DETERMINANTS OF STOCK MARKET REACTION TOWARD LEGAL
REQUIREMENT OF CSR IN INDONESIA
*Gatot Soepriyanto
Accounting Department Bina Nusantara (BINUS) University
Email: gsoepriyanto@binus.edu
**Rudy Suryanto
Accounting Department - Muhammadiyah University Yogyakarta (UMY)
Email:rudysuryanto@yahoo.com
Abstract
Prior study on stock market reaction toward mandatory Corporate Social Responsibility
(CSR) implementation law in Indonesia (article 74, Law No. 40/2007 on Limited
Liability Company) present evidence that the equity investors from firms whose deal with
or related to the management of natural resources, reacted positively to the passage of the
law. This suggests that investors view mandatory CSR implementation law as “a good
news” which in the long run may increase firm values. This study, therefore, aims to
investigate the economic determinants that drive positive market reactions, as we found
that the magnitude of the reactions were vary among companies taken as sample. We
address five hypotheses that investor reaction is explained by: (a) size of firms, (b)
profitability of firms, (c) leverage of firms, (d) how long the firm has been established
and (e) whether the firms in are engaged in mining or non-mining industry. These
hypotheses are investigated through cross-sectional regression analysis on firms that
directly affected by the CSR law. We conclude that the stock market reaction toward the
law is determined by size, leverage, age of firms and whether firms are engaged in
mining industry or not. It also concludes that investors react more (less) positive to small
(big) firms and high (low) leverage firms, suggesting that the investor consider the law as
“insurance” for company to sustain their operation.
Keywords: CSR Mandatory Law, Stock Market Reaction, Economic Determinants
1. Introduction
After a heated debate on whether mandatory corporate social responsibility (CSR)
implementation would benefit or harm the company, the House Representative (DPR)
finally approved the bill on July 20, 2007 – which require a company whose activities
deal with or related to the management of natural resources to carry out a CSR program.
This mandatory rule is enacted under article 74 of Law no. 1, 2007 on Limited Liability
Company (PT). Based on such research setting, Soepriyanto and Suryanto (2008)
examine stock price reactions to the passage of the bill – specifically to the share price of
firms that directly affected by the law (i.e. seven sub industries whose activities deal with
or related to the management of natural resources) and find some evidence that on
average, equity investors react positively to the approval of mandatory CSR
implementation law. Their result consistent with prior studies which suggested that CSR
will help investor to minimize exposed risk of the companies (Heugens, 2007, Chih. et.al,
2007, Sarre, 2001) and maintain companies’ reputation (Rowe, 2007). In summary, their
result supports the view that CSR program will contribute positively to the firm value.
Their study, however, posit another interesting question – what factors determined
the positive market reaction? Further examination into the magnitude of abnormal return
and three-day cumulative abnormal returns around the event date of their sample
companies also demonstrate a wide variation. We speculate that market did not take for
granted the information related to CSR, but they will relate the information to the
economic characteristic of each company such as size, profitability and leverage. This
research, therefore, aims to investigate the economic determinants that drive the market
reactions toward mandatory CSR implementation law
In general, we find that market consider company’s size, leverage, age and
industry in determining their response to legal requirement of CSR. Company’s age is
proved to be a significant determinant factor of market reactions, as well as company’s
size, leverage and type of industry. We find no significant association of profitability to
the market reactions, suggesting that the consequence of the legal requirement of CSR is
not related to company’s profitability.
This study is significant for a number of reasons. First, to seek the pattern of
investor’s reaction toward CSR practice and disclosure, since prior studies provide mix of
positive and negative association of CSR disclosure and share price performance.
Second, our study will be useful for regulator and policymaker to answer the question
whether mandatory CSR implementation law is beneficial or not beneficial to investors.
As such, our result lends a support to the view that mandatory requirement of certain
companies to carry out CSR will give investor “insurance” from any adverse effect of
environment and social issues.
The reminder of this paper is organised as follows; section 2 presents a brief
review on what prior studies have done, section 3 provides hypothesis development,
section 4 develop research methodology and explain sample data, section 5 discusses the
result and section 6 conclusion and limitation of this study.
2. Literature Review
The demand for companies to act in socially and environmentally manner had
started to emerge since 1970’s (Hines, 1991: 41). The demand now is getting stronger, as
society witnessed the damage caused by big corporations to its environment in term of
pollution, natural resource destruction, dangerous wastes, products safety and limitation
of workers’ right (Gray et.al, 1995). Many view that the irresponsible manners of the big
corporations above were partly caused by a narrow focus of the companies to maximize
its shareholder value, and neglecting the other stakeholders’ interest.
The narrow focus of companies is supported by neo classic economist’s
perspective that stated manager’s main objective is to make decisions in maximising
shareholders value, and let the government and other parties to take care of community
(Friedman, 1970). This argument then challenged by Freeman (2004) with his
“stakeholder theory” which states that management can only maximise shareholders
value by maintaining a good relationship with other stakeholders (Freeman, 2004). The
effort to balance the role of company to pursue profit and to contribute to its environment
is known as Corporate Social Responsibility (CSR).
In 1970, AICPA thought that there is a need for companies Social Accounting to
incorporate CSR in company annual report (Hacston & Milne, 1996). However till date,
there is no a generally accepted framework for such accounting. Though, some
international bodies had formulated the guidelines for companies to perform and report
CSR, such as UN Compact released by the United Nations, the Global Reporting
Initiative released by Sustainability Accounting International (SAI) but there is no
binding rule for the guidelines. Therefore, CSR programs and reporting remain voluntary.
Interestingly more companies now adopt CSR programs and report them in their
annual report (Stigson, 2002). Companies willing to invest in CSR and report them in
annual report, since they believe CSR programs are inline with their business objectives
(Stigson, 2002) and investors valued CSR programs (Bird, 2007, Mackey, et. al, 2007).
Indonesia had moved forward by enacted Law no 40/2007 in which mandated for
certain companies to budget and allocate some money in CSR programs. By this law,
Indonesia might be the first country to regulate CSR practice so it’s become mandatory.
Wilmhurst (2000) stated that government might intervene to the business process through
regulation, whenever community safeties are in danger. Indonesian public recently had
outraged by a number of pollution and major natural destruction done by companies, such
as the case of Inti Indo Rayon in North Sumatera, Newmont in Minahasa and Lapindo
Brantas in Sidoarjo. By examining news related to the passage of the mandatory CSR
implementation law, Soepriyanto and Suryanto (2008) find that in general investor
response positively to the regulation suggesting that investor views the CSR law as “good
news”.
Study on firm characteristic that may affect firms CSR program is categorized
into 2 streams. First stream is evaluating what factors determine the amount and nature of
CSR disclosures and second streams is revealing what CSR impact to company
performance, usually in term of profitability or market price (Kusumawati, 2007).
For the first stream studies, in general, researchers found that there is a low
commitment of Indonesian companies to perform CSR. Henny and Murtanto (2001)
found that CSR disclosures in Indonesia were relatively low, only 42.32% of total items
in the CSR disclosure checklist. That finding is far below the level of CSR disclosure in
developed countries revealed by Guthrie and Parker (1990). Guthrie and Parker (1990)
found level of CSR disclosure in UK (98%), US (85%) and Australia (56%). Although,
there is a big gap between CSR practice and disclosure in Indonesia and in developed
countries, there is a consistent finding of factors determined the amount and nature of
CSR disclosure in various countries as summarized below.
TABLE 1:
Summary of Empirical Evidence on Factors Determined the Amount and
Nature of CSR Disclosure
No
Authors, Year
Variable
Association
Result
1
Belkoui and Karpik (1989)
2
Roberts (1992)
3
4
Patten 1991
Gray et al (1995)
Positive
Negative
Positive
Negative
Positive
Positive
Positive
Positive
Positive
Positive
Significant
Significant
Significant
Significant
Not Significant
Significant
Not Significant
Not Significant
Significant
Significant
5
Hacston and Milne (1996)
Positive
Positive
Significant
Significant
Significant
6
Henny and Murtanto (2001)
Positive
Positive
Not Significant
Significant
7
Marwata (2001)
8
Zubaidah and Zulfikar (2005)
9
Sembiring (2005)
10
Anggraeni (2006)
Size
Level of Leverage
Profitability
Leverage
Size
Age
Profitability
Profitability
Size
Type of Industry (high vs
low profile)
Country of origin
Size
Type of Industry (high vs
low profile)
Profitability
Type of Industry (high vs
Low Profile
Size
Structure of ownership
Age of listing
Size
Profitability
Auditor Reputation
Size
Type of Industry
Profitability
Leverage
Management equity
Type of Industry
Size
Leverage
Profitabilty
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Positive
Negative
Positive
Positive
Positive
Positive
Positive
Significant
Not Significant
Not Significant
Significant
Significant
Significant
Significant
Significant
Not Significant
Not Significant
Significant
Significant
Not significant
Not Significant
Not Significant
From Table 1 above, it shows that there is strong association between size and type of
industry with CSR practice and disclosures, and there is a weak association between
profitability, leverage, structure of ownership with CSR practice and disclosures.
In contrast, there are variations in the result of second stream studies of CSR
impact to company’s performance as summarized in Table 2 below:
TABLE 2:
Summary of Empirical Evidence on CSR Impact to Company’s Performance
No
1
2
3
4
5
6
Authors, Year
Measurement
Result
Belkoui (1976) in Ahmed et.
al (2000)
Shane and Spicer (1983)
Luthfi (2001)
Purwanti (2001)
Rasmiati (2001)
Zuhroh & Sukmawati (2003)
Stock Price
Significant
Abnormal Return
Stock Price
Stock Trade Volume
Stock Trade Volume
Stock Trade Volume
Significant
Not Significant
Not Significant
Not Significant
Significant
Luthfi (2001), Purwanti (2001) and Rasmiati (2001) findings are in consistent with the
other result. Zuhroh & Sukmawati (2003) explained in their research that the difference
was due to Luthfi (2001), Purwanti (2001) and Rasmiati (2001) used annual reports of
1998 and 1999 in which Indonesia had financial crisis, therefore, there were potential
biases in their findings.
3. Hypotheses Development
Prior study (Soepriyanto & Suryanto, 2008) finds that market react positively to
legal requirement stipulated in article 74, Law 40/2007 which mandates Indonesian
companies whose activities deal with or related to the management of natural resources to
carry out CSR programs. The market respond positively to the regulation as illustrated by
abnormal return and three-day cumulative abnormal returns that significantly different
from zero, on the event date related to the passage of the regulation.
Some explanations on why market reacted positively to the regulation are (1)
more CSR programs are conducted and reported by companies in mining and other
natural-resource related companies might lowered companies’ expose risk (Sarre, et. al
2001), (2) the cost of that programs is below the expected benefit for example the
addition CSR cost will lower claims from community and NGO (Mackey, et al. 2007,
Bird et al, 2007) , (3) some mining companies (high-profile industry) have undergone
some CSR projects voluntarily, so they will have no problems when CSR becomes
mandatory (4) the additional activities and reporting required by the law will increase the
accuracy of market expectation, lower information asymmetry, and lower market surprise
(Na’im & Rakhman, 2000).
The implication of mandatory CSR implementation, however, is addition of cost.
The addition of cost on CSR may have an impact toward company performance. Hence,
investor will determine their decision by looking how company manages the additional
cost. The better company manages the additional cost, the better market will react. Thus,
in mandatory CSR implementation law event day, one could expect that the strength and
the direction of the abnormal returns would be determined by several contextual factors
a. Size
Previous studies suggest that companies with bigger size tend to disclose more of
CSR information. The bigger company size will make company exposure to social and
environmental risks are higher, since the company will deal with many parties through its
activities and products (Hackston & Milne, 1996). The bigger size also made companies
to be more politically visible (Belkoui & Karpik, 1989). Hence, the bigger size
companies have to make more effort to build their image and be perceived have concerns
to community, otherwise the companies will expose to political and social sanctions.
Based on that argument, the mandatory CSR law will not affect much to the companies
which have bigger size, since they’ve already experienced to perform and report
information of CSR beforehand. Thus, it will increase the accuracy of market expectation
and lower market surprise (Na’im & Rakhman, 2000). Smaller companies, on the other
hand, do not have such experience and expertise or probably they never conduct any CSR
program before. Therefore, the first hypothesis for this study is:
H1:
In response to mandatory CSR implementation law, the stock market will react
more positively to smaller firms whose activities deal with or related to the management
of natural resources.
b. Profitability
The implication of CSR legislation may incur additional costs. Companies which
have high profitability may not be affected much by such additional cost. The high
profitability will provide a company more flexibility to manage and report CSR programs
(Hackston & Milne, 1996). Therefore, more profitable firms are more likely to be able to
afford to implement the CSR programs. As such, the second hypothesis in this study is:
H2:
In response to mandatory CSR implementation law, the stock market will react
more positively to more profitable firms whose activities deal with or related to the
management of natural resources.
c. Leverage
Leverage is used as a proxy for firm’s riskiness. Mandatory CSR implementation
law is claimed to provide benefit in the form of “social and environment license” by
maintaining a good relationship with other stakeholders (Freeman, 2004). As discussed in
preceding section, more companies now implement CSR programs and report it in their
annual report. Companies willing to invest in CSR, since they believe CSR programs are
inline with their business objectives (Stigson, 2002) that may create long term
sustainability in their business processes. When a firm creates long term sustainability in
their business processes, it is expected that the risk of the firm will also reduce.
Therefore, the third hypothesis in this study is:
H3:
In response to mandatory CSR implementation law, the stock market will react
more positively to higher leverage firms whose activities deal with or related to the
management of natural resources.
d. Age
The relationship between the age of company and the market reaction toward
CSR legislation can be seen from two aspects, which are political visibility and CSR
experience. The older company age, the more political visibility the company has
(Belkoui and Karpik, 1989). The company with political visibility is expected to
performed more CSR programs than the company which is not. On the other hand, the
older companies are expected to have more experience in performing CSR, so when CSR
programs become mandatory they are relatively prepared than the newer companies.
Thus, the fourth hypothesis on this study is:
H4: In response to mandatory CSR implementation law, the stock market will react more
positively to older firms whose activities deal with or related to the management of
natural resources.
d. Industry
As stipulated by article 74, Law no 40/2007 on PT, the obligation to carry out
CSR program is for Indonesian companies whose activities deal with or related to the
management of natural resources. As the mining firms often associated with the
exploitation to the natural resources that may cause a significant environmental damage,
hence we would like to know specifically whether those firms experience more positive
market reaction toward mandatory CSR implementation news. Therefore, the fifth
hypothesis of this study is:
H4: In response to mandatory CSR implementation law, the stock market will react more
positively to mining firms whose activities deal with or related to the management of
natural resources.
4. Research Design and Methodology
4.1 Empirical Model
4.1.1 The Cross Sectional Regression Model
As the objective of this study is to investigate the determinant of market reaction
upon the obligation of CSR implementation as prescribed by article 74 of Law no. 40,
2007, a cross sectional analysis is employed. The cross sectional regression model was
used to examine the relationship between stock price movements represented by
abnormal return or cumulative abnormal returns (CAR) from the event window and the
range of variables outlined in the hypothesis development section that were predicted to
influence price reaction. The models used were as follows:
ARit   i  1 LOGTA   2 ROE   3 LEV   4 AGE   5 IND   it
CAR3   i  1 LOGTA   2 ROE   3 LEV   4 AGE   5 IND   it
(1)
In the model above, the dependent variables are the abnormal return (AR) and CAR,
while the independent variables are the firm’s specific characteristics such as LOGTA,
ROE, LEV, AGE and IND. All statistical tests use White’s (1980) consistent covariance
estimator. Table 3 summaries the variable interests in this research, which are described
below:
[INSERT TABLE 3 HERE]
a. LOGTA
LOGTA is the natural logarithm of the total assets of the firm. LOGTA is used as a proxy
of a firm’s size. The coefficient of LOGTA is expected to be negative.
b. ROE
The second hypothesis was tested with the estimated coefficient of ROE. ROE is a proxy
variable for profitability of the firm. It is the state of financial health of the firm. ROE is
calculated by dividing the firms’ earnings after interest and tax by the shareholders
equity. The coefficient of ROE is expected to be positive.
c. LEV
The third hypothesis was tested with the estimated coefficient of LEV. LEV is the level of
leverage of the firm. LEV is used as a proxy for firm risk as measured by dividing the
long term debt and total assets of the firm. The coefficient LEV is expected to be positive.
d. AGE
The fourth hypothesis was tested with the estimated coefficient of AGE. AGE is the age
of firms and used as a proxy for a firm’s political visibility and experience in conducting
CSR. It is calculated by deducting current year (2007) with the firm’s establishment year.
The coefficient AGE is expected to be positive.
e. IND
The fifth hypothesis was tested with the estimated coefficient of IND. IND is a dummy
variable for type of firms in our sample. The dummy variable is coded 1 if the firm is a
mining firm and 0 otherwise. The coefficient IND is expected to be positive.
4.1.2 The Standard Market Model
In order to examine the hypothesis, we also used the standard market model to
calculate AR and CAR as our dependent variable in Equation (1). This model allows us
to measure the effect of a particular event on the share return of the firms. To estimate the
abnormal return for the day related to the approval of mandatory CSR implementation
law, a standard market model is used (see Equation 2).
Rit   i   i Rmt   it 1, 2
(2)


Equation 2 is operationalised to estimate the OLS parameters,  i and  i . The estimation
period used in this study covers 200 days prior to day -1. The abnormal returns
surrounding each event are determined based on Equation 3.


ARit  Rit  ( i   i Rmt ) 3
(3)
In addition to a daily event window, a 3-day event window (-1 to +1) is calculated. It is
assumed that the length of the event window is enough to capture possible expectation or
1
Rit and Rmt are calculated using the following equation: Rit = (Pit – Pit-1)/ Pt , Rmt = (Mt - Mt-1) / Mt,
where Pit is the share price of firm i at time t; Pit-1 the share price of firm i at time t-1, Mt is the market index
of at time t; Mt-1 is the market index at time t-1
2
In equation 1, Rit is the security return for firm i on day t, Rmt is the market return on IDX composite
index (IHSG) on day t,
i
and
 i are
the Ordinary Least Square (OLS) coefficients and
 it
is the
disturbance term (residual)


In equation 2, AR is the abnormal return for firm i on day t and  i and  i are the OLS estimates of
market model parameters for firm i.
3
information leakage before the event, while not being too long to face problems with
confounding events falling within the event window.
Cumulative abnormal returns
(CARit) for each firm are computed by summing up the firm’s abnormal return during the
event window (Equation 4).
CARit 
1
N
tN
ε
t 1
4
it
(4)
4.2 Data and Sample
[INSERT TABLE 4 HERE]
Table 4 shows the sample selection procedure. We started with 35 firms listed in
IDX that categorized as firms whose activities deal with or related to the management of
natural resources. Then we filtered out 1 firm due to inactive share price movement, 9
firms because of insufficient share price data and 4 firms with confounding events. This
finally limits our sample into 19 firms. Our share price data is obtained from Indonesia
Share Market Database (ISMD) while accounting data is collected from OSIRIS
database. To calculate LOGTA, ROE and LEV we use the year data immediately prior to
the event day (2006 data).
[INSERT TABLE 5 HERE]
Table 5 illustrate that the sample used in this study is distributed across 4
industries and 7 sub industry based on IDX industry classification.
4
The sample
In equation 3, CARit is the cumulative abnormal return for firm i in time t and N is the number of days in
the event widow.
constitutes 10 firms from Mining industry (47%), 8 firms from Basic Material and
Chemical industry (38%), 1 firm from Infrastructure, Utility and Transportation industry
(5%) and 2 firms from Agriculture industry (10%).
5. Empirical Results
5.1 Descriptive Statistics
Table 6 reports descriptive statistics for abnormal returns and selected firm
characteristics for the 21 sample firms. Panel A and B show the descriptive statistics for
daily abnormal return (AR) and 3-day cumulative abnormal returns (CAR) surrounding
the passage of CSR mandatory implementation law. Meanwhile, Panel C describes the
descriptive statistics for independent variables.
Panel A in Table 6 suggests that the daily AR from day -1 to day +1 for July 20
event window (the approval of the PT bill that included mandatory CSR implementation)
is positive. The AR also remained positive when the returns are accumulated for 3 days
as illustrated in Panel B. Panel C in table 4 depicts the independent variables for our
sample. In terms of size as shown by the LOGTA variable, firms in the sample are quite
similar in size as indicated by mean (median) of sample LOGTA is 15.13 (15.06).
Furthermore, the sample consists of quite profitable firms as mean (median) of ROE
variable is 0.14 (0.11). Moreover, Panel C in table 4 illustrates that the sample primarily
consists of medium leverage firms as shown by LEV variable with mean (median) of
0.26 (0.18), which means that the firms in the sample have approximately 26% long term
debt in proportion to its total equity. In terms of AGE the company’s age mean (median)
is 30.48 (27.00) year.
5.2 Correlation Matrix
Table 7 reports the Pearson correlation matrix for independent variables used in
the cross sectional model. It shows that there is a positive correlation between variable
LOGTA and ROE. This indicates that larger firms in the sample reports higher ROE than
smaller firms suggesting that larger firms are more profitable than smaller firms.
Meanwhile, the correlation between variable ROE and LEV is negative. This suggests
that more profitable firms have less leverage and thus less risky firms.
5.3 Empirical Results
Table 8 Panel A and Panel B reports the cross sectional regression results of daily
Abnormal Return (AR) and the 3-day CAR (CAR3) on independent variables. The test
variables are LOGTA which is measured by the natural logarithm of the total assets of the
firms, ROE represents the profitability of the firms measured by profitability; LEV
represents riskiness of the firms, measured by the long term debt divided by the total
assets; AGE represents age of the firm measured by current year (2007) deducted by
firm’s date of establishment and IND as representation for type of industry – whether
mining firms or non mining firms, which is a dummy variable that is coded as 1 for
mining firm and 0 otherwise.
In Panel A table 8, the results of the cross sectional regression with AR as
dependent variable indicate that all tested variables move as predicted, with different
level of significance. The coefficient of LOGTA is negative with t-statistic (p-value) of 1.35 (0.09) that is significant at 10% level using one tailed test. The coefficient of LEV is
positive with t-statistic (p-value) of 2.08 (0.03) and significant at conventional level of
5% level using one tailed test. For AGE variable, its coefficient is positive and significant
at with t-statistic (p-value) of 2.69 (0.009) and significant at 1% level using one tailed
test. Meanwhile, the coefficient of IND is positive and significant with t-statistic (pvalue) of 1.74 (0.05) and significant at 5% level using one tailed test. Finally, the
coefficient of variable ROE is not significant with t-statistic (p-value) of -0.61 (0.273),
suggesting that no profitability effect influence the variation in the abnormal return.
Panel B in table 8 shows the cross sectional regression results of CAR3 on the
firm’s characteristics variables. The results suggest of the CAR3 regression on all tested
variables (LOGTA, LEV, AGE and IND) move in the same manner as the results of
regression, where AR is the dependent variable, without major different in the
significance level except for LOGTA. The coefficient of LOGTA variable is still negative
with higher t-statistic (p-value) of -2.31 (0.019) and significant at 5% level (one-tailed),
while the coefficient of variable LEV is still positive with t-statistic (p-value) of 2.59
(0.011) and stays significant at 5% level (one tailed). The coefficient of variable AGE
remains positive with t-statistic (p-value) of 4.52 (0.000) and remains significant at 1%
level using one tail test. Meanwhile, the coefficient of IND is still positive and significant
with t-statistic (p-value) of 1.99 (0.033) and significant at 5% level using one tailed test.
Finally, the coefficient of ROE became positive and insignificant with t-statistic of 0.53.
Therefore, both for AR and CAR3 cross sectional model, the results provide support for
H1, H3, H4 and H5.
5.4. Diagnostic Checks
Several statistical problems may impair the inferences drawn from the results
reported in Tables 8. Therefore, diagnostic tests are performed to determine if reported
results are affected by the statistical problems.
Heteroscedasticity
It has been assumed in the classical linear regression model (CLRM) that the variance of
the errors is constant. If the errors do not have a constant variance, then the errors are
heteroscedastic. If this happen, the standard errors could be wrong, hence, any inferences
could be misleading. Using White Heteroskedasticity test (1980) it is found that the
results do not seem to be influenced by heteroscedasticity problem (F-stat = 0.50).
Autocorrelation
It has been implicit that the disturbance error terms in the CLRM are not correlated with
one another. If the errors are correlated with one another, it would be known as
autocorrelation. Using the Breusch-Godfrey Serial Correlation LM test, it is found that
the residual of the test in Table 8 of this study are not correlated with one another (F-stat
= 1.81), therefore the results do not seem to be effected by autocorrelation.
Multicolinearity
In ordinary least square (OLS) estimation, one implicit assumption is that the dependent
variables are not correlated with one another. By observing the correlation matrix in
Table 7 it can be seen that there is no serious multicolinearity problem that may affect the
reported results.
Normality
It is important to note that in order to conduct single or joint hypotheses; the normality
assumption on residuals is required. For this purposes, the Bera-Jarque test is conducted
to detect whether or not the models used in this study violate the normality assumption.
The results suggest that the residual of the model is normally distributed as the JarqueBera statistics show 1.05 which insignificant at the 0.05 percent level.
6. Conclusion and Limitation
The purpose of this research was to examine the determinant and contextual factor
of market reaction to the news related to the passage of mandatory CSR implementation
law as stipulated in article 74, Law no. 40/2007 on PT. This study extends Soepriyanto
and Suryanto (2008) study who find a positive stock market reaction to the approval of
mandatory CSR law. The variation of abnormal return is expected to be differed based on
a several firm’s characteristics and contextual factor, such as the size, the profitability,
the leverage level, age of the firms and whether firms engaged in mining industry or not.
It is hypothesised that the abnormal return will be more positive for firms that profitable,
high leverage, older firms and engaged in mining industry. It is also hypothesised that the
abnormal return will be more positive to smaller firms. Using cross sectional regression
analysis, the results of this study provide support for the hypothesis.
The results of the cross sectional regression, then, shows that there is strong
association between market reactions toward the legal requirement of CSR and
company’s age. Market react more positively to older companies, as market perceived
that the older companies have better experience in conducting CSR program.
We also document that market used company’s size, leverage and type of industry
as determinant factors in responding to the legal requirement of CSR. Interestingly,
market reacts more positively in smaller firms, suggesting that market expect smaller
firms to perform CSR. In general, previous studies show that bigger firms have more
CSR practice and disclosure. This result does not oppose that general views, but
somewhat complete them. As market expects that bigger firms have already performed
and disclosed CSR programs, such regulation would not affect much. On the other hand,
given the low practice and disclosure of CSR in smaller firms, market view that such
regulation will give smaller firms a pressure to conduct and disclose CSR, which is
valued by market.
The similar explanation is given to why market reacts more positively to higher
leverage firms. Previous studies show that higher leverage firms will disclose less CSR
information in their annual report, suggesting that higher leverage firms view CSR as an
expense that should be minimised. The legal requirement of CSR, therefore, will give
pressure to higher leveraged firms to also perform CSR. Hence, this result suggests that
market also expect higher leverage firms to perform CSR.
We also find that type of industry, which is whether firms are in mining industry
or not, is significant as determinant factor. The law explicitly mentions companies in
whose deal with natural resources to perform CSR (e.g. mining firms), while also
mentioning the same obligation to companies whose related to the management of
natural resource. The last category is less clear therefore, market reaction to the
companies in last category is less strong than in first category.
We find that there is no significant association between market reaction and
profitability, suggesting that market view the consequence of the legal requirement of
CSR is not associated to company’s profitability.
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TABLE 3
Summary of the Independent Variables in the Cross Sectional Model
Variables
LOGTA
Predicted
Sign
-
ROE
+
LEV
+
AGE
+
IND
+
Description
A proxy variable for size. It is measured by the natural
logarithm of a firm’s total asset.
A proxy variable for profitability. It is measured by
Return on Equity Ratio (Net Income/Total Shareholder
Equity).
A proxy variable for leverage. It is measured by long
term debt/total asset.
A proxy for a firm’s political visibility and experience
in conducting CSR. It is measured as the age of firms
Dummy variable, which indicates the type of firms that
require conducting CSR in our sample. It is coded as a
1 for mining firms and 0 otherwise.
TABLE 4:
Sample Selection Procedure
Sample Size
Initial Sample
Less:
35 firms
1 firms
9 firms
Firms with inactive share price movement
Firms with insufficient share price data
Firms with confounding events (pre and post 3 days of
announcement day)
Total Sample for H1-H5
4 firms
21 firms
TABLE 5:
Sample Distribution
Industry
Sub
Industry
Coal
3
Total
Basic materials &
chemical
Mining
Oil &
Gas
4
10
Metal
Timber
3
4
Pulp &
Paper
4
8
Infrastructure,
utility and
transportation
Agriculture
Energy
Plantation
1
1
2
2
TABLE 6:
Descriptive Statistics for Abnormal Return and CAR of 21 Sample Firm on Event Window
Day
Mean
Median
SD
Q1
Q3
Min
Max
Panel A: Daily AR for July 20 event window
N = 21
-1
0
0.0310
0.0217
0.0085
-0.0018
0.0825
0.0802
-0.0076
-0.0146
0.0381
0.0184
-0.0315
-0.0399
0.3392
0.3394
+1
0.0069
0.0081
0.0217
-0.0103
0.0192
-0.0361
0.0516
Panel B: CAR3 for July 20 event window
N = 21
-2 to 0
0.0560
-0.0031
0.1615
-0.0122
0.0591
-0.0424
0.6743
-1 to +1
0 to +2
0.0595
0.0325
0.0087
0.0035
0.1665
0.0957
-0.0131
-0.0130
0.0452
0.0768
-0.0372
-0.0890
0.7303
0.3829
15.06
0.11
0.18
1.48
14.19
16.58
12.19
17.68
0.19
0.01
0.29
-0.13
0.62
0.22
0.04
0.46
0.00
0.62
27.00
0.00
19.92
21.50
34.50
6.00
106.00
0.51
0.00
1.00
0.00
1.00
Panel C: Independent Variables
N = 21
LOGTA
ROE
LEV
15.13
0.14
0.26
AGE
IND
30.48
0.48
Where LOGTA is the natural logarithm of total asset of the firms as a proxy for firms’ size, ROE is the return on
equity of the firms as a proxy of profitability, LEV is the leverage level of the firms, AGE is the age of the firms
and IND is the Dummy Variable, which coded as 1 for mining firms and 0 for non-mining firms.
TABLE 7:
Correlation Matrix for Independent Variables in the Cross Sectional Model
LOGTA
ROE
LEV
AGE
IND
LOGTA
1.000
0.301
0.367
-0.076
0.119
ROE
LEV
AGE
IND
1.000
-0.208
-0.019
0.423
1.000
0.230
-0.228
1.000
-0.185
1.000
Where LOGTA is the natural logarithm of total asset of the firms as a proxy for firms’ size, ROE is the return on
equity of the firms as a proxy of profitability, LEV is the leverage level of the firms, AGE is the age of the firms
and IND is the Dummy Variable, which coded as 1 for mining firms and 0 for non-mining firms.
TABLE 8
Cross Sectional Regression Results of AR and CAR3 for 21 Mandatory CSR
Implementation Firm on Firm Characteristics
ARit   i  1LOGTA   2 ROE   3 LEV   4 AGE   5 IND   it
CARit   i  1LOGTA   2 ROE   3 LEV   4 AGE   5 IND   it
Panel A: Cross Sectional Regression of AR with independent variables N=21
Variables
Parameter
Predicted Sign
Estimates
Standard Error
0.0918
0.0960
Intercept

LOGTA
ROE
LEV
AGE
IND
F-Statistic = 3.55





+
+
+
+
-0.0089
-0.0243
0.0745
0.0008
0.0244
0.0066
0.0392
0.0358
0.0003
0.0140
t-statistic
0.9559
p-value
0.17825
-1.3535
-0.6193
2.0827
2.6917
1.7411
0.0995***
0.2732
0.0288**
0.00925*
0.05265**
1.779302
-2.31362
0.536175
2.58803
4.519408
1.999846
0.0493
0.01885**
0.30045
0.01125**
0.0003*
0.03345**
R2 = 0.5776
Adjusted R2 = 0.4152
Panel B: Cross Sectional Regression of CAR3 with independent variables
N=21
0.254024
0.142766
Intercept

-0.02269
0.009808
LOGTA

0.031254
0.058291
ROE
+

0.137611
0.053172
LEV
+

0.002117
0.000469
AGE
+

0.041734
0.020869
IND
+

F-Statistic = 7.81
R2 = 0.7503
Adjusted R2 = 0.6542
1)
*,**, ***, significant at 1%, 5% and 10% level using one tailed test , respectively.
2)
All t-statistic and significance level are based on White (1980) standard errors.
Where AR is the abnormal return at day 0, CAR3 is the 3 days cumulative abnormal return at day -1 to day +1, LOGTA is the
natural logarithm of total asset of the firms as a proxy for firms’ size, ROE is the return on equity of the firms as a proxy of
profitability, LEV is the leverage level of the firms, AGE is the age of the firms and IND is the Dummy Variable, which coded
as 1 for mining firms and 0 for non-mining firms.