Empirical findingsMy study applies this new quantile approach to the Japanese manufacturing sector from 1986 to 2013 (Kondo 2016). I hardly find any stronger selection effects in larger cities (i.e. no stronger left-truncation of productivity distribution in larger than smaller cities). Conversely, as shown in Figure 2, higher average productivity in larger cities is mostly explained in terms of the right-shift of the productivity distribution, suggesting that agglomeration economies do better explain spatial productivity differences in the Japanese manufacturing sector. In addition, the dilation effect of agglomeration economies shows greater variations across sectors. On the one hand, more-productive firms enjoy greater benefits from agglomeration in some sectors, while, on the other hand, less-productive firms enjoy greater benefits from agglomeration in other sectors.
Figure 2 TFP distributions between above- and below-median employment density for all sectors
(a) 1986-2000
b) 2001-2013
Notes: Kondo (2016). The solid (dashed) line is the productivity distribution of cities with above-median (below-median) employment density. Estimation results by sector are available in Kondo (2016).
Another key testable prediction of Combes et al. (2012) is whether benefits from agglomeration economies decrease as interregional accessibility increases. For example, information and communications technology facilitates the exchange of ideas between geographically distant cities, decreasing the potential benefits of agglomeration economies. I find that benefits from agglomeration economies in the Japanese manufacturing sector have decreased in the recent decade. This suggests that when regional economies are integrated more tightly as communication and transportation costs decrease, the productivity advantages of agglomeration also decrease.
Toward further empirical studiesThe main findings in the Japanese manufacturing sector are similar to those of Combes et al. (2012), which concluded that firm selection does not play a crucial role in determining spatial productivity differences in the French manufacturing sector. By contrast, focusing on the pre-war Japanese silk reeling industry, Arimoto et al. (2014) concluded that selection played a key role in explaining why aggregate productivity in silk industrial clusters was higher than that in non-clusters. They also found that relatively less-productive firms enjoyed greater benefits from agglomeration economies, which indicates that the productivity distribution for the silk industrial cluster was less dispersed than that in non-clusters. These findings are in contrast to Combes et al. (2012) and those presented here.
Limited evidence in this literature is not enough to conclude that selection does not matter in spatial productivity differences. This literature requires further empirical studies worldwide, since there may be more counterexamples in other countries and time periods. Considering why differences arise across countries and time periods will deepen our understanding of agglomeration economies and help us draw better implications for urban and regional policies.
Finally, in Kondo (2017), I developed a new Stata command, estquant, which implements the quantile approach suggested by Combes et al. (2012) on Stata. I hope that the estquant command helps researchers apply this new approach and extend the empirical evidence in this literature.
ReferencesArimoto, Y, K Nakajima and T Okazaki (2014), “Sources of productivity improvement in industrial clusters: The case of the prewar Japanese silk-reeling industry”, Regional Science and Urban Economics 46: 27–41.
Combes, P-P and L Gobillon (2015), “The empirics of agglomeration economies”, in G Duranton, J V Henderson and W C Strange (eds), Handbook of Regional and Urban Economics, 5, Amsterdam: Elsevier, Chapter 5: 247–348.
Combes, P-P, G Duranton, L Gobillon, D Puga and S Roux (2012), “The productivity advantages of large cities: Distinguishing agglomeration from firm selection”, Econometrica 80(6): 2543–2594.
Kondo, K (2016), “Testing for agglomeration economies and firm selection in spatial productivity differences: The case of Japan”, RIETI, Discussion Paper 16-E-098.
Kondo, K (2017), “Quantile approach for distinguishing agglomeration from firm selection in Stata”, RIETI, Technical Paper 17-E-001.
Melitz, M J (2003), “The impact of trade on intra-industry reallocations and aggregate industry productivity”, Econometrica 71(6): 1695–1725.
Melitz, M J and G I P Ottaviano (2008), “Market size, trade, and productivity”, Review of Economic Studies 75(1): 295–316.