6 The mode of the distribution is about 50 m (about 14 floors). Since countries are likely to have more buildings below 50 m than above 50 m, and since the distribution of buildings is smooth after 50 m, this suggests that the data set mostly captures buildings above 50 m. Thus, the data set is likely less reliable for buildings below 50 m. Given that the satellite-based data that we use should capture buildings up to 50 m, we believe that we still capture most of the buildings in the world when combining the two datasets. Nonetheless, we can investigate if some regions of the world might have better data. Comparing the six continents (as defined by the United Nations), the mode is around 45-50 m for Africa, 50-55 m for Asia, 40-45 m for Europe, 15 m for North America, 45 m in Oceania and 45 m in South America. Therefore, we are confident that we can use information from the Emporis database to update WSF3Dv1 and create WSF3Dv2.

# **C Methods**

**C1. Classifying countries into 4 groups: heights vs. areas, high-rises vs. low-rises** First of all, we “residualize” the log of the outward based volume per capita (i.e., built-up area x 2.5 meters) with respect to log per capita GDP and its square (using country population as weights). As such, the residuals that we obtain capture to what extent a country uses a lot of area per capita given its income level, i.e., relative to the world’s quadratic fit. In layman’s terms, we obtain for each country the absolute distance from the country’s value to the world’s relation. A positive (negative) value implies more (less) outward volume per capita relative to the world.

Second, we similarly residualize the log of the upward volume per capita (= total volume - outward volume). The resulting residuals capture to what extent a country has a lot of (low-rise + high-rise) heights per capita given its income level. In layman’s terms, we obtain for each country the absolute distance from the country’s value to the world’s relation. A positive (negative) value implies more (less) upward volume per capita relative to the world.

Third, countries with higher area residuals tend to have higher heights residuals (correlation _≈_ 0.7), implying that countries that are “good” in one dimension are also “good” in the other dimension. However, since we are interested in the comparative rather than absolute advantage of each country, we residualize the height residuals with respect to the area residuals (using country populations as weights). In layman’s terms, we obtain for each country the distance from the country’s value to the world’s relation. A positive (negative) value implies more (less) heights per capita (rather than area per capita) relative to the world. The final residuals thus indicate to what extent a country is relatively pro-height or pro-area for a given income level.

We proceed likewise for high-rises vs. low-rises, first residualizing high-rise and low-rise volumes with respect to income, then residualizing the high-rise residuals with respect to the low-rise residuals. The final residuals indicate to what extent a country is relatively pro-highrises rather than relatively pro-low-rises for a given income level.

## **C2. Relation between city economic development and city building stocks**

To study how city building stocks vary with income, we use the 2015 city GDP data of Zhou et al. (2022) who provide “real GDP” data (2017 dollars) based on unbounded night lights data. We use this data in the absence of global city income data. City GDP is total lights within a UC boundary.