4. Empirical Results
4.1 Descriptive Statistics and Bivariate Correlation
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4. Empirical Results
4.1 Descriptive Statistics and Bivariate Correlation
Table 1 provides descriptive statistics for the variables used in the analysis. To prevent the results from distortion by outliers, I winsorize the sample except Bloomberg ESG disclosure score at the bottom and top 0.5% levels. There are 21,426 firm-year ESG disclosure scores in the full sample, which represent 3,305 firms. Apart from the combined score, I also collect separate environment, social, and governance score in Bloomberg ESG database. It yields 4,586 firm-year E score representing 694 firms, 12,747 firm-year S score representing 2,012 firms, and 21,258 firm-year G score representing 3,297 firms. The mean values of disclosure scores, ESG score, E score, S score, and G score, are 17.13, 21.04, 15.84, and 51.26, respectively. The average total risk is 0.026, the average systematic risk is 0.011, and the average idiosyncratic risk is 0.022. The average firm in the sample has a NCSKEW of 0.086, a KZ index of -14.864, a WW index of -0.248, a SA index of -3.283, and a year return of 0.143. In addition, the mean value of control variables such as ln(size), measured as natural log of total asset, is 7.268 (about 1.433 billion), market-to-book ratio is 3.519, leverage ratio is 0.550, and return on assets is 0.003.
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Table 2 presents the Pearson correlations. I find a high correlation between total risk and systematic risk (0.48) and idiosyncratic risk (0.97), which are similar to the correlation in Jo and Na (2012). Besides, among a total of 7 risk measures and financial constraint measures, 5 out of 7 measures show a negative and significant correlation
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with ESG disclosure score. The correlation between ESG score and total risk is -0.29, systematic risk is -0.08, idiosyncratic risk is -0.30, WW index is -0.19 and SA index is -0.35. However, ESG score and NCSKEW and KZ index are weakly correlated (correlation below 0.1). The correlation matrix presents that ln(size) is highly correlated with ESG score (0.59) and other control variables. Therefore, in the following regressions, I report regressions with and without ln(size) factor respectively. Finally, other control variables are weakly correlated. Multicollinearity problems are unlikely to happen in the model.
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4.2 Empirical Results
The Effect of ESG on firm risks
Table 3 reports results from regression analysis of the relation between ESG score and total risk, systematic risk, and idiosyncratic risk. Firm characteristics that might affect risks such as ln(size), MB, LEV, ROA are controlled. The use of lagged ESG score and other controlled variables is to predict future risks. I expect a negative relationship between ESG score and financial risk measures. Columns 1, 3, and 5 include ln(size) factor. High correlation between ln(size) and ESG score might cause multicollinearity problem. In these three regressions, only column 3 has a negative coefficient on ESG score. To prevent from multicollinearity, I redo the regressions without ln(size) factor in columns 2, 4, and 6. In these 3 regressions, all the coefficients on ESG score are negative and significant at 1% level, which matches my prediction and is in line with prior studies. I believe that ESG engagement can eliminate
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information asymmetry by enhancing corporate transparency. Besides, engaging in ESG activities can build a strong connection between ESG firms and stakeholders. It also helps to attract good quality employees and make product differentiation among competitors. All these factors potentially contribute to reducing financial risk. Table 3 provides strong evidence that higher ESG disclosure score can lower financial risks.
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I further investigate from what portal can ESG engagement affect a firm’s idiosyncratic risk. Idiosyncratic risk such as the high possibility of stock price crash, and difficult access to finance, which causes serious downside risks to a firm. Therefore, I use NCSKEW, KZ index, WW index, SA index to proxy downside risks to examine the relationship between ESG disclosure score and downside risks. Table 4 reports the results of the regression. Columns 1 and 3 include ln(size) factor in the regressions and it shows a negative coefficient of ESG disclosure score. However, after removing ln(size) factor due to the concern of multicollinearity, I find a positive correlation between ESG score and NCSKEW and KZ index. The 𝑅2 is comparatively small in NCSKEW regressions, which is similar to the 𝑅2 in Kim et al. (2014).
In the real business world, the greater the market frictions are, the higher the cost of external financing. Improving corporate transparency can solve market frictions problem. With credible internal control system and disclosure reports, ESG firms are able to raise funds at a lower cost either from equity market or from bond market.
Therefore, ESG firms can better capture investment opportunities. In contrast, firms with lower ESG score may end up forgo investment opportunities and damage
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shareholder value due to serious financial constraints. I argue that lower ESG score firms face higher downside risk than higher ESG score firms do. In WW and SA regressions (columns 5 and 6), I find that ESG score is negatively correlated with the downside risk measures and significant at 1% level. I do not control ln(size) variable in WW and SA index regression (Columns 5 and 6) because natural log of asset is considered in the calculation of the indexes.
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The Effect of ESG individual components on firm risks
In this section, I examine the effect of ESG individual components on firm risks.
I collect Bloomberg environmental, social, and governance disclosure score and combine it with total risk, systematic risk, and idiosyncratic risk to run regressions respectively. Environmental issue includes CO2 emissions, recycled policy, renewable energy usage, etc.; social issue includes minorities in workforce policy, training policy, human rights policy, etc.; governance issue consists of board structure, the independence of the board, etc. I find that each individual ESG components contributes to lower firm risks. E, S, G scores are negatively correlated with all 3 risk measures at 1% level. It suggests that higher disclosure score can build positive reputation among stakeholders and enhance its transparency, which reduce firm risks. Moreover, In columns 4, 8, and 12, it shows that environment and governance engagement have a more pronouncing effect to risks if a firm discloses all 3 dimension scores. Besides, the relationship between ROA and risk are negative and significant in all regressions.
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The relationship between ESG individual components and downside risks are reported in table 6. From column 9 to 12 (WW index measure) and 13 to 16 (SA index measure), I find significantly negative coefficients on all individual disclosure scores.
ESG is a protection to firms’ downside risk. Higher disclosure score firms can enjoy better access to finance and are less subject to financial distress. They not only have the ability to borrow but also to issue new equity. More external financing choices supports the survival of higher disclosure score firms. Hence, they can pursue a growing prospect in the future. However, in NCSKEW regressions, the coefficients on ESG individual scores are contrary to my expectation. The relationship between ESG score and KZ index is not clear in table 6.
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For NCSKEW and KZ index measures, we cannot conclude that higher ESG (individual) scores bring lower risks to enterprises. Therefore, I further investigate if a firm contribute to environment, social, and governance dimensions at the same time, could individual score play an important role to risks. And if so, which dimension has a more pronouncing effect? I present the results in table 7. In NCSKEW regressions, the coefficient on ESG, E, and S scores are still positive. I argue that only when the annual disclosure score is growing steadily can prevent the stock price from falling
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sharply, and thus avoid stock price crash risk. However, since the ESG disclosure score fluctuates every year, the risk reducing effect of ESG cannot be fully performed. In contrast, in the case of KZ index, when I limit the sample to firms engaging in all three dimensions of ESG, I find that contribution from each individual component can lower financial constraint significantly in column 7 to 12. This gives two implications. First, participating in ESG activities comprehensively is a more efficient way to lower firm risk than partial ESG engagement. The reducing risk effect can be correctly conveyed when firms contribute to environment, society, and governance simultaneously. Second, E score is the least among three individual scores. Therefore, when I limit the sample to firms engaging in all three dimensions of ESG, it yields a similar firm-year sample with individual E score sample. This explains the importance of environment contribution to risk reduction.
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The Effect of ESG on Stock Return
The results above in this study provide strong evidence that ESG engagement can reduce firm risks. Besides, the reducing risk effect is contributed to lower financial constraints, which is a measure of downside risk. Lower financial constraints allow firms to enjoy better access to finance and borrowing at lower costs. Firms can undertake profitable investments and pursue superior financial performance. Therefore, in this section, I examine whether firms with more extensive ESG activities can achieve higher stock return caused by downside risk reduction. The result is reported in table 8.
I expect a positive coefficient of variable score * risk in Eq. (11), which implies under
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the same level of risk, higher ESG score firms with lower downside risk can enjoy higher stock return. However, in columns 1, 5, and 9, none of the 3 ESG score regressions has significant coefficients on score* financial risk. Therefore, I conclude the relationship between score * risk and return is far from clear. The transfer effect of ESG from risk to return is uncertain in total risk, systematic risk and idiosyncratic risk regressions. I believe this result is due to two opposite effects countering each other.
Lower downside risks that ESG engagement brings can raise future stock return.
However, lower risks usually accompany lower stock return in the real financial market.
As table 8 shows, the overall effect of the two contradicting forces is not clear. For other control variables in my regression, we find that financial risk and LEV are positively correlated with return. Ln(size) and MB are negatively correlated with return.
The findings are consistent with prior studies.