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Abstract
This paper investigates the contribution of FDI to firms’ technical efficiency based on a two-stage empirical method. Using panel data for 674 firms belonging to the Tunisian manufacturing sector and observed over the period 1997–2001, a bootstrap procedure is applied to correct for serial correlation affecting DEA technical efficiency scores estimated in a first stage. Results obtained from second-stage regressions show that FDI presence at the firm level has a positive effect on its technical efficiency. However, horizontal FDI spillovers are not evidenced while sectoral export activity represents a potential source of technology spillovers for local firms.
Notes
1For Aitken and Harrison Citation(1999), the competition effect would also generate an opposite effect on local firms’ efficiency. According to the authors, a ‘market stealing effect’ could be induced by the entry of foreign firms. In such circumstances, local firms will be crowded out from the market or at least loose a large part of their market share. Hence, local firms’ production scale will be reduced and their technical efficiency will decrease.
2Following Audretsch and Feldman Citation(1996) and Crespo and Fontoura Citation(2007), regional effects are to be considered as a determinant factor for FDI technology spillovers. The argument is based on the distance effect explaining both inward FDI and international trade. We consider that, in the Tunisian case, regional effects do not play so strongly since 80% of Tunisian FDI inflows come from the European Union. It is worth noting that the predominance of European investments in Tunisia is quasi structural. Geographical proximity but also cultural and language proximity explains European inward FDI in Tunisia. Moreover, the preferential trade agreement signed in 1995 by Tunisia and the European Union has reinforced Tunisian inward FDI coming from this region.
3In Cohen and Levinthal Citation(1989), R&D is not only considered important for innovation but also for learning.
4Even though R&D spending in Tunisia reaches 1.25% of Tunisian GDP in 2009 and it is projected to reach 1.5% in 2014.
5The authors found a positive association between American and Asian company's presence in downstream sectors in Romania and the productivity of Romanian local firms in the supplying industries. The same correlation turns out to be negative when European investors are present in downstream sectors. These results are mainly attributed to the preferential trade agreement signed by Romania with the European Union. The agreement gives incentives to American and Asian investors to use Romania as a platform country in order to comply with the rules of origin for their sales on the European market. In these conditions, the nationality of investors creates a greater potential for FDI spillovers.
6Beside trade policy, intellectual property protection (IPP) is another determinant environmental factor that affects FDI spillovers. Depending on IPP, foreign investors will decide whether they will invest in local production (high IPP) or reduce their investments to a simple distribution presence (low IPP). In the latter case, few or negligible FDI spillover will be induced. Moreover, foreign firms’ entry in a host country will depend on the guarantee offered by local authorities concerning the respect of intellectual property rights. As explained by Markusen, a moral hazard problem arises when the MNE hires a ‘local agent’ to represent it in the host country. Since the local agent knows the technology used by the MNE, he could defect to start his own business. This situation could prevent the MNE from adopting a direct implementation in the host country (for more details on these aspects, see for example Markusen, Citation2001, and Javorcik, Citation2004a). With regard to the Tunisian case and despite recent efforts made by authorities to comply with the international copyright rules through the implementation of an appropriate legislation, it seems very hard to give any empirical measure specific to the Tunisian IPP.
7According to data provided by Tunisian Foreign Investment Promotion Agency (FIPA).
8The number of foreign firms moved from 124 in 1997 to 137 in 2001. At the same time, the number of local firms moved from 550 in 1997 to 537 in 2001.
9To estimate order m output efficiency scores and the corresponding standard deviation values for different values of m, we adopt the Matlab program proposed by Simar in the appendix of his article. As the exercise is hard to perform for panel data, order m output efficiency scores were computed on mean values of the basic variables (output and inputs). Matlab 6.0 was used and a specific algorithm was defined for re-sampling in order to realize the Monte Carlo loop.
10To avoid overloading the paper with detailed results and graphics, we decided to limit our presentation to a more aggregated analysis.
11Qualified labour also enhances firms’ technical efficiency when only local firms are considered but to a lesser extent (see model 5).
12According to the authors, low competitive firms could export and continue to pass through the international market ‘screen’ as long as they don't reach a maximum costs threshold ‘not tolerated’ by this market. When this threshold is reached, these firms will be excluded.
13In the sense of Ekholm et al. (Citation2003).
14Two years after the preferential trade agreement signed with the European Union.
15Launched in 1996.
16NSE is an annual enquiry operated by Tunisian National Institute of Statistics (INS) and conducted on a representative sample of firms. The enquiry is entitled ‘Enquête Nationale sur les Activités Economiques’ and it is realized through a questionnaire.
17Following the French terminology used by INS, an equivalent to the wholesale price index is the ‘Indice des Prix de Ventes Industrielles’.
18Following the NSE database, capital stock book value corresponds to the sum of lands, buildings, technical and industrial equipments, transport material and other immobilisations including computers.
19The study is entitled ‘Démographie des Equipements dans les Industries Manufacturières Tunisiennes’.
20We tried to compute the firm-specific depreciation rate from NSE database. However, as for some firms, we found inconsistent values of depreciation rate (more than 1), we decided to use a uniform rate of depreciation.
