Abstract
This paper investigates the heterogeneity within the group of foreign direct investors by analyzing the relation between parents’ productivity, the degree of domestic competition and the characteristics of their affiliates. Our results show that there is no unique recipe. Foreign direct investors may benefit differently depending on the economic environment in which they operate. Building an original 10-year panel dataset of Italian investors, we find that larger manufacturing parents tend to have more, larger and more productive affiliates in a higher number of destinations as well as being more productive in terms of total factor productivity. Having affiliates in high income countries or in both high and low income countries is associated with a productivity premium vis-à-vis investors in low income countries. Parent sector characteristics such as technology and degree of competition are also associated with productivity in a non-monotonic way. Low income country investors are found to be relatively more productive when operating in more competitive low technology sectors, while the opposite holds true for high income country investors, which become more productive when operating in less competitive high technology sectors.
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Notes
For a comprehensive review see, e.g., (Melitz and Redding 2014).
To complete the database, we also add geographical information, such as the distance between destination countries and Italy, using data from CEPII, and both GDP and GDP per capita taken from the World Development Indicators of the World Bank. Finally, the producer price indexes from Eurostat are used as 2-digit sector deflators.
Data from the MET survey are also employed in other recent studies: Brancati et al. (2016) study firms’ internationalization factors other than productivity and find evidence of increased innovation and learning from past export experience; Giovannetti and Marvasi (2016) and Giovannetti et al. (2015) investigate the relation between internationalization and global value chain participation in the food sector and for small and medium enterprises.
A detailed dataset description is provided in the appendix. Further technical explanations are available upon request.
Additionally, data on affiliates were not available for the years 2009 and 2010. In order to avoid a loss of information, we interpolated the investor-level affiliates indicators for the missing years. Note that interpolating the missing years implies assuming that the firm did not temporarily stop being a foreign direct investor. Given our focus on investors’ characteristics rather than on entry and exit, we are unlikely to introduce any significant bias in the analysis.
The share of exporters in the original MET survey is about 25%. The number of investors, as well as the other main descriptive figures, are in line with existing evidence on Italian FDI; see Gattai (2015) for a survey.
Robustness checks have been performed estimating TFP with several alternative methods (i.e. fixed effects, GMM and standard LP). Additionally, to further validate our results, we performed several other robustness checks on alternatively constructed datasets. First, we restricted the sample to all the affiliates of investors with at least 1 industrial affiliate. Second, we further restricted the analysis by also excluding all non-industrial affiliates. All these robustness checks have been performed on the main final dataset as well as on: (i) the non-interpolated final dataset (hence excluding all affiliate indicators for the years 2009 and 2010); (ii) the entire available Ice-Reprint database; and (iii) the entire non-interpolated Ice-Reprint database. The structure of our final dataset is robust and the baseline findings are confirmed. See the Robustness checks section for details.
For an overview see De Loecker and Goldberg (2014).
Operating profits are computed as sales minus variable costs, which in turn include costs for raw materials, energy and salaries. Unit costs are computed as variable costs divided by output, where output is the value of sales deflated using the producer price index.
For details on the estimated sectoral input coefficients of the production function and tests for constant returns to scale, see the appendix.
We divide low income and high income countries at 15,000 dollars, which is slightly less than half of the Italian GDP per capita.
Additional tables showing profit elasticities by sector and clustering of sectors with respect to their level of profit elasticity, together with the average technological level, are available in the appendix.
Results are based on OLS estimations. This is standard in the literature and, in particular, in evaluating productivity premia associated with certain characteristics. Fixed effects and dynamic panel estimations are not viable due to low within-variability.
Recall that Models (3) and (4) only control for sector PE with no further sectoral controls.
We also did some further checks that had a minor impact on results. Regarding the treatment of affiliates, we also checked our results on investors with at least 1 industrial affiliate: results are consistent. Moreover, we also have 2 datasets as regarding interpolation of the missing investors data (i.e. with or without): again the change in results is marginal. All the checks have been performed on each of the 4 TFP estimates (i.e. WRDG, LP, GMM, FE). All in all, this gives a total of 48 replications of the analysis. Results for the robustness checks not reported in the paper nor in the appendix are available upon request.
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The authors thank Davide Castellani, Lapo Filistrucchi and Marco Sanfilippo for useful discussions and the participants to: Italian Economic Association conference, Napoli, October 2015 and to seminars at the Universitá di Firenze and Universitá di Padova, Bank of Italy, University Sophia Antipolis in Nice, Università Politecnica delle Marche for useful comments on previous versions. Remaining errors are ours. Financial support from the from the Regione Autonoma Sardegna (LR7 2011, “Project Analysis of Competitiveness of Sardinia Production System”) is gratefully acknowledged.
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Giovannetti, G., Marvasi, E. & Ricchiuti, G. Does the Same FDI Fit All? How Competition and Affiliates Characteristics Affect Parents’ Productivity. Ital Econ J 5, 369–402 (2019). https://doi.org/10.1007/s40797-019-00103-1
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DOI: https://doi.org/10.1007/s40797-019-00103-1