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Firm innovation and spillovers in Italy: Does geographical proximity matter?

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Abstract

Using a sample of Italian manufacturing firms over the period 2004–2006, a knowledge production function is estimated to investigate the role of knowledge spillovers in the innovative performance of Italian manufacturing firms. Sales of innovative products are used as measure of firm innovative performance. The role of innovative spatial externalities are analysed through the use of spatial econometric techniques. Overall, the results show evidence of a positive role of geographical proximity in innovation.

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Notes

  1. At firm level, Carboni (2013a, b) used spatial econometric techniques to investigate related issues: the importance of geographical and sectoral proximity in promoting R&D investment and R&D collaboration among Italian manufacturing firms; Baltagi et al. (2016 and 2015) investigated the role of spatial spillovers in the productivity of Chinese chemical firms over 2004–2006 and of China’s electric and electronic manufacturing firms over 2004–2007, respectively; Lamieri and Sangalli (2013) allowed for spatial dependence in both dependent variable and error terms across firms in the evaluation of the impact of patents on the total factor productivity (TFP) of Italian manufacturing firms in 2010; Cardamone (2017) used a sample of Italian manufacturing firms over the period 2004–2006 to analyse the role of R&D in firm productivity by employing a spatial autoregressive model.

  2. The survey is compiled on the basis of information collected by means of a questionnaire sent to a sample of Italian manufacturing firms and is complemented with balance sheet data. The survey includes all firms with a minimum of 500 employees and a sample of firms with between 11 and 500 employees selected on a three-dimension stratification: geographical area, Pavitt sector and firm size. Although the survey covers the 2004–2006 period, some parts of the questionnaire refer to 2006 only.

  3. Sales are deflated through the production price index which is available for each sector according to the Ateco (Italian edition of Nace) classification of economic activities. For the tangible fixed assets, values have been deflated by using the average production price indices of the following sectors: machines and mechanical appliances, electrical machines and electrical equipment, electronics and optics and means of transport. R&D investments are deflated by considering the producer price index for industrial products. The source of the price indices is Istat.

  4. The status of ‘exporting’ is assigned on the basis of the positive answer on the question if the firm exported in 2006 in the Xth wave of the UniCredit–Capitalia survey.

  5. Descriptive statistics (Table 4) and correlation matrix (Table 5) are reported in the appendix. Variance inflation factors for independent variables of Eq. (1) are also computed; they are lower than 3 suggesting that there is no multicollinearity among the explanatory variables.

  6. Moreover, a spatial error model (SEM) could be used in order to control for the fact that a random shock to a firm in a specific location l, i.e. a shock in the error u of a firm at a location l, could be transmitted to other firms located nearby: \(\mathbf{y} = \mathbf{X }{\varvec{\upbeta }} + \mathbf{u}, \mathbf{u}= \uplambda \mathbf{Wu}+ {\varvec{\upvarepsilon }}\).

  7. http://clisun.casaccia.enea.it/Pagine/Comuni.htm (last accessed: July 2015). Data regarding the town “Due Carrare”, which were not available in the ENEA dataset, are taken from http://www.tuttitalia.it/veneto/87-due-carrare/ (last accessed: July 2015).

  8. The STATA command spmat provided by Drukker et al. (2013) was used to compute the haversine distance matrix. For the estimations, the R spdep and sphet packages (Bivand et al. 2013; Bivand and Piras 2015; Piras 2010) were employed.

  9. In a min-max normalized matrix, each element is divided by the minimum of the largest row sum and column sum of the matrix. This normalization avoids some issues which arise when row-sum standardization is used (Kelejian and Prucha 2010).

  10. In order to investigate the effect of R&D spillovers on innovative output, also the spatial lag of R&D was introduced in the model. In other words, \({{\varvec{W}}}\cdot {{\varvec{RDinv}}}\) is included in equation (1), where W is the spatial weighting matrix and RDinv is the vector of the R&D indicator. However, results, available upon request, show that geographical R&D spillovers have no significant impact on innovation of Italian manufacturing firms.

  11. It should also be mentioned that in the period under scrutiny, 2004–2006, the transfer of scientific knowledge from universities to the business sector in Italy was a very recent phenomenon (Pietrabissa and Conti 2005; Piccaluga and Balderi 2006; Netval 2008).

  12. Likelihood ratio (LR) tests are employed to compare the spatial Durbin model (SDM) with the spatial autoregressive model and the spatial error model. LR test results show that we cannot reject the hypothesis that the SAR and the SEM describe the data better than the SDM: statistical test value is equal to 6.53 (p-value = 0.686) in the first case and 8.36 (p-value = 0.498) in the second case. Results on the SEM model are reported for comparison in Table 7 of the appendix. The lambda coefficient is not significant. Hence, there is no evidence in favour of spatial dependence in unobservable factors.

  13. Both the maximum likelihood (ML) and the two stage least squares (2SLS) estimators are used to estimate the model. Results obtained considering the two approaches do not substantially vary.

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Authors and Affiliations

  1. Department of Economics, Statistics and Finance “Giovanni Anania”, University of Calabria, 87036, Arcavacata di Rende, CS, Italy

    Paola Cardamone

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Correspondence to Paola Cardamone.

Appendix

Appendix

See Tables 4, 5, 6 and 7.

Table 4 Descriptive statistics.
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Table 5 Correlation matrix
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Table 6 Heckman two-step estimation results
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Table 7 Estimation results. Dependent variable: (log of) innovative sales in 2006. Spatial error model, ML and GS2SLS estimates
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Cardamone, P. Firm innovation and spillovers in Italy: Does geographical proximity matter?. Lett Spat Resour Sci 11, 1–16 (2018). https://doi.org/10.1007/s12076-017-0193-y

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