Download What Italian economic growth mean in a global new knowledge era

What Italian economic growth mean in a global new knowledge era
Rita Lima1
Abstract
This paper studied the concept of knowledge within theories concerning regional economic growth
and economic development. According to the suggested growth model at subnational and national
level, I estimated that while traditional education is important in Italy, large innovation capacities
are more important for lagging area of the country, i.e. Sicily. Thus, a greater part of Italy's
technology deployment would be strongly influenced by regional governments, regional industry,
and regional policy in developing innovations and learning capabilities. In policy terms, it may be
increasingly forged networks for sharing knowledge and accessing complementary expertise with
other truly located –independent global centres of activity.
Key Words: Human Capital, R&D, Knowledge-Based Economy, Growth Model, GMM-SYS
estimator.
Rita Lima, Directorate for methodology and statistical process design, Italian National Institute
of Statistics, Rome, ITALY. Email: [email protected].
1
This article gives the views of the author, and not the position of Italian National Institute of Statistics..
1. Introduction
This paper seeks to place the interregional growth disparity discourse within the context of theories
concerning the “knowledge-based economy” (Foray & Lundvall, 1996; Abramowitz & David,
1996). Both Stiglitz and Lamberton have noted that there can be an “‘economics of information”
still has to reckon with the lack of any consensus as to what specifically it should cover (Stiglitz,
2000; Lamberton, 1998). Furthermore, Arrow has commented that “It has proved difficult to frame
a general theory of information as an economic commodity, because different kinds of information
have no common unit that has yet been identified” (Arrow, 1973). However, at the Big Data era,
concerning with the accumulation of knowledge triggered by the fusion of digital technology with
traditional technology, the knowledge-based economy is not only characterized by increasing
investments in education (in particular higher education), software and R&D ( Cooke et al., 2007) .
It depends on a complex set of relationships among enterprises, universities and government
research institutes which could encourage innovation and technology development (Leydesdorff,
and Meyer, 2003). These new developments in the spatial organization of social flows and networks
of activity and knowledge pose very significant questions how the use of knowledge could generate
tangible and intangible value increasing the economic growth in developing countries (Djeflat,
2009; Birkinshaw, 2014; World Bank 2013, 2016).
Hence it is very difficult to trace the path of knowledge diffusion in the modern era.
Many efforts might be made at various levels to better understand connections between changes in
knowledge communication and technology diffusion and the emergence and development of spatial
disparities in developing countries (OECD, 1997; Miettinen, 2002)
Thus as knowledge is cumulative - so the more an individual or an organization knows, the easier it
is to acquire new knowledge - a determinate geographical area benefits from the transfer of
technology in proportion to its level of
“absorptive capacity” requiring that it's founded,
essentially, on the ability of its economic units to acquire and internalize knowledge (Gong and
Keller, 2003), stimulate the occurrence of R&D and the establishment of innovation networks, as
well as engage skilled human capital in its activities (Narula, 2002; Crespo-Cuaresma, 2004; Abreu
et al., 2011).
The national absorptive capacity, as consequence, is strongly linked not only to the innovative
propensity of a country by but also to the physical proximity (and density) between geographical
areas through which the less innovative and less developed area (follower) could converge towards
the development levels of the most innovative area (leader) (Grossman e Helpman, 1991; Howit,
2000) and the leaders are able to organize, develop, grow and take advantage of labor‐linking
technologies (Basu, 2016). This is particularly important among lagging countries where their
regional development depends on how:
-
they relate, interact, and forge links with global cornerstones of innovative capacities as
elements of a collective system of knowledge (Andriessen, 2005; Abreu et al., 2011), as well
as
-
they produce, distribute, and use knowledge and ideas (the most important factors of
production and renewal) (Zucker et al., 2007; Antonelli et al., 2011).
In this sense, the regional innovation ability, in term of the link between the quality of the human
capital and the extent of resources invested in R&D , may come to be seen as a key source of policy
experimentation, especially in a context characterized by the progressive globalization of the capital
markets and by the hyper acceleration of transmission processes of technology. Thus knowledge
disparities affect spatial disparities in growth rates and wealth. As Liefner (2009) have noted
“Knowledge disparities and related economic disparities widen most quickly in developing
countries that open up for trade, foreign direct investment and related technology transfer. During
phases of fast technological catch-up, the absorption of technology in the technologically leading
regions within newly industrialized countries is more powerful and faster than the trickle down of
technology into lagging regions”. Under such policy circumstances, the paper would activate some
policy suggestions that respond to the
“strategic goal for the Union in order to strengthen
employment, economic reform and social cohesion”2 on the basis of an econometric model as soon
as the big data will turned into the post-knowledge era (Birkinshaw, 2014). The remainder of the
paper is organized as follows. Section 2 describes the general conceptual foundations, presents the
data and describes the econometric modelling framework. Section 3 reports the main empirical
findings and Section 4 concludes with the policy and strategy implications.
2. Conceptual foundations, data and the econometric modelling framework
2.1 Conceptual foundations
There are no doubts that we are currently living in an era defined by “an economy in which the
production, distribution and use of knowledge (R&D and human capital) is the main driver of
growth, wealth creation, and employment across all industries3”. Its role was already confirmed in
the models like Romer (1987; 1990), Grossman and Helpman (1991), Aghion and Howitt (1998);
Barro and Lee (1993) and Barro and Sala-i-Martin (2004).
For Venturelli (2000): “a nation without a vibrant labor force does not possess the knowledge base
to succeed in the [Knowledge] Economy, and must depend on ideas produced elsewhere.”
Despite the growing interest in analyze the impact of knowledge governance on growth (Echevarría
and Amaia, 2006; Goel, 2008; Galindo and Mendez 2013; Aghion and Griffith, 2015), at the
regional level, there is a need to better understand the mechanisms underlying regional growth
patterns (Capello and Nijkamp, 2004; Andersson and Karlsson, 2007; Stimson et al., 2011).
Recently, differences in regional growth are potentially explained by different conditions for
creating, accumulating and – crucially – transmitting knowledge (Setterfield, 2010) within the
locally based university-industry-government ecosystems.
Instead, in a period of globalization (including financial crises) with an increasing personal mobility
and exchange of ideas, as well as knowledge (like capital) tends to travel beyond boundaries,
formation and knowledge will have declining returns and big data will really give long term returns.
2
3
http://ec.europa.eu/europe2020/europe-2020-in-a-nutshell/targets/index_en.htm
Department of Industry Training & Research, in, Andrews, 2004, p4.
Hence, in a world in which information is ubiquitous and knowledge increasingly shared, the
companies that want to gain a competitive edge will have to look for new sources of advantage
(Basu, 2016).
The implication is that the knowledge-based dynamics based on the tri-lateral relations (fig.1)
could include the entry of new players in knowledge production in an increasing multiplicity of
global linkages and in a fast-growing interconnections between companies, research organizations,
universities and countries. In this contest, spatial proximity per se may be of no value (Strambach,
2015) .
Figure 1. The knowledge-based economy
Source: Leydesdorff, L., & M. Meyer.(2003).
In addressing this challenge, for example, when analyzing the knowledge-based economy in Italy,
one can ask whether more synergy can be explained when looking at the level of the whole country
(e.g., in terms of the North-South divide) or at the level of each regions? At the same time, there is
evidence of a persistence of an uneven spatial distribution of research and innovative activities. On
the one hand, “the administrative borders of nations and regions result from the construction of
political economies in the 19th century; but on the other hand, the niches of synergy that can be
expected in a knowledge-based economy are bordered also; for example, in terms of metropolitan
regions (e.g., Milan–Turin–Genoa)” (Knoblich, 2015).
On the one hand, it involves the interactions between the actors of an innovation system (such as
manufacturing firms, research laboratories, business services or academic institutions) based on a
geographic area that cover a wide spatial range of supposed interactions.,
On the other hand since knowledge will always be important key factor underpinning the future
economic development and growth trajectory of lagging regions within countries, a physical
proximity (and density) speeds the flow of ideas, especially when a significant part of intangible
knowledge is often tacit and social networks tend to be strong. Thus, regions characterized by a
denser clustering of industries should exhibit agglomeration economies that lead to higher R&D
productivity and, thus, to higher levels of innovation output.
What is the evidence in Italy? The paper intended to address a policy debate on how the evolution
of the knowledge era could help both lagging and leading regions for achieving successful national
development. In particular, the paper proposed a linear relationship between economic growth and
knowledge (in term of human capital and investment in R&D) focusing on Sicily and Italy
(excluding the Island) in the hope that the omission of the Island could identify regional best
practices for a post-knowledge-based economy and capture the role of neighboring effects in
national growth. The choice of the geographical unit was constrained by data availability (see
appendix A for more details).
However, among Italian regions:
-
Sicily is being the one of the less developed regions of the country that suffered the
consequences of the world economic recession of 2008-2009 more heavily than the other
Italian regions (SVIMEZ 2014). These factors couple with other specific aspects seriously
restaining the economic performance of the region, among which: inadequate entrepreneurial
culture, low technology potential and innovation propensity, scarce services to firms, weak
financial system, insufficient promotion of internationalization processes, weak linkages with
global markets and networks (SVIMEZ 2014).
-
its geographical localization eliminate or at least significantly reduce the effects of “spatial
contagion” in regional growth behavior that could inevitably reverberate on the national
economic growth thanks to the diffusion of technological knowledge and the accumulation of
human capital4.
All in all, Italy shows an increasing income disparities across regions combined with increased
technology gaps in comparison with the European leading countries with respect to knowledge and
innovation5.
2.2 Data
The estimation of the econometric equation was carried out using a balanced panel dataset for two
countries (Sicily and Italy, with the exclusion of the Island) over the period 1970-2011 for a total of
84 observations. Unfortunately, it is a reduced balanced panel in which the time series observations
on the cross-section are rather limited. That constitutes an important problem and the inference
based on the estimates should be valued with great caution. Basically this choice was driven by the
effective availability of regional time series, which is often heavily involved in a revision
procedure. See appendix A for more details.
The variables considered were the following:
-
the annual growth rate of the national Gross Domestic Product (GDP) per capita
against the German GDP per capita, as an indicator of the growth gap (GAPGE) in tailoring
policies to promote long-term output growth, improved productivity, innovation and
knowledge and catch-up with more advanced nations. In fact, Germany is a country leader
4
Among others, see the works of Arbia and Paelinck (2003); Bollino and Polinori (2007).
Draghi M. (09/11/2006), Instruction and economic growth, Lectio magistralis on occasion of the inauguration of 100th
academic year, Economics Faculty Università La Sapienza. In a recent conference Ignazio Visco (2014), treating the
problem of the Italian economic crisis, stated that: «The main causes of the Italian economic stagnation are to be
researched in the mediocre growth of that which economists call total factor productivity, which fundamentally depends
on human capital and the capacity of innovation and organisation of the businesses».
5
in innovational measures, environments, and outputs (Innovation Union Scoreboard, 2011)3
with the highest development rate (Broekel , 2015; European Commission, 2015).
-
the proportion of graduates in technical-scientific degrees as a percentage of the
active population aged 25-64 (LAURFL), as proxy of human capital stock, since this isn't
exclusively formed within a scholastic system but also through work experience (Blundell et
al., 1999).
-
the business enterprise R&D expenditure that is represented by the investment of
public and private enterprises as a percentage of the amount of gross fixed capital
accumulation (RSINV), aimed at fostering technological innovation and improving the
physical productivity of all factors (Romer, 1987; Grossman e Helpman, 1991; OECD;
2013).
All the above variables, expressed as logarithms, could be interpreted as elasticity, which means
they approached the percentage variation of the dependent variable (GAPGE) associated with a
variation of the explicative (RSINV and LAURFL).
2.3 The econometric modelling framework
Starting from the ample relative literature on regional differences in the availability and the quality
of local inputs, as well as geographically bounded knowledge spillovers (Greunz , 2003; Baici and
Casalone 2005, Andreano et al. 2013; Huggins et al., 2014), the proposal model consisted of
estimating the following equation for the region i = Sicily, Italy (excluding Sicily) over the period
1970-2009:
(1)
Yi =  o  1X it   2 Zi +  t +  it
where Y is the observation on the dependent variable (the logarithmic difference of the proportion
of national GDP per capita against the national German GDP, GAPGE); X represents a 1xk vector
3
The Innovation Scoreboard is an instrument for the annual evaluation of development level of innovation systems of the members of the European.
At a regional level the Regional Innovation Scoreboard is calculated.
of independent variables observed for region i in period t (the logarithms of the proportion of
technical-scientific graduates as a percentage of the active population aged 25 to 64 years
(LAURFL) and the investment of private and public companies as a percentage of the respective
gross fixed capital formation (RINSV); μt is the time-specific effect that is constant across the cross
section units; Zi is the country effect, a separate constant associated with a different region that is
assumed to be iid N (Baltagi, 1995; Greene, 1997); εit is the error term iid Normal that is assumed
uncorrelated neither with the regressors nor with Zi; 0 is the constant part of the individual effects;
this means that the actual constant term for each country is equal to 0+Zi. The key insight is that if
the country effect does not change over time, then any changes in the dependent variable must be
due to influences other than these fixed characteristics.
By inserting the variables in (1), the starting point for the analysis was therefore expressed:
(2)
logGAPGE it =  0 + 11logLAURFL it + 12logRINSV it +  2 Zi +  t +  it
Here three methods of estimating parameters have been under consideration using to decide which
model might be more suitable. The first method provided for the application of the classic OLS
(Ordinary Least Squared) regression model that allows the use together with all available
observations (pooled) to measure the causality (in economic sense) from the proxy of the human
capital (LAURL and RINV) to GAPGE, without checking for unobserved among countries. This
model is a baseline for comparison with more complex estimators although the risk that the
coefficients will be correlated with the error term εit, the estimates will be biased and there will be
some form of endogeneity among variables. To produce better (more reliable) results it is not
sufficient include year dummies in the pooled OLS regression. It still wouldn’t capture the effects
of varying intercept in the cross-sectional units dimension even if panel corrected robust standard
errors are then calculated in presence of heteroscedasticity5.
5
For details on the estimators, see MacKinnon and White (Journal of Econometrics, 1985); Davidson and MacKinnon
(Econometric Theory and Methods, Oxford, 2004).
The second method, using the longitudinal dimension of the data, was based on the estimate of a
fixed effects panel model with country dummy variables (the principal alternative of pooled OLS
model) to identify if human capital is localized in Sicily or not. In the case of the fixed effects, the
slope coefficients might restrict to be constant over both units and time, and allow for an intercept
coefficient that varies by unit or by time. This model is often termed the LSDV (Least-Squares
Dummy Variable) model, since it is equivalent to including (N-1) dummy variables in the OLS
regression of y on X (including a units vector). In the fixed effects approach, any hypotheses are
made on the country effects beyond the fact that they exist—and that can be tested. As a
consequence, once these effects are swept out by taking deviations from the country means, the
remaining parameters can be estimated. The last method was a Dynamic Panel Data specification
(DPD). The distortion of small samples is notably reduced and the precision of the estimate in the
presence of a persistent series is increased, when the first step System-Generalize Method of
Moments estimator (SYS- GMM)6 suggested by Blundell and Bond (1998) is used taking level and
differences as instruments. The fundamental idea of the GMM-SYS estimator is to calculate a
system of equations both in first-order differences and in levels (not present in the GMM-FD
estimator), where the instruments used in the equations in levels are the lagged first-differences of
the series. This is an alternative tactic for sweeping out the country effects and constructing more
DPD efficient estimates. The use of GMM-SYS combines the standard set of equations in firstdifferences with suitably lagged levels as instruments, with an additional set of equations in levels
with suitably lagged first-differences as instruments. Although the levels of Yit are necessarily
correlated with the country-specific effects, the first-differences ΔYit are not correlated with them,
permitting lagged first-differences to be used as instruments in the levels equations. As an empirical
matter, the validity of these additional instruments were tested using standard Sargan tests of overidentifying restrictions, or using Difference Sargan or Hausman comparisons between the firstdifferenced GMM and system GMM results (see Arellano and Bond, 1991).
6
Here, a GMM stadium estimator was chosen as those at two stadia suffer the problem of limited effectiveness of the
used variables and they present a strong distortion in limited samples (Bond and Windermeijer, 2002).
3. Results
The empirical evidences were obtained by modeling growth using the simplest pooled regression,
the fixed effects model (also known as the within estimator) and the dynamic model specification.
The estimated regression coefficients were obtained using the open source software Gretl. Although
the essentiality of the specification (which presents only two repressors’ other than the constant)
and the limited sample size , the first model (using all available observations together without
territorial dummy ) reveled a statistically significant contribution of the incidence of graduates from
the technical-scientific groups to the active population to the increase of national growth,
confirming the principal role in reducing the gap in terms of GDP per capita between Italy and
Germany. This is the results of the notable massive hemorrhage of the brightest and most talented
people leaving our country (the brain drain) started in the past and that is also in present in the
pursue of better life standards, higher pay as well as better and more challenging work
opportunities.
Also the R&D estimate was statistically significant with a positive sign contribution to national
growth, indicating a direct relationship between the national propensity to invest in knowledge and
innovation and the distance in growth from the more technological-economic frontier country. This
is highlighted by the scarce attitude in investing in innovation as percentage of GDP compared to
German ones (from 0.52% of GDP in Italy and 1.67% in German in 2002 to 0.66% and 1.89% of
GDP in 2011) (European Commission, 2015).
The estimate, therefore, confirmed that the Italian economy is characterized by important limited
opportunity to implement innovative techniques and models of production and consumption that are
already proven elsewhere, also excluding regions (like Sicily), remaining much more below the EU
average R&D expenditure.
Even if the education system in Italy is almost completely governed at national level, the gap
between Italy (excluding Sicily) and Sicily could mainly depend on specific factors of different
geographical localization (the heterogeneity cross-section not observed). In Sicily an inadequate
infrastructure (in terms of transport, telecommunications and efficient energetic networks, water
supplies, environmental services, etc) sums up to a considerable expenditure in education in
comparison with the reported number of students, to a low number of graduates - particularly in
scientific and technological disciplines - and to a likewise low level of investment in R&D. As it
might expect, the Hausman test’s null—that the territorial effects are the same between them—was
soundly rejected (F(1, 80)= 866.457 with a p-value<0.05) in favour of the fixed effects model.
Furthermore, the null hypothesis that the dt variables were zero (F(40, 39) = 2.0778, p-value
0.0121) was rejected.
The analysis of the results of the fixed effects panel model without temporal effects has given the
following estimates:
(3)
logGAPGE = -0.330 - 0.363logLA URFL  0.824logRI NSV n = 84, LSDV - R 2 = 0.962
(0,0358) (0,0486)
(0,0038) (HACs.e.)
Comparing the results with pooled regression, the specifications with and without fixed effects were
not always consistent. Firstly, the average distance between the national GDP per capita against the
German one was lowers in the first specification than in the second one (the intercept, respectively,
goes from– 0.779 to – 0.330). Also the role of the specialized labor force, although had the same
sign in the two specifications, became less important with fixed effects model to enable sufficient
growth: a unitary reduction of the proportion of graduates with technical-scientific degrees
compared to the active population between the ages of 25 and 64 could determine an GDP growth
increased by 0.08 in comparison with the frontier country.
Finally, the coefficient of RINV was negative, so that a reduction in investment in R&D would
bring about an increase in the gap of the Italian GDP per capita from that of Germany, confirming,
as verified widely in literature, a relationship between innovative capacity and growth (Romer,
1987). Therefore, human capital investment is significantly important to increase the capacity to
investigate the technological frontier, but also to receive and use readily the innovations introduced
by others (Helpman, 2006). Also in this specification, taking into account not only of the spatial
effects, dt improved the model in term of the goodness of fit (the LSDV R-squared goes from
0.9623 to 0.9911) and confirmed that many of the temporal effects were statistically significant in
the explanation of growth gap between countries.
That’s means that the geographical proximity plays a key but not unique significant role in the
relationship between innovation, knowledge and growth: time effects could be another significant
component. Unfortunately, certain instability of the parameters characterized the estimates: the
coefficients of LAURFL and LRINV changed both sign and intensity.
The difference between the specifications with and without significant temporal effects also
concerned with the geographical effects (the coefficients of the territorial dummy): the coefficient
of Italy (excluding Sicily) was, respectively, –0.4363 and –0.1957 while the one of Sicily was,
respectively, –0.8827 and –0.6430.
Although the low value of the correlation index between the explicative (corr(logLAURFL,
logRINV)=0.2489) would imply the exclusion of multicollinearity between the variables, the
estimated variance inflation factors (VIF) (superior to 1 but inferior to 10) suggested the presence of
a slight multicollinearity and estimated inefficiencies for the LSDV estimators.
However, even if in literature there are not directly comparable coefficients with these results and
the sample period isn't sufficiently wide enough, the gapy in development of our country compared
to the Geraman standard seems to be significantly conditioned by the strong differences that exist
between Sicily and the whole country in terms of human capital and expenditure in R&D. This
would confirm the absolute necessity in assuming as central objectives of rebalancing the regional
internal gaps in order to give efficacy to the politics finalized in the complete recovery of
competitiveness of our country (Bugamelli et al., 2012).
Finally the GMM-SYS estimation of DPD was carried out to reduce risks of spurious regression or
correlations between explanatory variables. The LM-tests statistics, based on AR(1) and AR(2)
models, performed very well while the Sargan test of overidentifying restrictions accepted the null
hypothesis that all selected instruments are valid. Even so the coefficient of the lagged dependent
variable was not significant (p-value >0.05), the static fixed effects model with standard HAC
errors would seem the best specification for the model of economic growth.
4. Conclusions and implications of economic policy
The analysis presented in this paper was based on a specific theoretical model and used specific
econometric methods as well as small set of economic variables in the empirical application.
I believe that this framework could be extended to other groups of countries or regions and can be
used for purposes other than the one set up in this paper.
On one hand , the finding confirmed the role of the several business policies that encourage firms to
invest more in R&D and in human capital (through higher skilled labor force) and suggested that for
catching-up area , i.e Sicily, the accumulation of human capital is important but it explains
only part of the variation across areas in GDP and its rate of growth, while for those countries
where economic growth is innovation based, like the rest of the country there are still considerable
vulnerabilities to a robust knowledge-based economy development6. These are partly due to the
nation's industrial composition, with more employment in traditional industries that conduct less
R&D and large proportions of small firms, often located in dense industrial districts in the Northern
Italy.
On the other hand, this paper would start a line of research on what it may come after the
knowledge era. Under the rapid technological development and the growing availability of big data
platforms and big data analytics tools, for lagging areas this means rapid inflow of technology and
spatial-economic concentration that increases and fuels both economic growth and spatial
disparities. Therefore, the effective deployment of technology and improved operational techniques
invariably involves changes in human capital requirements.
6
Although most of the Italian regions are low users or absorbers of innovation according to the 2014 Regional
Innovation Scoreboard data.
Actually, the gap in term of stock of human capital (in term of highly educated workers) appeared
to have contributed much less to regional growth than the diffusion of R&D, and this accounted for
much of the weaker productivity performance of Sicily respect the rest of the country.
Many specific aspects could burden the Sicilian economic activities such as, for example, the higher
degree of fragmentation on labor productivity, an unresolved problem of brain drain (with the
complementary inability to attract skilled labor force from abroad), the local innovation system
mainly based on micro enterprises still not oriented towards high value-added sectors (as most local
enterprises are small and face few incentives to cooperate), a greater vulnerability of a financial
system to the recent financial crisis, the presence of organized crime together with a consistent
informal and shadow economy and a very low levels and low quality of infrastructure, especially in
the transport sector.
It is worth mentioning that in the last years, Sicily has advanced from the status of a modest
innovator to a moderate innovator (2011 Innovation Scoreboard data).
Furthermore, in 2013 Regione Siciliana was engaged in the process of developing the Smart
Specialisation strategy 2014-2020. At the end of 2013, the following operational objectives have
been chosen for regional policy initiatives in promoting research, innovation and technology
transfer: a) strengthen the role of research and innovation as an engine for regional development; b)
increase the effectiveness and impact of research and innovation, concentrating resources in some
strategic areas and in large projects; c) enhancing the role of human capital for the purpose of
strengthening the regional innovation system.
The findings could be turned into policy strategies to stimulate high quality research and enable
innovative methods. In Italy R&D activity is lower than in other industrialized countries; national
universities produce fewer scientists and engineers per capita and industry links to the universities
are weak. In this respect, interventions would be directed to set up higher education institutions as
well as R&D organizations, promote immigration of talented students and skilled personnel from
lagging areas and encourage public policy addressing common EU challenges in an effective and
results oriented manner.
At the regional and/or local level efforts would involve not only education and training systems,
technology transfer programs and financing R&D, but also raise questions of relationships and
practices, institutional links with customers and suppliers, inter-firm networks, attitudes and publicprivate cooperation, trade organizations and other industry and regional associations ( Broekel,
2015).
These networks may consist of people from different and geographically distributed units of
professionals from different organizational units who have a common interest in certain work
related topics and share their knowledge on a regular basis (Andriessen, 2005).
In conclusion, all policies designed to achieve this kind of
investment priority will
be
complemented with adequate investment in knowledge. “Reform the patent system … Renegotiate
the intellectual property regime, with a view to creating better access for the poor to knowledge
that already has paid for itself many times over in the West. Consider anew the institutions that call
knowledge into being, in hopes of making them more efficient … And by all means, continue to
innovate boldly in new industries … But first, rebuild the education systems of the old industrial
nations and create new ones for developing nations.”. (Helpman, 2004).
Appendix A
Estimates of the resident population. This is the resetting of population figures recalculated using different
time series at different levels of geographical aggregation and variables such as gender and age. It regards. 1)
for the period 1952-1971, La Sapienza University in Rome: Department of Demographic Science. 1983.
“Ricostruzione della popolazione residente per sesso, età e regione”, Fonti e Strumenti n. 1); 2) for the period
1972-1981, “Popolazione e bilanci demografici per sesso, età e regione, Ricostruzione per gli anni 19721981”, Supplemento al bollettino mensile di statistica, Anno 1985, n. 14, Istat; 3) for the period 1982-1991,
“Ricostruzione della popolazione residente per età e sesso nelle province italiane”, Speciale Informazioni n.
17, 1996 , Istat; 4) for the period 1991-2001, “Ricostruzione della popolazione residente per età e sesso nei
comuni italiani”, informazioni n. 13, 2006, Istat.
Estimates of Gross domestic product (GDP), net Imports of goods and services and Gross capital formation.
These are the series reconstructed of the economic Goods and services account (values at current prices)
from the integrated system of regional accounts by SVIMEZ (2014).
Estimates of R&D expenditure in enterprises (in millions of Euros). These are the database of ISTAT’s
surveys on enterprises with a systematic matching between statistical and administrative sources. The
administrative sources used by ISTAT to identify enterprises with R&D potential include: the Anagrafe
Nazionale delle Ricerche (National Research Registry), managed by the Ministero dell’Istruzione,
dell’Università e della Ricerca (Ministry of Education, University and Research); the European Patent
Office, which lists Italian enterprises applying for patents; the list of Italian enterprises participating in
research projects funded by the European Commission framework programs and, since 2007, the list of
enterprises presenting the Italian Tax Authority with applications to use tax credits for R&D expenses. Until
1991, figures on R&D spending include both intra-muros and extra-muros expenses. Since 1992, the total
only includes intra-muros spending; this results in a clear decrease of spending, especially until 1994. At
national level the series take into consideration the following publications 1) for the period 1970-1990;
“Indagine statistica sulla ricerca scientifica effettuata in Italia”; 2) for the period 1991-1997 “Indagine
statistica sulla ricerca scientifica e lo sviluppo sperimentale nelle imprese e Indagine statistica sulla ricerca
scientifica e lo sviluppo sperimentale negli enti della pubblica amministrazione”; 3) from 1998 “ Rilevazione
statistica sulla ricerca e sviluppo nelle imprese”; 4) for the period 1998-2000, “Indagine statistica sulla
ricerca e sviluppo negli enti e nelle istituzioni”; 5) for 2001, “ Rilevazione statistica sulla R&S negli enti e
nelle istituzioni pubbliche e private”; 6) from 2002;” Rilevazione statistica sulla R&S nelle istituzioni
pubbliche e Rilevazione statistica sulla R&S nelle istituzioni private non profit”. At regional level, for the
peiod 1979-2009 the expenditure is reconstructed by the Statistical Service and Economic Analysis of the
Regional Statistics Offices. It should be finally highlighted that for the period 1970 - 1978 the regional
estimates is based on the annual growth rate of the national time series. The time series data of the economic
account and of R&D expenditure in enterprises are considered at prices constant to 2005, to adjust for effects
of inflation.
Estimates of graduates in scientific disciplines and technology disciplines. Data for the pre-2003 period
report the number of old university degree graduated students(4, 5 and 6 years degree); in addition to, with
reference to the ministerial decree n.509 dated 3 November 1999 - signed by the Ministry of University and
Scientific and Technological Research - "Regulation laying down rules on athenaeum teaching autonomy",
the data reported bring together the number of graduated students of new university degree (the first cycle, 3
years degree, the second cycle degree, 2 years specialisation course and single cycle degree only, 5 and 6
years). The second cycle degree admission is conditional on the prospective student having successfully
completed the 3 years of the first cycle of university degree. At national level the time series are based on the
following publications 1) for the period 1970-1997, ““Rilevazione sulle Università”, Istat; 2) for the period
1998-2009, “Rilevazione sulle Università”, MIUR. At regional level, the time series are based on the
following publications : 1) for the years 1970-1986 and for the years 1991-1996, “Annuario Statistico
dell’Istruzione”, Istat, 2) for the years 1998-2009, “Indagine sull'Istruzione Universitaria” , MIUR. For the
period 1987-1990 the computed regional figures on graduates are estimated considering the national growth
rate of graduates while for 1997 the average of the period 1996-1998 is used as estimate of the missing data.
Estimates of the economically active population (labour force) in the age group 25- 64. For the period 19772009 the regional and national series are taken from the " Rilevazione trimestrale sulle forze di lavoro (until
2003)” , Istat and “Rilevazione sulle forze di lavoro (since 2004)” . Istat; from time series 1993-2003 data, as
a result of a new reconstruction, which takes into account the revision of population in the period between
1991-2001 censuses. For the period 1970-1976 the time series data are obtained by applying the incidence of
this age group of reference estimated by O. Vitali (1970) to total population. Persons aged 14 are included
until 1992; persons aged 15 and over are included since 1993.
Table 1. Main descriptive statistics (log form of variables)
Variable
GDP
LAUREAFL
RSINV
GAPGE
GDP
LAUREAFL
RSINV
GAPGE
GDP
LAUREAFL
RSINV
GAPGE
Mean
SE
Italy
2.28
0.02
-0.80
0.03
0.84
0.02
-0.05
0.00
Italy (excluding Sicily)
2.29
0.02
-0.80
0.03
0.86
0.02
-0.64
0.02
Sicily
2.11
0.01
-0.85
0.02
0.19
0.05
-0.21
0.00
Variance
Skewness
Kurtosi
0.01
0.03
0.02
0.00
-0.54
0.91
-0.55
-0.76
-0.94
-0.53
-0.76
0.21
0.01
0.04
0.02
0.02
-0.53
0.90
-0.55
0.98
-0.96
-0.56
-0.73
-0.13
0.01
0.02
0.12
0.00
-0.80
0.91
-0.10
0.27
-0.53
-0.08
-0.71
-0.92
Bibliography
Abramowitz, M., & P. A. David. (1996). Measuring Performance of Knowledge-Based Economy.
In Employment and Growth in the Knowledge-Based Economy (pp. 35 Paris: OECD.
Abreu, M., Grinevich, V., Kitson, M., Savona, M. (2011), Absorptive Capacity and Regional
Patterns of Innovation. DIUS research report 08-11. Department for Innovation Universities and
Skills, UK.
Anghion P. e Howitt P. (1998), Endogeneous Growth Theory, Cambridge MA; MIT Press
Aghion Ph. and Griffith R:, (2005), Competition and Growth: Reconciling Theory and Evidence,
Cambridge, MA: MIT Press.
Anderson M. and Karlsson C. (2007), Knowledge in Regional Economic Growth—The Role of
Knowledge Accessibility, Industry and Innovation, 2007, vol. 14, issue 2, pages 129-149
Andreano M.S., Laureti L., Postiglione P. (2013):Economic Growth in MENA countries: Is there
convergence
in
per-capita
GDPs?,
Journal
of
Policy
Modeling,
dx.doi.org/10.1016/j.jpolmod.2013.02.005.
Andriessen, J. H. Erik (2005), Archetypes of Knowledge Communities, in Communities and
Technologies 2005. Peter v. d. Besselaar et al. (eds), 191-213. Dordrecht, The Netherlands: Springer.
Antonelli, C., Patrucco, P. and Quatraro, A. (2011) “Productivity growth and pecuniary knowledge
externalities: An empirical analysis of agglomeration economies in European regions”, Economic
Geography, 87, pp. 23-50.
Arellano M. and Bond S, (1991), Some Tests of Specification for Panel Data: Monte Carlo
Evidence and an Application to Employment Equations, Review of Economic Studies (1991) 58,
277-297 0034-6527/91/00180277$02.
Arrow, K.J. (1973). Information and Economic Behavior. In Collected Papers of Kenneth J. Arrow
–Vol.4: The Economics of Information. Cambridge (USA): Belknap Press of Harvard University
Press.
Avveduto, S. and M.C. Brandi, (2004), Le migrazioni qualificate in Italia, Studi Emigrazione, 156
(771-1016).
Baici E., Casalone G. (2005) Has human capital accounted for regional economic growth in Italy?
A panel analysis on the 1980-2001 period, SEMEQ Department - Faculty of Economics University of Eastern Piedmont, Working Papers 101.
Barro, Robert J. & Lee, Jong-Wha, (1993). International comparisons of educational attainment,
Journal of Monetary Economics, Elsevier, vol. 32(3), pages 363-394, December.
Barro, Robert J. & Sala-i-Martin, Xavier, (2004), Economic Growth, MIT Press, 2004
Basu, K. Globalization of labor markets and the growth prospects of nations. Journal of Policy
Modeling (2016), http://dx.doi.org/10.1016/j.jpolmod.2016.05.009 .
Birkinshaw, J. (2014) Do we stil need managers?, Business Strategy Review, 2 Jun 2014 DOI:
101111/j.1467-8616.2014.01042X- London Business School.
Blundell, R., Dearden, L., Meghir, C. e Sianesi, B. (1999), Human Capital Investment: The Returns
from Education and Training to the Individual, the Firm and the Economy, Fiscal Studies vol. 20,
no. 1, pp. 1–2.
Broekel T. (2015) Do Cooperative Research and Development (R&D), Subsidies Stimulate
Regional Innovation Efficiency? Evidence from Germany, Regional Studies, 49:7, 1087-1110, DOI:
10.1080/00343404.2013.812781.
Bugamelli M., Cannari L., Lotti F. e Magri S. (2012), “Il gap innovativo del sistema produttivo
italiano: radici e possibili rimedi”, Questioni di Economia e Finanza (Occasional Papers) 121,
Bank of Italy, Economic Research and International Relations Area.
Capello R. and Nijkamp, P. (2004), “The Theoretical and Methodological Toolbox of Urban
Economics: From and Towards Where?”, in R. Capello and P. Nijkamp (eds.), Urban Dynamics
and Growth: Advances in Urban Economics, Elsevier, Amsterdam, pp. 1-30.
Cooke P., De Laurentis C Todtling F. and Trippl M. (2007) Regional Knowledge Economies:
Markets, Clusters & Innovation, Cheltenham, Edward Elgar.
Crespo-Cuaresma, J., Foster, N., Scharler, J. (2004). On the Determinants of Absorptive Capacity:
Evidence from OECD Countries. ONB Workshops N. 2/2004.
Del Monte A. (2000), “Diffusione dell’ITC e sviluppo del Mezzogiorno”, XXVI Convegno
dell’Industria, Palermo, 27-28 settembre.
Djeflat, Pr. Abdelkader (2009), Building Knowledge Economies for job creation, increased
competitiveness, and balanced development, Worldbank Draft.
Echevarria, Cruz A. & Iza, Amaia, 2006. "Life expectancy, human capital, social security and
growth," Journal of Public Economics, Elsevier, vol. 90(12).
European
Commission
(2015),
Innovation
http://ec.europa.eu/enterprise/policies/innovation/policy/.
Union
Scoreboard
2014,
Foray, D., & B.-A. Lundvall. (1996). The Knowledge-Based Economy: From the Economics of
Knowledge to the Learning Economy. In OECD Documents: Employment and Growth in the
Knowledge-Based Economy (pp. 11-32). Paris: OECD.
Galindo Miguel Á., Méndez‐Picazo M.Teresa, (2013) "Innovation, entrepreneurship and economic
growth", Management Decision, Vol. 51 Iss: 3, pp.501 – 514.
Goel, R. K., Payne J. E., & Ram.. 2008. R&D expenditures and U.S. economic growth: A
disaggregated
approach.
Journal
of
Policy
Modeling,
30,
237–250.
http://dx.doi.org/10.1016/j.jpolmod.2007.04.008
Gong G. and Keller W. (2003), “Convergence and polarization in global income levels: a review of
recent results on the role of international technology diffusion”, Research Policy, vol. 32, n. 6.
Greunz , L. 2003. Geographically and technologically mediated knowledge spillovers between
European regions. Annals of Regional Science, 37: 657–680.
Grossman G.M e Helpman E., (1991), Innovation and Growth in the Global Economy, Cambridge:
MIT Press.
Helpman E. (2006), The Mystery of Economic Growth, Belknap Pr ess .
Howitt, P. (2000), Endogenous Growth and Cross-Country Income Differences, American
Economic Review, 90(4): 829-846.
Huggins, R., Izushi H. Prokop D.l and Thompson P., (2014), Regional Competitiveness, Economic
Growth and Stages of Developmen,. Zbornik radova Ekonomskog fakulteta u Rijeci, časopis za
ekonomsku teoriju i praksu - Proceedings of Rijeka Faculty of Economics, Journal of Economics
and Business, Vol. 32, No. 2, 2014, pp. 255-283. Available at SSRN:
http://ssrn.com/abstract=2554326.
Knoblich, R. (2015) Theory Talk #68: ‘Loet Leydesdorff on the Triple Helix: How Synergies in
University-Industry-Government Relations can Shape Innovation Systems, Theory Talks,
http://www.theorytalks. org/2015/03/theory-talk-68.html (11-3-2015).
Iammarino S. (2001), Sviluppo e diffusione dell’ITC: L’Italia negli Anni novanta, Studi e Note di
Economia, 2: 13-44.
ISTAT (2011), Migrazioni internazionali e interne della popolazione residente;
http://www.istat.it/it/files/2012/12/Migrazioni_popolazione-residente.pdf.
ISTAT (2014), Noi Italia. 100 statistiche per capire il Paese in cui viviamo.
Lamberton D M (1998) Information Economics Research: Points of Departure.
Information Economics and Policy 10 : 325-330.
Leydesdorff, L., & M. Meyer. (2003). The Triple Helix of University-IndustryGovernmentRelations: Introduction to the Topical Issue. Scientometrics, 58 (2), 191-203.
Liefner I. (2009), Spatial Disparities of Knowledge Absorption, Technological Change, and
Prosperity: Theoretical Considerations and Empirical Evidence from China; in “Berlin Workshop
Series 2009”, The International Bank for Reconstruction and Development/The World Bank; 7180.
Lynch L. M. (2004), Reorienting training policies to meet the challenger of information and
communication technologies, in Paganetto (a cura di) Knowledge economy, information
technologies and growth, Ashgate Publishing Company, Aldershot, England.
Miettinen R. (2002), National Innovation System: Scientific Concept or Political Rhetoric, Edita,
Helsinki.
Narula, R. (2002) , Understanding Absorptive Capacities in an “Innovation Systems” Context:
Consequences for Economic and Employment Growth. DRUID Working Paper nr. 04-02.
OECD (2013), Supporting Investment in Knowledge Capital, Growth and Innovation.
OCSE (2014), Education at a Glance.
OECD. (2014). “Is migration really increasing?” OECD Migration Policy Debates May 2014.
Retrieved October 18, 2014.
OECD, (1997), National Innovation Systems, OECD Publications, Paris.
Romer Paul M. (1990), Endogenous Technological Change, Journal of Political Economy, 1990,
vol. 98, no. 5, pt. 2]
Romer Paul M. (1987), Growth Based on Increasing Returns Due to Specialization." A.E.R. Papers
and Proc. 77 (May 1987): 56-62.
Svimez (2014), Rapporto 2003 sull’economia del Mezzogiorno, Bologna, Il Mulino.
Setterfield M.(ed.), 2010. Handbook of Alternative Theories of Economic Growth, Edward Elgar
Publishing, number 12814.
Stiglitz J E (2000) The Contributions of the Economics of Information to Twentieth Century
Economics. Quarterly Journal of Economics (140): 1441-78.
Stimson R., Stough Roger R. and Nijkamp P. (2011), Endogenous Regional Development:
Perspectives, Measurement and Empirical Investigation, Journal of Regioal science, Vol. 51, Issue
5 December 2011, Pages 1022–1024.
Strambach S., (2015). Combining knowledge bases in transnational innovation - microfoundations
and the geography of organization, Innovation Studies 2015/10, Lund University, CIRCLE - Center
for Innovation, Research and Competences in the Learning Economy.
Van den Belt, H., & A. Rip. (1987). The Nelson-Winter-Dosi Model and Synthetic Dye Chemistry.
In W. E. Bijker, T. P. Hughes & T. J. Pinch (Eds.), The Social Construction of Technological
Systems. New Directions in the Sociology and History of Technology (pp. 135-158.). Cambridge
MA: MIT Press.
World Bank, 2013. World Development Report 2013: Jobs. Washington, DC: World Bank.
World Bank, 2016. World Development Report 2016: Digital Dividends. Washington, DC: World
Bank.
Zucker, L. G., Darby, M. R., Furner, J., Liu, R. C. and Hongyan, M. (2007) “Minerva unbound:
knowledge stocks, knowledge flows and new knowledge production”, Research Policy, 36, pp. 850863.