Political Science A path through The Default Library ======================================== Who gets what, when and how: power, institutions, elections, war and peace, studied with evidence. Step 1. Political Theory: Justice, Power, Legitimacy and Liberty ---------------------------------------------------------------- Why: Justice, power and legitimacy, from Plato and Confucius to Rawls. Four questions every society answers whether it knows it or not: who rules, why anyone obeys, how free people should be, and what a fair share looks like. Thinkers from Athens, Qufu, Pataliputra and Tunis gave different answers, and each is still in use. select{font-family:-apple-system,sans-serif;font-size:.9rem;min-height:44px;max-width:100%;min-width:0;border:1px solid var(--line);border-radius:10px;background:var(--hi);color:var(--ink);padding:6px 8px} .row{display:flex;flex-wrap:wrap;gap:8px;align-items:center;margin:6px 0} .row>*{min-width:0} .chips button{min-width:0} A traffic light turns red at 3 a.m. on an empty street, and you stop. Nobody is watching. Why? Because it is the law, because it is sensible, because everyone else stops, because someone could be coming. Now scale that up: why do millions of people hand over a slice of their pay every year, accept a verdict from a stranger in a robe, or send their sons and daughters to fight when an official signs a paper? Political theory is the study of that ordinary miracle, and of whether it is justified. It is not the same as political science that counts votes. It asks what a government is for, when it has the right to command, and what we owe each other. The strongest answers come from every continent, and a good course reads them side by side rather than treating Europe as the default. The course in one box Who takes itPolitical science, philosophy and law students, usually in year 1 or 2. It is the opening course of Oxford's PPE politics paper, MIT's Course 17 and Sciences Po, and the heart of Oxford's Jurisprudence. English majors meet the same ideas in novels. What it is forGiving you the concepts to say why a law, a tax or a regime is fair or unfair, rather than only that you like it. Comes afterNothing. It is the starting point. Leads toComparative Politics (35/13), and for law students Constitutional Law (35/03) and International Law & Human Rights (35/10). The ideas here also feed Political Economy (35/17). What this piece gives youYou will not settle the debates; nobody has. You will recognise six big answers, who gave them and what each one's critics say. It takes no side on today's partisan fights. Easier first reads: The Social Contract: Why Obey a State at All? (18/14), What Is Freedom? (18/11) and Justice: From Plato to Rawls, Nozick and Sen (05/13). This piece goes further and lets you test the arguments. Why have a state at all? Nature, contract and the fish Start from a thought experiment: no government. What would life be like? Thomas Hobbes, writing during the English civil war, said in Leviathan (1651) it would be a "war of all against all", with life "solitary, poor, nasty, brutish, and short". Not because people are wicked, but because they fear each other, and the safest move against a fearful neighbour is to strike first. The only escape is to give up your freedom to a sovereign strong enough to keep everyone in awe. The same worry appears elsewhere. The Indian tradition of statecraft speaks of matsya-nyaya, "the law of the fishes", where the big fish eat the small. Kautilya's Arthashastra (attributed to the Mauryan minister of about 300 BCE; Patrick Olivelle's 2013 translation argues the text took its present shape around the first century CE from older material) makes the king, and his rod of punishment, the cure. Ibn Khaldun, in the Muqaddimah (1377), argued that people need a restraining authority because aggression is natural to them. Aristotle in his Politics disagreed in a different way: humans are by nature political animals who flourish only in a community, so the state is not an invention but a fulfilment. The social contract tradition says a state is justified by an agreement, real or imagined. John Locke (Second Treatise, 1689) thought people in nature already have rights to life, liberty and property, and form governments to protect them, so a government that violates them may be resisted. Jean-Jacques Rousseau (The Social Contract, 1762), opening with "Man is born free, and everywhere he is in chains", argued legitimacy comes only from the general will of the people governing themselves. Hobbes wanted a strong sovereign; Locke a limited one; Rousseau a self-governing one. Hobbes's logic is a game you can run. Each person chooses to keep the peace or to grab. If everyone keeps the peace each gets 3; a grabber facing a peaceful person takes 4 and leaves them 0; two grabbers fight and each get 1. Grabbing wins in every pairing, so peace collapses. Now add a sovereign who punishes grabbers. How much punishment is enough? Interactive · Hobbes's trap Penalty on a caught grabber Chance a grab is caught Share keeping the peace at the start Payoffs: peace–peace 3 each; grab vs peaceful 4 and 0; grab–grab 1 each. Expected penalty = penalty × chance. Strategies spread in proportion to their advantage (a replicator model, 200 rounds). A toy: it ignores the cost of the sovereign, and who watches the watcher. The answer is neat: an expected penalty above 1 is enough, whatever the starting mix, because it removes the temptation exactly. But notice what the toy leaves out. Who punishes the sovereign? That is Locke's question, and the reason constitutions limit power. Who should rule, and for whose sake? Plato's Republic (about 375 BCE) answered: those who know. Rule should belong to philosopher-guardians trained to see the good, as a ship should be steered by a navigator, not by a vote of the crew. Aristotle's Politics asked two more practical questions of every regime. How many rule: one, few or many? And for whose benefit: the common good, or the rulers' own? Combine them and you get six constitutions, three good and three corrupted. Interactive · Aristotle's six regimes One rules A few rule The many rule For everyone's good For the rulers' own gain Aristotle, Politics III. His term for rule of the many in their own interest is "democracy", which he saw as rule of the poor majority; "polity" is his name for the good form of rule by the many. Notice the question under Aristotle's grid: not "who is in charge?" but "is power used for everyone?" Other traditions ask the same. Confucius (551 to 479 BCE) held that government begins with the ruler's own conduct; a leader who is himself upright needs few orders. Mencius, his follower, said the people matter most, the state next, the ruler least, and that a ruler who abuses the people loses the Heaven-given mandate to rule, an idea that justified rebellion in Chinese history. Kautilya's king serves the welfare of his subjects, whose happiness is his, though the Arthashastra is frank about spies, deception and war in a way that Confucian texts avoid. Machiavelli, centuries later, separates what rulers should be from what they must do to keep power. Each of these is a view about the same two variables. Why people obey: power, legitimacy and the tipping point Power is the ability to make others do things. Legitimacy is the belief that the power is rightful. Max Weber, in lectures of 1919 (Politics as a Vocation), defined the state as the body that claims the monopoly of legitimate force within a territory, and gave three sources of legitimacy: tradition ("it has always been so"), charisma (a leader's personal appeal) and legal-rational rules (offices and procedures). Ibn Khaldun added a cycle: a dynasty rises on asabiyya, the solidarity of a tight group, then its rulers grow comfortable, solidarity fades, and after about three generations, which he counted as roughly 120 years, a hardier group replaces it. Legitimacy is not only a feeling. It works as a prediction about everyone else. Mark Granovetter's threshold model (1978) says each person has a threshold: they will join a protest if enough others already have. Some people join first at nothing; some join at ten others; some need half the street. Whether a crowd tips depends on the whole distribution, not the average. The economist Timur Kuran used this idea to explain why East Germany's regime seemed stable until the autumn of 1989 and then fell. Interactive · Who joins the crowd? Thresholds 0, 1, 2 ... 99 Same, but the "1" needs 2 Bell-shaped (use sliders) Average threshold Spread of thresholds 100 people. Each joins when the number already in the crowd reaches their threshold. The first mode is Granovetter's own example: nudging one person's threshold from 1 to 2 turns a full riot (100) into a crowd of one. Bell-shaped thresholds are spaced by the normal distribution. A model, not a forecast. The moral of the model is that a regime's support can be much thinner than it looks, and also that a crowd's size can hide how fragile it is. Legitimacy lives in expectations about other people, which is why regimes work hard at appearing strong, and why sudden changes surprise even the people living through them. Liberty: where does my freedom end? John Stuart Mill's On Liberty (1859) gave the most famous rule: the only purpose for which power can rightfully be used against a member of a community, against their will, is to prevent harm to others. Their own good, physical or moral, is not a sufficient warrant. This is the harm principle. Isaiah Berlin (1958) sorted freedom into negative liberty (freedom from interference) and positive liberty (the capacity to run your own life), warning that talk of the "real self" can excuse tyranny. Philip Pettit and the republican tradition, from Rome to Madison, defined freedom as non-domination: you are unfree if someone merely can interfere at will, even if they are kind. Confucian thought sees the person as made by relationships and duties, so freedom detached from role and community seems thin. Nozick treats each person as the owner of their body and property. These lenses give different answers to concrete questions. Choose a case below. The answers are how a thinker in each tradition would typically reason, our extrapolation, not quotations. Interactive · Five lenses on one law Case Lenses: Mill (harm principle), Nozick (self-ownership and property), Rawls (equal basic liberties, fair terms), Confucian (roles, virtue and harmony), republican (freedom as non-domination, after Pettit). Each lens is a family of views, with internal disagreements. The point is not to find the winning lens but to see that people who agree on "freedom is important" can disagree on a helmet law for honest reasons: they are counting different things. Justice: the veil, the entitlement and the flute John Rawls (A Theory of Justice, 1971) asked what rules we would choose for society if we did not know our place in it: rich or poor, clever or slow, man or woman, believer or not. Behind this veil of ignorance, he argued, we would choose equal basic liberties and allow inequalities only if they work to the benefit of the least advantaged: the difference principle. He thought a cautious chooser would follow the "maximin" rule, maximising the worst outcome. John Harsanyi replied that a rational chooser would maximise the average instead. The truth may lie between, and your own fear of the bottom sets where. Interactive · Behind the veil Four invented societies, each with five income groups (thousands per year). You will be placed in one group at random. How much you fear landing at the bottom Average only (Harsanyi) Worst case (maximin) Incomes are illustrative. "Equal-share value" is the sure income you would swap for the gamble, using constant-risk-aversion utility: fear 0 is the plain average, 1 is the geometric mean (log utility), 10 or more is treated as the pure minimum. Robert Nozick, in Anarchy, State, and Utopia (1974), objected from another direction. Justice is not a pattern of shares but a history of how they came about: if holdings were acquired fairly and transferred freely, the result is just, whatever it looks like. His example: start from any distribution you like, and suppose a million fans each freely pay 25 cents to watch the basketball star Wilt Chamberlain play. He ends up rich. To keep the pattern you would have to keep stopping free exchanges. Critics replied that markets rest on rules the community sets (Rawls's "background justice"), and that G. A. Cohen argued the freedom of the poor is also reduced by the property system. Interactive · Wilt Chamberlain in a village Fans who pay Each fan's payment (out of 100 coins) A village of 100 people, each starting with 100 coins (perfect equality, Gini 0). The Gini coefficient runs from 0 (equal) to 1 (one person has all). Amartya Sen (The Idea of Justice, 2009) came at it from a third angle. We do not need a perfect blueprint of a just society to know that famine or slavery is unjust; we need ways to compare real options. He asks what people are actually able to do and be (their capabilities), not only what they own. His example: three children quarrel over a flute. The first can play it, the second is poor and has no toys, the third made it. A utilitarian, an egalitarian and a libertarian each have a good reason to pick a different child, and none is simply wrong. Sen's moral is that plural, reasoned argument matters more than a single formula. Whose voices were left out of the contract? Great theories have blind spots, and some of the best political theory is the critique of the blind spots. Mary Wollstonecraft's A Vindication of the Rights of Woman (1792) argued that women's supposed unfitness for citizenship was produced by an education built to make them pleasing instead of rational, so the argument for equal rights applied to them too. Mill's The Subjection of Women (1869) made the same case from liberal premises. Karl Marx and Friedrich Engels (Communist Manifesto, 1848) argued that the question "what is a fair contract?" hides that workers must sell their labour to survive, so political equality can coexist with deep economic unfreedom; defenders of markets reply that Marx's predictions failed and that voluntary exchange raises the poorest. (31 Political Economy covers that debate.) Carole Pateman (The Sexual Contract, 1988) and Susan Moller Okin (Justice, Gender, and the Family, 1989) asked why theories of justice rarely examined the household. Charles Mills (The Racial Contract, 1997) argued that the social contract was in practice a contract among whites that excluded others. Communitarians such as Michael Sandel (Liberalism and the Limits of Justice, 1982) said Rawls's chooser, stripped of identity, is not a real person. Gandhi's Hind Swaraj (1909) questioned whether modern states, even self-governing ones, deliver freedom. These challenges did not end the older traditions, which have replies; they widened the conversation about who counts. What a graduate just sees Every government answers four questions (who rules, why we obey, how free, how fair), whether it says so or not. A state can be justified as nature's fulfilment (Aristotle), a bargain (Hobbes, Locke), a general will (Rousseau), or a protector (Kautilya). Each carries a different limit on power. Ask "how many rule, and for whom?" before arguing about names. Aristotle's two questions still sort most regimes. Legitimacy is a belief about what others believe. Regimes can look stable and be brittle, as in a threshold cascade. "Freedom" has at least three meanings (no interference, capacity, no domination), and people can honestly disagree while using the same word. Justice theories answer differently: a fair procedure (Rawls), a fair history (Nozick), or fair real options (Sen). Ask which one an argument is using. A thought experiment is a tool for isolating a variable, and its strength depends on what it assumes away. Who is left out of a theory is as informative as who is in it. Where it shows up next Comparative Politics (35/13): Aristotle's grid becomes the study of presidential and parliamentary systems, democracies and autocracies; Weber's legitimacy becomes the question of why some regimes last. Constitutional Law (35/03) and International Law & Human Rights (35/10): Locke's limits and Mill's harm principle live on in bills of rights and proportionality tests. Political Economy (35/17): Rawls, Nozick and Marx are the three poles of the argument about markets and fair shares. In the world and in books: every constitutional debate, every tax argument, and every novel about a tyrant or a rebellion. Literature students find these arguments in dystopias and in epics from the Mahabharata to Orwell. If you want to go deeper 📘 Textbook. Will Kymlicka, Contemporary Political Philosophy: An Introduction (Oxford, 2nd edition, 2002). The classic survey of utilitarianism, liberal equality, libertarianism, communitarianism, feminism and multiculturalism. 📘 Textbook. Adam Swift, Political Philosophy: A Beginners' Guide for Students and Politicians (Polity; later editions). Clear and problem-led. 📘 Gentler. Michael Sandel, Justice: What's the Right Thing to Do? (2009). Everyday dilemmas leading into the big theories. 🌐 Free course. Yale Open Courses, PLSC 114 Introduction to Political Philosophy (Steven B. Smith): Plato to Rawls, with lectures and transcripts. Also Sandel's Justice course from Harvard. The takeaway Political theory gives names to the questions every society answers: why a state, who should rule and for whom, why people obey, how free we should be, and what is fair. No single tradition has the last word, and the best answers have been argued across Athens, China, India, the Islamic world and modern Europe; the best critics asked who was left out. Try this: next time you hear an argument about a law, name what it assumes. Is it about harm to others, about fairness of procedure, about a fair history, or about what people can actually do? Then ask which of the other three it leaves out. Sources & Further Reading Sources. Primary texts are in the public domain: Plato's Republic, Aristotle's Politics (III), the Analects and Mencius, Hobbes's Leviathan (1651, ch. 13), Locke's Second Treatise (1689), Rousseau's Social Contract (1762), Wollstonecraft (1792), Marx and Engels (1848), Mill (On Liberty 1859; Subjection of Women 1869). Later works are described, not quoted: Weber (Politics as a Vocation, 1919), Berlin, "Two Concepts of Liberty" (1958), Rawls (1971), Nozick (1974), Pettit (1997), Sen (2009), Granovetter, "Threshold Models of Collective Behavior" (American Journal of Sociology, 1978), Kuran, "Now Out of Never" (World Politics, 1991), Pateman (1988), Okin (1989), Mills (1997), Sandel (1982). Arthashastra: Olivelle, King, Governance, and Law in Ancient India (Oxford, 2013), which dates the finished text to the early centuries CE; the traditional attribution to a Mauryan minister about 300 BCE is contested. Ibn Khaldun's dynastic cycle: Muqaddimah (1377), with the three-generation, 120-year figure as he gave it. Every number in the interactives was computed in node (the Hobbes toy, the threshold cascade, the equal-share values and the Gini coefficients); incomes and populations are invented for illustration. 🌐 Yale Open Courses: PLSC 114 Introduction to Political Philosophy 🌐 Harvard: Justice with Michael Sandel 📘 Kymlicka, Contemporary Political Philosophy Step 2. Comparative Politics: Why Countries Are Governed So Differently ----------------------------------------------------------------------- Why: Why countries are governed so differently. Same humans, same problems, wildly different rulebooks. Comparative politics asks who can fire whom, how many hands sit on the lever, and why some democracies last while others quietly stop being one. In July 2022 a prime minister in the United Kingdom was forced out in a matter of days, because his own party stopped supporting him. In the same year a president in Brazil, whose term was fixed by the constitution, could be removed only by impeachment or an election. A president in France could dissolve the National Assembly; a chancellor in Germany could not dissolve the Bundestag at will. All four countries are democracies. They are not governed the same way, and the difference decides what a crisis looks like. Open any newspaper and the stories are about people: who won, who resigned, who is blocking whom. A political scientist sees the same stories as outputs of rules. Change who can remove the leader and the same leader behaves differently. This piece is about those rules, and about the older question underneath them: why do some countries manage to keep a democracy for generations while others lose it? This describes how political systems work. It does not take sides in any country's current politics, and the regime labels below are tools for thinking, not verdicts. The course in one box Who takes itEvery political science major, usually in year 1 or 2. Programmes such as MIT's Course 17, Harvard Government, Oxford's Politics in PPE and Sciences Po all put comparative politics beside political theory, international relations and methods. Economics, law and history students often take it as an elective. What it is forExplaining why countries differ in how they are governed, and what those differences cause. Comes afterPolitical Theory (35/12). Leads toInternational Relations (35/14), Elections, Parties & Public Opinion (35/15), Public Policy (35/16) and Political Economy (35/17). What this piece gives youYou will not be able to write a constitution. You will be able to look at a country's rulebook and predict where the pressure points are. Easier first reads: How Government Actually Works (18/01), Democracy: The Many Flavors (18/02) and How Democracies Erode and How They Hold (18/12). This piece goes further, and lets you test the claims on small models. A state is a monopoly on force, and it comes in degrees Before asking how a country is governed, ask whether it is governed at all. Max Weber's definition, from his 1919 lecture "Politics as a Vocation", is the one most courses start with: a state is the organisation that successfully claims a monopoly on the legitimate use of physical force in a territory. Two words carry the weight. Successfully: a decree nobody can enforce does not make a state. Legitimate: a gang that holds the same streets by threat is not one, because people do not accept its right to. That gives the first idea in the field, state capacity: the ability of a state to collect taxes, enforce laws, deliver services and keep order across its whole territory. It is a matter of degree. A state can be tyrannical and weak (it can jail critics in the capital but cannot collect taxes in the provinces), or mild and strong (Denmark). Charles Tilly's famous line, "war made the state and the state made war", sums up one influential story of where capacity came from in Europe: rulers who needed money to fight built tax collectors, armies and records, and those became the state. Scholars of Latin America (Miguel Centeno, for one) and of Africa point out that the path was different there, so this story does not explain every region. Why does this come first? Because every other contrast, democracy against dictatorship, federal against unitary, is about how power is used, and a state that cannot reach its own territory has little power to organise. Political scientists also warn that a democracy needs a state to be a democracy: elections cannot be run, counted and enforced where nobody can guarantee safety at the polling station. Three ways to build an executive Every democracy has to answer one design question: how does the government get its power, and what can end it? There are three answers in common use. Presidential (United States, Mexico, Brazil, Argentina, Indonesia, Nigeria, South Korea): the president and the legislature are elected separately, each for a fixed term. Neither can end the other's life by a vote, except by exceptional impeachment. Latin America is the home of presidentialism: most of its republics copied the US model in the nineteenth century. Parliamentary (United Kingdom, Germany, India, Japan, Canada, South Africa, Spain): voters elect the legislature, and the legislature makes the government. The prime minister survives only while a majority backs them, and can usually call an early election. Power is fused, not separated. Semi-presidential (France, Portugal, Poland, Taiwan, Russia on paper): a directly elected president with real powers and a prime minister answerable to parliament. The term comes from Maurice Duverger, 1980. When president and parliament majority belong to different camps, France calls it cohabitation, which happened in 1986, 1993 and 1997–2002. The classic argument about which is safer was made by Juan Linz in a 1990 essay, "The Perils of Presidentialism". His case: two actors each claim to be the people's true agent ("dual legitimacy"), fixed terms leave no peaceful way out of a stalemate, and winner-take-all makes losing frightening. The failure he had in mind was Chile in 1973. Others reply that the evidence is mixed. José Antonio Cheibub's 2007 book argued that the apparent fragility owes much to presidential democracies being found where armies were already politically powerful, not to the constitution itself, and that presidential democracies with parties able to form coalitions (as in Brazil) can work. The honest summary is that presidentialism raises risks in some conditions and does not doom any country. Interactive · Predict, then reveal: the crisis test Pick a crisis, guess what happens in each system, then read the answer. Typical constitutional design only; real countries add their own twists. Executive's party loses its majority Legislature loses faith in the leader Leader deeply unpopular, years left Presidential Parliamentary Semi-presidential Notice the pattern in the answers. Parliamentary systems have an exit built into the rulebook: lose the majority and the government ends. Presidential ones do not, so a crisis has to be resolved by bargaining, or, in the worst cases, outside the rules. That is the price of fixed terms, and the benefit is predictability. Federal or unitary: counting the veto players A second design choice is where power sits geographically. In a unitary state (France, Japan, China, Chile) the national government holds sovereignty and may create or reshape regions. In a federation (the United States, Germany, India, Brazil, Mexico, Argentina, Nigeria, Switzerland) regions hold constitutionally protected powers that the centre cannot simply remove. The United Kingdom and Spain sit in between, devolving powers to Scotland, Wales, Northern Ireland and to Spain's autonomous communities. George Tsebelis made the effect precise with the idea of veto players (2002): any actor whose agreement is required to change the rules, such as a second chamber, a constitutional court, a regional government or a coalition partner. Each additional veto player shrinks the set of policies that all of them would accept over the status quo. His prediction: the more veto players, and the farther apart they are, the more stable policy becomes, for better (it protects minorities and prevents lurches) or worse (it blocks needed reform). Try it. Players live on a left-right line; each will accept a change only if it moves policy closer to their ideal point than the status quo is. Interactive · Veto players 1 player 2 players 3 players 4 players Status quo position How far apart the players are Illustrative one-dimensional model. Player positions: 35, 55, 20, 80 (spread scales them around 50). Winset = intersection of each player's interval [x − |x − SQ|, x + |x − SQ|]. With the status quo at 50 and the players spread on both sides of it, the set of acceptable changes is empty: someone is worse off whichever way policy moves. Move the status quo to an edge and a window opens, because everyone wants to move away from an extreme. Squeeze the players together (a unified government) and almost any change that suits them passes. This is the reason unified one-party control produces sharp policy swings in some countries and gridlock in others: it is the veto players, not the party names, that matter. A spectrum, not a switch: hybrid regimes "Democracy" and "dictatorship" are not two boxes. The Yale political scientist Robert Dahl broke democracy into two dimensions, contestation (can opposition compete and win?) and inclusion (who gets to take part?). Modern measures keep that idea: indices from V-Dem (Sweden), Freedom House (United States) and the Economist Intelligence Unit (UK) all rate the same countries on several scales, and they disagree at the margins, especially about where "democracy" ends. Hybrid regimes live in that margin. Steven Levitsky and Lucan Way (2002) called them competitive authoritarian: elections are real enough that the opposition can sometimes win, but the playing field is tilted. The incumbent controls the media, the courts, the money, the electoral authority. Nobody is shot at the polls; the contest is just unfair. V-Dem's "Regimes of the World" scheme splits the world into closed autocracies, electoral autocracies, electoral democracies and liberal democracies, and counts a country as an electoral democracy only if its elections are clean and free enough that the opposition could win. Interactive · Place a country on the map Contested elections (0 = none, 10 = fully free) Liberties and rule of law (0 = none, 10 = strong) Illustrative thresholds, not V-Dem's actual rules: elections below 2 = closed autocracy, below 4.5 = electoral autocracy, 4.5 to 6.5 = grey zone (competitive authoritarian if liberties are below 5), 6.5 and above = electoral democracy, and liberal democracy if liberties reach 6.5. The zone in the middle is where most of the scholarly argument happens, and it matters in practice: a country in the grey zone can drift either way, and it is often hard to tell from the inside which way it is going. Why democracies emerge, and what wealth really does In 1959 Seymour Martin Lipset observed that rich countries tend to be democracies. The natural story ("modernization theory"): as countries get richer, they get more educated, urban and middle-class, and those people demand a say. For decades that story was treated as the answer. Then came a sharper test. Adam Przeworski and Fernando Limongi (1997) asked which event wealth explains: democracies starting, or democracies surviving? Their finding, widely cited since: transitions to democracy happen at all income levels, roughly at random with respect to income, but once a country is a democracy, higher income makes it far more likely to last. Their headline version was that no democracy had ever fallen in a country richer than Argentina in 1975, about $6,000 per head in 1985 prices (as reported in summaries of the paper; later datasets have moved the threshold). Carles Boix and Susan Stokes (2003) replied that wealth does help cause transitions in the long run, and the argument is still alive. Here is why the distinction is subtle. Both stories produce the same picture in a snapshot: rich countries are mostly democratic. A toy model shows it. Each of 300 countries starts with a random income and random regime and runs for 100 years. Income grows 2% a year. Each year an autocracy may turn democratic (base chance 2%), and a democracy may fall (base chance 3%). Use the sliders to let wealth raise the chance of becoming a democracy, to let wealth protect democracies, or both. Interactive · Two ways wealth could matter Wealth does nothing Wealth causes transitions Wealth only protects Both Wealth raises chance of becoming a democracy Wealth protects democracies from falling An invented model, not data. Chance of becoming a democracy per year = 2% × (income ÷ 10)a; chance of a democracy falling = 3% × (10 ÷ income)b; both capped between 0.1% and 60%. Income in thousands at start log-uniform from 1 to 30. Fixed random seed, 300 countries, 100 years. With both sliders at zero the bars sit roughly level, apart from random noise. Now look at the three bars under "Wealth only protects" and under "Wealth causes transitions". They look alike. A correlation between income and democracy across countries cannot tell the two stories apart; you need to follow countries over time, which is exactly what Przeworski did. This is a recurring lesson in the field, and the reason Research Methods in Political Science (35/18) exists. How democracies break now: from the inside For most of the twentieth century a democracy ended with soldiers at the palace: Chile 1973, Argentina 1976, and the long run of coups in Africa. Coups still happen: Mali (2020 and 2021), Burkina Faso (2022) and Niger (2023) are recent examples. But a large part of today's decline works differently, and political scientists have names for it. Nancy Bermeo (2016) calls it executive aggrandizement: elected leaders weaken checks on their own power one legal-looking step at a time, by packing courts, rewriting electoral rules, tilting media regulation. Levitsky and Ziblatt's 2018 book How Democracies Die argues that the guardrails are informal norms (mutual toleration, forbearance) as much as formal rules. Peru's 1992 autogolpe, in which a sitting president closed congress with army support, is the dramatic version; most modern cases are quieter. How common is the pattern? According to the V-Dem Democracy Report 2026, covering 2025, there were 87 democracies and 92 autocracies among 179 countries, with about 74% of the world's population living in autocracies, and 44 countries autocratizing (as reported in coverage of the report; the institute's counting rules differ from Freedom House's or the EIU's, so the totals shift between indices). Whatever the index, the contested question is not whether the rules matter but what keeps leaders and parties inside them. Two things the evidence supports. First, democracies are much more likely to end where incomes are low and growth is poor (the survival result above). Second, the strongest barrier is rarely a clause in a constitution; it is parties and elites who accept losing an election and still expect to compete again. That second thing is hard to engineer, and it is why the same constitutional text works in one country and not in another. What a graduate just sees A state is its capacity to enforce, not its decrees. Ask first whether the rules can actually be applied across the territory. Who can remove the head of government, and how, tells you more about a crisis than the leader's character. Fixed terms give stability and take away the exit. Parliamentary systems fall more easily and recover faster. Policy is sticky in proportion to the number and distance of veto players, whatever party is in power. Regimes sit on a spectrum, and the grey zone between democracy and autocracy is where much of today's politics happens. A snapshot correlation between wealth and democracy cannot say what causes what. Follow countries over time. Democracies today more often erode from the inside than fall to a coup. Indices such as V-Dem, Freedom House and the EIU are measurement choices; check what an index counts before quoting a total. Where it shows up next International Relations (35/14): whether a country is a democracy, and how strong its state is, shapes how it behaves in a crisis, and the democratic-peace debate depends on regime classification. Elections, Parties & Public Opinion (35/15): the electoral rule that sits on top of each executive design decides how many parties exist. Public Policy (35/16) and Political Economy (35/17): veto players explain why reforms stall, and state capacity explains why laws on paper do not become outcomes. In the world: a country desk at a foreign ministry or bank, a risk analyst, a journalist covering a coup or a constitutional crisis, an election observer, an NGO working on governance. If you want to go deeper 📘 Textbook. William Roberts Clark, Matt Golder and Sona Nadenichek Golder, Principles of Comparative Politics (CQ Press, 4th edition, 2024). Heavily assigned; built around testable models rather than country tours. 📘 Textbook. Robert Dahl, On Democracy (Yale University Press, 1998; revised 2015). Short, and the starting point for the democracy-as-spectrum view. 📘 Gentler. Steven Levitsky and Daniel Ziblatt, How Democracies Die (Crown, 2018), and Daron Acemoglu and James Robinson, Why Nations Fail (Crown, 2012). Both written for general readers. 🌐 Free course. MIT OpenCourseWare 17.50 Introduction to Comparative Politics (Fall 2022, Chappell Lawson): why democracy emerges and survives, how institutions affect development, with lecture slides. The takeaway Countries are governed differently because their rules answer three questions differently: can the state enforce, who can remove the leader, and how many actors must agree to change anything. Democracy and autocracy are the ends of a spectrum with a crowded grey zone in the middle, wealth seems to protect democracies more than it creates them, and today's backsliding comes more often through legal-looking steps than through tanks. Try this: next time a country is in the news for a political crisis, find out two things before reading the commentary: who can remove the head of government, and by what vote? Then see how much of the story the rule already explains. Sources & Further Reading Sources. Weber, "Politics as a Vocation" (1919); Tilly, "War Making and State Making as Organized Crime" (1985) and Coercion, Capital and European States (1990); Duverger's term semi-presidentialism (1980); Linz, "The Perils of Presidentialism", Journal of Democracy 1(1), 1990; Cheibub, Presidentialism, Parliamentarism, and Democracy (Cambridge, 2007); Tsebelis, Veto Players (Princeton, 2002); Dahl, Polyarchy (1971); Levitsky and Way, "The Rise of Competitive Authoritarianism", Journal of Democracy 13(2), 2002; Lipset, "Some Social Requisites of Democracy", APSR 1959; Przeworski and Limongi, "Modernization: Theories and Facts", World Politics 1997 (the $6,055 threshold, 1985 dollars, comes from secondary summaries; later datasets put the threshold higher); Boix and Stokes, "Endogenous Democratization", World Politics 2003; Bermeo, "On Democratic Backsliding", Journal of Democracy 2016; Levitsky and Ziblatt, How Democracies Die (2018). Counts of democracies and autocracies: V-Dem Democracy Report 2026 (87 democracies, 92 autocracies, 179 countries, 74% of world population in autocracies, 44 autocratizing), as reported in coverage of the report. The interactives are invented models, computed live; the numbers they show were checked in node. 🌐 MIT OCW 17.50 Introduction to Comparative Politics 🌐 V-Dem Institute: Democracy Reports and data 📘 Clark, Golder & Golder, Principles of Comparative Politics 📘 Levitsky & Ziblatt, How Democracies Die Step 3. Probability & Statistics: Reasoning with Uncertainty ------------------------------------------------------------ Why: The statistics behind every poll and study. Nothing you measure is exact, and no part comes out the same twice. Statistics is the discipline of knowing how much to trust a number. A machinist turns a hundred steel shafts to "10 millimetres". Measure them with a good micrometer and no two are the same: 9.981, 10.012, 10.004. Nothing is wrong. That scatter is a fact of every process, every material and every sensor. Now stack five of those parts end to end in a gearbox. Will the stack still fit the housing? A pollster calls a thousand people and says 52% will vote yes, give or take three. A drug trial says "significant". A forecast says 30% chance of rain. Every one of these is the same question: given scatter, how much should I trust this number? A graduate of probability and statistics doesn't see a single number any more. They see the shape of the scatter behind it. This piece builds that sight with eight things you can run: dice, tolerances, polls, a drug-trial trap, a belief that updates, and a gearbox stack. The course in one box Who takes it: almost every engineering and science student, usually in the second year; computer scientists meet it again before machine learning. What it's for: deciding what to believe, and how far to trust a measurement, when outcomes are uncertain. Comes after: Calculus I (34/01), which helps with continuous distributions, though the ideas here need little more than arithmetic. Leads to: Machine Learning (34/32) needs it directly. It also shows up in Materials Science & Manufacturing (34/13: process control and defects), Systems Engineering (34/22: reliability) and Computer Security (34/33: randomness and guessing). Honest line: you won't be able to design a clinical trial after this. You will see why "significant" doesn't mean "important", why a bigger sample beats a cleverer guess, and why most real quantities look like a bell curve. For easier first reads, see the library's Probability: How to Not Fool Yourself (12/03), Statistics for Reading the World (12/05) and Bayes' Theorem (12/17); this piece goes further into the engineering side. Random variables: a shape with a centre and a spread A random variable is a number that comes out of a process you can't predict one run at a time. The key move is to stop asking "what will it be?" and ask "what is the shape of what it could be?" Roll two dice and add them: 7 is six times as likely as 2 or 12, so the shape is a tent. Roll many times and the tent appears. Interactive · Roll dice, grow a shape Dice added together Roll 10 timesRoll 1,000 timesClear Without JavaScript: two dice add to 7 with probability 6/36 and to 2 or 12 with probability 1/36 each. The mean total is 7 and the standard deviation is about 2.42. Two numbers summarise a shape. The mean is its balance point: 7 for two dice. The standard deviation (the "spread") says how far values typically stray from the mean: about 2.4 for two dice, which means totals usually land between roughly 5 and 9. The variance is the standard deviation squared, and it is the one that adds up neatly when you combine independent things. standard deviation = √( average of (value − mean)² ) Now the shape that matters most. Measured quantities (shaft diameters, heights, sensor noise) tend to form the normal curve, the bell. Its great convenience is a fixed rule: about 68.3% of values fall within one standard deviation of the mean, 95.4% within two, and 99.7% within three. Engineers use it every day. Say the shaft's spec is 10.00 mm ± 0.05 mm. Slide the process around and see what share of parts get thrown away. Interactive · How many parts fail the spec? Average diameter Spread (standard deviation) Without JavaScript: a centred process with standard deviation 0.025 mm (the spec limits are exactly ±2 standard deviations) scraps about 4.55% of parts. Cut the spread to 0.01 mm and about 0.00006% are scrapped. Shift the average 0.02 mm off-centre with spread 0.02 mm and about 6.7% are scrapped. Two lessons graduates keep. First, spread, not average, decides quality: a process centred perfectly but wide still scraps parts. Second, being off-centre costs a lot even when the spread is fine. That is why factories track both. The central limit theorem: why the bell keeps appearing Why should so many things be bell-shaped? The answer is one of the most useful theorems in mathematics. Take almost any random process, however lopsided, and average a few dozen independent draws. The averages pile up in a bell, centred on the true mean, with spread shrinking like 1/√n. Early forms are due to de Moivre in 1733 (for coin flips) and Laplace, whose general version dates from about 1810. Pick a lopsided source and drag the sample size. Interactive · Average anything, get a bell Flat (0 to 1) Very lopsided Coin (0 or 1) Draws averaged together: n Without JavaScript: for the lopsided source (mean 1, standard deviation 1) the average of n = 30 draws has standard deviation 1/√30 = 0.183, and the 3,000 averages form a close-to-normal bell around 1. At n = 1 you see the raw source. By n = 30 even the lopsided one is a near-bell. The prediction (the curve) uses only two numbers from the source, its mean and its standard deviation. The shape of the source is forgotten. That is why a measurement made of many small independent errors is normal, and why so much of statistics can pretend things are bell-shaped. standard error of an average = σ / √n In words: the average of n draws wobbles less than one draw, by the square root of n. To halve the wobble you need four times the data, not twice. It is the quietest expensive fact in all of experimental science. Sampling and confidence intervals: how sure is a poll? You can't ask everyone, so you ask n people and treat the share saying yes as an estimate. How far off might it be? The standard error from the last section gives the answer. For a share p the wobble is √(p(1−p)/n), and a 95% confidence interval is the estimate plus and minus about two of those. For n = 1,000 and a share near half, that is ±3.1 percentage points: the familiar "margin of error". The wording is subtle, and the interactive makes it concrete. "95% confidence" is a statement about the method, not about one interval: if you ran the procedure again and again, about 95 intervals in 100 would capture the true value. Here the truth is secretly 52%. Run 100 polls. Interactive · 100 polls, one truth People asked in each poll Run 100 polls Without JavaScript: with 1,000 people per poll the margin of error is about 3.1 points; run 100 polls and about 95 of the intervals cover the true value. With 4,000 people the margin halves to about 1.5 points. Watch two things. Roughly five in a hundred intervals miss the truth even when everything is done correctly. And quadrupling the sample from 1,000 to 4,000 only halves the interval. A poll of a country and a poll of a town need the same sample size for the same precision, as long as the sample is random. The real danger is not the margin of error; it is a sample that isn't random at all, which no formula fixes. P-values and Bayes: two ways to weigh evidence A hypothesis test asks whether data are surprising if nothing is going on. The p-value is the probability, assuming nothing is going on, of seeing a result at least as extreme as yours. Flip a coin 100 times and get 60 heads: for a fair coin the chance of a split at least that lopsided in either direction is about 5.7%. It is not the probability that the coin is fair, and it is not the probability that the result was "due to chance". The American Statistical Association issued a rare formal statement on this in 2016, with six principles. Two of them: p-values do not measure the probability that a hypothesis is true, and a p-value does not measure the size or importance of an effect. The classic trap is testing many things. At the usual cut-off of 0.05, a test of a true "nothing" comes out "significant" one time in 20 by luck. Test twenty unrelated things and the chance that at least one lights up is 1 − 0.9520, about 64%. Try it: each press tests twenty effects that are, by construction, nothing. Interactive · Test twenty things that do nothing Run 20 testsReset the tally Without JavaScript: twenty tests of pure noise at the 0.05 level give at least one false "discovery" about 64% of the time, and one in 20 tests on average. If you report only the one that lit up, you have a headline and no finding. This is part of why many published results fail to repeat: in the Open Science Collaboration's 2015 attempt to replicate 100 psychology studies, 97% of the originals had statistically significant results but only 36% of the replications did. Bayes' rule answers a different question: given the data, how should my belief move? Start with a prior belief, multiply by how well each possibility explains the data, and renormalise. Say you test parts and want the defect rate. Know nothing at first (any rate from 0% to 100% equally likely) and add parts one at a time. Interactive · Learn a defect rate from parts Add a good partAdd 10 good partsAdd a defective partReset Without JavaScript: after 20 good parts and no defects, a flat starting belief gives a most-plausible defect rate near 0%, an average of 4.5%, and a 90% credible range of roughly a quarter of a percent to 13%. Twenty clean parts cannot rule out a defect rate near one in eight. Frequentists and Bayesians disagree about the philosophy: one treats probability as long-run frequency and never assigns it to a hypothesis, the other as degrees of belief that data update. In practice both are used, often on the same project, and on big data sets they usually agree. The Bayesian version has one practical advantage here: after twenty clean parts it honestly says "we still can't rule out one in eight". Correlation, regression and the trap of causation Correlation (called r) measures how tightly two quantities move together along a straight line, from −1 to +1. Regression fits the best line through the cloud (by least squares, as in Linear Algebra, 34/04). Both are summaries, and any summary can hide the story. In 1973 the statistician Francis Anscombe made four data sets with, to two decimals, the same mean, the same spread, the same r (0.82) and the same line (y = 3 + 0.5x). Look at each. Interactive · Four data sets, one set of numbers Set 1Set 2Set 3Set 4 Without JavaScript: all four Anscombe sets have mean x = 9, mean y = 7.50, correlation 0.816 and line y = 3.00 + 0.50x. Set 1 is a noisy line, set 2 a curve, set 3 a line with one outlier, and set 4 a single far point that creates the whole slope. The numbers agree and the stories do not: one noisy line, one curve, one line with a rogue point, one vertical stack held up by a single point. Always plot first. The other trap is the famous one. Ice-cream sales and drownings rise together, because summer heat drives both. A confounder (a hidden third factor) makes two things move together without either causing the other. The remedy graduates know is randomised assignment: if a coin decides who gets the treatment, nothing else can be systematically linked to it. Where you can't randomise, causal claims need much more than a line through a cloud. Engineering: tolerance stacks, reliability and Monte Carlo Five parts, each 10 mm ± 0.1 mm, line up in an assembly. What is the worst the total can be out? Worst-case stacking adds the tolerances: ±0.5 mm. It is safe and expensive, because it assumes every part is at its extreme in the same direction at once. If the parts vary independently, variances add, so the standard deviations add in quadrature: root-sum-square, √5 × 0.1 = ±0.224 mm. For five parts that is under half the worst-case figure. When the formulas get hard, engineers use Monte Carlo: let the computer build the assembly ten thousand times with random parts and count how often it fails. Here each part's tolerance is treated as three standard deviations. Choose how many parts are stacked. Interactive · Monte Carlo tolerance stack Parts in the stack Build 10,000 assemblies again Without JavaScript: five parts at ±0.1 mm (three standard deviations) give a worst-case limit of ±0.5 mm, a root-sum-square limit of ±0.224 mm, and in simulation only about 0.3% of assemblies exceed the root-sum-square limit; none come close to 0.5 mm. The honest caveat is that root-sum-square assumes the parts vary independently and are centred. A tool wearing in the same direction on every part breaks that assumption, and so does one supplier's batch. Engineers disagree about when to trust statistical stacking over worst-case; safety-critical designs often insist on worst-case. Reliability follows the same arithmetic. A system that needs all of its parts is a series system, and probabilities multiply: ten components that each work 99% of the time give 0.9910 ≈ 90.4% for the whole. Add a backup in parallel and failure needs both to fail: two units that each work 90% of the time give 1 − 0.1² = 99%. The two rules drive why aircraft duplicate systems and why complicated machines need very reliable components. What a graduate just sees Every number has a spread behind it; the first question is "how scattered?", not "what is it?". Spread, not the average, decides how many parts are scrapped, and centring matters too. Averages of many independent things are bell-shaped, whatever the source looked like. Halving the uncertainty costs four times the data. A 95% interval is a claim about a method: it misses one time in twenty by design. A p-value is "how surprising is this if nothing is going on", not "how likely is it true". Test twenty things and one will look significant; ask how many were tried. Plot the data; correlation is not causation, and only randomising breaks a confounder. Independent errors add in quadrature; series reliabilities multiply. Where it shows up next Machine Learning (34/32): training is fitting a model to noisy data; ideas of overfitting, error bars and Bayes are this course made bigger. Materials Science & Manufacturing (34/13): process capability and scrap rates are the normal curve against a spec limit. Mechanics of Materials (34/11) and Machine Design (34/19): safety factors exist because loads and strengths are distributions, not numbers. Systems Engineering (34/22): reliability budgets multiply series probabilities and add redundancy to push them back up. Computer Security (34/33) and Algorithms (34/24): good random numbers, randomised algorithms, and the odds of guessing a secret. If you want to go deeper 📘 Douglas Montgomery and George Runger, Applied Statistics and Probability for Engineers (Wiley; 7th edition). The standard engineering course text, with plenty of tolerance, quality and reliability problems. 📘 Joseph Blitzstein and Jessica Hwang, Introduction to Probability (CRC Press; 2nd edition, 2019). Best for deep intuition about probability itself, from Harvard's Stat 110. 📖 David Spiegelhalter, The Art of Statistics (2019): the most readable route for learning alone, built around real cases and honest about uncertainty. 🌐 MIT OpenCourseWare 18.05, Introduction to Probability and Statistics (Spring 2022): free notes and problem sets, with Bayesian and classical statistics side by side. The takeaway Nothing you measure is exact, so every number is a shape. Summarise a shape by its centre and its spread, trust averages more as the square root of the sample, read a confidence interval as a promise about a method, treat a p-value as a measure of surprise, and plot before you believe a correlation. Try this: next time you see a poll or a "significant" headline, find the sample size and ask how many things were tested. Then, in the dice widget above, add four dice and watch the tent turn into a bell. Sources & Further Reading Sources. The normal-curve rule (68.3%, 95.4%, 99.7%), the central limit theorem, confidence intervals, the binomial test, the Beta posterior, root-sum-square stacking and series/parallel reliability are standard textbook results (Montgomery and Runger; MIT 18.05); every number shown was recomputed in code. Wasserstein and Lazar, "The ASA's Statement on p-Values: Context, Process, and Purpose", The American Statistician (2016). Open Science Collaboration, "Estimating the reproducibility of psychological science", Science (2015): 97% of originals significant against 36% of replications. Anscombe, "Graphs in Statistical Analysis", The American Statistician (1973). Early forms of the central limit theorem: de Moivre (1733) and Laplace (c. 1810), as surveyed in recent histories such as Ulyanov, "From classical to modern central limit theorems" (arXiv 2405.19828). Go deeper. The books and course above; Wasserstein and Lazar's statement is free and short (doi.org/10.1080/00031305.2016.1154108). Step 4. Microeconomics: Why Nothing Is Free and Every Price Is a Message ------------------------------------------------------------------------ Why: Incentives and markets: half of political economy. Everything costs what you gave up, choices happen one step at a time, and a price is the signal that makes strangers cooperate. Until it is wrong, and then you can see exactly how wrong. A coffee shop raises its flat white from 3.50 to 3.80 and, somehow, still looks busy. A ride-hailing app doubles its fares in the rain and, ten minutes later, there are more cars on the road. A city starts charging drivers to enter its centre, and the jam thins out without a single new rule about who may drive. Nobody commanded any of this. A price moved, and thousands of people each did a small, selfish, sensible thing. An economics graduate watches those three scenes and sees the same few ideas working: everything costs something you gave up, decisions happen one step at a time, and a price is a signal that tells buyers to hold back and sellers to show up. That toolkit is microeconomics: how individuals and firms choose, and what happens when those choices meet in a market. It is also the friendliest door into the whole subject, because you already use it every time you shop. The course in one box Who takes itEveryone on an economics, business or marketing degree, usually in year 1 as "principles" and again in year 2 as "intermediate microeconomics", with calculus. MIT puts its principles course (14.01) at the gateway to Course 14-1; business schools such as Wharton teach the same toolkit as "managerial economics". What it is forPredicting how people and firms respond when prices, taxes, rules or competitors change, and judging whether the result is good for anyone besides the people making the trade. Comes afterCalculus I (34/01): "one more" is a derivative. Leads toMacroeconomics (34/43), Game Theory (34/45), Behavioral Economics (34/46), International Trade & Finance (34/47), Public Economics (34/49), Strategy & Competition (34/54), Marketing Management (34/57) and Pricing (34/61). What this piece gives youYou won't be able to estimate a demand curve from real data. You will see why a price cap makes queues, why a tax costs more than it raises, and why a market with five firms behaves differently from one with one. Easier first reads: Where Money Actually Comes From (04/02), Pricing Without Fear (04/03) and Capitalism: How Markets, Prices and Private Property Organise an Economy (31/01). This one puts numbers and sliders on the same ground. All the markets below are illustrative, not data. Scarcity and opportunity cost: the real price is what you give up Wants have no ceiling; time, land, labour and money do. That gap is scarcity, and it makes every choice a trade. The honest price of anything is its opportunity cost: the best thing you gave up to get it. A two-hour film costs the ticket plus whatever else those two hours could have been. A university degree's biggest cost is often the wages you didn't earn. Economists draw this as a production possibilities frontier: the most of one good you can make for each amount of another. It bows outward because resources aren't equally good at everything. The first fields moved from wool to wheat are the ones best suited to wheat; the last fields are poor wheat land. So each extra unit costs more than the one before. This rising cost, not any law of nature, is what lets prices carry information. Interactive · A farm's frontier Wheat grown Illustrative farm: wool = 100 − wheat²/100. The cost of the next unit is wool(now) − wool(after one more wheat). At 40 wheat the farm can have 84 wool. One more wheat costs 0.81 wool. At 80 wheat the same extra unit costs 1.61 wool, twice as much. Nothing got worse; you just ran out of the easy fields. Remember this shape whenever someone says "just do more of it". Thinking at the margin: only the next unit matters People rarely choose all or nothing. They choose one more: one more hour of study, one more loaf baked, one more floor on the building. The margin is the edge where the decision lives. The rule an economics graduate carries: keep doing something while its marginal benefit is at least its marginal cost, and stop when the next unit would cost more than it brings in. do more while marginal benefit ≥ marginal costfor a seller facing a market price, marginal benefit is just the price A bakery sells loaves at a fixed price. Its cost of the next loaf rises as the oven gets crowded and staff go onto overtime (an illustration, not a real bakery). The slider below is the owner's decision. Watch the profit hump: the top is exactly where the cost of the next loaf meets the price. Interactive · How many loaves? Price per loaf Loaves baked Illustrative: total cost = q + 0.05q², so the next loaf costs about 1 + 0.1q. At a price of 6 the best output is 50 loaves and profit is 125. At 40 loaves the owner is leaving money on the table; at 60 she is baking loaves that cost more than they sell for. A higher price pushes the best output up: at 10 it is 90. Lining up each price with its best output is exactly how a supply curve is built. Supply and demand: the price that clears the market Demand says that, other things equal, buyers want less as the price rises. Supply says that sellers offer more as it rises (the bakery just showed why). Draw both against price and they cross at one point: the equilibrium price, where the quantity buyers want equals the quantity sellers offer. Above it there is a surplus and sellers cut prices; below it there is a shortage and buyers bid up. The price is pulled to the crossing without anyone setting it. The key habit: separate moving along a curve (the price changed) from shifting it (something else changed: tastes, income, input costs, a frost). Try three shocks in a market whose starting point is price 30, quantity 40. Interactive · Shift the curves Reset Demand rises (a fad) Supply falls (a frost) Both Demand shift (units at every price) Supply shift (units at every price) Illustrative: demand Q = 100 − 2P, supply Q = 2P − 20, then shifted sideways. A fad lifts both price (35) and quantity (50). A frost lifts the price to the same 35 but cuts the quantity to 30. Same price, opposite quantities: a graduate reads the quantity to tell whether demand or supply moved. When both shift by 20 the price jumps to 40 and the quantity barely changes, because the two effects on quantity cancel. Elasticity: how loudly buyers react A price rise always lowers the quantity demanded. The question that matters is how much. Price elasticity of demand is the percentage fall in quantity for a 1% rise in price. If it is below 1 in size, demand is inelastic: buyers shrug (salt, insulin, petrol in the short run). If it is above 1, demand is elastic: buyers walk (one brand of cereal, a restaurant meal). elasticity = % change in quantity ÷ % change in pricesellers' revenue rises with price when demand is inelastic, and falls when it is elastic A real case: from January 2014 Mexico taxed sugary drinks at one peso a litre, commonly described as roughly a tenth of the price. Purchases of the taxed drinks fell about 6% over 2014, and about 9% in the lowest-income households (Colchero and colleagues, BMJ, 2016). If the whole tax reached shelf prices, that is an elasticity around 0.6: buyers responded, but less than one-for-one. It is a rough reading, since pass-through to prices varied. Interactive · What does a 10% price rise do? Staple (0.3) Unit elastic (1) Restaurant meal (2) One brand (3) Elasticity (size) Price Illustrative constant-elasticity demand: Q = 100 × (P ÷ 10)−e. Real elasticities differ by market, time horizon and how many substitutes exist. With elasticity 0.5, a 10% price rise cuts quantity by about 4.7% and raises revenue by about 4.9%. At 2, the same rise cuts quantity by 17.4% and revenue by 9.1%. That is why a monopolist never charges where demand is inelastic: it could earn more by charging more. Surplus and the triangle nobody gets A trade happens only if it makes both sides better off. Consumer surplus is the gap between what a buyer would have paid and what they did pay. Producer surplus is the gap between what a seller received and the least they would have taken. In the market above, each is worth 400, so the total is 800. A competitive market, left alone and free of side effects, produces exactly the trades that make that total as large as it can be. Now interfere. A tax drives a wedge between what buyers pay and sellers keep, so some trades that were worth doing no longer happen. A price ceiling below the equilibrium makes sellers supply less than buyers want. Either way the lost trades are the deadweight loss: surplus that went to nobody. It is a triangle, so it grows with the square of the wedge. Interactive · A tax, or a price cap Tax per unit Price ceiling Tax per unit Same market as above (price 30, quantity 40, surplus 800). For the cap, the best case is assumed: the buyers who value the good most get it. Real rationing is usually worse. A tax of 10 shrinks the quantity from 40 to 30, raises 300, and destroys 50 of surplus. Double the tax to 20 and the deadweight loss does not double, it quadruples to 200, while revenue rises only to 400. A ceiling at 20 leaves 20 units on sale where buyers want 60: a shortage of 40, a loss of 200 even in the best case, and an invitation to queue, bribe or build a black market. Note what a tax can do that a cap cannot: it raises money that can be spent. The next piece in this thread, Public Economics (34/49), is about who ends up actually paying it. Market structures: from a thousand sellers to one "Supply and demand" assumes sellers are too small to move the price. That holds for a farmer selling wheat, not for an electricity grid or a search engine. Economists line up a spectrum. At one end is perfect competition: many sellers, identical goods, so each takes the price as given and price is driven to marginal cost. At the other is monopoly: one seller, who can choose the price, and who restricts quantity to keep it high. In between are oligopoly (a few firms who watch each other) and monopolistic competition (many firms with slightly different products, like restaurants). The simplest model that shows the whole spectrum is Cournot's, from 1838: firms choose quantities, each knowing the others do the same. The more firms, the more each fears flooding the market, and the closer price falls to cost. Interactive · How many rivals? Firms compete on quantity Perfect competition Number of firms Illustrative: demand P = 100 − Q, each firm's marginal cost 20. One firm sets 40 units at 60. Cournot with n firms: Q = 80n ÷ (n+1). One firm charges 60 and sells 40; two firms get 46.7; five get 33.3; twenty get 23.8, nearly the competitive 20. Buyers gain as firms are added (consumer surplus goes from 800 with one firm to about 2,900 with twenty), and the deadweight loss shrinks from 800 to about 7. This is the economic case for competition policy, and the reason Strategy & Competition (34/54) is a whole course. Entry matters more than the headcount, though: a market with one firm but easy entry can behave better than a market with three that are protected. Externalities and public goods: when prices miss something Prices work when the buyer and seller bear all the costs and benefits of the trade. An externality is a cost or benefit that lands on someone else: smoke from a factory, a neighbour's loud party, or, on the good side, a beekeeper's bees pollinating the orchard next door. The market quantity is too high for harms and too low for benefits, because the decision-maker ignores the third party. The classic fix is a Pigouvian tax (A. C. Pigou, 1920): charge the polluter the damage they cause, so the private price includes the social cost. London's congestion charge, which began on 17 February 2003 at five pounds a day (the standard rate is now £18 as of January 2026), is a price on road space for exactly this reason. Ronald Coase argued in 1960 that, where bargaining is cheap and rights are clear, the parties can sort it out themselves with no tax at all. Both are alive in every environmental debate. Interactive · A factory that pollutes Damage per unit made, to neighbours No policy Tax equal to the damage Same market as before. Making each unit costs the neighbours the amount on the slider. With damage of 10 per unit, the market still makes 40 units but the best quantity is 30. The ten extra units each cost society more than they are worth: a loss of 50. A tax of 10 moves the market to exactly 30. The other kind of miss is a public good: something nobody can be excluded from and that one person's use doesn't use up (a lighthouse, clean air, a vaccine's herd effect). Each person would like someone else to pay, so private markets undersupply it. When a resource is shared but rivalrous, a commons, Garrett Hardin's 1968 "tragedy of the commons" warned of overuse. Elinor Ostrom's field work, in Governing the Commons (1990) and her 2009 Nobel Prize, showed that communities from Swiss pastures to fisheries often build rules that avoid it without privatising or handing it to the state. The honest version is "it depends on the rules". More on this in Public Economics (34/49). What a graduate just sees Every choice has an opportunity cost, and it usually rises: the first unit is cheap, the thousandth is not. Decisions happen at the margin. Sunk costs and averages are the wrong numbers to look at. A price is information. When it moves, ask which curve shifted, and read the quantity to tell. Whether a price rise helps a seller depends on elasticity, which depends on substitutes and time. A price cap makes a shortage and a tax makes a wedge; the deadweight loss grows with the square of the wedge. "More competitors" and "freer entry" push price toward cost; fewer push it away. When someone else bears the cost or gets the benefit, the market quantity is wrong, and the fix is to make the price carry it. Markets can fail, but "the government fixes it" is a claim to check too: compare real alternatives, not an ideal against a market. Where it shows up next Macroeconomics (34/43): the same supply-and-demand logic, scaled up to the price level, interest rates and the whole labour market. Game Theory (34/45): when the firm count is small, strategy replaces the price-taking assumption. The oligopoly dial above is a simple game. Public Economics (34/49): externalities, public goods and who bears a tax become the whole subject. In the world: a surge price on a ride app, a carbon price, a minimum wage debate, antitrust cases against big platforms, and every "should we charge for this?" meeting in a company. If you want to go deeper 📘 Textbook. N. Gregory Mankiw, Principles of Microeconomics (Cengage). The most widely assigned introduction in the US and much of the world: clear, short chapters. 📘 Textbook. Daron Acemoglu, David Laibson and John List, Economics (Pearson), or Paul Krugman and Robin Wells, Microeconomics (Worth). Two modern alternatives: the first leans on evidence and experiments. 📘 Gentler. Charles Wheelan, Naked Economics (W. W. Norton). Almost no maths; the ideas as stories. 🌐 Free course. MIT OpenCourseWare 14.01 Principles of Microeconomics (Fall 2023): lectures and problem sets. Also CORE Econ's The Economy and Khan Academy Microeconomics, both free. The takeaway Every choice costs the best alternative you gave up, and it is made one step at a time. Prices carry that cost between strangers: when they are right, markets turn scattered selfishness into trades that make both sides better off. When they are wrong (a cap, a tax, a monopolist, a neighbour who bears the smoke), something is lost, and the skill is to find which wedge is the problem. Try this: this week, price one thing you buy in terms of what you gave up, and find your margin. Would you pay for one more? Then watch a price move at a shop or an app and ask which curve shifted. Sources & Further Reading Sources. The ideas are standard textbook microeconomics (Mankiw; Acemoglu, Laibson and List). Every number in the interactives uses invented, illustrative markets and was computed in node: farm frontier 84 to 83.19 (cost 0.81) at 40 wheat and 36 to 34.39 (1.61) at 80; bakery best output 50 with profit 125 at price 6, 90 at price 10; equilibrium 30 and 40, shocks 35 and 50 (fad), 35 and 30 (frost), 40 and 40 (both); elasticity 0.5 gives −4.65% quantity and +4.88% revenue, elasticity 2 gives −17.36% and −9.09%; tax of 10 gives quantity 30, revenue 300, deadweight loss 50, tax 20 gives 200 and revenue 400; ceiling 20 gives quantity 20, shortage 40, loss 200; Cournot with 1, 2, 5, 20 firms gives price 60, 46.7, 33.3, 23.8 and consumer surplus 800 to about 2,900; externality of 10 gives optimum 30 and loss 50. Mexico's 2014 sugary-drink tax: Colchero, Popkin, Rivera and Ng, BMJ 352:h6704 (2016), which reports a 6% fall in taxed-drink purchases in 2014 and 9% in the lowest-income households; the one-peso-a-litre rate is as reported in public summaries and the "about a tenth of the price" is approximate. London congestion charge: began 17 February 2003 at £5, £18 from 2 January 2026 (Transport for London, via Wikipedia). Coase 1960 (Nobel 1991), Pigou 1920, Hardin 1968 (Science), Ostrom 1990 (Nobel 2009), Cournot 1838 are standard history of economic thought. 🌐 MIT OCW 14.01 Principles of Microeconomics 🌐 CORE Econ, The Economy 📘 Mankiw, Principles of Microeconomics 📘 Wheelan, Naked Economics Step 5. Research Methods in Political Science: How Anyone Knows Anything About Politics --------------------------------------------------------------------------------------- Why: Experiments, natural experiments and case studies: how political scientists know. Do mailers raise turnout? Does a minimum wage cost jobs? Does a close election hand a party the next one? Political science is full of causal claims, and this course is about the difference between a claim you can believe and one that only sounds good. In 1998, a pair of political scientists, Alan Gerber and Donald Green, did something unusual. They took about 30,000 registered voters in New Haven, Connecticut, and chose by lottery who would get a knock on the door, a phone call or a mailer urging them to vote. After the election they checked who had actually voted. People who were reached by canvassers turned out at a rate about 8.7 percentage points higher than comparable people who were not; mailers added about half a point each; phone calls did nothing detectable. The striking part is not the result but the method. Before that, most claims about what gets people to vote came from comparing people who were contacted with people who were not, and those two groups differ in countless ways. The lottery removed the problem. Almost every question in political science has this shape: we see two things go together, and want to know whether one causes the other. The methods course is a toolkit for that question. The course in one box Who takes itEvery political science major, usually in year 2, in the first or second research-methods course. MIT's Course 17, Harvard Government and Sciences Po all require one or more; graduate programmes go much further. What it is forJudging whether a claim about politics is backed by evidence, and designing the evidence that could back it. Comes afterProbability & Statistics (34/05). Leads toIt supports every empirical subject in the major: Elections, Parties & Public Opinion (35/15), Public Policy (35/16) and Comparative Politics (35/13). Its economics sister is Econometrics (34/44). What this piece gives youYou will not be able to run a regression discontinuity analysis. You will see what each method promises, what it assumes, and what a failure looks like. Easier first reads: Probability & Statistics (34/05), Statistics for Reading the World (12/05), The Biases That Fool Everyone (17/02) and How to Spot Bullshit (17/03). The fundamental problem of causal inference Suppose a town starts a youth-training programme and a year later its participants earn more. Did the programme do it? To know, you would need to see the same people in the same year without the programme. That world never exists. Every person is observed under one condition only. Statisticians call this the fundamental problem of causal inference: the effect is the gap between what happened and what would have happened, and the second is never seen. The natural workaround is to compare people who got the treatment with people who did not. That fails when the groups differ in a hidden way that also affects the outcome, a confounder. If more motivated people sign up for the programme, and motivated people earn more anyway, the comparison credits the programme with the motivation. Try it below. The hidden trait raises the outcome; you choose how strongly it also drives who gets treated. Interactive · The hidden trait How much the hidden trait raises the outcome How much the trait drives who gets treated People chooseCoin flip assigns Illustrative: 1,000 simulated people; the treatment truly adds 2 to the outcome. The estimate is simply the average outcome of the treated minus that of the untreated. Without JavaScript: with the defaults, self-selection makes the treatment look like it adds about 3.9 when the truth is 2; assigning by coin flip brings the estimate back near 2. With self-selection and a strong hidden trait, the estimate runs well above the truth. Switch to the coin flip and the answer lands near 2 whatever you set, because chance cannot be correlated with anything. That is the whole logic of the experiment, and everything else in this piece is a way to approximate it when you cannot flip the coin. Experiments and natural experiments A randomised experiment assigns the treatment by lottery, so on average the groups are alike in every way, measured or not. Gerber and Green's 2000 study in the American Political Science Review was an early large field experiment in political science; since then, researchers have randomised leaflets, observers at polling stations, community-development funds and information campaigns, in places from Benin to Brazil to Uganda. Their limits are well known: you cannot randomly assign a country's constitution, many treatments are unethical or impossible, and an effect found in New Haven in 1998 may not hold in another place or year. When you cannot randomise, you can sometimes find a place where the world did it for you: a natural experiment. Daniel Posner's study of two African peoples, the Chewa and the Tumbuka, is a classic. A colonial border split each group between Malawi and Zambia. Their cultures are essentially the same on both sides, yet in Malawi they are political rivals and in Zambia allies. The difference, Posner argued, is size: in Malawi the Chewa are roughly 28 percent of the population and the Tumbuka 12 percent, big enough to form rival blocs; in Zambia each is under 7 percent, too small, so politicians appeal to them together as easterners. The border, drawn without regard to politics, did the assigning. A natural experiment is only as good as the claim that the assignment was "as if random", and that claim has to be argued case by case. Difference-in-differences Another approach: find a group hit by a policy and a group that was not, and compare how each changed. The comparison group's change stands in for what would have happened to the treated group anyway. The classic example is David Card and Alan Krueger (1994). In April 1992 New Jersey raised its minimum wage from $4.25 to $5.05 an hour, while neighbouring Pennsylvania did not. Surveying fast-food restaurants in both states before and after, they found employment in New Jersey did not fall relative to Pennsylvania; it rose slightly, by an estimated 2.75 full-time-equivalent jobs per restaurant. The finding was contested, and economists still argue about it, but the design became a standard tool. Card shared the 2021 economics Nobel prize partly for this work. effect = (treated after − treated before) − (comparison after − comparison before)works only if both groups would have moved in parallel without the policy Interactive · The parallel trends assumption True effect of the policy Treated group's extra trend, independent of the policy treated   comparison   dashed: counterfactual Invented numbers: the treated group starts at 50, the comparison group at 40, and the comparison group gains 2 over the period. Slide the extra trend away from zero and watch the estimate become wrong by exactly that amount. The groups start at different levels, which does not matter; what matters is whether they were already moving apart. Researchers check this by plotting several periods before the policy. If the lines run parallel before, parallel afterwards is believable; it is never proved. Regression discontinuity Some rules create sharp lines: a scholarship for scores above 80, a programme for towns above 5,000 people, a seat for the candidate with more votes. Compare units just above and just below the line, and they are almost identical, so the line works like a lottery for those near it. The design was introduced by Donald Thistlethwaite and Donald Campbell in 1960, for scholarships and students' career plans. In political science, David Lee (2008) used it for the US House: in districts where the Democrat won or lost by a hair, who won was close to a coin flip, so comparing these districts shows what it is worth to win once. You must decide how wide a window around the line to use. A narrow window gives clean comparisons but few points, so the estimate is noisy. A wide window gives many points but includes ones far from the cutoff, where the outcome curves for other reasons. Try both extremes. Interactive · How wide a window? True jump at the cutoff Window half-width Illustrative: 400 simulated units; the outcome curves upward to the right of the cutoff, which is why a wide window misleads. Lines are fitted separately on each side within the window. Experts use refined versions (weighted fits and data-driven windows), but the trade-off is the same. Two cautions: the estimate speaks only for units near the line, and it fails if people can sort themselves across it, as when officials manipulate a vote count or a test score. Case studies and process tracing Not every question has a hundred cases. Why did a particular revolution succeed? Why did one country avoid civil war? Here researchers study a few cases in depth. Process tracing is the discipline of this: you lay out the chain of events that your theory predicts, then look for evidence at each link. Stephen Van Evera (1997) sorted evidence into four kinds, depending on how a clue behaves if the theory is true or false. David Collier popularised process tracing in political science, defining it as the systematic examination of diagnostic evidence. Straw in the wind: nudges belief a little, neither strong support nor strong refutation. Hoop: the theory must pass it. Failing counts heavily against; passing proves little. Smoking gun: strong support if found. Not finding it proves little. Doubly decisive: confirms the theory and eliminates rivals if passed; badly hurts it if failed. Rare. Interactive · Which kind of clue? Straw in the windHoopSmoking gunDoubly decisive Next clue An invented case (not a real minister or firm), built to illustrate the four tests. The main dispute here is old and still live. In 1994 Gary King, Robert Keohane and Sidney Verba, in Designing Social Inquiry, argued that qualitative research should follow the logic of quantitative inference. Henry Brady and David Collier's reply in Rethinking Social Inquiry (2004) argued that case-based work has its own strengths, and process tracing is part of that answer. Many political scientists now combine the two, using a regression to find a pattern and cases to check the mechanism. Measuring a concept like democracy Before you can test whether democracy causes growth, you must decide what "democracy" is and how to count it. Dahl-style definitions stress competitive elections and broad participation; others add the rule of law, a free press and civil liberties. The big datasets make different choices. Polity, begun by Ted Robert Gurr in the late 1960s, scores states from −10 to +10, with 6 to 10 counted as democracy. The Economist Intelligence Unit's index, first published in 2006, adds up 60 indicators in five categories into a 0 to 10 score and sorts countries into four types (full democracy, flawed democracy, hybrid regime, authoritarian regime). V-Dem, launched in 2014, publishes more than 470 indicators across seven principles of democracy, back to 1789. Freedom House publishes its own yearly ratings. They agree about the extremes and disagree in the middle, and the disagreements are not mistakes; they reflect different definitions. Gerardo Munck and Jay Verkuilen (2002) criticised several indices for being too narrow or hiding choices about weights. Some scholars prefer a simple yes/no rule, such as one that counts a country as a democracy only if power has actually changed hands through elections. Interactive · Four definitions, one country Country ACountry BCountry CCountry DCountry E Five fictional countries and four invented rules, to show how definitions change classifications. These are not the rules of Polity, V-Dem, the EIU or Freedom House. Without JavaScript: a country with competitive elections and universal suffrage but no free press, courts or turnover of power counts as a democracy under a minimal rule and not under the others. Try Country C: elections are competitive and power has changed hands, but not everyone may vote. The rules disagree. Any finding about "democracy and X" depends on which was used, which is why careful papers show their results under more than one measure. The replication debate Social science has had a reckoning over whether findings hold up. In 2015 the Open Science Collaboration re-ran 100 psychology studies and reported that about 36 percent of the replications produced a statistically significant result, with effects about half the original size. Political science has had its own cases. In December 2014 Science published a study by Michael LaCour and Donald Green claiming that a short conversation with a gay canvasser durably changed views on same-sex marriage. In May 2015 David Broockman, Joshua Kalla and Peter Aronow showed that the data were irregular and appeared to be fabricated, and Science retracted the paper on 28 May 2015. The case was not about a weak method but about a fraud, and the lesson drawn was that data and code should be shared so that others can check. The quieter problem is flexibility. A researcher with many outcomes, subgroups and ways to code a variable can almost always find something with p below 0.05. Studies of published results in several social sciences, political science among them, have reported more findings just under the 0.05 line than chance would give. The remedies are pre-registering the analysis plan before seeing the data, journals that accept papers for their questions rather than results ("registered reports"), and sharing data and code. Some qualitative scholars have argued that transparency rules built for statistics fit interviews and archives badly, and the field is still working that out. Interactive · Many tests of nothing Number of outcomes tested (all truly zero) 1,000 simulated studies in which nothing is going on; a result counts as "significant" when it clears the usual two-sided 5 percent bar. Without JavaScript: with 14 outcomes that are all truly zero, the chance at least one looks significant is about 51 percent. Fourteen tests of nothing give about even odds of a "finding". The tighter bar, dividing 0.05 by the number of tests (the Bonferroni correction), keeps false alarms near 5 percent in total, at the cost of missing smaller real effects. None of this makes a single study worthless; it means one significant result among many tries deserves less trust than the same result from a single planned test. What a graduate just sees A correlation is an invitation to ask what else could produce it. Ask who selected into the group. Every causal claim hides a counterfactual: what would have happened otherwise? Randomisation works because chance is correlated with nothing. Everything else is an attempt to imitate it. A natural experiment is only as good as the argument that the assignment was "as if random". Difference-in-differences needs parallel trends; check the years before the policy. Near a cutoff, units are alike; far from it, they are not. Narrow windows trade bias for noise. A few cases can test a theory if you ask what each clue would look like if the theory were false. "Democracy" is a definition before it is a number. Ask which measure was used. Many tries inflate false findings. Plans written before the data, and open data, deflate them. Where it shows up next Elections, Parties & Public Opinion (35/15): field experiments on turnout and RDD on close elections are standard tools there. Public Policy (35/16): evaluating a programme is a causal-inference problem; see the counterfactual in the Progresa example. Comparative Politics (35/13): cross-country comparisons depend on how concepts like democracy are measured. Econometrics (34/44): the same methods with the economist's emphasis. In the world: reading a news report on a "study finds", pollsters and campaign teams running A/B tests, and think tanks and ministries asking for evidence before spending. If you want to go deeper 📘 Textbook. Kevin Kellstedt and Guy Whitten, The Fundamentals of Political Science Research (Cambridge University Press): a standard first methods text for the discipline. 📘 Textbook. Alan Gerber and Donald Green, Field Experiments: Design, Analysis, and Interpretation (Norton, 2012), and Thad Dunning, Natural Experiments in the Social Sciences (Cambridge University Press, 2012). 📘 Gentler. Joshua Angrist and Jörn-Steffen Pischke, Mastering 'Metrics (Princeton, 2015): the logic of these methods with almost no algebra. 🌐 Free course. MIT OpenCourseWare 17.871, Political Science Laboratory (Charles Stewart, Spring 2012): learn quantitative political research by doing projects. The takeaway Political science asks causal questions about worlds we see only once. Each method, from the lottery to the cutoff to the single case, is a way to build a believable stand-in for the world that did not happen, and each carries an assumption you can name. Trust a claim in proportion to how well that assumption holds. Try this: the next time a headline says something "linked to" something else, write down one hidden trait that could cause both, and what comparison would rule it out. Sources & Further Reading Sources. Gerber and Green, "The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout: A Field Experiment", APSR 94(3), 2000, 653-663 (about 29,400 to 30,000 voters, New Haven, 1998; canvassing effect 8.7 points among those reached and mail 0.5 per mailing as summarised by J-PAL). Posner, "The Political Salience of Cultural Difference", APSR 98(4), 2004, 529-545 (population shares as reported in the Wilson Quarterly summary). Card and Krueger, American Economic Review (1994): New Jersey minimum wage $4.25 to $5.05 in April 1992, estimated +2.75 FTE; Card's 2021 Nobel. Thistlethwaite and Campbell (1960); Lee, "Randomized experiments from non-random selection in U.S. House elections", Journal of Econometrics (2008). Van Evera, Guide to Methods for Students of Political Science (1997); Collier, "Understanding Process Tracing", PS: Political Science & Politics (2011); King, Keohane and Verba (1994); Brady and Collier (2004). Democracy measures: Polity (Gurr; scale -10 to +10), EIU Democracy Index (2006 on; 60 indicators in five categories), V-Dem (2014; more than 470 indicators in the 2020 release; coverage from 1789), Munck and Verkuilen (2002) (via Wikipedia entries on each). OSC, Science (2015): 36 percent of replications significant. LaCour and Green, Science (December 2014); Broockman, Kalla and Aronow, "Irregularities in LaCour (2014)", 19 May 2015; retracted 28 May 2015. The bunching of results just under p = 0.05 is reported in the meta-research literature (for example Gerber and Malhotra, 2008). All simulations here are the author's own and use invented data. 🌐 MIT OpenCourseWare 17.871 📘 Angrist & Pischke, Mastering 'Metrics 📘 Gerber & Green, Field Experiments Step 6. Elections, Parties & Public Opinion: Rules That Turn Votes into Power ----------------------------------------------------------------------------- Why: Voters, parties, polls and electoral systems. The same ballots can give one party a landslide or three parties a coalition. Which one happens depends on arithmetic you can learn in an afternoon, and on what polls can and cannot see. At the United Kingdom's general election on 4 July 2024, the Labour Party won 33.7% of the votes and 411 of the 650 seats, about 63% of the House of Commons. Reform UK won 14.3% of the votes and 5 seats, under 1%. Turnout was 59.7%. Nobody cheated and nobody miscounted. That is simply what the rule, one winner per constituency, does with votes. In a country that uses proportional representation, the same votes would have produced a very different parliament. And in both cases, in the months before the vote, pollsters had tried to tell the country what would happen using a few thousand phone calls or online panels. This piece is about those two machines: the one that turns votes into seats, and the one that tries to measure votes before they are cast. Plus the oldest question of all: why people vote the way they do, or at all. This describes how elections and polling work. It does not say which party or system is better; each electoral system trades something off, and every example is given to show a mechanism. The course in one box Who takes itEvery political science major, usually in year 2, and students of sociology, economics, law and journalism. Programmes such as MIT's Course 17, Oxford's Politics in PPE, Sciences Po and Harvard Government teach electoral systems, parties and public opinion in a core course, often with a methods course beside it. What it is forUnderstanding how rules, parties and attitudes combine to decide who governs, and how to read a poll or a result without being fooled. Comes afterComparative Politics (35/13) and Probability & Statistics (34/05). Leads toPublic Policy (35/16) and Research Methods in Political Science (35/18). What this piece gives youYou will not be able to run a campaign or design a survey. You will be able to predict what an electoral rule does to the number of parties, say why a poll can be off, and understand why a two-point lead is not a certainty. Easier first reads: How Voting Systems Shape Outcomes (18/03), Democracy: The Many Flavors (18/02) and The Political Spectrum, Fairly Presented (18/05). Seats are made by rules: plurality versus proportional Every electoral system answers one question: how do votes become seats? There are three big families. In plurality systems (the United Kingdom, Canada, India, the United States for most offices), each district elects one person, and whoever gets the most votes wins, even with less than half. In proportional representation (PR) systems (the Netherlands, Spain, Brazil's lower house, South Africa, Israel and most of continental Europe), a party's share of seats follows its share of votes, using a formula. Mixed systems combine the two (Germany, New Zealand, Mexico). Two PR formulas are common. The D'Hondt method (Victor d'Hondt, 1878; used for example in Spain and Portugal) gives seats one at a time to the party with the highest quotient votes ÷ (seats won + 1). Sainte-Laguë (André Sainte-Laguë, 1910; used for the German Bundestag and in New Zealand) divides by 1, 3, 5 and so on, which is a little kinder to small parties. A threshold (5% in Germany) excludes parties that fall below it. Political scientists measure the damage a system does to proportionality with the Gallagher index (Michael Gallagher, 1991): the square root of half the sum of squared gaps between each party's vote share and seat share. Zero is a perfect match. They also count how many parties matter with the effective number of parties (Laakso and Taagepera, 1979): one divided by the sum of squared shares. Four equal parties give 4; one party with 90% gives about 1.2. Below, ten districts, four parties, and an invented set of votes show all three systems side by side. Interactive · One set of votes, three rules Shift voters between A and B in every district (points) PR threshold (national vote %) Invented data for ten single-member districts and four parties; a PR system with 10 seats nationally is compared with 10 single-member districts. Shifting moves votes from A to B (negative: B to A) in each district, never below zero. Party D is strong in just one district. At the default setting, party A has 33.5% of the votes and wins 7 of 10 seats under plurality, while party C, with 24.2% spread evenly, wins none. The PR systems give A 4 seats (D'Hondt) or 3 (Sainte-Laguë) and C 2 or 3. Slide the shift and watch a small change in votes swing several seats. That sensitivity is the mechanism behind the "landslide on a minority of votes" in the UK example. Raise the threshold to 15% and see D drop out. Real systems add many details (district size, open or closed lists, bonus seats), and each has its defenders: plurality gives clear single-party governments and a local member; PR gives fairer representation and more coalitions. Duverger's law: why plurality squeezes parties The French jurist and sociologist Maurice Duverger noticed, in Les partis politiques (1951), that countries with plurality elections in single-member districts tend to have two main parties, while PR tends toward many. He gave two reasons. A mechanical effect: a party that wins 15% of the votes everywhere wins no seats, as party C just did. And a psychological effect, which is the stronger one in modern work: voters see this and avoid "wasting" a vote on a party that cannot win, so they back their favourite of the two who can. The tendency is not a law; India and Canada have regional parties that win locally, and Duverger's logic works district by district rather than nationally. You can see the psychological effect in a small model. Voters sit on a left-to-right line (more are near the middle). Candidate A stands at −1.0 and candidate B at 0.3. Candidate C's position is yours to set. Voters sincerely back the closest candidate. Then the voters of whichever candidate is last in the polls start to doubt: a share of them switch to their preferred of the other two. Try a plurality vote, and then a two-round runoff, in which the top two meet again in a second round. Interactive · Strategic voting and the spoiler One round (plurality) Two-round runoff Candidate C's position (A is at −1.0, B at 0.3) Share of the last-place candidate's voters who switch Voters are normally distributed around 0 (the median voter). A at −1.0, B at 0.3. Plurality: most votes wins. Runoff: the top two meet again and voters back the closer one. "Closest to the median" is the candidate who would beat each rival head to head (the Condorcet winner). With C at 0.8 and nobody switching, A wins with 36% under plurality, even though B, the candidate closest to the middle, would beat either rival in a head-to-head vote. Let half of C's voters switch, and B wins. A runoff produces the same outcome without anyone needing to guess: C's voters back B in round two. Move C to 0.5, next to B, and it is B that gets squeezed out. This is why parties that share a space worry about "splitting the vote", why many countries pair presidential elections with runoffs (France, Brazil, Argentina, Chile, Colombia), and why electoral rules change how parties form. Why people vote as they do Anthony Downs's An Economic Theory of Democracy (1957) gave the field its simplest model. If voters sit on a line and each backs the closer candidate, two vote-seeking candidates are pulled toward the position of the median voter, the voter with half the electorate on either side. Duncan Black proved the underlying result in 1948. Try it, and then add the realistic twist: a voter who feels too far from both candidates stays home. Interactive · The median voter, and what pushes candidates apart Candidate 1 position Candidate 2 position Voters' average position Voter stays home if candidates are farther than Voters follow a bell curve (standard deviation 1) around the chosen average. A setting of 6 means nobody abstains. Drag candidate 1 toward the middle: they gain, and the other candidate must follow. If both end at the same spot, the vote splits evenly. Then lower the "stays home" slider: the candidates at the extremes lose voters who prefer neither, which is why turnout and the pull toward the centre trade off. Real elections do not collapse to the middle, for many reasons: parties have activists and loyal supporters, voters care about more than one issue, and in multiparty systems each party aims at its own group. Political scientists also ask what drives the vote in practice. The Michigan school's The American Voter (1960, Campbell, Converse, Miller and Stokes) found that party identification, a long-term emotional attachment often learned in the family, predicts the vote better than issue positions. Philip Converse (1964) found many voters held opinions that were not tightly connected across issues. Morris Fiorina (1981) emphasised retrospective voting: reward or punish the government for how things went. Christopher Achen and Larry Bartels (Democracy for Realists, 2016) argued that voters respond to events they should logically ignore, and cited a study suggesting that shark attacks on the New Jersey shore in 1916 cost Woodrow Wilson votes. That claim did not hold up: Anthony Fowler and Andrew Hall (2018) re-examined every fatal shark attack in the US and presidential returns from 1872 to 2012 and found little compelling evidence of an effect. Achen and Bartels have replied that their broader argument stands. It is a good example of why the field now checks striking findings against more data. Turnout: why anyone votes at all Downs raised an awkward problem. Voting costs time, and the chance that one vote decides an election is tiny. William Riker and Peter Ordeshook (1968) wrote the voter's calculation as R = pB − C + D: the chance of being decisive times the benefit, minus the cost, plus a sense of duty or expression (D). Without D, almost nobody would vote. A rough size: even in the most favourable case, an election dead-level on average, the chance that one vote is the tie is about √(2 ÷ (π N)) for N voters. For a million voters that is about 0.08%, or 1 in 1,250, and real elections are rarely so close, so the true number is far smaller. Yet hundreds of millions vote. Turnout differs sharply between countries, and rules explain part of that. In Australia, voting at federal elections has been compulsory since 1924, and turnout has rarely fallen below 90%. Belgium, the oldest compulsory system (1893), has about 90%. Argentina adopted compulsory voting in 1912, Brazil in 1932, and Peru, Bolivia and Uruguay also enforce it. Fines, or small bureaucratic penalties such as the difficulty of getting a passport, are the enforcement. In the UK in 2024, with voluntary voting, turnout was 59.7%. Scholars debate whether compulsory voting changes results or only adds reluctant voters, and whether people grow into the habit. Polling and its errors A poll asks a few hundred or thousand people and infers the opinions of millions. The inference works because of a result you met in Statistics: if you draw a truly random sample of size n from a population in which a share p holds an opinion, your estimate will land within about 1.96 × √(p(1 − p) ÷ n) of the truth 95% of the time. For p = 50% and n = 1,000, that is ±3.1 points; for n = 4,000 it is ±1.55. Halving the margin costs four times the sample. And the margin on the lead between two candidates is roughly double. The catch is the word "truly random". The classic failure is the Literary Digest poll of 1936, which mailed ballots to about 10 million people drawn from its own subscribers, telephone directories and car registrations, got 2.38 million back, and predicted a landslide win for Alf Landon. Franklin Roosevelt won with 60.8% of the vote. A huge sample did not help, because it was a biased one, and the people who strongly disliked Roosevelt were more likely to answer. A modern version is non-response: people who do not pick up the phone differ from those who do. The American Association for Public Opinion Research (AAPOR) task force on the 2020 US election found that national polls overstated Joe Biden's lead by about 3.9 points and state polls by 4.3, the largest national polling error in about 40 years, and could not pin the cause on a single factor (non-response was the leading suspect). The UK's 2015 polling inquiry, led by Patrick Sturgis, blamed unrepresentative samples too. Simulate it. Each click draws 1,000 polls from a population where one candidate really has the share you choose. Then add a bias, the tilt that non-response introduces. Interactive · A thousand polls Sample size per poll True share for candidate X (%) Bias from non-response (points, against X when negative) Draw 1,000 new pollsHuge but biased Each poll samples n people at random from a population in which X's true share, plus the bias, is the chance of a "yes". Margin of error is 1.96 √(p(1−p)/n) around the true share. Leave the bias at zero: about 95% of polls land within the margin of error, and with a 52% truth and n = 1,000 around one poll in ten calls the wrong winner. Now press "Huge but biased": five thousand people per poll, bias of 4 points. The margin of error shrinks to under 1.4 points, yet almost no poll lands within it and nearly all call the wrong winner. Sampling error shrinks as samples grow; bias does not. Good pollsters weight their samples to match known population figures, and aggregators average many polls, but a shared bias passes through both. Polarisation, measured "Polarisation" is used for several different things. Ideological polarisation is how far apart people's positions on issues are. Affective polarisation is how much people dislike and distrust the other side. They need not move together. The most-cited cross-country study, by Levi Boxell, Matthew Gentzkow and Jesse Shapiro, measured affective polarisation (negative feeling toward opposing parties) in twelve OECD countries over four decades. The United States showed the largest rise; five other countries rose by less, and six fell. In other words, there is no worldwide trend that everyone shares, and the American pattern is the exception more than the rule, though the causes are debated. Other measures exist: the V-Dem project, for example, publishes an expert-coded indicator of political polarisation for each country-year, and survey projects ask people how warmly they feel toward the parties. Different indices count different things, so check what an index measures before comparing countries. Researchers also disagree about causes: some point to the sorting of parties by identity, some to media, some to electoral rules (plurality tends to create two big opposing camps; PR, more cross-cutting coalitions), and none of these explains everything. What a graduate just sees The electoral rule is part of the result. Ask how votes become seats before arguing about who "won". Plurality tends toward two parties (Duverger), and small shifts in votes can swing many seats. Proportional systems track votes closely, and the formula (D'Hondt, Sainte-Laguë) and threshold decide who is just in or out. A party that splits a camp can hand the seat to the other side; runoffs and ranked ballots exist to fix that. Candidates are pulled toward the median voter, and held away from it by activists and abstention. For one voter the chance of deciding is tiny, which is why turnout is partly duty and partly rules. A poll's margin of error covers only sampling luck. Bias, from non-response or an unrepresentative sample, is a separate and often bigger risk. "Polarisation" can mean disagreement or dislike, and measures differ across countries. Where it shows up next Public Policy (35/16): coalitions, parties and veto players shape what gets proposed and passed. Research Methods in Political Science (35/18): sampling, bias and causal claims, the tools behind every poll and every "voters do X" finding. Probability & Statistics (34/05): margin of error, sample size and bias are the same ideas you meet there. In the world: campaign and polling firms, election administration and observation (the OSCE, the Carter Center), journalists who report polls, civil servants who must govern with a coalition, and anyone who has to read a result and a forecast sensibly. If you want to go deeper 📘 Textbook. Michael Gallagher and Paul Mitchell (eds.), The Politics of Electoral Systems (Oxford University Press, 2005). Country-by-country coverage of how each system works. 📘 Textbook. Arend Lijphart, Patterns of Democracy (Yale University Press, 1999; 2nd edition 2012). Compares 36 democracies on electoral systems, party systems and executives. 📘 Gentler. Nate Silver, The Signal and the Noise (Penguin, 2012), a readable account of forecasting, including election forecasts and why they miss; and Achen and Bartels, Democracy for Realists (Princeton, 2016), a harder read but written for general audiences, for the case that voters are less issue-driven than we assume. 🌐 Free course. MIT OpenCourseWare 17.50 Introduction to Comparative Politics covers electoral systems, party systems and voting alongside regimes. For electoral systems in particular, the ACE Electoral Knowledge Network (run by International IDEA and other partners) has a free encyclopaedia with an Electoral Systems section. The takeaway Votes become power through a rule, and the rule shapes how many parties exist, who splits whose vote and how far a small swing travels. Voters are pulled by party identity, by how things went, and by where the candidates stand, and polls measure them well only when the sample is random: size shrinks luck, not bias. Try this: next election you hear about, find the rule first (one winner per district or proportional?) and the margin of error on any poll. Then ask how far the "lead" is from the margin, and what share of votes the winner needed to win. Sources & Further Reading Sources. UK 4 July 2024 general election: Labour 33.7% of votes and 411 of 650 seats; Reform UK 14.3% and 5 seats; turnout 59.7% (as compiled on Wikipedia from the House of Commons Library and Electoral Commission; checked against the 650-seat total). Duverger, Les partis politiques (1951); Laakso and Taagepera, "Effective Number of Parties", Comparative Political Studies 12(1), 1979; Gallagher, "Proportionality, Disproportionality and Electoral Systems", Electoral Studies 10(1), 1991; Downs, An Economic Theory of Democracy (1957); Black (1948); Campbell et al., The American Voter (1960); Converse (1964); Fiorina, Retrospective Voting in American National Elections (1981); Riker and Ordeshook, "A Theory of the Calculus of Voting", APSR 62(1), 1968; Achen and Bartels, Democracy for Realists (2016); Fowler and Hall, "Do Shark Attacks Influence Presidential Elections?", Journal of Politics 80(4), 2018. Compulsory voting: Australia since 1924, Belgium since 1893, Argentina 1912, Brazil 1932 (as summarised on Wikipedia's compulsory-voting page). Literary Digest 1936: about 10 million mailed, 2.38 million returned; Landon predicted at 57.08%, Roosevelt won 60.8% (Wikipedia, with non-response as the main cause). AAPOR Task Force on 2020 Pre-Election Polling (2021): national polls overstated Biden's lead by 3.9 points, state polls by 4.3, the largest national error in about 40 years. UK polling inquiry: Sturgis et al., report for the British Polling Council and Market Research Society (2016). Boxell, Gentzkow and Shapiro, "Cross-Country Trends in Affective Polarization" (NBER Working Paper 26669). V-Dem Democracy Report 2026 as reported. Every number an interactive shows was computed in node. 🌐 ACE Electoral Knowledge Network 🌐 MIT OCW 17.50 Introduction to Comparative Politics 📘 Gallagher & Mitchell, The Politics of Electoral Systems 📘 Lijphart, Patterns of Democracy Step 7. International Relations: Why Fear Needs No Villain ---------------------------------------------------------- Why: War, peace, trade and the institutions between states. There is no government above governments. From that one fact, scholars derive arms races, deterrence, alliances and the long argument over whether trade, institutions or shared values can buy peace. A country announces a new missile-defence system. It is, its government says, purely defensive. The neighbour reads the same news and sees a shield that could let the first country strike without fearing the reply. So it orders more missiles. The first country sees those and orders more of its own interceptors. Nobody in this story needs to be aggressive. Everyone is trying to be safe, and everyone ends up less safe and poorer. That pattern is so common that international relations (IR) scholars gave it a name, and built a discipline around the conditions that produce or soften it. This piece lays out the ideas a graduate keeps: why anarchy matters, what the main schools say in their own best terms, how deterrence works and fails, why wars happen even though they are costly, and where the democratic-peace argument stands today. This describes how scholars explain relations between states. It does not tell you which country is right in any current conflict, and each school is given at its strongest. The course in one box Who takes itEvery political science major, usually in year 2, and students of international studies, economics, history and law. Programmes such as MIT's Course 17, Harvard Government, Sciences Po and the London School of Economics teach it as a core subject, often split into theory and a second course on security or political economy. What it is forExplaining why states fight, cooperate, trade and build institutions, and what changes the odds. Comes afterComparative Politics (35/13) and Game Theory (34/45). Leads toInternational Law & Human Rights (35/10) and the international side of Political Economy (35/17). What this piece gives youYou will not be able to advise a foreign ministry. You will be able to read a crisis and say which kind of explanation is at work: fear, interest, ideas or power. Easier first reads: The International Institutions: What They Can and Can't Do (18/10) and Game Theory (34/45). This one goes further into the theories and puts small models in your hands. Anarchy: no one above the states Inside a country, if someone steals your car you can call the police, and the police answer to a government that holds a monopoly of force (see 35/13). Between countries there is no police force that can arrest a government. In IR the word for that is anarchy. It does not mean chaos. It means the absence of a ruler above the units. States still make treaties and often keep them; they just cannot be certain a treaty will be kept, and no one can be forced to keep it except by other states. Kenneth Waltz's Theory of International Politics (1979) turned this into a full theory: leave aside what particular leaders or governments are like, and look only at the structure: anarchy, plus the way power is spread among states. Even without any villain, he argued, that structure pushes states to look after their own security. The older tradition, from Thucydides' account of the Peloponnesian War (fifth century BCE) through Hans Morgenthau's Politics Among Nations (1948), said much the same thing in different terms: the strong do what they can, and the weak accept what they must. Other traditions noticed the same problem long before. The Arthashastra, a text of statecraft from India attributed to Kautilya (its dating is debated, but it belongs to the centuries around the turn of the era), describes a "circle of states" in which a neighbour is a natural enemy and a neighbour's neighbour a natural friend. The Chinese Warring States period (475 to 221 BCE) produced similar thinking. IR theory was largely written in Europe and North America after 1945, and scholars such as Amitav Acharya and Barry Buzan have argued since 2010 for a more global account that draws on these older sources too. The security dilemma: fear without villains John Herz named it in 1950, and Herbert Butterfield described the same trap at about the same time: steps a state takes to make itself safer make others less safe, so they respond, and the first state ends up less safe than before. Robert Jervis's 1978 article "Cooperation Under the Security Dilemma" showed that how bad it gets depends on technology: if offensive and defensive weapons can be told apart, and defence has the advantage, states can protect themselves without threatening anyone. If offence has the advantage, whoever moves first wins, and every state is tempted to strike or to arm. A game theorist reads that as a payoff table, and you can see the point on a small model. Two states each choose to Hold or Arm. If both hold, each gets 3. If both arm, each gets 1 (costly and no safer). If one arms and the other holds, the armer gets 2 + 1.5o and the holder gets 3 − 4.5o, where o is the offence advantage from 0 to 1. Slide o and watch which game appears. Interactive · How offence advantage changes the game Offence advantage (0 = defence wins, 1 = offence wins) Payoffs are illustrative, symmetric, and written as (row, column). Equilibria are computed by checking every cell for a profitable deviation. Three worlds appear. When defence is strong (o below about 0.44), arming does not pay, and Hold is the best reply to everything: safe peace. In the middle band (from about 0.44 to two-thirds) there are two stable outcomes, mutual holding and mutual arming, and which one you get depends on whether each side trusts the other. This is the "stag hunt" that Rousseau described. Above two-thirds, arming is best whatever the other does, and both end up in the worse outcome: the prisoner's dilemma. That is the security dilemma at its sharpest. Historians still argue how much of the naval race between Britain and Germany before 1914 was this spiral and how much was German ambition (the Fischer debate), which is a reminder that the model describes one pattern, not every war. Four lenses: realism, liberalism, constructivism and the critics Schools of IR are best understood as answers to one question: what drives state behaviour? Here is each in its strongest form. Realism. Power and security drive states. Defensive realists such as Waltz say states should seek enough power to be safe; offensive realists such as John Mearsheimer (The Tragedy of Great Power Politics, 2001) say anarchy pushes great powers to maximise power because they can never be sure of others' intentions. Cooperation is possible but fragile. Liberalism. Interests are not fixed by anarchy, and three things change them: trade and interdependence, international institutions, and the kind of government a state has. Its roots go back to Kant's Perpetual Peace (1795); a modern version is Robert Keohane's After Hegemony (1984), which argues that institutions lower the cost of making and checking agreements. Constructivism. Ideas and identities are part of the structure. Alexander Wendt's 1992 article "Anarchy Is What States Make of It" argues anarchy does not by itself force self-help: if states see each other as friends, rivals or enemies, they behave accordingly, and these roles are built by practice and can change. The same number of missiles means different things in the hands of a friend and of an enemy. Critical theories. Marxist and dependency approaches (Immanuel Wallerstein, Robert Cox) say the key units are classes and the capitalist world economy, not only states. Feminist IR (Cynthia Enloe, J. Ann Tickner) asks whose security is being counted and how gender shapes who counts as a "state actor". Postcolonial writers ask why the field's founding stories are mostly European ones. These are not four teams in a league table. Scholars blend them, and a single event can be read through each. Pick one and compare. Interactive · One event, four lenses A neighbour doubles its army Two rivals trade heavily A new international court is set up Realism Liberalism Constructivism Critical Deterrence: making a threat believable Deterrence is persuading someone not to do something by threatening a cost that outweighs the gain. Thomas Schelling's The Strategy of Conflict (1960) and Arms and Influence (1966) made it a core part of the field. The point most people miss is that deterrence is not about how big the threat is. It is about whether the other side believes you will carry it out. A threat you would regret carrying out is a weak threat. Put numbers on it. An attacker gains G if the attack goes unanswered, and pays a cost C if the defender retaliates. The attacker thinks the defender will retaliate with probability p. The attacker's expected value is (1 − p)G − pC. It is deterred when that is below zero, i.e. when p exceeds G ÷ (G + C). With G = 10 and C = 30, the defender only needs to be believed 25% of the time. Interactive · How believable must the threat be? Attacker's gain if unanswered (G) Cost to attacker if retaliated against (C) Attacker's belief that the defender will retaliate (p) This is why bigger is not always better. A threat so terrible that carrying it out would also destroy the defender is a bluff everyone can see through, which is why states build things that make the threat credible: tripwire troops that guarantee a response, treaty commitments, second-strike forces that survive a first blow. Cold War strategists called the mutual version "mutual assured destruction". It is a standing argument among scholars how much nuclear deterrence in fact kept the peace between 1945 and 1991 and how much was luck: the Cuban missile crisis of October 1962 is cited by both sides of that argument. Why costly wars still happen: the bargaining puzzle War destroys wealth and lives. If both sides can foresee that, they should be able to agree on a deal that gives each what it would have won, minus the costs. James Fearon (1995) called this the puzzle: war should be avoidable, yet it happens. His answer: it takes one of a few conditions to break the bargain: private information and the incentive to bluff, commitment problems (a rising power cannot promise not to exploit its strength later), or issues that cannot be divided. The model is tiny. Say the prize is worth 1. State A thinks it would win with probability pA; State B thinks A would win with probability qB. War costs A cA and B cB. A will accept a deal giving it at least pA − cA; B will offer up to qB + cB. A deal exists unless pA − qB is greater than the combined costs. Interactive · Is there a deal? Agree on odds, ordinary costs Both think they would win Same, but trade makes war costly A's belief that A wins (%) B's belief that A wins (%) War cost to A (% of prize) War cost to B (% of prize) This also frames the old claim that trade prevents war. Norman Angell argued in The Great Illusion (1910) that war could not pay for modern industrial economies; war came in 1914 anyway. Liberals reply that trade raises the cost of war (the sliders' third preset) and defenders of the claim point to later evidence. Realists answer that interdependence also creates dependence that states try to escape or use as a weapon (Albert Hirschman, 1945), and that what matters is whether leaders expect a short, cheap war. Institutions are the second liberal answer: the UN (1945), NATO (1949), the EU's predecessors (1951 and 1957) and the WTO (1995) can share information and make commitments harder to break. Mearsheimer's 1994 article "The False Promise of International Institutions" replies that institutions mostly reflect the interests of powerful states. Both sides cite evidence, and the debate is not settled. The democratic-peace debate One of the most studied patterns in the field is that democracies hardly ever fight each other. The idea goes back to Kant (1795); the modern statistical case starts with Melvin Small and J. David Singer in 1976 and Michael Doyle's 1983 articles, and was strengthened by Maoz and Russett in 1993. Notice what it claims and does not claim: dyadic peace (between two democracies), not that democracies are peaceful in general. Democracies fight non-democracies, and have fought many wars of conquest and colonial wars. Why might it be true? Two main explanations. Institutions: leaders who answer to voters pay a price for failure, so they pick fights more carefully and are more transparent. Norms: democracies expect each other to settle disputes by compromise. Scholars like Sebastian Rosato (2003) reject both, arguing the mechanisms do not work as claimed. Others say the pattern comes from something else: shared alliances during the Cold War, trade, or US dominance. A "territorial peace" argument reverses the arrow: settled borders produce both peace and democracy. A statistical point decides how much weight to give the claim. The absence of war between two groups means little if there were few pairs to fight. Try it: of the 179 countries V-Dem counted for 2025, it classified 87 as democracies. How many country pairs are democracy-democracy? And if wars fell on pairs at random, how likely is it that none hit a democratic pair? Interactive · The base-rate test Democracies among 179 countries Number of wars (invented, for the demo) Simplified: wars land on pairs at random, which real wars do not (neighbours fight more). Democracy count for 2025 from the V-Dem Democracy Report 2026, as reported; other indices count differently, and long ago there were far fewer democracies. At 87 democracies, about a quarter of all pairs are democratic, so 30 random wars would all miss them only about once in thousands of trials. With 5 democracies it would happen about 98% of the time. So the finding is informative only for the periods when democracies were numerous, and critics point out that the post-1945 sample is small and entangled with the Cold War. Supporters reply that controlling for contiguity, alliances, wealth and trade still leaves the effect. The status of the claim: widely accepted as a pattern, still disputed as a cause. What a graduate just sees Between states there is no police. Many outcomes that look like malice are fear plus uncertainty. The security dilemma is worst when offence has the advantage and when attack and defence look alike. A threat works by being believed, not by being big. Ask what makes it credible. If war is costly and both sides know it, a deal usually exists. When war comes, ask which condition broke the deal: bluffing, a commitment problem or optimism. Every big theory is a lens. Realism, liberalism, constructivism and the critics each highlight different things, and a single crisis needs more than one. "Democracies don't fight each other" is a claim about pairs, and its force depends on how many pairs there were. Interdependence can restrain states or be used as a lever against them. Both happen. Much of the standard theory grew in a few countries. Check whose history it is drawn from. Where it shows up next International Law & Human Rights (35/10): the rules states make for themselves, and the puzzle of why they are mostly obeyed without enforcement. Political Economy (35/17): trade, sanctions and finance as tools of statecraft and sources of dependence. Game Theory (34/45): the stag hunt, prisoner's dilemma and bargaining models used here come straight from it. In the world: foreign ministries, intelligence analysis, defence and arms control, the UN and regional bodies (African Union, ASEAN, OAS, EU), journalists and think tanks, and every business that must price geopolitical risk. If you want to go deeper 📘 Textbook. Jeffry Frieden, David Lake and Kenneth Schultz, World Politics: Interests, Interactions, Institutions (W. W. Norton). Built around bargaining and incentives, and the required text for the MIT course below. 📘 Textbook. John Baylis, Steve Smith and Patricia Owens, The Globalization of World Politics (Oxford University Press, many editions). Strong on the full range of theories, including critical ones. 📘 Gentler. John Mearsheimer, The Tragedy of Great Power Politics (W. W. Norton, 2001; updated edition 2014), for the realist view in its starkest form, and Joseph Nye, Soft Power (PublicAffairs, 2004), for a liberal counterweight. Both are written for general readers. 🌐 Free course. MIT OpenCourseWare 17.41 Introduction to International Relations (Spring 2023, Richard Nielsen): causes of conflict and cooperation, crisis bargaining, nuclear strategy, economic relations, law and human rights. The takeaway With no ruler above the states, safety has to be built by the states themselves, and the steps one takes can look like threats to another. Realists stress power, liberals trade, institutions and regimes, constructivists ideas and identity, and critics ask whose order this is. A threat deters only if it is believed, wars happen when bargaining breaks down, and the democratic peace is a real pattern whose cause is still argued. Try this: next time a government says a military step is "purely defensive", write down how it looks from the other side of the border, then ask what each lens would say. If the two answers differ, you have found the security dilemma. Sources & Further Reading Sources. Waltz, Theory of International Politics (1979); Morgenthau, Politics Among Nations (1948); Herz (1950) and Butterfield (1951) on the security dilemma; Jervis, "Cooperation Under the Security Dilemma", World Politics 30(2), 1978; Mearsheimer, The Tragedy of Great Power Politics (Norton, 2001; updated edition 2014); Kant, Perpetual Peace (1795); Keohane, After Hegemony (1984); Wendt, "Anarchy Is What States Make of It", International Organization 46(2), 1992; Schelling, The Strategy of Conflict (1960); Fearon, "Rationalist Explanations for War", International Organization 49(3), 1995; Angell, The Great Illusion (1910); Hirschman, National Power and the Structure of Foreign Trade (1945); Mearsheimer, "The False Promise of International Institutions", International Security 19(3), 1994/95; Small and Singer (1976), Doyle (1983), Maoz and Russett (1993) and Rosato (2003) on the democratic peace; Acharya and Buzan, Non-Western International Relations Theory (2010). Democracy count: V-Dem Democracy Report 2026 (87 democracies out of 179 countries, 2025), as reported in coverage of the report. Course contents: MIT OCW 17.41 syllabus. Every number an interactive shows was computed in node: equilibrium bands at offence advantage 4/9 and 2/3; deterrence threshold G/(G+C) = 25% at G = 10, C = 30; bargaining ranges; C(87,2) ÷ C(179,2) = 23.5%; (1 − 0.235)30 = 0.03%. 🌐 MIT OCW 17.41 Introduction to International Relations 📘 Frieden, Lake & Schultz, World Politics 📘 Mearsheimer, The Tragedy of Great Power Politics 📘 Baylis, Smith & Owens, The Globalization of World Politics Step 8. Econometrics: How to Find What Caused What -------------------------------------------------- Why: Regression and causal inference. Correlation is cheap. Econometrics is the craft of getting from "these move together" to "this made that happen", and of knowing how badly you could be fooled on the way. A city opens a new tram line, and a year later house prices along it have risen. Did the tram do it? Maybe. Or maybe the city built the line where it already expected the area to boom. Or a new university opened nearby the same year. Every headline that says "X is linked to Y" hides this question, and most of the time the headline cannot answer it. An economist sees the same pattern in a minimum-wage law, a school-class size, a tax change, a drug trial. The data show a link. The job is to say what the link would have been if nothing else had been going on. That is econometrics: statistics built for questions where you usually cannot run the experiment, and where the people in your data chose their own circumstances. The course in one box Who takes itEconomics majors, usually in year 2 or 3, straight after a first statistics course. Public policy, political science, finance and business-analytics programmes teach close cousins. Programmes such as MIT's Course 14-1, the LSE and Chicago all require it. What it is forEstimating the effect of one thing on another from messy real-world data, with an honest statement of how wrong the estimate might be. Comes afterProbability & Statistics (34/05). Leads toDevelopment Economics (34/48), and it is the working tool in Behavioral Economics (34/46), Marketing Research & Analytics (34/59) and Machine Learning (34/32). What this piece gives youYou will not be able to run a regression package after this. You will see why a coefficient can lie, what a clever study does to stop it lying, and why a "significant" result is weaker evidence than it sounds. It goes further than Probability & Statistics (34/05), whose last section stops at "correlation is not causation"; here is what to do about it. The library's How to Spot Bullshit (17/03) is a gentler start. All datasets here are invented and labelled illustrative. Regression is the best-fitting line Take twelve students: hours studied, and the score each got. Plot them and the dots drift upward. A regression draws the one straight line that sits closest to all the dots. "Closest" has a precise meaning: for each dot, measure the vertical gap to the line (the residual), square it, and add up the squares. The line with the smallest total is the ordinary least squares (OLS) line. Squaring means one big miss counts far more than several small ones; the picture shows each squared miss as a literal square. score = a + b × hours + errorb is the slope: the score gap between two students who differ by one hour Interactive · Fit the line yourself (illustrative data) Slope b (points per hour) Intercept a (score at 0 hours) Snap to the best line Twelve invented students. The best line is found by solving the least-squares equations; no other line has a smaller sum of squared misses. The best line here is about 47 + 3.5 × hours (the readout gives it exactly). The coefficient b says: students who studied an hour more scored about 3.5 points higher. Notice what that sentence does not say. It compares different students. It does not say that making one student study an extra hour would raise her score by 3.5. That gap, between a comparison and a cause, is the rest of the course. Omitted variables: add the confounder, watch the effect change Suppose you regress wages on years of schooling in a big survey and find that each extra year goes with higher pay. Is that what schooling does? People with more drive or talent may both stay in school longer and earn more anyway. If so, the schooling coefficient is also soaking up the talent effect. This is omitted-variable bias, and it has a tidy rule: bias ≈ (effect of the omitted factor on the outcome) × (how strongly it moves with your variable)if either is zero, there is no bias In the lab below, the true effect of one more year of schooling is exactly 3 (thousand dollars). Talent also raises pay, and a dial sets how strongly talent pushes people toward more school. Switch the model between ignoring talent and including it. Interactive · The hidden confounder (illustrative simulation, 1,500 people) How strongly talent pushes people to stay in school Ignore talent Control for talent Randomise schooling above-average talent   below-average talent True schooling effect = 3.0; talent adds 8 per standard deviation. Wage = 10 + 3·school + 8·talent + noise. "Randomise" means a coin-flip-like process assigns schooling regardless of talent. Without JavaScript: with talent pushing schooling at strength 1.2, ignoring talent gives a schooling coefficient near 4.7 instead of the true 3.0; controlling for talent recovers about 3.0; randomising schooling also recovers about 3.0. Ignoring talent at strength 1.2 gives about 4.7, over half again too large. Control for talent and it falls to about 3. The cost of this fix: you must measure the confounder. Most real confounders (motivation, family connections, how well-run the firm is) sit in no dataset. That is why economists spent decades arguing about what regressions could prove. Randomise: the coin that cuts every hidden link Press the third button. When schooling is assigned by a process unrelated to talent, the link between them is broken, and even the naive regression that ignores talent lands on 3. A coin flip is independent of everything: motivation, family, luck, things nobody thought to measure. That is why a randomised experiment is the gold standard, and why 2019's Nobel prize in economics went to Abhijit Banerjee, Esther Duflo and Michael Kremer for experimental approaches to reducing poverty. But economists rarely get to flip the coin: you cannot randomly assign years of schooling, a recession or a minimum wage. The next three ideas are ways to find a coin that nature or a rule has already flipped. Together they are called the credibility revolution, a phrase from Joshua Angrist and Jörn-Steffen Pischke's 2010 essay, answering Edward Leamer's 1983 complaint that econometrics needed to "take the con out" of itself. Difference-in-differences: borrow the other group's trend In 1992 New Jersey raised its minimum wage and neighbouring Pennsylvania did not. David Card and Alan Krueger surveyed fast-food restaurants in both states before and after (American Economic Review, 1994). Looking only at New Jersey before and after would confuse the law with anything else changing that year. Instead they used Pennsylvania as the stand-in for "what would have happened anyway". Their finding, that employment did not fall in New Jersey, contradicted the textbook prediction at the time. It has been disputed ever since: Neumark and Wascher re-analysed it with payroll records and found job losses, and the field's overall reading is still argued over. The design is not in dispute. Card shared the 2021 Nobel prize in part for it. effect = (treated after − treated before) − (comparison after − comparison before)subtract the comparison group's change to remove the common trend Interactive · Difference-in-differences (illustrative numbers) Common trend (hits both groups) True effect of the policy Treated group was already drifting differently Treated group starts at 20, comparison at 24 (invented units). Dashed line: where the treated group would have gone without the policy, if it followed the comparison's trend. With a common trend of +1.5 and no true effect, the treated group still rises by 1.5, so a naive "before and after" would claim a policy success. The difference-in-differences correctly says zero. The design's weak point is the third slider: it assumes the two groups would have moved in parallel without the policy. If the treated group was already on a different path, the estimate is off by exactly that gap. Good papers show the two lines tracking each other for years before the policy. Instruments and cutoffs: two more ways to find a coin An instrumental variable is something that nudges the thing you care about but has no other route to the outcome. Angrist (1990) studied whether military service in the Vietnam era changed later earnings. Men who chose to serve differ from those who did not, so a plain comparison is confounded. But the draft lottery assigned numbers by birthday at random, and a low number made service much more likely. The lottery number is a coin that pushes people into uniform. Its effect on earnings, divided by its effect on service, isolates the effect of service for the people who served because of the lottery. His study found that service lowered earnings for the veterans concerned. effect of service = (earnings gap by lottery group) ÷ (service gap by lottery group)the "Wald" ratio: reduced form over first stage A worked number, invented to show the arithmetic: suppose 35% of low-number men served and 20% of high-number men, a first stage of 0.15; and low-number men earned $300 less a year on average. Then the estimated effect of serving is −300 ÷ 0.15 = −$2,000 a year. Dividing by a small first stage blows up noise, which is why weak instruments are dangerous. A regression discontinuity uses a cut-off. Israeli school classes are capped at 40 pupils under a rule attributed to the twelfth-century scholar Maimonides, so a school with 40 pupils has one class and a school with 41 splits into two small classes of about 20. Angrist and Lavy (1999) compared schools just either side of those thresholds, where enrolment is close to a coin flip, to estimate what class size does to test scores. Slide the window below: narrow windows are fairer but noisier. Interactive · Regression discontinuity (illustrative data) Window around the cut-off (± points) True jump at the cut-off 400 invented students; those scoring 60 or more get a support programme. Outcome rises 0.3 per score point anyway, so just comparing everyone above with everyone below overstates the jump. Standard errors: how much would the answer wobble? Every estimate comes from one sample, and a different sample would have given a different number. The standard error is the typical size of that wobble. For a regression slope it shrinks with the square root of the sample size, grows with the noise around the line, and shrinks when x varies a lot: SE(b) ≈ (noise around the line) ÷ ( spread of x × √n )four times the data halves the wobble The rule of thumb: an estimate more than about two standard errors from zero is called statistically significant at the 5% level. Significant means "hard to produce by sampling wobble alone if the true effect were zero". It does not mean big or important, and it does not mean true. Run 300 studies of a real but modest effect below and see who gets stars. Interactive · Run 300 studies (illustrative simulation) Sample size per study True slope Run 300 new studies significant at 5%   not significant At n = 50 and a true slope of 0.3, a little over half of the studies reach significance, and those average about 0.40, a third above the truth, because only the lucky overestimates clear the bar. Drop to n = 10 and a true slope of 0.05 and the few stars (about 8%) average roughly 0.7, fourteen times too big. Published results are mostly the significant ones, so they exaggerate. That is one reason newer replications so often find smaller effects. P-hacking: test enough things and something will glow A test at the 5% level is wrong by chance 1 time in 20 when nothing is going on. Test 20 unrelated outcomes and the chance that at least one glows is 1 − 0.9520 = 64%. A researcher who tries several outcomes, subgroups or control sets and reports the one that "worked" is p-hacking, whether on purpose or just by wandering a garden of forking paths. Simmons, Nelson and Simonsohn (2011) showed how easy it is: using flexible, ordinary-looking choices they "found" that listening to a song about being older made students younger by about a year and a half. Economists are not exempt: Brodeur and co-authors (2016) found an unnatural pile-up of results just below the 0.05 line in top economics journals. Interactive · A study of nothing (illustrative simulation) Outcomes you test (all truly unaffected) Run one studyRun 500 studies Each outcome compares two groups of 50 drawn from the same distribution, so every "effect" is noise. A cell lights up when |t| passes the 5% cut-off (1.98). Defences: decide the analysis before seeing the data (pre-registration), report every test you ran, adjust the threshold when you run many, and above all prefer designs, like the ones above, where the answer does not depend on which controls you picked. What a graduate just sees A regression coefficient is a comparison between different people, not a lever you can pull. Before believing "X raises Y", ask what else moves with X and also moves Y. The bias is its effect times its link to X. Adding controls can only fix confounders you measured. A coin flip fixes the ones you did not. Every natural-experiment study is a hunt for a coin: a lottery, a border, a cut-off, a law that changed for some and not others. Difference-in-differences needs parallel trends; ask to see the years before the policy. "Significant" means hard to get from wobble alone. It says nothing about size, and a small-sample star probably overstates. If you can run twenty tests, one will light up. Ask how many were run. Where it shows up next Development Economics (34/48): randomised trials and natural experiments are how economists test what actually helps poor countries. Behavioral Economics (34/46): the replication record in that piece depends on this one, from effect sizes to p-hacking. Marketing Research & Analytics (34/59): "did the campaign cause the sales, or did summer?" is difference-in-differences. Machine Learning (34/32): prediction asks "what will happen"; econometrics asks "what would happen if I changed it". They need each other. In the world: central banks and tax agencies, tech companies' A/B test teams, public-health trials, and every think-tank report that says "causes". If you want to go deeper 📘 Textbook. James Stock and Mark Watson, Introduction to Econometrics (Pearson). A standard undergraduate text, strong on causal thinking and applications. 📘 Textbook. Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach (Cengage). The other standard choice, more algebra and more worked data. 📘 Gentler. Joshua Angrist and Jörn-Steffen Pischke, Mastering 'Metrics: The Path from Cause to Effect (Princeton, 2015). Written for people who are not specialists, built around exactly the designs above. 🌐 Free course. MIT OpenCourseWare 14.32 Econometrics (Spring 2007, Joshua Angrist): undergraduate course with lecture notes and problem sets. The takeaway A regression gives you a comparison, and a comparison is only a cause if nothing else differs between the groups. Econometrics is a toolkit for making that true or nearly true (randomise, difference out a trend, use a lottery, zoom in on a cut-off), plus the humility to report how much your number would wobble and how many ways you tried. Try this: the next time a headline says "X linked to Y", name one confounder that could drive both, and ask what coin flip, border or cut-off could separate them. Sources & Further Reading Sources. Least squares, omitted-variable bias, standard errors and the Wald estimator are standard textbook material (Stock & Watson; Wooldridge; Angrist & Pischke). Card & Krueger, "Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania," American Economic Review 84(4), 1994; the Nobel Prize in Economic Sciences 2021 went to David Card (half) and Joshua Angrist and Guido Imbens (half, "for their methodological contributions to the analysis of causal relationships"). Angrist, "Lifetime Earnings and the Vietnam Era Draft Lottery," 1990; Angrist & Lavy, "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement," 1999; regression discontinuity was introduced by Thistlethwaite & Campbell in 1960. Angrist & Pischke, "The Credibility Revolution in Empirical Economics," Journal of Economic Perspectives 2010; Leamer, "Let's Take the Con out of Econometrics," 1983. Simmons, Nelson & Simonsohn, "False-Positive Psychology," 2011; Brodeur, Lé, Sangnier & Zylberberg, "Star Wars: The Empirics Strike Back," 2016. The 2019 economics Nobel went to Banerjee, Duflo and Kremer. Every number an interactive shows was computed in node from the code on this page: the best line is 47.0 + 3.53 × hours; omitted-variable bias at strength 1.2 is 8 × 1.2 ÷ (1.2² + 4) = 1.76 in theory (1.67 in this sample); 1 − 0.9520 = 64.2%. All datasets are invented. 🌐 MIT OCW 14.32 Econometrics 📘 Angrist & Pischke, Mastering 'Metrics 📘 Stock & Watson, Introduction to Econometrics Step 9. Constitutional Law: The Rules for the People Who Make the Rules ----------------------------------------------------------------------- Why: Constitutions and the courts that guard them. Every country has to decide who may tell the government "no", and what happens when a parliament passes a law that breaks the highest law. The American, European and Latin American answers are different inventions, and each one fits the history that produced it. A legislature passes a law. A citizen says it violates her rights. Who decides? The legislature itself has an obvious interest in saying no. The executive, who enforces the law, has an interest too. So societies have invented an outsider: a judge, or a special court, whose job is to say that the law, though properly passed, is void. Where that power sits, who may invoke it, and what a ruling does afterwards are the main differences between constitutional systems. In the United States any court can refuse to apply an unconstitutional law. In Germany, only one court can strike it down. In Mexico, a man can go to a federal judge in person and ask for protection, called an amparo, but the ruling will traditionally protect him alone. These are three solutions to the same problem. This piece describes how constitutional systems work and is not legal advice; constitutions and court rules differ by country and change, and some systems (Mexico's judiciary, for one) have been reformed recently, so check current sources. The course in one box Who takes itLaw students in year 2 (the US first-year or second-year core; a core course in Europe and Latin America), plus political-science students. Constitutional law is also taught as "public law" or "derecho constitucional", and comparative constitutional law is an elective in many programmes. What it is forUnderstanding the highest rules in a country: how power is organised and limited, what rights are protected, and who has the last word. Comes afterLegal Systems of the World (35/01). Leads toCriminal Law & Procedure (35/07), Administrative Law (35/09), International Law & Human Rights (35/10) and European Union Law (35/11). It is also a core course in Political Science (see Comparative Politics, 35/13). What this piece gives youYou will not be able to brief a constitutional case. You will see the main designs for judicial review, why a Mexican amparo and a German constitutional complaint work differently, and what an amendment rule says about a country. Easier first reads: Rights, Constitutions & the Rule of Law (18/04) and How a Law Gets Made (18/07). Why a constitution: rules about the rule-makers A constitution does two jobs. It organises power (who makes laws, who enforces them, how courts are chosen, how regions fit in) and it limits power (rights that even a majority cannot simply vote away). Those two jobs require one more feature: it must be harder to change than an ordinary law. A constitution that parliament could rewrite by a simple vote would limit nothing. That is why every serious constitution has special rules for its own amendment, which we return to at the end. Constitutions come in many sizes. The US Constitution of 1789 has seven articles and 27 amendments. Brazil's 1988 Constitution is a large document that goes into detail on social rights. Mexico's 1917 Constitution, born of the Revolution, was one of the first to include social rights such as labour protections. The United Kingdom has no single codified constitution; its fundamental rules are spread across statutes, court decisions and conventions, and Parliament is sovereign. The question "who has the last word?" gets a different answer in each. Judicial review: who decides when a law breaks the constitution? The American story starts with Marbury v. Madison, decided on 24 February 1803 (5 U.S. (1 Cranch) 137). Chief Justice John Marshall, writing for the Court, held that a provision of the Judiciary Act of 1789 gave the Supreme Court a power the Constitution did not, and that a law contradicting the Constitution could not stand. His reason, in one line: "It is emphatically the province and duty of the judicial department to say what the law is." The Constitution does not say in terms that courts may strike down laws; Marshall derived it from the Constitution's status as supreme law, an argument that Alexander Hamilton had already made in Federalist 78. The result is a diffuse model: any court, deciding any ordinary lawsuit, can refuse to apply a statute that violates the Constitution, and the Supreme Court has the last word. Europe took a different path. The Austrian jurist Hans Kelsen argued that having every judge decide constitutional questions would produce chaos and that the job needed one special court. Austria's 1920 constitution created such a court, the first in a form Kelsen designed; Czechoslovakia's 1920 constitution also provided for one, which first sat in 1921. After the Second World War, Germany (Basic Law of 1949, with the Federal Constitutional Court beginning work in 1951), Italy, and later Spain and Portugal built their own. Under this concentrated model, ordinary judges cannot strike down a statute; if they think one is unconstitutional, they stop the case and refer the question up. When the constitutional court annuls a law, it is gone for everyone. Kelsen called the court a "negative legislator". Many countries mix the two. Brazil lets any judge set aside an unconstitutional law in a case, while the Supreme Federal Court also hears direct challenges whose results bind everyone. Colombia's Constitutional Court (created by the 1991 constitution) hears abstract challenges and reviews rights decisions from lower courts. France, which long distrusted judges overruling parliament, created the Conseil constitutionnel in 1958 to check bills before promulgation, and since 2010 lets litigants raise constitutional questions about laws already in force. The United Kingdom keeps parliamentary sovereignty: courts can declare a statute incompatible with human rights (Human Rights Act 1998), but the statute remains valid until Parliament changes it. Interactive · A law is challenged. What happens? Simplified summaries of each system's main route. Each country has several, with deadlines and conditions. Mexico's judicial institutions were restructured by a 2024 constitutional reform; check current rules. Amparo: rights you can take to court yourself Latin America's characteristic invention is a fast, simple court action for someone whose constitutional rights are being violated. It began in Mexico. Manuel Crescencio Rejón designed the first version in the 1841 constitution of the state of Yucatán, and Mariano Otero carried it to the federal level in the Acta de Reformas of 1847. It was repeated in the federal constitutions of 1857 and 1917 (article 107 in the current one). Otero's rule, the Otero formula, says an amparo ruling protects only the person who asked for it. The law may remain on the books, and a neighbour whom it also hurts has to file her own case. The idea spread. Argentina's Supreme Court recognised amparo by decision in the Siri case (1957), followed the next year by Kot; a statute followed in 1966, and the 1994 constitutional reform wrote it into article 43. Colombia's 1991 constitution created the tutela (article 86), a rapid action that any person can bring before any judge. Chile has a recurso de protección for rights violations. Spain and Germany reached a similar place from Kelsen's direction: Spain's recurso de amparo and Germany's Verfassungsbeschwerde let individuals bring complaints to the constitutional court after other courts have ruled. The United States has no stand-alone equivalent; individuals raise constitutional claims inside ordinary lawsuits, in criminal defences, in habeas corpus petitions, or in civil-rights suits against officials. Compare the effect of a ruling. Under the Otero formula, 100 people may be hurt by a law, but if only 20 sue, 20 are protected. Under Kelsen's model, once the constitutional court annuls the law, everyone is protected. Mexico's 2011 reforms opened a route to a general declaration of unconstitutionality after repeated rulings, with a demanding voting threshold, so the formula is no longer absolute. The trade-off is real: relative effects respect the legislature and the individual case, but leave people unprotected unless they can afford to sue; general effects protect everyone but give a court the power to remove a law. Interactive · One bad law, a crowd of people Relative effect (Otero formula) General effect (law annulled) People the law affects Share who file a case in time Arithmetic on invented numbers to show the structure. Real systems have filing deadlines, class and collective actions, and government practice of following rulings. Rights and their limits: tiers of scrutiny and proportionality No right is absolute. Free speech yields to defamation law; property to taxation. So the question in a rights case is always: when is a restriction justified? Systems have developed two main structures for answering. US courts apply tiers of scrutiny. The more important the right (or the more suspect the classification), the harder the government's job. Under rational-basis review, a law survives if it is rationally related to a legitimate aim. Under intermediate scrutiny, the aim must be important and the means substantially related to it. Under strict scrutiny, the aim must be compelling and the law narrowly tailored, which in practice means no less restrictive way would do. The famous footnote 4 of United States v. Carolene Products (1938) is usually credited with hinting that laws burdening certain rights and minorities deserve closer inspection. Germany, and many courts influenced by it (including Canada's, after R. v. Oakes in 1986, and the European Court of Human Rights), use proportionality. The court asks four questions in order: is the aim legitimate? is the measure suitable to achieve it? is it necessary, meaning is there a less restrictive way that would work as well? and is it proportionate in the strict sense, meaning do the benefits outweigh the harm to the right? The German Federal Constitutional Court's Lüth decision (1958) is a classic starting point for the idea that rights are not only shields against the state but also values that shape the whole legal order. Proportionality has a built-in balance; tiers have a built-in sorting step. Critics of proportionality say it lets judges substitute their own weighting for the legislature's; critics of tiers say the sorting is arbitrary and hides the real weighing. Both camps are serious. Interactive · Test a restriction two ways Scenario (invented): a city bans all marches on its main road at rush hour. Set how strong the city's reason is, how much it cuts into the right, and whether a lighter option (such as lane closures) would do. Political speech Commercial advertising Business licensing Strength of the city's reason (1 to 5) Severity of the restriction (1 to 5) A lighter alternative exists No lighter alternative A toy model of the structure of each test, with invented thresholds; it does not predict any court's result. Splitting power: separation and federalism Constitutions also fight tyranny by dividing power. Montesquieu's The Spirit of the Laws (1748) argued that liberty is safest when the powers to make, enforce and judge laws are held by different hands. James Madison, in Federalist 51 (1788), turned it into a design principle: "Ambition must be made to counteract ambition." Different bodies, with different interests, check each other. Constitutions arrange the pieces differently. In a presidential system, an elected president and the legislature have separate mandates and can end up in opposition; the US, Mexico, Argentina, Brazil, Chile and Colombia are examples. In a parliamentary system (Germany, Spain), the government depends on the confidence of the legislature, so there is less separation but fewer deadlocks. France is semi-presidential. And power is divided vertically, too: federal states (the US, Mexico, Argentina, Brazil, Germany) give regions constitutional powers that the centre cannot simply take away; unitary states (France, Chile, Colombia) delegate power downwards. Spain sits between, with autonomous communities. The US presidential veto shows the mechanics. The Constitution (Article I, section 7) lets the president veto a bill; Congress can override with a two-thirds vote in each chamber. Notice that the 60-vote threshold that often decides what passes in the Senate is not in the Constitution at all. It is a Senate rule, which the Senate can change. Interactive · Can the opposition get its bill past a veto? Share of seats held by the president's opponents (both chambers) US Congress sizes: House 435 seats, Senate 100. Assumes every member votes the party line and all are present; real votes are messier, and a 50-50 Senate is broken by the vice-president. Changing the constitution: how hard should it be? The amendment rule is a constitution's way of describing how much it trusts the present. If it is too easy to change, the document protects nobody. If it is too hard, it freezes the arrangements of a past generation and pressure builds up elsewhere, often in judicial interpretation. The US Constitution requires two-thirds of both houses of Congress (or a convention called by two-thirds of the states), then ratification by three-quarters of the states: 38 of 50. Only 27 amendments have passed since 1789, the first ten in 1791. Germany needs two-thirds of the Bundestag and two-thirds of the Bundesrat votes (Basic Law, Article 79(2)); and Article 79(3) puts some principles, such as human dignity and the federal, democratic and social character of the state, beyond amendment altogether. Brazil requires three-fifths of the members of each house in two rounds of voting (Article 60), and also protects a core of "stone clauses" from change. Mexico requires two-thirds of the legislators present in Congress and then approval by a majority of the state legislatures (Article 135). Spain's ordinary route needs three-fifths of each chamber, and a stricter route with a referendum exists for the most basic provisions. Courts can also police the limits. India's Supreme Court held in Kesavananda Bharati (1973) that Parliament's power to amend does not extend to destroying the constitution's "basic structure". And when a country tries to replace its constitution altogether, it can fail: in Chile, voters rejected a proposed new constitution in a plebiscite on 4 September 2022, with 61.87% voting against. Interactive · Could this amendment pass? Legislators in favour Second body in favour Main route only; shares are of the body described (members, members present, or states, as the country's rule says). Some countries add referendums or other routes. Thresholds are examples for learning, not advice. What a graduate just sees Ask who has the last word on a constitutional question, and what their ruling does to the law afterwards. "Diffuse" (any judge) and "concentrated" (one court) are two designs for judicial review, and many countries mix them. An amparo-style remedy brings the constitution to the individual; the price is that relative effects leave the law standing for everyone else. A right is a claim plus a test for when it can be limited, and the test (tiers or proportionality) shapes outcomes. Separation of powers is built from veto points. Count them to predict how hard it is to change anything. An amendment rule shows how much a constitution trusts the present. Easy rules invite rewriting; hard rules invite creative interpretation. Rules of a legislature (like the 60-vote Senate threshold) are not the same as rules of the constitution. Where it shows up next Administrative Law (35/09): the constitutional limits on agencies, and the courts that check them. Criminal Law & Procedure (35/07): the rights of the accused are mostly constitutional rights. International Law & Human Rights (35/10) and European Union Law (35/11): when a regional court or treaty sits above the national constitution, or a constitution makes a treaty equal to it. Comparative Politics (35/13): how electoral rules and the executive-legislature relationship shape stability. In the world: every news story about a court striking down a law, a government refusing to obey a ruling, or a country drafting a new constitution. If you want to go deeper 📘 Textbook. Erwin Chemerinsky, Constitutional Law: Principles and Policies (Wolters Kluwer/Aspen; use the latest edition). A standard US law-school text. 📘 Textbook. Vicki C. Jackson and Mark Tushnet, Comparative Constitutional Law (Foundation Press). The standard comparative casebook, including Kelsenian courts and proportionality. 📘 Gentler. Akhil Reed Amar, America's Constitution: A Biography (Random House, 2005), a readable story of the US text; and Mark Tushnet, Advanced Introduction to Comparative Constitutional Law (Edward Elgar) for the wider world. 🌐 Free course. MIT OpenCourseWare 17.245 Constitutional Law: Structures of Power and Individual Rights (Spring 2013), covering judicial review, federalism and separation of powers through US cases. For the texts of the world's constitutions, the Constitute project publishes them in searchable form. The takeaway A constitution organises and limits power and is harder to change than ordinary law. Someone has to say when a law breaks it: any judge in the American model, one special court in Kelsen's, or a mix. Latin America added the amparo, which brings the constitution to the individual. And every design embodies a trade-off between the legislature's power, the individual's protection and the court's authority. Try this: find the amendment clause of a constitution you care about (the Constitute site has them) and work out how many people have to say yes. Then ask whether that country amends its constitution often or rarely. Sources & Further Reading Sources. Marbury v. Madison, 5 U.S. (1 Cranch) 137 (decided 24 February 1803), opinion by Chief Justice Marshall; the quoted sentence is from the opinion. Federalist 51 (1788) and 78. Kelsen and the Austrian and Czechoslovak courts: Wikipedia, "Constitutional court" (Czechoslovak constitution of 1920, court convened 1921; Austrian court; German Basic Law 1949). Amparo history (Rejón, Yucatán 1841; Otero, Acta de Reformas 1847; the Otero formula in the 1857 and 1917 constitutions): academic and Mexican congressional sources. Argentina's Siri (1957), Kot (1958), Law 16,986 (1966), article 43 (1994) and Colombia's tutela (article 86): Spanish Wikipedia, "Acción de amparo". German Basic Law Article 79(2) and (3): the Federal Ministry of Justice's English translation. Brazil Constitution Article 60 (three-fifths of each house, two rounds). Mexico Constitution Article 135 (two-thirds of those present and a majority of the state legislatures). Chile's 2022 plebiscite: 61.87% reject, 4 September 2022 (Wikipedia, citing the electoral service). Tiers of scrutiny, proportionality, Carolene Products (1938), Lüth (1958), R. v. Oakes (1986), Kesavananda Bharati (1973): standard constitutional-law texts. US congressional sizes and veto rules: Article I. This piece describes how constitutional systems work and is not legal advice. 📘 Jackson & Tushnet, Comparative Constitutional Law 📘 Amar, America's Constitution: A Biography 🌐 MIT OCW 17.245 Constitutional Law 🌐 Constitute: the world's constitutions Step 10. Public Policy: How Problems Become Policies, and What Then Goes Wrong ------------------------------------------------------------------------------ Why: How problems become policies. A city charges fifteen cents for a plastic bag and the bags nearly vanish. A famous job-training scheme is announced with fanfare and fizzles in a maze of approvals. Public policy is the study of why the first happens, why the second is more common, and how to tell which is which. In March 2002 Ireland began charging shoppers 15 cents for every plastic bag at the till. Reports at the time, from the government and the press, said use fell by about 90 percent within weeks. Notice what did not happen: no inspector visited homes, no one was jailed, and nobody forbade bags. A small price, collected by the shop, changed what millions of people did without thinking about it. Now picture the opposite. A national programme is announced to create jobs in a poor district. Years later the money has been spent on meetings and approvals, and almost no jobs exist. Nobody lied, nobody stole. Every office that had to agree did agree, slowly and partly. A good idea met a long chain of people who each had other priorities. Public policy as a subject asks four things about any such case. Why did this problem get attention when others did not? What tool did the government choose, and why that one? Did the plan survive contact with the people who had to carry it out? And did it work, which means: compared with what would have happened anyway? The course in one box Who takes itPolitical science and public-affairs majors, usually in year 3. Schools of public policy (the Kennedy School, LSE, Sciences Po, the Hertie School and others) teach the same core as the heart of a master's degree, mixed with economics and statistics. What it is forUnderstanding how a social problem becomes a law, a tax or a programme, and judging whether that policy worked. Comes afterComparative Politics (35/13) and Public Economics (34/49). Leads toNo later subject in the curriculum requires it. It sits beside Administrative Law (35/09), which asks how governments are held to the law, and Research Methods in Political Science (35/18), whose tools judge whether a policy worked. What this piece gives youYou will not be able to write a cost-benefit analysis. You will be able to read a news story about a new tax, ban or scheme and ask the five questions a policy analyst asks. Easier first reads: How a Law Gets Made (18/07), How Government Actually Works (18/01) and Where Public Money Goes (18/08). The policy cycle: a useful map that is not the territory Textbooks draw policy as a loop: a problem is noticed, put on the agenda, a solution is designed, a decision is made, the decision is carried out, and the results are evaluated, which sends you back to the start. The idea goes back to Harold Lasswell's 1956 account of the "decision process" in stages, and nearly every introduction to public policy still opens with some version of it. Its value is that it gives you questions to ask at each point. Its weakness is that real policy does not happen in order. Charles Lindblom argued in 1959 that officials do not survey all options and pick the best; they "muddle through", making small changes from where they already are. Solutions often exist before problems: a team has a programme in a drawer and waits for a crisis to attach it to. The cycle is a checklist, not a law of nature. Interactive · The cycle, walked through one real case The case is Ireland's 2002 plastic bag levy. The stages are the textbook ones; the case details are simplified. Sources differ on exact usage figures, so treat "about 90 percent" as the figure reported at the time. Without JavaScript: Ireland's 2002 levy moved from visible litter, to a design choice (a fee at the till rather than a ban), to implementation by shops, to a reported fall of about 90 percent in use, and a later increase of the fee after use crept up. Agenda setting: why some problems get attention and others wait Thousands of real problems exist at any time. Only a few get a minister's, a legislator's or the public's attention. John Kingdon, in Agendas, Alternatives, and Public Policies (1984), described three streams flowing separately. The problem stream: indicators, crises, a disaster on the news. The policy stream: ideas floated by experts and officials, most of which go nowhere. The politics stream: the national mood, elections, lobbying, a change of government. When the three meet, a "policy window" opens, and a policy entrepreneur who has a solution ready can push it through. Windows close again, often within months. A second account, by Frank Baumgartner and Bryan Jones in Agendas and Instability in American Politics (1993), says policy is mostly stable and then changes suddenly, a pattern they call punctuated equilibrium. Attention is scarce, so a problem can be ignored for years and then, when its image changes (from "a medical matter" to "a threat to children", say), move from one committee to a very different one. Interactive · Three streams and a window Problem: how loud is the crisis Policy: how ready is a workable solution Politics: how receptive are decision-makers A metaphor, not a measured model: the threshold of 6 is invented to make the idea playable. Kingdon himself describes the streams as largely independent and the window as partly chance. Play with it. A loud crisis with no ready solution gets hearings and hand-wringing; a ready solution with no crisis waits in a drawer. The practical lesson is Kingdon's: people who want change prepare the policy stream in the quiet years, so that when a window opens they are not drafting from scratch. Instruments: regulation, taxes, spending, information and nudges Once a problem is on the agenda, the government chooses a tool. Political scientists group them roughly as: regulation (a rule, a ban, a standard), taxes and prices (change what a choice costs), spending and subsidies (pay for a service or reward a behaviour), information (warnings, labels, campaigns) and nudges (changing the default or the way choices are shown, without banning anything). Each acts on a different lever, and the same goal can be pursued with any of them. To cut sugar intake, Mexico taxed sugary drinks from January 2014 at one peso per litre; a study in the BMJ estimated purchases fell about 6 percent over 2014 and about 12 percent by December relative to earlier trends. The UK chose a levy on manufacturers' sugary drinks, in force from April 2018, which pushed producers to reformulate. Others rely on labels or school-meal rules. The core economic point about price instruments is that a tax lets each firm or person choose where to cut, and so reaches the same target more cheaply than a uniform rule. Suppose three factories must together cut pollution by a fixed amount, and cutting costs each of them a different amount. A uniform standard makes each cut the same share, including the one for whom it is dearest. A tax makes everyone cut until the last unit costs the same as the tax, so the cheap cutters cut more. Try it. Interactive · Same target, two instruments Total cut required (units) How different the factories' costs are Three factories whose cost of the next unit cut rises in proportion to how much they have already cut: slopes 4/r, 4 and 4r (r is the slider). Costs are in arbitrary money units. Textbook model; real regulators do not know these slopes, which is the standard argument for prices and tradable permits. With r = 1 the factories are identical and the two tools cost the same. As the spread grows, the tax pulls ahead. The caveats matter as much as the result: the model assumes the regulator can set the right tax without knowing costs (it cannot, so it adjusts), and it ignores who pays. A flat tax on fuel hits a poor household harder, which is why many carbon taxes come with rebates. Nudges are the cheapest instrument and the most oversold. A well-known 2003 study by Eric Johnson and Daniel Goldstein found that countries where people must opt out of organ donation reported far higher consent than those where they must opt in. Later work cautions that consent is not donation: hospital systems and family permission matter as much as the default. Implementation: the gap between a law and what happens Jeffrey Pressman and Aaron Wildavsky's 1973 book Implementation (subtitled "How Great Expectations in Washington Are Dashed in Oakland; or, Why It's Amazing That Federal Programs Work at All") followed a federal job-creation scheme in Oakland, California. Their lesson: a programme that needs agreement from many separate offices has many chances to fail. Each "clearance" is a point where someone can delay or veto. If each has a high chance of agreement, the chain still frays, because probabilities multiply. P(success) = p × p × … × p = pnn clearances, each passed with chance p; 0.9 ten times gives about 35 percent Interactive · The clearance chain Chance each office agrees Offices that must agree A simple multiplication that assumes the offices decide independently. Real offices are not independent, and later scholars argued that coordination and bargaining can raise the odds. Pressman and Wildavsky's own point was the warning, not the exact number. A second source of gaps is the person at the counter. Michael Lipsky's Street-Level Bureaucracy (1980) argued that teachers, police officers, caseworkers and nurses make policy in practice: they have more cases than time, so they ration attention and bend rules. Policy on paper is what the capital writes. Policy in practice is what thousands of workers do when the line outside is long. Good designers ask: who has to do this, with what time, and what happens if they simply cannot? Evaluation: what would have happened anyway? The evaluator's central question is about a world nobody saw: what would have happened without the policy? This unseen outcome is the counterfactual. Comparing a group before and after a policy is the obvious test, and it is often badly wrong, because things change on their own: the economy grows, a disease wanes, a school year ends. Mexico's Progresa programme, begun in 1997, gave poor families cash if their children went to school and clinics. Because it could not reach every community at once, the government drew lots for the first wave: of 506 communities in the evaluation, about 320 got the money first and about 186 waited (sources differ by one). Surveys every six months compared the two groups. Enrolment, especially in secondary school, rose in the treated communities. The programme was renamed Oportunidades in 2002 and later Prospera, and cash-transfer schemes with conditions spread to many other countries, Brazil's among them. The 2019 economics Nobel prize, shared by Abhijit Banerjee, Esther Duflo and Michael Kremer, honoured this style of experimental evaluation of anti-poverty policy. Interactive · Before-and-after versus a comparison group True effect of the programme (points of enrolment) What would have happened anyway (trend, points) Invented, illustrative numbers: both groups start at 60 percent enrolment and both are hit by the same background trend. Real estimates carry sampling error, which this toy leaves out. Set the trend to +5 and the effect to 4. A naive before-and-after comparison claims a 9-point success. The comparison group reveals that 5 of those points would have come anyway. Set the trend negative and a working programme looks like a failure. The lesson is the one Progresa's designers acted on: a policy is judged against a counterfactual, and the best counterfactual is a comparison group chosen by chance. The methods piece (Research Methods in Political Science, 35/18) takes this much further. Evidence-based policy, and its limits "Evidence-based policy" means choosing policies because rigorous evaluations found they work. Governments have built institutions for it: in the UK, the What Works Network was launched in 2013 as a set of centres that synthesise evidence for areas such as crime, education and ageing. A cautionary example is "Scared Straight", a programme that takes at-risk teenagers into prisons to frighten them. It was popular and seemed obviously sensible. Systematic reviews of randomised trials found it did not reduce offending and, on average, probably increased it. Evidence overturned common sense. The approach has limits, and its own defenders list them. Published results overstate. Journals prefer significant findings, and small studies that happen to land high are the ones that get published. Stefano DellaVigna and Elizabeth Linos (Econometrica, 2022) compared 126 trials run by two US "nudge units" with 26 academic papers: the academic papers averaged an effect of 8.7 percentage points on take-up, and the units' own trials 1.4 points. Publication bias, they estimated, explains much of the gap. Other researchers disagree on how large the true effect of nudges is: one 2022 meta-analysis found a moderate effect, and a reanalysis argued that after correcting for publication bias there is no clear evidence of any. What worked there may not work here. A result from Kenyan schools or a New Haven neighbourhood tells you about that place; whether it carries to Lagos, Lima or Leeds depends on how the programme works and who delivers it. Angus Deaton and Nancy Cartwright made this argument forcefully in 2018. Even the famous Kenyan school-deworming study (Miguel and Kremer, 2004) has been re-examined and argued over by later researchers, with reviews disagreeing about its size. And what is measured gets gamed. Where a number becomes the target, people optimise the number. Interactive · Why published effects run high True effect (percentage points) People per group in each trial 600 simulated trials of a programme that raises take-up from 10 percent. Only trials that are positive and significant at the usual 5 percent level count as "published". Illustrative simulation with a fixed random seed. With a true effect of 1 point and 400 people per group, the few trials that clear the bar overstate the truth by several times. Make the trials bigger and the exaggeration fades. This is why evaluators ask for pre-registered trials, large samples and replications, and why a single dramatic study is a reason for interest, not a policy. What a graduate just sees The policy cycle is a checklist of questions, not a description of how things happen. Solutions often wait for problems. A problem gets attention when a crisis, a ready solution and a receptive government line up. Prepare in the quiet years. The same goal can be pursued with a rule, a price, a payment, a label or a default. Ask which lever is being pulled and who pays. A price lets people who find cutting cheap do more of it, which usually reaches a target more cheaply than a uniform rule. Probabilities multiply: a plan that needs many separate yeses is far less likely to survive than each yes suggests. Policy is made at the counter as well as the capital. Ask who has to do this, and with what time. "It worked" means "it did better than what would have happened anyway". Ask for the comparison group. Published effects are inflated. One striking study is a lead, not proof; and what worked in one place may not work in another. Where it shows up next Research Methods in Political Science (35/18): the tools for finding a counterfactual, from field experiments to natural experiments. Administrative Law (35/09): the courts' side of implementation, asking when an agency's rule or decision can be challenged. Public Economics (34/49) and Behavioral Economics (34/46): the economics behind taxes, public goods and nudges. In the world: a city councillor deciding between a ban and a fee, a ministry of health asking for a pilot before a national rollout, an NGO choosing which programme to fund. Congestion charges (Singapore began its scheme in 1975, London in 2003) are the same story again: a price chosen, an agenda window, an implementation challenge, an evaluation. If you want to go deeper 📘 Textbook. Thomas Birkland, An Introduction to the Policy Process (Routledge): the standard undergraduate text on the cycle, agenda setting and theories of change. 📘 Textbook. Paul Cairney, Understanding Public Policy (Palgrave): short, critical, and strong on why the cycle is a simplification. 📘 Gentler. Abhijit Banerjee and Esther Duflo, Poor Economics (PublicAffairs, 2011): policy evaluation told through stories from India, Kenya, Mexico and elsewhere. 🌐 Free course. MIT OpenCourseWare 14.73, The Challenge of World Poverty (Banerjee and Duflo, Spring 2011): lectures that apply evaluation to real programmes. The takeaway Policy is a chain: a problem gets attention, a tool is chosen, many people carry it out, and someone asks what would have happened anyway. Each link can break, and each can be examined. Read any new policy by asking which link you are being asked to take on trust. Try this: this week, take one policy in the news, a tax, a ban or a scheme, and write down its goal, its instrument, who has to carry it out, and what comparison would show whether it worked. Sources & Further Reading Sources. Policy cycle: Lasswell, The Decision Process (1956); Lindblom, "The Science of Muddling Through", Public Administration Review (1959). Agendas: Kingdon (1984); Baumgartner and Jones (1993). Ireland's levy: introduced 2002 at 15 cents, a drop of about 90 percent reported by the government at the time (BBC News, 2002 and other reports; per-person figures differ between sources, and critics note bags are reused as bin liners). Mexico's soda tax: Colchero et al., BMJ (2016), about 6 percent fall in 2014 and about 12 percent in December 2014. UK Soft Drinks Industry Levy: announced 2016, in force 6 April 2018. Nudges: Johnson and Goldstein, Science (2003); DellaVigna and Linos, Econometrica 90(1), 2022, 81-116 (1.4 vs 8.7 points; 126 trials vs 26 papers); the Mertens et al. meta-analysis and the Maier et al. correction, PNAS (2022). Implementation: Pressman and Wildavsky (1973); Lipsky (1980). Progresa: renamed Oportunidades 2002; evaluation of 506 communities, about 320 treatment (J-PAL and Attanasio, Meghir and Santiago, Review of Economic Studies); 2019 Nobel to Banerjee, Duflo and Kremer. What Works Network: launched 2013 (UK government). Scared Straight: Cochrane systematic review of randomised trials (Petrosino and colleagues). Deaton and Cartwright, "Understanding and misunderstanding randomized controlled trials", Social Science & Medicine (2018). Miguel and Kremer, Econometrica (2004). Sections on the nudge-unit and trial simulation are the author's own illustrations. This piece describes how policy works; it is not advice on any particular policy. 🌐 MIT OpenCourseWare 14.73 📘 Banerjee & Duflo, Poor Economics 📘 Kingdon, Agendas, Alternatives, and Public Policies Step 11. Political Economy: Why the Rules Decide Who Gets Rich -------------------------------------------------------------- Why: Why some nations prosper: states, markets and institutions. Two towns share a name and a border; one is far richer than the other. Two countries find the same oil; one builds a fund, the other a feud. Political economy asks how rules, power and incentives shape who prospers, and the honest answer is that the field still argues about it. Stand at the border fence in Nogales. On the Arizona side, household incomes are far higher, children finish school, and the roads and electricity work. A few metres away in Sonora, Mexico, people share the same desert, much of the same ancestry and food, and many of the same relatives. Daron Acemoglu and James Robinson open their 2012 book Why Nations Fail with this pair of towns. Their answer: it is not geography or culture, it is the rules each side lives under, and who made them. Not everyone accepts that answer, and the story of why is the story of this subject. Political economy sits where economics and politics meet. Economics asks how people respond to incentives; politics asks who sets the incentives, and for whom. Put them together and you can ask why some governments tax heavily and others lightly, why some oil states are rich and stable and others are neither, and why a few countries in East Asia grew from poverty to wealth in a generation. The course in one box Who takes itPolitical science and economics majors, usually in year 3 or 4. Oxford's PPE, MIT's political science and economics departments, Sciences Po and Harvard's government programme all teach versions of it, often as "political economy of development" or "comparative political economy". What it is forExplaining how institutions, interests and elections shape economic outcomes, and how economic conditions shape politics in return. Comes afterMicroeconomics (34/42), Game Theory (34/45) and Comparative Politics (35/13). Leads toIt is a capstone-style subject; nothing later requires it. Related ground: Development Economics (34/48), International Trade & Finance (34/47) and Public Economics (34/49). What this piece gives youYou will not be able to estimate an institutional effect. You will see six big claims in the field, the strongest case for each, and the strongest objection. Easier first reads: The Mixed Economy (31/05) and The Great Debates (31/06), which cover systems rather than mechanisms, and Institutions: The Invisible Rules That Shape Us (30/05). Institutions and growth: the rules of the game The economic historian Douglass North defined institutions as "the rules of the game" in a society: formal ones (a constitution, property law, a court) and informal ones (norms, expectations). North, who shared the 1993 economics Nobel prize, argued that what separates rich from poor countries is less the resources they hold than the rules that tell people whether effort will be rewarded. If a farmer expects the harvest to be seized, she plants less. If an inventor expects a copy to be sold on the next street, he invents less. The rival explanations are worth stating fairly. Geography: tropical disease, poor soil and distance from trade routes (Jeffrey Sachs, Jared Diamond). Culture: beliefs about trust and work, in a tradition that runs back to Max Weber. Policy: whether a government's choices on trade, spending and money encourage growth. The institutions view says these other things matter, but mostly through the rules people live under. The debate is about how much, and whether institutions are the deep cause or one link in a longer chain. Extractive and inclusive institutions, and the critics Acemoglu and Robinson's version is bold. Inclusive economic institutions protect property for ordinary people, enforce contracts, and let anyone start a business; they rest on inclusive political institutions that spread power widely. Extractive institutions let a narrow elite take what others produce, and the elite resists change, because new technology or fair competition could threaten it. Their historical argument includes colonial settlement: where European settlers died quickly of disease, colonisers set up systems to extract wealth rather than settle, and those systems persisted after independence. In 2024 Acemoglu, Simon Johnson and Robinson shared the economics Nobel prize for this line of work. The criticism comes from several directions, and it is serious. Jeffrey Sachs argues that the book underplays geography, technology and international politics, and notes that Singapore and South Korea grew fast without being democracies. Jared Diamond argues geography matters more and asks where good institutions come from in the first place. Francis Fukuyama says the inclusive/extractive binary is too crude and that China's growth sits awkwardly with the theory. William Easterly says the case studies lack statistical support; Arvind Subramanian raises reverse causation, since rich countries can afford better institutions. A fair summary: most economists accept that institutions matter; there is real disagreement about whether the inclusive/extractive split explains as much as the book claims. Interactive · A toy of expropriation and investment Share of output the ruling elite takes your elite take   10% take Illustrative toy, not Acemoglu and Robinson's model: a Solow economy (output = capital to the power 0.33; people invest 25 percent of what they keep; capital wears out at 5 percent a year) in which the elite seizes a share of output and consumes it. Both economies start with the same capital. Without JavaScript: in the toy, a 40 percent take leaves income per person after 100 years at about 82 percent of the level under a 10 percent take, and an 80 percent take leaves the economy with no growth at all. Notice two things. Output after 100 years falls as the take rises, because people keep less to invest. But the elite's own long-run take is largest at a share of roughly two-thirds, not at 100 percent: a ruler who takes everything starves the thing he lives on. That is Mancur Olson's point about the "stationary bandit": even a self-interested ruler wants a tax base that lasts, and what restrains him is his need for a future. What the toy cannot show is what changes a ruler's incentive, which is exactly what the book, and its critics, argue about. The resource curse It seems natural that oil, gas or diamonds should make a country rich. Yet in 1995 Jeffrey Sachs and Andrew Warner reported that countries with more natural-resource exports tended to grow more slowly. Political scientists added a political story: Michael Ross argued in 2001 that oil in particular is associated with less democracy, because a government funded by oil rent does not need to tax its citizens, and citizens who are not taxed demand less say. Economists point to "Dutch disease", named after the effects of the Groningen gas field found in 1959: a resource boom lifts the exchange rate and hurts other exports like factories and farms. Resource prices also swing wildly, which makes budgets whipsaw. It is a tendency, not a law. A 2008 study by Brunnschweiler and Bulte argued that resource abundance, as opposed to export dependence, is not bad for growth, and a later study suggested price volatility rather than abundance drives the problems. Norway founded its petroleum fund in 1990, and its rule lets the government spend only up to about 3 percent of the fund each year. Botswana, a diamond exporter, is often cited as another success. Institutions again: both had working rules before the money came. Interactive · A windfall you can save or spend Share of the windfall saved in good years spending   oil revenue Illustrative: one random 30-year oil price path (fixed seed) with revenue = 100 × price. Spending = average revenue + (1 − saved) × the gap from average; the fund earns 3 percent. A negative fund means the government has borrowed. With nothing saved, spending follows the price: boom, then bust, and projects half-built when the price falls. With everything above average saved, spending is flat. But the fund then depends on a good estimate of "average", and a long run of low prices drains it. That is why real rules, Norway's included, are debated constantly. Redistribution and the median voter In a democracy, if the poor outnumber the rich, why do they not vote to take more from the rich? The classic model comes from Allan Meltzer and Scott Richard (1981), building on Duncan Black (1948) and Anthony Downs (1957). Put voters on a line by income. If everyone votes on a single question, the winning position is that of the median voter, the one with exactly half the voters on each side. In a society where average income is above the median (a few very rich people pull the average up), the median voter gains from taxing everyone and paying out equally. So more inequality, the model says, should mean more redistribution. tax rate preferred = (1 − own income ÷ average income) ÷ efficiency costcapped between 0 and 100 percent; the median voter's preference wins Interactive · The median voter chooses a tax Inequality (spread of log incomes) Cost of taxing (higher = more waste) Poorest share who do not vote Illustrative: incomes follow a lognormal distribution with average 1. Each voter picks the tax rate that maximises their own after-tax income plus the equal transfer, with waste growing with the square of the rate. Not calibrated to any country. Without JavaScript: with the default settings the decisive voter earns about 0.73 of the average and prefers a tax of about 27 percent; if the poorest 40 percent cannot vote, the preferred rate falls to zero. Raise inequality and the decisive voter's preferred tax rises; exclude poor voters and it falls. Real data are harder on the model. Studies find some support (one found more aggressive redistribution where the median citizen would gain, across 79 countries) and some contradictions: the decisive voter in some referendums was not the median one, and many unequal democracies redistribute little. Explanations on offer include turnout and money that favour the rich, voters' beliefs about fairness and mobility, and the way parties package issues. The model is a clear starting point rather than a verdict. Varieties of capitalism Not all market economies work alike. In Varieties of Capitalism (2001), Peter Hall and David Soskice divided rich countries into liberal market economies (the United States, Britain, Ireland, Australia, Canada and New Zealand) and coordinated market economies (Germany, Japan, Sweden, Austria and others). In the first, firms coordinate mainly through markets and contracts. In the second, they rely more on negotiation, industry bodies and long relationships. Their claim is about complementarities: a feature in one area works best alongside matching features in the others. Patient bank finance suits long job tenure; firm-specific skills suit firms that will not poach each other's workers. Flexible hiring and stock markets suit general skills that move between employers. Interactive · Do the pieces fit? Stock-market financeBank and insider finance Easy hiring and firingLong tenure, union bargaining General skills (schools, degrees)Firm-specific skills (apprenticeships) This shows the theory's logic, not data. Real countries mix; France, Italy and Spain do not fit either type cleanly, and Hall and Soskice treated the two as ends of a spectrum. Hall and Soskice predicted that liberal economies lean to radical innovation (software, biotech) and coordinated economies to incremental improvement (machine tools, cars). Critics reply that the framework underplays change (Germany has reformed its labour market since the 1990s), treats actors as locked into institutions, and rests partly on the United States as an outlier. It remains a standard way to see that "capitalism" has several designs. It connects to the library's The Mixed Economy (31/05). State-led development in East Asia In the 1960s South Korea was poor; a standard economics textbook says its output per person then grew about 7 percent a year for more than thirty years, and one working paper puts income per head in 1962 at under a hundred US dollars. The state did not leave this to the market alone. In Korea, Japan and Taiwan, governments steered credit to chosen industries, pushed firms to export, and protected infant industries. Chalmers Johnson's study of Japan's trade ministry (1982), Alice Amsden on Korea (1989) and Robert Wade on Taiwan (1990) described a "developmental state". The World Bank's East Asian Miracle (1993) credited mostly market-friendly policies with a smaller role for intervention, an interpretation its critics disputed. The same tools failed elsewhere: many governments that picked winners picked losers. What differed, scholars argue, is that East Asian states tied support to performance (exports, not just promises) and had capable bureaucracies. The free-market view holds that the countries succeeded in spite of intervention, or that the lesson is openness to trade and high investment. This is not settled. What is not in dispute is the arithmetic of compounding, which explains why decades of fast growth transform a country. Interactive · Compounding Growth in income per person, per year Years Pure arithmetic: income after n years = (1 + g)n. Real growth is uneven. At 7 percent a year, income per head doubles about every ten years and is about 7.6 times larger after thirty. At 2 percent it is 1.8 times larger. A country at 2 percent for a generation looks much the same to its citizens; at 7 percent it looks like a different country. What a graduate just sees Rules matter at least as much as resources: ask who set them and who benefits. Even a selfish ruler needs a tax base. What restrains plunder is the ruler's need for a future, or others' power to check him. Institutions are a strong explanation with strong critics. Say which claim you mean: "matters" or "matters most". Resource wealth is a risk, not a curse: price swings, an exchange rate that squeezes other industries, and a state that does not need its citizens' taxes. In a democracy, the median voter's preference is a force. When it fails to predict policy, ask who actually votes and who actually gets heard. Capitalism has several designs, and features tend to come in matching bundles. Industrial policy has a record of both successes and failures: look for what made the successes accountable. Small differences in growth rates compound into different worlds. Where it shows up next Development Economics (34/48): the same questions about poor countries, from the economist's side. International Trade & Finance (34/47): tariffs, exchange rates and industrial policy in the world economy. Public Economics (34/49): taxes and the welfare state, where the median voter model gets used. Research Methods in Political Science (35/18): how scholars try to test claims like "institutions cause growth". In the world: sovereign-wealth-fund rules, debates on industrial policy in Europe, the US and China, and why governments in unequal democracies do or do not redistribute. If you want to go deeper 📘 Textbook. Torsten Persson and Guido Tabellini, Political Economics: Explaining Economic Policy (MIT Press, 2000): the standard formal treatment; demanding. 📘 Textbook. Peter Hall and David Soskice (eds.), Varieties of Capitalism (Oxford University Press, 2001): the book behind one idea, with the case chapters. 📘 Gentler. Daron Acemoglu and James Robinson, Why Nations Fail (Crown, 2012). Read it alongside a critic, such as Jared Diamond's or Francis Fukuyama's review. 🌐 Free course. MIT OpenCourseWare 14.75, Political Economy and Economic Development (Benjamin Olken, Fall 2012). The takeaway Prosperity depends heavily on rules and on who has the power to change them. Institutions, resources, voters and development strategies each explain something, and each has a serious critic. Hold the claims as claims, and ask of any one of them what evidence would settle it. Try this: pick a country you know and name one institution that protects ordinary people's property or contracts, and one that lets the powerful avoid it. Ask who would lose if it changed. Sources & Further Reading Sources. North, Institutions, Institutional Change and Economic Performance (1990); Nobel 1993. Acemoglu and Robinson, Why Nations Fail (2012; Nogales; critics Sachs, Diamond, Fukuyama, Subramanian, Easterly as summarised on the book's Wikipedia entry); the 2024 economics Nobel to Acemoglu, Johnson and Robinson. Sachs and Warner (1995); Ross, "Does Oil Hinder Democracy?" World Politics (2001); Brunnschweiler and Bulte (2008); Groningen field found 1959. Norway: oil fund established 1990, spending limit about 3 percent a year (Wikipedia, "Government Pension Fund of Norway"). Median voter: Black (1948), Downs (1957), Meltzer and Richard (1981); empirical results mixed (Milanovic 2000; Levitt 1996; Brunner and Ross 2010, per the Wikipedia entry on the median voter theorem). Olson, "Dictatorship, Democracy, and Development", APSR (1993). Varieties of capitalism: Hall and Soskice (2001); critiques by Crouch, Taylor and others as summarised on the Wikipedia entry. East Asia: Johnson, MITI and the Japanese Miracle (1982); Amsden, Asia's Next Giant (1989); Wade, Governing the Market (1990); World Bank, The East Asian Miracle (1993). Korea's growth: a macroeconomics textbook (Krugman and Wells) says about 7 percent a year for more than 30 years; a working paper reports per-head income rising from about US$87 in 1962 to US$5,199 in 1989 (approximate; definitions differ). The toy models and the compounding table are the author's own, computed and checked. This piece describes how economies and politics interact; it is not investment or policy advice. 🌐 MIT OpenCourseWare 14.75 📘 Hall & Soskice, Varieties of Capitalism 📘 Acemoglu & Robinson, Why Nations Fail Step 12. Game Theory: When Your Best Move Depends on Mine --------------------------------------------------------- Why: Strategy, voting paradoxes and collective action. Two shops pick prices, two countries pick tariffs, a hundred bidders pick bids. Game theory is the maths of choosing when everyone else is choosing too, and it explains why smart people can trap themselves. Two petrol stations face each other across a road. If both charge a fair price, both make a decent profit. If one cuts its price, it steals the customers and makes more, for a while. So each is tempted to cut. Both cut. Both now earn less than before, and neither can raise prices alone without losing everyone. Nobody was stupid. Each did the sensible thing given what the other might do. That pattern, a trap built entirely out of sensible choices, is the first thing game theory teaches you to see. It then teaches how to get out of the trap: by repeating the game, by changing who moves first, by changing the rules. The same toolkit explains the design of radio-spectrum auctions and the matching of kidney donors. The course in one box Who takes itEconomics majors in year 2, straight after microeconomics. Business schools teach it inside strategy courses, and computer science and political science programmes teach it too, because algorithms and elections are games. What it is forPredicting what rational, self-interested players will do when each one's best move depends on the others, and designing rules so the result is a good one. Comes afterMicroeconomics (34/42). Leads toStrategy & Competition (34/54); it also underlies the auctions and pricing in Pricing (34/61) and the rules-of-the-game thinking in Public Economics (34/49). What this piece gives youYou will not be able to solve a multi-stage game with incomplete information. You will see why a dominant strategy can leave everyone worse off, why a threat only works if carrying it out is in your interest, and why an auction format changes how people bid. Easier first reads: the library's The Social Contract (18/14) asks why we accept rules and a state at all, and Anarchism (31/04) covers the commons problem in words. This piece puts numbers on that kind of trap. Players, strategies, payoffs, and the Nash equilibrium A game has three ingredients: the players (who decides), the strategies (what each can do) and the payoffs (what each gets for every combination of choices, in whatever they care about: money, time, votes, survival). Two players with two options each fit in a small grid, the payoff matrix. One player picks the row, the other the column, and each cell holds two numbers: the row player's payoff first, the column player's second. Two ideas solve most such grids. A dominant strategy is one that is best for you whatever the other does: if it exists, play it. When neither player has one, look for a Nash equilibrium: a pair of choices where neither player could do better by changing their own choice alone. John Nash proved in 1950 that every finite game has at least one, possibly in mixed strategies (rolling dice between options). Nash shared the 1994 economics Nobel prize with John Harsanyi and Reinhard Selten for it. Nash equilibrium: each player's choice is a best response to the other'snobody gains by switching alone; it says nothing about whether the outcome is good Interactive · The payoff-matrix workbench Prisoner's dilemma Stag hunt Chicken Matching pennies Which side to drive Each cell: your payoff (row player), then the column player's. Edit the numbers; the analysis updates. Shaded = a best response for that player; outlined cell = Nash equilibrium. Without JavaScript: in the prisoner's dilemma (5, 3, 1, 0), defecting is dominant for both players, the only Nash equilibrium is both defecting for 1 each, and both cooperating would give 3 each. Try the presets and read the verdicts. Chicken and the driving game have two pure equilibria and a mixed one at 50:50; matching pennies has no pure equilibrium at all, only the mixed one, which is why a goalkeeper and a penalty-taker must be unpredictable. In matching pennies, if the row player leans even slightly toward heads, the column player exploits it. The prisoner's dilemma: why smart players choose badly Merrill Flood and Melvin Dresher devised it at the RAND Corporation in 1950, and Albert Tucker gave it its story: two suspects, questioned separately. Stay silent (cooperate) and both get a light sentence. Betray the other (defect) and you walk free while he gets the worst. Both betray and both get a heavy sentence. With points instead of years, the standard payoffs are: temptation 5, mutual cooperation 3, mutual defection 1, being betrayed 0. Whatever the other does, defecting pays you more: 5 beats 3 if she cooperates, 1 beats 0 if she defects. So both defect and both get 1, when both cooperating would have given 3 each. The equilibrium is stable and bad. That is the logic of arms races, overfishing, price wars and the two petrol stations. Now play it. Ten rounds against a computer opponent whose rule you can pick; you know the game is exactly ten rounds long. Interactive · Play ten rounds Tit for tat Always defect Always cooperate Grudger Coin flip CooperateDefectRestart Payoffs per round: both cooperate 3 each; you defect on a cooperator 5 to 0; both defect 1 each. Tit for tat copies your last move; the grudger cooperates until you defect once, then never again. Against tit for tat, the best ten-round total is 32: cooperate nine times, defect on the last round when it can no longer be punished. Against always-defect, nothing beats 10. Against always-cooperate you can take 50. The right play depends on who is on the other side, which is the whole subject in one sentence. Repeated games: when tomorrow changes today Meet the same person again and again and the logic changes: defecting today invites revenge tomorrow. In 1980 the political scientist Robert Axelrod invited game theorists to send in programs for a repeated prisoner's dilemma, each program playing every other for 200 moves. The winner, in this tournament and in a second, larger one, was the simplest entry: tit for tat, sent by the psychologist and game theorist Anatol Rapoport. It cooperates first, then copies the opponent's last move. Axelrod's 1984 book The Evolution of Cooperation drew the lesson: successful strategies were nice (never defect first), provocable (punish defection at once), forgiving (return to cooperation) and clear (easy to read). Tit for tat never beats the partner it faces; the best it can do in a single match is tie. It won by getting good scores out of nearly everyone. Run your own small tournament. Six kinds of players meet in a 200-round round robin; add copies of Always-defect and see whether the winner changes. Interactive · A round-robin tournament (a toy field) Always-defect entrants No mistakes 5% of moves slip Entrants: Always cooperate, Tit for tat, Grudger, Pavlov (cooperate only if both did the same last round), Coin flip, plus the Always-defect copies. Everyone meets everyone else for 200 rounds; bars show the average points per round. Payoffs 5, 3, 1, 0. A toy field, far smaller than Axelrod's. With no Always-defect, the nice, retaliating strategies fill the top. Add more defectors and the field gets harsher: the defectors climb to the top once enough naive cooperators and coin-flippers are there to be exploited, while their scores against each other stay at 1. Success in a repeated game depends on who else is playing. Turn on the 5% slips and tit for tat loses ground too: after one accidental defection two tit-for-tat players can lock into a spiral of mutual retaliation. Later work found more forgiving variants, such as generous tit for tat, and the Pavlov rule do better in noisy worlds. Cooperation without a referee needs a future worth protecting, which is why long-term relationships, reputations and repeated dealings support trust that one-off meetings cannot. Coordination games: when you just need to match Not every game is a conflict. In the driving game, both of you want to pick the same side; which side hardly matters. Load "Which side to drive" above: two equilibria, both good, and nothing in the payoffs says which. Conventions (driving on the left in the UK, Japan and India, on the right in most of the rest of the world) are equilibria picked by history. The stag hunt adds risk: both hunting stag gives 4 each, but hunting hare alone is safe at 3. Two equilibria again, one better but riskier. Thomas Schelling, in The Strategy of Conflict (1960), noticed that when payoffs cannot choose, players use focal points: the obvious, salient option, like meeting "at the clock" in a station. Many real-world failures, from bank runs to standards wars, are coordination games stuck on the bad equilibrium. Sequential games: the credible threat When players move in turn, you solve the game backwards: start at the end, see what the last mover would do, and work back. Take a company deciding whether to enter a market held by an incumbent. The incumbent says: "If you enter, I'll start a price war." Should the entrant believe it? Only if fighting is actually best for the incumbent after entry happens. If, once the entrant is in, the war would cost the incumbent more than sharing the market, the threat is empty. A rational entrant sees that and enters. Reinhard Selten's refinement of Nash equilibrium, subgame-perfect equilibrium, rules such empty threats out. Interactive · Entry, threats and commitment Incumbent's profit if it fights after entry No commitment Commit to fight (contract penalty) Payoffs (entrant, incumbent): stay out (0, 10); enter and incumbent accommodates (4, 5); enter and incumbent fights (−2, your slider). The commitment option makes accommodating cost the incumbent 3 (so it pays only 2). The slider has a threshold. With a war worth less than 5 to the incumbent, the threat is empty and the entrant walks in. Raise the war's payoff above 5, or use the commitment button, and the threat becomes credible, the entrant stays out, and the incumbent never has to fight. Commitment devices (spare factory capacity already built, a contract that punishes backing down, a reputation) have this strange property: by taking away your own options they can make you better off. Auctions: bid your value, or shave it An auction is a game where the rules decide the payoffs. In a sealed-bid second-price auction, the highest bidder wins but pays only the second-highest bid. William Vickrey analysed it in 1961 (it had been used by stamp collectors since 1893). Here, bidding exactly your true value is a dominant strategy: bidding more risks paying above your value, bidding less risks losing an item you would have profited from. In a first-price auction you pay what you bid, so you shave your bid below your value, trading a bigger profit if you win against a smaller chance of winning. With four bidders whose values are equally likely to be anywhere in a range, the equilibrium bid is three-quarters of your value (in general, (n−1)/n of it). Interactive · Four bidders, two formats (3,000 simulated auctions) Your bid as a share of your value second-price   first-price Values for all four bidders are drawn uniformly between 0 and 100. The three rivals bid their full value in the second-price format and three-quarters of it in the first-price format. Your average profit is per auction, including those you lose. Slide the share. In the second-price format your profit peaks at 100% of your value; in the first-price format it peaks near 74%. Also watch the seller's average revenue: it is about 60 in both formats, a first glimpse of the revenue equivalence theorem. Real auctions differ from this textbook case. When bidders are unsure of the item's true value (oil leases, spectrum) the winner tends to be the one who overestimated most, the winner's curse. Mechanism design: design the game, then let people play it Game theory asks: given the rules, what will players do? Mechanism design turns it around: what rules would make self-interested players do what we want? Two famous cases. Spectrum auctions. Governments own radio frequencies and need to hand them to phone companies. For decades they used beauty contests and lotteries. From July 1994 the US Federal Communications Commission instead ran a "simultaneous multiple round" auction, where many related licences are sold together over many rounds and bidders see each other's bids. Theorists including Paul Milgrom and Robert Wilson helped shape the design, and they shared the 2020 economics Nobel prize for improvements to auction theory and new auction formats. In 2000 the UK's 3G auction raised £22.5 billion from five licences. Kidney exchange. Many people who need a kidney have a willing donor who is the wrong blood type. Selling kidneys is illegal almost everywhere, so prices cannot match people. Alvin Roth, Tayfun Sönmez and Utku Ünver designed algorithms (their 2004 paper in the Quarterly Journal of Economics) in which pair A's donor gives to pair B's patient while B's donor gives to A's: a swap, extended to longer cycles and to chains started by an altruistic donor. The first paired exchange recorded was in South Korea in 1991 and the first in the US in 2000; by 2012 the US National Kidney Registry had organised a 60-surgery chain, and by 2014 one of 70. Roth shared the 2012 economics Nobel prize with Lloyd Shapley for the theory of stable allocations and the practice of market design. Game theory predicts what people do under the rules. Mechanism design chooses the rules. What a graduate just sees Before asking what someone will do, ask what they would do if they expected you to do each of your options. A dominant strategy needs no guess about the other player. If it exists, it settles the game. A Nash equilibrium is stable, not good. The prisoner's dilemma's equilibrium leaves both worse off than cooperating. Repetition changes the game: cooperation can be rational when the future is long enough and defection is punishable. A threat counts only if carrying it out is in your interest when the time comes. Otherwise build a commitment or expect to be called. When payoffs do not choose between equilibria, the focal point does: salience, convention, a shared story. In a second-price auction, bid your value. In a first-price auction, shave your bid. The rules decide how people behave. When a market cannot use prices, you can still design the rules. Where it shows up next Strategy & Competition (34/54): firms setting prices, building capacity and deciding whether to enter are playing the games above. Pricing (34/61): discounts, price matching and surge pricing are games with customers and competitors. Behavioral Economics (34/46): the next piece asks when real people do not play the equilibrium, as in the ultimatum game. Machine Learning (34/32) and Computer Networks (34/27): game-playing AIs and congestion control both apply these ideas. In the world: online-advertising auctions, spectrum sales, school and hospital matching, climate treaties, arms control. If you want to go deeper 📘 Textbook. Robert Gibbons, Game Theory for Applied Economists (Princeton, 1992). Short and widely used in economics programmes. 📘 Textbook. Martin Osborne, An Introduction to Game Theory (Oxford). A fuller undergraduate treatment with many worked games. 📘 Gentler. Avinash Dixit and Barry Nalebuff, The Art of Strategy (Norton, 2008), and Robert Axelrod, The Evolution of Cooperation (1984). Real cases, almost no algebra. 🌐 Free course. Open Yale Courses ECON 159: Game Theory (Ben Polak, 24 lectures, recorded Fall 2007), covering Nash equilibrium, backward induction and signalling. The takeaway When your best move depends on others' moves, look for dominant strategies, then for equilibria, and remember that a stable outcome can be a bad one. Repetition, commitment and well-designed rules are the three ways out, and each is a way of changing the game rather than the players. Try this: this week, spot a situation where you and someone else are each waiting for the other to move first or to give in, and ask what the payoff grid looks like and whether it is a game you can change. Sources & Further Reading Sources. Dominance, Nash equilibrium, mixed strategies, backward induction and the second-price result are standard textbook game theory (Gibbons; Osborne; Polak's Yale lectures). The prisoner's dilemma was devised by Flood and Dresher at RAND in 1950 and named by Tucker (Wikipedia, "Prisoner's dilemma"). Nash, "Equilibrium points in n-person games," PNAS 36, 1950; the 1994 economics Nobel prize went to Nash, Harsanyi and Selten. Axelrod's tournaments (c. 1980) and 1984 book: tit for tat, by Anatol Rapoport, won both; matches were 200 moves; the death-spiral under noise and generous tit for tat are as described in Wikipedia's "Tit for tat". Schelling, The Strategy of Conflict (1960). The second-price auction was described by Vickrey in 1961 and used by stamp collectors from 1893. The FCC has held spectrum auctions since July 1994 using a simultaneous multiple round design; the UK's 2000 auction raised £22.5 billion from five 3G licences (Wikipedia, "Spectrum auction"). The 2020 prize went to Milgrom and Wilson, the 2012 prize to Roth and Shapley. Kidney exchange history from Wikipedia's "Kidney paired donation"; Roth, Sönmez & Ünver, "Kidney Exchange," QJE 2004. Every number an interactive shows was computed in node from the code on this page: ten-round best score against tit for tat 32, against always-defect 10, against always-cooperate 50; the four-bidder first-price optimum is near 0.74 (theory 0.75); the average winning price is near 60 in both formats; the toy tournament's ranking depends on the chosen field and is not Axelrod's result. 🌐 Open Yale Courses ECON 159 Game Theory 📘 Dixit & Nalebuff, The Art of Strategy 📘 Axelrod, The Evolution of Cooperation Step 13. International Law & Human Rights: Rules Between Nations Without a World Police --------------------------------------------------------------------------------------- Why: International law and human rights. Nobody can arrest a state. Yet ships dock, planes land, borders mostly hold and a handful of dictators have ended up in the dock. How law works when the only lawmakers are the ones it binds. select{font-family:-apple-system,sans-serif;font-size:.9rem;min-height:44px;max-width:100%;min-width:0;border:1px solid var(--line);border-radius:10px;background:var(--hi);color:var(--ink);padding:6px 8px} .row{display:flex;flex-wrap:wrap;gap:8px;align-items:center;margin:6px 0} .row>*{min-width:0} .opt{display:block;width:100%;text-align:left;border-radius:12px;margin:6px 0} Park an embassy car in front of a fire hydrant in almost any capital in the world and the ticket will quietly go nowhere. The diplomat inside cannot be arrested or sued in the country where they serve, under a treaty (the 1961 Vienna Convention on Diplomatic Relations) that nearly every state has joined. No police force makes anyone keep it. States keep it because each of them wants its own diplomats treated the same way abroad. That is the whole puzzle of this subject in one parking space. Inside a country, law has a legislature that writes it, courts that apply it and police who enforce it. Between countries there is no world parliament, no court every state must obey, and no world police. The international lawyer Louis Henkin put the surprising result well in 1979: almost all nations observe almost all principles of international law and almost all of their obligations almost all of the time. The headlines are about the exceptions. This piece is about why the rule is so often the rule. The course in one box Who takes itLaw students in their later years (an elective in many US law schools; a standard course, Derecho Internacional Público, in the five-year degrees of Spain and Latin America), and political science and international relations majors. What it is forTelling a rule that states are bound by from a mere aspiration, and knowing which body, if any, can say so. Comes afterConstitutional Law (35/03): you need to know what a domestic legal order is before asking what changes when there are many. Leads toEuropean Union Law (35/11), the deepest case of states sharing sovereignty, and International Relations (35/14). What this piece gives youYou will not be able to argue a case at the Hague. You will see where the rules come from, why a veto matters, which court hears what, and why compliance is common even without police. It describes how legal systems work and is not legal advice; treaties change and countries differ. Easier first reads: The International Institutions: What They Can and Can't Do (18/10) and Rights, Constitutions & the Rule of Law (18/04). Where the rules come from: treaties, custom and principles Every legal system has to answer one question first: what counts as law? Statute and precedent answer it at home. Between states, the answer is written in Article 38 of the Statute of the International Court of Justice (1945), which lists what the Court applies: treaties, custom, and general principles of law recognised by civilised nations (an old phrase, now read as principles found in the world's legal systems, such as good faith). Court decisions and scholars' writings are only "subsidiary means" of finding the law. A treaty binds the states that join it, because of a rule so basic it is the first rule of the subject: pacta sunt servanda, agreements must be kept (Article 26 of the 1969 Vienna Convention on the Law of Treaties). A treaty usually says when it comes into force, often after a number of ratifications. Custom is harder to see. It is a practice states follow because they believe the law requires it. Two ingredients: what states actually do (state practice) and why they do it (opinio juris, the sense of legal obligation). The International Court asked for practice that is "extensive and virtually uniform", including among the states most affected, in the North Sea Continental Shelf cases (1969). Custom matters because it binds even states that never signed anything. A state that objects from the start and keeps objecting (a persistent objector) can escape a new custom, but not the few peremptory norms (jus cogens), such as the bans on genocide and slavery, which no treaty can override (Article 53 of the Vienna Convention). Interactive · Is it custom yet? Share of states that act this way Most-affected states do it Most-affected states don't "Because the law requires it" Courtesy or convenience A teaching toy: score = practice × (1 if the most-affected states join, else 0.6) × (1 if from a sense of legal duty, else 0.25). Real tribunals weigh evidence, not scores. At 70 or above: likely custom; 40 to 70: emerging or doubtful; below 40: not custom. Why does "courtesy or convenience" matter? Because ships of every flag stay out of each other's way at sea, but that alone is a habit; custom needs the belief that doing otherwise would be a breach. Now the treaty side: a treaty is not law for anyone until enough states say yes. Interactive · When does a treaty start to bind? Treaty Vienna Convention on the Law of Treaties (1969) Rome Statute of the ICC (1998) Law of the Sea Convention (1982) Paris Agreement (2015) Ratifications so far Share of world emissions those parties cover (Paris only) Thresholds: Vienna Convention 35 ratifications (Art. 84); Rome Statute 60 (Art. 126); Law of the Sea 60, then a 12-month wait (Art. 308); Paris Agreement 55 parties covering at least 55% of emissions (Art. 21). Real entry into force also counts days after the last deposit. Each of the three families of states treats the result differently at home. This is the first big comparison in the subject: when a treaty binds the country, does it also bind its courts and citizens automatically? FamilyHow treaties enter domestic law United States (common law)A treaty the Senate approves by two-thirds is "supreme law of the land" (Constitution, Art. VI), but courts treat many as non-self-executing: they need a statute before a private person can invoke them. The US has not joined the Rome Statute and has signed but not ratified the American Convention on Human Rights. Latin America (civil law)Often generous. Argentina's 1994 constitution gives listed human rights treaties constitutional rank (Art. 75 no. 22); Colombia's courts speak of a bloque de constitucionalidad (Art. 93); Brazil's 2004 amendment gives human rights treaties approved by supermajority the rank of constitutional amendments; Mexico's 2011 reform puts treaty rights alongside the constitution's own (Art. 1). Continental EuropeFrance's 1958 constitution puts ratified treaties above statutes (Art. 55); Germany ranks general rules of international law above statutes (Art. 25) while treaties generally have statute rank. The UK is dualist: a treaty needs a statute, which is why it passed the Human Rights Act 1998. The Charter's bargain: force is banned, the Council decides The biggest treaty of all is the UN Charter, signed at San Francisco on 26 June 1945 and in force from 24 October 1945. Its central rule is Article 2(4): states must refrain from the threat or use of force against another state's territorial integrity or political independence. It had two exceptions: self-defence against an armed attack (Article 51), and action authorised by the Security Council under Chapter VII. That second exception is the Charter's bargain, and it shows why the subject is half law and half politics. The Council has 15 members, five of them permanent: China, France, Russia (which took the Soviet seat), the United Kingdom and the United States. A resolution needs nine "yes" votes, and under Article 27(3) a negative vote by any permanent member blocks it: the veto. A permanent member that merely abstains does not block it, by long practice. The design is deliberate: in 1945 the founders thought a rule that the great powers could outvote would be a rule they would leave. Interactive · Can this resolution pass? United States United Kingdom France Russia China Elected members voting yes (of 10) Rule (Charter Art. 27): at least 9 yes votes of 15, and no permanent member voting no. Abstentions count as neither. Notice what the veto does not do: it does not make the underlying rule disappear. A blocked resolution leaves Article 2(4) in place; it only removes the Council as the body that can authorise enforcement. When the Council is stuck, the General Assembly can debate and condemn (its resolutions are recommendations, not law), and the International Court can give opinions. In March 2022 the Court, by 13 votes to 2, ordered Russia to suspend its military operations in Ukraine while the case was heard. The order was binding in law and was not followed in fact. Both halves of that sentence are part of the subject. Courts for states and for people: the ICJ and the ICC Two courts in the Netherlands are constantly confused. The International Court of Justice (ICJ, the "World Court", at the Peace Palace in The Hague) is the UN's main judicial organ. Fifteen judges, elected for nine-year terms, settle disputes between states, and give advisory opinions when UN bodies ask. The catch is consent: a state can be sued only if it has accepted the Court's jurisdiction, by treaty clause, by a special agreement, or by a standing declaration. Nicaragua v. United States (1986), where the Court found the US had breached customary law by supporting the Contras and mining Nicaraguan harbours, shows both sides: a state can be judged, and the judged state can ignore it. Judgments are meant to be enforced through the Security Council (Charter Art. 94), where the veto sits. The International Criminal Court (ICC) is a different animal: a treaty court created by the Rome Statute (adopted 17 July 1998, in force 1 July 2002) that tries individuals for genocide, crimes against humanity, war crimes and aggression. It is a court of last resort: under complementarity, it acts only when national courts are unable or unwilling to genuinely investigate. The ICC says 125 states were parties in mid-2026; several states have filed notices of withdrawal that take effect in 2027. The US, China, Russia and India are not parties. Supporters say it ended the idea that rank protects; critics say its docket has been uneven and its cases dependent on state cooperation for arrests. Both statements describe real features: the Court has no police of its own. Interactive · Which forum? Predict, then reveal Next case Human rights courts: Europe, the Americas and Africa Human rights law turns the traditional picture around. Treaties between states used to be about what states owed each other. After the horrors of the Second World War, the Universal Declaration of Human Rights (General Assembly, 10 December 1948; adopted 48 to 0 with 8 abstentions) said every person has rights against their own government. The Declaration is not a treaty. Two treaties made it binding in 1966 (in force 1976): the Covenants on Civil and Political Rights and on Economic, Social and Cultural Rights. The global system mostly works through committees that review reports. The sharper teeth are regional. SystemFounding textWho can bring a caseCourt Europe (Council of Europe)European Convention on Human Rights, signed 1950, in force 1953Any individual, directly, after exhausting national remedies (since 1998)European Court of Human Rights, Strasbourg The Americas (OAS)American Declaration (1948); American Convention (1969, in force 1978)Individuals go to the Commission (Washington) first; the Commission or a state brings the case to the CourtInter-American Court, San José, Costa Rica Africa (AU)African Charter on Human and Peoples' Rights (1981, in force 1986)The Commission (Banjul); direct individual access to the Court only if the state made a special declarationAfrican Court, Arusha, Tanzania The European system is the most used: its judgments are routinely followed, partly because they are supervised by the Council of Europe's ministers, and partly because Europeans' own courts cite them. Russia was expelled from the Council of Europe in March 2022 and ceased to be a party to the Convention in September 2022. The Inter-American Court's landmark is Velásquez Rodríguez v. Honduras (1988): a student who disappeared at the hands of state agents. The Court held that a state must prevent, investigate and punish such violations, even when it did not itself commit a particular one. Later it developed "conventionality control": national judges should check domestic law against the Convention. The African Charter is distinctive in listing the rights of peoples and the duties of individuals alongside individual rights. Its Court's reach is thinner: of the few states that accepted direct individual petitions, several (including Rwanda, Tanzania, Benin and Côte d'Ivoire) have since withdrawn the declaration. The same pattern appears elsewhere: courts are strongest where governments choose to leave their doors open. Rights are also not the same everywhere. The Charters differ on which rights, how they are limited, and whose duties come with them. A fair summary is that they share a core (life, freedom from torture, fair trial) and differ at the edges. Enforcement without a world police If nobody enforces, why comply? Lawyers and political scientists give overlapping answers, and they do not all agree on the weights. Reciprocity: a state that breaks a treaty invites others to break it back. Reputation: a state known to cheat finds fewer partners next time, and nearly every state is in a long series of repeated dealings. Countermeasures and sanctions: injured states can respond in proportion; the Security Council can impose sanctions. Domestic courts and politics: in many countries judges apply international law, and a government answers to voters, press and its own officials. Habit and shared interest: air traffic, mail and borders work because everybody gains from the rule. Realists such as Hans Morgenthau and, more sceptically, Jack Goldsmith and Eric Posner argue that states follow law when it matches their interest and that the rule's apparent force is mostly coincidence. Institutionalists and Henkin's tradition argue the rule itself changes states' calculation and what they can justify. The data cannot settle this, because the easy cases are chosen by the states themselves. What both sides agree on: the cases of defiance are costly and rare, and powerful states can resist more cheaply. Interactive · Should this state break the rule? Gain from breaking it Chance the breach is noticed and attributed Cost of retaliation if noticed Value of a good reputation for keeping promises No veto (retaliation fully applies) Holds a veto (Council sanctions off the table) Expected net gain = gain − chance × (retaliation + reputation lost). In this toy half of retaliation runs through the Security Council, so a veto-holder escapes that half. Invented numbers, in arbitrary units: it shows how the factors interact, not what any real state would do. Drag the chance of being noticed to 100% and the reputation value up: even a big gain stops paying. Drag it to 10% and the same rule becomes a suggestion. That is why transparency (inspections, reporting, a free press) does so much work in international law: it raises the chance of being noticed. Who judges the judges: universal jurisdiction, immunity and the critics One more route fills part of the gap. Some crimes (torture, genocide, war crimes) are so serious that any state's courts may try them wherever they happened: universal jurisdiction. In October 1998 British police arrested the former Chilean ruler Augusto Pinochet in London on a Spanish judge's warrant; the House of Lords held that a former head of state could not claim immunity for torture, and he was eventually sent home on health grounds rather than tried abroad. A US court in Filártiga v. Peña-Irala (1980) heard a Paraguayan family's claim over a torture death. The offence was committed abroad by a foreigner. It cuts both ways. Immunity protects serving heads of state and diplomats from foreign courts, to let governments function (the ICJ confirmed it in the Arrest Warrant case, 2002). Critics from Africa and the Global South have charged that universal jurisdiction and the ICC are used against weaker states while great powers escape; defenders answer that the answer to selective justice is more justice, and that national courts, including Argentina's under universal jurisdiction, have opened cases the home courts would not. The honest summary: international law is a real, imperfect, evolving legal order, strongest where states have chosen to bind themselves and weakest where power and law collide. What a graduate just sees Ask first: which source, treaty or custom? A treaty binds only parties; custom can bind everyone; jus cogens binds even over a treaty. Consent is the master key: jurisdiction, treaties and judgments all begin with a state saying yes. Reading a news item about a court, ask "which court, who can sue, who is the defendant?" before anything else. The ICJ tries states, the ICC tries people. A veto is not a ruling that the rule is gone, only that the Council will not enforce it. Compliance is the norm and defiance the news; most of the work is done by reciprocity, reputation and being watched. The regional human rights systems have different founders, different doors, and different records. "International" is not one thing. Whenever a state is accused, the first reply is usually jurisdiction or immunity, not the facts. Learn the objection as well as the claim. Where it shows up next European Union Law (35/11): the same ideas pushed further. The EU's court made its treaties directly effective for individuals, which is exactly what an ordinary treaty does not do. International Relations (35/14): realists, liberals and constructivists disagree about precisely the compliance question above. Constitutional Law (35/03): how a constitution lets treaties in (see the table above) is part of every national legal system. In the world: trade disputes at the WTO, maritime boundaries and fishing rights, refugee protection, sanctions, war crimes investigations, climate treaties, and the ICC's Ukraine and Palestine investigations. If you want to go deeper 📘 Textbook. Malcolm Shaw, International Law (Cambridge University Press, 9th edition, 2021). A widely assigned, comprehensive survey. 📘 Textbook. James Crawford, Brownlie's Principles of Public International Law (Oxford, 9th edition, 2019). The other standard choice, strong on sources and state responsibility. 📘 Gentler. Philippe Sands, East West Street (2016): how two lawyers, Lauterpacht and Lemkin, invented "crimes against humanity" and "genocide", told as a family detective story. 🌐 Free course. The United Nations Audiovisual Library of International Law: free lectures by leading international lawyers, plus the texts of the treaties themselves. The takeaway International law is law made by the states it binds: treaties by consent, custom by practice believed to be obligatory. The UN Charter bans force and gives enforcement to a Council with five vetoes; the ICJ judges states if they consent; the ICC tries individuals when national courts will not; regional courts give people direct rights where governments allow. With no police, it works through reciprocity, reputation and being watched, which is why it usually holds and sometimes fails where it matters most. Try this: next time international law is in the news, write down three things before reading the story: which source (treaty or custom), which court or body, and whether that body's jurisdiction depends on the accused state's consent. Sources & Further Reading Sources. Statute of the ICJ, Art. 38; Vienna Convention on the Law of Treaties 1969 (Arts. 26, 53, 84); UN Charter (Arts. 2(4), 27, 51, 94); ICJ, North Sea Continental Shelf (1969), Nicaragua v. United States (1986), Arrest Warrant (2002), and the order of 16 March 2022 in Ukraine v. Russian Federation (13 to 2); Rome Statute (adopted 17 July 1998, in force 1 July 2002; 125 states parties per the ICC Assembly of States Parties Presidency, June 2026, with withdrawals by Burkina Faso, Mali and Niger taking effect in 2027); ECHR, American Convention and African Charter texts; Council of Europe resolution and Russia's cessation of membership (March and September 2022); Velásquez Rodríguez v. Honduras (Inter-American Court, 1988); African Court Art. 34(6) declarations and the withdrawals of Rwanda, Tanzania, Benin and Côte d'Ivoire (African Court and Pretoria Centre for Human Rights statements); Henkin, How Nations Behave (1979); Goldsmith & Posner, The Limits of International Law (2005). Constitutional article numbers are from the national texts and should be checked against current versions. The interactives' numbers are toy models computed in node; thresholds from the treaty articles. 🌐 UN Audiovisual Library of International Law 📘 Shaw, International Law; Crawford, Brownlie's Principles of Public International Law 📘 Sands, East West Street Step 14. Public Economics: What Governments Can and Can't Fix ------------------------------------------------------------- Why: Taxes, public goods and the welfare state. Why tax at all, who really pays it, what redistribution costs, how inequality is measured, and why the state fails in its own ways. Look at your last pay slip, or at a shop receipt. Part of every price you pay and every wage you earn goes to a government, and the road you walked on, the water you drank and the vaccine your grandparents took were paid for that way. In Denmark that share is about 45% of what the whole economy produces; in Mexico, 18%. In Brazil the richest tenth gets close to 60% of income; in Sweden, under 30%. Nobody voted to make these numbers exactly what they are, yet they shape everything from how long you wait for a doctor to whether your child can climb out of poverty. A public economics graduate learns to ask two questions of any policy. Does it fix a real failure of markets, and who ends up paying and gaining once everybody has reacted? The first is a question about efficiency, the second about fairness, and the discipline's honesty comes from keeping them separate and keeping a third, awkward one in view: governments fail too. The course in one box Who takes itEconomics students as a field course in year 2 or 3 (MIT lists it as 14.41, Public Finance and Public Policy), and public-policy schools as part of the core. Business students meet its tax half in their finance courses. What it is forJudging what governments should do in an economy, what taxes and benefits really do to people, and what they cost in lost output. Comes afterMicroeconomics (34/42): surplus, elasticity and externalities do all the work. Leads toNo subject in the library requires it yet. It is the policy end of the Economics major, next to Macroeconomics (34/43) and Development Economics (34/48). What this piece gives youYou won't be able to design a tax code. You will see why who writes the cheque is not who pays, why cutting inequality and growing the pie can pull against each other, and how much richer countries actually redistribute. Easier first reads: How Taxes Actually Work (03/07), Where Public Money Goes (18/08), The Mixed Economy (31/05) and Climbing the Ladder: What the Data Says About Social Mobility (30/04). Why governments are in the economy at all: public goods Markets are good at things that can be bought, one buyer at a time. They are bad at things whose benefits spill over to everyone. A public good is non-excludable (you can't keep non-payers out) and non-rival (my use doesn't reduce yours): clean air, a lighthouse, defence, a vaccination campaign. Each person would prefer that someone else pays, and the result is that a private market supplies too little. This is the free-rider problem, and it is the oldest justification for taxation: a way to make everyone pay for something everyone wants. The game below is the classic experiment. Four players each hold 20 tokens. Whatever goes into the shared pot is multiplied by 1.6 and split equally, so each token you give returns 0.4 to you and 0.4 to each of the others. Interactive · The shared pot You give The others give, on average Tokens = money. Payoff = 20 − your gift + 0.4 × (all gifts). Invented game parameters. Whatever the others do, you always do better by giving nothing: every token you give costs you 1 and returns 0.4. But if all four give nothing, each ends with 20, while if all four give everything, each ends with 32. The best private choice and the best group result point in opposite directions. In real experiments people usually start by giving around half, and contributions drift down over repeated rounds, though a core of givers remains; allowing players to punish free riders keeps contributions higher. Governments do the "punishment": they make paying compulsory. Elinor Ostrom (Nobel 2009) added that communities can often solve the problem themselves, so tax is one solution, not the only one. Insurance and information: why some markets need a mandate Insurance is a market for sharing risk. It can fall apart when buyers know more about their own risk than the seller does: adverse selection, the problem George Akerlof described for used cars in 1970 and which won him a share of the 2001 Nobel Prize. If the insurer charges the average price, the low-risk customers find it too dear and leave; the pool gets sicker; the price rises; more leave. Governments respond with mandates (everyone must be in), public insurance, or subsidies. Interactive · The insurance spiral Voluntary Everyone must join How much people value cover relative to their expected cost Illustrative: 1,000 people with expected yearly costs from 0 to 999, evenly spread. Each will pay up to the multiple times their own expected cost. The premium is the average cost of those who buy. If people value cover at exactly their expected cost (multiple 1), a voluntary market collapses to almost nobody. At 1.3 the premium settles at 812 and only 37.5% buy. At 2 it settles at about 666 and two thirds buy. Even a keen market never covers everyone. A mandate fixes the premium at the pool average, 500, and covers everyone, but it forces in those who value cover at less than that price: 38% at multiple 1.3. Mandates trade a market failure for a compulsion, which is why the argument over them (Switzerland and the Netherlands require everyone to buy private health cover; the UK and Canada fund care from taxes instead) is fierce. Who really pays a tax The law says who must send the money. Tax incidence asks who ends up poorer. Put a tax on sellers and they raise the price; put it on buyers and they bid less. In both cases the price buyers pay and the price sellers keep move apart by the tax, and the split depends only on who can walk away more easily. The side that is less responsive to price carries more of the burden. share paid by buyers = supply elasticity ÷ (supply elasticity + demand elasticity)elasticities in size. Same answer whoever the law names. Short: the stickier side pays Interactive · A 10-unit tax on a 100-unit good Law names the sellers Law names the buyers Buyers' price sensitivity (demand elasticity) Sellers' price sensitivity (supply elasticity) Small-tax approximation in a linear market. The 10 and 100 are invented. With demand elasticity 0.5 and supply 1.5 buyers pay 75% of the tax, whichever side the law names. That is why economists are suspicious of a payroll tax "paid half by the employer": the split on the form is a legal fiction, and the usual finding is that workers bear most of it through lower wages. And it is why a tax on something people cannot easily give up, like fuel in the short run, raises revenue and mostly falls on the buyer. Efficiency and equity: the leaky bucket Taxes that move money from richer to poorer lose some on the way, through administration and, more importantly, because people change what they do. The economist Arthur Okun called this the leaky bucket (1975): carry a bucket of money from rich to poor and some leaks out. How big is the leak? It depends on how much people cut work, move income or avoid tax when the rate rises, which economists summarise as the elasticity of taxable income, e. In a simple model where the tax base shrinks by (1 − t)e, revenue t(1 − t)e peaks at a rate of 1 ÷ (1 + e): beyond that, a lower rate would collect more. This is the Laffer curve. Interactive · How high can a rate go? Tax rate on extra income How strongly people respond (e) Textbook toy: taxable income = (1 − t)e. Real estimates of e vary a lot between people, countries and time horizons, and are debated. With e = 0.5 revenue peaks at a rate of 67%; with e = 1, at 50%. Most of the argument is not about the curve, which almost nobody denies, but where real countries sit on it and how large e is for high earners. Equity-minded economists answer that a leak does not mean the bucket should stay empty; efficiency-minded ones that growth is the best antipoverty tool. Both statements are true. The disagreement is over weights and measured responses, so ask for both. Inequality, and how it is measured Inequality is easy to feel and hard to summarise. The standard picture is the Lorenz curve: line everyone up from poorest to richest and plot the share of income held by the bottom 10%, 20%, and so on. Perfect equality is a straight diagonal; the more it sags, the less equal. The Gini coefficient (Corrado Gini, 1912) is the area between the diagonal and the curve, doubled, so that 0 means everyone has the same and 1 means one person has everything. It is one number, and it hides shapes: two very different distributions can have the same Gini. For example, a society where half the people have nothing and the other half share equally has a Gini of 0.5, exactly like one where the bottom three quarters hold a quarter of the income and the top quarter three quarters. Top shares tell the other half of the story. Interactive · Build a Lorenz curve Equal Sweden / Norway-like India South Africa Share of income held by the bottom 50% Share of income held by the top 10% Three-slice approximation (bottom 50%, middle 40%, top 10%), so it understates the true Gini. The default is the global distribution in the World Inequality Report 2026 (top 10% about 53%, bottom half about 8%, pre-tax income); presets are shares reported for those countries in the same report, rounded. The World Inequality Report 2026 (Dec 2025) puts the global top 10% at about 53% of income and the bottom half at about 8%. By country, it reports South Africa's top 10% near 66% and Brazil, Mexico, Chile and Colombia near 60%, India, Thailand and Türkiye above 50%, and Sweden and Norway under 30%. A caution on the other measure: the World Bank's Gini for South Africa is 54.1 (2022), Brazil 50.3 (2024), Mexico 42.6 (2024), the United States 41.8 (2024), Germany 33.7 (2022), Sweden 29.3 (2023), Slovenia 24.7 (2023). These measure income or consumption per person from household surveys, which tend to miss the very top; the report's figures use tax and national accounts to see more of it. The two kinds of number will not agree and neither is wrong. The welfare state across countries The modern welfare state grew from Bismarck's social insurance laws in 1880s Germany and from Britain's 1942 Beveridge Report, which named five "giants" to slay: want, disease, ignorance, squalor and idleness. Today governments differ in how large they are and in how they spend: through cash for pensions and the unemployed, services like health and school, or tax breaks. The OECD (rich-country club) measures the size of the state's take as tax revenue as a share of GDP: 34.1% on average in 2024, from 18.3% in Mexico to 45.2% in Denmark. The same OECD database measures inequality before taxes and transfers ("market income") and after ("disposable income"). The gap is what the tax-and-benefit system does. Pick a country. Interactive · What does the state change? Tax revenue incl. social contributions, % of GDP, 2024 (OECD Revenue Statistics 2025). Gini from the OECD Income Distribution Database, latest year available (2022 to 2024), whole population. The OECD treats public pensions as transfers, not market income, so ageing countries show larger cuts. Nordic and continental European countries start with market-income Ginis around 0.43 to 0.52 and end at 0.28 to 0.33, a cut of a third or more. The United States starts about as unequal as the UK and France (0.51) and ends at 0.39. Mexico barely moves: 0.416 to 0.400, a cut of 4%, consistent with its small tax take. Notice also that the rich-country spread is mostly in how much they redistribute, not in how unequal the market leaves them. The sociologist Gøsta Esping-Andersen's Three Worlds of Welfare Capitalism (1990) sorted rich states into liberal, conservative and social-democratic types; most scholars now use more families, and regard the groups as tendencies. Government failure: the same logic, pointed the other way If markets can fail, so can the people who fix them. Public choice economics (James Buchanan, Nobel 1986, and Gordon Tullock) applies the same assumptions to voters, politicians and civil servants as to shoppers: they respond to incentives. Mancur Olson's The Logic of Collective Action (1965) explained why small groups with a lot at stake organise better than large groups with a little each. A subsidy that gives each of 50 firms 2 million and costs each of 100 million taxpayers 1 will be lobbied for by the firms and ignored by the taxpayers (illustrative numbers). Anne Krueger called the effort spent winning such favours rent-seeking (1974). Other failures are about knowledge: planners seldom know what local people know; programmes outlast their purpose; and well-meant rules cause side effects. The sound reply is not "government never works", which smallpox (declared eradicated by WHO in 1980) refutes, but Harold Demsetz's warning against the "nirvana fallacy" (1969): comparing a real market with an ideal government, or the reverse, instead of one real option with another. A graduate asks, for each policy, who gains, who pays, who is watching, and what happens after the first round of reactions. What a graduate just sees Public goods go unsupplied because everyone prefers to free-ride; compulsion or good rules fix that. When buyers know less than sellers, or the reverse, markets can unravel. Insurance is the case study. The law says who sends the cheque; elasticities say who pays. The less flexible side bears more. Redistribution has a leak, and the size of the leak is an empirical question, not an ideology. A Gini is one number. Ask for top shares, the bottom half and the time frame. Rich countries differ less in market inequality than in how much their taxes and benefits change it. Governments fail from incentives, not just stupidity: concentrated gains beat diffuse costs. Compare real policies, not a flawed market with a perfect state or the reverse. Where it shows up next Macroeconomics (34/43): the budget deficit, the debt ratio and the multiplier are this subject at the scale of the whole economy. Development Economics (34/48): why many poorer countries collect far less tax (Nigeria 8% of GDP, Kenya 16%, Mexico 18%, in the OECD's global series) and what that does to schools and roads. Behavioral Economics (34/46): defaults and nudges in pension and tax design. In the world: a sugar tax, a carbon price, a minimum pension, a budget speech, the debate over a wealth tax or a universal income. If you want to go deeper 📘 Textbook. Jonathan Gruber, Public Finance and Public Policy (Worth/Macmillan). The usual undergraduate text, strong on evidence from natural experiments. 📘 Textbook. Harvey Rosen and Ted Gayer, Public Finance (McGraw-Hill). Another standard choice, a bit more theory. 📘 Gentler. Anthony Atkinson, Inequality: What Can Be Done? (Harvard University Press, 2015), or Branko Milanovic, Global Inequality (Harvard University Press, 2016): clear on the data, with proposals set out for and against. 🌐 Free course. MIT OpenCourseWare 14.41 Public Finance and Public Policy (Fall 2024). Also World Inequality Report 2026, free online. The takeaway Governments belong in an economy where prices miss something: public goods nobody will buy alone, insurance that unravels, spillovers that land on others. Who writes the cheque for a tax is not who pays it. Redistribution has a leak that is real and measurable, inequality has to be measured with more than one number, and the state is subject to the same incentives it tries to correct. Try this: this week, take one tax or subsidy you have heard of and write down who legally pays, who you think really pays, and which side of the market could walk away more easily. Sources & Further Reading Sources. Concepts: standard textbook public finance (Gruber; Rosen and Gayer). OECD, Revenue Statistics 2025 (December 2025): OECD average 34.1% of GDP in 2024 (an estimate: unweighted average change for 36 countries applied to 2023), range 18.3% (Mexico) to 45.2% (Denmark); countrywide figures here are 2024 values for France 43.5, Italy 42.8, Sweden 41.4, Germany 38.0, UK 34.4, Brazil 33.7, United States 25.6, Korea 25.3, Chile 20.5; Kenya 15.6 (2024) and Nigeria 8.2 (2023) from the OECD's global revenue series. OECD Income Distribution Database, Gini before and after taxes and transfers: Denmark 2022 0.442 / 0.276; France 2023 0.517 / 0.299; Italy 2023 0.509 / 0.325; Sweden 2024 0.431 / 0.289; Germany 2023 0.497 / 0.307; UK 2023 0.522 / 0.367; Brazil 2022 0.523 / 0.451; Korea 2023 0.392 / 0.323; US 2023 0.506 / 0.394; Chile 2022 0.491 / 0.448; Mexico 2022 0.416 / 0.400. World Bank Poverty and Inequality Platform Gini (SI.POV.GINI), latest, accessed November 2026. World Inequality Report 2026 (World Inequality Lab, December 2025), headline shares as reported by Al Jazeera, 10 December 2025 (global top 10% 53%, bottom half 8%; South Africa 66% and 6%; Sweden and Norway top 10% under 30%, bottom half about 25%) and by Indian press for India (top 10% about 58%, bottom half about 15%); check the report itself for exact figures. Gini definition and the same-Gini example: Corrado Gini 1912, as described on Wikipedia. Mexico's tax, public goods experiments, Fehr and Gächter 2000: summary in Wikipedia articles. Akerlof, "The Market for 'Lemons,'" QJE 1970 (Nobel 2001); Ostrom, Governing the Commons (1990; Nobel 2009); Buchanan Nobel 1986; Olson 1965; Okun, Equality and Efficiency (1975); Krueger 1974; Demsetz 1969; Beveridge Report, published 1 December 1942 (some sources date it November 1942); Bismarck's social insurance in the 1880s; Esping-Andersen 1990; WHO declared smallpox eradicated in 1980. Computed in node: public goods game payoffs 20 (nobody gives) and 32 (all give); insurance equilibrium 812 / 37.5% at 1.3, 666 / 66.7% at 2, forced-in share 50 ÷ multiple; incidence shares es ÷ (es + ed); Laffer peak 1 ÷ (1 + e) = 67% at 0.5, 50% at 1; Ginis from the three-slice curve: global 0.59, South Africa 0.68, India 0.56, Sweden/Norway 0.32. 🌐 MIT OCW 14.41 Public Finance and Public Policy 🌐 World Inequality Report 2026 📘 Gruber, Public Finance and Public Policy 📘 Atkinson, Inequality: What Can Be Done? Step 15. Social Psychology: How Other People Change What We Do -------------------------------------------------------------- Why: Conformity, obedience and group identity. The classic experiments say that ordinary people go along, obey and look away. The re-checked versions say something more careful, and more interesting: the situation matters a great deal, and so does what people think the situation is. In 1964 a young woman was murdered in Queens, New York, and a newspaper told the country that thirty-seven neighbours watched and did nothing. The story became the opening page of nearly every social psychology textbook. It has also, in the decades since, turned out to be wrong in most of the details that made it shocking. That arc, a vivid story, a famous experiment, a tidy lesson, then a slow correction, is a fair summary of the whole subject. Social psychology asks how the presence of other people changes what one person thinks, says and does. Its best ideas survive. Many of its best-known demonstrations were less clean than the textbooks said. A graduate of this course keeps both: the ideas, and the habit of asking how a classic study was actually done. The course in one box Who takes itPsychology majors, usually in year 2; it is one of the core areas in the British Psychological Society's accreditation list and in the American Psychological Association's guidelines for the undergraduate major. Sociology, business and law students often take it as an elective. What it is forExplaining how groups, authority, first impressions and beliefs about other people shape behaviour, and how to tell a solid finding from a famous one. Comes afterResearch Methods in Psychology (35/19), which is where you learn why a single study proves little. Leads toOrganizational Behavior (34/53) and Consumer Behavior (34/58), and it sits beside Behavioral Economics (34/46) and Developmental Psychology (35/24). What this piece gives youYou will not be able to run an experiment. You will know six classic findings, what each actually showed, what its critics say, and how well it has held up. Easier first reads: The Biases That Fool Everyone (17/02) and Why You Understand Less Than You Think (17/10). Conformity: Asch and the line that was obviously wrong Solomon Asch's question was simple: will people state something they can plainly see is false, just because a group does? In his experiments (reported from 1951), a volunteer sat with several others, all but one of them secretly working with the experimenter, and judged which of three lines matched a standard line. The answer was obvious. On 12 of the 18 trials the confederates, one after another, gave the same wrong answer, and the real participant answered near the end. Alone, people made fewer than 1% errors. In the group, about 36% of answers on the critical trials went along with the wrong majority. About three quarters of participants conformed at least once, and about a quarter never did. That last number is the one textbooks tend to leave out: most answers were still correct, and a survey of textbooks in 2015 found that only one of twenty major texts reported that most responses defied the majority. The size of the group matters up to a point. A single dissenting-looking companion barely moves people; two or three make a real difference; beyond about three or four the effect levels off. The tool below draws that curve and lets you run a toy group of fifty simulated volunteers. Interactive · How many confederates does it take? People in the room giving the wrong answer Run 50 simulated volunteers Curve is approximate and rounded, following the commonly reported shape of Asch's group-size results; secondary sources differ by a point or two. The 50 volunteers are a toy model with a made-up spread of suggestibility (some people never conform), not data. Without JavaScript: with three or more people giving the wrong answer, about a third of answers conformed in Asch's experiments; with one, only a few per cent. Why did they conform? Asch interviewed them. Some doubted their own eyes, some knew the group was wrong but did not want to stand out, and a few came to see the lines the group's way. Psychologists later split this into informational influence (the group probably knows something) and normative influence (disagreeing has a social cost). John Turner and colleagues argued that the data fit a third idea better: we follow those we see as "people like us". Conformity also varies by place and time. A 1996 meta-analysis by Bond and Smith, covering 133 studies in 17 countries, found more conformity in more collectivist societies and a decline in the United States over the decades after the 1950s. Obedience: Milgram and the shock machine In 1961–62 at Yale, Stanley Milgram told volunteers they were helping a study of learning. A "teacher" read word pairs to a "learner" in the next room and gave an electric shock for each mistake, rising in 15-volt steps to 450 V. The learner, an actor, protested and eventually fell silent. If the teacher hesitated, the experimenter in a grey coat gave a fixed series of prods, the first being "Please continue." In the best-known condition, 26 of 40 men (65%) went all the way to 450 V. The number was shocking, and so was Milgram's reading of it: ordinary people will harm a stranger when an authority tells them to. But Milgram ran many variations, and they are the real finding. Obedience was not a trait of the people; it moved with the situation, from nearly everyone to hardly anyone. Interactive · One experiment, many results Percentages who went to the maximum 450 V, as reported for Milgram's variations (about 40 participants each). Telephone instructions are often given as 20.5% or 21%. Without JavaScript: baseline 65%; learner in the same room 40%; learner's hand forced onto the plate 30%; instructions by telephone about 21%; run-down office 47.5%; two fellow "teachers" refuse 10%; a fellow "teacher" who delivers all the shocks 92.5%. The critics are serious. Gina Perry, who went through Milgram's archive for her 2012 book Behind the Shock Machine, argued that the published account did not match what she found, including that only about half of participants fully believed the shocks were real, and that two thirds of those who did believe disobeyed. Alex Haslam and Stephen Reicher argue that people did not simply obey: they went on when the prod appealed to science ("The experiment requires you to continue") and stopped when it sounded like a bare order. Their reading is "engaged followership": participants identified with the study's goal, not with the learner. A 2009 partial replication by Jerry Burger, which stopped at 150 V for ethical reasons, found rates close to Milgram's at that point. The shock is real; the "people are robots" reading is not. Attribution: why we blame the person and excuse ourselves You see a driver cut across three lanes. You think: what a jerk. When you do it, the exit was suddenly there and the mirror was blocked. Attribution theory, started by Fritz Heider in the 1950s, studies how we explain behaviour: by something in the person (a disposition) or something in the situation. The consistent human habit is to over-read the person. Lee Ross named it the fundamental attribution error in 1977. The classic demonstrations: in 1967 Edward Jones and Victor Harris had people read an essay praising or attacking Fidel Castro, and told them the writer had been assigned the side. Readers still guessed the writer leaned that way. In Ross, Amabile and Steinmetz's 1977 quiz game, students were assigned by coin toss to write hard questions or to answer them. The questioners looked cleverer to everyone, including the answerers, though the role had made it so. Harold Kelley's 1967 covariation model gives a tidy rule for reasoning well. Ask three questions about the behaviour: does everyone do it (consensus), does this person do it to everything (distinctiveness), does this person do it every time (consistency)? Interactive · Sam laughs at a comedian. Why? Consensus: do other people laugh at this comedian? Yes, most doNo, hardly anyone Distinctiveness: does Sam laugh at other comedians too? No, only this oneYes, at nearly all Consistency: does Sam laugh at this comedian every time? Yes, every timeNo, only tonight Kelley's three textbook patterns are shown; the other five combinations are ambiguous and the model says so. Later studies (McArthur, 1972) found that people tend to under-use the consensus information. Without JavaScript: low consensus, low distinctiveness and high consistency points to the person; all three high points to the comedian; low consensus, high distinctiveness and low consistency points to the circumstances of tonight. Culture matters here too. In a well-known study Michael Morris and Kaiping Peng (1994) found that American and Chinese participants explained the same animated fish scene differently, Americans leaning on the fish's inner traits and Chinese participants on the group around it. It fits the larger point that much of social psychology was built on university students in a few rich countries, which Joseph Henrich, Steven Heine and Ara Norenzayan called WEIRD samples (Western, Educated, Industrialised, Rich, Democratic) in 2010. Attitudes and persuasion: when behaviour changes belief Common sense says attitudes drive behaviour. A good deal of the time it runs the other way, and Leon Festinger's cognitive dissonance theory (1957) says why: holding two clashing thoughts is uncomfortable, and the cheapest fix is often to change the belief, not the act. The famous test (Festinger and Carlsmith, 1959) had 71 male Stanford students do dull peg-turning, then asked some to tell the next person it was fun, for either $1 or $20. Which group came to like the task more? Interactive · Predict, then reveal Afterwards, who said the boring task had really been enjoyable? The $1 groupThe $20 groupNeither: no difference Pick one, then read the result. Without JavaScript: the $1 group rated the task as more enjoyable than the $20 group, the opposite of what simple reward would predict. The $1 group did, and the $20 group only slightly more than people who were never asked to lie. Dissonance theory says $1 is too little to justify the lie, so the mind adjusts the belief; $20 is justification enough. The theory has had rivals (Daryl Bem's self-perception account) and refinements, and the "free-choice" variant, in which people supposedly like what they chose more, has been criticised for a statistical flaw. But the core pattern, that acts shape beliefs especially when we feel responsible for them, is one of the more durable ideas in the field. On persuasion itself, Richard Petty and John Cacioppo's elaboration likelihood model (1986) says there are two routes: a central one, where people weigh arguments when they are motivated and able, and a peripheral one, where cues such as a confident speaker or a famous face do the work. Strong arguments matter most when people are paying attention. When they are not, packaging does. And attitudes predict behaviour only loosely: Richard LaPiere's 1934 trip with a Chinese couple found almost every hotel and restaurant served them, while most of the same businesses later said by mail that they would not. What people say and what they do can differ. Groups and prejudice: how little it takes to make "us" In the early 1970s Henri Tajfel divided schoolboys in Bristol into two groups by a trivial rule (they were told it was a preference for paintings by Klee or by Kandinsky) and then asked each boy to hand out points to anonymous members of the two groups. The boys knew none of them and would gain nothing. Yet they favoured their own group, and often chose options that gave their group fewer points in total as long as the gap over the other group got bigger. This minimal group paradigm underlies social identity theory: part of who we are comes from the groups we belong to, and we want our group to look good against others. Interactive · Hand out the points You are in the "blue" group. Choose one column: points for an anonymous blue member (top) and an anonymous "green" member (bottom). Choose a pair. An invented matrix in the spirit of Tajfel's, not his actual numbers. Real participants were schoolboys, and what they picked on matrices designed to pull the strategies apart is what the study reported. Without JavaScript: in the matrix, giving 13 points to both groups gives each 13; giving 7 to your group and 1 to the other leaves a gap of 6 but costs your group 6 points. Muzafer Sherif's Robbers Cave study (Oklahoma, 1954) is the famous other half: twenty-two boys at a summer camp, split into two groups, competed for prizes and soon despised each other, then made peace when forced to cooperate on shared goals. The conclusion that competition breeds hostility and common goals reduce it is widely taught. The critique is that the story is tidier than the record: earlier versions of the study in 1949 and 1953 did not produce the same result (in one, the boys turned on the experimenters), and Michael Billig has argued that the staff were effectively a third group steering events. What has held up better is Gordon Allport's 1954 contact hypothesis: contact between groups reduces prejudice under conditions such as equal status and shared goals, supported by a large meta-analysis of hundreds of studies by Thomas Pettigrew and Linda Tropp. Some newer popular ideas deserve caution. The Implicit Association Test is widely used to measure unconscious bias, but its scores are only modestly stable from one session to the next and predict individual behaviour weakly, and changing a score has not reliably changed behaviour. It may tell you about group-level patterns better than it tells you about a person. Bystanders: the story and the evidence The Kitty Genovese story, as taught for decades, said thirty-eight people watched a murder from their windows and did nothing, out of apathy. In 2007 Rachael Manning, Mark Levine and Alan Collins went back to the record: far fewer people had a clear view, many who heard noise did not realise what was happening, and at least one person called the police. In 2016 the New York Times itself called its original reporting flawed and said it had grossly exaggerated the number of witnesses. The better evidence is laboratory. In 1968 John Darley and Bibb Latané had students talk by intercom, one at a time, with others in separate rooms; one of the "others" seemed to have a seizure. Students who believed they were the only listener reported it before the fit ended 85% of the time. With one other possible listener the figure was 62%, and with four others, 31%. Latané and Darley called the mechanism diffusion of responsibility: with others around, each person feels less personally responsible. Interactive · One person, or the whole room? The 85, 62 and 31 are from Darley and Latané (1968), the proportion reporting before the fit ended. The "someone helps" row is arithmetic that assumes each listener acts independently, which real groups, who also watch each other, will not. Without JavaScript: each person is less likely to act in a group (85%, 62%, 31%), but if five listeners each had a 31% chance, the chance that at least one acts is about 84%, close to the lone person's 85%. Look at what the arithmetic says. The effect is real for each individual, but the group as a whole may still produce help about as often as one person would. That is why later work added caveats. A 2011 meta-analysis by Peter Fischer and colleagues found the bystander effect strong in non-dangerous emergencies and weaker in dangerous ones, and weaker when bystanders are not strangers. A 2020 study by Richard Philpot and colleagues of more than 200 real CCTV recordings of street conflicts in the UK, the Netherlands and South Africa found that in over 90% someone intervened, and more bystanders meant more chance of help. The bystander effect is real, but people are not generally apathetic. What held up The Open Science Collaboration (2015) tried to repeat 100 psychology studies; about 36% of the repeats were statistically significant against 97% of the originals, and the repeats showed effects about half the size. Social psychology fared worse than cognitive psychology (about a quarter replicated against about a half). Treat the list below as a rough report card, not a verdict on any one study. Held up well:solid Asch-style conformity in its broad direction; anchoring-like context effects; the contact hypothesis; the bystander effect in non-dangerous settings. Held, but smaller or messier:shrank Milgram's obedience (the rate depends on setup, and the interpretation changed); cognitive dissonance in its simplest forms; the fundamental attribution error (strong in the lab, culture-bound in size). Failed or collapsed:failed Many social-priming effects, such as words about old age making people walk slower; power posing changing hormones; ego depletion. See Research Methods in Psychology (35/19). Disputed:disputed The Stanford Prison Experiment, which lasted six days in 1971, said guards turned cruel by role. Thibault Le Texier (2018) reported from the archive that the guards were coached on how to behave, so it is better read as a demonstration of what people do when told what is expected than as evidence of a natural tendency. What a graduate just sees Most people in an Asch room still gave the right answer; the effect is real and also smaller than the legend. Obedience swings from 10% to 90% depending on the setup, so ask what the situation is, not what kind of person someone is. When judging someone else, check the situation first; when judging yourself, you already do. Acts change beliefs: if you were paid little for something you did, you will find reasons it was worth it. A trivial label is enough to start "us and them"; shared goals are better at undoing it. In a crowd each person feels less responsible, but a named person asked directly usually helps. A famous study is a lead, not a fact: look for the replications, the sample, the critics. Where it shows up next Organizational Behavior (34/53): group decisions, conformity in meetings, authority and safety culture. Consumer Behavior (34/58) and Behavioral Economics (34/46): social proof, defaults, norms, and which of the "nudges" are weaker than advertised. Clinical Psychology (35/27): stigma, group therapy and attribution styles in depression. In the world: jury deliberation, online pile-ons, workplace safety, military ethics, and how emergency courses teach "you, in the red coat, call 999". If you want to go deeper 📘 Textbook. Elliot Aronson, Timothy Wilson and Robin Akert, Social Psychology (Pearson, many editions), or David Myers, Social Psychology (McGraw-Hill). Both are standard undergraduate texts; check for a recent edition that covers replication. 📘 Gentler. Gina Perry, Behind the Shock Machine (2012) on Milgram, and Gina Perry, The Lost Boys (2018) on Robbers Cave. 🌐 Free text. The free Noba psychology textbooks have a module on Conformity and Obedience, written for introductory courses. 🌐 Free course. MIT OpenCourseWare 9.00SC Introduction to Psychology (Fall 2011) is the free introductory course. The takeaway Other people shape what we say, do and believe, and the situation can matter more than character. But the best-known demonstrations are more qualified than their reputations: most people resisted in Asch's rooms, Milgram's results depended on the setup, the Genovese crowd was exaggerated, and several famous effects shrank on re-testing. Try this: the next time someone's behaviour annoys you, write down two situational explanations before you settle on a trait. Sources & Further Reading Sources. Asch, "Opinions and Social Pressure," Scientific American 1955, and the 1951 monograph (about 36% of critical responses conforming; about 74% at least once; control error under 1%); a 2015 survey of twenty textbooks on Asch (and an earlier one in 1990); Turner on referent informational influence; Bond and Smith, Psychological Bulletin 1996. Milgram, Journal of Abnormal and Social Psychology 1963 and Obedience to Authority (1974): variations as summarised in Wikipedia, "Milgram experiment"; Perry 2012; Haslam, Reicher and Birney on engaged followership; Burger, American Psychologist 2009. Jones and Harris 1967; Ross 1977; Ross, Amabile and Steinmetz 1977; Kelley 1967; McArthur 1972; Morris and Peng 1994; Henrich, Heine and Norenzayan, Behavioral and Brain Sciences 2010. Festinger and Carlsmith, 1959 (71 male Stanford students); Petty and Cacioppo 1986; LaPiere 1934. Tajfel, Billig, Bundy and Flament, "Social categorization and intergroup behaviour," European Journal of Social Psychology 1971; Sherif et al., Intergroup Conflict and Cooperation: The Robbers Cave Experiment (1961); Billig; Allport, The Nature of Prejudice (1954); Pettigrew and Tropp, 2006. Darley and Latané, Journal of Personality and Social Psychology 8 (1968), 377–383; Manning, Levine and Collins, American Psychologist 2007; the New York Times acknowledgement of 2016; Fischer et al., Psychological Bulletin 2011; Philpot et al., American Psychologist 2020. Open Science Collaboration, Science 2015; Le Texier, American Psychologist 2019. Calculations on this page (the 84% and 85% group figures, the toy simulation) were run in node. This piece describes research and is not a guide to any individual's behaviour. 📘 Perry, Behind the Shock Machine 📘 Aronson, Wilson and Akert, Social Psychology 🌐 Noba Project