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Exploring the link between innovation and growth in Chilean firms


We investigate the relation between the introduction of innovation and subsequent firm growth employing a dataset representative of the Chilean productive structure. By means of quantile treatment effects (QTE), we estimate the effect of the introduction of innovation by comparing firms with a similar propensity to innovate for different quantiles of the firm growth distribution. Our results indicate that process innovation positively affects sales growth for those firms located at the 75th and 90th percentiles. Contrarily, product innovation appears not to be a driver of firm performance. We also find that process innovation benefits mature firms at higher quantiles while it positively affects young firms located at low-medium quantiles.

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  1. 1.

    The presence of these specificities concerning the innovation process in less developed countries may also lead to the mismeasurement of innovation output associated with indicators of standard use. According to Cassoni and Ramada-Sarasola (2012), “since new products do not constitute the core of innovation in non-developed economies, standard innovation output indicators - the share of innovative sales; the accumulated number of patents - would not act as good proxy variables whenever most firms are process-innovative” (p.143).

  2. 2.

    Since the third wave of the ELE provides firm revenues for 2012 in the third wave, we employ 2012 instead of 2013 in the analysis in order to have more homogeneous time intervals.

  3. 3.

    It corresponds approximately to 31,000 USD (1 UF = 39 USD). Firm sizes are stratified according to the following segmentation: micro (800–2400 UF), small (2400–25,000 UF), medium (25,000–100,000 UF) and large (100,000 or more).

  4. 4.

    This cleaning procedure concerning the growth rates led to the exclusion of 57 large firms that experienced extremely high (positive and negative) growth rates. In particular, these large firms have a striking drop (close to 100 %) in their sales in 2009 while these recover in 2012. Therefore, they have extremely high growth episodes for this period (2009–2012). These are probably the results of measurement errors, anomalies in the data concerning those firms or possible M&A that we do not observe. However, since the setting of the 500 % threshold could be considered somewhat discretionary, we conducted the analysis also using a lower and a higher threshold of, respectively, 400 and 600 % and results do not change.

  5. 5.

    Note that we smooth the growth rate over several years since we observe sales data over non-homogeneous time intervals (years 2007, 2009 and 2012); thus, to make the growth rates comparable, we compute an annual average.

  6. 6.

    As Audretsch et al. (2014) pointed out, the indicators used to measure growth are not neutral with respect to empirical results. Different measures describe different patterns of growth (Delmar et al. 2003). In order to check the sensitivity of our results, we employed an alternative growth rate measure based on Birch (1987) which helps reduce the bias toward larger firms (absolute growth) and small firms (relative growth rate). The results are available upon requests.

  7. 7.

    We must stress that, while the second and third waves feature detailed information concerning R&D activities, the first one collects data on R&D which are not strictly comparable with those present in other waves, and, therefore, we restrain from investigating the association between conducting R&D and subsequent firm growth.

  8. 8.

    We choose this cut-off based on recent studies (Coad et al. 2013; Navaretti et al. 2014; Kantis et al. 2016; Coad et al. 2016). However, the estimates presented in the next sections are replicated using a smaller sample of young firms defined as having no more than 8 years old. The results, available from the authors upon request, do not change substantially.

  9. 9.

    Note that all firms in the sample answer this question irrespectively of their innovation status. Productive development policies comprehend a wide array of policy tools such as tax exemptions for training programmes, subsidies aimed at increasing exports, programmes to strengthen the links between small and large firms, and tax credits for the acquisition of machinery.

  10. 10.

    In order to check the robustness of the QR estimates, we employed a recent method proposed by Parente and Santos Silva (2016) which allows to obtain standard errors asymptotically valid under heteroskedasticity and misspecification. The estimates are similar whether we estimate the asymptotic covariance matrix or we estimate the covariance matrix by bootstraping. The results are available upon request.

  11. 11.

    Note that, for the entire split-sample analysis presented in the paper, we do not test whether the coefficients of the innovation variables are significantly different among the two subgroups of firms.

  12. 12.

    Santoleri (2015), employing the ELE, shows similar results on firm age and product innovation.

  13. 13.

    In a previous version of this paper, we also provided nearest neighbour matching (NNM) estimations since we were not performing the split-sample analysis for young and mature firms. Compared to PSM, the NNM showed better covariate balancing. However, given that this technique requires more observations than the PSM, we decided not to perform it since this would lead to a substancial loss of observation due to the impossibility of finding an adequate match.

  14. 14.

    As already mentioned, we do not test whether the difference in the coefficients for young and mature firms is statistically different. Hence, the estimates regarding the split-sample exercise must be interpreted as statistically significant differences in the returns to innovation within each subgroup and not across them.


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The authors would like to thank Federico Tamagni, Angelo Secchi, Stefano Bianchini, Le Li, Nanditha Mathew, Ernest Migueléz, Andrin Spescha, Lorenzo Napolitano and Emanuele Pugliese as well as two anonymous referees for their helpful comments and suggestions. We are also deeply indebted to Werner Bönte (the editor). Thanks are also due to the participants to the DRUID Academy conference at Bordeaux University and to the 3rd Ph.D. workshop in Economics of Innovation, Complexity and Knowledge at Collegio Carlo Alberto in Turin.

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Correspondence to Pietro Santoleri.



Table 10 Sample distribution by industry and size
Table 11 Summary statistics and variable description
Table 12 Pairwise correlation matrix
Table 13 First-step of the PSM estimates: logit model

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Santi, C., Santoleri, P. Exploring the link between innovation and growth in Chilean firms. Small Bus Econ 49, 445–467 (2017).

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  • Innovation
  • Firm growth
  • Chile
  • Firm age
  • Quantile treatment effects

JEL Classification

  • C14
  • C21
  • C22
  • D22
  • O31