Abstract
Using local labor systems (LLSs) data, we assess the effect of the local productive structure on employment growth in Italy during the period 1981–2008. Italy represents an interesting case study because of the high degree of spatial heterogeneity in local labor market performances and of the presence of strongly specialized LLSs (industrial districts). Building on semi-parametric geoadditive models, our empirical investigation allows us to identify important nonlinearities in the relationship between local industry structure and local employment growth to assess the relative performance of industrial districts and to control for unobserved spatial heterogeneity.
JEL Classification R11, R12, C14
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Notes
- 1.
- 2.
A similar specification has been used by Paci and Usai (2008) and Mameli et al. (2008). These authors also extend this model by introducing other explanatory factors (such as human and social capital) into the model framework, but they conclude that the baseline model (1) does not suffer from problems connected to omitted variables. On the basis of these evidences and because of the lack of complete information on further explanatory variables for the whole sample period, we do not consider additional factors in our empirical analysis.
- 3.
Cingano and Schivardi (2004) observe that the evidence of a negative effect of MAR externalities may be due to the choice of the employment growth as dependent variable. They show that, within the same sample, if the total factor productivity (TFP) growth is used in place of the employment growth as dependent variable, the sign of the MAR coefficient turns out to be positive. TFP measures have also been used in other recent studies on Italy (Cainelli et al. 2013), Spain (De Lucio et al. 2002), and Europe (Dettori et al. 2012). Although it is an unquestionable improvement of the analyses on the effects of agglomeration economies, the choice of productivity measures often creates additional inconvenience for researchers in terms of data availability. Paci and Usai (2008), for example, stated that the use of productivity measures may lead researchers to consider more aggregated geographical levels, with negative consequences in terms of assessment of local externalities (Dekle 2002; De Lucio et al. 2002) and of selection biases (Henderson 2003; Cingano and Schivardi 2004). For these reasons and in consideration of the fact that we are interested in evaluating long-term effects of agglomeration economies, we decided to use employment growth as variable of outcome in our analysis. Census data on employment at LLS level for a large number of sectors, indeed, allow us to consider a time span of about thirty years. Moreover, the use of employment growth also allows us to verify the existence of differences between Manufacturing and Service sectors, whereas studies on TFP only analyze Manufacturing sectors due to the difficulty of measuring TFP levels in service sectors.
- 4.
As a first step in our empirical analysis, we have estimated the log-linear model (1) and obtained results very much in line with previous evidence reported for the case of Italy in studies which used LLS as territorial units of analysis (Paci and Usai 2008; Mameli et al. 2008) (these findings are available upon request). However, the results of a RESET test clearly informed us that the log-linear model is mis-specified due to the assumptions on the functional form.
- 5.
The technique used in this chapter to estimate semi-parametric geoadditive models is widely discussed in Basile et al. (2013).
- 6.
As it is well known, ISTAT provides data on the number of employees and of establishments in manufacturing and services sectors over the period 1981–2008 by considering two different classifications of LLS, namely the 784 LLSs identified with the 1991s census data and the 686 LLSs identified with 2001s census data. As mentioned above, we use the 2001 classification (686 LLSs) for each decennial census considered in our analysis (1981–1991, 1991–2001, 2001–2008). However, we have also assessed whether the results of our analysis are robust to the choice of the LLS classification. Specifically, we have replicated the regression analysis using data on the 784 LLS (the 1991 criterion) for all the census periods. The results obtained (available upon request) are qualitatively very similar to those reported in the paper.
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Acknowledgements
We thank Giulio Cainelli (University of Padova), Diego Puga (University of Madrid), and the other participants of the AIEL (Italian Association of Labour Economics) conference in Santa Maria Capua Vetere (Italy) for their interesting comments. We are also grateful to two referees who with their comments helped us to reformulate the analysis. We are responsible for any remaining errors.
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Basile, R., Donati, C., Pittiglio, R. (2015). Agglomeration Economies and Employment Growth in Italy. In: Mussida, C., Pastore, F. (eds) Geographical Labor Market Imbalances. AIEL Series in Labour Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55203-8_6
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