Spatial segregation of low- and high-wage workers is a persistent economic issue with broad social implications. Using social security data and an AKM wage decomposition, this paper examines spatial wage inequality in West Germany. Spatial inequality in log wages rose sharply between 1998 and 2008, mainly due to increased variance in worker pay premiums across regions (48%) and stronger positive spatial assortative matching of workers and establishments (40%), i.e. colocation. Changes in establishment wage premia are mostly unrelated to rising colocation whereas labor mobility even reduced it. Instead, growth in worker pay premiums among stayers was concentrated in regions where high-wage workers and high-wage establishments were overrepresented already in the 1990s and, thus, magnified pre-existing colocation leading to ‘colocation without relocation’. Germany’s rising trade surplus, especially with Eastern Europe, boosted stayers’ worker pay premiums in those ex-ante high-wage regions and fully explains rising colocation.

Previous research indicates that firm innovativeness can either be determined by a firm’s position within the network dimension or by its position within the geographical dimension. Integrative studies addressing both distinct and combined proximity effects remains rare (cf. Whittington et al. 2009). Thus, we address in this Chapter the following research question: Are firm-level innovation outcomes positively or negatively related to network positioning effects, geographical co-location effects or combined proximity effects; and if the latter case is true, are the combined effects substitutional or complementary in nature? Panel data count models with fixed and random effects were used to analyze a firm’s innovative performance as measured by patent application counts. This last empirical analysis is organized as follows: We start with a short introduction in Sect. 12.1. Next, we provide a brief discussion of theoretical background in Sect. 12.2. In Sect. 12.3 we introduce our conceptual framework and derive our hypotheses. In Sect. 12.4 we introduce the data and methods used. Next, we outline the estimation strategy and report our empirical results in Sect. 12.5. Finally, we discuss our findings and conclude with a number of critical remarks in Sect. 12.6.

This article conducts a plant-level study of the factors affecting foreign direct investment (FDI) inflow to a large opening economy endowed with specific factor advantages. We conclude that the distribution of FDI in Russian regions depends on market access and can be most notably described by the knowledge-capital framework. Factor endowments built by natural resources are more successful in explaining the location decisions of export–platform affiliates. The impact of natural resources depends on how the availability of these resources is measured. The results reject the crowding out effects of resource FDI and prove co-location mode, when service investments are attracted to resource-rich regions. Labour cost advantages better explain the preferences of non-trading service affiliates.

Empirical evidence on the evolution of innovation networks within high-tech industries is still scant. We investigate network formation processes by analyzing the timing of firms to enter R&D cooperations, using data on laser source manufacturers in Germany, 1990-2010. Network measures are constructed from a unique industry database that allows us to track both the formation and the termination of ties. Regression results reveal that a firm's knowledge endowment (and cooperation experience) shortens the duration to first (and consecutive) cooperation events. The previous occupation of strategic network positions is closely related to the establishment of further R&D cooperations at a swift pace. Geographic co-location produces mixed results in our analysis.

This paper analyzes the spatial distribution of foreign direct investment (FDI) across regions in Russia. Our analysis employs data on Russian firms with a foreign investor during the 2000-2009 period and links regional statistics in the conditional logit model. The main findings are threefold. First, we conclude that market-related factors and the availability of natural resources are important factors in attracting FDI. Second, existing agglomeration economies encourage foreign investors. Third, the findings imply that service-oriented FDI co-locates with extraction industries in resource-endowed regions.

This paper conducts an empirical study of the factors that affect the spatial distribution of foreign direct investment (FDI) across regions in Russia; in particular, this paper is concerned with those regions that are endowed with natural resources and market-related benefits. Our analysis employs data on Russian firms with a foreign investor during the 2000-2009 period and linked regional statistics in the conditional logit model. The main findings are threefold. First, we conclude that one theory alone is not able to explain the geographical pattern of foreign investments in Russia. A combination of determinants is at work; market-related factors and the availability of natural resources are important factors in attracting FDI. The relative importance of natural resources seems to grow over time, despite shocks associated with events such as the Yukos trial. Second, existing agglomeration economies encourage foreign investors by means of forces generated simultaneously by sector-specific and inter-sectoral externalities. Third, the findings imply that service-oriented FDI co-locates with extraction industries in resource-endowed regions. The results are robust when Moscow is excluded and for subsamples including only Greenfield investments or both Greenfield investments and mergers and acquisitions (M&A).

Empirical and theoretical contributions provide strong evidence that firm-level performance outcomes in terms of innovativeness can either be determined by the firm’s position in the social space (network effects) or by the firm’s position in the geographical space (co-location effects). Even though we can observe quite recently first attempts in bringing together these traditionally distinct research streams (Whittington et al. 2009), research on interdependent network and geographical co-location effects is still rare. Consequently, we seek to answer the following research question: considering that the effects of social and geographic proximity on firm’s innovativeness can be interdependent, what are the distinct and combined effects of firm’s network and geographic position on firm-level innovation output? We analyze the innovative performance of German laser source manufacturers between 1995 and 2007. We use an official database on publicly funded R&D collaboration projects in order to construct yearly networks and analyze firm’s network positions. Based on information on population entries and exits we calculate various types of geographical proximity measures between private sector and public research organizations (PRO). We use patent grants as dependent variable in order to measure firm-level innovation output. Empirical results provide evidence for distinct effect of network degree centrality. Distinct effect of firm’s geographical co-location to laser-related public research organization promotes patenting activity. Results on combined network and co-location effects confirms partially the existence of in-terdependent proximity effects, even though a closer look at these effects reveals some ambiguous but quite interesting findings.