Università degli Studi di Salerno
CELPE
Centro di Economia del Lavoro e di Politica Economica
Rosamaria D’AMORE, Roberto IORIO, Giuseppe LUBRANO LAVADERA
University of Salerno - CELPE
Exploring the Relationship Between Human Capital and Innovation at
the Firm Level: A study on a Sample of European Firms
Corresponding author
riorio@unisa.it
Discussion Paper 144
1
2
Index
Abstract ....................................................................................................................................................... 5
Introduction ................................................................................................................................................. 7
1.
Human Capital, R&D and Innovation: Looking for Microeconomic Literature………………………. ... 8
2.
Data and Objective of the Empirical Analysis; Bivariate and Multivariate Analysis .......................... 10
3.
Instrumental variable ......................................................................................................................... 18
4.
Synthesis of results ........................................................................................................................... 20
5.
Conclusions ....................................................................................................................................... 21
References ............................................................................................................................................... 22
Appendix………………………………………………………………………………………….........................24
3
Scientific Commitee
Adalgiso AMENDOLA, Floro Ernesto CAROLEO, Marcello D’AMATO, Cesare IMBRIANI, Pasquale PERSICO
CELPE - Centro di Ricerca Interdipartimentale di Economia del Lavoro e di Politica Economica
Università degli Studi di Salerno
Via Giovanni Paolo II, 132 - 84084 Fisciano, I- Italy
http://www.celpe.unisa.it
E-mail celpe@unisa.it
4
Exploring the Relationship Between Human Capital and Innovation at the
Firm Level: A study on a Sample of European Firms
Rosamaria D'AMORE, Roberto IORIO, Giuseppe LUBRANO LAVADERA
University of Salerno - CELPE
Abstract
In this paper, we explore the relationship between the human capital “embodied” in the workforce
and the innovative capabilities of the firm, adopting an international comparative perspective. In fact
data come from a survey (EFIGE) run in seven European countries during the 2007-2009 period.
They are analysed with several models of multivariate analysis also with the support of a semiparametric model. Our results show a positive relationship between the ratio of graduated
employees and the percentage of turnover from innovative products, being the share of personnel
employed in R&D constant. This relationship is not linear: we find decreasing marginal returns for
human capital and R&D. We then find a complementarity between human capital and R&D: the
strength of the link between human capital and innovation is higher when the firm’s R&D increases.
We also find some significant differences in the intensity of the human capital/innovation link across
different countries.
Keywords
Human capital; R&D; Innovation;
JEL
D22; O32; J24
5
6
Introduction
The importance of human capital for growth has been largely emphasized. This link has its roots at
the firm level, where it is easy to assume a relationship between the education of the workforce
and the innovative capacity of the firm: more educated employees may introduce more
innovations, and such employees are needed to import and manage innovations created by other
firms. Indeed, the link between human capital and innovation has been largely investigated at a
macroeconomic level, with a special emphasis on the externalities, but we believe this has not
been done sufficiently at the firm level. This paper aims to contribute to filling this gap.
The relationship between education and innovation is evident if we consider employees with high
education levels working in R&D laboratories, but the link between R&D and innovation at the firm
level has received more attention: we enquire whether a higher educated workforce is important for
innovation, even excluding R&D activities. The relationship between education and innovation is
evident if we consider employees with high education levels working in R&D laboratories, but the
link between R&D and innovation at the firm level has received more attention: we enquire whether
a higher educated workforce is important for innovation, even excluding R&D activities. Empirically,
we try to answer this question: is there a relationship between the share of university graduate
employees with tertiary education on one side and the probability of introducing product innovation
and the percentage of turnover derived from innovative product on the other side, assuming that
the share of employees directly occupied in R&D activities is constant (even controlling for several
other factors that may have an effect on the innovation)? If such a relationship exists, we ask
whether it is linear or whether it is better approximated by a non-linear function.
Our theoretical framework is rooted in the knowledge production function. This function states that
innovation at the firm level is related to the cognitive capital present in the firm itself, which is
calculated not only by the expenses formalized in R&D but also by the level of internal human
capital (Audretsch and Feldman, 2004).
Moreover, we analyse the interaction between these two components. It is possible to hypothesize
both a complementarity and a substitutability relationship. We can expect that the higher the
human capital embodied in the workforce is, the larger the effect of R&D on innovation. A qualified
workforce may multiply the innovative potential coming from research laboratories; this is a
relationship of complementarity. Alternatively, it is possible to expect that if R&D expenditure is
absent or low, the innovative strength is particularly attributed to the qualified personnel not
working in laboratories. This is a relationship of substitutability: the lower the expenditure in R&D
is, the higher the effect of human capital on innovation. Therefore, we ask whether the relationship
between human capital and R&D are complementary or substituted at the firm level.
This paper also attempts to shed light on the differences of the effect of human capital on
innovation from an international perspective. We ask the following question: if human capital
embodied in the workforce has an effect on the innovativeness of the firm, is this effect significantly
different between different countries?
The empirical analysis is conducted on data from a survey (EFIGE) run in seven European
countries during the 2007-2009 period. This survey is better described in the third section.
To address these issues, we performed a regression analysis comparing several different
techniques. To test for the linearity of the relationship between the elements of cognitive capital
and the innovativeness of the firm, a semi-parametric analysis was also used.
The article is structured as follows: the second section presents a review of the relevant literature
on the relationship between human capital, R&D and innovation; the third section describes the
data (first sub-section), presents the results of the univariate and bivariate analysis (second subsection) and the multivariate analysis, followed by a synthesis of the results (third sub-section). Our
conclusions end the paper.
7
1. Human Capital, R&D and Innovation: Looking for Microeconomic
Literature
The link between human capital, R&D and innovation has usually been considered from a
macroeconomic point of view. In the theory of growth, the importance of human capital and R&D is
almost immediately acknowledged, but in the ‘classical’ Solow model of growth (1956), they remain
in a sort of ‘black box’. Mankiw, Romer and Weil (1992), extending Solow’s model with the
inclusion of human capital, managed to explain almost two-thirds of the variability of the growth
rate among different national economies. The new theory of growth particularly emphasises the
importance of human capital (Lucas, 1988) and R&D (Romer, 1990 a, b) for growth. Even if the
models of these authors have a microfoundation, the context remains macroeconomic.
An important theoretical and empirical breakthrough was the introduction of the knowledge
production function of Griliches (1979); the scope of the theoretical formulation is explicitly
microeconomic: in this formulation innovation is the output and knowledge is the input. Although
the latter is a term with a clear meaning in economics, this needs to be explained empirically. As
Audretsch and Feldman (2004) underline, citing Cohen and Klepper (1991 and 1992), the main
source of knowledge in firms is generally considered R&D, which is therefore the term that
underpins most empirical investigations; other elements to which the knowledge ‘translates’ vary
with the specific objective of the study. In Audretsch and Feldman’s (2004) formulation, the
knowledge production includes, in addition to R&D, human capital as an input of innovation. In the
empirical studies such formulation of the knowledge production function is rarely estimated at the
firm level. It is usually extended to industries, geographic areas or countries, highlighting the role of
spillovers and externalities; the innovation output of each firm depends only partly on internal
sources of knowledge. Knowledge functions largely depend on the research done by other firms,
by the public and private research centres and on the human capital in the geographically
contiguous area of the firm (for an analysis of the Italian case, see Audretsch and Vivarelli, 1996).
Therefore, when the link between the input and output of knowledge is studied at the firm level, the
result is often weak, while, if the unit of analysis is larger, the relationship becomes clearer.
In the decades since the introduction of the theoretical model of the knowledge production function,
there have been many important contributions, even empirical, that substantiate this approach
(Griliches and Mairesse, 1983; Hall and Mairesse, 1995; Crepon, Duguet and Mairesse, 1998).
The idea is that a company, an industry or a geographical area (Jaffe, 1986; Acs, Audretsch and
Feldman, 1992; Feldman, 1994) must invest in R&D expenses (input) to increase the production of
innovations (output). These, in turn, imply an increase of the added value through product
innovations and productivity, particularly through process innovations. In the past, the original
formulation was significantly and sufficiently enriched through the consideration of the effects of
feedback (Kline and Rosenberg, 1986), as well as the realization that knowledge spillovers can
take root only in the presence of a sufficient level of absorptive capacity (Cohen and Levinthal,
1989). This is, in fact, an adequate level of internal knowledge resources that can ‘absorb’ the
external knowledge. According to Mangematin and Nesta (1999), firms with a high level of
absorptive capacity will be in a better position to assimilate and utilize external knowledge to
increase the innovative performance.
Therefore, in the sophisticated context of the evolutionary theory of the firm, the idea of Nelson and
Phelps (1966) - which was born in the macroeconomic field - that ‘internal’ knowledge is needed to
absorb new knowledge produced outside, is reclaimed, showing a kind of inverse causal process
between intellectual capital and innovation. Despite these important theoretical improvements, the
setting of this trend of studies remains focused on the role of R&D as a primary factor: R&D is
capable of generating innovation to support productivity, the competitiveness of products, and
economic growth.
8
Compared to the profusion of studies on the effects of human capital in the macroeconomic sphere
or on the effects of R&D at the firm level, there are less frequent studies on the effects of human
capital on innovation at the firm level (Schneider, Guenther and Brandenburg, 2010). In many
cases, the highlighted link is indirect, in the sense that human capital is seen as a prerequisite for
investment in other factors or changes in firms that in turn lead to innovation. For example, in a
study using Italian data, Arrighetti, Landini and Lasagni (2011) refer to a vision of a firm based on
capabilities and stress the propensity to invest in intangible assets. Such assets, which have a
strong impact on innovation and firm performance, depend on the level of human capital in the firm
as well as on firm size, organizational complexity and many firm-specific factors. Abowd et al.
(2002), using US data, show that human capital affects the productivity of businesses directly or in
a complementary role with respect to the most advanced technologies, business models and
organizational practices. Piva, Santarelli and Vivarelli (2005), using Italian data, highlight the link
between organizational change and the demand for employees with high levels of skill. Blundell et
al. (1999), in their literature review about the returns on human capital at the macroeconomic
(representing the entire economy) and microeconomic (representing the firm and individual) levels,
underline the dual role of a highly educated and skilled workforce: they are able to adapt to new
tasks and technologies and are a direct source of innovation, because education increases an
employee’s ability to be innovative in his job. They also report the results of several empirical
studies, such as that of Bosworth and Wilson (1993), which suggests strong links between the
employment of graduates, including professional scientists and engineers, and the adoption and
use of high-level technologies in a firm. Besides, they underline the role of on-the-job training as a
component of human capital and, aside from innovation, they also consider the effects of human
capital on productivity and profitability. The importance of qualified human resources, together with
R&D, to enhance the firm’s absorptive capacity and therefore its innovative performance is also
underlined by Lund Vinding (2006) and Muscio (2007).
Some papers, similarly to ours, take into account both the human capital and R&D at the firm level.
Ballot, Fakhfakh and Taymal (2001), analysing data from France and Sweden, consider the effect
of R&D and human capital on firm performance as measured by the value added and find a
positive effect. They also highlight the interaction between these two factors, showing a positive
effect. The second study, like ours, is based on EFIGE data. The share of university graduate
employees is linked with the introduction of an innovation in a firm as well as with the number of
patents filed at the European Patent Office (the relationship found is positive); expenditure on R&D
is not included in the same estimate of the determinants of innovation but is placed in relation to
human capital in the sense that the latter (measured with the share of graduates) positively affects
the expenditure on R&D. The paper by D’Amore, Iorio and Lubrano Lavadera (2014), which
analyses Italian data (a rotating panel of Italian firms for a period of nine years), shares the same
theoretical background and some central empirical questions as those in this study and they find
similar results: a statistically significant and positive relationship between the number of graduates
and the number of employees in R&D on the one hand and the likelihood of introducing both a
product and a process innovation on the other. They also study the interaction term between
graduates and R&D and they find that it has a negative sign: an effect of ‘substitution’ between the
two components of the cognitive capital of the firm seems to prevail and therefore the relationship
between human capital and innovation is stronger, whereas the level of R&D is lower. The
difference in that paper compared to this study is that the quadratic terms for human capital and
R&D are not introduced and this may be one reason for the different result in this paper (in also
considering quadratic terms, we anticipate that we will find a complementarity between human
capital and R&D).
9
2. Data and Objective of the Empirical Analysis; Bivariate and
Multivariate Analysis
For our analysis, we used data from the EFIGE survey. The EFIGE (European Firms in a Global
Economy) is an international research project under the auspices of the European Commission. A
large survey with six sections was submitted to a sample of 14,911 firms in seven European
countries: 3,019 in Italy, 2,975 in France, 2,973 in Germany, 2,832 in Spain, 2,142 in the United
Kingdom, 488 in Hungary, and 482 in Austria. The stratification of the sample was done according
to the size and business sector, taking into account the main geographical areas of each country.
The questions are related to the 2007-2009 period.
The goal of this study is to correlate the innovativeness of the firms with their cognitive capital. As a
measure of innovativeness, we take two variables into consideration, both of which are derived
from two specific questions of the EFIGE survey: the first is a dummy variable that assumes a
value of 1 if the firm introduced any product innovations in the 2007-2009 period and 0 otherwise;
the second variable is the average percentage of turnover from innovative product sales in the
same years1. As a measure of cognitive capital, we use the share of graduates and personnel
engaged in R&D. We investigated whether and to what extent human capital and R&D are related
to innovation at the firm level as well as the non-linearity of the relationship and the effects of the
interactions between the two components of cognitive capital on innovation. Referring to the review
of the literature of the previous chapter, we can say that our theoretical reference is the function of
knowledge production à la Audretsch and Feldman (2004). In this framework, innovation at the firm
level (I) is a function of R&D (RD), the internal human capital (HK) and an error term. The nonlinear formulation of the function implies the non-constant effects of R&D and human capital on
innovation and the existence of the effects of interaction between the two inputs (the effect of one
depends on the size of the other). Formally,
Ii = αRDβiHKγiεi
We analysed the relationship between innovation and intellectual capital with bivariate and
multivariate statistical techniques. Table 1 presents the list of the variables included in the different
analyses, with their names, definitions, mean values, standard deviations, minimum and maximum
values.
1
In the EFIGE questionnaire, only those who introduced a product innovation may indicate the percentage of turnover
derived from innovative product sales; therefore, for those who did not introduce any product innovations, we assumed
that the percentage was zero.
10
Variables
innoprod
Definitions
Dummy =1 if the 'firm introduced any product
innovation in the period 2007-2009
innoturn_prop
innoturn
gradperc
gradperc2
rdperc
rdperc2
Xgradrdperc
extceo
workforce
export
Italy
France
Spain
Germany
Austria
Hungary
UK
pavitt1
pavitt2
pavitt3
pavitt4
grad_Italy
grad_France
grad_Spain
grad_Germany
grad_Austria
grad_Hungary
N
Percentage of turnover deriving from
innovative product sales
percentage of university graduates in the
workforce
2
(gradperc)
percentage of employees involved in R&D
2
(rdperc)
gradperc* rdperc
Instrument: Dummy = 1 if the chief executive
office (CEO) is a manager recruited outside
the firm.
Number of employees in the firm’s home
country
Dummy = 1 if the firm export
Dummy = 1 if the firm is located in Italy
Dummy = 1 if the firm is located in France
Dummy = 1 if the firm is located in Spain
Dummy = 1 if the firm is located in Germany
Dummy = 1 if the firm is located in Austria
Dummy = 1 if the firm is located in Hungary
Dummy = 1 if the firm is located in UK
Supplier Dominated firms
Scale-Intensive firms
Specialized-Suppliers firms
Science-based firms
Italy*gradperc
France*gradperc
Spain*gradperc
Germany*gradperc
Austria*gradperc
Hungary*gradperc
mean
sd
min
max
0.491
0.500
0
1
0.102
0.188
0
1
10.18
18.80
0
100
9.453
13.50
0
100
271.6
7.820
250.757
122.1
914.3
13.77
1067
478.4
0
0
0
0
10000
100
10000
10000
0.0460
0.209
0
1
65.09
102.0
10
500
0.580
0.205
0.201
0.192
0.199
0.0300
0.0331
0.140
0.265
0.500
0.189
0.0460
1.413
1.790
2.021
2.290
0.177
0.510
14759
0.494
0.403
0.401
0.394
0.399
0.171
0.179
0.347
0.441
0.500
0.392
0.210
5.631
6.859
6.858
8.111
2.099
4.250
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
100
100
100
100
80
100
Table 1 Definitions and descriptive statistics of the variables
Notes
§Variables excluded by regressions to avoid the perfect collinearity trap
The lines indicate dummies that sum to one.
Table 2 gives an introductive, general picture of the innovative performance of the firms included in
the sample: the first column shows the percentage of firms that claim to have introduced product
innovations (that is, when the value of innoprod is 1), the second column shows the mean
percentage of turnover derived from innovative products (the mean value of the variable innoturn),
and the third column shows the same mean as before but only considers innovative firms.
Percentage of firms
introducing
product innovation
(who declared innoprod =1)
Average value of innoturn—all
firms
Average percentage of turnover from
innovative product sales—
only innovative firms
49.1%
10.2%
21.2%
Table 2 - Percentage of innovative firms
11
Table 3 shows the correlation between the variables concerning the innovative performance
(innoprod and innoturn) and the variables expressing the intellectual capital of the firms (gradperc
and rdperc).
innoturn
gradperc
rdperc
innprod
0.56***
0.15***
0.18***
innoturn
0.19***
0.22***
gradperc
0.26***
Table 3 Pearson correlations
Notes
***Significant at 1%
All correlations are positive and significant at 1%. Thus, there is a positive relationship between the
components of the cognitive capital and the innovativeness of the firms. On the other hand, there is
a strong relationship between the number of graduates and the number of employees in R&D: this
is obvious considering that, among those involved in R&D, there is a high percentage of graduates
with science degrees. To highlight the effect of each of the two components of cognitive capital on
the innovation, it is necessary to perform a multivariate analysis, which also takes into account a
number of other ‘control’ variables that are correlated both to innovation and the cognitive capital of
the firm.
In the multivariate analysis, as dependent variables we considered the variables expressing the
innovative performance (innoprod and innoturn); as independent variables under study we
considered the variables expressing the intellectual capital (gradperc and rdperc)2, their quadratic
terms and their interaction (respectively: gradperc2, rdperc2 and Xgradrdperc); we controlled for
the firm dimension (workforce), for its exporting capacity (export) and we also introduced dummy
variables for Pavitt sector and for countries3. In one of the estimated models we also introduced
the interaction between the dummy variables for countries and gradperc (grad_[name country])
and in the analysis with instrumental variable we took extceo as an instrument for gradperc.
In the regressions with innoprod as a dependent variable, we adopted the probit model because
this variable is dichotomous; the dependent variable innoturn is a percentage, assuming therefore
positive values only from 0 to 100. Such data may be properly treated with a Tobit model (as
suggested by Long, 1997) or a Generalized Linear Model (as suggested by Papke and
Wooldridge, 1996)4; we also estimated a classical OLS linear regression model. Moreover, we
have to consider that the process that determines whether a firm is innovative may be different
from the process that determines the percentage of innovative turnover. A Heckman selection
model estimates the two different but linked processes. We considered the same covariates for the
two steps, assuming that they have a different effect in the two processes5. In studying the
relationship between human capital and innovation, problems of endogeneity and reverse causality
may arise as our data are cross-sectional. Therefore, we also estimated probit (for innoprod), linear
and Tobit regressions (for innoturn) with instrumental variables.
Robust estimates were always performed when possible.
We estimated several models: Model 1 is the ‘basic’ model, including only first degree terms for
human capital and R&D variables without interactions plus the control variables illustrated above.
Model 2 adds the interaction and quadratic terms of human capital and R&D variables to the
2
As underlined before, gradperc and rdperc are partly overlapping: a (presumably large) part of the R&D personnel is
graduated and a part of the graduated people is employed in R&D function. Therefore, if both variables are included in a
multivariate analysis, we could conclude that the coefficient of gradperc indicates the effect on innovation of an increase
in the percentage of the graduated not employed in R&D and that the coefficient of rdperc indicates the effect on
innovation of an increase in the percentage of not graduated people employed in R&D.
3
We tested other control variables in the models, but the results were non-significant or were missing in several
observations, therefore reducing the number of observations without significantly modifying the effectiveness of the
estimates.
4
In STATA 14, we adopted the options family (binomial) and link (logit).
5
Because of the computational difficulties in estimating the maximum likelihood specification of the Heckman selection
model, we adopted the two-step specification.
12
previous model. Model 3 adds the interaction terms between the human capital variable and the
dummy variables for the countries to the previous model.
In our comments, we considered 5% as the threshold of significance, but we noted all cases in
which the coefficients and tests were below the 1% level. On the other hand, we signalled when a
threshold of 10% would be however overcome.
In the following section, we report the formulas for each model (for the sake of simplicity, when the
dependent variable is innoturn, we report only the linear model) and the result of the estimates for
every model, followed by a brief comment.
We begin our analysis with Model 1, the basic model.
Model 1
-1
Φ (innoprodi) = β0 + β1 gradperc i +β3 rdperc i + β3workforce + β4 export + β5-7 (Pavitt dummies) + β8-13
(country dummies) +ε i
innoturni = β0 + β1 gradperc i +β3 rdperc i + β3workforce + β4 export + β5-7 (Pavitt dummies) + β8-13 (country
dummies) +ε i
Dependent variable
gradperc
rdperc
workforce
export
Dummy Country
Dummy Pavitt
Const
(1)
probit
innoturn
***
0.009
(0.001)
***
0.014
(0.001)
***
0.001
(0.000)
***
0.494
(0.023)
YES
YES
***
-0.389
(0.038)
(2)
OLS
(3)
Tobit
(4)
GLM
(5)
Heckman
innoprod
***
0.175
(0.017)
***
0.242
(0.017)
0.001
(0.002)
***
4.630
(0.314)
YES
YES
***
5.474
(0.529)
sigma cons
***
0.319
(0.029)
***
0.476
(0.030)
***
0.017
(0.003)
***
13.776
(0.731)
YES
YES
***
-18.662
(1.129)
***
32.886
(0.507)
***
***
0.008
(0.001)
***
0.011
(0.001)
0.000
(0.000)
***
0.301
(0.020)
YES
YES
***
-1.577
(0.031)
0.240
(0.045)
***
0.361
(0.065)
-0.001
(0.006)
*
6.628
(2.834)
YES
YES
0.822
(9.211)
13727
14.729
(8.387)
13727
13
1001.4
8.62e-206
13
234.9
8.85e-43
mills lambda
N
2
adj. R
rmse
r2
r2_p
df_m
chi2
p
14046
0.0835
13
1278.2
2.58e-265
13727
0.092
17.90
0.0934
13727
13
0.0238
13
2.11e-171
3.09e-238
Table 4 Determinants of product innovation and of turnover from innovative product sales – Model 1 for different
estimations
Notes
Standard errors in parenthesis and * p < 0.05, ** p < 0.01, *** p < 0.001
The first column of Table 4 reports the results of the probit estimate for innoprod as a dependent
variable; the other columns report the results of the OLS, Tobit, GLM and Heckman selection
model for innoturn as a dependent variable (we report only the results of the second step of the
Heckman selection model)6. The variables representing the cognitive capital of the firm show
6
Innoprod is the dependent variable of the probit representing the first step; the independent variables are the same as
in the probit model shown in Table 3. The number of cases is almost the same, except for the few (339 over 14,046) firms
13
similar results, regardless of the measure of innovation and the estimated model. The positive and
significant (at the 1% level) sign for rdperc was largely expected: firms with a higher ratio of
employees involved in R&D have a higher degree of innovativeness (a higher probability of
introducing an innovation and a higher impact on the firm’s returns from the innovations). The sign
for gradperc is always positive and significant (at the 1% level) too: this means that, even
controlling for the employees in R&D, a higher ratio of university graduate employees is associated
with a higher level of innovativeness. As for the control variables, we observe that exporting firms
are more innovative than non-exporting ones; the effect of the size (expressed by the number of
employees) is significantly positive with regard to the capacity to innovate, while the effect on the
innovation turnover is significant (at 1%) and positive only in the Tobit model. Regarding the Pavitt
classification, we may conclude that, as expected, the specialized suppliers and science-based
firms are more innovative than supplier-dominated firms. If we suppose that the process for
generating an innovation is different from the process of determining the percentage of innovative
turnover, the Pavitt classification has no significant effect on this second process as expressed by
the results of the second step of the Heckman selection model. The correctness of the hypotheses
of the non-identity of the two processes, therefore, with regard to the need to estimate a selection
model, is estimated by the Mills lambda test. This has a significance threshold of 5%, and this
hypothesis should be rejected (the test is significant only at 10%). However, we anticipate that for
more complex models like Models 2 and 3, the test becomes significant at 1%. Therefore, the
presence of an estimate with the Heckman selection model is largely justified.
The signs and significance of the coefficients for the country dummy variables show that firms in
the United Kingdom have a higher probability of introducing product innovation than all the other
countries except Austria, and this result is significant at 1%. Regarding the percentage of turnover
derived from innovations, the prevalence of British firms is confirmed against France, Spain,
Germany and Hungary (at the 1% level, except for the Heckman selection model, which shows
similar results but at a lower level of significance for Spain, Germany and Hungary). The positive
sign for Austrian firms with innoturn is remarkable, even though the 5% level of significance is
reached only in the Tobit model (which is at 10% in the other three models).
We turn now to Model 2, which adds to Model 1 the interaction term of gradperc and rdperc
(Xgradpercrdperc) and their quadratic terms (gradperc2 and rdperc2).
Model 2
-1
Φ (innoprodi) = β0 + β1 gradperc i + β2 gradperc2 i + β3 rdperc i + β4 rdperc2 i + β5 Xgradrdperc i + β6
workforce + β7 export + Β8-10 (Pavitt dummies) + β11-15 (country dummies) +ε i
innoturni = β0 + β1 gradperc i + β2 gradperc2 i + β3 rdperc i + β4 rdperc2 i + β5 Xgradrdperc i + β6 workforce + β7
export + Β8-10 (Pavitt dummies) + β11-15 (country dummies) +ε i
which have 1 for innoprod and have missing innoturn, which was dropped from the first step of the Heckman selection
model. As the variables are the same and the cases are almost the same, the results are very similar. Therefore, we
preferred not to show the results of the first step of the Heckman selection model. The same is true for Models 2 and 3.
14
Dependent variable
gradperc
gradperc2
rdperc
rdperc2
Xgradrdperc
workforce
export
Dummy Country
Dummy Pavitt
Const
(1)
probit
innoturn
***
0.018
(0.002)
***
-0.000
(0.000)
***
0.052
(0.002)
***
-0.001
(0.000)
-0.000
(0.000)
***
0.001
(0.000)
***
0.423
(0.024)
YES
YES
***
-0.528
(0.038)
(2)
OLS
***
0.159
(0.032)
-0.001
(0.001)
***
0.654
(0.034)
***
-0.006
(0.000)
**
0.003
(0.001)
0.002
(0.002)
***
3.752
(0.314)
YES
YES
***
4.499
(0.516)
sigma cons
(3)
Tobit
(4)
GLM
innoprod
***
***
0.438
0.010
(0.060)
(0.002)
***
**
-0.003
-0.000
(0.001)
(0.000)
***
***
1.486
0.034
(0.061)
(0.002)
***
***
-0.015
-0.000
(0.001)
(0.000)
0.002
0.000
(0.001)
(0.000)
***
0.020
0.000
(0.003)
(0.000)
***
***
11.393
0.250
(0.723)
(0.020)
YES
YES
YES
YES
***
***
-21.760
-1.667
(1.131)
(0.031)
***
32.153
(0.501)
***
0.378
(0.097)
*
-0.002
(0.001)
***
1.451
(0.223)
***
-0.014
(0.002)
0.002
(0.001)
*
0.013
(0.006)
***
10.277
(2.137)
YES
YES
*
-21.186
(8.591)
***
mills lambda
N
2
adj. R
rmse
2
R
2
Pseudo R
df_m
chi2
p
(5)
Heckman
14046
0.113
16
1837.0
0.000
13727
0.119
17.64
0.120
13727
16
0.0315
16
7.43e-241
0.000
13727
31.878
(7.170)
13727
16
1494.4
8.16e-309
16
198.3
1.72e-33
Table 5 Determinants of turnover from innovative product sales – Model 2 for different estimations
Notes
Standard errors in parenthesis and * p < 0.05, ** p < 0.01, *** p < 0.001
The first column of Table 5 reports the results of the probit estimate for innoprod as a dependent
variable; the other columns report the results of the OLS, Tobit, GLM and Heckman selection
model for innoturn as a dependent variable.
The values of the pseudo R-square (for the probit estimate) and of the adjusted R-square (for
linear regression) for Model 2 (0.113, and 0.119, respectively) are larger than the values obtained
for Model 1 (0.083 and 0.092, respectively); thus, the introduction of the quadratic and interaction
terms of gradperc and rdperc increases the goodness of fit and the explicative power of the model.
The results of the estimation show the nonlinearity of the relationship between the human capital
embodied in the workforce and the R&D intensity of the firm on one side and the innovativeness of
the firm on the other. In fact, the terms of the first degree of gradperc and rdperc are positive and
significant while their square terms are negative and are almost in every case significant at 1% (the
only exceptions concern gradperc2, which is significant at 5% in the Heckman selection model and
is not significant in the OLS, while the 10% threshold would be passed). This means that the
increase in the probability of innovation and in the percentage of innovative turnover is larger when
the percentage of graduate employers is smaller.
The same holds true for the percentage of employers involved in R&D activities. In other words,
these results are consistent with decreasing returns, both for the human capital embodied in the
workforce and the firm’s R&D intensity.
15
The interaction term between gradperc and rdperc is negative but not significant for innoprod. For
innoturn, the sign is positive and significant (at 1% only in the OLS model; in the Heckman
selection model, it would pass a threshold of 10%). Thus, with a higher R&D intensity, an increase
in the percentage of university graduate employees is associated with a stronger increase in the
innovative turnover. At the same time, when the percentage of university graduate employees
increases, an increase in the percentage of R&D employees is associated with a stronger increase
in the innovative turnover. In other words, it is possible to consider a multiplicative effect between
human capital and R&D in terms of firm innovativeness.
In addition to the usual regression analysis, we also implemented a semi-parametric analysis specifically, a local polynomial regression (Cleveland, Grosse and Shyu, 1992) with the same
variables as the parametric analysis above. The control variables enter parametrically, and the
variables of interest (gradperc and rdperc) are regressed non-parametrically. The results are
graphically represented in Graph 1 of the Appendix: gradperc, rdperc and a linear predictor of the
independent variables are placed on the axes; the clearly visible saddle confirms the nonlinearity of
the relationship between human capital and R&D on the one side and innovation on the other. This
analysis supports the idea that a quadratic specification of the model can explain a large part of the
effect of gradperc and rdperc on innovativeness.
We now analyse Model 3, which adds the interactions of country dummy variables with gradperc to
the variables included in Model 2.
Model 3
-1
Φ (innoprodi) = β0 + β1 gradperc i + β2 gradperc2 i + β3 rdperc i + β4 rdperc2 i + β5 Xgradrdperc i + β6
workforce + β7 export + Β8-10 (Pavitt dummies) + β11-15 (country dummies) + β16-20 (dummies grad_[name
country]) +ε i
innoturni = β0 + β1 gradperc i + β2 gradperc2 i + β3 rdperc i + β4 rdperc2 i + β5 Xgradrdperc i +
+ β6 workforce + β7 export + Β8-10 (Pavitt dummies) + β11-15 (country dummies) + β16-20 (dummies grad [name
country]) +ε i
This model has both the dummy variables for the seven countries and their interactions with the
percentage of graduate employees. We impose that the relationship between gradperc and
innoturn is represented by different regression lines for different countries. These lines have
different vertical intercepts (expressed by the intercepts of the dummy variables for countries) and
different slopes (expressed by the coefficients of the interaction terms between gradperc and the
dummy variables for countries).
We therefore test whether the magnitude of the relationship between human capital and the
innovativeness of the firm is significantly different across different countries.
16
Dependent variable
gradperc
gradperc2
rdperc
rdperc2
Xgradrdperc
workforce
export
Italy
France
Spain
Germany
Austria
Hungary
grad_Italy
grad_France
grad_Spain
grad_Germany
grad_Austria
grad_Hungary
Dummy Pavitt
Const
(1)
probit
innoturn
***
0.022
(0.003)
***
-0.000
(0.000)
***
0.052
(0.002)
***
-0.001
(0.000)
0.000
(0.000)
***
0.001
(0.000)
***
0.422
(0.024)
***
-0.249
(0.045)
***
-0.341
(0.046)
***
-0.317
(0.048)
***
-0.254
(0.047)
**
0.180
(0.091)
***
-0.257
(0.087)
-0.002
(0.003)
**
-0.007
(0.003)
-0.003
(0.003)
***
-0.009
(0.003)
-0.008
(0.008)
-0.007
(0.004)
YES
***
-0.566
(0.042)
(2)
OLS
***
0.288
(0.055)
-0.001
(0.001)
***
0.650
(0.034)
***
-0.006
(0.000)
**
0.003
(0.001)
0.002
(0.002)
***
3.723
(0.313)
1.160
(0.683)
***
-2.546
(0.659)
-1.214
(0.653)
**
-1.727
(0.654)
2.242
(1.296)
-0.467
(1.344)
*
-0.168
(0.066)
*
-0.149
(0.062)
**
-0.145
(0.055)
**
-0.161
(0.058)
0.082
(0.157)
*
-0.179
(0.088)
YES
***
3.342
(0.575)
sigma cons
(3)
Tobit
(4)
GLM
innoprod
***
***
0.602
0.013
(0.088)
(0.002)
***
**
-0.004
-0.000
(0.001)
(0.000)
***
***
1.480
0.034
(0.061)
(0.002)
***
***
-0.015
-0.000
(0.001)
(0.000)
0.002
0.000
(0.002)
(0.000)
***
0.020
0.000
(0.003)
(0.000)
***
***
11.322
0.248
(0.722)
(0.020)
-0.481
0.055
(1.354)
(0.038)
***
***
-8.641
-0.197
(1.406)
(0.041)
***
*
-5.980
-0.086
(1.411)
(0.039)
**
**
-4.066
-0.118
(1.338)
(0.038)
**
6.085
0.122
(2.341)
(0.065)
*
-6.354
-0.063
(3.108)
(0.088)
*
*
-0.214
-0.006
(0.100)
(0.002)
-0.171
-0.003
(0.092)
(0.002)
-0.145
-0.004
(0.086)
(0.002)
**
-0.246
-0.004
(0.086)
(0.002)
-0.118
0.000
(0.213)
(0.005)
-0.251
-0.005
(0.165)
(0.004)
YES
YES
***
***
-23.374
-1.706
(1.269)
(0.035)
***
32.124
(0.500)
***
0.503
(0.114)
*
-0.003
(0.001)
***
1.368
(0.217)
***
-0.013
(0.002)
*
0.002
(0.001)
0.012
(0.006)
***
9.459
(2.079)
1.427
(1.553)
***
-7.760
(2.050)
*
-3.585
(1.801)
**
-4.880
(1.664)
4.881
(2.733)
-3.253
(2.807)
**
-0.252
(0.082)
*
-0.156
(0.077)
*
-0.154
(0.078)
*
-0.168
(0.075)
-0.001
(0.223)
-0.157
(0.120)
YES
*
-19.696
(8.499)
***
mills lambda
N
2
adj. R
rmse
2
R
2
Pseudo R
df_m
chi2
P
(5)
Heckman
14046
0.114
22
1837.0
0.000
13727
0.121
17.62
0.122
13727
22
0.0317
22
2.29e-238
0.000
13727
29.333
(7.012)
13727
22
1507.8
6.50e-306
22
224.6
1.65e-35
Table 6 Determinants of product innovation and of turnover from innovative product sales – Model 3 for different
estimations
Notes
Standard errors in parenthesis and * p < 0.05, ** p < 0.01, *** p < 0.001
17
The first column of Table 6 reports the results of the probit estimate for innoprod as a dependent
variable; the other columns report the results of the OLS, Tobit, GLM and Heckman selection
model for innoturn as a dependent variable.
We find that in the United Kingdom the magnitude is higher than in the other countries; regarding
the probability of introducing an innovation, this result is significant for France (at 5%) and
Germany (at 1%). Regarding the percentage of innovative turnover, this result is significant for Italy
(at 1%), France, Spain and Germany (at 5%). We refer to the results of the Heckman selection
model because the significance of the Mills lambda test (1%) ensures that this kind of model
should be preferred to other models. In any case, all the other models confirm the negative and
significant (at 5%) sign for Italy. However, in the GLM model, the signs for the other countries are
not significant at 5% (Germany would be significant at 10%).
3. Instrumental Variables
In the text above, we usually preferred to use the term relationship between human capital and
innovativeness rather than effect of the former on the latter. This is because the causal relationship
between human capital and innovation may be twofold: as reported in the introduction and
confirmed by several contributions in the literature, more educated employees may introduce more
innovations, but it is also true that they are needed to absorb and manage innovations. For this
inverse causal relationship, it is possible to consider the skill biased technical change theory —
that is, the introduction of more sophisticated technologies requires an upgrade of the employees’
skills (Sanders, 2004). Therefore, more human capital may imply more innovation, but more
innovation may require more human capital. This twofold relationship may imply endogeneity in the
relationship and thus a correlation with the residuals of the regression model and bias in the
estimate. To overcome this problem, we use an instrumental variable estimation. We use the
variable extceo for the percentage of graduate employees as an instrument. Extceo indicates if the
management of the firm is external (manager recruited from outside the firm) or internal (belonging
to the same family who owns the firms or a manager appointed within the firm)7. In fact we find that
external management has a higher propensity to hire more qualified employees (the variable
exteceo is significantly correlated with gradperc and the relationship exists even in a multivariate
analysis when controlling for the same control variables of the analyses above) and that the
positive impact of the external management on innovation happens through a higher propensity to
assume more qualified workers (including extceo among the covariates of Model 1, it results not
significant -at 5%- but, if gradperc is excluded from the covariates, extceo becomes significant).
Therefore, extceo meets the requirements of an instrumental variable: it is correlated with the
covariate, which may suffer from endogeneity (gradperc); however, having an impact on the
dependent variable only through that covariate, it does not result in a correlation with the residuals.
We estimated Model 1 both for innoprod (probit model) and innoturn (linear regression and Tobit)
with extceo as an instrumental variable for gradperc.8
It is not possible to estimate Model 2 with instrumental variables — being instrumental variables for
gradperc2, we should have the quadratic term of extceo, but it is perfectly collinear with the first
degree term as it is a binary variable. Moreover, if the variable gradperc is endogenous with
7
This distinction concerns only “family firms” (firm directly or indirectly controlled by an individual or family-owned entity).
The “non family firms” are therefore excluded from the analysis with instrumental variables.
8
As seen before when analysing the results of Model 1 without an instrumental variable, at a 5% level of significance,
there is no reason to choose a Heckman selection model over an OLS linear regression. We did not estimate the
Heckman selection model with instrumental variables.
18
respect to innoprod and innoturn, the interactions between gradperc and the country dummy
variables (introduced in Model 3) are also endogenous, and we need to instrument them; they
could be instrumented by the interactions between extceo and the country dummy variables, but
the dichotomic nature of extceo generates a high degree of multicollinearity in the estimate.
Therefore, we cannot reliably estimate the different effects of human capital in different countries
with instrumental variables.
(1)
probit
innoturn
***
0.051
(0.019)
0.565
(0.409)
rdperc
0.004
(0.005)
workforce
export
0.345
(0.110)
Dependent variable
gradperc
(2)
OLS
(3)
Tobit
(4)
GLM
(5)
Heckman
1.449°
(0.768)
0.030
(0.023)
0.130
(0.375)
0.140*
(0.071)
0.224°
(0.133)
0.003
(0.004)
0.226
(0.065)
0.001
(0.000)
**
0.000
(0.004)
0.014
(0.007)
*
-0.000
(0.000)
0.002
(0.003)
***
3.750
(1.145)
*
10.785
(2.198)
*
0.270
(0.063)
***
4.718
(1.026)
Innoprod
*
*
*
imills1
12.251
(0.521)
Dummy Country
YES
YES
YES
YES
YES
Dummy Pavitt
YES
YES
YES
YES
YES
2.671
(2.410)
-25.320
(4.640)
-1.690
(0.137)
5.847
(2.226)
-1.113
(0.769)
3.477*
(0.018)
2.391*
(0.021)
9538
9538
9538
0.343
14.98
0.344
13
14
4551.5
0.000
13.968***
0.008
Const
arthrho
_cons
lnsigma
_cons
alpha cons
***
-0.605
(0.080)
-0.489
(0.277)
***
2.396
(0.020)
lns cons
lnv cons
N
2
adj. R
rmse
2
R
2
Pseudo R
df_m
chi2
p
First stage
Endogenous
9743
9538
0.027
18.22
0.0285
13
1048.6
6.23e-216
13
529.4
8.34e-105
19.934***
0.937
3.11°
*
13
664.2
1.55e-133
2.10
*
Table 7 Determinants of product innovation and of turnover from innovative product sales – Instrumental variable
estimations for Model 1
Notes
Standard errors in parenthesis and p°<0,10, p* < 0.05, ** p < 0.01, *** p < 0.001
The first column of Table 7 reports the results of the probit estimation with instrumental variables,
having innoprod as dependent variable; the other columns report the results of the instrumental
19
variable estimation for OLS, Tobit, GLM, Hechkman selection model, having innoturn as
dependent variable. In this table we evidenced the coefficients significant at 10% too.
The discussion of the results may begin with the endogeneity tests: even though the hypothesis of
the endogeneity of innovation with respect to a firm’s human capital has a theoretical and logical
background, the statistical tests do not appear to confirm such a hypothesis. According to the Wald
test, the hypothesis of exogeneity for the probit and Tobit model cannot be rejected with 5% as a
significance threshold (in the probit model, the hypothesis is rejected with a 10% threshold). If the
hypothesis of exogeneity is not rejected, the ordinary probit and Tobit models should be preferred
to the corresponding estimates with instrumental variables; however, even with the hypothesis of
exogeneity, the instrumental variable estimates are consistent, although inefficient. Moreover, it
must be underlined that the results of the Wald test dramatically change, even with slight variations
in the model (even excluding one single covariate). On the contrary, the Hausman test for the
linear regression for innoturn clearly rejects (at a 1% level) the hypothesis of exogeneity: the
estimate with the instrumental variables of the linear regression model therefore has full statistical
justification in confronting the correspondent model without an instrumental variable.
Despite the expected high standard errors of the IV-probit model because of its inefficiency,
gradperc has a significant (at 1%) and positive effect on innoprod. The effect of gradperc on
innoturn is positive and significant (at 1%) in the IV-linear regression model, while the inefficiency
of the IV-Tobit model may explain its non-significance at 5% (however, it would be significant at
10%). It seems therefore possible to conclude, even when controlling for possible endogeneity,
that an increase in the percentage of graduate employees increases the probability of obtaining
one or more product innovations and the effectiveness of those innovations in terms of the share of
turnover deriving from their sales. It is worth nothing that rdperc is significant only in the IV-linear
regression (it has a 10% level of significance in the Tobit model). When controlling for endogeneity,
the firm’s R&D appears to be less able than human capital to explain the firm’s innovativeness.
4. Synthesis of Results
The results exposed above, which were obtained with different techniques of regression analysis,
allowed us to answer the questions posed in the introduction.
For the first question about the existence of a relationship between the human capital embodied in
the workforce (expressed by the share of graduate employees) and the innovativeness of the firm
(expressed by its probability of introduce product innovation and the percentage of turnover
deriving from innovative product sales), we can say that the relationship is positive and significant,
even when controlling for the R&D intensity (expressed by the percentage of employees involved
in R&D activities) and for other relevant variables.
Such a relationship is not linear: our results suggest an increasing but concave relationship both
between human capital and innovativeness and between R&D intensity and innovativeness.
We also asked about the type of interaction that exists between these two components of the firm’s
‘cognitive capital’. We find a multiplicative effect, indicating a complementarity between human
capital and R&D: when R&D is higher, the strength of the relationship between human capital and
innovativeness increases.
Another result is that the intensity of the relationship between human capital and innovativeness is
significantly different across different countries. Because of the cross-sectional nature of our data,
these results should be read more in terms of ‘relationship’ than ‘effect’, but the analysis with the
instrumental variable supports their interpretation as a positive effect of the human capital
embodied in the workforce on the innovative capability of the firm.
20
5. Conclusions
Several studies theorize or empirically test the link between human capital and economic growth at
a macroeconomic level. Studies investigating this relationship at a microeconomic level are less
frequently seen. At the firm level, the link between human capital and innovation is often seen as
indirect in the sense that a skilled workforce is considered a precondition for the elements (R&D
investments in information technology, business organization, etc.) that generate innovation. The
intention of this work is to empirically verify whether there is a direct relationship between the skills
of the workforce and the innovative capacity of the firm, even when ‘controlling’ for other crucial
factors for innovation (especially R&D). The analysis, conducted on data from firms in seven
European countries in the 2007-2009 period, reveals that an increase in the share of graduate
employees in the firm increases the likelihood of introducing a product innovation and the share of
turnover deriving from such innovations; the human capital and R&D intensity at the firm level
show decreasing returns, but they also reciprocally have a multiplicative effect: the effectiveness,
in an innovative sense, of the ‘human capital’ embodied in the workforce is higher in firms where
the ratio of employees employed in R&D services is higher; i.e., a better-educated workforce has a
multiplicative effect on the R&D intensity of the firm. We find, therefore, a complementarity
between human capital and R&D at the firm level. We also find that the intensity of the relationship
between human capital and innovativeness at the firm level is significantly different across
countries.
This study, of course, has some limitations from the cross-sectional nature of the data to the lack of
detailed information on the innovations. The information on the level of education of the workforce
is also limited (only the distinction between graduates and non-graduates is made, and therefore
the type of degree or the exact level attained is not included). Nevertheless, even with the
limitations just mentioned, this analysis offers interesting results, both because the topic has been
rarely explored and because the results may have important implications in terms of policy. It
appears evident that the education of the workforce is crucial for the industry as it may boost its
innovative capacity, both for a direct effect and through a multiplicative effect on the R&D function.
It is therefore clear that the way to reverse the downward trend in the competitiveness of European
firms is to increase attention and resources for investment, both public and private, in human
capital.
21
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Appendix
Graph 1
24
CELPE’s Discussion Papers
2017, 143 Cristian BARRA, Roberto ZOTTI
Bank Performance, Financial Stability and Market Competition: do Cooperative and NonCooperative Banks Behave Differently?
2016, 142 Adalgiso AMENDOLA
Riforma del terzo settore e ruolo delle associazioni di servizio per lo sviluppo
2016, 141 Cristian BARRA, Giovanna BIMONTE, Luigi SENATORE
Innovation Processes and Environmental Safety
2016, 140 Damiano FIORILLO, Luigi SENATORE
Self Image and Environmental Attitude and Behavior
2016, 139 Maria Rosaria GAROFALO
Oltre il conflitto tra efficienza ed equità: regole e misure di policy per l’uguaglianza di genere
2016, 138 Adalgiso AMENDOLA
Economia ed etica: razionalità economica, mercato e impresa
2015, 137 Antonella BELLINO, Giuseppe CELI
The Migration-Trade Nexus in the Presence of Vertical and Horizontal Product Differentiation
2015, 136 Adalgiso AMENDOLA, Roberto DELL’ANNO, Lavinia PARISI
Happiness, Inequality and Relative Concerns in European Countries
2015, 135 Giuseppina TESTA
The Effect of the Euro Competition Over Innovation Decisions and Labor Productivity
2014, 134 Antonio CARDONE
Capitale e Ricchezza
2014, 133 Giovanna BIMONTE, Luigi SENATORE
An Overview on the Application of the Coalitional. Games in Cancer Diagnosis
2014, 132 Domenico MADDALONI
The Warfare-Welfare Nexus. An Ecological-Evolutionary Conceptual Framework for the Analysis
of the Rise and Decline of National Public Welfare Systems
2014, 131 Massimiliano BENCARDINO, Giorgia IOVINO
Analysing and Managing Urban Sprawl and Land Take
2014, 130 Carmen AINA, Fernanda MAZZOTTA, Lavinia PARISI
Bargaining or Efficiency within the Household? The Case of Italy
2014, 129 Adriana BARONE, Annamaria NESE
Body Weight and Academic Performance: Gender and Peer Effects
2013, 128 Adriana BARONE, Annamaria NESE
Family Structure, Children and Night Work: Italy vs. Sweden
2013, 127 Annamaria FERRAGINA
The Impact of FDI on Firm Survival and Employment: A Comparative Analysis for Turkey and Italy
2013, 126 Adalgiso AMENDOLA, Roberto DELL’ANNO
Social Exclusion and Economic Growth: An Empirical Investigation in European Economies
2013, 125 Anna PAPACCIO
Bilateralism and Multilateralism: a Network Approach
2013, 124 Claudio PINTO
Complexity of Treatment, and Changes in Efficiency and Productivity for Directly Managed Italian
Hospitals
2012, 123 Giorgia IOVINO
Il Mezzogiorno e la sfida delle energia rinnovabili
2012, 122 Roberto DELL’ANNO, Stefania VILLA
Growth in Transition Countries: Big Bang versus Gradualism
2012, 121 Lavinia PARISI
The Determinants of First and Second Marital Dissolution. Evidence from Britain
25
2010, 120 Salvatore FARACE, Fernanda MAZZOTTA
Bivariate Probit Models for Analysing how “Knowledge” Affects Innovation and Performance in
Small and Medium Sized Firms
2010, 119 Marcello D’AMATO, Christian DI PIETRO
The Evolution of Wealth Distribution in a Model of Educational Investment with Heterogenous
Agents
2010, 118 Paolo COCCORESE
Banks as 'fat cats': Branching and Price Decisions in a Two-Stage Model of Competition
2010, 117 Sergio DESTEFANIS, Giovanni PICA
The Wage Curve an Italian Perspective
2010, 116 Adalgiso AMENDOLA, Joshy EASAW, Antonio SAVOIA
Inequality in Developing Economies: The Role of Institutional Development
2010 115 Gianluigi COPPOLA
Health, Lifestyle and Growth
2010 114 Teresa AMODIO
Methods of Analysis of Local Tourist Systems
2009 113 Fernanda MAZZOTTA
The Effect of Parental Background on Youth Duration of Unemployment
2009 112 Matteo FRAGETTA
Identification in Structural Vector Autoregressions Through Graphical Modelling and Monetary
Policy: A Cross-Country Analysis
2009 111 Bruna BRUNO, Damiano FIORILLO
Why without Pay? The Intrinsic Motivation between Investment and Consumption in Unpaid
Labour Supply
2009 110 Maria Rosaria GAROFALO, Annamaria NESE
Social Preferences and the Third Sector: Looking for a Microeconomic Foundation of the Local
Development Path
2008 109 Giorgia IOVINO
Gis, ricerca geografica e pianificazione urbanistica: un’applicazione sul centro storico di
Benevento
2008 108 Bruna BRUNO
La donna flessibile e il lavoro opportuno
2008 107 Damiano FIORILLO
Offerta di Attività Gratuita in Italia: una analisi micro-econometrica
2007, 106 Shane Niall O'HIGGINS, Marcello D'AMATO, Floro Ernesto CAROLEO, Adriana BARONE
Gone for Good? Determinants of School Dropout in Southern Italy
2007, 105 Ugo COLOMBINO, Annamaria NESE
Preference Heterogeneity in Relation to Museum Services
2007, 104 Giuseppe CELI, Mario SPORTELLI
Harrod’s Dynamics and the Kaldor-Thirlwall Export-led Growth
2007, 103 Francesca BETTIO, Fernanda MAZZOTTA, Giovanni SOLINAS
Costs and prospects for home based Long Term Care in Northern Italy: the Galca survey
2007, 102 Lisa CROSATO, Sergio DESTEFANIS, Piero GANUGI
Technology and Firm Size Distribution: Evidence from Italian Manufacturing
2007, 101 Guglielmo Maria CAPORALE, Alexandros KONTONIKAS
The Euro and Inflation Uncertainty in the european Monetary Union
2006, 100 Francesco Paolo VOLPE
Principio democratico e giustizia nell'amministrazione
2006, 99 Niall O’HIGGINS
Still With Us After All of These Years: Trends in Youth Labour Market Entry, Home-Leaving And
Human Capital Accumulation in Italy 1993-2003
26
2005, 98
2005, 97
2005, 96
2005, 95
2005, 94
2005, 93
2005, 92
2005, 91
2005, 90
2004, 89
2004, 88
2004, 87
2004, 86
2004, 85
2004, 84
2004, 83
2004, 82
2004, 81
2004, 80
2004, 79
2004, 78
2003, 77
2003, 76
Floro Ernesto CAROLEO, Gianluigi COPPOLA
The Impact of the Institutions on Regional Unemployment Disparities
Carlo ALTAVILLA, Antonio GAROFALO, Concetto Paolo VINCI
Is the Discouraged Worker Effect Time-Varying?
F. BUSATO, B. CHIARINI, P. DE ANGELIS, E. MARZANO
Capital Subsidies and Underground Production
Lucio Valerio SPAGNOLO, Mario CERRATO
No euro please, We’re British!
Roberto BASILE, Mauro COSTANTINI, Sergio DESTEFANIS
Unit root and cointegration tests for cross-sectionally correlated panels. Estimating regional
production functions
Sergio DESTEFANIS, Raquel FONSECA
Matching Efficiency and Labour Market Reform in Italy. A Macroeconometric Assessment
Cesare IMBRIANI, Antonio LOPES
Banking System Efficiency and the Dualistic Development of the Italian Economy in the Nineties
Carlo ALTAVILLA, Antonio GAROFALO, Concetto Paolo VINCI
Designing the Optimal Lenght of Working Time
Marco MANACORDA, Barbara PETRONGOLO
Regional Mismatch and Unemployment: Theory and Evidence from Italy, 1977-1998
Roberta TROISI
Teoria dell’impresa e responsabilità parapenale: le implicazioni organizzativo-gestionali
Roberta TROISI
Enti non profit: tipologie ed opzioni organizzative
Lavinia PARISI
La povertà: una rassegna sul confronto tra due approcci. Capability vs. Unidimensionalità
Giuseppe CELI
Quality Differentiation, Vertical Disintegration and the Labour Market Effetcs of Intra-Industry
Trade
Niall O’HIGGINS
Recent Trends in Youth Labour Martkets and Employment Policy in Europe and Central Asia
Carlo ALTAVILLA, Floro Ernesto CAROLEO
Evaluating Asimmetries in Active Labour Policies: The Case of Italy
Floro Ernesto CAROLEO, Francesco PASTORE
La disoccupazione giovanile in Italia. La riforma dei sistemi d'istruzione e di formazione
professionale come alternativa alla flessibilità numerica per accrescere l'occupabilità
Francesco PASTORE, Izabela MARCINKOWSKA
The Gender Wage Gap among Young People in Italy
Elisabetta MARZANO
Dual Labour Market Theories And Irregular Jobs: IsThere a Dualism Even in The
Irregular
Sector?
Corrado ANDINI
Unemployment and Welfare Partecipation in a Structural VAR: Rethinking the 1990s in the United
States
Floro Ernesto CAROLEO
Fondamenti teorici della rigidità salariale nell'ambito dei "Non Market clearing Models"
Adalgiso AMENDOLA, Floro Ernesto CAROLEO, Gianluigi COPPOLA
Regional Disparities in Europe
Fernanda MAZZOTTA
Flessibilità, povertà e istruzione: un approccio Sen – istituzionale
Adalgiso AMENDOLA, Annamaria NESE
Mobilità intergenerale nel livello d'istruzione nella società femminile italiana ed endogenità del
titolo di studio in un modello di partecipazione alla Forza Lavoro.
27
2003, 74 Antonio LOPES
Innovazione nel Sistema Creditizio del Mezzogiorno negli Anni Novanta
2003, 73 Sergio DESTEFANIS, Vania SENA
Public Capital and Total Factor Productivity New Evidence from the Italian Regions
2003, 72 Giuseppina AUTIERO, Bruna BRUNO
Social Preferences in Wage Bargaining: a Neocorporatist Approach
2003, 71 Gianluigi COPPOLA, Maria Rosaria GAROFALO, Fernanda MAZZOTTA
Industrial Localisation and Economic Development. A Case Study
2002, 70 Francesco GIORDANO, Fernanda MAZZOTTA
Salario di Riserva, Probabilità di Occupazione ed Efficacia dell’Istruzione Universitaria: un’Analisi
sugli Studenti dell’Università di Salerno
2002, 69 Giuseppe RUSSO
Istituzioni del Mercato del Lavoro e Occupazione: dai Costi di Aggiustamento all’Appropriabilità
2002, 68 Floro Ernesto CAROLEO, Francesco PASTORE
Training Policy for Youth Unemployed in a Sample of European Countries
2002, 67 Maria Rosaria GAROFALO, Maria Rosaria SUPINO
Il Disegno Normativo del Welfare Municipale in Italia come Strumento per lo Sviluppo Economico
e l’Allargamento delle Opportunità Occupazionali. Una Lettura Neoistituzionale della L. 328/00
2002, 65 Pietro SENESI
Cyclical dynamics under continuous time equilibrium
2001, 64 Marcello D'AMATO, Vincenzo GALASSO
E' la Riforma Dini Politicamente Sostenibile?
2001, 63 Sergio DESTEFANIS, Ornella Wanda MAIETTA
Assessing the Productive Efficiency of Non-Profit Organisations: a Comparative Analysis
2001, 62 Floro Ernesto CAROLEO, Francesco PASTORE
How fine targeted is ALMP to the youth long term unemployed in Italy?
2001, 61 Paolo COCCORESE
Strategic Advertising for Entry Deterrence Purposes
2001, 60 Alessandra AMENDOLA
Modelling Asymmetries in Unemployment Rate
2001, 59 Sergio DESTEFANIS
Differenziali Territoriali di Produttività ed Efficienza negli Anni '90: i Livelli e l'Andamento
2001, 58 Giuseppina AUTIERO, Fernanda MAZZOTTA
Job Search Methods: the Choice between the Public and the Private Sector
2001, 57 Giuseppina AUTIERO, Bruna BRUNO, Fernanda MAZZOTTA
A Correspondence Analysis of Labour Market Institutions
2000, 56 Giuseppina AUTIERO
Governmental Organized Learning and Coordination Problems: The case of Japan in1950s
2000, 55 Giuseppina AUTIERO, Fernanda MAZZOTTA
The Choice of Search Methods: Some Empirical Evidence from Italy
2000, 54 Giuseppe CELI
The Impact of International Trade on Labour Markets. The Case of Outward Processing. Traffic
between the European Union and Central Eastern European Countries.
2000, 53 Giuseppe RUSSO, David VEREDAS
Institutional Rigidities and Employment on the Italian Labour Market: the Dynamic of the
Employment in the Large Industrial Firms.
2000, 52 Floro Ernesto CAROLEO
Le Politiche per l'Occupazione in Europa: una Tassonomia Istituzionale
2000, 51 Andrew NEWELL, Francesco PASTORE
Regional Unemployment and Industrial Restructuring in Poland
1999, 50 Giuseppe CELI, Alasdair SMITH
Quality Differentiation and the Labour Market Effects of International Trade.
1999, 49 Giuseppe CELI
Vertical and Horizontal Intra-Industry Trade: What is the Empirical Evidence for the UK?
28
1999, 48 Cesare IMBRIANI, Filippo REGANATI
Productivity spillovers and regional differences: some evidence on the italian manufacturing
sector.
1999, 47 Adalgiso AMENDOLA, Annamaria NESE
L’impatto del background familiare sul livello d’istruzione dei figli.
1998, 46 Adalgiso AMENDOLA, Annamaria NESE
Mobilità intergenerazionale nella società femminile italiana.
1998, 45 Floro Ernesto CAROLEO, Fernanda MAZZOTTA
Youth Unemployment and youth employment policies in Italy.
1997, 44 Annamaria NESE
Mobilità intergenerazionale in Italia
1997, 43 Adriana BARONE, Concetto Paolo VINCI
Fairness: un concetto relativo nell’analisi del mercato del lavoro
1997, 42 Adriana BARONE, Concetto Paolo VINCI
Wage differentials and factor intensity reversals
1997, 41 Rosa CAPOLUPO
L’ipotesi di convergenza nel recente dibattito sulle teorie della crescita
1997, 40 Rosa CAPOLUPO
Endogenous Vs exogenous models of growth: the convergenze debate
1997, 39 Fernanda MAZZOTTA, Annamaria NESE
Transizioni "In and Out" il mercato del lavoro in Italia: un’analisi microeconometrica
1997, 38 Fernanda MAZZOTTA
Disoccupazione e probabilità di occupazione in Italia: un’analisi su microdati
1997, 37 Maria Rosaria GAROFALO, Bruna BRUNO
Equivalenza istituzionale" dei modelli di contrattazione sul salario: oltre il dibattito tra
centralizzazione e decentramento
1997, 36 Adalgiso AMENDOLA, Floro Ernesto CAROLEO, Gianluigi COPPOLA
Differenziali territoriali nel mercato del lavoro e sviluppo in Italia
1996, 35 Adalgiso AMENDOLA
Istituzioni e mercato del lavoro. Deregolazione, occupazione e paradigma istituzionale
1996, 33 Annamaria NESE
Modelli microeconometrici per l'analisi della domanda abitativa
1996, 32 Annamaria NESE
Test semiparametrici per modelli parametrici
1996, 31 Giuseppe CELI
Vertical intra-industry trade and skill intensity in Europe: a cross sector analysis
1996, 30 Sergio DESTEFANIS
Nominal rigidities and real activity. A cross-industry analysis for Italy, 1951-93
1996, 29 Cesare IMBRIANI, Filippo REGANATI
International technology transfer into the italian manufacturing sector
1996, 28 Stefania di SERAFINO, Alberto GANDOLFI
The choice of the Government optimal procurement mechanism: the first-price sealed bid auction
with one and/or two winners.
1996, 27 Raul de LUZENBERGER
Redistribution, and public debt with liquidity constraints
1996, 26 Bruno CHIARINI
Un modello VAR per la domanda di lavoro
1995, 25 Maria Rosaria CARILLO, Alberto ZAZZARO
Innovazione tecnologica e distruzione di capitale umano in un modello neo- shumpeteriano
di
crescita.
1995, 24 Raul de LUZENBERGER
Macroeconomia e politiche redistributive: il caso di vincoli di liquidità
1995, 23 Annamaria NESE
Tenure choice and demand for housing in Italy
1995, 22 Filippo REGANATI
La struttura delle preferenze per prodotti orizzontalmente differenziati.
1995, 21 Sergio DESTEFANIS, Michele LA ROCCA, Cosimo VITALE
Forecasting train ticket sales with linear model-based approaches and with edats
29
1995, 20 Stefania di SERAFINO
Bounded rationality and incomplete contracts
1995, 19 Adalgiso AMENDOLA, Floro E. CAROLEO, Maria Rosaria GAROFALO
Istituzioni, mercato del lavoro e decentramento delle decisioni.
1995, 18 Niall O’HIGGINS
Why did the youth training scheme reduce the wages of young people? A story of human capital
formation, reservation wages and job offers.
1994, 17 Antonio CARDONE
Misure di efficienza: alcuni aspetti teorici
1994, 16 Maria Rosaria GAROFALO, Luisa ZANCHI
Neo-corporativismo, centralizzazione e dispersione inter-settoriale dei salari.
1994, 15 Sergio DESTEFANIS
Allowing for frequency of negotiation in the aggregate wage equation. A study for Italian
manufacturing, 1973/92.
1994, 14 Marcello D'AMATO
Tariffs for a foreign industry with market power under incomplete information on demand
1994, 13 Raul DE LUZENBERGER, Cesare IMBRIANI, Giancarlo MARINI
Sustainability Issues in the process of European Integration
1994, 12 Riccardo MARSELLI, Antonio CARDONE
Interdipendenza tra regioni: un'analisi su dati di Panel
1993, 11 Adriana BARONE, Concetto Paolo VINCI
Dilemma del prigioniero e persistenza della disoccupazione involontaria
1993, 10 Maria Rosaria CARILLO
Mutamenti strutturali ed offerta di lavoro.
1993, 9 Niall O'HIGGINS
The effectiveness of YTS in Britain: an analysis of sample selection in the determination of
employment and earnings
1993, 8 Giuseppe CELI
Politiche valutarie ed integrazione commerciale: l'esperienza dello SME negli anni '80
1992, 7 Paolo COCCORESE
Attività innovativa e configurazione industriale
1992, 6 Maria Rosaria GAROFALO, Gian Luigi CORINTO
La Razionalità dell'Allocazione del Tempo di Lavoro in Agricoltura. Con un'Applicazione Empirica
ad un Sistema Locale attraverso la PL.
1992, 5 Adalgiso AMENDOLA, Maria SCATTAGLIA
Disoccupazione e Tassi di Attività nel Mezzogiorno
1992, 4 Floro Ernesto CAROLEO
La Disoccupazione Strutturale
1991, 3 Giovanni URGA
Dynamic Models of Labour Demand in the Italian Industrial Sector: Theories and Evidence from
Panel Data
1991, 2 Adalgiso AMENDOLA
Teoria dei Contratti Impliciti. Rigidità del Salario e Disoccupazione
1991, 1 Guido CELLA
Sulla Integrazione Produttiva Interregionale: il Caso del Mezzogiorno
30
CELPE – Centro di Ricerca in Economia del Lavoro e di Politica Economica
Università degli Studi di Salerno
ISSN 1970-4259
Depositato ai sensi di Legge
31