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Title: The investment location choices of multinational enterprises in Central and Eastern Europe : the multi-level data and discrete choice methodology approach
Author: Rasciute, Simona
ISNI:       0000 0004 2676 4092
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2008
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This thesis examines the principal economic factors explaining firms' foreign direct investment (FDI) location decisions into 13 Central and Eastern European countries (CEECs) between 1997 and 2007 using discrete choice econometric methods. The first part employs Meta-analysis to systematically summarise, integrate and synthesise the results of empirical studies that analyse two main reasons why multinational enterprises (MNEs) locate their investment abroad: access to foreign markets and reducing productions costs. A large number of factors related to model specifications, dataset characteristics and methodologies in the primary studies explain the variation in the estimates of the market size and labour costs effects on FDI across the studies. Furthermore, the existing empirical literature on the market size effect on FDI is prone to publication bias more than the literature on the labour costs effect on FDI, as papers with statistically significant and larger market size effect on FDI are more inclined to be published in international journals. The second part employs four alternative discrete choice methodologies, including the Mixed logit (ML) model and the Latent Class (LC) model approaches to capture the main locational determinants of over a 1000 individual firm-level FDI location decisions in 13 CEECs between 1997 and 2007. The results show that the choice where abroad to invest does not only depend on the opportunities offered by foreign markets and industries but also on investing firms' individual characteristics. These results support the presence of heterogeneity in the investment location decisions, which is not only revealed by statistically significant interaction terms, but also by statistically significant standard deviations of the random parameters in the ML model and statistically significant class-specific explanatory variables in the LC model.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available