Abstract
This paper explores the relation between supply-chain participation and the internationalization of firms. We show that even small and less productive firms, if involved in production chains, can take advantage of reduced costs of entry and economies of scale that enhance their probability of exporting. The empirical analysis is carried out on an original database, obtained by merging and matching balance-sheet data with data from a survey on over 25,000 Italian firms, which include direct information on the involvement in supply chains. We find a positive and significant relation between being part of a supply chain and the probability of exporting, as well as the intensive margin of trade. The number of foreign markets served (the extensive margin), on the other hand, does not seem to be affected. We also investigate whether being in different positions along the chain, i.e., upstream or downstream, matters, and we find that downstream producers tend to benefit more. Our results are robust to different specifications, estimation methods, and the inclusion of the control variables typically used in heterogeneous firms models.
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Notes
In accordance with the official EU definition, in the rest of the paper, small enterprises are denoted as those with less than 50 employees.
By “relationship specific,” we mean that the value of the assets or investments is higher inside a particular relationship than outside of it.
An interesting example can be found in the value chain certification of the famous Italian brand “Gucci,” which has certified its suppliers and subcontractors. The certification involves over 600 firms from Tuscany. As a consequence, these firms, improving their reputation, have also increased their access to credit (Il Sole 24 ore online, “Intesa San Paolo e Gucci alleate per favorire l'accesso al credito delle PMI,” January 17, 2013).
The sample reduction is mainly due to microfirms and small firms for which balance-sheet data are unavailable or inconsistent across the two data sources (2-digit sector and/or region do not match). After the merge, the share of firms below 50 employee decreases to 75.3 from 86.2 %. Moreover, we lose a large number of firms in services: The share of manufacturing increases to almost two-thirds after the merge from about one-half before the merge.
The involvement in a specific production process is identified in the survey with a firm’s identification with a specific supply chain, which is different from the sector they belong to.
The aspect of participation in the final product has been added for consistency with our definition, given that it is likely to signal the “contribution of the firm to specific forms of production.”
The use of the alternative proxy for supply chain participation is also discussed when we introduce the robustness checks in Sect. 3.4, and detailed results are provided in Appendix B (Electronic Supplementary Material), and Tables B3 and B4.
The TFP estimation is based upon the Solow residuals from an econometric specification derived from a Cobb–Douglas production function. We estimated the TFP at the sectoral level, using the Levinshon and Petrin (2003) methodology, with intermediate inputs as proxies for unobservable productivity shocks. Further details on the estimation methods are provided in the “Appendix”.
Pieri and Zaninotto (2013), in a study on the Italian machinery tool industry, find that: “the most efficient builders of MTs choose integrated structures, while less efficient firms choose to outsource part of their production process by buying intermediate inputs from other firms.” (p. 413).
The construction of this variable is based upon the answer to a specific question of the survey, in which a firm is asked whether it has been involved in international activities over the past 3 years. Direct and indirect exports have been considered for the purpose of this analysis. This choice is consistent with the consideration that firms along the supply chain, upstream or downstream, have different degrees of proximity to the final market.
For consistency, the network variables that we include in the regressions are mutually exclusive. Hence, while some firms are involved in different types of networks simultaneously (e.g., local and domestic, domestic and global, or local and global), our definitions are such that each firm is univocally attributed to the wider type of network.
The matrix of correlations is available in Appendix B, Table B1, showing no concerns of collinearity between our variables.
Results are robust to the inclusion of each regressor separately and consistent also/even when the model is estimated on the whole sample of 25,090 firms (i.e., not merged with balance-sheet data). As a robustness check, all the estimations presented in the paper have been performed also on the whole sample of 25,090 firms (without checking for the TFP and FDIs). For space reasons, results are available in Appendix B, Tables B6–B8.
Replacing the SEs dummy with the logarithm of the number of employees produces similar results, with the coefficient of the latter being positive. Regressions with the SEs dummy, however, are more consistent with the following analysis, in which we split the sample between SEs and MLEs.
Note that using the initial productivity level and the change in productivity helps also to avoid concerns over a possible simultaneity bias with the dependent variables. Moreover, there is general consensus among trade economists that the direction of causality mainly goes from productivity to export, via self-selection effects à la Melitz (2003).
Average marginal effect and marginal effect at the mean, respectively.
The prediction is considered to be correct if the predicted probability is >50 % and the firm is indeed exporting or if the predicted probability is below 50 % and the firm is not exporting (Hosmer and Lemeshow 2000).
The full set of results for six different regressions for small firms (up to 50 employees) and six for larger firms (from 50 employees) are reported in Tables 10 and 11 in the Appendix. For simplicity, we report regressions up to 50 employees for SEs and over 50 employees for large firms. Above 50 employees, the two sets of regressions produce very similar results. Regressions for all the different thresholds are available from the authors.
The negative and significant effect found for firms with at least 300 employees (Column 6, Table 11) should be taken with caution, since in this subgroup most firms are already exporting (62 %), the number of observations is rather small, and the confidence interval is quite large.
Detailed results from the regressions with the interaction term, not reported here for reasons of space, are available in Appendix B, Table B2. In addition, we also run separate regressions for firms in a supply chain. For this subsample, the initial level of TFP is expected to show a lower correlation with the probability of exporting, and, indeed, the coefficients from the regressions are found to be lower and non-significant. However, the number of observations is rather small, making the introduction of an interaction term more appropriate.
The extensive margin index goes from 0 (non-exporters) to 8. The different destinations for which we have data are as follows: EU, EXTRA-EU, North America, China, India, rest of Asia, South America, other.
In our case, the definition of binary variables is preferable to the use of the actual shares of total sales. In fact, the latter is likely to contain measurement errors, i.e., the observed shares are only indicative and extreme values are indeed prevalent in the sample.
The results of these robustness checks, not included here for reasons of space, are available in Appendix B, Tables B3–B8.
To run regressions on the whole sample, however, we cannot control for TFP.
Though service firms are likely to take advantage from supply chain participation, in our sample, they are less represented both in absolute terms (they are one third of the merged sample) and especially as far as supply chain involvement is concerned (only <17.7 % of firms in a supply chain operate in services, and <7.3 % of firms in services report to be part of a supply chain). Finally, most of the services firms in our sample are localized (i.e., not easily exportable) services, including business services and transportation.
The propensity score matching models and the ATTs estimations have been performed also on the whole survey as a robustness check. Estimated ATTs are similar (slightly higher) to those reported in the paper, but the matching procedure was more problematic. Details are available in Appendix B, Tables B9, and B10.
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Acknowledgments
We would like to thank two anonymous referees, Tadashi Ito and the participants to the Royal Economics Society Conference (Manchester, April 7–9, 2014); the Italian Trade Study Group Conference (November 2013); the 15th European Trade Study Group Conference (September 2013); the 10th c.Met05 Workshop (July 2013) and seminars at University of Florence, and IDE-JETRO for their comments on previous drafts of the paper. Financial support from the Regione Sardegna for the project CIREM “Analysis of competitiveness of Sardinia’s production system” is gratefully acknowledged.
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Appendix
Appendix
1.1 Data and variables description
The main source of information is a survey conducted by the MET (Monitoraggio Economia e Territorio s.r.l.). The survey contains information on 25,090 Italian firms for the year 2011, with some information also referring to the period 2009–2011. This sample of firms has been built using a stratification procedure by size, sector and region of the firms, to ensure representativeness at a national level. Firms in the dataset belong to different sectors of manufacturing and services and are located in all Italian regions. The information contained in the survey is mostly qualitative and ranges from employment to investments, innovation, and internationalization. To also have quantitative information (particularly for the TFP estimation), we match and merge the MET survey and the balance-sheet information from AIDA (Bureau Van Dijk) and the ICE-Reprint data (confining to the foreign direct investments information). After matching the information for each firm from the survey with the balance-sheet data and checking the consistency of a number of firm identifiers (mainly the 2-digit sector and the region), we are left with 10,459 firms for which the matching procedure has been successful. Further controls and the necessity to estimate the TFP reduce the sample size to 7,590 firms, which represent our final dataset. The main variables we employ are described in Table 8.
1.2 Total factor productivity estimation
The TFP estimation is based on the Solow residuals from an econometric specification derived from a Cobb–Douglas production function. This measure of the TFP, strictly related to the economic theory and rooted on clear assumptions, triggers a number of empirical issues, mainly due to the endogeneity of the observed data (del Gatto et al. 2011; van Beveren 2012). As a robustness check, we estimate the TFP in three different ways using a fixed-effects estimation (FE), the general method of moments (GMM), and the Levinsohn and Petrin (2003) approach (LP). Exploiting information from our merged database, we build a panel of indicators to estimate TFP on data covering the period 2007–2011. Overall, the three TFP estimates are robust and show a good degree of overlap (Table 9). In the paper, however, we only present the results based on the LP estimates, more appropriate for our analysis, since they explicitly take into account firms’ intermediate inputs.
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Giovannetti, G., Marvasi, E. & Sanfilippo, M. Supply chains and the internationalization of small firms. Small Bus Econ 44, 845–865 (2015). https://doi.org/10.1007/s11187-014-9625-x
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DOI: https://doi.org/10.1007/s11187-014-9625-x