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


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).


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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


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


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)


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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