Mardones, C., & Zapata, A. (2018). Impact of public support on the i

Economics of Innovation and New Technology
ISSN: 1043-8599 (Print) 1476-8364 (Online) Journal homepage: http://www.tandfonline.com/loi/gein20
Impact of public support on the innovation
probability in Chilean firms
Cristian Mardones & Annabella Zapata
To cite this article: Cristian Mardones & Annabella Zapata (2018): Impact of public support on
the innovation probability in Chilean firms, Economics of Innovation and New Technology, DOI:
10.1080/10438599.2018.1546548
To link to this article: https://doi.org/10.1080/10438599.2018.1546548
Published online: 19 Nov 2018.
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ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
https://doi.org/10.1080/10438599.2018.1546548
Impact of public support on the innovation probability in Chilean
firms
Cristian Mardones
and Annabella Zapata
Department of Industrial Engineering, University of Concepción, Concepción, Chile
ABSTRACT
ARTICLE HISTORY
This research evaluates the impact of public funding on the probability of
performing various innovative activities in Chilean firms during the period
of 2007–2014, using binary choice models applied to a pseudo-data panel.
The results indicate that public support for innovation has a positive and
statistically significant effect on internal and external Research and
Development (R&D) and also increases the probability of having a
formal R&D department. However, the results are not robust regarding
innovation for products, services, processes, logistics, marketing and
organizational innovation. On the other hand, collaborative actions were
shown to positively impact the realization of all types of innovation and
stimulate internal and external R&D. Thus, it is concluded that public
support programs could be more successful if they promote cooperation
with public and / or private actors.
Received 14 May 2018
Accepted 2 November 2018
KEYWORDS
Pseudo-panel; probit; R&D
JEL CODE
C23; O3
1. Introduction
The innovation process is complex because of the diversity of ways that firms can innovate. However,
it is possible to distinguish different types of innovations, which are classified as technological and
non-technological. Product, service and process innovations are technological innovations; meanwhile marketing and organizational innovations are non-technological innovations (Geldes, Felzensztein, and Palacios 2017).1
R&D plays a fundamental role in innovation, and although the concept is treated as a whole, three
activities can be distinguished, including basic innovation, applied innovation and experimental
development. Additionally, it is possible to distinguish between internal R&D that is systematically
carried out in the firm to increase knowledge and use it to develop and introduce innovations,
while external R&D is acquired from public or private research organizations as well as from other
firms or units established abroad.
Firms can make innovative efforts for a number of reasons, such as having greater competition in
the market, improving efficiency, and raising product quality, among other types of efforts. However,
these efforts are hindered by internal and external factors, including the high costs of investing in
innovation and the difficulty of obtaining public and / or private support.
Therefore, there are numerous programs in different countries that seek to promote innovative
activities and boost R&D, although in most cases, there is no scientific evidence to determine the
effectiveness of these programs. Thus, this research seeks to evaluate the public policies associated
with the financing of innovation in Chile since there are a large number of existing funding instruments, which makes it necessary to prove their effectiveness.2 Specifically, it seeks to determine
CONTACT Cristian Mardones
crismardones@udec.cl
Edmundo Larenas 219, 4th floor, Concepción, Chile
© 2018 Informa UK Limited, trading as Taylor & Francis Group
Department of Industrial Engineering, University of Concepción,
2
C. MARDONES AND A. ZAPATA
whether public support raises the probability of realizing various types of innovations and the probability of undertaking R&D activities in firms. For this, a pseudo-panel methodology is used because in
Chile, as in other developing countries, there is no availability of data that follows the same firms over
time in innovation topics.
This study complements the international literature that analyzes government incentives to drive
innovative activities with data panel at the firm level. For example, Dai and Cheng (2015) used the
generalized propensity score matching method with panel data obtained for Chinese firms
between 2005 and 2007 to determine the optimal level of public subsidies for investment in R&D.
Guan and Yam (2015) investigated the effect of subsidies, loans and government support for entrepreneurship based on the exemption or reduction of tax payments using a survey conducted in
Beijing between 1993 and 1995, concluding that subsidies have an insignificant impact on innovative
sales, but loans and support for entrepreneurship have a positive effect. Young and Cheol (2010) analyzed panel data from small and medium-sized Korean firms and determined that subsidies helped
SMEs to overcome financial barriers to R&D, reduce capital costs and share the risk of these activities
with the government. Lee (2011) assessed the effect of public support on R&D using firm-level data in
six countries (i.e. Canada, Japan, Korea, Taiwan, India and China); their results show that public
support has a complementary effect on the R&D of private firms with low technological base and
are highly competent in the market; however, the effect is opposite for firms that present a quick
growth in sales, are not part of collaborative agreements for R&D and belong to some industrial
cluster. González and Pazó (2008) used data from Spanish manufacturing firms between 1990 and
1999 and determined that firms receiving public support are more likely to carry out R&D activities.
Huergo, Trenado, and Ubierna (2015) showed that after receiving public support for R&D activities,
the firms are more likely to continue these activities with their own resources in later years. Afcha
(2012) found that public subsidies have a positive effect on external R&D, but the effect is negative
on internal R&D; furthermore, these authors determined that internal R&D is positively influenced by
external sources of knowledge and that external R&D is negatively affected by these types of
cooperation. Acosta, Coronado, and Romero (2015) determined that public support plays a fundamental role in the decision to invest in R&D and that the probability of engaging in these activities
increases with the size, age of firms, number of university personnel and cooperation networks with
other firms. King and Woolley (2014) showed that government subsidies in the United Kingdom
increase innovation in firms regardless of their size. Cappelen, Raknerud, and Rybalka (2012) used
panel data from 1999 to 2004 in Norway to determine that firms that receive tax credits develop
new production processes; in some cases, they develop new products for the firm but do not contribute to patenting or introducing new products to the market. Furthermore, Cappelen, Raknerud,
and Rybalka (2012) found that firms that collaborate with others are more likely to be successful
in their innovative activities. Berubé and Mohnen (2009) found that Canadian firms that obtain tax
credits and subsidies are much more innovative than firms that only obtain tax credits and, furthermore, that firms that receive subsidies are able to introduce more innovations to the market and are
more successful at commercializing them. Antolín-López et al. (2015) analyzed the relationship
between different types of public incentives that promote innovative activities in 29 European
countries and determined that programs that encourage participation in fairs and collaboration
between firms are efficient tools to promote product innovation in new firms. While subsidies to
acquire infrastructure and policies that reduce taxes on R&D expenditures encourage innovation in
established firms, they also found that firm size affects the probability of obtaining public support
in established firms and that R&D expenditure positively affects this probability both in new and
established firms.
In the Latin American context, Crespi, Maffioli, and Melendez (2011) evaluated programs that
provide financial incentives for R&D in Colombia using data from between the years 1995 and
2007, thus concluding that the benefited firms increased employment, labor productivity and
product innovation. Hall and Maffioli (2008) assessed the government funds for technological development in Argentina, Chile, Brazil and Panama. Their results showed that technological development
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
3
funds do not displace private investment. On the contrary, in many cases, the R&D inversion is
increased meaningfully. In addition, in the case of Panama and Chile, they found that the programs positively affected the introduction of innovations for products, processes, sales and
employment.
Most of the empirical studies carried out in Chile have estimated structural models of the CDM
type (see Crepon, Duguet, and Mairessec 1998) that relate R&D expenditure, innovation and productivity. For example, Benavente (2005) used this type of model with cross-sectional data obtained
from the first three versions of the Innovation Survey, which allowed him to conclude that the firms
that receive public financing are more productive and innovative. Bravo-Ortega, Benavente, and Á
González (2014) also using cross-section data conclude that public financing complements private
resources for R&D, that investing more in R&D increases the probability of exporting, and also that
export orientation and R&D activities increase productivity. Álvarez, Bravo-Ortega, and Navarro
(2011) determine that higher R&D expenditure generates slow and uncertain productivity gains in
Chilean firms. Álvarez, Bravo-Ortega, and Zahler (2015) find a positive effect of technological and
non-technological innovation on labor productivity in the manufacturing and services sector.
However, the present study does not use a structural model but rather uses an ex – post evaluation approach that tries to identify the effect of public financing on certain outcome variables,
which is why the cross – section data of the various surveys of innovation are used to build a
pseudo-panel. Thus, this study is important since World Bank (2008) points out that there has not
been a systematic approach to monitor and evaluate innovation programs and policies in Chile.
To date, only some ex – post evaluations have been carried out at the request of the Budget
Office (DIPRES) or through studies financed by the Inter – American Development Bank to analyze
specific programs. For example, Benavente and Crespi (2003) concluded that the PROFO program
improved management, productivity and access to other financing programs, but did not contribute
to product or process innovation. Benavente, Crespi, and Maffioli (2007) showed that the FONTEC
program did not contribute to the creation of new products although it improved the interaction
with external sources of knowledge and the obtaining of financing. Tan (2009) showed that the
FAT and PROFO programs had a positive impact on sales, labor productivity, wages and exports.
Álvarez, Crespi, and Conrado (2012) concluded that the FONTEC program generated a positive
impact on employment, remunerations and productivity.
2. Innovation policy in Chile
In Chile, the innovation policy3 is developed by the Corporation for the Production Promotion
(CORFO), which is a state agency that originally aimed at the industrialization of the country.
Although for many years CORFO financed several technological diffusion and innovation programs,
only since 2005 all these instruments were grouped through InnovaChile (World Bank 2008). Currently, CORFO seeks to support the creation of innovative firms through different programs and
instruments that help finance, growth and internationalization of national firms, and also develops
initiatives that boost and promote a culture of entrepreneurship.
Among the main programs or instruments of CORFO, the following can be mentioned: bank loan
guarantees for SMEs provided by the State, the Opportunity Zones Investment Support Contest, the
Regional Entrepreneurship Support Program, the known Global Entrepreneurs Competition like StartUp Chile, support for business incubators in higher education institutions, the creation of funds to
finance mentoring networks, the promotion of social enterprises with the so-called ‘B Corps’, funds
for the creation of technological extension centers, the opening of Start-Up Chile offices at a regional
level, creation of seed capital funds, financing for the opening of co-working in different cities, and
the creation of early investment funds for dynamic ventures. On the other hand, CORFO develops
various events to strengthen innovation and entrepreneurship, supports the creation of critical
mass and dissemination of knowledge, as well as funds training to promote change in innovation
and entrepreneurship ecosystems.
4
C. MARDONES AND A. ZAPATA
Additionally, due to the concentration of entrepreneurial activity in the capital of the country, local
resource allocation committees were created to articulate the various private sector development
initiatives with regional development strategies, and also, the Territorial Integrated Program was generated to support the coordination of regional development with local governments and other institutions (Goldberg and Palladini 2008).
According to Benavente, Crespi, and Maffioli (2007), the evolution of the national innovation strategy in Chile has been remarkable, becoming one of the most dynamic in Latin America. However, the
participation of the private sector in national innovation efforts is quite limited, which is explained by
market failures such as the impossibility of fully appropriating the benefits from innovation, information asymmetries, moral hazard that limit financing access and the intangibility of the knowledge
accumulated in R&D that prevents its use as a guarantee for loans and network externalities.
Finally, it can be mentioned that according to Agosin, Grau, and Larrain (2010) Chile’s industrial
policy underwent a temporary transition from horizontal policies that sought to correct market failures that affected the entire economy (such as bank loan guarantees for SMEs provided by the State
and financing innovative projects) to an emphasis on specific industrial clusters in which already exist
or could exist comparative advantages (program of attraction of direct foreign investment in sectors
of high technology and Fundación Chile which is a public-private institution that seeks the development of new markets). However, these industrial policy instruments involved few resources to generate substantial changes in the productive structure and the proliferation of programs made it
difficult for any program to achieve the scale required to achieve success. Currently, due to the successive changes in government, we have returned to more horizontal policies.
3. Data
The research is based on data obtained from the different versions of the Innovation Survey.4 This
survey shows inputs, results and strategies for innovation and also characterizes the factors that
influence the capacity of innovation and the economic performance of Chilean firms. Currently,
nine versions of this survey are available that are representative by the economic sector, region
and size of firms in each period. The data have a repeated cross-sectional structure since the firms
surveyed are not the same for each period nor is it possible to track over time some of the
subsets. In addition, the variables of interest for the present study are not fully available in the first
four versions of the survey; therefore, it was decided to work only with the last five versions. As a
result, the consolidated database used for this research contains observations covering the period
2007–2014. Table 1 summarizes the variables used in the study and how they were coded.
The consolidated database shows that the most innovative activity performed by Chilean firms
over the years is organizational innovation (21%), followed by process innovation (18.9%), product
innovation (11.8%), service innovation (10.9%), logistics innovation (9.6%) and marketing innovation
(8.1%). In addition, 10.3% of the firms carry out internal R&D, 4.5% carry out external R&D, and 6.6%
have a formal R&D department, while 8.6% of the firms collaborate with partners in innovation. On
the other hand, 15.2% of the firms acquire external knowledge (i.e. patents, licenses or know-how
agreements), and 29.2% acquire some equipment, machines or software to funding the innovative
activities. Furthermore, it should be noted that 5.4% of the firms state that they have obtained a
public financing instrument to carry out innovative activities.5 Many of these instruments aim to
promote innovation in SMEs.
Figure 1 shows the number of firms surveyed according to the technological classification used by
Eurostat.6 In the particular case of Chile, the low number of firms classified in the higher-level of technological manufacturing sectors as well as in the knowledge intensive services (KIS) sectors is very
evident. On the contrary, the majority of manufacturing firms are classified in low-technology
sectors and service firms in the less knowledge-intensive services (LKIS) sectors. However, the
most recent surveys show that the number of firms with a higher technological profile and knowledge intensity have increased.
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
5
Table 1. Variables used in the study.
Variable
Product innovation
Service innovation
Process innovation
Logistics innovation
Marketing innovation
Organizational innovation
Internal R&D
External R&D
Department of R&D
Public support
Cooperation
Eurostat classification of economic
activities
Size
Holding
Type of property
External knowledge
Equipment for innovation
Total sales
Exports
Number of workers
Source: Own elaboration.
Definition
Dummy = 1 if the firm introduced product innovation in the last two years or 0 otherwise
Dummy = 1 if the firm introduced service innovation in the last two years or 0 otherwise
Dummy = 1 if the firm introduced process innovation in the last two years or 0 otherwise
Dummy = 1 if the firm introduced logistical innovation in the last two years or 0 otherwise
Dummy = 1 if the firm introduced marketing innovation in the last two years or 0 otherwise
Dummy = 1 if the firm introduced organizational innovation in the last two years 0 or
otherwise
Dummy = 1 if the firm performed R&D in the firm itself in the last two years or 0 in another
case
Dummy = 1 if the firm performed R&D outside the firm in the last two years or 0 otherwise
Dummy = 1 if the firm has a formal unit, department or laboratory of R&D or 0 otherwise
Dummy = 1 if the firm has used public support for innovation or 0 otherwise
Dummy = 1 if the firm has carried out cooperative actions for innovation or 0 in otherwise
11 dummy variables = 1 if the firm belongs to a particular economic activity or 0 otherwise
3 dummy variables = 1 if variables that identify the size of the firm according to sales (i.e. if
the signature is large, medium or small) or 0 otherwise
Dummy = 1 if the signature is part of a holding firm or 0 otherwise
3 dummy variables that identify the type of ownership of the firm (i.e. foreign, private or
mixed ownership) or 0 otherwise.
Dummy = 1 if the firm acquired external knowledge in the form of patents, licenses or knowhow agreements, or 0 otherwise
Dummy = 1 if the firm acquired equipment, machines or software for innovation or 0
otherwise
Total sales in thousands of Chilean pesos
Dummy = 1 if the firm made exports or 0 otherwise
Total employees (i.e. contractors, fees and subcontractors)
Figure 2 shows that the greatest R&D expenditure intensity is in the High-tech, KIS3, KIS4 and Lowtech sectors. However, these intensities are quite variable through the surveys, which reflects a lack of
constant efforts to carry out R&D in Chilean firms.
The above cannot be attributed to a drastic change in the characteristics of the firms that are part
of the sample, since, as can be seen in Figure 3, the size of the firms has remained relatively constant
in the different versions of the survey.
Figure 1. Number of firms surveyed according to Eurostat technology classification.
6
C. MARDONES AND A. ZAPATA
Figure 2. R&D expenditure intensity of firms surveyed according to the Eurostat technology classification.
4. Methodology
The difficulty and cost of periodically surveying the same firms prevents many developing countries
from having panel data. However, repeated cross-sectional surveys are often available, which include
different firms in each period. In this scenario, Deaton (1985) suggests dividing the population into
groups or cohorts based on certain characteristics and making estimates using the average of the
cohorts instead of individual observations. This alternative is known as pseudo-panel data, which
does not suffer from attrition of the sample, and also allows for observations for much longer
periods of time in comparison to a genuine panel data (Baltagi 2005).
To illustrate the estimation of a pseudo-panel, we need a set of T independent cross-sections represented by (1):
yit = xit′ b + mi + nit ,
t = 1, . . . , T
(1)
where β is the parameter vector of interest, and xit denotes a vector of explanatory variables of K
dimension. The subscript i refers to firms, and since it is assumed that the set of available data is a
series of independent cross-sections, there are N firms in each period.
Figure 3. Percentage of firms surveyed according to size and version of the innovation survey.
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
7
To obtain an estimator of β, it can define C cohorts that group together firms that share common
characteristics. Each firm belongs exactly to a group, and averaging the individual observations in
each cohort provides by (2):
y ct = x ′ct b + m
ct + nct ,
c = 1, . . . , C; t = 1, . . . , T
(2)
where y ct is the average of yit on all firms belonging to the cohort c in period t, and the same logic
applies to all other variables in the model. Thus, the resulting dataset is a pseudo-panel with repeated
observations for T periods and C cohorts.
Moffitt (1993) states that repeated cross-sectional data estimates can be interpreted as a procedure of instrumental variables. To illustrate this idea, a simple extension of (1) is used, in which
the individual effect mi is decomposed into a cohort effect mc and into the individual deviation of
this effect as follows:
mi =
C
mc zci + hi
(3)
c=1
where zci is a dummy variable that takes the value of 1 if unit i is a member of cohort c and is 0 otherwise. Replacing (3) in (1) yields
yit = xit′ b +
C
mc zci + hi + nit
(4)
c=1
If mi is correlated with xit , it can be expected that hi and xit are correlated too. Because of this expectation, estimating (4) with ordinary least squares would yield inconsistent estimates for β. However,
assuming that the instruments for xit are not correlated with hi + nit , an estimator of instrumental
variables can produce consistent estimates for β and mc . A natural choice is to define dummy variables of time, Dst = 1 if s = t and is 0 otherwise, to interact with cohort dummies as instruments
for xit , so a linear predictor is derived in a reduced form, as follows:
xit =
C T
g1,ct zci Dst +
c=1 s=1
C
g2,c zci + vit
(5)
c=1
The linear predictor for xit is given by x̂it = x ct , which corresponds to the average value of xit in the
cohort c and period t. Thus, we obtain the instrumental variable estimator for β, which is given by
−1
C T
C T
′
b̂IV =
(x ct − x c )xit
(x ct − x c )yit
(6)
c=1 t=1
c=1 t=1
To ensure the consistency of b̂IV , the instruments for xit are required to vary with t and not be (i.e.
asymptotically) correlated with hi y nit when the size of the cohorts tends to infinity.
According to Moffitt (1993), it is possible to extend this same approach of instrumental variables to
the binary choice model in which a latent variable yit∗ is defined as follows:
yit∗ = xit′ b + mi + nit ,
t = 1, . . . , T
(7)
yit = 1 ,
if
yit∗ . 0
(8)
yit = 0 ,
if
yit∗ ≤ 0
(9)
In this model, only an additional normality assumption is required for mi + nit .
It should be noted that in the pseudo-panel methodology there is a trade-off between the number
of cohorts and the number of individuals per cohort. For example, if cohorts were constructed by
economic activity, region and firm size, the number of observations in each cohort would be significantly reduced, which implies that the sample averages of each cohort are not accurate estimates of
8
C. MARDONES AND A. ZAPATA
the population averages of the cohorts. In contrast, a pseudo-panel constructed with a smaller
number of cohorts includes more individuals per cohort, which allows obtaining a smaller pseudopanel with more precise observations. Verbeek and Nijman (1992) studied the consistency properties
of the previous estimators, finding that nc 1 is an essential condition for the consistency of the
estimator.
Therefore, it was decided to use the Eurostat classification to construct the cohorts7 since this
allows obtaining groups with more homogeneous units according to the technological intensity of
the firms belonging to every economic activity.
5. Results
This section presents the results of econometric models that evaluate the impact of public financing
on innovation and R&D activities. It should be noted that six empirical specifications are estimated
that include different explanatory variables, which allows testing the robustness of the estimates.
Specifically, in order to analyze the effect on the probability of carrying out some type of innovation,
model 1 includes public financing as an explanatory variable; model 2 also controls by cooperation
and economic activity according to Eurostat classification; model 3 also controls by firm size (large,
medium and small, leaving as a base the microenterprises), belonging to a holding company and
the type of property (private, foreign and mixed, leaving as a public firm base); model 4 also adds
a control for the holding of an R&D department, realization of internal R&D and realization of external
R&D; model 5 additionally controls for expenses in external knowledge and expenses in equipment
for innovation; and model 6 also controls for total sales, number of workers and exports.
Some studies present evidence of endogeneity in the cooperation variable, so it is necessary to
use some strategy to face this problem such as the estimation of a bivariate probit model or
through instrumental variables. In this sense, it can be mentioned that the methodology used in
this study does not directly use the cooperation variable, but Moffit’s proposal uses instrumental variables to construct the cohorts in the pseudo-panel (see equation 4). Thus, in this study the
cooperation variable is already instrumentalized, which allows controlling for some possible endogeneity. However, the grouping variables must satisfy the appropriate conditions for an estimator
of instrumental variables to be consistent. This requires that the instrument be relevant, that is,
that it be properly correlated with the endogenous explanatory variables of the model. If they are
weakly correlated with the explanatory variables, the estimators could have a low performance
due to the problem of weak instruments.
Therefore, in this study an F test was performed to detect weak instruments, obtaining a statistic of
16.2, which exceeds the criterion established by Stock and Yogo (2005) of a value lower than 10, for
this reason a weak instrument hypothesis is rejected. In addition, all models were re-estimated
excluding the variable cooperation and also bivariate probit were estimated to analyze how the
results obtained changed, in both cases it is concluded that the estimated effects of public
financing are maintained (they are not reported due to space limitations). Consequently, it is possible
to affirm that after addressing different strategies, the effects found in section 5.1 and 5.2 are maintained by the pseudo-panel model, and also that the instrumental variables used to construct the
cohorts are relevant for controlling endogeneity of the variable cooperation.
5.1. Impact on the different types of innovation
The results presented in Table 2 indicate that public support only has a positive and statistically significant impact at 1% on product innovation in three of the six proposed specifications. This impact is
most significant when the model only includes public support as an explanatory variable, then the
magnitude decreases but continues to be significant when controlled by economic activity, size
and type of ownership of the firms. However, the effect of public support ceases to be significant
when controlling for variables related to R&D activities, acquisition of equipment and external
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
9
Table 2. Results of the impact of public support on product innovation.
Public support
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
3.094**
[0.241]
1.717**
[0.313]
2.345**
[0.249]
0.287
[0.171]
0.221*
[0.0974]
−0.146
[0.132]
0.104
[0.0667]
−1.501
[1.156]
−0.193
[0.107]
−0.521**
[0.185]
−0.195
[0.153]
−0.453**
[0.0829]
−0.300
[0.241]
1.150*
[0.447]
1.543**
[0.392]
−0.0443
[0.198]
−0.00503
[0.124]
−0.543**
[0.157]
−0.183
[0.0947]
−3.687
[12.22]
−0.542**
[0.151]
−0.340
[0.217]
−0.394*
[0.189]
−0.560**
[0.102]
−0.588*
[0.256]
4.274**
[1.102]
3.217**
[0.825]
4.370**
[1.367]
−1.466**
[0.342]
−2.524
[1.715]
2.497
[1.957]
−4.605*
[2.058]
1.200
[0.806]
1.591**
[0.510]
−0.0426
[0.198]
−0.145
[0.164]
−0.475**
[0.160]
−0.202*
[0.0948]
−1.967
[2.246]
−0.593**
[0.178]
−0.424
[0.217]
−0.282
[0.182]
−0.538**
[0.106]
−0.441
[0.268]
3.656**
[1.191]
2.071
[1.073]
3.034*
[1.528]
−1.245**
[0.367]
−2.752
[1.669]
0.583
[2.028]
−6.414**
[2.437]
1.622
[1.232]
0.337
[1.067]
−2.525
[1.352]
0.500
[0.861]
1.885**
[0.653]
0.139
[0.214]
−0.301
[0.205]
−0.389*
[0.164]
−0.202*
[0.0984]
−29.01
[25.26]
−0.620**
[0.227]
−0.364
[0.225]
0.0298
[0.234]
−0.380**
[0.122]
−0.228
[0.284]
0.0722
[1.737]
−1.978
[2.023]
−1.084
[2.121]
−1.718**
[0.584]
2.894
[2.968]
8.201*
[4.172]
−2.672
[3.282]
2.209
[1.999]
0.688
[1.256]
−3.233
[2.102]
−3.438**
[1.170]
3.421**
[1.189]
−1.382**
[0.0192]
21875
0.010
−1.421**
[0.0487]
21875
0.022
−2.365
[1.863]
21875
0.026
−1.158
[1.958]
21875
0.026
−3.411
[2.519]
21875
0.027
3.294
[1.881]
3.335**
[0.981]
0.265
[0.229]
−0.541
[0.226]
−0.151
[0.190]
−0.222
[0.135]
−32.75
[25.43]
−0.837*
[0.343]
−0.520*
[0.259]
0.252
[0.253]
−0.129
[0.176]
0.180
[0.359]
−1.893
[1.865]
−2.939
[2.023]
−2.597
[2.165]
−1.136
[0.880]
2.494
[3.635]
7.171
[5.327]
−5.263
[4.269]
6.652*
[2.909]
−3.295
[2.567]
−4.798*
[2.264]
−4.205**
[1.231]
3.996**
[1.243]
3.36e-09
[2.06e-09]
0.000817
[0.000658]
0.293
[0.636]
−2.131
[3.297]
21875
0.027
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
R&D department
Perform internal R&D
Performs external R&D
External knowledge
Equipment for innovation
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
10
C. MARDONES AND A. ZAPATA
knowledge for innovation, total sales, exports and number of workers. This result is somewhat disappointing since in 2007 the OECD recommended that Chile promote productive diversification
through an effective innovation policy that would allow it to expand and consolidate its long-term
growth.8 On the other hand, it is interesting to note that cooperative actions with other firms or institutions to carry out innovation activities have a positive and statistically significant impact on all the
specifications that include this explanatory variable. It is worth mentioning that in Chile the R&D
capacities tend to be highly concentrated within universities and public research institutes, but
there is a low transfer of technologies from these institutions to the private sector. In addition, it is
observed that the acquisition of external knowledge has a negative and statistically at 5%. It also
notes that the acquisition of machinery, equipment and software for innovation has a positive and
statistically significant effect at 5%.
The results presented in Table 3 demonstrate that public support does not contribute to service
innovation since the estimated coefficients are not robust in the different specifications, showing
positive and significant effects only in two specifications. However, cooperative actions have a positive and statistically significant effect at 1% for this type of innovation. This result can be related to the
findings of Álvarez, Bravo-Ortega, and Zahler (2015) who, conclude that cooperation is a more important determinant for expenditure on innovative activities carried out in the services sector when analyzing the determinants of inputs, innovative outputs and labor productivity in the services sector and
the manufacturing sector. On the other hand, exports have a negative and statistically significant
impact at 5%, this last result is quite reasonable since Chilean firms tend to export more goods
than services, so an exporting firm should be less likely to innovate in services.
In terms of process innovation, the results presented in Table 4 show that the use of public support
has a positive and statistically significant impact on the first three specifications that are controlled by
economic activity, size and type of property, but in the following specifications, the effect changes
sign and it is no longer significant. The cooperative actions again show a positive and statistically significant effect at 1% in all specifications that include this explanatory variable. Knowledge-intensive
financial services, Other knowledge-intensive services, and Less knowledge-intensive market services
have a negative and statistically significant impact on all specifications, this is reasonable because
service sectors are on average less likely to innovate in processes than the rest of the economic activities. Regarding size and property type, there is no statistical evidence that any of these characteristics
increases or decreases the probability of process innovation. Performing internal R&D and acquisition
of machines, equipment and software for innovation have a statistically significant and positive
effect, while the acquisition of external knowledge (i.e. patents, licenses and know-how) tends to
reduce the probability of innovating in processes.
The results related to logistics innovation shown in Table 5 that public support does not have a
robust impact on this type of innovation. In addition, it is not possible to observe a robust effect
of cooperative actions since although the coefficients are positive in all the specifications, only
four specifications have statistical significance. There is no economic activity that is more or less
likely to innovate in logistics. The acquisition of external knowledge has a negative impact on this
type of innovation, while the purchase of machinery, equipment and software for innovation has a
positive effect, in both cases with coefficients that are statistically significant at 1% or 5%. The negative effect of the acquisition of patents, licenses and / or know-how indicates that external knowledge
becomes a barrier to logistics innovation. One possible explanation for the lack of impact on logistics
innovation is the immense geographical concentration of the country, which means that successful
firms that start in other regions could end up migrating to the capital for access to a larger market and
capital of risk.9
Table 6 shows that the impact of public support on marketing innovation is not robust in the six
estimated specifications. Cooperative actions increase the probability of innovating in marketing with
coefficients that are statistically significant at 1% in all specifications that include this variable. In
addition, exports have a negative and statistically significant impact at 1%. This could be explained
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
11
Table 3. Results of the impact of public support on service innovation.
Public fundiong
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
2.054**
[0.251]
−0.100
[0.317]
3.054**
[0.247]
0.0457
[0.181]
−0.303*
[0.118]
−0.588**
[0.156]
−0.672**
[0.0890]
−0.775
[0.447]
0.0998
[0.0920]
−0.378*
[0.154]
−0.551**
[0.166]
−0.357**
[0.0756]
−0.133
[0.209]
1.221**
[0.420]
1.521**
[0.357]
0.238
[0.207]
0.0348
[0.138]
−0.0256
[0.173]
−0.0910
[0.112]
−1.360**
[0.434]
0.197
[0.139]
−0.0543
[0.158]
−0.159
[0.162]
−0.123
[0.0957]
0.193
[0.224]
−1.226
[1.048]
0.660
[0.750]
0.633
[1.302]
1.931**
[0.324]
−11.43**
[1.628]
−9.903**
[1.767]
−8.775**
[1.984]
1.043
[0.744]
1.709**
[0.469]
0.226
[0.210]
0.164
[0.183]
0.0623
[0.177]
−0.0694
[0.115]
−0.930*
[0.454]
0.381*
[0.176]
−0.152
[0.168]
−0.136
[0.174]
−0.0558
[0.0994]
0.319
[0.235]
−1.680
[1.134]
−0.274
[0.992]
−0.735
[1.449]
1.658**
[0.362]
−10.94**
[1.658]
−11.50**
[1.938]
−7.177**
[2.374]
−1.280
[1.269]
2.709**
[1.007]
−4.647**
[1.390]
0.601
[0.808]
1.403**
[0.532]
0.272
[0.213]
0.194
[0.216]
0.0868
[0.179]
−0.0483
[0.117]
−0.932
[0.476]
0.481*
[0.211]
−0.0893
[0.176]
−0.0990
[0.185]
−0.000475
[0.108]
0.305
[0.249]
−1.887
[1.616]
−0.0789
[1.672]
−0.926
[1.919]
1.819**
[0.385]
−10.22**
[1.950]
−10.61**
[2.249]
−7.362**
[2.423]
−1.901
[1.707]
3.228**
[1.121]
−2.965
[2.068]
−0.702
[0.817]
0.531
[0.833]
−1.361**
[0.0195]
21875
0.004
−1.335**
[0.0482]
21875
0.026
9.235**
[1.793]
21875
0.034
9.731**
[1.929]
21875
0.035
8.981**
[2.009]
21875
0.035
2.357
[1.966]
2.359**
[0.903]
0.310
[0.233]
−0.0358
[0.235]
−0.00425
[0.203]
−0.231
[0.152]
−0.880
[0.481]
−0.328
[0.323]
−0.454*
[0.211]
−0.0983
[0.201]
−0.217
[0.166]
−0.0393
[0.328]
−1.013
[1.847]
−1.048
[1.702]
0.0926
[2.050]
−0.0199
[0.778]
−3.478
[2.922]
−0.424
[4.097]
0.0444
[3.811]
2.839
[2.642]
−0.234
[2.583]
−5.442*
[2.204]
−1.828*
[0.904]
1.657
[0.906]
4.13e-10
[2.10e-09]
0.000952
[0.000610]
−1.789**
[0.660]
2.178
[3.043]
21875
0.035
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
R&D department
Perform internal R&D
Performs external R&D
External knowledge
Equipment for innovation
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
12
C. MARDONES AND A. ZAPATA
Table 4. Results of the public support impact on process innovation.
Public support
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
3.069**
[0.221]
0.804**
[0.281]
1.704**
[0.234]
−0.232
[0.172]
−0.139
[0.0924]
−0.308**
[0.112]
−0.186**
[0.0609]
−1.699*
[0.763]
−0.513**
[0.0999]
−0.544**
[0.139]
−0.515**
[0.139]
−0.810**
[0.0736]
−0.649**
[0.221]
0.900*
[0.376]
1.719**
[0.340]
−0.271
[0.193]
−0.220
[0.113]
−0.383**
[0.134]
−0.209*
[0.0842]
−1.795*
[0.864]
−0.571**
[0.136]
−0.577**
[0.158]
−0.558**
[0.151]
−0.874**
[0.0899]
−0.650**
[0.233]
−1.160
[0.900]
−1.159
[0.671]
−1.557
[1.116]
−0.366
[0.282]
1.730
[1.514]
2.805
[1.645]
−0.00166
[1.832]
−0.0632
[0.648]
1.537**
[0.430]
−0.275
[0.195]
0.210
[0.151]
−0.337*
[0.138]
−0.140
[0.0870]
−1.664
[1.122]
−0.141
[0.163]
−0.546**
[0.168]
−0.691**
[0.171]
−0.772**
[0.0938]
−0.718**
[0.243]
−0.517
[0.968]
−0.218
[0.868]
−1.274
[1.239]
−0.992**
[0.318]
2.965
[1.546]
2.780
[1.817]
4.345*
[2.191]
−4.735**
[1.093]
4.254**
[0.894]
−1.871
[1.191]
−0.689
[0.693]
1.659**
[0.489]
−0.132
[0.198]
−0.133
[0.180]
−0.216
[0.141]
−0.152
[0.0893]
−1.430
[1.738]
−0.362
[0.194]
−0.547**
[0.175]
−0.481*
[0.191]
−0.644**
[0.101]
−0.433
[0.256]
−4.895**
[1.386]
−4.988**
[1.505]
−5.959**
[1.634]
−1.098**
[0.340]
7.282**
[1.802]
7.917**
[2.099]
5.983**
[2.238]
−1.791
[1.512]
3.753**
[1.008]
−3.611*
[1.797]
−3.486**
[0.745]
3.589**
[0.767]
−1.072**
[0.0171]
21875
0.009
−0.873**
[0.0431]
21875
0.022
−1.212
[1.633]
21875
0.023
−2.866
[1.749]
21875
0.024
−3.231
[1.797]
21875
0.025
−0.667
[1.668]
1.720*
[0.795]
−0.127
[0.209]
−0.168
[0.196]
−0.143
[0.163]
−0.163
[0.122]
−1.493
[1.785]
−0.315
[0.293]
−0.559**
[0.204]
−0.443*
[0.203]
−0.551**
[0.147]
−0.316
[0.317]
−5.613**
[1.509]
−5.038**
[1.525]
−6.447**
[1.687]
−0.663
[0.685]
6.252*
[2.662]
5.899
[3.718]
4.533
[3.420]
−1.567
[2.358]
3.742
[2.247]
−3.645
[1.923]
−3.536**
[0.814]
3.538**
[0.831]
2.33e-10
[1.80e-09]
0.000352
[0.000555]
0.421
[0.558]
−1.996
[2.681]
21875
0.025
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
R&D department
Perform internal R&D
Performs external R&D
External knowledge
Equipment for innovation
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
13
Table 5. Results of the impact of public support on logistics innovation.
Public support
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
0.480
[0.276]
−1.193**
[0.345]
1.324**
[0.279]
−0.321
[0.218]
−0.252*
[0.117]
−0.288*
[0.133]
−0.300**
[0.0766]
−0.719
[0.395]
−0.235*
[0.109]
−0.325*
[0.151]
−0.153
[0.147]
−0.517**
[0.0809]
−0.366
[0.237]
−0.657
[0.466]
0.909*
[0.412]
−0.125
[0.247]
−0.114
[0.146]
−0.0435
[0.164]
−0.166
[0.108]
−1.007*
[0.432]
−0.0261
[0.160]
−0.322
[0.174]
−0.0455
[0.168]
−0.279**
[0.105]
−0.156
[0.257]
3.597**
[1.129]
2.797**
[0.798]
3.319*
[1.404]
0.874*
[0.344]
−6.796**
[1.735]
−5.520**
[1.905]
−6.191**
[2.173]
0.528
[0.807]
1.749**
[0.531]
−0.0553
[0.254]
0.443*
[0.199]
0.0543
[0.171]
−0.0156
[0.114]
−0.905
[0.463]
0.408*
[0.198]
−0.301
[0.190]
−0.270
[0.196]
−0.202
[0.111]
−0.148
[0.272]
3.530**
[1.254]
3.012**
[1.090]
3.353*
[1.607]
0.101
[0.390]
−5.691**
[1.785]
−4.093
[2.094]
1.219
[2.668]
−5.115**
[1.406]
2.359*
[1.123]
−2.972
[1.537]
−0.692
[0.878]
1.001
[0.608]
0.0789
[0.258]
0.439
[0.235]
0.143
[0.175]
0.0326
[0.117]
−0.779
[0.494]
0.597*
[0.235]
−0.145
[0.201]
−0.125
[0.214]
−0.0357
[0.120]
−0.118
[0.289]
2.194
[1.746]
2.505
[1.847]
1.955
[2.086]
0.451
[0.411]
−3.107
[2.129]
−0.896
[2.424]
0.952
[2.701]
−5.951**
[1.917]
3.622**
[1.257]
0.560
[2.279]
−2.450**
[0.900]
2.053*
[0.923]
−1.332**
[0.0204]
21875
0.000
−1.203**
[0.0513]
21875
0.008
1.897
[1.916]
21875
0.011
0.813
[2.074]
21875
0.013
−1.063
[2.154]
21875
0.014
3.813
[2.097]
3.039**
[0.985]
0.257
[0.278]
0.167
[0.261]
0.150
[0.202]
−0.0760
[0.155]
−0.812
[0.499]
−0.308
[0.351]
−0.515*
[0.235]
−0.0152
[0.231]
−0.155
[0.179]
−0.263
[0.368]
3.140
[2.086]
1.619
[1.895]
3.025
[2.299]
−1.352
[0.847]
4.057
[3.197]
10.81*
[4.482]
6.813
[4.128]
1.487
[2.888]
−3.282
[2.770]
−2.028
[2.413]
−3.877**
[1.012]
3.457**
[1.014]
3.60e-09
[2.26e-09]
0.000704
[0.000660]
−2.171**
[0.727]
−8.484*
[3.344]
21875
0.015
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
R&D department
Perform internal R&D
Performs external R&D
External knowledge
Equipment for innovation
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
14
C. MARDONES AND A. ZAPATA
Table 6. Results of the impact of public support on marketing innovation.
Public support
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
1.675**
[0.276]
−0.0123
[0.350]
3.114**
[0.264]
0.101
[0.200]
−0.0798
[0.122]
−0.313*
[0.159]
−0.0521
[0.0776]
−1.202
[0.981]
−0.167
[0.117]
−0.136
[0.164]
−0.0965
[0.164]
−0.502**
[0.0960]
−0.660
[0.365]
−1.177*
[0.487]
3.535**
[0.439]
0.0911
[0.234]
−0.111
[0.152]
−0.673**
[0.191]
−0.371**
[0.109]
−1.440
[1.767]
−0.354*
[0.173]
−0.205
[0.192]
−0.250
[0.189]
−0.726**
[0.121]
−0.978*
[0.393]
3.915**
[1.230]
1.324
[0.876]
4.096**
[1.534]
−1.087**
[0.371]
4.456*
[1.958]
6.112**
[2.147]
1.944
[2.438]
−0.905
[0.869]
3.823**
[0.561]
0.120
[0.237]
0.0829
[0.203]
−0.650**
[0.196]
−0.330**
[0.112]
−2.047
[5.263]
−0.208
[0.206]
−0.210
[0.206]
−0.328
[0.208]
−0.706**
[0.126]
−0.998*
[0.410]
4.090**
[1.353]
1.436
[1.203]
4.281*
[1.747]
−1.396**
[0.423]
4.919*
[2.007]
6.677**
[2.459]
4.651
[2.935]
−1.826
[1.434]
1.003
[1.159]
−1.082
[1.583]
−1.209
[0.940]
3.356**
[0.623]
0.156
[0.240]
0.318
[0.250]
−0.644**
[0.198]
−0.279*
[0.116]
−2.003
[3.485]
0.0693
[0.258]
−0.103
[0.216]
−0.367
[0.211]
−0.663**
[0.135]
−1.132**
[0.418]
5.837**
[2.047]
3.865
[2.032]
6.201**
[2.404]
−1.116*
[0.445]
3.993
[2.361]
5.592
[2.933]
3.684
[3.010]
−4.232*
[1.956]
2.122
[1.324]
2.101
[2.380]
0.401
[0.999]
−0.719
[1.008]
−1.505**
[0.0214]
21875
0.003
−1.586**
[0.0534]
21875
0.021
−8.733**
[2.084]
21875
0.023
−9.322**
[2.287]
21875
0.024
−10.30**
[2.350]
21875
0.024
−2.008
[2.204]
3.050**
[1.066]
0.155
[0.256]
0.288
[0.279]
−0.339
[0.232]
−0.178
[0.162]
−2.197
[4.047]
0.706
[0.424]
0.0969
[0.266]
−0.198
[0.234]
−0.212
[0.207]
−0.492
[0.493]
2.684
[2.223]
3.886
[2.101]
3.336
[2.431]
1.419
[0.966]
−3.432
[3.588]
−7.192
[5.300]
−6.010
[4.748]
−5.755
[3.278]
3.838
[3.014]
3.300
[2.635]
0.831
[1.113]
−1.429
[1.113]
8.93e-10
[2.41e-09]
0.000285
[0.000741]
2.337**
[0.766]
−1.711
[3.609]
21875
0.025
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
R&D department
Perform internal R&D
Performs external R&D
External knowledge
Equipment for innovation
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
15
Table 7. Results of the impact of public support on organizational innovation.
Public support
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
1.699**
[0.221]
0.153
[0.281]
2.650**
[0.231]
−0.117
[0.167]
0.0355
[0.0890]
−0.240*
[0.110]
−0.266**
[0.0626]
−0.385
[0.266]
−0.102
[0.0866]
−0.119
[0.118]
−0.379**
[0.132]
−0.287**
[0.0627]
−0.380
[0.197]
0.672
[0.376]
2.127**
[0.333]
0.0701
[0.190]
0.121
[0.111]
−0.138
[0.133]
−0.122
[0.0867]
−0.454
[0.296]
−0.131
[0.127]
−0.0925
[0.132]
−0.366*
[0.143]
−0.304**
[0.0815]
−0.259
[0.210]
−1.534
[0.913]
−1.925**
[0.664]
−1.227
[1.133]
0.138
[0.285]
−3.472*
[1.481]
−2.040
[1.620]
−4.465*
[1.811]
0.606
[0.660]
2.249**
[0.423]
0.0799
[0.191]
0.344*
[0.149]
−0.0957
[0.136]
−0.0762
[0.0896]
−0.289
[0.317]
0.0769
[0.154]
−0.0841
[0.141]
−0.436**
[0.156]
−0.251**
[0.0847]
−0.258
[0.219]
−1.431
[0.977]
−1.774*
[0.867]
−1.311
[1.251]
−0.192
[0.315]
−2.925
[1.505]
−2.059
[1.765]
−1.821
[2.155]
−2.254*
[1.076]
1.871*
[0.889]
−1.631
[1.196]
0.147
[0.707]
2.049**
[0.477]
0.137
[0.193]
0.279
[0.180]
−0.0471
[0.138]
−0.0604
[0.0918]
−0.160
[0.340]
0.0933
[0.188]
−0.0368
[0.148]
−0.356*
[0.165]
−0.177
[0.0924]
−0.198
[0.231]
−2.756
[1.417]
−2.826
[1.483]
−2.705
[1.668]
−0.111
[0.335]
−1.413
[1.746]
−0.237
[2.036]
−1.469
[2.198]
−2.006
[1.482]
2.137*
[1.001]
−0.866
[1.785]
−1.344
[0.721]
1.250
[0.737]
−0.911**
[0.0166]
21875
0.003
−0.948**
[0.0429]
21875
0.013
3.874*
[1.610]
21875
0.014
3.342
[1.724]
21875
0.014
2.861
[1.778]
21875
0.015
−0.741
[1.692]
1.580*
[0.794]
0.139
[0.205]
0.390*
[0.197]
0.0855
[0.161]
0.0560
[0.125]
−0.229
[0.345]
0.639*
[0.294]
0.185
[0.183]
−0.304
[0.180]
0.0461
[0.142]
0.140
[0.299]
−3.875*
[1.553]
−2.279
[1.512]
−3.741*
[1.730]
1.453*
[0.700]
−6.562*
[2.645]
−8.345*
[3.729]
−7.634*
[3.450]
−4.517
[2.364]
4.012
[2.270]
0.687
[1.926]
−0.720
[0.791]
0.532
[0.803]
2.20e-10
[1.82e-09]
−0.000351
[0.000556]
1.451*
[0.575]
8.190**
[2.702]
21875
0.015
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
R&D department
Perform internal R&D
Performs external R&D
External knowledge
Equipment for innovation
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
16
C. MARDONES AND A. ZAPATA
Table 8. Results of the impact of public support on the performance of internal R&D.
Public support
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
4.580**
[0.239]
3.726**
[0.317]
2.183**
[0.255]
0.307
[0.183]
0.278**
[0.102]
−0.0426
[0.141]
0.100
[0.0718]
−0.338
[0.415]
0.285**
[0.0951]
−0.0133
[0.161]
−0.535**
[0.193]
−0.287**
[0.0864]
0.166
[0.206]
3.172**
[0.428]
1.579**
[0.385]
0.153
[0.211]
0.116
[0.129]
−0.184
[0.165]
0.0313
[0.0972]
−0.866
[0.449]
−0.0702
[0.140]
−0.213
[0.184]
−0.468*
[0.203]
−0.454**
[0.104]
0.0226
[0.222]
−0.777
[1.112]
−1.337
[0.834]
−0.222
[1.387]
−0.475
[0.333]
1.434
[1.926]
5.455**
[2.050]
3.486
[2.264]
2.550**
[0.493]
1.412**
[0.393]
0.0435
[0.214]
0.119
[0.129]
−0.294
[0.168]
−0.0303
[0.0986]
−0.937*
[0.450]
−0.178
[0.145]
−0.217
[0.183]
−0.501*
[0.196]
−0.539**
[0.108]
−0.117
[0.227]
0.553
[1.412]
−0.203
[1.142]
1.477
[1.638]
−0.723*
[0.368]
0.561
[2.187]
4.330
[2.507]
3.491
[2.549]
1.242
[0.733]
−0.974
[0.737]
2.550**
[0.493]
1.413**
[0.393]
0.0405
[0.214]
0.117
[0.130]
−0.300
[0.170]
−0.0364
[0.103]
−0.928*
[0.452]
−0.185
[0.149]
−0.219
[0.184]
−0.507*
[0.198]
−0.543**
[0.110]
−0.124
[0.229]
0.581
[1.420]
−0.196
[1.144]
1.518
[1.652]
−0.751
[0.393]
0.525
[2.195]
4.254
[2.538]
3.539
[2.561]
1.263
[0.742]
−0.985
[0.739]
−0.00133
[0.00677]
−1.561**
[0.0199]
21875
0.024
−1.659**
[0.0508]
21875
0.034
−2.369
[2.026]
21875
0.035
−2.615
[2.062]
21875
0.036
−2.594
[2.065]
21875
0.036
4.249**
[0.646]
1.923**
[0.448]
0.254
[0.223]
0.313*
[0.141]
−0.00406
[0.232]
0.220
[0.149]
−0.699
[0.474]
0.378
[0.311]
−0.0291
[0.217]
−0.222
[0.238]
−0.130
[0.228]
0.395
[0.407]
0.606
[1.981]
0.911
[1.316]
0.516
[2.172]
0.259
[1.137]
−1.009
[3.394]
0.676
[5.002]
−2.749
[4.956]
0.857
[0.779]
−0.773
[0.789]
0.00960
[0.00977]
4.84e-09**
[1.32e-09]
−0.000881
[0.000568]
0.208
[0.807]
−1.370
[3.860]
21875
0.037
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
External knowledge
Equipment for innovation
Number IPRs owned
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
because Chile is mainly characterized by the export of commodities, fruits, wine, food or other products with low technological complexity that do not require significant marketing innovations.
The results related to organizational innovation presented in Table 7 show that the coefficients are
not robust in the different specifications. The implementation of cooperative actions has a positive
and statistically significant impact in all estimated specifications. In addition, organizational
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
17
Table 9. Results of the impact of public support on the implementation of external R&D.
Public support
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
3.355**
[0.299]
3.139**
[0.399]
1.363**
[0.328]
−0.290
[0.369]
0.266*
[0.131]
0.125
[0.172]
0.149
[0.0927]
0.0623
[0.412]
0.106
[0.134]
0.0862
[0.205]
0.0964
[0.199]
−0.163
[0.111]
0.319
[0.244]
3.020**
[0.571]
2.006**
[0.558]
−0.151
[0.412]
0.132
[0.169]
0.121
[0.211]
0.105
[0.129]
0.429
[0.487]
0.132
[0.206]
−0.315
[0.253]
−0.0382
[0.229]
−0.187
[0.144]
0.421
[0.272]
−3.130*
[1.427]
−4.250**
[1.111]
−4.696**
[1.808]
−0.704
[0.465]
2.279
[2.578]
1.277
[2.884]
3.453
[2.958]
1.773**
[0.657]
1.795**
[0.550]
−0.259
[0.410]
0.0832
[0.170]
−0.0228
[0.214]
0.0289
[0.130]
0.439
[0.484]
−0.0735
[0.210]
−0.299
[0.254]
−0.103
[0.232]
−0.273
[0.144]
0.276
[0.276]
−3.270
[1.866]
−4.525**
[1.610]
−4.145
[2.117]
−1.357**
[0.525]
3.525
[2.969]
3.476
[3.641]
6.162
[3.397]
0.234
[0.963]
0.214
[0.973]
1.771**
[0.657]
1.806**
[0.553]
−0.262
[0.411]
0.0811
[0.170]
−0.0309
[0.216]
0.0183
[0.136]
0.461
[0.491]
−0.0826
[0.213]
−0.305
[0.256]
−0.113
[0.235]
−0.279
[0.145]
0.264
[0.279]
−3.215
[1.882]
−4.518**
[1.616]
−4.072
[2.137]
−1.405*
[0.555]
3.448
[2.983]
3.286
[3.723]
6.272
[3.422]
0.278
[0.979]
0.191
[0.980]
−0.00225
[0.00830]
−1.917**
[0.0254]
21875
0.015
−2.041**
[0.0657]
21875
0.019
−0.311
[2.678]
21875
0.022
−1.457
[2.763]
21875
0.024
−1.408
[2.767]
21875
0.024
2.330**
[0.889]
2.014**
[0.633]
−0.198
[0.419]
0.152
[0.187]
0.0497
[0.295]
0.0854
[0.198]
0.560
[0.541]
0.0829
[0.426]
−0.258
[0.305]
−0.0393
[0.287]
−0.162
[0.299]
0.392
[0.520]
−2.924
[2.601]
−3.982*
[1.853]
−4.145
[2.843]
−1.194
[1.479]
3.165
[4.577]
2.213
[6.849]
4.674
[6.809]
0.262
[1.019]
0.151
[1.045]
0.000156
[0.0125]
1.48e-09
[1.85e-09]
−0.000248
[0.000799]
−0.0195
[1.042]
−1.400
[5.158]
21875
0.024
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
External knowledge
Equipment for innovation
Number IPRs owned
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
innovation is positively influenced by exports. It is important to remark that the state agency CORFO
has tried to generate a boost in the areas of innovation, networking business and administrative training through the subcontracting of public agencies and private partners (agents), but also through a
network of regional offices CORFO directly manages a set of instruments dedicated to the promotion
of innovation and regional development.
18
C. MARDONES AND A. ZAPATA
Table 10. Results of the impact of public support on the probability of having a formal R&D department or laboratory.
Public support
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
3.234**
[0.270]
3.809**
[0.377]
1.924**
[0.292]
0.744**
[0.182]
0.951**
[0.0966]
0.368*
[0.143]
0.512**
[0.0763]
0.324
[0.335]
0.890**
[0.0926]
0.318
[0.168]
−0.821**
[0.268]
0.105
[0.0927]
0.358
[0.230]
2.024**
[0.535]
1.353**
[0.480]
−0.00533
[0.214]
0.468**
[0.128]
−0.0628
[0.171]
0.166
[0.105]
−0.366
[0.389]
0.232
[0.154]
0.201
[0.193]
−0.663*
[0.264]
−0.0929
[0.115]
−0.151
[0.250]
0.577
[1.308]
1.489
[1.043]
1.433
[1.641]
−1.477**
[0.394]
3.764
[2.512]
7.674**
[2.586]
11.50**
[2.987]
1.761**
[0.640]
1.191*
[0.500]
−0.148
[0.218]
0.509**
[0.130]
−0.165
[0.175]
0.109
[0.107]
−0.570
[0.398]
0.184
[0.169]
0.148
[0.192]
−0.628**
[0.231]
−0.212
[0.122]
−0.312
[0.254]
3.196*
[1.624]
3.929**
[1.374]
4.218*
[1.892]
−1.354**
[0.437]
1.288
[2.788]
3.761
[3.250]
9.721**
[3.338]
2.422**
[0.890]
−2.248*
[0.896]
1.781**
[0.641]
1.175*
[0.499]
−0.138
[0.219]
0.514**
[0.130]
−0.146
[0.178]
0.130
[0.113]
−0.598
[0.401]
0.206
[0.172]
0.153
[0.192]
−0.607**
[0.233]
−0.198
[0.124]
−0.285
[0.259]
3.021
[1.648]
3.844**
[1.376]
3.986*
[1.932]
−1.265**
[0.463]
1.415
[2.795]
4.039
[3.272]
9.506**
[3.355]
2.323*
[0.904]
−2.188*
[0.899]
0.00445
[0.00789]
−1.716**
[0.0224]
21875
0.013
−2.036**
[0.0618]
21875
0.037
−6.559*
[2.578]
21875
0.043
−6.278*
[2.632]
21875
0.044
−6.277*
[2.632]
21875
0.044
3.664**
[0.939]
2.092**
[0.654]
0.176
[0.241]
0.680**
[0.155]
0.458
[0.272]
0.561**
[0.179]
−0.370
[0.473]
1.369**
[0.435]
0.526*
[0.234]
−0.101
[0.281]
0.576*
[0.287]
0.949
[0.492]
0.612
[2.200]
4.446**
[1.701]
0.295
[2.366]
2.304
[1.414]
−7.308
[4.374]
−11.84
[7.015]
−6.850
[6.482]
2.640*
[1.041]
−2.822**
[1.060]
0.0271*
[0.0111]
6.14e-09**
[1.86e-09]
−0.00125
[0.000756]
2.120*
[0.956]
3.292
[4.685]
21875
0.045
Cooperation
High-technology industry
Medium-high-technology industry
Medium-low-high technology industry
Low-tech-technology industry
Knowledge-intensive market services
High-tech knowledge-intensive services
Knowledge-intensive financial services
Other knowledge-intensive services
Less knowledge-intensive market services
Other less knowledge-intensive services
Large firm
Medium firm
Small firm
Holding
Private property
Foreign property
Mixed property
External knowledge
Equipment for innovation
Number IPRs owned
Total sales
Number of workers
Exports
Constant
Number of obs.
Pseudo R2
Source: Own elaboration.
Note: (*) significant at 5% and (**) significant at 1%.
5.2. Impact on R&D activities
In relation to the R&D activities carried out by Chilean firms, the effect of public support on internal
and external R&D performance is presented in Tables 8 and 9, respectively. Both types of R&D show
that public support for innovation has a positive and statistically significant impact at 1% in all
ECONOMICS OF INNOVATION AND NEW TECHNOLOGY
19
estimated specifications. Additionally, in both cases, it is possible to observe that carrying out cooperative actions with different actors increases the probability of carrying out R&D, and this effect is
statistically significant in all the estimated specifications. Unfortunately, it is not possible to obtain
conclusive results regarding size, property type and other innovative variables because the coefficients are not robust in the different specifications and the statistical significance for internal and
external R&D activities is not maintained. For internal R&D, the results indicate that total sales increase
chances of carrying out these activities. As for external R&D, it is possible to find a negative, statistically significant and robust effect in the medium sized firm.
On the other hand, using public support tends to increase the probability of having a formal R&D
department or laboratory in the firms. The results presented in Table 10 show that the impact of
public support is positive and significant in all estimated specifications. Cooperative actions again
have a positive and statistically significant effect. Firms belong to medium-high-technology industry
have a positive, statistical significant and robust effect on the probability of having an R&D department. The acquisition of external knowledge has a positive effect on the probability of having an
R&D department, while the purchase of machinery, equipment and software adversely affects this
probability. In addition, the results indicate that total sales and exports increase chances of having
an R&D department.
6. Conclusions
There is no doubt that innovation is a fundamental pillar of economic growth, which is why in Chile
many public programs have been created to encourage and promote this activity. However, there is
little evidence of the actual effect of these programs. Therefore, this study evaluates the impact of
public support on various innovative and R&D activities carried out by Chilean firms during the
period of 2007–2014. This information is useful because it can provide suggestions for redesigning
public policies that currently promote innovation and R&D in Chilean firms.
Using binary choice models with pseudo-data, it is concluded that public support encourages
internal and external R&D, demonstrating its effectiveness in promoting this type of activity. Financial
support also increases the probability that firms will have a formal R&D department or unit, allowing
firms to conduct research activities and develop innovations autonomously. However, it is not possible to demonstrate that public support affects product, service, process, logistics, marketing and
organizational innovations since the coefficients are not robust in magnitude, sign or statistical significance. These mixed results are coincident with Guo, Guo, and Jiang (2016) who point out that the
empirical evidence on the effects of public financing on R&D activities is inconclusive since some
studies identify positive effects, while other studies find limited or no effects on spending on innovative R&D and outputs.
The study also shows that collaborative action with various public and / or private actors has a
positive impact on the realization of all types of innovation and stimulates internal and external
R&D activities. These results provide important policy implications since one way for public innovation programs to be more successful would be to promote cooperation among different actors.
Finally, it is important to mention that the acquisition of patents, licenses and know-how agreements has a negative effect on the different types of innovation (except marketing innovation). It
is interesting to highlight this result given that several public programs that seek to boost innovation
in Chile provide financing to carry out patent procedures.
As a future extension of this study, it would be interesting to identify the effect of public funding
on inputs and outputs from innovative process performed by Chilean firms and include new versions
of the Innovation Survey to increase temporal period coverage.
Notes
1. It is not necessary for the firm to carry out all types of mentioned innovations to be considered innovative.
20
C. MARDONES AND A. ZAPATA
2. In the early 1990s, public instruments for the promotion of innovation were scarce, and existing funding was
mainly directed at academic research and technological institutes. However, in recent years, Chile has experienced an accelerated learning process by building a complete and comprehensive portfolio of instruments targeting firms and institutions across all economic sectors (OECD 2007).
3. According to the Oslo Manual (OECD 2005), innovation policy is an amalgamation of science and technology
policy with industrial policy.
4. The questionnaire of the innovation survey conducted in Chile follows the guidelines established in the OSLO
Manual (OECD 2005).
5. The uses of various public programs that seek to promote the country’s innovative activity have been consulted in
the different versions of the Innovation Survey (i.e. CORFO, FIA, INNOVA CHILE, INNOVA BIO-BIO and FONDEF).
However, as the format of this survey has varied over time, it is not possible to obtain specific information on
the use of each of these programs in each version of the survey. Therefore, this study assesses the joint effectiveness of these programs to determine if they are really encouraging innovation and R&D in Chilean firms.
6. Eurostat uses a disaggregation of the manufacturing industry according to technological intensity (high-technology, medium-high-technology, medium-low-technology and low-technology). Following a similar approach Eurostat defines the following sector as knowledge-intensive services (Knowledge-intensive market services excluding
high-tech and financial services KIS1, High-tech knowledge-intensive services KIS2, Knowledge-intensive financial
services KIS3 and Other knowledge-intensive services KIS4) or as less knowledge-intensive services (Less knowledge-intensive market services LKIS1, Other less knowledge-intensive services LKIS2).
7. Eurostat classification of economic activities according to technological intensity and based on NACE Rev. 2 at 2digit level.
8. Goldberg and Palladini (2008) mention CORFO’s activities are based on the idea that the State should only intervene when there are clear market failures and that all programs should be driven by demand, which is demonstrated through ownership and co-financing of the private sector, therefore CORFO does not discriminate
between economic activities or geographical regions in the allocation of its resources.
9. https://techcrunch.com/2016/10/16/a-look-into-chiles-innovative-startup-government/
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by Corporación de Fomento de la Producción (CORFO) [INNOVA CHILE N° 15||PES-44389, 2016].
ORCID
Cristian Mardones
http://orcid.org/0000-0002-9389-7368
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