Maury_Corp_Final

Corporate Social Responsibility, Risk Diversification, and Firm Value:

Does Corporate Social Responsibility Diversify Risk

Maury Rubin

Department of Economics

University of Western Ontario

[email protected]

April 21, 2016

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Corporate Social Responsibility, Risk Diversification, and Firm Value:

Does Corporate Social Responsibility diversify Risk

Abstract

The purpose of this study is to explore whether firms that employ corporate social responsibility

tactics see bottom-line improvements to their firm’s performance through risk diversification. The

literature review of this topic shows that “internal firm diversification” contributes positively to

the reduction of idiosyncratic risk which in turn contributes to positive financial performance, and

as such, this paper posits that investing in corporate social responsibility is a form of “internal firm

diversification.” Using a corporate social responsibility rating as well as its lagged value as

independent variables (Li, Class, 2016) and control variables for idiosyncratic risk (Mishra, and

Modi, 2012) we observe that corporate social responsibility contributes positively to a firm’s

financial performance in a variety of measures.

Key words: Corporate Social Responsibility, CSR, Diversification, Financial Performance, Firm

Risk, Idiosyncratic Risk, Tobin’s Q,

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1. Introduction

There has been in the past decade extensive coverage of the topic surrounding the impact of

corporate social responsibility on firm performance. After all, corporate social responsibility

initiatives are actions that advance social good beyond that which is required by law (Kang,

Germann, Grewal, 2016), and can have an ambiguous effect on a firm’s bottom line performance.

Thus this topic has been studied from various angles to thoroughly understand what if any

contributions corporate social responsibility has to the firm and if it is even worth pursuing.

In their 2016 paper, Kang, Germann, and Grewal posit four mechanisms by which corporate social

responsibility leads or is linked to positive firm performance. They suggest that corporate social

responsibility is 1) a result of having extra resources, a sign of a successful firm, 2) a sign of good

management, which leads to a successful firm, 3) a penance mechanism to atone for past corporate

social irresponsibility, and 4) an insurance mechanism that builds a reservoir of goodwill. Of these

four mechanisms, only one stands out as making a strong strategic cases for taking on the

labourious task of implementing corporate social responsibility measures. By extension of

mechanism (4), this paper questions whether corporate social responsibility actually acts as an

insurance mechanism and if it does, can this idea be extended to determine whether corporate

social responsibility reduces idiosyncratic risk (company specific risk).

This paper examines the premise of a variety of papers that conclude that corporate social

responsibility leads to an overall reduction in idiosyncratic risk through diversification. If

corporate social responsibility leads to an increase in diversification what then are the effects of

corporate social responsibility on the firm’ s financial performance.

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I will begin this analysis with a literature review outlining what has been discovered in the past

with regards to corporate social responsibility and diversification, as well as diversification and

firm performance. I will then run six regressions using a metric of firm performance as the

dependent variable and a corporate social responsibility rating, a lagged corporate social

responsibility rating, and company specific control variables as the independent variables. After

holding for the company specific methods of diversification, I will be able to conclude what if any

effect corporate social responsibility has on firm performance as a diversification tool.

2. Literature Review and Hypotheses

As discussed previously, the purpose of this discussion is to determine what effect corporate

social responsibility measures have on the financial performance of the firm when we account

for diversification effects. The idea of linking corporate social responsibility to diversification is

not novel; it has been suggested previously, and it has been shown that corporate social

responsibility does lower idiosyncratic risk via diversification. In portfolio theory, diversifying

one’s portfolio is a means by which one lowers “idiosyncratic risk’. In this paper’s context,

diversification refers to firms investing in non-core business behaviours as a means to diversify

their own risk. Mishra and Modi for instance note in their paper Positive and Negative Corporate

Social Responsibility, Financial Leverage, and Idiosyncratic Risk that “although extant theory

predicts a complex relationship between CSR and financial returns stakeholder theory and the

RBV of the firm suggest some clear pathways through which positive (negative) CSR may help

firms reduce (increase) their financial risk.” (Mishra, Modi, 2012). In a similar vein, Attig,

Ghoul, Guedhami, and Suh cite in their 2012 paper Corporate Social Responsibility and Credit

Ratings, “Consistent with this view, Lee and Faff (2009) and Boutin-Dufresne and Savaria

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(2004) document that low CSR firms exhibit significantly higher idiosyncratic risk. And finally,

Luo and Bhattacharya (2009) show that firms with high corporate social performance are able to

lower undesirable idiosyncratic risk.

Now that a link has been drawn between corporate social responsibility and diversification,

can we draw the same links between diversification and financial performance? Interestingly

Lang and Stultz (1993) find that firm diversification and Tobin’s q are negatively related. They

find that firms that increase their number of sales segments have lower “q’s” than firms that keep

their number of segments constant. In contrast Tanriverdi and Lee (2008) find that within-

industry diversification increases marginal returns. This paper will posit that corporate social

responsibility measures act as a “within –industry” diversification measure and should therefore

positively effect financial performance.

In the regressions, when we hold for the effects of diversification we should only be left

with the effects of corporate social responsibility measures on the firm’s performance. The

following hypothesis is formed.

H1a: If corporate social responsibility measures act a method of

diversification, then a higher corporate social responsibility rating will lead

to better financial performance (ie. Higher net income, lower leverage ratio)

H1b: If corporate social responsibility measures do not act a method of

diversification, then a higher corporate social responsibility rating will not

lead to better financial performance (ie. No or ambiguous effect on net

income/ leverage ratio)

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H2a: If corporate social responsibility factor measures act as a method of

diversification, then a higher lagged corporate social responsibility rating

will lead to better financial performance (ie. Higher net income, lower

leverage ratio)

3. Data and Summary Statistics

3.1 Databases

The main variable needed to test the hypothesis is a rating for corporate social responsibility. To

get this measure the KLD database was accessed. This database is an annual data set of positive

and negative environmental, social, and governance (ESG) performance indicators applied to a

universe of publicly traded companies. Additionally, it is necessary to obtain fundamental financial

information. Firm fundamentals from 1960 – 2016 were obtained from the Compustat Capital IQ

database.

3.2 Test variables

CSR_Ratingit

In SAS, a variable CSR_rating is generated by taking the sum of the positive community, diversity,

employee relations, environment, human rights, and product points and subtracting the sum of the

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negative community, diversity, employee relations, environment, human rights, and product

points.

Lag1CSRit-1

There may be effects coming from the corporate social responsibility employed in the previous

period. Therefore, the CSR_Rating is lagged by one time period to formulate Lag1CSR.

3.3 Control Variables

This is aiming to observe corporate social responsibility as a measure of diversification. As

such, control variables must be employed in order to account for firm specific risk

diversification. The following variables have been used in a variety of papers discussing firm

diversification. Descriptions of these variables can be found in appendix 1.

Research and Development Intensity (RnDit )

Spending on research and development can lead to firm and product innovation which may reduce

a firm’s idiosyncratic risk. Further Luo and Bhattacharya (2009) argue that R&D is an important

strategic lever affecting the influence of corporate social responsibility of firms. Therefore, R&D

must be controlled for and it equals total R&D expense divided by total firm revenue.

Advertising Intensity (ADit)

Because firms can use advertising to to inform customers and investors about their performance,

products, and services, advertising can help lower idiosyncratic risk. Advertising is controlled for

by dividing total advertising expense by total revenue.

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Firm age (Firm_ageit)

A firm’s age can effect idiosyncratic risk beacuse older firms may be more adept at identifying

and lowering their firm specific risk. Firm age is calculated by subtracting observation i’s date

from the first year of the firm’s public existence.

Return on Assets (ROAit)

Return-on-assets is a measure that provides investors information regarding the profitability of

the firm and can influence the idiosyncratic risk of a firm. ROA is calculated as net income

before extraordinary items divided by total assets.

Market to book ratio (Mktbookit)

The market to book ratio is a common metric by which investors judge firms. It is calculated as

the market value of equity divided by the book value of equity.

Size (Sizeit)

Larger firms may be able to reduce idiosyncratic risk faster and or better than smaller firms. Size

is calculated as the log of total assets. A similar measure was used in Saleh, Zulkifi, and Muhamad

(2007)

Dividends (DVPDit)

Paying dividends is a sign of a firm’s financial health, and therefore suggests that it can reduce its

idiosyncratic risk. Dividends payout is represented as the total dividends paid.

Cash (CHit)

Cash on the balance sheet is assumed to be excess cash rather than cash that is necessary to run

the everyday operations of the business. It follows that firms with more cash have the ability to

invest in actions that can help lower idiosyncratic risk.

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3.4 Dependent Variables

A summary of the dependent variables that represent a firm’s financial performance.

Descriptions of these variables can be found in appendix 1

Tobins Q (TobinsQit)

Tobin’s Q is a measure of the firm’s assets in relation to its total market value. It is commonly

used to determine if a company is over or under valued because the ratio suggests whether the firm

is in actuality worth more or less than than the cost of its assets. If the hypothesis about corporate

social responsibility is correct, we should expect to see Tobin’s Q increase when corporate social

responsibility initiatives increase. Tobin’s Q is calculated as the market value of equity plus the

book value of assets minus the sum of book value of common equity and deferred taxes, all divided

by the book value of assets.

Leverage (Debt_to_assetit)

Leverage ratios are common tools used to assess the financial well being of a firm. Firms that are

highly levered run the risk of bankruptcy as well as long run insolvency. If our hypothesis is

correct, and corporate social responsibility initiatives diversify risk, we should see the debt to asset

ratio lower when corporate social responsibility initiatives are undertaken. The leverage ratio is

computed by taking the total debt and dividing by total assets.

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Solvency (Solvit)

Firms and shareholders are concerned with solvency as it represents the firm’s long term financial

health. Firms that become insolvent are unable to payback their long term liabilities and will

eventually become a gone concern. If our hypothesis is correct we would expect corporate social

responsibility initiatives to increase the solvency ratio because the initiatives should lower the

firm’s idiosyncratic risks. The solvency ratio is calculated by dividing net income plus depreciation

by total short term and long term debt.

Operating Cash Flow (OANCFit)

Operating cash flow represents the amount of cash generated by the normal operations of the

business. As a performance measure, operating cash flow is important to the firm because it

indicates whether a company is able to generate sufficient cash flow to maintain and grow its

operations. If the hypothesis is correct corporate social responsibility initiatives should increase

the operational cash flows of the firm. A similar measure was used in McGuire, Sundgren, and

Schneeweis (1988).

Net Income (NIit)

Net income is the firm’s bottom line. It represents the total income generated by the firm less taxes,

interest expenses and extraordinary items. The firm uses its net income to reinvest in the firm, or

pay out dividends to its shareholders. Therefore, firms are extremely interested in growing this line

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item. If the hypothesis is correct, corporate social responsibility initiatives should increase the net

income account.

Total Revenue (Revtit)

Total revenue represents the total sales of the firm. The firm uses the money generated by saes to

service pay its employees, service debt, pay taxes, and run the business. If the hypothesis is true,

total revenue should grow with the increase in corporate social responsibility initiatives.

3.5 Summary Statistics

Table 1 presents the summary statistics for all the test variables, the control variables, and the

dependent variables. The mean and standard deviation of CSR_rating is 0.75 and 3.19

respectively, and the mean and standard deviation of the lagged CSR rating is 0.77 and 3.2

respectively.

Table 2 then presents the correlations between the variables as well as the Pearson correlation

coefficients. The preliminary correlations confirm some of what the hypothesis 1a suggests.

CSR_rating is positively and significantly correlated with net income, solvency, revenue, and

operating cash flow, and it is negatively and significantly correlated with leverage. The

CSR_rating was negatively correlated with Tobin’s Q however this correlation was insignificant.

The correlation between the lagged CSR variable and the financial performance metrics are the

same as the normal CSR variable.

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4. Findings and Discussion

The regressions found in Tables 3 through 8 were run in SAS, and they form the basis for the focus

of this paper. In Tables 3 trough 8 the output of the regression displays clear evidence that when

specific firm diversification is held constant, the diversification effects stemming from corporate

social responsibility practices helps increase the financial performance of a firm. Specifically, in

model 1 of the regression analysis CSR_rating behaves according to Hypothesis 1a. That is, when

measures of firm performance are regressed on CSR_Rating, CSR_rating contributes positively

and significantly to Tobin’s Q (0.095), total revenue (319.830), net income (38.99751), solvency

(5.11178), and operating cash flow (66.54262). Additionally, CSR_rating contributes negatively

to the debt to asset ratio (-0.01096), which is a positive outcome for the firm. Therefore, we should

accept H1a that states that corporate social responsibility leads to better firm performance through

its ability to lower idiosyncratic risk.

Next, the same regressions were run with the addition of a lagged value for corporate social

responsibility. Previously, this paper hypothesized that a lagged corporate social responsibility

rating would increase firm performance (H2a). The regressions however revealed that the lagged

corporate social responsibility rating led to a decrease in net income (-13.06717), leverage (-

0.00769), revenue (-437.66553), solvency (-1.47468 (not significant)), operating cash flow (-

35.57132) and an increase in Tobin’s Q (0.00388 (not significant)). The parameter estimates on

the CSR_rating however behaved the same as it did in model 1. Though these results contradict

hypotheses 2a, they are actually quite intuitive. If a firm increases corporate social responsibility

efforts in the previous period, if will generally have a cost effect leading to a negative impact on

some of out financial measures. However, thorough diversification in this period, corporate social

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responsibility measures contribute positivity to the firm’s financial performance and offset the

costs of implementation.

Limitations and further direction

The findings are consistent with pervious studies that look at the connection between corporate

social responsibility and performance. However, if this study were to extend further more work

would need to be done to establish a causal relation between corporate social responsibility and

the various financial performance metrics.

As well I would focus on one or two key performance metrics and then find strong instrumental

variables to ensure there are no endogeneity issues.

Further Hull and Rothenberg (2008) note that the relationship between CSR and financial

performance is difficult to establish because it is more complex than a linear relation. I would in

the future clearly identify which variables would need to be held constant in order for the

diversification effects of corporate social responsibility to truly be significant.

5. Conclusion

This paper’s objective was to further the discussion surrounding the usefulness of corporate

social responsibility as a factor that can increase firms’ financial performance. A link was drawn

between the use of corporate social responsibility scores and the reduction of idiosyncratic risk,

as well as the link between idiosyncratic risk and firm performance. Together, this lead to the

hypothesis that if other forms firm specific risk diversifying measures are held constant the risk

diversification virtues of corporate social responsibility would be evident in the regressions on

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firm performance. After running regressions on various firm financial metrics, hypothesis 1a was

accepted, and we were able to observe the positive effects of corporate social responsibility on

the financial metrics. Additionally, the same models were run with an additional lagged

corporate social responsibility rating and in most cases, this variable contributed negatively to

the selected financial metrics.

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References: Attig, Najah, Sadok El Ghoul, Omrane Guedhami, and Jungwon Suh. "Corporate Social Responsibility and Credit Ratings." SSRN Electronic Journal SSRN Journal pp 679. Web. "Corporate Sustainability Performance and Idiosyncratic Risk: A Global Perspective." - Lee. Pp.682 Web. 21 Apr. 2016. Hull, Clyde, and Sandra Rothenberg. "Firm Performance: The Interactions of Corporate Social Performance with Innovation and Industry Differentiation." 2008. Pp. 786 Web. 21 Apr. 2016. Kang, Charles, Franl Germann, and Rajdeep Grewal. "Washing Away Your Sins? Corporate Social Responsibility, Corporate Social Irresponsibility, and Firm Performance." AMA Journals. Pp.60 Web. 21 Apr. 2016. Lang, Larry, and Stulz. "Tobin's Q, Corporate Diversification and Firm Performance." Pp.1 . Web. 21 Apr. 2016. Luo, Xueming, and C.b Bhattacharya. "The Debate over Doing Good: Corporate Social Performance, Strategic Marketing Levers, and Firm-Idiosyncratic Risk." Journal of Marketing 73.6 (2009): Pp.5-7. Web. McGuire, Sundgren, and Schneeweis. "Corporate Social Responsibility And Firm Financial Performance." JSTOR(1988): pp. 861 Web. Mishra, Saurabh, and Sachin B. Modi. "Positive and Negative Corporate Social Responsibility, Financial Lever." Age, and Idiosyncratic Risk. pp.431 Web. 21 Apr. 2016. Saleha, Mustaruddin, Norhayah Zulkiflib, and Rusnah Muhamadc. An Empirical Examination of the Relationship between Corporate Social Responsibility Disclosure and Financial Performance in an Emerging Market Mustaruddin Saleh a Web. Tanriverdi, Huseyin, and Chi-Hyon Lee. "Within-Industry Diversification and Firm Performance in the Presence of Network Externalities: Evidence From the Software Industry." ResearchGate. Pp. 381 Web. 21 Apr. 2016.

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Table 1. Descriptive Statistics

Variable N Mean Median Standard Deviation Min Max

Csr_rating 80294 0.7598202 -1 3.197673321 -9 18

lag1CSR 77190 0.7702423 -1 3.208044159 -9 18

roa 62711 -0.0116311 0.040663168 1.42686622 -276.0222222 26.03349093

rnd 79325 1.2350422 0 104.82394 -90.38461538 25684.4

ad 79325 0.0104253 0 0.254009645 0 60.0896861

mktbook 65008 3.7808317 1.861526865 184.9659854 -4027.243304 44843.55816

size 80294 6.796648 6.84097478 2.295605686 -6.907755279 15.14290377

firm_age 80294 17.1660523 14 13.42296236 0 56

Debt_to_asset 80294 0.1031694 0 0.258825806 0 30.2

solv 29924 6.2629027 0.150481837 614.0195264 -27011 77379.09091

tobinsq 59242 2.357958 1.349368209 26.8629185 0.107349868 3001.077575

NI 69465 243.5738018 21.047 1590.76063 -99289 104821

OANCF 52843 590.8179356 60.431 3284.524809 -110560 129731

CH 73003 538.7759871 38.732 3805.676781 -279.141 159353

REVT 79940 3712.05 432.0825 14601.7722 -2510 483521

DVPD 80294 9.1524294 0 104.0197089 0 10778

Table 2. Correlation Table

Table 1. reports correlations between key variables that we use in this paper for sample period of 1960-2016. Table 2. reports the Pearson

correlation coefficients; the table denotes the Probability > |r| under H0: Rho=0

Variable Csr_rating lag1CSR roa rnd ad mktbook firm_age Debt_to_assetsolv tobinsq NI OANCF CH REVT DVPD sizeCsr_rating 1.000 0.798 0.029 -0.007 -0.001 -0.001 0.278 -0.040 0.028 -0.002 0.194 0.219 0.169 0.264 0.065 0.426lag1CSR 0.798 1.000 0.049 -0.004 -0.001 -0.002 0.114 -0.079 0.021 -0.003 0.142 0.171 0.154 0.180 0.050 0.318roa 0.029 0.049 1.000 -0.022 -0.019 0.002 0.034 -0.101 0.040 -0.236 0.018 0.010 0.005 0.011 0.092rnd -0.007 -0.004 -0.022 1.000 0.008 0.000 -0.008 0.011 -0.001 0.001 -0.003 -0.003 -0.001 -0.003 -0.001 -0.011ad -0.001 -0.001 -0.019 0.008 1.000 0.000 -0.010 0.013 -0.003 0.003 -0.001 -0.001 -0.002 -0.001 -0.004 -0.014mktbook -0.001 -0.002 0.002 0.000 0.000 1.000 -0.003 0.009 0.000 0.077 0.001 0.000 -0.001 0.001 0.001firm_age 0.278 0.114 0.034 -0.008 -0.010 -0.003 1.000 0.068 0.007 -0.019 0.157 0.153 0.105 0.235 0.019 0.425Debt_to_asset -0.040 -0.079 -0.101 0.011 0.013 0.009 0.068 1.000 -0.008 0.014 0.002 0.008 -0.003 0.034 -0.035 0.068solv 0.028 0.021 0.040 -0.001 -0.003 0.000 0.007 -0.008 1.000 -0.012 0.013 0.010 0.012 0.008 0.022tobinsq -0.002 -0.003 -0.236 0.001 0.003 0.077 -0.019 0.014 -0.012 1.000 -0.001 -0.005 -0.004 -0.007 -0.075NI 0.194 0.142 0.018 -0.003 -0.001 0.001 0.157 0.002 0.013 -0.001 1.000 0.514 0.321 0.592 0.284OANCF 0.219 0.171 0.010 -0.003 -0.001 0.000 0.153 0.008 0.010 -0.005 0.514 1.000 0.384 0.598 0.319CH 0.169 0.154 0.005 -0.001 -0.002 -0.001 0.105 -0.003 0.012 -0.004 0.321 0.384 1.000 0.438 0.122 0.300REVT 0.264 0.180 0.011 -0.003 -0.001 0.001 0.235 0.034 0.008 -0.007 0.592 0.598 0.438 1.000 0.119 0.416DVPD 0.065 0.050 -0.001 -0.004 0.019 -0.035 0.122 0.119 1.000 0.148size 0.426 0.318 0.092 -0.011 -0.014 0.001 0.425 0.068 0.022 -0.075 0.284 0.319 0.300 0.416 0.148 1.000

Variable Csr_rating lag1CSR roa rnd ad mktbook firm_age Debt_to_assetsolv tobinsq NI OANCF CH REVT DVPD sizeCsr_rating _ <.0001 <.0001 0.0604 0.7437 0.7195 <.0001 <.0001 <.0001 0.6482 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001lag1CSR <.0001 _ <.0001 0.3287 0.7531 0.6864 <.0001 <.0001 0.0003 0.5368 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001roa <.0001 <.0001 _ <.0001 <.0001 0.6531 <.0001 <.0001 <.0001 <.0001 <.0001 0.0213 0.2313 0.0044 <.0001rnd 0.0604 0.3287 <.0001 _ 0.0274 0.991 0.019 0.0012 0.8307 0.7898 0.4123 0.475 0.7217 0.4034 0.7693 0.0029ad 0.7437 0.7531 <.0001 0.0274 _ 0.9954 0.0045 0.0004 0.6467 0.4273 0.8791 0.8627 0.6878 0.6785 0.3069 <.0001mktbook 0.7195 0.6864 0.6531 0.991 0.9954 _ 0.4419 0.0227 0.9936 <.0001 0.7282 0.9155 0.8813 0.8968 0.8795firm_age <.0001 <.0001 <.0001 0.019 0.0045 0.4419 _ <.0001 0.2104 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001Debt_to_asset <.0001 <.0001 <.0001 0.0012 0.0004 0.0227 <.0001 _ 0.1761 0.0006 0.6252 0.0534 0.3902 <.0001 <.0001 <.0001solv <.0001 0.0003 <.0001 0.8307 0.6467 0.9936 0.2104 0.1761 _ 0.0504 0.0209 0.0754 0.0348 0.1805 0.0001tobinsq 0.6482 0.5368 <.0001 0.7898 0.4273 <.0001 <.0001 0.0006 0.0504 _ 0.7512 0.3084 0.3513 0.0932 <.0001NI <.0001 <.0001 <.0001 0.4123 0.8791 0.7282 <.0001 0.6252 0.0209 0.7512 _ <.0001 <.0001 <.0001 <.0001OANCF <.0001 <.0001 0.0213 0.475 0.8627 0.9155 <.0001 0.0534 0.0754 0.3084 <.0001 _ <.0001 <.0001 <.0001CH <.0001 <.0001 0.2313 0.7217 0.6878 0.8813 <.0001 0.3902 0.0348 0.3513 <.0001 <.0001 _ <.0001 <.0001 <.0001REVT <.0001 <.0001 0.0044 0.4034 0.6785 0.8968 <.0001 <.0001 0.1805 0.0932 <.0001 <.0001 <.0001 _ <.0001 <.0001DVPD <.0001 <.0001 0.7693 0.3069 <.0001 <.0001 <.0001 <.0001 _ <.0001size <.0001 <.0001 <.0001 0.0029 <.0001 0.8795 <.0001 <.0001 0.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 _

Table 3. This table presents OLS regression results for the sample period 1960-2016. In model 1 and 2 the dependent variable is Tobins Q. In model 2 a lagged version of CSR_rating is used to determine CSR_rating’s lagged effect.

Parameter Estimates Model 2

Variable Label DF Parameter

Estimate Standard

Error t Value Pr > |t| Intercept Intercept 1 3.41744 0.03562 95.93 <.0001

Csr_rating 1 0.08892 0.00580 15.34 <.0001

lag1CSR 1 0.00388 0.00566 0.69 0.4925

rnd 1 -0.00008584 0.00007430 -1.16 0.2480

ad 1 0.12775 0.03090 4.13 <.0001 firm_age 1 -0.00862 0.00080093 -10.76 <.0001

roa 1 -2.92191 0.02725 -107.24 <.0001

mktbook 1 0.00010776 0.00004723 2.28 0.0225

size 1 -0.20012 0.00562 -35.61 <.0001

DVPD 0 0 . . .

CH 1 0.00001718 0.00000307 5.59 <.0001

Root MSE 2.18836 R-Square 0.2292 Adj R-Sq 0.2290



Estimate Standard

Error t Value Pr > |t| Intercept Intercept 1 3.48715 0.03861 90.32 <.0001 Csr_rating 1 0.09555 0.00369 25.90 <.0001

rnd 1 -0.00008590 0.00008248 -1.04 0.2977

ad 1 0.13069 0.03433 3.81 0.0001 firm_age 1 -0.00912 0.00086890 -10.50 <.0001

roa 1 -2.84497 0.02952 -96.36 <.0001

mktbook 1 0.00012289 0.00005248 2.34 0.0192

size 1 -0.20909 0.00615 -34.02 <.0001

DVPD 0 0 . . .

CH 1 0.00001839 0.00000341 5.40 <.0001

Root MSE 2.43165 .

R-Square 0.1931 Adj R-Sq 0.1930

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Table 4.

This table presents OLS regression results for the sample period 1960-2016. In model 1 and 2 the

dependent variable is REVT(revenue). In model 2 a lagged version of CSR_rating is used to

determine CSR_rating’s lagged effect.



Estimate Standard

Error t Value Pr > |t| Intercept Intercept 1 -12615 217.13936 -58.10 <.0001

Csr_rating 1 685.48032 34.62235 19.80 <.0001

lag1CSR 1 -437.66553 33.76259 -12.96 <.0001

rnd 1 0.10992 0.46282 0.24 0.8123

ad 1 75.60353 192.41447 0.39 0.6944 firm_age 1 56.34040 4.82209 11.68 <.0001

roa 1 -1052.31850 160.93182 -6.54 <.0001

mktbook 1 -0.00931 0.29305 -0.03 0.9747

size 1 2172.71984 33.93992 64.02 <.0001

DVPD 0 0 . . .

CH 1 1.57885 0.01827 86.41 <.0001

Root MSE 1363 R-Square 0.2967 Adj R-Sq 0.2965



Estimate Standard

Error t Value Pr > |t| Intercept Intercept 1 -12488 209.65967 -59.56 <.0001

Csr_rating 1 319.83572 19.82969 16.13 <.0001 rnd 1 0.08196 0.45759 0.18 0.8578 ad 1 70.19936 190.37562 0.37 0.7123 firm_age 1 65.45549 4.66178 14.04 <.0001 roa 1 -1100.87893 155.68976 -7.07 <.0001

mktbook 1 -0.00356 0.29001 -0.01 0.9902 size 1 2136.21765 33.06569 64.61 <.0001 DVPD 0 0 . . . CH 1 1.58090 0.01805 87.59 <.0001 Root MSE 13491 R-Square 0.2945 Adj R-Sq 0.2944

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Table 5.


dependent variable is SOLV(solvency measure). In model 2 a lagged version of CSR_rating is

used to determine CSR_rating’s lagged effect.



Estimate Standard

Error t Value Pr > |t| Intercept Intercept 1 19.34887 17.42129 1.11 0.2667 Csr_rating 1 5.11178 1.42980 3.58 0.0004 rnd 1 0.00204 0.02157 0.09 0.9247 ad 1 1.40060 9.83233 0.14 0.8867 firm_age 1 -0.31006 0.27614 -1.12 0.2615 roa 1 102.32534 11.69171 8.75 <.0001 mktbook 1 0.00004658 0.01358 0.00 0.9973 size 1 -1.31987 2.46157 -0.54 0.5918 DVPD 0 0 . . . CH 1 0.00139 0.00136 1.03 0.3051 Root MSE 630.84250 R-Square 0.0036 Adj R-Sq 0.0034



Estimate Standard

Error t Value Pr > |t| Intercept Intercept 1 18.53388 17.81241 1.04 0.2981 Csr_rating 1 6.22922 2.21437 2.81 0.0049 lag1CSR 1 -1.47468 2.21468 -0.67 0.5055 rnd 1 0.00258 0.02170 0.12 0.9055 ad 1 1.48913 9.89448 0.15 0.8804 firm_age 1 -0.33691 0.28212 -1.19 0.2324 roa 1 108.74644 12.36242 8.80 <.0001 mktbook 1 -0.00000250 0.01366 -0.00 0.9999

size 1 -1.20031 2.50605 -0.48 0.6320 DVPD 0 0 . . . CH 1 0.00142 0.00137 1.03 0.3008 Root MSE 634.70999 R-Square 0.0038 Adj R-Sq 0.0034

21

Table 6.


dependent variable is OANCF(operating cash flow). In model 2 a lagged version of CSR_rating

is used to determine CSR_rating’s lagged effect. Parameter Estimates Model 1


Estimate Standard

Error t Value Pr > |t| Intercept Intercept 1 -1787.85302 52.24653 -34.22 <.0001

Csr_rating 1 66.54262 4.86737 13.67 <.0001

rnd 1 0.00762 0.09985 0.08 0.9391

ad 1 22.55975 41.55703 0.54 0.5872

firm_age 1 6.82122 1.03869 6.57 <.0001

roa 1 -130.60970 34.39530 -3.80 0.0001

mktbook 1 0.00243 0.06329 0.04 0.9694

size 1 297.24762 7.91772 37.54 <.0001

DVPD Cash Dividends Paid 0 0 . . .

CH Cash 1 0.28654 0.00396 72.29 <.0001




Estimate Standard


Csr_rating 1 94.52944 8.00381 11.81 <.0001

lag1CSR 1 -35.57132 7.92319 -4.49 <.0001 rnd 1 0.01021 0.10094 0.10 0.9194

ad 1 23.25908 41.98094 0.55 0.5796

firm_age 1 6.20187 1.07054 5.79 <.0001

roa 1 -127.23763 35.52832 -3.58 0.0003

mktbook 1 0.00198 0.06392 0.03 0.9753

size 1 303.93954 8.10919 37.48 <.0001

DVPD 0 0 . . . CH 1 0.28708 0.00401 71.55 <.0001


22

Table 7.


dependent variable is DEBT_TO_ASSET (leverage). In model 2 a lagged version of CSR_rating

is used to determine CSR_rating’s lagged effect.



Estimate Standard

Error t Value Pr > |t| Intercept Intercept 1 -0.05383 0.00328 -16.40 <.0001 Csr_rating 1 -0.01096 0.00031041 -35.31 <.0001 rnd 1 0.00001724 0.00000716 2.41 0.0161 ad 1 0.00213 0.00298 0.72 0.4744 firm_age 1 -0.00010564 0.00007297 -1.45 0.1477 roa 1 -0.07098 0.00244 -29.12 <.0001 mktbook 1 0.00001203 0.00000454 2.65 0.0080 size 1 0.02956 0.00051760 57.11 <.0001 DVPD 0 0 . . . CH 1 -0.00000303 2.825417E-7 -10.73 <.0001


Parameter Estimates


Estimate Standard


Csr_rating 1 -0.00456 0.00053457 -8.53 <.0001 lag1CSR 1 -0.00769 0.00052130 -14.74 <.0001

rnd 1 0.00001867 0.00000715 2.61 0.0090 ad 1 0.00246 0.00297 0.83 0.4068 firm_age 1 -0.00028253 0.00007445 -3.79 0.0001

roa 1 -0.06030 0.00248 -24.27 <.0001

mktbook 1 0.00001213 0.00000452 2.68 0.0073 size 1 0.02925 0.00052404 55.81 <.0001 DVPD 0 0 . . . CH 1 -0.00000287 2.821283E-7 -10.17 <.0001


23

Table 8.


dependent variable is NI (net income). In model 2 a lagged version of CSR_rating is used to

determine CSR_rating’s lagged effect.



Estimate Standard


Csr_rating 1 38.99751 2.37066 16.45 <.0001 rnd 1 0.00694 0.05471 0.13 0.8990 ad 1 11.94375 22.75960 0.52 0.5997 firm_age 1 3.77264 0.55732 6.77 <.0001 roa 1 109.22691 18.61287 5.87 <.0001 mktbook 1 0.00864 0.03467 0.25 0.8033 size 1 138.73108 3.95304 35.09 <.0001 DVPD 0 0 . . . CH 1 0.12803 0.00216 59.33 <.0001 Root MSE 1612.83488 R-Square 0.1466 Adj R-Sq 0.1465

Parameter Estimates


Estimate Standard


Csr_rating 1 50.38043 4.14563 12.15 <.0001

lag1CSR 1 -13.06717 4.04268 -3.23 0.0012

rnd 1 0.00804 0.05542 0.15 0.8846

ad 1 12.35317 23.03944 0.54 0.5918

firm_age 1 3.53431 0.57739 6.12 <.0001

roa 1 115.66242 19.26975 6.00 <.0001

mktbook 1 0.00843 0.03509 0.24 0.8102

size 1 141.31321 4.06392 34.77 <.0001

DVPD 0 0 . . .

CH 1 0.12766 0.00219 58.35 <.0001


24

Appendix 1: Variable Definitions and measures

Variable Definition

CSR

CSR_Rating KLD scores (total strengths minus total concerns) aggregated across the

categories of community, diversity, employee relations, environment, human

rights, and product.

Lag1CSR CSR_Rating, lagged one time period.

Firm Performance

TobinsQ The market value of equity plus the book value of assets minus the sum of book

value of common equity and deferred taxes, all divided by the book value of

assets.

Debt_to_asset A leverage ratio: Total debt divided by total assets

Solv A measure of solvency: Net income plus depreciation divided by total debt

OANCF Operating cash flow

NI Firm’s net income after extraordinary items

Revt Firm’s total revenue resulting from all segments

Firm Characteristics

RND R&D intensity: Total research and development expenses divided by total

revenue.

AD Advertising intensity: Total advertising expense divided by total revenue

Firm _age Firm’s age since its IPO

ROA Return on assets: Net income before extraordinary items and discontinued

operations divided by total assets.

Mktbook Market value of equity divided by the book value of equity

Size Log of total assets

DVPD Total dividends paid

CH Total cash

25

Appendix 2: Code

*Create a library where these files will reside;

LIBNAME paper 'H:\Documents\My SAS Files\';

data variables;

set paper.Control_variables;

run;

*create control variables;

*ROA;

*RND

*ad intensity

*market to book

size

firm age;

data variables;

set variables;

if DVPD=. then DVPD=0;

if xad=. then xad=0;

if xrd=. then xrd=0;

if DT=. then DT=0;

if fyear="#N/A" then delete;

if CONM="#N/A" then delete;

roa= IBC/AT;

rnd=xrd/revt;

ad=xad/revt;

mktbook=csho*PRCC_C/CEQ;

size=log(AT);

year=year(datadate);

26

Debt_to_asset=DT/AT;

solv=NI/DT;

tobinsq=((csho*PRCC_C)+AT-TXDB-CEQ)/AT;

run;

*getting company age;

*getting the first year of the company;

*sort data by year;

proc sort data=variables; by CUSIP year;

run;

data agetemp;

set variables;

if first.CUSIP;

by cusip;

first_year=year;

drop year;

run;

*sort data by cusip;

proc sort data=variables; by cusip; run;

*merge temp and temp 2 and create a variable firm age;

27

data merged;

merge variables agetemp;

by cusip;

firm_age =(year) - (first_year);

run;

*delete duplicate data;

proc sort data=merged nodup;

by CUSIP year;

run;

data merged;

set merged;

if TIC = ' ' then delete;

run;

*getting CSR stuff;

*merging the CSR data table with the Link table by TICKER;

data csr;

set paper.csr2;

TIC=Ticker;

drop Ticker;

if Tic="#N/A" then delete;

if Tic=" " then delete;

run;

proc sort data=csr; by TIC year; run;

28

data link;

set paper.linktable;

year=year(DATADATE);

run;

proc sort data=link; by TIC year; run;

data merge1;

merge link csr;

by Tic year;

run;

*generating the CSR rating variable and droping observations which do not have a csr rating;

data merge1;

set merge1;

Csr_rating = ( com_str_num- com_con_num)+( div_str_num- div_con_num)+( emp_str_num-

emp_con_num)+( env_str_num- env_con_num)+( hum_str_num-

hum_con_num)+( pro_str_num- pro_con_num);

if CSR_rating;

run;

proc sort data=merged; by TIC; run;

proc sort data=merge1; by TIC; run;

data final;

merge merged merge1;

by tic;

run;

proc sort data=final; by TIC year;run;

29

data final;

set final;

if CSR_rating =' ' then delete;

mktbook=csho*PRCC_C/CEQ;

if firm_age=. then delete;

if size=. then delete;

if fyear="#N/A" then delete;

if CONM="#N/A" then delete;

lagCSR=lag(CSR_rating);

difyear = dif(year);

if difyear =1 then lag1CSR=lagCSR;

if difyear= 0 then lag1csr=lagCSR;

drop difyear;

drop lagCSR;

run;

data final2;

set final;

if lag1csr="#N/A" then delete;

run;

*regressions;

proc reg data=final;

model tobinsq = csr_rating RND

AD

Firm_age

ROA

30

Mktbook

Size

DVPD

CH;

run;


model revt = csr_rating

RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;


model solv = csr_rating

RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;


model oancf = csr_rating RND

AD

31

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;


model debt_to_asset = csr_rating RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;


model ni = csr_rating RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;

32

*regressions;


model tobinsq = csr_rating lag1csr

RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

33

CH;

run;


model revt = csr_rating lag1csr

RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;


model solv = csr_rating lag1csr

RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;


model oancf = csr_rating lag1csr

RND

AD

Firm_age

ROA

34

Mktbook

Size

DVPD

CH;

run;


model debt_to_asset = csr_rating lag1csr RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;


model ni = csr_rating lag1csr RND

AD

Firm_age

ROA

Mktbook

Size

DVPD

CH;

run;

35

ods listing close;

proc corr data=final;

var csr_rating lag1csr roa rnd ad mktbook firm_age debt_to_asset solv tobinsq NI

oancf ch revt DVPD size;

ods output PearsonCorr=Corroutp1;

run;

proc means data=final n median mean std min max;

var csr_rating lag1csr roa rnd ad mktbook size firm_age debt_to_asset solv tobinsq NI oancf ch

revt DVPD year ;

ods output summary=stats;

run;

36