This Report summarizes research on the effects of globalization in three directions. The first part conducts an empirical study on factors affecting the spatial distribution of foreign direct investment (FDI) across regions in Russia. In particular, this study is concerned with those regions that are endowed with natural resources and market-related benefits. Our analysis employs data on Russian firms having 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).
The second part examines the relevance of the self-selection into import hypothesis and studies how Russian manufacturing firms respond to importing with product innovations, R&D expenditures, and technology upgrades. The discussion is guided by the theoretical model for heterogeneous firms engaged in international trade which predicts that more productive firms generate higher profit gains and can afford higher entry costs, while trade liberalization encourages the use of more progressive technologies and brings higher returns from R&D investments. We test the theory using a panel of manufacturing Russian firms surveyed in 2004 and 2009, and use import entry indicators to identify the causal effects on various direct measures of technology upgrades. We report significant self-selection of larger and more productive manufacturing firms into import. We found that the likelihood of a firm investing in R&D depends on prior experience in import. Imported input stimulates R&D less than machinery import. Continuous traders are more likely to introduce all three types of innovation. However, we cannot identify any impact of government or foreign ownership on learning-by-importing effects. A firm's location in the border region is irrelevant for the power of links between trade and innovative behaviour.
The third part empirically analyses the agglomeration-related productivity premium at the enterprise level of the manufacturing industry in Russia. A settlement is counted as part of an urban agglomeration in two cases: that of a large, central city and that of a town located within 50 kilometers of the central city. Data obtained from a 2009 manufacturing enterprise survey are used, along with linked data on hosting regions and cities. We employ a multilevel model, which allows us to consider firm, urban and regional heterogeneity and test two possible explanations of the productivity advantages of firms in urban agglomerations – own-sector and all economic activity concentration in the city and the surrounding region. The results suggest that Russian plants in urban agglomerations enjoy 17-21% higher labor productivity. This gain arises as a result of urbanization and external scale economy – the agglomeration of firms belonging to different industries at both the urban and the regional levels of analysis. We also found that productivity gained from urban agglomeration is the highest in towns with populations of 100,000 to 250,000 people. Localization and clustering – the own-sector concentration of plants in the city – is not associated with higher labor productivity. The structure and size of the surrounding economy always matter: in contrast to urban clusters, regional own-industry clustering satisfactorily explains the productivity premium, suggesting that efficient clustering requires a scale economy larger than only a city. The region’s trade openness almost doubles the productivity premium of a firm in an urban agglomeration. All of our results are robust to changes in estimation technique, sample structure and choice of spatial objects.