the simple average of employment (or labor productivity) in the beginning and ending years. This bounds the resulting growth rate between – 2 and +2, thereby significantly reducing the influence of outliers.<sup>9</sup> We cluster the heteroskedasticity-corrected errors at the city level.

Among the control variables, _FIRM_ is a vector of firm-level controls, including the share of foreign ownership, the ownership share of the largest shareholder, and dummies for both the size and age of the firm.<sup>10</sup> One function of these dummy variables is to help control for the sorting of firms across cities based on their observable characteristics – the possibility that firms with characteristics that are favorable to fast growth may systematically sort into, for example, big cities.<sup>11</sup> _Ekt_ is a vector of the underlying BE at the city level. While firm-level BE indicators are available from WBES, we do not directly use individual answers because they reflect choices made by firms and that are, therefore, endogenous. We instead follow the literature by using the local average across firms of the BE indicators at the city-industry level as a proxy for the local BE (Dollar et al. 2005; Hallward-Dreimeier et al. 2006; Aterido et al., 2011; Xu 2011).<sup>12</sup> In total, there are slightly more than 6,000 unique city-industry cells. This approach of constructing the BE

> 9 Since the dependent variables are bound between -2 and +2, non-linear Tobit estimation of equation (1) may be used. However, it is not necessary when one is mainly interested in the marginal effect. The Tobit model requires “commitment to functional form and distributional assumptions, about which we do not usually feel strongly” (p 197198, Angrist and Pischke, 2009), while the OLS has the virtue of “simplicity, automation, and comparability across studies” (p197, Angrist and Pischke, 2009). We have experimented with estimation using the Tobit model, finding qualitatively similar results to those which are based on OLS.

> 10 We include two firm-size dummies.  The first is for firms which initially employ between 20 and 100 workers, and the second for firms that initially employ more than 100 workers.  We also employ two firm-age dummies – one for firms aged between 6 and 10 years, and the other for firms that are older than 10 years.

> 11 A concern in the urban economics literature is that firms with good unobservable characteristics may sort into big cities.  That is, there is a sorting of, say, more capable firms, or more technologically-advantaged firms, into large cities. To investigate whether this may be the case, we use the affiliation of high-tech industries by firms as a proxy of firm quality or technology, and see if firms located in big cities are more likely to be in high-tech industries. Hightech industries are those in metals and machinery, electronics, chemicals and pharmaceuticals, non-metallic and plastic materials, auto and auto components. The remaining manufacturing firms are engaged in relatively low-tech activities, such as textiles, leather, garments, food, wood and furniture, and other manufacturing. This exercise is only feasible for manufacturing firms, and for this exercise we thus only use the manufacturing firms. The regression results that relate the high-tech dummy with the In Big City dummy while controlling for country dummies show clearly there is no statistically significant correlation between a firm’s tech status and whether or not it is located in a big city; and the magnitude is small. We thus proceed to interpret the In Big City as likely representing the benefits of agglomeration.

> 12 In computing the mean for a firm, the observation for the firm itself is excluded to avoid endogeneity. We allow the BE to vary at the industry level since, for instance, regulatory harassment and entry/exit barriers tend to differ across industries as well as across cities. In terms of the number of firms, the median city-industry cell has eight firms.

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