The effect of internal control quality, governance structure and ownership concentration

on R&D intensity: A China Study

Abstract

Purpose – The purpose of this study is to examine the effect of internal control quality, strength of corporate governance structure and ownership concentration on R&D intensity in Chinese listed companies belonging to the small and medium-size enterprises (SEMs) and the Growth Enterprise Market (GEM).

Design/methodology/approach – This study employs a sample comprising the Chinese publicly-listed small and medium-size enterprises (SEMs) and the Growth Enterprise Market firms (GEMFs) over the 2008–2011 period.

Findings – Based on a sample of 1,276 firm-year observations over the 2008–2011 period we find that the firms’ quality of internal control is significantly and positively associated with R&D intensity. Additionally, we find that executive characteristics such as directors’ education and monetary compensation are both significantly and positively associated with R&D intensity. Firm type such as GEM listed firms and state owned enterprises (SOEs) are also significantly and positively associated with R&D intensity while ownership concentration of firms is negatively associated with R&D intensity. Our results are robust to alternative measures of internal control quality that use one and second lagged periods, two stage least Square Regression, and difference-in-difference test.

Originality/value – We extend prior research by providing evidence that the quality of internal control has a positive impact on Chinese firms’ R&D investments. This is important given the influence of the internal control regulatory framework such as Basic Standard of Enterprises Internal Control issued by the Chinese government in 2008 on Chinese listed companies. This study provides evidence of the economic consequences of the implementation of internal control standards in China in 2008.

Keywords Internal control, R&D intensity, China, qualifications, monetary compensation, SEMs, GEM listed firms, SOEs.

Research type Research paper

1. Introduction

Research and development (R&D) investment is critical in that it can impact a

firm’s ability to compete with its peers (Ji et al. 2016). Further, empirical research has

suggested that a firm’s corporate governance structure can affect its R&D intensity

(e.g. Honore et al 2015; Kor 2006; James and McGuire 2016; Dong and Gou 2010).

The purpose of this study is to examine the effect of internal control quality on R&D

intensity of Chinese listed firms belonging to the small and medium-size enterprise

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(SEMs) and Growth Enterprise Market (GEM) sectors. As part of our empirical

analysis, we investigate whether executive characteristics, firm type, and ownership

concentration have an effect on firms’ R&D intensity. The empirical results of this

study therefore extend the literature on the effect of internal control and board and

firm-level characteristics on firms’ R&D intensity.

We are motivated to undertake this study for several reasons. First, there is limited

empirical research investigating the determinants of R&D investment of Chinese

firms (e.g. Sun and Wen 2007; Dong and Gou 2010; Lin et al 2011). Particularly, there

is little research in studying internal control and R&D intensity issues in Chinese

publicly-listed small and medium-size enterprises (SEMs) and the Growth Enterprise

Market (GEM) firms. Second, empirical studies have found that CEO compensation is

associated with R&D intensity in U.S. firms (e.g., Deutsch 2007; Hermann et al 2010)

and European firms (e.g., Honore et al 2015), but there is limited research that has

examined this issue in the Chinese context. Third, Barker and Mueller (2002) suggest

that a CEO’s formal education had no significant association with R&D expenditure.

Other studies (see e.g. Lin et al. 2011) find that CEO education level is positively

associated with firm’s level of innovation while Kouaib and Jarboui (2016) find that

CEO education is significantly positively associated with reducing R&D expenditure.

Inconsistent results in prior studies relating to the determinants of R&D expenditure

suggest that the further work in this area is required. In the Chinese context, prior

studies (e.g., Dai and Cheng 2015; Boeing et al 2016; Peng and Ke 2016) find that

industry type in the manufacturing sector is associated with R&D investment. This

study will extend prior studies in the Chinese context on R&D investment by

examining the association of firm type viz-a-viz small and medium-size enterprises

(SEMs) and Growth Enterprise Market (GEM) firms on firms’ level of R&D

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expenditure.

Based on a sample of 1,276 firm-year observations of Chinese listed firms over the

2008–2011 period, we find that firms’ quality of internal control is significantly and

positively associated with R&D intensity. We also observe that various director

characteristics and firm-level factors impact firms’ R&D intensity. Specifically, we

find that directors’ level of education and monetary compensation are both

significantly and positively associated with R&D intensity. Firm type is also a

significant determinant of firms’ R&D intensity. Specifically, GEM listed firms and

state owned enterprises (SOEs) are significantly and positively associated with R&D

intensity however equity concentration of firms is negatively associated with R&D

intensity.

This study makes the following contributions. First, we extend prior research by

providing evidence that the quality of internal control has a positive impact on

Chinese firms’ R&D investments. This is important given the influence of the internal

control regulatory framework such as Basic Standard of Enterprises Internal Control

issued by the Chinese government in 2008 on Chinese listed companies. Specifically,

quality of internal control can significantly impact the economic and competitive

position of firms. This study provides evidence of the economic consequences of the

implementation of internal control standards in China in 2008. Second, we find that

education, monetary compensation of board members and firm type and ownership

concentration significantly impact firms’ R&D intensity. This study adds to the

literature on these issues in a Chinese business context. This is particularly important

given the high level of state ownership of firms in China, the strong political influence

on audit and internal control structure, and also the strategies of board members and

the evolving corporate governance structure in China (Ji et al. 2016). Understanding

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how different institutional contexts drive firm-level R&D investment is important

given the link between innovation and economic growth (James and McGuire 2016).

This paper proceeds as follows: Section 2 provides a brief review of internal

control system in Chinese listed firms; Section 3 develops our hypotheses; Section 4

discusses the research design; Section 5 summarizes and analyzes the empirical

results; and Section 6 concludes the paper.

2. Internal control framework for listed firms in China

The internal control regulatory framework in China is regulated by the Basic

Standard of Enterprises Internal Control (BSEIC) and was jointly issued by the

Ministry of Finance (MOF), the China Security Regulatory Commission (CSRC), the

National Audit Office (NAO), China Banking Regulation Commission, and the China

Insurance Regulation Commission (CIRC) in 2008. Internal control is defined as “a

process in which an entity's board of directors, management and other personnel

strives to achieve the objectives of internal control” (BSEIC, Article 3). The

objectives of this process “are to ensure, to a reasonable extent, the legality of

operations, the security of assets and the authenticity and integrity of financial

reporting of an enterprise, improving the business efficiency and effects and helping

the enterprise realize its development strategy” (BSEIC, Article 3). A firm is required

to set up an effective internal control framework based on the following five elements:

internal environment, risk assessment, control activities, information and

communication, and internal supervision in accordance with Article 5 of this standard.

In 2010, three enterprise internal control guidelines: Guidelines for Application of

Enterprise Internal Controls, Guidelines for Evaluation of Enterprise Internal

Controls, and Guidelines for Auditing of Enterprise Internal Controls were issued by

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the Chinese government for assisting firms to establish an effective internal control

system. The Guidelines for Application of Enterprise Internal Controls applies to

companies listed inside and outside China from January 1, 2011, companies listed on

the main board of the Shanghai and Shenzhen Stock Exchanges from January 1, 2012,

and companies listed on the SME board and the ChiNext board. All listed companies

and the relevant non-listed large- and medium-sized enterprises required to make

effective preparations for the implementation of those guidelines according to No. 11

[2010] of the MOF’s announcement. 

The Guidelines for Application of Enterprise Internal Controls (GAEIC) has eight

different application guidelines for different aspects of an enterprise’ internal control

system such as (1) Organizational structure, (2) Development strategies, (3) Human

resources, (4) Social responsibilities, (5) Enterprise culture, (6) Fund activities, (7)

Procurement business, and (8) Asset management. Some of application guidelines

require the enterprises to conduct R&D activities. For instant, the enterprises are

required to accelerate the high-tech development and transformation of traditional

industries (No. 4, Article 14) and shall focus on the risks such as lack of core

technology in intangible assets, unclear ownership, backward technology, and

existence of major technology safety risks, which may lead to legal disputes or lack of

sustainable development ability of the enterprise (GAEIC, No. 8, Article 3). 

3. Hypotheses development

3.1 Internal Control Quality and R&D Intensity

Effective internal control leads to high-quality financial reporting by reducing

uncertainty in the preparation of financial reports and in ensuring that transactions and

financial arrangements are effectively captured in those reports. Ineffective internal

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control, on the other hand, can potentially lead to an increase in business risk, a

reduction in contracting efficiency and increased agency risk (Ji et al. 2016). Effective

internal control drives accounting quality, suppresses market frictions in the form of

information asymmetry and agency related problems and improves firms’ governance

structure around the use of expenditure on research and development. Higher quality

internal accounting control systems form the basis for high-quality financial reporting

since effective internal controls may prevent both procedural and estimation errors, as

well as opportunistic earnings management. Weak internal control systems may

potentially lead to sub-optimal investments and allows management to extract rents

from stakeholders arising from heightened information asymmetry, which in turn may

lead to capital rationing and reduced expenditure on discretionary R&D. The reason

for this is that firms that have weaker internal control systems face higher costs in

acquiring external funds, and are less likely to have the discretionary expenditure to

undertake R&D. Persistent information asymmetry between managers and

stakeholders could potentially initiate ill-informed and inefficient investment

decisions that lead to lower-than-optimal profits, market values and, hence, lower

access to external funding. This in turn will motivate firm management to retain funds

rather than investing in R&D. A lack of transparency arising from poor internal

control quality may also make it difficult for capital suppliers or for a firm’s auditors

to monitor firms’ use of its funds.

Overall, it is not unreasonable to expect that internal control quality may

positively affect funding opportunities for the firm which in turn may positively

affects a firm’s level of R&D expenditure. We thus develop the following (directional)

hypotheses:

H1: Internal control quality is positively associated with firms’ level of R&D

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intensity.

3.2 Corporate Governance Structure and R&D

Dong and Gou (2009) provide evidence that strength of governance structure

including the characteristics of the board of directors and top management play an

important role in firms’ R&D investment decisions. They show that the proportion of

independent directors on the board is positively associated with R&D intensity. We

also assess the relation between various corporate governance attributes pertaining to

the CEO and chairman and firms’ level of R&D expenditure. These governance

attributes are: duality in roles between the general manager and chairman of the

board, and the CEO’s level of education, compensation, age and gender. Prior research

shows that effective corporate governance can serve to monitor firms investment

decisions (Ferreira & Matos 2008; Jensen 1986) and can also reduce external

financing costs (Anderson et al. 2004; Chang et al. 2009). Similarly, the governance

attributes of the chairman and CEO may serve to monitor firms capital, and level of

R&D expenditure.

CEOs with higher education levels are more likely to use their knowledge and

skills to create investment opportunities for the firm via increased R&D. Higher

education levels of the CEO would facilitate effective information gathering and use

regarding the firm’s strategy for innovation and how R&D could drive growth and

allow the firm to compete against its peers. Higher levels of education may assist the

CEO in making prudent decisions regarding innovation in the face of earnings

fluctuations. The overall monitoring and governance around investments in R&D are

likely to be assisted through CEOs education level which would assist in ensuring that

competitors are not privy to proprietary information relating to firms’ innovations.

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Dong and Guo (2009) find that incentive compensation and the age of

management influence their propensity to invest in R&D. The reason for this is that

young managers and well compensated mangers tend to take more risks than old

managers and mangers less well remunerated (Barker and Mueller 1992). A similar

rationale is applicable for male a opposed to female managers or directors.

Overall, it is not unreasonable to expect that CEO and chairman characteristics

may affect how discretionary funds are used by the firm which will be manifested in

firms’ level of R&D expenditure. We thus develop the following non-directional

hypothesis:

H2: Strength of governance structure is positively associated with firms’ R&D

intensity.

3.3 Firm type and R&D

We assess the relation between firm type and its R&D intensity. Dong and Guo

(2009) assert that firms with a high level of state ownership face less competition

compared to their peers, are more risk adverse and thus less incentivized to invest in

R&D. Specifically, we assess boards of firms belonging to the small and medium-size

enterprises (SEMs) and the Growth Enterprise Market (GEM) sectors, and whether

boards of state-owned enterprises are more likely to invest in R&D. These sectors are

important economically and they are heavily reliant on R&D to compete in the rapidly

growing Chinese market. Many of these firms could be considered to be in the

introduction or growth phases of life cycle development and as such, it is timely to

assess how their R&D activities relate to internal control quality, governance structure

and shareholder concentration which are also under development.

In China, the Growth Enterprise Market (GEM) Board is set up for those small-

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and-medium firms, growth enterprises, and high technology companies to raise their

business resources. Prior studies suggest that firm size is matter for understanding the

innovation management behaviour (e.g. Dong and Gue 2009; Philips and Zhdanov

2012; Anwar and Sun 2015; Hong et al. 2016; Yu 2017). Some scholars argue that

large firms have higher investment in R&D compared to small firms as the large firms

have more resources and expertise including networks to draw upon (e.g., Rubera and

Kirca 2012) and less resource constraints and more autonomy in decision-making

(Hong et al. 2017). The empirical results suggest that this is not always the case. For

example, Dong and Gue (2009) find small firms are more innovative and have more

R&D investment than larger firms in China. Further, Anwar and Sun (2015) find that

the impact of foreign direct investment in R&D in China’s manufacturing industries is

greater in GEM sector firms. Yu (2017) find that R&D investment has a stronger

impact on GEM sector firms than on larger non-GEM firms in terms of its impact on

stock returns. In the U.S., Philips and Zhdanov (2012) find that small firms are

motivated to innovate through R&D more so than large firms as the large firms can

derive innovation through takeovers or mergers. Iturriaga et al (2016) find a negative

correlation between firm size and R&D investment using a sample of 19 developed

countries with five most R&D-intensive industries,

In addition, Dong and Guo (2009) assert that firms with a high level of state

ownership face less competition compared to their peers, are more risk adverse and

thus less incentivized to invest in R&D. Boeing (2016) find both minority state-owned

firms and privately-owned firms received more R&D grants compared to majority

state-owned firms. Based on the aforementioned evidence, it is likely that firms

belonging to the GEM sector in China are more likely to invest in R&D. Conversely,

state-owned owned firms are less likely to invest in R&D based on their reliance on,

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and access to resources through government connections. We thus develop the

following (directional) hypotheses:

H3a: Firms belonging to the GEM sector invest more in R&D compared to non-GEM

sector firms.

H3b: State owned firms invest less in R&D compared to non-state owned firms.

3.4 Ownership concentration and R&D

Prior studies find firms’ ownership concentration is associated with R&D

investment) but no conclusive evidence was found (Iturriaga et al. 2016). Lee and

O’Neill (2003) find Japanese firms have a more concentration ownership than U.S.

firms’ as their majority shareholders are banks, insurance companies and suppliers.

Those Japanese firms are found to have more R&D investment than their U.S.

counterparty. Using a sample of 19 developed countries with five most R&D-

intensive industries, Iturriaga et al (2016) find that corporate ownership

concentration is positively associated with R&D investment. In contrast, Kastl et

al. (2013) find lager firms with more decentralised tend to have more R&D

investment in the Italian manufacturing industry. Using an Italian sample, Bragoli

et al. (2016) find no significant difference between public limited firms and private

limited firms, which are characterised by a more concentration ownership in terms

of their investment on R&D. In China, Dong and Guo (2009) find that as

managerial ownership of shares increases, firm R&D intensity initially decreases,

and then increases consistent with an inverted parabolic curve. We also assess the

relation between ownership concentration of firms’ shares and their propensity to

invest in R&D. Institutional investors are considered risk-averse and less likely to

encourage firm management to invest in R&D (Dong and Guo 2009 Due to the

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consistent evidence and the results from the China study (i.e., Dong and Guo

2009), the following hypothesis is proposed.

H4: Firms with more dispersed ownership concentration invest less in R&D.

4. Research design

4.1. Sample selection and data source

Our initial sample comprised the Chinese publicly-listed small and medium-size

enterprises (SEMs) and the Growth Enterprise Market (GEM) firms over the 2008–2011

period. The sample was reduced to 1,276 firm-year observations after excluding firms

with missing data. Table 1 provides the sample observations and distribution based on

industry and year. CEO attributes were hand collected from firms’ annual reports.

Insert Table 1 here

4.2 Dependent variable

Our dependent variable is R&D intensity (RDIINC) which includes expenditures of

investment on new technology, new products, and other research and development

projects. The R&D investment expenditures were hand collected from firms’ annual

reports. We compute R&D intensity (RDIINC) as R&D expenditure scaled over total

revenue.

4.3 Independent variables

Our main independent variable is internal control quality (INCONTROL).

INCONTROL is measured using an internal control index provided by the Shenzhen

Dibo Internal Control Database. The Dibo internal control index is calculated based

on performance of each firm on the following five internal control factors: internal

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control strategies, operation efficiency, reporting quality, legal compliance, and asset

safety. The higher the index value, the higher the internal control quality.

We measure strength of corporate governance structure by assessing whether

the role of the board chairman and managing director are performed by different

persons. Other governance variables include CEO education and CEO monetary

compensation, CEO age and CEO gender. Each of these variables was obtained from

Shenzhen GTA Company-developed China Stock Market and Accounting Research

(CSMAR) database. Shareholder concentration is measured as the sum of the square

of the top 3 shareholders of the company's share proportion. The definition of each of

the independent variables is provided in Table 2.

Insert Table 2 here

4.4 Control variables

Our analysis includes several control variables that have been identified as key

determinants of R&D intensity in prior research (see Dong and Gou 2010). These

control variables are provided in Table 2.

4.5 Regression model

Our OLS regression model, which examines the association between our

independent variables and R&D intensity (RDIINC) is estimated as follows:

RDIINC=a0+a1 INCONTROL+a2UNIFY +a3 EDU+a4 COMPENSA+a5 CEOAGE+a6CEOGEND+a7 MARKETTYPE+a8 LISTAGE+a9 INDEPENCEI+a10 STATEDU+a11OWNOER+a12 ROA+a13 ASSET +a14 LEV +a15TURNOVER+a16CASHI +a17 ENVIRN+a18 INDUS+a19YEAR+ε

4. Empirical results

5.1. Descriptive statistics

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Table 3 reports the descriptive statistics for the dependent variable (RDIINC),

independent variable (INCONTROL, UNIFY, EDU, COMPENSA, CEOAGE,

CEOGEN, and MARKETTYPE, STATEDU and OWNOER) and control variables

(LISTAGE, INDEPENCEI, ROA, ASSET, LEV, TURNOVER, CAHI, ENVIRN,

INDUS and YEAR). The dependent variable RDIINC has a mean (standard deviation)

of 0.042 (0.414). INCONTROL has a mean (standard deviation) of 681.49 (60.03).

The mean, standard deviation, median and range of other independent and control

variables are also presented in Table 3. Finally, there is a reasonable level of

consistency between the means and medians, reflecting normality of distributions.

Insert Table 3 here

5.2. Correlation results

The Pearson pairwise correlation results are reported in Table 4. We find

significant (p < .05) positive correlations (with predicted signs) between RDIINC and

each of our four measures of internal control. Significant and positive correlations are

found between RDIINC and UNIFY, EDU and COMPENSA (p < .01). A significant

and negative correlation exists between RDIINC and CEOAGE. A significant and

positive correlation exists between RDIINC and MARKETTYPE while RDIINC is

significantly and negatively correlated with STATEDU and OWNOER. We also find

significant (p < .05) correlations (with predicted signs) between RDIINC and several

of our control variables LISTAGE, ROA, ASSET and LEV. Table 4 also shows that

only moderate levels of collinearity exist between the independent variables. Finally,

we compute variance inflation factors (VIFs) when estimating our base regression

model to test for signs of multi-collinearity between the independent variables. We

find that no VIFs exceed five, so multi-collinearity is not problematic in our study

(Hair et al., 2006).

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Insert Table 4 here

5.3. Regression results

5.3.1 OLS regression – internal control

The OLS regression results for the association between internal control and the

R&D intensity are presented in Table 5 (with coefficient estimates, t-statistics and p-

values provided).① The coefficients for industry sector and year effects are not

reported for the sake of brevity.

Table 5 shows that the regression coefficient for INCONTROL is positively and

significantly associated with the R&D intensity (RDIINC) at p < 0.05 or better. Our

results therefore provide strong support for H1, showing that strength in internal

control leads to greater investment in R&D for Chinese listed firms. In terms of

economic significance we find that, on average, a one-standard deviation increase in

strength in internal control results in an increase in the R&D intensity for our sample

firms of around 0.20% or 20 basis points.

We find that strength of corporate governance attributes encompassing board

chairman duality, level of education, compensation, age and duality are variably

significantly positively associated with RDIINC. In particular, we find that level of

education, and compensation are both positive and significantly associated with

RDIINC. H2 is thus partially supported by our results. We also find support for H3a

(and 3b) as firms belonging to the GEM sector (or state-owned firms) are positive and

significantly associated with RDIINC. With respect to ownership concentration, we

find that firms with a more concentrated equity ownership, as reflected by the

ownership of the top three shareholders, invest less in R&D. A significant and

① We note that t-statistics are based on the Huber/White/Sandwich estimator of standard errors (e.g. Wooldridge 2010). We also winsorized (reset) all continuous variables at the 1st and 99th percentiles to mitigate the effect of outliers significantly influencing our empirical results. Finally, p-values are reported based on one-tailed tests for directional hypotheses and two-tailed tests otherwise.

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negative association exists between OWNOER and RDIINC. Thus H4 is supported by

the results.

We also observe that some of the regression coefficients for the control variables

are significantly associated with R&D intensity in several of our regression models. In

particular, the regression coefficients for ASSET, LEV and TURNOVER) are

statistically significant (p < 0.10 or better with predicted signs).

Insert Table 5 here

5.3.2. Tobit regression – internal control

We also employ a tobit regression model to test the association between

INCONTROL and R&D intensity. We employ a tobit model in the event there is either

right or left-censoring in the dependent variable that may exist if all of the variables

above or below a particular threshold are set at that threshold but may be higher or

lower. We see that our regression model holds when employing a tobit model. Table 6

shows that the regression coefficient for INCONTROL is positively and significantly

associated with the R&D intensity (RDIINC) at p < 0.05 or better. Again H1 is

supported by these results.

Insert Table 6 here

5.3.3. OLS regression – other variables

OLS regressions are then performed to test the association between internal control

and the R&D intensity for subgroups where the CEO and general manager perform

the same role (UNIFY=1) and where they do not perform the same role (UNIFY=0).

Results are presented in Table 7. Table 7 shows that for the subgroup where there is

duality of roles between the CEO and general manager, the regression coefficient for

INCONTROL is positively and significantly associated with the R&D intensity

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(RDIINC) at p < 0.05. These results provide further support for H2, showing that

strength in internal control leads to greater investment in R&D for firms characterized

by separation of duties between the CEO and managing director. According to agency

theory advocates, the combined function can significantly impair the board’s most

important functions of monitoring, disciplining and compensating senior managers

and could significantly impair board oversight, governance and disclosure policies

(Kang et. al. 2007). Duality also enables the CEO to engage in opportunistic

behaviour that may exacerbate information asymmetry between management and

shareholders and the firm and bondholders (Fama and Jenson, 1983; Gul and Leung,

2004). Firms with separation of duties between the CEO and managing director are

likely to have the necessary monitoring and oversight in place that can assist in

achieving efficiency in investments relating to R&D.

We also observe that some of the regression coefficients for the control variables

are significantly associated with R&D intensity. In particular, the regression

coefficients for EDU, COMPENSA, MARKETTYPE, STATEDU, OWNOER,

ASSET, LEV, and TURNOVER) are statistically significant (p < 0.10 or better with

predicted signs). These results provide further support for H3 and H4.

5.3.4. Tobit regression – other variables

We also employ a tobit regression model to test the association between

INCONTROL and R&D intensity for each of the subgroups with and without

separation of separation of duties between the CEO and managing director. Results

are shown in Table 8. Similar results are obtained with that depicted in Table 8.

Insert Table 8 here

5.4 Robustness checks and additional analysis

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5.4.1 Lagged independent variable

We then assess the association between internal control strength and R&D investment

using a lagged (one period or two period) measure of INCONTROL. Our results are

robust providing support for H1.

Insert Table 9 here

5.4.2 Two stage least Square Regression

It is possible that our baseline regression results reported in Table 6 could be

impacted by endogeneity (i.e. simultaneity and/or reverse causality) leading to biased

or inconsistent regression coefficient estimates (Woolbridge 2002). Findings on OLS

regressions of explanatory variables on the ICOE may vary leading to difficulties in

interpretation in the presence of endogeneity. To negate the potential effect of

endogeneous relations between the variables included in our models, we perform

instrumental variable (2SLS) analysis. It may be possible that the variability in the

R&D intensity is not due to strength of internal control but rather determined by other

firm specific unobservable factors or omitted variables (Larcker and Rusticus 2007;

2010). As part of our robustness checks, we perform instrumental variables (2SLS)

regression analysis (e.g. Larcker and Rusticus 2010; Wooldridge 2010).

The results of the 2SLS regression analysis are presented in Table 10. In particular,

the results of the 2SLS regression model show that that the regression coefficients for

INCONTROL is positively and significantly associated with RDIINC (p < 0.05),

which provides further support for H1.

Insert Table 10 here

5.4.2 Difference-in-difference test

As a final robustness check of the potential issue of endogeneity affecting our

baseline regression results (see Table 5), we conduct a difference-in-difference (DID)

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analysis (e.g. Gippel et al. 2015). In particular, we empirically analyze whether

changes in regulations in China concerning internal controls of firms in 2010 either

magnified or moderated the effect of INCONTROL on RDIINC.

In analyzing the effect of the regulatory change in 2010 on the association between

strength of internal control and firms’ investment in R&D, we therefore expand our

regression model to include a GFC dummy variable (coded as 1 for the 2010 and

subsequent years, and 0 otherwise) and its respective interaction term with

INCONTROL (i.e. INCONTROL*YEAR). We are particularly interested in the

regression coefficient of the INCONTROL*YEAR interaction term as it captures the

DID of our outcome variable (i.e. RDIINC) between the treatment and control firms

before and after implementation of the regulatory change. The aim of the DID is to

study the impact of an exogenous event (i.e. the regulation) on a treatment group

compared to a control group (Gippel et al. 2015). The significance and sign of the

regression coefficient of the INCONTROL*YEAR interaction term will then allow us

to determine whether the joint effect of the regulatory change and INCONTROL has

altered a firm’s R&D investment following the regulatory change.

The regression results for the DID analysis are reported in Table 11. The regression

coefficient for INCONTROL is positively and significantly associated with RDIINC

thus providing more support for H1. Overall, these findings provide further evidence

of a causal negative linkage between strength of internal control and R&D intensity.

Insert Table 11

6.0 Conclusions

We examine the effect of internal control quality, governance characteristics, firm

type and ownership concentration on R&D intensity of Chinese listed firms belonging

to the small and medium-size enterprise (SEMs) and Growth Enterprise Market

18

(GEM) sectors. As part of our empirical analysis, we investigate whether CEO

educational level and monetary compensation and the nature of firms belonging to the

small and medium-size enterprise (SEMs), Growth Enterprise Market (GEM) firms

and state owned enterprise (SOEs) sectors have an effect on firms’ R&D intensity. We

also assess whether the top three shareholder concentration significantly impacts firms

propensity to engage in R&D. The empirical results of this study therefore extend the

literature on the effect of internal control and board and firm-level characteristics on

firms’ R&D intensity.

Based on a sample of 1,276 firm-year observations over the 2008–2011 period,

we find that the firms’ quality of internal control is significantly and positively

associated with R&D intensity. Additionally, we find that directors’ education and

monetary compensation are both significantly and positively associated with R&D

intensity. GEM listed firms and state owned enterprises (SOEs) are also significantly

and positively associated with R&D intensity while ownership concentration of firms

is negatively associated with R&D intensity. Our results are robust to alternative

measures of internal control quality that use one and second lagged periods.

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21

Table 1: Sample distribution based on industry and year

Industry Type 2008 2009 2010 2011 Total %Utilities 4 6 10 16 36 2.8Integrated industry 19 25 45 94 183 14.3Industrial industry 124 169 242 466 1001 78.5Commercial industry 6 11 16 23 56 4.4Total 153 211 313 599 1276 100

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Table 2: Variable Definitions

Variable type Variable name Variable definition and value descriptiondependent variable RDIINC R&D intensity， Ratio of R & D investment to business

revenue，independent variable

INCONTROL internal control quality ， China listed companies internal control index of DIB

INCONTROLL1 internal control quality of one delayed periodINCONTROLL2 internal control quality of second delayed period

Governance characteristics of board

UNIFY Dummy variable, if the general manager is concurrently chairman of the board ，UNIFY =1, otherwise UNIFY =0

EDU CEO education level, EDU =1 when CEO's educational level is technical secondary school, EDU =2 when CEO's educational level is college, EDU =3 when CEO's educational level is undergraduate, EDU =4 when CEO's educational level is master's degree, EDU =5 when CEO's educational level is doctor.

COMPENSA CEO monetary compensation, the natural logarithm of the monetary compensation for the CEO in the year

CEOAGE The age of CEO, the actual number of years of birth to the year of observation of the CEO

CEOGEND Dummy variable, If CEO's gender is male，CEOGEND =1, otherwise CEOGEND =0

Company characteristics

MARKETTYPE Dummy variable, if the enterprise is the GEM listed companies，MARKETTYPE =1, otherwise MARKETTYPE =0

LISTAGE The number of years the company has been listed, of the company from the listing year to the observation year

INDEPENCEI The proportion of independent directors is equal to the ratio of the number of independent directors to the total number of directors.

STATEDU Actual controller nature, if the actual controller of listed companies is the government agencies, STATEDU =1, otherwise STATEDU =0

OWNOER Equity concentration, the sum of the of the square of the top 3 shareholders of the company's share proportion

Leverage total liabilities scaled by total assetsROA Return on assets, net profit / total assetsASSET The natural logarithm of total assets at the end of this periodLEV leverage ratio, total liabilities / total assetsTURNOVER turnover ratio，Ratio of operating income to total assetsCASHI Ratio of net cash flow from operating activities to total assets

Regional environment, industry and annual control variables

ENVIRN Overall business environment index score， data sources：Wang Xiaolu, Fang Gang, Li Feiyue, China sub provincial enterprises operating environment index 2011 report, Beijing: CITIC publishing house, January 2012.

INDUS Industry control variables, the first category of industry classification standards of China Securities Regulatory Commission, The sample is involved in utilities, real estate, integrated, industrial, commercial industry

YEAR Year Dummy variable， including in 2008, 2009, 2010 and 2011

Instrumental variables

INCONTROLMEAN

Take the mean value of the internal control quality variables according to the enterprise's annual, industry and local area

23

Table 3: Descriptive statistics

variable mean Stan. Dev. Min. p25 p50 p75 Max.

RDIINC 0.0422 0.0414 0.0000 0.0184 0.0334 0.0503 0.2574

INCONTROL681.490

960.0360

479.3691

664.8600

692.3500

712.5900

840.8999

UNIFY 0.3456 0.4758 0.0000 0.0000 0.0000 1.0000 1.0000EDU 3.3919 0.8918 1.0000 3.0000 4.0000 4.0000 5.0000COMPENSA 12.8318 0.7678 10.3062 12.3884 12.8485 13.3166 14.7320CEOAGE 47.0329 6.3017 32.0000 43.0000 47.0000 50.0000 66.0000CEOGEND 0.9303 0.2548 0.0000 1.0000 1.0000 1.0000 1.0000MARKETTYPE 0.1403 0.3474 0.0000 0.0000 0.0000 0.0000 1.0000

LISTAGE 3.5024 1.6395 2.0000 2.0000 3.0000 5.0000 8.0000INDEPENCEI 0.3666 0.0474 0.3333 0.3333 0.3333 0.4000 0.5560

STATEDU 0.1983 0.3989 0.0000 0.0000 0.0000 0.0000 1.0000OWNOER 0.1738 0.1083 0.0177 0.0920 0.1513 0.2361 0.5219ROA 0.0630 0.0462 -0.0649 0.0336 0.0583 0.0867 0.2172ASSET 21.0597 0.7232 19.6950 20.5411 20.9869 21.4863 23.4187LEV 0.3311 0.1863 0.0283 0.1772 0.3097 0.4743 0.7623TURNOVER 0.6696 0.3862 0.1537 0.4193 0.5835 0.8084 2.4257CASHI 0.0439 0.0762 -0.1627 0.0009 0.0426 0.0908 0.2397ENVIRN 3.1120 0.1173 2.7600 3.0500 3.0900 3.1900 3.3350

24

Table 4: Pearson Correlation Matrix

　variable RDIINC INCONTROL INCONTROLL1

INCONTROLL2

INCONTROLMEAN

UNIFY EDU COMPENSA CEOAGE CEOGEND MARKETTYPE

RDIINC 1.0000 　 　 　 　 　 　 　 　 　 　INCONTROL 0.0826*** 1.0000 　 　 　 　 　 　 　 　 　INCONTROLL1 0.1036*** 0.4087*** 1.0000 　 　 　 　 　 　 　 　INCONTROLL2 0.1152** 0.3045*** 0.4218*** 1.0000 　 　 　 　 　 　 　INCONTROLMEAN 0.0972*** 0.7110*** 0.3253*** 0.2266*** 1.0000 　 　 　 　 　 　UNIFY 0.1003*** 0.0498* 0.0652* 0.0252 0.0322 1.0000 　 　 　 　 　EDU 0.1985*** 0.0066 -0.0220 -0.0006 0.0020 0.0355 1.0000 　 　 　 　COMPENSA 0.0823*** 0.2546*** 0.2867*** 0.2410*** 0.1731*** 0.1109*** 0.0587** 1.0000 　 　 　CEOAGE -0.0497* 0.0004 0.0462 0.0368 -0.0329 0.1859*** -

0.1809*** 0.0728*** 1.0000 　 　CEOGEND -0.0449 0.0148 0.0586 0.0889* -0.0241 0.1149*** 0.0099 0.0188 -0.0152 1.0000 　MARKETTYPE 0.2378*** 0.0043 0.0001 0.0000 0.0338 0.1003*** 0.0427 -0.0229 -0.0293 0.0220 1.0000

LISTAGE -0.1247*** 0.1134*** 0.2340*** 0.3541*** 0.1498*** -

0.1142*** 0.0289 0.0612** 0.0167 0.0314 -0.3235***

INDEPENCEI 0.0276 0.0278 0.0181 0.0265 0.0218 0.1316*** 0.0066 -0.0245 0.0148 -0.0674** 0.0021

STATEDU -0.0634* -0.0645** -0.0475 -0.0134 -0.1180*** -0.2374*** 0.0857*** 0.0168 0.0567** 0.0744**

* -0.1330***

OWNOER -0.1407*** 0.0599** 0.0167 -0.0345 -0.0514* 0.0150 -0.0148 0.0001 0.0105 -0.0420 -0.0806***

ROA 0.1646*** 0.4642*** 0.3545*** 0.3056*** 0.2889*** 0.0600** 0.0692** 0.3355*** -0.0144 -0.0172 0.0059

ASSET -0.1937*** 0.2910*** 0.3354*** 0.2965*** 0.1926*** -0.0706** -0.0158 0.2624*** 0.0117 0.0387 -0.1606***

LEV -0.3906***

-0.1452*** -0.1586*** -0.1753*** -0.1143*** -

0.1682*** -0.0333 -0.1230*** -0.0220 0.0542* -0.3575***

TURNOVER -0.3540*** 0.0092 -0.0150 -0.0269 -0.0145 -

0.1031*** -0.0310 0.0700** -0.0147 0.0509* -0.2673***

CASHI -0.0012 0.1122*** 0.0012 0.1024** 0.0336 -0.0703** 0.0119 0.1251*** 0.0337 -0.0313 -0.1584***ENVIRN 0.0066 -0.0237 -0.0092 -0.0090 -0.0320 0.0940*** -0.0561** 0.1286*** - -0.0495* -0.0511*

25

0.0691**

***, **, *indicates 1%、5% and 10% significance level.

Table 4: Pearson Correlation Matrix (cont.)

　variable LISTAGE INDEPENCEI STATEDU OWNOER ROA ASSET LEV TURNOVE

R CASHI ENVIRN

LISTAGE 1.0000 　 　 　 　 　 　 　 　 　INDEPENCEI -0.0065 1.0000 　 　 　 　 　 　 　 　STATEDU 0.1102*** -0.1113*** 1.0000 　 　 　 　 　 　 　OWNOER -0.1861*** 0.0848*** 0.1687*** 1.0000 　 　 　 　 　 　ROA 0.0396 -0.0518** -0.0770*** 0.0894*** 1.0000 　 　 　 　 　ASSET 0.2069*** -0.0145 0.1274*** 0.1584*** 0.0429 1.0000 　 　 　 　LEV 0.3125 *** 0.0078 0.1832*** 0.0257 -0.4138*** 0.3829*** 1.0000 　 　 　TURNOVER 0.1636*** -0.0085 0.0521* 0.1769*** 0.0399 0.1210*** 0.3431*** 1.0000 　 　CASHI 0.1657 *** -0.0546* 0.1096*** 0.0903*** 0.4430*** -0.0272 -0.1124*** 0.1146*** 1.0000 　ENVIRN 0.0110 -0.0490* -0.2337*** -0.0152 0.0584** -0.0775*** 0.0039 0.1384*** 0.0696*** 1.0000

***, **, *indicates 1%、5% and 10% significance level.

26

Table 5: Internal control quality and R&D intensity (OLS)

variableRDIINC

Coef. t P>t_cons 0.0452047 0.96 0.337INCONTROL 0.0000348* 1.70 0.089UNIFY 0.0018051 0.79 0.428EDU 0.0071512*** 6.23 0.000COMPENSA 0.0030871** 2.10 0.036CEOAGE -0.0001357 -0.82 0.410CEOGEND -0.0058261 -1.47 0.141MARKETTYPE 0.0082872** 2.47 0.014LISTAGE 0.0000334 0.05 0.963INDEPENCEI 0.0296503 1.39 0.164STATEDU 0.0058245** 2.05 0.040OWNOER -0.0376895*** -3.75 0.000ROA 0.0372932 1.19 0.233ASSET -0.0048796*** -2.76 0.006LEV -0.0465144*** -5.75 0.000TURNOVER -0.0217047*** -7.34 0.000CASHI -0.0142147 -0.90 0.368ENVIRN 0.0172085* 1.85 0.064INDUS ControlYEAR ControlF value (Prob > F) 23.20(0.0000)Adj R-squared 0.2860Obs 1276

***, **, * represent 1%、5%, and 10% significance levels

27

Table 6: Internal control quality and R&D intensity (Tobit)

variableRDIINC

Coef. t P>t_cons 0.0399740 0.85 0.398INCONTROL 0.0000350* 1.70 0.089UNIFY 0.0018253 0.80 0.425EDU 0.0070268*** 6.10 0.000COMPENSA 0.0030850** 2.08 0.037CEOAGE -0.0001306 -0.79 0.430CEOGEND -0.0063855 -1.61 0.109MARKETTYPE 0.0086377*** 2.56 0.010LISTAGE 0.0001267 0.17 0.863INDEPENCEI 0.0251620 1.17 0.241STATEDU 0.0055168* 1.93 0.054OWNOER -0.0394049*** -3.89 0.000ROA 0.0457756 1.46 0.145ASSET -0.0049908*** -2.81 0.005LEV -0.0462157*** -5.68 0.000TURNOVER -0.0227027*** -7.57 0.000CASHI -0.0166886 -1.05 0.294ENVIRN 0.0202977** 2.17 0.030INDUS ControlYEAR ControlLog likelihood 2393.3415LR chi2 459.22Prob > chi2 0.0000Obs 1276

***, **, * represent 1%、5%, and 10% significance levels

28

Table 7: The effect of COE duality (OLS)

　　variableRDIINC RDIINC　UNIFY=0 　UNIFY=1

Coef. t P>t Coef. t P>t_cons 0.0189422 0.36 0.723 0.0766139 0.76 0.447INCONTROL 0.0000535** 2.22 0.027 -0.0000067 -0.17 0.864EDU 0.0067441*** 4.91 0.000 0.0072018*** 3.43 0.001COMPENSA 0.0035268** 2.08 0.038 0.001395 0.47 0.639CEOAGE 0.0002315 1.14 0.256 -0.0006425** -2.23 0.026CEOGEND -0.0050653 -1.20 0.231 -0.0070577 -0.64 0.523MARKETTYPE 0.0093134** 2.14 0.032 0.0061013 1.13 0.260LISTAGE 0.0002219 0.26 0.795 -0.000403 -0.28 0.777INDEPENCEI 0.0488432* 1.73 0.084 -0.0010918 -0.03 0.975STATEDU 0.0065428** 2.15 0.032 0.0027981 0.36 0.718OWNOER -0.0320743*** -2.73 0.006 -0.0528039** -2.52 0.012ROA 0.0103502 0.27 0.785 0.0781537 1.36 0.176ASSET -0.0065991*** -3.23 0.001 0.0005707 0.16 0.872LEV -0.0469845*** -4.85 0.000 -0.0479633*** -3.20 0.001TURNOVER -0.0172449*** -5.04 0.000 -0.0313613*** -5.28 0.000CASHI -0.0311633* -1.70 0.089 0.0261116 0.84 0.402ENVIRN 0.0264754** 2.45 0.015 -0.002816 -0.15 0.881INDUS 　Control 　ControlYEAR 　Control 　ControlF value (Prob > F) 15.33(0.0000) 9.64(0.0000)

Adj R-squared 0.2743 0.3018Obs 835 441

***, **, * represent 1%、5%, and 10% significance levels

Table 8: The effect of COE duality (OLS)

29

variableRDIINC RDIINC　UNIFY=0 　UNIFY=1

Coef. t P>t Coef. t P>t_cons 0.0140133 0.26 0.793 0.0734257 0.74 0.458INCONTROL 0.0000544** 2.25 0.025 -0.0000054 -0.14 0.887EDU 0.0065584*** 4.77 0.000 0.0072103*** 3.49 0.001COMPENSA 0.0035315** 2.07 0.039 0.0013218 0.45 0.651CEOAGE 0.0002492 1.22 0.223 -0.0006542** -2.31 0.021CEOGEND -0.0060219 -1.42 0.156 -0.0060524 -0.56 0.579MARKETTYPE 0.0097410** 2.24 0.025 0.0062227 1.17 0.242

LISTAGE 0.0003593 0.42 0.676 -0.0003622 -0.26 0.795INDEPENCEI 0.0405509 1.42 0.156 -0.0014011 -0.04 0.967STATEDU 0.0066106** 2.16 0.031 0.0016429 0.22 0.830OWNOER -0.0342242*** -2.90 0.004 -0.0527257** -2.57 0.011ROA 0.0217721 0.57 0.567 0.0758723 1.34 0.180ASSET -0.0068093*** -3.32 0.001 0.0005573 0.16 0.873

LEV -0.0465441*** -4.79 0.000 -0.0482186*** -3.28 0.001

TURNOVER -0.0182504*** -5.25 0.000 -0.0316890*** -5.43 0.000

CASHI -0.0366094** -1.99 0.047 0.0313335 1.02 0.307ENVIRN 0.0305301*** 2.80 0.005 -0.0017868 -0.10 0.923INDUS 　Control 　ControlYEAR 　Control 　ControlLog likelihood 1581.2245 829.8968LR chi2 298.5 178.84Prob > chi2 0.0000 0.0000Obs 835 441

***, **, * represent 1%、5%, and 10% significance levels

30

Table 9: The effect of internal control delayed periods 1 and 2 (OLS)

variable RDIINC RDIINCCoef. t P>t Coef. t P>t

_cons 0.1030719** 1.98 0.048 0.0864673 1.40 0.164INCONTROLL1 0.0000363* 1.81 0.071 　 　 　INCONTROLL2 　 　 　 0.0000527** 2.27 0.024UNIFY 0.0040935 1.56 0.120 0.0020571 0.65 0.515EDU 0.0058564*** 4.61 0.000 0.0054306*** 3.58 0.000COMPENSA 0.0013237 0.84 0.400 0.0023650 1.29 0.198CEOAGE -0.0001533 -0.83 0.406 -0.0002724 -1.23 0.220CEOGEND -0.0052597 -1.17 0.242 -0.0003190 -0.06 0.952MARKETTYPE 0.0074443 1.29 0.198 　-- 　-- 　--LISTAGE -0.0009373 -1.04 0.299 -0.001891* -1.66 0.099INDEPENCEI 0.0246329 1.04 0.300 0.0345807 1.22 0.222STATEDU 0.0047411 1.57 0.116 0.0046023 1.29 0.198OWNOER -0.0259790** -2.22 0.027 -0.0122958 -0.87 0.387ROA 0.0757581** 2.35 0.019 0.0219353 0.59 0.557

ASSET -0.0059514*** -3.11 0.002 -0.0051589** -2.39 0.017

LEV -0.0392087*** -4.34 0.000 -

0.0419781*** -3.94 0.000

TURNOVER -0.0180770*** -5.85 0.000 -

0.0170107*** -4.88 0.000

CASHI -0.0241520 -1.39 0.166 -0.0230194 -1.09 0.275ENVIRN 0.0123182 1.18 0.239 0.0094350 0.75 0.453INDUS 　Control 　ControlYEAR 　Control 　ControlF value (Prob > F) 15.29(0.0000) 9.53(0.0000)

Adj R-squared 0.3099 0.2954Obs 701 408

***, **, * represent 1%、5%, and 10% significance levels

31

Table 10: The effect of control variables (2SLS)

variable RDIINCCoef. t P>t

_cons 0.0401939 0.85 0.395INCONTROL 0.0000693** 2.11 0.035UNIFY 0.0017548 0.77 0.441EDU 0.0072378*** 6.29 0.000COMPENSA 0.0030522** 2.07 0.038CEOAGE -0.0001304 -0.79 0.429CEOGEND -0.0060054 -1.51 0.130MARKETTYPE 0.0083890** 2.50 0.013LISTAGE -0.0000242 -0.03 0.974INDEPENCEI 0.0278513 1.30 0.193STATEDU 0.0059721** 2.10 0.036OWNOER -0.0377311*** -3.75 0.000ROA 0.0168257 0.48 0.629ASSET -0.0055756*** -3.02 0.003LEV -0.0460134*** -5.67 0.000TURNOVER -0.0216535*** -7.31 0.000CASHI -0.0121537 -0.77 0.444ENVIRN 0.0171653* 1.84 0.065INDUS 　ControlYEAR 　ControlF value (Prob > F) 23.22(0.0000)Adj R-squared 0.2843Obs 1276

***, **, * represent 1%、5%, and 10% significance levels

32

Table 11 Results of Difference in Difference test (OLS)

　variable RDIINC RDIINC　UNIFY=0 　UNIFY=1Coef. t P>t Coef. t P>t

_cons 0.0447322 0.84 0.403 0.0881627 0.89 0.374INCONTROLDUMMY 0.0060782 1.30 0.193 0.0083071 1.05 0.293

YEARDUMMY -0.0041601 -1.09 0.278 0.0039069 0.60 0.546INCONTRLO*YEAR 0.0014529 0.27 0.785 -0.0061166 -0.71 0.479EDU 0.0065145*** 4.76 0.000 0.0071849*** 3.43 0.001COMPENSA 0.0035547** 2.10 0.036 0.0014100 0.48 0.634CEOAGE 0.0002054 1.01 0.313 -0.0006175** -2.15 0.032CEOGEND -0.0047387 -1.12 0.261 -0.0075617 -0.69 0.493MARKETTYPE 0.0098218** 2.28 0.023 0.0063059 1.17 0.244LISTAGE 0.0003777 0.44 0.657 -0.0003395 -0.24 0.811INDEPENCEI 0.0487977* 1.73 0.084 -0.0011872 -0.03 0.972STATEDU 0.0063840** 2.11 0.036 0.0022928 0.30 0.766OWNOER -0.0321552*** -2.73 0.006 -0.0523333** -2.51 0.012ROA 0.0167164 0.47 0.641 0.0567554 1.04 0.298ASSET -0.0062380*** -3.17 0.002 -0.0001111 -0.03 0.975LEV -0.0493582*** -5.23 0.000 -0.0477677*** -3.28 0.001TURNOVER -0.0170535*** -4.99 0.000 -0.0317048*** -5.40 0.000CASHI -0.0367494** -2.04 0.042 0.0265042 0.86 0.392ENVIRN 0.0259733** 2.41 0.016 -0.0032094 -0.17 0.864INDUS Control ControlF value (Prob > F) 16.19(0.0000) 10.16(0.0000)Adj R-squared 0.2767 0.3043Obs 835 441

33