Development Economics: Why Some Countries Are Rich, and What Has Actually Helped

On a world map of average income, two peninsulas stand out. South Korea and North Korea share a language, a climate, a history up to 1945 and most of their genes. Their incomes today are wildly different. A single border drawn across one place did that, which is hard to explain by weather or by who the people are.

Or take Ghana and South Korea. By the Maddison Project's estimates (2023 edition, in 2011 international dollars), Ghana's income per person in 1960 was about $2,200 and Korea's about $1,500. By 2022 Korea was at about $41,300 and Ghana at about $4,200. Nobody sat down and chose that. Development economics is the attempt to find out what produced it, and what a government, an aid agency or a charity can actually do about it.

Why some countries are rich: geography, institutions, human capital

Three families of explanation compete, and each has a serious case.

Geography. Where a country sits shapes its costs. John Gallup, Jeffrey Sachs and Andrew Mellinger argued in 1998 that geography affects growth directly, through transport costs (landlocked countries far from the coast pay more to trade), disease burdens (the tropics carry more) and farm productivity. Jared Diamond's Guns, Germs, and Steel (1997) made a broader version of the case about continents and crops.

Institutions. The rules of the game matter more. Daron Acemoglu, Simon Johnson and James Robinson argued in "The Colonial Origins of Comparative Development" (2000, published 2001) that where Europeans could not survive diseases they built extractive institutions, which lasted; where they could settle in numbers they built ones that protected property. Mortality among early settlers, they said, predicts institutions today, and institutions predict income. The three won the 2024 Nobel prize in economics for their work on how institutions shape prosperity. Korea against North Korea is the standard picture.

Human capital. People matter more. Edward Glaeser, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer (2004) argued that schooling is the deeper driver and that institutions follow growth; poor countries, they said, have often escaped poverty through sensible policies, sometimes under dictators, and improved their political institutions later.

School	Its strongest case	The standard objection
Geography	Coast access and disease burden predict income	Korea and North Korea share geography; places change with technology
Institutions	Settler mortality predicts institutions, and institutions predict income	Critics question the mortality data, and ask whether it works only through institutions
Human capital	Schooling predicts growth and precedes institutional change	Rich countries can afford more schooling, so the causation can run the other way

Most economists weigh all three. The live argument is over which is deepest: what causes the others. That has not been settled, and each side's best evidence is read differently by the others.

Growth miracles and stalls

Whichever school you favour, the arithmetic is shared. Income growing at g per cent a year doubles in about 70 ÷ g years. At 2% a country doubles in 35 years; at 7%, in ten. Small gaps in growth, sustained, produce giant gaps in income: that is the whole reason the subject exists.

Read the table the way a graduate does. Korea (5.4% a year), Taiwan (5.3%), Botswana (5.6%) and China (4.8%) grew 18 to 29 times richer from 1960 to 2022. Vietnam and India grew about six times richer. Ghana (1.1% a year) grew about 1.9 times; Niger shrank slightly. Argentina managed 1.2% over the period, and Brazil and Mexico were flat through the 1980s, the "lost decade". Venezuela's income per person fell from about $17,100 in 2010 to about $5,300 in 2022, which is a collapse, not a stall. Even the United States, at 1.9% a year, became 3.2 times richer.

One caution: the "middle-income trap" (countries growing quickly and then stalling) is a popular phrase, and economists dispute how real it is as a separate pattern. The data show stalls, and the explanations fight.

Measuring poverty: where the line is drawn

To say how many people are poor, you need a line. The World Bank's international poverty line began in 1990 at about a dollar a day and has been re-based as price data improved: $1.90 (2011 prices), $2.15 (2017 prices) and, from June 2025, $3.00 a day in 2021 prices, with higher lines of $4.20 and $8.30 for lower-middle and upper-middle-income countries. The dollars are adjusted for what money buys locally (purchasing power parity, as in International Trade & Finance (34/47)), so a "dollar a day" is not a dollar's worth of food at the exchange rate.

On the $3.00 line, the World Bank's September 2026 update says about 811 million people live in extreme poverty, roughly one in ten, down from about 2.3 billion in 1990. Progress has slowed in the past decade, and extreme poverty is increasingly concentrated in Sub-Saharan Africa and in conflict-affected, fragile countries. Critics argue the line is far too low: Peter Edward of Newcastle University, for example, argues for a figure around $7.40 a day. Where you put the line changes the story.

Two things the slider teaches. Raising the median income lowers poverty a lot, which is why growth matters. But the same median with more inequality puts more people below a low line. Both levers move the count; neither is the whole story.

The randomista turn: Banerjee, Duflo and Kremer

For decades the big questions were answered with cross-country regressions, and the answers were fragile: there are too few countries and too many things that differ between them. In the 1990s and 2000s a group of economists took a different route: run experiments. Pick villages, schools or households at random for a programme, leave the rest as a comparison, and measure the difference. Abhijit Banerjee, Esther Duflo and Michael Kremer shared the 2019 Nobel prize in economics for this experimental approach to alleviating global poverty. Their sceptical friends called the movement the "randomistas".

The logic is plain: random assignment makes the two groups alike on average, so a difference afterwards is caused by the programme. What stops it being easy is noise. A small trial might miss a real effect, and a lucky one might exaggerate it. That is what statistical power is, and you can feel it.

Try 0.2 with 100 people per group: only about 29% of trials detect a real, modest effect, and the ones that do overstate it, averaging about 0.37. A "significant" result from a small study is a biased reading of the truth. That is why economists ask for large samples and replication.

Now guess what trials found. Each of these four ran in Kenya or across several countries, and each tested something many people expected to work.

The critics: what Deaton said

Angus Deaton, who won the Nobel prize in economics himself in 2015 for work on consumption and poverty, was an early and sharp critic. In a 2009 paper with a title that is itself an argument, "Instruments of development: Randomization in the tropics, and the search for the elusive keys to economic development", he argued that RCTs and similar methods often misread what makes assignment random, that average effects hide variation, and that "experiments have no special ability to produce more credible knowledge" than other methods. His conclusion was that project evaluation by RCT is unlikely to advance understanding of economic development, and he welcomed a move toward testing theoretical mechanisms.

The randomistas' reply: small, answerable questions add up. You may not learn why Korea grew, but you can learn whether a net should be free or priced, and the answer is usable. The result is a split in the field about what counts as knowledge:

Question	Experimentalists	Deaton's side
What can we know?	Specific effects of specific programmes	Mechanisms and theory matter more
Does it generalise?	Replicate in many places	Context and heterogeneity can undo it
Does it answer big questions?	Not directly; it does answer practical ones	Then the big questions go unanswered

Neither side has won; most working development economists use both kinds of evidence.

Aid, cash and microfinance

Aid. In 1970 the UN General Assembly set a target for rich countries to give 0.7% of their national income as aid. The argument about whether aid works is older than the target. Jeffrey Sachs argues that aid, if it tackles disease, low farm yields and high transport costs, can break a poverty trap. William Easterly argues that top-down aid distorts incentives and does little. Dambisa Moyo argues that aid breeds corruption and dependency and that African countries should lean on private capital. Statistical studies of aid and growth give mixed results; early ones often found little or negative effects and later ones more positive but inconclusive.

Cash. An alternative to projects is simply giving poor households money. In a large Kenyan experiment, Dennis Egger, Johannes Haushofer, Edward Miguel, Paul Niehaus and Michael Walker (published 2022) found a local transfer multiplier of about 2.4, minimal price inflation and "large positive spillovers on non-recipient households and firms". Cash did not simply raise local prices and hurt the neighbours.

Microfinance. Small loans to the poor, associated with Muhammad Yunus and Grameen Bank, were hailed as a breakthrough. Six randomised evaluations in six countries (Banerjee, Karlan and Zinman, 2015) found a "consistent pattern of modestly positive, but not transformative, effects". Microcredit is a useful tool and not a miracle.

The sober picture: economists have good evidence on some small things (free nets, cash), mixed evidence on big things (aid), and humility about the biggest question of all.

What a graduate just sees

• Differences in income are enormous and mostly about growth rates sustained over decades: 70 ÷ g gives the doubling time.
• Geography, institutions and human capital are all real; the dispute is over which is deepest.
• A "poverty line" is a choice, in dollars adjusted for local prices, and moving it changes the count.
• Growth lifts the most people out of poverty; but the same average can hide very different inequality.
• A random assignment gives an honest comparison, but a small trial is noisy and "significant" results exaggerate.
• An RCT answers "did this work here?"; it does not automatically answer "will it work there?"
• What people expect to work often does not: charging a small fee for nets, free textbooks, big microcredit effects.
• Cash transfers have held up better than many feared, with local spillovers rather than inflation.

Where it shows up next

• Econometrics (34/44): the credibility revolution (natural experiments, instruments, trials) is the toolkit this course is built on.
• International Trade & Finance (34/47): whether a poor country should open its borders or protect an infant industry is one of the oldest fights in development.
• Public Economics (34/49): cash transfers, taxation and public goods in low-income countries are the same questions as in rich ones, with fewer resources.
• In the world: a job in a ministry, a development bank, a research group such as J-PAL (co-founded by Banerjee and Duflo), or an NGO choosing what to fund; and every news story about aid cuts, poverty counts or a new "miracle".

If you want to go deeper

• 📘 Textbook. Debraj Ray, Development Economics (Princeton University Press, 1998). The rigorous standard, strong on theory.
• 📘 Textbook. Michael Todaro and Stephen Smith, Economic Development (Pearson). A widely assigned, broader survey with country cases.
• 📘 Gentler. Abhijit Banerjee and Esther Duflo, Poor Economics (2011): the randomista view, told through the poor people's choices. Pair it with Angus Deaton, The Great Escape (2013), for the sceptical view.
• 🌐 Free course. MIT OpenCourseWare 14.73 The Challenge of World Poverty (Spring 2011, Duflo and Banerjee), an undergraduate course covering growth, famine, schooling, microfinance and aid. Also Marginal Revolution University's Development Economics video course (Tyler Cowen and Alex Tabarrok).