Open Journal Systems

(1)

Analysis of the Relationship Between Corporate Sustainability and Banks’ Financial Performance During the Covid-19 Pandemic

Albert Gunawan* and Yunieta Anny Nainggolan

School of Business and Management, Institut Teknologi Bandung, Indonesia Email: christoforus_ [email protected]

Abstract - This study aims to understand the relationship between corporate sustainability and banks’ financial performance during the Covid-19 pandemic. There are 506 banks from 56 countries observed. For data analysis, the author utilized panel data regression. From 32 models, corporate sustainability indicators are significant in 25 models. However, the findings show a mixed sign of corporate sustainability impact on banks’ financial performance. There is a robust finding that corporate sustainability indicators are significant and negatively correlated with the financial performances, which are ROE, ROA, and Tobin’s Q.

Nonetheless, a different relationship is found with the NPL Ratio. Thus, even though the result is mixed, the finding strongly inclined to reject the hypothesis that corporate sustainability could boost banks’ financial performance. The author analyzes the effect of the Covid-19 Pandemic in two ways: using the government stringency index and using the interaction variable. The government stringency index shows a negative impact on banks’ financial performance. Thus, there are robust findings that the government response to the Covid-19 amplifies the negative effect of corporate sustainability on banks’ financial performance.

Keywords – Corporate Sustainability, banks’ financial performance, the Covid-19 pandemic

I. INTRODUCTION

The research of the relationship between corporate sustainability and corporate financial performance has had a prominent place in the literature for the past 40 years [1].

Prior empirical studies show mixed evidence on the relationship between corporate sustainability and financial performance despite an existing theory that support the positive relationship between those two variables [2]. In 2019, the world was shocked by the emergence of a new virus, the Coronavirus Diseases 2019 or Covid-19. During the Covid-19 pandemic, the relation of corporate sustainability and banks’ financial performance became questioned again. Although corporate sustainability initiatives are usually encouraged to improve and sustain long-term financial performance, whether companies should invest in corporate sustainability to help during difficult times remains controversial. Therefore, a thorough understanding of corporate sustainability’s effect on banks’

financial performance during the Covid-19 pandemic is needed to enrich the literature by exploring new facts and examine the related theory. The research offers a contribution to the literature by providing a global analysis of the corporate sustainability and financial performance of an essential homogenous industry, which is the banking

industry, covering the period of global crisis due to the Covid-19 pandemic and the government policies.

II. METHODOLOGY

The author focuses on analyzing corporate sustainability and banks’ financial performance during the Covid-19 pandemic. The dependent variables are Return to Assets (ROA), Return to Equity (ROE), Tobin’s Q, and NPL Ratio. The independent variables are ESG score, E score, S score, G score, and interaction variables which calculated as corporate sustainability indicator multiplied by the government stringency index. The control variables are equity-to-asset, loan-to-deposit, total assets, government stringency index, GDP growth, and countries dummies.

The author collected secondary data of 609 banks from 58 countries as the initial observation. The author gathered five years of data from 2016-2020 because most ESG data availability for banks mostly began in 2015. The list of firms categorized as Bank and Financial Service by Refinitiv. The secondary data are collected from several sources: Refinitiv, banks’ annual report, International Financial Statistics, OxCGRT, and countries official website. Using Refinitiv and banks’ annual report, the author collected Return on Asset, Return on Equity, NPL Ratio, Tobin’s Q, ESG score, E-score, S-score, and G- score. For the control variables, the author also used Refinitiv, except for Gross Domestic Product (GDP) growth data and the Government Stringency Index. The data of GDP is collected through International Financial Statistics and countries official website. The Government Stringency Index is provided by the OxCGRT that can be accessed from Our World in Data website [3].

After collecting the initial data, the author observed the data validity, such as the business operation and availability of ESG score data. Several firms categorized as a bank, but do not offer banking services. Those firms focus more on other financial services, such as leasing.

Several firms also have no ESG score, which the author could not analyze the relationship of the variables. Thus, the author removes the data. After filtering the data, only 506 banks from 56 countries have the data required. The author uses panel data regression to analyze the available data.

The author uses a panel data regression to test the hypothesis. Before regress the data, the author run the classical assumption test, which are normality test, multicollinearity test, heteroscedasticity test, and

(2)

autocorrelation test. Then, the author determine which model is suitable to estimate the data using the Chow Test, Hausman Test, and Lagrange Multiplier test.

This study employs the Shapiro-Wilk (SW) test of normality. This test has been used in previous studies [4]

[5]. Similar to references [4], [6], [7], [8], multicollinearity is tested using variance inflation factor. The Breusch- Pagan test will be run to test the presence of heteroscedasticity as previous studies done [4] [8] . Lastly, the presence of autocorrelation can be identified using Wooldridge test. Wooldridge test uses residuals from a regression in first-differences.

For testing hypotheses, the author uses a panel regression model. The model is built based on the literature review. This model is an adaptation from the previous researches [8] [9] [10]. In this research, there are two main models. The first one only uses one independent variable, which is ESG score or E score or S score or G score as the corporate sustainability indicator. In the second model, the author adds interaction variable which multiplies corporate sustainability with the government stringency index to understand the effect of the government response to the Covid-19 deeper. Below are the estimation models:

Model 1

𝐶𝐹𝑃_𝑖𝑗𝑡= 𝐶 + 𝛽₁× 𝐶𝑆𝑃_{𝑘𝑗𝑡−1}+ 𝛽₂× 𝐺𝑅_𝑐𝑡+ 𝛽₃× 𝐸𝐴_𝑗𝑡+ 𝛽₄× 𝐿𝐷_𝑗𝑡+ 𝛽₅× 𝑇𝐴_𝑗𝑡+ 𝛽₆× 𝐺𝐷𝑃_𝑐𝑡+ 𝛽₇×

𝐶𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠 + 𝜀_𝑗𝑡 (1)

Model 2

𝐶𝐹𝑃_𝑖𝑗𝑡= 𝐶 + 𝛽₁× 𝐶𝑆𝑃_{𝑘𝑗𝑡−1}+ 𝛽₂× (𝐶𝑆𝑃_{𝑘𝑗𝑡−1}× 𝐺𝑅_𝑐𝑡)+ 𝛽₃× 𝐺𝑅_𝑐𝑡+ 𝛽₄× 𝐸𝐴_𝑗𝑡+ 𝛽₅× 𝐿𝐷_𝑗𝑡+ 𝛽₆× 𝑇𝐴_𝑗𝑡+ 𝛽₇× 𝐺𝐷𝑃_𝑐𝑡+ 𝛽₈× 𝐶𝑜𝑢𝑛𝑡𝑟𝑖𝑒𝑠 + 𝜀_𝑗𝑡 (2)

In the equation above, i = 1, 2, 3, and 4, so that CFP1

is Return on Assets (ROA), CFP2 is Return on Equity (ROE), and CFP3 is Tobin’s Q, and CFP4 is Nonperforming loans (NPL ratio). CFPijt represents CFP for bank j in time- period t. Also, k is 1 to 4, so that CSP1 to CSP44 measures ESGjt-1, Ejt-1, Sjt-1, Gjt-1, representing the corporate sustainability indicator measured as ESG-score, Environmental-score, Social-score, Governance-score, respectively for bank j in time-period t-1. Specifically for model 2, (CSPkjt-1 x GRct) is an interaction variable that multiplies each corporate sustainability indicators with the government stringency index, namely ESG score*Stringency Index, E score*Stringency Index, S score*Stringency Index, and G score*Stringency Index.

GRct represent the pandemic specific variable which is the Oxford Covid-19 Government Response Tracker (OxCGRT) that measure the stringency index. In this research, the author simplifies the variable name as the government stringency index. Each country has different stringency index, which represented by C in the variable.

Also, t is the time-period.

There are three bank specific control variables, which are bank leverage, bank liquidity, and bank size, which represented by equity-to-asset, loan-to-deposit, and total

assets, respectively. In the equation above, the variables are for bank j in time-period t. GDPjt represent the gross domestic product growth for each country in time-period t.

Countries is the country dummy which represent the country fixed effect. εjt represents the error term.

III. RESULTS

A. Descriptive Statistics

To analyze the relationship between corporate sustainability and banks’ financial performance, the author uses variables divided into three categories. First, dependent variables consist of Return on Assets (ROA), Return on Equity (ROE), Tobin’s Q (TOBIN), and Nonperforming loan Ration (NPL). Second, independent variable consists of ESG score, E score, S score, G Score, and interaction variables that are multiplication of corporate sustainability indicators with the government stringency index. Lastly, control variables consist of bank leverage (equity-to-asset), bank liquidity (loan-to-deposit), bank size (total assets), pandemic-specific variables (government stringency index), macroeconomics (GDP growth), and country fixed effect. The author develops a descriptive statistic for all variables to gather more insight for the analysis.

TABLE I DESCRIPTIVE STATISTICS

Table 4.1 shows the research’s number of observations, mean, standard deviation (Std. Dev.), minimum, and a maximum of all variables. Unit measurement of (1) ESG score, (2) E score, (3) S score, and (4) G score are decimal and using the prior year score (t-1), (5) equity-to-asset is a ratio, (6) loan-to-deposit is a ratio, (7) total assets is in USD billion, (8) governance stringency index is decimal, (9) GDP growth is decimal, and (10) country fixed effect is dummy variable which represents each country. The samples are 506 banks from 56 countries during the fiscal year 2016- 2020.

As demonstrated in Table I, all the variables are more than 2.400 observations, which is large enough to be analyzed. During five years, the mean of banks’ financial performance shows a positive result. However, some banks suffer from unprofitable business operations, as indicated by negative ROA and ROE in the minimum column.

Interestingly, several banks, such as in Japan and Qatar, claim that there is no nonperforming loan. The low NPL ratio reflects a good credit risk management. Unlike ROA, ROE, and Tobin’s Q, the lower the NPL Ratio, the preferable it is.

Variable Labels Observation Mean Std. Dev. Min Max

Return on Assets ROA 2.520 1,3493 1,1186 -5,5631 12,0825 Return on Equity ROE 2.519 12,7355 8,9139 -92,3261 81,0551

Tobin's Q TOBIN 2.496 0,1365 0,1070 0,0000 1,4270

Nonperforming Loan Ratio NPL 2.456 0,0308 0,0647 0,0000 1,0297

ESG ESG 2.224 44,8580 20,4605 0,0000 94,3071

E E 2.224 26,4859 31,1075 0,0000 97,4330

S S 2.224 45,4572 23,8222 0,0000 97,2430

G G 2.224 51,3812 21,7299 0,0000 99,3762

Equity-to-asset EA 2.521 0,1034 0,0368 0,0000 0,3828

Loan-to-deposit LD 2.502 1,0912 7,0369 0,0143 342,2281

Total assets TA 2.522 197,8626 505,8126 1,2203 5110,3540 Stringency Index GR 2.530 11,4448 23,2674 0,0000 75,4586

GDP Growth GDP 2.530 0,0314 0,0657 -0,9991 0,4748

(3)

Moving to the independent variables, the highest score obtained by Governance Score (G Score), which is 99,3762. The G score also has the highest average. This is sure enough because most of firms have concerned about governance for a long time and enforced by the authority all around the world [8]. The combination of Environment, Social, and Governance only scored 94,3071 in the maximum column, putting it in the fourth position.

However, comparing the average score, the environment score has the lowest score, which is 26,4859. E score also has the highest standard deviation, which means the data spread widely. The low environment score could indicate that banks are less active in disclosing their information on the impact of the action on the environment.

For the control variables, only GDP growth has a negative value. Total assets have a large standard deviation, which is 505,8126. From this, we could understand that sizes of the banks are varied. The stringency index shows that the highest score is 75,4586.

As mentioned in the previous chapter, the stringency index could not be used as a parameter to determine the effectiveness of the policy. Instead, this indicator is used to understand how the government responds to the Covid-19 pandemic [3]. Thus, before the Covid-19 pandemic, the author gives 0 as the value.

B. Correlation Matrix

The author develops a correlation matrix for the variables to examine the multicollinearity issue. Using correlation matrix, correlation coefficients between each variable can be identified in a summarized way. Below is the table.

TABLE II CORRELATION MATRIX

The highest correlation coefficient as shown in Table 2 is 0,85. According to reference [11] in regression analysis, a multicollinearity problem will arise if the correlation coefficient among variables exceeds the rule of thumb level, which is 0,8. However, the author also calculates mean Variance Inflation Factors (VIF) as an alternative to testing the multicollinearity problem for each model. VIF can provide a better and more accurate result for models with more than two explanatory variables.

C. Relationship Between Corporate Sustainability and ROA

The author developed four models to understand the effect of corporate sustainability on banks’ ROA, which consider different independent variables, namely ESG score, E score, S score, and G score. Table 3 shows the panel data regression result that estimates the relationship between corporate sustainability and Return on Assets (ROA). There are several steps to regress the data, including normality test, multicollinearity test, heteroskedasticity test, and autocorrelation test.

There are 2.201 observations (N). The models’

probability is 0, which means the models are significant.

The R-squared within varied between 0,277 to 0,2814. The author also includes the country fixed effect.

Before regress the panel data, the author tests the normality, multicollinearity, heteroskedasticity, autocorrelation, and estimation model. First, the author tests the normality using the Shapiro-Wilk method. All models do not pass the test because the p-value is less than 0,05. However, the problem will not affect the credibility of the research because the data size is large [8]. Second, all models pass the multicollinearity test using the variance inflation factor (VIF) test because the results are less than 10. The mean VIF is between 1,15 to 1,22. The highest VIF is GDP growth, which is 2,87 (in models 1-4) and the lowest is the loan-to-deposit, which is 1,12 (in model 4).

Third, all models encounter heteroskedasticity problems since the P-value in the Breusch-Pagan tests are less than 0,05. The result is zero for all models. The author uses clustered standard error to overcome the problem, which is part of robust standard error. Using clustered standard error method, heteroskedasticity and autocorrelation problems could be treated. Fourth, the author decided on the estimation model using the Chow test, Hausman Test, and Lagrange Multiplier test. The author estimated models 1-4 using fixed-effect because the p-value in the Chow test rejects the null hypothesis, so does in the Hausman test.

The coefficient in models 1-4 is significant. Models 2 and 4 coefficient is significant with 5% significance level, while model 1 and 3 is significant with 10% significance level. All the coefficient shows a negative sign. The fourth model has the highest coefficient which is -1,7151, while the lowest is -1,5052. Thus, the coefficient in the fourth model has a larger magnitude.

Three out of four independent variables in models 1-4 are firmly significant with a 1% significance level and negatively correlated with the dependent variable. The significant independent variables are ESG score, E score, and G score. As stated in Table 3, the ESG score has the most negative impact on financial performance, which is - 0,0071, followed by the social score and environment score. This result could be interpreted that, ceteris paribus, one score increase in ESG score will reduce ROA by

ROA ROE TOBIN NPL ESG E S G EA LD TA GR GDP

ROA 1,0000 ROE 0,8336 1,0000 TOBIN 0,6817 0,4367 1,0000 NPL -0,0940 -0,1510 -0,0906 1,0000 ESG -0,1062 0,0026 -0,1580 0,0771 1,0000 E -0,1831 -0,0331 -0,2710 0,1223 0,8500 1,0000 S -0,0968 -0,0013 -0,1546 0,1064 0,9268 0,8199 1,0000 G -0,0271 0,0276 -0,0251 -0,0289 0,7310 0,4146 0,4468 1,0000 EA 0,5792 0,1832 0,6233 -0,0178 -0,2774 -0,4167 -0,2686 0,0821 1,0000 LD 0,0483 0,0760 0,0084 -0,0112 0,0091 0,0249 0,0238 -0,0264 0,0565 1,0000 TA -0,1561 -0,0446 -0,2322 -0,0326 0,4099 0,4665 0,3384 0,2946 -0,3183 -0,0721 1,0000 GR -0,1769 -0,2171 -0,1154 0,0000 0,0766 0,0501 0,0885 0,0381 -0,0304 -0,0077 0,0212 1,0000 GDP 0,2723 0,2580 0,1056 0,0122 -0,0118 -0,0189 0,0207 -0,0512 0,1185 0,0740 -0,0332 -0,4531 1,0000

(4)

TABLE III

PANEL (A) DEPENDENT VARIABLE: RETURN TO ASSET (ROA)

The table presents panel data regression to analyze the relationship between the ROA and corporate sustainability. The dependent variable is RAE, which is a ratio. The independent variables for models 1, 2, 3, and 4 are ESG score, E score, S score, and G score, respectively. Unit measurement of (1) ESG score, (2) E score, (3) S score, and (4) G score are decimal and using the prior year score (t-1), (5) equity-to-asset is a ratio, (6) loan-to-deposit is a ratio, (7) total assets is in USD billion, (8) governance stringency index is decimal, (9) GDP growth is decimal, and (10) country fixed effect is dummy variable which represents each country. The samples are 506 banks from 56 countries during the fiscal year 2016-2020. The Chow, Hausman, and Langrage Multiplier (LM) test are used to estimate the regression, whi ch is pooled least square, fixed effect, or random effect. All the models are not normal, but because the sample size was big, the study’s credibility is not influenced. Mean VIF is to detect multicollinearity problems.

Breusch-Pagan (BP) test is to detect heteroskedasticity. To overcome heteroscedasticity and autocorrelation problems, the author used clustered standard error. *, **, ***

denote statistical significance level at 1 percent, 5 percent, and 10 percent, respectively.

0,0071. The S score is -0,0060, meaning that, ceteris paribus, one score increase in S score will reduce ROA by 0,0060. Meanwhile, the E score, ceteris paribus, one score increase in E score will reduce ROA by 0,0035. However, the relationship between G score and ROA is insignificant.

For the control variables, equity-to-asset (EA), loan- to-deposit (LD), total assets in billion (TA), and government stringency index (GR) are significant. All equity-to-asset and government stringency indexes are significantly affecting the ROA with a 1% significance level. The loan-to-deposit variable is significant with 5%

significance level. The total assets are significant at 1% for models 1, 3, and 4. In model 2, the total assets are significant at a 5% significance level. However, the GDP growth is not significant in all models.

From four out of five significant control variables, only the stringency index negatively affects the ROA. The stringency index has a coefficient between -0,0061 to - 0,0056, meaning that, ceteris paribus, one score increase in stringency index could decrease ROA between 0,0056 to 0,0061. The equity-to-asset has a dominant positive relationship with the ROA, followed by loan-to-deposit and total assets. One percent increase in the equity-to-asset could, ceteris paribus, increase ROA by 0,224201 to 0,227162. As the banks’ revenue mainly from interest, by having one percent loan-to-deposit higher, they could improve the ROA between 0,0084877 to 0,0089898. If the bank larger, by having one billion more assets, ceteris paribus, the ROA will increase around 0,0004.

D. Relationship Between Corporate Sustainability and ROE

The author developed four models to understand the effect of corporate sustainability on banks’ ROE, which consider different independent variables, namely ESG score, E score, S score, and G score. Table 4 shows the panel data regression result that estimates the relationship between corporate sustainability and Return on Equity (ROE). There are several steps to regress the data, including normality test, multicollinearity test, heteroskedasticity test, and autocorrelation test.

The R-squared within varied between 0,1819 to 0,1851.

The author also includes the country fixed effect.

Before regress the panel data, the author tests the normality, multicollinearity, heteroskedasticity, autocorrelation, and estimation model. First, the author tests the normality using the Shapiro-Wilk method. All models do not pass the test because the p-value is less than 0,05. However, the problem will not affect the credibility of the research because the data size is large [8]. Second, all models pass the multicollinearity test using the variance inflation factor (VIF) test because the results are less than 10. The mean VIF is between 1,15 to 1,22. The highest VIF is GDP growth, which is 2,87 (in models 5-8) and the lowest is the loan-to-deposit, which is 1,12 (in model 8).

Third, all models encounter heteroskedasticity problems since the P-value in the Breusch-Pagan tests are less than 0,05. The result is zero for all models. The author uses clustered standard error to

Chow Hausman LM

Mean VIF BP Test

Variables Labels VIF Coef. P-value VIF Coef. P-value VIF Coef. P-value VIF Coef. P-value

Constant -1,5052 0,067*** -1,7716 0,031** -1,5346 0,061*** -1,7151 0,040**

ESG ESG 2,29 -0,0071 0,004*

E E 3,02 -0,0035 0,006*

S S 2,52 -0,0060 0,009*

G G -0,0021 0,137

Equity-to-asset EA 2,14 22,5821 0,000* 2,16 22,7162 0,000* 2,14 22,5340 0,000* 2,14 22,4201 0,000*

Loan-to-deposit LD 1,13 0,8597 0,044** 1,13 0,8990 0,038** 1,13 0,8488 0,045** 1,12 0,8876 0,041**

Total assets TA 2,00 0,0004 0,006* 2,07 0,0004 0,010** 1,89 0,0004 0,008* 1,73 0,0004 0,005*

Stringency Index GR 1,67 -0,0056 0,000* 1,67 -0,0059 0,000* 1,68 -0,0056 0,000* 1,66 -0,0061 0,000*

GDP Growth GDP 2,87 0,1411 0,641 2,87 0,1178 0,701 2,87 0,1727 0,560 2,87 0,1453 0,629

Country fixed effect C

N Probability R-squared within R-squared between R-squared overall

0,1493 0,277 0,1562

0,2785 2.201 0 0

- 0

1,19 0,00

0 0

1,22 0,00

2.201 0 0

0 -

1,20 0,00

0 0 -

1,15 0,00

Model 1 Model 2 Model 3 Model 4

0,2126 Yes

Yes Yes Yes

0,2814

0,2173

2.201 0

0,1578 0,2192 2.201

0 0,2811 0,1569 0,2181

(5)

TABLE IV

PANEL (B) DEPENDENT VARIABLE: RETURN TO EQUITY (ROE)

The table presents panel data regression to analyze the relationship between the ROE and corporate sustainability. The dependent variable is ROE, which is a ratio. The independent variables for models 5, 6, 7, 8 are ESG score, E score, S score, and G score, respectively. Unit measurement of (1) ESG score, (2) E score, (3) S score, and (4) G score are decimal and using the prior year score (t-1), (5) equity-to-asset is a ratio, (6) loan-to-deposit is a ratio, (7) total assets is in USD billion, (8) governance stringency index is decimal, (9) GDP growth is decimal, and (10) country fixed effect is dummy variable which represents each country. T he samples are 506 banks from 56 countries during the fiscal year 2016-2020. The Chow, Hausman, and Langrage Multiplier (LM) test are used to estimate the regression, which is pooled least square, fixed effect, or random effect. All the models are not normal, but because the sample size was big, the study’s credibility is not influenced. Mean VIF is to detect multicollinearity problems.

Breusch-Pagan (BP) test is to detect heteroskedasticity. To overcome heteroscedasticity and autocorrelation problems, the author used clustered standard error. *, **, ***

denote statistical significance level at 1 percent, 5 percent, and 10 percent, respectively.

overcome the problem, which is part of robust standard error. Using clustered standard error method, heteroskedasticity and autocorrelation problems could be treated. Fourth, the author decided on the estimation model using the Chow test, Hausman Test, and Lagrange Multiplier test. The author estimated models 1-4 using fixed-effect because the p-value in the Chow test rejects the null hypothesis, so does in the Hausman test.

The coefficient in models 5-8 is not significant. All independent variables in models 5-8 are significant and negatively correlated with the dependent variable. The ESG and E score is significant with a 1% significance level, S score is significant with a 5% significance level, while G score is significant with a 10 % significance level. As stated in Table 4, the ESG score has the most negative impact on financial performance, which is -0,0617, followed by the social score, environment score, and governance score.

This result could be interpreted that, ceteris paribus, one score increase in ESG score will reduce ROE by 0,0617.

The S score is -0,0442, meaning that, ceteris paribus, one score increase in S score will reduce ROE by 0,0442.

Meanwhile, the E score, ceteris paribus, one score increase in E score will reduce ROE by 0,0347. Lastly, one score increase in G score, will decrease ROE by 0,0238.

For the control variables, equity-to-asset (EA), loan- to-deposit (LD), and government stringency index (GR) are significant. All equity-to-asset and government stringency indexes are significantly affecting the ROE with a 1% significance level. The loan-to-deposit variable is significant with a 5% significance level. However, the GDP growth is not significant in all models.

From three out of five significant control variables, only the stringency index negatively affects the ROE. The

stringency index has a coefficient between -0,0628 to -0,0670, meaning that, ceteris paribus, one score increase

in stringency index could decrease ROE between 0,0628 to 0,0670. The equity-to-asset has the biggest positive relationship with the ROE, followed by loan-to-deposit.

One percent increase in the equity-to-asset could, ceteris paribus, increase ROE by 101,100 to 104,0549. As the banks’ revenue mainly from interest, by having one percent loan-to-deposit higher, they could improve the ROE between 0,058048 to 0,061855.

E. Relationship Between Corporate Sustainability and Tobin’s Q

The author developed four models to understand the effect of corporate sustainability on banks’ Tobin’s Q, which consider different independent variables, namely ESG score, E score, S score, and G score. Table 5 shows the panel data regression result that estimates the relationship between corporate sustainability and Tobin’s Q. There are several steps to regress the data, including normality test, multicollinearity test, heteroskedasticity test, and autocorrelation test.

Before regress the panel data, the author tests the normality, multicollinearity, heteroskedasticity, autocorrelation, and estimation model. First, the author tests the normality using the Shapiro-Wilk method. All models do not pass the test because the p-value is less than

Chow Hausman LM

Mean VIF BP Test

Constant -0,4767 0,904 -2,7363 0,486 -1,1235 0,783 -2,0099 0,604

ESG ESG 2,29 -0,0617 0,008*

E E 3,02 -0,0347 0,006*

S S 2,52 -0,0442 0,031**

G G 1,35 -0,0238 0,062***

Loan-to-deposit LD 1,13 5,8379 0,014** 1,13 6,1855 0,012** 1,13 5,8048 0,014** 1,12 6,0605 0,015**

Total assets TA 2,00 0,0019 0,111 2,07 0,0017 0,157 1,89 0,0018 0,134 1,73 0,0018 0,111

GDP Growth GDP 2,87 -0,3908 0,856 2,87 -0,6438 0,774 2,87 -0,1369 0,948* 2,87 -0,3850 0,858

0 0 0 0

- - -

1,19 1,22 1,20 1,15

0,00 0,00 0,00 0,00

Yes Yes Yes Yes

2.201 2.201 2.201 2.201

0 0 0 0

0,0266 0,0279 0,0277 0,0284

0,1851 0,1834 0,1840 0,1819

0,0083 0,0099 0,0087 0,0093

(6)

TABLE V

PANEL (C) DEPENDENT VARIABLE: TOBIN’S Q

The table presents panel data regression to analyze the relationship between the Tobin’s Q and corporate sustainability. The dependent variable is Tobin’s Q, which is a decimal.

The independent variables for models 9, 10, 11, 12 are ESG score, E score, S score, and G score, respectively. Unit measurement of (1) ESG score, (2) E score, (3) S score, and (4) G score are decimal and using the prior year score (t-1), (5) equity-to-asset is a ratio, (6) loan-to-deposit is a ratio, (7) total assets is in USD billion, (8) governance stringency index is decimal, (9) GDP growth is decimal, and (10) country fixed effect is dummy variable which represents each country. The samples are 506 banks from 56 countries during the fiscal year 2016-2020. The Chow, Hausman, and Langrage Multiplier (LM) test are used to estimate the regression, which is pooled least square, fixed effect, or random effect. All the models are not normal, but because the sample size was big, the study’s credibility is not influenced. Mean VIF is to detect multicollinearity problems. Breusch-Pagan (BP) test is to detect heteroskedasticity. To overcome heteroscedasticity and autocorrelation problems, the author used clustered standard error. *,

**, *** denote statistical significance level at 1 percent, 5 percent, and 10 percent, respectively.

0,05. However, the problem will not affect the credibility of the research because the data size is large [8]. Second, all models pass the multicollinearity test using the variance inflation factor (VIF) test because the results are less than 10. The mean VIF is between 1,15 to 1,22. The highest VIF is GDP growth, which is 2,87 (in models 9-12) and the lowest is the loan-to-deposit, which is 1,12 (in model 12).

Third, all models encounter heteroskedasticity problems since the P-value in the Breusch-Pagan tests are less than 0,05. The result is zero for all models. The author uses clustered standard error to overcome the problem, which is part of robust standard error. Using clustered standard error method, heteroskedasticity and autocorrelation problems could be treated. Fourth, the author decided on the estimation model using the Chow test, Hausman Test, and Lagrange Multiplier test. The author estimated models 1-4 using fixed-effect because the p-value in the Chow test rejects the null hypothesis, so does in the Hausman test.

The coefficient in models 9-12 is not significant. Three out of four independent variables in models 9-12 are significant and negatively correlated with the dependent variable. The significant independent variables are ESG score, S score, and G score. The ESG score is significant with 5% significance level, the S score is significant with 1% significance level, and the G score is significant with 10% significance level. As stated in Table 5, the ESG score has the most negative impact on financial performance, which is -0,00051, followed by the social score and environment score. This result could be interpreted that, ceteris paribus, one score increase in ESG score will reduce Tobin’s Q by 0,00051.

The S score is -0,00042, meaning that, ceteris paribus, one score increase in S score will reduce Tobin’s Q by 0,00042.

Meanwhile, the G score, ceteris paribus, one score increase in G score will reduce Tobin’s Q by 0,00021. However, the relationship between E score and Tobin’s Q is insignificant.

For the control variables, equity-to-asset (EA), total assets in billion (TA), government stringency index (GR), and Gross Domestic Product (GDP) growth are significant.

All equity-to-asset, total asset in billion, and government stringency indexes are significantly affecting the Tobin’s Q with a 1% significance level. The GDP growth are significant at 5% for models 9-12. However, the loan-to- deposit is not significant in all models.

From four out of five significant control variables, the stringency index and GDP growth negatively affects the Tobin’s Q. The stringency index has a coefficient around - 0,0005, meaning that, ceteris paribus, one score increase in stringency index could decrease Tobin’s Q between 0,0005. Meanwhile one percent GDP growth meaning that, ceteris paribus, the Tobin’s Q will decrease 0,0517 to 0,0540. The equity-to-asset has a dominant positive relationship with the Tobin’s Q, followed by total assets.

One percent increase in the equity-to-asset could, ceteris paribus, increase Tobin’s Q by 0,006154 to 0,006262. If the bank larger, by having one billion more assets, ceteris paribus, the Tobin’s Q will increase around 0,00003.

Chow Hausman LM

Mean VIF BP Test

Constant 0,0449 0,383 0,0236 0,620 0,0425 0,398 0,0329 0,514

ESG ESG 2,29 -0,0005 0,012**

E E 3,03 -0,0001 0,377

S S 2,52 -0,0004 0,009*

G G 1,35 -0,0002 0,061***

Loan-to-deposit LD 1,13 0,0537 0,146 1,13 0,0562 0,123 1,13 0,0528 0,154 1,12 0,0554 0,130

Total assets TA 2,00 0,00003 0,000* 2,07 0,00002 0,000* 1,89 0,00003 0,000* 1,73 0,00003 0,000*

GDP Growth GDP 2,87 -0,0540 0,024** 2,87 -0,0539 0,028** 2,87 -0,0517 0,026** 2,87 -0,0540 0,029**

0 0 0 0

- - -

1,19 1,22 1,20 1,15

0,00 0,00 0,00 0,00

Yes Yes Yes Yes

2.196 2.196 2.196 2.196

0 0 0 0

0,0751 0,0671 0,0760 0,0586

0,1825 0,0295 0,1886

0,0346

0,1886 0,1845

0,0339 0,0247

(7)

TABLE VI

PANEL (D) DEPENDENT VARIABLE: NPL Ratio

The table presents panel data regression to analyze the relationship between the Nonperforming loan (NPL) Ratio and corporate sustainability. The dependent variable is NPL Ratio, which is a decimal. The independent variables for models 13, 14, 15, 16 are ESG score, E score, S score, and G score, respectively. Unit measurement of (1) ESG score, (2) E score, (3) S score, and (4) G score are decimal and using the prior year score (t-1), (5) equity-to-asset is a ratio, (6) loan-to-deposit is a ratio, (7) total assets is in USD billion, (8) governance stringency index is decimal, (9) GDP growth is decimal, and (10) country fixed effect is dummy vari able which represents each country. The samples are 506 banks from 56 countries during the fiscal year 2016-2020. The Chow, Hausman, and Langrage Multiplier (LM) test are used to estimate the regression, which is pooled least square, fixed effect, or random effect. All the models are not normal, but because the sample size was big, the study’s credibility is not influenced. Mean VIF is to detect multicollinearity problems. Breusch-Pagan (BP) test is to detect heteroskedasticity. To overcome heteroscedasticity and autocorrelation problems, the author used clustered standard error. *, **, *** denote statistical significance level at 1 percent, 5 percent, and 10 percent, respectively.

F. Relationship Between Corporate Sustainability and NPL Ratio

The author developed four models to understand the effect of corporate sustainability on banks’ Nonperforming Loans (NPL) Ratio, which consider different independent variables, namely ESG score, E score, S score, and G score.

Table 6 shows the panel data regression result that estimates the relationship between corporate sustainability and NPL ratio. There are several steps to regress the data, including normality test, multicollinearity test, heteroskedasticity test, and autocorrelation test.

Before regress the panel data, the author tests the normality, multicollinearity, heteroskedasticity, autocorrelation, and estimation model. First, the author tests the normality using the Shapiro-Wilk method. All models do not pass the test because the p-value is less than 0,05. However, the problem will not affect the credibility of the research because the data size is large [8]. Second, all models pass the multicollinearity test using the variance inflation factor (VIF) test because the results are less than 10. The mean VIF is between 1,15 to 1,21. The highest VIF is GDP growth, which is 2,85 (in models 13-16) and the lowest is the loan-to-deposit, which is 1,12 (in model 13, 14, 16). Third, all models encounter heteroskedasticity problems since the P-value in the Breusch-Pagan tests are less than 0,05. The result is zero for all models. The author uses clustered standard error to overcome the problem, which is part of robust standard error.

Using clustered standard error method, heteroskedasticity and autocorrelation problems could be treated. Fourth, the author decided on the estimation model using the Chow test, Hausman Test, and Lagrange Multiplier test. The author estimated models 13-16 using random-effect because the p-value in the Chow test rejects the null hypothesis but accept the null hypothesis in the Hausman test.

The coefficient in models 13, 15, and 16 is significant with 10% significance level. The coefficient shows a positive relationship with the NPL Ratio. The fifteenth model has the highest coefficient, which is 0,072, while the lowest is in model 16. Thus, the coefficient in the fifteenth model has a larger magnitude.

Two out of four independent variables in models 13- 16 are significant with 10% significance level and negatively correlated with the dependent variable. The significant independent variables are ESG score and S score. As stated in Table 6, the ESG score has the most negative coefficient on NPL Ratio, which is -0,0002, followed by the social score. This result could be interpreted that, ceteris paribus, one score increases in ESG, or S score will reduce NPL by around 0,0002. This means that ESG score and S score are positively significant to the financial performance of banks because the lower the NPL, the preferable it is.

For the control variables, only loan-to-deposit that significant with a 5% significance level. The loan-to- deposit appears to have a negative relationship with NPL Ratio around -0,0022 to -0,0024. This could be interpreted that, ceteris paribus, the loan-to-deposit could decrease the NPL Ratio between 0,0022 to 0,0024. The other control variables are not significant to the dependent variable.

Chow Hausman LM

Mean VIF BP Test

Constant 0,0209 0,051*** 0,0145 0,186 0,1931 0,072*** 0,0196 0,067***

ESG ESG 2,29 -0,0002 0,055***

E E 3,00 -0,00003 0,765

S S 2,51 -0,0002 0,054***

G G 1,36 -0,0001 0,115

Equity-to-asset EA 2,11 0,0166 0,815 2,14 0,0159 0,823 2,12 0,0143 0,840 2,12 0,0164 0,816

Loan-to-deposit LD 1,12 -0,0024 0,021** 1,12 -0,0022 0,039** 1,13 -0,0024 0,022** 1,12 -0,0023 0,029**

Total assets TA 2,00 -1.70e-06 0,501 2,07 -3.96e-06 0,163 1,89 -2.56e-06 0,244 1,73 -3.06e-06 0,189

Stringency Index GR 1,66 0,00002 0,541 1,66 7.45e-06 0,831 1,67 0,00002 0,558 1,65 9.42e-06 0,779

GDP Growth GDP 2,85 0,0245 0,227 2,85 0,0247 0,229 2,85 0,0255 0,212 2,85 0,0244 0,227

0 0 0 0

0,5488 0,3556 0,8200 0,4586

0 0 0 0

1,19 1,21 1,19 1,15

0,00 0,00 0,00 0,00

Yes Yes Yes Yes

2.148 2.148 2.148 2.148

0,3969 0,3932 0,3944 0,3967

0,0065 0,0044 0,0066 0,0056

0,3604 0,3565 0,3582 0,3596

(8)

TABLE VII

PANEL (E) DEPENDENT VARIABLE: RETURN ON ASSET (ROA)

The table presents panel data regression to analyze the relationship between the ROA and corporate sustainability considering the government response to the Covid-19 Pandemic. The dependent variable is ROA, which is a ratio. The independent variables are corporate sustainability indicators and interaction variable (corporate sustainability multiplies by stringency index). Unit measurement of (1) ESG, E, S, G, ESGS, ES, SS, and GS are decimal, (2) equity-to-asset is a ratio, (3) loan-to-deposit is a ratio, (4) total assets is in USD billion, (5) GR is a decimal, (6) GDP growth is decimal, and (7) country fixed effect is dummy variable which represents each country. The samples are 506 banks from 56 countries during the fiscal year 2016-2020. The Chow, Hausman, and Langrage Multiplier (LM) test are used to estimate the regression, which is pooled least square, fixed effect, or random effect. All the models are not normal, but because the sample size was big, the study’s credi bility is not influenced. Mean VIF is to detect multicollinearity problems. Breusch-Pagan (BP) test is to detect heteroskedasticity. To overcome heteroscedasticity and autocorrelation problems, the author used clustered standard error. *, **, *** denote statistical significance level at 1 percent, 5 percent, and 10 percent, respectively.

G. Relationship Between Corporate Sustainability and ROA Considering the Government Response to the Covid-19 Pandemic

The author developed four models to understand the effect of corporate sustainability on banks’ ROA considering the government response to the Covid-19 pandemic, which employ four another different independent variable besides ESG score, E score, S score, and G score. The independent variables are an interaction variable of Corporate Sustainability multiplied by the Government Stringency Index, namely ESG score*Stringency Index, E score*Stringency Index, S score*Stringency Index, and G score*Stringency Index.

Table 7 shows the panel data regression result that estimates the relationship between corporate sustainability and Return on Assets (ROA) considering the government response to the Covid-19 pandemic. There are several steps to regress the data, including normality test, multicollinearity test, heteroskedasticity test, and autocorrelation test.

Before regress the panel data, the author tests the normality, multicollinearity, heteroskedasticity, autocorrelation, and estimation model. First, the author tests the normality using the Shapiro-Wilk method. All models do not pass the test because the p-value is less than

0,05. However, the problem will not affect the credibility of the research because the data size is large [8]. Second, all models pass the multicollinearity test using the variance inflation factor (VIF) test because the results are less than 10. The mean VIF is between 1,17 to 1,38. The highest VIF is the government stringency index, which is 7,75 (in models 20) and the lowest is the loan-to-deposit, which is 1,12 (in model 20). Third, all models encounter heteroskedasticity problems since the P-value in the Breusch-Pagan tests are less than 0,05. The result is zero for all models. The author uses clustered standard error to overcome the problem, which is part of robust standard error. Using clustered standard error method, heteroskedasticity and autocorrelation problems could be treated. Fourth, the author decided on the estimation model using the Chow test, Hausman Test, and Lagrange Multiplier test. The author estimated models 17-20 using fixed-effect because the p-value in the Chow test rejects the null hypothesis, so does in the Hausman test.

The coefficient in models 17-20 is significant. Model 18 and 20 coefficient is significant with 5% significance level, while models 17 and 19 are significant with 10%

significance level. All the coefficient shows a negative sign. The eighteenth model has the highest coefficient which is -1,7898, while the lowest is in model 17. Thus, the coefficient in the eighteenth model has a larger magnitude.

In model 17, ESG score and ESG score*Stringency Index are negatively significant with 5% and 1%

significance level, respectively. The government response

Chow Hausman LM

Mean VIF BP Test

Constant -1,5336 0,062*** -1,7898 0,029** -1,5465 0,059*** -1,7309 0,039**

ESG ESG 2,55 -0,0059 0,024**

ESG*Stringency Index ESGS 7,12 -0,0001 0,009*

E E 3,33 -0,0029 0,032**

E*Stringency Index ES 2,20 -0,00003 0,186

S S 2,81 -0,0048 0,044**

S*Stringency Index SS 5,93 -0,0001 0,012**

G G 1,59 -0,0016 0,264

G*Stringency Index GS 7,67 -0,00005 0,061***

Loan-to-deposit LD 1,13 0,8038 0,059*** 1,13 0,8771 0,046** 1,13 0,7850 0,059*** 1,12 0,8660 0,047**

Total assets TA 2,00 0,0006 0,002* 2,07 0,0005 0,007* 1,89 0,0005 0,006* 1,73 0,0005 0,001*

Stringency Index GR 7,02 -0,0025 0,102 2,41 -0,0053 0,000* 5,87 -0,0027 0,074** 7,75 -0,0038 0,012**

GDP Growth GDP 2,88 0,0700 0,815 2,88 0,0840 0,788 2,88 0,1063 0,710 2,88 0,1077 0,723

N Probability R-squared within R-squared between

R-squared overall 0,2205 0,2166 0,2247 0,2121

0,2843 0,2794 0,285 0,2784

0,1599 0,1556 0,1637 0,1491

2.201 2.201 2.201 2.201

0 0 0 0

0,00 0,00 0,00 0,00

Yes Yes Yes Yes

1,38 1,17 1,35 1,36

0 0 0 0

- - - -

0 0 0 0