Firm’s innovation strategies and employment: new
evidence from Uruguay
Hugo Laguna
Carlos Bianchi
INSTITUTO DE ECONOMÍA
Serie Documentos de Trabajo STITUTO DE ECONOMÍA
ISSN:
ISSN:
1510-9305
1688-5090
Mayo, 2020
DT 06/2020
(en papel)
(en línea)
We acknowledge the microdata access given by the National Agency of Research and Innovation, Uruguay.
This article is based on the Master Thesis in Economics (FCEA-UDELAR) of Hugo Laguna. We thank
Carlos Casacuberta, Bibiana Lanzilotta, and Adriana Peluffo for useful comments. All remaining errors are
ours.
Forma de citación sugerida para este documento: Laguna, H. Bianchi, C. (2020) “Firm’s innovation
strategies and employment: new evidence from Uruguay”. Serie Documentos de Trabajo, DT 06/2020.
Instituto de Economía, Facultad de Ciencias Económicas y Administración, Universidad de la República,
Uruguay.
Firm’s innovation strategies and employment: New evidence from
Uruguay.
Hugo Laguna*
Carlos Bianchi**
Abstract
A large and rich body of literature has shown that the relationship between innovation
and employment is complex and dynamic in nature. From a firm’s level analysis, recent
researches have shown heterogeneous empirical patterns for developed and developing
countries. This paper contributes by inquiry in the role of innovation strategies as
determinants of the firm’s employment growth in a Latin American small middleincome country. Adapting econometric structural models currently in vogue, we discuss
the effects of three innovation strategies (Make, Buy, Make&Buy) on the firm’s
workforce growth. In line with the literature, we identify a significant positive relation
between product innovation associated with Make and Make&Buy strategies, however,
on the contrary to most recent research we find a positive and significant effects of
process innovation associated to Buy strategies. Considering technological, sectoral and
firm characteristics, our findings show a clear positive effect of any innovation strategy
in the growth of the firm’s workforce. Meanwhile, no innovative strategies negatively
affect workforce growth. Our findings contribute by deepening the understanding of the
firm level determinants of employment in developing countries. We analyze our result
in the light of a recent but extensive evidence on the relationship between innovation
and employment at firm’s level in Uruguay. In particular, we discuss the traditional
explanation on the firm’s technological behavior in Latin America, to discuss the effects
on employment of integrative innovation strategies in Uruguay.
JEL Codification: O33, D22, J23
Key words: innovation strategies, employment, Latin America, Uruguay
(*) Facultad de Ciencias Económicas y de Administración, Universidad de la República,
Uruguay, email: hugolaguna@gmail.com
(**) Instituto de Economía, Universidad de la República, Uruguay, email:
cbianchi@iecon.ccee.edu.uy
1
Resumen
Una amplia y rica literatura ha mostrado que la relación entre innovación y empleo es de
naturaleza compleja y dinámica. Investigaciones recientes han mostrado patrones empíricos
heterogéneos a nivel de la firma, tanto para países desarrollados como en desarrollo. Este
estudio analiza el rol de las estrategias de innovación como determinante del crecimiento del
empleo a nivel de la firma en un país latinoamericano de ingresos medios. Se discute el efecto
de cuatro estrategias de innovación (Make, Buy, Make&Buy) sobre el crecimiento del empleo,
adaptando modelos econométricos estructurales largamente utilizados en investigaciones
previas. En línea con la literatura, se identifican relaciones positivas y significativas entre
innovación en productos, asociada con la estrategia Make&Buy. No obstante, a diferencia de lo
encontrado en las investigaciones recientes, se encuentra un efecto positivo y significativo de la
innovación en procesos, asociada con la estrategia Buy. Tomando en consideración las
características tecnológicas y sectoriales de las firmas, los resultados encontrados muestran un
efecto positivo claro de las estrategias de innovación sobre el crecimiento del empleo en las
mismas. Adicionalmente, la ausencia de estrategias de innovación afecta negativamente el
crecimiento del empleo. Estos hallazgos van en la dirección de mejorar la comprensión de los
determinantes del empleo a nivel de la firma en los países en desarrollo. Para eso discutimos los
resultados a la luz de una reciente, pero extensa acumulación de estudios sobre innovación y
empleo a nivel de firma para Uruguay. En particular, se discute la explicación tradicional sobre
el comportamiento tecnológico de la firma en América Latina.
Palabras clave: estrategias de innovación, empleo, América Latina, Uruguay.
Código JEL: O33, D22, J23
2
1. Introduction
Innovation is a process of creative destruction, where new things and ways to do things
replace older one, affecting the resources and capabilities related to the development,
production and commercialization of such things (Schumpeter 1942). It triggers
structural changes that involve reallocation processes of resources that are observable
at different analytical levels. The destructive and creative effects of this process has
gained particular attention related to the potential effects on employment and skills
demand (Dachs et al. 2017; Catela et al. 2015).
Facing the global crisis of 2008 and the diffusion of ICT innovations, research on the
effects of innovation in employment has dramatically grown (Frey 2019; Acemoglu and
Restrepo 2018; Autor 2015). However, the relationship between technical change and
employment is a classic topic that has received attention from diverse research streams,
based on different theoretical basis and at different aggregation levels (Dosi and
Mohnen 2019; Calvino and Virgillito, 2018; Vivarelli 2014; Freeman et al. 1992). This
wide and large body of literature has demonstrated that there is a complex, no linear
relationship between innovation and employment which effects are neither
homogenous nor immediate (Herstad and Sandven 2019; Kancs and Siliverstovs 2019;
Piva and Vivarelli 2018).
Empirical research has been mostly based on the study of the theoretically expected
effects of different types of innovation outcomes in employment (for literature revision
see: Calvino and Virgillito 2018; Vivarelli 2014). Following the main distinction
between product and process innovation, these works have identified labor saving and
creating effects of innovation outcomes. In stylized facts, it is expected that process
innovations lead to efficiency gains (labor productivity) savings employments.
Conversely, if the firm is able to establish lower prices creating a demand increase,
process innovation may positively affect employment (Coad and Rao 2011). On other
hand, product innovation may trigger compensation effects due the market expansion
of the firm, which in turn create new employment demand. However, product
innovation can also present negative externalities that can reduce employment. Rather
than create new markets, product innovation can displace old products either from the
innovative firm (cannibalization effect) or from its competitors (business stealing
effect) (Vivarelli 1995 and 2013).
Based on these concepts, the debate on the effects of innovation in employment at firm
level has gained great attention (Barbieri et al., 2019; Bianchini and Pellegrino 2019;
Cirera and Sabetti 2019; Herstad and Sandven 2019; Hou et al. 2019; Harrison et al.
2014; Giuliodori and Stucchi 2012; Lachenmaier and Rottmann 2011; Coad and Rao,
2011; Bogliacini and Pianta 2010; Jamandreu 2003; Pianta 2003). These works have
contributed to identify and understand some general patterns among heterogeneous
findings from developed economies. In a general manner, product innovation shows
positive effects on employment growth, in particular when considering big firms acting
in high-tech sectors. On the other hand, process innovation shows neutral or negative
effects on employment (Calvino and Virgillito 2018; Vivarelli 2014). Moreover, recent
researches shed light on heterogeneous effects according to macroeconomics cycle,
market structure and sectoral composition of the economies (Díaz et al. 2020; Lim and
Lee 2019; Dachs et al. 2017). Regarding developing economies, results show a similar
landscape, with an intensive positive relation between product innovation and
3
employment and mainly neutral effect of process innovation on employment (Baensch
et al. 2019; Cirera and Sabetti 2019; Crespi et al, 2019; Pereira and Tascir 2019; Mitra
2019; Castillo et al. 2014).
Despite this large and rich body of literature, there is relatively few empirical works
analyzing the firms’ behavior determining innovation outcomes that canalize the effects
of innovation in employment (Barbieri et al. 2019; Triguero et al. 2020; Zuniga and
Crespi 2013; Evangelista and Savona 2003). Following these authors, we pose that
innovation outcomes are observable mechanisms related to no-observable deliberate
actions of the firm –i.e. strategies-, acting in a high uncertainty context. The literature
on the topic has usually associated innovation strategy to product or process innovation
(Peters 2004; Pianta 2001). However, there is a critical conceptual difference between
these concepts. Innovation strategies refers to deliberate actions to achieve
performance’s improvements in a more or less open way (Triguero et al. 2020;
Criscuolo et al. 2018), which are partially observable through the innovation activities
that the firm conduct (Breemersch et al 2019; Barletta et al. 2016). These activities,
which include R&D, technology acquisition and collaboration, are the main
determinants of the type of innovation outcomes (Cohen 2010).
Following Penrosean contributions, the firm is conceived as an agent that embrace a set
of resources, organizing it to transform technical innovations in production practices to
improve firm performance (Lazonick 2016; Dodgson 2017). To achieve the aimed
improvements, a critical decision refers to what extent the firm focuses its innovation
strategy in internal activities (Make), in external knowledge acquisition (Buy) or both
(Make&Buy) (Cassiman and Veugelers, 2000). We analyze the effects of these
strategies on the growth of the workforce and skills’ demand of the firm.
We consider the effects these strategies on employment regarding the situation of no
innovative firms. Evidence on the firm’s innovation behavior in Latin America has
widely show that firms face several barriers to innovate and even do not perceive the
potential benefits of innovation regarding their regular market position (Grazzi and
Pitrobielli 2016).
Based on previous contributions from Latin America, we test the effects of different
types of innovation on firm’s employment. In doing so, we adapt the estimation method
developed by Harrison et al. (2008 and 2014) and early adapted by Zuniga and Crespi
(2013), using instrumental variables to test the effects of innovation strategies on the
growth of the workforce and the skills’ demands in a panel data set containing 4,126
observations from Uruguayan firms during 2007-2015. Relatedly, in line with the
literature (Bogliaccino and Pianta 2010) we test likely heterogeneous effects of
innovation strategies according the technology intensity of the sector and the size of the
firm.
This article contributes with the ongoing debate on the effects of innovation on
employment. In particular, it improves previous conceptualization on the relationship
between innovation strategies and the type of innovation outcomes.
The results show consistent differences between innovative and no innovative
strategies. Firms that conducted any type of innovation strategies show a positive and
significant impact of them on workforce growth. In line with previous empirical
4
findings, we corroborate a positive impact on workforce growth of the Make, Buy and
Make&Buy strategies (Zuniga y Crespi 2013, Aboal et al. 2011a and 2011b) These
findings have been also corroborated according skilled and unskilled workers and
considering the sectoral technology intensity. However, unlike previous research, our
findings reveal strongest effects of the integrative strategy: Make&Buy.
Moreover, when comparing the evidence for the Uruguayan case in the backdrop of the
Latin American extant literature, we corroborate the singular positive effect of the
strategy Buy, strongly associated to process innovation. It allows us to discuss the
relevance of innovation strategy as a firm’s growth firm strategy in a developing
context.
2. Innovation strategies and types of innovation
The innovation strategy of the firm refers to a deliberate effort of the firms that
rationalize their objectives and how to intend to pursue them (Nelson 1991). Some of
them are more explicit while others are part of the tacit organizational knowledge. We
pose that innovation strategies are observable through the mix of knowledge and
practices adopted by the firm via internal and external searching activities and trial and
error practices to improve firm’s performance, e.g. productive improvements, market
advantages (Criscuolo et al., 2018; Veugelers and Cassiman, 2006). Innovation
strategies usually imply a bundle of heterogeneous innovation activities, which have
quite different effects on the innovation capabilities of the firms’ workforce.
Firms conducting innovation strategies based on internal activities oriented to create
knowledge, mostly R&D, likely obtain productivity gains (Crepon et al 1998; OrtegaArgilés et al. 2011) and potential market advantages (Hall and Vopel 1996) through
product innovation. Job creating effects attributable to innovation strategies based on
R&D has been mostly identified in big and micro firms from technologically dynamic
sectors (Calvino and Virgillito 2018).
Relatedly, since the seminal contributions of Cohen and Levinthal (1989 and 1990) it is
largely recognized that R&D also affects positively the firms’ absorptive capacities and
interactive learning. In this regard, recent empirical researches has shed light on the
heterogeneous effects of the openness degree of the innovation strategy on the firm on
employment and skills’ demand (Bello-Pintado and Bianchi 2020; Triguero et al.
2019).
Moreover, innovation strategies include diverse activities beyond R&D – i.e. external
technology acquisition; training, design –, oriented by standardized search for
improvements (routines) (Barletta at al. 2016), that critically affect the performance of
the firm (Som et al. 2015; Vega-Jurado et al. 2009).
These type of innovation has historically predominated in Latin American firms (Katz
2004) usually determining process innovation outcomes (Crespi et al. 2019), oriented
to efficiency gains and enhancing competitiveness that allow the firm maintain the
market position (Cassoni and Ramada-Sarasola 2015). These activities, rather than
R&D, are in core of the firms’ innovation patterns identified in Latin American
economies, which are characterized by a high proportion of small and median firms
5
acting in traditional branches in both, service and manufacturing (Dutrenit et al. 2019;
Barletta et al. 2016).
Sectoral dynamics affects firms’ innovation strategy through technological and
institutional microeconomic factors related to the technological cycles, knowledge basis
and appropriability conditions (Dachs et al. 2017; Bogliacino and Pianta 2010). In this
regard, the literature on service innovation (Aboal et al. 2015) has shown that
innovation on this sector is less standardized and strongly based on customer
interaction rather than R&D. These activities contribute to create new service products,
but especially in customized improvements of old services. Therefore, as Dachs et al.
(2017), point out, customization would imply lesser cannibalization and business
stealing effects than in manufacturing, but low intensive market creation effects on
employment. However, empirical research has identified heterogeneous effects due to
the technological intensity of the sector both in service and manufacturing (Yang and
Lin 2008; Evangelista and Savona 2003; Piva and Vivarelli 2002).
Moreover, firms’ strategies and their effects on employment are determined by sectoral
variables related to the global organization of production and the national productive
specialization (Breemersch et al. 2019; Dachs et al. 2017; Giuliodori and Stucchi 2012).
Despite the agricultural and extractive sectors, Latin American economies are
concentrated in manufacturing of commodities and traditional services, with critical
productivity gaps respect dynamic economies (Catela et al. 2015; Grazzi and Pietrobielli
2016) and a small critical mass of innovative firms (Yoguel and Robert 2010; Berrutti
and Bianchi 2019). In this context, Latin American firms have usually been dependent
of foreign knowledge, being the acquisition of knowledge embodied in machinery and
equipment oriented to process innovation outcomes the most usual innovation activity
in the region (IDB 2010; Katz 2004).
Evidence show that embodied technological acquisition involves knowledge search and
adoption practices, that require qualitative and quantitatively different workforce than
R&D based strategies (Bello-Pintado and Bianchi 2020; Barbieri et al. 2009; Conte and
Vivarelli 2007). In this regard, in developed countries, the acquisition of capital goods
for innovate has been associated to process innovation outcomes with potential
negative effects on workforce (Barbieri et al. 2019). However, the dynamic of
displacement and compensation mechanisms observed in developed countries is not
necessarily expected in developing ones. In economics structures based on low
productivity traditional activities the compensation mechanisms based on market
creation are questionable (Crespi et al. 2019) But, in other way, displacement
mechanisms associated to labor saving efficiency gains due process innovation, can be
compensated by productivity gains and enhancing competitiveness effects that allow
firms survive (Cirera and Sabetti 2019; Pereira and Tascir 2019; Mitra and Jha 2016).
In order to capture the effects of different innovation strategies in employment through
the firm’s innovation activities, Zuniga and Crespi (2013) use the stylized typology
coined by Cassiman and Veuguelers (2006). They distinguished three types of
strategies: (i) Make: internal technology development based on R&D activities; (ii) Buy:
external knowledge acquisition through embodied or disembodied knowledge; and (iii)
Make & Buy: mix strategies (Cassiman and Veugelers, 2000; Veugelers and Cassiman,
1999).
6
Finally, we should recognize the inherent limitation of the firm’s level analysis of the
relation between innovation and development. It is well known that firm’s innovation
outcomes (product or process) result from a complex process affected by institutional,
macroeconomic and technological factors that are usually exogenous of the firm’s
behavior. Moreover, the mechanisms operating between innovation outcomes and
employment described above strongly depend on demand features, market structure
and competition (Breemersch et al. 2019; Kancs and Siliverstovs 2019). In this regard,
recognized the limitations associated with the firm level analysis to capture the
displacement and compensation effects (Barbieri et al. 2019), we pose that innovation
strategies determine innovation outcomes and ultimately affect innovation effects in
the firm’s workforce quantity and quality.
3. Empirical background and hypotheses statements
Empirical researches have consistently corroborated heterogeneous effects of
innovation on employment at firm’s level. Growing evidence makes possibly to identify
some general patterns but also shows non-conclusive evidence on the effects of
innovation outcomes on firm’s employment growth and on the mechanisms operating
between them (Calvino and Virgillito 2018; Vivarelli 2014).
Considering European cases, Harrison et al. (2014) find a positive effect of product
innovation (new product sales) on employment in both manufacturing and services in
France, Germany, Spain and the UK. However, even compensated by old product
market expansion, they find that process innovation shows negative impact on firm’s
workforce. Very similar general results, using the same empirical strategy, have
obtained by Hou et al. (2019) for France, Germany, The Netherlands and China. These
results are partially aligned with evidence from Italy showed a robust effect of the R&D
investment in manufacturing firm’s employment but do not significant effect of
innovation investment in external acquisitions on employment (Barbieri et al. 2019;
Piva and Vivarelli 2005). However, previous findings from Germany and Spain had
shown a positive impact of product innovation but also a positive, even higher, effect of
process innovation outcomes (Giuliodori and Stucchi 2012; Lachenmaier and
Rottmann 2011).
In addition, except for Germany (Lachenmaier and Rottmann 2011), empirical evidence
from Europe show heterogeneous effects according technology intensity of the sector
and the firm’s size. Considering manufacturing firms, Barbieri et al. (2019) and
Pellegrino et al. (2018) find evidence of positive effects of R&D expenditures on firm’s
workforce in Italy and Spain, but only for high-tech firms, and negative effects of
embodied technological acquisition in the small and medium firms. In addition,
Bianchini and Pellegrino (2019) identified a dynamic positive effect of persistence in
product innovation outcomes in Spain manufacturing firms, being it stronger for SMEs
than for big firms. On the contrary, they do not find significant effects of process
innovation outcomes on workforce growth. Regarding the service sector, Evangelista
and Savona (2003) find a positive effect of gross innovation investment in firm’s
employment, in particular of R&D investment. However, they find this result for small
firms acting in knowledge intensive business service (KIBS), while, on the contrary,
7
they found a negative effect of gross innovation investment in big firm’s employment,
particularly in capital intensive and financial sectors.
There is few but growing evidence from non-western central countries. Researches
from Asian emergent countries corroborate the results from developed economies:
product innovation outcomes are consistently associated to workforce growth while
process innovation shows mainly no significant effects (Hou et al. 2019; Lim and Lee
2019; Yang and Lin 2008). Moreover, Yang and Lin (2008) show that these effects have
skill biased effects, favoring highly skilled workforce growth. Similar results have been
obtained using a pooled database of firms from Africa, Eastern Europe, Central Asia
and Middle-East (Cirera and Sabetti 2019). However, these authors call the attention
on the intensity of the product innovation effects on employment according the income
level of the country, suggesting than firms in lower income countries will obtain more
intensive effects of product innovation.
3.1 The Uruguayan case in the Latin American context
The study on the linkages between technical change and employment has a long
tradition from varied theoretical and methodological approach in Latin American
studies (Haddad, and Hewings 1999; Robert et al. 2010). Moreover, as part of the
growing interest on the topic worldwide, it has recently gained growing attention in
Latin America.
As usual in Latin American economies, heterogeneity prevails. However, regarding the
evidence on the effects of product and process innovation, findings from this region
also show some rough general patterns in line with the compensation and displacement
effects stated in the literature and the evidence from developed and emergent
countries. Against this backdrop, evidence from Uruguay show results that are nontotally convergent with the regional findings. (Table 1).
Based in the approach of Harrison et al. (2008 and 2014) a number of studies have
corroborated positive effects of product innovation on firm employment in
manufacturing firms in Argentina, Chile, Colombia, Costa Rica and Uruguay (Crespi et
al. 2019; Pereira and Tascir 2019; Mejia and Arias-Granada 2014; Zuniga and Crespi
2013; Crespi and Tascir 2011; Benavente and Lauterbach 2006) and in service firms in
Colombia and Uruguay (Mejia and Arias-Granada 2014; Aboal et al. 2011b).
On another hand, several studies (Pereira and Tascir 2019; Mejia and Arias-Granada
2014; Benavente and Lauterbach 2006) did not found significant effects of process
innovation in firm employment, but negative effects of this type of innovation outcome
has been observed for Chile (Crespi and Tacsir 2011) and positive effects in Argentina
and Costa Rica (Castillo et al. 2014; Crespi and Tacsir 2011).
More recent multi-country evidence in the topic shows similar findings but suggesting
singular results for the Uruguayan case. Using OLS estimators, Crespi et al. (2019) find
consistent evidence of a positive effect of product innovation in firm’s employment in
Argentina, Chile, Costa Rica and Uruguay. Moreover, this finding is corroborated for
the four countries through IV estimation models.
8
In the same vein, using both OLS and IV estimates, they mostly find non-significant
evidence of impacts of process innovation outcomes on firm’s employment. Exceptions
that they observe to this results are negative significant effects of process innovation on
firms’ employment for the whole manufacturing sample in Chile and Uruguay, in
particular in small manufacturing firms in Uruguay. However, when using IV
estimators, this results is only observed for the whole sample of manufacturing firms in
Uruguay, and with particular effects on high tech firms.
These authors argue that divergences can be attributable to non-relevant effects of
process innovation on productivity that in turn do not trigger labor saving effects in or,
in the contrary as is usually attributed to product innovation, process innovation can be
showing expansion market effects that overcompensate displacement effects.
On another hand, when analyzing the effects of innovation in skill composition of the
workforce in Argentina and Uruguay, Crespi et al. (2019) find positive effects of
product innovation on skilled and unskilled employment in Argentina and Uruguay.
Moreover, they find weak but significant negative effects of process innovation on
unskilled employment in Argentina.
Another recent research, analyzing product and process innovation for aggregated data
from 14 Latin American countries, have corroborated a positive relationship between
product innovation and employment and non-significant effects of process innovation.
In addition, analyzing the relative weight of regulation in favor of labor reward, they
also show that more labor friendly regulation (which includes Uruguay) reduce the
effects of product innovation on employment (Baensch et al. 2019).
From the perspective of the innovation strategies, some of the main references for our
research found positive effects of all innovation strategies (Make, Buy, and Make&Buy)
for manufacturing and services firms from Argentina, Chile and Uruguay. Finding also
stronger effects in big firms and high-tech sectors. Previous research focused on the
Uruguayan case, also following similar approaches, had obtained similar results.
(Peluffo and Silva 2017, Aboal et al. 2011a, 2011b). However, Aboal et al. (2011a and
2011b) did not find effects of the Make strategy in services except by SMES and KIBS
firms.
9
Table 1: Previous research on relationship innovation and employment for Uruguay
Work
Period
Method
Results
Harrison model;
MCO-VI
(+) total employment on product innov. in the whole sample, small, low-tech and hightech firms;
(+) skilled/unskilled employment on product innov. in the whole sample;
(-) total employment on process innov. in the whole sample and high-tech firms;
(-) unskilled employment on process innov. in the whole sample.
Peluffo and Silva (2017)
20002012
VI-GMM
(+) Product innov;
(+) productivity enhancing innov;
(+) Skilled employment on Innov. outcomes.
Zuniga and Crespi (2013)
19982009
Harrison
adapted;
VI
Aboal et al. (2011a)
20042009
Harrison model;
MCO-VI
(+) total, skilled/unskilled employment on buy; make and buy in the whole sample,
small and kibs firms;
(+) employment on make strategy in small firms;
(-) unskilled employment on make strategy.
Aboal et al. (2011b)
19982009
Harrison model;
MCO-VI
(+) total, skilled/unskilled employment on innov. strategies in the whole sample, small,
and kibs, high-tech and low-tech firms;
(+) unskilled employment on buy and make and buy strategies in low-tech firms.
Crespi et al. (2019)
19982009
model
(+) Innov. strategies;
(+) Small firms; low-tech; high-tech;
(+) skilled/unskilled employment on Innov. Strategies.
Source: Authors.
Note: It is worth noticing that other quoted research (Aboal et al. 2015 and Crespi and Tascir 2011) also include results from Uruguayan firms, but they are
replicated in articles included in this table (Aboal et al. 2011; Crespi et al. 2019).
10
Summing up, according with previous research evidence, we expect a positive
relationship between firm innovative strategies and the growth of the firm workforce and
we do not expect to observe negative effects of innovation activities in firm’s
employment.
H1: Innovative strategies show a positive effect on the firm’s workforce growth in
Uruguay.
However, considering the prevalence of heterogeneous effects of innovation types and
strategies, the extant evidence show that the firm’s growth is strongly associated with
innovation based on R&D activities (product innovation), which in turn are a critical
determinant of internal capabilities. Therefore, we expect a stronger effect of the
strategies that include Make activities, i.e. Make and Make&Buy.
H2 Firms that conduct Make or Make&Buy innovative strategies show more intensive,
positive, effects in workforce growth that those that conduct only Buy strategies.
We test H1 and H2 for the whole sample and also for different subsamples that allow us
to capture potential disparate effects according to sectoral technology intensity and the
size of the firms.
Finally, considering the effect of innovation in the firms’ employment composition, in
line with prevalence evidence from both regional and international previous research, we
expect a stronger effects of innovation strategies on the growth of the skilled firm’s
workforce. Therefore, we pose that:
H3: Firms that conduct innovative strategies show a bigger growth of skilled
workforce than unskilled one.
4. Methodology
In order to test our hypotheses, we adapt the model developed by Harrison et al. (2008
and 2014), following Zuniga and Crespi (2013) to integrate the analysis of innovative
strategies. The multiproduct model of Harrison et al. (2008, 2014) allows differentiating
effects of innovation on employment, distinguishing according the innovation outcome:
process or product innovation. Moreover, the approach based on innovative strategies
(Zuniga and Crespi 2013) allows analyzing the effects of the innovation strategies on the
innovation outcomes, considered as mechanisms that affect the workforce growth.
The model of Harrison et al. (2008, 2014) is based on a labor demand function, where
the rate of growth of the firm’s workforce is affected by the type of innovation. According
to the literature, efficiency gains in the production of old products and the efficiency
changes associated to process innovation negatively affect the workforce growth. On the
contrary, the growth rate in the production of old products will positively affects
employment due market expansion effect. In the same vein, a positive effect of the rate of
growth of the new products production is expected. Relatedly, the production expansion
due to new products also positively affects workforce’ growth.
11
The equation 1 from Harrison et al. (2008, 2014) shows the relationship between the
workforce growth, efficiency gains due process innovation and the growth of sales of new
and old products.
wg =
0+
1
process + old + new +
(1)
Where: wg is the workforce growth rate; process is a dummy variable indicating process
innovation; old is the nominal growth rate of the sales due to old products; new is the
nominal growth rate of the sales due to new products; α0 is the average parameter of
efficiency growth in the production of old products; α1 is the average parameter of
efficiency growth in the production of old products due to process innovation; β is the
relative efficiency parameter between new and old products production; μ are
unobservable factors -i.e. productivity shocks and changes in the products prices-.
Zuniga and Crespi (2013) adapted a reduced form of the model of Harrison et al. (2008,
2014), where they substitute the innovation outcomes (product or process) by innovation
strategies. Moreover, aiming to capture the net effect of the innovation strategies on
workforce (wg_net), these authors substitute the dependent variable of equation (1) by
the difference between wg, the growth rate due to old products (old) and the sectorial
price growth index (π).
wg_net =
0+
m
+
b
+
&
& +
(2)
The innovation strategies are three excludent dummy variables, where: make captures if
the firm conducts internal R&D; buy captures if the firm acquires external – embodied
and disembodied – knowledge; m&b captures if the firm conducts both.
In order to control endogeneity problems, Zuniga and Crespi (2013) use a structural
model approach in two steps. It allows testing orthogonality between innovation
strategies and the error term in equation 2. Since innovation strategies depend on firm’s
growth, which in turn is also affecting the error term of equation 2, these variables are
potentially endogenous.
Using instrumental variables, the first step of the model includes two equations where
innovation strategies predict product (3) or process (4) innovations.
new =
0+
m
process = δ0+δm
+
+δb
b
+
+δ
&
&
& +
& +
(3)
(4)
Since we have more instrumental (make; buy; m&b) than endogenous (new; process)
variables, we could test instrument validity using the Sargan’s test. Not rejecting the null
hypothesis implies that the innovation strategies are orthogonal regarding the error term
(Hou et al. 2019).
12
Finally, in the second step, the effect of innovation strategies in workforce is estimated
incorporating predicted values from (3) and (4) in equation (1).
In order to test our hypotheses, we split the data-base and, using the same econometric
approach, we capture the effects of innovation strategies on the growth of the workforce
according the most relevant features highlighted in the literature.
4.1 Data and variables
We use three waves (2007-2015) of the Uruguayan Innovation Survey (UIS). UIS, based
on the Oslo Manual (OECD 2005), is representative of firms with more than five
employees acting in the manufacturing industry and selected services activities1.
Regarding our empirical strategy, our final data set is an unbalanced panel including
4,126 observations from firms that were surveyed in at least two consecutive waves.
50.5% of observations belong to manufacturing sector while the rest of them act in
service.
Table 2 resumes the main variables used in the analysis. We concisely report the
construction method of dependent and explicative variables base on the questionnaire
survey.
Table 2: Variable names and description.
Dependent variable
wg_net
Net workforce growth rate
Sales growth rate of new products
new
Average annual net workforce growth rate,
calculated by wg – (old – π). Average
annual net workforce growth rate of skilled
(skilled_gnet) and unskilled
(unskilled_gnet) labor is defined
analogously.
Average annual sales growth rate of new
products, computed as new =
innsales*(1+sales), where innsales is the
share of sales due to product innovations.
process
Process innovation if firm introduce process innovation
only or organizational change innovation
only.
wg
Workforce growth rate
Average annual workforce growth,
calculated by (ln(workforcet)ln(workforcet-1))/3
skilled_wg
Growth rate of skilled labor
Average annual workforce growth,
calculated by (ln(skilled_workforcet)ln(skilled_workforcet-1))/3
ISIC classification Rev. 3: Manufacturing includes division from 15 to 37; Selected services
include the divisions: 40, 41, 50, 51, 55, 60 to 67, 71-74, 85, 90 and 92.
1
13
unskilled_wg
Growth rate of unskilled labor
Sales growth rate of old products
old
Sales
Average annual sales growth rate
Prices growth rate
π
Average annual workforce growth,
calculated by (ln(unskilled_workforcet)ln(unskilled_workforcet-1))/3
old = sales - new
Average annual sales growth rate
calculated by (ln(salest)-ln(salest-1))/3
Average annual Index of prices growth
rate. The Index is computed based on GDP
deflator (implicit price deflator) for
manufacture and service sector.
Variables of interest
make
=1 if firm conducted internal R&D.
Make dummy
=1 if firm reports external R&D,
acquisition of capital goods, hardware and
software or technology transfer,
consultancy, training, engineering and
industrial design, organization and
management design.
buy
Buy dummy
m&b
Make and Buy dummy
=1 if firm reports both activities
Control variables
i.year
i.isic
Wave dummy
Industrial dummy
A set of UIS wave dummy variables.
A set of industrial dummy variables.
Sub sample variables
small
Small firm
htech
High-technology-intensive
Knowledge-intensive business
services
kibs
=1 if firm has up to 50 employees at the
end of the survey wave.
=1 if firm belongs a sector activity
classified as high technology
according to the OECD (2011)
classification.
=0 for nonclassified firms; =1 for
traditional services firms (include the
ISICs divisions: 40, 55, 60, 61, 63, 71, 85);
=2 for kibs firms (include the ISICs
divisions: 64, 72 to 74), adapted from
Aboal et al. 2011a.
Source: Authors.
5. Results
Descriptive statistics present the heterogeneous innovation strategies conducted by the
Uruguayan firms, showing a similar share of product and process innovative firms (See
Table A3 and
14
Table A4, appendix). In line with regional patterns, no-innovative firms predominate, in
both manufacturing and service sectors, and there is noticeable differences between
innovative and no-innovative firms. The former are bigger and show a greater
participation of skilled employees in the workforce than the latter. In addition, the
average growth of the workforce is negative or close to zero in the whole manufacturing
sample, but it turns positive when considering only innovative firms. On the other hand,
relatedly to structural tendencies in the world economy, the service sector is growing,
showing a positive average growth of the workforce. On other hand, within innovative
firms, descriptive results show the association between the strategy buy and process
innovation outcomes, but product innovators follow both make and buy strategies (See
Table A1 and Table A2, appendix).
Regarding econometric estimates, our results corroborate the positive and significant
effect of innovative strategies on the growth of firm’s workforce, i.e. H1 can be accepted.
Table 3 presents the estimates of equation 2, showing positive and strong effects of all
innovation strategies on firms’ workforce growth. This result is consistent considering
both size and sector of the firm. In line with the studies on innovation strategies and
employment in Latin America, the estimated coefficients are noteworthy high (Zuniga
and Crespi 2013; Aboal et al. 2011a and 2011b) but, unlike these previous research, our
results reveal stronger effects of integrative Make&Buy strategies. Within manufacturing
sector, there are not relevant differences in the intensity of the effects in small and big
firms. On the contrary, small service’s firms show more intensive effects than big ones.
Table 3: Innovation strategies in manufacturing and service firms (2010-2015).
Sector
Regression
Dependent Var.
Constant
make (dummy)
buy (dummy)
mnb (dummy)
R2
Standard error
n
Sargan
p-value
Manufacturing firms
Total
Small
MCO
MCO
wg_net
wg_net
-0.038***
0.001
(0.013)
(0.017)
0.337***
0.288**
(0.090)
(0.114)
0.236***
0.247***
(0.021)
(0.032)
0.360***
0.412***
(0.030)
(0.060)
0.241
0.282
0.284
0.259
1,336
746
0.908
0.407
0.635
0.816
Service firms
Total
Small
MCO
MCO
wg_net
wg_net
0.017
0.033
(0.042)
(0.049)
0.321**
0.506**
(0.134)
(0.245)
0.272***
0.330***
(0.025)
(0.043)
0.518***
0.586***
(0.045)
(0.075)
0.263
0.322
0.299
0.270
1,299
705
1.072
0.875
0.585
0.646
Source: Authors based on UIS.
Notes: 1- Standard errors in parentheses. 2- All regressions include industrial dummy variables (2
digits) and year dummy variables. 3- * p<0.1, ** p<0.05, *** p<0.01.
Moreover, instrumental variables estimates of equation 1 corroborate previous results
(See Table A5 and Table A6, appendix). Considering sector and size of the firm, there are
consistent positive effects of all innovation strategies in product innovation outcomes.
On the contrary, there is no significant relationship between any innovation strategy and
process innovation (See Table A5 and Table A6, appendix). Validity of the instrumental
variables is confirmed since results do not reject Sargan’s null hypothesis.
15
These results give us to reject H2, in spite of international evidence the effect of
innovation in workforce’ growth do not show relevant differences between innovation
strategies. According to the literature, we split the sample in order to inquire about
specific effects considering labor demand determinants (size and sector of the firm) and
labor composition (skills), which allows testing H3.
In doing so, we use a sectoral taxonomy currently use in the field (Dachs et al. 2017),
which distinguishes four sectors: Low-tech manufacturing, high-tech manufacturing,
traditional services and KIBS (Table 4). Results are similar to observed in whole sample,
rejecting a linear relationship between some innovation strategy and the sectoral
intensity of technology. However, high-tech manufacturing firms show more intensive
effects of the strategy Make, which, in contrary show low significance regarding KIBS’s
firms.
Table 4: Innovation strategies manufacturing and service firms according
to sectoral technology intensity (2010-2015).
Manufacturing firms
Regression
Dependent Var.
Constant
make (dummy)
buy (dummy)
mnb (dummy)
R2
Standard error
n
Sargan
p-value
High-tech
Low-tech
MCO
wg_net
0.001
(0.028)
0.518**
(0.202)
0.193***
(0.043)
0.339***
(0.047)
0.218
0.289
288
1.933
0.164
MCO
wg_net
-0.049***
(0.014)
0.303***
(0.097)
0.246***
(0.024)
0.367***
(0.039)
0.249
0.282
1,048
0.426
0.808
Service firms
Traditional
KIBS
services
MCO
MCO
wg_net
wg_net
-0.053
0.019
(0.046)
(0.044)
0.386*
0.293
(0.203)
(0.181)
0.255***
0.264***
(0.037)
(0.035)
0.486***
0.490***
(0.054)
(0.082)
0.281
0.225
0.296
0.290
560
624
0.347
1.273
0.841
0.529
Source: Authors based on UIS.
Notes: 1- Standard errors in parentheses. 2- All regressions include industrial dummy variables
(2 digits) and year dummy variables. 3- * p<0.1, ** p<0.05, *** p<0.01.
These results are also corroborated when considering the quality of labor demand (see
Table A7 and Table A8, appendix). Considering size of the firm and sectoral
technological intensity, the positive effects of innovation in workforce’ growth is
confirmed for both skilled and unskilled workforces. Moreover, while in manufacturing
firms, the intensity of the effect of each strategy are always stronger for skilled workforce,
this result is not observed for service firms. Therefore, we can only partially confirm H3.
6. Main findings and conclusions
The main finding of this research is the consistently and robustly identification of a
positive and significant relationship between all innovation strategies and workforce
growth (Table 5). This finding, even expectable, is critical to inform the current debate
16
on employment and technical change. In particular, to differentiate micro effects at firm
level, mostly associated to the firm’s dynamics than macro effects where, as the Uruguay
case show, a general falling of employment is observed (OPP 2019).
In addition, results corroborate that Make&Buy is the strategy that shows more intensive
effects on workforce growth, rather than only Make, as have shown previous research
(Aboal et al. 2011a and 2011b). However, it is possible to observe that the strategy Make
shows the most intensive effects when considering manufacturing firms acting in hightech sectors.
Even though it is possible to identify different intensities in the relationship between
firm strategies and workforce’ growth according firms’ size and sectoral technology
intensity, evidence seems favor the interpretation of a big and positive effect of
innovation on employment rather than highlight heterogeneity (Table 5).
Table 5: Innovation strategies and workforce growth.
Manufacturing firms
Strategy
Total
workforce
growth
Skilled
workforce
growth
Unskilled
workforce
growth
Total
Selected Services firms
Small
HighTech
LowTech
Total
Small
KIBS
Trad.
M
(+)***
(+)**
(+)**
(+)***
(+)**
(+)**
(+)*
(+)
B
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
M&B
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
M
(+)***
(+)***
(+)**
(+)***
(+)**
(+)
(+)*
(+)*
B
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
M&B
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
M
(+)***
(+)**
(+)**
(+)***
(+)**
(+)***
(+)
(+)
B
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
M&B
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
(+)***
Source: Authors based on UIS.
Note: 1- *** p<0.01, ** p<0.05, * p<0.1.
Our results show that innovative firms have a higher probability to increase their
workforce, due the different innovation activities that they have conducted. The
disparate effects of different strategies are observed according sector and size, but the
most salient results is the positive influence of innovation in firm’s employment.
In the light of these results, further research should inquire deeply on a potential
displacement effect from no innovative firms to innovative ones. Moreover, an accurate
measure of innovation strategies, opposing market technology purchase vs R&D based
activities only, should improve the conclusion that, on the contrary to the international
evidence, in small developing countries, technology acquisition associated to enhancing
competitive process are strongly associated to firms’ growth.
17
Finally, in line with recent contribution (Dosi and Mohnen 2019), when considering the
growing and varied production on the topic worldwide, the micro effect should be
framed into the global trade demand changes.
18
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23
Appendix
Table A1: Innovation Strategies in manufacturing firms. Period 2007-2015.
Share of firms follow each type of strategy by type of
firm (%)
Buy
Make
Make/Buy
1
0
0
Process only innovators
82
2
15
Product innovators
47
5
48
All firms
29
2
15
Non innovators
Source: Authors based on UIS.
Notes: Buy strategy: The firm acquired external R&D, capital goods, hardware and software or
technology transfer, consultancy, training, engineering and industrial design, organization and
management design. Make strategy: the firm reports internal R&D. Non innovators: firms
that not obtain process or product innovations. Process only innovators. Firms that obtain
process only innovations. Product innovators: firms that have introduced product innovations.
Table A2: Innovation Strategies in service firms. Period 2007-2015.
Share of firms follow each type of strategy by type of
firm (%)
Buy
Make
Make/Buy
1
0
0
Process only innovators
86
3
12
Product innovators
56
4
40
All firms
28
1
10
Non innovators
Source: Authors based on UIS.
Notes: Buy strategy: The firm acquired external R&D, capital goods, hardware and software or
technology transfer, consultancy, training, engineering and industrial design, organization and
management design. Make strategy: the firm reports internal R&D. Non innovators: firms
that not obtain process or product innovations. Process only innovators. Firms that obtain
process only innovations. Product innovators: firms that have introduced product innovations.
24
Table A3: Descriptive statistics for manufacturing firms (2007-2015).
Mean
Median
Type of firm
Non-innovators
0,56
Process only innovators
0,20
Product innovators
0,23
Number of employees
Non-innovators
62,08
23
Process only innovators
139,32
65
Product innovators
148,61
73,50
All firms
98,03
39
Share of skilled workforce
Non-innovators
0,09
0,03
Process only innovators
0,12
0,07
Product innovators
0,15
0,10
All firms
0,11
0,06
Workforce growth (average annual rate)
Non-innovators
(0,02)
(0,01)
Process only innovators
0,01
0,02
Product innovators
0,01
0,01
All firms
(0,01)
Skilled workforce growth (average annual rate)
Non-innovators
(0,09)
(0,03)
Process only innovators
0,00
0,00
Product innovators
0,03
0,02
All firms
(0,05)
(0,01)
Unskilled workforce growth (average annual rate)
Non-innovators
(0,02)
0,00
Process only innovators
0,01
0,02
Product innovators
0,01
0,02
All firms
0,00
0,00
Sales growth (nominal) (average annual rate)
Non-innovators
0,06
0,07
Process only innovators
0,10
0,10
Product innovators
0,09
0,10
All firms
0,08
0,08
Prices growth
Non-innovators
0,09
0,08
Process only innovators
0,08
0,08
Product innovators
0,09
0,09
All firms
0,09
0,08
sd
Min
Max
113,45
208,03
252,28
180,41
3
3
5
3
1.104
2.316
2.465
2.465
0,15
0,13
0,16
0,15
-
1,00
1,00
1,00
1,00
0,10
0,09
0,08
0,10
(0,35)
(0,26)
(0,33)
(0,35)
0,30
0,24
0,26
0,30
0,49
0,37
0,34
0,44
(1,65)
(1,42)
(1,15)
(1,65)
1,44
1,38
1,44
1,44
0,12
0,11
0,11
0,12
(0,50)
(0,43)
(0,48)
(0,50)
0,69
0,46
0,61
0,69
0,15
0,13
0,11
0,14
(0,53)
(0,54)
(0,37)
(0,54)
0,69
0,59
0,52
0,69
0,06
0,06
0,06
0,06
(0,11)
(0,11)
(0,25)
(0,25)
0,24
0,24
0,21
0,24
Source: Authors based on UIS.
25
Table A4: Descriptive statistics for service firms. (2007-2015).
Mean
Median
Type of firm
Non-innovators
0,61
Process only innovators
0,21
Product innovators
0,18
Number of employees
Non-innovators
108,71
29
Process only innovators
286,32
79
Product innovators
290,54
77
All firms
178,14
42
Share of skilled workforce
Non-innovators
0,19
0,05
Process only innovators
0,24
0,11
Product innovators
0,35
0,28
All firms
0,23
0,09
Workforce growth (average annual rate)
Non-innovators
0,01
0,01
Process only innovators
0,02
0,03
Product innovators
0,04
0,04
All firms
0,01
0,02
Skilled workforce growth (average annual rate)
Non-innovators
(0,04)
0,00
Process only innovators
0,03
0,02
Product innovators
0,04
0,05
All firms
(0,01)
0,00
Unskilled workforce growth (average annual rate)
Non-innovators
0,01
0,01
Process only innovators
0,03
0,02
Product innovators
0,05
0,04
All firms
0,02
0,02
Sales growth (nominal) (average annual rate)
Non-innovators
0,10
0,10
Process only innovators
0,12
0,13
Product innovators
0,13
0,13
All firms
0,11
0,11
Prices growth
Non-innovators
0,10
0,11
Process only innovators
0,10
0,11
Product innovators
0,09
0,11
All firms
0,10
0,11
sd
Min
Max
355,00
794,79
736,81
559,08
2
5
5
2
9.373
9.973
7.470
9.973
0,27
0,27
0,31
0,29
-
1,00
1,00
1,00
1,00
0,11
0,11
0,09
0,11
(0,34)
(0,33)
(0,31)
(0,34)
0,33
0,32
0,33
0,33
0,46
0,41
0,39
0,44
(1,68)
(1,63)
(1,59)
(1,68)
1,67
1,64
1,46
1,67
0,19
0,17
0,24
0,20
(1,10)
(0,49)
(0,99)
(1,10)
0,83
0,73
0,96
0,96
0,14
0,13
0,14
0,14
(0,50)
(0,54)
(0,48)
(0,54)
0,66
0,55
0,60
0,66
0,05
0,05
0,06
0,06
(0,13)
(0,13)
(0,08)
(0,13)
0,58
0,30
0,41
0,58
Source: Authors based on UIS.
26
Table A5:
Sector
Whole sample
Regression
MCO
Probit
MCO
Probit
MCO
Probit
MCO
Probit
Var. Depend
new
process
new
process
new
process
new
process
-0.040***
-9.579
-0.015
-11.036
-0.057*
-10.633
-0.042***
-5.839
Constant
Small
High-tech
Low-tech
(0.015)
-248.683
(0.018)
-305.361
(0.033)
-633.753
(0.015)
-97.943
make
(dummy)
0.322***
8.925
0.279***
9.937
0.518***
(a)
0.286***
5.334
(0.052)
-248.683
(0.063)
-305.361
(0.143)
(0.055)
-97.943
buy (dummy)
0.242***
9.818
0.261***
10.768
0.209***
10.713
0.249***
6.083
(0.016)
-248.683
(0.020)
-305.360
(0.041)
-633.753
(0.018)
-97.943
0.370***
8.886
0.411***
9.608
0.364***
9.519
0.371***
5.255
(0.022)
-248.683
(0.033)
-305.360
(0.042)
-633.753
(0.026)
-97.943
mnb (dummy)
R2
0.271
0.343
0.263
0.274
Standard error
0.253
0.213
0.279
0.245
n
1,336
1,330
746
721
288
278
1,048
Source: Authors based on UIS.
Notes: 1- Standard errors in parentheses. 2- All regressions include industrial dummy variables (2 digits) and year dummy variables. 3- * p<0.1, ** p<0.05, ***
p<0.01.
27
1,044
Table A6:
Sector
Whole sample
Regression
MCO
Probit
MCO
Probit
MCO
Probit
MCO
Probit
Var. Depend
new
process
new
process
new
process
new
process
0.002
-6.240
0.003
0.097***
-88.013
(0.112)
-6.705
153.042
-0.000
(0.109)
-10.689
603.384
-5.749
124.146
0.351***
5.890
0.555***
10.207
0.374***
(0.071)
-88.014
(0.093)
0.265***
6.087
0.301***
(0.018)
-88.013
(0.023)
0.507***
5.247
0.565***
(0.027)
-88.013
(0.038)
Constant
make
(dummy)
buy (dummy)
mnb (dummy)
Small
KIBS
603.384
10.587
603.384
9.411
603.384
(0.037)
(0.096)
0.242***
(0.028)
0.448***
(0.034)
Traditional services
5.285
153.044
6.284
153.042
5.049
153.042
(0.110)
0.370***
(0.111)
0.272***
(0.025)
0.526***
(0.047)
R2
0.304
0.389
0.319
0.268
Standard error
0.267
0.223
0.262
0.267
n
1,299
1,293
705
673
560
560
624
Source: Authors based on UIS.
Notes: 1- Standard errors in parentheses. 2- All regressions include industrial dummy variables (2 digits) and year dummy variables. 3- * p<0.1, ** p<0.05, ***
p<0.01.
Table A7: Innovation strategies and workforce composition. (2010-2015).
28
6.143
124.147
6.024
124.146
5.669
124.146
618
Manufacturing firms
Sector
Workforce composition
Regression
Dependent var.
Constant
Total
Service firms
Small
Total
Small
Skilled
Unskilled
Skilled
Unskilled
Skilled
Unskilled
Skilled
Unskilled
MCO
MCO
MCO
MCO
MCO
MCO
MCO
MCO
skilled_gnet unskilled_gnet skilled_gnet unskilled_gnet skilled_gnet unskilled_gnet skilled_gnet unskilled_gnet
-0.173***
-0.030**
-0.156***
0.009
-0.087
0.112*
-0.104
0.179***
(0.028)
(0.014)
(0.042)
(0.018)
(0.093)
(0.060)
(0.110)
(0.064)
make (dummy)
0.420***
0.309***
0.381***
0.237**
0.384**
0.348**
0.467
0.716***
(0.101)
(0.090)
(0.128)
(0.117)
(0.160)
(0.176)
(0.289)
(0.221)
buy (dummy)
0.311***
0.233***
0.386***
0.239***
0.336***
0.263***
0.417***
0.334***
(0.035)
(0.022)
(0.058)
(0.034)
(0.038)
(0.027)
(0.070)
(0.045)
0.445***
0.345***
0.527***
0.379***
0.549***
0.522***
0.642***
0.629***
mnb (dummy)
(0.042)
(0.031)
(0.088)
(0.065)
(0.056)
(0.049)
(0.112)
(0.081)
R2
0.149
0.221
0.146
0.246
0.134
0.218
0.139
0.287
Standard error
0.512
0.293
0.566
0.272
0.522
0.341
0.573
0.309
n
1,336
1,325
746
736
1,299
1,268
705
681
Sargan
0.148
0.450
0.260
0.561
1.030
0.680
1.143
1.264
p-value
0.929
0.798
0.878
0.755
0.598
0.712
0.565
0.531
Source: Authors based on UIS.
Notes: 1- Standard errors in parentheses. 2- All regressions include industrial dummy variables (2 digits) and year dummy variables. 3- * p<0.1, ** p<0.05, *** p<0.01.
Table A8: Innovation strategies and workforce composition. (2010-2015).
29
Sector
high-tech
Workforce
composition
Low-tech
KIBS
Traditional services
Skilled
Unskilled
Skilled
Unskilled
Skilled
Unskilled
Skilled
Unskilled
MCO
MCO
MCO
MCO
MCO
MCO
MCO
MCO
skilled_gnet
unskilled_gnet
skilled_gnet
unskilled_gnet
skilled_gnet
unskilled_gnet
skilled_gnet
unskilled_gnet
-0.090*
0.005
-0.196***
-0.040***
-0.154*
-0.039
-0.100
0.116*
(0.054)
(0.028)
(0.030)
(0.014)
(0.086)
(0.051)
(0.089)
(0.061)
make (dummy)
0.656**
0.506**
0.373***
0.272***
0.411*
0.483
0.429*
0.274
(0.283)
(0.213)
(0.105)
(0.096)
(0.236)
(0.323)
(0.221)
(0.182)
buy (dummy)
0.282***
0.176***
0.319***
0.247***
0.305***
0.249***
0.310***
0.264***
(0.070)
(0.043)
(0.039)
(0.025)
(0.064)
(0.041)
(0.049)
(0.037)
0.459***
0.332***
0.437***
0.347***
0.570***
0.480***
0.399***
0.536***
Regression
Dependent Var.
Constant
mnb (dummy)
(0.067)
(0.050)
(0.053)
(0.040)
(0.073)
(0.064)
(0.085)
(0.084)
R2
0.179
0.203
0.150
0.227
0.144
0.214
0.116
0.209
Standard error
0.462
0.296
0.523
0.292
0.525
0.358
0.483
0.320
n
288
284
1,048
1,041
560
538
624
615
Sargan
0.207
0.224
0.138
0.429
0.229
3.989
3.878
1.559
p-value
0.902
0.894
0.933
0.807
0.892
0.136
0.144
0.459
Source: Authors based on UIS.
Notes: 1- Standard errors in parentheses. 2- All regressions include industrial dummy variables (2 digits) and year dummy variables. 3- * p<0.1, ** p<0.05, *** p<0.01.
30