Rosmiati Jafar
| Basuki | Windijarto | Rahmat Setiawan* | Arka Prabaswara
© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
This research aims to examine the impact of environmental, social, and governance (ESG) disclosure on cost of debt in non-financial companies listed on the Indonesia Stock Exchange across ten different industries. This study utilizes a sample comprising 288 non-financial companies from ten different industries listed on the Indonesia Stock Exchange for the period from 2017 to 2022, all of which have published sustainability reports (SR), resulting in 742 observations. Regression analysis using Ordinary Least Squares (OLS) is employed to examine the impact of companies' ESG disclosures on costs of debt. The robustness of these findings is assessed and confirmed through four distinct analytical models, namely OLS Standard Error, SSC Model, Fixed Effect Model, and Random Effect Model. The research outcomes indicate that increased environmental, social, and governance (ESG) disclosure is associated with a reduction in the cost of debt. These findings exhibit robustness across diverse industries and remain consistent when employing various statistical methodologies.
ESG disclosure, cost of debt, environmental, social, and governance, sustainability, developing country
In the global pursuit of development, world leaders formally adopted the Sustainable Development Goals (SDGs) agenda on September 25, 2015. The SDGs constitute a comprehensive agenda designed to perpetuate ongoing enhancements in the economic well-being of communities, sustain the social fabric of societies, safeguard the environment, foster inclusive development, and institute governance mechanisms capable of preserving improvements in the quality of life from one generation to the next. The implementation of the SDGs involves the active participation of all economic actors, encompassing both governmental entities and organizations/companies [1-4].
The heightened awareness of corporate social responsibility has garnered significant attention, as evidenced by rapid growth in scholarly research [5-8]. Consequently, companies are increasingly adopting business practices that extend beyond mere profit maximization, incorporating ethical considerations and sustainability issues into their operations [9]. In this evolving landscape, there is a reassessment of traditional business models [10], with a growing emphasis on approaches that actively contribute to the preservation of the Earth's ecosystem and its natural equilibrium [11].
ESG represents an extension and enhancement of the concepts underlying the green economy, corporate social responsibility, and responsible investment. According to Eccles [12], ESG disclosure is often viewed by many investors and creditors as a proxy for evaluating the quality of a company's management [13, 14]. This disclosure serves as a form of corporate transparency, holding the potential to mitigate information asymmetry [15, 16] between the company and its stakeholders, particularly investors and creditors from lending institutions [4, 17].
The integration of all ESG dimensions plays a crucial role in facilitating a lending institution's evaluation of opportunities, risks, transparency, and the future performance of the respective company [18]. As a result, lending institutions heavily rely on ESG reports and metrics to assess and quantify company performance indicators [19]. Enhanced information transparency not only aids in diminishing information asymmetry but also contributes to companies maintaining a positive image in the eyes of stakeholders. Companies are perceived as having long-term goals and adhering to regulations, and this adherence to ESG disclosure is associated with a potential reduction in the cost of debt [20-23].
R.Q: Does ESG disclosure have a negative effect on the cost of debt?
The research question was addressed using data from non-financial companies listed on the Indonesia Stock Exchange during the period from 2017 to 2022. The findings of the study offer robust evidence, indicating that companies with higher levels of ESG disclosure experience a lower cost of debt. These results suggest that ESG practices play a significant role in influencing the creditworthiness of a company as perceived by lending institutions. The argument posited is that lending institutions incorporate corporate ESG information into their lending decisions to assess two critical types of risks associated with these companies: default risk and reputation risk [5, 24, 25].
This research makes a significant contribution to the literature on environmental, social, and governance (ESG) by presenting empirical findings on the impact of ESG on the cost of debt, specifically within the context of developing countries, with a particular focus on Indonesia. Notably, existing research that explores the influence of ESG on the cost of debt has predominantly been conducted in the contexts of developed countries, such as America and Europe [4, 5, 22, 26, 27]. This research thus fills a crucial gap by shedding light on how ESG considerations affect the cost of debt in the unique setting of a developing country like Indonesia.
This research also holds significance as the pioneering study to furnish empirical evidence across ten non-financial industries. These industries encompass Energy, Basic Materials, Consumer Cyclicals, Consumer Non-Cyclicals, Healthcare, Industrials, Infrastructures, Property and Real Estate, Technology, and Transportation and Logistics. The findings reveal that, with the exception of the Technology industry, all other industries demonstrate evidence supporting the idea that ESG disclosure can lead to a reduction in the cost of debt. The scarcity of technology companies in Indonesia is proposed as a potential explanation for the absence of conclusive results in this particular industry. It is acknowledged that a limited sample size can introduce challenges in testing, preventing the findings from providing specific insights for the Technology industry due to its relatively small representation.
2.1 Stakeholder theory
Stakeholder theory fundamentally posits that a company is not solely driven by its self-interests; rather, it is obligated to deliver benefits to its stakeholders. The term 'stakeholder' is expansive in its scope, as highlighted by Harmoni [28]. Bowmann-Larsen and Wiggen [29] offer a definition of stakeholders as all individuals and groups with a 'critical eye' on company actors. Stakeholders, according to Freeman and McVea [30], are groups or individuals who possess the capacity to influence or be influenced by the processes involved in achieving an organization's goals.
Stakeholder theory asserts that companies bear a responsibility beyond solely maximizing profits for shareholders; they are also accountable for delivering benefits to society, the social environment, and the government—collectively referred to as stakeholders. Companies are tasked with cultivating positive relationships with stakeholders by addressing their desires and needs [31], particularly those stakeholders wielding influence over crucial resources essential for the company's operational activities, such as labor, suppliers, and the government [32]. One approach to maintaining these relationships and safeguarding the interests of all parties involved is through the practice of ESG disclosures [33].
ESG disclosure serves as a mechanism for transparently conveying information about a company's stance and actions concerning economic, environmental, and social aspects. Through the practice of ESG disclosures, stakeholders can directly assess the company's performance, influencing their decisions and contributions to the company. This influence extends to various aspects, including stakeholder decisions related to supporting the company and providing access to funding [34-36].
2.2 Legitimacy theory
Legitimacy theory, developed by Guthrie and Parker [37] in 1989, posits the existence of a social contract that explains the relationship between society and companies. According to Suchman [38], legitimacy is the general perception or assumption that the actions of an entity are desirable, appropriate, or fitting within some socially constructed system of norms, values, beliefs, and definitions. Legitimacy theory asserts that organizations are continually striving to ensure that their activities align with the prevailing societal boundaries and norms [39-41].
Legitimacy can be viewed as a mutual relationship between a company and society. In this dynamic, society grants legitimacy to a company, allowing it the right to establish facilities and conduct business activities. In return, the company is expected to provide benefits to the surrounding community and, at the very least, avoid causing harm, such as environmental damage. This social contract emphasizes the reciprocal nature of legitimacy, wherein a company is entrusted with certain privileges by the community, and in exchange, it is obligated to act responsibly and contribute positively to its social and environmental context.
Companies can acquire and sustain legitimacy from society by employing ESG disclosures [42]. Such disclosures serve as a means of communication from the company to stakeholders, demonstrating the company's adherence to prevailing societal norms. This practice indirectly reinforces the legitimacy gained from society and shapes stakeholder perceptions. These perceptions, in turn, impact the company's value in the eyes of stakeholders, including lending institutions, as it reflects a commitment to social responsibility [43]. The transparency provided by ESG disclosures contributes to building and maintaining trust with stakeholders, further enhancing the company's legitimacy.
Companies that have successfully established legitimacy within the community often experience smoother operations. This enhanced legitimacy translates into easier access to resources, including, notably, access to cost-effective funding and lower cost of debt [34-36]. The trust and positive perception earned from the community and stakeholders contribute to the company's overall resilience, facilitating its ability to secure resources necessary for sustained and efficient operations, particularly in terms of financial support and favorable debt terms.
2.3 Effect of ESG disclosure on cost of debt
The concept of sustainability performance entails a company's broader orientation that extends beyond short-term profit maximization [44]. It involves considering the impact of a company's operations on all stakeholders, encompassing communities, society, and the environment [30]. The motivation behind corporate ESG disclosure can be associated with legitimacy theory. Corporate legitimacy, as posited by this theory, is viewed as a vital organizational resource crucial for the company's survival, bestowed upon the organization by society.
Company ESG disclosure serves as a signal that communicates the business's commitment to meeting stakeholder expectations [39]. In the context of legitimacy theory, an increase in company ESG disclosure is expected to result in a lower cost of debt. This is attributed to the fact that ESG disclosure reinforces the company's commitment to and relationships with stakeholders, fostering a sense of mutual trust. As a consequence, the risk of disruptions to company operations is reduced, enabling the company to maintain profitability and thereby lowering the risk of default [45].
Enhanced ESG disclosure is often viewed as more compliant with regulations [44, 46] and contributes to reducing information asymmetry problems [16]. Consequently, this transparency is associated with a lower cost of debt [20]. Companies that actively engage in ESG disclosures are perceived by creditors as having a lower risk of default. This positive perception leads to a favorable evaluation of creditworthiness, allowing these companies to secure lower or cheaper costs of debt.
Given the findings from prior research, such as Apergis et al. [26] on S&P 500 companies, which indicated a negative effect of ESG scores on the cost of debt, and Eliwa et al. [5], whose research explored the impact of both ESG disclosure and ESG performance on the cost of debt, revealing a negative influence, the first hypothesis for this research can be formulated as follows:
H1: ESG disclosure has a negative effect on the cost of debt.
3.1 Data sources and samples
This research employs data from 288 non-financial companies listed on the Indonesia Stock Exchange for the period spanning 2017 to 2022. The sample consists of companies that have published sustainability reports (SR), resulting in a dataset with 742 observations. Measurements for the ESG variable are sourced from these sustainability reports. Additionally, the OSIRIS database is utilized for variables derived from financial reports and company annual reports. The exclusion of financial firms from the sample is a deliberate choice aimed at preventing biases in the results. This exclusion is justified by the highly regulated nature of the financial industry, encompassing SR-related regulations, and potential incomparability in terms of debt financing costs [5, 47, 48].
3.2 Dependent variable
The cost of debt (COD) is defined as the minimum level of profit demanded by lenders on company investments financed with debt. In alignment with previous research methodologies, the calculation of COD in this study involves dividing the interest expense by the total long-term debt. This measurement approach is consistent with methodologies employed in various studies, such as those conducted by Francis et al. [49], Gray et al. [50], Persakis and Iatridis [51], and Pittman and Fortin [52]. The summary of the measurements is presented in Table 1.
3.3 Independent variable
To measure the level of ESG disclosure, this study employs sentences as the unit of analysis, following the approach recommended by Li [53]. The assessment uses a checklist based on guidelines issued by the Global Reporting Initiative (GRI), an international organization under the auspices of the United Nations that provides standards for sustainability reporting for organizations, companies, and other institutions. GRI standards assist companies in measuring, understanding, and communicating their performance in terms of economic, environmental, and social impacts. This study covers 94 GRI items related to ESG information disclosure. Specifically, it includes 32 items under Environment (GRI 300), 40 items under Social (GRI 400), and 22 items under Governance (GRI 102) [27]. All GRI disclosure items can be accessed at www.globalreporting.org.
The ESG measurement involves using the ESG Score, which is calculated by comparing the amount of ESG information disclosed by a company with the total number of disclosure items. Each indicator is assigned a value of 1 for companies making ESG disclosures in accordance with the indicators, and a value of 0 for companies not making ESG disclosures for each respective indicator. The summary of the measurements is presented in Table 1.
Table 1. Summary of abbreviations, variables, operationalization, and expected sign
|
Abbreviations |
Variable |
Operationalization |
Expected Sign |
|
COD |
Cost of Debt |
Interest expense divided by total long-term debt |
|
|
ESGD |
ESG Disclosure |
Comparison of the amount of ESG disclosed by the company with the total number of disclosure items |
Negative |
|
SIZE |
Firm Size |
Natural logarithm of total assets |
Negative |
|
AGE |
Firm Age |
Natural logarithm of company age |
Negative |
|
LEV |
Leverage |
The ratio of total debt to total assets |
Negative |
|
ROA |
Return on Asset |
Net profit divided by total assets |
Negative |
3.4 Control variable
Firm size (SIZE) is considered as a variable for analysis. Past studies have consistently indicated that larger companies tend to have better risk management capabilities due to their robust financial and strategic positions [54, 55]. Larger companies are often associated with lower risks, leading to a smaller cost of debt, and conversely. Consistent with Gholami et al. [22], the measurement of company size can be operationalized using the natural logarithm of total assets.
Firm age (AGE) refers to the length of time a company has been in existence. It represents the duration from the initiation of operational activities until the company can establish itself or sustain its presence in the business world. Following the approach suggested by Pukthuanthong et al. [56], firm age can be measured using the natural logarithm of the company's age.
Leverage (LEV) is a metric that gauges the extent to which a company utilizes debt in its expenditures. The relationship between leverage and cost of debt (COD) is typically negative, as higher leverage signifies greater dependence on debt financing, leading to an increase in COD [57]. In alignment with the approach suggested by Nicolò et al. [58], leverage can be quantified by dividing long-term debt by total assets.
Return on assets (ROA) is a financial metric that assesses a company's capacity to generate profits utilizing its resources, including assets, capital, or sales [59-61]. Companies with higher levels of profitability are generally perceived as having an enhanced ability to fulfill their debt obligations [62]. Consequently, an inverse relationship is anticipated between return on assets (ROA) and cost of debt (COD). ROA quantified by dividing net profit by total assets [63-66]. The summary of the measurements is presented in Table 1.
3.5 Econometric models
The following is the regression model used in this research:
$C O D_{i, t}=\beta_0+\beta_1 E S G D_{i, t}$ (1)
$\begin{gathered}\operatorname{COD}_{i, t}=\beta_0+\beta_1 E S G D_{i, t}+\beta_2 S I Z E_{i, t}+\beta_3 A G E_{i, t} +\beta_4 L E V_{i, t}+\beta_5 R O A_{i, t}+\varepsilon_{i. t}\end{gathered}$ (2)
In this research, Ordinary Least Square (OLS) regression techniques are employed to test hypotheses, utilizing STATA17 software. The data used in the study has undergone classical assumption tests, representing the initial requirement for conducting panel data regression analysis [67]. Additionally, the research conducted various other tests to ensure the robustness of the main results. Following the methodology of prior research [68, 69], the study utilized four alternative regression estimates, including OLS with robust standard error and Driscoll-Kraay standard error (SCC) regression. The analysis also incorporated fixed effect and random effect models to enhance the robustness of the results [67].
4.1 Descriptive statistics
In this research, a preliminary test for normality is conducted before further testing. The exclusion of outliers is implemented to reduce inference errors, enhancing the accuracy of statistical analysis [70]. Additionally, the study performs tests for multicollinearity using the Variance Inflation Factor (VIF) and Pearson correlation coefficient [67].
The VIF results presented in Table 2 indicate low multicollinearity between variables for all non-financial firms, as evidenced by VIF values falling within an acceptable range. The highest VIF value is observed for SIZE at 1.31, while the lowest is for LEV at 1.06. When examining VIF values for each industry, the highest is for SIZE at 4.71 in the Transportation and Logistics industry, and the lowest is for AGE at 1.01 in the Property industry. Overall, the research model does not exhibit multicollinearity problems among the independent variables, as indicated by VIF values consistently below 10 [70]. This suggests that the variables are not highly correlated, and the model is not compromised by multicollinearity issues.
Table 2 presents descriptive statistical values, offering insights into the average level of cost of debt (COD) for non-financial companies in Indonesia, which is reported at 10 percent. The highest COD is observed in the Consumer Non-Cyclical industry at 75 percent, while the lowest is in the Basic Materials industry at 0.01 percent. The industry with the highest average COD is Transportation and Logistics at 14 percent, whereas the Energy industry has the lowest average COD at 7 percent.
Additionally, the average level of ESG disclosure for non-financial companies in Indonesia is relatively low, standing at approximately 36 percent. The highest ESG disclosure value is in the Basic Materials industry at 89 percent, while the Consumer Cyclical industry has the lowest ESG disclosure value at 5 percent. The Energy industry boasts the highest average ESG disclosure value at around 41 percent, whereas the Transportation and Logistics industry reports the lowest average ESG disclosure value at approximately 28 percent. Descriptive statistics for all variables, both for the overall sample and each industry type, are detailed in Table 2.
Table 3 presents the Pearson correlation coefficients for all variables, and it is observed that they are all below 0.90. This suggests the absence of multicollinearity problems between the variables, aligning with the guidelines provided by Hair [70].
Table 2. Descriptive statistics
|
|
N |
Minimum |
Maximum |
Mean |
Std. Dev |
VIF |
|
All Non-Financial Firm |
||||||
|
COD |
743 |
0.0001 |
0.7529 |
0.1066 |
0.1065 |
|
|
ESGD |
743 |
0.0532 |
0.8936 |
0.3634 |
0.1387 |
1.06 |
|
SIZE |
743 |
24.8485 |
33.6552 |
29.662 |
1.7642 |
1.31 |
|
AGE |
743 |
1.0986 |
4.7536 |
3.4830 |
0.6316 |
1.15 |
|
LEV |
743 |
0.0004 |
1.8911 |
0.2171 |
0.1956 |
1.16 |
|
ROA |
743 |
-0.6312 |
0.9209 |
0.0433 |
0.1175 |
1.21 |
|
Basic Material |
||||||
|
COD |
137 |
0.0001 |
0.5677 |
0.1162 |
0.1132 |
|
|
ESGD |
137 |
0.1064 |
0.8936 |
0.3801 |
0.1617 |
1.23 |
|
SIZE |
137 |
25.3401 |
32.6006 |
29.4079 |
1.7584 |
1.49 |
|
AGE |
137 |
1.0986 |
4.6634 |
3.5620 |
0.5805 |
1.13 |
|
LEV |
137 |
0.0004 |
0.6563 |
0.1759 |
0.1589 |
1.31 |
|
ROA |
137 |
-0.4991 |
0.2491 |
0.0307 |
0.0776 |
1.12 |
|
Consumer Cyclical |
||||||
|
COD |
85 |
0.0022 |
0.6579 |
0.1019 |
0.0919 |
|
|
ESGD |
85 |
0.0532 |
0.6383 |
0.3486 |
0.1146 |
1.15 |
|
SIZE |
85 |
24.8493 |
31.0953 |
28.2766 |
1.6596 |
1.51 |
|
AGE |
85 |
1.9459 |
4.2626 |
3.3550 |
0.6398 |
1.51 |
|
LEV |
85 |
0.0053 |
30.1008 |
0.2293 |
0.2364 |
1.76 |
|
ROA |
85 |
-1.5302 |
4.7763 |
0.0163 |
0.0979 |
1.85 |
|
Consumer Non-Cyclical |
||||||
|
COD |
143 |
0.0002 |
0.7529 |
0.1160 |
0.1210 |
|
|
ESGD |
143 |
0.1596 |
0.7766 |
0.3787 |
0.1221 |
1.11 |
|
SIZE |
143 |
24.9392 |
32.8263 |
29.4224 |
1.5680 |
1.25 |
|
AGE |
143 |
1.7917 |
4.7535 |
3.6152 |
0.5913 |
1.27 |
|
LEV |
143 |
0.0028 |
0.7742 |
0.1865 |
0.1605 |
1.38 |
|
ROA |
143 |
-0.2510 |
0.5265 |
0.0660 |
0.1167 |
1.38 |
|
Energy |
||||||
|
COD |
104 |
0.0164 |
0.5881 |
0.0744 |
0.0491 |
|
|
ESGD |
104 |
0.1383 |
0.8511 |
0.4150 |
0.1646 |
1.44 |
|
SIZE |
104 |
25.0811 |
32.7540 |
29.9517 |
1.5673 |
1.84 |
|
AGE |
104 |
1.7917 |
4.6347 |
3.4660 |
0.5941 |
1.46 |
|
LEV |
104 |
0.0072 |
0.9406 |
0.2752 |
0.2120 |
1.78 |
|
ROA |
104 |
-0.3156 |
0.5925 |
0.0715 |
0.1609 |
1.65 |
|
Healthcare |
||||||
|
COD |
41 |
0.0013 |
0.7208 |
0.1086 |
0.1159 |
|
|
ESGD |
41 |
0.1809 |
0.7021 |
0.3889 |
0.0992 |
1.31 |
|
SIZE |
41 |
27.4156 |
30.9357 |
29.0295 |
1.0593 |
1.26 |
|
AGE |
41 |
3.0910 |
4.6443 |
3.8960 |
0.4168 |
1.17 |
|
LEV |
41 |
0.0085 |
0.5135 |
0.1138 |
0.1234 |
1.47 |
|
ROA |
41 |
-0.2793 |
0.9209 |
0.1014 |
0.1636 |
1.24 |
|
Industrials |
||||||
|
COD |
44 |
0.0175 |
0.4544 |
0.1242 |
0.0923 |
|
|
ESGD |
44 |
0.1809 |
0.7128 |
0.3042 |
0.1271 |
1.12 |
|
SIZE |
44 |
26.5838 |
33.6551 |
29.8228 |
2.1310 |
1.32 |
|
AGE |
44 |
2.4849 |
4.3567 |
3.6383 |
0.4342 |
1.82 |
|
LEV |
44 |
0.0128 |
0.5046 |
0.1593 |
0.1267 |
1.52 |
|
ROA |
44 |
-0.1677 |
0.1808 |
0.0447 |
0.0633 |
1.35 |
|
Infrastructure |
||||||
|
COD |
88 |
0.0040 |
0.6624 |
0.1025 |
0.0848 |
|
|
ESGD |
88 |
0.1383 |
0.6277 |
0.3269 |
0.1109 |
1.15 |
|
SIZE |
88 |
25.6022 |
33.2557 |
30.4132 |
1.7427 |
1.82 |
|
AGE |
88 |
1.9459 |
4.2341 |
3.3683 |
0.6287 |
1.57 |
|
LEV |
88 |
0.0098 |
1.2103 |
0.3105 |
0.2255 |
1.59 |
|
ROA |
88 |
-0.6312 |
0.1558 |
0.0131 |
0.0928 |
1.24 |
|
Property and Real Estate |
||||||
|
COD |
64 |
0.0006 |
0.6210 |
0.0912 |
0.1090 |
|
|
ESGD |
64 |
0.1277 |
0.7553 |
0.3290 |
0.1355 |
1.47 |
|
SIZE |
64 |
24.8485 |
31.7495 |
29.3098 |
1.6244 |
1.71 |
|
AGE |
64 |
1.0986 |
4.2341 |
3.0912 |
0.7617 |
1.01 |
|
LEV |
64 |
0.0010 |
0.5236 |
0.2287 |
0.1404 |
1.23 |
|
ROA |
64 |
-0.0651 |
0.2756 |
0.0064 |
0.0573 |
1.24 |
|
Technology |
||||||
|
COD |
13 |
0.0067 |
0.4195 |
0.1109 |
0.1087 |
|
|
ESGD |
13 |
0.1809 |
0.5213 |
0.3486 |
0.1170 |
2.27 |
|
SIZE |
13 |
28.6319 |
31.4258 |
29.7580 |
1.1112 |
1.81 |
|
AGE |
13 |
2.3025 |
3.8501 |
3.2837 |
0.6336 |
2.80 |
|
LEV |
13 |
0.0036 |
0.4382 |
0.0915 |
0.1424 |
4.35 |
|
ROA |
13 |
-0.1047 |
0.2054 |
0.0686 |
0.0862 |
2.01 |
|
Transportation and Logistics |
||||||
|
COD |
23 |
0.0039 |
0.6384 |
0.1487 |
0.1393 |
|
|
ESGD |
23 |
0.1809 |
0.4468 |
0.2840 |
0.0639 |
1.59 |
|
SIZE |
23 |
25.7492 |
32.6515 |
28.8893 |
2.2439 |
4.71 |
|
AGE |
23 |
1.9459 |
32.2063 |
3.1933 |
0.7752 |
3.54 |
|
LEV |
23 |
0.0195 |
1.0470 |
0.3787 |
0.3009 |
1.38 |
|
ROA |
23 |
-0.5782 |
0.5995 |
0.0077 |
0.2194 |
1.03 |
Table 3. Pearson correlation
|
|
COD |
ESGD |
SIZE |
AGE |
LEV |
ROA |
|
COD |
ESGD |
SIZE |
AGE |
LEV |
ROA |
|
All Non-Financial Firm |
Industrials |
||||||||||||
|
COD |
1 |
|
|
|
|
|
COD |
1 |
|
|
|
|
|
|
ESGD |
-0.243 |
1 |
|
|
|
|
ESGD |
-0.385 |
1 |
|
|
|
|
|
SIZE |
-0.253 |
0.284 |
1 |
|
|
|
SIZE |
-0.155 |
0.187 |
1 |
|
|
|
|
AGE |
-0.101 |
0.158 |
0.318 |
1 |
|
|
AGE |
0.245 |
-0.163 |
0.254 |
1 |
|
|
|
LEV |
-0.216 |
0.032 |
0.239 |
-0.067 |
1 |
|
LEV |
-0.303 |
0.159 |
0.000 |
-0.513 |
1 |
|
|
ROA |
-0.122 |
0.158 |
0.110 |
0.167 |
-0.361 |
1 |
ROA |
-0.457 |
0.253 |
0.263 |
-0.246 |
-0.040 |
1 |
|
Basic Material |
Infrastructure |
||||||||||||
|
COD |
1 |
|
|
|
|
|
COD |
1 |
|
|
|
|
|
|
ESGD |
-0.264 |
1 |
|
|
|
|
ESGD |
-0.297 |
1 |
|
|
|
|
|
SIZE |
-0.237 |
0.222 |
1 |
|
|
|
SIZE |
-0.440 |
0.334 |
1 |
|
|
|
|
AGE |
-0.167 |
0.322 |
0.537 |
1 |
|
|
AGE |
0.015 |
0.219 |
0.473 |
1 |
|
|
|
LEV |
-0.211 |
0.111 |
0.073 |
0.139 |
1 |
|
LEV |
-0.339 |
0.180 |
0.336 |
-0.125 |
1 |
|
|
ROA |
0.016 |
0.129 |
0.249 |
0.131 |
-0.595 |
1 |
ROA |
-0.140 |
0.037 |
0.061 |
-0.048 |
-0.327 |
1 |
|
Consumer Cyclical |
Property and Real Estate |
||||||||||||
|
COD |
1 |
|
|
|
|
|
COD |
1 |
|
|
|
|
|
|
ESGD |
-0.264 |
1 |
|
|
|
|
ESGD |
-0.278 |
1 |
|
|
|
|
|
SIZE |
-0.237 |
0.222 |
1 |
|
|
|
SIZE |
-0.197 |
-0.547 |
1 |
|
|
|
|
AGE |
-0.167 |
0.322 |
0.537 |
1 |
|
|
AGE |
0.062 |
0.035 |
0.097 |
1 |
|
|
|
LEV |
-0.211 |
0.111 |
0.073 |
0.139 |
1 |
|
LEV |
-0.190 |
0.274 |
0.247 |
-0.053 |
1 |
|
|
ROA |
0.016 |
0.129 |
0.029 |
0.131 |
-0.595 |
1 |
ROA |
-0.343 |
0.097 |
0.323 |
0.043 |
-0.197 |
1 |
|
Consumer Non-Cyclical |
Technology |
||||||||||||
|
COD |
1 |
|
|
|
|
|
COD |
1 |
|
|
|
|
|
|
ESGD |
-0.216 |
1 |
|
|
|
|
ESGD |
-0.309 |
1 |
|
|
|
|
|
SIZE |
-0.365 |
-0.221 |
1 |
|
|
|
SIZE |
-0.330 |
0.016 |
1 |
|
|
|
|
AGE |
-0.146 |
-0.068 |
0.252 |
1 |
|
|
AGE |
-0.041 |
-0.044 |
-0.024 |
1 |
|
|
|
LEV |
-0.168 |
0.264 |
0.266 |
-0.164 |
1 |
|
LEV |
-0.249 |
-0.378 |
-0.378 |
-0.563 |
1 |
|
|
ROA |
-0.084 |
-0.061 |
0.123 |
0.398 |
-0.377 |
1 |
ROA |
-0.670 |
0.587 |
0.118 |
0.258 |
0.245 |
1 |
|
Energy |
Transportation and Logistics |
||||||||||||
|
COD |
1 |
|
|
|
|
|
COD |
1 |
|
|
|
|
|
|
ESGD |
-0.340 |
1 |
|
|
|
|
ESGD |
-0.530 |
1 |
|
|
|
|
|
SIZE |
-0.379 |
0.422 |
1 |
|
|
|
SIZE |
-0.202 |
-0.367 |
1 |
|
|
|
|
AGE |
-0.403 |
0.440 |
0.478 |
1 |
|
|
AGE |
-0.313 |
-0.049 |
0.801 |
1 |
|
|
|
LEV |
-0.219 |
-0.113 |
0.269 |
-0.066 |
1 |
|
LEV |
-0.537 |
0.059 |
0.445 |
0.372 |
1 |
|
|
ROA |
-0.182 |
0.302 |
0.189 |
0.187 |
-0.518 |
1 |
ROA |
-0.074 |
0.094 |
-0.059 |
-0.059 |
-0.093 |
1 |
|
Healthcare |
|
|
|
|
|
|
|
||||||
|
COD |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
ESGD |
-0.322 |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
SIZE |
-0.093 |
0.050 |
1 |
|
|
|
|
|
|
|
|
|
|
|
AGE |
0.023 |
-0.203 |
-0.337 |
1 |
|
|
|
|
|
|
|
|
|
|
LEV |
0.053 |
-0.409 |
-0.328 |
0.222 |
1 |
|
|
|
|
|
|
|
|
|
ROA |
-0.226 |
0.348 |
-0.031 |
-0.047 |
-0.356 |
1 |
|
|
|
|
|
|
|
|
Bold Correlation is significant at the 0.05 level (2-tailed). |
|||||||||||||
4.2 Effect of ESG disclosure on cost of debt
The regression results presented in Table 4 for Model 1 and Model 2, as well as the results in Table 5 for various non-financial industries, support Hypothesis 1 (H1). These findings indicate a significant negative influence of ESG disclosure on the company's cost of debt. This pattern is consistent across all industries except the technology industry, where the results do not align with the hypothesis.
The lack of significant results in the technology industry may be attributed to the small sample size, which limits the ability to draw meaningful conclusions or observe statistical significance. The technology industry in Indonesia is relatively nascent, and the limited number of companies in the industry that publish sustainability reports may contribute to this challenge.
Table 4. Hypothesis testing and result (OLS regression)
|
|
(1) |
(2) |
|
Constant |
0.172*** |
0.406*** |
|
|
(15.84) |
(6.68) |
|
ESGD |
-0.182*** |
-0.128*** |
|
|
(-6.83) |
(-4.78) |
|
SIZE |
|
-0.007*** |
|
|
|
(-3.09) |
|
AGE |
|
-0.004 |
|
|
|
(-0.66) |
|
LEV |
|
-0.127*** |
|
|
|
(-6.29) |
|
ROA |
|
-0.145*** |
|
|
|
(-4.36) |
|
Obs |
742 |
742 |
|
R2 |
0.059 |
0.147 |
|
Adj R2 |
0.058 |
0.132 |
|
*, **, *** significant at the 10, 5, and 1 percent levels |
||
|
( ) : t-statistics values |
||
Table 5. Regression results for each industry
|
|
Basic Material |
Consumer Cyclical |
Consumer Non-Cyclical |
Energy |
Healthcare |
|||||
|
|
(1) |
(2) |
(1) |
(2) |
(1) |
(2) |
(1) |
(2) |
(1) |
(2) |
|
Const |
0.159*** |
0.298* |
0.176*** |
0.453** |
0.197*** |
0.885*** |
0.117*** |
0.215** |
0.255*** |
0.904 |
|
|
(6.46) |
(1.73) |
(5.65) |
(2.55) |
(6.08) |
(4.76) |
(9.39) |
(2.32) |
(3.59) |
(1.37) |
|
ESGD |
-0.112* |
-0.110* |
-0.212** |
-0.166** |
-0.214*** |
-0.136* |
-0.102*** |
-0.050* |
-0.376** |
-0.400* |
|
|
(-0.15) |
(-1.73) |
(-2.50) |
(-1.84) |
(-2.62) |
(-1.67) |
(-3.65) |
(-1.67) |
(-2.12) |
(-1.92) |
|
SIZE |
|
0.001 |
|
-0.010 |
|
-0.022*** |
|
0.000 |
|
-0.018 |
|
|
|
(0.13) |
|
(-1.44) |
|
(-3.24) |
|
(0.034) |
|
(-0.96) |
|
AGE |
|
-0.036** |
|
0.004 |
|
-0.015 |
|
-0.025*** |
|
-0.019 |
|
|
|
(-2.13) |
|
(0.24) |
|
(-0.84) |
|
(-3.033) |
|
(-0.41) |
|
LEV |
|
-0.170** |
|
-0.077 |
|
-0.070 |
|
-0.094*** |
|
-0.180 |
|
|
|
(-2.55) |
|
(-1.43) |
|
(-1.01) |
|
(-3.66) |
|
(-1.01) |
|
ROA |
|
-0.200 |
|
-0.031 |
|
-0.065 |
|
-0.087*** |
|
-0.130 |
|
|
|
(-1.59) |
|
(-0.23) |
|
(-0.685) |
|
(-2.68) |
|
(-1.06) |
|
Obs |
137 |
137 |
85 |
85 |
143 |
143 |
104 |
104 |
41 |
41 |
|
R2 |
0.025 |
0.129 |
0.070 |
0.135 |
0.047 |
0.165 |
0.116 |
0.320 |
0.104 |
0.155 |
|
Adj R2 |
0.018 |
0.096 |
0.059 |
0.080 |
0.040 |
0.135 |
0.107 |
0.285 |
0.081 |
0.034 |
|
|
Industrials |
Infrastructure |
Technology |
Transportation and Logistics |
Property and Real Estate |
|||||
|
|
(1) |
(2) |
(1) |
(2) |
(1) |
(2) |
(1) |
(2) |
(1) |
(2) |
|
Const |
0.209*** |
0.262 |
0.177*** |
0.677*** |
0.211* |
1.669 |
0.467*** |
1.008* |
0.165*** |
-0.072 |
|
|
(6.14) |
(1.40) |
(6.50) |
(4.52) |
(2.16) |
(1.64) |
(4.06) |
(1.83) |
(4.72) |
(-0.262) |
|
ESGD |
-0.279*** |
-0.174* |
-0.227*** |
-0.126* |
-0.287 |
0.366 |
-1.154*** |
-1.277*** |
-0.224** |
-0.195* |
|
|
(-2.70) |
(-1.74) |
(-2.88) |
(-1.69) |
(-1.08) |
(1.139) |
(-2.86) |
(-2.95) |
(-2.28) |
(-1.73) |
|
SIZE |
|
0.001 |
|
-0.020*** |
|
-0.049 |
|
-0.015 |
|
0.008 |
|
|
|
(0.09) |
|
(-3.322) |
|
(-1.62) |
|
(-0.69) |
|
(0.03) |
|
AGE |
|
-0.011 |
|
0.028* |
|
-0.038 |
|
-0.001 |
|
0.009 |
|
|
|
(-0.287) |
|
(1.85) |
|
(-0.58) |
|
(-0.025) |
|
(0.552) |
|
LEV |
|
-0.224* |
|
-0.075* |
|
-0.474 |
|
-0.186** |
|
-0.180* |
|
|
|
(-1.93) |
|
(-1.74) |
|
(-1.29) |
|
(-2.17) |
|
(-1.81) |
|
ROA |
|
-0.619*** |
|
-0.149 |
|
-0.797* |
|
-0.041 |
|
-0.777*** |
|
|
|
(-2.82) |
|
(-1.61) |
|
(-1.94) |
|
(-0.40) |
|
(-3.18) |
|
Obs |
44 |
44 |
88 |
88 |
13 |
13 |
23 |
23 |
64 |
64 |
|
R2 |
0.148 |
0.364 |
0.088 |
0.322 |
0.096 |
0.631 |
0.280 |
0.580 |
0.077 |
0.231 |
|
Adj R2 |
0.128 |
0.280 |
0.077 |
0.281 |
0.014 |
0.368 |
0.246 |
0.457 |
0.062 |
0.165 |
*, **, *** significant at the 10, 5, and 1 percent levels
( ): t-statistics values
However, despite the inconclusive results in the technology industry, the overall findings of this study align with previous research conducted in the context of developed countries. Notably, the results are consistent with studies such as those by Apergis et al. [26] on S&P 500 companies and Eliwa et al. [5] on 15 EU countries. These consistent findings across different contexts strengthen the robustness and generalizability of the observed negative relationship between ESG disclosure and the cost of debt.
In line with legitimacy theory, the findings of this study suggest that the disclosure of a company's ESG performance contributes to a reduction in the cost of capital (COC) by influencing the perceptions of financial resource providers [22]. ESG disclosure plays a role in diminishing information asymmetry between companies and lenders, facilitating lending institutions in evaluating the risk of borrower default [4]. The research results indicate that companies, both in developed and developing countries, receive favorable treatment from lenders when they exhibit higher transparency in reporting ESG information. This favorable treatment manifests in the form of lower costs of capital, underscoring the importance of reduced information asymmetry in shaping financial relationships.
Indeed, companies with higher ESG disclosure are perceived as having lower business risk. ESG disclosure enhances a company's commitment to and relationships with stakeholders, fostering a sense of mutual trust. This, in turn, contributes to a lower risk of disruptions to company operations. The establishment of strong relationships and trust with stakeholders positions the company to weather challenges more effectively. As a result, these companies are better equipped to sustain profitability, reducing the risk of default. This aligns with the notion that a robust ESG framework not only addresses environmental and social concerns but also contributes to overall risk management and resilience in the face of uncertainties [45].
4.3 Robustness test
Tables 6 and 7 provide regression results utilizing four panel data regression models, demonstrating the robustness of the analysis. Regression analysis, employing different models, serves as a robustness test and confirms the consistency of the results with the main findings. This approach enhances the reliability and validity of the study by showcasing that the observed relationships between ESG disclosure and the cost of debt persist across various model specifications. The use of multiple regression models contributes to a more comprehensive understanding and reinforces the confidence in the reported outcomes.
Table 6. Robustness test with OLS standard error and SCC model
|
|
OLS Standard |
SCC Model |
||
|
|
(1) |
(2) |
(1) |
(2) |
|
Constant |
0.172*** |
0.407*** |
0.144*** |
0.376*** |
|
|
(14.59) |
(5.94) |
(45.92) |
(13.86) |
|
ESGD |
-0.182*** |
-0.128*** |
-0.199*** |
-0.139*** |
|
|
(-7.13) |
(-5.66) |
(-18.55) |
(-18.06) |
|
SIZE |
|
-0.007*** |
|
-0.006*** |
|
|
|
(-3.15) |
|
(-4.09) |
|
AGE |
|
-0.004 |
|
-0.003 |
|
|
|
(-0.54) |
|
(-0.37) |
|
LEV |
|
-0.127*** |
|
-0.128*** |
|
|
|
(-7.06) |
|
(-31.34) |
|
ROA |
|
-0.145*** |
|
-0.147*** |
|
|
|
(-4.21) |
|
(-8.94) |
|
Obs |
742 |
742 |
742 |
742 |
|
R2 |
0.059 |
0.147 |
0.069 |
0.149 |
|
*, **, *** significant at the 10, 5, and 1 percent levels |
||||
|
( ) : t-statistics values |
||||
Table 7. Robustness test with fix effect model and random effect model
|
|
FEM |
REM |
||
|
|
(1) |
(2) |
(1) |
(2) |
|
Constant |
0.126*** |
0.074 |
0.146*** |
0.476*** |
|
|
(15.44) |
(0.17) |
(15.09) |
(4.77) |
|
ESGD |
-0.055** |
-0.070*** |
-0.087*** |
-0.061*** |
|
|
(-2.53) |
(-2.90) |
(-4.24) |
(-3.04) |
|
SIZE |
|
-0.007 |
|
-0.009** |
|
|
|
(-0.49) |
|
(-2.44) |
|
AGE |
|
0.091** |
|
-0.012 |
|
|
|
(2.03) |
|
(-1.27) |
|
LEV |
|
-0.208*** |
|
-0.168*** |
|
|
|
(-8.01) |
|
(-8.25) |
|
ROA |
|
-0.072*** |
|
-0.093*** |
|
|
|
(-2.60) |
|
(-3.68) |
|
Obs |
742 |
742 |
742 |
742 |
|
R2 |
0.014 |
0.141 |
0.097 |
0.148 |
|
*, **, *** significant at the 10, 5, and 1 percent levels |
||||
|
( ) : t-statistics and z-statistics values |
||||
The primary objective of this paper is to enhance our comprehension of the implications of ESG disclosure within the context of developing countries. The focus is particularly on investigating whether Indonesian companies receive favorable treatment from lending institutions in the form of reduced costs of debt capital as a result of their ESG disclosure practices. Drawing on a sample of non-financial companies, comprising 742 observations and segmented into ten industries, our findings indicate that companies stand to benefit from high levels of ESG disclosure. Specifically, this benefit manifests in the form of easier access to funding, translating into a lower cost of capital charged by lending institutions.
The findings of this study have both academic and practical implications. New insights into the relationship between ESG practices and the cost of debt suggest that ESG practices offer benefits not only for companies in developed countries but also for those in developing countries. Consequently, these findings may provide valuable considerations for regulators and policymakers contemplating the adoption of ESG practices within their respective contexts. In the context of Indonesia, the results of this study could inform stricter policies related to ESG disclosure. While ESG disclosure is currently regulated under Indonesia's Financial Services Authority Regulation No. 51 of 2017, this regulation lacks clear rewards and penalties, resulting in numerous public companies in Indonesia still not disclosing ESG information. Therefore, these findings could support the establishment of more effective reward and penalty systems, given the demonstrated positive impact of ESG activities and disclosures on various stakeholders. Furthermore, companies can leverage these findings to enhance their ESG disclosures, foster better practices, and potentially benefit from related advantages, such as reduced debt costs.
Although this research makes a significant contribution, it still has some limitations. First, this study focuses solely on ESG reporting derived from corporate sustainability reports. Due to the lack of discipline among some companies in Indonesia in publishing sustainability reports, this may result in a limited dataset. Future research might explore additional sources of ESG information, such as company websites, annual reports, and AGMs, to gain a more comprehensive understanding of ESG practices and their impact on the cost of debt. Second, this study employs a single measurement to assess the cost of debt. Future research could utilize alternative measurements, such as bond yields, to achieve more comprehensive results. Additionally, future studies may consider incorporating other variables, such as the cost of equity, to examine the impact of ESG disclosure from an investor's perspective.
[1] Buallay, A. (2019). Is sustainability reporting (ESG) associated with performance? Evidence from the European banking sector. Management of Environmental Quality: An International Journal, 30(1): 98-115. https://doi.org/10.1108/MEQ-12-2017-0149
[2] García-Sánchez, I.M., Raimo, N., Marrone, A., Vitolla, F. (2020). How does integrated reporting change in light of COVID-19? A revisiting of the content of the integrated reports. Sustainability (Switzerland), 12(18): 7605. https://doi.org/10.3390/su12187605
[3] Nicolo, G., Zanellato, G., Manes-Rossi, F., Tiron-Tudor, A. (2021). Corporate reporting metamorphosis: Empirical findings from state-owned enterprises. Public Money and Management, 41(2): 138-147. https://doi.org/10.1080/09540962.2020.1719633
[4] Raimo, N., Caragnano, A., Zito, M., Vitolla, F., Mariani, M. (2021). Extending the benefits of ESG disclosure: The effect on the cost of debt financing. Corporate Social Responsibility and Environmental Management, 28(4): 1412-1421. https://doi.org/10.1002/csr.2134
[5] Eliwa, Y., Aboud, A., Saleh, A. (2021). ESG practices and the cost of debt: Evidence from EU countries. Critical Perspectives on Accounting, 79: 102097. https://doi.org/10.1016/j.cpa.2019.102097
[6] Jones, S., Frost, G., Loftus, J., Van Der Laan, S. (2007). An empirical examination of the market returns and financial performance of entities engaged in sustainability reporting. Australian Accounting Review, 17(41): 78-87. https://doi.org/10.1111/j.1835-2561.2007.tb00456.x
[7] Manes-Rossi, F., Nicolò, G., Tiron Tudor, A., Zanellato, G. (2020). Drivers of integrated reporting by state-owned enterprises in Europe: A longitudinal analysis. Meditari Accountancy Research, 29(3): 586-616. https://doi.org/10.1108/MEDAR-07-2019-0532
[8] Vitolla, F., Raimo, N., Rubino, M. (2019). Appreciations, criticisms, determinants, and effects of integrated reporting: A systematic literature review. Corporate Social Responsibility and Environmental Management, 26(2): 518-528. https://doi.org/10.1002/csr.1734
[9] Nirino, N., Miglietta, N., Salvi, A. (2020). The impact of corporate social responsibility on firms’ financial performance, evidence from the food and beverage industry. British Food Journal, 122(1): 1-13. https://doi.org/10.1108/BFJ-07-2019-0503
[10] Kim, K.H., Kim, M.C., Qian, C. (2018). Effects of corporate social responsibility on corporate financial performance: A competitive-action perspective. Journal of Management, 44(3): 1097-1118. https://doi.org/10.1177/0149206315602530
[11] Franceschelli, M.V., Santoro, G., Giacosa, E., Quaglia, R. (2019). Assessing the determinants of performance in the recycling business: Evidence from the Italian context. Corporate Social Responsibility and Environmental Management, 26(5): 1086-1099. https://doi.org/10.1002/csr.1788
[12] Eccles, R.G., Serafeim, G., Krzus, M.P. (2011). Market interest in nonfinancial information. Journal of Applied Corporate Finance, 23(4): 113-127. https://doi.org/10.1111/j.1745-6622.2011.00357.x
[13] Fernandez, W.D., Burnett, M.F., Gomez, C.B. (2019). Women in the boardroom and corporate social performance: Negotiating the double bind. Management Decision, 57(9): 2201-2222. https://doi.org/10.1108/MD-08-2017-0738
[14] Tamimi, N., Sebastianelli, R. (2017). Transparency among S&P 500 companies: An analysis of ESG disclosure scores. Management Decision, 55(8): 1660-1680. https://doi.org/10.1108/MD-01-2017-0018
[15] Botosan, C.A. (1997). Disclosure level and the cost of equity capital. Accounting Review, 72(3): 323-349.
[16] Hail, L., Leuz, C. (2006). International differences in the cost of equity capital: Do legal institutions and securities regulation matter? Journal of Accounting Research, 44(3): 485-531. https://doi.org/10.1111/j.1475-679X.2006.00209.x
[17] Vitolla, F., Raimo, N., Rubino, M., Garzoni, A. (2019). How pressure from stakeholders affects integrated reporting quality. Corporate Social Responsibility and Environmental Management, 26(6): 1591-1606. https://doi.org/10.1002/csr.1850
[18] Albarrak, M.S., Elnahass, M., Salama, A. (2019). The effect of carbon dissemination on cost of equity. Business Strategy and the Environment, 28(6): 1179-1198. https://doi.org/10.1002/bse.2310
[19] Huber, B.M., Comstock, M. (2017). ESG reports and ratings: What they are, why they matter? Oversight role: A practical guide. The Corporate Governance Advisor, 25(5): 1-12.
[20] Cheng, B., Ioannou, I., Serafeim, G. (2014). Corporate social responsibity and access to finance. Strategic Management Journal, 35: 1-23. https://doi.org/10.2139/ssrn.1847085
[21] El Ghoul, S., Guedhami, O., Kwok, C.C.Y., Mishra, D.R. (2011). Does corporate social responsibility affect the cost of capital? Journal of Banking and Finance, 35(9): 2388-2406. https://doi.org/10.1016/j.jbankfin.2011.02.007
[22] Gholami, A., Sands, J., Shams, S. (2022). Corporates’ sustainability disclosures impact on cost of capital and idiosyncratic risk. Meditari Accountancy Research. https://doi.org/10.1108/MEDAR-06-2020-0926
[23] Hubbard, R.G. (1998). Capital-market imperfections and investment. Journal of Economic Literature, 36(1): 193-225.
[24] Weber, O., Diaz, M., Schwegler, R. (2014). Corporate social responsibility of the financial sector - strengths, weaknesses and the impact on sustainable development. Sustainable Development, 22(5): 321-335. https://doi.org/10.1002/sd.1543
[25] Weber, O., Scholz, R.W., Michalik, G. (2010). Incorporating sustainability criteria into credit risk management. Business Strategy and the Environment, 19(1): 39-50. https://doi.org/10.1002/bse.636
[26] Apergis, N., Poufinas, T., Antonopoulos, A. (2022). ESG scores and cost of debt. Energy Economics, 112: 106186. https://doi.org/10.1016/j.eneco.2022.106186
[27] Gjergji, R., Vena, L., Sciascia, S., Cortesi, A. (2021). The effects of environmental, social and governance disclosure on the cost of capital in small and medium enterprises: The role of family business status. Business Strategy and the Environment, 30(1): 683-693. https://doi.org/10.1002/bse.2647
[28] Harmoni, A. (2013). Stakeholder-based analysis of sustainability report: A case study on mining companies in Indonesia. International Conference on Eurasian Economies, 40: 204-210. https://doi.org/10.36880/c04.00704
[29] Bowmann-Larsen L., Wiggen O. (2004). Responsibility in World Business: Managing Harmful Side-Effects of Corporate Activity. USA: United Nations University Press.
[30] Freeman, R.E.E., McVea, J. (1984). A stakeholder approach to strategic management. In SSRN Electronic Journal. https://doi.org/10.2139/ssrn.263511
[31] Vitolla, F., Raimo, N. (2018). Adoption of integrated reporting: Reasons and benefits—A case study snalysis. International Journal of Business and Management, 13(12): 244. https://doi.org/10.5539/ijbm.v13n12p244
[32] Hörisch, J., Freeman, R.E., Schaltegger, S. (2014). Applying stakeholder theory in sustainability management: Links, similarities, dissimilarities, and a conceptual framework. Organization and Environment, 27(4): 328-346. https://doi.org/10.1177/1086026614535786
[33] Hörisch, J., Schaltegger, S., Freeman, R.E. (2020). Integrating stakeholder theory and sustainability accounting: A conceptual synthesis. Journal of Cleaner Production, 275. https://doi.org/10.1016/j.jclepro.2020.124097
[34] Fonseka, M., Rajapakse, T., Richardson, G. (2019). The effect of environmental information disclosure and energy product type on the cost of debt: Evidence from energy firms in China. Pacific Basin Finance Journal, 54: 159-182. https://doi.org/10.1016/j.pacfin.2018.05.001
[35] Kleimeier, S., Viehs, M. (2016). Carbon disclosure, emission levels, and the cost of debt. SSRN Electronic Journal, 1-42. https://doi.org/10.2139/ssrn.2719665
[36] Orens, R., Aerts, W., Cormier, D. (2010). Web-based non-financial disclosure and cost of finance. Journal of Business Finance and Accounting, 37(9-10): 1057-1093. https://doi.org/10.1111/j.1468-5957.2010.02212.x
[37] Guthrie, J., Parker, L.D. (1989). Corporate social reporting: A rebuttal of legitimacy theory. Accounting and Business Research, 19(76): 343-352. https://doi.org/10.1080/00014788.1989.9728863
[38] Suchman, M.C. (1995). Managing legitimacy: Strategic and institutional approaches. In The Academy of Management Review. Academy of Management Stable.
[39] Deegan, C. (2002). Introduction: The legitimising effect of social and environmental disclosures - A theoretical foundation. Accounting, Auditing & Accountability Journal, 15(3): 282-311. https://doi.org/10.1108/09513570210435852
[40] Odriozola, M.D., Baraibar-Diez, E. (2017). Is corporate reputation associated with quality of CSR reporting? Evidence from Spain. Corporate Social Responsibility and Environmental Management, 24(2): 121-132. https://doi.org/10.1002/csr.1399
[41] Reverte, C. (2009). Do better governed firms enjoy a lower cost of equity capital?: Evidence from Spanish firms. Corporate Governance, 9(2): 133-145. https://doi.org/10.1108/14720700910946587
[42] Ng, A.C., Rezaee, Z. (2015). Business sustainability performance and cost of equity capital. Journal of Corporate Finance, 34: 128-149. https://doi.org/10.1016/j.jcorpfin.2015.08.003
[43] Venkataraman, S., Williams, R. (2020). ESG risks material in 33% of Moody’s 2019 private-sector issuer rating actions. Moody’s. https://www.responsible-investor.com/esg-briefing-esg-risks-material-in-one-third-of-moody-s-2019-credit-ratings/.
[44] Henisz, W.J., McGlinch, J. (2019). ESG, material credit events, and credit risk. Journal of Applied Corporate Finance, 31(2): 105-117. https://doi.org/10.1111/jacf.12352
[45] Jones, T.M. (1995). Instrumental stakeholder theory: A synthesis of ethics and economics. The Academy of Management Review, 20(2): 404-437.
[46] Dhaliwal, D.S., Li, O.Z., Tsang, A., Yang, Y.G. (2011). Voluntary nonfinancial disclosure and the cost of equity capital: The initiation of corporate social responsibility reporting. Accounting Review, 86(1): 59-100. https://doi.org/10.2308/accr.00000005
[47] Jafar, R., Basuki, B., Windijarto, W., Setiawan, R. (2024). Family control and corporate performance: The role of independent commissioners in reducing agency problems. Gestão & Produção, 31: e7923. https://doi.org/10.1590/1806-9649-2024v31e7923
[48] Xu, H., Xu, X., Yu, J. (2021). The impact of mandatory CSR disclosure on the cost of debt financing: Evidence from China. Emerging Markets Finance and Trade, 57(8): 2191-2205. https://doi.org/10.1080/1540496X.2019.1657401
[49] Francis, J., LaFond, R., Olsson, P., Schipper, K. (2005). The market pricing of accruals quality. Journal of Accounting and Economics, 39(2): 295-327. https://doi.org/10.1016/j.jacceco.2004.06.003
[50] Gray, P., Koh, P.S., Tong, YH. (2009). Accruals quality, information risk and cost of capital: Evidence from Australia. Journal of Business Finance and Accounting, 36(1-2): 51-72. https://doi.org/10.1111/j.1468-5957.2008.02118.x
[51] Persakis, A., Iatridis, G.E. (2017). The joint effect of investor protection, IFRS and earnings quality on cost of capital: An international study. Journal of International Financial Markets, Institutions and Money, 46: 1-29. https://doi.org/10.1016/j.intfin.2016.10.001
[52] Pittman, J.A., Fortin, S. (2004). Auditor choice and the cost of debt capital for newly public firms. Journal of Accounting and Economics, 37(1): 113-136. https://doi.org/10.1016/j.jacceco.2003.06.005
[53] Li, F. (2010). The information content of forward-looking statements in corporate filings – A naïve bayesian machine learning approach. Journal of Accounting Research, 48(5): 1049-1102. https://doi.org/10.1111/j.1475-679X.2010.00382.x
[54] Alexander, D.L., Thistle, P.D. (1999). Market power, efficiency and the dispersion of systematic risk. Review of Industrial Organization, 14(4): 377-390.
[55] Benlemlih, M., Girerd-Potin, I. (2017). Corporate social responsibility and firm financial risk reduction: On the moderating role of the legal environment. Journal of Business Finance and Accounting, 44(7-8): 1137-1166. https://doi.org/10.1111/jbfa.12251
[56] Pukthuanthong, K., Walker, T.J., Thiengtham, D.N. (2013). Does family ownership create or destroy value? Evidence from Canada. International Journal of Managerial Finance, 9(1): 13-48. https://doi.org/10.1108/17439131311298511
[57] Goss, A., Roberts, G.S. (2011). The impact of corporate social responsibility on the cost of bank loans. Journal of Banking and Finance, 35(7): 1794-1810. https://doi.org/10.1016/j.jbankfin.2010.12.002
[58] Nicolò, G., Zampone, G., Sannino, G., De Iorio, S. (2022). Sustainable corporate governance and non-financial disclosure in Europe: does the gender diversity matter? Journal of Applied Accounting Research, 23(1): 227-249. https://doi.org/10.1108/JAAR-04-2021-0100
[59] Setiawan, R., Gestanti, L. (2022). CEO characteristics, firm policy, and firm performance. International Journal of Business and Society, 23(1): 371-389.
[60] Wijaya, L.I., Narsa, I.M., Irwanto, A., Setiawan, R. (2021). Are investors' desires for dividend increases stronger than dividend initiation? International Journal of Trade and Global Markets, 14(6): 559-579.
[61] Singh, S.K., Basuki, B., Setiawan, R. (2021). The effect of non-performing loan on profitability: Empirical evidence from Nepalese commercial banks. The Journal of Asian Finance, Economics and Business, 8(4): 709-716.
[62] Houqe, M.N., Ahmed, K., Richardson, G. (2020). The effect of environmental, social, and governance performance factors on firms’ cost of debt: International evidence. International Journal of Accounting, 2050014: 1-30. https://doi.org/10.1142/S1094406020500146
[63] Hamrouni, A., Boussaada, R., Ben Farhat Toumi, N. (2019). Corporate social responsibility disclosure and debt financing. Journal of Applied Accounting Research, 20(4): 394-415. https://doi.org/10.1108/JAAR-01-2018-0020
[64] Setiawan, R., Rachmansyah, A.B. (2019). Firm characteristics, macroeconomic variables and cash holdings in Indonesia and Singapore. International Journal of Innovation, Creativity and Change, 9(8): 265-286.
[65] Wijaya, L.I., Narsa, I.M., Irwanto, A., Setiawan, R. (2022). Is information asymmetry the ground of investor sentiment in the Indonesian capital market? International Journal of Business and Globalisation, 32(1): 78-95.
[66] Hidayatulloh, M.S., Setiawan, R. (2020). Cash flow, investment, and internationalisation strategy. International Journal of Innovation, Creativity and Change, 13(4): 889-900.
[67] Gujarati, D.N., Porter, D.C. (2009). Basic Econometrics. 5th Edition, McGraw Hill Inc., New York.
[68] Al-Duais, S.D., Qasem, A., Wan-Hussin, W.N., Bamahros, H.M., Thomran, M., Alquhaif, A. (2021). Ceo characteristics, family ownership and corporate social responsibility reporting: The case of Saudi Arabia. Sustainability (Switzerland), 13(21): 12237. https://doi.org/10.3390/su132112237
[69] Petersen, M. (2007). Robust estimation in the linear model. The Annals of Statistics, 2(3). https://doi.org/10.1214/aos/1176342717
[70] Hair, J.F., Black, W.C., Rabin, B.J., Anderson, R.E. (2010). Multivariate Data Analysis: A Global Perspective, 7th ed., Pearson, New Jersey, NJ. https://doi.org/https://doi.org/10.1002/ gsj.1199