🗓️ Session 3: Working with data — stylized facts & the theory-ladenness of observation
Abstract
This session introduces working with data in ecological economics: how to construct stylized facts and why observation is always theory-laden. In the accompanying group work, you find data and present preliminary results on the situation of your selected country or region.
🖥️ Lecture Slides
📽️ Open the slides in a separate tab
Open the slides, click the ☰ button in the upper right corner, and choose Tools → PDF Export Mode. Then print the page (Cmd/Ctrl + P) and save it as a PDF.
📊 Data sources for the figures
Every data figure in the lecture is built from openly available data, and the table below says exactly where each one comes from. Use it to check a claim from the lecture — and, more importantly, to pull the same indicators for your own country analysis.
On an Our World in Data chart page, the Download tab gives you the underlying data as a CSV for all countries and years, not just the ones shown. That is usually the fastest route to a comparable number for your country.
| Figure in the lecture | Where the data come from |
|---|---|
| Income grows, but in a very unequal fashion | Maddison Project Database (via Our World in Data) |
| Income poverty remains a global problem | World Bank Poverty and Inequality Platform (PIP) |
| There has been progress with respect to income poverty | Moatsos, “cost of basic needs” poverty (via Our World in Data) · World Bank PIP, as above |
| Reducing income poverty due to growth of GDP?! | The same two sources as the previous two rows, joined |
| GDP correlates with many development indicators | Years of schooling · Child mortality · Adult literacy · Medical doctors |
| The extraction of raw materials keeps increasing | UN IRP Global Material Flows Database (background). A scriptable but narrower alternative is the UN SDG Indicators Database, series EN_MAT_DOMCMPT |
| GDP correlates with energy use and with emissions | Energy use per capita · CO2 emissions |
| Some countries achieved decoupling of GDP from emissions | Consumption-based CO2 and GDP per capita |
| Composite indicators also correlate with GDP | Human Development Index · Augmented HDI (Prados de la Escosura) · Maddison Project Database, as above |
| No country manages both at once | Fanning, O’Neill, Hickel & Roux (2022), national trends data |
Every figure above is built by two R scripts, from the data linked in the table. They live in the analysis/ folder of the course repository, together with a README explaining how to run them. You are welcome to change a colour, a year range or a country selection — or to lift the approach for your own poster.
🍩 The country doughnuts
The two doughnut diagrams in the lecture come from the University of Leeds project A Good Life For All Within Planetary Boundaries. Its National Snapshots and Country Trends pages will draw the same diagram for your country, and the underlying figures can be downloaded from the same download page as the scatter plot above.
🧭 The Atlas of Economic Complexity
The Atlas of Economic Complexity (Harvard Growth Lab) is not behind any figure in this lecture, but it is the obvious next stop for a country analysis. It holds 50+ years of trade flows for 228 countries: what a country exports and imports, to and from whom, how that basket has shifted over time, and where it sits in the complexity rankings.
- Country profiles — start here for your country
- Rankings — economic complexity, and growth projections
- Data downloads — the underlying data, if you want to compute something yourself
A caveat worth carrying into the poster: economic complexity says something about the productive structure of an economy, not about whether that structure is ecologically sustainable or socially just. A high-complexity export basket and a transgressed material footprint are entirely compatible — which is itself a result worth pointing at.
Write down every judgement call. Which year, which indicator, which source, which of several competing series. That list is where the theory-ladenness of your own analysis becomes visible — and it belongs on your poster.
Check what a number actually measures before you compare it. Poverty lines change (the World Bank moved from 2011 to 2021 purchasing power parities, so the lines are now $3.00 and $4.20 rather than $1.90 and $3.20), and material extraction is not material consumption. Two defensible sources can tell two different stories about the same country.
📚 Follow-up Reading
- Gräbner-Radkowitsch (2026b) — The Theory-Ladenness of Observation, in the EUF Method and Inquiry hub.
- Gräbner-Radkowitsch (2026a) — Accessing Development Data in R. Only relevant if you work with R; you can safely skip it otherwise.
🏆 Further readings
On how a measurement choice makes the result:
- Reddy and Pogge (2010) — the classic critique of the dollar-a-day poverty line.
- Jerven (2013) — how development statistics are actually produced, and what that means for anyone using them.
On the doughnut, and on its critics:
- Fanning et al. (2022) — the study behind the scatter plot in the lecture.
- Fanning and Raworth (2025) — the 2025 successor, with 35 indicators up to 2022, at the global level.
- Gräbner-Radkowitsch and Petersen (2026) — an introduction to doughnut economics (in German).
- Winter (2026) — a critical reading of the doughnut diagram itself.
On objectivity:
- Harding (2015) — the source of the two passages on strong objectivity quoted in the lecture.