Module 4: Poverty, Inequality, and Development¶

Slumdog Millionaire (2008) Esther Duflo & Abhijit Banerjee

Overview¶

In 2011, India's Planning Commission declared that anyone spending more than 28.65 rupees per day in a rural area was not poor. The number provoked outrage. Members of Parliament tried surviving on the amount for a day and failed. Newspapers printed photographs of what 28.65 rupees could buy: a cup of tea and a couple of rotis. The Tendulkar poverty line became one of the most debated numbers in Indian economic history — not because the statisticians were incompetent, but because the question "who is poor?" turns out to be far more difficult than it appears.

This module takes you into the economics of poverty, inequality, and development. You will learn how economists define and measure poverty, why two countries with the same average income can have vastly different living standards, how the informal economy sustains hundreds of millions of workers outside the reach of labor law and social protection, and what rigorous evidence tells us about which interventions actually reduce deprivation. Along the way, you will encounter a fundamental tension that runs through all of development economics: between the grand ambition to end poverty and the painstaking, experiment-by-experiment work of figuring out what actually works.

We anchor this module in India for good reason. India is simultaneously one of the world's fastest-growing major economies and home to the largest number of people living in multidimensional poverty. It has the world's biggest social safety net program (NREGA), the largest biometric identity system (Aadhaar), and an informal economy that employs roughly 86% of its workforce. If you want to understand poverty and development economics, India is not just one case study among many — it is the central case.

Key Concepts¶

Poverty Line — an income or consumption threshold below which a person is classified as poor
Absolute Poverty — deprivation measured against a fixed standard (e.g., $2.15/day)
Relative Poverty — deprivation measured relative to the median standard of living in a society
Inequality — the unequal distribution of income, wealth, or opportunity across a population
Gini Coefficient — a number from 0 to 1 measuring income inequality (0 = perfect equality, 1 = maximum inequality)
Human Development Index (HDI) — a composite measure of health, education, and income
Informal Economy — economic activity outside formal regulation, taxation, and social protection
Microfinance — small loans and financial services for low-income people excluded from formal banking
Randomized Controlled Trial (RCT) — an experiment that randomly assigns treatment to measure the causal effect of an intervention
Capability Approach — Amartya Sen's framework defining well-being as the freedom to achieve valued functionings
Economic Mobility — the ability of individuals or families to move up or down the income distribution over time
Social Safety Net — government programs that protect people from falling into extreme deprivation
Education Returns — the increase in lifetime earnings from each additional year of schooling
Child Labor — work by children that deprives them of education, health, and childhood
Gender Gap — systematic differences in economic outcomes between men and women

Defining and Measuring Poverty¶

Absolute vs. Relative Poverty¶

Economists distinguish two fundamentally different ways of thinking about poverty. Absolute poverty measures deprivation against a fixed standard: can a person afford enough food to sustain basic caloric intake? Do they have access to shelter, clean water, and basic healthcare? The World Bank's international poverty line of $2.15 per day (2017 purchasing power parity) is an absolute measure — it does not change as the economy around you grows.

Relative poverty, by contrast, defines being poor in relation to those around you. The European Union, for instance, classifies anyone earning less than 60% of their country's median income as "at risk of poverty." By this measure, a person in Denmark earning $15,000 a year could be poor, while a person in rural Bihar earning far less might not be — because the Danish threshold is set relative to a much higher national standard.

This distinction matters enormously for policy. India's poverty debate has always been fought on absolute terms: how many people fall below a minimum threshold of consumption? But as the economy grows and a new middle class emerges, the politics of poverty increasingly become about relative deprivation — the street vendor in Mumbai watching luxury SUVs pass his stall may consume more calories than his grandfather, but the gap between his life and what he sees around him has widened.

Poverty Lines: From Tendulkar to Rangarajan¶

India has had a long and contentious history of setting its official poverty line. The Tendulkar Committee (2009) set the line at monthly per capita consumption expenditure of Rs 816 in rural areas and Rs 1,000 in urban areas — the figures that produced the notorious Rs 28.65 per day number. Critics argued the line was absurdly low, capturing only the very bottom of deprivation.

The Rangarajan Committee (2014) proposed a higher threshold: Rs 972 for rural and Rs 1,407 for urban areas per person per month. Under the Rangarajan line, India's poverty rate jumped from the Tendulkar estimate of about 22% to nearly 30% — roughly 93 million additional people were reclassified as poor by moving the line upward.

Indian Context

The poverty line debate illustrates a deeper point: measurement is not neutral. Where you draw the line determines how many people are counted as poor, which in turn determines how much resources the government allocates to anti-poverty programs, and which states receive more central funds. In India, the difference between the Tendulkar and Rangarajan estimates — roughly 90 million people — is larger than the entire population of Germany. The seemingly technical question of "where to set the line" is profoundly political.

Multidimensional Poverty¶

Increasingly, economists and policymakers recognize that poverty is not just about income. The Multidimensional Poverty Index (MPI), developed by Sabina Alkire and James Foster at the Oxford Poverty and Human Development Initiative, measures deprivation across three dimensions — health, education, and living standards — using ten indicators: nutrition, child mortality, years of schooling, school attendance, cooking fuel, sanitation, drinking water, electricity, housing, and assets.

India's progress on multidimensional poverty has been dramatic. NITI Aayog's 2024 report, based on the National Family Health Survey, found that 248 million people exited multidimensional poverty between 2013-14 and 2022-23. The national MPI headcount ratio fell from 29.17% to 11.28%. The steepest declines occurred in the poorest states — Bihar, Uttar Pradesh, and Madhya Pradesh — suggesting that targeted programs in sanitation, cooking fuel, and nutrition are reaching those at the bottom.

Yet the MPI also reveals what income measures miss. A family might earn above the poverty line but still lack access to clean water or send their children to a school without functioning toilets. Multidimensional poverty measurement captures these simultaneous deprivations in a way that a single rupee figure cannot.

Inequality: Who Gets What?¶

The Gini Coefficient and the Lorenz Curve¶

While poverty asks "how many people fall below a minimum threshold?", inequality asks "how is the total pie distributed across the entire population?"

The most commonly used measure of inequality is the Gini coefficient, derived from the Lorenz curve. The Lorenz curve plots the cumulative share of income earned by the cumulative share of the population, ranked from poorest to richest. If income were perfectly equally distributed, the Lorenz curve would be a straight diagonal line — the bottom 20% would earn 20% of income, the bottom 50% would earn 50%, and so on. In practice, the curve bows below this diagonal: the bottom 50% of India's population earns far less than 50% of total income.

The Gini coefficient is the area between the Lorenz curve and the line of perfect equality, expressed as a number between 0 (perfect equality) and 1 (one person holds all income). India's consumption Gini is around 0.35-0.37, which appears moderate compared to South Africa (0.63) or Brazil (0.53). But this figure, based on consumption surveys, almost certainly understates true inequality because the very rich are poorly captured in household surveys. Research using income tax data suggests India's income Gini may be considerably higher — some estimates place it above 0.50.

Indian Context

The World Inequality Lab's 2022 report estimated that the top 10% of Indians earn 57% of total national income, while the bottom 50% earn just 13%. If these estimates are accurate, India is among the most unequal large economies in the world — more unequal than the United States and approaching levels seen in South Africa. The gap between the consumption survey data (which suggests moderate inequality) and the income data (which suggests severe inequality) is itself a measurement puzzle that economists are actively debating.

Why Inequality Matters¶

Does inequality matter if absolute poverty is declining? Economists are divided on this question, but several channels make inequality economically consequential beyond its moral dimensions.

First, inequality can reduce economic mobility. When wealth is highly concentrated, the children of the rich have access to better education, healthcare, nutrition, and networks — giving them a head start that compounds over generations. The "Great Gatsby curve," identified by economist Miles Corak, shows that countries with higher income inequality tend to have lower intergenerational mobility.

Second, extreme inequality can undermine institutions. When a small elite controls a disproportionate share of resources, they can shape political rules to protect their position — what Acemoglu and Robinson call "extractive institutions." This can lock in patterns of inequality that persist for decades or centuries.

Third, inequality at very high levels may reduce aggregate economic growth by limiting the human capital investment of those at the bottom. A talented child in a village in Jharkhand who cannot afford quality schooling represents a loss not just for that child but for the economy as a whole.

The Informal Economy¶

One of the most distinctive features of developing economies is the enormous size of the informal economy — economic activity that takes place outside formal legal, regulatory, and tax structures. Street vendors, domestic workers, construction laborers hired on daily wages, autorickshaw drivers, home-based garment workers — these workers are not covered by labor laws, do not receive employer-provided insurance or pensions, and are invisible in most administrative data.

In India, the informal economy is not a marginal sector. According to the Periodic Labour Force Survey and National Statistical Office estimates, roughly 86% of India's total employment is informal. Even within the formal sector — registered firms subject to labor regulation — a substantial share of workers are employed informally, without written contracts or social security. The result is that the overwhelming majority of Indian workers lack the protections that textbook models of the labor market assume.

Indian Context

Dharavi, the Mumbai settlement that features prominently in Slumdog Millionaire, is often called Asia's largest slum. But it is also a bustling informal manufacturing hub with an estimated annual economic output exceeding $1 billion. Dharavi's workshops produce leather goods, pottery, recycled plastics, textiles, and food products — all without formal business registration, quality certification, or labor contracts. The informal economy in Dharavi is not a sign of failure; it is a survival strategy and, for many, a pathway to modest prosperity in a context where formal employment is scarce and formal regulations are burdensome for small operators.

The informal economy poses a fundamental dilemma for policymakers. On one hand, informality means workers have no safety net when a crisis hits — as the COVID-19 lockdown of March 2020 devastatingly demonstrated, when millions of informal workers in Indian cities suddenly lost their livelihoods and walked hundreds of kilometers back to their villages. On the other hand, heavy-handed formalization — imposing taxes and regulations on tiny enterprises — could destroy the very livelihoods it aims to protect.

Human Development: Beyond Income¶

The Human Development Index¶

In 1990, Pakistani economist Mahbub ul Haq, working with Amartya Sen, introduced the Human Development Index (HDI) as an alternative to GDP per capita for measuring a country's progress. The HDI combines three dimensions: health (measured by life expectancy at birth), education (mean and expected years of schooling), and income (gross national income per capita). The resulting index ranges from 0 to 1.

India's HDI in the latest Human Development Report stands at 0.644, placing it in the "medium human development" category. This is well below its GDP ranking — India is the world's fifth-largest economy by GDP but ranks 134th in HDI. The discrepancy reveals a crucial insight: aggregate economic output tells you almost nothing about whether that output translates into healthy, educated, fulfilling lives for ordinary citizens.

Within India, the variation is staggering. The HDI of Kerala (approximately 0.78) is comparable to that of countries like Mexico or Brazil, while the HDI of Bihar (approximately 0.57) resembles sub-Saharan African nations. These are states within the same country, governed by the same constitution — yet a child born in Kerala can expect to live roughly eight years longer and receive several more years of schooling than a child born in Bihar.

The Capability Approach¶

The intellectual foundation for looking beyond income was laid by Amartya Sen's capability approach. Sen argued that development should not be measured by how much money people have but by what they are able to do and be — their capabilities. A person who earns above the poverty line but cannot read, or who is well-fed but cannot participate in political life, or who has income but lacks the freedom to leave an abusive household, is deprived in ways that income alone does not capture.

Sen distinguished between functionings (actual achievements — being nourished, being literate, participating in community life) and capabilities (the freedom to achieve those functionings). Two people with identical incomes may have very different capabilities: a woman in a society that restricts her mobility has fewer capabilities than a man with the same income in the same society.

Key Insight

The capability approach reframes the central question of development economics. Instead of asking "how much does this person earn?", it asks "what is this person free to do and to become?" This shift in perspective has profoundly influenced how international organizations measure progress — the HDI, the MPI, and the Sustainable Development Goals all bear Sen's intellectual fingerprint.

What Works? RCTs and Evidence-Based Policy¶

The Randomization Revolution¶

For decades, development economics was dominated by "big questions" — does foreign aid work? Does trade liberalization reduce poverty? Should governments invest in infrastructure or human capital? These questions generated passionate debate but little consensus, because it was nearly impossible to isolate the causal effect of any single policy from the thousands of other factors changing simultaneously.

In the 1990s and 2000s, a group of economists — most prominently Abhijit Banerjee, Esther Duflo, and Michael Kremer — proposed a radically different approach: instead of arguing about big theories, test specific interventions with randomized controlled trials. An RCT randomly assigns some people to receive an intervention (the treatment group) and others to continue as before (the control group). Because assignment is random, any difference in outcomes between the two groups can be attributed to the intervention itself, not to pre-existing differences.

The approach, borrowed from medical trials, transformed the field. Rather than debating whether "education" reduces poverty, researchers could test whether providing free textbooks, hiring additional teachers, deworming children, or offering conditional scholarships had a measurable effect on learning outcomes. Each RCT answered a narrow, specific question — but the accumulation of hundreds of such experiments built a knowledge base that grand theories could not.

Indian Context

Some of the most influential RCTs in development economics were conducted in India. Pratham, an Indian NGO, partnered with J-PAL (the Abdul Latif Jameel Poverty Action Lab, co-founded by Banerjee and Duflo at MIT) to test the "Teaching at the Right Level" approach in Indian schools. The intervention grouped children by learning level rather than age and used volunteer tutors to bring struggling students up to basic reading and arithmetic competency. RCTs across multiple Indian states showed dramatic improvements in learning: children who received the intervention were significantly more likely to read a simple paragraph and perform basic subtraction. This evidence convinced the Indian government to adopt the approach in several states, eventually reaching millions of children.

Microfinance: Hype and Evidence¶

The story of microfinance illustrates both the promise and the limits of evidence-based development. In the 1970s, Muhammad Yunus, a Bangladeshi economist, began lending tiny sums — sometimes as little as $27 — to poor women in rural Bangladesh who had no access to formal banking. His Grameen Bank model, which used group lending and social pressure to ensure repayment, achieved repayment rates above 95% and appeared to transform lives. Yunus won the Nobel Peace Prize in 2006, and microfinance was celebrated as a revolution in poverty alleviation.

Then the RCTs arrived. Banerjee, Duflo, and collaborators conducted a rigorous evaluation of microfinance in Hyderabad, India, published in the American Economic Journal: Applied Economics in 2015. They found that access to microcredit increased borrowing and business investment, but had no measurable effect on household consumption, health, education, or women's empowerment over an 18-month to three-year period. Similar results emerged from RCTs in other countries. Microfinance was not harmful, but it was not the transformative force its advocates had claimed.

The lesson was not that microfinance is useless — it provides a valuable financial service to people excluded from formal banking. The lesson was that no single intervention is a silver bullet, and that rigorous testing is essential for separating genuine impact from wishful thinking.

Education, Health, and Gender¶

Education Returns¶

Education is one of the strongest predictors of escaping poverty. Each additional year of schooling increases an individual's earnings by approximately 8-13% globally, though the returns vary by country and level of education. In India, the returns to higher education are particularly steep: completing a university degree roughly doubles average earnings compared to completing only secondary school.

But the quantity of education is only part of the story. India has achieved near-universal enrollment in primary school, yet learning outcomes remain alarmingly low. The Annual Status of Education Report (ASER) consistently finds that a substantial share of children enrolled in school cannot perform basic grade-level tasks — a phenomenon researchers call the "schooling without learning" crisis. A child who attends school for eight years but cannot read a simple story or perform two-digit subtraction has received a credential, not an education. This is why Pratham's "Teaching at the Right Level" intervention, validated by RCTs, focuses on learning rather than mere enrollment.

Child Labor¶

Poverty and child labor are tightly interwoven. When a family cannot meet its basic needs, the short-term economic logic of sending children to work rather than school can feel inescapable — even when the long-term cost, in terms of foregone education and lifetime earnings, is enormous. India has made significant progress: the share of children aged 5-14 engaged in economic activity has fallen substantially over the past two decades. But child labor persists, particularly in agriculture, domestic work, and small-scale manufacturing. The 2011 Census recorded nearly 10.1 million child workers aged 5-14, and the true figure — including invisible domestic labor and family enterprise work — is likely higher.

Indian Context

The mid-day meal scheme, one of India's largest social programs, serves free lunches to roughly 120 million children in government and government-aided schools. Originally designed to improve nutrition, the scheme also functions as an attendance incentive — families are more likely to send children to school (rather than to work) when a guaranteed meal reduces the opportunity cost of attendance. This is a concrete example of how social safety nets and education policy interact: the mid-day meal addresses poverty, child labor, and education returns simultaneously.

The Gender Gap¶

The economic gender gap is one of the most persistent features of developing economies. In India, the female labor force participation rate is among the lowest in the world — the PLFS 2023-24 reports it at roughly 41.7% (using the usual status definition, including subsidiary work), compared to 78.8% for men. Even among women who work, earnings are substantially lower: the gender wage gap in India, controlling for education and occupation, ranges from 20% to 40% depending on the measure and sector.

The gender gap in economic participation is driven by a web of factors: social norms that assign women to domestic responsibilities, safety concerns about travel and workplaces, inadequate childcare infrastructure, and discriminatory hiring practices. But it also has profound economic consequences. McKinsey Global Institute estimated in 2015 that bridging the gender gap in labor force participation could add $700 billion to India's GDP by 2025. When half the population faces barriers to full economic participation, the loss is not just to those individuals but to the entire economy.

Connecting to the Film¶

Scene Connection: Slumdog Millionaire (2008)

Danny Boyle's Oscar-winning film follows Jamal Malik, an orphan from the slums of Mumbai, as he competes on Who Wants to Be a Millionaire? The film is a visual encyclopedia of this module's concepts: absolute poverty in the slum sequences, the bustling informal economy of Dharavi, child labor in the begging-ring and sweatshop scenes, the gender gap in Latika's constrained trajectory compared to Jamal's, and the question of whether economic mobility in India is driven by capability, luck, or something else entirely.

Read the full film analysis

Connecting to the Nobel Laureate¶

Nobel Connection: Esther Duflo & Abhijit Banerjee (2019)

Banerjee and Duflo, partners in life and in research, shared the 2019 Nobel Prize in Economic Sciences (with Michael Kremer) for their experimental approach to alleviating global poverty. Their work — conducting hundreds of RCTs across dozens of countries, many of them in India — transformed development economics from a field of grand theories and armchair debates into one of rigorous, experiment-by-experiment evidence. Their J-PAL South Asia lab in Chennai has become a global hub for testing what works in the fight against poverty.

Read the Nobel Focus

Interactive Explorations¶

Try these browser-based simulations to see the module's concepts in action:

Simulation	What to Try	Link
GDP Calculator	Compute GDP and see what gets measured — and what does not	Open
GDP Components Breakdown	See how consumption, investment, government spending, and net exports divide the pie	Open
Efficiency-Equity Spectrum	Explore the trade-off between maximizing the pie and dividing it fairly	Open
Human Capital ROI Calculator	Estimate the return on investment from additional years of education	Open

Simulations from Introduction to Economics by Dan McCreary (CC BY-NC-SA 4.0)

Key Takeaways¶

Poverty can be measured in absolute terms (a fixed threshold) or relative terms (compared to the median) — the choice of measure shapes policy and politics.
Multidimensional poverty captures deprivations in health, education, and living standards that income alone misses — India's MPI shows dramatic recent progress.
The poverty line is not a neutral technical number — where you draw it determines who counts as poor and how much resources flow to them.
The Gini coefficient and Lorenz curve measure inequality across the entire distribution, revealing gaps that average income figures conceal.
India's informal economy employs roughly 86% of workers, making it the dominant mode of work — not a marginal sector to be formalized away.
The Human Development Index reveals that economic growth does not automatically translate into better lives — India ranks far lower in HDI than in GDP.
Sen's capability approach redefines development as expanding what people are free to do and be, not just how much they earn.
RCTs transformed development economics by testing specific interventions rather than debating grand theories — many landmark studies were conducted in India.
Microfinance is a useful financial service but not a silver bullet — rigorous evidence shows modest effects on poverty, challenging earlier hype.
The gender gap in labor force participation costs India not just in terms of equity but in forgone economic output worth hundreds of billions of dollars.

Think Like an Economist¶

Reflection Prompts

India reduced its multidimensional poverty headcount by 248 million people between 2013-14 and 2022-23, yet income inequality (as measured by tax data) appears to have widened over the same period. Is it possible for poverty to fall while inequality rises? Under what conditions? Is this a success or a failure?
An NGO wants to improve education outcomes in a rural Indian district. It has the budget for one intervention: either distributing free textbooks to all students or hiring additional tutors to work with children who are behind grade level. Using the logic of RCTs and evidence-based policy, how would you advise them to decide?
Consider a street food vendor in Mumbai earning Rs 500 per day. She is above the Tendulkar poverty line and above the Rangarajan poverty line. But she has no health insurance, her children attend a poorly functioning government school, and she could lose her livelihood tomorrow if the municipality clears her stall. Is she poor? What does your answer reveal about the limits of income-based poverty measurement?

Next Steps¶

You have now explored how economists measure poverty, why inequality matters, and what rigorous evidence tells us about fighting deprivation. But development also requires growth — and growth in the twenty-first century is increasingly driven by technology, innovation, and the digital economy. In Module 5, we turn to innovation, technology, and economic growth — exploring how ideas become engines of prosperity, and who gets left behind when the engine accelerates.

Test yourself: Take the Module 4 Quiz | Explore further: Module 4 Resources