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ICT and R&D as inputs or efficiency determinants? Analysing Italian manufacturing firms (2007–2009)

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Abstract

Are Information and Communication Technology (ICT) and Research & Development (R&D) productive inputs or efficiency determinants? This is the topic of this paper, which analyses a sample of 2691 Italian manufacturing firms over the period 2007–2009. Data are from a merged EFIGE–AIDA dataset. The empirical setting is based on a production function estimated through the stochastic frontier approach. ICT and R&D are used once as inputs, once as efficiency determinants. The results show that the elasticities of production with respect to ICT and R&D investments are quite high (0.08 for ICT and 0.04 for R&D) when they enter into the model only as inputs. We also documented that ICT and R&D contribute positively to explain the efficiency scores.

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Notes

  1. Solow (1987) states as follows: “… what everyone feels to have been a technological revolution, a drastic change in our productive lives, has been accompanied everywhere, including Japan, by a slowing-down of productivity growth, not by a step up. You can see the computer age everywhere but in the productivity statistics.”

  2. The choice of using a short time period is because we want to use merged information deriving from both EFIGE and AIDA. We know that this can lead to bias problem even considering that in the analysed period covers years of crisis.

  3. SF approach is preferred in this work also with respect to semiparametric methods proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003) and also to GMM estimations (Blundell and Bond 1998), because all these methods belong to the class of non-frontier techniques (Del Gatto et al. 2011). This implies all these methodologies share the assumption that production is always full efficient in terms of technology, while the SF approach allows to decompose the productivity in technological change and efficiency change.

    It is worth noticing that one advantage of approach proposed by Olley and Pakes (1996) is the flexible characterization of productivity because it only assumes that it accords to a Markov process (van Biesebroeck 2008), but potential weakness is the nonparametric approximation. Moreover, the cited semiparametric method can produce estimates suffering from collinearity (Ackerberg et al. 2006). As regard the GMM method, it is flexible in generating instruments in order to avoid endogeneity problems. However, it need for a long panel, at least four time periods are required (we have only three periods in our empirical analysis) and, if instruments are weak, this method risk underestimating the coefficients (van Biesebroeck 2008).

  4. We estimate also Cobb-Douglas production functions and, by implementing the LR test, we reject this specification in favour of the translog form.

  5. The number of observations is determined by the no-missing values in 2007–2009 and by the fact that we use lagged variables in order to limit endogeneity problems.

  6. See Gandhi et al. (2013) for the identification of the production function. This work analytically explains that raw materials do not enter in the production function when output is measured as value added.

  7. We do not have information about ICT and R&D stocks.

  8. We suppose that the percentages of ICT and R&D investments do not significantly change in consecutive years.

  9. Given that we depart from a translog production function and in order to make it linear, all continuous variables are in logs.

  10. The first model is the application of SF as proposed by Battese and Coelli (1992), the second one is the application of the specification as shown in Battese and Coelli (1995).

  11. We do not report the results of these log-likelihood ratio (LR) tests that, however, are available on request.

  12. Under the null hypothesis, there is the absence of inefficiency in the sample. The test-statistic LR is equal to {−2 ln[L(H0)/L(H1)]}. The degrees of freedom are given by the number of parameters exceeding in the alternative hypotheses with respect to the null one. The critical values are tabulated in Kodde and Palm (1986). We reject the null hypothesis at 1 % for all the models considered.

  13. We use the Spearman rank correlation index.

  14. We do not report the estimation of the nested model that is available on request.

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Acknowledgments

This paper was written when the author was post-Doc visiting student at the Royal Docks Business School, University of East London, Docklands Campus (4-6 University Way, London, E16 2RD, UK). The author receives a Research Fellowship from the Regione Calabria and EU Commission. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of EU Commission and Regione Calabria.

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    Graziella Bonanno

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Bonanno, G. ICT and R&D as inputs or efficiency determinants? Analysing Italian manufacturing firms (2007–2009). Eurasian Bus Rev 6, 383–404 (2016). https://doi.org/10.1007/s40821-015-0035-z

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