29 _more_ educated urban residents. However, the difference is not significant.<sup>38</sup> Angrist et al. (2019) provide a measure of education quality that is built on globally harmonized test scores, obtained for all areas of a country, for 101 countries (c. 2020).<sup>39</sup> If we use it as a proxy for urban education (human capital) quality, we find significantly lower values in NRXGDP countries ((2)). However, if we use the learning adjusted years of education,<sup>40</sup> we again find no significant differences at the country level ((3)).

Another imperfect proxy for education quality is a country’s return to education. Abstracting from human capital demand factors, a lower education quality should reduce the wage gains from higher educational attainment. We use the I2D2 data (1990-2017) and follow the methodology of Lagakos et al. (2018) and Jedwab et al. (2020a) to estimate the returns to education for urban areas, as well as the returns to experience for urban areas.<sup>41</sup> More precisely, for individual _i_ and country-year-sample _t_ , we use OLS to estimate the following model for _each_ country one by one (for 18-67 year-old “urban” workers only): The dependent variable is the log of monthly earnings ( _lnWit_ ). As in Lagakos et al. (2018), experience is categorized into seven bins ( _expite_ ): [5-9 years] (which we call 5), [10-14] (10), [15-19] (15), [20-24] (20), [25-29] (25), [30-34] (30), and [35+] (35). The omitted bin is [0-4] (0). We include the number of years of education ( _eduit_ ) and country-year-sample fixed effects ( _θt_ ).<sup>42</sup> Finally, we omit samples without at least 10 observations in each bin.

We use _γ_ as a measure of the returns to education. To estimate the returns to work experience we follow Jedwab et al. (2020a). For each bin one by one (5, 10, etc.), we

> 38Likewise, if we use city-level data as we did in Section 1., we only find very weak correlations between the PCC residuals estimated using tradable employment and the residuals based on the number of years of education. These results are not shown but are available upon request. Thus, urban tradable employment and urban human capital seem _unrelated_ for a given city size and the level of urban economic development.

> 39From the notes of the Excel database containing the data, we learn that the “database harmonizes scores across major international student achievement testing programs measured in TIMMS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment.” Url for the database: https: //www.worldbank.org/en/publication/human-capital$ _\_ sharp$Data, last accessed 03-29-2022.

> 40From the notes of the Excel database containing the data (same Url as above), we learn that the “Learning-Adjusted Years of School are calculated by multiplying the [...] years of school by the ratio of most recent harmonized test scores to 625, where 625 corresponds to advanced attainment [...].”

> 41We calculate _potential work experience_ – which we call “experience” in the rest of the analysis – as follows: (i) For individuals with at least 12 years of education, we assume children start school at age 6 and calculate experience as age - years of education - 6; (ii) For individuals with less than 12 years of education, we assume that experience before age 18 is inconsequential and calculate experience as age - 18.

42Most countries having several samples, we use individual weights divided by the size of the sample.