Inequality, Wealth Distribution, and Poverty

Every society has rich people and poor people. The interesting questions are: How big is the gap? Why does it exist? Is it growing? And when does a normal gap become a dangerous one? This chapter gives you the tools to answer all four — and to avoid the single most common mistake people make when they talk about inequality.

Income vs. wealth: the distinction everything depends on

Before anything else, you must separate two ideas that people constantly blur together.

Income

A flow of money over a period of time — what you earn in a year. Wages, a business's profit, interest, dividends (a share of company profits paid to owners), rent received, and government transfers like pensions.

Wealth (or net worth)

A stock of value at a single moment — everything you own minus everything you owe. Your home equity, savings, stocks, the value of your business, minus your mortgage, loans, and credit-card debt.

Why does this matter so much? Because wealth is always far more concentrated than income, and for a clear mechanical reason: wealth compounds. Once you own assets, those assets earn income (rent, dividends, interest), which you can save and turn into more assets, which earn more income. Money makes money. Wages do not compound that way.

This also explains odd-looking cases. A newly qualified doctor can have a high income but negative wealth (large student debt, no assets yet). A retired schoolteacher who paid off her home decades ago can have a low income but real wealth. Confusing the two leads to nonsense conclusions — so keep them apart.

How we measure inequality

You can't manage what you can't measure. Economists use a few standard tools.

The Gini coefficient (named after statistician Corrado Gini) squeezes the whole distribution into one number between 0 and 1. 0 means perfect equality (everyone has exactly the same). 1 means perfect inequality (one person has everything, everyone else has nothing). It is built from the Lorenz curve, which plots the cumulative share of people against the cumulative share of income they collectively hold.

 % of income
 100 |                                   * (45° "equality" line)
     |                              *   /
     |                         *      /  ← the bigger this gap,
     |                    *        /       the more unequal
     |               *         _/  ← actual Lorenz curve
     |          *        __--
     |     *      __--
   0 |*__--______________________________
     0           % of people          100

 Gini = (area between the two lines) / (total area under the 45° line)

Gini is handy because it's a single comparable number. The US household-income Gini is roughly 0.48 in 2024, up from about 0.43 in 1990 — a clear, sustained rise. But Gini has blind spots: it's most sensitive to changes in the middle of the distribution and can hide what's happening in the extreme tails. It also says nothing about absolute living standards — a poor country and a rich country can share the same Gini.

So economists pair it with share and percentile measures, which are more intuitive:

• Top 1% / top 10% share — what slice the richest group takes.
• 90/10 ratio — income at the 90th percentile divided by income at the 10th. (A "percentile" is your rank out of 100; the 90th percentile out-earns 90% of people.)
• Palma ratio — the top 10%'s share divided by the bottom 40%'s share, designed to spotlight the tails Gini blurs.

Why is inequality rising? The main causes

Four big forces, often working together.

1. Skill-biased technological change (SBTC). Computers and automation are brilliant at replacing routine middle-skill work (filing, assembly-line tasks, basic bookkeeping) but they make highly educated workers more productive. The result is job polarization: growth at the top (engineers, managers) and bottom (care, hospitality), with a hollowed-out middle. This widens the skill premium — the extra pay a skilled worker commands. The college wage premium rose sharply after about 1980, and most economists treat this as the leading explanation for the modern surge.

2. Globalization and trade. When firms can offshore production or import cheaper goods, less-skilled workers in rich countries face direct competition. The clearest evidence is the "China shock" after China joined the World Trade Organization in 2001: research by Autor, Dorn, and Hanson showed that US manufacturing regions exposed to Chinese imports suffered deeper, longer-lasting job and wage losses than older trade theory predicted. Trade and technology reinforce each other.

3. Returns on capital outrunning growth — Piketty's "r > g." In Capital in the Twenty-First Century (2013–14), economist Thomas Piketty argued that when the rate of return on capital (r) — profits, dividends, interest, rent — is higher than the economy's overall growth rate (g), existing and inherited wealth grows faster than wages and output. Over generations, wealth concentrates automatically.

Be precise about what's contested. Piketty's data on long-run inequality (with Saez and Zucman, in the World Inequality Database) is widely respected. His theory — the iron law of r > g — is debated. Critic Matt Rognlie showed much of the rising "capital share" is really housing and real estate, not factories and machines; others question whether r reliably beats g after taxes and depreciation.

4. Inheritance. Wealth begets wealth across generations. Oxfam (January 2025) estimated that roughly 36% of billionaire wealth is inherited, that every billionaire under 30 inherited their fortune, and that around 60% of billionaire wealth traces to inheritance, monopoly, or cronyism. Over the next 30 years, about 1,000 billionaires are expected to pass more than $5.2 trillion to heirs, largely untaxed. This is the modern fear of a hereditary elite — Piketty's "patrimonial capitalism." Other amplifiers include assortative mating (high earners marrying each other), declining union membership, and "winner-take-all" superstar effects in tech and finance.

Poverty: absolute vs. relative

"Poverty" means two genuinely different things, and confusing them wrecks arguments.

The absolute-poverty story is the greatest economic triumph of our era. Extreme poverty fell from roughly 36% of humanity in 1990 to about 9–10% by the late 2010s — the fastest, largest poverty reduction in history, driven above all by China and India. (When the World Bank raised the line to $3.00/day in 2025, the count rose to around 838 million — a higher threshold catches more people, but the long-run downward trend is unmistakable.)

Mobility: does the gap trap you?

Inequality at one moment matters less if people move up and down freely. Intergenerational mobility measures the chance a child ends up in a different income rank than their parents. Here the data is sobering.

The Great Gatsby Curve (named by economist Alan Krueger in 2012) plots inequality against immobility across countries — and finds a clear pattern: more unequal countries tend to have less mobility. The Nordic nations, Canada, and Australia are low-inequality and high-mobility. The US is high-inequality and low-mobility, which quietly undercuts the "American Dream."

Raj Chetty's Opportunity Insights team, using tax records, found a US child born in the bottom income fifth has only about a 7.5% chance of reaching the top fifth — and that mobility varies enormously by place. Most strikingly, "absolute mobility" (the chance of out-earning your own parents) fell from about 90% for Americans born in 1940 to roughly 50% for those born in 1980.

Should we redistribute? The honest debate

The case against is best captured by Arthur Okun's "leaky bucket" (1975). Carrying money from rich to poor, he argued, spills some on the way: administrative costs plus incentive effects — high taxes can blunt the urge to work and invest, and poorly designed transfers can reduce recipients' work effort. Take $1,000 from the top and perhaps only $500 reaches the bottom.

The case for is that equality and growth can be partners. IMF economists (Berg and Ostry, 2011; Ostry and colleagues, 2014) found that more-equal societies tend to sustain longer growth spells, and that moderate redistribution generally does not harm growth — only extreme redistribution does. The mechanisms: severe inequality can depress consumer demand, waste talent by under-investing in poor children (the mobility link again), and breed political instability and rent-seeking.

Some inequality is normal; extremes are the danger

Zero inequality is neither possible nor desirable. People differ in effort, skill, risk-taking, and simple age (a 25-year-old earns less than a 50-year-old at the same job). Rewards create the incentives that drive work, saving, and innovation. The real question is about extremes.

History offers a warning and a puzzle. Simon Kuznets (1950s–60s) proposed the Kuznets curve: inequality rises then falls as a country industrializes — an inverted U. It fit the US "Great Compression", when inequality fell sharply from about 1929 to 1948 (Depression, World War II, strong unions, high top tax rates) and stayed low through the 1950s–60s. But the curve broke after 1980: the US top-1% share climbed back toward its Gilded Age (1910s–20s) peak. The decline was not automatic, so economists now talk of "Kuznets waves." Both the Gilded Age and the Roaring 1920s ended in upheaval — Progressive-era reforms and the 1929 crash — which is exactly why extreme concentration worries people: it tends to capture politics, erode equality of opportunity, corrode trust, and fuel populist backlash.

The global twist: two curves moving in opposite directions

Zoom out to the whole planet and a paradox appears. Inequality between countries has fallen (poor nations growing faster), even as inequality within many rich countries has risen. Branko Milanovic captured this in the famous Elephant Curve.

 Income
 gain  |                                          * ← global top 1%
  (%)  |   Asia's rising                         /     (trunk tip up)
       |   middle class      ___                /
       |      (back) ___    /   \___           /
       |        __--    \__/        \         /
       |     __-                     \  ___  /
       |   _-      rich-country middle \/   ← stagnation (the dip)
       |__/______________________________________________
         poorest        global income rank        richest

Over roughly 1988–2008, the global middle (especially Asia's new middle class — the elephant's back) gained hugely; the working/lower-middle class of rich countries around the 80th percentile stagnated (the dip in the trunk); and the global top 1% soared (the raised tip). That stagnant dip is the economic root of much of today's political anger in wealthy nations.

Key Takeaways

• Income is a yearly flow; wealth is a stock of assets minus debts — and wealth is always far more concentrated because it compounds.
• Gini gives one number (0 = equal, 1 = unequal) but hides the tails; pair it with top-share and 90/10 percentile measures.
• The main drivers are skill-biased technology, globalization (the China shock), capital outrunning growth (Piketty's contested r > g), and inheritance.
• Absolute poverty ("can you survive?") has collapsed worldwide; relative poverty ("can you participate?") rises with inequality and never disappears.
• Mobility may matter more than the gap itself — and more-unequal countries are usually harder to climb (the Great Gatsby Curve).
• Redistribution's "leaky bucket" is real but smaller than once feared; some inequality is healthy, but extremes threaten politics, opportunity, and stability.
• Globally, between-country inequality is falling while within-rich-country inequality rises — the Elephant Curve in one picture.