Abstract
Using a large sample of Italian small and medium-sized enterprises (SMEs), we investigate the effect of business cooperation realized through a “network contract” on the economic performance of network members. We find that establishing a formal business network has a positive effect on a firm’s gross margin ratio and exports, but not on profits. The advantages of this type of networking are stronger in the cases of the following: smaller firms; firms operating in traditional markets; firms operating in turbulent markets; firms located in less developed areas; and firms not part of an industrial district. The characteristics of a network (such as its size, geographical dispersion, and the sectorial diversity of its members) also have an impact on firm performance.
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Notes
See also Nunes et al. (2017), who adopt a similar approach to analyzing the relationship between formal and informal networks and innovation for a sample of Portuguese firms.
The information is self-reported, since firms are asked if they cooperate with other firms in areas such as expanding product spectrum, sales, financing opportunities, etc.
It is also possible to detach some workers from one firm to another within the network, as well as to organize joint recruitment procedures and joint hiring of workers.
For example, in France firms can set up an Economic Interest Grouping (EIG), a legal entity adopted also by the European Union in the form of the European Economic Interest Grouping (EEIG), with the aim of promoting the formation of international networks of firms. With respect to EIGs and EEIGs, the Italian network contract is more flexible and allows member firms to retain their independence and autonomy. For more details, see Ferrari (2010).
Note that, for each firm in the AIDA database that enters a network agreement, we have information about the whole network, even if for some members we do not have financial data.
There was a jump in 2012, and the small number of firms participating in the networks in the years immediately after the introduction of the law is one drawback of our analysis.
While ROA is one of the most commonly used measures of profitability, we also computed ROS (EBIT margin over total sales) and ROE (EBIT margin over equity), obtaining similar findings for all the regressions. Results are available upon request.
A higher share reflects the fact that the firm, instead of organizing most of the activities in-house, relies on contracts with third parties for the supply of inputs and components.
For the sake of comparison, Table 4 reports the results of simple OLS models which pool all firms and year observations. While the FE and OLS estimates are not very different in the case of value added, the impact of networking on exports is almost twice as large in the OLS models. Similarly, the impact on ROA is negative and statistically significant according to the OLS estimates, while it is always insignificant in all the FE models. Overall, these differences suggest that uncontrolled firm characteristics may have biased some of the results obtained in the literature. Hence, unless differently stated, the rest of the paper focuses on the results of fixed-effect models.
We thank an anonymous referee for having raised this issue.
For exporters, the average share of sales in foreign markets is 24%.
The same result emerges if we add to the model in Table 4, column (8), the interacted term NET×EXP among the regressors. The coefficient on the interacted terms turns out to be positive and significantly different from zero. Results are available upon request.
We are indebted to an anonymous referee for suggesting this angle of analysis to us.
The coefficient (0.0067) is larger than the one reported for the whole sample (0.0055), and the statistical significance is higher as well.
The approach of grouping firms according to four homogeneous socioeconomic subsystems is very common for empirical studies focusing on Italy.
Volatility is computed at the NACE three-digit classification (over 350 sectors), using the entire AIDA dataset, including the years before the introduction of the network contracts, in order to minimize endogeneity concerns.
The Pavitt’s taxonomy is widely used to classify sectors according to their main innovation characteristics. Bogliacino and Pianta (2016) have recently extended the Pavitt’s taxonomy in order to classify both manufacturing industries and service sectors.
Since time-invariant controls (i.e., network characteristics) are not changing over time, we cannot use the fixed effect estimator. However, the fact that firms are pre-selected among those firms entering a network mitigates the potential endogeneity problems.
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Cisi, M., Devicienti, F., Manello, A. et al. The advantages of formalizing networks: new evidence from Italian SMEs. Small Bus Econ 54, 1183–1200 (2020). https://doi.org/10.1007/s11187-018-0127-0
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DOI: https://doi.org/10.1007/s11187-018-0127-0