DETERMINANTS OF THE
ITALIAN LABOR
PRODUCTIVITY: A QUANTILE
REGRESSION APPROACH
M. Velucchi, A. Viviani, A. Zeli
New York University and European University of Rome
Università di Firenze
ISTAT
Roma, November 21, 2011
INTRODUCTION
Some Stylized Facts:
• 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:
• low labor productivity,
• small firm size
• 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 economic
growth
THE AIM
• Focus on Italian firms’ labor productivity in recent years (19982007).
• 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.
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)
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.
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.
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 BalanceSheet 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.
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.
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.
THE MODEL
THE MODEL
THE MODEL
MANUFACTURING VS.
SERVICES
GLS: MANUFACTURING(M1), SERVICES(S1),
MANUFACTURING NON LINEAR(M2),S
ERVICES NON LINEAR(S2).
QUANTILE
REGRESSIONS:
MANUFACTURING
QUANTILE
REGRESSIONS: SERVICE
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.
CONCLUSIONS (2/2)
We find that
• The medium estimates obtained with GLS do not capture the
complex dynamics and heterogeneity of the Italian firms labor
productivity.
• 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.
THANKS FOR YOUR ATTENTION