Tax Incentives and R&D Activity: Firm-Level Evidence from Taiwan
Chia-Hui Huang
Department of Economics
Aletheia University
E-mail: chhuang@mail.au.edu.tw
Chih-Hai Yang∗
Department of Economics
National Central University
E-mail: chyang@mgt.ncu.edu.tw

Abstract
This paper investigates the effect of tax incentives on R&D activities in Taiwanesemanufacturing firms. Specifically, we assess the potential R&D-enhancing effect onrecipients of R&D tax credits compared with their non-recipient counterparts. Moreover,the potential difference in the R&D-enhancing effect between high-tech and non-high-techfirms is also examined. Utilizing a firm-level panel dataset during 2001 and 2005,
empirical results obtained by propensity score matching show that recipients of R&D tax
credits appear on average to have 93.53% higher R&D expenditures and a 14.47% higher
growth rate for R&D expenditures than non-recipients with similar characteristics. The
R&D-enhancing effect of R&D tax credits is not found to be particularly relevant tohigh-tech or non-high-tech firms. We further employ a generalized method of moment(GMM) of the panel fixed model to control for the endogeneity of R&D tax credits andfirm heterogeneity in determining R&D expenditure. Various estimates based on the entire
sample and high-tech-firms are quite similar and there is a significantly R&D-enhancing
effect of R&D tax credits. This result suggests that the R&D preferential policy has
induced more R&D expenditure by firms in Taiwan. While the existence of the
R&D-enhancing effect brought on by tax incentives is intuitive, the estimates can provide
insightful implications for the R&D tax policy.

Paper Presentation at the 2009 Comparative Analysis of Enterprise Data (CAED)
Conference, Tokyo, October 2-4, 2009.
Key words: R&D, Tax, Propensity Score Matching
JEL classification: H25, H32, K34, O32, O38∗

Corresponding author: Department of Economics, National Central University. 300 Jhongda Road, Jhongli
City, 320, Taiwan. Tel.: +886-3-4226903. E-mail: chyang@mgt.ncu.edu.tw

1. Introduction
The importance of innovation on economic growth has been highlighted by endogenous
growth theories and evidenced in many existing studies.1 Therefore, many countries’
governments have tried to create a favorable innovation environment and protective
regularity, aiming to promote firms’ innovative activities and consequently to contributeto sustainable economic growth. However, innovative activity is essentially full of
uncertainty as well as time- and money-consuming. Innovation is also recognized to havesome characteristics of a public good, preventing the market from providing sufficientquantities of R&D from the perspective of social return. To bridge the gap between theprivate and social rate of return and foster industrial R&D activity, various policy
measures have been launched to stimulate industrial innovation. Specifically, the R&D
tax credit has become increasingly popular in some developed countries such as the U.S.,
Canada, and some OECD countries since the early 1980s.2
Taiwan, one of the best performers among latecomers, has been very successful innarrowing the technological gap with its counterparts among leading countries during thepast two decades, especially in the electronics industry. In terms of innovation input,
Taiwan has devoted a gradually increased ratio of R&D spending to GNP that went from
1.62% in 1990 to 2.62% in 2007, which is similar to those of the U.S. and Germany.3
Taiwanese firms’ patents have also grown extremely fast both domestically and in the U.S.
A study of “influential patenting” by the technology consulting firm CHI, Inc. placed
Taiwan 4th in the world in terms of the quantity of its U.S. patents in 2000 and it hasremained at this ranking. Trajtenberg’s (2001) study on an international comparison ofpatents granted in the U.S. also showed that Taiwan ranks high in terms of patents per
capita, compared with the G7 and the other “Asian Tigers”. This achievement is quite rare1

See Acemoglu et al. (2006) for a comprehensive survey on theoretical as well as empirical literature of theinnovation-economic growth nexus.
2
For a survey of tax treatment of R&D around the world, please see Table 1 in Hall and Van Reenen (2000).
3The R&D expenditures devoted by the business enterprise sector accounted for 68.8% in 2007.
1

in the developing world and has almost uniformly failed to happen outside of Asia orwithin Asia. During the process of technological development, the Statute for Upgrading
Industries (SUI) that applies tax incentive, subsidies, and supporting measures to assist
innovative activity is considered one of Taiwan’s key industrial technology policies (Lienet al. 2007).
Since economists have been generally skeptical regarding the efficacy of tax incentives,
and the tax implementations of the SUI will expire in the end of 2009, it inspires alegitimate question of concern: Has the R&D-preferential policy induced greater R&D
expenditures and a higher growth rate of R&D expenditure from firms in Taiwan? Fromthe perspective of public finance, the tax bases erosion attributed to R&D incentives ispossible one cause of fiscal shortage. Whether or not the limited government resources
should be used to encourage innovation depends on the efficacy of these measures on
inducing much greater innovations and then contributing to sustainable growth.
Given the knowledge economy, knowledge-based industries are thought of as the mostimportant industries to support sustainable economic growth, and therefore many
countries are aggressive in developing these knowledge-based industries (usually
high-tech). High-tech industries are generally more R&D intensive and are also theprimary recipients of R&D tax credits in the U.S. (Wu, 2008) as well as in Taiwan. It
inspires another interesting and important issue: Does the R&D-inducement effect, if
any, differ between high-tech and other industries in Taiwan? Paff (2005) finds thatstate-level R&D incentives do not appear to have equal incentive effect across industries,
and

the

industries she

examined are

high-tech,

R&D intensive industries(biopharmaceuticals and prepackaged software industries). In practice, many so-called
traditional industries (usually less R&D-intensive) put up criticism, as R&D tax credits
work more favorably for high-tech firms in Taiwan. Actually, innovative behavior isstrongly related to the technological environment surrounding a firm’s location.

2

In industries with high technological opportunity, the technological environment facedby these firms which are relatively fertile results in a more prevalent and higher R&D
expenditure among firms. Alternatively, the appearance of innovation is relatively rare in
the environment of low technological opportunity, implying that the patterns of innovative
activity within that industry should be less R&D intensive and fewer firms should engage
in R&D. To enforce the policy of R&D tax credits more efficiently, one possible
improvement is the setting of various tax credits across industries. Therefore, assessing thepotential difference of the R&D-inducement effect of R&D tax credits across industries
provides insightful implications for policy implementations.
One primary difficulty encountered when using firm-level data to evaluate theeffectiveness of an R&D tax credit within a country is the variation between firms in theeffectiveness of the credit (Bloom et al. 2003). Most existing studies, by using linear
regressions, treat tax incentive as an exogenous variable. However, the differences in tax
position and expectations regarding future R&D expenditure induce an endogenous
problem on choice of tax credit application. To assess the effect of R&D tax credits, it isimportant to correct for the selection bias, because the recipients of tax credits may differ
systematically in some firm characteristics from non-recipients (Czarnitzki et al. 2004). In
this paper we adopt a non-parametric technique of propensity score matching (PSM)
method developed by Heckman et al. (1997, 1998) to correct the possible selection bias.
Moreover, firm heterogeneity is potentially a key factor in influencing the assessment of
R&D-inducement effect of R&D tax credit.
Hall and Van Reenen (2000) find that the way in which the R&D tax credit creates
heterogeneous emerges in many ways and often perverse incentives has been a key featureof the debate on the desirability of R&D tax credits. While the PSM approach can deal
with the selection bias problem, it does not take the unobservable firm heterogeneity into
account. Fortunately, there is detailed information concerning the amount of deduction of3

R&D of business tax as well as firm characteristics, enabling us to adopt the Generalized
Method of Moments (GMM) for panel data model in order to evaluate the impact of R&D
tax credits on a firms’ R&D activity. Using the adequate instrumental variable to deal withthe endogenous variable provides asymptotically efficient estimators even under a weak
assumption on the disturbance and is robust in the presence of heteroscedasticity acrossfirms, showing a correlation of disturbances within firms over time.
This paper evaluates the impact of tax credits for R&D and its growth in Taiwanesemanufacturing firms. Although this question is not new to the literature, this study has
distinct novelties that can contribute to the empirical literature. First, the question, of how
and to what extent tax credits stimulate industrial R&D, has attracted widespreadinternational attention among economists with limited empirical studies focused on
developed countries such as the U.S., Canada, and France. However, tax incentive policies
are worthwhile considerations in not only developed economies, but also for
newly-industrialized and developing countries. Taiwan has successfully achieved
substantial technological development over the last two decades, and its outstanding
performance in innovations makes it an excellent case to investigate the tax incentives
issue. Our firm-level evidence from Taiwan provides new evidence to the existing
literature that concentrates on advanced countries.
Second, we further separate samples into high-tech and non high-tech firms to examine
the potential differences in the effectiveness of an R&D tax credit. It is widely criticizedthat a uniform tax credit system for all industries is not appropriate, because the degree of
R&D activity could differ substantially across industries under various types oftechnological opportunities. Therefore, examining the potential difference in theeffectiveness of R&D tax credits across industries provides insightful reference for thefurther revision of R&D tax credits. Third, this study employs the propensity scorematching (PSM) method to correct the selection bias endogeneity of the innovative4

activities and R&D tax credits. This enables us to compare the R&D activities of R&D tax
credit recipients and non-recipients. Owing to the comprehensive nature of theinformation on the amount of R&D tax credits contained in our dataset we further
examine the marginal, rather than the treatment, effect of R&D tax credits on firms’ R&D
expenditures. In this context, this study employs the technique of GMM of panel fixed
effect to deal with the problem of unobservable firm heterogeneity and then assesses theeffect of R&D tax credits on the firms’ R&D efforts more appropriately.
The remainder of this paper is organized as follows. The next section provides a brief
review of the literature on R&D tax credits. Section 3 introduces Taiwanese R&D
incentive measures and describes the data used in this study. Section 4 presents theempirical model and then examines the R&D-inducement effect of an R&D tax credit byusing the propensity score matching method. The findings from further investigation into
the marginal effect of R&D tax credits on R&D across industries are presented in Section
5. The final section concludes with the main results and their policy implications.

2. Literature Review
While the importance of R&D in economic development and sustainable growth iswell-recognized, economists generally think that private R&D is under-investment interms of social optimal level due to imperfect appropriability conditions of new
knowledge (Davis et al., 2000) and financing gaps induced by asymmetric information(Hall, 2002 ). Therefore, governments generally adopt various policy instruments to foster
industrial R&D activity directly or indirectly, such as tax incentives, subsidies,government R&D labs, and investing in higher education.
Two primary policy tools applied by governments to stimulate firms’ R&D are direct
subsidies and tax incentives. While direct subsidies (either R&D contracts or R&D grants)
can increase private R&D investment significantly, they may simply substitute for some5

R&D investment that the performing firms otherwise would have prepared to undertake(Wu, 2005) – that is, it crowds out firm-financed R&D expenditure and ultimately has no
impact whatsoever on such activities (Wallsten, 2000).4 Moreover, Wu (2005) argues thatpublic R&D subsidies may negatively affect private R&D investment by reducing the
upward pressure on the prices of such R&D inputs as the wages of scientists and engineers.
Alternatively, the instrument of tax credits reduces the cost of private R&D and seems to
be a market-oriented mechanism, because it leaves the choice of how to conduct andpursue R&D programs to enterprises. Although an R&D tax credit is just one of several
policy instruments on R&D and is far from the panacea for failure in the market ofinnovation, it has become a common strategy in many countries compared withgovernment direct subsidies and/or conducting the R&D program directly (Klette et al.,2000).5
The wide and increasing prevalence of R&D tax credits has attracted a high degree ofconcern among economists and policy makers regarding whether and/or how effective aretax incentives for R&D. An emerging amount of studies have utilized various
methodologies to evaluate the effect of the tax system on R&D behavior (cost) since theearly 1980s. Hall and Van Reenen (2000) provide a comprehensive summary of theliterature and indicate that a dollar in tax credit for R&D stimulates about a dollar ofadditional R&D expenditure. As the tax treatment of R&D becomes more lenient, theyalso argue that it is likely that countries will increasingly turn to the tax system and awayfrom direct grants.
There are also some studies on tax incentives examining the impact of fiscal incentives
on the level of R&D investment, which mainly concentrate on country or state level.
Based on nine OECD countries (Australia, Canada, France, Germany, Italy, Japan, the U.S.
4

For the studies of direct subsidies on the R&D activities, please see Özçelik, E and E. Taymaz (2008) for acomprehensive survey.
5
For the advantages and disadvantages of both tax incentives and direct subsidies for R&D, please see Kletteet al. (2000) for a detailed discussion.
6and the U.K.), Bloom et al. (2002) find that tax incentives are effective in increasing R&D
intensity after controlling for permanent country-specific characteristics, world macro
shocks, and other policy influences. A 10% fall in the cost of R&D stimulates a 1% and
10% rise in the level of R&D in the short and long run, respectively. Guellec and Van
Pottelsberghe (2003) investigate the impact of R&D tax incentives on business R&D inseventeen OECD countries and reach similar results showing that tax incentives caneffectively stimulate business R&D. Wu (2005) employs data from six states (Arizona,
California, Colorado, Illinois, Massachusetts, and New Jersey) in the U.S. to examine theeffects of state R&D tax credits on private R&D expenditure within each state. The author
shows that the presence of an R&D tax credit results in 75 to 118 more R&D dollars per
capita. It suggests that the establishment of a state R&D tax is effective in stimulating
more company R&D expenditures, but it does not address the cost of implementing suchtax credit programs.6
Firm-level evidence on the efficacy of tax incentives is quite rare, owing primarily todata limitations. Koga (2003) examines the effectiveness of R&D tax credits on Japanesemanufacturing firms over the period 1989-1998 and especially explores the role of firmsize. He finds that R&D tax credits mainly stimulate R&D investment in large firms rather
than medium firms. The estimated tax price elasticity for large firms and medium firms
are -1.036 and -0.118, respectively. R&D tax credits might be less effective in Japan thanin western countries. Examining the same issue for Canadian manufacturing firms overthe 1997-2003 period, Baghana and Mohnen (2009) reach a contrasting result comparedwith the findings of Koga (2003).The estimated short-run price elasticity of R&D is-0.142 for small firms and not significantly different from zero for large firms. The authors
claim that the reason is partly due to of the deadweight loss associated with level-based
6

Wu (2008) further examines the effects of state R&D tax credits on growth in the U.S. high-technology
sector. The results show that the initiation of a state R&D tax credit has significant and positive effects on thehigh-technology establishments per 1,000 of population and the high-technology share of businessestablishments. It highlights the importance of the role of state R&D tax incentives in technology-based
economic development.
7

R&D tax incentives that is particularly acute for large firms. Paff (2005) examines firms’
R&D expenditure in response to an R&D tax credit rate increase in the U.S during
1994-1996 and 1997-1999. Empirical estimates obtained from difference-in-difference
provide some evidence that firms increase R&D expenditure significantly, while R&D
incentives do not appear to have equal incentive effects across industries. It sheds light on
the unequal role played by tax credits for R&D investment across industries. Moreover,she finds much higher tax price elasticity than the existing literature’s estimates near unity.
One possible reason is that the firms she examines are biopharmaceutical and software
firms which are highly R&D-intensive.
Different from previous studies focusing on the tax price elasticity, Czarnitzki et al.
(2005) use a non-parametric matching approach to compare the R&D expenditure ofrecipients of tax credits with a hypothetical situation in the absence of R&D credits. Fromevidence in a large sample of Canadian manufacturing firms, they find that R&D tax
credits have a positive impact on firms’ R&D decisions to conduct R&D as well as firms’
improved performance.

3. Taiwan’s R&D Incentives and Data Description
3.1 Taiwan’s R&D Tax Credits
Over the past three decades, Taiwan’s government has undertaken several measures toencourage firms to devote more to innovative activity and to promote their technologicalcapability. The most well-known industrial technology policy in recent years is the Statutefor Upgrading Industries (SUI), which was put into practice on January 1, 1991.7
Contained within the SUI, there are various articles concerning tax incentives, including:accelerated depreciation (Article 5), investment tax credits for R&D, personnel training,
automation and pollution control (Article 6), investment tax credits for the newly
7

Before 1991, Taiwan’s main industrial technology policy was law regarding: The Enactment of
Encouragement Investment.
8emerging, important and strategic industry shareholders (Article 8) as well as five-year
holiday or shareholder investment tax credits for the newly emerging, important andstrategic industries (Article 9). While the first two are functionally encouraging, the last
two are industry specific. The SUI also includes tax credits for those investing in scanty
areas (Article 7), aiming to improve the overall investment environment.
Article 6 is the most important policy measure that is particularly relevant to R&D tax
credits. More specifically, this tax incentive is the long-standing instrument adopted toencourage firms to promote their R&D activities in Taiwan. Figure 1 depicts trends inaggregate R&D spending and the amounts of R&D tax credits in Taiwan from 1992
onward. It is apparent that the amount of R&D expenditure increased steadily from
NT$94.828 billion in 1992 to NT$280.980 billion in 2005. Correspondingly, the amountof R&D tax credits has increased more than ten times from NT$1.529 billion in 1992 to
NT$16.318 billion in 2005. Compared with the steadily increasing trend of R&D
expenditure, the accelerating trend on the usage of R&D tax credits since the late 1990s
seems to suggest that R&D tax credit is quite relevant to R&D investment.
[Insert figure 1 approximating here]
While the previously mentioned literature, such as Koga (2003) and Baghana andMohnen(2009),

reachedinconsistentfindings

intherelationshipbetween

R&D-inducement effect of tax incentives and firm size, the policy tool of R&D tax credit
is widely criticized as only being beneficial for large firms rather than small andmedium-sized enterprises (SMEs) in Taiwan.8 More importantly, the policies of R&D tax
credits in favor of specific industries or firms, which are devoted to specific events, cause
the problem of tax base erosion and destroy the fairness of the burden of taxation amongfirms. Specifically, Taiwan has seen serious fiscal difficulties (the government debt
reached NT$4 trillion in 2007), including the pressure from tax shortages. On the other
hand, Article 6 of SUI accounts for approximately one third of the NT$100 billion of total
8

In practice, SMEs account for about 97% of Taiwan’s manufacturing sector in terms of firm numbers.
9

tax revenue loss for the Taiwanese government annually (Lien et al. 2007). As a result, thequestion of whether public support encourages firms to engage in R&D activity has
recently become an important and hot issue in Taiwan. This debate is particularly topical,
as the Statute for Upgrading Industries will expire at the end of 2009 and a new policy isrequired to be put in place. How does the policy contained in the SUI affect firms’
innovation behavior? Should the government extend this statute or terminate it asscheduled? These questions are topical and important from the perspective of both publicfinance and technology policy.
Aggregate evidence shows that the SUI has stimulated firms’ R&D activities andeconomic growth, e.g. Wang and Chen (1995, 2000), Lee et al. (2005), and Yang et al.
(2006), supporting the claim that this statute should be extended. However, as indicated by
Yang et al. (2006) in that the tax incentives cause the problem of tax distortion, it istherefore important to clarify the R&D-inducement effect of R&D tax incentives.9
Although the discussions regarding the SUI and macroeconomic policy have attracted
widespread interest in Taiwan, the question of how R&D tax credits affect firms’ R&D
investment has not been thoroughly addressed, suggesting the need for more empiricalevidence at the firm level to fill this gap as well as to provide new evidence to the existing
literature.
3.2 Data Source
On examining the potential impact of tax incentives on firms’ R&D activities, one
primary obstacle encountered is the availability of detailed information on firms’ usage of
R&D tax credits. Due to this limitation, we therefore utilize a panel dataset of
manufacturing firms which are listed on the Taiwan Stock Exchange (TSE) over the
2001-2005 period. One point worth noting is that firms listed on TSE are medium andlarge enterprises rather than small firms. Information on firm characteristics is obtained by

9

Lien et al. (2007) use the macroeconomic model to examine tax credits’ economic benefits and costs andpropose some directions adjustment of the tax credits policy drawn from their analyses.
10matching from various data sources. R&D expenditure and other firm-specific variables,
including employment, the date of establishment, fixed capital stock, advertisingexpenditure, and profitability, are acquired from the databank constructed by the Taiwan
Economic Journal (TEJ) company.10 The deduction of R&D of business tax and export
data are taken from the annual finance reports of individual enterprise in each year. Finally,
the information on outward foreign direct investment (FDI) is drawn from the notification
data from the Investment Commission of Ministry of Economic Affairs. By eliminating a
few firms with incomplete data for all the relevant variables, we obtain an unbalanced
panel data of 621 enterprises, yielding an overall sample of 3,031 observations. In thisstudy we further separate the full sample into two subgroups: one is the electronics
industry, representing the high-tech industry, and the other represents other non-techindustries. While this classification seems to be ad hoc, it is acceptable as it coincides withthe cutting point using the average industry R&D intensity as the criteria. We therefore
have 1,377 and 1,654 observations for high-tech and non-high-tech firms, respectively.
Table 1 summarizes the variable definitions and basic statistics.
[Insert Table 1 approximately here]
Before turning to the empirical estimations, the features of the R&D tax credits taken up
by the sample firms are briefly introduced. Table 2 shows some basic statistics forrecipients and non-recipients of R&D tax credits in terms of the whole sample, high-tech,and non-high-tech firms, respectively. We clearly see that both average R&D expenditureand the growth rate of R&D expenditure of recipients of tax credits are substantiallyhigher than those of non-recipients, suggesting that the recipients have a better innovation
performance than non-recipients in terms of R&D activity. It also implies that there is apotential R&D-enhancing effect for firms receiving the R&D tax credit. In addition, theratio of receiving R&D tax credits, in terms of firm number, within non-high tech firms is

10

The Taiwan Economic Journal is a commercial company that has a fine reputation for collecting andsummarizing the information for companies listed on the Taiwan Stock Exchange. The TEJ databank isreliable and widely adopted by most universities in Taiwan as well as financial sector firms. This data bankcontains comprehensive information for balance sheets, financial statements, annual reports, and so on.
11only 12.82% which is much lower than that of their high-tech counterparts at 65.65%.
Furthermore, the figures in Table 2 reveal that larger and younger firms are apt to receive
the R&D tax credits. However, firms with high capital intensity are not inclined to receive
the R&D tax credits, as they have mainly applied for tax credits for automation andpollution control, rather than R&D tax credits, and this has especially been the case with
non-high-tech firms.
[Insert Table 2 approximately here]
The preliminary descriptive analyses show that the R&D tax credits seem to act as an
inducement to Taiwanese manufacturing firms to engage in R&D activities over the
2001-2005 period. However, R&D tax credits are not the main tax credit measure taken up
by firms with high capital intensity. It also highlights the importance of firm heterogeneity
affecting the impact of tax incentives on R&D investment.

4. Do R&D Tax Credits Induce More R&D?
4.1 Empirical Setting and Estimating Technique
Unlike previous studies which estimate the tax price elasticity of R&D, we first employ
the PSM approach to examine the effectiveness of R&D tax credit in Taiwan - that is, we
ask the question: What had the firm done in the absence of R&D tax credits? From thepoint of econometrics, the PSM approach can effectively deal with the endogenous
problem when we evaluate the R&D-inducement effect of tax credits. The effect of R&D
tax credit on R&D is much like a “treatment effect”. It means that we investigate thequestion of what would a treated firm (recipients of R&D tax credits) with given
characteristics have done if it had not been treated. However, the treated firms are usually
not selected randomly from a population, but are self-selected based on certain criteria,
inducing the comparison of simple averages of a treatment group and a control group toyield biased estimates of the treatment effect. The propensity score matching (PSM)

12method developed by Heckman et al. (1997, 1998) provides as an appropriate approach.
The concept of a PSM approach compares treated firms with a selected non-recipientgroup with similar characteristics rather than all non-recipients. The estimation steps are as
follows. First, let a dummy variable T equal 1 if the treatment is received; 0 otherwise. We
assume:Y = β ′ X + εi00ii
Yi = 
′Yi1 = β1 X i + γ + ε i

if Ti = 0

(1)

if Ti = 1

Here, Y denotes R&D activity, β and X are the respective vectors of parameters (including
a constant term) and exogenous variables. Term ε is a random error term with zero
expectation. This term picks up the effects of unobserved factors that may affect theoutcome, but are uncorrelated with the variables X.
We let g1i indicate the R&D expenditure (or growth rate of R&D expenditure, G_RD)
of firm i if it did not receive R&D tax credits in the initial year, but did so after 1 year that is, treatment occurs and T equals 1. Alternatively, assume g 0i denotes the R&D
expenditure (or growth rate of R&D expenditure) for firms which did not receive R&D
tax credits during the period (treatment does not occur, T=0). The average treatment effectis therefore:E ( g1i − g 0i | Ti = 1) = E ( g1i | Ti = 1) − E ( g 0i | Ti = 1)

(2)

One problem obviously arises: while the outcome of the treated firms (recipients of
R&D tax credits) is observable, it is not the case for the non-recipients - that is, g 0i is
unobservable. What would these firms have realized if they had not received the treatment?
The above causal inference relies heavily on construction of counterfactual observations.
Therefore, we need rich data on firms that have similar observable characteristics in theinitial period, but did not receive R&D tax credit during the period. The average R&D
activity E(g 0i | Ti = 1) is measured by E(g 0i | X i , Ti = 0) instead.
13

To construct a valid control group, Rosenbaum and Rubin (1983) suggest matching on
the propensity score with the probability of receiving a treatment conditional on thecovariates. Thus, we assume selection in the program is governed by the latent regression:
Ti* = δ ′Z i + u i

Ti = 1 if Ti* > 0 , 0 otherwise(3)Here, δ denotes coefficient and Z denotes a vector of determinants influencing a firm’s
decision regarding to R&D tax credits. This enables us to compute the probability of afirm’s decision to adopt R&D tax credits. With the propensity score of choosing to receive
R&D tax credits, we can run the matching algorithm and find the appropriate
counterfactual.
Within the matching process, the most important issue is to balance the distribution of
the pre-treatment observable characteristics between the treatment and control groups. We
therefore adopt Leuven and Sianesi’s (2003) approach to proceed with the estimation. To
ensure the validity of the results, three different criteria are adopted to determine theoptimal matching: kernel matching, nearest-neighbor matching method, and caliper
matching.11
4.2 Empirical Results
We first estimate the Logit model to get the propensity of a firm’s decision on whether
or not to apply for the R&D tax credits. The second step is to use the predicted propensity
scores to match R&D tax credit recipients with non-recipients which possess similar
observable firm characteristics. In the first stage of the Logit regression, seven variables
are included: a dummy variable for the firm’s previous R&D status (D_RDSTA),
logarithm of firm size (lnSIZE), firm age (AGE), logarithm of capital to labor ratio (lnKL),
one year lag of advertisement intensity (ADVR1), one year lag of profitability (PROFIT1),
and the export ratio (EXPR). Table 3 displays the estimation results for all manufacturingfirms, high-tech firms, and non-high-tech firms.

11

For an extensive discussion of matching methods, see Heckman et al. (1998).
14

[Insert Table 3 approximately here]
Estimates obtained by using the entire sample, high-tech firms, and non-high-tech firms
are quite similar, but the impacts of determinants are less significant for non-high-techfirms. The propensity of manufacturing firms to apply for R&D tax credits is found to bepositively associated with previous R&D status, firm size, profitability, and the export
ratio. The results suggest that R&D experience, larger, more profitable, andexport-oriented firms have a higher probability of self-selection for becoming R&D tax
credit users. This is economically intuitive owing to the fact that firms may possess thesecharacteristics which make them particularly innovation-prone, i.e., more likely to engage
in R&D. However, firm age, capital intensity, and advertisement intensity appear to havenegative impacts on the propensity to apply for R&D tax credits. This suggests that young
firms which engage in R&D tend to rely more heavily on deduction of R&D of businesstax. On the other hand, the negative impact of capital intensity on the propensity for R&D
tax credits is partly because firms with higher capital intensity tend to apply tax credits forautomation and pollution control rather than for R&D tax credits.
We next retrieve the propensity scores from the Logit model to match R&D tax credit
recipients with non-recipients under similar observable characteristics. The outcome
variables are the growth rate of R&D expenditure and a firm’s current R&D expenditure.
Table 4 shows the estimation results for all manufacturing firms, reporting the differencesin the growth rate of R&D expenditure and R&D expenditures for recipients andnon-recipients of R&D tax credits.
[Insert table 4 approximately here]
Column (1) shows the treatment effect of R&D tax credits on the growth rate of firms’
R&D investment. After controlling the non-random selection of the treatment groups, thematching results for recipients versus non-recipients of R&D tax credit suggest that thegrowth rate of R&D expenditure is significantly positive except for the nearest neighbor15matching method during the first year following the receipt of tax credits. The results
suggest a positive impact of policy incentive on firms’ R&D activity, and that recipients of
R&D tax credits reach an R&D growth rate ranging from 15.11% to 16.52% higher thantheir non-recipient counterparts, on average.
From an alternative point of R&D activity in terms of current R&D expenditure,various matching methods show that all the coefficients are positive and statisticallysignificant at the 1% level in column (2). It is surprising to see that recipients of R&D tax
credits experience a dramatically higher level of R&D expenditure than their non-recipient
counterparts, ranging from 89.79% to 95.98%. This result suggests the existence of astrong R&D-enhancing effect brought on by the R&D tax credits.
Using the technique of PSM approach, Czarnitzki et al. (2005) find that about 29% offirms that have used R&D tax credits would not have conducted R&D in the absence of
this program in Canada. While the innovation outputs we discuss are different from thosein Czarnitzki et al. (2005), our findings lend evidence that R&D preferential policy really
has induced additional engagement of R&D investment in Taiwan.
Does the R&D-inducement effect differ between high-tech and non-high-tech industries?
Turning to the separated estimates for high-tech firms and non-high-tech firms, Table 5
displays the results obtained using various matching methods. While the estimated resultsoverall suggest a significantly positive impact on R&D growth and R&D expenditure forboth high-tech and non-high-tech firms, the corresponding R&D-inducement effect seems
to vary substantially between them. Various matching methods reach more consistent
results for non-high-tech firms, and recipients of R&D tax credits will have a higher (32%)
R&D growth and a higher (64%) R&D expenditure than non-recipients.
[Insert table 5 approximately here]
Results obtained from various matching methods differ significantly. The R&D
inducement effect obtained by nearest-neighbor matching is dramatically higher than16those obtained by the other two matching methods. Within high-tech firms, thecorresponding inducement effects on R&D growth and R&D investment are about 20%
and 86%, respectively. This suggests that the treatment effect of R&D tax credits on afirm’s R&D growth seems to be stronger for non-high-tech firms than for their high-tech
counterparts. In contrast, the R&D-inducement impact on R&D expenditure seems to behigher for high-tech firms, probably from the fact that high-tech firms usually devote more
outlays on R&D investment and they pay more attention to applying for R&D tax credits.
Drawn from previous analyses, public R&D incentives will not crowd out firms’ R&D
investment in Taiwan, while private R&D expenditure increases are significantly induced
by R&D tax credits. The inducement effect appears to be accelerating, because recipientsof R&D tax credits experience a much higher growth rate on R&D expenditure. Moreover,
we find that the impact of R&D tax credits differs across industries, which is consistent
with findings in Paff (2005).
3.3 Assessing the Matching Quality
As discussed in subsection 3.1, the basic concept of the PSM approach is inconstructing the matched control group in the context of the matching analysis. In thematching process, it relies on the idea of balancing the sample of tax credit participants
and comparable non-participants. Remaining differences in the outcome variable between
two groups are attributed to the treatment (Heckman et al. 1997). This reveals that the
distribution of the pre-treatment observable characteristics between the treatment andcontrol groups is the key factor to determine whether the matching results are reliable. We
therefore assess the balancing of the distribution of covariates used in the propensity
scores estimation within each of the matching exercises. The detailed results for thematching statistics are shown in Tables 6 and 7.
[Insert table 6 and table 7 approximately here]
Table 6 reports the standard t-test for the equality of mean sample values along with its

17

p-value. Drawn from the figures of the t-tests, we fail to reject the null hypothesis that themean is equal between the treatment and control groups for all variables in various
matching methods. This result indicates that the recipients of R&D tax credits and thematched non-recipients are not significantly different from each other concerning the setof variables used for matching, implying that the treated and the matched control groups
have similar characteristics on average.
Table 7 reports the standardized bias, the joint significance tests, and pseudo-R2 within
the matching process. Figures in Panel A are the reduction in the absolute bias obtained
after matching the control and treatment units. Because the bias has been significantly
reduced from 88% to 100%, it suggests that the matching procedure is effective.12 In
addition, the mean absolute bias in the matched sample ranges between 1.6 and 1.9,
whereas it is nearly 50 in the unmatched sample. Based on the suggested criterion
proposed by Sianesi (2004), we check the joint statistical significance of the covariates
and the pseudo-R2 of the propensity score in the estimation procedures for the unmatched
and matched samples. As shown in the second bottom row of Panel B, the pseudo-R2
comes to a value close to zero in the propensity score estimation that uses the recipient
firms and the matched control units. Finally, the results of the LR-test also provide
evidence that the matching has successfully eliminated any systematic observabledifferences between the treated and control groups. In sum, the above statistical tests lend
strong support to the validity of the previous matching results.

5. Further Investigation into the Marginal Effect of R&D Tax Credits
The main advantage of the PSM approach is that it helps to reduce sampling selection
bias arising from observable differences between the treatment and control groups, while
it suffers a serious drawback in that it does not control for the unobservable characteristics
across firms that may exist between recipients and non-recipients of R&D tax credits.
12

For the criteria on the effectiveness of matching process, please refer to Heckman et al. (1997, 1998)18

Fortunately, one advantage of our dataset is that it contains the amount of R&D tax credits,
enabling us to examine the marginal rather than the treatment effect of R&D tax credits on
firms’ R&D activities. Thus, we further include the taxation remit of the R&D tax credits
and employ the technique of GMM for panel data model to examine the marginal effect oftax credits on firms’ R&D expenditure.
5.1 Empirical Specification and Estimation Techniques
To estimate the impact of tax credits on firms’ R&D spending, this study refers toprevious studies that discuss the determinants of R&D and then specifies the following
simple log-linear equation:
ln RDit = β0 + β1 ln RDTAX it + β 2 ln SIZEit + β 3 AGEit + β 4 ln KLit
+ β 5 ln AD1it + β6 PROFIT1it + β7 ln EXPit + β 8 ln FDI it + ε it

(4)

The dependent variable lnRDit denotes the logarithm of R&D spending of firm i in year t.
As for the explanatory variables, theoretical and empirical studies have identified various
determinants. Because this study aims to examine the marginal effect of the R&D tax
credits on firms’ R&D expenditure, the taxation remit of a firm’s R&D tax credits RDTAX
in logarithmic form (lnRDTAX) is the primary variable in equation (4).
The explanatory variables include the following three firm characteristics; namely, firm
size (SIZE), firm age (AGE), and capital intensity (KL). The size of a firm is measured by
the logarithm of employment. Large firms usually have obvious advantages in terms of
their ability to support R&D. The famous Schumpeter hypothesis points out that firms
wielding monopolistic power (usually larger firms) tend to engage in innovation and thishypothesis has been supported by many empirical studies.13 Alternatively, Audretsch andAcs (1991) find that small firms tend to outweigh large firms in terms of innovation
performance when operating in a more technology-intensive environment. As for the
potential impact of the firm’s age on R&D, there is a potential learning-by-doing effect oninnovation and that incumbent firms have an advantage over their younger counterparts in
13

For example, please see Lerner (1995) and Hall and Ziedonis (2001).
19

terms of R&D management. Therefore, a positive relationship between firm age and R&D
is expected. The term lnKL denotes a firm’s capital intensity, measured as the logarithm ofphysical capital per employee. A firm with higher capital intensity usually engages inmore R&D to improve its production process in Taiwan. We therefore observe a positive
association between capital intensity and R&D.
Advertising expenditure (AD) is an important strategy conducted by firms in order to
respond to the competition in markets, and this variable is employed to proxy the
toughness in market competition. The financing of innovation has been widely studied in
the previous literature.14 Thus, a firm’s profitability (PROFIT) is also included to measure
the availability of internal financial resources and is expected to have a positive impact ona firm’s innovation. To avoid the simultaneous causality between a firm’s conduct andperformance, both above variables enter the equation in the form of a lag of one year.
Under the circumstance towards globalization, firms have more opportunities to acquire
technological knowledge through knowledge spillovers in international markets and thenspur their additional R&D activity.15 We therefore include exports (EXP) and outward
foreign direct investment (FDI) as explanatory variables.
5.2 Estimation of the Results
One econometric problem that suffers in the estimation procedure is the endogenous
causality between R&D tax credit and R&D investment, which is also the reason why wepreviously adopted the PSM approach. Therefore, we use the Wu-Hausman test to detect
the existence of the endogenous problem. If no endogeneity is found, then we employ thetechnique of the panel fixed effect model to deal with the unobserved firm heterogeneity.
Correspondingly, when endogeneity is detected, the GMM method provides an alternativetechnique. Using the adequate instrumental variable to deal with the endogenous variable,
this approach provides asymptotically efficient estimators even under a weak assumption
14
15

For the importance of financing on firms’ R&D, please see Hall (2002) for a comprehensive survey.
Please see Yang et al. (2009).
20

on the disturbance and is robust in the presence of heteroscedasticity across firms, whichshows a correlation of disturbances within countries over time.
The testing results indicate that there is an endogenous causality between R&D tax
credits and a firm’s R&D investment for the entire sample and the category of high-techfirms, but this endogenous problem is not significant among non-high-tech firms.16
Therefore, we adopt the technique of the GMM to conduct the empirical estimation for theentire sample and high-tech firms.17 In addition, the technique of the panel fixed effectmodel is employed to estimate the impact of tax credits on R&D expenditure for
non-high-tech firms. Table 8 reports a series of estimates of the R&D equation specified inequation (4). Left and middle columns are estimates obtained from the GMM fixed effectmodel for all firms and high-tech firms, respectively. The right column of Table 8 displays
the results of the fixed effect model for non-high-tech firms.
[Insert Table 8 approximately here]
To what extent does the R&D tax credit stimulate private R&D expenditure? We firstlook at the variable of concern in this study: lnRDTAX. The estimated coefficients for thevariable of R&D tax credits are positive and statistically significant at least at the 5%
statistical level in all estimates, after controlling for other potential influences. The result is
consistent with the previous findings in the PSM estimates that R&D tax credits do foster
firms to increase their R&D investment. The estimated elasticity of R&D with respect to
R&D tax credits is 0.197 for all firms, 0.149 for high-tech firms, and 0.081 for
non-high-tech firms. While the marginal effects of R&D tax credits on R&D expenditure
for high-tech and non-high-tech firms are estimated using different econometrictechniques, the higher coefficient attached with R&D tax credit variable in column (2)16

To save space, the estimating results of Wu-Hausman are not shown in this study, but are available uponrequest from the authors.
17
We adopt a one-year lag variable of R&D tax credits as the instrumental variable for R&D tax credits.
Before discussing the impacts of determinants, we have carefully assessed the effectiveness of the
instrumental variable. Using the F-test developed by Staiger and Stock (1997), the statistic value shows thatthe null hypothesis is rejected, suggesting that the instrumental variable used in this study is effective.
21

suggests that the tax policy is more relevant to high-tech firms’ R&D expenditures. Thisfinding is consistent with that in previous PSM estimates.
The estimated coefficient on lnRDTAX suggests that a one dollar taxation remit of R&D
tax credit induces 0.197 dollars more of R&D expenditure in Taiwan. The marginal effectof R&D tax credits on a firm’s R&D activities is similar to the finding in Lan and Wang
(1992),18 suggesting that the R&D preferential policy of tax credit has induced additional
R&D investment engaged by firms. However, the effect of the R&D tax credits seems to
be more relevant to high-tech rather than non-high-tech firms.
With respect to observed characteristics, the estimates show similar results for both of
the samples of all firms and high-tech firms. The positive impact of firm size on R&D
expenditure supports Schumpeter’s hypothesis that large firms (usually having market
power) are more inclined to have the wherewithal to exploit innovations. The results also
show that capital intensity serves as a crucial factor of innovation propensity. The amountof export (lnEXP) is found to have a significantly positive impact on firms’ R&D, lending
evidence to the importance of international linkage on domestic R&D activity. However,
the results of the fixed effects model for non-high-tech firms are quite poor.
To provide a more precise observation on the question of the importance of the tax
incentive on fostering a firm’s R&D activities, this study further estimates equation (4)using data year-by-year to obtain the elasticity of R&D with respect to R&D tax credits
for individual year. Table 9 summarizes the results.
[Insert table 9 approximately here]
The estimated results for various years are quite similar, suggesting that the impacts ofdeterminants did not change substantially during the 2001-2005 period. More importantly,
the estimated elasticity are 0.184 in 2001, 0.346 in 2002, 0.304 in 2003, 0.324 in 2004,
and 0.361 in 2005. These results suggest that one dollar of taxation remit for investment in18

Lan and Wang (1992) investigate the impact of R&D tax incentives on R&D expenditure by using 124
Taiwanese manufacturing industries. Their results suggest that the elasticity of R&D with respect to R&D tax
credits is 0.166 on average.
22

R&D leads the sample firms to increase their spending on R&D by 0.184 dollars 2001 andthis gradually increases to 0.361 dollars in 2005. According to the R&D data contained inour sample firms that are listed on TSE, it implies that the induced R&D expenditure
brought on by R&D tax credits ranged from NT$9.399 million in 2001 to NT$30.495
million in 2005.
While the induced R&D expenditure has been calculated based on the estimatedelasticity, it does not give a clear picture: “To what extent is firms’ R&D investment
induced by tax credits”? We therefore calculate the average share of R&D spending which
was induced by R&D tax credits, in order to lend an insightful implication. From the pie
charts in Figure 2 we clearly observe that the corresponding shares to R&D spending
range from 5.30% to 13.91%. This result indicates a considerable role played by tax
incentive policies on private R&D in Taiwan.
[Insert Figure 2 approximately here]
To summarize, the above analyses give strong evidence for the positive impact of R&D
tax credits on private R&D. Because the way of calculating R&D elasticity is different
from that in Hall and Van Reenen (2000) and Bloom et al. (2003), we cannot compare theimpact of a tax incentive on the level of R&D expenditure in Taiwan with that in OECD
countries directly. Even though the econometric estimates show a considerable
R&D-inducement effect brought on by the tax incentives in Taiwan, it is an encouraging
result from the government’s point of view having implemented policy tools, mainly the
Statute for Upgrading Industries (SUI), to encourage and promote private R&D.

6. Concluding Remarks and Policy Implications
Although the effectiveness of fiscal incentives for private R&D has attracted
widespread interest in many developed countries, it has received much less attention in the
newly industrialized economies (NIEs) and developing countries. Taiwan is one of the23most successful NIEs regarding innovative performance in the world over the past two
decades, especially in the technological field of electronics. One possible reason is that the
government has long-standing tax incentives adopted to foster private R&D. Economists
have traditionally been skeptical over the efficacy of any fiscal provisions. Is the R&D tax
credit really an effective mechanism to stimulate firms to invest more R&D? This issue isparticularly important and topical to Taiwan, because the tax credit policy attached in SUI
will expire at the end of 2009. The government must decide in the near future whether toextend the tax credit policy or make a change in part, if not completely.
This paper aims to evaluate the impact of tax credits for R&D activity in Taiwan.
Different from previous studies focusing on estimating the tax price elasticity of R&D
based on the information of R&D cost, we first adopt the propensity score matching tosimulate the scenario of how the treatment of R&D tax credits affects firms’ R&D activity.
The PSM approach helps to correct the selection bias that is not well dealt with inprevious studies. In the second step, to control for the unobservable firm heterogeneity
between treated and control groups which may affect R&D activity, this study employs
the technique of the GMM for a panel data model to estimate the elasticity of R&D withrespect to tax credits.
Using a panel dataset of 621 enterprises listed on the Taiwan Stock Exchange over theperiod 2001-2005, the empirical findings are summarized as follows. First, the PSM
estimates show that R&D tax credits induce a higher (14.47%) growth rate of R&D
expenditure and a higher (93.53%) R&D expenditure on average. It suggests that there isno crowding-out effect of public R&D support on private R&D, whereas there is a strong
impact of R&D tax credits on private R&D. Second, the R&D-inducement effect of tax
credit is found to differ between high-tech and non-high-tech firms. Specifically,
non-high-tech recipients of R&D tax credits seem to have a higher growth rate of R&D
expenditure than recipients among high-tech firms. On the other hand, high-tech recipients

24

experienced a high R&D expenditure level compared to non-recipients with similar
characteristics. Third, estimates obtained by the technique of the GMM for the panel data
model show that the elasticity of R&D with respect to taxation remit is 0.197 on average,
and it varies substantially over the years. Specifically, the induced R&D expenditure
brought on by tax credits accounts for 5.30% to 13.91% of sample firms’ R&D in various
years. This suggests the considerable impact of tax credit on private R&D in Taiwan.
Drawn from above analyses, several policy implications are inspired. First, as the
R&D-inducement effect of R&D tax credits is found to be significantly positive in all
estimates, it suggests that the R&D preferential policy is indeed an effective policy tool tofoster private R&D activity, supporting previous aggregate evidence. Second, as theeffectiveness of R&D tax credits has been supported in this study, this policy tool can becontinuously implemented after SUI expires. Third, since the impact of R&D tax credits isdifferent between high-tech and non-high-tech firms, the government should think of how
to adjust the reduction rate of taxation for R&D investment across industries.

25

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29

Figure 1 R&D Expenditure and R&D Tax Credits, 1992-2005

billions

billions
R&D

R&D Tax Credits

300

18

260.851250

280.9816.31816240.8214224.428200

190.52

197.631204.97412

11.499176.45510
156.321

150

8.973

137.955100

94.82887.853

125.031114.682

6.694

103.617

6
4.142

4.09

3.871

4

50
2.418
1.519
0.822

2.425

2

1.632

1.193

0

0
1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Data Source: Indicators of Science and Technology, Taiwan, various issues.
Yearbook of Tax Statistics, Republic of China, various issues.

30

Figure 2 Share of Induced R&D by Tax Credits to Total R&D, 2001-2005
5.30%c10.43%

2001

2002

10.11%

13.91%

2003

2004

11.56%

2005
Notes: a.
represents R&D spending which was induced by R&D tax credits.
b.
represents R&D spending for sample firms.
c. The share of average R&D spending induced by R&D tax credits on the average
R&D spending.

31

Table 1 Variable Definitions and Basic Statistics 2001-2005
Variables
Definition
All Firms
High-Tech Firms
G_RD
Growth rate of R&D expenditure (%)
9.94
24.58
(171.292)(151.176)
RD
R&D expenditure (NT$ million)
214.537408.269(781.716)(1112.215)
RDTAX
R&D tax credits (NT$ million)
73.220133.182(525.203)(725.679)
D_RDTAX
Dummy variable
0.368
0.656
A firm with R&D tax credits =1(0.482)(0.475)SIZE
Firm Size: number of employees
976.3531169.513(1972.7)(2470.633)
AGE
Firm Age: surveyed year minus the starting year
24.74017.664(12.567)(9.052)KL
Capital intensity: the ratio of fixed capital to employee
5.392
2.736
(NT$ million per capital)(10.861)(4.920)AD
Advertising expenditure (NT$ million)
41.43038.684(232.834)(278.969)
PROFIT
Ratio of profit to sales (%)
10.7352.138(352.028)(521.935)
FDI
Value of outward FDI (NT$ million)
220.010303.809(1079.022)(1391.737)
EXP
Export value (NT$ million)
6178.962
10949.64(24489.65)(34792.41)
OBS
Sample size
3031
1377
Note: The means and standard errors are calculated by pooling data for the 2001-2005 period.

32

Non-High-Tech Firms-2.09(185.39)53.250(181.950)
12.503(121.577)
0.128(0.334)815.542(1412.519)
33.081(10.906)
7.602(13.613)43.716(185.951)
17.892(16.654)150.475(716.425)
2211.891(7590.328)
1654

Table 2

Firm Details for Recipients and Non-recipients of R&D Tax Credits,
2001-2005
All firms

High-tech firms

Non-high-tech firms

User

Non-user

User

Non-user

User

Non-user424.359

92.260493.435

245.500

129.80741.99521.77%

3.09%

23.63%

26.40%

13.94%-4.45%

1298.989

788.3321307.777905.264

1261.519

749.976

19.90428.523

16.69819.51233.52832.981

2.826

6.887

2.582

3.030

3.864

8.152

Number of

1116

1915

904

473

212

1442

observations(36.82%)(63.18%)(65.65%)R&D expenditure(NT$ million)
Growth of R&D
Number of
employees
Age of firm
Capital Intensity(NT$ million)33(34.35%) (12.82%)(87.18%)

Table 3

D_RDSTAlnSIZE

AGE

lnKL

ADVR1

PROFIT1

EXPR

CON

Pseudo R2
Log likelihood

Propensity to R&D Tax Credit Recipients－Logit Model
All Firms

High-Tech Firms

Non-High-Tech Firms

3.636***

3.391***

3.401***(0.460)(0.603)(0.717)0.305***

0.146**

0.377***(0.0466)(0.061)(0.088)-0.046***-0.029***-0.005(0.005)(0.008)(0.009)-0.544***-0.154-0.342(0.115)(0.160)(0.217)-1.344***-2.244**-0.642(0.391)(1.024)(0.466)0.021***

0.025***

0.008(0.004)(0.005)(0.008)0.007***

0.010***-0.006*(0.002)(0.002)(0.003)-3.482***-3.599***-5.587***(0.618)(0.802)(1.132)

0.211

0.117

0.111

-1277.043-706.299-468.960

Notes: (1) Figures in parentheses are standard deviations. (2) ***, **, and * represent
statistical significance at the 1%, 5%, and 10% levels, respectively. (3) The dummy
variable D_RDSTA, which equals unity for firms, exhibits positive R&D
expenditures during the past four years.

34

Table 4

Treatment Effect of R&D Tax Credits for All Firms
R&D Growth

RD

(1)

(2)

Kernel0.165*

0.948***

bwidth=0.06(1.88)(4.46)

Nearestneighbor
Caliper
caliper=0.01

0.118

0.960***(1.05)(4.49)

0.151*

0.898***(1.67)(4.10)

Notes: (1) Figures in parentheses are t-values. (2) ***, **, and * represent statisticalsignificance at the 1%, 5%, and 10% levels, respectively. (3) The propensity scorefunction includes D_RDSTA, lnSIZE, AGE, lnKL, ADVR1, PROFIT1, and EXPR

35

Table 5 Treatment Effect of R&D Tax Credits for High-tech and Non-high-tech Firms
High-Tech Firms

Non-High-Tech Firms

R&D Growth

RD

RD GrowthRD

Kernel0.126

0.769***

0.305***

0.687***

bwidth=0.06(1.31)(2.95)(2.18)(2.41)

Nearestneighbor
Caliper
caliper=0.01

0.341***

1.042***

0.346

0.575

(3.72)(4.51)(1.55)(1.50)

0.145*

0.775***

0.310***

0.660***(1.74)(3.35)(2.14)(2.24)

Notes: (1) Figures in parentheses are t-values. (2) ***, **, and * represent statisticalsignificance at the 1%, 5%, and 10% levels, respectively. (3) The propensity scorefunction includes D_RDSTA, lnSIZE, AGE, lnKL, ADVR1, PROFIT1, and EXPR

36

Table 6 Matching Covariates Balancing Property: Summary Statistics and Test Statistics for All Firms
Caliper
KernelNearest NeighborMeant-testMean
t-testMean
t-testVariables
SampleTreated Controltp>|t|Treated Controltp>|t|Treated Control
tp>|t|D_RDTAX Unmatched
0.995
0.721
19.56
0.000
0.995
0.721
19.56
0.000
0.995
0.721
19.56
0.000
lnSIZE
AGE
lnKL
ADVR1
PROFIT1
EXPR

Matched

0.995

0.992

0.83

0.406

0.995

0.995

0.00

1.000

0.995

0.994

0.24

0.812

Unmatched

6.325

5.943

8.35

0.000

6.325

5.943

8.35

0.000

6.325

5.943

8.35

0.000

Matched

6.344

6.327

0.36

0.718

6.344

6.318

0.55

0.584

6.346

6.3359

0.20

0.838

Unmatched19.90428.523

-18.2

0.000

19.90428.523-18.220.000

19.90428.523-18.220.000

Matched20.82921.067

-0.51

0.611

20.82920.792

0.08

0.938

20.84420.994

-0.32

0.750

Unmatched

3.120

3.351

-11.6

0.000

3.120

3.351

-11.60

0.000

3.120

3.3513-11.600.000

Matched

3.127

3.112

0.80

0.424

3.127

3.098

1.53

0.126

3.127

3.108

1.01

0.314

Unmatched

0.039

0.077

-5.8

0.000

0.039

0.077

-5.86

0.000

0.039

0.077

-5.86

0.000

Matched

0.041

0.045

-0.88

0.379

0.041

0.043

-0.53

0.599

0.041

0.045

-0.86

0.392

Unmatched

21.998

8.210

1.09

0.275

21.998

8.210

1.09

0.275

21.998

8.210

1.09

0.275

Matched22.03821.992

0.07

0.944

22.03821.984

0.08

0.932

21.96621.985

-0.03

0.976

Unmatched62.63134.788

21.42

0.000

62.63134.788

21.42

0.000

62.63134.788

21.42

0.000

Matched62.59463.598

-0.72

0.469

62.59461.895

0.50

0.617

62.58463.353

-0.55

0.581

Notes: The p-value of the t-test represents the equality of means in the treated and control groups.

37

Table 7

Matching Covariates Balancing Property: The Standardized Bias and the
Joint Significance Tests for All Firms
Panel A
KernelCaliper

NearestNeighbor

Variables

Sample% bias

D_RDTAX

Unmatched

85.5

Matched

0.9

Unmatched

31.

Matched1.5

Unmatched-73.4

Matched-2.0

Unmatched-45.1

Matched3.0

Unmatched-23.4

Matched-2.9

Unmatched

4.7

Matched

0.0

Unmatched

83.5

Matched-3.0

lnSIZE
AGE
lnKL
ADVR1
PROFIT1
EXPR|bias|% bias|bias|

85.5
98.9

0.0
2.2

100.0

0.3

93.1

5.8

99.6

-1.7

87.2

0.0

92.7

2.1

-1.3

98.3

3.8

91.5

-2.8

88.0

4.7
99.6

83.5
96.4

97.4

-23.4

4.7
99.7

0.8

-45.1

-23.4
87.6

99.7

-73.4

-45.1
93.3

0.2
31.7

-73.4
97.2

|bias|

85.5

31.7
95.4

% bias

-0.0

99.9

83.5
97.5

-2.3

97.2

Panel B
KernelCaliper

NearestNeighbor|bias| summary statistics:

BEFOREAFTERBEFOREAFTERBEFORE

AFTER

Mean

49.6091.908

49.6091.726

49.6091.616

Std. Dev.

31.7381.172

31.7382.026

31.738

1.419

Maximum

85.4643.034

85.4645.775

85.464

3.841

Minimum

4.659

0015

4.659

0

4.659

0006

2

Pseudo R

0.210

0.001

0.210

0.001

0.210

0.001

LR test p-value

0.000

0.857

0.000

0.866

0.000

0.940

Notes: % bias is the standardized bias as suggested by Rosenbaum and Rubin (1985)
reported together with the achieved percentage reduction in |bias|.

38

Table 8 R&D Effects of R&D Tax Credits

lnRDTAXlnSIZE
AGE
lnKL
lnAD1
PROFIT1
lnEXP
lnFDI
Constant# of obs.

(1)

(2)

(3)

All Firms

High-Tech Firms

Non-High-Tech Firms

Panel Fixed GMM

Panel Fixed GMM

Fixed Effects

0.197**

0.151**

0.081***(0.085)(0.068)(0.024)0.397*

0.426*

0.485*(0.214)(0.244)(0.275)0.035

0.054*

0.023(0.031)(0.031)(0.041)0.441*

0.646***

0.270(0.241)(0.247)(0.416)-0.0090.011-0.044**(0.011)(0.011)(0.023)-0.005-0.002-0.007(0.005)(0.005)(0.010)0.054**

0.122***

0.040(0.024)(0.031)(0.036)-0.001-0.0050.006(0.007)(0.007)(0.013)2.983

2.546*

2.426(1.912)(1.480)(3.191)

3031

1377

1654

Notes: Figures in parentheses are standard deviations. ***, **, and * represent
statistical significance at the 1%, 5%, and 10% levels, respectively.

39

Table 9

R&D-Inducement Effects of R&D Tax Credits in Various Years
2001

2002

2003

2004

lnRDTAX

2005

0.184***
0.346***
0.304***
0.324***
0.361***(0.051)(0.060)(0.052)(0.048)(0.046)lnSIZE
0.783***
0.732***
0.557***
0.671***
0.560***(0.183)(0.179)(0.162)(0.153)(0.162)AGE-0.067***-0.026-0.028-0.027-0.031**(0.016)(0.017)(0.016)(0.015)(0.015)lnKL-0.565-0.731**-0.707**-0.677**-0.383(0.370)(0.359)(0.342)(0.318)(0.334)
lnAD1
0.036
0.034
0.080***
0.048**
0.073***(0.035)(0.031)(0.030)(0.027)(0.028)PROFIT1
0.058***
0.030***
0.031***
0.033***
0.35E-03*(0.012)(0.011)(0.012)(0.011)(0.196E-03)
lnEXP
0.339***
0.257***
0.326***
0.303***
0.281***(0.050)(0.048)(0.046)(0.042)(0.045)
lnFDI
0.012
0.009
0.035
0.031
0.017
(0.030)(0.027)(0.028)(0.028)(0.032)
Constant
1.136
1.943
1.758
1.250
1.873
(1.512)(1.512)(1.443)(1.319)(1.312)
2
R
0.379
0.373
0.392
0.428
0.405
# of obs.
603
606
608
606
608
Notes: Figures in parentheses are standard deviations. ***, **, and * represent
statistical significance at the 1%, 5%, and 10% levels, respectively.

40