Turbolence underneath the big calm:
what is happening behind the ﬂat trend of productivity in Italy
Giovanni Dosi† , Marco Grazzi†, Chiara Tomasi†, and Alessandro Zeli‡† LEM, Scuola Superiore Sant’Anna, Pisa, Italy‡ ISTAT, Roma, Italy
Data
The research we present here draws upon the Micro.3 databank developed by the Italian Statistical Office(ISTAT)1. The database, that covers the period 1989-2004 (2005 and 2006 are getting available at the time weare writing this extended abstract), contains information about 134625 Italian firms and represents adevelopment of the former Micro.1 dataset, which was the census of all the Italian firms with 20 employees
or more conducted by ISTAT over the period 1989-1997 (see Dosi, 2007, and the works cited therein for asurvey of results on that dataset). Over the period covered by the data there are missing values partly due tothe fact that some firms may exit from the database as they reduce their size and fall below the 20 employees
threshold. The existence of missing values makes Micro.1 an unbalanced panel data-set. Despite that, thevalidity of the database is largely supported by its census nature, which avoids possible biases in the datacollection process. In addition, as reported in Bartelsman et al. (2004), though manufacturing firms with lessthan 20 employees account for about 88% of the total Italian firm population, firms with more than 20employees cover almost 70% of the total employment. Micro.3 contains standard variables appearing infirms’ financial statement. In this work, for brevity, we focus on manufacturing sectors only and we consider
2 and 3 digit industrial sectors, according to the European NACE classification. The Micro.3 database has
been merged with the ISTAT's external trade register (COE) and with a novel patent database. The COE
database contains information on all trade exchanges involving Italian ﬁrms. As such we can always
associate to every firm in our database the correct trade status and ensure that we are not introducing any
selection bias. Further, we exploit a patent database that contains the number of registered patents (bothUSPTO and EPO). The patent database contains information on patents granted to Italian firms starting in1949 for the USPTO and 1978 for the EPO, thus even before the beginning of the period considered forMicro.3.
Preliminary Results
International comparisons (OECD, 2008) shows that Italy registered a very poor performance with respect toother OECD countries. Italy ranked last in terms of growth of GDP per hour worked over the period 19952006 (see OECD, 2008, p. 17). Italy registered a zero growth in the years 2001-2005 and an average annual
growth below 1% in the previous period, 1995-2000. Only Spain did worse in this subperiod. In a number ofOECD countries labor productivity accelerated in the second half of the 1990s but slowed again in the firsthalf of the new millennium. Italian performance on value added per worker was substantially lower than theOECD average in both sub-periods, so that over the longer time span 1995-2006 it ranks last.
To start with, we show that the aggregate statistics that one can build on Micro.3 are indeed coherent with thenational statistics released by the OECD or Eurostat 2. Once that such comparability of Micro.3 and aggregate
1This research could have not been possible without the valuable collaboration of the Italian Statistical Oﬃce (ISTAT- IndustrialStatistics Division).The data are made available to our team under the mandatory condition of censorship of any individualinformation.
2 In this respect a methodological note is due. Aggregate statistics for productivity typically report a measure, value added perworker, that is the ratio between the whole value added of the economy (or of one of its sectors) and the sum of all workers
employed. In this sense one is not able to assess the contribution of a firm (or a set of them) to the productivity of one sector or ofthe whole economy. On the contrary, exploiting the micro-data as those contained in Micro.3, not only one can (re)produce thesame measure (by summing up value added and worker and then taking the ratio) but one can also estimate the average of the
productivity of firms in a sector and its variance.

statistics is ascertained, we start exploiting the higher information content provided by the microdata. In
particular, as far as the levels of productivity are concerned, this allows us to test the hypothesis of anincrease in the average labor productivity over time. As said, due to the huge heterogeneity in the distributionof productivity - thus reflected in big standard deviations - it is not possible to come to such conclusion on
the basis of comparisons of averages. Further the distributions of productivity displays a substantial departurefrom normality (Dosi and Grazzi, 2006; Bottazzi et al., 2007). Thus, in order to verify if two samples, in ourcase the distributions of productivity in two different years, differ in their central tendency we will perform a
non-parametric test, namely the Fligner-Policello test (Fligner and Policello II, 1981). This test does notassumes neither normality nor equal variances and it can be interpreted as a test of stochastic equality
between the two distributions, that is we statistically verify if the distribution of productivity in one year isdominating the distribution of productive in another year. As it could be expected the evidence is notencouraging. In the post-euro subsample, 1999-2004, for most sectors it is not possible to conclude that
productivity was higher in 2004 than in other years. Not only, most of the years in the post-euro period reporta higher number of sectors for which productivity in 2004 was lower than in the previous years; this isaccounted for by the positive and significant values of the test statistics. Consider for instance, year 2004
versus 1999, the first after the euro introduction. The number of sectors, 4, in which productivity is higher in1999 exactly equals those in which the productivity in 2004 is higher. In conclusion, the analysis of thedynamics of productivity reveals that, with respect to the period 1991-2004, there has been a considerable
stagnation, once we consider constant price values, of value added per worker. Even more worrying, it is notpossible to statistically discard the hypothesis that in some sectors labor productivity was higher in the pastthan in 2004. Such stagnation, somewhat counterintuitevely, goes together with the increase in the dispersion
in the productivity distribution at the sectoral level. Firms in the first decile for productivity are five to six
times more productive than firms in the lowest decile, and this gap is not shrinking over time. On thecontrary, in some sectors there is evidence of increasing differences.
Hence, it would appear that the increasing gap between most and least productive is due to specificcharacteristic of firms, what one might call their constituent “identity card”. The richness of the information
contained in Micro.3 allows us to perform analyses that enable us to carefully identify those dimensions of afirm’s performance on which these distinctive characteristics might exert their influence upon. To this end,
we observe how export activity and innovativeness contribute to boost the intra-industry differences. One
particularly suited way to investigate what drives such dynamics of increasing diversities of performance isby means of quantile regression. Exploratory analyses have indeed revealed that, selecting firms on the basisof these crucial dimensions (i.e. exporting and patenting) yields a relatively small subsample of firms thatreport a substantial difference in terms of productivity with respect to the industry average, meaning that theeffect of a regressor on the dependent variable will not necessary be the same at all level of the conditional
distribution of the dependent variable. Quantile regression enables us to ascertain if and by what extent thisimpact is varying and in this respect it also permits to to identify and highlight the existing heterogeneity.
References
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