Using a rich data set of the Dutch manufacturing sector between 1995 and 2010, we investigate the effect of foreign direct investment (FDI) on domestic new firm entry. The emerging empirical literature has focused on the direct relationship between FDI and entry, but has not explored the mechanisms behind the observed effect. Drawing on a simultaneous equations model, our analysis features both the direct effect of FDI as well as indirect effects through two channels: industry competition and wages. We estimate the parameters through 3SLS and take into account the endogeneity of competition and wages with respect to entry. Our results show that there is a significant negative direct effect of FDI on entry. At the same time, FDI decreases competition and increases wage levels, which then impact entry positively and negatively, respectively. The total effect of FDI is negative, but small and virtually disappears after one year. Policy implications are discussed.
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The assumption maintained throughout this paper is that lower (higher) industry concentration is regarded as proxying more (less) competition. This is a widely used approach in relevant studies. For the theoretical foundation of our choice, please see Appendix A.
See Lipsey (2004) for a summary of the literature on FDI and wages.
Statistiek financiën van grote (niet-financiële) ondernemingen in Dutch.
Statistiek financiën kleine ondernemingen in Dutch.
As of 2000, SFGO and SFKO merged into a single data set; the so-called statistics on finances of non-financial enterprises (NFO-statistiek financiën van niet-financiële ondernemingen in Dutch). However, SFGO as such is still available.
Gemeentelijke basisadministratie persoonsgegevens in Dutch.
SBI stands for “standaard bedrijfsindeling” which corresponds to the Dutch version of the NACE industry classification.
This value is based on model 1 in Table 3 where no lag structure is imposed. Alternative models with different lag lengths and the breakdown of industries based on technological intensity culminate in different number of observations.
All unreported results are available from the authors on request.
Details on this issue for the US are available at URL: goo.gl/SfuHTH (retrieved on 09/10/2013).
More information on the competition legislation within the EU is available at URL: goo.gl/dGTaqW (retrieved on 09/10/2013).
Note that in this study, we only consider FDI of firms with total assets of at least 22.7M€ and entry rates are predominantly measured through small firms with total assets below 22.7M€. This means that we only investigate effects of FDI in larger firms on the entry of primarily small firms in order to solve the potential endogeneity of the former.
A few studies (e.g., Almeida 2007; Heyman et al. 2007) use matched employer-employee data to address this issue. The results in both papers show that higher wage premiums in foreign firms, although existent, are lower than previously thought. The data in this study, however, does not allow for such a matching procedure.
ZZPs (zelfstandige zonder personeel in Dutch, i.e. independent professional without personnel) are those people who work for themselves but do not hire any other employees.
The Dutch tax regime has been structured in such a way that there are large financial benefits for freelancing: one often has to pay no taxes at all in the first few years. As a result, many people have opted to start one-(wo)man businesses and become a ZZP-er.
Industries in our data set are very narrowly defined: at the five-digit. Two of our 252 industries (in a given year) consist of only one-(wo)man firms. Therefore, when these firms are excluded in the robustness checks, these two industries drop out of our sample. The original sample size is then reduced by two observations from 3784 to 3782 in the robustness analysis. In contrast, the remaining 250 industries consist of both one-(wo)man and other firms and we can still compute all of our industry-level variables in a similar way as before. This means that all of these 250 industries remain in the sample used in the robustness analysis.
Eurostat uses the following aggregation of manufacturing industries according to technological intensity: high technology, medium high technology, medium low technology, and low technology. We combine the first two categories and label it as high tech, and subsequently merger the last two categories and label it as low tech.
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We wish to thank the editor and two anonymous referees for helpful comments, as well as Utz Weitzel for his valuable suggestions and supervision during the research project. We also gratefully acknowledge financial support from the Dutch Ministry for Economic Affairs and excellent data assistance from colleagues at the Statistic Netherlands (CBS). Any remaining errors are our own.
Appendix A: Concentration-Competition Link
The association between market concentration and competition is well-grounded in industrial organization theory (see Tirole 1988) and particularly in the structure-conduct-performance (SCP) paradigm introduced by Bain (1968). The SCP paradigm suggests that industries characterized by low concentration constrain the conduct of firms with respect to pricing and advertising policies, innovation, etc. Firms comply with the prevailing prices and earn normal profits in the long run. In contrast, a higher level of concentration provides them more freedom in market conduct choices, and is assumed to facilitate collusive activities and anti-competitive practices. High concentration is then taken as an indication of weak competition resulting in high prices and high price-cost margins. Put differently, there is an inverse relationship between the degree of industry concentration and competition (Scherer and Ross 1990).
Since the introduction of the SCP paradigm, numerous empirical studies have resorted to the indicators of concentration to proxy competition, notably the Herfindahl index (see Valta 2012; Xu 2012; Amess and Roberts2005). If a declining trend is detected in the index over time, this is reflective of that particular industry becoming more competitive. Conforming to previous studies, we also use the Herfindahl index as our proxy for competition. In the literature, alternative measures of competition are also offered, for instance, by Boone (2008), and these measures are compared in their ability to reflect changes in competition (Boone et al. 2007). Nonetheless, with the data we had access to, the Herfindahl index was the most appropriate choice for this study.
Appendix B Data
Appendix C: Robustness analyses
Excluding one-(wo)man firms
To check the robustness of our findings, we estimated our system of equations by using different subsamples. First, we define a sample excluding one-(wo)man firms (i.e., ZZPs).Footnote 13 This sample is an unbalanced panel of 3782 industry-year observations.
One-(wo)man businesses operate under special regulatory and tax conditions in the Netherlands.Footnote 14 Since they account for almost 15% of all firms and half of the new firm entries per year and over the sample period in our data set, it is worthwhile to check whether previously discussed results are sensitive to the exclusion of these firms. Estimates derived from this subsample are displayed in models (1) through (3) of Table 5 in the same way as before.Footnote 15 A quick look at these alternative models suggests that the results are by and large in agreement with those obtained from the analysis of the whole sample.
High- vs. low-technology industries
The above analysis assumes homogeneity of manufacturing industries. However, industries with different underlying structures may have particular sensitivities to FDI. Due to the differences in, for example, the degree of innovation capacity, or the sources and directions of technical change, the effects of FDI on firm entry may not be uniformly distributed across industries. This predominantly applies to the differentiation between high- and low-tech sectors. In addition, Table 3 shows that there is a significantly positive effect of R&D intensity on entrepreneurship, suggesting a possible role of technological competencies of different industries. Therefore, it would be worthwhile to test developed hypotheses in Section 2 considering technology advantages of firms. Thus, similar to Barbosa and Eiriz (2009), we replicate our analysis separately using subsamples of high- and low-tech Dutch manufacturing industries. The breakdown according to technological intensity is based on NACE Rev. 1.1. which is provided by Eurostat.Footnote 16 High-tech group covers the codes 24, 29-35 (except for 35.1) and low-tech group covers 15-23, 25-28, 35.1, 36, and 37. Appendix E displays the list of industries assigned to each subsample in detail.
The estimation results are presented in Table 6 for both the whole sample and sample excluding one-wo(man) businesses. Comparing the results, we find striking differences in the way FDI affects gross entry. Specifically, the negative direct effect of FDI only prevails in the low-tech group, and it disappears in the high-tech sector. To illustrate, 10% increase in FDI measured as the share of foreign employment reduces gross entry rates in low-tech sectors by 0.71% and 0.64% in the samples including and excluding one-(wo)man businesses, respectively. This suggests that domestic innovative and absorptive capacity in knowledge intensive industries is (more) complementary to new technologies and know-how of foreign firms. This complementarity may provide sufficient incentives for entrepreneurs to enter high-tech industries and seemingly compensate for the negative consequences of FDI which is found to be more applicable to low-tech industries. Our finding for high- and low-tech industries is similar to the finding of Barbosa and Eiriz (2009), who did not trace any significant effect of FDI on firm entry for the high-tech subsample.
HHI almost has no effect on entry in high-tech while its coefficient has a significant positive sign in low tech, suggesting that higher concentration stimulates entry only into the latter group. Wages are negatively linked to gross entry rates in both subsamples, which is in line with our main results. Finally, Table 6 also shows that while the total FDI effect is positive for high-tech industries, it is negative for low-tech ones.
Appendix D: Economic significance
Here, we discuss the economic significance of our estimates. Table 7 displays the size effects of a subset of the models included in Tables 3 and 5. The economic effects are calculated as the percentage change in the dependent variable with respect to a 10% increase in the relevant independent variable. Values in columns (1) to (3) are based on models (1) to (3) in Table 3 drawing on the whole sample. Columns (4) to (6) correspond to the economic effects of the results presented in Table 5, the estimation for robustness check. We see that a 10% increase in FDI coincides with an increase in market concentration of 1.1%. The effect of FDI on wages is economically larger. A 10% increase in FDI is associated with an increase of 5.5% in industry wages. The channel effects of concentration and wages on entry are both smaller. A 10% increase in industry wages translates into a decrease of 3.6% in gross entry rates. For market concentration, an increase of 10% translates into an increase of 4.43% in entry rates. A similar picture emerges in the subsequent columns (3) to (6). In summary, the economic effects of FDI on wages are the largest among the relationships depicted in Table 7. The effects of wages on entry are a bit smaller, which slightly diminishes the power of this channel. Additionally, these reduced effects of wages on gross entry rates are neutralized by the counter effect of the second channel, market concentration. In other words, although the indirect effect of FDI on entry through the wage channel might initially appear troublesome, the effect is: (i) is weakened on its way through the channel and (ii) is offset by the counter effects between FDI, market concentration, and entry rate.
Appendix E: Dutch Manufacturing
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Abolhassani, M., Danakol, S.H. Wage and competition channels of foreign direct investment and new firm entry. Small Bus Econ 53, 935–960 (2019). https://doi.org/10.1007/s11187-018-0115-4
- Firm entry
- Market concentration
- Foreign direct investment