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Quality of Foreign Direct Investment, Knowledge Spillovers and Host Country Productivity: A Framework of Analysis

Pradhan, Jaya Prakash (2006): Quality of Foreign Direct Investment, Knowledge Spillovers and Host Country Productivity: A Framework of Analysis.

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The existing research on knowledge‐spillovers from foreign direct investment (FDI) has invariably treated all foreign firms as homogeneous and of equal importance for the development of host countries. However, in actual market situations foreign firms are basically non‐homogeneous and of varying qualities as far as the potential for knowledge‐spillover are concerned. Foreign firms differ in terms of export‐orientation, intensity to undertake local R&D activities, vertical integration, generating demands for local raw materials, and entry modes. Non‐inclusion of such quality dimensions of FDI into the spillover analysis is certainly an important limitation of the existing literature. This paper has explored different notion of FDI quality and argued that it should be included in the empirical studies on spillover analysis. This paper has develop an empirical framework for inclusion of quality dimensions in exploring FDI related spillovers on host country productivity and propose a percentile criterion to distinguish between low and high quality FDI firms in empirical exercises. Since there are several dimensions of FDI quality, the study suggest that the researchers can utilize the principal component analysis (PCA) to build a composite quality index to define low and high quality FDI firms. The empirical exercise on the construction of FDI quality index and related spillover variables for the Indian manufacturing sector shows that there are considerable differences exists between the spillovers variables associated with high and low quality FDI firms. This difference is more pronounced at individual industries level. Unless the differences that are present across foreign firms in terms of quality are brought into the spillover analysis, the obtained results are likely to give misleading conclusions.

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