137c . 2 DO TAX CREDITS HELP R&D? A BIBLIOMETRIC ANALYSIS OF THE LITERATURE Abstract This paper...
Transcript of 137c . 2 DO TAX CREDITS HELP R&D? A BIBLIOMETRIC ANALYSIS OF THE LITERATURE Abstract This paper...
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DO TAX CREDITS HELP R&D? A BIBLIOMETRIC ANALYSIS OF THE
LITERATURE
Elies Seguí-Mas Universitat Politècnica de València
Faustino Sarrión-Viñes Universitat Politècnica de València
María del Mar Marín Sánchez Universitat Politècnica de València
Subject area : c) Management and organization.
Keywords : credit tax; R&D, innovation, bibliometric analysis.
137c
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DO TAX CREDITS HELP R&D? A BIBLIOMETRIC ANALYSIS OF THE
LITERATURE
Abstract
This paper investigates the literature of tax incentives in R&D through a bibliometric
analysis during the period 1993-2014, using the Web of Science database. This has
allowed observing the most studied areas of research in this field.
Using Bibexcel and Pajek, we shows the main authors, the more active journals in this
field, the most cited papers and the co-occurrence of authorship, keywords and co-
citations and geolocalization of researchers. To strengthen the results and gain a
greater understanding of the current state of the literature on tax credits we study the
contents of core articles.
Resumen
La presente investigación analiza la literatura de los incentivos fiscales en la I + D a
través de un análisis bibliométrico durante el período 1993-2014, utilizando para ello la
base de datos de la Web of Science.
Usando Bibexcel y Pajek, se describen los principales autores, las revistas más activas
en este campo, los artículos más citados y la co-ocurrencia de autoría, palabras clave
y citas, además de la geolocalización de los investigadores. Para reforzar los
resultados y obtener una mayor comprensión de la situación actual de la literatura
sobre los incentivos fiscales en la I+D se ha realizado un estudio del contenido de los
artículos.
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1. INTRODUCTION
R&D tax credit has generated great research interest in the last decade. Most of them
are focused in evaluating the effectiveness of the R&D tax credit, but the results of the
effectiveness of tax incentives for R&D of empirical research are diverse, since in some
cases there is not empirical evidence of their effectiveness. Also in the current literature
the various types of public support are analyzed encourage business R&D, be mainly
(1) Tax credit (2) subsidies for R&D (3) contracts to R&D.
R&D grants and R&D contracts are direct subsidies; both are one of the most way that
government intervene in private R&D activities. So much grants and contracts
persuade additional private R&D investment by increasing the private marginal rate of
return (Wu, 2005). Some investigations (David et al. 2000 or Watkins et al. 2008)
analyzed the potential effect replacement subsidies, for example, research had already
been planned prior to the grant. Also, as argued by Wu (2005), subsidies may have a
negative effect on private investment in R&D by introducing the upward pressure on
the prices of R&D inputs. Grants for R&D typically available to the company at the time
of the initial investment (unlike the tax credits that are usually a posteriori) also to be
granted by the administration and fulfill any requirements provide a signal quality
project, which may attract potential investors (Busom et al 2014).
With regard to tax incentives reduce the cost of private R&D. One of the most principal
difference between subsidies is that tax credits is neutral with respect to sector,
industry or business since tax credits decrease considerably the discretionary
decisions involved in plan selection (Czarnitzki, et al 2004). Among the tax incentives
only used (corporation tax) highlight reserves for investment, accelerated depreciation
and investment tax credits, although it is necessary to note that its use varies by
country (Romero et al. 2007).
Following one of the definitions that describe Russo (2004) and also it is used by
Canadian government and Organization for Economic Cooperation and Development,
R&D is the activity that devises new applications or increases the stock of knowledge.
The R&D activity is composed by (1) basic research, (2) applied research and (3)
experimental development. Basic research, the specific practical applications are in the
back burner, where the essential is to advance scientific knowledge. The applied
research instead focuses on to advance knowledge of specific practical applications
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and experimental development is to create or improve products, processes etc (Russo,
2004).
In some revised instead of using the term R&D items the term R&E (research and
experimentation) is used, this is because to highlight the significant advances in
technology, rather than incremental improvements, product engineering, etc, used the
term R&E (Tassey, 2007).
This article analyses the tax credits in the R&D research published in a wide range of
journals over a 21-year period (1990-2014) and it attempts to use cited references to
identify/provide: (1) the leading authors and journals that directly (by publication) and
indirectly (by citation) contribute to the tax credits in R&D literature, (2) the core articles
that influence the international literature, (3) the co-occurrence authorship, keywords
and citations among articles, (4) the thematic area of the co-citation and (5) a map
showing where the most productive authors are in the world.
This paper contains four sections. The first section offers a brief introduction to tax
credits and the aim of this study. The second section introduces the bibliometric
techniques used in this paper. This article explains the analysis results of the study.
Finally, the study concludes with a discussion of the results, limitations and implications
for future research.
2. METHODOLOGY:
2.1 Bibliometric analysis
Bibliometric analysis is a research technique that uses quantitative and statistical
analyses to describe the distribution patterns of research articles with a given topic and
a given time period (Diodato, 1994).
There are two common methodological approaches to quantify information flow. The
first approach uses a publication as a whole or its attributes, such as author’s name,
keywords, citations, etc. The second approach consists in identifying the links between
objects, their co-occurrences and networks (Gupta and Bhattacharya, 2003).
In the first approach, scalar techniques are generally used. Such techniques are based
on direct counts (occurrences) of specific bibliographic elements, such as articles
(Gupta and Bhattacharya, 2003), and provide the major characteristics of various
actors’ (individual researchers, countries, fields, etc.) research performance (Verbeek
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et al., 2002), as well as its evolution and trends over time (Gupta and Bhattacharya,
2003).
This approach is considered a satisfactory measure of scientific production (Martin,
1996), but can be regarded as only a partial indicator of contributions to knowledge
(Martin 1996).
In the second approach, an analytical procedure is used. This procedure identifies the
relations (co-occurrences) of specific items, such as the number of times that keywords
(co-word), citations (co-citation) and authors (co-authorship) are mentioned together in
publications in a particular research field (Gupta and Bhattacharya, 2003).
A co-word analysis is based on the assumption that a paper's keywords offer an
adequate description of its content and of links between topics. Two keywords co-
occurring within the same document denote a link between them (Cambrosio et al.,
1993).
A co-citation analysis counts the frequency with which any paper of a given author is
co-cited with another in references of cited documents (Bayer et al., 1990). It assumes
that the more frequently those two authors are cited together, and the more similar their
patterns of co-citations are with others, the closer the relationship between them (White
and Griffith, 1981).
Co-authorship is the most recognized expression of intellectual collaboration in
scientific research. It implicates the participation of two authors or more in conducting
research, which leads to scientific output of greater quality or larger quantity than can
be achieved by an individual (Hudson, 1996).
In order to acquire a global view of the tax credits in R&D literature, we used herein a
combination of both techniques (scalar and analytical). The use of a bibliometric
analysis to evaluate and monitor research performance has become widespread
(Tijssen, 1992).
2.2 Data collection
Web of science (WoS) was the database that we conducted the search. WoS is
composed of (1) Science Citation Index Expanded, (2) Social Sciences Citation Index
(SSCI), (3) Arts & Humanities Citation Index (A&HCI), (4) Conference Proceedings
Citation Index- Science (CPCI-S) and (5) Conference Proceedings Citation Index-
Social Science & Humanities (CPCI-SSH). In this work we used SSCI and A&HCI from
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1993 to 2014. The search started in 1993 because it was the first article that meets the
defined search.
The source of scientific documents is composed only of the research articles published
in a journal; the induction for its choice was motivated by several reasons, (1) they
have been submitted to a critical review and to the approval of fellow researches and
(2) have passed a certification process (Callon, Courtial and Penan, 1993).
WoS database is probably the most relevant database for bibliometric analyses.
Nowadays it covers all the publications and corresponding citations from more than
12,000 professional journals, which probably constitute the core of the international
scientific serial literature for a lot of fields (Garfield, 1979; Moed et al. 1985; Tijssen
1992; WoS, 2014).
The search criteria include the joint appearance of one of the terms tax credit,
innovation and R&D, and its variants, in the categories title, abstract and keywords.
Forty articles were retrieved from WoS for the study period. The topic of study is very
specific, so the results are in line with those expected.
To analyze this dataset, this work used bibliometrics as the method and also Bibexcel
(2014-03-25). The first step was to convert the Web of Knowledge file into a Bibexcel
Dialogue format, which was completely exported in plaintext with CR. To display the
network, the Pajek software (2.05) was used as the data analysis and the visualization
tool for our research.
3. RESULTS OF BIBLIOMETRIC ANALYSIS.
This section contains the results of the bibliometric analyses made by the 83 authors in
the 40 articles published in the dataset covering the 1993-2014 study period, that it has
resulted with the search criteria.
3.1 Analysis of the period 1993-2014
Currently, is the international literature interested in tax credits for R&D? One form of
observing the interest of this field, it is to analyze the number of publications in recent
years.
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Figure 1. Number of articles in WoS from 1993 to 2014.
Source: Compiled from WoS.
In this period, a total of 40 articles were published in WoS, but the distribution is not
constant, the majority of works were published more recently (the last decade). This
result shows the dynamism of the studied matter and the involvement of academic
investigators to help advance in this field.
Articles were published mostly in English, 37 (93%) versus 3 (8%) published in
Spanish.
3.2 What articles are considered core in literature ?
Table 1 provides a ranking of the most cited articles in this field. The most cited article
is “Market incentives and pharmaceutical innovation” by Yin, published in 2008 in
Journal of Health Economics, which obtained 30 cites. This work has the most average
per year; by this measure the effect of the passage of time is counteracted. A total of
40 articles were cited 167 times, one hundred forty one without self-citations, the
average citation per article is 4.18 and h-index is 9. The h-index is an indicator
developed by Hirsch. It is based on a list of publications sorted in descending order by
the number of times cited, for example one h-index of 9 means that there are 9 articles
that have 9 or more cites (WoS, 2015).
Twenty five (62.5%) of the 40 articles received at least one citation, and 7 (17.5%)
were cited more than 10 times.
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Table 1: Most cited articles.
Rank Title Authors
1 Market incentives and pharmaceutical innovation Yin Journal of Health Economics
2 Are firms that receive R&D subsidies more innovative? Bérubé and Mohnen Canadian Journal of Economics
3 Tax incentives for innovation: time to restructure the R&E tax credit Tassey Journal of Technology Transfer
4 Evaluating the impact of R&D tax credits on innovation: A microeconometric study on Canadian firms Czarnitzki; Hanel and Rosa
5 The effects of state R&D tax credits in stimulating private R&D expenditure: A cross-state empirical analysis Wu Journal of Policy Analysis and Management
6 A cost-benefit analysis of R&D tax incentives Russo Canadian Journal of Economics
7 Measuring the cost-effectiveness of an R&D tax credit for the UK Griffith; Redding and Van Reenen
8 Is a higher rate of R&D tax credit a panacea for low levels of R&D in disadvantaged regions? Harris; Li and Trainor
9 Expanding the R&E tax credit to drive innovation, competitiveness and prosperity Atkinson Journal of Technology Transfer
10 Strengthening the competitiveness of united-states microelectronics Gover Ieee Transactions on Engineering Management
Source: Compiled from WoS.
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3.3 Most productive journals
The 40 articles appeared in 27 different journals. The journals with more than two
articles are presented in Table 2, of which the most productive are Research Policy
(n=4) and Innovation-Management Policy & Practice (n=4). Of all the published articles,
50% (20 of 40) were featured among the top seven.
The scope between journals are heterogeneity, since some journals are concentrated
in taxes like Fiscal Studies, others are more concentrated in innovation like Innovation-
Management Policy & Practice, and others are specialist in economy, like Research
Policy or Journal of Technology Transfer. The heterogeneity of scopes shows the
interest in various fields by this area.
The impact factor of these journals reaffirms the conclusions reached above. Journals’
quartiles are between s 1 and 4 in the respective categories. This fact evidences the
great impact of these journals and this information, together with the growing number of
studied articles (Figure 2), and indicates that prestigious journals are interested in this
research topic.
Table 2. Journal citation frequency (more than two publications per journal)
Rank Source Titles Record
Count
% of
40 Quartile
Impact
Factor Scope
1 Innovation-Management
Policy & Practice 4 10% Q4 0.439
Innovation research, policy analysis
and best practice in companies
2 Research Policy 4 10% Q1 2.598
Innovation, technology, research and
economic, social, political and
organizational processes
3 Journal of Technology
Transfer 3 8% Q2 1.305 Technology transfer
4 Small Business Economics 3 8% Q1/Q2 1.641
Entrepreneurship, self-employment,
family firms, small and medium-sized
firms, and new venture creation
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Canadian Journal of
Economics Revue
Canadienne d Economique
2 5% Q3 0.641 In all areas of economics
6 Fiscal Studies 2 5% Q4 0.319 The government action influence in
the economy
7 Hacienda Pública Española 2 5% Q4 0.250 Theoretical and applied economic
research
Source: Compiled from WoS (2015).
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3.4 Most productive authors and institutions they b elong to
Eighty three different authors participated in 40 articles, of which 77 (93%) published a
single article. Table 3 shows the top authors that they have published more than two
articles.
The high percentage of authors with a single work, and the fact that the number of
works has increased in the last four years (Figure 1) shows that this is a ascending
topic. A lot of authors are interested in this field, but they still have no well-developed
research line. Most of the authors belong to USA, these can be observing better in
Figure 2.
Table 3. Most productive authors, country and institution they belong to (2 works or
more).
Rank No. Author Institution Country
1 2 Paff Penn State Berks a college of The Pennsylvania State
University USA
2 2 Hemphill University of Michigan Australia
3 2 Ernst Centre for European Economic Research Germany
4 2 Watkins Lehigh University. College of Business and
Economics USA
5 2 Link The University of North Carolina USA
6 2 Romero-Jordan University Rey Juan Carlos Spain
Source: Compiled from WoS (2015).
3.5 Geolocation
To analyze the city and country where the authors work, through authors’ institutional
addresses by geolocation is another way to observe the literature structure. The
geographic situation of authors’ belongs was analyzed to investigate whether the
degree of impact of credit tax in R&D was Spanish, European or global.
Figure 2 (by GPS) evidences that the researchers who investigated the impact of credit
tax in R&D were located mainly in Europe and North America (very few cases in other
continents). USA is the most productive country (n=20), a half of articles were publish
in this country. It evidences the importance of this field in USA. If European authors
were analyzed, we found that the largest group of researchers were located in Spain
(n=6), in second place came England and Germany (n=3).
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The extensive number of countries, which researched in this field, reflects the high
prestige and impact of this research topic. This is directly related to the most productive
investigators since the countries of the lead investigators were also the main producers
of the articles that analyzed this field.
Figure 2. Geolocation of where authors work.
Source: Compiled from GPS Visualizer.
3.6 Co-authorship analysis
Another form to analyze the structure of literature is analyzing the co-authorship; this
method is the most formal demonstration of intellectual collaboration in scientific areas
(Acedo et al. 2006). A co-authorship occurs when two researchers or more collaborate
to produce an article. Some authors, like Hudson (1996), argue that this collaborations
cause a higher quality or a larger quantity of articles than if conducted by only one
investigator. Acedo et al. (2006) explain that the number of authors that signed a work
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had changed in the last decades. Nowadays is more usual that one articles is signed
by various authors.
The study of Glanzel's (2002) and Acedo et al. (2006) describe how multi-authored
works seem to have a stronger impact, since they are more likely to be cited and attract
more citations than those articles written by one.
In this study, 13 articles were written by one author, 9 articles by two authors, but most
were written by three authors or more, 18 articles. At first this seems to indicate that the
works in this field are often written by more than one author, which seems to indicate
the collaboration between researchers, but co-authorship analysis shows although
several authors collaborate in an article this does not happen more times, except
Watkins and Paff (2 articles together).
To analyze this situation was necessary to purge the entire database by hand to
ensure the reliability of the results, this was due to detected errors in the in the data file,
mainly in names and surnames due to the inclusion of one surname or two, or special
characters for different languages, major errors were detected in Latin names of
researchers, which meant that Bibexcel did not correctly interpret the database.
Figure 3 shows the result of co-authorship analysis (using Pajek with the Kamada-
Kawai algorithm, by separated components). In this figure, we observe a lot of
collaborations by authors, but only one that is repeated more than once. Paff and
Watkins obtained 2 collaborations, but the rest of authors showed only one
collaboration.
If these results involved an institution that investigators worked in, a relationship was
observed between the number of collaborations among researchers and those
belonging to the same institution or institutions in the same country. For example, Paff
and Watkins belong a different university but both are in USA, or Delgado-Rodríguez,
Romero-Jordan, Alvarez-Ayuso and De Lucas-Santos belongs to different universities
in the same country.
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Figure 3 Co-authorship.
Source: The author’s own.
3.7 Centrality
In order to reinforce the conclusions obtained in the analysis of co-authorship was
made centrality analysis. This centrality analysis allowed to observe the authors’
relevance for the structure of the collaboration network also if this network of the
scientific community of its discipline was incommunicado, or not (Ronda-Pupo and
Guerras-Martín, 2013). To calculate centrality algorithms exist different forms, in this
study we used degree, betweenness and closeness.
These three algorithms led to the same conclusion. Degree is a centrality algorithm
that it shows the number of different researchers with which an author connects
directly, varying degrees of collaboration are represented among them (Valderrama, et
al; 2007 Freeman, 1979). In this study the degree of centrality in the network was
0.042 -very low- (Figure 4). This result confirms the theory of this field exist a poor
degree of collaboration among the authors.
The second algorithm that we used to observe the centrality was the betweeness. It is
based on the closest distance among the authors in the network structure (Freeman
1979; Ronda-Pupo et al., 2013). Valderrama, et al (2007) describe that this method
allow to observe what extent an researcher is located between the –or among other-
researchers of the network, thus allowing interconnection, which denotes the ability to
control and access information flows and the researchers’ prestige. Once again the low
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centrality pattern of the network (.002) is repeated. This results indicates not exist
authors that have the best ability to access and control information flows.
The third and final algorithm that we used is the closeness index, which describes a
researcher’s speed of interaction with other researchers of the network, allowing to
observe the "closeness" of each author with other authors (Valderrama, et al; 2007). In
this study the software Pajeck network analysis did not yield the proximity index for low
network connectivity.
Figure 4 Centrality measure: Degree
Source: The author’s own.
3.8 Co-word analysis
A keyword analysis, which examines the content of scientific works or works of other
types (Berelson 1952; Kassarjian 1977), can be used for different reasons, among the
most common (1) identify topics and preferred statistical approaches (Helgeson et al.
1984),and (2) identifies trends (Yale et al. 1988; Roznowski 2003). A keyword analysis
reduces the keywords space to a set of network graphs that explain the strongest
associations among keywords or descriptors (Coulter et al. 1998). We analyzed co-
keywords (or co-descriptors) to describe and discover the interactions between
different keywords in the literature that analyze the credit tax in R&D.
We used Bibexcel and Pajek to analyze the co-occurrence of keywords in the
descriptors or keywords in each article. Following the process described by Persson et
al. (2009) we did this analysis than can be observe in Figure 5a. We represented it by
Pajek (Fruchterman-Reingold 2D algorithm). The co-occurrence matrix is formed by the
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co-occurrence frequency of two keywords in which these two keywords appear
together in the keyword field of each work. Several keywords are interconnected in this
figure, where the volume of the spheres is a vector (Figure 5a), which represents the
frequency of keyword occurrence in the core literature.
Figure 5 shows the analysis of the keywords and their interconnections, and depicts a
network graph that represents the most usual keywords in the tax credit in R&D
literature. Before to realize the analysis, was necessary to refine by hand the database,
to ensure the reliability of keyword counts. The main errors were detected in spelling
errors or the inclusion, or not, of the plural of words to not distort the results; for
example, the word “tax credit” or “tax credits” the solution was to homogenize all words
in plural. The most usual keywords in this field (in relative weights in the number of
articles), were innovation (18), credits (8) and firms (8). They appear in the centre of
the cluster and connect most of others keywords, and thus represent the importance of
these words in the field of tax credits in R&D. The keyword “tax credit” has a less
frequency and co-occurrence than innovation, as we decrease the frequency to 3 to
observe it. The co-occurrence of this keyword (Figure 5c) is to innovation (n = 3), which
shows the relationship of these two words in this field.
Figure 5a Keyword Co-occurrence (minimum of 2).
Source: The author’s own.
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Figure 5b. Keyword Frequency (minimum of 6 frequencies).
No. Keyword
15 Innovation
8 Credits
8 Firms
7 Development tax credits
6 Productivity
6 Growth
Source: The author’s own.
Figure 5c. Keyword Co-occurrence (minimum of 3).
No. Keyword Keyword
6 Credits Innovation
6 Firms Innovation
4 Countries Innovation
4 Innovation Panel
3 Firms Productivity
3 Development tax credits Innovation
3 Development tax credits Firms
3 Development subsidies Innovation
3 Credits Panel
3 Development tax credits Research-and-development
3 Growth Innovation
3 Countries Credits
3 Countries Industry
3 Basic research Innovation
3 Industry Innovation
3 Countries Panel
3 Development subsidies Firms
Source: The author’s own.
3.9 Co-citation
The co-citation map displayed in Figure 6 has been created with Pajek, following the
process described by Persson et al. (2009). Through the analysis of co-occurrences of
citations between articles of this study, it provides us insight into the breadth and
importance of the most cited literature in the tax credit in R&D literature.
In Figure 6 we eliminated the low frequencies (minimum 3) to increase its visualization.
The co-citation map indicates the core literature used in this field. Documents are
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represented by the first author and year of publication. The most cited article is Hall et
al., (2000) in Research Policy (in 23 articles). Bloom et al., (2002) and Hall et al.,
(2000) are the most repeated references of the core articles (n=12 co-citations), which
can be considered the main reference source for the core literature.
The cluster algorithm produced three clusters. In the right- hand side of the figure there
is a cluster formed by a four articles (blue cluster). This cluster is divided in two parts,
first, Atkinson et al., (2007) and Tassey et al., (2007) both analyze the gaps and needs
for change in tax credits to boost R & D, and the other group formed by Grihiches et al.,
(1986) and Mansfield et al., (1980) are more concentrated in returns of investing in
R&D.
In the left-hand side of the figure there is a dense cluster formed by a big number of
articles (yellow cluster). In general terms, it can be considered that the main principal
topic is the impact and effectiveness of tax incentives on R & D. In this dense cluster,
the most cited of all cluster is Hall et al., (2000), and the most co-cited are Hall et al.,
(2000) and Bloom et al., (2002). Both analyze the impact and effectiveness of tax
incentives on R&D. Hall et al., (2000) conclude that a dollar in tax credit for R&D
increase a dollar of additional R&D, and Bloom et al., (2002) conclude that a 10% fall in
the cost of R&D increase just over a 1% rise in the level of R&D (in the short-run), and
in the long-run just under a 10% rise. But not all works analyze this specific aspect,
others analyze the impact of subsidies in R&D (Busom et al., 2000 or Gonzalez et al.,
2005) or the public R&D spending (David et al., 2000).
The green cluster, which is the closest to the yellow cluster, in fact the article of
Czarnitzki et al., (2011) is connected with Hall et al., (2011). In general terms this
cluster has a similar theme to yellow cluster, since these articles analyze the impact of
subsidies in R&D or the public R&D spending too. For example, Takalo et al., (2010)
analyze the interaction of public and private funding of innovative venture in adverse-
selection based funding constraints, also Cerulli et al., (2010) and Gelabert et al.,
(2009) analyze the effect of public subsidies on R&D. The article of Czamitzki et al.,
(2011) is connected with Hall et al., (2011), if you observe the thematic of both are
similar, because they analyze the impact of tax credit on R&D. For these reasons we
can sort out this cluster like a sub-cluster of yellow cluster.
This map gives us an insight into the breadth and importance of who is the most cited
literature in the core tax credits in R&D literature.
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Figure 7 Co-citation (Minimum of 3)
Source: The author’s own.
4. STUDY OF CONTENTS
The first step has been to describe the leading authors and journals that directly and
indirectly contribute to the tax credits in R&D literature, we detected the core articles
that influence the international literature, the co-occurrence authorship, keywords and
citations among articles, the thematic area of the co-citation and a map showing where
the most productive authors are in the world. Although to strengthen the results and
gain a greater understanding of the current state of the literature on tax credits is
necessary to conduct a thorough study of the contents of these articles both from the
point of view of the methodology used and the main lines of research.
The detailed study of forty articles highlights the abundance of empirical literature
analyzing the effects of financial public policies (mainly subsidies and tax credits). If we
focus on the study of tax incentives main measure (1) the cost-effectiveness of the
incentives and (2) the additional private R&D spending.
Yang et al. (2012) investigated the effect of tax incentives on R&D activities, in a panel
of 576 manufacturing firms in Taiwan. In their study concluded that the companies who
applied tax credits had a superior R&D expenditure (53.08%) that companies that
hadn´t utilized, also affected to its growth (especially for electronics firms). In the
Spanish case, the work of Romero and Sanz (2007) analyze the effectiveness of tax
incentives for investment in R&D over the period 1990 to 2001. In this paper the
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effectiveness of the measure is confirmed, they argue that these measures are
adequate to reduce the price of R&D, in fact prove that there is a gross investment
between 1.24 and 1.26 monetary units for each additional monetary unit of spending.
Although they remark that the ability of the tax system, to encourage investment in
R&D, it is moderate (price elasticity between -0.98 and -1.01). Also Wu (2005) with an
empirical study analyzed the effects of R&D tax credits on private R&D expenditure.
The statistical results showed that to encourage private R&D, credit programs of R&D
state an effective measure. Chiaga et al. (2012) in contrast to other studies on tax
incentives, mainly the effect of the tax credits are analyzed in a given time or for a
temporary period; this study analyzes the effect at different stages of the life cycle of
the companies. It is stressed a greater impact on innovative activity in the stage of
stagnation than in growth.
Regarding cost-effectiveness of the tax credit, the study of Griffith et al (2001) analyzed
the economic impact of new R&D tax credit, in United Kingdom. They concluded that in
the long run, the increase in GDP (growth domestic product) far outweighs the costs of
the tax credit, this being much smaller effect in the short term.
Other study that analyzed the impact of R&D tax credits on innovation is Czarnitzki et
al. (2011). This article studies these effects in a micro econometric study on Canadian
firms. In this study the results are compared with a hypothetical situation in which was
observed the result of a series of indicators without incentive tax credits. The results
show that the tax credits increase the production of innovation; companies have a
higher probability of introducing innovations to market. Although it remarks that private
benefits expected of public support for R&D may be overvalued.
The next step in understanding the actual situation of the literature on tax credits is the
analysis of the methodology used in the studies. With regard to non econometric
estimates include the (1) case studies, which through surveys of a sample of the
population or specific sector of activity, or by making comparisons to changes in policy
or interviews stakeholders analyze the effects of tax credits on R&D. Examples of
these techniques are, the Atkinson's study (2007) which describes the situation of the
tax credit on R & E in the US, for it describes the impact, the evolution and the need to
change some policies. To improve the competitiveness of US companies more
competitive proposes: (1) regular credit rate to 40% (2) expand the alternative
simplified credit and (3) extension of the flat credit for R&D projects. Cappelen et al
(2012) compared the effects of a new tax measure to boost R & D in Norway, on the
21
probability to innovate and patent. They concluded that the tax measure increasing
rates of innovation in enterprises.
With regard to econometric estimates major parametric methods. The empirical
methodology used in the studies analyzed are mixed, as in other studies like Hemphill
(2009), when the object of study is the effectiveness of tax on R&D, the methodology
used is usually (1) measure being the sensitivity of the level of R&D spending to the
reduction in R&D price, such as the price elasticity of R&D. An example of the use d
these techniques is the study of Griffith et al (2001), in which the economic impact of
new tax credits for R&D is analyzed through existing econometric estimates on the
price elasticity of taxes R&D and the effect of R&D on productivity. And (2) measure
being an estimate of the benefit-cost ratio of the tax credit, for example for each
monetary unit spending generates roughly one unit of gross investment. Romero et al.
(2007) detected that for every additional unit of output generated between 1.24 to 1.26
units of gross investment.
In literature nonparametric methods are also used, which are aimed at analyzing
whether subsidies and tax credits are complements or substitutes. Busom et al (2014)
analyze whether subsidies and tax credits are complementary or substitute, in this
study also examines whether the actual use of these measures is related to funding
constraints. Conclude that both measures are not perfect substitutes.
Different policies used in sectors and countries may be the reason why the studies
focus on studying only one region, country or a particular sector, with very few works
where there is comparative to the supranational level. An example of supranational
analysis is the study by Leyden et al (2002) in which he analyzes the historical
evolution of fiscal policies to R&D, both in the US and in 22 other industrialized
countries, among which is Spain. It also analyzes the empirical evidence on the
effectiveness of tax incentives for R&D as well as a review.
5. CONCLUSIONS
The turbulent economic landscape that is undergoing most western countries, which
are constantly being implemented cuts in public spending and investment. It becomes
necessary to analyze public spending or investments should not cut to predict an early
economic recovery.
22
In this study we analysed the literature that study the public policies (mainly, tax credit
or subsidies) in R&D. Most analyzed studies indicate the existence of a positive effect,
reinforcing the idea of not cutting back on incentives for R & D. It is important to note
that the system of tax incentives for R & D is very different in each country; the
comparability of studies and results becomes a difficult task for any firm conclusions.
Regarding the Spanish case; the tax incentives for R&D have not been supersize
configuration changes, which could point the commitment of state incentives for this
type.
The analysis of articles published by year shows that this field is a rising topic. Because
the search criteria were very specific and search WoS, the number of articles dealing
with this area is not very wide.
The most productive authors found were Paff, Hemphill, Ernst, Watkins, Link and
Romero-Jordan, but they only have 2 articles in this field, it evidences the few
concentration of authors in this field. The co-authorship confirms this situation, since
that there are no well-developed collaborating groups. The most cited works were Yin
(2008) and Bérubé and Mohnen (2009).
Many journals have published articles about this topic, which denotes the vitality of the
subject matter. Among the most active, Innovation-Management Policy & Practice and
Research Policy fell in the JCR fourth and first -respectively-, which shows its influence
for the international scientific community.
The co-occurrence map provides insight into the breadth and importance of keywords.
The result of this analysis was contrary to expectations, since the word "R&D" or "tax
credits" were not the most repeated keywords, were "Credits" and "Innovation" and
"Firms", although they were among the most used.
The geolocalization analysis showed that the researchers who investigated the impact
of credit tax in R&D were located mainly in Europe and North America (very few cases
in other continents).
This investigation is not without its limitations as far as the search; selection and data
analysis are concerned. One limitation is the possible non-inclusion of one of the key
articles considered, or more, in the database used, which was not due to lack of
methodology.
23
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