Sociology A path through The Default Library ======================================== How societies hold together, sort people, change and come apart, measured and argued over from every continent. Step 1. Classical Social Theory: What Holds a Society Together, and What Pulls It Apart --------------------------------------------------------------------------------------- Why: Marx, Durkheim, Weber, Du Bois and the questions they set. Seven thinkers, from fourteenth-century Tunis to Atlanta in 1903, asked the questions every sociologist still starts from. The useful way to read them is not as saints or villains, but as seven lenses, each with a blind spot. Two strangers join a club on the same day. One is a founder's nephew with a spare room and a good surname; the other is a new hire with a skill the club needs and no connections. Within a year the club has a rule book, two cliques, a treasurer nobody trusts and a leader whom some members would follow anywhere. Nobody planned any of it. Social theory begins with the feeling that such patterns are real, that they push on individuals who did not choose them, and that they can be explained. The founders of the discipline disagreed about almost everything else: what holds a group together, whether conflict is the engine or the disease, whether to explain people from the outside or the inside. They did not all come from Paris and Berlin, and the canon that leaves out Ibn Khaldun, Harriet Martineau and W. E. B. Du Bois is one that sociologists themselves now call incomplete. This piece takes them in their own terms and then lets their critics answer. The course in one box Who takes itSociology majors, usually in year 1 or 2; theory is a required strand in programmes shaped by the American Sociological Association's guidance and the UK's QAA benchmark for sociology. History, politics and anthropology students often take a version. Theory courses differ in who they include: some stop at Marx, Durkheim and Weber, and more now add the thinkers here. What it is forGiving you the questions and vocabulary (class, solidarity, authority, alienation, the colour line) that every later sociology course assumes. Comes afterNothing: this is the major's entry point. Leads toContemporary Social Theory (35/77), Stratification & Inequality (35/78) and Deviance, Crime & Social Control (35/83); it also feeds Sociological Research Methods (35/76), which asks how anyone checks these claims. History majors meet it as The social theory historians argue with. What this piece gives youYou will not have read the primary texts, which are long and hard. You will know what each thinker asked, the one idea that carries their name, one thing it explains and one thing it misses. Easier first reads: The Pecking Order: Why Every Society Has Hierarchy (30/01) and Class, Status and Power: Marx, Weber and Bourdieu (30/02), which covers class in depth; this piece goes elsewhere in each thinker. Ibn Khaldun: why strong groups rise and soft ones fall Ibn Khaldun (1332–1406) was born in Tunis and worked as a judge, diplomat and adviser across North Africa, Andalusia and Egypt, in a world of rising and falling dynasties. In 1377, in a remote fortress in what is now Algeria, he finished the Muqaddimah, the introduction to a world history, and argued that history needed a new science: the study of umran, human social organisation, with its own regularities that explain events better than the character of kings. His central concept is asabiyyah, group feeling or solidarity: the bond that makes people defend and follow one another without being paid. He saw it strongest in groups living hard lives together, such as desert and mountain peoples, who can conquer a rich, settled city. But rule brings comfort, taxes and delegation. The conquerors' grandchildren grow used to luxury and hire others to fight, solidarity thins, and a new hardy group takes over. As usually summarised, he held that a dynasty rarely lasts beyond three generations of about forty years, around 120 years. Interactive · How long does a dynasty last? Group feeling at the founding (0–100) Softening per generation (comfort, hired help) Group feeling of the hardy challenger A toy model: each generation (about 40 years) keeps a fixed share of the last one's group feeling, and the dynasty falls in the first generation where it drops below the challenger's. These are invented numbers for the idea, not Ibn Khaldun's own equations. Without JavaScript: with group feeling 90, losing 30% per generation, against a challenger at 60, the dynasty runs 90, 63, then 44 and falls in the third generation, about 120 years in. Ibn Khaldun also wrote about taxes (rates that are too high shrink the tax base), the division of labour and why cities concentrate skills. The critics' main points: his cycle fits the Maghrib he knew better than centralised bureaucratic states such as the Ottoman or Chinese empires; "dynasty in three generations" is a pattern and not a law; and calling him the "first sociologist" can say more about European habits of naming founders than about his work. Many historians prefer to read him as a thinker in the Islamic tradition of history and jurisprudence who asked a sociological question six centuries before the word existed. Marx: a society is organised around who makes and who owns Karl Marx (1818–1883) saw that what a society produces, and who controls the means of producing it, shapes its laws, its beliefs and its conflicts. This is the "materialist" part: ideas matter, but they grow in conditions. In Capital (vol. 1, 1867) he asked where profit comes from if goods trade at their value. His answer is that a worker sells her labour power, and a day's work produces more value than her wage replaces. The unpaid part is surplus value; the ratio of surplus to paid labour he called the rate of surplus value (Capital vol. 1, chapters 9–10). The employer's drive to lengthen or intensify the working day is, in this account, built into the system, not a matter of bad character. Class and the three-way contrast with Weber and Bourdieu are in Class, Status and Power (30/02); here is the arithmetic. Interactive · The working day, divided Length of the working day (hours) Hours of work that produce the wage's worth Marx's accounting with invented numbers, to show the idea. Whether value is created by labour at all is disputed (see below), so treat this as a statement of his claim, not a measurement. Without JavaScript: in a 10-hour day where 4 hours produce the worth of the wage, 6 hours are surplus, the rate of surplus value is 150%, and 60% of the day is unpaid in Marx's terms. Marx's critics are many and from every direction. The marginalist economists, led by Eugen von Böhm-Bawerk in the 1890s, rejected the labour theory of value and the logic of the "transformation" from values to prices. Predictions of a steadily shrinking middle and a revolution in the most industrialised countries did not happen, and revolutions came in largely agrarian ones. In the twentieth century, states ruling in his name raised hard questions about how far his ideas answer for them, which his defenders and opponents argue about to this day. Others say he treated colonies as footnotes; recent scholars, such as Kevin Anderson, point to his later writing on India, Ireland and Russia as a more complex view. What survived in sociology is the habit of asking who owns, who decides and whose interests an arrangement serves. Durkheim: social facts and the bonds that regulate us Émile Durkheim (1858–1917) wanted sociology to be a science with its own subject matter: social facts, ways of acting and thinking that exist outside any one individual and press on them, like a language, a legal code or a custom of mourning (The Rules of Sociological Method, 1895). In The Division of Labour in Society (1893) he argued that small, similar communities hold together by resemblance (mechanical solidarity) and complex ones by interdependence (organic solidarity), as in a body whose organs differ. His test case was Suicide (1897). Using official statistics from European countries, he showed that the rate of suicide was steady within a society and differed between groups: for example, it was higher in Protestant than in Catholic populations. He argued that this is a social fact and needs a social explanation. He proposed two dimensions: how strongly people are tied to a group (integration) and how firmly norms guide their wants (regulation). Too little integration gives egoistic suicide; too much gives altruistic; too little regulation, as in sudden boom or bust, gives anomic; too much gives fatalistic. The tool below is a map of the four, a concept diagram and not a risk calculator. It describes societies and groups, not any person. Interactive · Durkheim's two dials Integration: how tightly people belong Regulation: how firmly norms guide wants A teaching diagram. Durkheim's hypothesis is that social pressure toward each type is lowest in the middle of both dials; the scores here are the distance past the midpoint, scaled 0–100, with no data behind them. If you or someone you know is struggling, findahelpline.com lists services by country. Without JavaScript: low integration points to egoistic pressure, very high to altruistic; low regulation to anomic, very high to fatalistic; the middle of each dial is the least pressured. The critique is long and instructive. Maurice Halbwachs (1930) reworked the statistics and found the picture more complicated. Jack Douglas (1967) argued that official suicide figures record how coroners, families and churches label deaths, so differences between groups may reflect differences in labelling. And Durkheim compared whole populations, then reasoned about individuals; the later name for that slip is the ecological fallacy (see Sociological Research Methods, 35/76). He also wrote of "primitive" societies from secondhand reports that anthropologists would not accept today. What lasts is the claim that rates, not just cases, deserve explaining, and that belonging and rules both matter. Weber: authority, meaning and the cage of rules Max Weber (1864–1920) answered Marx differently: material interests matter, but so do ideas, and sociology must understand (Verstehen) what actions mean to the people doing them. His tool was the ideal type, a deliberately pure, exaggerated model (the bureaucrat, the prophet) that helps you see how real cases depart from it. In Politics as a Vocation, a lecture in Munich in January 1919, he defined the state as the body that successfully claims the monopoly of legitimate physical force within a territory. And he asked why people obey. His three pure types of legitimate authority are traditional (it has always been so), charismatic (devotion to an exceptional person) and legal-rational (rules, offices and procedures that bind even the holder). Interactive · Which kind of authority? Traditional Charismatic Legal-rational Pick a type. Next case Without JavaScript: a headman's son inheriting by custom is traditional; a preacher followed for her extraordinary gifts is charismatic; an official obeyed because a written rule says her office may demand payment is legal-rational. Weber's best-known book argument, The Protestant Ethic and the Spirit of Capitalism (1904–05), said that an ascetic, work-as-calling outlook in certain Protestant traditions helped create the disciplined spirit modern capitalism needed. Modern bureaucracy, he feared, would then trap its members in an "iron cage" (Talcott Parsons's 1930 phrase for his image; a recent translation says "shell as hard as steel"). The thesis has been contested ever since. Historians such as R. H. Tawney said capitalism was already growing in Catholic cities; in 2009 Sascha Becker and Ludger Woessmann argued that in nineteenth-century Prussia, Protestant regions' advantage came through literacy and schooling, which Protestant reformers promoted, rather than through an ethic of work. Weber's studies of China and India are also criticised for treating them as the West's opposite. What stays is the idea that authority is a belief people hold, and that the same office can rest on very different foundations. Simmel: the shape of a group changes what happens in it Georg Simmel (1858–1918) asked a different question: not what society is made of, but what forms social life takes, whatever the content: competition, exchange, secrecy, conflict, the stranger. His essay "The Metropolis and Mental Life" (1903) argued that city dwellers cope with the flood of stimuli with reserve and calculation. He is best known to students for the effect of number. A pair (a dyad) is fragile: if one leaves, the group is gone, and each member holds a veto over its life. Add a third person and everything changes: the group can survive a loss, a mediator can calm a quarrel, two can ally against one, and a third can profit from a conflict between the others. Interactive · What a bigger group adds People in the group Counts are arithmetic: pairs n(n−1)/2, three-person subgroups n(n−1)(n−2)/6, all possible subgroups of two or more 2n−n−1. Lines are drawn up to 12 people. Without JavaScript: 2 people have 1 relationship; 3 have 3 pairs and 1 three-person subgroup; 10 have 45 pairs and 120 triads; 30 have 435 pairs. His second idea is the intersection of social circles: modern individuals belong to many groups (a family, an occupation, a faith, a club), and what makes each person distinct is the particular combination. The critics: his essays are brilliant and unsystematic, with few data; Durkheim and Weber built schools, and Simmel, who was long kept off a full professorship in Germany, built readers. His influence went through the Chicago School of urban sociology in the United States, and into network analysis, which counts and maps exactly those ties. Martineau and Du Bois: look first, count, and listen Martineau first. Harriet Martineau (1802–1876), from Norwich in England, was a journalist and novelist who lost most of her hearing in youth and wrote her way to financial independence. In 1834–36 she travelled through the United States and published Society in America (1837) and, a year later, How to Observe Morals and Manners (1838), a guide to studying a society you do not belong to. In 1853 she translated and condensed Auguste Comte's Positive Philosophy (Comte, who coined the word "sociology", approved her version), and it made his work readable in English. Her method, in plain terms: look at institutions and everyday life, not only at laws and leaders; talk to people of every rank; and judge a society against the principles it claims for itself. Applied to America, it turned the nation's creed of equality into a ruler, and measured slavery and the legal position of women against it. The tests are still taught as good practice: compare what people profess with what they do; do not trust only your hosts; know your own prejudices. The critics say she often relied on the people she was introduced to and on a moral sense she took for granted, and that her stage-by-stage view of civilisation, common in her century, ranked other societies in ways sociologists now reject. For a century the discipline mostly forgot her; since the late twentieth century, sociologists have returned her to the founders' list. Then Du Bois. W. E. B. Du Bois (1868–1963) was the first Black American to earn a Harvard doctorate, and the author of what is often called the first sociological case study of a Black community in the United States. Commissioned by the University of Pennsylvania, he and his assistant Isabel Eaton spent about sixteen months, from August 1896 to December 1897, going door to door in Philadelphia's Seventh Ward, and they conducted more than 5,000 interviews. The Philadelphia Negro (1899) set out employment, housing, crime, education and social class in the ward, and argued that the conditions it found came from discrimination and history, not from any inborn difference. He then ran the Atlanta University studies from the late 1890s and made data graphics for the 1900 Paris Exposition. The sociologist Aldon Morris, in The Scholar Denied (2015), argues that Du Bois and his Atlanta colleagues founded a rigorous scientific sociology which the white-led discipline sidelined. In The Souls of Black Folk (1903) he named a lived experience, double consciousness: "this sense of always looking at one's self through the eyes of others." He also wrote that "the problem of the twentieth century is the problem of the color-line." Theory and measurement went together: the same man who wanted to count the line wanted to describe how it felt to cross it. His critics, including later Du Bois himself, pointed to the elitism of the "Talented Tenth" idea of 1903, which put hope in an educated few. Scholars debate what "double consciousness" fully means and how far it travels beyond the American case; later thinkers, such as Frantz Fanon, drew on related ideas in a colonial setting. Seven lenses side by side ThinkerCore questionCarries the nameThe usual objection Ibn KhaldunWhy do dynasties rise and fall?AsabiyyahFits the Maghrib better than other states MartineauDoes a society live by its own principles?Observe morals and mannersRelied on hosts and a stage view of progress MarxWho owns, and who works for whom?Surplus valueLabour theory of value; failed predictions DurkheimWhat binds individuals into a society?Social fact, anomieEcological fallacy; official statistics WeberWhy do people obey, and what do actions mean?Ideal type, three authoritiesThe Protestant ethic thesis is contested SimmelWhat forms does social life take?Dyad and triadBrilliant but unsystematic Du BoisWhat is the colour line, measured and lived?Double consciousnessThe Talented Tenth; scope of the concept One thing to notice: most of the entries are about how people are classed (by property, by status, by group size, by race) and about whether the classing is natural or made. Later courses, Stratification & Inequality (35/78) and Race, Ethnicity & Migration (35/79), are that question pursued with data. What a graduate just sees Groups have a life of their own: rates, rules and cycles that no member chose. Solidarity is strongest when a group is hard-pressed, and it erodes with comfort: ask what a rising group has that a settled one has lost. Ask who owns, who decides, and who gains from an arrangement that is described as natural. Authority is a belief: the same order is obeyed for tradition, devotion to a person, or rules, and each fails differently. Add a third person and the dynamics change; in large groups the number of possible relationships outruns anyone's attention. A rate is not the story of any one person: do not slide from "this group has more" to "this person is". The canon was chosen by people; ask who was left out and who gets to count as a founder. Where it shows up next Contemporary Social Theory (35/77): Goffman, Bourdieu, Foucault and others who answer, correct or reject these seven. Stratification & Inequality (35/78): Marx's class, Weber's status and Du Bois's colour line, measured with the tools of Sociological Research Methods (35/76). Deviance, Crime & Social Control (35/83): Durkheim's claim that a society needs rules, and what happens when they weaken. In the world: an HR manager reading a bureaucracy; a founder thinking about when a start-up stops being a movement; a journalist explaining why a protest group fell apart; a historian choosing which social theory to argue with. If you want to go deeper 📘 Textbook. George Ritzer and Jeffrey Stepnisky, Classical Sociological Theory (McGraw-Hill, many editions), and Craig Calhoun and others, Classical Sociological Theory (Wiley-Blackwell, several editions). Both are standard course texts with extracts and commentary; check for a recent edition and for how many non-European thinkers each includes. 📘 Gentler. Aldon Morris, The Scholar Denied (University of California Press, 2015), on Du Bois and the Atlanta school. The primary texts also reward a taster: Du Bois, The Souls of Black Folk (1903) is in the public domain, and the abridged Muqaddimah (translated by N. J. Dawood, Princeton University Press) is an accessible way into Ibn Khaldun. 🌐 Free course. Open Yale Courses, SOCY 151, Foundations of Modern Social Theory (Iván Szelényi, 2009). It runs through Marx, Weber and Durkheim, with lectures on earlier political thinkers first. It leaves out the other four in this piece. 🌐 Free text. OpenStax, Introduction to Sociology 3e, whose early chapters introduce the main theoretical perspectives. The takeaway Classical social theory is a set of lenses, not a set of answers. Ibn Khaldun saw solidarity rise and fade, Martineau tested a nation against its own creed, Marx followed ownership, Durkheim followed belonging, Weber followed meaning and obedience, Simmel followed the shape of the group, and Du Bois measured the colour line and described how it feels. Each lens shows something the others miss, and each has a critic worth reading. Try this: pick a group you belong to (a team, a family, a club) and describe it in one sentence each through Marx, Durkheim and Weber. Note which sentence was the hardest to write; that is the lens you use least. Sources & Further Reading Sources. Ibn Khaldun, The Muqaddimah (1377), trans. Franz Rosenthal (Princeton, 1958); the "three generations of about forty years" summary as given in the standard reference literature (Wikipedia, "Asabiyyah"); the model on this page is invented. Martineau, Society in America (1837), How to Observe Morals and Manners (1838), The Positive Philosophy of Auguste Comte (1853). Marx, Capital vol. 1 (1867), chapters 9–10; Böhm-Bawerk, Karl Marx and the Close of His System (1896); Kevin Anderson, Marx at the Margins (2010). Durkheim, The Division of Labour in Society (1893), The Rules of Sociological Method (1895), Suicide (1897); Halbwachs, Les causes du suicide (1930); Douglas, The Social Meanings of Suicide (1967). Weber, Politics as a Vocation (lecture of 28 January 1919), The Protestant Ethic and the Spirit of Capitalism (1904–05), Economy and Society (1922); Becker and Woessmann, "Was Weber Wrong? A Human Capital Theory of Protestant Economic History," Quarterly Journal of Economics 2009. Simmel, "The Metropolis and Mental Life" (1903) and Sociology (1908). Du Bois, The Philadelphia Negro (1899: about sixteen months of fieldwork from August 1896 to December 1897 and more than 5,000 interviews, as stated on Wikipedia, "The Philadelphia Negro"), The Souls of Black Folk (1903); Morris 2015. The counts and the toy models on this page were run in node. This piece describes schools of thought and is not advice; the suicide discussion is about statistics on societies, and findahelpline.com lists support by country. 📘 Morris, The Scholar Denied 📘 Ritzer and Stepnisky, Classical Sociological Theory 🌐 Open Yale SOCY 151 Step 2. Sociological Research Methods: How Sociologists Know What They Know --------------------------------------------------------------------------- Why: Surveys, interviews, ethnography and how sociologists know. A claim about society is only as good as the way it was collected: who was asked, how, who answered, who was watching and who decided the question. Six ideas that let you read a study, and doubt it usefully. A headline says that 52% of a town supports longer library hours. A second says 46%. Both come from honest surveys run in the same week. Neither pollster lied. One reached more young people than the other, or asked a slightly different question, or counted the "don't knows" differently, and the gap is the whole subject of this course in miniature. Sociology cannot put a society in a laboratory. It has to ask, watch, count and compare, usually without being able to assign people to conditions. That makes method not a technicality but the argument: most disagreements between sociologists turn out to be disagreements about evidence. A graduate of this course reads a study from the bottom up, starting with who is in the data. The course in one box Who takes itSociology majors in year 1 or 2; the American Sociological Association's guidance on the major and the UK's QAA benchmark statement for sociology both treat research methods, quantitative and qualitative, as core. Most programmes teach it in two parts: survey and statistics, then interviews and ethnography. What it is forJudging what a piece of social research can and cannot show, and designing a small study of your own that does not fool you. Comes afterStatistics (34/05), for sampling error and regression. Leads toStratification & Inequality (35/78), Cities, Communities & Social Networks (35/81) and Deviance, Crime & Social Control (35/83), which all rely on the data the methods produce. It pairs with Classical Social Theory (35/75), which supplies the questions. What this piece gives youYou will not be able to run a national survey. You will know how a sample goes wrong and how weighting repairs it, how wording moves answers, how interviews are coded, what the best-known ethnography dispute was about, and how much of the field's record has replicated. Easier first reads and neighbours: Elections, Parties & Public Opinion (35/15) on polls, Research Methods in Psychology (35/19) and Research Methods in Political Science (35/18) on experiments and causal inference, which this piece does not repeat. Sampling and weighting: who ended up in the data You cannot ask everyone, so you ask a sample and hope it stands for the rest. The mathematics of this is kind: a truly random sample of 1,000 people gives a margin of error near plus or minus 3 points, whatever the size of the country. The trouble is the word truly. You need a list of the population (a sampling frame), a chance mechanism for choosing from it, and answers from the people chosen. Failure at any of the three skews the picture, and a big sample does not cure a skewed one. The classic case is the Literary Digest poll of 1936, told in Elections, Parties & Public Opinion (35/15): millions of ballots, the wrong winner. Today's version is nonresponse. Many surveys now hear back from a small share of those invited, and some kinds of people answer more than others. Pollsters repair this with weighting: if the census says 40% of adults are young but only 25% of respondents are, each young respondent counts for more than one person, and each older one for less. The usual weight is the group's share of the population divided by its share of the sample. Real surveys weight on several variables at once (age, sex, education, region), often by iterating until all the margins match, a method called raking. Interactive · Survey a town, then weight it An invented town: 40% of adults are young, 60% older. In truth, 70% of the young and 40% of the older support extending library hours (52% overall). Young people answer surveys less often. Set how often each group replies. Young people who reply Older people who reply People invited Draw a random sample Illustrative data, not a real town. The first block of numbers is the long-run expectation; the second is one random draw from it, so it varies when you press the button. Weighting assumes that within each group the people who reply resemble those who do not, which is the assumption that cannot be checked from inside the survey. Without JavaScript: with young people replying 20% of the time and older people 50%, a survey expects 46% support (the truth is 52%). Weighting the groups back to 40/60 recovers 52%. Look at what you paid for the repair. Weighted answers vary more, because a few respondents now count for a lot: the effective sample size shrinks (Kish's formula, the square of the sum of the weights divided by the sum of the squares), and the margin of error widens. And the repair works only for what you weighted on. If the older people who reply are keener on libraries than the older people who do not, weighting by age cannot see it. Well-run surveys state their response rate, their weights and their frame. The long-running ones that sociologists lean on include the General Social Survey (United States, since 1972), the World Values Survey (since 1981), the European Social Survey (since 2002), and regional barometers such as Latinobarómetro and Afrobarometer (since the 1990s). Questions are instruments: wording, order and answer options A question is a measuring device, and like any device it has a calibration. The same underlying opinion can give different readings with a different frame. Sociologists distinguish reliability (do repeated measurements agree?) from validity (are you measuring what you claim?), and they worry that the concept came first and the measure was an afterthought. Even "unemployed" is a decision: the International Labour Organization counts someone as unemployed if they have no work, are available, and have looked for it recently, which is not the same as being registered at an employment office, so two countries' rates can differ for definitional reasons. The Pew Research Center, which publishes its question-wording experiments, offers clean examples. Pick one below. Interactive · Same people, different question Figures are from Pew Research Center's published question-wording experiments (see Sources for the year of each). Each pair was asked of comparable random samples. Without JavaScript: 68% favoured military action in Iraq (January 2003) but 43% when casualties were mentioned; 51% favoured a law letting doctors give terminally ill patients "the means to end their lives" but 44% one saying "assist ... in committing suicide" (2005). Three lessons recur. Context: mentioning a cost or consequence changes the answer, which is not always bias, since the second version may be the more informed one. Words with baggage: "welfare" and "assistance to the poor" do not draw the same support. Order: a question can colour the next one, so well-designed surveys randomise the order or put general questions before specific ones. The practical rule is to report the exact wording, and to treat any single percentage as the answer to that question, not to "what people think". Interviews and coding: turning talk into evidence A survey is good at how many; an interview is good at what it means and why. In depth interviews, researchers ask open questions of a few dozen people, record and transcribe them, and then do the work that defines qualitative research: coding. They read the transcripts, label the passages with themes, and refine a codebook: a list of codes with definitions and examples. The aim is not a statistical estimate. It is an account of the mechanisms and categories in people's own terms, strong enough to be tested elsewhere. Traditions differ: grounded theory (Glaser and Strauss, 1967) builds the codes from the data upward, and thematic analysis (Braun and Clarke, 2006) is a widely taught step-by-step version. How do you know two researchers would code alike? One answer is intercoder agreement: two people code the same passages independently and you compute how often they match after allowing for chance. The common statistic is Cohen's kappa (1960): agreement beyond chance, from 0 (no better than chance) to 1 (perfect). Try it. Ten invented snippets from people who moved to remote work, four codes, and a second coder whose choices are fixed. Interactive · You are the second coder Codebook. A = Autonomy (control over time or place). I = Isolation (missing people). C = Cost (money). B = Boundaries (work spilling into home). Code all ten snippets to see how you agree. Clear my codes Snippets are invented, not from real interviews. A real study would have far more text, and a first, messier codebook that the two coders revise together. Without JavaScript: a snippet about wanting someone to notice when you struggle could be coded Autonomy or Isolation; one about affording rent by working from a village could be Cost or Autonomy. Codes overlap, and that is the point of the exercise. Not everyone accepts kappa for this work. Researchers from the content-analysis tradition treat agreement figures as the mark of rigour. Braun and Clarke argue that in reflexive thematic analysis the researcher's interpretation is the instrument, so measuring agreement between coders misses what coding is for, and what matters is a clear account of how the themes were reached. Both camps agree on one thing: write down your rules, and say how many interviews you did and how you chose them. Ethnography and participatory research: being there, and who owns the question Ethnography means spending long periods inside a setting: watching, joining in, taking field notes, and writing up what you saw. Bronisław Malinowski's Argonauts of the Western Pacific (1922), from years in the Trobriand Islands, set a standard in anthropology; William Foote Whyte's Street Corner Society (1943), from a Boston neighbourhood in the late 1930s, did so in sociology; and W. E. B. Du Bois's door-to-door work in Philadelphia (see Classical Social Theory, 35/75) combined counting with listening. The strength is meaning and mechanism: you see what people do, not just what they say they do. The weakness is generalisation: one site, one researcher, a presence that may change what is seen, and notes that no one else can read. The best-known dispute of recent years shows what is at stake. Alice Goffman's On the Run (University of Chicago Press, 2014) described how young Black men in a poor Philadelphia neighbourhood lived under constant threat of arrest, drawing on years of fieldwork that began when she was an undergraduate. It was widely praised and won attention far outside sociology. Critics, among them the legal ethicist Steven Lubet and the journalist Jesse Singal, asked whether some episodes could be corroborated, whether she had reported accounts she was told as events she saw, and whether her description of her own role in one episode was accurate. Goffman replied in writing, and defended the book's purpose of letting marginalised people describe their own lives, rather than meeting an official-records test. An anonymous long critique that circulated on academic listservs was reviewed by her university, which found it without merit. Her adviser said he had checked some informants' identities. Other sociologists said they did not think she fabricated data but that heard accounts should have been presented as such. The honest summary is that the dispute stayed about how an ethnographer should handle evidence that cannot be checked, since the notes are confidential and protect people from arrest. Participatory action research asks a different question: who is the research for? The Colombian sociologist Orlando Fals Borda (1925–2008), who helped found one of Latin America's first sociology faculties at the National University of Colombia in 1959 and wrote La Violencia en Colombia (1962) on a decade of political violence, worked in the 1970s and after with peasant and Caribbean-coast communities. He and others developed a method in which community members help choose the questions, collect the evidence and use the results, with knowledge returned to them. In his words, researchers should "not monopolise your knowledge nor impose arrogantly your technique." Supporters say insiders know which questions matter and catch what outsiders miss. Critics ask who counts as "the community", whether it can romanticise local views, and how conflicting views are handled. The idea has spread well beyond Colombia, in development, health and education research. From pattern to cause: aggregation, confounding and natural experiments Experiments (see Research Methods in Psychology (35/19) and Research Methods in Political Science (35/18)) are the cleanest route from pattern to cause, and sociologists often cannot run them. Two things they can do are read data at the right level and look for accidents of history that act like experiments. The first is a trap from Durkheim's time. In 1950 the sociologist William S. Robinson showed that in the 1930 US census the states with more foreign-born residents had lower illiteracy (a correlation near −0.5), while at the level of individuals, the foreign-born were somewhat more likely to be illiterate (about +0.12). The state pattern came from where immigrants settled, not from who among them could read. Inferring about individuals from group data is the ecological fallacy, and it hangs over any use of regional statistics. The tool below builds the reversal from scratch. Interactive · A reversal between regions and people Six invented regions, with group A making up 5% to 90% of each. Inside every region, a person in A has a rate d points above someone not in A. But A members also settle where the background rate is lower. Gap inside every region, d (points) How strongly A settles in low-rate regions, c Invented regions and rates: region rate = 40 + (d − c) × (share of A), in per cent. The point is the arithmetic of the reversal, not any real group. Without JavaScript: with d = +5 and c = 20, group A's members have a rate 5 points higher in every region, yet regions with more of A have lower rates, a slope of −15 points across the full range of shares. The second tool is the natural experiment: an event or rule that sorts people almost as if by lot. John Snow's study of cholera in London in the 1850s is the classic: households got water from two companies, one drawing from a sewage-polluted stretch of the Thames and one from cleaner water upriver, on streets where the two were mixed with no other pattern. Households on the first company's supply died of cholera at roughly eight times the rate in his best-known table for 1854 (315 against 37 deaths per 10,000 houses). The sociological examples are the same in kind: a draft lottery, a school-entry cut-off date, a lottery for housing vouchers. The randomised US Moving to Opportunity experiment (1994–98), which offered some families vouchers to move to lower-poverty neighbourhoods, found in a 2016 analysis by Raj Chetty, Nathaniel Hendren and Lawrence Katz that children who moved when young earned more as adults. Natural experiments are persuasive because the sorting wasn't chosen by the people, and limited because they answer narrow questions about narrow groups. How well it has held up: the replication record Sociology shares psychology's worries, with some differences. Much of its evidence is observational and comes from shared datasets, so the question is often not "does a repeat experiment reproduce it?" but "do different analysts get the same answer from the same data?" A striking test, led by Nate Breznau and colleagues (2022, in PNAS), gave 73 research teams the same cross-country survey data and the same question: does immigration reduce public support for social policy? The teams made reasonable but different choices about measures and models, and their estimates ran from large negative effects to large positive ones. A commentary pointed out that most results clustered near zero, which is a fair reading too. The lesson is not that the answer is arbitrary, but that a single published analysis understates the uncertainty. For experiments the record is mixed in the usual way: a 2018 project by Colin Camerer and colleagues repeated 21 social-science experiments published in Nature and Science; about 13 of the 21 (62%) replicated in the original direction, with effects on average about half the original size. Field practice has shifted accordingly: pre-registered analyses, shared data and code, and journals that ask for them. Sociologists have also learned to separate three things a headline blurs: a robust description ("the gap is this large, by this measure"), a causal claim ("this produced that") and a normative claim ("so we should"). Methods can support the first firmly, the second with care, and the third not at all. What a graduate just sees Before the result, ask who was in the data: the frame, the sampling method and who replied. A bigger sample shrinks random error, not bias. A million self-selected answers is still a self-selected million. Weighting repairs what you can measure and widens your error bars; it cannot see what you did not weight on. A percentage answers a question, not a topic: look up the exact wording. Qualitative work earns trust by its audit trail (codebook, number of interviews, how cases were chosen), not by sample size. A pattern across regions need not hold within them: ask whether the claim is about places or people. Look for the accident that acts like a coin toss, and ask what question it can actually answer. Where it shows up next Stratification & Inequality (35/78): measuring income, class and mobility is a measurement choice at every step. Cities, Communities & Social Networks (35/81): network data, name generators and neighbourhood effects need all of the above. Deviance, Crime & Social Control (35/83): police records and victim surveys measure different things, and the gap between them is a research finding. In the world: market researchers, civil-service analysts, UX researchers and journalists who read a poll all use this same checklist. If you want to go deeper 📘 Textbook. Earl Babbie, The Practice of Social Research (Cengage, many editions), a standard US text, and Alan Bryman, Social Research Methods (Oxford University Press, several editions), standard in the UK. Both cover surveys, interviews, ethnography and mixed methods; check for a recent edition. 📘 Gentler. Howard S. Becker, Tricks of the Trade: How to Think about Your Research While You're Doing It (University of Chicago Press, 1998), short, wise and practical about how sociologists actually work. 🌐 Free text. OpenStax, Introduction to Sociology 3e, whose chapter on sociological research covers surveys, field research and secondary data. 🌐 Free guide. The Pew Research Center's Writing Survey Questions, with the wording and order experiments used above. I could not confirm a single free full course on social research methods, so this guide and the OpenStax chapter are the free route. The takeaway What sociology knows is shaped by how it asked. Know who is in the data and how they were weighted, treat a percentage as the answer to a particular question, code interviews with rules you can show, use ethnography and participatory work for meaning and for whose question it is, read group data and individual data separately, and expect different analysts to differ. Try this: the next time you see a poll or a "study shows" headline, find the exact question wording, the sample size and who was sampled. If you cannot find all three, treat the number as a lead, not a fact. Sources & Further Reading Sources. Pew Research Center, "Writing Survey Questions" (open versus closed questions; Iraq, January 2003: 68% favour and 25% oppose against 43% and 48% with casualties; end-of-life wording, 2005: 51% against 44%; "domestic policy or foreign policy" January 2002: 52% and 34% against 33% and 52% with "the war on terrorism"; December 2008 satisfaction question: 88% dissatisfied after the Bush approval item against 78% without; 2008 vote issue, closed 58% against 35% open-ended). Kish on effective sample size (design effect from unequal weights). Glaser and Strauss, The Discovery of Grounded Theory (1967); Braun and Clarke, "Using thematic analysis in psychology," Qualitative Research in Psychology 2006; Cohen, "A coefficient of agreement for nominal scales," Educational and Psychological Measurement 1960. Malinowski 1922; Whyte 1943. Goffman, On the Run (2014): the account of the dispute follows the reporting summarised on Wikipedia (Lubet, Singal, the University of Wisconsin–Madison review, Duneier's statement), and the book itself; I have not read the unpublished critique. Fals Borda (1925–2008), La Violencia en Colombia (1962), the 1959 faculty, and the quoted line as given on Wikipedia, "Orlando Fals Borda". Robinson, "Ecological correlations and the behavior of individuals," American Sociological Review 1950 (−0.53 and +0.12 as reported; later corrected to about −0.46, per Wikipedia, "Ecological fallacy"). Snow, On the Mode of Communication of Cholera (1855), with the "about fourteen times" figure from Wikipedia, "John Snow". Chetty, Hendren and Katz, American Economic Review 2016. Breznau et al., PNAS 119(44), 2022. Camerer et al., Nature Human Behaviour 2018. ILO definition of unemployment. Start years of surveys are from each programme's own site. The sampling, coding and region numbers were run in node. Study descriptions are for teaching; this is not advice. 📘 Becker, Tricks of the Trade 📘 Babbie, The Practice of Social Research 🌐 Pew: Writing Survey Questions Step 3. Probability & Statistics: Reasoning with Uncertainty ------------------------------------------------------------ Why: The statistics behind every survey and census. 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. Social Psychology: How Other People Change What We Do ------------------------------------------------------------- Why: How other people change what we do, from the psychology side. 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 Step 5. Contemporary Social Theory: Stage, Capital, Power, Category and Choice ------------------------------------------------------------------------------ Why: Goffman, Bourdieu, Foucault, feminist and postcolonial theory, and their critics. After the founders, sociology split into schools that disagree about almost everything: whether society is a stage, a market of advantages, a net of power, a set of categories, a web of relations or the sum of choices. Each is good at seeing something the others miss. You walk into a job interview. You smooth your hair, choose a tone of voice, decide how much of your last job to admit to. The interviewer has also prepared a face. Meanwhile the interviewer's firm has a type of person it recognises as "a good fit", and you may or may not have been trained since childhood to look like one. Behind both sits a legal and institutional machinery of CVs, references and tests that sorts everyone, which none of you chose, and a pile of categories (gender, race, class, nationality) that shape who sits on which side of the desk. Each of those sentences belongs to a different school. This course covers six answers to "how does society work?" that came after Marx, Durkheim and Weber: Goffman on the stage, Bourdieu on capital and habitus, Foucault on power and knowledge, feminist theory on gender and its intersections, postcolonial and Southern theory (with Fei Xiaotong's ripples) on where theory itself comes from, and rational-choice sociology on choices that add up to structures. The aim is not to pick a winner but to learn which tool fits which problem. The course in one box Who takes itSociology majors, usually in year 2 or 3; theory is a core strand in programmes shaped by the American Sociological Association's guidance and the UK's QAA benchmark, with the LSE and other departments offering a "contemporary theory" course after the classics. Anthropology, politics, media and gender studies students often take it too. What it is forGiving you six working vocabularies (frontstage, habitus, discipline, intersectionality, coloniality, incentives) and the judgment to know when each helps. Comes afterClassical Social Theory (35/75). Leads toGender, Family & Sexuality (35/80) and Social Movements & Collective Action (35/82); it also colours Stratification & Inequality (35/78) and Race, Ethnicity & Migration (35/79). What this piece gives youYou will not have read Foucault or Bourdieu, whose books are hard. You will know each school's central idea, what it explains, what its best critics say, and have played with a model of five of them. Easier first reads: Class, Status and Power: Marx, Weber and Bourdieu (30/02), Institutions: The Invisible Rules That Shape Us (30/05) and Status in the Age of Followers (30/06). Goffman: the stage that everyday life is played on Erving Goffman (1922–1982), a Canadian-born sociologist, built his first account of the idea during fieldwork on a hotel's staff on the island of Unst in the Shetlands in the late 1940s. The Presentation of Self in Everyday Life (an Edinburgh monograph in 1956; the American edition in 1959) treats ordinary encounters as performances. People manage the impression they give, in a front region (the restaurant floor, the consulting room, the video call with the camera on) and relax in a back region (the kitchen, the staff room, the mute button). Waiters, doctors and teenagers all keep a gap between the two. In Interaction Ritual (1967) he described face-work: the small repairs people make so that nobody is humiliated, such as pretending not to hear a stomach rumble. In Asylums (1961) he showed what happens when the back region is abolished: in a "total institution" such as a psychiatric hospital, prison or barracks, residents sleep, work and play in one place under one authority, and their sense of self is stripped. Stigma (1963) described the work done by people whose "spoiled identity" is visible or might be discovered. In his American Sociological Association presidential address, written in 1982 and published in 1983, he argued that face-to-face interaction is its own domain, the interaction order, with rules worth studying in themselves. The critics are practical. Alvin Gouldner (1970) said Goffman's world is a bland middle-class one of impression management with the history, money and power left out. Others note that his evidence is observation and anecdote, not sample or experiment, so it suggests rather than proves. Defenders reply that he never claimed life is only theatre: his point was that the theatre has real rules, that breaking them has real costs, and that the people who pay most are those with the least room to perform. Bourdieu: capital, habitus and the quiet advantages of taste Pierre Bourdieu (1930–2002) started as an ethnographer in Algeria, studying the Kabyle, and then turned the same eye on France. His idea is that advantage comes in several currencies. Economic capital is money and property. Cultural capital is knowledge, manner and credentials: the accent that sounds educated, ease in a museum, a degree. Social capital is the network you can call on. Symbolic capital is prestige, being recognised as worth listening to. They can be converted, with friction. The second concept is habitus: the dispositions you absorb from your upbringing, such as tastes, posture, what feels "for people like us". It lets people act without thinking in ways that fit their position. A field (art, law, academia, finance) is a structured arena in which people compete for the capital that counts there. In Distinction (1979, English 1984), built on French surveys of about 1,200 people in the 1960s, he argued that taste is not innocent: preferences for food, music and art sort people into positions, and the powerful turn their tastes into a mark of worth. Bourdieu's picture of the social space has two axes: the total volume of capital you hold, and its composition, whether it is mostly economic or mostly cultural. The tool below places a position on those axes. The labels are schematic, in the spirit of his diagrams, not a data fit. Interactive · Where do you sit in social space? Economic capital (0–10) Cultural capital (0–10) A schematic diagram: volume = (E + C) / 2, composition = E − C. The example occupations are the kind of position Bourdieu's French survey placed in each region, not a measurement of anyone. Without JavaScript: high on both axes (large volume) places you among the dominant; mainly economic, among business owners; mainly cultural, among professors and artists; low on both, among those with little of either. The critiques are well known. The first is determinism: habitus can seem to leave little room for change or surprise. Defenders say it is a theory of reproduction with its own room for conversion. The second is whether the French pattern travels. Richard Peterson and Roger Kern (1996) argued that in the US the high-status listener is increasingly an omnivore, who likes many genres, rather than a snob with one narrow highbrow taste. Michèle Lamont (1992), comparing the US and France, found upper-middle-class people drew their boundaries as often on moral qualities as on culture. Tony Bennett and colleagues (2009) found that British data supported much of Bourdieu's view of class and taste, with revisions, especially on gender and ethnicity. Foucault: power that works through knowledge and being watched Michel Foucault (1926–1984) saw power not as a thing held by rulers, but as a web of relations that runs through schools, hospitals, prisons and offices, and that produces as well as forbids. Discipline and Punish (1975; English 1977) opens with the torture and execution of Damiens in Paris in 1757, and then a prison timetable from the 1830s, as two eras' ways of governing bodies. He argues that modern societies shifted from spectacular punishment to discipline: timetables, exams, ranks and records that train people into docility by constant observation. His symbol is Jeremy Bentham's panopticon (1791), a prison with a central watchtower from which any cell can be seen, while the prisoners can never tell whether they are being watched. The effect, Foucault said, is that the prisoner "becomes the principle of his own subjection": he polices himself. He extended this to biopower (the management of populations through health, birth and death rates, in The History of Sexuality, vol. 1, 1976) and, in lectures in 1978–79, to governmentality, the arts of governing conduct. His other core claim is power/knowledge: knowledge of people (psychiatry, criminology, statistics) is both a result of power and a tool of it. Here is the mechanism in its barest form. It is a rational-choice caricature of an idea Foucault would put in terms of norms people absorb, but it shows why uncertain observation is so cheap. Interactive · A tower and a few guards People under watch People the guards can check per round Penalty if caught (points) Gain from breaking a rule (points) Invented numbers. “Hidden” means nobody can tell whom the guards are checking; they treat the chance of being checked as k / N. “Visible” means people can see who is being checked, so only those people comply. Real discipline, in Foucault’s account, also works through habit and self-image, which this toy does not model. Without JavaScript: with 100 people, 10 checks, a penalty of 12 and a gain of 1, the chance of being checked is 10% and the expected penalty is 1.2, above the gain of 1: everyone complies when watching is hidden; if checks are visible, only the 10 people being watched comply. The critics: Jürgen Habermas (1985) and Nancy Fraser (1981) said that if all power is bound up with knowledge, Foucault gives no basis for saying why any resistance is better than what it resists. Historians, such as Andrew Scull on psychiatry, challenged his accounts of the "great confinement" of the mad. The panopticon was rarely built as drawn; critics say modern surveillance is more often many cameras and databases than one tower, which others reply is exactly his point. Foucault himself described his books as tools to be used and revised, and sociologists have used them for studies of schools, medicine, policing and data. Feminist theory: whose standpoint, and how categories combine Feminist sociology is not one school. Simone de Beauvoir's The Second Sex (1949) supplied the founding line, "one is not born, but rather becomes, a woman": gender is made, not given. Dorothy Smith (1974) argued that sociology had been written from the standpoint of men in the ruling professions, and began from women's everyday work, a standpoint approach that later writers such as Nancy Hartsock and Sandra Harding developed into a claim that people on the margins can see features of society the comfortable miss. Arlie Hochschild named emotional labour in The Managed Heart (1983), her study of flight attendants paid to produce a feeling in others, and the "second shift" of unpaid housework in 1989. Raewyn Connell's account of masculinities, and Judith Butler's Gender Trouble (1990), which treats gender as performative, a pattern repeated until it looks natural, show how varied the field is. Kimberlé Crenshaw coined intersectionality in a 1989 article from a legal case, DeGraffenreid v. General Motors (1976), in which Black women said the company's hiring and later layoffs hurt them as Black women. The court declined to treat them as a distinct group, observing that GM hired white women and hired Black men, so there was no sex or race discrimination. Crenshaw's point: if the law looks at one axis at a time, it misses harm at the crossing. Patricia Hill Collins (Black Feminist Thought, 1990) developed a wider account of overlapping systems. The tool uses invented callback rates to show the arithmetic. Interactive · Averages that hide the crossing Callback rate for Black women (%) Illustrative numbers, with the four groups equal in size: white men 30%, white women 26%, Black men 20%, and Black women set by the slider. They are not from any study. “Additive” means the race gap among men plus the gender gap among white people, applied to Black women. Without JavaScript: with callback rates of 30, 26, 20 and 12 for white men, white women, Black men and Black women, women average 19 and men 25 and the white group 28 and the Black group 16, but the additive prediction for Black women is 16, so their actual 12 shows a further 4-point gap that neither single-axis average shows. Criticism comes from inside and outside. Inside: Elizabeth Spelman (1988) asked who "women" means when the category is built from some women's experience, and Leslie McCall (2005) set out three ways researchers handle categories, from rejecting them to comparing across them. Butler's view and standpoint theory pull in different directions. Outside: critics doubt that a standpoint gives privileged knowledge, which they say makes claims hard to test, and some argue intersectionality becomes hard to measure or reduces people to identity categories. Supporters answer that quantitative researchers can test it with interaction terms, as the toy does, and that it began as a precise legal point. Postcolonial and Southern theory: where theory is made Who gets to be the theorist, and whose experience counts as "society in general"? Frantz Fanon's The Wretched of the Earth (1961) analysed colonialism as a system that shaped minds as well as economies. Edward Said's Orientalism (1978) argued that European scholarship and literature built a flattering, static picture of "the Orient" that helped justify rule. Gayatri Spivak's essay "Can the Subaltern Speak?" (1988) asked whether those at the bottom of a colonial order can be heard in the language of those above. In Latin America, Raúl Prebisch (1950) and the Economic Commission for Latin America argued that countries exporting raw materials and importing manufactures faced a long-run decline in their terms of trade, and Fernando Henrique Cardoso and Enzo Faletto (Dependencia y desarrollo en América Latina, written in the late 1960s; English 1979) argued that development there was shaped by the position of local elites within a global system, allowing some "associated-dependent" growth. Raewyn Connell's Southern Theory (2007) says the sociological canon rests on the experience of a few European and North American societies, offered as universal, and looks at social thought produced in the colonised world, such as Paulin Hountondji's work in West Africa and Ali Shariati's in Iran. Interactive · The terms-of-trade squeeze Yearly fall in the price of exports relative to imports Years A toy of the Prebisch–Singer hypothesis: if a country’s export prices fall by g per cent a year against its imports, buying the same machine takes (1 − g)−t times the exports after t years. The start (100 sacks of coffee for a tractor) is invented. Studies of real commodity prices find declines that depend on the period and the commodity; one often-cited estimate is about 0.6% a year for 1900–1986. Without JavaScript: a yearly fall of 0.6% over 50 years means about 135 sacks of coffee for a tractor that cost 100, a 35% rise. Dependency theory's record is argued. Its critics point to South Korea and Taiwan, which climbed through manufactured exports and state guidance, and to commodity cycles that have also raised prices for periods. Defenders note that those cases also relied on special conditions, and that Cardoso himself never said development was impossible. Said's critics, such as Aijaz Ahmad (1992) and Bernard Lewis, argued that Orientalism lumps together very different scholars and itself treats "the West" as one block. Connell's critics ask whether "the South" is itself too broad a label. The same worry about borrowed categories sits behind Fei Xiaotong (1910–2005), who studied under Bronislaw Malinowski at the London School of Economics and wrote Peasant Life in China (1939), his thesis on a Jiangsu village. His From the Soil (Xiangtu Zhongguo, 1947–48; English edition 1992) drew a contrast. Western social life, he said, is often like bundles of firewood: people belong to clear groups, with equal standing inside and rules at the edge. Chinese rural society is closer to ripples from a stone dropped in water, the "differential mode of association" (chaxu geju): each person is the centre of their own circles of kin and friends, and obligations weaken with distance. Two people's circles overlap but are never the same, and the same stranger can be close to one person and far from another. bundlesmembersripples around me The picture is a schematic of Fei's contrast. He drew it as an ideal-type comparison, not a claim that all Chinese or all Western life fits. Critics note that Fei wrote from a few villages in southern Jiangsu and that the contrast with "the West" is stylised, since Western life is full of ripples (networks, favours) and Chinese life of bundles (work units, lineages, companies). Fei himself kept studying change. After being attacked in the Anti-Rightist Campaign of 1957 and returning to research later, he turned to small towns and rural industry. His idea has fed research on guanxi, personal connections, and on how obligations work when institutions are weak. Rational choice: explaining the many from the choices of each Rational-choice sociology starts from individuals with preferences and constraints, and asks what structures follow from their choices. James Coleman (1926–1995) gave it its best-known sociological statement, Foundations of Social Theory (1990), and a diagram, the macro-micro-macro "boat": a social condition shapes individuals' situations, individuals choose, and their choices add up to a new social outcome. Weber's Protestant ethic is his example: a religious doctrine (macro) changes individuals' values (micro), who act in new ways in markets, producing capitalism (macro). Gary Becker, who won the Nobel Memorial Prize in Economics in 1992, applied the approach to marriage, crime and education. Mancur Olson's The Logic of Collective Action (1965) showed why large groups with shared interests often fail to act (see Social Movements & Collective Action, 35/82). Coleman's study of trust is a neat case: to trust someone is to take a bet with a gain if they are honest and a loss if they are not. Interactive · Should I trust them? Chance they are trustworthy (%) Gain if they are honest Loss if they betray Invented numbers. The decision rule is Coleman’s: trust if p × gain exceeds (1 − p) × loss. The presets for p are illustrative guesses, not measurements. Without JavaScript: with a gain of 10 and a loss of 20, trust pays only if the chance of honesty is above 20 / (10 + 20), which is about 67%. The model makes a point clear: the key variable is the chance, and it is not set by the individual. It is set by the social structure around them: reputation, kin ties, repeated dealings, a third party who vouches (Coleman's social capital). That is how rational choice and network sociology meet. The critics are strong. Donald Green and Ian Shapiro (1994) argued that rational-choice work in political science often fits stories after the fact and is rarely tested against alternatives. Psychologists and behavioural economists, following Herbert Simon and Daniel Kahneman and Amos Tversky, show that people satisfice, use shortcuts and care about fairness: in the ultimatum game, many reject unfair offers at a cost to themselves. Others, such as Pierre Bourdieu, hold that "choice" hides the way tastes and options are made by position. Defenders reply that the approach is meant to be a clear baseline, that preferences can include fairness and duty, and that its virtue is that its assumptions are on the table where they can be criticised. What a graduate just sees Everyday encounters have a front and a back, and much of social life is managing the gap. Advantage comes in several currencies, and taste and manner are among them: ask what is being displayed and who it signals to. Watching does not need to be constant to work; it needs to be uncertain and credible. Knowledge of people is not neutral: who counts, measures and names a group shapes how it is treated. Look at crossings: an average by gender or by race can hide a gap that only appears when you combine them. Ask whose experience a theory was built from before asking whether it fits. A structure is made of choices, and choices are made inside a structure: ask what each person was choosing between. Where it shows up next Gender, Family & Sexuality (35/80): standpoint, performativity, emotional labour and the division of unpaid work. Social Movements & Collective Action (35/82): rational-choice puzzles (why join?) against identity and framing accounts. Stratification & Inequality (35/78) and Race, Ethnicity & Migration (35/79): Bourdieu's capitals, intersectional categories and global dependency. In the world: a UX designer thinking about what users perform, an HR analyst checking pay gaps by combined groups, a policy analyst asking whose data a model rests on, a trade negotiator hearing the old Prebisch argument again. If you want to go deeper 📘 Textbook. George Ritzer, Contemporary Sociological Theory and Its Classical Roots (McGraw-Hill), and Anthony Elliott, Contemporary Social Theory: An Introduction (Routledge). Both are standard course texts; check for a recent edition and for how much each gives to non-Western and feminist theory. 📘 Gentler. Erving Goffman, The Presentation of Self in Everyday Life (1959), short and readable, and Raewyn Connell, Southern Theory (Polity, 2007), the clearest statement of why the question of where theory is made matters. 🌐 Free reference. The Stanford Encyclopedia of Philosophy entry on Foucault is a careful, free overview. 🌐 Free text. OpenStax, Introduction to Sociology 3e introduces the main perspectives. I could not confirm a free full course on contemporary theory, so this and the reference above are the free route; Open Yale's SOCY 151 covers the classics only. The takeaway Contemporary theory offers six tools: Goffman for the stage of everyday life, Bourdieu for the currencies of advantage, Foucault for power that works through knowledge and being watched, feminist theory for gender and the crossing of categories, postcolonial and Southern theory (with Fei's ripples) for whose experience counts, and rational choice for the way choices add up. Each explains some things well, and each has critics worth taking seriously. Try this: take one workplace or school situation you know and describe it with three of the six tools, in one sentence each. See which tool makes you notice something the others did not. Sources & Further Reading Sources. Goffman, The Presentation of Self in Everyday Life (1956/1959), Asylums (1961), Stigma (1963), Interaction Ritual (1967), "The Interaction Order," American Sociological Review 1983; Gouldner, The Coming Crisis of Western Sociology (1970). Bourdieu, Distinction (1979; English 1984), "The Forms of Capital" (1986); Peterson and Kern, American Sociological Review 1996; Lamont, Money, Morals, and Manners (1992); Bennett et al., Culture, Class, Distinction (2009). Foucault, Discipline and Punish (1975), The History of Sexuality vol. 1 (1976), the 1978–79 Collège de France lectures; Bentham's panopticon letters (1791); Habermas, The Philosophical Discourse of Modernity (1985); Fraser, "Foucault on Modern Power," Praxis International 1981. de Beauvoir 1949; Smith 1974; Hochschild 1983, 1989; Butler 1990; Crenshaw, University of Chicago Legal Forum 1989; DeGraffenreid v. General Motors Assembly Division, 413 F. Supp. 142 (E.D. Mo. 1976); Collins 1990; Spelman 1988; McCall, Signs 2005. Fanon 1961; Said 1978; Spivak 1988; Prebisch 1950; Cardoso and Faletto 1969/1979; Connell 2007; Ahmad, In Theory (1992); the “about 0.6% a year” estimate of Grilli and Yang, World Bank Economic Review 1988, as commonly cited. Fei, Peasant Life in China (1939) and From the Soil (1947–48; trans. Gary Hamilton and Wang Zheng, 1992); dates of his life and the 1957 campaign per Wikipedia, "Fei Xiaotong". Coleman 1990; Becker (Nobel 1992); Olson 1965; Green and Shapiro 1994. The model arithmetic on this page was run in node. This describes schools of thought and is not advice. 📘 Connell, Southern Theory 📘 Ritzer, Contemporary Sociological Theory and Its Classical Roots 🌐 Stanford Encyclopedia: Foucault Step 6. Stratification & Inequality: Measuring Who Gets What ------------------------------------------------------------ Why: Class, status, mobility and inequality, measured. A single number can describe a whole society’s inequality, and hide most of what matters. The skill is knowing what each measure shows, what it leaves out, and where the measuring stops and the arguing begins. Line up every adult on Earth by income, poorest at the left and richest at the right. The World Inequality Report 2022 did exactly that with data for 2021 and found that the poorest half of humanity received about 8.5% of the world's income, while the richest tenth received about 52%. The same line-up for wealth is starker still: the bottom half owned about 2%, the top tenth about 76%. Those two sentences are the whole subject in miniature. They are a measurement: someone had to decide what counts as income, which people to count, and how to compare a Brazilian real with an Indian rupee. They are also a starting point for argument, because people who agree on every digit still disagree about whether such a line-up is a problem, whose fault it is, and what to do. This piece teaches the measuring, and keeps the arguing clearly labelled as arguing. Throughout, the figures carry their year and source. Survey-based inequality statistics are not interchangeable across countries, and the biggest mistake a newcomer makes is to read a table of Gini numbers as if every row were measured the same way. The course in one box Who takes itSociology majors, usually in year 2 or 3. US programmes following the American Sociological Association's guidance on the major, and UK programmes built on the QAA benchmark statement, treat stratification as a core area; economics and public-policy students meet the same data in labour and public economics courses. What it is forDescribing how resources, status and opportunity are spread across people and groups, measuring that spread, and testing explanations for it. Comes afterClassical Social Theory (35/75) and Sociological Research Methods (35/76), with Statistics (34/05) underneath. Leads toRace, Ethnicity & Migration (35/79) directly, and it feeds Public Policy (35/16) and Political Economy (35/17). What this piece gives youYou will not run a household survey. You will be able to read a Lorenz curve and a Gini, say what they hide, tell income from wealth, read a mobility matrix, and compare caste and class. Easier first reads, which this piece does not repeat: Class, Status and Power (30/02), Castes, Estates and Classes (30/03) and Climbing the Ladder (30/04, which covers Chetty's mobility results and the Great Gatsby curve in depth). The Lorenz curve and the Gini: one number for a whole shape Picture the population lined up from poorest to richest. Now plot, for each point along that line, the share of all income held by everyone up to that point. At the left, almost nothing; at the right, 100%. If everyone had the same income the plot would be a straight diagonal. Real societies sag below it. That sagging curve is the Lorenz curve, drawn by the American statistician Max Lorenz in 1905. The Italian statistician Corrado Gini turned the sag into one number in 1912. The Gini coefficient is the area between the diagonal and the Lorenz curve, divided by the whole triangle under the diagonal. Zero means perfect equality; one means one person holds everything. A Gini of 0.30 is not "30% unequal"; it is a way of saying how far the curve bows. Gini = (area between diagonal and Lorenz curve) ÷ (area under the diagonal)0 = everyone equal · 1 = one person has it all · many sources print it ×100, as 41.8 The tool below builds a Lorenz curve from four real-looking points: what the poorest fifth gets, what the richest fifth gets, and what the richest tenth gets. Pick a country preset or drag the sliders yourself. The page computes the curve's Gini from those points; a real national figure uses the whole survey, so the two differ a little. Interactive · Build a Lorenz curve Poorest fifth receives Richest fifth receives Of which, the richest tenth Presets use World Bank Poverty and Inequality Platform shares, latest year available (shown on each button). The curve joins your points with straight lines, so it cuts corners and the estimated Gini comes out several points lower than the official one (which uses the whole survey); the official figure is shown for comparison. Without JavaScript: the US in 2024 (World Bank) had a Gini of 41.8, with the poorest fifth receiving 5.1% of income or consumption and the richest fifth 47.1%. Slovenia in 2023 had 24.7, with 10.0% and 34.8%. Two warnings travel with every Gini. First, it is blind to where on the curve the inequality sits: very different distributions can share the same value. Second, what is being measured matters as much as the arithmetic. Income before or after taxes and transfers, household or per person, income or consumption: each choice moves the number. What the Gini hides: tops, bottoms and the shape between A country can have the same Gini as another and a very different top. This is why researchers also report shares: how much goes to the bottom tenth or fifth, and how much to the top tenth or one per cent. José Gabriel Palma's ratio, the top tenth's share divided by the bottom forty per cent's, was proposed because he found that the middle half of most distributions looks surprisingly alike and the real differences sit in the tails. Surveys have a second problem: the very rich rarely answer them, and when they do they under-report. Researchers at the World Inequality Database (Thomas Piketty, Emmanuel Saez, Gabriel Zucman and colleagues) therefore combine surveys with tax records and national accounts to estimate the top. Their figures for the top are higher than survey-only figures, and the two approaches are an active methodological dispute, not a settled matter. For India, the World Bank's 2023 survey-based Gini of 23.0 is built on consumption, which is much more equal than income; the WID estimates that the top 10% held about 56% of national income in 2014 (Chancel and Piketty, 2019). Both numbers are "right" about different things. Explore a dozen countries below. Switch the measure and see how the order changes. Interactive · Same countries, four measures World Bank Poverty and Inequality Platform, latest survey year per country (year in brackets). Surveys differ: consumption-based for India; income-based for most others. Not strictly comparable. The ratio is the richest fifth's share divided by the poorest fifth's. Without JavaScript: Gini 54.1 (South Africa, 2022), 50.3 (Brazil, 2024), 41.8 (US, 2024), 33.7 (Germany, 2022), 24.7 (Slovenia, 2023). Income is one thing, wealth is another Income is a flow: wages, profits, benefits arriving over a year. Wealth is a stock: houses, savings, shares, pensions, minus debts. Wealth is always more concentrated than income, because it accumulates, it is inherited, and people with spare income can buy assets that grow. Households with big mortgages can have negative net wealth while earning a decent salary, which is why the bottom half's wealth share is near zero. Interactive · Who gets what, income against wealth World Inequality Report 2022 executive summary: global figures for 2021, using market exchange rates adjusted for purchasing power. "Middle 40%" is what remains after the top 10% and bottom 50%. Without JavaScript: global income 2021, top 10% about 52%, bottom 50% about 8.5%; global wealth, top 10% about 76%, bottom 50% about 2% (World Inequality Report 2022). Evidence and argument separate here. The evidence is the distribution. The arguments begin with why: Thomas Piketty's Capital in the Twenty-First Century (2013, English 2014) argued that when the return on capital exceeds economic growth, wealth concentrates. Critics have disputed the data (the Financial Times questioned some of his series in 2014), the law-like claim, and whether returns behave as the theory needs. Defenders replied with revised data and extended series. Economists who study the same figures still disagree on how much comes from technology, trade, taxes, institutions, market power or inheritance. Mobility: the same ladder seen as a matrix A snapshot of inequality does not say how people move within it. If a society is very unequal but everyone's children shuffle freely, a lifetime perspective looks fairer than the snapshot. If the same families stay at the same rungs, the snapshot understates the problem. Sociologists measure movement with a mobility matrix: sort families into fifths by income, sort their grown children the same way, and tabulate where children from each parental fifth end up. Without any movement the matrix would have 100% on the diagonal. If rank were inherited not at all, every cell would show 20%, because each child lands in a random fifth. Real countries sit between. The key parameter is the rank-rank correlation between parents' and children's positions. In Raj Chetty and colleagues' 2014 study of US tax records, it was about 0.34; Chetty et al. also reported that a child born in the bottom fifth had about a 7.5% chance of reaching the top fifth, against 20% under random shuffling. The simple model below, set to 0.34, gives about 8%, close to that observed figure. Interactive · A mobility matrix you can tune Rank-rank correlation (how much rank is inherited) Computed live from a bivariate-normal model of parent and child rank; real matrices are not exactly this shape, and the presets other than 0.34 are illustrative, not measurements. Rows are the parents' fifth (1 = poorest); columns are the child's fifth. Without JavaScript: with a rank correlation of 0.34, a child of the poorest fifth in this model has about an 8% chance of reaching the richest fifth and about 35% of staying in the poorest fifth, against 20% for each under pure shuffling. What the matrix does not tell you is why. Chetty's team found large differences across US places, and the strongest correlates of upward mobility were less segregation, better schools, more two-parent households, and more social ties across class lines. Those are correlates; sociologists and economists debate how far each is cause. And the matrix measures rank, not welfare: a society where everyone is richer can have the same matrix as a stagnant one. The Great Gatsby curve, and why it is a clue and not a verdict Plot countries' inequality against how strongly parental income predicts children's income and you get an upward slope: more unequal countries tend to be less mobile. Alan Krueger named it the Great Gatsby curve in 2012, using Miles Corak's data. Climbing the Ladder (30/04) tells that story and tabulates the numbers; here is what the measurement lens adds. The curve is built from perhaps a couple of dozen rich countries, with inequality measured years before mobility, and with mobility estimates that depend on how each national study defined income and age. The OECD's 2018 report, A Broken Social Elevator?, reported that families at the bottom tenth needed about two generations to reach average income in Denmark, five in the United Kingdom, seven in China and India, nine in Brazil and eleven in Colombia (as reported by secondary summaries of the OECD figures; the OECD's own average across member countries was about four to five). Those are projections from a simple model, not forecasts. The causal reading: when the gaps between rungs are wide, advantaged parents have more to invest and more to lose, so advantage is passed on. Inequality causes immobility. The sceptical reading: a handful of countries is a small sample; a third factor (education systems, labour-market rules, immigration, family structure) could shape both. The correlation may survive without the causal story. Caste and class: two ways to lock a ladder Class is a position you hold; caste is a category you belong to. In the sociological sense, a caste system fixes membership at birth, restricts marriage to within the group (endogamy), links rank to ideas of purity or worth, and in its strict forms limits the occupations open to each group. A class system has none of these formal locks: in principle anyone may move, and the ladder is made of money, schooling and connections. Real societies are mixtures, and every comparison is partial. SystemFixed at birth?MarriageLegal status today Modern class (most countries)No, but parents' position predicts stronglyFree, with class homogamy in practiceFormally equal citizens Caste in India (jati, within the four varna)YesMostly within the group (endogamous)Untouchability abolished by Article 17 of the 1950 Constitution; affirmative-action quotas for Scheduled Castes and Tribes Estates (France before 1789)Yes, with limited ennoblementRank-matched in practiceAbolished 1789 Burakumin in JapanDescent-based stigmaDiscrimination in marriage documentedStatus abolished 1871; remedial laws 1969 to 2002; Act to promote elimination of discrimination, 2016 Race as "caste" (US, apartheid South Africa)Yes, by law in the pastBanned across lines, now struck down or repealedLegal bars removed (US 1960s, South Africa 1991); gaps persist Take India, where the evidence is richest. In the 2011 census Scheduled Castes were 16.6% of the population and Scheduled Tribes 8.6%. India has not published a full caste census since 1931; Bihar's state survey (published October 2023) counted about 63% of its people as Other Backward Classes or Extremely Backward Classes and about 19.7% as Scheduled Castes, and in April 2025 the national government announced that caste would be enumerated in the next census. Research on mobility finds both change and persistence. Hnatkovska, Lahiri and Paul (2013) found that gaps in education and wages between Scheduled Castes and Tribes and others narrowed between 1983 and 2005; a correspondence study by Thorat and Attewell (2007) sent matched job applications with names signalling caste or religion and found that employers called back applicants with Dalit or Muslim names less often. The tool below shows the arithmetic that makes group inequality persist even when within-group variation is huge. It is a pure statistical illustration of how a gap in group averages turns into over- and under-representation at the top and bottom. It does not say what causes any real gap. Interactive · A gap in averages, a gap at the extremes Group B's share of the population Gap in average position (in standard deviations) Illustrative model: both groups have normally distributed outcomes with the same spread, and Group B's average is lower. No real dataset; it shows how averages and tails relate. Without JavaScript: with a 20% group whose average sits half a standard deviation lower, that group makes up about 32% of the poorest fifth and about 10% of the richest fifth. Does the quota system answer the problem it addresses? Here the evidence and the positions come apart. Defenders of caste-based reservations argue that exclusion was by group, so remedy must be by group; that representation in universities, government and politics matters in itself; and that Scheduled Caste graduates still face discrimination in hiring. Critics offer several different objections: that class-based criteria would target need better, that a "creamy layer" within backward groups captures the benefits (the Supreme Court's 1992 Indra Sawhney ruling excludes it for OBC quotas and capped reservations at about 50%), or that a merit principle should govern selection. India added a 10% quota for economically weaker sections of the unreserved categories in 2019; the Supreme Court upheld it by a 3 to 2 majority in November 2022. These are arguments about justice, and the data in this section can inform each side without deciding between them. B. R. Ambedkar's Annihilation of Caste (1936, written for a conference that cancelled his speech) argued that caste could not be treated as a mere economic layer, because it divides labourers from one another and is sustained by belief; many Marxist writers in India held that class struggle was primary and caste a form of it. The disagreement is still live in the field: is caste a status order, a class order, or both? Measuring is not deciding The facts above are compatible with several philosophies of what, if anything, to do. A graduate keeps them separate and can state each one fairly: Egalitarian. Inequalities that come from luck, birth or circumstance are unjust (luck egalitarians such as G. A. Cohen and Ronald Dworkin), or large gaps corrode equal standing among citizens (relational egalitarians such as Elizabeth Anderson). Look at the gaps and at how they arose. Prioritarian and Rawlsian. Benefits to the worst off count for more; for John Rawls (1971), inequalities are justified only if they improve the position of the least advantaged. Look at the bottom. Sufficientarian. Harry Frankfurt argued that what matters morally is that everyone has enough, not that everyone has the same. Look at whether anyone falls below the line. Entitlement (libertarian). For Robert Nozick (1974), a distribution is just if it came about through just acquisition and voluntary transfer, whatever pattern results. Look at the process, not the shape. Desert and meritocratic. Rewards should track effort and contribution; the target is unfair barriers, not unequal outcomes. Michael Young, who coined "meritocracy" in 1958, meant it as a warning that a society which believes its ranking is earned will treat the bottom harshly. Each position has serious critics, and each looks at different statistics. A top-share figure speaks most to the egalitarian; a poverty line to the sufficientarian; a mobility matrix to the meritocrat. When a speaker moves from "the gap is X" to "so we must Y" without saying which philosophy carries the step, the second part is not a finding. What a graduate just sees A Gini is one number for a whole curve; two countries can share it and differ at the top or the bottom. Before comparing two inequality figures, ask: income or consumption, before or after tax, household or person, which year, which survey. Surveys miss the very rich; tax-based estimates of top shares are higher, and that method dispute is real. Wealth is much more concentrated than income, worldwide: about 76% of it sits with the top tenth, against about 52% of income (2021). Mobility is a matrix, not a slogan: ask where children of the poorest fifth end up, and compare that with 20% under pure shuffling. A gap between group averages produces big gaps in the tails, even with huge overlap. Caste and class overlap but differ: one is a category fixed at birth and marriage, the other a position. "The gap is this" and "so we should do that" are different sentences, and the second always needs a value premise. Where it shows up next Race, Ethnicity & Migration (35/79): the group gaps here are measured with categories that were themselves made, which is the next piece's subject. Public Policy (35/16) and Political Economy (35/17): tax and transfer design, minimum wages and affirmative action all come from this evidence plus a value judgement. Statistics (34/05): the survey weights, confidence intervals and rank correlation behind every table above. In the world: the World Bank's inequality tables, the OECD's Society at a Glance, an economist's wealth-tax proposal, an Indian court case on quotas. If you want to go deeper 📘 Textbook. David Grusky (ed.), Social Stratification: Class, Race, and Gender in Sociological Perspective (Routledge/Westview, several editions), the standard reader; and the free OpenStax Introduction to Sociology (3rd ed.), chapters on global and US stratification. 📘 Textbook for the numbers. Anthony Atkinson, Inequality: What Can Be Done? (Harvard University Press, 2015), a careful economist's account with proposals clearly marked as proposals. 📘 Gentler. Branko Milanovic, Global Inequality: A New Approach for the Age of Globalization (Harvard University Press, 2016), which explains the elephant chart and the world's income ladder. 🌐 Free data. World Inequality Database, Our World in Data on economic inequality, and Opportunity Insights for US mobility atlases. 🌐 Free course. MIT OpenCourseWare 14.74 Foundations of Development Policy (Spring 2009) for how poverty and inequality are measured in developing countries. The takeaway Inequality is measured with curves, shares and matrices, and each hides something: the Gini hides the shape, surveys hide the very rich, snapshots hide movement. The same figures sit under several moral philosophies, so a good sociologist reports the gap, says how it was measured, and labels the step to "should" as a different kind of claim. Try this: pick one inequality statistic you have seen in the news and find out its year, its unit (income, consumption or wealth), its source and what it leaves out. Sources & Further Reading Sources. World Bank Poverty and Inequality Platform, indicators SI.POV.GINI and SI.DST.* (latest survey year per country, retrieved November 2026). World Inequality Report 2022 (Chancel, Piketty, Saez, Zucman), executive summary: global income and wealth shares for 2021. Chancel and Piketty, "Indian income inequality, 1922–2015" (2019). Chetty, Hendren, Kline and Saez, Quarterly Journal of Economics 2014 (rank-rank 0.34; 7.5%). Krueger 2012 and Corak 2013 on the Great Gatsby curve. OECD, A Broken Social Elevator? (2018). Hnatkovska, Lahiri and Paul, Journal of Human Resources 2013. Thorat and Attewell, Economic and Political Weekly 2007. Census of India 2011; Bihar caste-based survey (October 2023); Government of India decision of 30 April 2025. Indra Sawhney v. Union of India (1992); Janhit Abhiyan v. Union of India (2022). Ambedkar, Annihilation of Caste (1936). Rawls 1971; Nozick 1974; Frankfurt 1987; Young 1958. 📘 Grusky (ed.), Social Stratification 📘 Milanovic, Global Inequality 🌐 World Inequality Database Step 7. Cities, Communities & Social Networks: Why Where You Live Is Who You Meet --------------------------------------------------------------------------------- Why: Cities, neighbourhoods and the networks that connect people. A city is a pile of buildings and also a pattern of who lives beside whom and who knows whom. Sociologists learned to read both patterns, and found that mild preferences can build hard walls, and that the people you barely know matter more than your friends. Two people on the same street can have almost nothing in common, and two people on opposite sides of a continent can be four handshakes apart. Both facts are about the same thing: the shape of social ties, laid over a map. You see it when you look for a job. The person who tells you about the opening is rarely your best friend, who knows the same people you do. It is a former colleague, a friend of a cousin, someone you last saw at a wedding. You see it when you notice that a neighbourhood has "turned", and nobody remembers deciding that it would. And you see it in the fact that more than half the world now lives in a city, most of them in places no architect drew. This course asks how people sort themselves in space, how the places they end up in shape them back, and how the web of ties between them behaves. Its tools are old (a map of a city from the 1920s) and new (a network of hundreds of millions of accounts). Its best models are small enough to play with, and they are all below. The course in one box Who takes itSociology majors, usually in year 2 or 3 (urban sociology, community, "networks"); geography, planning, public-policy and data-science students take versions of it. Programmes differ: American ones start with the Chicago school, British ones with planning and housing, and increasingly all of them start from cities of the Global South. What it is forExplaining how people end up where they live and who they know, and how both shape their chances. Comes afterSociological Research Methods (35/76), which is where you learn what a map of neighbourhood averages can and cannot show. Leads toIt is read beside Race, Ethnicity & Migration (35/79), Stratification & Inequality (35/78) and Deviance, Crime & Social Control (35/83), and feeds Public Policy (35/16), because housing and neighbourhoods are policy. What this piece gives youYou will not be able to draw a city plan or run a network analysis. You will know the classic Chicago picture and its critics, why most new city-dwellers live in settlements they built themselves, how Schelling's segregation model works and what it cannot show, and why weak ties and small worlds are the maths of "it's who you know". Easier first reads: Climbing the Ladder: Social Mobility (30/04), Thinking in Systems (17/06) and From Königsberg to Your GPS (12/18, on graphs). The Chicago school: a city as an ecosystem In the 1910s and 1920s the University of Chicago had a city outside its window that was growing faster than anyone could count: a million immigrants and a great migration of Black families from the American South, all arriving in the same few decades. Robert Park, a former journalist, and Ernest Burgess asked their students to go out and map it. Park borrowed the vocabulary of plant ecology: groups invade an area, they compete for it, and one succeeds another. Their collected work appeared as The City (1925). Burgess drew the picture that made the school famous: the concentric zone model. A city grows outward from its business centre in rings. Around the centre is a zone in transition of cheap rooms and industry, where the newest, poorest arrivals land. Next come the workers' homes, then better housing, then a commuter ring. As people prosper they move outward, and the next wave takes their place. The detail that mattered most: in Clifford Shaw and Henry McKay's studies (1942) the zone in transition had the highest delinquency rates decade after decade, whichever immigrant group lived there. The rate belonged to the place, not to the people. That is the founding claim of the sociology of neighbourhoods. Two other ideas from Chicago stayed. Louis Wirth (1938) argued that urban life follows from three things: size, density and social variety. A big, dense, mixed place means many shallow contacts, and Wirth feared it left people anonymous. Herbert Gans's study of an Italian-American neighbourhood in Boston, The Urban Villagers (1962), answered that for many city people the village never went away, and Jane Jacobs (1961) described the "eyes on the street" that make a busy sidewalk safe. Nearly every urban theory since has been a reply to one of these. Interactive · Three maps of one city Schematic diagrams of the three textbook models, not maps of a real city. Burgess 1925; Hoyt 1939; Harris and Ullman 1945. Without JavaScript: Burgess drew five rings out from a business centre; Hoyt (1939) argued that cities grow in wedges along transport lines and by income; Harris and Ullman (1945) said cities grow around several centres at once. The Chicago picture has serious critics. Homer Hoyt (1939) found that rich and poor areas form wedges along roads and rails rather than rings; Chauncy Harris and Edward Ullman (1945) said cities grow around many centres. The bigger problem is method. W. S. Robinson showed in 1950 that a pattern across areas can be quite different from the pattern across people (the ecological fallacy): a neighbourhood with a high crime rate does not mean its typical resident commits crimes. And the Chicago picture was built from one fast-growing American industrial city; W. E. B. Du Bois had already mapped Philadelphia's Black Seventh Ward in The Philadelphia Negro (1899), with far more attention to how racial exclusion, not "natural" competition, shaped where people could live. Neighbourhoods that watch themselves: collective efficacy If the place matters, what about the place does? Robert Sampson spent decades answering that in Chicago. In a 1997 study in Science, Sampson, Stephen Raudenbush and Felton Earls surveyed 8,782 residents in 343 neighbourhood clusters and measured collective efficacy: neighbours' mutual trust together with their willingness to step in (to stop kids skipping school, say, or to break up a fight). Neighbourhoods with higher collective efficacy had less violence, even after allowing for poverty and instability. It was a shift from "slums breed crime" to something more precise and less insulting: some poor neighbourhoods have the glue and some do not. The honest limits: the study is a snapshot, so it cannot fully separate glue causing safety from safety allowing glue; and trust measured by survey is not the same everywhere. What it contributed is a way of thinking: a neighbourhood is more than the sum of its residents' incomes. This idea leads straight into Deviance, Crime & Social Control (35/83). Cities of the Global South: the settlements that build themselves "Global South" is a loose, contested label for lower- and middle-income countries, many of them in the southern hemisphere; some writers prefer other terms. Whatever it is called, this is where urban growth is. In 2018 the United Nations projected that the world would go from 55% urban to 68% by 2050, with most of the increase in Asia and Africa. And much of that growth is informal: homes built without permission on land the builder does not formally own, connected to services the builder arranges. The scale is large. The UN's SDG tracking (indicator 11.1.1) estimated that about 1.12 billion people lived in slums or informal settlements in 2022, about a quarter (24.8%) of the world's urban population, and 130 million more than in 2015. Regional totals vary from one edition of the report to the next, a reminder that these are model-based estimates. NumberSourceCaution About 1.12 billion in slums or informal settlements (2022)UN SDG report 2025, indicator 11.1.1Definition covers inadequate housing as well as informal land; regional splits change between editions Kibera, Nairobi: 170,070 counted (2009)Kenya national census 2009, as usually citedPopular claims of a million or more were common before it. Recheck the figure before you repeat it 55% urban in 2018; 68% projected for 2050UN World Urbanization Prospects 2018A projection; countries define "urban" differently Three views compete. Janice Perlman's The Myth of Marginality (1976), on Rio's favelas, found residents were workers and organisers fully part of the city's economy, not drop-outs from it. Hernando de Soto's The Mystery of Capital (2000) argued that the poor already own enormous assets, trapped because they hold no legal title; give them title and they gain credit. Studies of Peru's titling programme (Erica Field, 2007) found that titles changed how households used their time, and did less to open bank credit than the theory predicted. A third view, Mike Davis's Planet of Slums (2006), emphasised exploitation, and AbdouMaliq Simone's writing on Johannesburg described "people as infrastructure": where roads and pipes fail, people's relationships do the work of carrying goods, news and credit. The same self-building appears in organised form. In Karachi, the Orangi Pilot Project (from 1980, led by Akhtar Hameed Khan) helped residents lay their own sewers, at a fraction of the cost of public contractors. And since Jennifer Robinson's Ordinary Cities (2006), many urbanists argue that theories built on Chicago and London should be tested against Lagos, Mexico City and Mumbai too, not exported to them. Schelling's model: mild preferences, sharp segregation In 1971 the economist Thomas Schelling (later a Nobel laureate, 2005) published a model small enough to run with coins on a chessboard. Two kinds of people live on a grid with some empty squares. Each person is happy if at least some fraction of their neighbours are like them. An unhappy person moves to a spot where they would be happy. Nobody wants to live in a segregated city. Nobody is asked to. Watch what happens. Interactive · Schelling's moving game Each person wants at least this share of neighbours like them New random cityOne roundRun until stable group A group B empty A 20 × 20 grid, 10% empty, half of each group. "Like me" counts the up-to-eight surrounding squares. Unhappy people move to a randomly chosen empty square where they would be happy, which is one common variant; Schelling's own version differed in detail. Fixed random seed, so a given setting always gives the same city. Without JavaScript: in this model, if everyone wants at least a third of neighbours like them, the average share of like neighbours rises from about 50% to about 70%; at 60% it goes above 95%; at 75% or more, people get stuck and cannot find anywhere to go. Here is the lesson, and the limit. Slide the threshold to 35%: people are content to be in the minority, two thirds different from them. Run it. Average like-for-like neighbours moves from about half (the random start) to about 70%. At 60% you get near-perfect separation, in a handful of rounds. The segregation is much stronger than anyone wanted, because each move changes the neighbourhood for everyone around, which triggers the next move. Schelling called this the problem of micromotives and macrobehavior (his 1978 book title). What it does not show is as important. The model shows that preferences could produce segregation; it does not show that they did. Real cities add everything the grid leaves out: unequal incomes, prices, lenders, landlords and police. In the United States, segregation was also built by law and policy (restrictive covenants, redlining, the siting of public housing), as Richard Rothstein documents in The Color of Law (2017); apartheid South Africa's Group Areas Act (1950) enforced it outright. Survey work by William Clark and others also found that people's stated preferences are a mixture, and that what households say they want does not map neatly onto Schelling's threshold. Treat the model as a warning that "nobody chose this" does not mean "nobody did anything", and not as an explanation of any real city. For a second playable version, see Nicky Case's Parable of the Polygons (below). Weak ties: why acquaintances find you the job Mark Granovetter's 1973 paper "The Strength of Weak Ties" is one of the most cited in sociology. His starting point was a survey of professional workers in a Boston suburb for his book Getting a Job (1974). Most who found their job through a personal contact had got the lead from someone they saw only occasionally or rarely. Why? Your close friends know the same people you do and have heard the same news. An acquaintance sits in another circle and hears things you cannot. A weak tie is a bridge: the only short path between two clusters. The tool below makes the geometry visible. Sixty people form six tight groups of ten (everyone in a group knows everyone else). Person 0, highlighted, wants to hear news from outside their own group. Add random acquaintances between groups and watch how many people lie within three steps. Interactive · Add weak ties Extra acquaintances linking different groups within three steps of person 0 further away An invented network of six cliques of ten, with links added at random (fixed seed). It illustrates the idea; it is not Granovetter's data. Without JavaScript: with no weak ties, person 0 can reach only their own group of ten (9 other people). With 2 weak ties the figure is 29 people within three steps; with 16 it is 49; with 24 almost everyone can be reached. There is a modern test. Karthik Rajkumar and colleagues (2022, Science) ran experiments on LinkedIn's "People You May Know" for over 20 million users, and found that moderately weak ties produced the most job moves, better than both very close contacts and very distant ones. Granovetter's idea held, and became more specific. A caution: the strength of a weak tie depends on culture. Fei Xiaotong's From the Soil (1947) described Chinese rural society not as groups but as ripples spreading from a stone: relationships measured by distance from the self, with obligations shrinking outward. Networks work in every culture; what a tie is and what it owes differs. Small worlds: few steps, tight clusters In 1967 Stanley Milgram asked people in the American Midwest and in Boston to forward a folder to a named stockbroker near Boston, through acquaintances only. Of the 296 chains started in the follow-up study (Travers and Milgram, 1969), 64 reached the target, taking on average about 5.2 intermediaries. "Six degrees of separation" took its name from John Guare's 1990 play. Critics, notably Judith Kleinfeld (2002), pointed out that most chains never finished, so the true figure could be larger. Peter Dodds, Roby Muhamad and Duncan Watts re-ran it by email in 2003 across 13 target countries, and again most chains were lost, but the estimates came out at roughly five to seven steps. In 2011 a Facebook study (Backstrom and colleagues) put the average distance at 4.74 across about 721 million accounts. The puzzle is why. Real networks are clustered: your friends tend to know each other. That alone would make worlds large, with long chains of overlapping circles. Duncan Watts and Steven Strogatz (1998) showed that rewiring only a few links to random distant places collapses the distances while the clusters stay intact. A few weak ties do the work of a thousand. Try it. Interactive · Rewire the ring Share of links rewired to random places Watts-Strogatz model: 200 people on a ring, each linked to the 3 nearest on each side. Only the shortcut links are drawn. Path length and clustering are computed live on the generated network (fixed seed). Without JavaScript: with no rewiring the average path between two people is about 17 steps; with 1% of links rewired it drops to about 7.5 (44% of the original) while clustering is still about 96% of its starting value; with every link rewired it is about 3.2 but clustering is nearly gone. The same idea sits behind how disease, rumours and fashions spread, and it is the reason a few well-placed links can help or hurt a whole society. It is also a hint at a limit: a small world says that short paths exist, not that people can find them without a map or that the path carries anything useful. Being four steps from a CEO does not mean you can reach one. What a graduate just sees A place has properties its residents do not: Shaw and McKay's high-delinquency zone stayed high as whole populations came and went. A map of areas is not a map of people: before you say "this neighbourhood is X", ask whether it is the ecological fallacy. "Nobody wanted this" does not mean no one did anything: Schelling's moves produce more separation than anyone asked for, and real segregation also has laws, banks and landlords in it. About a quarter of city-dwellers worldwide live in informal settlements, and the builders of those homes are often the most active people in the city. For news and opportunity, your acquaintances matter more than your best friends, and the middle of the range of tie strength may beat both ends. A handful of random links turns a big world small, so short paths are cheap and finding them is hard. Treat a "huge slum" headline number as an estimate. Kibera's census count was a fraction of the popular figure. Where it shows up next Race, Ethnicity & Migration (35/79): residential segregation, ethnic enclaves and the policy history behind them. Stratification & Inequality (35/78): neighbourhood effects on children's outcomes, and how networks carry advantage. Deviance, Crime & Social Control (35/83): social disorganisation and collective efficacy are theories of crime as much as of neighbourhoods. Public Policy (35/16): zoning, slum upgrading versus titling, transport, and housing vouchers. In the world: how a recommendation system finds "people you may know", how a public-health team traces contacts, how a planner decides between upgrading and relocating an informal settlement. If you want to go deeper 📘 Textbook. Richard LeGates and Frederic Stout (eds), The City Reader (Routledge, many editions), is the standard anthology of Park, Burgess, Wirth, Jacobs and later writers. For networks, David Easley and Jon Kleinberg, Networks, Crowds, and Markets (Cambridge University Press, 2010). 📘 Gentler. Duncan Watts, Six Degrees (2003), and Jane Jacobs, The Death and Life of Great American Cities (1961). For the Global South, Mike Davis, Planet of Slums (2006), and Jennifer Robinson, Ordinary Cities (2006). 🌐 Free play. Nicky Case's Parable of the Polygons is a free interactive Schelling model; the Easley and Kleinberg book is free to download as a draft. 🌐 Free course. Matthew Jackson's Social and Economic Networks has been offered free to audit on Coursera; check that it is currently running. I could not confirm a single standard free urban-sociology course. The takeaway Places and ties both have structure that individuals do not choose. Mild preferences can pile up into hard segregation, close friends tell you what you already know while acquaintances bring news, and a few bridges make a big world small. Most of the world's new city-dwellers build their homes themselves. Models like Schelling's and Watts-Strogatz show what can happen; the evidence about a real city needs its history. Try this: list the last three useful things you learned (a job lead, a recommendation, a repair person) and write down who told you and how well you know them. Count how many were weak ties. Sources & Further Reading Sources. Park, Burgess and McKenzie, The City (1925); Shaw and McKay, Juvenile Delinquency and Urban Areas (1942); Wirth, "Urbanism as a Way of Life" (1938); Hoyt (1939); Harris and Ullman (1945); Robinson, "Ecological Correlations and the Behavior of Individuals" (1950); Gans (1962); Jacobs (1961); Du Bois (1899); Sampson, Raudenbush and Earls, Science 277 (1997); UN SDG Report 2025 Extended Report, Goal 11; UN World Urbanization Prospects 2018; Perlman (1976); de Soto (2000); Field, "Entitled to Work," QJE (2007); Davis (2006); Simone, "People as Infrastructure," Public Culture (2004); Robinson (2006); Schelling, "Dynamic Models of Segregation," Journal of Mathematical Sociology (1971) and Micromotives and Macrobehavior (1978); Rothstein (2017); Granovetter, AJS 78 (1973) and Getting a Job (1974); Rajkumar et al., Science 377 (2022); Fei Xiaotong, From the Soil (1947); Travers and Milgram, Sociometry (1969); Kleinfeld (2002); Dodds, Muhamad and Watts, Science (2003); Backstrom et al. (2011/2012); Watts and Strogatz, Nature 393 (1998). All interactive numbers are computed live on invented data and are illustrations. 📘 Easley and Kleinberg, Networks, Crowds, and Markets 🌐 Parable of the Polygons Step 8. Race, Ethnicity & Migration: Who Made the Categories ------------------------------------------------------------ Why: Race, ethnicity and migration, and how the categories were made. Racial and ethnic labels are written by censuses, courts and laws, and they change from country to country. The same is true of how states answer the question of how newcomers should fit in. In the 1930 United States census, the form offered "Mexican" as a race. It was the only census that ever did. By 1940 Mexican Americans were back to being counted as white, and since 1980 the census has asked a separate question about Hispanic origin. Nobody's ancestry changed between 1930 and 1940. The category did. That is the central lesson of this subject. Race and ethnicity are among the most consequential facts in many people's lives, and the labels people use for them are, to a large extent, the product of laws, censuses, courts and history, and they differ from country to country. Saying so is not saying that race is "not real" in its effects: a category that governs where you may live, whom you may marry or how you are treated by a police officer has real consequences whatever its origin. It is saying that to study those consequences, you first have to ask who made the categories, and for what. This piece also covers the other half of the subject, migration: how many people actually live outside the country where they were born, where they go, what economists and sociologists have found about the effects, and how different states have answered the question of how newcomers should fit in. It is a contested field. Each position is given in terms its holders would accept, and the evidence is kept apart from the arguments. The course in one box Who takes itSociology majors, usually in year 3, and students of political science, anthropology, geography and public policy. Programmes in the United States, the United Kingdom, Brazil, South Africa and France teach it with their own national cases at the centre, which is part of the lesson. What it is forUnderstanding how groups are named and counted, how those labels shape life chances, and how people, states and labour markets handle movement across borders. Comes afterStratification & Inequality (35/78), whose group-gap arithmetic this piece assumes. Leads toIt sits beside Gender, Family & Sexuality (35/80) and Deviance, Crime & Social Control (35/83), and feeds Public Policy (35/16), International Relations (35/14) and, in the History major, The Early Modern World, 1450–1800 (35/88). What this piece gives youYou will not be able to design a census. You will see how four countries count their people, how migration is measured, and the main arguments about integration. Easier first reads: Castes, Estates and Classes (30/03), which covers the colonial casta system, and Institutions (30/05). Categories are made, not found W. E. B. Du Bois opened The Souls of Black Folk (1903) with the line that "the problem of the twentieth century is the problem of the color-line", and spent a lifetime arguing that the line was drawn by societies, not by biology. Michael Omi and Howard Winant's Racial Formation in the United States (1986) turned that into a framework: racial categories are continually made and remade by states, social movements and everyday interaction. The census, the court and the classroom are where the line is written down. Genetics bears on this in a particular way. In 1972 the geneticist Richard Lewontin found that about 85% of the variation in the human genes he studied lay within populations, and only a small share between continental groups. The geneticist A. W. F. Edwards replied in 2003 that Lewontin's point, while true gene by gene, does not mean that ancestry cannot be inferred: using many genes at once, people can be sorted by geographic ancestry with high accuracy. Both statements are correct. What they do not support is the nineteenth-century idea of a few discrete "races" with essences. Human variation is mostly gradual across geography. In 2023 a US National Academies report recommended that genetic research stop using race as a stand-in for genetic ancestry and describe people with measured ancestry instead. Whether "race" and "ancestry" should be kept apart in medicine and in social science is debated; the sociological claim is narrower: the social categories are not the genetic clusters. The constructionist case (Du Bois, Omi and Winant): categories are social facts that organise real advantage, so study how they were built and how they work. A critique from inside sociology (Rogers Brubaker, Ethnicity Without Groups, 2004): treating "groups" as the basic units can make them seem more solid than the people in them are, so research should follow how people use categories, not assume the groups exist. The structural case (Douglas Massey and Nancy Denton, American Apartheid, 1993): residential segregation by race was produced by policy and practice and drives other inequalities. An older class-first case (William Julius Wilson, The Declining Significance of Race, 1978): in the late twentieth century, class position increasingly shaped Black Americans' outcomes. These are scholars arguing about weights, not about whether discrimination existed. Rules for deciding who belongs Every classification needs a rule. Here is how a few rules from the history of the United States handle the same family. Choose each grandparent's ancestry, and see what each rule says about the grandchild. (This is historical illustration, not a way to classify anyone today.) Interactive · One family, different rules Tap each grandparent to switch between European-descended and African-descended ancestry (a simplification: real ancestry is often richer). Rule sources: Virginia Racial Integrity Act (1924) defined "white" as having no trace of non-Caucasian blood, with an exception for up to one-sixteenth American Indian ancestry; the 1967 case Loving v. Virginia struck down its ban on marriage across the line. The census has allowed people to choose more than one race since 2000. Without JavaScript: with one African-descended grandparent of four, the 1924 Virginia rule makes the grandchild "colored", a simple majority rule makes the grandchild "white", and the 2000-onward census lets the person tick both. Other places use quite different rules. US states set their own rules at different times, which is why the same person could be one race in one state and another across the border. South Africa's Population Registration Act of 1950 sorted every person into White, Coloured, Bantu (later Black African) or Indian/Asian, and decided membership mostly by appearance and general acceptance in the community, with descent added in 1967; it was repealed in 1991. Brazil asks people to name their own colour, and for decades the answers have come from a wide vocabulary: when the 1976 household survey asked an open question, respondents gave more than 130 different colour terms. One country, four ways to count Switch between the censuses below. Each shows what the latest national count looked like, which categories were offered, and what is not asked. Interactive · What the census lets you say US: Census Bureau, 2020 census, race alone; Hispanic origin (18.7%) is a separate question and can be of any race. Brazil: IBGE, Census 2022, self-declared colour or race (rounded). South Africa: Statistics South Africa, Census 2022 (shares computed from the published counts; the census had an estimated undercount, which makes group shares uncertain). France: no ethnic or racial count; foreign-born share from the World Bank/UN DESA 2024 series. Without JavaScript: US 2020, 61.6% White alone, 12.4% Black alone, 10.2% two or more races. Brazil 2022, 45.3% pardo, 43.5% white, 10.2% preto. South Africa 2022, 81.4% Black African, 8.2% Coloured, 7.3% White. The United States has counted by race since 1790, when the first census sorted free people by sex and colour and counted enslaved people separately (under the Constitution's three-fifths clause, which counted each enslaved person as three-fifths of a person for representation). Since 1977 federal rules (OMB Directive 15) have set the standards for the whole government: race as a handful of categories, Hispanic origin as an ethnicity. The 2020 census counted 33.8 million people as multiracial, up from about 9 million in 2010, and the Census Bureau attributes most of that jump to improvements in how the questions were designed and coded, not to a sudden change in the population. In March 2024 the standards were revised: one combined question on race and ethnicity, and a new category for Middle Eastern or North African origin. Brazil has asked about colour since the first census in 1872 (not in every census since). The answer is self-declared and the categories are colour words: branca, preta, parda, amarela and indígena. In 2022 the pardo category (roughly "brown", covering many mixed backgrounds) became the largest, at 45.3%, ahead of white at 43.5%, for the first time since 1991. Gilberto Freyre's idea of a Brazilian "racial democracy" (1930s) held that mixing had avoided American-style racism; research since the 1950s, including the UNESCO studies, documented large racial gaps in income and education. Since a 2012 law, federal universities reserve half their places for public-school graduates, with sub-quotas for Black, mixed and Indigenous students in proportion to each state's population; a 2014 law reserves 20% of federal civil-service posts for Black and mixed people. Because quota places are valuable, several universities added committees that check applicants' appearance, which is controversial in its own right. Defenders say a self-declared category invites misuse; critics say the state judging a person's looks reproduces the old classifying. South Africa used the apartheid categories to assign where people could live, work and marry. After 1994 the same four labels were kept for a different purpose, to measure and redress disadvantage under laws such as the Employment Equity Act (1998) and broad-based black economic empowerment. The 2022 census counted about 62.0 million people. Defenders of keeping the categories say race is still the structure of disadvantage and you cannot repair what you do not measure. Critics, including some from within the redress tradition, say racial categories keep the apartheid classification alive, favour the better-off members of the groups and should give way to measures of actual disadvantage. The government's own policy debate on this is ongoing. France has no census question on race or ethnicity. Article 1 of the 1958 Constitution commits the Republic to equality "without distinction of origin, race or religion", and the 1978 data-protection law (Informatique et Libertés) forbids, with exceptions, collecting data on racial or ethnic origin. In November 2007 the Constitutional Council struck down a clause that would have allowed studies measuring "diversity of origins" on ethnic and racial grounds. The statistical agencies instead use nationality, country of birth and the birthplace of parents; the INED/INSEE Trajectoires et Origines surveys (2008 to 2009, and 2019 to 2020) are the main source on discrimination. Supporters of the French approach argue that counting people by group creates and hardens the groups, and that the state should treat every citizen as a citizen. Critics, among them the sociologist Patrick Simon, argue that you cannot measure or prove discrimination you refuse to count, and that "colourblind" statistics leave minorities invisible. Both sides say they are defending equality. The disagreement is about whether equality requires counting. The United Kingdom has asked an ethnic-group question in every census since 1991. Germany, with its history in mind, has been cautious about ethnic categories and uses "migration background". Mexico added a question on Afro-descendant identity in its 2020 census. India does not ask about race, and asks about caste only for Scheduled Castes and Tribes. Every one of these is a decision, and each has defenders and critics. How many people move, and where International migration looks vast in the news and is smaller than most people guess. The United Nations Population Division counts the migrant stock: people living in a country other than where they were born (or, for some countries, a country other than their citizenship). By its 2024 estimate there were about 304 million international migrants, about 3.7% of the world's people. In 1990 it was about 154 million, or 2.9%. In 2020, 281 million, or 3.6%. The stock includes refugees and people who moved decades ago. Interactive · The world's migrants, over time UN DESA International Migrant Stock: 1990 (about 154 million, 2.9%), 2020 (281 million, 3.6%) and 2024 (304 million, 3.7%). Shares are of world population. Migrant stock is not the yearly flow: it counts everyone currently living abroad. Without JavaScript: 2.9% in 1990, 3.6% in 2020, 3.7% in 2024, about 1 person in 27. The largest destination in 2024 was the United States, with more than 52 million foreign-born residents, followed by Germany (about 16.8 million), Saudi Arabia (about 13.7 million), the United Kingdom (about 11.8 million) and France (about 9.2 million). Europe and North America together host about half of all migrants. The shares of populations vary much more than the totals do, as the next tool shows. In the Gulf, foreign workers outnumber citizens: the World Bank series puts the foreign-born at 74% of the UAE in 2024. In China and India it is a fraction of one per cent. Guessing the foreign-born share Surveys find that people in many countries overestimate the immigrant share of their own population by a wide margin. Try it for yourself: pick a country, set your guess with the slider, then reveal. Interactive · Guess, then reveal Your guess: share of residents born abroad Reveal World Bank (from UN DESA), International migrant stock as % of population, 2024. Definitions differ by country (foreign-born or foreign citizens), and for Gulf states most migrants are temporary workers. Without JavaScript: foreign-born share in 2024 was about 74% in the UAE, 22% in Canada, 21% in Sweden, 20% in Germany, 15% in the US, 3% in Japan, under 1% in Brazil and India. Why people move, and what the evidence says about effects The nineteenth-century geographer E. G. Ravenstein described migration as shaped by "push" and "pull" factors; later theory added that wage gaps are not enough. Douglas Massey and colleagues argued that families send members abroad to diversify risk, that networks of earlier migrants cut the cost and risk of coming, and that migration then feeds itself. The rich-poor wage gap across a border is large; most people nonetheless stay home, because moving is costly and people prefer the places and families they know. What the evidence shows (United States, 2016). A National Academies of Sciences, Engineering, and Medicine panel reviewed the research and concluded that, over the long run, immigration has little effect on the overall wages of native-born workers, with some possible losses for the least-educated natives and for earlier immigrants. On public finances, first-generation immigrants cost more at the state and local level (mostly schooling for their children), while second-generation Americans are among the strongest net contributors. Specific findings are disputed. When about 125,000 Cubans arrived in Miami in the 1980 Mariel boatlift, David Card (1990) found no visible effect on wages of low-skilled workers there. George Borjas (2017) re-analysed the same events with a narrower comparison and found a large wage drop for high-school dropouts. Michael Clemens and Jennifer Hunt (2019) argued that a change in the survey's sample composition explained Borjas's result. Researchers still disagree on how large any short-term effect is on the least-skilled, though few now find large effects on the average native worker. Positions are a separate matter. Joseph Carens (1987) argued that borders are like feudal privilege and that liberal principles imply something close to open borders. Michael Walzer (1983) argued that communities have a right to decide who joins, like clubs and families, though they owe duties to refugees. David Miller (2016) defends limits on the grounds that national communities have legitimate shared obligations. Others stress humanitarian duty (the 1951 Refugee Convention) or economic nationalism. Evidence about wages does not settle any of these positions; each rests on a view about what a state owes to citizens and strangers. Assimilation, multiculturalism and the other options The older sociology of immigration, the Chicago school (Robert Park, 1920s) and Milton Gordon (1964), expected newcomers to move steadily toward the mainstream, in language first, then jobs, then marriage. Later work found more varied paths. Alejandro Portes and Min Zhou (1993) described segmented assimilation: some immigrant children rise into the middle class, some are pushed into poor and disadvantaged neighbourhoods, and others do best by holding on to their community's networks and values. Richard Alba and Victor Nee (2003) argued instead that the mainstream itself changes, so assimilation is the blurring of boundaries and not a one-way absorption. Civic assimilation (the French republican model). Equal citizenship means public life is common ground; religion and origin are private. The 2004 law banning conspicuous religious symbols in state schools is defended as protecting that common ground and students' freedom. Multiculturalism (Canada). Canada adopted a multiculturalism policy in 1971 and passed the Multiculturalism Act in 1988. The philosopher Will Kymlicka (1995) argued that equal citizenship can require recognising group differences, such as language, religious holidays and Indigenous self-government, because a neutral state still has a culture. The critics of multiculturalism. Brian Barry (2001) argued from liberal egalitarianism that group rights risk trapping individuals inside their communities. In October 2010 Angela Merkel said that attempts to build a "multikulti" society had failed, and in February 2011 David Cameron said that "state multiculturalism" had failed, both mainly about integration and extremism. Interculturalism. A third position, used in Quebec and promoted by the Council of Europe (2008 White Paper), stresses dialogue and interaction between cultures within a shared public language and civic culture. The empirical question under these debates is whether diversity affects trust. Robert Putnam (2007) found that, in the US, people in more diverse neighbourhoods reported lower trust, including of their own group; he argued that this was a short-run effect that societies overcome. A 2014 review by Tom van der Meer and Jochem Tolsma found a negative association in the literature that was typically weak and varied by measure. A 2006 meta-analysis of 515 studies by Thomas Pettigrew and Linda Tropp found that contact between groups under favourable conditions generally lowers prejudice. These are findings, and none of them tells a state whether to pursue assimilation or multiculturalism. What a graduate just sees A census category is a decision: ask who wrote it, when, and what it was used for. The same family can fall into different categories under different rules, and a country's rules change within a lifetime. Not counting by race (France) and counting by race (the US, South Africa, Brazil) are both arguments about how to pursue equality. Most genetic variation lies within populations; ancestry is real, and it is not the same as race. About 3.7% of the world lives outside its birth country (2024), and the share depends on whether the stock or the yearly flow is meant. The share of foreign-born residents ranges from under 1% to three quarters of a country. "Immigrants lower wages" and "immigrants don't lower wages" are both too crude: effects differ by group, time and place. "Assimilation" and "multiculturalism" are two answers to a question every state must face; evidence about trust and contact informs each but does not decide between them. Where it shows up next Stratification & Inequality (35/78): group gaps and the arithmetic of tails; this piece explains how the groups are defined. Gender, Family & Sexuality (35/80) and Deviance, Crime & Social Control (35/83): intersecting categories, and how policing and sentencing data are classified. Public Policy (35/16) and International Relations (35/14): visas, asylum law, quotas and how states bargain over migration. In the world: a census form, a job-application audit study, a university quota committee, a border debate, a remittance office. If you want to go deeper 📘 Textbook. Michael Omi and Howard Winant, Racial Formation in the United States (Routledge, 3rd ed. 2014), the standard statement of the constructionist view; for a broader survey, the free OpenStax Introduction to Sociology (3rd ed.) chapters on race and ethnicity. 📘 Textbook for migration. Stephen Castles, Hein de Haas and Mark Miller, The Age of Migration (Bloomsbury/Red Globe Press, multiple editions). 📘 Gentler. Hein de Haas, How Migration Really Works (Viking, 2023), which tests common beliefs against data. 🌐 Free data. Our World in Data: Migration and the UN Population Division's International Migrant Stock tables. 🌐 Free course. Khan Academy: Society and culture has short lessons on race, ethnicity, migration and demographics. The takeaway Racial and ethnic categories are made by laws and censuses, and they differ by country, so the first question about any group statistic is how the group was defined. Migration is smaller and more varied than the news suggests, its measured effects are real but modest and disputed at the margins, and how states answer the integration question is a matter of values that evidence can inform but not settle. Try this: find your own country's census or official form, count the categories, and note one that was added or removed in your lifetime. Sources & Further Reading Sources. US Census Bureau, 2020 census results on race and Hispanic origin and the 2021 note on the multiracial count; OMB Statistical Policy Directive 15 revision (March 2024). IBGE, Censo 2022 (December 2023 release). Statistics South Africa, Census 2022. Population Registration Act 1950 (repealed 1991). Loi 78-17 of 6 January 1978; Conseil constitutionnel decision 2007-557 DC (15 November 2007); INED/INSEE TeO surveys. UN DESA, International Migrant Stock 2024, via the Migration Policy Institute; World Bank indicator SM.POP.TOTL.ZS (2024). Du Bois 1903; Omi and Winant 1986; Lewontin 1972; Edwards 2003; National Academies 2023; Brubaker 2004; Massey and Denton 1993; Wilson 1978. National Academies 2016; Card 1990; Borjas 2017; Clemens and Hunt 2019. Carens 1987; Walzer 1983; Miller 2016. Portes and Zhou 1993; Alba and Nee 2003; Kymlicka 1995; Barry 2001. Putnam 2007; van der Meer and Tolsma 2014; Pettigrew and Tropp 2006. 📘 de Haas, How Migration Really Works 📘 Omi and Winant, Racial Formation in the United States 🌐 Our World in Data: Migration Step 9. Gender, Family & Sexuality: What Is Measured and What Is Argued ----------------------------------------------------------------------- Why: Gender, family and sexuality across societies. Families, fertility and gender gaps vary enormously across societies and every figure depends on what is counted. The measuring can be stated plainly; the moral questions are a different kind of argument. In Niger, the average woman has about six children. In South Korea, she has fewer than one. Both facts are true in the same year, 2024, on the World Bank's series, and they describe the biggest change to family life in human history: the number of children people have, and when they have them, has fallen across almost every society, at very different speeds. Gender, family and sexuality are the parts of life where sociology is most often asked to explain what is "natural". The honest answer from the evidence is layered. Some patterns are very widespread; the forms they take vary a great deal; and who counts as a family, a woman, a man or a couple is itself written differently in different laws and cultures. It is also a field where people arrive with convictions rooted in religion, politics and personal experience. This piece gives the measurements, from several regions and with dates, and keeps the descriptive findings apart from the moral positions, each of which is stated in terms its holders would recognise. The course in one box Who takes itSociology majors, usually in year 3; also students of anthropology, gender studies, economics and public health. Programmes shaped by the ASA guidance on the major and the UK QAA benchmark usually treat gender and the family as core areas, often as separate courses. What it is forUnderstanding how societies organise reproduction, care, partnership, work and identity, and how those arrangements differ and change. Comes afterContemporary Social Theory (35/77), where Goffman, Bourdieu, Foucault and feminist and postcolonial theory are introduced. Leads toIt sits beside Race, Ethnicity & Migration (35/79) and feeds Public Policy (35/16); it connects to Developmental Psychology (35/24) and Social Psychology (35/25). What this piece gives youYou will not be able to run a demographic survey. You will know how family forms differ, how to read a fertility rate, why "the gender gap" depends on what is measured, how a wage gap is decomposed, and how sexuality is measured and argued over. Easier first reads: Class, Status and Power (30/02) and Institutions (30/05). Families: the same jobs, many designs Every society has to raise children, care for the old and the sick, pass on property or rights and manage sex and marriage. How these are bundled into households differs. Sociologists and anthropologists distinguish the nuclear family (parents and children), the extended family (several generations or siblings together), and many ways of tracing descent: through fathers (patrilineal), mothers (matrilineal) or both. Some well-known stories need correcting. The belief that industrialisation destroyed a once-universal extended household was challenged by the historian Peter Laslett and the Cambridge Group, who showed that in England the household was already small and nuclear centuries before the factory. The demographer John Hajnal (1965) described a "Western European marriage pattern" of late marriage and many people never marrying, in a region west of a line from St Petersburg to Trieste, found by the 1700s; it is a regional history, not a law of modernisation. Matrilineal societies exist from the Akan of Ghana to the Minangkabau of Sumatra and the Khasi of Meghalaya in India, and show that "patriarchal family" is not the only historical form. In China, Fei Xiaotong's From the Soil (1947) described Chinese social relations as ripples spreading out from the self through kinship, unlike the "bundles of sticks" of Western associations. Marriage itself varies. In George Murdock's 1967 Ethnographic Atlas, most of the societies recorded permitted polygyny, though in practice most marriages even there were monogamous. Today polygyny is legal in a range of countries, many with Muslim majorities, and illegal in others. Age at first marriage is a clear marker of difference. Interactive · Age at first marriage World Bank series from UN Population Division data: singulate mean age at marriage, women and men, in the survey year shown. Years differ by country, so compare them as a rough guide only. Without JavaScript: women's average age at first marriage was 17.2 in Niger (2015), 21.4 in India (2016), 27.5 in the United States (2010) and 32.1 in Norway (2018). Fertility: the arithmetic of replacement The total fertility rate (TFR) is the average number of children a woman would have if she lived through today's age-specific birth rates. In low-mortality countries a TFR of about 2.1 keeps each generation the same size (a little over two because not every child is a girl who survives to have children); where child mortality is higher, replacement is higher. The World Bank's series (from UN data) gave a world TFR of about 2.19 in 2024. In 1950 it was close to five. next generation ≈ this generation × (TFR ÷ 2.1)ignores migration and changes in mortality; 2.1 is the usual replacement level in low-mortality countries The tool below runs that arithmetic for three generations. The effect of a rate that looks only a little under 2.1 is slow; the effect of a rate under 1 is dramatic. Interactive · Three generations of a TFR Total fertility rate Presets are the World Bank's 2024 values (UN-based). The cohort model ignores migration and mortality changes and is a simplification, not a forecast. Without JavaScript: at a TFR of 0.75 (South Korea, 2024), 100 people are followed by about 36 children, 13 grandchildren and 5 great-grandchildren in a closed population; at 5.93 (Niger), by about 282, 797 and over 2,200. Why has fertility fallen? Several explanations compete, and most researchers think more than one is right. Gary Becker's economic account says that as wages and the value of education rise, parents choose fewer children and invest more in each (the "quantity-quality" trade-off). Demographers of the second demographic transition (Ron Lesthaeghe, Dirk van de Kaa) point to changing values about autonomy and self-realisation, and to contraception. Peter McDonald's gender-equity argument holds that fertility falls furthest where women have gained equality in schools and workplaces but family life stays traditional, which could help to explain very low rates in South Korea, Japan and Southern Europe. Cost-of-living accounts stress housing and childcare. On policy, reviews of cash and leave incentives (for example, Anne Gauthier's 2007 review) typically find modest effects on completed fertility, mostly on timing. The United Nations' 2024 projections have the world population peaking at around 10.3 billion in the 2080s, then declining. Gender gaps: it depends what you measure "The gender gap" is not one quantity. Women are far behind men in some measures, ahead in others, and the gaps do not line up by country. The World Economic Forum's Global Gender Gap Index measures the relative gap between women and men on economics, education, health and politics. Its 2025 edition found that 68.8% of the global gap had been closed, led by Iceland (92.6%, its sixteenth year at the top), and estimated 123 years to parity at the current pace. A well-known caveat is that this index compares women with men inside each country, not women's absolute standing: a poor country with small gaps can score well. Other indices (UNDP's Gender Inequality Index) include absolute health and income terms. Interactive · Five gaps, fifteen countries World Bank World Development Indicators: women in national parliaments (2025), labour-force participation (ILO modelled estimates, 2025), life expectancy (2024) and sex ratio at birth (2024). Participation counts anyone working or seeking work, including unpaid family farm work. Without JavaScript: women hold 63.8% of seats in Rwanda and 4.2% in Nigeria (2025); women live 7.0 years longer than men in South Africa and 0.6 in Nigeria (2024); the sex ratio at birth is 1.10 boys per girl in China, against about 1.05 worldwide (2024). Try each measure. Women's labour participation is higher in Niger and Nigeria than in India or Pakistan, because in much of the poorest agriculture nearly everyone works; it says little about pay, ownership or power. Life expectancy runs the other way: women outlive men almost everywhere, by a gap that biology (men's higher mortality from heart disease, accidents and violence, and risk behaviours) and social conditions both shape. The sex ratio at birth shows where preferences for sons have been acted on: the natural ratio is about 1.05 boys per girl, and values well above it, as in China (1.10 in 2024), point to sex-selective abortion. The economist Amartya Sen's 1990 essay "More than 100 million women are missing" started the debate over the cumulative toll of such practices; later estimates differ with method and have been revised. The wage gap: raw, adjusted and the argument over what to adjust The raw (or unadjusted) pay gap compares typical pay for all women and all men. It is a fact about the labour market as a whole. The adjusted gap compares women and men who match on hours, occupation, industry and experience. It is closer to a measure of pay differences for "the same job". The two answer different questions, and the argument is not about the arithmetic but about the controls. Interactive · Peel the gap, one control at a time Illustrative, invented numbers: a raw gap of 20 points, split into pieces in rough proportion to how published decompositions in rich countries are shaped. They are not data for any country. Economists such as Francine Blau and Lawrence Kahn (2017) do these decompositions with real data. Without JavaScript: in this illustration a raw gap of 20 points shrinks to 14 when matched on hours, 8 when also matched on occupation and industry, and 6 when also matched on experience, leaving an unexplained 6. "Control for the differences." Compare like with like: much of the raw gap comes from women on average working fewer paid hours, in different occupations, with more career interruptions. What remains after controls is the best estimate of unequal pay for equal work; the rest reflects different choices or constraints that need different remedies. "The controls are the outcome." Hours, occupation and interruptions are not neutral; they reflect who does unpaid care, which jobs punish part-time work, and norms that steer people. The Nobel-winning economist Claudia Goldin (2023) argued that much of today's gap comes from "greedy" jobs that pay disproportionately for long, inflexible hours, which penalise whoever does most of the caring. Controlling for these hides the mechanism. There is a measurable event behind this. Henrik Kleven, Camille Landais and Jakob Søgaard (2019) followed Danish parents using administrative data: women's and men's earnings tracked each other until the first child, then diverged, leaving a long-run earnings gap of around 20% that is attributable to children. They estimated that child penalties accounted for most of the remaining gender earnings inequality in Denmark by 2013 (about 80%, up from about 40% in 1980). Denmark has generous parental leave and childcare, so the point is not that the gap is a Danish problem but that it shows up even there. Which policies would narrow it (paid leave for fathers, childcare, flexible hours, anti-discrimination law) is a political question, and the evidence on each is itself debated. Are women and men different? Two honest answers Psychologists measure differences between two groups with an effect size, d: the gap between the averages divided by the typical spread within groups. A d of 0.2 is called small, 0.5 medium and 0.8 large. In 2005 Janet Hyde reviewed meta-analyses of psychological gender differences and concluded that about 78% of the effect sizes were small or close to zero, summarised as the "gender similarities hypothesis". Some traits do show larger gaps: throwing distance, physical strength and some interests (people versus things) show moderate to large d. Critics (Marco Del Giudice, Tom Booth and Paul Irwing, 2012) replied that looking at one trait at a time understates the picture. When many personality traits are combined, the overall difference can be much larger, with a multivariate d around 2.7 in their analysis of a personality questionnaire. Defenders of Hyde's view respond that such combined measures depend on the model and the sample, and that overlap in each real-world task is still very large. Both sides use the same data; they disagree on which summary matters. The tool below shows what a given d means in practice. Interactive · What a difference of d means Effect size d Two normal distributions with equal spread, equal group sizes, and means d standard deviations apart. A model, not data for any trait. Without JavaScript: at d = 0.2, the two distributions overlap about 92%, a randomly chosen member of the higher-scoring group beats a member of the other group about 56% of the time, and the higher-scoring group supplies about 59% of the top tenth. A related dispute: Gijsbert Stoet and David Geary (2018) found that the share of women among science graduates was lower in more gender-equal countries, which they called a "gender-equality paradox" and read as evidence that freer choice leads to more divergent preferences. Critics, such as Richardson and colleagues (2020), argued that the pattern depends on how graduation and equality are measured and that economic necessity pushes women toward STEM careers in poorer countries. Margaret Mead's Sex and Temperament (1935), which reported very different gender roles in three New Guinea societies, is often cited for the argument that roles are made by culture; its fieldwork has been challenged (as Derek Freeman did with her Samoa book in 1983), though the broad finding that roles vary widely across societies stands on much other evidence. Sexuality: attraction, behaviour and identity "How many people are gay?" has no single answer because sexuality has at least three dimensions: who one is attracted to, what one does, and how one describes oneself. They overlap but are not the same, and the labels people use depend on history and language. Alfred Kinsey's 1948 volume on men and the 1953 volume on women introduced a seven-point scale from 0 (exclusively heterosexual) to 6 (exclusively homosexual) to capture the range. His samples were criticised: a 1954 review by Cochran, Mosteller and Tukey for the American Statistical Association found serious problems with his sampling. Modern surveys use probability samples, but results still depend on wording and on how safe people feel answering. Interactive · A definition decides the number Count anyone at or above this Kinsey-style score Invented distribution for 1,000 people, to show how the cut-off changes the headline figure. It is not data. Real surveys that ask about identity, attraction and behaviour report different figures for the same population. Without JavaScript: in this invented example of 1,000 people, counting only the highest score gives 3% while counting any score above zero gives 12%. Real figures show the same thing. In the 2021 census of England and Wales, 3.2% of people aged 16 and over (about 1.5 million) identified as gay, lesbian, bisexual or another sexual orientation, and 7.5% did not answer the question. Gallup's 2023 telephone survey in the United States found 7.6% of adults identified as LGBTQ+. The two numbers differ because of method, wording, country and age mix, not because one is wrong. Where do sexual orientations come from? Three views are common. Constructionists, following Michel Foucault's The History of Sexuality (1976), argue that the categories "homosexual" and "heterosexual" as identities are modern, and that other societies organised same-sex behaviour by age, rank or ritual instead. Essentialist or biological accounts argue that orientation is stable and has biological correlates. A large 2019 genetic study by Andrea Ganna and colleagues (477,522 people, Science) found that many genetic variants of small effect together explain somewhere between 8% and 25% of the variation in whether someone has had a same-sex partner, with no single "gay gene" and no way to predict an individual. Interactionist accounts combine the two: biology shapes dispositions, and society shapes which dispositions become identities. Historians such as John Boswell and David Halperin have argued for a long time about how far the constructionist claim holds. Societies have recognised more than two genders and various sexualities in their own terms: hijra communities in South Asia (India's Supreme Court recognised a legal third gender in the 2014 NALSA judgment), muxes among the Zapotec of Oaxaca, fa'afafine in Samoa, kathoey in Thailand and two-spirit people, a pan-Indigenous term adopted in North America in 1990. Scholars caution against treating these as one "third gender" category: each has its own history, and members describe themselves in their own words. Law, dated. Same-sex marriage is legal nationwide in 38 countries (a count from early 2026), beginning with the Netherlands in 2001. India's Supreme Court struck down the colonial-era Section 377 as applied to consenting adults in Navtej Singh Johar v. Union of India (6 September 2018). Other courts and parliaments have followed, including Botswana (2019), Singapore (2022) and Namibia (2024), while Uganda's 2023 Anti-Homosexuality Act increased penalties. ILGA World, which tracks the law, counts more than sixty UN member states that still criminalise consensual same-sex acts. The moral positions are a separate matter. Defenders of full legal equality argue that sexual orientation is not a choice that harms others and that equal dignity requires equal rights, including marriage. Many religious traditions, among them Catholic, Orthodox and some Islamic, Jewish and Protestant ones, hold that marriage is a union of a man and a woman ordered to children and that sexual ethics rest on revelation or natural law, whatever the biology; others within those traditions have revised their teaching. Liberal-neutralist views hold that the state should leave consenting adults' private lives alone and protect religious liberty too. Evidence on origins or prevalence is claimed by every side but does not decide between them. What a graduate just sees The "traditional family" is not one thing: households, descent and marriage vary, and the small nuclear household is older in some places than industry. A TFR is a rate, not a headcount; below about 2.1 a population shrinks without migration, slowly at 1.8 and quickly at 0.8. Fertility has fallen nearly everywhere; the reasons are debated, and cash incentives usually have modest effects. "The gender gap" must be named: pay, participation, power, health, or the sex ratio at birth. Gaps in one do not predict gaps in another. Raw and adjusted pay gaps answer different questions; the dispute is about the controls. An effect size d tells you the overlap: even a medium difference leaves two groups heavily overlapping. How many people are gay, lesbian or bisexual depends on whether you ask about identity, attraction or behaviour. Evidence and moral positions travel separately: a genetic study or a survey count settles neither a marriage law nor a religious teaching. Where it shows up next Race, Ethnicity & Migration (35/79): fertility and family patterns of migrants, and how gender and race categories intersect. Public Policy (35/16): parental leave, childcare, pension and anti-discrimination design all rest on these measurements. Developmental Psychology (35/24) and Social Psychology (35/25): how children learn gender roles and how stereotypes work. In the world: a pension forecast built on a fertility assumption, a pay-transparency law, a census question on sexual orientation, a court case on marriage. If you want to go deeper 📘 Textbook. The free OpenStax Introduction to Sociology (3rd ed.) chapters on gender, sex and sexuality and on marriage and family; and Raewyn Connell, Gender: In World Perspective (Polity, 3rd ed. 2015), which covers several regions. 📘 Textbook for the economics. Claudia Goldin, Career and Family: Women's Century-Long Journey toward Equity (Princeton University Press, 2021), the book behind her 2023 Nobel, which tells the story with data. 📘 Gentler. Stephanie Coontz, Marriage, a History: How Love Conquered Marriage (Viking, 2005), a readable sweep of how marriage has changed. 🌐 Free data. Our World in Data on fertility and the UN Population Division's World Population Prospects. 🌐 Free course. Khan Academy: Society and culture has short lessons on gender, family and demographics. The takeaway Families, fertility and gender gaps vary enormously, and every figure depends on what is counted: a TFR is a rate, a gender gap depends on the measure, a pay gap depends on the controls, and a sexuality count depends on the definition. The measurements can be stated plainly with their dates, and the moral questions that follow belong to a different kind of argument. Try this: take a claim such as "women earn X% less" or "Y% of people are gay" and write down what is being measured, in which year and for which people, before you decide what it means. Sources & Further Reading Sources. World Bank World Development Indicators (SP.DYN.TFRT.IN, SP.DYN.SMAM.FE/MA, SG.GEN.PARL.ZS, SL.TLF.CACT.FE/MA.ZS, SP.DYN.LE00.FE/MA.IN, SP.POP.BRTH.MF; latest year per country as shown), drawing on UN Population Division and ILO estimates. UN World Population Prospects 2024. World Economic Forum, Global Gender Gap Report 2025. Laslett 1972; Hajnal 1965; Murdock 1967; Fei Xiaotong 1947; Becker; Lesthaeghe and van de Kaa; McDonald; Gauthier 2007; Sen 1990. Kleven, Landais and Søgaard, NBER working paper 24219 (2018) and AEJ: Applied 2019; Goldin 2021 and the 2023 Nobel Memorial Prize in Economic Sciences; Blau and Kahn 2017. Hyde 2005; Del Giudice, Booth and Irwing 2012; Stoet and Geary 2018 and critiques; Mead 1935 and Freeman 1983. Kinsey 1948 and 1953; Cochran, Mosteller and Tukey 1954; Foucault 1976; Ganna et al., Science 2019. ONS, Census 2021 sexual orientation bulletin; Gallup, March 2024. NALSA v. Union of India (2014); Navtej Singh Johar v. Union of India (2018); ILGA World. 📘 Goldin, Career and Family 📘 Connell, Gender: In World Perspective 🌐 Our World in Data: Fertility rate Step 10. Deviance, Crime & Social Control: Who Counts as Doing Wrong, and Who Counts It --------------------------------------------------------------------------------------- Why: Crime, deviance, policing and punishment. Crime is the one social fact that is defined by law, found by police, recorded by officials and reported by people who choose whether to bother. A sociologist learns to ask about every one of those steps. A newspaper headline says robberies fell 35% in a year. Is that because there were fewer robberies, or because fewer victims phoned the police? Both can be true, and which one is true changes what the government should do. The figure that looks so firm is the end of a chain of human decisions: someone decided to act, someone decided the act was a crime, someone decided to report it, someone decided to record it. That chain is what this course teaches you to see. It does not say crime is "just a social construct" (a person who has been mugged knows better) and it does not treat official numbers as a plain count. It asks why societies have rules at all, why some people break them, why some people and places get caught more, and what punishment does. The topic is one where readers arrive with strong views, so this piece gives the evidence with its dates and keeps the facts apart from the arguments about what to do. The course in one box Who takes itSociology majors, usually in year 3, and the core of criminology programmes (in Britain and the Commonwealth often a separate degree; in the US a sociology or criminal-justice major). Law and social-policy students take versions of it. What it is forExplaining why rule-breaking happens, who gets labelled and punished, and how we know how much crime there is. Comes afterClassical Social Theory (35/75) and Sociological Research Methods (35/76). Leads toIt sits beside Criminal Law & Procedure (35/07), which explains the legal side, Cities, Communities & Social Networks (35/81) and Public Policy (35/16); in the Sociology major it comes before the advanced seminars. What this piece gives youYou will not be able to evaluate a policing programme. You will know the main theories of why people offend, how to read a crime statistic, how imprisonment rates compare worldwide, and what the evidence says, and does not say, on several policing debates. Easier first reads: How to Spot Bullshit (17/03), Institutions (30/05) and Rights, Constitutions and the Rule of Law (18/04). Durkheim: why a society always has crime Émile Durkheim, writing in The Rules of Sociological Method (1895), made a claim that sounds shocking and is meant to be dry: crime is normal. Every known society has it. A society with no crime at all, he said, is not imaginable, because even a community of saints would treat small faults the way we treat crimes. Crime, on this view, marks a boundary: the moral rules of a group are made visible when someone breaks them and the group reacts. Durkheim did not call crime good, and he did not mean any particular amount was fine. A rate that is unusually high for a society is a sign that something is wrong with it. His book Suicide (1897) was the method in action. Suicide looks like the most private act, but its rates differ systematically between groups and stay stable inside each society from year to year. He argued the rates reflect two forces, social integration and moral regulation, and named four kinds: egoistic (too little integration), altruistic (too much), anomic (too little regulation, as in sudden economic booms and busts) and fatalistic (too much). Anomie, a state of normlessness, became his lasting contribution. Critics note that his data were crude, that official suicide figures vary by how coroners record deaths, and that the famous Protestant-Catholic gap is weaker and more varied than he said. The lesson that survived is the method: look at rates, and ask what in the society produces them. Strain, learning and control: three answers to "why do they do it?" Later theories divide into families depending on what they take for granted. Strain theories assume everyone wants roughly the same things and ask what happens when the means are blocked. In "Social Structure and Anomie" (1938), Robert Merton argued that a society that preaches success for all but spreads the legitimate means unevenly pushes some people toward other routes. He listed five ways people respond, and a person can use different ones in different parts of life. Interactive · Merton's five responses Does the person accept the society's goal (say, financial success)? Do they use the approved means (say, a steady job)? Accepts the goal? YesNoReplaces it Uses the approved means? YesNoReplaces them Merton's typology, 1938. His example for "retreatism" has been criticised, and the scheme describes a US-style success goal; other societies may have different goals. Without JavaScript: accepting both goal and means is conformity; accepting the goal but not the means is innovation (which includes crime); rejecting the goal but keeping the means is ritualism; rejecting both is retreatism; replacing both is rebellion. Learning theories say offending is learned like other behaviour. Edwin Sutherland's differential association (1939) held that people become offenders by learning, from the people they are close to, more definitions favourable to breaking the law than against. Sutherland also coined "white-collar crime" (1939), pointing out that criminology had studied the poor while ignoring offences by the respectable. Control theories flip the question. Travis Hirschi (Causes of Delinquency, 1969) argued that the interesting thing is not why people offend, since temptation is common, but why most do not. His answer is the social bond: attachment to others, commitment to conventional goals, involvement in conventional activities, and belief in the rules. Later, Gottfredson and Hirschi (1990) proposed low self-control as the common cause; many criminologists accept that self-control matters and dispute that it is the whole story. Marxist and conflict theorists, and feminist criminologists, ask the questions the others skip: who writes the laws, whose harms count, and why women's offending and women's victimisation were ignored for so long. Each school has its own best evidence, and the field has not picked a single winner. Most researchers use several: strain for pressure, learning for pathways, control for restraint. Place and opportunity: crime is concentrated A different approach stops asking about offenders and counts where and when. Lawrence Cohen and Marcus Felson's routine activity theory (1979) says a crime needs three things to meet in space and time: a motivated offender, a suitable target and no capable guardian. Change daily life (more homes empty in the daytime, more portable goods) and crime changes without anyone's character changing. Concentration is the other pillar. In Minneapolis, Lawrence Sherman and colleagues (1989) found that about 3% of addresses produced half of all calls to the police. David Weisburd (2015) argued this is something like a law: in city after city, a few percent of street segments account for about half the crime, and the pattern holds over time. It connects to Robert Sampson's findings on neighbourhoods in Cities, Communities & Social Networks (35/81): collective efficacy and the physical setting both matter. It also shows why a city's crime rate is a poor guide to the risk on your own street. Labelling: when the label becomes part of the cause Another turn in the 1950s and 1960s moved attention from the act to the reaction. Edwin Lemert (1951) separated primary deviance, which may be fleeting and explained away, from secondary deviance, which happens once a person takes the label on as an identity and others treat them accordingly. Howard Becker's Outsiders (1963) put the idea sharply: deviance is not a quality of an act but the result of other people applying a rule and a label to someone. The point is not that nothing wrong happens. It is that who gets labelled depends on power, visibility and who is watching. A well-known field test of label effects is Devah Pager's audit study in Milwaukee (2003). Pairs of young men with matched resumes applied for the same entry-level jobs. Among white applicants, 34% of those with no record got a callback, against 17% of those with a drug conviction on their record. Among Black applicants it was 14% with no record and 5% with one. The record cut callbacks by about half for both, and a Black applicant without a record did slightly worse than a white applicant with one. Separate follow-up studies of young people (for example the Rochester Youth Development Study, Bernburg and Krohn, 2003) found that formally processed youths later offended more than similar youths who were not, though critics point out that the youths who were processed may have differed in ways the studies could not measure. The same logic runs through data. Where police look, they find; where they find, records pile up; where records pile up, police are sent. Kristian Lum and William Isaac (2016) showed this by simulating a predictive-policing tool on Oakland's drug-offence data: because it learned from past arrests, it kept sending officers to the same neighbourhoods, though survey-based estimates suggested drug use was spread much more widely. Ensign and colleagues (2018) described such "runaway feedback loops" in general terms. The tool below is a toy version. It does not show that real police departments behave like this; it shows what happens if records drive patrols and nothing else corrects them. Interactive · Two neighbourhoods, one true rate How strongly patrols follow past records A's share of the true offending A's share of records at the start A toy model: 20 offences a day across two neighbourhoods; the chance an offence is recorded rises with the share of patrols sent there, and patrol share follows (past records)^k. k = 1 is "in proportion to records", k above 1 concentrates patrols on the "worst" area. Invented parameters, expected values, no noise. Without JavaScript: if both neighbourhoods have the same true offending (50%) but A starts with 55% of past records, patrols proportional to records leave A with about 52% after a year, and patrols that concentrate on the "hot" area (k = 2) push it to about 76%. Crime statistics and the dark figure Criminologists call the crime that never reaches the record the dark figure (from the German Dunkelfeld). Police-recorded crime counts only what is reported and then logged. Victimisation surveys fill in the rest: since the 1970s in the United States (the National Crime Victimization Survey) and since the early 1980s in England and Wales (now the Crime Survey for England and Wales), households are asked what happened to them, whether or not they told anyone. The two sources often disagree in level and sometimes in trend. In 2014 the UK Statistics Authority withdrew its official "National Statistics" label from police-recorded crime in England and Wales after doubts about recording practice. Whether victims report depends on the crime and on trust. The US Bureau of Justice Statistics found that 42% of robberies were reported to police in 2023, down from 64% in 2022, and 72% of motor-vehicle thefts in 2023, down from 81%. If the true number of robberies had stayed the same, police figures would have shown a drop of about a third that year with no change in the street. Homicide is the exception that proves the rule: a body is hard to hide, so it is the best-measured crime. The UN Office on Drugs and Crime estimated about 458,000 homicide victims worldwide in 2021, with the highest regional rates in Africa and the Americas and the lowest in Europe and Asia. Interactive · Same street, different statistic True robberies in a year1,000 Share of victims who report to police US robbery, 2022 (64%)US robbery, 2023 (42%)Everything reported The 1,000 is invented and held fixed so you can see the reporting effect alone; the 64% and 42% are BJS's reporting shares for robbery (Criminal Victimization, 2023). A real change in reporting would not leave the true count unchanged. Without JavaScript: with 1,000 real robberies, a 64% reporting rate gives 640 reports; at 42% it gives 420, a fall of 34%, with no change in actual robbery. Reporting also varies by country, by how much people trust the police, and by what the law counts. So comparing the "crime rates" of two countries from police records mostly compares their police and their definitions. This is why serious comparisons use victimisation surveys or homicide. Incarceration around the world Societies punish in different amounts. The World Prison Brief (Institute for Crime & Justice Policy Research at Birkbeck, University of London) collects the number of people held in prisons per 100,000 residents for nearly every country. The chart below reproduces a selection as read from the World Prison Brief's ranking on 2 November 2026; each country's reference year differs (it is shown on its WPB page) and figures are revised regularly. Interactive · Prisoners per 100,000 people Selected countries, World Prison Brief highest-to-lowest ranking, accessed 2 November 2026. Each country has its own reference year. China's WPB figure may cover sentenced prisoners only, so it understates total detention. El Salvador's figure follows a state of exception declared in March 2022. Without JavaScript: El Salvador 1,659; Cuba 794; United States 542; Türkiye 504; Rwanda 489; Brazil 439; Mexico 202; England and Wales 140; China 119; Germany 69; Norway 54; India 36; Nigeria 36; Japan 33; Yemen 14 (per 100,000, WPB ranking read on 2 November 2026). Read it with care. Rates are not crime rates: a country can imprison many people for few crimes or the reverse. They depend on what is criminalised (drug laws are a large share in some countries), sentence lengths, pre-trial detention (large in much of Latin America and South Asia), and whether the state records detention outside the prison system. Different readers draw different conclusions. Those who argue for imprisonment point to incapacitation and deterrence; those who argue against point to costs, to what a record does to a person's life (see Pager above) and to evidence that longer sentences deter little compared with the certainty of being caught. The El Salvador case shows the split sharply: its government says the state of exception collapsed gang violence, while human-rights organisations document arbitrary detentions and deaths in custody. Both claims can be checked against evidence and neither settles the question of what should be done. Policing debates, told even-handedly The sociological stance is to take each policy claim as an empirical question. Pick an approach to see what supporters say, what evidence exists and what critics say. Interactive · Seven approaches Without JavaScript: the approaches are more officers, hot-spot patrols, order-maintenance ("broken windows"), community and procedural-justice policing, body-worn cameras, alternative responders, and reducing the role of police. Two cautions run through all of them. First, most US studies are about the US and may not carry over to Mexico City, Lagos or Glasgow. Policing means different things in different places: Japan's neighbourhood koban boxes, Latin America's "mano dura" (firm hand) policies, Britain's "policing by consent", which is often attributed to Robert Peel but whose attribution historians question. Second, "does it reduce crime?" is only one question. Whether people are treated fairly, whether the burden falls evenly and what it costs are separate questions, and people weigh them differently. What a graduate just sees Every crime figure is a chain of human decisions; ask who reported it, who recorded it and what changed. A society always has rule-breaking; what changes is its amount, its form and who is blamed. Strain, learning and control are three different questions, not three rival answers to one. Crime is concentrated in a few places and times, so the average hides most of the story. A label can be part of the cause: a conviction can halve a job callback, and records can steer the next patrol. A fall in recorded crime could be a fall in reporting; homicide and victim surveys are the cross-checks. Imprisonment rates differ by a factor of fifty between countries, and say as much about law and politics as about crime. Where it shows up next Criminal Law & Procedure (35/07): what the law counts as a crime and how a trial is run. Public Policy (35/16): how a "crime problem" gets turned into a programme, and how programmes are evaluated. Statistics (34/05) and Research Methods (35/76): rates, base rates, selection and the ecological fallacy. In the world: police analytics, probation and re-entry services, victim support, court reform, journalism about crime. If you want to go deeper 📘 Textbook. Mike Maguire, Rod Morgan and Robert Reiner (eds), The Oxford Handbook of Criminology (Oxford University Press, several editions), or Tim Newburn, Criminology (Routledge, several editions). Both are standard in UK and Commonwealth courses. 📘 Gentler. Howard Becker, Outsiders (1963), which is short and readable; Devah Pager, Marked (2007); Patrick Sharkey, Uneasy Peace (2018), on the fall of violence in US cities. 🌐 Free data. The World Prison Brief (country pages with dates); the BJS and UNODC statistics pages. 🌐 Free course. The Open University's Critical criminology and the social sciences on OpenLearn is free; the BMCC Criminology OER hub has free open course materials. The takeaway Crime is a social fact in two senses: every society has rule-breaking, and every crime figure passes through human choices about reporting and recording. The main theories ask different questions (what pressures, what is learned, what restrains), crime is concentrated in a few places, labels can become causes, and punishment rates differ enormously for reasons beyond crime itself. Policing debates are best handled by asking what the evidence for each approach is, and which questions it answers. Try this: the next time you see a crime statistic in the news, write down who must have reported it, who recorded it and what else could have changed besides the amount of crime. Sources & Further Reading Sources. Durkheim, Rules of Sociological Method (1895) and Suicide (1897); Merton, American Sociological Review 3 (1938); Sutherland (1939, 1949); Hirschi (1969); Gottfredson and Hirschi (1990); Cohen and Felson, ASR 44 (1979); Sherman, Gartin and Buerger, Criminology 27 (1989); Weisburd, Criminology 53 (2015); Lemert (1951); Becker (1963); Pager, American Journal of Sociology 108 (2003); Bernburg and Krohn, Criminology 41 (2003); Lum and Isaac, Significance 13 (2016); Ensign et al., PMLR 81 (2018); BJS, Criminal Victimization, 2023 (September 2024); UNODC, Global Study on Homicide 2023; UK Statistics Authority (2014); World Prison Brief (prisonstudies.org, accessed 2 November 2026). The policing summaries draw on Sherman and Weisburd (1995), Braga et al.'s Campbell reviews of hot-spots policing, Kelling and Wilson (1982), Sampson and Raudenbush (1999), Chalfin et al. (2022), Ariel et al. (2015), Yokum et al. (2019), Dee and Pyne (2022). Interactive numbers are computed live; the two-neighbourhood and funnel models use invented parameters. 📘 Becker, Outsiders 🌐 World Prison Brief Step 11. Social Movements & Collective Action: Why Anyone Turns Up ------------------------------------------------------------------ Why: How movements form, win and fail. Everyone agrees the water price is unfair, and almost everyone would rather someone else went to the protest. How movements get around that, and how often they win, is one of the most argued-over questions in the social sciences. In April 2000 the people of Cochabamba, Bolivia, shut down their city over a water bill. A private company had taken over the water system and prices had jumped. Within weeks the contract was cancelled. Nobody had a great deal of money or an army, and the grievance was not new: people in many cities have paid too much for water. What was new was that tens of thousands of people, who all had a reason to stay home, did not. That is the puzzle at the centre of this course. Grievances are everywhere and movements are rare. If a protest succeeds, everyone gets the benefit, including the people who stayed in bed. So why would anyone go? A graduate of this course keeps a set of answers, a healthy suspicion of neat stories about "the power of the people", and a clear sense of what the evidence says about how often protest works. The course in one box Who takes itSociology and political-science majors, usually in year 3 or 4; the American and British programmes treat it as an advanced elective, and many include its game-theory core (the Olson problem) in methods or theory courses. What it is forExplaining who protests, who organises, why movements start when they do, and what makes them win or fail. Comes afterContemporary Social Theory (35/77) and Game Theory (34/45), whose prisoner's-dilemma logic gives the field its hardest question. Leads toIt feeds Public Policy (35/16), Elections, Parties & Public Opinion (35/15) and Comparative Politics (35/13); it is read beside Social Psychology (35/25) for the psychology of crowds. What this piece gives youYou will not be able to organise a campaign. You will see Olson's problem and how it is solved, why resources and opportunities decide as much as anger does, how Granovetter's thresholds make crowds jump from nothing to everything, and what the best-known data on nonviolent versus violent campaigns do and do not show. Easier first reads: How Political Change Actually Happens (18/06), The Social Contract (18/14) and Thinking in Systems (17/06). Olson's puzzle: why a shared goal does not organise itself Before 1965 many scholars assumed that people with a common interest would act together to pursue it. In The Logic of Collective Action the economist Mancur Olson argued the opposite. If the result is a public good (cleaner air, a higher wage for the whole industry, a regime change), no one can be kept from enjoying it, so each person gains most by letting others pay the cost. In a big group, one person's contribution barely changes the outcome, and so the rational move is to free-ride. Olson's conclusion was that large groups will not organise unless they offer selective incentives: benefits for members only, such as insurance through a union or discounts through an association, or penalties for those who do not take part. your return from giving 1  =  m / n + sm is the factor the group's pooled effort is multiplied by, n is the group size, s is any private bonus you get for joining. You join if this is more than 1. The tool is the standard "public goods game" that economists use as a stand-in for Olson's logic. Each of n people can put in one unit. The pot is multiplied by m and shared equally. If everyone contributes, everyone ends up with m units instead of 1. But your own unit comes back to you only as m/n. Interactive · The free-rider's arithmetic Size of the group Multiplier on the pooled effort (m) Private bonus for joining (s), in units A textbook model with invented numbers. It describes a self-interested player, which real people only partly are: in laboratory versions of this game, many people contribute, though contributions fade with repetition. Without JavaScript: with 10 people and m = 3, you get back 0.3 of each unit you give, so free-riding pays, even though everyone giving would triple everyone's holdings. With 1,000 people you get back 0.003 unless a private bonus makes up the difference. Play with it and the pattern is the one Olson found. Small groups can often cooperate: when n is below m, each person gets back more than they put in. Large groups need something extra. The theory has famous critics. Elinor Ostrom (Governing the Commons, 1990) documented many communities that managed shared resources for generations without a state or a market, using monitoring, reputation and graduated sanctions. Sociologists point out that people also join for identity, solidarity and moral reasons, which Olson's version leaves out. Gerald Marwell and Pamela Oliver (The Critical Mass in Collective Action, 1993) argued that contributions are interdependent, so a committed few can make participation worthwhile for everyone else. Olson's real contribution is the question: whenever a movement exists, ask what solved the problem. Resource mobilisation: grievances are everywhere, organisations are rare Through the 1950s and 1960s, theories of protest were mainly about people: frustrated, disconnected or irrational. That did not fit the civil rights movement, whose participants were well-organised church members. In 1977 John McCarthy and Mayer Zald argued in American Journal of Sociology for a new frame: grievances are fairly constant, so what changes is the supply of resources (money, volunteers, meeting places, skills, legitimacy) and the organisations that can turn them into action. Movements, on this view, are shaped as much by who funds and organises as by who is angry. Early movement groups, like churches, unions, student groups or village councils, already hold the networks that carry the message. The theory has been criticised for under-rating anger and for suggesting that professional organisations drive movements. Frances Fox Piven and Richard Cloward (Poor People's Movements, 1977) argued that the power of the poor lies in disruption, and that formal organisation often dilutes it. Both views have evidence. A third useful addition is Bert Klandermans's work on participation (the "supply and demand" of protest): even if a movement exists, someone becoming a participant has to pass through stages, and each stage loses people. Interactive · From sympathiser to person on the street Hear about the event Agree it is worth doing, given they heard Still willing once they weigh the cost and risk Manage to turn up (time, travel, childcare) Invented numbers for 100,000 sympathisers; the four-stage idea follows Klandermans and Oegema (1987). Real conversion rates are measured in surveys, and vary a great deal. Without JavaScript: with 100,000 sympathisers and conversion rates of 60%, 50%, 40% and 50%, about 6,000 turn up. Doubling any one stage doubles the turnout, which is why organisers work on the weakest stage. Opportunities and frames: when the door opens and what people think is at stake Resources are not enough. Doug McAdam's Political Process and the Development of Black Insurgency (1982) and Charles Tilly's work stressed the political opportunity structure: elites split, allies appear, repression becomes costly, a court case or election creates an opening. Tilly also described each society's repertoire of contention, the familiar menu of ways to protest (petitions, strikes, marches, boycotts, occupations) that changes slowly and differs between places. The third element is meaning. David Snow and colleagues (1986) showed that movements win people by framing: diagnosing a problem, naming who is at fault and offering something people can do. "Grievance" is made, not found. Scholars of Latin America and Asia added something the American models lacked. Sonia Alvarez and Arturo Escobar (1992) argued that many movements in the region were about identity, culture and everyday survival (neighbourhood kitchens, indigenous territory, human-rights groups) rather than bargaining over resources inside a stable state. Orlando Fals Borda's participatory action research, from Colombia, goes further: the people who suffer a problem are the researchers of it. Granovetter's thresholds: the riot that needs one more person Why do crowds turn from nothing into everything? In 1978 Mark Granovetter proposed that each person has a threshold: the number of others already acting that would make them join. A hothead has a threshold of 0. A cautious person might want to see 40 others first. His example: a hundred people, with thresholds 0, 1, 2, 3 and so on up to 99. The person with threshold 0 starts; that triggers the one with threshold 1; then 2; and the cascade takes everyone. Now change one thing: nobody has a threshold of 1 (the 1 becomes a second 2). The first person acts, the second person still wants to see two, and the cascade stops at 1. Two crowds with almost identical average attitudes; one empty street, one full. Interactive · Run the cascade Thresholds 0 to 99 Same, but no one at 1 Random crowd Average threshold Spread (standard deviation) Draw another crowd A hundred people, each joining when the number already acting reaches their threshold. Granovetter's two examples are exact. The random crowd uses normally distributed thresholds, rounded and floored at 0, with a fixed seed per draw; it is illustrative. Without JavaScript: thresholds 0 through 99 give 100 participants; the same crowd with one person moved from 1 to 2 gives 1. A random crowd with average 10 and spread 5 gives 100; with average 20 and spread 4 gives 0. Two lessons. First, you cannot read a crowd's behaviour off its average opinion: the spread, and the presence of a few instigators, matter. Second, from outside a cascade looks like a mood. Many sudden revolutions were "unpredicted" in this sense. Timur Kuran's Private Truths, Public Lies (1995) argued that people hide their real preferences under repression, so the thresholds are invisible until the cascade begins; that is his explanation of why 1989 in Eastern Europe caught nearly everyone, including experts, by surprise. The model is a simplification: it ignores who knows whom (see Cities, Communities & Social Networks, 35/81) and assumes everyone sees everyone. Nonviolent and violent campaigns: what the data show and the critics In Why Civil Resistance Works (2011), Erica Chenoweth and Maria Stephan collected 323 major campaigns for regime change or self-determination between 1900 and 2006. A campaign was counted as a success only if it achieved its goal within a year of its peak and the result was a direct consequence of the campaign. The headline: major nonviolent campaigns succeeded about 53% of the time, violent ones about 26%. Their explanation is about participation: nonviolent campaigns are easier to join (the barrier is lower and the risk smaller), so they draw larger and more varied crowds, which brings defections from the regime's own police, army and business supporters. A well-known side result is that no campaign in the data that gained the active participation of 3.5% of the population had failed. Chenoweth later called it a rule of thumb, not a law. Interactive · The numbers, and what 3.5% means A country with this many people (millions) Success rates from Chenoweth and Stephan (2011) and, for the recent figures, Chenoweth's later statements (rounded; "about"). The 3.5% figure is her observed pattern in the 1900 to 2006 data, not a law. Without JavaScript: 1900 to 2006, nonviolent about 53% and violent about 26% success; more recently Chenoweth has said the nonviolent rate fell to roughly a third and the violent to about 10%. 3.5% of 10 million people is 350,000. The critics, stated fairly. Some argue about selection: nonviolent resistance may be chosen where it has a chance, and violent rebellion where the regime leaves no choice, so comparing success rates compares different situations (Rendel Lehoucq, 2016, and others). Others point to coding and measurement choices: who counts as a campaign, what counts as success, and how to classify campaigns that mix methods, as South Africa's did. A third line is the radical flank effect: Herbert Haines (1984) found that when a militant wing appeared in the US civil rights movement, funding and support for the moderate groups rose, so the two may have helped each other. And the facts have moved. Chenoweth herself reported that nonviolent success has fallen since about 2010, and suggested that governments have learnt, digital organising makes quick mass turnout easier but builds less lasting organisation, and campaigns skip the slow work. A fair summary: in the data we have, nonviolent campaigns have done better than violent ones on average, the reasons are debated, and the advantage was smaller in recent years. Movements from every continent Most textbook examples come from the United States and Western Europe, which gives a skewed picture. Here are sixteen movements from five continents. Dates are for the movement's best-known phase, and "result" is brief and incomplete, because causes of big political change are always mixed. Interactive · Pick a region Without JavaScript: the movements are listed by region: Africa (anti-apartheid, Green Belt, Tunisia, Sudan), Asia (Salt March, Chipko, People Power, Tiananmen), the Americas (Montgomery, Plaza de Mayo, Cochabamba, Zapatistas, Chile 1988), Europe (Solidarity), Oceania (New Zealand suffrage), plus Syria 2011. Look across the list and several patterns appear that fit the theory. Churches, unions, markets and villages did the organising (resources). Many began when something shifted at the top: a split among generals, a court ruling, an election (opportunity). Many used familiar local forms: a march, a boycott, a petition, a strike. And the end of the story is often not the end: Tunisia's and Sudan's openings were later reversed, and Syria's turned into a civil war. Winning the moment is different from winning the settlement. What a graduate just sees When a group of people share a goal and nothing happens, ask what keeps them from acting. When something does happen, ask what solved the problem. Anger is common and organisation is rare, so look at who has money, meeting places and networks, not only who is aggrieved. A crowd is a distribution of thresholds, not an opinion: the spread and the first few movers decide. Sudden revolutions are often surprising because private preferences are hidden until the cascade starts. In the 1900 to 2006 data, nonviolent campaigns won more often than violent ones, but the reasons, the comparison and the recent trend are all disputed. Winning an uprising is not the same as winning the outcome that follows. Where it shows up next Public Policy (35/16): how a movement turns attention into an agenda, and what organised groups do once inside. Elections, Parties & Public Opinion (35/15): why turnout is itself an Olson problem. Comparative Politics (35/13) and Political Economy (35/17): when regimes fall, and what follows. In the world: union organising, a neighbourhood campaign for a crossing, a consumer boycott, a viral hashtag that does or does not become an organisation. If you want to go deeper 📘 Textbook. Donatella della Porta and Mario Diani, Social Movements: An Introduction (Wiley-Blackwell, 3rd edition 2020), is the standard European text. Mancur Olson, The Logic of Collective Action (Harvard University Press, 1965), is short and still the core. 📘 Gentler. Erica Chenoweth, Civil Resistance: What Everyone Needs to Know (Oxford University Press, 2021); Peter Ackerman and Jack DuVall, A Force More Powerful (2000); Timur Kuran, Private Truths, Public Lies (1995) for the surprise of cascades. 🌐 Free data. The NAVCO dataset of nonviolent and violent campaigns is publicly available from Chenoweth and colleagues (search "NAVCO Harvard Dataverse"). 🌐 Free course. I could not confirm a single standard free university course on social movements, so I am not naming one. Open course platforms (MIT OpenCourseWare, Open University's OpenLearn, Coursera audit mode) are worth searching for "social movements" or "civil resistance". The takeaway Shared interests do not organise themselves, because each person gains by letting others act. Movements exist when something solves that: selective benefits, small-group trust, identity, organisation or a cascade of thresholds. In the data we have, nonviolent campaigns beat violent ones more often than not, and critics and recent results qualify that. Movements arise on every continent with resources, openings and local forms of protest. Try this: think of something your street, school or workplace wants and does not have. Write down who benefits, who would pay the cost of fixing it, and what would make the first five people go. Sources & Further Reading Sources. Olson (1965); Ostrom (1990); Marwell and Oliver (1993); McCarthy and Zald, American Journal of Sociology 82 (1977); Piven and Cloward (1977); Klandermans and Oegema, American Sociological Review 52 (1987); McAdam (1982); Tilly (1978); Snow, Rochford, Worden and Benford, ASR 51 (1986); Escobar and Alvarez (eds), The Making of Social Movements in Latin America (1992); Granovetter, "Threshold Models of Collective Behavior," AJS 83 (1978); Kuran (1995); Chenoweth and Stephan (2011), with figures as reported by NPR (2019) and in a Harvard Kennedy School Epicenter interview with Chenoweth; Lehoucq, "Does Nonviolence Work?" Acta Politica (2016); Haines, Social Problems 32 (1984). Movement dates are standard history and are summarised, not exhaustive. Interactive numbers are computed live on invented data except where marked. 📘 della Porta and Diani, Social Movements: An Introduction 📘 Chenoweth, Civil Resistance: What Everyone Needs to Know Step 12. 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