China: the employment figures measure the number of “staff and workers”, also referred to as “formal employment”, rather than total employment. This leads to underreporting of employment, especially in rural areas, as it excludes, amongst others, workers employed in township and village enterprises. In as far as the share of “staff and workers” in total employment is not orthogonal to size, this will introduce a bias in our results.<sup>6</sup> Subject to this caveat, Figures 7 and 8 show that China looks very different from India. Once a threshold of around 150 employees per square kilometer is reached,<sup>7</sup> agglomeration economies start dominating in India, whereas the opposite happens in China. For Chinese locations with a density above 150 employees per square kilometer, service employment growth becomes strongly decreasing with size, indicating important congestion costs.<sup>8</sup> Along that dimension, China looks more like the U.S., where congestion costs also dominate for locations above the 150 employees per square kilometer threshold. Given that the overall level of infrastructure is better in China than in India, this finding is consistent with the interpretation of frictions holding back the growth of medium-density locations in India, but not in China.

Although in terms of the tradeoff between agglomeration economies and congestion costs in highdensity places China and the U.S. look similar (and different from India), there is another dimension along which the U.S. looks different from both China and India. As can be seen from Figure 7 and Figure 8, the difference in growth rates between fast-growing places and slow-growing places in India and China is much larger than in the U.S. In other words, the spatial distribution of economic activity in both India and China is changing much faster than in the U.S., which underscores the importance of this type of spatial analysis in developing economies.

To further compare the U.S. and India, we compute the counterfactual employment growth of Indian districts if the relationship between density and growth were the one we estimate for the U.S. The result is represented in Figure 9, where the left-hand panel shows the predicted growth rates of Indian districts, based on the estimates for India, and the right-hand panel shows the counterfactual

> 6Data for China come from the China City Statistical Yearbooks with prefecture-level cities as the unit of observation. A second caveat, in addition to the one already mentioned, is that services refer to the “tertiary sector” implying a broader definition than the one used for India and the U.S. We use this broader category because of changes in the definitions of different service subsectors over the time period under consideration. Using alternative definitions of services in China does not change the qualitative results though.

> 7In the figures this corresponds to a log employment density of 5. Because the Chinese service data are not exactly comparable to those of India (on the one hand, they are more inclusive by considering the tertiary sector, and on the other hand, they are less inclusive because they only measure “formal” employment), not too much should be read into the exact level of this threshold.

> 8Note that in our data aggregate tertiary employment went down in China between 2000 and 2007. Indeed, one of the effects of liberalization was a reduction in the share of “formal” employment (i.e., a reduction in “staff and workers”).

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