• No results found

The Influence of Environmental, Social and Corporate Governance on a Firm its Credit Rating

N/A
N/A
Protected

Academic year: 2021

Share "The Influence of Environmental, Social and Corporate Governance on a Firm its Credit Rating"

Copied!
29
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

The Influence of Environmental, Social and

Corporate Governance on a Firm its Credit Rating

MARK VAN DER LEI*

Master Thesis MSc. Finance Rijksuniversiteit Groningen

Supervisor: Dr. Lammertjan Dam

Word count: 11.227

June 21, 2013

ABSTRACT

In this research we test the effect of Environmental, Social and Corporate Governance on a firm its Credit rating. We use an Ordinary Least Squares and a Logit regression analyses to empirically test the effect of Environmental, Social and Corporate Governance on a firm its Credit rating, controlling for firm characteristics. We perform this regression analyses for a panel data set consisting of 193 S&P500 firms during the period of 2006 to 2010. We find a positive and significant relation between Environmental, Social and Corporate Governance and a firm its Credit rating. There is a difference of one Credit rating grade between the Environmental, Social and Corporate Governance performance of the lowest quartile and the highest quartile of firms. Although the relation is significant, it is relatively small and seems economically of little importance.

Keywords: Corporate Social Responsibility, Credit Rating, Cost of Capital, Environmental, Social and Corporate Governance.

JEL codes: G21, G32, M14

*

(2)

[2]

I. Introduction

In this research we investigate the influence of Environmental, Social and Corporate Governance (ESG) on a firm its Credit rating. Environmental and social concerns are of growing importance in firms and society. When one opens a random newspaper one will surely read something about the global warming, child labor or environmental pollution due to business activities. Do firms have environmental and social responsibilities towards society? Should firms harm profits and engage in responsible business activities? Or is it possible to combine the best of both worlds by ‘Doing good and doing well?’ (Margolis, Elfenbein and Walsh, 2009). That is the question in the fast emerging and developing field of responsible finance and investing.

In this research we relate a firm its ESG performance to risk and to be more specific, the influence of ESG on a firm its Credit rating. This is a research which is to our knowledge not yet conducted by other researchers in this way. There are several papers pointing out that ESG yields a lower risk (e.g. Soppe, 2004; El Ghoul, Guedhami, Kwok and Mishra, 2011; Menz, 2010). According to these papers our hypothesis is that good ESG leads to a higher Credit rating. This research is of importance to the field of responsible finance and investing in that we try to empirically prove that Corporate Social Responsibility leads to lower risk and thus could lead to a lower cost of capital. We test this hypothesis empirically by applying an Ordinary Least Squares- and a Logit regression analysis. In this analysis we make use of a panel data set consisting of 193 S&P500 firms during the period 2006 to 2010.

(3)

[3]

Before we turn to our empirical research we give a more comprehensive view on the current state of literature in the field of Corporate Social Responsibility in section II.

Literature Review. Next is a description of our data and descriptive statistics in section III. Data and Descriptive Statistics. Hereafter, we represent our methodology and the results of

our empirical research in section followed by the discussion in section IV. Methodology and

Results. Finally we summarize our main findings in section V. Conclusion.

II. Literature Review

In this section we discuss the current state of literature following three relations of ESG with the firm. We start with a broad view on the influence of ESG on a firm its overall performance, followed by the influence of ESG through capital markets on the cost of capital and finally we end with the specific relation of ESG with Credit ratings.

A. ESG and Firm Performance

The implication of Environmental Social Governance (ESG) in a firm its business activities are of growing concern. The concept of ESG is also known as Corporate Social Responsibility (CSR), triple bottom line, sustainability or corporate citizenship (Mercer, 2007). There has been done much research in the past 30 years in this relatively new field of finance (Margolis and Walsh, 2003). According to Heal (2004) corporate social responsibility is best described by the strategy that reduces differences between social and private costs. These social costs occur when a firm its business activities harm society and the firm benefits from this.

(4)

[4]

non-financial issues as social and environmental sustainability. Hence, better corporate social performance not only leads to higher costs but also to a better financial performance of a firm. Following from this discussion there is done much research concerning the relation between ESG and a firm its financial performance. In recent years two meta-analyses are conducted, one by Orlitzky, Schmidt and Rynes (2003) and one by Margolis et al. (2009). Orlitzky et al. (2003) find little evidence that including social and environmental factors in firms decision making leads to a better financial performance. Nevertheless they find too many different results in previous studies to draw any generalizable conclusions. Margolis et al. (2009) on the other hand find a small positive correlation between ESG and financial performance. We can conclude from this that there is some evidence that there should be a positive relation but it is still debatable. These meta-analyses present a wide range of different studies about how ESG can influence a firm its financial performance.

An example of how ESG could lead to better financial performance is that a firm with high ESG-standards could attract more qualified and motivated employees (Phillips, Hager and North, 2007). Furthermore companies with high ESG-standards have a better relation with public institutions (Godfrey, 2005) and create a positive ‘moral capital’ which enhances a firm its value. Other ways in which firm value could be higher through high ESG-standards are for instance: avoiding environmental-fines, higher sales due to a good reputation or making a firm more productive. In this study we research the influence of ESG to improve firm value by lower risks.

We can conclude from this section that the question on whether high ESG-standards lead to better financial performance is still debatable and could only be proven through empirical research. The critical point here is that the benefits aligned with the high ESG-standards must be greater than the costs to enhance a firm its value. In the next section we turn to another way in which high ESG-standards could be beneficial, namely through the capital market.

B. ESG and Capital Markets

Capital markets have an influence on a firm its cost through the cost of capital. The cost of capital is a weighted average of a firm its cost of equity and the cost of debt.1 Hence, ESG lowers a firm its cost of capital by either influencing the cost of equity or the cost of debt. El Ghoul et al. (2011) find this negative relation between ESG and the cost of capital.

1 = ( ∗ ) + ( ) ∗ (1 − ) With is equity, is debt, is return on equity, is return on

(5)

[5]

They show that responsible employee relations and environmental concerns are the most important drivers of ESG. First, we will discuss the transmission of ESG by the equity market and subsequently the transmission of ESG by the debt market.

According to the transmission of ESG by the equity market there are two theories. The theory of Social Responsible Investing (SRI) (Heinkel, Krause and Zechner, 2001) and the theory of shareholder activism (Smith, 1996; Dimson, Karakas and Li, 2012). Heinkel (2001) provides an equilibrium model stating that SRI leads to lower equity capital returns for stocks with better ESG performance. This equilibrium model implies that ethical investing by a small group of investors leads to lower stock prices for unethical firms which thus require a higher return on equity. If the costs of equity are high enough it becomes desirable for unethical firms to invest in more ethical business activities due to SRI. A key determinant of this model is however that at least 20% percent of investors should be ethical. This threshold value is currently not met since only about 10% of US investors are ethical (Heinkel et al., 2001). Another problem with this theory is that equity is according to the pecking order theory of Myers and Majluf (1984) not used very often as a source of external financing. Thus the exposure of a firm its cost of capital to the effect described by Heinkel et al. (2001) is limited. When turning to the shareholder activism theory (Smith 1996) the above described problems of the SRI theory seem to be tackled (Menz, 2010). Shareholder activism relies on the voting power of ethical shareholders to improve the ESG performance of a firm and make the firm more ethical and responsible. More responsible firm behavior leads to lower risk and thereby a lower cost of equity (Dimson et al., 2012). Although to influence management to make ethical decisions in a firm there are a substantial amount of ethical investors required. However, there are only a few firms in which there is a substantial amount of ethical investors. This implies that the influence of shareholder activism is also limited. According to these two theories there is evidence that equity markets can influence the firm its cost of capital by enhancing ESG, although there are too few ethical investors yet.

(6)

[6]

and of great importance on whether ESG is transferred by the cost of debt, is whether lenders pay attention to ESG. According to Esty, Knoop and Sesia (2005), banks do pay attention to environmental and social concerns and conduct them as real risk variables which may affect their creditors. An example of the awareness of banks for environmental and social issues is the adoption of the Equator Principles by several leading banks. According to Scholtens and Dam (2007), the Equator Principles are a signal for banks to show responsible conduct. Hence, banks are likely to consider ESG as risk variables. Thirdly firms need to refinance their mature debt every now and then. This implies that firms are more dependent on the credit market than on the equity market. The dependency on the credit markets gives the lender more power to impose ESG-requirements on the borrower, giving banks and institutional investors a good position to influence the degree of ethics in a firm its business activities.

Concluding there is evidence that ESG leads to a lower cost of capital. According to the literature the transmission by the cost of equity is limited to the amount of ethical investors and whether equity is used as a source of financing. The credit market seems to have more potential to impact the cost of capital. In the next section we will take a closer look at the relation between ESG and a firm its risk.

C. Influence of ESG on Credit Ratings

Credit ratings are a measure of credit risk. Credit risk is the risk that a firm will go bankrupt and will not pay back its obligation to the creditor. Hence credit risk is higher if a firm has a higher probability of default. A higher credit risk exposes the creditor to more risk and therefore should give a higher return (Fama and MacBeth, 1973). When applying this theory to a firm its cost of debt, we can state that a higher Credit rating implies a lower cost of debt due to lower credit risk.

(7)

[7]

Governance is a part of ESG we assume that other ESG variables might also reduce credit risk. Surprisingly enough there is to our knowledge no research about the influence of ESG on Credit ratings yet.

Nevertheless there are researches pointing out that there should be a relationship between ESG performance and the firm its risk and the cost of debt. Bassen, Hölz and Schlange (2006) and El Ghoul et al. (2011) find that a better ESG performance leads to lower risk and thus a lower cost of capital. Bassen et al. (2006) state that lower corporate responsibility does lead to lower risk although it does not lead to higher performance. Because of this conclusion, one could say that corporate social responsibility is needless. Although Bassen et al. (2006) do state that a complete lack of corporate responsibility leads to unnecessary high risk and this results in a higher cost of capital. El Ghoul et al. (2011) confirm the finding of lower cost of capital due to better corporate responsibility and point out that improving responsible employee relations and environmental policies substantially lowers a firm it risk. When turning to the cost of debt, Goss and Roberts (2011) find that firms with the worst ESG-score pay up to 20 basis points more interest on their debt in comparison to the best performing firms. They state that the difference of 20 basis points is too small to be of economic importance in most firms because the improvement of corporate responsibility is accompanied with high costs. Menz (2010) confirms this by stating that the ESG performance is not yet incorporated in bond pricing although he does find enough evidence to state that there is a negative relation between ESG performance and risk.

(8)

[8]

III. Data and Descriptive Statistics

A. Sample Description and Data Sources

The data we use in this research is obtained from Thomson and Reuters Datastream. Datastream gives access to a large range of financial and non-financial information for listed companies. The data we use in this research can be divided in three parts according to their type and data availability. These parts are: the Credit ratings, the ESG-ratings and the firm characteristics. Concerning the availability, Datastream gives some limitations. In particular for the ESG-ratings and Credit ratings since they have just became accessible in Datastream. Datastream reports ESG-ratings back until the fiscal year of 2005 for around 4000 listed companies in the United States and Europe. The availability of Credit ratings is also limited to the fiscal year of 2005. Credit-ratings are only available for listed companies that are rated by rating agency Fitch. Firm characteristics are not limited in Datastream and easily available for almost all listed companies.

The ESG-ratings in Datastream are from another Thomson and Reuter business, namely ASSET4. ASSET4 gives objective and systematically ordered information according Environmental, Governance and Social (ESG) factors. The database contains 750+ individual data points which are combined in 250+ key performance indicators. A weighting of this key performance indicator provides us with a score of the four pillars of ESG namely the economical-, environmental-, governance- and social pillar. ASSET4 also provides an overall equal weighted score of ESG which is together with the four pillars of ESG used in this research to measure the Corporate Social Responsibility of a firm.

(9)

[9]

Concerning the availability of data this research is conducted for the United States during the period from 2006 until 2010. The listed firms we use are all part of the S&P500. In

Table II the construction of the dataset is summarized. As can be observed in Table II the

biggest loss of firms is due to the unavailability of Credit rating data and ESG-rating data which are almost perfectly correlated. The loss of data points by firm characteristics is mainly due to a lack of information on the Interest coverage control variable, this loss is about 50 firms. As can be observed in Table II there are 193 firms left after deducting all the missing information for S&P500 firms. For these 193 firms all variables are available during the period 2006 to 2010. This gives us a panel data set of 965 data points.

Table I

Fitch Credit Rating Specifications

A firm its Credit rating is a score for the creditworthiness used for long-term debt issuance, assigned by Fitch and reported in Datastream (ECSLO05V). The Credit ratings range from AAA (highest score) to D (lowest score). RATING is used for the OLS-regression and ranging from 24 (highest score) to 1 (lowest score). GRADE is used for the Logit-regression and is the investment or speculative grade following from the Credit rating assigned by Fitch.

Fitch Rating Datastream ECSLO05V RATING GRADE

(10)

[10] Table II

Sample Construction of S&P 500 Firms for the Period 2006 to 2010

Number of firms lost due to missing Fitch Credit rating information is perfectly correlated with the missing Asset4 information and therefor equal. Loss of firms by missing control variables is mainly due to missing interest coverage ratios.

Number of firms Number of firms lost S&P500 Firms 500

Number of firms having complete Fitch rating information 253 247

Number of firms having complete Asset4 information 253 0

Number of firms having complete Firm characteristics data 193 60

B. Independent variables

In this section we describe the independent variables we use in this research. We start with the ESG-variables, subsequently the firm characteristics and end with the descriptive statistics.

B.1. Environmental, Social and Governance Variables

(11)

[11] Table III

Variable Definitions and Data Sources of the ESG Variables and Firm Characteristic

Variables Predicted Definitions and Data sources

Sign

ESG Variables:

ESG-score + An equal weighted score of the four ASSET4 scores which gives a good indication of a company its ESG/CSR Performance (Datastream, A4IR).

Economic-score + The economic pillar measures a company its capacity to generate sustainable growth and a high return on investment through the efficient use of all its resources. It is reflection of a company's overall financial health and its ability to generate long term shareholder value (Datastream, ECNSCORE).

Environmental-score + The environmental pillar measures a company its impact on the environment. It reflects how well a company uses best management practices to avoid environmental risks and capitalize on environmental opportunities in order to generate long term shareholder value (Datastream, ENVSCORE).

Social-score + The social pillar measures a company its capacity to generate trust and loyalty with its workforce, customers and society. It is a reflection of the company's reputation and the health of its license to operate, which are key factors in determining its ability to generate long term shareholder value (Datastream, SOCSCORE).

Governance-score + The corporate governance pillar measures a company its systems and processes, which ensure that its board members and executives act in the best interests of its long term shareholders. It reflects a company's capacity, to direct and control its rights and responsibilities through the creation of incentives in order to generate long term shareholder value (Datastream, GCVSCORE).

Firm Characteristics:

Leverage - Leverage ratio, total debt divided by total equity (Datastream, WC08236).

Return on assets + Return on assets ratio, net income before extraordinary items dived by total assets (Datastream, WC08326).

Loss - Loss is marked one if net income (Datastream, WC01551) is negative in the prior and current fiscal year, otherwise zero.

(12)

[12]

Size + Firm size, the natural log of total assets (Datastream, WC02999).

Capital intensity + Capital intensity, gross, property plant and equipment (Datastream, WC02301) divided by total assets (Datastream, WC02999).

Financial utility + Financial utility, marked one if the firm has a financial utility otherwise zero. A firm has a financial utility if the SIC-code (Datastream, WC7021) starts with a 6.

B.2. Firm Characteristics

The variables we use in our regression to control for firm characteristics are in Table

III under Firm Characteristics, and the predicted signs are explained below. The variables we

use to control for firm characteristics are derived from previous studies on Credit ratings (e.g. Horrigan, 1966; Kaplan and Urwitz, 1979; Ziebart and Reiter 1992; Ashbaugh-Skaife et al., 2006; Alali, Anandarajan and Jiang, 2012). These variables are the leverage ratio, return on assets, the loss in prior and current fiscal year, the interest coverage ratio, the firm its size, Capital intensity ratio and the firm its financial utility.

The firm its leverage-, returns on assets- and interest coverage ratio we use to measure a firm its default risk. A higher leverage ratio gives a higher default risk and therefor a lower Credit rating, therefor this relation should be negative. For return on assets and the interest coverage ratio it applies that a higher ratio gives a lower default risk and thus a higher Credit rating. In addition to these three ratios for default risk we also use loss in prior and current fiscal year as a categorical variable, marked 1 if a firm has a loss in the prior and fiscal year and 0 otherwise. We use this variable since a firm with losses has a greater default risk. Hence, no losses give a lower default risk and thus a higher Credit rating. We include size as a control variable since larger firms face lower risk and therefor have a higher Credit rating. The capital intensity ratio controls for differences in firms its asset structure. Firms with a higher capital intensity ratio have a lower default risk and thus a higher Credit rating. The last control variable included is the firm its utility, since financial companies are regulated strictly they are expected to have a higher Credit rating relative to other companies. A firm its utility is marked 1 if it is a financial company and 0 otherwise. Hence, there should be a positive relation with a firm its Credit rating.

(13)

[13]

B.3. Descriptive Statistics

In Table IV are the descriptive statistics of Fitch Credit rating-, the ESG- and the control variables. The Credit rating has an average equal to 16.95, which means an average Credit rating of BBB+. The 25% quartile is equal to 15.5 and the 75% quartile is 19 which means that most of the firms are fairly close to the average and that 75% of the firms has a rating higher than BBB-. According to the skewness and kurtosis Credit rating is normally distributed.

Concerning the Investment grade we observe that 84% of the firms have an investment grade and that the distribution is skewed to the right. All figures indicate that a large majority of the firms have an investment grade. The ESG variables show an average ESG-score of 72.11 and a standard deviation of 24.99, this implies that there is quiet some variance. The distribution is a bit skewed to the right which means that there are more firms above the average than beneath it. The four ESG pillars follow quiet a similar pattern concerning the average and the standard deviation except for the Governance-score. The Governance-score is somewhat higher than the other ESG-scores. The scores in it selves have no clear definition but the variance in the score is lower which is remarkable. This could be the result of the fact that there is paid a lot of attention to corporate governance by most of the firm and that the scores of governance therefore, are high and close to each other. Hence, the distribution of the

Governance-score is quiet.

According to the control variables we do not observe remarkable things. We do see that 6.7% of the firms in the dataset are financials which probably have a higher Credit rating. And that 3.9% of the firm have losses in the prior and the current year. Besides, we observe that most of the control variables are not normally distributed which is of no importance for our research.

Table V represents the correlations between the independent variables and dependent

variable. Regarding the correlations between the Credit rating and all the dependent variables we see significant correlations with the right expected sign as represented in Table III, except for the Capital intensity. The sign of the Capital intensity is negative and significant, we will take a closer look at this in the result section. Furthermore we see that Size has a high correlation of 0,416 with Credit rating. The correlations of the ESG-scores with the Credit

(14)

[14]

Furthermore we observe relatively high and significant correlations (0.329 – 0.882) between the different ESG-scores. This would imply that a firm with a good environmental-

score has for instance also a high social-score. Another straightforward but interesting

observation is that the Economic-score which indicates for responsible and stable financial decisions is highly correlated with the Return on assets. These high correlations can lead to orthogonality problems in our regression analyses but we will come back to that in section IV.

Methodology and Results.

Table IV

Company Descriptive Statistics for 193 US Firms for the Period 2006-2010

Credit rating is the Credit rating assigned by Fitch represented in Table I and Investment Grade is assigned to

Credit ratings higher than BB+ also represented in Table I. Size is equal to the log of the total assets. Return on

assets is the return divided by the assets. Loss is the occurrence of loss in the prior and current fiscal year (1 if

loss and 0 otherwise). Leverage is the net debt divided by total equity. Interest coverage is the ratio of EBIT divided by interest expenses. Financial utility is marked 1 if the firm has a financial utility and 0 otherwise. The

Capital intensity is total property, plant and equipment divided by the total assets. ESG-score is the average

score of the four ESG-pillars. Economic-score is the score for the economic sustainability. Environmental-score is the firms score for the impact on the environment. The Governance-score is the firm its corporate governance score and the Social-score represents the firm its social responsibility score. More detailed descriptions of the variables are in Table III.

Variables Mean Standard

(15)

[15]

RATING is the Credit rating of the firm, ESG_SCORE is the overall ESG-score, ECN_SCORE is the Economic score, ENV_SCORE is the Environmental score, SOC_SCORE is the Social score, GCV_SCORE is the Governance score, LEV is the Leverage ratio, ROA is the Return on assets ratio, LOSS is the Loss in prior and current fiscal year (1 if loss and 0 otherwise), INT_COV is the

Interest coverage ratio, SIZE is the Size of the firm equal to the log of the total assets, CAP_INT is the Capital intensity ratio and FIN_UT is the Financial utility of the firm. More detailed descriptions

of the variables are in Table III.

Note: Marked bold if correlation is significant at 2.5% level.

Variables

RATING ESG_SCORE ECN_SCORE ENV_SCORE GCV_SCORE SOC_SCORE SIZE ROA LOSS LEV INT_COV FIN_UT CAP_INT

RATING 1 ESG_SCORE 0.343 1 ECN_SCORE 0.289 0.729 1 ENV_SCORE 0.352 0.882 0.473 1 GCV_SCORE 0.166 0.649 0.329 0.552 1 SOC_SCORE 0.273 0.894 0.547 0.762 0.539 1 SIZE 0.416 0.270 0.137 0.343 0.128 0.235 1 ROA 0.285 0.238 0.431 0.114 0.066 0.175 -0.083 1 LOSS -0.185 -0.106 -0.231 -0.025 -0.017 -0.044 -0.035 -0.371 1 LEV -0.356 -0.251 -0.256 -0.187 -0.245 -0.160 -0.172 -0.182 0.110 1 INT_COV 0.151 0.140 0.201 0.079 0.098 0.091 0.046 0.335 -0.250 -0.218 1 FIN_UT 0.083 -0.301 -0.164 -0.218 -0.218 -0.314 0.147 -0.070 -0.054 0.154 -0.021 1 CAP_INT -0.111 0.092 0.017 0.104 0.090 0.074 -0.009 -0.007 -0.020 0.171 0.038 -0.039 1 Table V

(16)

[16]

IV. Methodology and Results

A. Methodology

To conduct our empirical research on the effects of ESG-performance on Credit ratings. We do so because of the difference in scale of the dependent variable, namely ordinal and nominal.

In the models of section IV.B. Ordinary Least Squares Regression Results we use

Credit rating as dependent variable. Credit rating is an ordinal variable ranging from 24

(Highest) to 1 (lowest) which describes the risk of a firm. In an optimal situation we need to make us of an Ordered Logit regression as Ashbaugh-Skaife et al. (2006) perform in their research. Although the coefficients of an Ordinary Least Squares regression (OLS) are better and easier to interpret. The assumptions of OLS still hold except for the non-interval dependent variable. Because of this we choose to use the OLS method to capture the marginal effects of changes in ESG-scores on a firm its Credit rating.

In the models of section IV.C. Logit Regression Results we use the Investment grade as a dependent variable. Investment grade is divided in two categories namely investment grade and speculative grade. We do so because many bond portfolio managers are only allowed to invest in investment grade bonds (Grinblatt and Titman, 2002). Therefore the outcomes of this analysis make economically sense to firms because their cost of debt would increase substantially if they lose their investment grade. According to the dichotomous characteristic of the dependent variable we use a Logit regression. Additionally we use these Logit regression models as a robustness check for the OLS results.

We test the statistical effect of the ESG-performance on a firm its Credit rating by looking at four different regression models. The two main models we use are Equation 1 and

Equation 2. These two models we use in both the OLS regression analyses and the Logit

(17)

[17]

= + _ + _ + _ + _ +

+ + + + _ + _ + _ +

(Equation 1) 2.

Equation 2 is constructed of the overall ESG-performance and the control variables.

Note that in the Logit regression Credit rating (RATING) is again replaced by Investment

grade (GRADE) as dependent variable.

= + _ + + + + + _

+ _ + _ +

(Equation 2) 2.

Next to these two main models we added Equation 3 and Equation 4 for robustness checks and for comparison of the results. Note that in the Logit regression Credit rating (RATING) is again replaced by Investment grade (GRADE) as dependent variable.

= + + + + + _ + _

+ _ +

(Equation 3) 2.

= + _ + _ + _ + _ +

(Equation 4) 2.

B. Ordinary Least Squares Regression Results

In this section we describe the results of the OLS regressions analyses. The results of the OLS regression are presented in Table VI.

The first column of Table VI represents the full model which includes all variables. The model has an adjusted R-squared of 0.374 with a probability of 0.000 and thus is highly significant according to the F-statistic. The second column in Table VI represents the full model in which ESG-variables are combined in the overall ESG-score. In this model we see a slightly smaller but also a significant adjusted R-squared of 0.364. Hence the second model

2. i Represents the firm and t stands for the year. RATING is the Credit rating of the firm and GRADE is the firm

its Investment grade. ESG_SCORE is the overall ESG-score, ECN_SCORE is the Economic score,

ENV_SCORE is the Environmental score. SOC_SCORE is the Social score, GCV_SCORE is the Governance

score, LEV is the Leverage ratio, ROA is the Return on assets ratio, LOSS is the Loss in prior and current fiscal

(18)

[18]

explains a little less of the variation in a firm its Credit rating. Equation 3 and Equation 4 are also significant and confirm the findings of Equation 1 and Equation 2 and make the results more robust.

When we look at the firm characteristics we find four variables highly significant on the 1% significance level, respectively the firm its Size, Return on assets, Leverage and the firm its Financial utility. Capital intensity is only significant at the 10% significance level and thus not very significant. According to the sign of these significant firm characteristics we see that the expected sign is equal to the observed sign in Equation 1 and Equation 2, except for the less significant Capital intensity. This could probably be explained by the positive relation between Leverage, which has a negative influence on a firm its Credit rating, and Capital

intensity. We do not pay further attention to this because this variable is not very significant

and has a small impact on the firm its Credit rating.

Looking at the coefficient of the significant firm characteristics we observe that Size has the biggest impact on a firm its Credit rating. A firm which is one standard deviation bigger (0.455) gives an improvement in the Credit rating of about one grade. The impact of a one standard deviation higher Return on assets and leverage (respectively, 6.8% and 16.4%) have an impact of respectively +0.6 and -0.6. Thus these two variables explain around a half of a grade of variance in the Credit rating. When looking at the impact of a one standard deviation change in a firm its Financial utility (0.25) we obtain a change of +0.4 in the Credit

rating. Although a firms utility could only be one or zero we could state that a firm which has

a financial utility has a 1.5 grade higher Credit rating, which is quiet large.

The results of the control variables of Equation 1 and Equation 2 are confirmed by

Equation 3, which only uses firm characteristics to explain the variance in a firm its Credit rating. Even some more variables get significant although explanatory power of Equation 3 is

somewhat lower with an adjusted R-squared of 0.337.

Turning to the ESG variables we obtain somewhat different outcomes in different models. First of all the model with the overall ESG score, Equation 2, gives us a highly significant

F-statistic. This implies that the overall ESG-score has a significant influence on a firm its Credit rating. In the full model, Equation 1, with the four ESG variables, we see that only the Environmental-score is significant which is due to the high correlations between the

(19)

[19] Notes:

Equation 1: RATING = f (Firm characteristics, ESG-variables). Equation 2: RATING = f (Firm characteristics, ESG-variable). Equation 3: RATING = f Firm characteristics).

Equation 4: RATING = f (ESG-variables).

Significance level at 0.10; 0.05 and 0.01 are indicated with *,** and ***.

Table VI

The Effect of ESG Variables on US Firms Credit Rating (2006-2010) using OLS. With ESG-Variables as Independent Variable and Credit Rating as Dependent Variable, Controlled for Firm

Characteristics

Credit rating is the Credit rating assigned by Fitch represented in Table I. Size is expressed by the log of the total

assets. Return on assets is the return divided by the assets. Loss is the occurrence of loss in the prior and current fiscal year (1 if loss and 0 otherwise). Leverage is the net debt divided by total equity. Interest coverage is the ratio of EBIT divided by interest expenses. Financial utility is marked 1 if the firm has an financial utility and 0 otherwise. The

Capital intensity is total property, plant and equipment divided by the total assets. ESG-score is the average score of

the four ESG-pillars. Economic-score is the score for the economic sustainability. Environmental-score is the firms score for the impact on the environment. The Governance-score is the firm its corporate governance score and the

Social-score represents the firm its social responsibility. More detailed descriptions of the variables are in Table III.

Variables Predicted Estimated coefficient

sign Eq. 1 Eq. 2 Eq. 3 Eq. 4

(20)

[20]

To overcome this problem and observe the relation between the different ESG variables and Credit ratings we ran additional regressions. These regressions can be found in Appendix A and represents four models with the control variables and each ESG-variable individually. In

Equation 5 until Equation 8 we observe that the Environmental score and the Social-score

have a significant impact on Credit rating on a 1% significance level. The Economic-score and the Governance-score are significant on a 10% significance level and thus are less convincing to have an impact on Credit rating. The observed signs of the significant ESG variables and the overall ESG-score with the Credit ratings are all positive and in line with what we would expect.

Looking at the coefficient of the overall ESG-score we see a 0.0207 increase in Credit

rating for every point the ESG-score increases, this makes economically little sense. Hence, if

a firm improves its ESG-score by one standard deviation (24.99) the Credit rating increases 0.5 grade which is economically speaking not a large impact but could make a difference. The impact of an improvement of the ESG-score gets larger when one moves from the 25% quartile to the 75% quartile an improvement of ESG (39.30 points) yields an improvement of 0.8 grade. Since an improvement of the ESG-score from the 25% quartile to the 75% quartile is accompanied with years of effort and thus great costs this is not very likely to happen. From this we can conclude that there is a significant impact of a firm its ESG-score on a firm its Credit rating although it is not very large.

When turning to the impact of the ESG-variables individually (Appendix A) we can see which variables improve a firm its Credit rating most. Starting with the Economic-score we observe a relatively small impact of this score on the firm its Credit rating (0.0090). This could again be due to a distortion of a high correlation within the independent variables. This time Return on assets is highly correlated with the Economic-score and the model is disturbed by the problem of non-orthogonality described earlier. In an additional regression we have run without Return on assets, The Economic-score the coefficient gets slightly higher. The highly significant Environmental-score on the other hand has an impact of 0.0202 which is quiet equal to the impact of the overall ESG-score and has the highest impact of the ESG variables on the Credit rating. The impact of the Governance-score and the Social-score are respectively 0.0155 and 0.0162, smaller than the impact of the Environmental-score and larger than the Economic-score. Concluding from this one could say that improvements in the

Environmental-score yield the most, followed by improvements in the Social-score and the Governance-score. Improvements in the Economical-score yield the least but are most

probably also displayed by the Return on assets which does yield to improve the Credit

(21)

[21] C. Logit Regression Results

In this section we describe the results of the Logit regression analyses. The results of the Logit regression are represented in Table VII. Table VII includes the two major models used for testing the hypothesis and are complemented by the individual ESG-variables in

Table 9 in the appendix. Results of this Logit regression show the importance of the variables

on whether a firm has an investment grade or not. The first Column of Table VII represents the full model including all four ESG variables and the control variables to control for firm characteristics (Equation 1) and has a McFadden R-squared of 0.252 which is significant according the LR-statistic. In the second column represents the full model including the overall ESG score and the firm characteristics (Equation 2) and has a McFadden R-squared equal to Equation 1 of 0.0252 and is also significant.

In the Logit regressions we find all firm characteristics significant within the 10% significance level. The Size, Leverage, Financial utility and Capital intensity seem to be the most significant in both Equation 1 and Equation 2. This more or less confirms the results of the OLS analyses. The Loss and the Interest coverage are significant at the 5% level and the

Return on assets is significant on a 10% level. The latter is remarkable because return on

assets was significant at 1% level in the OLS analyses. The signs of the significant firm characteristics are equal to the expected signs except for the sign of Interest coverage coefficient and the sign of the Capital intensity coefficient. The sign and significance of the

Interest coverage variable negligible because the coefficient is almost equal to zero. The sign

of the Capital intensity is just as with the OLS regression not in accordance with the expected sign, which should be positive according to Alali et al. (2012). Hence, concerning the firm characteristics we find a quiet similar pattern as with the OLS analyses and we find even more variables significant in explaining a firm its investment grade or not.

Turning to the overall ESG-score we find a positive and significant influence on the firm its

Investment grade (Equation 2). Thus a higher ESG score gives a higher probability of having

an Investment grade. This is in accordance with the results we found in the OLS analyses and thus gives us more robust results. When looking to the ESG variables in Equation 1 we find a significant impact on the 1% level of the Environmental-score and the Social-score. These are the similar ESG variables that where most significant in Equation 5 until Equation 8 in

Appendix A. Appendix B shows the individual effects of the ESG-variables on the firm its

(22)

[22] Table VII

The Effect of ESG Variables on US firms Credit Rating (2006-2010) using Logit Regressions. With ESG-Variables as Independent Variable and Investment Grade as dependent Variable,

Controlled for Firm Characteristics

Investment Grade is assigned to Fitch Credit ratings higher than BB+ represented in Table I. Size is expressed as

the log of the total assets. Return on assets is the return divided by the assets. Loss is the occurrence of loss in the prior and current fiscal year (1 if loss and 0 otherwise). Leverage is the net debt divided by total equity. Interest coverage is the ratio of EBIT divided by interest expenses. Financial utility is marked 1 if the firm has an financial utility and 0 otherwise. The Capital intensity is total property, plant and equipment divided by the total assets. ESG-score is the average score of the four ESG-pillars. Economic-score is the score for the economic sustainability. Environmental-score is the firms score for the impact on the environment. The

Governance-score is the firm its corporate governance score and the Social-score represents the firms social

responsibility. More detailed descriptions of the variables are in Table III.

Notes:

Equation 1: GRADE = f (Firm characteristics, ESG-variables). Equation 2: GRADE = f (Firm characteristics, ESG-variable).

Significance level at 0.10; 0.05 and 0.01 are indicated with *,** and ***.

Variables Predicted Estimated coefficient

sign Equation 1 Equation 2

(23)

[23]

Although the coefficients are not easily interpretable when using a Logit regression, we do observe that the coefficients of the ESG-variables are relatively small comparing to the coefficients of the firm characteristics. This is in harmony with the results found with the OLS analyses.

D. Discussion

Comparing the results of this study with previous studies in this field we find a lot of similarities. First of all we can conclude that a better ESG-score and thus better corporate responsible performance leads to a higher Credit rating. Both the OLS and the Logit regression results represent a significant positive relation between a firm its corporate responsible performance and the firm its Credit rating. Past research (e.g. Bassen et al., 2006; El Ghoul et al., 2011; Goss and Roberts, 2011; Menz, 2010) points into this direction and this research confirms that Corporate Social Responsibility is significantly lowering a firm its risk. When turning to the models with the individual ESG-variables we overall see that the

Environmental-score and Social-score are most of the times significant and have the largest

values of the four ESG-pillars. Higher Environmental- and Social-score yield the most in lowering a firm its credit risk. This finding is in accordance with the finding of El Ghoul et al. (2011) which states that improving responsible employee relations, environmental policies, and product strategies contributes substantially to reducing firm its cost of capital. Responsible employee relations and product strategies are part of a firm its Social-score and environmental policies are included in the Environmental-score. Hence, this makes our findings robust with previous studies.

(24)

[24]

marginal economical influence of ESG on a firm its cost of capital is confirmed by Bassen et al. (2006) and Goss and Roberts (2011). When putting the results of this research and literature together it seems that the influence of ESG on a firm its Credit rating is not very large and seems not yet of economic influence. When broadening the discussion even more one could state that 'Doing good by doing well' (Margolis et al., 2009) is not very likely by the transmission of lowering risk by ESG.

Limitations of this study could be the measurement problems concerning the ESG-performance and the credit risk. The measurement of both concepts is debatable. Credit risk is measured by Credit rating agencies which are paid by the company which needs a rating. This system seems sensible for bribing since the agency is paid by the company which wants a Credit rating as high as possible (Bolton, Freixas and Shapiro, 2012). Even if this is no problem the Credit rating agency might not always see every risk and thus make a wrong credit risk assessment. Except for some mistakes by Credit rating agencies in the past, for example Lehmann brothers (2008) and Greece (2009), overall Credit ratings seem a fairly good measurement of credit risk. The measurement of ESG or CSR is a bit more complicated since there is no single definition of this concept. The ideas of what should be included in ESG are different in different continents and among different researchers (Kates, Parris and Leiserowitz, 2005). It seems obvious that different views on ESG give a different outcome on how responsible companies are. We have chosen for the most widely used concept of ESG including: Economic, Environmental, Governance and Social issues. If this is the best measure of ESG is debatable. Next to these two limitations concerning the method we use, this research does not aim at the influence of ESG on a firm its performance. We only measure the influence of ESG on credit risk and not the implication of the lower credit risk on firm performance. This is a conscious decision we made and could be the topic of future research.

V. Conclusion

(25)

[25]

yet researched to our knowledge. In this research we assume that ESG performance and risk have a negative relation and research this by looking at the influence of a firm its ESG-score on a firm its Credit rating. We do this for 193 S&P500 firms in the time period of 2006 until 2010.

We find in both the Ordinary Least Squares and the Logit regression analyses that ESG performance has a significant influence on a firm its Credit rating controlled for firm characteristics which confirms our hypothesis. On average we find that an one point improvement in a firm its overall ESG-score leads to a 0.0207 improvement of a firm its Credit rating. To make this tangible, a firm needs to improve its ESG-score with 50 points to improve its Credit rating with one point and this is actually a very small influence. In accordance with earlier findings (Bassen et al., 2006; Goss and Roberts, 2011) we find ESG to be of a significant influence on Credit ratings but probably not of economic importance. This means that the cost of an improvement in ESG will probably not be covered by the benefits of a higher Credit rating. When turning to the four pillars of ESG we find that the Environmental-score and Social-score have the largest influence on a firm its Credit rating and the Economic-score and the Governance-score to a lesser extent. Although the coefficients do not exceed the coefficient of the overall ESG-score and thus are also quiet small. This is in accordance with the findings of El Ghoul et al. (2011).

(26)

[26]

References

Alali, F., Anandarajan, A., Jiang W., 2012. The effect of corporate governance on firm’s credit ratings: further evidence using governance score in the United States. Accounting and Finance 52, 291–312

Ashbaugh-Skaife, H., Collins, D.W., LaFond, R., 2006. The effects of corporate governance on firms’ credit ratings. Journal of Accounting and Economics 42, 203-243.

Bassen, A., Hölz, H. M., Schlange, J., 2006. The Influence of Corporate Responsibility on the Cost of Capital An Empirical Analysis. A research paper of the Deutsche Bank in corporation with Universität Hamburg.

Bolton, P., Freixas, X., Shapiro, J., 2012. The Credit Ratings Game. The Journal of Finance, Volume 67, Issue 1, 85–111.

Cyert, R. M., March J. G., 1963. A Behavioral Theory of the Firm. Prentice Halls, Englewood Cliffs. 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 & Finance, Volume 35, Issue 9, 2388-2406. Esty, B., Knoop, C., Sesia A., 2005. The Equator Principles: An Industry Approach to Managing

Environmental and Social Risks. Harvard Business School Case Study 9, 205–214. Dimson, E., Karakas O., Li, X., 2012. Active Ownership. Working paper.

Fama, E. F., MacBeth, J. D., 1973. Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, Volume 81, No. 3, 607-636.

Freeman, R. E., 1984. Strategic management: A stakeholder perspective. Pitman Publishing Inc., Boston.

Friedman, M., 1970. The social responsibility of business is to increase its profits. The New York Times Magazine, September 13, 1970.

Godfrey, P., 2005. The Relationship Between Corporate Philanthropy and Shareholder Wealth: A Risk Management Perspective. Academy of Management Review 30, 777–798.

Goss, A., Roberts, G. S., 2011. The Impact of Corporate Social Responsibility on the Cost of Bank Loans. Journal of Banking & Finance, Volume 35, Issue 7, 1794–1810

Grinblatt, M., Titman, S., 2002. Financial Markets and Corporate Strategy, second ed. McGraw-Hill Irwin, New York.

Heal, H., 2004. Corporate Social Responsibility, An Economic and Financial Framework. Columbia Business School, presented at the 2004 Annual Conference of the Monte Paschi Vita.

Heinkel, R., Krause A., Zechner J., 2001. The Effect of Green Investment on Corporate Behavior. Journal of Financial and Quantitative Analysis 35, 431–449.

(27)

[27]

Jensen, M., 2001. Value Maximization, Stakeholder Theory, and the Corporate Objective Function. Journal of Applied Corporate Finance 14, 8–21.

Kaplan, R., Urwitz, G., 1979. Statistical models of bond ratings: a methodological inquiry. Journal of Business 52, 231–261.

Kates R. W., Parris T. M., Leiserowitz A. A., 2005. What is sustainable development? Goals, indicators, values, and practices. Environment: Science and Policy for Sustainable Development, Volume 47, Issue 3, 8-21.

Mackelprang, A. J., 1970. Missing data in factor analysis and multiple regression, Midwest Journal of Political Science Volume 14, No. 3, 493-505.

Margolis, J. D., J. P., Walsh, 2003. Misery Loves Companies: Rethinking Social Initiatives by Business. Administrative Science Quarterly 48, 268–305.

Margolis, J. D., Elfenbein H. A., Walsh J. P., 2009. Does It Pay to Be Good? A Meta-Analysis and Redirection of Research on the Relationship Between Corporate Social and Financial Performance. Working Paper.

Menz, K. M., 2010. Corporate Social Responsibility: Is it Rewarded by the Corporate Bond Market? A Critical Note. Journal of Business Ethics 96, 117–134

Mercer, 2007. The Language of Responsible Investment, An Industry Guide to Key Terms and Organisations. Mercer Investment Consulting, London.

Myers, S. C., Majluf, N. S., 1984. Corporate Financing and Investment Decisions when Firms have Information Investors do not have. Journal of Financial Economics 13, 187–221.

Orlitzky, M., Schmidt F., Rynes S., 2003. Corporate Social and Financial Performance: A Meta-Analysis. Organization Studies 24, 403–411.

Phillips, Hager & North, 2007. Does Socially Responsible Investing Hurt Investment Returns? Research Paper, Phillips, Hager & North Investment Management Ltd., Vancouver.

Scholtens, B., 2006. Finance as a Driver of Corporate Social Responsibility. Journal of Business Ethics 68, 19–33.

Scholtens, B., Dam, L., 2007. Banking on the Equator. Are Banks that Adopted the Equator Principles Different from Non-Adopters? World Development, Volume 35, Issue 8, 1307–1328.

Smith, M. P., 1996. Shareholder Activism by Institutional Investors, Evidence from CalPERS. Journal of Finance 51, 227–252.

(28)

[28]

Appendix A

The Effect of ESG Variables on US Firms Credit Rating (2006-2010) using OLS. With Individual ESG-Variables as Independent Variables and Credit Rating as Dependent

Variable, Controlled for Firm Characteristics

Credit rating is the Credit rating assigned by Fitch represented in Table I. Size is equal to the log of the total assets. Return on assets is the return divided by the assets. Loss is the occurrence of loss in the prior and current fiscal year

(1 if loss and 0 otherwise). Leverage is the net debt divided by total equity. Interest coverage is the ratio of EBIT divided by interest expenses. Financial utility is marked 1 if the firm has an financial utility and 0 otherwise. The

Capital intensity is total property, plant and equipment divided by the total assets. ESG-score is the average score of

the four ESG-pillars. Economic-score is the score for the economic sustainability. Environmental-score is the firms score for the impact on the environment. The Governance-score is the firm its corporate governance score and the

Social-score represents the firms social responsibility. More detailed descriptions of the variables are in Table III.

Variables Predicted Estimated coefficient

sign Eq. 5 Eq. 6 Eq. 7 Eq. 8

Constant 1.2868 3.7018 0.2849 2.2260 Firm characteristics Size + 2.1704 *** 1.7490 *** 2.2049 *** 1.9812 *** Return on assets + 9.0981 *** 9.3318 *** 10.3726 *** 9.3293 *** Loss - -0.5610 -0.7432 -0.6706 -0.6966 Leverage - -3.7458 *** -3.6574 *** -3.6984 *** -3.8346 *** Interest coverage + -0.0013 -0.0012 -0.0014 -0.0012 Financial utility + 0.9562 * 1.5027 ** 0.9697 1.3906 ** Capital intensity + -0.4469 -0.5964 * -0.4833 -0.5045 ESG Variables Economic-score + 0.0090 * Environmental-score + 0.0202 *** Governance-score + 0.0155 * Social-score + 0.0162 *** Model specification Adjusted R-squared 0.342 0.375 0.341 0.356 Probability (F-Statistic) 0.000 0.000 0.000 0.000 Number of firms 193 193 193 193 Years 5 5 5 5 Notes:

Equation 5: RATING = f (Firm characteristics, ECN-variable). Equation 6: RATING = f (Firm characteristics, ENV-variable). Equation 7: RATING = f (Firm characteristics, GCV-variable). Equation 8: RATING = f (Firm characteristics, SOC-variable).

(29)

[29]

Appendix B

The Effect of ESG Variables on US Firms Investment Grade (2006-2010) using Logit Regressions. With Individual ESG-Scores as Independent Variables and Investment

Grade as Dependent Variable, Controlled for Firm Characteristics.

Notes:

Equation 5: GRADE = f (Firm characteristics, ECN-variable). Equation 6: GRADE = f (Firm characteristics, ENV-variable). Equation 7: GRADE = f (Firm characteristics, GCV-variable). Equation 8: GRADE = f (Firm characteristics, SOC-variable).

Significance level at 0.10; 0.05 and 0.01 are indicated with *,** and ***.

Investment Grade is assigned to Fitch Credit ratings higher than BB+ represented in Table I. Size is equal to the log of

the total assets. Return on assets is the return divided by the assets. Loss is the occurrence of loss in the prior and current fiscal year (1 if loss and 0 otherwise). Leverage is the net debt divided by total equity. Interest coverage is the ratio of EBIT divided by interest expenses. Financial utility is marked 1 if the firm has an financial utility and 0 otherwise. The Capital intensity is total property, plant and equipment divided by the total assets. ESG-score is the average score of the four ESG-pillars. Economic-score is the score for the economic sustainability.

Environmental-score is the firms Environmental-score for the impact on the environment. The Governance-Environmental-score is the firm its corporate governance

score and the Social-score represents the firm its social responsibility. More detailed descriptions of the variables are in

Table III.

Variables Predicted Estimated coefficient

sign Eq. 5 Eq. 6 Eq. 7 Eq. 8

Referenties

GERELATEERDE DOCUMENTEN

Aangezien het areaal moerige gronden en minerale gronden tezamen 2 a 3% is toegenomen kan worden gesteld dat het areaal veengronden met maximaal 2 a 3% is afgenomen, maar dit

The benefit of the community envisioning has already show as it raised the criterion of social conformism (sense of community); Social participation on the web needs to be

Although most of the research efforts have been performed to analyse the effect of degradation mechanisms, very limited research has been carried out on the countermeasures

Verder is het feit dat de uitoefening van het stakingsrecht enkel in de Europese context wordt beperkt door verkeersvrijheden problematisch voor werknemers die voor werkgevers werken

In health care this implies that a centralized (pooled) clinic that serves all patient types may achieve shorter waiting times than a number of decentralized (unpooled)

This paper considers the reciprocal implementation of three planning approaches, new urbanism, new ruralism and green urbanism, to reflect on multifunctionality and draws

To measure the relation between formal and informal environmental management control systems (EMCS) and the score on the TB, and the moderating effect of the processing

Credit rating announcements published before the financial crisis show large negative cumulative average abnormal returns for downgrades in all event windows except for