||IV estimation -<br>historic|= 1 if estimates reflect IV<br>estimation, using historic or<br>geological instruments||0.177|0.382|
|---|---|---|---|---|---|
|Firm<br>Heterogeneity|Industry control<br>Firm size control|= 1 if estimates include an<br>industry dummy variable<br>= 1 if estimates include a firm<br>size control|study does include<br>industry or firm size<br>controls|0.275<br>0.366|0.446<br>0.482|
|Worker|Local area human<br>capital|= 1 if study includes a local<br>area human capital measure|study does not control for|0.189|0.392|
|Sorting|Individual-level<br>human capital|= 1 if study includes worker-<br>level controls|human capital|0.261|0.439|

Note: Number of observations for each variable is equal to 1,242. We normalize patents by publication year, common practice in the patent literature, by using the residual from a regression of citations on publication year<sup>5</sup> . We reported unweighted means and standard errors. Country-income is captured by a dummy variable reflecting whether the country is non-highincome country, using the mid-point of the study’s data sample.<sup>6</sup>

# Urban cost data

We also provide novel estimates of urban cost elasticities across developing and developed countries. While Ahlfeldt and Pietrostefani (2019) provide a good synthesis of the available literature, they focus on developed countries. We use three measures of city-level urban costs: congestion (hours lost due to traffic), pollution (PM2.5 emissions) and crime (homicide rate).

Congestion information is sourced from TomTom, which provides real time traffic statistics for over 600 million users around the world. Congestion reflects the average annual additional hours spent driving in rush hours in 2018 and is available for 337 cities, of which 69 cities are in developing countries. Pollution, measured using PM2.5 emission levels, is sourced from the World Health Organization and is available from 2008 to 2015. We use pollution data for 2014, the most recent year covering a sample of 298 cities (with 78 in developing countries). We exclude China from the pollution analysis because of potential data reporting concerns highlighted in Greenstone et al. (2020). Crime is measured using the homicide rate obtained from United Nations Office on Drugs and Crime (UNODC) global study on homicide (2019) and is available from 2003 to 2017. We utilize crime data for 2015, the latest year comprising 124 cities (with 63 in developing countries). Summary statistics of these variables are provided in Table 2. Figure 4 shows how pollution, congestion and crime vary across levels of economic development.

City-level population density is calculated as the population per square kilometer of _built up density_ using 2015 data from the Global Health Settlement (GHS). Unfortunately, this data is only available at irregular intervals (1975, 1990, 2000 and 2015), which combined with limited time horizon of our urban cost measures, limits us to a cross-section analysis. We also examine robustness to population density from country census data that are available for approximately half the cities in our baseline sample.

> 5 An alternative approach of demeaning citations using the mean citations of publications in that year is not possible, due to few observations in some publication years.

> 6 The analysis is robust to defining country income using the first or last period of the study sample.

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