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How do firms combine different internationalisation modes? A multivariate probit approach

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Abstract

Most of the literature on the relationship between firm’s participation in international markets and firm heterogeneity focuses on export and foreign direct investment. This paper considers a wider range of forms of internationalisation that firms could combine into different patterns. With the purpose of analysing the selection of heterogeneous firms into different internationalisation patterns, we jointly model the decisions on the forms of internationalisation through a multivariate probit. This model allows us to avoid any a priori assumption about the firm’s behaviour. In this context we study the complementarity/substitutability relationships between forms of internationalisation. The results obtained show that: (i) neglecting some forms could lead to an incomplete representation about the firm’s internationalisation strategy, (ii) different firm’s characteristics influence the choice of internationalisation pattern, i.e. some types of firm are more prone to choosing one type of process over another, (iii) complementarity between forms of internationalisation seems to be preferred over substitution, but some heterogeneity also arises in this context.

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Notes

  1. See Basile et al. (2003); Benfratello and Razzolini (2009); Castellani and Zanfei (2007); Tomiura (2007); Bougheas and Görg (2008); Oberhofer and Pfaffermayr (2012).

  2. Capitalia was one of the largest Italian banks. It was recently acquired by the Unicredit group.

  3. See Greene (2003) for a brief textbook exposition.

  4. “Survey on Italian Enterprises. Report on the productive system and industrial policy”, Observatory on Small and Medium Enterprises, Capitalia, IX Report, 2005 (in Italian).

  5. See Benfratello et al. (2008); Bianco and Nicodano (2006); Filatotchev et al. (2003); Hall et al. (2009); Minetti and Zhu (2011); Parisi et al. (2006).

  6. Some authors (see, e.g. Aw and Lee 2008; Oberhofer and Pfaffermayr 2012) find that firm age have a crucial role in explaining the firms decision to serve foreign markets. We also include, in a first exercise, the age of the firm in the regressions together with the other covariates but its coefficient was never significant. For this reason and because of the presence of missing data in this variable that should lead to lost observations from our sample, we decide to not include it in our final model. As for other results in our analysis (see next in the paper), the lack of any effect of firm age on the decision to serve foreign market could have one possible explanation in the absence of micro-firms in our sample, which usually are younger than larger firms.

  7. The Pavitt taxonomy is a classification of economic sectors based on technological opportunities, innovations, R&D intensity, and knowledge. For details on categories, see Table 8 of the “Appendix”.

  8. The survey collects data on the amount of R&D expenditures and investment in ICT, but we did not use them due to the large amount of missing data.

  9. The model has been estimated using STATA (version 9) ml command, with a self-supplied code for the log-likelihood calculation, and the modules mdraws and mvnp developed by Cappellari and Jenkins (2006).

  10. Because marginal effects are a non-linear combination of model parameters, standard errors (not reported) were estimated by the Delta method and significance was tested by a Wald test. Detailed results are available from the authors upon request.

  11. To check the robustness of estimates to the specification of the size variable, we also estimate the model using the number of employees instead of the four dummies for the size. The marginal effect (at mean) of the size is positive and significant on each internationalization choice and increasing at different level of size, as in the presented results. The sign of the other coefficients and MEMs are all confirmed.

  12. In a previous estimation exercise not reported here, with no industrial district indicator and a dummy for investments in fixed capital instead of capital intensity, we found a strong positive association between locations in the North of Italy and exports.

  13. In order to check the robustness of this result for TFP, we use a Tornquist-type index number to measure the firm’s TFP (Caves et al. 1982) an approach already used in the context of analysis of firms’ internationalization (Delgado et al. 2002; Girma et al. 2005). We re-estimate the model by using this TFP measure: coefficient does not change sensibly and its significance does not change at all.

  14. The estimates of MEMs referred to the remaining probabilities are available from the author upon request.

  15. Conditional and joint probabilities are highly nonlinear in both parameters and covariates, which prevent a tractable analytical solution of MEMs and standard errors (SEs) that are then calculated using simulation and numerical gradients. In particular, we simulate 500 sets of parameters from an asymptotic multivariate normal distribution, each time calculating the predicted probability, and its numerical derivatives with respect to the relevant covariates evaluated at the means of all covariates. We thereby obtain 500 sets of PPR and MEMs, and sample SEs are calculated as estimates of the SEs for the PPR and MEMs.

  16. Obviously, a number of other combinations of the four internationalisation modes could have been considered, but here we limit the analysis to those that we deem as the most remarkable.

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Authors and Affiliations

  1. Department of Statistics, University of Bologna, Via Belle Arti, 41, 40126, Bologna, BO, Italy

    Pinuccia Calia & Maria Rosaria Ferrante

Authors
  1. Pinuccia Calia
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  2. Maria Rosaria Ferrante
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Correspondence to Maria Rosaria Ferrante.

Appendix

Appendix

See Tables 6, 7, 8, 9, 10, and 11.

Table 7 Distribution by industry and size, initial and final sample
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Table 8 Variables definition (for covariates: in brackets the reference category used in estimated models)
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Table 9 Sample means and standard deviation of covariates
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Table 10 Estimates of correlation coefficients
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Table 11 Multivariate probit estimates—SML using GHK simulator with R = 300 replications
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Calia, P., Ferrante, M.R. How do firms combine different internationalisation modes? A multivariate probit approach. Rev World Econ 149, 663–696 (2013). https://doi.org/10.1007/s10290-013-0162-5

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