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Caution to place makers: Greater firm density does not always promote incumbent firm health

Caution to place makers: Greater firm density does not always promote incumbent firm health

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Governments around the world are fostering industrial ‘clusters’, hoping to create agglomeration economies. Using the political division of Germany in 1949, this column argues that heightened firm density can raise costs for incumbent firms in addition to the often-cited agglomeration benefits. This is important for policymakers contemplating efforts to promote their local areas by targeted cluster initiatives and bidding to attract large firms. Policy efforts that are neutral in orientation – such as physical infrastructure investments or improving the generation and dissemination of knowledge – may be more effective alternatives.

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A common theme in economic geography is that increasing returns to scale at the local level are essential for explaining the geographical distribution of economic activity. These agglomerative forces are often cited as a rationale for policy intervention to attract new firms to areas. Tight geographic concentration, however, can also raise countervailing costs as firms compete for local inputs. This makes the gains from increased spatial concentration around incumbent firms uncertain. Causal identification in this setting is challenging due to selection effects: economic models typically assume that firms consider the relative costs and benefits of locations and choose the best candidate. This choice process suggests that empirical correlations of changes in local firm concentration and incumbent firm performance or survival are likely to be biased from the true relationship. This endogeneity problem can be overcome with random assignment of locations to entrants. One setting that overcomes the selection bias is where location choice is driven by non-economic factors. Such quasi-experimental variation is rare in regional economics and cannot be generated in controlled experiments (Holmes 2010, Greenstone et al. 2010), but this form of variation is very valuable.

In a forthcoming paper (Falck et al., 2013), we consider this question in an historical setting with quasi-experimental characteristics – the division of Germany after the second world war. By 1949, the three western zones occupied by the UK, France and the US formed the Federal Republic of Germany. The eastern part developed into a satellite state of the Soviet Union and most believed in 1949 that this eastern zone would adopt the Soviet Union’s socialist system. The fear of expropriation (or worse) prompted many firm owners to flee to West Germany. We study this relocation in the context of the machine-tool industry. In total, a fifth of the machine-tool industry present in East Germany migrated during a narrow window of 1949-56. This was a one-time event, as no comparable prior or subsequent migrations occurred within the industry across German regions. This produced a shock representing on average an 8% increase in total industry size for receiving zones.

The relocation of the German machine tool industry

Machine-tool producers are defined as producers of power-driven machines that are used to produce a given work piece by cutting, forming or shaping metal. Based on Who Makes Machinery, a buyer’s guide issued annually since the 1930s, we identify 394 incumbent firms with pre-war experience in the UK or US zones. We are able to follow the fate of these firms after the relocations from East Germany occur. We also identify 33 relocating firms. These firms were often quite strong before the war, and they quickly regained their former strength in their new locations after moving (Buenstorf and Guenther 2011). We can further compare the impact of these relocating firms with new entrants that choose their location more opportunistically and based upon existing conditions (Glaeser and Kerr 2009), again measured through the buyer’s guide on an annual basis.

What factors were important for the destination choices of relocating firms? These choices were mainly driven by non-economic factors that were independent of local industrial conditions. As an example, Figure 1 illustrates the location choice of seven ‘relocators’ from the region of Chemnitz-Ore mountains in eastern Germany. The shading of the left map reflects ‘dialect/cultural similarity’ (Falck et al. 2012), the shading of the right map reflects product similarity within the machine tool industry. The first observation is that distance is a factor – in that the seven fi