a commonly-used proxy for income, Nighttime Lights (NTL) per capita.<sup>5</sup> We process DMSP-OLS data for three years (1992, 2000, and 2012) for the areas matching with the GHS-UCDB urban areas used in the rest of the paper. Column (5) reports the results using Nighttime Lights per capita instead of the previous income measure. Our preferred specification is given by the between and within estimator in Column (2). Estimates are similar when using instrumental variables and Nighttime Lights as a proxy for income. Income and population elasticities can be inferred from the results of the regression.<sup>6</sup> As a city’s population doubles, its built-up surface increases by 34.7% when keeping income constant and controlling for unobserved city and year characteristics. This number is lower than previous estimates reported in the literature (Angel, et al., 2016) and shows that cities have not extended their built-up as much as their population growth would have suggested. As a city’s income doubles, the built-up surface increases by 10.4% when keeping population constant and controlling for unobserved city and year characteristics.

As a final robustness check, we run the same regression as in Table 4 using population, income and builtup from the Functional Urban Area (FUA) database developed by the OECD and the European Commission. The spatial entities represent the commuting areas of the urban centers in 2015, which are geographically larger than our urban extents as they often contain multiple urban centers. Annex 2 presents a similar table as in Table 4. The new coefficients are similar to the ones estimated using our urban centers.

**Table 5 : Determinants of built-up surface across income groups**

|**Dependent variable:**<br>**built-up**|**All**<br>**countries**|**Low income**|**Lower**<br>**middle**<br>**income**|**Upper**<br>**middle**<br>**income**|**High income**|
|---|---|---|---|---|---|
|Ln city Income|0.104***<br>(0.00483)|0.0488*<br>(0.0192)|0.0891***<br>(0.0134)|0.107***<br>(0.00594)|0.101***<br>(0.0187)|
|Ln Population|0.347***<br>(0.0176)|0.343***<br>(0.0474)|0.308***<br>(0.0371)|0.251***<br>(0.0207)|0.281***<br>(0.0261)|
|FE|City + Year|City + Year|City + Year|City + Year|City + Year|
|Controls|No|No|No|No|No|
|R-squared|0.583|0.597|0.612|0.611|0.549|
|N. of observations|28341|2139|10959|11124|4119|
|Standard errors in parentheses * p < 0.05,|** p < 0.01, **|* p < 0.001||||

> 5 While Defense Meteorological Satellite Program/Operational Linescan System (DMSP-OLS) was originally designed for military use, DMSP-OLS has been widely used in social science because of its low-light imaging capabilities to detect nighttime artificial lighting (Elvidge et al., 1997). Henderson et al. (2012) empirically prove the concept of using an annual composite of DMSP-OLS as a proxy measurement of economic growth across the national and/or local level. Many studies extensively use nighttime lights to estimate the national and sub-national GDP. These estimates are critically useful for developing countries and/or conflict-affected areas where official statistics are unavailable or limited.

> 6 Other possible specifications were considered but not reported in this note. A dynamic panel specification was considered but 3 years of data was not sufficient to report strong results for a dynamic panel.

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