Determinants and Effects of Foreign Direct Investment: Evidence from German Firm-Level Data
Claudia M. Buch, J. Kleinert, A. Lipponer
Economic Policy,
No. 41,
2005
Abstract
Foreign direct investment is an essential aspect of ‘globalization’ yet its empirical determinants are not well understood. What we do know is based either on poor data for a wide range of nations, or good data for the US and Swedish cases. In this paper, we provide evidence on the determinants of the activities of German multinational firms by using a newly available firm-level data set from the Deutsche Bundesbank. The specific goal of this paper is to demonstrate the relative role of country-level and firm-level determinants of foreign direct investment. We focus on three main questions: First, what are the main driving forces of German firms’ multinational activities? Second, is there evidence that sector-level and firm-level factors shape internationalization patterns? Third, is there evidence of agglomeration effects in the foreign activities of German firms? We find that the market access motive for internationalization dominates. Firms move abroad mainly to gain better access to large foreign markets. Cost-saving motives, however, are important for some manufacturing sectors. Our results strongly suggest that firm-level heterogeneity has an important influence on internationalization patterns – as stressed by recent models of international trade. We also find positive agglomeration effects for the activities of German firms that stem from the number of other German firms that are active on a given foreign market. In terms of lessons for economic policy, our results show that lowering barriers to the integration of markets and encouraging the formation of human capital can promote the activities of multinational firms. However, our results related to the heterogeneity of firms and agglomeration tendencies show that it might be difficult to fine-tune policies directed at the exploitation of synergies and at the creation of clusters of foreign firms.
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Understanding CSR Champions: A Machine Learning Approach
Alona Bilokha, Mingying Cheng, Mengchuan Fu, Iftekhar Hasan
Annals of Operations Research,
2099
Abstract
In this paper, we study champions of corporate social responsibility (CSR) performance among the U.S. publicly traded firms and their common characteristics by utilizing machine learning algorithms to identify predictors of firms’ CSR activity. We contribute to the CSR and leadership determinants literature by introducing the first comprehensive framework for analyzing the factors associated with corporate engagement with socially responsible behaviors by grouping all relevant predictors into four broad categories: corporate governance, managerial incentives, leadership, and firm characteristics. We find that strong corporate governance characteristics, as manifested in board member heterogeneity and managerial incentives, are the top predictors of CSR performance. Our results suggest policy implications for providing incentives and fostering characteristics conducive to firms “doing good.”
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