The chapter opens not with a definition but with six testimonies. They are worth reading closely, because the definition of development the chapter eventually gives is assembled out of exactly the things they mention.

Read together, the six say something a single income statistic cannot. The Ugandan woman is describing the loss of voice before she gets to the loss of food. The Moldovan woman is describing shame. The Ethiopian is describing vulnerability. Only the Brazilian mentions income directly, and even there it is bundled with medicine and clothing. The slide draws the conclusion in one line:

That single sentence is the chapter's thesis. If poverty has several dimensions, then a development policy that raises income while leaving the other dimensions untouched has not, by this definition, succeeded.

Three scopes, not three opinions

Development economics is not a political stance within economics; it is a different scope. The slide sets it beside two neighbours, and the comparison is examinable as a comparison.

Key features of developing economies

These four features are the reason the neoclassical apparatus cannot simply be transplanted. Each one is a textbook assumption being switched off.

The slide's summary line: development economics studies the economic, social, and institutional mechanisms that raise living standards at scale.

The important role of values

Development is not value-free — judgements of "improvement" are inherently normative. Policy involves trade-offs between efficiency, equity, growth, stability and sustainability, and the slide is explicit that the focus on poverty and freedom rests on normative commitments to dignity and justice. This is not a disclaimer; it is a claim that the subject matter cannot be described without choosing what counts as better.

Economies as social systems

Four claims, each of which rules out a purely economic answer:

• Economies are embedded in institutions, culture, history, and politics.
• Development policy depends as much on institutional variables as on capital and labour.
• Policies that work in rich countries often fail in developing contexts.
• A holistic, interdisciplinary approach is therefore required.

The traditional economic measure

National development = growth in GNI or income per capita, with income as the main driver of utility. Chapter 2 will show you how this is measured and why the measurement is harder than it looks. The point here is narrower: this is the definition the rest of section 1.3 is arguing against.

The new view: Sen's capability approach 可行能力

Amartya Sen's reformulation replaces income with freedom as the thing to be maximised. Development is the expansion of human capabilities and substantive freedoms. Four terms carry the whole argument:

Some key capabilities

The slide splits them into "beings" (states of existence) and "doings". Each list is a checklist rather than a hierarchy.

The three core values of development

This is the most examinable list in the chapter. Three values, each with a short definition and a stated goal.

Income and happiness: Figure 1.2

The slide plots happiness (vertical) against income per head in US$ per year (horizontal, running to about $35,000), one dot per country, using Layard's data. Three numbered conclusions are printed on it:

The scatter is a steep rise at low income and a near-flat cloud at high income. Ukraine sits at the bottom of the vertical scale, in the low 30s; the high-happiness cluster (roughly 80–100) contains countries spread across a very wide income range, which is the second conclusion made visible — the same happiness level is reached at $10,000 and at $35,000.

The central role of women

Women's empowerment is both a core goal and a key driver of development — it improves child health, education, and long-run human capital. The slide states it as a two-way relationship, and the distinction matters: even if you cared nothing about equity between men and women, the growth evidence would still make female education one of the highest-return investments available.

The three objectives of development

1. Increase the availability of life-sustaining goods Food, shelter, health, protection
2. Raise overall levels of living Higher incomes, more jobs, better education, greater attention to cultural and human values
3. Expand the range of economic and social choices By freeing people from servitude and dependence

These three objectives are the three core values restated in operational form — the same three ideas, said once as values and once as aims.

Eight goals adopted by the United Nations in 2000, with a 2015 deadline. They are worth knowing as a list, because the SDGs that replaced them are an extension of the same structure.

Table 1.1 — the goals and their targets

The full table runs to two slides. The targets are the measurable half of each goal:

From the MDGs to the SDGs

The MDG era ended in 2015. In September of that year UN leaders adopted the 2030 Agenda, launching 17 Sustainable Development Goals to be achieved over the following fifteen years. The SDGs are broader than the MDGs — they apply to all countries rather than only to developing ones, and they add goals on inequality, energy, cities, and climate that the MDGs never covered.

Conclusions

The chapter closes on three points: the importance of development economics; the inclusion of non-economic variables in designing development strategies; and the effort to achieve the MDGs. The slide's closing quotation is "…One future — or none at all." The instructor also assigned the documentary Why Poverty?, Episode 1: Poor Us, which is the same argument as section 1.1 in film form.

Section A tests the definitions you would be asked to write out. Section B tests the distinctions that are easy to blur — functionings against capabilities, the three values against the three objectives. Section C asks for the extended answers.

The World Bank's scheme is the one used in practice: countries are ranked on GNI per capita and sorted into four bands. The thresholds are revised every year, so the slide's 2025 numbers are the ones to quote.

The slide also shows the alternative grouping the course uses when geography matters more than income: by region. The two groupings disagree on purpose. A country can be upper-middle-income and in Sub-Saharan Africa, or low-income and in East Asia, and which grouping is useful depends on the question — income explains what a government can afford, region explains what its neighbours are doing.

GNI and GDP

Two accounting identities that get confused constantly. The difference is who is being counted, not what is being counted.

The expenditure approach to GDP

Y = GDP in dollars · C = consumption · I = investment · G = government purchases · NX = net exports = exports − imports

This is the national income accounting identity. It is useful because it breaks GDP into components according to their purpose: goods and services can be consumed, invested by the private sector, bought by the government, or shipped abroad. NX can be positive or negative — a country that exports more than it imports has positive net exports; the United States, which imports more than it exports, has negative net exports and correspondingly a trade deficit.

Measuring changes over time

The slide's own illustration: US nominal GDP was $14.4 trillion in 2008 and only $7.4 trillion in 1995. Did prices change? Did quantities change? Some of both — and the whole point of real GDP is to answer the question that the nominal figure leaves open. The instructor's note frames it as the familiar (total revenue) = price × quantity: nominal GDP is the revenue line, real GDP holds the price term still.

Comparing incomes across countries needs a conversion factor, and the obvious one — the market exchange rate — answers the wrong question. It tells you what a dollar buys in currency markets; what you want to know is what it buys in the local market. In poor countries, non-traded goods and services (a haircut, a meal, a bus ride, housing) are far cheaper than they are in rich ones, so converting at the market rate systematically understates real incomes in poor countries.

What the difference looks like in the data

The instructor's table (slide 10) puts the two conversion methods side by side. The pattern is systematic: the poorer the country, the larger the upward revision from switching to PPP.

The ratio column is the story. For high-income countries PPP is barely different from the market rate — the United States is 1.03×, because it is the base. For low-income countries the revision is around two-and-a-half to three times, and the largest ratios in the table belong to India (3.88×), Egypt (3.77×) and Pakistan (3.69×). The low-income group average rises from $775 to $2,127 — a country that looks desperately poor at market rates looks merely poor at PPP.

The HDI is Sen's capability approach made operational: three dimensions — health, knowledge, and a decent standard of living — combined into one number between 0 and 1. The classification bands are:

The slide also stresses that the HDI can be calculated for groups and regions within a country, not just for countries — it varies among groups, across regions, and between rural and urban areas. That matters: the whole point of a holistic measure is that a national average can hide the thing you care about.

The old HDI, worked: Bangladesh in 2007

This is the slide's own example, with every intermediate number. The old HDI builds a sub-index for each dimension and then takes the arithmetic mean.

The goalposts are visible inside the formulas: life expectancy runs from 25 to 85 years, literacy and enrolment from 0 to 100%, and income from ln(100) to ln(40,000). The income index is logarithmic precisely because of the diminishing-returns result from Figure 1.2 in Chapter 1.

What is new in the New HDI

The UNDP rebuilt the index in November 2010. There are two headline changes and several smaller ones.

The smaller changes are just as examinable:

The effect of the whole package is that the New HDI can identify not only whether a country is developing, but also whether various significant groups within that country are participating in the development.

The New HDI, worked: China in 2010

Discussion of the HDI

Table 2.4 — HDI and its components, selected countries (2018)

The last column is the most interesting one. It is the country's GNI rank minus its HDI rank, so a positive number means the country does better on human development than its income alone would predict. Turkey is +43 and Bangladesh +20; Egypt is −80 and Brazil −40. The HDI's own advantage statement — "a country can do much better than might be expected at a low level of income" — is exactly what those positive numbers are showing.

The extremes, as of the instructor's 2026 update

The world average in 2023 is 0.72. Burundi and South Sudan are tied at rank 180 as printed on the slide. Note that Norway and Switzerland share 0.970 and are separated only by rounding.

The section's subtitle is the point: there are shared characteristics, but the diversity within them is enormous. The slides develop each characteristic with data, and the data is where the examinable detail lives.

Figure 2.4 — shares of global income, 2024

Nominal GDP, current US dollars. Seven shares, which sum to 100%:

Table 2.6 — the 12 most and least populated countries, 2024

Table 2.10 — the urban population, 2024

The developed/developing split and the regional breakdown are two separate partitions, so the regional rows do not sum to the world total. The check that does hold: 1,259 + 6,901 = 8,160.

Table 2.11 — employment by sector, 2004–2008 (%)

The single most useful table for the structural-change argument in Chapter 3: it shows agriculture absorbing most of the labour force in poor countries while contributing a small share of GDP — the gap between the two columns is the productivity difference Lewis's model is built on.

M = male, F = female, GDP = share of GDP (2008). Source: World Bank, World Development Indicators 2010, tabs. 2.3 and 4.2. Note on the slide: Ethiopian agricultural employment reflects limited coverage.

Table 2.8 — crude birth rates around the world, 2022

Crude birth rate 粗出生率 = births per 1,000 population per year. The slide lists roughly 150 countries; what matters for revision is the shape of the distribution, and where China and the other large developing countries sit.

The pattern is the demographic transition: the highest rates are concentrated in Sub-Saharan Africa, the lowest in Europe and East Asia. China, Thailand and Brazil now sit in the same band as the United States and the United Kingdom.

Table 2.7 — primary school enrolment and pupil-teacher ratios, 2022

Four cells in the printed table were not legible on the slide render and are marked "—" rather than guessed. The legible ratios run 37 → 28 → 14 for the income groups and 20 → 22 → 33 → 36 for the regions, so the missing values sit inside that band.

Figure 2.4 — under-5 mortality rates, 1990 and 2023

Deaths per 1,000 live births. Two of the eight bar labels and the high-income 1990 value were not legible on the slide. The direction is unambiguous: the largest absolute fall is in low-income countries (128 → 38), which is the same distance travelled as lower-middle-income countries but from a much higher base.

The comparison is not between rich and poor countries today, but between poor countries today and what today's rich countries looked like when they began industrialising. The eight differences are what make "just do what Britain did" unworkable.

This section asks a harder question than the rest of the chapter. Chapter 2 up to now has described how countries differ and measured the difference. §2.6 asks why the differences exist at all — and in particular why some countries are poor for reasons that go back centuries. It is new in the 13th edition and the slides never mention it, but it is the frame the whole field now uses, and it is what Chapter 3's dependence school was fumbling towards without the evidence to back it up.

The section's organising device is Figure 2.9, a schematic with twenty-two numbered arrows connecting climate, colonial history, inequality and institutions to present-day income. The chain underneath it runs like this:

The four families of explanation

The reversal of fortune

This is the section's sharpest empirical point, and it is a falsification test rather than a correlation. If geography were fundamental to development prospects, the argument runs, then the areas that were most prosperous before colonisation should still be the most prosperous now.

The explanation is uncomfortable. Where local populations were larger, denser and more organised, it was easier for colonisers to take over the existing structures and collect tribute — so the institutions built there favoured extracting existing wealth over creating new wealth. Where populations were sparse, taking over was harder and colonists had to settle and produce, which required institutions that encouraged investment: constraints on executives, protection from expropriation.

The mechanism for how this persisted is the difference between formal and informal rules. As North stresses, a constitution can be rewritten in a day; the informal norms around it — how people expect contracts to be honoured, whether officials are expected to take bribes — "usually change only ever so gradually". The Congo is the textbook's illustration: the rule of Leopold II is arguably an ultimate cause of the Mobutu regime after independence.

Engerman and Sokoloff: endowments, inequality, and the two Americas

The second strand explains why the New World diverged internally. What the land could grow determined who worked it:

The mechanism connecting inequality to slow development has a name: the social conflict theory of institutions. Where inequality was extreme there was less investment in human capital and in public goods, and less movement toward democratic institutions — with causality running both ways, since education and institutions reinforce each other. The textbook is careful to add that this is not a story about the English being gentler colonisers, given the treatment of Native Americans and of slaves in the southern colonies.

Section B is the calculation section — the two HDI worked examples and the PPP conversion. Section C reproduces the instructor's own multiple-choice questions.

Sustained growth is recent

Sustained increases in standards of living are a recent phenomenon. Modern economic growth emerged only in the most recent two or three centuries. Until about 12,000 years ago humans were hunters and gatherers; the agricultural revolution around 10,000 BC brought settlements and eventually cities, but even the sporadic peaks of achievement that followed were characterised by low average living standards — wages in ancient Greece and Rome were roughly equal to wages in fifteenth-century Britain or seventeenth-century France, all of them well before modern growth began.

The instructor's figure plots average world GDP per capita from 1 to 2010 (Maddison's data). It is flat for most of its length and then turns up sharply. His framing of the timescale is worth remembering: compress the 130,000 years since modern humans appeared into a single day, and the era of modern growth would have begun only in the last three minutes.

The Great Divergence 分化

The magnitudes are the thing to remember:

Since 1700, living standards in the richest countries have risen from roughly $500 per person to approaching $45,000 — a factor of 90 in a period that is a flash in human history.

Modern growth around the world

After the Second World War, growth in Germany and Japan accelerated sharply — Japan averaging nearly 6% per year between 1950 and 1990 — and both settled at roughly three-quarters of the US level. The United Kingdom was the richest country in the late nineteenth century, then slipped because it grew substantially slower than the United States; since 1950 the two have grown at more or less the same rate, with UK income staying at about three-quarters of the US level. Brazil accelerated until 1980 and then stagnated; China and India have had the reverse pattern.

In per capita terms the ratios are: Japan and the United Kingdom about 3/4 of the United States, Brazil and China about 1/5, and Ethiopia only about 1/40.

Reasons to expect convergence

1. Technology transfer Enables developing countries to leapfrog earlier stages — no need to rediscover the steam engine
2. Diminishing returns to factor accumulation An extra machine adds more output where machines are scarce — so the poor should grow faster

The slide's own caution: divergence occurred for two centuries from the start of the industrial revolution, but the most recent data demonstrate that, on average, (re-)convergence is now underway. "Expect convergence if conditions are similar" is the qualification that later becomes conditional convergence.

Four periods of the convergence record

Growth convergence ≠ absolute income convergence

This is the chapter's sharpest empirical point, and it is easy to get wrong. Over 1990–2017:

China and India grew far faster than the high-income OECD countries. But because they started from such a low base, the absolute gap in per capita income between them and the advanced economies still widened substantially. Relative convergence — closing the ratio — is not the same thing as absolute convergence, which would mean closing the difference.

Data source on the slide: Penn World Table.

A broad sample of countries

Over 1960–2007, growth rates across countries ranged from −23% to +16% per year. Some countries exhibited a negative growth rate; others sustained nearly 6%; most sustained about 2%. Per capita GDP in 2007 varied by a factor of about 64. The lesson the slide draws is the familiar one: small differences in growth rates result in large differences in standards of living over a few decades.

The chapter organises the field into four families. Each is a claim about what causes underdevelopment, and therefore about what should be done.

Rostow's stages of growth

The classic statement of the linear-stages view: the transition from underdevelopment to development can be described in terms of a series of steps or stages through which all countries must proceed. The sequence is what gives the family its name, and the assumption that the sequence is universal is what the later critiques attack.

The Harrod-Domar growth model

The model the stages view needed to make its arithmetic work. Its appeal to scholars and politicians is that it is very simple — and its origin is the Marshall Plan of 1948–51, where the question was precisely how much investment a country needed to hit a growth target. Two economists developed it independently in the 1940s: Roy Harrod (England) and Evsey Domar (MIT).

The derivation

Start from the identity between saving and investment. If the saving rate is s and the capital-output ratio is c — the amount of capital needed for one unit of increase in GDP — then:

Saving equals the saving rate times income; that saving finances investment; investment adds to the capital stock; and the capital required is c times the increase in output.

Rearranging (3.6) gives the model's central result — the rate of growth of GDP:

In the absence of government, the growth rate of national income will be directly related to the savings ratio and inversely related to the economy's capital-output ratio. That is the whole policy content of the model: to grow faster, save more or make capital more productive.

What determines c?

The slide pushes on the capital-output ratio, since it is the more interesting of the two parameters.

Empirically, over 1970–2019 capital-output ratios followed clearly divergent patterns across economies (Penn World Table): Japan and South Korea saw a steady long-term increase driven by sustained capital deepening; the US ratio remained relatively stable; most Latin American and African economies spiked sharply in the 1980s amid economic distress and then declined; India and Indonesia dipped in the 1980s before returning to a gradual upward trend.

The fixed-coefficient production function

The model's assumptions are easiest to see in the production function. Capital (K) and labour (L) are always used in a fixed proportion; there are constant returns to scale, so doubling both doubles output. The slide's example: to produce 100 tons of cement a year a country needs $10 million of capital and 100 workers.

With that functional form, the isoquants are right-angled rather than smooth: to produce a given level of output you need at least a certain amount of both inputs, and extra quantities of one alone do nothing. The kink of each isoquant lies on the ray K/L = 1/10, which is why the model implies capital and labour must grow at the same rate.

Criticisms of the stages model

The instructor's note adds the practical objection: in reality, more saving and investment is not a necessary condition for economic growth.

The structural-change family studies the transformation of an agricultural economy into a more industrialised one, using the tools of neoclassical price and resource-allocation theory and econometrics. Two landmarks: W. Arthur Lewis's "two-sector surplus labour" theory, and Hollis B. Chenery and coauthors' "patterns of development" empirical analysis. Lewis — Saint Lucian, Nobel Memorial Prize 1979 — is the one the chapter develops in detail.

The agricultural sector

Fixed capital K_A and unchanging technology t_A, so production varies only with labour input L_A. Four properties:

1. Decreasing returns in terms of labour Adding workers to fixed land adds less and less output
2. Zero return to labour after a certain point This is surplus labour: production does not increase with extra labour
3. All workers share the production equally So the wage equals the average product, not the marginal product

Mechanically, the story runs: draw a production function that becomes flat at a certain point; from it derive the MP curve, which is also the labour demand curve and which turns zero at the same point where the TP curve becomes flat. There is no labour supply curve — employment is up to the point where MP becomes zero, and the wage is determined by the average product, because there is no labour market in the conventional sense. It is a subsistence economy, and W_A = TP_A / L_A.

The industrial (modern) sector

The same construction, with two changes. Draw a production function; derive the MP curve, which is again the labour demand curve. But the labour supply is horizontal — perfectly elastic — and the wage in the modern sector is higher than the agricultural wage. Labour supply and labour demand together determine the wage level and employment in the industrial sector.

The reason the supply is horizontal is the whole point: while surplus labour exists in agriculture, the modern sector can hire as many workers as it wants at the going wage, because those workers are producing nothing where they are. The wage only has to be slightly above the agricultural wage to attract them.

The two sectors together: migration and industrialisation

Put the panels side by side and the model becomes a theory of development. Modern-sector growth raises the demand for labour; the higher wage pulls workers out of agriculture; the modern sector expands and the economy grows. This self-sustaining growth will not last forever, though:

1. Surplus labour is eventually absorbed Once the modern sector has hired everyone who was producing nothing
2. Further withdrawal reduces agricultural output Because the next workers to leave were producing something
3. The marginal product of agricultural labour rises above zero More land per person remaining
4. Cheap labour is gone; the industrial wage must rise Which means an upward-sloping labour supply curve

Criticisms of the Lewis model

The dependence school denies the premise of both families above: that development is a matter of getting the quantities right. It argues that developing countries are held where they are by external and internal institutional and political constraints — and that the obstacles are therefore structural, not technical.

The neocolonial dependence model 新殖民主义

An indirect outgrowth of Marxist thinking. Its elements:

The dualistic-development thesis 二元发展论

The second strand, which is about structure rather than power. Four propositions:

1. Superior and inferior elements can coexist A modern sector and a traditional one can exist side by side in the same economy
2. The coexistence is chronic It does not resolve itself over time; it is a stable state, not a transition
3. The degrees of superiority or inferiority tend to increase The gap widens rather than narrowing
4. The superior element does little or nothing to pull up the inferior Which is the direct contradiction of the Lewis model's prediction

Criticisms and limitations

The instructor's note records the school's own prescription in one word: autarky is best — cutting the ties to the core rather than negotiating better terms within them.

The counterrevolution challenges the statist 计划经济 model — the assumption, shared by the stages models and the dependence school alike, that the state is the agent of development. It comes in three flavours, which the slide distinguishes carefully.

Main arguments

1. Denies the efficiency of intervention Planning does not improve on the market outcome
2. Points up state-owned enterprise failures Public firms are inefficient — the case for privatisation
3. Stresses government failures Rent-seeking, capture, and the political economy of policy

And one theoretical point, which is the bridge to the Solow model: traditional neoclassical growth theory — with diminishing returns — cannot sustain growth by capital accumulation alone.

Developed in the mid-1950s by Robert Solow of MIT, and the basis for the Nobel Prize he received in 1987. The slide's framing of the question it answers is worth keeping: in 1960 South Korea and the Philippines were similar in many respects — per capita GDP about $1,800 and $2,200 respectively, less than 15% of the US level; populations of about 25 million, half of working age; similar fractions working in industry and agriculture. Between 1960 and 2007 their paths diverged dramatically: the Philippines grew at about 1.7% per year, South Korea at just under 6%, reaching nearly $24,000 against the Philippines' $5,000. The Solow model is the starting point for understanding that difference.

What it adds to the production model

Assumptions: the neoclassical production function

The Cobb-Douglas production function

Most popular function form for production. Assume A is determined exogenously; it has decreasing returns to capital.

Note that the slides use the square-root case specifically: both exponents are 0.5, so α = 0.5 throughout. That matters for the steady state below.

Dividing through by L: the per-worker form

The model is solved in per-worker terms. Divide both sides by L:

And in general form: y = Af(k).

The capital accumulation equation

This is the second equation of the Solow model:

s = saving rate · y = output per worker · n = population growth rate · d = depreciation rate

The growth of the capital-labour ratio relies on three things: savings, population growth and depreciation. The two equations — y = Af(k) and Δk = sy − (n+d)k — together determine the equilibrium.

The Solow diagram

Three lines on axes of k (horizontal) and y (vertical): the production function y = Af(k), the saving curve sy below it, and the straight line (n+d)k from the origin.

At the steady state

k and y are constant. Are K and Y also constant? No. Remember that Y = yL and K = kL, and L is growing at a rate of n. Therefore Y and K grow at a rate of n. The instructor's example: if y = 10 and L is growing at 2%, then Y is growing at 2%.

The closed form is worth knowing, because it makes the comparative statics immediate. Setting sy = (n+d)k and substituting y = Ak^α:

Different initial conditions: the catch-up argument

Suppose two countries X and Z have the same A, f, s, n and d, but country X has larger k and y than country Z. Which country grows faster?

Can a country grow in the long run?

Short-run versus long-run is the whole content of this question. In the short run the economy may not be at the steady state, and k and y may grow. In the long run the economy reaches the steady state, and k and y are stable. So capital accumulation cannot sustain growth — and the chapter is explicit that this is a drawback, because empirically economies appear to continue growing over time.

Strengths and weaknesses of the Solow model

Countries will be rich to the extent that they have a high rate of investment, a high TFP level, and a low rate of depreciation. The model's own verdict on capital accumulation is blunt: because of diminishing returns, an economy accumulating capital sees the marginal product of capital decline, until eventually the additional output produced by investment is only just enough to offset wear and tear. At that point growth stops.

The test case is Robert Barro, 1991, "Economic growth in a cross section of countries," Quarterly Journal of Economics — a paper using regression techniques, on 98 countries over 1960–1985, asking whether incomes converge.

Why no unconditional convergence?

1. The Solow model assumes the same steady state for every country If countries differ in s, n, d or A, they have different steady states and there is no reason for them to converge to each other
2. Many other variables affect growth Such as initial human capital — which is why Barro's second regression controls for them
3. A causality problem in the statistics Is it that saving causes growth of GDP, or growth of GDP causes saving?

The instructor's closing slide on this section shows the growth rate of labour productivity in constant 2017 USD for China, Germany, Japan and the USA — the same comparison at the productivity level rather than the income level, and with the same pattern of faster growth in the catch-up economies.

The chapter closes by refusing to pick a winner. The conclusions the slides draw:

Relevance of growth theories to today's developing countries

The slide is candid about the limits of everything in the chapter:

Two exercises were worked in class and appear at the end of the deck with full answers. They are the closest thing available to a worked exam question, and both are reproduced here verbatim.

The calculation sections reproduce the instructor's two exercises with his own answers. Work them before revealing.

Each chapter is built from the instructor's own deck, slide by slide. Where a slide carried only an image — a figure, a table, or a formula stored as a picture — the content was read off the rendered slide rather than guessed at, and where a cell could not be read it is marked as illegible rather than filled in.

These are the topics that follow in a standard development-economics sequence. They are listed so the shape of the rest of the course is visible, and each will be built out to the same five-block structure as the three chapters above when the decks arrive.

The instructor closed the Chapter 3 slides with the title pages of two research papers. They are not examinable material, but they mark the direction of his own interest and both are about China, which is where the course's examples keep landing.

The course slides were built on an earlier edition — their footers read Copyright © 2012 Pearson Addison-Wesley — so if you are working from the 13th edition the chapter numbers match but the section numbers often do not. Nothing is missing; some of it has moved into appendices, and some of it has moved into a different chapter.

Each item below is something you should be able to do from memory, on paper, without the slides. They are ordered by how likely they are to appear as an exam question, based on what the instructor chose to gloss, bold, or work through in class.