🗓️ Session 5: Free trade — blessing or curse?

Author

Affiliation

Claudius Gräbner-Radkowitsch

EUF and JKU

Published

21 10 2026

Modified

21 10 2026

Abstract

This session examines trade: the classical case for free trade, from Smith and Ricardo, and the challenges to it — unequal terms of trade, technological lock-in, and the externalization of ecological costs across countries. The environmental conflicts this produces on the ground were planned for the second half, but moved to the next session for lack of time (see Environmental conflicts, Session 6).

🖥️ Lecture Slides

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Link to the Tweedback Session

The computations behind the trade examples (slides 8–12)

1. Adam Smith: absolute advantage (slides 8–9)

The setup. Germany 🇩🇪 and Japan 🇯🇵 each have 100 units of labour (L). If a country uses all of its labour on one good, it can produce:

	L	🥨 (all labour)	🍱 (all labour)
🇩🇪	100	20	10
🇯🇵	100	16	12

From this follows how much labour one unit of each good costs:

🇩🇪: one 🥨 costs $100/20 = 5$ L, one 🍱 costs $100/10 = 10$ L.
🇯🇵: one 🥨 costs $100/16 = 6.25$ L, one 🍱 costs $100/12 \approx 8.33$ L.

So 🇩🇪 is absolutely better at pretzels (5 L < 6.25 L) and 🇯🇵 is absolutely better at bento (8.33 L < 10 L). Each country has an absolute advantage in one good.

Relative value. The last column of the slide’s left table shows what one pretzel is worth within each country, measured in bento. In 🇩🇪, the labour for one 🥨 (5 L) could instead produce half a 🍱 ($5/10 = 0.5$). In 🇯🇵, the labour for one 🥨 (6.25 L) could produce $6.25/8.33 = 0.75$ 🍱. Equivalently, divide the two production possibilities: $10/20 = 0.5$ and $12/16 = 0.75$.

No trade. Without trade, each country must produce both goods itself. The slide assumes that each country splits its labour evenly, 50 L per good:

🇩🇪: $50/5 = 10$ 🥨 and $50/10 = 5$ 🍱
🇯🇵: $50/6.25 = 8$ 🥨 and $50/8.33 = 6$ 🍱
World (🇺🇳): $10 + 8 = 18$ 🥨 and $5 + 6 = 11$ 🍱

Trade. Now each country puts all its labour into the good in which it has the absolute advantage: 🇩🇪 makes 20 🥨 and no 🍱; 🇯🇵 makes 12 🍱 and no 🥨. World output rises to 20 🥨 and 12 🍱, which is 2 🥨 and 1 🍱 more than before. Not a single extra hour was worked: the gain comes entirely from the division of labour.

	No trade 🥨	No trade 🍱	Trade 🥨	Trade 🍱	Change 🥨	Change 🍱
🇩🇪	10	5	20	0	+10	−5
🇯🇵	8	6	0	12	−8	+6
🇺🇳	18	11	20	12	+2	+1

The corridor in which both win (slide 9). Specialisation alone leaves 🇩🇪 without bento and 🇯🇵 without pretzels, so the two have to exchange. Suppose 🇩🇪 sends $x$ 🥨 to 🇯🇵 and receives $y$ 🍱 in return. Each country should end up with more of both goods than it had without trade:

🇩🇪 keeps $20 - x$ 🥨, which must exceed 10, so $x < 10$; and it receives $y$ 🍱, which must exceed 5, so $y > 5$.
🇯🇵 receives $x$ 🥨, which must exceed 8, so $x > 8$; and it keeps $12 - y$ 🍱, which must exceed 6, so $y < 6$.

Hence $8 < x < 10$ and $5 < y < 6$. This is the slide’s corridor: 🇩🇪 trades roughly 8.1–9.9 🥨 for 5.1–5.9 🍱. Within it, the $+2$ 🥨 and $+1$ 🍱 of extra world output are shared between the two countries, and how they are shared depends on where in the corridor the exchange lands.

Note how the corridor lines up with the relative values: the implied exchange rate $y/x$ lies between $5/10 = 0.5$ and $6/8 = 0.75$ bento per pretzel. That is, the price at which the two countries trade must lie between their two domestic relative values. Below 0.5, 🇩🇪 would be better off making bento itself; above 0.75, the same holds for 🇯🇵 and pretzels.

2. David Ricardo: comparative advantage (slides 10–12)

The setup. This is Ricardo’s original example. The table now shows labour requirements, i.e. how many hours it takes to produce one unit:

	🍷 (wine)	🧥 (cloth)
🇵🇹	80	90
🏴󠁧󠁢󠁥󠁮󠁧󠁿	120	100

Portugal needs fewer hours for both goods, so it has an absolute advantage in both. On Smith’s logic there would be no reason to trade. Ricardo shows that there is one anyway.

Who is relatively better at what (slide 11). Compare Portugal’s labour requirements with England’s, good by good:

🍷: $80/120 \approx 0.67$, so Portugal needs only 67% of England’s labour.
🧥: $90/100 = 0.9$, so Portugal needs 90% of England’s labour.

Portugal’s lead is much larger in wine, so its comparative advantage lies in 🍷, and England’s (its smallest disadvantage) in 🧥.

Opportunity costs. The same result follows from asking what each country gives up. The 90 hours Portugal spends on one 🧥 could instead produce $90/80 = 1.125$ 🍷. In England, the 100 hours for one 🧥 could instead produce $100/120 \approx 0.833$ 🍷. So cloth is relatively cheaper in England (it costs 0.833 🍷 there versus 1.125 🍷 in Portugal), and by the same token wine is relatively cheaper in Portugal (it costs $80/90 \approx 0.89$ 🧥 there versus $120/100 = 1.2$ 🧥 in England). Note that what matters is not how many hours a good takes in absolute terms, but how much of the other good one must forgo to produce it.

No trade (slide 12). Suppose each country produces one unit of each good:

🏴󠁧󠁢󠁥󠁮󠁧󠁿: $120 + 100 = 220$ hours for 1 🍷 and 1 🧥
🇵🇹: $80 + 90 = 170$ hours for 1 🍷 and 1 🧥
World: 2 🍷 and 2 🧥

Specialisation. Now each country uses the same hours only for the good in which it has the comparative advantage:

🇵🇹: $170/80 = 2.125$ 🍷 and no 🧥
🏴󠁧󠁢󠁥󠁮󠁧󠁿: $220/100 = 2.2$ 🧥 and no 🍷
World: 2.125 🍷 and 2.2 🧥, which is 0.125 🍷 and 0.2 🧥 more with the same total labour of 390 hours.

The terms of trade. As in Smith’s case, the two countries must exchange. Suppose England sends 1 🧥 to Portugal and receives $w$ 🍷 in return:

🏴󠁧󠁢󠁥󠁮󠁧󠁿 ends up with $2.2 - 1 = 1.2$ 🧥 and $w$ 🍷. It is better off than before if $w \geq 1$.
🇵🇹 ends up with 1 🧥 and $2.125 - w$ 🍷. It is better off than before if $2.125 - w > 1$, so $w < 1.125$.

Any exchange of 1 🧥 for between 1 and 1.125 🍷 thus leaves both countries with more than they had without trade. More generally, the price of cloth must lie between the two opportunity costs from above: above England’s 0.833 🍷 (otherwise England would rather make its own wine) and below Portugal’s 1.125 🍷 (otherwise Portugal would rather make its own cloth).

The take-away. Trade can be mutually beneficial even when one country is more productive in everything. What drives the gains are differences in opportunity costs, not in absolute productivity. This simple narrative remains at the core of many trade debates and analyses, which is why it is worth looking closely at the assumptions built into it: full employment, labour as the only input, no transport costs, and no ecological costs of production or shipping.

📊 Data sources for the figures

Every data figure in the lecture is built from openly available data. The table below says where each one comes from, so you can check a claim from the lecture and, more importantly, pull the same data for your own country.

Figure in the lecture	Where the data come from
South Korea: GDP per capita compared with other regions	Maddison Project Database 2023 (Bolt and van Zanden 2025), via Our World in Data
South Korea: exports by product group, 1962–2024	Atlas of Economic Complexity (The Growth Lab at Harvard University 2026b), exports by SITC section
Solar PV module prices vs. cumulative installed capacity	Our World in Data (Our World in Data 2026), combining Nemet (2009), Farmer and Lafond (2016) and IRENA
Solar PV module production by country, 2024	IEA-PVPS (2025), as reported by pv magazine. Entered by hand, as there is no download
The terms-of-trade problem	The Grilli-Yang commodity price index, 1900–2016 (Pfaffenzeller et al. 2007), in the version of Geronimi et al. (2018), extended with the World Bank Pink Sheet (World Bank 2026)
The country–product matrix (who exports what)	Atlas of Economic Complexity, all countries × 4-digit products (HS 1992), 2024
The product space	Layout and links from the Growth Lab’s product space network (The Growth Lab at Harvard University 2026a); node sizes from Atlas trade data
The product space for three country groups	As above, combined with the Atlas data for every country

🧭 Economic complexity for your country

The figures on technologically unequal exchange all come from the Atlas of Economic Complexity of the Harvard Growth Lab. You do not need any code to use it for your poster:

Country profiles: start here. For your country they show what it exports, how the export basket has changed since the 1960s, its rank in the Economic Complexity Index, and which products lie “nearby” in the product space.
Explore: treemaps, trade partners, the product space with your country’s exports highlighted, and time series, all downloadable.
Rankings: economic complexity and the Growth Lab’s growth projections.
Data downloads: the full underlying data, if you want to compute something yourself.

Three concepts from the lecture that you will meet there:

Revealed comparative advantage (RCA). A country has RCA ≥ 1 in a product if that product makes up a larger share of the country’s exports than it does of world trade. The country–product matrix in the lecture marks exactly these cells.
Diversity and ubiquity. Diversity is the number of products a country exports with RCA ≥ 1; ubiquity is the number of countries that export a given product with RCA ≥ 1. The Economic Complexity Index combines the two (Hidalgo 2021).
The product space links two products when countries tend to export both with RCA ≥ 1. Your country’s position in it shows which new products are within reach of the capabilities it already has (Hidalgo et al. 2007).

A caveat to carry into the poster: economic complexity describes the productive structure of an economy. It tells you nothing about whether that structure is ecologically sustainable or socially just. Look at what your country exports and at what this extraction or production costs on the ground, which is where Session 6 picks up.

Want to rebuild the figures yourself?

Every figure above is built by two R scripts, trade_01_download.R (fetches the data) and trade_02_figures.R (draws the figures). They live in the analysis/ folder of the course repository, together with a README that explains how to run them and how to fetch Atlas data for your own country with a few lines of R. You are welcome to change a country, a year or a colour, or to lift the approach for your own poster.

A suggestion for your country analysis

Look at data on your country’s trade and trade openness.
Complement this with data on its export composition, its economic complexity, and its position in the product space.
What is your country specialised in? How has this changed over time?
What are the prospects for the future: which products lie within reach, and how does this relate to the key challenge you have identified?
How much does the current policy mix take into account capability constraints?

📖 Up-front Reading

Please read before the session:

Chapters 18 and 19 in Daly and Farley (2011).

📚 Follow-up Reading

Kolcava et al. (2019)

🏆 Further readings

Dosi et al. (2015)
Hidalgo (2021)
Hidalgo (2023)

References

Bolt, J. and van Zanden, J. L. (2025) “Maddison-style estimates of the evolution of the world economy: A new 2023 update,” Journal of Economic Surveys 39(2): 631–671.

Daly, H. E. and Farley, J. C. (2011) Ecological economics: Principles and applications, 2nd Ed., Washington, DC: Island Press.

Dosi, G., Grazzi, M. and Moschella, D. (2015) “Technology and costs in international competitiveness: From countries and sectors to firms,” Research Policy 44(10): 1795–1814.

Farmer, J. D. and Lafond, F. (2016) “How predictable is technological progress?” Research Policy 45(3): 647–665.

Geronimi, V., Anani, E. and Taranco, A. (2018) Data for: Revisiting the Prebisch-Singer hypothesis of a secular decline in the terms of trade of primary commodities (1900–2016). A dynamic regime approach, doi:10.17632/j24mjpcrrz.1.

Hidalgo, C. A. (2021) “Economic complexity theory and applications,” Nature Reviews Physics 3(2): 92–113.

Hidalgo, C. A. (2023) “The policy implications of economic complexity,” Research Policy 52(9): 104863.

Hidalgo, C. A., Klinger, B., Barabási, A.-L. and Hausmann, R. (2007) “The product space conditions the development of nations,” Science 317(5837): 482–487.

IEA-PVPS (2025) Trends in photovoltaic applications 2025, International Energy Agency, Photovoltaic Power Systems Programme.

Kolcava, D., Nguyen, Q. and Bernauer, T. (2019) “Does trade liberalization lead to environmental burden shifting in the global economy?” Ecological Economics 163: 98–112.

Nemet, G. F. (2009) “Interim monitoring of cost dynamics for publicly supported energy technologies,” Energy Policy 37(3): 825–835.

Our World in Data (2026) Solar (PV) panel prices vs. Cumulative capacity, available at https://ourworldindata.org/grapher/solar-pv-prices-vs-cumulative-capacity.

Pfaffenzeller, S., Newbold, P. and Rayner, A. (2007) “A short note on updating the Grilli and Yang commodity price index,” World Bank Economic Review 21(1): 151–163.

The Growth Lab at Harvard University (2026a) Product space networks, doi:10.7910/DVN/FCDZBN.

The Growth Lab at Harvard University (2026b) The Atlas of Economic Complexity, available at https://atlas.hks.harvard.edu.

World Bank (2026) Commodity price data (The Pink Sheet), annual indices, real, available at https://www.worldbank.org/en/research/commodity-markets.

--- title: "🗓️ Session 5: Free trade — blessing or curse?" date: "2026-10-21" date-modified: last-modified # The page was s_05a_Trade.html while session 5 had two parts; keep old links # working. aliases: - s_05a_Trade.html --- ## Abstract This session examines **trade**: the classical case for free trade, from Smith and Ricardo, and the challenges to it — unequal terms of trade, technological lock-in, and the **externalization** of ecological costs across countries. The **environmental conflicts** this produces on the ground were planned for the second half, but moved to the next session for lack of time (see [Environmental conflicts](s_06a_Conflicts.html), Session 6). ## 🖥️ Lecture Slides  ```{=html} <div class="container" style="position: relative;overflow: hidden;width: 100%;padding-top: 56.25%;"> <iframe src="slides/EcolEcon26_L05_Trade.html" style="position: absolute;top: 0;left: 0;bottom: 0;right: 0;width: 100%;height: 100%;" allowfullscreen loading="lazy"></iframe> </div> ``` [📽️ Open the slides in a separate tab](slides/EcolEcon26_L05_Trade.html){.btn .btn-primary target="_blank"} ::: {.callout-tip} ## Would you rather have a PDF? Open the slides, click the ☰ button in the upper right corner, and choose **Tools → PDF Export Mode**. Then print the page (<kbd>Cmd</kbd>/<kbd>Ctrl</kbd> + <kbd>P</kbd>) and save it as a PDF. :::  - Link to the [Tweedback Session](https://tweedback.de/2naq) ::: {.callout-note collapse="true"} ## The computations behind the trade examples (slides 8–12) ### 1. Adam Smith: absolute advantage (slides 8–9) **The setup.** Germany 🇩🇪 and Japan 🇯🇵 each have **100 units of labour (L)**. If a country uses *all* of its labour on one good, it can produce: | | L | 🥨 (all labour) | 🍱 (all labour) | |----|-----|-----------------|-----------------| | 🇩🇪 | 100 | 20 | 10 | | 🇯🇵 | 100 | 16 | 12 | From this follows how much labour one unit of each good costs: - 🇩🇪: one 🥨 costs $100/20 = 5$ L, one 🍱 costs $100/10 = 10$ L. - 🇯🇵: one 🥨 costs $100/16 = 6.25$ L, one 🍱 costs $100/12 \approx 8.33$ L. So 🇩🇪 is absolutely better at pretzels (5 L < 6.25 L) and 🇯🇵 is absolutely better at bento (8.33 L < 10 L). Each country has an **absolute advantage** in one good. **Relative value.** The last column of the slide's left table shows what one pretzel is worth *within* each country, measured in bento. In 🇩🇪, the labour for one 🥨 (5 L) could instead produce half a 🍱 ($5/10 = 0.5$). In 🇯🇵, the labour for one 🥨 (6.25 L) could produce $6.25/8.33 = 0.75$ 🍱. Equivalently, divide the two production possibilities: $10/20 = 0.5$ and $12/16 = 0.75$. **No trade.** Without trade, each country must produce both goods itself. The slide assumes that each country splits its labour evenly, 50 L per good: - 🇩🇪: $50/5 = 10$ 🥨 and $50/10 = 5$ 🍱 - 🇯🇵: $50/6.25 = 8$ 🥨 and $50/8.33 = 6$ 🍱 - World (🇺🇳): $10 + 8 = 18$ 🥨 and $5 + 6 = 11$ 🍱 **Trade.** Now each country puts all its labour into the good in which it has the absolute advantage: 🇩🇪 makes 20 🥨 and no 🍱; 🇯🇵 makes 12 🍱 and no 🥨. World output rises to 20 🥨 and 12 🍱, which is **2 🥨 and 1 🍱 more** than before. Not a single extra hour was worked: the gain comes entirely from the division of labour. | | No trade 🥨 | No trade 🍱 | Trade 🥨 | Trade 🍱 | Change 🥨 | Change 🍱 | |----|-----|-----|-----|-----|-----|-----| | 🇩🇪 | 10 | 5 | 20 | 0 | +10 | −5 | | 🇯🇵 | 8 | 6 | 0 | 12 | −8 | +6 | | 🇺🇳 | 18 | 11 | 20 | 12 | +2 | +1 | **The corridor in which both win (slide 9).** Specialisation alone leaves 🇩🇪 without bento and 🇯🇵 without pretzels, so the two have to exchange. Suppose 🇩🇪 sends $x$ 🥨 to 🇯🇵 and receives $y$ 🍱 in return. Each country should end up with more of both goods than it had without trade: - 🇩🇪 keeps $20 - x$ 🥨, which must exceed 10, so $x < 10$; and it receives $y$ 🍱, which must exceed 5, so $y > 5$. - 🇯🇵 receives $x$ 🥨, which must exceed 8, so $x > 8$; and it keeps $12 - y$ 🍱, which must exceed 6, so $y < 6$. Hence $8 < x < 10$ and $5 < y < 6$. This is the slide's corridor: 🇩🇪 trades roughly 8.1–9.9 🥨 for 5.1–5.9 🍱. Within it, the $+2$ 🥨 and $+1$ 🍱 of extra world output are shared between the two countries, and *how* they are shared depends on where in the corridor the exchange lands. Note how the corridor lines up with the relative values: the implied exchange rate $y/x$ lies between $5/10 = 0.5$ and $6/8 = 0.75$ bento per pretzel. That is, the price at which the two countries trade must lie between their two domestic relative values. Below 0.5, 🇩🇪 would be better off making bento itself; above 0.75, the same holds for 🇯🇵 and pretzels. ### 2. David Ricardo: comparative advantage (slides 10–12) **The setup.** This is Ricardo's original example. The table now shows **labour requirements**, i.e. how many hours it takes to produce *one* unit: | | 🍷 (wine) | 🧥 (cloth) | |--------|-----------|------------| | 🇵🇹 | 80 | 90 | | 🏴󠁧󠁢󠁥󠁮󠁧󠁿 | 120 | 100 | Portugal needs fewer hours for *both* goods, so it has an absolute advantage in both. On Smith's logic there would be no reason to trade. Ricardo shows that there is one anyway. **Who is *relatively* better at what (slide 11).** Compare Portugal's labour requirements with England's, good by good: - 🍷: $80/120 \approx 0.67$, so Portugal needs only 67% of England's labour. - 🧥: $90/100 = 0.9$, so Portugal needs 90% of England's labour. Portugal's lead is much larger in wine, so its comparative advantage lies in 🍷, and England's (its *smallest disadvantage*) in 🧥. **Opportunity costs.** The same result follows from asking what each country gives up. The 90 hours Portugal spends on one 🧥 could instead produce $90/80 = 1.125$ 🍷. In England, the 100 hours for one 🧥 could instead produce $100/120 \approx 0.833$ 🍷. So cloth is relatively cheaper in England (it costs 0.833 🍷 there versus 1.125 🍷 in Portugal), and by the same token wine is relatively cheaper in Portugal (it costs $80/90 \approx 0.89$ 🧥 there versus $120/100 = 1.2$ 🧥 in England). Note that what matters is *not* how many hours a good takes in absolute terms, but how much of the other good one must forgo to produce it. **No trade (slide 12).** Suppose each country produces one unit of each good: - 🏴󠁧󠁢󠁥󠁮󠁧󠁿: $120 + 100 = 220$ hours for 1 🍷 and 1 🧥 - 🇵🇹: $80 + 90 = 170$ hours for 1 🍷 and 1 🧥 - World: 2 🍷 and 2 🧥 **Specialisation.** Now each country uses the same hours only for the good in which it has the comparative advantage: - 🇵🇹: $170/80 = 2.125$ 🍷 and no 🧥 - 🏴󠁧󠁢󠁥󠁮󠁧󠁿: $220/100 = 2.2$ 🧥 and no 🍷 - World: 2.125 🍷 and 2.2 🧥, which is **0.125 🍷 and 0.2 🧥 more** with the same total labour of 390 hours. **The terms of trade.** As in Smith's case, the two countries must exchange. Suppose England sends 1 🧥 to Portugal and receives $w$ 🍷 in return: - 🏴󠁧󠁢󠁥󠁮󠁧󠁿 ends up with $2.2 - 1 = 1.2$ 🧥 and $w$ 🍷. It is better off than before if $w \geq 1$. - 🇵🇹 ends up with 1 🧥 and $2.125 - w$ 🍷. It is better off than before if $2.125 - w > 1$, so $w < 1.125$. Any exchange of 1 🧥 for between 1 and 1.125 🍷 thus leaves both countries with more than they had without trade. More generally, the price of cloth must lie between the two opportunity costs from above: above England's 0.833 🍷 (otherwise England would rather make its own wine) and below Portugal's 1.125 🍷 (otherwise Portugal would rather make its own cloth). **The take-away.** Trade can be mutually beneficial even when one country is more productive in everything. What drives the gains are differences in *opportunity costs*, not in absolute productivity. This simple narrative remains at the core of many trade debates and analyses, which is why it is worth looking closely at the assumptions built into it: full employment, labour as the only input, no transport costs, and no ecological costs of production or shipping. ::: ## 📊 Data sources for the figures Every data figure in the lecture is built from **openly available data**. The table below says where each one comes from, so you can check a claim from the lecture and, more importantly, pull the same data for **your own country**. | Figure in the lecture | Where the data come from | |---|---| | South Korea: GDP per capita compared with other regions | [Maddison Project Database 2023](https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database) [@bolt2025], via Our World in Data | | South Korea: exports by product group, 1962–2024 | [Atlas of Economic Complexity](https://atlas.hks.harvard.edu/) [@growthlabatlas], exports by SITC section | | Solar PV module prices vs. cumulative installed capacity | [Our World in Data](https://ourworldindata.org/grapher/solar-pv-prices-vs-cumulative-capacity) [@owidsolar], combining @nemet2009, @farmer2016 and IRENA | | Solar PV module production by country, 2024 | @ieapvps2025, as reported by [pv magazine](https://www.pv-magazine.com/2025/10/17/thirty-five-countries-now-operate-gw-scale-annual-pv-markets/). Entered by hand, as there is no download | | The terms-of-trade problem | The Grilli-Yang commodity price index, 1900–2016 [@pfaffenzeller2007], in the [version of @geronimi2018](https://doi.org/10.17632/j24mjpcrrz.1), extended with the [World Bank Pink Sheet](https://www.worldbank.org/en/research/commodity-markets) [@worldbank2026pinksheet] | | The country–product matrix (who exports what) | [Atlas of Economic Complexity](https://atlas.hks.harvard.edu/), all countries × 4-digit products (HS 1992), 2024 | | The product space | Layout and links from the [Growth Lab's product space network](https://doi.org/10.7910/DVN/FCDZBN) [@growthlab2026productspace]; node sizes from Atlas trade data | | The product space for three country groups | As above, combined with the Atlas data for every country | ### 🧭 Economic complexity for your country The figures on technologically unequal exchange all come from the [Atlas of Economic Complexity](https://atlas.hks.harvard.edu/) of the Harvard Growth Lab. You do not need any code to use it for your poster: - [**Country profiles**](https://atlas.hks.harvard.edu/countries): start here. For your country they show what it exports, how the export basket has changed since the 1960s, its rank in the Economic Complexity Index, and which products lie "nearby" in the product space. - [**Explore**](https://atlas.hks.harvard.edu/explore): treemaps, trade partners, the product space with your country's exports highlighted, and time series, all downloadable. - [**Rankings**](https://atlas.hks.harvard.edu/rankings): economic complexity and the Growth Lab's growth projections. - [**Data downloads**](https://atlas.hks.harvard.edu/data-downloads): the full underlying data, if you want to compute something yourself. Three concepts from the lecture that you will meet there: - **Revealed comparative advantage (RCA).** A country has RCA ≥ 1 in a product if that product makes up a larger share of the country's exports than it does of world trade. The country–product matrix in the lecture marks exactly these cells. - **Diversity and ubiquity.** Diversity is the number of products a country exports with RCA ≥ 1; ubiquity is the number of countries that export a given product with RCA ≥ 1. The Economic Complexity Index combines the two [@hidalgo2021]. - **The product space** links two products when countries tend to export both with RCA ≥ 1. Your country's position in it shows which new products are within reach of the capabilities it already has [@hidalgoetalProductSpace2007]. A caveat to carry into the poster: economic complexity describes the **productive structure** of an economy. It tells you nothing about whether that structure is ecologically sustainable or socially just. Look at what your country exports *and* at what this extraction or production costs on the ground, which is where [Session 6](s_06a_Conflicts.html) picks up. ::: {.callout-note} ## Want to rebuild the figures yourself? Every figure above is built by two R scripts, `trade_01_download.R` (fetches the data) and `trade_02_figures.R` (draws the figures). They live in the [`analysis/` folder of the course repository](https://github.com/graebnerc/EcolEcon26/tree/main/analysis), together with a [README](https://github.com/graebnerc/EcolEcon26/blob/main/analysis/README.md#session-5-free-trade) that explains how to run them and how to fetch Atlas data for **your own country** with a few lines of R. You are welcome to change a country, a year or a colour, or to lift the approach for your own poster. ::: ::: {.callout-tip} ## A suggestion for your country analysis - Look at data on your country's trade and **trade openness**. - Complement this with data on its **export composition**, its **economic complexity**, and its position in the **product space**. - What is your country specialised in? How has this changed over time? - What are the prospects for the future: which products lie within reach, and how does this relate to the key challenge you have identified? - How much does the current policy mix take into account capability constraints? ::: ## 📖 Up-front Reading Please read **before** the session: - Chapters 18 and 19 in @daly2011. ## 📚 Follow-up Reading - @kolcava2019 ### 🏆 Further readings - @dosiGrazziMoschella2015 - @hidalgo2021 - @hidalgo2023 ## References ::: {#refs} :::