Determinants of the Italian Labor Productivity: A Quantile Regression Approach

1. Determinants of the Italian LaborProductivity: A Quantile RegressionApproach M. Velucchi, A. Viviani, A. Zeli New York University and European University of Rome Università di Firenze ISTAT Roma, November 21, 2011

2. Introduction • Some StylizedFacts: • Since the early ’90s, the Italian economy has been characterized by a relative decline in economic growth rates. • The economic literature discusses the roots of this feature of the Italian productive system: • lowlaborproductivity, • small firmsize • high specialization in traditional, low-tech sectors • low R&D expenditures and innovation. • The role of labor force’ skills and talent, innovation, investments and internationalization strategies are crucial for a successful firm in manufacturing products. • The heterogeneous performance of Italian firms’ labor productivity may depend on several firms’ characteristics and is extremely important in fostering the Italian economicgrowth

3. The Aim • Focus on Italian firms’ labor productivity in recent years (1998-2007). • Use an original panel recently developed by the Italian National Institute of Statistics at a micro level. • Test how a set of firms’ characteristics (physical and human capital investments, R&D expenditures, innovation and internationalization mode) influence the performance of Italian firms labor productivity in the period considered. • Run models on manufacturing and services, separately. Compare them.

4. A brief look to the literature • Galor (2005): productivity growth was the root for sustained economic growth and industrialization of western economies. • Productivity: source of the unprecedented rise in human welfare in the past century • However, productivity increases have not been constant over time (Eicher and Strobel, 2008). • Empirical literature discusses on both productivity measures and factors that may foster it (Hall et al., 2009) • Innovations (Griffith et al., 2004) as well as internationalization (Melitz, 2003; Mayer and Ottavivano, 2008) seem to play a crucial role in increasing the labor productivity and firms’ performance in a country. • Italian firms labor productivity is jeopardized across sectors, levels of technology and internationalization mode (Castellani and Giovannetti, 2010; Dosi et al. 2010)

5. Heterogeneity in Italian Labor Productivity • In this paper, we analyze the heterogeneous performance of Italian firms’ labor productivity and investigates the roots of the dynamics of the Italian firms’ labor productivity in recent years (1998-2007). • We disentangle the role of firms’ characteristics focusing on different quantiles, showing that GLS estimates do not capture the complex dynamics and heterogeneity of the Italian firms labor productivity.

6. How? • We use an original panel at a micro level from the Italian National Institute of Statistics. • We compare estimates from quantile regressions and panel approach. • We focus on manufacturing and services, separately. • We aim at finding evidence of a relationship between labor productivity and internationalization of firms, investments in intangible assets and innovation. • The original database and the quantile regression allow us to highlight that these relationships do not hold uniformly across quantiles. • Results show that GLS estimates do not fully capture the heterogeneity of the Italian labor productivity.

7. Dataset • Panel (from Italian National Institute of Statistics): • Data collected from four different surveys and administrative sources (Nardecchia et al., 2010): • Census of Italian firms, • SCI survey, on firms with more than 20 employees, • PMI survey that covers the firms with 20-100 employees • annual reports of incorporated firms collected by the Central Balance-Sheet Data Office of Italy. • Business transformations have been considered in the panel following a backward perspective (Biffignardi and Zeli, 2010) • It contains firms microdata for the period 1998-2007. • Entry and exit are not included. • This ia a catch-up panel. • It is a selection of a cross-sectional data-set from an archival source at time t in the past, and then locates the units in the present by subsequent observation. • Makes it possible the analysis of the behavior of a single firm (or a group of firms) over time. • The target population for the panel: firms with more than 20 employees.

8. The Approach • Predictions from most regression models are point estimates of the conditional mean of a response, given a set of predictors: the center of the conditional distribution of the response. • Assumption of normally distributed errors • Comparison between GLS estimates from a panel approach with quantile regressions estimates.

9. Why a Quantile Regression Approach? • Whilst the optimal properties of standard regression estimators are not robust to little departures from normality, quantile regression estimates are robust to outliers and heavy-tailed distributions. • The quantile regression estimator is invariant to outliers of the dependent variable that tend to infinity (Buchinsky, 1994). • Whilst OLS regressions focus on the mean, quantile regressions are able to describe the entire conditional distribution of the dependent variable. • High/low labor productivity firms are of interest and we wouldn’t dismiss them as outliers • No assumption on the error terms (i.i.d.): focus on firms’ heterogeneity and consider the possibility that estimated parameters vary at different quantiles of the conditional distribution.

10. The Model

11. The Model

12. The Model

13. Manufacturing vs. Services

14. GLS: Manufacturing(M1), Services(S1), Manufacturing non linear(M2),S ervices non linear(S2).

15. Quantile Regressions: Manufacturing

16. Quantile Regressions: Service

17. Conclusions (1/2) • We deal with the heterogeneous performance of Italian firms’ labor productivity and investigates how firms characteristics affect the dynamics of the Italian firms’ labor productivity in recent years (1998-2007). • We use an original panel recently developed by the Italian National Institute of Statistics at a micro level (firm level) including information from their balance sheets and internationalization activity. • We use a non linear Cobb-Douglas production function and a quantile regression approach. • We run models on manufacturing and services, separately.

18. Conclusions (2/2) • Wefindthat • The medium estimates obtained with GLS do not capture the complex dynamics and heterogeneity of the Italian firmslaborproductivity. • Labor productivity is very heterogeneous across the economy and the relationships between labor productivity and firms characteristics are not constant across quantiles. • Innovativeness and human capital, in particular, have a larger impact on fostering labor productivity of low productive firms than that of high productive firms. • Internationalization is more important for low productive firms than for highly productive firms, suggesting that low productive firms should expand their role in international markets to increase their productivity and that the expected effects are larger than for highly productive firms.

19. Thanks for yourattention