent poverty elsewhere. However, researchers remain divided about how important poverty traps are in explaining the range of poverty dynamics observed in various contexts. We build a theoretical model that integrates micro-, meso-, and macro-level poverty traps, allowing us to analyze the ways in which multiple layers of poverty traps interact and reinforce each other. Through this simulation model, markets and institutions arise endogenously and help certain individuals escape poverty, while others remain persistently poor. In addition to one’s own productivity and initial capital levels, we explore how individual opportunity and income can be heavily determined by market access and institutional factors beyond one’s control. Using simulation results from controlled experiments, we can identify the role played by meso- and macro-conditions (that correspond to local markets and country-wide institutions, respectively) in helping individuals escape poverty. Our results suggest that even in a parsimonious model—with optimizing, forward-looking agents operating in a world with only one trap at each level—local and national context matters immensely and combines to determine individual opportunity in complex ways.

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1 Introduction

While recent decades have seen approximately one billion people, escape poverty, certain countries, regions, and individuals remain poor generation after generation. Despite the overall decline in extreme poverty globally, the Economist notes that “the bad news is that poverty is becoming harder to tackle,” since World Bank estimates find that extreme poverty recently increased in Sub-Saharan Africa as well as the Middle East and North Africa (Economist , 2018 ). As a result, future success is uncertain and it may become even more difficult to achieve similar reductions in poverty in the future. In many cases, persistently poor individuals reside in remote regions where they use low-return technologies, have limited access to markets (including input, output, or labor markets), and where ineffective or corrupt governments fail to provide essential public goods, ensure secure property rights, or overcome coordination failures. As a result, it is important to analyze the conditions under which chronic poverty persists and the ways in which individual factors, local context, and national policies combine to perpetuate poverty. In this paper, we construct a poverty trap model combining insights from micro-, meso-, and macro-poverty trap studies and then use data generated by this model to explore the causes of individual poverty. Our model is based upon intertemporal utility maximizing, forward-looking agents who choose either a low- or high-return agricultural technology (our micro-trap) in a context where markets arise endogenously (our meso-trap, which is escaped when individuals choose to become traders, thus providing improved local market access to their neighbors) and government policies are determined endogenously (our macro-trap, where the median voter determines the level of public goods provision which can become more broadly beneficial and inclusive). We find evidence of poverty traps that are highly dependent on neighborhood and country characteristics. This implies that policy should be tailored to specific contexts, potentially including cash transfers in some cases alongside improved market access or public goods provision in others. To tackle the full range of causes of poverty, however, multiple policies will be required.

Two recent reviews of the poverty trap literature draw very different conclusions, with Kraay and McKenzie ( 2014 ) finding that poverty traps are “rare” and Barrett et al. ( 2016 ) concluding that there is “overwhelming” evidence that poverty traps frequently occur, as described more below. As is clear from reading these surveys, the empirical identification of poverty traps is inherently difficult, with multiple potential equilibria pulling data away from unstable thresholds, limited panel data, and complex and evolving interactions between individuals, local context, and national policies. Agent-based models can help inform and overcome these three challenges. In particular, it is especially useful for modeling interactions between individuals, neighborhoods, and countries. These interactions clearly matter, but their complexity means that theoretical models are less developed. The complications that result from integrating these layers of analysis lend itself well to agent-based models. Rich, detailed simulation data allow for empirical analysis that is otherwise impossible with real-world data. Additionally, our model allows us to analyze the importance of focusing on single or multiple equilibrium poverty traps. For example, with a range of initial conditions in a common layered model, we are able to show how some countries exhibit single equilibrium poverty traps, others multiple equilibrium traps, and others no poverty trap at all.

Our paper thus has two primary research questions. First, how do multiple layers of poverty traps (micro, meso, and macro) interact with and potentially reinforce each other? To answer this question, we build upon Barrett and Swallow’s ( 2006 ) definition of fractal poverty traps and develop a layered agent-based model. Our model has a poverty trap built into each level of analysis and endogenously determines which poverty traps are overcome in which contexts. Second, using data generated by our model, we ask how individual poverty depends on not only micro, but also meso- and macro-characteristics.

Our model highlights fractal poverty traps in order to analyze how multiple layers of poverty traps interact with and possibly reinforce each other. Barrett and Swallow ( 2006 ) develop an insightful informal model that we expand upon by formalizing each level of aggregation, where both single and multiple equilibrium poverty traps can coexist simultaneously at multiple levels of aggregation. We are interested in the welfare of individuals within households, but they make economic decisions within a context determined by larger meso- and macro-processes, and many of these factors evolve endogenously before further impacting individuals.

At the micro-level, we model individual poverty traps as occurring through lumpy production technologies, as individual farmers can use either a low-return technology or a high-return technology that requires a large fixed cost.

The meso-level is described by Barrett and Swallow ( 2006 ) as “communities, groups, networks, and local jurisdictions,” and these factors can influence poverty through coordination, cooperation, local public goods, and markets. Our meso-level poverty trap focuses on the endogenous appearance of traders, who give up production in order to transport goods from other producers to markets while earning income through transport fees. In our model, the multiple equilibria include either the failure of any traders to arise (either because nobody can afford to become a trader or because there is not sufficient demand due to geographic clusters of subsistence agriculture) or the existence of a trader that improves market access through mutually beneficial contracts.

At the macro-level, we focus on the endogenous determination of macro-economic policy. A growing literature indicates that institutions are a fundamental cause of wealth and poverty across nations and a potential macro-level poverty trap (Acemoglu et al. 2001 , 2005 ). The tax rate is chosen by the median voter, and the revenue is then used to provide a public good, which we model as a reduction in the cost of adopting the high-return technology. At the two extremes, a country may choose either low tax rates and low public goods provision or higher tax rates and high public goods provision. In the former, the government has little revenue and low public goods provision, meaning that only wealthy households are able to afford technology adoption and unequal opportunities persist. In the latter, higher taxes provide higher revenues and public goods provision, which makes the high-return technology accessible to more citizens.

For a given distribution of agents with fixed productivity and initial capital levels, households endogenously invest through time, markets arise endogenously through the decision of some agents to become traders, and voting decisions endogenously determine public goods provision. We then use our model to analyze our second research question, exploring the ways in which individual poverty is influenced by meso- and macro-contexts. To explore this question, we use our model to generate data in four scenarios: the baseline model described above, a situation where no traders are allowed to arise (meso-trap forced), a situation with no public goods provision (macro-trap forced), and one without markets or public goods (both traps enforced). Focusing on individual outcomes, we show that individual poverty traps depend on a range of exogenous (to the individual) factors. First, individuals with high productivity (capturing, for example, land quality) transition to the high-return technology in any scenario. Second, individuals with mid-range productivity levels fail to escape poverty when neither the meso- nor macro-trap can be overcome, but might do so when these traps are overcome due to neighborhood or country characteristics. We define this range as the geographic poverty trap and highlight the ways in which otherwise similar individuals may end up in very different equilibria based on context. We find that multiple equilibrium traps can appear in some layers of our model, while single equilibrium traps arise in others. Third, below a certain level of individual productivity, even the breaking of the meso- and macro-level traps is insufficient to help individuals escape poverty.

2 Poverty trap literature

The growing poverty trap literature provides valuable insights into the causes of poverty, developing a range of theoretical insights explaining why poverty falls in certain cases while persisting in others. Footnote 1 However, debates remain about the importance of poverty traps and the strength of the empirical evidence verifying their existence. In a recent survey of the literature, Kraay and McKenzie ( 2014 ) conclude that poverty traps “are rare and largely limited to remote or otherwise disadvantaged areas” (p. 129), such as rural areas of South Africa (Adato et al. 2006 ) as well as Kenya and Madagascar (Barrett et al. 2006 ). In contrast, Barrett et al. ( 2016 ) argue that existing research provides “overwhelming evidence that poverty traps exist” (p. 321). Both surveys highlight a range of theoretical and empirical studies, including ones that focus on micro-, meso-, and macro-poverty traps. Even while creating as simple a layered poverty trap model as possible, we find that context matters immensely and, in many cases, largely determines individual opportunity and outcomes.

There are several reasons why similar surveys of the poverty trap literature produce such different conclusions. First, definitions of poverty traps differ. A useful definition is provided by Barrett et al. ( 2019b ), who state that “the essence of a poverty trap is that equilibrium behavior leads predictably to expected poverty indefinitely, given preferences and the constraints and incentives an agent faces, including the set of markets and technologies (un)available to her” (p. 5). Within this framework, however, some authors focus only on multiple equilibrium poverty traps, while others additionally analyze single equilibria in which all individuals remain poor without an opportunity to reach a higher, non-poor equilibrium. With multiple equilibrium poverty traps, a threshold separates those that converge to the poverty steady state from those that grow toward a non-poor steady state. For example, Kraay and McKenzie ( 2014 ) only focus on multiple equilibria in their survey. Footnote 2 Given the existence of a threshold separating the two steady states, a short-term transfer can move households beyond the threshold and allow them to begin accumulating more wealth independently, thus providing long-run benefits without requiring further assistance (Rosenstein-Rodan 1943 ; Murphy et al. 1989 ; Sachs 2006 ). Thus, these multiple equilibrium poverty traps produce two important implications: initial conditions matter and one-time policy interventions can produce long-run benefits.

However, many households remain trapped in poverty even without the existence of a potential high-income steady state sitting just beyond their reach. This idea is captured by single equilibrium poverty traps, which exist when there is only a single steady state in which all individuals remain poor. In these contexts, short-term transfers will only provide short-term relief from poverty. Rather than being explained only by initial conditions, single equilibrium poverty traps place greater weight on structural factors that limit opportunities for these households—potentially including bad geography (Jalan and Ravallion 2002 ), disease (Sachs and Malaney 2002 ), poor institutions (Acemoglu et al. 2001 , 2005 ), or other factors that individual households cannot control. For this reason, they are also referred to as structural poverty traps (Barrett and Carter 2013 ). While evidence of multiple equilibria is most common in remote pastoral areas (Lybbert et al. 2004 ; Barrett et al. 2006 ), a large number of papers find evidence of single equilibrium poverty traps across a range of contexts (including Naschold 2012 ; Giesbert and Schindler 2012 ; Kwak and Smith 2013 ; McKay and Perge 2013 ; Naschold 2013 , and Quisumbing and Baulch 2013 ). In these cases, it is even more challenging to design effective anti-poverty policies, since they must tackle deeper, structural causes of poverty. Many studies consider both single and multiple equilibrium traps and Barrett et al. ( 2016 ) conclude that “there is overwhelming evidence that poverty traps exist, of both the single and multiple equilibria varieties” (p. 321) but that there is more to learn about the specific mechanisms causing these poverty traps. Footnote 3

We prefer to use poverty traps to refer to both multiple equilibria and single, low equilibrium situations, since in either case households can be stuck in poverty (whether in the low equilibrium or a low equilibrium) and because the term is popularly understood to mean someone who is stuck in poverty generation after generation (which can occur in both). Our model allows for multiple equilibria at the micro-, meso-, and macro-levels, thus integrating into the literature as broadly as possible. Even with the model remaining the same, the specific context determines whether agents in our model may exist in a structural poverty trap, a scenario with multiple equilibria, or no poverty traps at all, as discussed more below.

Second, because poverty traps are inherently difficult to identify empirically, Barrett et al. ( 2016 ) emphasize that “the absence of evidence is not evidence of absence,” noting that current empirical studies may thus fail to identify poverty traps even when poverty traps might exist, due to these data limitations and fundamental estimation challenges (p. 314). By using an agent-based model to generate data in a context in which several poverty traps are present by design, we are able to evaluate the contexts in which poverty traps exist.

Third, poverty trap studies of a single level of analysis—whether individuals, households, or countries—may not observe the ways in which different factors interact to cause poverty. Geographic factors can strongly influence household welfare and create geographic poverty traps (Jalan and Ravallion 2002 ). For example, access to roads and markets can improve individual welfare (Stifel and Minten 2017 ) and incentivize greater technology adoption and local economic growth (Berg et al. 2018 ; Stifel et al. 2016 ). At the national level, institutions influence individual incentives, property rights, access to education and health, and opportunity (Acemoglu et al. 2005 ; Acemoglu and Robinson 2013 ), indicating that similar households in different institutional environments may have drastically different poverty dynamics. To include this level of complexity, we develop a model of fractal poverty traps, which Barrett and Swallow ( 2006 ) define as occurring when “multiple dynamic equilibria exist simultaneously at multiple (micro, meso, and/or macro) scales of analysis and are self-reinforcing through feedback effects” (p. 3). Theoretical models provide a range of explanations for persistent poverty among certain households or countries, but existing empirical studies may miss important heterogeneity across different contexts. Footnote 4 We explore how much complexity can arise even with a relatively simple fractal poverty trap model. We find rich complexity even by only adding two levels to an individual poverty trap model, and adding more levels or additional types of traps at each level would increase this complexity considerably.

3 A fractal poverty trap model

This section introduces our three-level fractal poverty trap model. At the lowest level, individuals face a micro-level poverty trap through the choice of a low-return or a high-return technology with a fixed cost. At the meso- (neighborhood or regional) level, we focus on access to markets by introducing the endogenous appearance of traders, who give up production in order to focus on transporting goods from other farmers to markets in mutually beneficial ways. At the macro- (country) level, we use the median voter theorem to endogenously determine a tax rate which is used to fund public goods. The public goods reduce the costs of technology adoption in a way that is accessible and beneficial to a broader cross section of citizens and, as a result, can drive increased technology adoption, use of markets, and higher income levels.

While we focus our analysis on agricultural production and markets, the interpretation of this model could easily be extended to other applications where households face their own constraints (such as lumpy investments in agriculture, human capital, or migration), regional markets may or may not arise (due to social networks, poor infrastructure, low levels of trust, or costly transportation), and countries suffer from macro-level poverty traps (possibly from bad geography, poor institutions, lack of property rights, or weak legal systems). While we provide a specific story to motivate our model at each level, our main contribution is a flexible, fractal poverty trap model, which we believe could be applied to a variety of contexts.

3.1 Micro-level model

We base our micro-level model on Ikegami et al. ( 2019 ). Consider a representative individual with a fixed level of total factor productivity (TFP, \(\alpha \) ) and an initial capital level of \(k_{0}\) . Total factor productivity does not change with time but households invest in capital through time ( t ). Total factor productivity is a technology multiplier that measures the potential to earn income on a individual’s farm and, as such, it can represent household characteristics (such as ability) or farm characteristics (such as the quality of the land or the amount of rainfall).

Given their levels of TFP and capital, individuals choose how to maximize income in period t . Each individual earns income through agriculture ( \(y_{t}\left( \alpha ,k_{t}\right) \) ) and chooses between a low-return technology and a high-return technology with a fixed cost ( G ), that creates a non-convexity such that:

where \(0<\gamma _{\mathrm{L}}<\gamma _{\mathrm{H}}<1\) . In agriculture, for example, a high-return technology can include modern farming methods (which can require learning), high yielding seed varieties (which require fixed expenditures on seeds, fertilizer, and other inputs), or cash crops such as fruit trees or coffee plants (which require a large initial expenditure and may take several years to profit). While we focus on agriculture, this framework can easily be applied to human capital investments, migration, or other individual choices and income strategies. Poverty traps resulting from lumpy investments also require credit market failures and we do not allow for borrowing (Barrett and Carter 2013 ). Recent empirical evidence further suggests that access to credit allows certain individuals to transition to high-return technologies with fixed costs that are otherwise inaccessible to them (Banerjee et al. 2019 ).

3.2 Meso-level model

The meso-level is less commonly analyzed in the poverty trap literature, and we model it through the growth of traders that provide improved market access to farmers. Certain entrepreneurs choose to become traders by giving up agricultural production in order to earn money by charging fees to transport goods. As more efficient transportation options, traders can charge individual farmers fees in mutually beneficial agreements and, as a result, we interpret their presence as a neighborhood breaking a meso-trap.

Initially, all individual agricultural producers described in the micro-level model transport their own goods to market, possibly on foot or using draft animals. However, this transportation is costly and farmers lose some of their crop while en route. Assume that only a portion \(\left( \theta _{L}\right) \) of a farmer’s harvest reaches market if they transport it themselves. Becoming a trader requires sufficient wealth to purchase a pushcart (or bike, car, wagon, etc.) with which they can efficiently transport large amounts of goods to market. We model this through a fixed capital threshold \(\left( k_{T}\right) \) that a farmer must surpass before being able to cash in their capital for a pushcart. The pushcart can transport the crops of at least 15 people (the 15 neighbors in the grid space) and does so while maintaining a greater proportion of each farmer’s harvest \(\left( \theta _{H}>\theta _{L}\right) \) .

In each neighborhood without an incumbent trader, the farmer with the highest level of capital compares his potential income as a farmer with his potential income as a profit-maximizing trader. In order to predict one’s potential income as a trader, the individual evaluates each neighbor to determine the maximum fee that they would be willing to pay to a trader. After gathering this information from each of the fifteen other farmers in the neighborhood, the potential trader calculates the fee that would maximize their total revenue if they were to become a trader. If this is greater than what they would earn as a farmer (and they have sufficient capital), then they decide to become a trader, and we only allow one trader per neighborhood. We assume that a trader knows the potential fee that each of their 15 neighbors would be willing to pay but is only able to charge a single price for all potential clients, thus acting as a local monopolist charging the transportation fee \(\left( F\right) \) that maximizes total revenue. After the trader chooses their total revenue maximizing fee, the neighbors then decide whether to continue with independent farming or to accept the trader’s offer to transport their goods (which we call commercial farming). Traders and farmers use adaptive expectations for predicting future profits, fees, and capital levels in their neighborhood (to check if they could become a trader). In the second period they assume that those will remain the same as the first period, but for all subsequent periods they assume that the growth rate of those values will remain the same as their growth rates in previous periods. If an individual farmer lives in a neighborhood that already has a trader, they forego this calculation because we assume a monopoly.

3.3 Macro-level model

Our macro-level model is motivated by the recent institutions literature, which shows that historical events cause countries to develop persistent political and economic institutions, which then drive economic growth in some countries but not others (Acemoglu et al. 2005 ; Acemoglu and Robinson 2013 ). Acemoglu and Robinson ( 2013 ) state that growth-promoting, “inclusive economic institutions require secure property rights and economic opportunities not just for the elite but for a broad cross section of society” (p. 75). We assume inclusive political institutions where each country is a stable democracy in which each individual agent votes (non-strategically) for their preferred tax rate and the chosen tax rate is determined by the median voter theorem. Footnote 5 Within this political system, we allow for endogenously determined economic policies and find that, even in well-functioning democracies, macro-traps can arise that limit individual opportunity. Footnote 6

In our model, the median voter chooses their preferred tax rate, which is then used to fund the provision of a non-rivalrous and non-excludable public good in each time period. Each forward-looking individual agent votes for the tax rate that maximizes their own individual expected utility. A high tax rate would lead to greater government revenue and therefore more public goods in the next period, while a low tax rate would mean greater after-tax income available for private investments and current consumption. An individual’s choice would therefore depend not only on their own income and wealth levels (which would determine how valuable the public good would be for them relative to the reduction in their current income due to taxes) but also on the overall income level of their economy (if overall incomes are high, even a low tax rate would suffice to generate high government revenue). Consequently, the median tax rate and therefore the level of public goods provided will differ based on the underlying distribution of wealth, which is itself ultimately based on factors including initial conditions and geography in a country. As explained below, this macro-economic policy ranges from less inclusive—in which case low taxes provide little public goods provision that limit opportunities for the poor—to more inclusive—in which case higher taxes provide greater levels of public goods, which make high return technologies broadly accessible. These investments in public goods, in turn, influence whether households are able to escape micro-level poverty traps and whether markets develop at the meso-level.

3.3.1 Voting and tax revenue

We assume that individuals possess complete information about their own income levels, the aggregate income (or GDP) in their economy (which will determine the total tax revenue raised by any marginal tax), and the level of public goods provision resulting from any given level of tax revenue. With this information, each individual citizen votes for their preferred marginal tax rate \(\left( \tau _{i}\right) \) and the median voter’s preferred tax rate \(\left( \tau ^{*}\right) \) wins and is implemented that period. Based on the chosen marginal tax rate \(\left( \tau ^{*}\right) \) , the government collects a share \(\tau ^{*}\) of each individual’s income and collects total revenue \(R=\sum _{i}\tau ^{*}y_{i}\) .

3.3.2 Public goods

The government uses this revenue to pay for a per-period public good, which we model as a reduction in the fixed cost of adopting the high-return technology (a reduction in G ). By design, this investment is a public good (it is assumed to be non-rivalrous and non-excludable) that can be broadly beneficial while helping a range of households escape poverty, which could indirectly enable the growth of local markets. This investment can be interpreted in a few specific ways. First, a government may provide more education, which helps individuals learn more easily and adopt technologies more rapidly (Foster and Rosenzweig 2010 ) and can increase agricultural yields and incomes (Huffman 2001 ). Second, extension services can produce a range of economic benefits, including speeding technology adoption by providing farmers with more information (Evenson 2001 ). Third, general research and development can facilitate the adoption of new technologies by making them more suitable for a given region or providing localized information (Anderson and Feder 2004 ). Fourth, governments may subsidize technology adoption so as to improve growth while making technologies more affordable. Fifth, weather forecasting and plant epidemiological research is essential to farming and can be interpreted as public goods that a government provides annually, if not more frequently (Craft 1999 ; Rosenzweig and Udry 2019 ).

We assume that the per-period public goods provision depends on the total tax revenues collected according to \(G=\frac{0.45}{1+aR^{2}}\) . At one extreme, the government collects little tax revenue and provides limited public goods investment, which may result in high-return agriculture remaining unaffordable for the poor. We interpret this system as not inclusive, since it fails to help poorer citizens climb out of poverty, while wealthier citizens will be able to afford and benefit from high-return agriculture. At the other extreme, the government collects higher tax revenues and funds public goods so as to facilitate technology adoption. In line with Acemoglu and Robinson’s ( 2013 ) definition of inclusive economic institutions, this scenario benefits a broad cross section of society and can unlock greater individual agricultural productivity. Our interpretation also parallels the model in Arora and Chong ( 2018 ), where improved institutional quality corresponds with higher taxation and improved public goods provision. We interpret higher public goods quality as breaking free from a macro-trap (potentially caused by low-quality institutions) even though not every individual gains. Given our parameters and as seen in Fig.  1 , the fixed cost of high-return technology starts at 0.45 and declines as total tax revenue increases.

High-return technology fixed cost and tax revenue

This specific functional form provides a non-convexity in public goods provision and is based on assumptions that, at some level of public goods provision, there are economies of scale. These might arise because governments gain buying or monopsony power that lowers costs, provide services to bigger populations, or because some beneficiaries are easier to reach than others. Economies of scale in public goods provision are commonly assumed in a range of political economy studies on state size and public goods provision (Alesina and Spolaore 1997 ; Alesina et al. 2004 ; Stiglitz 2015 ). As a result, we assume a range over which public goods provision has increasing marginal impacts. Eventually, it becomes very costly to provide public goods because the remaining areas or individuals are the most difficult to reach or because of congestion or coordination problems (Alesina and Spolaore 1997 ). This increases the costs and decreases the marginal impact after a certain point. The macro-poverty trap is similar to the micro- and meso-traps, since the non-convexity generated by this additional feature can also trap societies in poor outcomes. Even though the median voter’s preferred policy is chosen, the median voter in certain regions will choose low taxes and low levels of public infrastructure, which can trap groups in poverty.

3.3.3 Macro-trap

To understand the macro-poverty trap, it may be helpful to consider how the aggregate level of income as well as its distribution affects the median tax rate and therefore the total revenue and the level of public goods provided.

First, aggregate national income is important. If aggregate incomes are low, then an unfeasibly high tax rate may be required to generate adequate revenue to help individuals adopt the high return technology. On the other hand, if aggregate incomes are high, a low tax rate may be sufficient in order to generate the revenue required for adequate provision of public goods. This results in a macro-poverty trap similar to the micro- and meso-traps since the non-convexity generated by this additional feature can also trap societies in poor outcomes. Even though the median voter’s preferred policy is chosen, the tax revenue could result in low levels of public infrastructure, which can trap groups in poverty.

Second, the distribution of income and wealth within a country can also affect the level of public goods provided. Very poor individuals who cannot adopt the high-return technology even with a high level of public goods vote for a 0% tax rate. Very rich individuals who can easily afford the high-return technology without any provision of public goods and traders who do not need the public goods themselves also vote for a 0% tax rate. Individuals between these two extremes vote for higher taxes because the provision of some level of public goods helps ease their transition to the high-return technology. Among them, poor individuals who are on the cusp of being able to afford the high-return technology vote for very high tax rates since they need a high-level of public goods in order to transition to the high-return technology. Consequently, a country where the distribution of incomes is very unequal where some individuals are very rich and several others very poor would lead to a situation with a low provision of public goods which in turn prevents the middle class from transitioning to the high-return technology. On the other hand, in a more equal society where most individuals are middle class, the median tax rate would be high enough to facilitate the provision of public goods that are adequate to allow those individuals to transition to the high-return technology. Footnote 7

Since the macro-trap is based on both aggregate income as well as its distribution, it is inextricably linked to initial conditions and the resulting individual-level income and wealth outcomes driven by micro- and meso-processes. In turn, the macro-economic situation also affects the micro-process of technology adoption by individual farmers since the macro-economically determined level of public goods provision affects the cost that individual farmers have to pay in order to transition to the high return technology.

3.4 Intertemporal utility maximization

Among farmers, individuals choose to either use the low- or high-return technology and to engage in independent (not using the trader) or commercial (using the trader) farming. Together, in each period households choose to maximize income among the four agricultural choices, where the commercial options are only available if there is a trader in one’s neighborhood:

Each individual can also choose to be a trader, in which case they earn: \(y_{t}=f_{\mathrm{Trader}}=\left( F_{t}\right) \left( \# of Clients_{t}\right) \) .

After maximizing income, \(\tau ^{*}y_{t}\left( \alpha ,k_{t}\right) \) is paid in taxes and the household determines optimal levels of consumption ( \(c_{t}\) ) and investment in capital ( \(i_{t}\) ) so as to satisfy the household budget constraint:

Capital grows according to:

where \(\delta \) is the rate of depreciation. Farmers will accumulate capital until they reach their respective steady state (which depends on their individual TFP and initial capital levels), while traders will maintain capital levels just above the threshold to become a trader \(\left( k_{T}=4\right) \) .

Each individual maximizes:

subject to:

Our agents value current consumption and future income, whether by a given individual or the next generation. We choose to have our agents face this overlapping generations problem for several reasons. First, this model can be applied to situations where individuals either make decisions valuing their own current and future welfare or their own current welfare and that of future generations. Either interpretation is captured through \(\phi \) , which measures how much an agent values current consumption relative to the expected income of their future self (or, alternatively, how much a given agent values the consumption of the current generation relative to the income of the next generation). As such, this model parallels models of family altruism across overlapping generations (as done by, for example, Lambrecht et al. 2006 ; Michel et al. 2006 ). Footnote 8 Second, this formulation is more reasonable with adaptive expectations. Given the high degree of uncertainty that exists in our fractal model due to meso- and macro-level changes relying on complex dynamics, it is difficult for individuals to hold rational expectations about the future and they may not wish to extrapolate beyond one period. In order to calculate future consumption, agents would need to have expectations about what would happen two periods ahead and if they care about future utility recursively (as is standard and equivalent to the infinite horizon problem), they would need to have expectations infinitely far into the future. In highly uncertain environments, adaptive expectations may be a more reasonable assumption than rational expectations (see, for example Heiner 1989 and discussions in Rosser 1997 , 1999 ). While we prefer this specification, we calibrate \(\phi \) so that our OLG steady state approximates those in the infinite horizon model (as shown in Online Appendix A).

We use this fractal poverty trap model to generate data which we analyze in the following sections. We run simulations for 300 countries through 100 periods, with each country consisting of 576 individuals across a \(24\times 24\) grid, each with a randomly drawn TFP \(\left( \alpha \right) \) and initial capital level \(\left( k_{0}\right) \) . Each country is divided into 36 neighborhoods (a \(6\times 6\) grid of neighborhoods), each of which includes a \(4\times 4\) grid of households. Otherwise, the analysis relies on numerical simulation of this model based on the parameters presented in appendix. We calculate a range of individual, neighborhood, and country characteristics and analyze their evolution through time. By design, our model encapsulates many potential outcomes, given the endogenous decisions of certain individuals to become traders and an endogenously evolving tax rate.

3.5 Stages of decision making

Our agents make decisions in five stages:

To farm or not to farm. Based on all available information at the start of the round, the agent with the highest level of capital in a neighborhood without a trader chooses whether to be a farmer or a trader. Specifically, the agent looks at each of their fifteen neighbors and determines what fee each one would be willing to pay a trader, and then compares their expected profit from farming with their potential profit as a trader. If they decide to be a trader, they post their fees at the end of this stage. If a neighborhood already has a trader, the trader decides whether to continue trading (and determines the optimal fee as above) or switch to being a farmer.

How to farm. Once a trader (if one exists in the neighborhood) posts fees, individual farmers, decide their type of farming that period. This involves choosing whether or not to use a trader simultaneously with whether or not to use the high-return technology.

How much to pay in taxes. After agents choose optimal production based on Steps 1 and 2 above, they are informed about the economy-wide GDP and are asked to vote for their preferred tax rate in the period. First, the tax rate determines disposable income today according to the above budget constraint. Second, today’s tax rate funds the next period’s public goods ( \(G_{t+1}\left( \tau ^{*}\right) \) ) and therefore affect future output.

Consume and save. Following the realization of today’s tax rate, agents consume and save based on the maximization problem outlined above, which weights a combination of current consumption and future income. At the end of this round, capital depreciates.

Following the end of all four stages, the process repeats itself.

3.6 Escaping poverty traps

Poverty traps exist when individuals remain poor indefinitely into the future and we first need to define what it means to be poor before exploring whether or not someone is trapped in poverty in our model. In many cases, an income-based poverty line is defined as the income level required for a minimum standard of living. While income-based poverty lines have many advantages, they create artificial breaks that classify essentially identical individuals near the threshold as either poor or non-poor. In a world of smooth income distributions, these negatives can prove problematic. In contrast, others define poverty lines based on behaviors, assets, or other traits (see, for example, Carter and Barrett 2006 ). As explained below, we define poverty based on individual behavior rather than income. Furthermore, poverty is commonly discussed in static terms, with a household either being poor or non-poor based on a current measure of income, consumption, or assets. However, it is important to distinguish a static poverty line, separating currently poor and non-poor households, from a dynamic poverty line, separating households that remain structurally poor and those that eventually escape poverty. As a dynamic poverty line, Barrett and Carter ( 2013 ) define the Micawber frontier as dividing “those agents ...who (in probability) converge to a high-income equilibrium, and those ...who in expectation collapse to a low-level, poverty trap equilibrium” (p. 980). Footnote 9 We use a similar concept as well.

In this paper, we focus our analysis primarily on whether or not individuals dynamically break out of the micro-based poverty trap, meaning that they transition from the low- to the high-return technology (and possibly to a trader) by the end of our simulation. We highlight use of the low-return technology as a poverty trap because it focuses on behavioral decisions, rather than arbitrary divisions in income levels that depend more on the parameterization of our model. We make two adjustments to the Micawber frontier analyzed in micro-level models by Barrett and Carter ( 2013 ) and Ikegami et al. ( 2019 ). First, both papers base their Micawber frontier on the technology that an individual uses in their steady state. Because our tax revenues and trader fees continue to display a little bit of dynamics (even after 100 rounds), we do not necessarily reach a stable steady state in many cases. As a result, we refer to the prevailing Micawber frontier and provide videos and images based on what the prevailing Micawber frontier would be in any given round. Second, we classify two lines that help emphasize the importance of meso- and macro-determinants of individual technology choices. The prevailing lower Micawber frontier is the individual TFP below which every individual engages in low-return farming (and above which at least some individuals do not). Footnote 10 The prevailing upper Micawber frontier is the individual TFP above which no individual engages in low-return farming (either engaging in high-return farming or being a trader). These two lines provide us with three potential groups. First, those below the prevailing lower Micawber frontier are stuck in poverty, regardless of one’s broader context, since their individual TFP levels are too low. This can be thought of us a structural poverty trap. Second, those between the two lines are in a geographic poverty trap, a concept introduced by Jalan and Ravallion ( 2002 ), since identical individuals might end up in or out of poverty based on meso- or macro-context. For example, breaking free from the poverty trap might be due to a combination of individual factors (such as a high individual productivity level), neighborhood factors (the existence of a trader, possibly with agreeable fees), and country factors (such as a tax rate that drives down the fixed cost of the high-return technology). Third, those above the prevailing upper Micawber frontier are non-poor regardless of context.

The transition out of poverty can take one of several paths. In our model, all individuals start poor in low-return independent farming. In this context, low initial capital levels cause farmers to rely on the low-return technology and they produce independently because they lack access to markets (represented through traders). Footnote 11 While everyone starts in low-return independent farming, some individuals escape to alternative livelihoods through a range of pathways. First, certain individuals with sufficiently high productivity levels will transition to high-return independent agriculture. Footnote 12 Second, wealthy high-return independent farmers may attain sufficient capital to become a trader, with their decision further depending on the potential willingness-to-pay of consumers in their neighborhood. Third, once a trader appears in one’s neighborhood, individual farmers might transition into low-return commercial farming, although this is rarely a final outcome. Given that these individuals still engage in low-return agriculture and their final income levels overlap considerably with low-return independent farming as shown below, we consider these individuals to still be in the poverty trap. After a few periods of low-return commercial farming, many individuals transition to high-return commercial farming, where they earn noticeably higher incomes, benefitting from both the high-return technology and the improved market access provided by traders.

We primarily focus on individual livelihoods as determining whether or not an individual is poor. For a given individual, making the transition out of the low-return technology means earning higher incomes. However, given our range of parameters, the entire distribution of our simulation results in considerable overlap across these distinct income sources. For example, the range of individual TFP levels means that we observe a wide range of income levels, even within a given income source. Figure  2 presents the density function of incomes by income source after 100 periods of our baseline model. Even by the end of our model, 64% of individuals remain trapped in poverty, with 63% engaging in low-return independent farming (with their range of incomes stretching from 0.3 to 1.1) and 0.5% choosing low-return commercial agriculture (with a range from 0.3 to 1.6). Otherwise, 15.7% use high-return independent farming and 18.6% reach high-return commercial farming, with a noticeably higher income distribution. Traders (2%) are not depicted because their income levels average 6.7 and reach far higher, making the figure harder to read for the farmers. This illustrates how there is more to income than one’s income source and we focus mainly on income source but also compare incomes in the remainder of our analysis.

PDF of income distribution by income source

Country #8 case study

Furthermore, individual incomes will change due to a particular neighborhood’s trader access and fees and a country’s chosen taxation rate and public goods provision. Because our model integrates considerable variation across individuals who operate within heterogeneous neighborhoods and countries, we obtain a complex range of outcomes and incomes that remains difficult to classify but, in many ways, makes it more applicable to the real world. Otherwise, we remain intentionally agnostic about specific poverty lines and focus our analysis on two concepts: (a) for individuals in specific contexts, transitioning out of low-return farming improves one’s welfare and (b) in a complicated and continuous range of incomes, higher incomes are better and specific poverty lines might differ by context.

3.7 Case studies: country examples

To further illustrate the potential outcomes arising within our model, we begin by presenting case studies. Figure  3 highlights country #8, where income levels are initially low and the low TFP endowment across individuals causes the entire country to remain trapped in poverty. The upper left diagram shows that the GDP per capita converges to around 0.72, while the remaining graphs show that the public good is never funded (zero taxation), nobody becomes a trader, and all individuals use the low-return technology. Thus, the country, every neighborhood, and each individual remains stuck in a single equilibrium poverty trap at each level of analysis.

Country #9 case study

In contrast, Fig.  4 highlights country #9, which experiences rapid initial growth, a brief stable period, and then a rapid escape to a higher level of GDP per capita. This transition occurs because the country escapes the macro-trap as taxes drive down the fixed cost of technology adoption (top right) and because neighborhoods begin to escape their meso-traps through the rise of traders (bottom left). These changes allow almost 80% of individuals to escape the poverty trap (bottom right). In this case, country-level factors drive the provision of public goods (through higher tax rates) before period 10 and over 60% of individuals immediately transition out of low-return agriculture. Then, traders arise shortly after, with a trader in every neighborhood by period 30, and additional farmers gradually escape through the end of our simulation. Together, the provision of public goods and rise of traders sparks rapid growth in GDP per capita starting around period 17. Within country #9, the country as a whole breaks out of the macro-trap, every neighborhood escapes the no-trader trap, and individuals face a multiple equilibrium trap with some escaping from the low-return technology, while others cannot.

Thus, while the model remains the same in both countries, we see a range of outcomes based on the set of individual productivity and initial capital levels. In some cases (country #8), entire countries remain trapped in poverty, with no traders and everyone using the low-return technology. Elsewhere (country #9), traders arise and tax rates increase to provide broadly beneficial public goods, both of which unlock rapid growth allowing most individuals to escape poverty. In this comparison, individual TFP values range from 0.80 to 1.04 in country #8 but 0.75 to 1.43 in country #9, with the higher end of the distribution helping drive the rise of traders and escape of most individuals, while those at the lower end are unable to escape poverty, even with access to traders and public goods.

4 Empirical analysis

4.1 evidence of poverty traps.

Before conducting our main analysis, we first present preliminary evidence on growth and changes in the income distribution through time. Across a range of methods, we find consistent evidence of divergence across multiple equilibria.

Quah ( 1996 , 1997 ) use cross-sectional data to analyze distributions of national income levels, finding evidence of “twin peaks” that indicate divergence across countries. We present similar distributions in Fig.  5 to present visual evidence regarding if and when convergence and/or divergence occurs. First, we compare the distribution of average incomes through time by plotting the probability density function in years 1, 21, 41, 61, 81, and 100—for the macro- (Fig.  5 a), meso- (Fig.  5 b), and micro-level (Fig.  5 c) data. In the macro- and meso-graphs, we find similar evidence of divergence across both countries and neighborhoods. In each, there is initially a unimodal distribution in which all countries or neighborhoods begin with low average income levels centered around 0.5. Through time, divergence occurs as a large peak develops around 0.75, while some countries and neighborhoods transition to higher income steady states ranging from around 1.5–2.5. It appears that most countries reach their steady state, since we see relative stability in the distribution between years 81 and 100, although the share of countries in the low-income steady state continues to fall slowly. The micro-evidence is similar, with the vast majority of individuals trapped at low incomes while some escape poverty through farming (earning incomes around 2–4) and a few becoming wealthy traders (incomes up to around 12).

PDF of GDP per capita through time

Many poverty trap models predict that those stuck in poverty will experience zero growth (due to an absolute poverty trap) or slower growth (due to a relative poverty trap). In the case of multiple equilibria, we expect to see no growth among those at a low-income steady state, rapid growth among those transitioning to the high-income steady state, and no growth again once the high-income steady state is reached. As done in Easterly ( 2006 ) and Kraay and McKenzie ( 2014 ), in Table  1 we present evidence on growth rates by initial income levels, which provides a convenient way to determine whether or not poor individuals, neighborhoods, or countries grow faster or slower in our model and whether or not a certain range experiences rapid growth. Using these methods, both Easterly ( 2006 ) and Kraay and McKenzie ( 2014 ) find little evidence of poverty traps, with positive growth among poor countries that is not considerably different from wealthier countries. Additionally, we present our results across different time periods to determine whether this growth is sustained and when it slows down. The average growth rates over all years increase by quintile. The initially poorest countries grow at 0.4% per year, while the initially wealthiest grow at 1.0% per year, resulting in divergence. Tracking growth rates through time, the poorest two quintiles experience early growth (3.6% and 3.8%) as they converge on their steady state, but growth rates slow to 0–0.1% by year 30. The initially wealthier countries experience both more rapid initial growth (4.8%) and more sustained growth that does not approach 0–0.1% until year 60. Thus, while all groups appear to reach their steady state, the initially wealthier countries grow more rapidly and longer as they transition toward their higher income levels.

In Fig.  6 , we plot average growth rates by initial income percentile as a way to provide a more detailed and visual depiction of these trends, as done in Piketty et al. ( 2017 ). Consistently across each level of analysis, the poorest two quintiles experience very low growth around 0.5% on average. Starting in the third quintile in both the macro- and meso-results, growth rates slowly increase and break 1% around the 90th percentile. In the micro-results, we see even stronger evidence of divergence and growing inequality. Those individuals with the lowest initial incomes grow enough to converge on the low-income steady state, while the majority of the distribution experiences low average annual growth rates of 0.4%. Average growth rates increase among the initially wealthiest, reaching 1.5% at the highest percentile.

Mean growth rate by initial income percentile

Thus, we see evidence of divergence at each level of analysis, with the initially wealthier individuals, neighborhoods, and countries growing fastest while transitioning to higher steady states.

4.2 Graphical analysis through time

Next, we graphically analyze transformations through time across a range of scenarios, highlighting the importance of individual as well as neighborhood and country characteristics. The following figures (and corresponding videos available online) depict individual job types: low-return independent farming (red), low-return commercial farming (green), high-return independent farming (blue), high-return commercial farming (black), and traders (yellow). Using the same random draw of individuals across all 300 countries used above, we run our simulation under four different scenarios: contexts where it is possible to escape both the meso- and macro-traps (upper right, the baseline case analyzed thus far), contexts where the macro-trap is forced by not allowing any taxation which results in no public goods provision (upper left), contexts where the meso-trap is forced by not allowing any individuals to become traders (bottom right), and contexts where the meso- and macro-traps are both forced (bottom left). Thus, each point depicts one of 172,800 individuals (576 in each of 300 different countries) and the same 172,800 individuals are depicted in each of the four scenarios. The four scenarios allow us to visualize the effects of the macro- trap (by comparing along a given row) or the meso-trap (by comparing along a given column). The vast majority of individuals are low-return independent farmers but these markers are smaller than the other types (a quarter of the size) to make the dynamic changes more visible.

To illustrate the transformations that occur throughout our model, we present graphs depicting the evolution of individual occupations (with the two Micawber frontiers), current capital, incomes, and tax preferences. The graphs are available as videos on our online appendix and a few single-period snapshots are provided below. We organize our results in this section according to several concepts.

4.2.1 Micawber frontiers and poverty traps

Figure 7 depicts individual job types along with the prevailing lower and upper Micawber frontiers, separating individuals definitely stuck in poverty (left zone), individuals whose outcome depends on context (middle zone), and non-poor individuals (right zone). Micawber frontiers are dynamic concepts evaluating what someone does in their steady state and, while we do not always reach a clear steady state in our model, the final round provides a good proxy for the steady state in our model. It is useful to begin with both traps enforced (lower left), which is analogous to similar micro-level models (Ikegami et al. 2019 ). Here, there is a clear, single Micawber frontier that separates low and high-return independent farmers based on their individual TFP. Without the opportunity to break through the meso- and macro-traps, neighborhood and country characteristics do not matter and, given our low range of starting values, neither does initial capital (except for a few early periods where the prevailing Micawber frontier slopes downward). As seen in Figs.  8 and 9 , there is a clear jump in both capital and income at the single Micawber frontier, but the steady-state levels are also increasing in TFP.

When countries can provide public goods but traders cannot arise within neighborhoods (lower right), we see several important changes. First, a geographic poverty trap appears between the two Micawber frontiers where, as explored more below, similar individuals may end up poor or non-poor based on the interactions between their own initial conditions and their meso- and macro-context. Footnote 13 The prevailing lower Micawber frontier falls, since the provision of public goods facilitates technology adoption and helps farmers with intermediate TFP levels transition out of poverty. Also, the prevailing upper Micawber frontier shifts right because the negative impact of taxes dominates the benefit of cheaper technology adoption for this range of TFP values.

When traders can arise within neighborhoods but countries cannot provide public goods (upper left), the prevailing lower Micawber frontier also falls in relation to both traps being enforced, opening up a geographic poverty trap. Footnote 14 This occurs as traders help more farmers transition from low-return independent, to low-return commercial, and finally to high-return commercial farming, a transition that is especially visible around periods 10–30. However, the prevailing upper Micawber frontier does not change.

Finally, the smallest poverty trap and largest geographic poverty trap occur when both the neighborhood and country-level traps can be overcome (upper right). Here, we see the biggest fall in the prevailing lower Micawber frontier, as trader access and public goods provision allow a wider range of individual TFP values to transition. However, even with both traps able to be overcome, there remains a group of individuals trapped in low-return farming due to their low TFP values. With the potential for taxation, the prevailing upper Micawber frontier shifts right, since again there is a range of farmers for whom the cost of taxes outweigh the benefit from public goods provision.

The geographic poverty trap is the largest when both the meso- and macro-trap can be broken and non-existent when neither can be broken (when only individual characteristics matter in our model). When only the macro-trap is forced, country-level characteristics should not matter but neighborhood-level characteristics that explain the presence of traders still cause considerable overlap. When only the meso-trap is forced, neighborhood-level characteristics should not matter but country-level characteristics that explain taxation and public goods provision still cause considerable overlap. Thus, the degree of overlap suggests that both meso- and macro-variables explain individual-level poverty.

Using our behavioral poverty trap definition based on whether or not farmers engage in low-return farming, overcoming meso- and macro-traps is seen to reduce individual poverty levels. Individuals are least likely to escape poverty when both traps are forced (lower left), where 92% of individuals still use the low-return technology in period 100. When both traps can be overcome (upper right), this number falls to 64%. Between these extremes, allowing countries to escape the macro-trap (73% in low-return farming) is better than allowing neighborhoods to escape the meso-trap (77% in low-return farming).

Furthermore, when taxes arise (right column), the upper Micawber frontier shifts to the right. This result is driven by higher taxes dissuading farmers near the threshold from adopting the high-return technology.

Evolution of individual occupations by TFP and initial capital—period #100

Evolution of individual current capital by TFP—period #100

Evolution of individual current income by TFP—period #100

In online appendix, we provide an illuminating period-by-period video that displays movements in the lower and upper Micawber frontiers through time.

4.3 Case studies: similar individuals in different contexts

To further highlight these issues, we compare four similar individuals who exist in different neighborhoods and countries in Fig.  10 . We focus within the geographic poverty trap, where one’s neighborhood and country influence what jobs an individual can pursue and what their income levels become. The individuals we selected have both initial capital and TFP values between 1.1 and 1.11.

We present graphs of individual income through time in each of the four scenarios, with plots of job type, the fixed cost of the high-return technology, trader access, and trader fees provided in Appendix Figs.  16 through 19 . With both traps enforced (bottom left), meso- and macro-context does not matter (since we do not allow for traders or public goods) and every individual reaches the same outcome. Given this TFP level, every individual remains trapped in low-return independent farming and earns an income level of 0.94. When we allow countries to escape the macro-trap while enforcing the meso-trap (bottom right), taxation increases and farmers now engage in high-return independent farming (though individuals A and C jump back and forth due to cycles in G ). While public goods provision facilitates the adoption of the high-return technology and pushes up incomes, the rate of taxation lowers incomes and, in this particular group, these individuals remain about as well of as they would be without taxation (with final incomes ranging from 0.91 to 0.97). When we instead allow neighborhoods to produce traders while enforcing the macro-trap (upper left), we see higher incomes with more heterogeneity. Access to traders begins in rounds 10–25 and access to traders helps these individuals transition from low-return independent farming to low-return commercial farming and finally to high-return commercial farming. However, while two individuals (A and D) continue to choose high-return commercial farming (with different incomes resulting from different trader fees), two individuals (B and C) transition to high-return independent farming, since the trader fees grow beyond their willingness to pay. However, comparing their outcomes with the scenario where both traps are enforced, this brief utilization of the trader allows these individuals to transition permanently to high-return independent farming, with incomes about 50% higher than they would have been without any access to a trader.

Finally, in our baseline scenario both traps can be overcome and we again see different outcomes across these similar individual farmers. For individual D, they reach high-return commercial agriculture as with only the macro-trap enforced. However, they now earn slightly higher incomes as a result of the lower trader fees. For individual C, they also engage in high-return commercial farming and earn a similar income as when only the macro-trap is enforced, though with some cycles resulting from changes in trader fees. Otherwise, individuals A and B perform similarly as when only the meso-trap is enforced, since no traders arise in their neighborhoods.

Income comparison for similar individuals

By focusing on identical individuals in different contexts, we see that one’s neighborhood and country matters crucially along with whether or not traps at each level can be overcome. In this example drawn from the geographic poverty trap, individual opportunity is determined by one’s context.

4.4 Summary

These results further illustrate the importance of one’s context in the study of poverty trap models. Our results are consistent with Barrett et al. ( 2016 ), who conclude that there is a range of evidence that poverty traps occur in a variety of contexts and include both single and multiple equilibrium traps. These findings also suggest that poverty is likely to be concentrated in regions with little market access and weak governments, a finding consistent with the conclusion made by Kraay and McKenzie ( 2014 ). In these cases, individuals face single equilibrium poverty traps and short-term cash assistance is unlikely to provide long-run benefits. Rather, deeper and more structural reforms are necessary. The baseline model illustrates this, with individual farmers who lack market access and public goods provision face a single equilibrium (or structural) poverty trap, while those fortunate enough to reside where meso- and macro-traps are overcome face a single non-poor equilibrium.

Thus, even if individual TFP cannot be determined easily, targeting policies based on meso- or macro-context may prove more effective. However, these reforms may prove to be the most difficult in isolated regions with weak governments. As seen in our Micawber frontiers above, certain individuals remain trapped in poverty unless policies are able to improve their TFP sufficiently. This implies that single policies will generally prove to be insufficient and that a combination of policies may be required to provide improved public goods, market access, loans to overcome dynamic thresholds, and more productive individual assets.

In related work, we are using this model to answer additional questions. First, we return to the idea that “the absence of evidence is not evidence of absence” and ask whether commonly used empirical tests of poverty traps are likely to find evidence of poverty traps, even when they exist. By using different methods for randomly selecting data from our simulated model, we can evaluate the performance of common empirical methods and the conditions required for them to reliably identify poverty traps accurately. Second, we introduce common policy interventions, such as cash or asset transfers, and evaluate their performance under different contexts.

5 Conclusions

This paper develops a fractal poverty trap model that allows us to explore the interactions between micro-, meso-, and macro-poverty traps. Our model is based on forward-looking utility maximizing individuals choosing their occupation and investment levels within endogenously developing markets and national economic policies. By design, our model allows for multiple equilibrium poverty traps at each level of analysis and we generate data with which we evaluate common empirical methods. At both the meso- and macro-levels, we find evidence of divergence, with many blocks and countries trapped in poverty, while others experience sudden surges in growth allowing them to escape to higher income levels. Focusing primarily on the micro-level, we find that individual opportunity depends largely on individual productivity, but also differs greatly based on the meso- and macro-context in which one lives. For those with little access to markets or public goods provision, individuals are likely to remain trapped at low income levels unless they are fortunate enough to have high individual productivity levels. For individuals with intermediate productivity levels, however, moving to a neighborhood with a trader or a country with public goods provision may allow them to transition out of poverty.

These results show that context matters for a large range of individuals in our simulation. A combination of policies is essential to move many of the poor out of poverty traps, including cash transfers but also structural changes promoting greater market access and public goods provision.

Furthermore, these results highlight the importance of context, but capturing the complexity of context poses a considerable challenge. While we attempted to integrate micro-, meso-, and macro-layers into a fractal poverty trap model as simply as possible, the analysis quickly becomes complex in ways that are hard to quantify empirically. For example, having one rich neighbor might be sufficient for a trader to arise, but their fees depend on neighborhood demand, which is based on a set of TFP levels and growing capital levels that interact in complex ways. At the macro-level, more rich fellow citizens mean that, all else equal, a lower tax rate will suffice for providing a desired level of public goods. However, rich fellow citizens might also vote for lower taxes. This complexity makes fractal poverty traps interesting and important to explore, but it also makes simple narratives challenging. In this paper, we develop a relatively simple fractal poverty trap model and provide a few inferences focused on the complex ways that individual productivity and local and national context interact to determine individual welfare.

Availability of data and material

The simulation code and the datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

Dynamic poverty trap models illustrate how initial conditions impede productivity, savings, and investment (see, for example, Galor and Zeira 1993 ; Bowles et al. 2016 ). A wide range of models provide theoretical justifications for the existence of poverty traps, including ones based on nutrition (Dasgupta and Debraj 1986 ), coordination failures (Rosenstein-Rodan 1943 ; Murphy et al. 1989 ), fixed capital investments (Banerjee and Newman 1993 ; Aghion and Bolton 1997 ; Carter and Barrett 2006 ; Barrett and Carter 2013 ), and lumpy human capital investments (Basu and Van 1998 ).

Kraay and McKenzie ( 2014 ) draw upon Azariadis and Stachurski ( 2005 ) to define a poverty trap as “a set of self-reinforcing mechanisms whereby countries start poor and remain poor: poverty begets poverty, so that current poverty is itself a direct cause of poverty in the future” (p. 127). In a footnote, they state that “others also refer to a single, poor, dynamic equilibrium as a structural poverty trap (for example, Barrett and Carter 2013 ; Naschold 2013 ), but we do not use this definition in our paper.” Banerjee and Duflo ( 2011 ) also focus on multiple equilibria, noting that a poverty trap occurs “whenever the scope for growing income or wealth at a very fast rate is limited for those who have little to invest, but expands dramatically for those who can invest a bit more” (p. 11). In contrast, they state that “if the potential for fast growth is high among the poor, and then tapers off as one gets richer, there is no poverty trap” (p. 11).

Other empirical evidence of poverty traps is found in agricultural areas (Dercon 1998 ; Lybbert et al. 2004 ; Carter and Barrett 2006 ), human capital investment (Emerson and Souza 2003 ; Das 2007 ), and assets (Zimmerman and Carter 2003 ; Carter and May 1999 , 2001 ; Adato et al. 2006 ). Similarly, the growing literature on the importance of institutions (Acemoglu et al. 2001 , 2005 ) and the historical determinants of development (Nunn 2008 , 2009 ) can be interpreted as causing low-income poverty traps. Barrett et al. ( 2019a ) highlight recent research on a range of mechanisms and their implications for policy.

An additional layer of complexity involves individual decision making across a range of choices, including education, health, jobs, and more. While many theoretical models focus on specific decisions, individuals live in diverse contexts and this complexity may influence the conditions under which individuals remain trapped in poverty. Empirical studies often address this challenge by developing asset indices. Our model focuses on analyzing fractal layers of poverty traps rather than a complex set of individual choices.

Morrow and Carter [2017] show how important income dynamics and voter’s information about income dynamics are to democratic elections, showing that limited upward mobility can rapidly increase support for redistribution in certain cases.

While we could explain macro-economic poverty traps through other factors, such as coordination failures, corruption, or the potential transition from a dictatorship to a democracy, this focus on taxation and public goods provision provides a relatively clear link with the remainder of our model. Explaining the transition to democracy and growth of s remains vitally important (Acemoglu and Robinson 2005 ), we leave aside this complexity for simplicity.

Appendix C includes more detailed explanations of these relationships using results obtained from the simulations of the model.

Lambrecht et al. ( 2006 ) use the log of current consumption while noting that the functional form for future income only needs to be continuous, differentiable, and concave.

The Micawber frontier or threshold is based on Zimmerman and Carter ( 2003 ), who based the name on Lipton ( 1994 ).

Our micro-model is based on Ikegami et al. ( 2019 ), who find a downward sloping Micawber frontier than then turns vertical, resulting from their higher range of initial capital levels. As depicted below, we use a lower set of starting initial capital levels and, as a result, our two prevailing Micawber lines correspond to their vertical section.

They can be thought of as subsistence farmers—although this term often conflates multiple traits and often lacks a specific, uniform meaning (Miracle 1968 )—and we classify them as low-return independent farmers to frame them explicitly within our model.

Without access to a trader, this scenario parallels the model of Ikegami et al. ( 2019 ), in which sufficiently productive individuals inevitably transition to the high-return technology, very low productivity individuals remain trapped using the low-return technology (a single equilibrium poverty trap), and an intermediate ability range uses the high-return technology if they begin with sufficiently high capital levels (a multiple equilibrium poverty trap).

Context does influence individual incomes outside of our geographic poverty trap zone, as seen in the current capital and current income graphs, for example, in the distribution of incomes below the prevailing lower Micawber frontier. However, our geographic poverty trap is defined based on individual behavior (or technology choice) rather than specific income levels.

It is also illuminating to observe the growth of traders, who need to maintain a level of capital above the minimum threshold (4 units of capital) but earn higher incomes from their fees. We chose the capital threshold to be feasible for high-TFP individuals, who will reach it automatically based on the high-return independent farming steady state, but can also feasibly jump to from the low-return independent farming steady state (as seen in the current capital graph when both traps are enforced). Starting in period 7, traders arise whenever the meso-trap can be overcome, with the first traders having the some of the highest TFP levels.

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Acknowledgements

We are grateful for the suggestions from an anonymous referee, and we also benefitted from feedback from participants at Lafayette College, Davidson College, Lawrence University, the Midwest Economics Association Annual Meeting, and the Eastern Economics Association Meetings.

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Both authors contributed to the study conception and design. Model simulations were performed by Shyam Gouri Suresh. Econometric analyses of simulated data were performed by Dylan Fitz. The first draft of the manuscript was written by Dylan Fitz, and both authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.

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Supplementary material 1 (mp4 18335 KB)

Supplementary material 2 (mp4 17205 KB)

Supplementary material 3 (mp4 23245 KB)

Supplementary material 4 (mp4 3873 KB)

Appendix A: Online Videos

On our websites, we include videos displaying the evolution of our core graphs by period. Specifically, we include full videos for the “Evolution of Individual Occupations by TFP and Initial Capital,” “Evolution of Individual Current Capital by TFP,” and the “Evolution of Individual Current Income by TFP.” These present the full dynamics leading up to the Period #100 graphs that are included in the paper.

Appendix B: The model

1.1 grid space and parameterization.

We randomly select a level of TFP \(\left( \alpha \right) \) and an initial capital level \(\left( k_{0}\right) \) for an individual placed in each cell of a 24 by 24 grid. For each country, we choose a minimum individual TFP level between 0.6 and 0.9 \(\left( \alpha _{\min }\sim U\left[ 0.6,0.9\right] \right) \) and a maximum value between 1 and 1.5 \(\left( \alpha _{\max }\sim U\left[ 1.0,1.5\right] \right) \) . Within that country, each individual receives a TFP value drawn from a uniform distribution between these values \(\left( \alpha \sim U\left[ \alpha _{\min },\alpha _{\max }\right] \right) \) . Each individual’s initial capital level is drawn in a similar manner, with \(k_{0\min }\sim U\left[ 0.05,0.50\right] \) , \(k_{0\max }\sim U\left[ 1.00,2.00\right] \) , and \(k_{0}\sim U\left[ k_{0\min },k_{0\max }\right] \) . Our numerical simulations are based on the following parameters (Table  2 ).

1.2 Linking overlapping generations model with infinite horizon model

In this section, we explain how we calibrated \(\phi \) in our overlapping generations model in a way that the steady-state capital level is similar in the OLG and infinite horizon models.

Infinite horizon model  In the infinite horizon model, an agent maximizes \(U=\max {\sum }_{t=1}^{\infty }\beta ^{t-1}u\left( c_{t}\right) \) such that \(c_{t}\le \alpha k_{t}^{\gamma }-G+\left( 1-\delta \right) k_{t}-k_{t+1}\) . Taking the first-order condition with respect to \(k_{t+1}\) , we have:

In the steady state, we have that \(u'\left( c_{t}\right) =u'\left( c_{t+1}\right) \) and that \(k_{t}=k_{t+1}=k_{t+2}=\cdots =k^{*}\) . Adding this to our first-order condition, we have:

Overlapping generations model   In our overlapping generations model, an agent maximizes \(U=\phi \ln \left( c_{t}\right) +\left( 1-\phi \right) \ln \left( y_{t+1}\right) \) such that \(c_{t}\le \alpha k_{t}^{\gamma }-G+\left( 1-\delta \right) k_{t}-k_{t+1}\) and \(y_{t+1}=\alpha k_{t+1}^{\gamma }-G\) . Taking the first-order condition with respect to \(k_{t+1}\) , we have:

In the steady state, we have that \(k_{t}=k_{t+1}=k_{t+2}=\cdots =k^{*}\) . Adding this to our first-order condition, we have:

We first note that there is no closed-from solution for steady-state capital in our OLG model. However, we can express \(\phi \) in a way that lets us calibrate our OLG model to the infinite horizon model by parameterizing \(\phi \) based on \(k^{*}\) . Letting \(T_{1}=\alpha k^{*\gamma }-G-\delta k^{*}\) , \(T_{2}=\alpha \gamma k^{*\gamma -1}\) , and \(T_{3}=\alpha k^{*\gamma }-G\) , we have that:

Considering the case where \(\beta =0.98\) and \(\delta =0.08\) and \(\alpha =1\) , we present two scenarios, based on individual occupations and potential public goods provision. An individual using the low-return independent farming with complete public goods provision has:

An individual using the high-return independent farming with no public goods provision has:

Therefore, we choose \(\phi =0.06\) as a value that results in steady-state capital levels that are approximately consistent with those from an infinite horizon model under various cases arising within our model.

Displaying occupations by neighborhood for country 9, period #1

Displaying occupations by neighborhood for country 9, period #10

Displaying occupations by neighborhood for country 9, period #100

1.3 Sample occupation maps

Appendix Figs.  11 , 12 , and 13 present individual occupations for a single country across all four scenarios. Within this single country, each of the 36 sixteen-individual neighborhoods is shown and we present the occupations for periods 1, 10, and 100. Every individual begins in low-return independent farming (red), but through time some individuals move into high-return independent farming (blue), low-return commercial farming (green), high-return commercial farming (black), trading (yellow).

Preferred tax rates

Tax rate by country—period #100

Appendix C: Preferred tax rates

To highlight the voting behavior driving the macro-level model, it is useful to visually present the preferred tax rates across various scenarios. As discussed in the text, the tax rate that an individual prefers depends on their cost of taxation (a reduction in disposable income) and their benefit from the public goods funded through taxation (a reduction in the fixed cost of the high-return technology). Since the level of public goods provision depends on average income levels (alternatively, we can also consider aggregate income levels), an individual’s choice of tax rate depends on their country’s average income level. Since the chosen tax rate is based on the median voter, the distribution of income also matters. We highlight these issues in two figures.

Income source by period for similar individuals

High-return technology fixed cost (G) by period for similar individuals

Trader (1 if no trader) by period for similar individuals

Trader fees (F) by period for similar individuals

First, Appendix Fig.  14 presents preferred individual-level tax rates based on the log of average income levels within a country (vertical axis) and the log of individual income levels (horizontal axis). Each country can be seen as a horizontal row (at its average income level) that consists of 576 individual dots with color-coded preferred tax rates. The figure is constructed using all individuals from all 300 countries based on the final period of our simulation. Appendix Fig.  14 highlights that preferred tax rates are closely related to individual income levels. Note that extremely poor individuals prefer not to have any taxes, since the reduction in disposable income dominates when an individual is unable to adopt the high-return technology even when the fixed cost is reduced. Traders receive the highest incomes, and they also prefer not to have any taxes, since they do not benefit directly from public goods because they do not pay the fixed cost of the high-return technology. Between these two extremes, farmers may have a range of net benefits from taxation that drive their individual tax preferences. Within this range, the preferred tax rate falls with individual income as the reduction in disposable income matters relatively more at higher individual income levels (thus increasing the cost) and a marginal increase in public goods provision is less likely to determine one’s choice of technology (thus decreasing the benefit).

Second, a country’s tax rate is chosen by the median voter and we highlight the median voter’s preferred tax rate in Appendix Fig.  15 . Using only the final period, this figure depicts the chosen tax rate for each country on the vertical axis and the corresponding average income level on the horizontal axis. At the very lowest average income levels, the median voter prefers no taxes and it is only at higher average income levels that taxes appear. Graphing the median voter’s preferred tax rate by average income levels reveals this trend, although there is not a one-to-one correspondence due to the complexity of the model, such as the distribution of incomes and access to traders. As discussed in the text of the paper, for example, more unequal countries are likely to choose lower tax rates (since the very poor and very rich prefer lower taxes), while more equal, middle-income societies are more likely to choose higher tax rates. Despite this heterogeneity, we generally observe an increase in the chosen tax rate before a gradual decline to a tax rate of 0.3 at higher average income levels. While some individuals do prefer tax rates of 0.7 and 0.8, these higher rates are not chosen in any of our countries.

Combining these points, we see that some level of equality and a moderate level of overall income will be needed for public goods provision to become feasible.

Appendix D: Similar individuals in different contexts—additional materials

To complement our graphs of income levels in the text, we additionally display income sources, public goods provision, trader access, and trader fees for the same set of individuals. For income source, 1 is low-return independent farming, 2 is high-return independent farming, 3 is low-return commercial farming, 4 is high-return commercial farming, and 5 is trading (Figs.  16 , 17 , 18 , and 19 ).

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Fitz, D., Gouri Suresh, S. Poverty traps across levels of aggregation. J Econ Interact Coord 16 , 909–953 (2021). https://doi.org/10.1007/s11403-021-00333-6

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Springing people from the poverty trap

Chronic poverty in the developing world can seem like an insoluble problem. But a long-term study from Bangladesh co-authored by an MIT economist presents a very different picture: When rural poor people get a one-time capital boost, it helps them accumulate assets, find better occupations, and climb out of poverty.

In particular, the study strongly suggests that poverty is not principally the product of people’s capabilities or attitudes. Rather, the very poor are usually mired in a poverty trap, in which an initial lack of resources prevents them from improving their circumstances. But the sudden acquisition of a productive asset — even, say, one cow — via a randomized asset transfer program can help spring the poor from that trap if it brings them above a basic wealth threshold. Instead of being farm laborers or domestic servants, rural people take up livestock rearing and more land cultivation, and sustain better incomes.

“The poor in these contexts are not unable to take on more productive employment, they simply lack the productive assets to do so,” says Clare Balboni, an assistant professor of economics at MIT and co-author of a published paper detailing the study’s findings.

The study adds evidence explaining what lies behind the success of “big push” antipoverty programs, which often center on significant one-time interventions. As the paper states, “big push policies which transform job opportunities represent a powerful means of addressing the global mass poverty problem.” Such programs have gained traction over the last 15 years or so.

The paper, “Why Do People Stay Poor?” appears in the May issue of the Quarterly Journal of Economics . The co-authors are Balboni, who is the 3M Development Assistant Professor of Environmental Economics in MIT’s Department of Economics; Oriana Bandiera, a professor of economics at the London School of Economics (LSE); Robin Burgess, a professor of economics at LSE; Maitreesh Ghatak, a professor of economics at LSE; and Anton Heil, a research manager at LSE.

Mind the gap

To conduct the study, the scholars examined data from a long-term survey project involving 23,000 households in 1,309 villages, administered by BRAC, a major NGO in Bangladesh. That project included a specific antipoverty program covering 6,000 poor rural households: Women in half of those households were offered a one-time asset transfer of about $500 and complementary training and support in 2007, while the rest served as a control group after 2011, with surveys of the households conducted in 2007, 2009, 2011, 2014, and 2018.

An earlier published paper, by some of this paper’s LSE-based co-authors, quantifies the experiment’s material gains. After four years, for women given a cow in 2007, earnings increased by 37 percent, consumption rose 10 percent, ownership of household durables increased 110 percent, and extreme poverty (those living on under $1.25 per day) declined 15 percent, compared to the control group.

In short, this intervention works. But why? The current paper closely scrutinizes the BRAC data to arrive at an explanation. The villages in the BRAC experiment have a “bimodal” wealth distribution: Some people have very few assets, while others have significantly more, with a gap in between the two levels. As it happens, when people in the poorest group receive a $500 asset, it leaves them in the gap between those levels.

The poor do not stay in that gap, however, after receiving that $500 asset. Tracking households over time, the researchers identified a striking pattern. The gap in between wealth levels is actually a threshold. People whose acquisition of the $500 allowed them to surpass that threshold gained income and wealth over time, while those below it remained poor.

Essentially, acquiring even one cow allowed members of very poor households to move from being under-employed laborers to working more with livestock and in land cultivation. It’s not that the poor did not want to work; hours worked actually rose when people had more work options. The study estimates that 98 percent of poor households consisted of wage laborers before the intervention, whereas about 98 percent would choose to devote some hours to livestock rearing, given enough assets.

“The poor are trapped in these occupations as a result of the fact that they are born poor,” Balboni says.

A growing interest in big pushes

The findings about the BRAC program in Bangladesh fit a burgeoning literature that has examined “big push” programs and their implications. And while Balboni focuses much of her research on environmental economics, other MIT scholars have also analyzed this subject.

In a paper published in late 2021, MIT economists Abhijit Banerjee and Esther Duflo, with doctoral student Garima Sharma, found that a similar BRAC program in rural India generated generated income increases of 30 percent while producing economic benefits at least four times the cost of the program (and possibly much more). Banerjee and Duflo have also examined evidence across the field on poverty trap dynamics.

In the case of the BRAC program in Bangladesh, the current study estimates that the economic misallocation resulting from the poverty trap in this setting is 15 times the one-time cost of taking households across the poverty threshold.

“We really need these big-push policies that tap into talent,” says Balboni, who also recently presented the paper in person to students in MIT’s MicroMasters Program in Data, Economics, and Development Policy.

Funding for the research was provided, in part, by The British Academy, as well as the U.K.’s Economic and Social Research Council-Department for International Development.

Breaking the Poverty Trap

How it works

One of the reasons the rich get richer, and the poor get poorer, is because of the lack of not knowing and ignorance hindering half the world, allowing the cycle of poverty to continue. Poverty trap is as a spiraling mechanism, that forces people to remain poor binding many to no hope of escaping. The poverty trap has been an ongoing cycle within generations even those close to me, that has tremendously taken a negative toll on society and my family for nearly decades.

Being a Haitian American descendant, I’ve not only seen that challenges my family faced, but my relatives back in Haiti are still facing till this day. I came to a deeper understanding it’s not late to save Haiti, and even the worldwide from poverty. We as a whole nation need to, enforce education, create business and increase jobs. Not only will these ideas change Haiti, but help their human development index world as a whole, and the induvial who are eager to leave poverty and are ready to end the inevitable cycle called the poverty trap. (Hubbard R. Glenn, 2009)

Most of the world has been taught to instill the importance of education to society, but in Haiti education is a luxury and if a child couldn’t afford the school they would get any seamless job to provide for their family. Which resulted in an uneducated population with limited economics opportunity for Haiti to advance. Many kids in Haiti today who have the chance to get an education still have a roadblock because of lack or resources for anyone to leverage their education. Which again initially causes the population of Haitians to be in poverty for decades. For example, a person in Haiti may never be able to save enough to escape poverty if they were to remain in a developing country with a small educated population but may have a better chance if they were able to move to a state with larger education population (Hoff Karla pg2, 2006). This lack of education is not only holding Haiti back. But making it harder to catch with up the world to advanced capitalist nations to leave the cycle. I stand firmly that useful education can positively transform every aspect of poverty. For instance, when I implied valuable training it’s indicating the need of more practical knowledge and skills their Haitians need to acquire that can grow its economy like learning to communicate which can help build a relationship then can get an investor to invest in their country. Another useful prime skill is learning how to leverage technology because it keeps on advancing, and more importantly, education can promote the young or even the old to become entrepreneurs and manage a business. Overall education in Haiti can promote agricultural knowledge, innovation, and efficient that can contribute incremental productivity to aid poverty in Haiti. (Perry, Guillermo,20016)

In the light of, a prosperous country they realize the influential key factor of starting a business is essential for growth. Haiti has been known to have instability, and lack of governance which make it harder for them to advance because the country also has a shortage of business this impact their economy to promote establishing business would help add wealth to their country it could ultimately have a chance of escaping the trap. Many people today even American fail to see the actual values of having a small business and even large corporations to grow the economy and to initially end poverty. Trade is one of the great forms to embed poverty because it allows distribution of income, improvement in capital mobility, and overall generate cash flow. (Hoff Karla,2016) Generally speaking, if Haiti was able to allocate more corporations to accumulate money in their economy, it could help gain funds to fix rebuild their countries from the terrible natural disasters and no longer be an emerging country. A researcher has even clarified the root cause of poverty is prune to a lack of access to markets and resources. Additionally, we wonder why food and other sources are costly not only in Haiti but in poorer parts of the country. That’s due to lack of business because it can initially tackle the base of the pyramid it can’t bring fair prices to the people instead of forcing them to pay more. Starting a business that makes profits is one thing, but meeting the social needs to you buyers is another thing. For instance, if we were to create a business that not only helps the locals, and the whole country, by generating an excellent water supply to help prevent disease from spreading, and also creating technology security business that can help other parts of the world by stopping hackers. Why only have business within a country I tremendously believe greatly to help other around the world because that would add value to other countries and Haiti would be more prominent know which then can lead other business opportunity to them to branch out of poverty. Given that, these ideas can eventually turn into a profit for Haiti to advance it can’t happen if someone don’t take action and start planting seeds, so poverty can be gone in the next ten years there needs to be a process and dedication, and remember a government can’t reform overnight

We’ve all heard saying give a man a fish; you feed him for a day; teach him how to fish, and he will be able to feed himself for the rest of his life. This saying is so powerful because if you give someone money just for charity, it comes then go, but give someone a job their money will continue to be steady and grow. Undoubtedly, creating jobs is the most effective way to eradicate poverty, but also is an essential part, that many government leaders seem to ignore. Why continue to give Haiti foreign aid if we can create jobs. Creating jobs, it will generate employment, increase employability, and make the labor markets more efficient. (Karnani) It’s not possible for any country reduced poverty significantly without experiencing economic growth, but economic growth has had a widely different impact on poverty reduction across countries. A one percent increase in per capita GDP can reduce income poverty by as much as four percent or as little as one percent. The exact link between economic growth and poverty reduction is mediated by job creation. (Karnani) For example countries like China and India has seen drastic, rapid economic growth for several years primarily because of the process economic reform was initiated, but that’s not the root cause. Despite the reform, unemployment was a significant problem, but as soon as they created employment it approximately caused their per capita GDP to rise. Of course, job increase is a great way to reduce poverty, but in all reality putting more money in the economy and spending, and buying can aid, the market. In true reality, the poor need productive jobs that lead to higher income, and alleviate poverty.( World Bank,2002)

In the final analysis, it’s clear that many different routes can stop poverty trap. Whether its education that can allow Haitian people not to be ignorant of what’s going on around them, and it equips their people with skills and value to be responsible citizens. Overall education enables each individual to reach their full potential. As well as, creating a business to demonstrate that it grows their GDP, and overall have a higher capacity to assemble and maintain the growing progress of running a business and. Finally, creating jobs makes a huge impact regardless of the person being illiterate they can still put their hands to work to generate income and add to Haiti’s economy.

Overall, poverty to others doesn’t have to deal with just money many people who are living in poverty believe they are rich because of their mindset it starts with the end in mind so to end poverty it’s what you think that needs to be changed. Yes, there is more that goes in ending the poverty in Haiti and around the world, but there is still hope, where there is a vision it’s possible to stop that cringy word called the poverty trap.

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Poverty Trap

A situation in which there is little incentive for workers earning a low income to earn extra income, because it would result in having to either pay higher tax and/or losing some of their benefit payments.

The low-income poverty trap refers to the phenomenon in which people living in poverty have difficulty escaping poverty because of structural barriers that prevent them from earning enough income from a job to meet their basic needs. These barriers can include lack of access to education and training, lack of affordable housing and transportation, lack of affordable childcare, and discrimination in the labour market.

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Poverty Anywhere is a Threat to Prosperity Everywhere

• 24 Apr 2024
• 12 min read

Poverty is the Parent of Revolution and Crime.  

 —Aristotle.  

In our interconnected world shaped by technology, trade, and communication, the assertion   

that "Poverty in any corner poses a danger to prosperity everywhere" carries significant resonance. Despite poverty often appearing as a localized concern, its impact extends far beyond borders, influencing economies, social frameworks, and the overall welfare of humanity on a global scale.

The International Labour Organization (ILO) even has this principle enshrined in their Declaration of Philadelphia. While prosperity might evoke images of flourishing economies and a comfortable standard of living, it cannot exist in isolation from the realities of global poverty.  

One of the most direct threats poverty poses is to global economic stability. Impoverished regions often lack the resources to invest in infrastructure, education, and healthcare. This creates a cycle of limited economic opportunities, hindering their ability to participate effectively in the global market. Furthermore, widespread poverty translates to a diminished consumer base, impacting the profitability of businesses in prosperous nations that rely on exports.  

Poverty encompasses more than just a lack of material resources; it encompasses inadequate access to education, healthcare, sanitation, and opportunities for economic advancement. The World Bank defines extreme poverty as living on less than USD 2.15 USD/day , but poverty's dimensions extend beyond income thresholds to encompass multidimensional factors like education, health, and social exclusion. According to the NITI Aayog, the poverty line is set at 1,286 rupees per month for urban areas and 1,059.42 rupees per month for rural areas.  

At the local level, poverty manifests in various forms, including hunger, inadequate housing, and limited access to education and healthcare. In impoverished communities, individuals face heightened vulnerability to diseases, malnutrition, and exploitation. Children from poor households often lack access to quality education , perpetuating cycles of poverty across generations. Moreover, poverty can breed social unrest and crime, further destabilizing communities and hindering economic growth.  

Poverty takes a significant toll on economic development , both domestically and globally. In economically disadvantaged regions, productivity losses due to illness, malnutrition , and lack of education diminish human capital, hindering economic growth potential .   

Moreover, poverty restricts market opportunities and consumer spending, stifling demand and hindering economic expansion. In the global context, poverty undermines international trade and investment, contributing to economic disparities between nations and impeding efforts toward global economic integration.  

In a local slum , families may be forced to live in overcrowded, unsanitary housing with limited access to clean water and proper sanitation. This can lead to the spread of diseases and exacerbate existing health problems.  The high cost of rent might force multiple families to share a single unit, limiting privacy and hindering hygiene. For example, Dharavi serves as a stark reminder of the living conditions faced by many families in slums worldwide. Overcrowding, inadequate sanitation, and limited resources continue to be pressing issues that need attention and solutions. Efforts to improve living conditions and provide better opportunities for slum dwellers are crucial for creating a more equitable society.   

The social consequences of poverty are profound and far-reaching. Poverty exacerbates social inequalities, marginalizing vulnerable groups and perpetuating cycles of deprivation. Moreover, poverty undermines social cohesion and stability , fueling resentment and discord within communities. In extreme cases, poverty can give rise to social unrest, conflict, and mass migration, with implications for regional stability and global security. For example, Afghanistan faces a severe humanitarian crisis and poverty, with nearly 28.8 million people in urgent need of support. The economic collapse, exacerbated by decades, has left millions of Afghans struggling against poverty and to meet their basic needs. Food insecurity is a critical issue, with 17.2 million people facing crisis or worse levels of food insecurity.  

A 2019 study by the United Nations Development Programme (UNDP) found a strong correlation between poverty, inequality, and violent conflict. This instability disrupts economies, hinders investment, and forces people to flee their homes, creating a refugee crisis that further burdens developed nations. For example, the Syrian Civil War, fueled in part by poverty and social inequality, led to a mass exodus of refugees to Europe, placing a strain on social services and security forces in host countries.  

Access to healthcare is a fundamental human right, yet poverty often deprives individuals of this essential service. In impoverished communities, limited access to healthcare facilities, medications, and trained healthcare professionals exacerbates health disparities and increases the prevalence of preventable diseases. Furthermore, poverty undermines public health interventions, hindering efforts to combat infectious diseases and promote maternal and child health. In many rural areas of Sub-Saharan Africa , poverty limits access to healthcare facilities. These regions often lack well-equipped clinics, hospitals, and trained medical professionals.  

Rural communities in India face a severe shortage of access to healthcare services. Public spending on healthcare is limited, and private healthcare primarily serves urban areas. Those in rural areas often travel long distances (up to 100 km) to access healthcare services. India suffers from a significant lack of qualified medical personnel in rural areas. The absence of efficient public health systems exacerbates the problem. High rates of poverty hinder access to healthcare. Nearly 90% of the population is not covered by insurance, and most costs are paid out of pocket or through loans. Rural areas experience disparities in health indicators due to poverty, including high rates of infant mortality, malnutrition, maternal mortality, low vaccination rates, and low life expectancy.   

Poverty creates a ripple effect that impacts many aspects of life, including education. Children from low-income families may not be able to afford good school, uniforms, or transportation, expenditure even if public education is free. This can prevent them from enrolling or fully participating in school.  

This lack of resources can hinder a child's ability to learn and keep them from achieving their full potential. It can also perpetuate the cycle of poverty, as children who don't receive a quality education may have fewer job opportunities later in life.  

Poverty and environmental degradation are closely intertwined, forming a vicious cycle of deprivation and ecological decline. Impoverished communities often rely on natural resources for their livelihoods, leading to overexploitation and environmental degradation. Moreover, inadequate infrastructure and sanitation facilities contribute to pollution and environmental health hazards, further exacerbating the burden on vulnerable populations.   

India's forests are under immense pressure due to deforestation driven by various factors, including agricultural expansion, logging, and infrastructure development. Tribal communities, often among the poorest in India, rely heavily on forests for their livelihoods, including for fuelwood, food, and medicinal plants. As forests shrink, these communities face increased poverty and loss of traditional knowledge , leading to a vicious cycle of deprivation and ecological decline. The struggle for survival can sometimes force them into unsustainable practices like illegal logging or encroachment on protected areas, further exacerbating environmental degradation.  

In an increasingly interconnected world, the impacts of poverty transcend national borders, reverberating across continents through trade, migration, and communication networks. Globalization has intensified economic interdependence, making prosperity contingent on the well-being of nations at all levels of development. Economic downturns in one region can have cascading effects on global markets, highlighting the interconnected nature of modern economies.  

Addressing poverty requires concerted efforts at the local, national, and international levels. International cooperation is essential for mobilizing resources, sharing expertise, and implementing effective poverty alleviation strategies . Initiatives like the United Nations Sustainable Development Goals (SDGs) provide a framework for collective action, aiming to eradicate poverty and promote shared prosperity by 2030. Moreover, international aid and development assistance play a crucial role in supporting impoverished communities and building resilient societies.  

Effective poverty alleviation strategies empower communities to become agents of change in their own development. Empowering marginalized groups, including women, indigenous peoples, and rural populations, is crucial for fostering inclusive growth and sustainable development. By investing in education, healthcare, and livelihood opportunities, communities can break free from the cycle of poverty and contribute to broader economic and social progress.  

"Poverty anywhere is a threat to prosperity everywhere" encapsulates the profound interconnectedness of global societies and economies. Poverty undermines human dignity, economic progress, and social cohesion, posing a threat to prosperity at both local and global levels. Addressing poverty requires holistic approaches that tackle its multidimensional manifestations, from economic deprivation to social exclusion and environmental degradation. By prioritizing poverty alleviation and fostering international cooperation, we can build a more equitable and prosperous world for all. As global citizens, we must recognize our shared responsibility in combating poverty and promoting sustainable development for future generations.   

Poverty is the Worst form of Violence.  

 —Mahatma Gandhi