Abstract
There is a widely held assumption that multinational enterprises (MNEs) generate benefits that spill over to the host economy, resulting in productivity growth. Several channels foster the diffusion of such spillovers. They include backward and forward linkages with local firms – through which multinational firms may encourage the entry and development of more efficient local suppliers and final goods producing firms (Markusen and Venables 1999), competition and demonstration effects (Wang and Blomstrom 1992; Glass and Saggi 2002), as well as movements of labour force from multinationals to local firms (Fosfuri et al. 2001). The transmission of spillovers from MNEs to domestic firms, however, is not automatic; rather, it is affected by several factors, most of which can be summarized in the concept of distance, broadly defined in order to encompass both the economic and the geographical dimension. Economic distance concerns relative backwardness and absorptive capacity and determines whether and to what extent local firms eventually benefit from Foreign Direct Investment (FDI)-induced spillovers (Findlay 1978; Glass and Saggi 1998).In this paper, Multinational Enterprises (MNEs) and Foreign Direct Investment (FDI) are used as synonymous.
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Notes
- 1.
In this paper, Multinational Enterprises (MNEs) and Foreign Direct Investment (FDI) are used as synonymous.
- 2.
In particular, Nicolini and Resmini (2010) found that MNEs operating in traditional labour intensive sectors generate intra-sectoral spillovers that accrue to medium firms in Bulgaria, and small firms in Poland and Romania, while MNEs operating in high-tech manufacturing sectors generate inter-sectoral spillovers accruing to small firms in all the considered countries and large firms in Romania. These results suggest that at aggregate level, FDI induced spillovers depend, on the one hand, on the composition of each manufacturing sector in terms of the average size of indigenous firms, and, on the other hand, on the industry structure of each region, not to mention the regional distribution of high and low tech foreign firms within each host country.
- 3.
Negative effects arise when the entry of a (foreign) firms into the domestic market increases competition, and less competitive indigenous firms leave the market.
- 4.
These data come from Amadeus database published by Bureau Van Djik, which besides standard financial information gives also details on several other qualitative and quantitative variables, such as the structure of the ownership, industry classification, and geographical location within countries. Only firms whose ownership can be properly identified have been included in the sample. To this respect, firms with a share of foreign ownership greater than 10% have been classified as foreign affiliates, while all other firms with a percentage of foreign ownership below 10% have been classified as domestic. Although our sample does not consider the entire population of firms operating in the considered countries over period, its representativeness is fairly good, as it is shown in the Appendix.
- 5.
- 6.
- 7.
- 8.
Taking advantages of two unique panel-data samples which include information on firm price indexes, Mairesse and Jaumandreu (2005) demonstrate that the elasticities of factors of production included in a simple Cobb-Douglass production function vary more with the estimation procedures than with the particular specification of the production function equation, being the latter a real output function, a revenue function deflated either by individual prices or industry price, and a not deflated revenue function. Therefore, the omitted variable bias claimed by other scholars seems to be negligible.
- 9.
This implies that firms with zero or negative investment can not be considered when estimating input coefficients. Alternatively, Levinsohn and Petrin (2003) suggest that material inputs can be used as a proxy for the firm’s reaction to productivity shocks.
- 10.
Two sectors, namely manufacturing of refined petroleum products (NACE 23) and recycling (NACE 37), were excluded because the small number of firms operating in these sectors made it impossible to apply the Olley and Pakes procedure.
- 11.
In so doing we recover the information on productivity of firms active in period t but with zero investments. In fact, omitting plants with zero investment would have meant omitting plants with low or declining productivity, thus introducing a sample bias in the next steps, i.e. the construction of an aggregate TFP measure and the analysis of the impact of FDI spillovers on it.
- 12.
This approximation is preferred by a number of scholars. See Petrin and Levinshon (2005) for an in-depth discussion of the advantages of this approximation with respect to other possible aggregations available in the literature.
- 13.
Several studies have emphasized that the transition process yields to both regional and sectoral changes. These studies belong to an emerging body of literature focusing on regional performance following transition, i.e. detecting loosing and winning regions (Traistaru et al. 2003).
- 14.
The initial level of TFP, the relative concentration of each manufacturing sector and the average size of firms are drawn from the Amadeus database. Imports, GDPs at sector levels, Production price indexes as well as information on human resources devoted to science and technology at regional level come from Eurostat.
- 15.
We use the latest available national Input–Output tables at two digit level for each country. This strategy implies that supplier and client relationships occur within sectors as well. This is not unrealistic, given the level of aggregation we work with. This concept can be clarified if we consider the following two sub-sectors, i.e. cotton fibres and cotton fabrics. Although they reflect different stages of the same production chain, these two sub-sectors belong to the same two digit manufacturing sector, i.e. textiles (Nace 17). We are aware that this specification did not allow us to fully capture intra-sectoral spillovers, which also stem from foreign activity taking place at the same production stage as domestic firms. These spillovers derive from imitation and/or demonstration effects, as well as from personnel training and mobility. However, it is likely that multinational firms try to minimize them, because they involve the transmission of specific knowledge to their local competitors (Haskel et al. 2007).
- 16.
According to input–output tables, each sector has at the same time both client and supplier relationships with other manufacturing sectors; the degree of intensity of these relationships is often very similar.
- 17.
Residuals, by definition, are the portion of the variation of the dependent variable not explained by the explanatory variables. Thus, in our case, they pick up the effects of FDI not related to the other explanatory variables on changes in TFP proxy.
- 18.
See the appendix for further details.
- 19.
As discussed in Sect. 3, this variable has been included in order to correct possible error measurement in the dependent variable, which relies on nominal values.
- 20.
The insignificance of the coefficients of the openness variable may also be explained by a complementary relationship between trade and FDI. This implies that the degree of openness of a region is already captured by the presence of foreign enterprises. A spurious correlation may also affect the specialization variable and the average size of firms: the larger the size of the firms, the more concentrated is the sector.
- 21.
The poor explanatory power of specification (4) may be explained by the fact that sectoral heterogeneity is already captured by explanatory variables; therefore, adding sectoral fixed effects may generate multicollinearity problems that worsen the goodness of fit of the model.
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Nicolini, M., Resmini, L. (2011). Productivity Spillovers, Regional Spillovers and the Role of by Multinational Enterprises in the New EU Member States. In: Kourtit, K., Nijkamp, P., Stough, R. (eds) Drivers of Innovation, Entrepreneurship and Regional Dynamics. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17940-2_6
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