Abstract
Italy ranked last in terms of manufacturing productivity growth according to OECD estimates over the last decade, with a flat, if not declining, trend. In this work we investigate the underlying firm-level dynamics of enterprises on the basis of a database developed by the Italian Statistical Office (ISTAT) covering the period 1989–2004 and containing information on more than 100,000 firms. Over this period not only have the indicators of the central tendency of the distribution of labor productivity not significantly changed, but also the whole sectoral distributions have remained relatively stable over time, with their support at least not shrinking, or even possibly widening, over time. This is even more surprising if one takes into consideration the “Euro” shock that occurred during the period investigated. On the contrary, we observe that inter-decile differences in productivity have been increasing. Further, heterogeneous firms’ characteristics (i.e. export activity and innovation) seem to have contributed to boosting such intra-industry differences. Given such wide heterogeneities we resort to quantile regressions to identify the impact of a set of regressors at different levels of the conditional distribution of labor productivity. One phenomenon that we observe is what we call a tendency toward “neo-dualism” involving the co-existence of a small group of dynamic firms with a bigger ensemble of much less technologically progressive ones.
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Notes
The database has been made available for work after careful censorship of individual information. More detailed information concerning the development of the database Micro.3 is given by Grazzi et al. (2009).
Limited liability companies (società di capitali) have to provide a copy of their financial statement to the Register of Firms at the local Chamber of Commerce.
Istat provides the time series for the Italian economy at: http://con.istat.it/default.asp.
Because of small number of observations, results for the coke and petroleum sector (NACE 23) are not reported.
The AEP density has the following functional form (Bottazzi and Secchi, forthcoming)
$$ f_{{\rm AEP}}(x;{{\mathbf{p}}}) = {\frac{1}{C}}\;\; e^{-\left( {\frac{1}{b_l}}\;\left|{\frac{x-m}{a_l}}\right|^{b_l}\;\theta(m-x)+ {\frac{1}{b_r}}\;\left|{\frac{x-m}{a_r}}\right|^{b_r}\;\theta(x-m) \right)} $$(1)where \({\mathbf{p}}=(b_l,b_r,a_l,a_r,m), \theta(x)\) is the Heaviside theta function and where the normalization constant reads C = a l A 0(b l ) + a r A 0(b r ) with
$$ A_k(x) = x^{{\frac{k+1}{x}}-1}\; \Upgamma \left( {\frac{k+1}{x}} \right). $$(2)The two positive shape parameters b r and b l , describe the tail behavior in the upper and lower tails, respectively; two positive scale parameters a r and a l , are associated with the distribution width above and below the modal value and one location parameter m, represents the mode. The AEP reduces to the exponential power distribution Subbotin (1923) when a l = a r and b l = b r .
The a l and a r are substantially stable and are not reported.
Smaller b corresponds to fatter tails.
Quite obviously, more disaggregated three-digit sectors have many fewer observations than the corresponding two-digit sectors in which they are nested. Thus in order to recover a higher number of observations we pool together observations at the beginning (1991–92) and at the end (2003-04) of the period of observation.
These results are largely invariant to the size of the firm. The same analysis applied to firms with more than 100 employees gives the same patterns.
The period 2000–04 is chosen to take advantage of the change in the data collection procedure (see Sect. 2), which made available financial statements for all limited liability companies.
The export variable is the average of a yearly dummy on export activity.
The measure is in terms of the number of four-digit sectors in which the firms operate as an exporter and as an importer.
Gross operative margin is valued added minus wages, salaries, and social insurances paid by the firm. We use this basic measure of profitability (GOM/total sales) as we expect it to be relatively less biased by accounting interferences than other indicators, for example net profits.
This specification has the advantage of reducing endogeneity problems between our main independent variables—export and innovation—and productivity growth, because both are predetermined. We also try to reduce possible bias due to unobserved heterogeneity by accounting for a number of firm’s characteristics (see Bernard and Jensen 1999, for a similar regression).
For the pre-Euro subperiod the export dummy takes value one if the firm was exporting in both 1991 and 1992, or, for the post-Euro subperiod, in both 2000 and 2001. Note that the export status is very stable over time. If a firm is exporting in a given year there is a 90% chance it will be exporting in the following year also.
The patent dummy takes a value of unity if the firm had registered a patent in at least one of the two first years, 1991 and 1992 or 2000 and 2001. We consider patents registered at the USPTO or at the EPO.
Note that, inevitably, our measure of productivity is not a physical one, but value added at constant prices. Granted that if exporters before the Euro found it possible to increase their Lira prices that could have showed up as a (spurious) augmentation in value added vis à vis non-exporters. Obviously that became impossible in the Euro era.
In particular, only in few sectors is holding patents related to higher productivity growth in the following period. Results are reported in Appendix B.
In particular, the variable “investment” is always available in the first subperiod, 1991–95, whereas in the second subperiod, 2000–04 it is only available for firms surveyed by Istat, the National Office of Statistics. That amounts to all firms above 100 employees and a representative sample of firms in the employment range 20–100.
Note that we select in our dataset either those firms that have exported both before and after the adoption of the common currency, or those that have served the domestic market in both periods.
The validity of the DID estimator relies on the assumption that the underlying trends in the outcome variable is the same for both treatment and control groups. To check for this assumption we compare the trend of exporters and domestic firms in productivity (level and growth) in the pre-Euro years. In our case the common trend assumption of DID holds starting from 1996 onwards.
Results do not change if different time intervals are used.
The word “dualism” has been used historically to denote the co-existence of “modern” and “traditional” sectors, with, supposedly, the industrialization process fostering the expansion of the former and the progressive disappearance of the latter.
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Acknowledgments
We benefitted from the comments of two anonymous referees and several participants in the conferences CAED 2009 Tokyo, ISS 2010 Aalborg, The Demography of firms and industries, Paris, and in particular John Sutton. We acknowledge financial support from the European Commission 6th FP (Contract CIT3-CT-2005- 513396), Project: DIME—Dynamics of Institutions and Markets in Europe and from the Institute for New Economic Thinking, INET inaugural grant #220. One of the authors (C.T.), gratefully acknowledges financial support by the Marie Curie Program Grant COFUND Provincia Autonoma di Trento “The Trentino Programme of research, training and mobility of post-doctoral researchers”. The views expressed in the paper are those of the authors and not those of their respective institutions.
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Appendices
Appendix A: Productivity levels and differences: three-digit analysis
Table 11 reports the same analysis on the levels of productivity performed in Sect. 3.1 and focuses on three-digit sectors in order to verify if the aggregated analysis at the two-digit level introduced any bias in the results. This is not the case and results are coherent with the two-digit level analysis. Comparing the years 2004 and 2000, there are indeed 10 sectors (out of the 61 that fulfill the data requirements) in which productivity is higher in 2004 than in 1999. But there are six for which the reverse is true; and for all the other sectors the differences in the distribution of productivity in the two years is not significant.
Consider now year 2004 versus 1995. Productivity is higher in 2004 for 20 sectors. Yet for 2/3 of our sample it is not possible to reject the null that the distribution of productivity has not shifted to the right. Thus, as it was for the analysis at the two-digit level (cf. Table 3), in order to recover some evidence of significantly different levels of productivity between two years, one has to compare the first and last year in the sample: in this case productivity is higher for most of sectors for which observations are available.
Appendix B: Productivity growth by sector
See Table 12.
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Dosi, G., Grazzi, M., Tomasi, C. et al. Turbulence underneath the big calm? The micro-evidence behind Italian productivity dynamics. Small Bus Econ 39, 1043–1067 (2012). https://doi.org/10.1007/s11187-011-9326-7
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DOI: https://doi.org/10.1007/s11187-011-9326-7