One country’s typical income is 25 times another’s

25× U.S. income per person compared with Ethiopia’s, 2022

Why? And why do whole economies sometimes shrink, or see prices race ahead? Those are the questions of macroeconomics: the study of the economy as a whole.

Open with the number, not the definition. Ask the room to guess the ratio before you reveal it: most guesses are far too low or far too high.

The 25.5 comes from the Maddison Project data in the book’s notebook (GDP per person in 2022 dollars). Then name the field: microeconomics studies single households, firms or markets; macroeconomics studies how their interactions add up to income, jobs and prices for a whole country. Macroeconomists still build on individual behavior (“microfoundations”), so the two fields are closer than separate courses suggest.

Tell the public-policy students why this matters to them: almost every big policy debate (minimum wages, stimulus, interest rates, deficits) is a macroeconomic question underneath.

Today: three questions

• The facts. What are the big facts about income, jobs, prices and unemployment?
• The model. How does an economic model turn a few assumptions into predictions?
• Long run, short run, policy. How do growth and fluctuations differ, and how can models inform policy without a “parallel universe”?

These are the three acts of the lecture and the three branches of the chapter mind map on the title slide. Act I documents four stylized facts with live data. Act II builds the smallest possible model, a labor market, and runs experiments on it. Act III uses the long history of U.S. income to separate the long run from the short run, and closes with what models can and cannot do for policy.

The questions and the facts

Act I

Fact 1: incomes differ enormously, across countries and over time

How much richer is the United States than Ethiopia today, and than the United States a century ago?

Pose both comparisons: across countries (U.S. vs. Ethiopia) and over time (U.S. today vs. 1922). Ask for a guess for each. A common guess for “a century ago” is “ten times”, which is also a claim the textbook makes; we will test it against the data.

Prepare them for the ratio scale: equal vertical distances mean equal percentage changes, so a country growing at a constant rate draws a straight line.

Six economies, 150 years: the gaps are huge and they change

Data: Maddison Project Database 2023 (Bolt and van Zanden 2024), in 2022 dollars with the BEA GDP deflator (FRED). Chart inspired by Fig. 1.1 in Jones (2024), Macroeconomics, 6th ed.

How to read it: GDP per person in 2022 dollars, on a ratio scale, for the textbook’s six economies. Hover to read a value.

Do live:

1. Point at the spread in any year: the richest has several times the income of the poorest.
2. Point at Japan: a fall during World War II, then very fast growth. China: nearly flat until 1950, then fast growth.
3. Click “linear scale”: poor countries are squashed near zero and early growth disappears. Click back. This is why we use ratio scales for growth.
4. Use the menu to add a group (e.g. East Asia), or South Korea: about 1,288 dollars per person in 1950, poorer than Japan in 1870, yet it passed Japan by 2020.

The data confirm one textbook claim and correct another

• U.S. vs. Ethiopia: 25.5× in 2022, but 59× around 2000. Ethiopia has grown fast since then.
• U.S. today vs. a century ago: 5.8× in the Maddison data, not “more than 10 times”. A tenfold gap takes us back to 1879.
• Steady growth draws a straight line on a ratio scale; a steeper line means faster growth.

Always check a claim against the data, and say which data and which year you used.

Reveal the answers to the opening guesses. The ratios come from the notebook’s “Key numbers” check of Fig. 1 (U.S./Ethiopia: 24.5 in 1950, 28.7 in 1980, 59.4 in 2000, 44.5 in 2010, 25.5 in 2022).

The “ten times richer than a century ago” claim fails in the Maddison data (5.8). With the BEA series used for Fig. 7 later, the same ratio is 8.1: different sources disagree, because estimating income a century ago is hard and sources measure prices differently. When sources disagree, report the range. This habit matters as much for policy analysts as for academics.

Fact 2: recessions destroy jobs, and the last two were the deepest

How much of the adult population lost work in the 2007–09 recession, and in the Covid-19 recession?

The employment-population ratio is the share of adults (16 and older in the U.S. data) who have a job. Ask: is a fall of 2 points big or small? Of 10 points? Collect guesses for each recession before showing the chart.

Every recession lowers the employment rate; 2020 lowered it by 10 points

Data: BLS Employment-Population Ratio (EMRATIO) and NBER recession dates, via FRED. Chart inspired by Fig. 1.2 in Jones (2024), Macroeconomics, 6th ed.

How to read it: monthly data since 1960; shaded bands are recessions. Hover over a recession’s first month and over its lowest point.

Point at: the long rise from about 56 percent in the early 1960s to a peak of 64.7 percent in April 2000 (more women joining the labor force); the dip in every recession; the slow recovery after 2009; the Covid-19 collapse and quick bounce, which still has not returned to the February 2020 level of 61.1 percent.

Data note to share: October 2025 is missing, because the BLS did not run its survey during the federal government shutdown. The chart joins September and November.

Covid-19 cut employment four times more than any recession before 2007

• Before 2007, no recession lowered the ratio by more than 2.4 points.
• 2007–09: −4.5 points, and a slow recovery.
• Covid-19: −9.9 points, from 61.1 to 51.2 percent in two months.

The two latest recessions really were the sharpest job losses in more than 50 years.

Each fall compares the ratio in the peak month with its lowest value up to two years after the trough, because employment often keeps falling after a recession ends.

Policy angle for discussion: in 2020 the ratio fell by 3.9 points in the U.S. (ILO yearly data) but only 1.2 in Germany and 0.3 in Japan, which paid firms to keep workers on reduced hours rather than lay them off. The book’s website version of this figure lets students add those countries. Ask: what are the costs and benefits of such job-retention schemes?

A falling employment rate does not always mean rising unemployment

The employment rate falls only when unemployment rises.

It also falls when people stop looking for work: \(e = p\,(1-u)\), the participation rate times the employed share of the labor force. In April 2020, about 70% of the fall came from higher unemployment and 30% from people leaving the labor force.

Definitions: employed E, unemployed U (no job but looking), labor force LF = E + U, adult population Pop. Unemployment rate u = U/LF, participation rate p = LF/Pop, employment-population ratio e = E/Pop. Dividing E = LF − U by Pop gives e = p(1 − u).

February 2020: p = 63.3 percent and u = 3.5 percent give e = 63.3 × 0.965 ≈ 61.1 percent. Taking logs, ln e = ln p + ln(1 − u), so the fall in e splits exactly into a participation part and an unemployment part: 71 percent and 29 percent in the notebook’s check.

Why it matters for policy: a falling unemployment rate can hide discouraged workers leaving the labor force. Watch both ratios.

Fact 3: inflation comes in waves

When was inflation highest in the rich countries, and did it ever stay low?

Inflation is the growth rate of the price level, π = 100 × (P_t − P_{t−1})/P_{t−1}. Ask students which decade they think had the highest inflation, and whether they have lived through high inflation themselves (most have now: 2021–22).

Inflation peaked in the 1970s, stayed low for 30 years, then jumped

Data: World Bank WDI consumer-price inflation (FP.CPI.TOTL.ZG); U.S.: BLS CPI (CPIAUCNS), via FRED; gaps from the World Bank Global Inflation Database. Chart inspired by Fig. 1.3 in Jones (2024), Macroeconomics, 6th ed.

How to read it: annual inflation for the U.S., the U.K., the euro area and Japan; the dashed line is zero (below it, prices fall: deflation). Hover over 2022 to compare the four.

Do live: add Germany (peak of only 7.0 percent in 1973) and then Argentina; the axis widens to thousands of percent because prices rose 3,080 percent in 1989 (hyperinflation).

Three eras of inflation, and one runaway case

• 1970s peaks: Japan 23% (1974), U.K. 24% (1975), U.S. 13.5% (1980).
• 1990–2019: about 2.5% a year in the U.S., U.K. and euro area; 0.5% in Japan, often with deflation.
• 2021–22: a jump everywhere; U.S. inflation was 8.0% in 2022.
• Argentina, 1989: 3,080%, so prices were multiplied by about 32 in one year.

Why inflation rises, why it falls and what central banks can do about it is a central question of the short-run chapters.

The era averages are the notebook’s “Key numbers” for Fig. 3. The 8.0 percent for 2022 is higher than the value printed in the textbook, probably because the full-year figure was not yet available when the book was written.

Inflation compounds: a year of π percent multiplies prices by 1 + π/100. So 3,080 percent multiplies prices by 31.8, and two years of 10 percent raise prices by 21 percent, not 20.

Fact 4: unemployment is much higher in Europe, but not always

Is unemployment in Europe really “nearly twice” the U.S. rate? Has it always been higher?

The unemployment rate is the share of the labor force that is looking for work but has no job. “Europe” in the chart is the euro area of 12 countries (European Commission AMECO data). Ask for a guess: how many times the U.S. rate is the European rate over the last 20 years?

Since the 1980s Europe’s unemployment has stayed above the U.S. rate

Data: BLS UNRATE; Japan: OECD LRHUTTTTJPA156S, via FRED; Europe: European Commission AMECO (ZUTN); others: ILO via the World Bank. Chart inspired by Fig. 1.4 in Jones (2024), Macroeconomics, 6th ed.

How to read it: yearly unemployment rates; the slider picks a period, which the chart shades, and the title reports Europe’s average rate relative to the U.S. rate over that period.

Do live: start at 2006–2025 (about 1.6 times). Move the period to 1960–1979: Europe’s rate was below the U.S. rate, on average just over half of it. Then use the menu to add Spain and Greece (above 26 percent in 2013, after the euro-area debt crisis) and Germany (from 10.5 percent in 2005 to 3.8 percent in 2025).

Europe’s unemployment was once lower; the gap opened around 1980

• 2006–2025: Europe’s rate averaged 1.6× the U.S. rate: high, but short of “nearly twice”.
• Europe first overtook the U.S. in 1979, and has been higher in every year since 1984, except 2020.
• Japan’s rate never rose above 5.4%.
• “Europe” hides large differences: Spain and Greece passed 26% in 2013; Germany fell below the U.S.

Why unemployment rates differ so much and so persistently is a question for the labor-market chapter.

Ask the policy students for hypotheses before the next chapter on labor markets: labor-market institutions (firing costs, unemployment benefits, wage bargaining), taxes on labor, the euro-area debt crisis. Note that the facts alone cannot tell these stories apart. That is the job of a model, which is where Act II begins.

Other big facts the chapter mentions but this lecture leaves for later: budget and trade deficits (the U.S. federal deficit reached 13.8 percent of GDP in 2020), currency crises and asset-price bubbles.

How a model works

Act II

Macroeconomics answers questions in four steps

1. Document the facts, with data: what we just did.
2. Build a model: a simplified economy written as a few equations.
3. Compare the model’s predictions with the facts, including the numbers, not just the signs.
4. Run experiments: change an input and read off the new outcomes.

A model is useful because it leaves things out: it keeps only the forces that matter for the question.

Step 3 is quantitative: a good model of rich and poor countries must predict not just that the U.S. is richer than Ethiopia, but roughly by how much.

Step 4 is what makes models valuable for policy. In the lab, a scientist can run a controlled experiment. A macroeconomist cannot rerun 2009 with a different policy; the model is where we run the experiment instead. We come back to this at the end.

Every model turns inputs into outcomes

You set the minimum wage at 12, two dollars above the market wage of 10. What happens to the wage, to employment and to unemployment? Write down your guess.

Before showing the figure, introduce the vocabulary on the board:

• parameters: inputs that stay fixed unless the model builder changes them in an experiment (a minimum wage set by law, how strongly workers respond to the wage);
• exogenous variables: inputs that may change over time along a path set in advance (population growing at 1 percent a year);
• endogenous variables: the outcomes the model solves for (the wage, employment).

Then ask for the prediction. Most students say “the wage rises to 12”; fewer get the employment and unemployment numbers.

Inputs go in, the model works out the outcomes

Live mini-model of the labor market. Adapted from Fig. 1.6 in Jones (2024), Macroeconomics, 6th ed.

How to read it: the left box holds the inputs (parameters and the minimum wage), the middle box is the model, and the right box shows the outcomes it computes. The slider sets the minimum wage.

Do live: start at a low minimum wage (nothing changes), then move it to 10, then 12, then 15. Ask after each step: did we choose the wage or employment? No: we only changed an input, and the model computed them. That is what “endogenous” means, and what running an experiment means.

A floor above the market wage creates unemployment

• Answer: the wage rises to 12, employment falls from 50 to 48, and 6 hours of work go unsold.
• A floor of 10 or less changes nothing: it is not binding.
• The outcomes are endogenous: you never set them, you only set the inputs.

A model’s outcomes are answers to “what if” questions about its inputs.

Compare with the predictions written down. The unemployment number surprises most students: 6, not 2. Each dollar above the market wage makes firms hire one hour less AND draws out extra hours from workers, so the gap grows faster than the wage. The next slides show where these numbers come from.

The labor-market model is three equations in three unknowns

\[L^s = \bar{a}\,w + \bar{\ell} \qquad\quad L^d = \bar{f} - w \qquad\quad L^s = L^d\]

Put supply and demand into market clearing and solve for the wage, then employment:

\[w^* = \frac{\bar{f}-\bar{\ell}}{1+\bar{a}} \qquad\qquad L^* = \frac{\bar{a}\,\bar{f}+\bar{\ell}}{1+\bar{a}}\]

With \(\bar{a}=2\), \(\bar{\ell}=30\), \(\bar{f}=60\): \(\;w^* = 30/3 = 10\) and \(L^* = 150/3 = 50\).

A bar marks a parameter. ā is how many extra hours workers offer when the wage rises by a dollar (the slope of supply); ℓ̄ is the hours offered even at a zero wage; f̄ is the hours the firm would hire at a zero wage.

Steps: āw + ℓ̄ = f̄ − w, so (1 + ā)w = f̄ − ℓ̄. Substitute w* into demand to get L. Check: the supply curve gives the same L.

Intuition: the wage is high when firms want many hours relative to what people offer anyway. Employment is a weighted average of f̄ and ℓ̄, with weight ā/(1 + ā) on f̄: the more responsive workers are, the closer employment gets to what firms would like to hire.

The full derivation, step by step, is in the book chapter’s Model overview.

Experiment: what happens when firms want more workers?

Firms’ demand for hours rises: \(\bar{f}\) goes from 60 to 63. Which way do the wage and employment move, and by how much?

Let students sketch the shift first: which curve moves, and in which direction? Then ask for the numbers. Hint: the wage takes the share 1/(1 + ā) of the shift.

A demand shift moves the wage and employment together

Supply-and-demand model of the labor market. Adapted from Fig. 1.8 in Jones (2024), Macroeconomics, 6th ed.

How to read it: employment L on the horizontal axis, the wage w on the vertical axis; supply slopes up, demand slopes down; the dot is the equilibrium, and the title reports w* and L*. The slider sets f̄, labor demand at a zero wage.

Do live: move f̄ from 60 to 63 and read the title. Then to 70: both the wage and employment rise. Move it down: both fall.

Supply and demand shifts have opposite effects on the wage

A rise of 3 in …	wage \(w^*\)	employment \(L^*\)	curve that moves
\(\bar{f}\): firms want more hours	10 → 11	50 → 52	demand, right
\(\bar{\ell}\): more hours offered	10 → 9	50 → 51	supply, right

A demand shift is shared between pay and jobs; a supply shift moves them in opposite directions.

Read the comparative statics off the solution: ∂w/∂f̄ = 1/(1 + ā) = 1/3 and ∂w/∂ℓ̄ = −1/(1 + ā) = −1/3. w* and L* are straight lines in f̄ and ℓ̄, so the coefficients are the effects. A rise in f̄ raises w* by 1/(1 + ā) and L* by ā/(1 + ā): with ā = 2, one third goes to pay and two thirds to jobs. A rise in ℓ̄ (for example population growth) raises employment but lowers the wage.

Ask: how would you test this? You would need to know whether a change in wages and jobs came from demand or from supply. That identification problem runs through all of empirical macroeconomics.

A binding minimum wage creates unemployment of \((1+\bar{a})\) hours per dollar

\[U = L^s(\bar{w}_{\min}) - L^d(\bar{w}_{\min}) = (1+\bar{a})\,(\bar{w}_{\min} - w^*) \qquad \text{when } \bar{w}_{\min} > w^*\]

Employment falls

by 1 hour for each dollar above \(w^*\), read off demand

Unemployment rises

by \(1+\bar{a} = 3\) hours per dollar: fewer hours hired and more hours offered

With a floor of 12: \(U = 3 \times (12-10) = 6\), the number on the live model.

Derivation: at the floor, supply is ā·w_min + ℓ̄ and demand is f̄ − w_min. Subtract and use f̄ − ℓ̄ = (1 + ā)w* from the solution. Employment is read off demand, because nobody can make the firm hire more hours than it wants at that wage.

No curve moves: the floor is a horizontal line, and the market slides up the demand curve. A floor below w* has no effect at all.

Small growth rates compound into large differences

\[y_t = y_0\,(1+g)^t \qquad\quad g = \left(\frac{y_T}{y_0}\right)^{1/T} - 1 \qquad\quad T_{\text{double}} \approx \frac{70}{100\,g}\]

Growth rate \(g\)	1%	2%	3%	5%
Years to double	70	35	23	14
Factor after 100 years	2.7	7.2	19.2	131.5

Each year’s growth applies to a level that already includes all past growth: the rule of 70 turns a growth rate into a doubling time.

Compound growth: substitute y_1 = y_0(1 + g) into y_2 = y_1(1 + g), and so on. Solving for g gives the average annual growth rate between two years. Logs turn compound growth into a straight line: ln y_t = ln y_0 + t ln(1 + g), which is why a ratio scale shows steady growth as a straight line.

Rule of 70: doubling means (1 + g)^T = 2, so T = ln 2 / ln(1 + g) ≈ 0.70/g. The exact doubling times are 69.7, 35.0, 23.4 and 14.2 years.

Ask: how much richer is a country after 100 years at 2 percent rather than 1 percent? 7.2 versus 2.7 times, almost three times as rich.

Long run, short run and policy

Act III

The long run: will income keep growing at 2 percent a year?

U.S. income per person has risen for 150 years. How fast, and how steadily?

Introduce potential output: what the economy would produce if all prices could adjust and all workers and machines were fully used. Ask for a guess of the average growth rate of U.S. income per person since 1870.

U.S. income per person grew about 1.9% a year for over 150 years

Data: BEA via FRED (GDPCA, B230RC0A052NBEA, A191RD3A086NBEA); CBO potential GDP (GDPPOT); before 1929 and other countries: Maddison Project 2023. Chart inspired by Fig. 1.7 in Jones (2024), Macroeconomics, 6th ed.

How to read it: real GDP per person in 2022 dollars on a ratio scale; the solid line is actual output and the dashed line is potential output. A straight line means steady growth.

Point at the three features that organize the whole textbook: the long run (the enormous, almost straight rise), the short run (the swings of actual output around potential, visible mainly in the Great Depression and World War II) and the future (will the line continue?).

Do live: use the menu to choose Japan (2.1 percent a year from 1870 to 2022) and Argentina (only 1.4 percent; its income fell from about half of the U.S. level in 1870 to less than a third in 2022).

Two percent a year, for 150 years, multiplies income by 17

• 1870: $4,634 per person; 2022: $77,942, a factor of 16.8.
• Average growth 1870–2025: 1.88% a year, so income doubles about every 70/1.9 ≈ 37 years.
• Half a point of extra growth for 150 years doubles relative income: roughly the U.S. against Argentina.
• A tenfold rise in the next century would need 2.33% a year.

The long run is about growth: why living standards rise, and why some countries grow faster than others.

The textbook rounds the growth rate to 2 percent and reports 4,200 dollars for 1870. Our 1870 value is about 10 percent higher because it uses newer historical estimates (Maddison) before 1929.

Small differences compound: in logs, relative income changes by about t(g_1 − g_2), so 0.005 × 150 = 0.75, a factor of e^0.75 ≈ 2.1.

At the historical rate, income would rise by a factor of about 6.4 in the next century; a tenfold rise needs 2.33 percent a year. Is that plausible? That question motivates the growth chapters (Chapters 3 to 6).

The short run: how far does output stray from potential?

In which years was U.S. output furthest below potential, and was it ever above?

Define the output gap: gap = 100 × (Y/Y^pot − 1), negative in recessions and positive in booms. Ask students to guess the deepest gap and the year.

Output gaps are small, except in a depression or a war

Data: BEA via FRED (GDPCA, B230RC0A052NBEA); CBO potential GDP (GDPPOT) from 1949, a smooth trend before. Chart inspired by Fig. 1.7 in Jones (2024), Macroeconomics, 6th ed.

How to read it: the gap for every year; the slider picks a year, and the title converts that year’s gap into dollars per person and for a family of four.

Do live: move to 1933, 1944, 1982 and 2009. Before 1949, potential output is a statistical trend rather than a CBO estimate, so read the early years with care.

A gap of a few percent is thousands of dollars per family

• Great Depression, 1933: −17%. World War II boom, 1944: +23%.
• Since 1949 no gap has exceeded about 6%; the deepest was 1982 (−6.2%).
• 2009: −4.7%, about $3,100 per person, or $12,300 for a family of four.

The short run is about fluctuations: why output sometimes falls below potential, and what policy can do about it.

The textbook reports a 2009 gap of about 6 percent (4,000 dollars per person). With today’s revised data it is 4.7 percent: statistical agencies revise their estimates of the past as new information arrives.

The short-run chapters (9 to 15) study why output deviates from potential and how monetary and fiscal policy respond.

Potential output is not the most an economy can produce

Potential output is the maximum output; actual output can only fall short of it.

Potential output is what the economy produces with flexible prices and normal use of resources. Output can exceed it: in 1944 it was 23% above potential, as wartime production pushed people and machines beyond normal use.

A positive gap is a boom, and booms tend to come with rising inflation, a theme of the Phillips-curve chapter. So both signs of the gap are a policy concern, not only recessions.

A minimum wage is a parameter that policy chooses

Should the minimum wage rise? Who gains and who loses?

What our model predicts

• A floor above \(w^*\) raises pay for those who keep their jobs.
• It cuts hours hired and creates unemployment, \((1+\bar{a})\) hours per dollar.
• A floor below \(w^*\) does nothing.

What it leaves out

• Firms with power to set wages, where a floor can raise employment.
• Effects on productivity, prices, turnover and training.
• Evidence: many studies find small job losses from moderate increases; the debate continues.

Use this to practice step 3 (compare the model with the facts) and to show that a simple model is a benchmark, not a verdict.

The competitive model predicts job losses. With monopsony (an employer that sets wages because workers have few alternatives), a moderate floor can raise both pay and employment. Influential empirical work includes Card and Krueger (1994) on fast-food jobs in New Jersey and Pennsylvania, and Cengiz, Dube, Lindner and Zipperer (2019), which finds that the number of low-wage jobs changed little after moderate state minimum-wage increases. Other studies find larger losses, especially for large increases or for young workers. Present the debate as open.

Discussion question for policy students: what would you need to measure to apply the model to your country? (How strongly labor supply and demand respond to the wage, and where the floor sits relative to the market wage.)

Macro policy has no parallel universe, so we use models

How much did the 2009 U.S. fiscal stimulus (about $800 billion) help?

• Unemployment rose by more than forecasters expected. Because the stimulus did not work? Or because the crisis was worse than anyone realized?
• To know, we would need a second United States without the stimulus. We will never observe it.
• A model is our best stand-in for that missing world: it states its assumptions, so others can check them.

Models do not end policy debates, but they make the disagreements precise.

This is the key limitation of step 4. Controlled experiments are the norm in much of science, but macroeconomic policy happens once, in one economy. That is why there is so much honest debate over macroeconomic policy: the counterfactual must be inferred, from models and from comparisons across countries and periods.

Ask: what evidence would convince you that the stimulus worked? (Comparisons across U.S. states with different amounts of spending, comparisons with other countries, model-based estimates of multipliers.) Fiscal policy is studied in the stabilization and government chapters.

“A model with one wage and no schooling is too simple to be useful”

A skeptic says: “Real labor markets have millions of jobs, skills and places. A model with a single wage tells us nothing.”

Reply: the simplification is the point. The model isolates one mechanism, says exactly what drives the result (\(\bar{a}\) and where the floor sits) and what to measure, and can then be tested against the facts and extended. Every model in this course works the same way.

A map at the scale of 1:1 is useless. Abstraction lets us focus on the forces that matter for a question. The test of a model is step 3: does it fit the facts it was built for, quantitatively? When it does not, we add the missing piece (skills, firms with wage-setting power, geography) and check again.

What to remember from this chapter

• Macroeconomics starts from big facts: huge income gaps, deep recessions, waves of inflation, persistent unemployment differences.
• A model turns parameters and exogenous variables into endogenous outcomes; experiments change the inputs.
• In the labor market, \(w^* = (\bar{f}-\bar{\ell})/(1+\bar{a})\); demand shifts move pay and jobs together, supply shifts move them apart; a binding minimum wage creates unemployment.
• Growth compounds: about 1.9% a year doubles income every 37 years (the long run); output gaps measure the short run.

Ask each student to write the one idea they found most surprising, and one question they want the course to answer. This mirrors the chapter’s review questions.

Walk the three branches of the mind map in about a minute each: the facts (cyan), how a model works (lavender), long run, short run and policy (green). Click the image to zoom in.

Big facts, small models: choose the inputs, solve for the outcomes, run the experiments the economy cannot run

Next: Chapter 2, Measuring the Macroeconomy

Chapter 2 explains how GDP, the measure behind most of today’s charts, is defined and measured.

Sources

• Fig. 1, GDP per person. Maddison Project Database 2023, Groningen Growth and Development Centre (Bolt and van Zanden 2024, doi:10.34894/INZBF2); BEA GDP deflator (A191RD3A086NBEA), via FRED. Inspired by Fig. 1.1 in Jones (2024).
• Fig. 2, employment-population ratio. BLS Current Population Survey (EMRATIO), via FRED; NBER business cycle dates. Inspired by Fig. 1.2 in Jones (2024).
• Fig. 3, inflation. World Bank WDI (FP.CPI.TOTL.ZG); BLS CPI (CPIAUCNS), via FRED; World Bank Global Inflation Database (Ha, Kose and Ohnsorge). Inspired by Fig. 1.3 in Jones (2024).
• Fig. 4, unemployment. BLS UNRATE and OECD LRHUTTTTJPA156S, via FRED; European Commission AMECO (ZUTN); ILO modelled estimates (SL.UEM.TOTL.ZS), via the World Bank. Inspired by Fig. 1.4 in Jones (2024).
• Figs. 6 and 8, labor-market model. Adapted from Figs. 1.6 and 1.8 in Jones (2024).
• Fig. 7, U.S. GDP per person and the output gap. BEA via FRED (GDPCA, B230RC0A052NBEA, A191RD3A086NBEA); CBO potential GDP (GDPPOT), via FRED; Maddison Project Database 2023. Inspired by Fig. 1.7 in Jones (2024).
• Minimum-wage evidence. Card, D. and A. Krueger (1994), American Economic Review 84(4); Cengiz, D., A. Dube, A. Lindner and B. Zipperer (2019), Quarterly Journal of Economics 134(3).
• Textbook. Jones, C. I. (2024). Macroeconomics, 6th ed. W. W. Norton.
• Book chapter, with the data, code and every figure. Data Science and Macroeconomic Models, Chapter 1.