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Foreign direct investment and firm performance: an empirical analysis of Italian firms

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Both empirical and theoretical literature show that multinational firms exhibit a competitive advantage before investing abroad. However, there are no clear empirical results regarding the ex post effects of foreign direct investment (FDI) on firm performance, partially due to the inadequacy of available firm-level data. We build a brand new firm-level dataset able both to represent the extent of Italian firms’ foreign activity and to provide reliable measures of key performance indicators, especially total factor productivity (TFP) and employment. We then use a propensity score matching procedure to analyze the causal relationship between FDI and firm performance. Firms investing abroad for the very first time, especially in advanced economies, show higher productivity and employment dynamics in the years following the investment: the average positive effect on TFP is driven by new multinationals operating in specialized and high-tech sectors, while the positive employment gains are explained by an increase of the white collar component. On average there are no negative effects on the parent firm’s blue collar component.

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  1. UNCTAD (2010, 2011).

  2. See Greenaway and Kneller (2007) for a comprehensive survey.

  3. The model prediction stems from the presence of a tradeoff between the fixed cost of setting up a new plant abroad and the variable cost of exporting goods to the foreign countries.

  4. Feinberg and Keane (2006) show that only 12 % of Canadian affiliates controlled by US multinationals resemble purely horizontal FDI and only 19 % purely vertical FDI; the remaining 69 % is the result of some complex integration strategy.

  5. Among others: foreign market dimension, transportation costs of intermediate and final goods, fixed cost of producing and assembling.

  6. For a more general overview of offshoring see also (Feenstra and Hanson 1996; Grossman and Rossi-Hansberg 2008), who consider the possibility for firms to outsource some stages of the value chain to foreign enterprises.

  7. Helpman et al. (2004) for a sample of US firms, show the presence of an advantage in terms of labor productivity in favor of multinationals compared to exporters (+15 %) and in favor of exporters compared to non-internationalized firms (+40 %). Girma et al. (2005) find the same productivity sorting for a sample of firms in the United Kingdom. Kimura and Kiyota (2006) show that Japanese exporters with some form of foreign production are more productive than domestic firms (+6.4 %). In Italy, Castellani and Zanfei (2007) confirm only the advantage for multinational firms over exporters; Castellani and Giovannetti (2010) add that these productivity premia are explained by a higher productivity of capital as well as managerial and clerical employment. The same results are confirmed for Germany (Wagner 2006; Arnold and Hussinger 2010) and France (Engel and Procher 2012).

  8. By contrast, the self-selection of exporting firms has been widely investigated. See Wagner (2007).

  9. Multinationals are a small fraction of active firms and usually acquire this status once; therefore it is not easy to obtain appropriate micro-data to evaluate this theoretical prediction.

  10. Some tasks are more difficult to offshore than others (i.e. post-sales support is easier to offshore than assembly of the final product), so the offshoring costs may vary.

  11. See Amiti and Wei (2009) for a description of some of these channels.

  12. Furthermore, productivity gains for Japanese firms are observed in the service sector, but not in manufacturing (Ito 2007).

  13. High-skill activities are located in the parent firm while low-skill ones are off-shored. Interestingly, this process does not lead to a decrease in overall employment.

  14. Unfortunately, the methodology developed by Muendler and Becker (2010) cannot be employed here since we do not observe employment in the foreign affiliates. See Crinò (2009) for an extensive survey of the effects on employment of multinational firms.

  15. Researchers can carry out statistical and econometric analysis on data collected in the Invind survey through the Bank of Italy BIRD System. See

  16. Orbis dataset is property of Bureau Van Dijk and contains information on close to 180 million private companies worldwide. See

  17. See Bentivogli et al. (2014) for a comprehensive survey of datasets providing information about the foreign activity of Italian firms.

  18. CADS is collected by the private company Cerved Group. Cerved data are largely used both by scholars and international institutions (e.g. IMF and OECD). See

  19. The procedure follows that of Bontempi et al. (2010).

  20. See CompNet Network (2014).

  21. The correlation is lower with the TPF estimated with the Gandhi et al. (2012) methodology, since the latter is the only one following a gross output approach with a translog production function instead of the standard Cobb–Douglas value added approach. See online Appendix B.

  22. See Wagner (2007) for a detailed analysis of the standard methodologies employed in the literature.

  23. Kolmogorov–Smirnov test results are available upon request.

  24. See Table 15 in the online Appendix A.

  25. An alternative approach employed in the literature is to test whether a rise in productivity may increase the propensity of firms to invest abroad.

  26. Matching may not correctly identify the idiosyncratic effect of foreign investment if the endogeneity depends both on self-selection and simultaneity between performance and FDI. An instrumental variable analysis is better suited to solving these forms of endogeneity. Unfortunately, finding an instrument that is exogenous with respect to performance and correlated with the choice of investing abroad is not an easy task and to the best of our knowledge the literature has not yet found a variable with these features.

  27. See Rosenbaum and Rubin (1983) for a formal proof.

  28. For an extensive discussion on the methodologies of matching and the concept of similarity see Blundell and Costa Dias (2000).

  29. Results for the rolling windows samples are available on request.

  30. The propensity score matching requires that, for each firm, there is a positive probability of being treated and a positive probability of not being treated. This condition must hold both for the treated and the control groups, which should be drawn from the same ex ante distribution. Then the propensity score matching is usually conducted only among firms that belong to the same support as regards the distribution of vector X (known as the common support condition).

  31. The estimates presented are based on the methodology of radius matching, especially useful for preventing errors in associating observations of the treated and untreated firms within the same industry-year. The caliper, as suggested by the literature, was set at one-fifth of the standard deviation of the propensity score. We also tested the robustness of our results employing the nearest neighbor matching procedure, obtaining similar estimates. For an overview of the different algorithms used in the literature see Caliendo and Kopeinig (2008).

  32. All the ATT estimates are obtained considering only firms that are observed over a broad interval of time before and after the key year \(t^*\), i.e. in the range \(t^*-2\), \(t^*+5\). In this way the results at different time horizons are easier to compare since we always use the same treated and control firms. Unfortunately following this procedure entails a loss of about 160 of new MNEs evaluated in \(t^*+1\) and 120 in \(t^*+3\). Nevertheless, our results are robust to relaxing the constraint of being always observed in the range \(t^*-2\), \(t^*+5\).

  33. These estimates may be biased as new multinationals may start transferring prices to tax-advantaged jurisdictions, over-invoicing input and under-invoicing output: this may lead to downward bias estimates of ex post gains in TFP and output and an upward bias of ex post gains in capital (labor is unaffected since it is measured as the number of workers).

  34. In this part of the analysis we do not constrain firms to be observed over the entire time span [\(t^*-2\), \(t^*+5\)] since we have information about the labor force composition only for a small subset of our sample. In the propensity score estimation we also control both for the white collar share and for the changes in blue and white collar workers before \(t^*\).

  35. The result is also broadly in line with the work of Serti and Tomasi (2008), which evaluates the effect of starting to export on firm performance.

  36. Traditional sectors: food, beverages and tobacco; textiles and apparel; leather and related products; wood and wood products; other manufacturing. Scale intensive sectors: paper products and printing; coke and refined petroleum products; chemical and pharmaceutical products; rubber and plastic products; non-metallic mineral products; metals and metal products. Specialized and high-tech sectors: machinery and equipment; electrical, electronic and optical products; transport equipment.

  37. See Tables 21, 22 and 23, in the online Appendix A.

  38. Detailed results for firms investing in emerging countries are available in Tables 24, 25 and 26 in the online Appendix A.

  39. This probability is estimated with a pooled probit which is based on a number of firm characteristics - i.e. TFP, size, age, a set of dummy variables that take value 1 if the firm belongs to the lowest 25th percentile of the distribution of cash flow, return on revenue, leverage rate and value added and all the possible interaction among these dummies-and sectoral controls that take into account the macroeconomic cycle and the time invariant characteristics of the sectors.

  40. Since counterfactual (domestic) firms are characterized by a lower survival rate than new-MNEs, estimating the effects of investing abroad conditional upon the survival of both groups of firms might be considered as a conservative approach in the analysis of the effects of FDI.

  41. Both the theoretical and the empirical findings on firm heterogeneity suggest considering a period not too close to the time of the true investment (\(t^{*}\)). In fact, we know that firms investing abroad must have an ex ante competitive advantage in line with the findings of Helpman et al. (2004); in a dynamic framework, this advantage may arise from a series of positive productivity shocks in the years preceding the investment (Ghironi and Melitz 2007). In our falsification exercise we randomly choose \(t^{'}\) at least 10 years before the actual investment, so even the evaluation at the longest time horizon considered (\(t^{'}+5\)) shall not be too close to true investment year \(t^{*}\).

  42. The falsification exercise implies a reduction of the sample period of the analysis (1988-2002) compared to the original one (1988–2011). To verify that the estimates of the falsification test were not driven only by the sample modification, we re-estimated the baseline ATT effects using the actual investment year \(t^{*}\) and the falsification test sub-period (1988–2002). The estimates obtained are significant and in line with those found for the whole sample. The results are available on request.


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Correspondence to Michele Mancini.

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This paper has been prepared within the Bank of Italy working group on the internationalization of Italian firms. We wish to thank Andrew Bernard, Matteo Bugamelli, Riccardo Cristadoro, Giuseppe De Arcangelis, Luca De Benedictis, Jan De Loecker, Massimo Del Gatto, Stefano Federico, Alfonso Rosolia, Marco Sideri, Jeffrey Wooldridge and the participants at the Italian Trade Study Group conference in Cagliari for the insightful comments.

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Borin, A., Mancini, M. Foreign direct investment and firm performance: an empirical analysis of Italian firms. Rev World Econ 152, 705–732 (2016).

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