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Innovation and productivity in SMEs: empirical evidence for Italy

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Abstract

Innovation in SMEs exhibits some peculiar features that most traditional indicators of innovation activity do not capture. Therefore, in this paper, we develop a structural model of innovation that incorporates information on innovation success from firm surveys along with the usual R&D expenditures and productivity measures. We then apply the model to data on Italian SMEs from the “Survey on Manufacturing Firms” conducted by Mediocredito-Capitalia covering the period 1995–2003. The model is estimated in steps, following the logic of firms’ decisions and outcomes. We find that international competition fosters R&D intensity, especially for high-tech firms. Firm size and R&D intensity, along with investment in equipment, enhances the likelihood of having both process and product innovation. Both these kinds of innovation have a positive impact on firm’s productivity, especially process innovation. Among SMEs, larger and older firms seem to be less productive.

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Notes

  1. For a survey of previous empirical results, see Mairesse and Sassenou (1991); Griliches (1998).

  2. See Hall and Mairesse (2006) for a comprehensive survey. Recent papers based on the CDM model include Benavente (2006) on Chile, Heshmati and Lööf (2006) on Sweden, Jefferson et al. (2006) on China, Klomp and Van Leeuwen (2001) on The Netherlands, Mohnen et al. (2006) on seven European countries and Griffith et al. (2006) on four European countries.

  3. Although the Mediocredito-Capitalia survey is not a panel itself, it contains repeated observation for a number of firms, which is enough to allow the estimation of a dynamic framework. See Sect. 3 of this paper for further information on the data.

  4. We require that sales per employee be between 2,000 and 10 million euros, growth rates of employment and sales of old and new products between −150% and 150%, and the R&D employment share less than 100%. We also replaced R&D employment share with the R&D to sales ratio for the few observations where it was missing. For further details, see Hall et al. (2008). In addition, we restrict the sample by excluding a few observations with zero or missing investment.

  5. We adopt the OECD definition for high- and low-tech industries. High-tech industries encompass high and medium-high technology industries (chemicals; office accounting and computer machinery; radio, TV and telecommunication instruments; medical, precision and optical instruments; electrical machinery and apparatus, n.e.c.; machinery and equipment; railroad and transport equipment, n.e.c.). Low-tech industries encompass low and medium-low technology industries (rubber and plastic products; coke, refined petroleum products; other non-metallic mineral products; basic metals and fabricated metal products; manufacturing, n.e.c.; wood, pulp and paper; food, beverages and tobacco products; textile, textile products, leather and footwear).

  6. We do not yet know how much of the difference is due to differences in sampling strategy across the different countries.

  7. Although we did not include these results in the paper for the sake of brevity, they are available from the authors.

  8. Note that this is a generalization of Heckman’s two-step procedure for estimation when the error terms in the two equations are jointly normally distributed. The test here is valid even if the distribution is not normal.

  9. For instance, a firm present in all three waves will have a “111” code, “100” if present in the first only, “110” if in the first and in the second only, and so forth. These codes are transformed into a set of six dummies (23 = 8 minus the 000 case and the exclusion restriction).

  10. Because of the large number of missing observations, we could not use a narrower definition of subsidies.

  11. The first is estimated probability of process and not product from the bivariate probit model in Table 4, and the second is the marginal probability of product innovation from the same model.

  12. The sample size in this table is 9,014, reduced from 9,674 in the main tables of the paper due to the absence of lagged capital (beginning of year capital) for some of the observations.

  13. The overlap of this sample with the sample used in the main body of the paper is 75%.

  14. For precise comparability with the earlier paper, in this table we estimated the process and product innovation equations using single probits rather than a bivariate probit. This is consistent, but not efficient, given the correlation between the two equations.

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Acknowledgements

We would like to thank the Mediocredito-Capitalia (now Unicredit) research department for having kindly supplied firm level data for this project. We thank also Susanto Basu, Ernie Berndt, Piergiuseppe Morone, Stéphane Robin, Mike Scherer, Enrico Santarelli, Alessandro Sembenelli, Marco Vivarelli and participants at the NBER Productivity Seminars, at the workshop “Drivers and Impacts of Corporate R&D in SMEs” held in Seville at IPTS. The views expressed by the authors do not necessarily reflect those of the Bank of Italy.

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Authors and Affiliations

  1. Department of Economics, University of California at Berkeley, 549 Evans Hall, Berkeley, CA, 94720-3880, USA

    Bronwyn H. Hall

  2. UNU-MERIT, Maastricht University, Maastricht, The Netherlands

    Bronwyn H. Hall & Jacques Mairesse

  3. NBER, Cambridge, MA, USA

    Bronwyn H. Hall & Jacques Mairesse

  4. IFS, London, UK

    Bronwyn H. Hall

  5. Economic Research Department, Bank of Italy, Via Nazionale 91, 00184, Rome, Italy

    Francesca Lotti

  6. CREST (ENSAE, Paris), 15, Boulevard Gabriel Peri, 92245, Malakoff Cedex, France

    Jacques Mairesse

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  1. Bronwyn H. Hall
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  2. Francesca Lotti
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  3. Jacques Mairesse
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Correspondence to Francesca Lotti.

Appendix: variable definitions and supplementary tables

Appendix: variable definitions and supplementary tables

R&D engagement: dummy variable that takes value 1 if the firm has positive R&D expenditures over the 3 years of each wave of the survey.

R&D intensity: R&D expenditures per employee, in real terms and in logs.

Process innovation: dummy variable that takes value 1 if the firm declares to have introduced a process innovation during the 3 years of the survey.

Product innovation: dummy variable that takes value 1 if the firm declares to have introduced a product innovation during the 3 years of the survey.

Innovator: dummy variable that takes value 1 if the firm has process or product innovation.

Share of sales with new products: percentage of the sales in the last year of the survey coming from new or significantly improved products (in percentage).

Labor productivity: real sales per employee, in logs.

Investment intensity: investment in machinery per employee, in logs.

Public support: dummy variable that takes value 1 if the firm has received a subsidy during the 3 years of the survey.

Regional–national–European–international (non EU) competitors: dummy variables to indicate the location of the firm’s competitors.

Large competitors: dummy variable that takes value 1 if the firm declares to have large firms as competitors.

Employees: number of employees, headcount.

Age: firm’s age (in years).

Size classes: [11–20], [21–50], [51–250] employees.

Age classes: [<15], [15–25], [>25] years.

Industry dummies: a set of indicators for a two-digit industry classification.

Time dummies: a set of indicators for the year of the survey.

Wave dummies: a set of indicators for firm’s presence or absence in the three waves of the survey.

High-tech firms: encompasses high and medium-high technology industries (chemicals; office accounting and computer machinery; radio, TV and telecommunication instruments; medical, precision and optical instruments; electrical machinery and apparatus, n.e.c.; machinery and equipment; railroad and transport equipment, n.e.c.).

Low-tech firms: encompasses low and medium-low technology industries (rubber and plastic products; coke, refined petroleum products; other non-metallic mineral products; basic metals and fabricated metal products; manufacturing, n.e.c.; wood, pulp and paper; food, beverages and tobacco products; textile, textile products, leather and footwear).

Capital stock: fixed capital stock, in real terms, computed by a perpetual inventory method with constant depreciation rate (δ = 0.05). The starting value is the accounting value as reported in firm’s balance sheets (see Tables 7, 89 and 10).

Table 7 A comparison of selected variables for France, Germany, Spain, the UK and Italy
Full size table
Table 8 A non-parametric selectivity test
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Table 9 Robustness check for step 2 and 3
Full size table
Table 10 Robustness check using lagged capital and ML estimation (9,014 observations)
Full size table

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Hall, B.H., Lotti, F. & Mairesse, J. Innovation and productivity in SMEs: empirical evidence for Italy. Small Bus Econ 33, 13–33 (2009). https://doi.org/10.1007/s11187-009-9184-8

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