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Stock Market Valuation of Corporate Social Responsibility

Indicators

Alan Gregory* Julie Whittaker** Xiaojuan Yan* This Version: November 2010

Discussion Paper No: 10/06

*Xfi Centre for Finance and Investment, University of Exeter ** Department of Management, University of Exeter

JEL Classifications:

Key Words: Corporate Social Responsibility, Socially Responsible Investment Returns, Valuation

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Stock Market Valuation of Corporate Social Responsibility

Indicators

Abstract

Renneboog et al. (2008) highlight the fact that whether or not corporate social responsibility (CSR) is priced is an open one. In this paper, using a comprehensive set of KLD indicators, we first show that there appears to be no robust evidence of either over or under performance by high-CSR firms over the long term. However, there are significant differences in factor loadings between high and low CSR stocks. Using an Ohlson-model framework, we go on to show that indicators of corporate social responsibility are valued by markets. In particular, indicators associated with diversity, employee relations and environment attract significantly positive value premia, and this result is robust to industry effects and the influence of intangible assets not captured in book values. Our evidence is consistent with high CSR firms having a lower cost of capital (Sharfman and Fernando, 2008) but also with an interpretation that high CSR firms may have lower expected adverse cash flow shocks.

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Stock Market Valuation of Corporate Social Responsibility

Indicators

Introduction

The aim of this research is to conduct a comprehensive study of the value, risk profile and long run returns of firms engaging in corporate social responsibility (CSR) strategies. Hitherto, investors, managers and other stakeholders have been faced with inconsistent signals concerning the returns likely to accrue to a “socially responsible” investment strategy, the risks of such a strategy, and the market valuation of firms that pursue CSR strategies, leading Renneboog et al. (2008) to conclude that the question of whether or not CSR is priced is an open one.

One line of research looks at the stock market performance of firms with high levels of CSR. Two recent papers by Derwall et al. (2005) and Kempf and Osthoff (2007) record very high alphas to strategies that are long in US firms that score highly in their chosen CSR metric and short in low-scoring firms. Guenster et al. (2006) investigate financial performance, finding that “eco efficient” US firms have a higher Tobin‟s q ratio than “eco-inefficient” firms. These findings are at variance with the recent evidence in Brammer et al. (2006) who document negative performance amongst high-CSR UK firms, and by Galema et al. (2008) who record no significant alphas but find a positive impact on book to market ratios. However, Fernando, Sharfman and Uysal (2009) show that “Green” and “Toxic” firms both have lower Tobin‟s Q ratios than do environmentally neutral firms.

Further, the evidence on high past returns seems difficult to reconcile with the evidence from studies of “socially responsible” mutual funds, which do not generally find significant differences in performance between those that follow a “socially responsible investment” (SRI) strategy and those that do not. For example, Hamilton, Jo and Statman (1993) investigated US SRI funds and found no significant difference in performance compared with conventional funds, a finding supported using cointegration testing by Reyes and Grieb (1998). Geczy, Stambaugh and Levin (2003) compared optimal portfolios of US funds with SRI objectives and those without,

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concluding that SRI constraints are costly when fund managers are skilled. A UK study by Gregory et al. (1997) suggested that SRI funds did not perform as well as other funds, but that this could be explained by their greater exposure to „small firms‟ risk rather than SRI criteria.

This approach has recently been extended by Kreander et al. (2005) who employ size-adjusted benchmarks for four European countries with broadly similar results. Bauer Koedijk, and Otten (2005), working with US, UK and German data found little significant difference in the performance of SRI and conventional funds, although they find some evidence of outperformance in UK international funds. Most recently, Gregory and Whittaker (2007) show that carefully controlling for risk/style effects, controlling for “home bias” in investment allocations, changes the finding in Bauer et al. (2005) of outperformance by UK SRI funds. They also document time-varying performance and greater persistence in the performance of SRI funds, concluding that whilst “ethical” investors do not lose out compared to “ordinary” investors, absolute returns are low and the style exposure of SRI funds is different to that of conventional funds.

The central dilemma is this. The evidence from research into fund managers seems to point to risk exposures differing between SRI and non-SRI funds, but no significant differences in Jensen‟s alpha once that has been allowed for. If anything, the evidence points towards under-performance (Renneboog et al., 2008). Yet some of the evidence from research at firm level seems to point to significant out-performance by “high CSR” firms, whilst the conclusions from studies of Tobin‟s Q seem to provide mixed results. One explanation of these lower returns but possibly higher valuations that is suggested by the findings of both Sharfman and Fernando (2008) and El Ghoul et al. (2010), is that high CSR firms have a lower cost of capital.

In this paper, we first show that the portfolios described in some of the papers above are not easily formed, and that this has implications for the reported findings. Second, when we form “best in class” portfolios that are investable, and are based on both overall CSR ratings and “best in class” (industry neutral) ratings, we show there are no significant differences in alpha between high-CSR and low-CSR firms. However,

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and consistent with Sharfman and Fernando (2008) and El Ghoul et al. (2010), we find that there is some evidence of lower factor-loading exposures in high CSR firms.

Our most significant contribution to the debate is the application of a rigorous theoretical framework to the valuation puzzle. Employing a version of the Ohlson (1995) model, we show that different aspects of CSR are valued quite differently by markets, with most having a significant positive impact on value. These effects at firm value level rather than the abnormal returns level is precisely what would be expected if a low level of CSR is liable to take the form of either expected cash flow shocks or a higher expected cost of capital. Taken as a whole, our results suggest that stakeholders in firms with high CSR scores do not lose out financially as a result of those firms adopting CSR policies, and indeed high CSR firms tend to be more highly valued than low CSR firms.

Background and Context

Interest in the link between corporate socially responsible strategies and corporate financial performance (CFP) has grown over the last quarter century. Initially the interest was displayed predominantly in the management literature as scholars sought to establish the business case for a broad stakeholder approach to management. Managers under increasing pressure to maximise returns to shareholders (Fligstein, 2001), were at the same time being pressed to be more socially responsible (Margolis and Walsh, 2003). Numerous empirical studies were conducted using a wide array of methodologies and data sets, but few using financial market data. Margolis and Walsh (2003) reviewed 127 studies published between 1972 and 2002 and found that most indicated some positive association between CSR and financial performance. In a meta-analysis of 52 studies, Orlitzky, Schmidt, and Rynes (2003) reached a similar conclusion, but found that CSR was more highly correlated with accounting-based measures of CFP than with market-based indicators. However, many of the early studies had very limited data sets on CSR factors to work with (Surroca, et al., 2010).

In the financial literature, empirical studies in this area first focused on the performance of SRI funds. These funds were established to provide retail investment vehicles for principled investors, and their distinctive feature was the negative and positive screening of stocks. Since portfolio theory suggests that if the universe of

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funds is restricted there will be a less favourable “efficient frontier” for the investment opportunity set, empirical work was motivated to establish whether SRI funds underperformed. As noted in the previous section, such research has found little evidence of inferior performance on a risk-adjusted basis for SRI funds, despite the restricted universe, but neither has it established superior performance to support the case that CSR pays. However, as Brammer et al. (2006) note, since the relevance of fund manager skill cannot be separated from underlying company performance, it is neither feasible to ascertain from studies of SRI funds whether there is a net cost or benefit to companies in being socially responsible, nor determine what the long run implications are to investors from considering CSR factors in their investment portfolios. Firm level studies are required to address these issues.

The need to attain an improved understanding of the relevance of CSR factors in company financial performance has become more pertinent as companies adopting CSR strategies have become more prevalent. There is a greater imperative for mainstream investors to understand the financial implications of such strategies. Indeed, Freshfields Bruckhaus Deringer (2005) have proposed a new legal interpretation of fiduciary duty which obligates investors to consider CSR strategies because of evidence that they could influence company financial returns and long term value. Furthermore, there is pressure on institutional investors to consider CSR through the introduction of disclosure measures by some governments (Sparkes, 2006), and the United Nations backed investor initiative „Principles for Responsible Investment‟ (PRI, 2010) has become influential in encouraging consideration of environment, social and governance factors in investment decisions.

Whereas there was once scepticism that CSR strategies could enhance profits, and fears that they could diminish them (Friedman, 1970, Jensen, 2001), now there is a greater understanding of how CSR strategies may translate into improved financial performance. For example, improving stakeholder relations can lead to greater loyalty, enhanced reputation (Freeman, 1984, Hillman and Keim, 2001), and create new sources of value (Freeman and Phillips, 2002). Furthermore, CSR strategies may improve resource use efficiency (Porter and van der Linde, 1995, Sharfman et al., 2008), and reduce a firm‟s exposure to regulatory risk (Heal, 2005, Mackey et al.

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2007). Nonetheless, it remains an empirical question whether companies‟ CSR strategies are managed in such a way as to improve financial performance.

Methodology and data

Conventional portfolio theory predicts that, in equilibrium, all firms will be fairly priced. A fundamental problem with the analysis of CSR returns is that it is not obvious whether CSR exposure is likely to be a priced systematic risk factor. One can conceive of scenarios where it may be. For example, given that oil price shocks are systematic in nature, firms that adopt renewable energy or low carbon strategies may have lower systematic risk than those that do not. On the other hand, some CSR exposures would not be expected to affect systematic risks, but rather would be expected to be of a specific risk nature. For example, avoiding pollution spills by installing suitable technologies avoids fines and clean-up costs, plus costs associated with poor publicity, but these are future cash flow effects rather than systematic risk effects.

In general, the point is that it is not obvious why all differences in CSR should show up in long run abnormal returns in any predictable fashion. Rather, if a firm has expected cash flow shocks that result from its CSR policies or lack of them (a further example might include benefits from attracting superior employees because of a firm‟s CSR record), and that these cash flow shocks are non-systematic in nature, conventional theory predicts that these will be embedded in the current stock price, so that whilst expected cash flows will be a function of CSR, expected returns may not be. Of course, expected returns may be lower if high CSR firms also have a lower cost of capital (Sharfman and Fernando, 2008; El Ghoul et al., 2010). The corollary is that if CSR indicators are a proxy for future cash flow differences, then they should show up in valuation models, but not necessarily in realised returns or, indeed, in measures of the implied cost of capital (ICC).

The basic valuation model we employ is based on the Peasnell (1982) or Ohlson (1995) framework as implemented in Barth, Beaver and Landsman (1992) and Barth, Beaver and Landesman (1998). Formally, suppressing firm sub-scripts for clarity, the Ohlson (1995) model can be stated as:

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8 t a t t t b x v P  1 2 (1) Where: 1 .   t t a t x rb x t

x is the net income in year t, bt1 is the opening book value, r is the cost of equity, so that a

t

x is abnormal earnings (or residual income) and vt is an “other information” parameter. Both abnormal earnings and “other information” are assumed to follow an autoregressive process such that

) 1 ( 1      r and (1 )(1 ) ) 1 ( 2        r r r , where ω and γ are the autoregressive parameters on abnormal earnings and “other information” respectively. In the model, ω and γ are assumed to have a value in the range 0 to 1 and some empirical support for these parameter constraints can be found in DeChow, Hutton and Sloan (1999). Alternatively, the Ohlson (1995) model can be stated in terms of earnings and book values thus:

t t t t t d v r r x r r b P (1 1 ) 1 (1 ) 2          (2)

If we simply assume that dividends are zero, then we can express theoretical value as a linear combination of book values, earnings and “other information”. Alternatively, we can drop the Ohlson (1995) “linear information dynamics” and derive a model based upon assumed growth rates in earnings and book values (Rees, 1997). This type of approach gives the theoretical underpinning for the class of model estimated in the “value relevance” literature, examples of which can be found in Barth, Beaver and Landsman (1992), Barth, Beaver and Landesman (1998), and Barth et al. (1998). As Barth, Beaver and Landsman (2001) point out, by estimating components of earnings and book value, and by allowing for industry effects, the strict linearity assumptions in the Ohlson (1995) model can be relaxed. Essentially, this literature investigates value relevance by examining whether the coefficient on some “other information” parameter (for example, brands in the case of Barth et al., 1998) is significantly different from zero.

In this paper, we use this research method to obtain a direct estimate of the value the market ascribes to CSR attributes, using the Barth et al., (1998) framework, where in our case the CSR attribute in question is treated as the “other information” parameter

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in the Ohlson model. Formally, this involves testing the significance of the coefficient on the CSR parameter in the model:

it it it it t t Yit it YR B EPS CSR V

       2 3 2008 1991 1 0 (3)

Where CSRitis the CSR indicator of interest, and YRYis a year dummy equal to one if

the observation is from year Y. We also run the model including industry fixed effects, where industries are the Fama-French ten industry classifications. However, we can also run the regression for each year independently, and test for significance of the CSR coefficient using the Z-tests from Barth et al. (1998). This allows us to examine time variation in market reaction to CSR indicators. This may be important as Guenster et al. (2006) provide evidence which suggests that the US market‟s valuation of environmental performance has increased through time, with high “eco-efficient” firms only being valued at a premium in later time periods. The Z-tests are:

   T j j j j k k t T Z 1 2 / / 1

1 where T is the number of years in the sample, tjis the

t-statistic for year j and kj is the number of degrees of freedom in year j, and;

Z2 = mean t / standard deviation t/

T1

. The Z1 test, from Healy, Kang and Palepu (1987), assumes independence of the residuals, whereas the Z2 test, of Bernard (1987) does not.

Note that conceptually, the beta coefficients in (3) are capturing both differences in the expected cost of capital between firms, and differences in expected income. Alternative approaches could employ the Lee et al. (1999) version of the Ohlson/Peasnell (OP) model,1 which can be written as:

2 1 2 1 2 2 1 1 ) ( ) 1 ( 1 1 e e t e t t e e t t e e t t t r g r g B r FROE B r r FROE B r r FROE B V                  (4)

Where Bt= book value of equity in year t, g is the long run growth rate, FROEtiis the forecasted return on equity for period t + i, computed as I/B/E/S forecast EPS for

1 Strictly, although Lee et al. (1999) claim that their model is based on Ohlson (1995) it is actually a version of the Peasnell (1982) model, as there is neither an “other information” parameter, nor a “linear information dynamic” in the model estimated.

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period t + i / book value of equity for period t + i -1. In principle, we could test to see whether price to value ratios were different between high and low CSR firms. Alternatively, we could estimate an alternative version of the Lee et al. (1999) model and solve for cost of capital:

2 2 1 2 2 1 1 1 1 e t e e t t e e t t t r TV B r r FROE B r r FROE B V            (5)

This latter is the approach taken in El Ghoul et al. (2010), who find that for two CSR indicators (environment and employee relations) high CSR firms have a lower equity cost of capital. We prefer the Barth et al. (1998) approach for several reasons. First, we are not dependent on analysts‟ forecasts which are known to exhibit bias (Hou, van Dijk and Zhang, 2010). Second, as we point out above, there will only be a cost of capital effect to the extent that risks are systematic, but we are also interested in expected cash flow effects. In treating CSR as “other information” in the OP model, significant coefficients will reflect both discount rate and cash flow effects, as any time and industry variation in the cost of equity should show up in the regression fixed effects industry and year dummies. Third, a recent paper by Surroca, Tribo and Waddock (2010) argues that CSR only shows up in regression estimates because they are a proxy for brand/innovation values. In the OP framework, we are able to test for this by including proxies for brands and innovations. Specifically, we run the Barth et al. (1998) regression model with research and development expenditure (R&D) and advertising expenditure included as RHS values. In short, the OP framework is much more flexible as it allows us to control for possible confounding effects in the Lee at al (1999) model.

Our measure of CSR is the widely used Kinder, Lyndenberg and Domini (KLD) data series. Clearly this data has been subject to criticism, but it has the merit of having a long time series of information (since 1991) and being readily available. Whilst we would have liked to cross-check our results against the Innovest data employed in Derwall et al. (2005) and Guenster et al. (2006), Innovest declined to make their data

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available for our research.2 The KLD data takes the form of a series of zero-one variables for a number of strengths and weaknesses across different categories of CSR indicators. A full description of the data can be found on the KLD website, but an important feature of the data is that the number of strengths and weaknesses is not symmetrical within any CSR indicator, and the number of strengths and weaknesses differ between indicators. Furthermore, over time indicators can change as new concerns emerge, so that the number of strengths and weaknesses can change between years. For example, the Human Rights measure has for some early years included concerns reflecting activity in South Africa and Northern Ireland, neither of which are regarded as relevant by KLD in later years. A summary of the indicators and their definitions is reported in Appendix 1.

Our first problem is to normalize this data so that it is comparable across firms and across industries. There are several approaches that have been adopted in the recent literature. First, Kempf and Osthoff (2007) normalize the data for each CSR indicator on a zero to one scale, weighting strengths positively and concerns negatively. We follow that approach here as our first method of classifying firms. By contrast, in respect of the KLD environmental score Fernando et al. (2009) classify firms into one of four categories: “Green” firms, which have only strengths; “Toxic” firms, which have only concerns; “Gray” firms, which have both strengths and concerns; and “Neutral” firms, which have neither. This categorization can be extended across other CSR indicators, which provides an alternative classification method which can be employed as a robustness check on our portfolio returns. Finally, some studies (e.g. El Ghoul et al., 2010) simply sum positive and negative CSR scores. This is broadly similar to the Kempf and Ostoff (2007) approach, although by virtue of the normalisation process the latter has the advantage of making scores comparable between alternative metrics and years.

For our portfolio returns tests, we use these classifications to assign firms to portfolios each year. Using the Kempf and Osthoff (2007) method, cut-offs for portfolio formation need to be established. Kempf and Osthoff choose the tenth and ninetieth percentiles for portfolio formation, though sensitise their results using other cut-offs.

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Like them, we form overall portfolios and portfolios for Fama-French ten industry groups. Unfortunately, we find that the normalized scores do not lend themselves to clean break points. In extreme cases, we find examples for some CSR-indicator/industry/year groups where there is no variation in scores between firms. We initially attempted to construct such decile groupings, setting the break point at the top 10% or bottom 10% or less if the firm in the next centile had the same CSR score as the decile firm, but where all firms within these deciles have the same score, continuing beyond the decile until a clean break point was found.3 However, the result was some highly idiosyncratic portfolios with no representation for some industries and very small numbers of stocks in some others.

As it was hard to escape the conclusion that such portfolios would most likely not be investable, we re-set the “best” or “high CSR” portfolio cut-off to the upper quartile. We apply this cut-off to both overall and industry-level portfolios. Thus with regard to the latter classification, a stock has to have a CSR score that is unambiguously in the upper quartile of that industry‟s score for inclusion in our portfolio. However, we do not insist on such tightly defined portfolios for the lower end, principally to avoid these portfolios being driven by a small number of exceptionally low scoring stocks. An example is offered for clarity.

Taking the first year for which KLD data is available, Figure 1 shows the (not untypical) overall “Community” score profile:

Community: Freq Percent Cum.

.375 18 2.82 2.82

.5 445 69.64 72.46

.625 135 21.13 93.58

.75 38 5.95 99.53

.875 3 0.47 100.00

Figure 1. Illustrative example of “Community” normalized score for 1991

3 Note that there is a potential programming trap for the unwary here. If firms are simply ranked by score and decile break points formed using the number of firms in the sample, firms will be assigned on the basis of their prior ranking before the score sort.

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Using our chosen upper quartile cut-off, the final two rows of 41 firms are classified as “high” CSR firms. Rather than limit the low CSR set to the bottom 18 firms, we classify both the lower two tiers of firms to the “low CSR” category, leaving the central grouping as a “Rest” portfolio. We do this as we believe that driving our results off a small number of low CSR score firms is potentially misleading. Finally, we form portfolios only where there are at least two CSR classifications of stock available. It turns out that these requirements prove particularly onerous in respect of the Human Rights indicator, a point to which we return below. However, our portfolio return results are robust to two alternative classifications. First, we test the obvious alternative of defining our “low CSR” firms on the basis of the same cut-off measure used to establish the “high CSR” set. Second, we use the Fernando et al. (2010) “green” and “toxic” definitions for each of our CSR portfolios. Our results are qualitatively similar. Note that this cut-off problem is unique to the portfolio returns tests, as the valuation regression tests use the actual normalized scores and do not employ portfolio cut-offs.

We form value and equally-weighted portfolios for the overall sample, and also for an industry-neutral sample. Conceptually, the industry-neutral sample replicates a “best in class” type investment strategy, as selecting stocks on their overall scores could give rise to an industry bias. This is because some industries might, by their nature, tend to have greater exposure to some types of KLD-indicator concerns. For example, energy companies are more likely to experience more environmental concerns than financial companies. To construct our industry-neutral portfolio for each CSR indicator we follow Kempf and Osthoff (2007) and value weight our low and high CSR industry portfolio returns using CRSP industry weights.

We then test our “High CSR”, “Low CSR”, and “Long High – Short Low CSR” (LS) portfolios using both the Fama and French three-factor and Carhart four-factor models, which can be summarized as:

W t +pit

R R s SMB h HML w ML

R

Rptftpp mtftp tp tp (6)

Where the factors are: the market risk premium, (Rm – Rf); the size factor, SMB; the

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all of which are from Ken French‟s website. The three factor model is a special case of the four-factor model in which the coefficient on wp is constrained to be zero.

A difficulty with any KLD portfolios is the changing composition of the time series portfolios. Until 2001, only S&P500 and Domini 400 index stocks were included. In 2001, and again in 2003, the number of firms was expanded significantly to include Russell 1000 and then Russell 300 firms respectively. Kempf and Osthoff (2007) deal with this problem by limiting their universe to only firms which were members of the original constituent indices. A disadvantage with this approach is that it throws away a large number of observations. Furthermore, changes in the nature of the KLD CSR indicators could mean significant changes in the “high” and “low” portfolios, and changes in industry weights through time could produce a similar effect. For these reasons, we perform a robustness check using the Ferson and Schadt (1996) model.

The version of the Ferson and Schadt (1996) model we estimate is:

1

1

+ it 0    p p mt ft p t mt ft ft pt R R R z R R R       (7)

where zt-1 is a vector of instruments for the information available at the beginning of

month t (i.e the end of month t-1) and zt-1 = Zt-1 - E(Z) is a vector of deviations of

instruments from their unconditional means. Instruments used are: the lagged dividend yield, the lagged treasury bill rate, the term structure (difference between long gilt rate and the treasury bill rate), the default premium and a January dummy.

Results

We start with some descriptive statistics. Table 1 summarizes the market value, book value and net income data together with normalized CSR scores, advertising and R&D data. For all the CSR variables we have 23,856 firm-year observations. Cross-matching KLD with Compustat yields a lower number of observations for our two proxies for intangible assets. Specifically, we have 9,343 firm-year observations on advertising and 12,258 for R&D. Our main tests use the full sample of firm-year observations available, but our final tests (reported in Table 9) use only those firms where both proxies can be observed. With regard to the normalized CSR scores,

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Environment exhibits the lowest cross-sectional variability relative to its mean, whilst

Diversity has the highest.

Table 2, Panel A shows correlation coefficients between normalized CSR scores, with Pearson correlations being below the diagonal and Spearman rank correlations being above the diagonal. Generally CSR measures tend to be positively correlated, and there are significant correlations between CSR scores, with the 0.406 correlation between Human Rights and Community being the highest, and the 0.231 correlation between Community and Diversity being the next highest. The lowest correlation is between Environment and Human Rights where there is a significant negative correlation of -0.134.

Table 2, Panel B reports these correlations for those observations where both

Advertising and R&D are available. There is some evidence here that, consistent with Surocca et al. (2010), intangible asset expenditures and CSR are correlated. Both

Advertising and R&D show significant positive correlations with Community (0.256 and 0.237 respectively), with R&D also having a correlation of 0.246 with Employee. Intriguingly, Environment has a negative correlation with both Advertising and R&D, whilst Advertising is also negatively correlated with Product. This may indicate that one of the functions of advertising activity is to overcome negative images of the firm‟s Product shortcomings or environment concerns. The very low correlation between Product and R&D is perhaps surprising given that one of the KLD Product strength indicators is “R&D/Innovation”.

Summary statistics for portfolio returns are reported in Table 3. Panel A reports the means and standard deviations for the all-industry portfolios, whilst Panel B reports the results for the “best in class” or “industry neutral” portfolios. We report number of observations in the first column, followed by the value-weighted returns in the next three columns and equally weighted returns in the last three columns. Within each type of weighting, we show first the mean returns for the low CSR portfolio, then those for the high CSR portfolio, and last the returns for a portfolio long in high CSR stocks and short in low CSR stocks, the “long-short” (LS) portfolio. As would be expected given the well-documented size effect, equally weighted returns are higher than value-weighted returns. In the case of value-weighted returns, we see that high

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CSR returns are slightly higher than low CSR returns for all but Diversity and

Product, although none of the differences are significant. On an equally-weighted basis, returns are higher for all but Environment and Human Rights, but again none of these differences are significant. When we investigate returns using the industry-neutral approach (Panel B), we again find generally small differences and none of these are significant. However, Human Rights is the exception, with some fairly extreme returns differences emerging, although these are not significant. Notably, the number of observations is lower, at 168 rather than the 216 monthly observations for the other indicators, and that the high CSR group has a strikingly high standard deviation of monthly returns, particularly in the case of the equally weighted portfolios.

This prompts further investigation. The central problem with Human Rights is that the vast majority of firms have a “neutral” rating in Fernando et al. (2010) terms. Only 61 firms have only Human Rights Strengths whilst 1,210 have only Human Rights Weaknesses, and just under 10% of our sample have no records for Human Rights. Furthermore, this indicator exhibits less stability than others because of changes in its composition through time.4 Forming industry-neutral “best in class” portfolios with such small numbers of observations seems potentially misleading, and therefore this CSR indicator is dropped from our further analyses.

In an investigation of the performance of CSR-classified stocks, our first tests are the abnormal returns tests reported in Tables 4.1 to 5.3. The tables are organized as follows. Tables 4.1 to 4.3 refer to the “all industry” portfolios. In these tests we report the results of regressing the high CSR “all-industry” portfolio minus the low CSR industry neutral portfolio (the LS portfolio) on the Fama-French model factors (Table 4.1), the four-factor or Carhart model (Table 4.2) and finally the Ferson-Schadt (1996) Conditional CAPM factors (Table 4.3). The portfolios are equally weighted in Panel A and value weighted in Panel B. Beginning with a broad-brush overview, for none of the models do we observe any consistent evidence of significant differences in the alphas of the LS portfolios, and although the LS Community CSR portfolio out-performs on an equally-weighted basis, this result is sensitive to the

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factor model employed. However, we see clear evidence of differences in risk-exposures to factors that are robust across models.

In the equally-weighted results in Panel A of Table 4.1, firms that score highly in terms of Community and Environment have significantly lower betas than low CSR firms, a result that carries through into Panel A of Table 4.2 and Table 4.3. Our finding here with regard to Environment is consistent with the results reported in Sharfman and Fernando (2008). On an equally-weighted basis, high Community and

Diversity firms also have a lower exposure to the SMB factor, irrespective of whether a three or four factor model is employed. High Diversity, Employment and Product

portfolios have a lower exposure to the HML factor, whilst the high Community

portfolio has a significantly higher exposure to this factor. These results are consistent in both three factor (Table 4.1) and four factor (Table 4.2) models. Finally, in Panel A of Table 4.2 we see that high Community, Environment and Product CSR portfolios have a higher exposure to the momentum (WML) factor. Finally, there are differences in exposures to the conditioning factors.

On a value-weighted basis (Panel B of Tables 4.1 to 4.3), inferences change slightly. LS CSR portfolios sorted on Community and Diversity have a consistently lower beta across all three models. LS Employee CSR portfolios have a higher beta when the three factor or Ferson-Schadt model is employed, but not in the case of the four factor model. Both Community and Diversity LS portfolios have a lower exposure to the SMB factor, irrespective of whether a three or four factor model is employed, but the higher exposure for the Product portfolio is only observed in the three factor model results. Perhaps the most striking result is the highly significant reduction in exposure to the HML factor observed for every indicator except Community, where the effect is positive. Again, these exposure differences are robust to the model employed. Table 4.2 Panel B also shows a lower exposure to the momentum (WML) effect for both

Community and Product portfolios. Finally, Table 4.3 suggests some exposure to time variation in interest rate risk and default risk in the LS employee relations portfolio in particular.

It is interesting to compare our results from this exercise with those in Table 6 of El Ghoul et al. (2010), who find a lower ex ante cost of capital to be associated with

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CSR measures relating to employee relations, environment and product, with the latter two recording the highest effects. Our findings, based on observed rather than expected returns, are consistent with such effects, but suggest they are driven primarily by lower HML exposures.

When we analyze the performance using “best in class”, or industry neutral CSR portfolios, we see no evidence of any significant alphas, no matter whether portfolios are formed on an equally-weighted or value-weighted basis, and no matter which model is employed (Tables 5.1 to 5.3). Our results in this regard differ from those reported in Kempf and Osthoff (2007) who report strong positive abnormal returns of up to 8.7% per annum. Of course, there are major differences between our studies. First, as we record above, we are not able to form clean decile portfolios, and so we use a different portfolio formation rule based on upper quartile performance. We understand that Kempf and Osthoff are aware of this issue and actually used an allocation rule to form the portfolios, which we do not follow in this paper.5 However, a further reason for the differences in our results is that we include all stocks in the KLD universe whereas Kempf and Osthoff (2007) deal with the structural change in the KLD sample by include only the original universe of firms (those in the S&P 500 or Domini 400 indices). Our way of dealing with the expansion in the data set is to include a conditional regression model, but as a robustness check when running our main tests described below, we examine the effect of limiting our analysis to the universe of S&P 500 stocks.6

In the detailed results, many of the conclusions drawn with regard to the all-industry portfolios apply, but there are differences. We now observe lower betas for all our LS CSR portfolios when equally weighted returns (Panel A of Tables 5.1, 5.2 and 5.3) are used, though the significance level are weaker and the effect is not significant for

Product, and only ever significant at the 10% level for Diversity. SMB exposure is

5

We would like to thank Peer Osthoff for the following clarification, which we quote with permission: “If you have 20 companies and you want to form the top 10% portfolio and the scores for second and third company are equal e.g.: 2, 1, 1, 0, Then you invest the 10000$ the following way. 5000$ for the first company with score 2, 2500$ for each company with the score 1 - you equally distribute the remaining 5000$ on all companies which have the same score. If you form the value-weighted portfolio you distribute the remaining 5000$ with regard to the market capitalization of the stocks.”. 6 Ideally, we would have liked to include Domini 400 firms. Unfortunately, the marker for index membership stops being reported by KLD from 2006 on, and despite repeated requests to KLD we have not been able to obtain lists of constituents at the start of years 2006-9.

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again lower for Community and Diversity, but now Employee is significantly lower as well. However, HML differences are altogether less significant with only the

Employee LS portfolio exhibiting significantly less exposure. On a value-weighted basis, we see further evidence of different results when comparing the all-industry results with those from an industry-neutral perspective. First, we see a marked reduction in adjusted R-squared statistics. Indeed, for the Ferson-Schadt regressions we now see that the F-statistics are only significant for the Community and Diversity

regressions. The three-factor model regression for the Employee regression also fails to be significant. Most strikingly, the HML exposure now switches from being significantly negative to being insignificant, or even being significantly positive in the case of Community and Environment. This suggests that most of the benefit in terms of lower risk exposures in higher CSR portfolios comes from inter-industry effects rather than intra-industry effects, a point that is particularly relevant to the analysis that follows.

The main results of this paper are from the OP valuation framework employed in Barth et al. (1998). We run these regressions with both year and Fama-French ten industry results, including each CSR indicator of interest individually, finally ending up with the “All” column which simultaneously examines the valuation coefficients on each of these CSR scores. Consistent with other studies using the Ohlson framework, we record positive and highly significant coefficients on both net book value (NBV) and net income (NI) for all these regressions. All the regressions are run using a robust Huber/White/sandwich estimate of standard errors, where observations are clustered on years.

Looking at these results in more detail we see that all of the indicators individually have a significant positive impact on value except Product.. Each of Community,

Diversity, and Employee, has a positive impact significant at the 1% level using a two-tailed test, with Environment being significant at the 5% level. The Adjusted R-squared figures are tightly clustered around 60%. However, we know from Table 2 that some of these CSR scores are quite highly correlated, which motivates the regression shown in the final column of Table 6. In this model Community and

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positively valued (and significant at the 1% level), while Product is now positive with marginal significance.

In Tables 7 and 8 we provide two robustness checks on our findings. First, mindful of the fact that the structure of the KLD database changes in 2001 and 2003, in the spirit of Kempf and Osthoff (2007) we run our regressions on the sample of firms that are members of the S&P 500 at the start of the year in question. These results are shown in Table 7. Again, we run the regressions on each CSR indicator individually and then in combination. The absolute values of the coefficients on Community,

Diversity, and Employee are found to be smaller than those observed for the full sample, and Community becomes insignificant. However, the coefficient on the

Environment score more than doubles, and that on the Product score more than triples. When we include all the CSR scores simultaneously in the regression, the major change is that Environment is now positively valued. As a further check, we run a regression on the mean of these normalized CSR scores, as described in Kempf and Osthoff (2007).7 The coefficient on this variable, K&O_Mean, is highly significant in both cases.8

In Table 8 we run the Barth et al. (1998) methodology using individual year regressions with Fama-French ten industry dummies. The results are entirely consistent with those obtained from the panel regressions. Both the Z-test measures show that all of our CSR scores have a significant impact on value in the same direction implied by the panel regressions. The fact that the mean coefficient on each score are between those obtained from the panel regressions is as we would expect given the changes in the composition of the KLD universe through time. An advantage of the annual regressions is that it allows observation of time variation in the data. Whilst, as the standard deviations in Table 8 imply, there is considerable time variation in the coefficients, the general pattern for Employee and Diversity is consistent. Community starts with insignificant negative loadings in the early years, and Environment tends to become increasingly significant in later years. Last, if we look at the mean score measure of Kempf and Osthoff (2007), we see that significant

7 This is the “Comination1” variable in their paper.

8 Note that we do not report these results as our interest is in examining the way different CSR measures impact upon value. Nonetheless, an average CSR measure of this type provides a useful robustness check.

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positive valuation coefficients are found in every year post 1994. In general, our results are consistent with investors placing greater value on CSR indictors in later years, particularly from 1995 onwards.

The importance of Intangible Assets

The argument in Surocca et al. (2010) is that there is a virtuous circle that exists between corporate financial performance (CFP) and CSR performance (CRP). Their claim is that “investing in CRP improves intangibles that lead to superior levels of CFP, which in turn must be reinvested in intangibles in order to improve CRP” (Surocca et al., 2010, p.464). This raises an interesting question, as if intangible assets and CSR inter-act with one another in this way, it may be that what we are observing in CSR merely acting as a proxy for intangible assets in the valuation regressions. It is well known that expenditure on intangible assets, in the form of advertising and R&D expenditures, is associated with an increase in firm value (Chauvin and Hirschey, 1994; Green, Stark and Thomas, 1996; Lev and Sougiannis, 1996; Gu and Li, 2010). Accordingly, we re-run our valuation regressions by including proxies for intangibles. Specifically, we re-estimate the Barth et al. (1998) regression as: it it it it it t t Yit it YR B EPS ADV R D CSR V

         5 4 3 2 2008 1991 1 0 & (8)

Where ADVit is the advertising expenditure, and R&Dit is the R&D expenditure for

firm i in year t. Again, we run the regressions with industry fixed effects and Huber-White-sandwich estimators of variance.

The results are reported in Table 9. Once more, we start with the base model in column 1. As expected, this shows that both advertising expenditures and R&D expenditures are positively valued by the market. In this sample, it appears that advertising has no significant impact on value, which in terms of the OP framework implies that all the benefit from advertising is realised in the current period. However, consistent with the evidence in Lev and Sougiannis (1996) and Green et al. (1996), current R&D expenditure raises market value by a multiple of 2.83 times current expenditure, which Green et al. (1996, p.198) show can be interpreted in terms of the current market value of the current stock of R&D capital. As we add each of

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the CSR normalized score variables individually, we see first that all except Product

retain their explanatory power in the regressions.

From a regression that incorporates all the CSR score variables simultaneously, we observe that allowing for the mediating effects of intangible assets does appear to change our results. As in Tables 6 and 7, Community becomes insignificant whilst

Diversity and Employee retain their significant positive coefficients. However,

Environment now has a highly significant positive impact upon value, whereas

Product is no longer significant.

Finally, the economic impact of changing the CSR policy of a firm can be judged by combining the coefficient estimates from Tables 6, 7 and 9 with estimates of the standard deviations of the sub-sample normalized CSR scores, so that Table 10 shows a calculated economic “impact” which is the share price change in $ associated with a one standard deviation increase in the normalized CSR score. Consistently, the greatest positive impact is associated with a one standard deviation increase in the

Diversity score, which increases Price by $1.96 once the effects of intangibles are allowed for. An increase in the Environment score raises Price by $1.068, whilst improving the Employee score by one standard deviation increases Price by $0.814.

Conclusion

The principal contribution of this paper has been to show that CSR performance appears to be valued by markets. Most of these valuation effects are robust to both industry effects and the inclusion of proxies for intangible assets, suggesting that if the “virtuous circle” argument of Surocca et al. (2010) holds, then both intangibles (at least in the form of R&D expenditures) and some CSR indicators (notably diversity, employee relations, and environment) positively contribute to a firm‟s market value.

A major advantage of our approach is the employment of a theoretically robust valuation model, consistent with Ohlson (1995) and Peasnell (1982). The model is able to allow for both expected cash flow effects and expected cost of capital effects. As we discuss, we would expect that firms with good CSR records might benefit from both a lower cost of capital and lower expected adverse cash flow impacts. Whilst we do not attempt to disaggregate those two effects, we note that our analysis of realized

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returns shows some evidence of lower beta and book-to-market exposures amongst high CSR stocks, and that this evidence is consistent with the analysis in Sharfman and Fernando (2008) and El-Ghoul et al. (2010). We note that some of the risk differences observed in realized returns seem to be attributable to industry effects. However, the fact that we still observe valuation differences attributable to CSR performance after controlling for industry and intangible asset investments is consistent with the expected future financial performance of high CSR firms being greater than that of low CSR firms.

Overall, the message from this paper seems clear. Firms that improve their CSR performance are, on average, rewarded by the market with higher valuations. This may be because of cost of capital effects (as suggested by El Ghoul et al., 2010) or because of cash flow effects, or a combination of both. The findings of this paper do not imply that portfolios formed on the basis of CSR scores can out-perform. Indeed, we provide evidence that they have not done so historically. Rather our evidence implies that in general, positive changes in CSR performance should be associated with positive abnormal returns.

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Table 1. Summary Statistics

Variable Firm-Year Observations Mean Std Dev

Price 23856 32.706 32.670 NBV 23856 14.169 16.551 NI 23685 1.611 2.898 Community 23856 0.401 0.072 Diversity 23856 0.296 0.110 Employee 23856 0.455 0.085 Environment 23856 0.547 0.065 Human Rights 23856 0.620 0.125 Product 23856 0.481 0.077 Advertising 9343 0.752 1.401 R&D 12258 0.828 1.160

Data cover an 18 year period ending in 2008. Variables are: Price, the share price in $ (market capitalization divided by the number of shares in issue); NBV, the net book value per share; NI, the net income per share; Community, the normalized KLD CSR measure for community relations indicators; Diversity, the normalized KLD CSR measure for diversity indicators; Employee, the normalized KLD CSR measure for employee relations indicators; Environment, the normalized KLD CSR measure for environmental indicators; Human Rights, the normalized KLD CSR measure for human rights indicators; Product, the normalized KLD CSR measure for product indicators; Advertising, the advertising expenditure per share, and; R&D, the expenditure on research and development expenditure per share.

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Table 2 Correlations between CSR indicators

Panel A: Correlations for observations where CSR indicators are available

Variable Community Diversity Employee Environment Human Rights Product Community 1.000 0.190 0.242 0.126 0.423 0.024 Diversity 0.231 1.000 0.168 0.050 0.044 -0.108 Employee 0.175 0.174 1.000 0.087 0.308 0.075 Environment -0.019 0.040 0.054 1.000 -0.109 0.107 Human Rights 0.406 -0.025 0.184 -0.134 1.000 0.105 Product -0.017 -0.161 0.079 0.159 0.105 1.000

Panel B: Correlations for observations where both CSR indicators and intangible expenditures are available

Variable Community Diversity Employee Environment Human Rights

Product Advertising R&D

Community 1.000 0.225 0.291 0.137 0.337 0.014 0.261 0.163 Diversity 0.294 1.000 0.242 0.095 -0.009 -0.075 0.238 0.115 Employee 0.243 0.273 1.000 0.073 0.271 0.046 0.120 0.285 Environment -0.031 0.081 0.049 1.000 -0.113 0.115 -0.121 -0.068 Human Rights 0.368 -0.064 0.180 -0.169 1.000 0.136 0.129 0.143 Product -0.038 -0.155 0.055 0.163 0.125 1.000 -0.139 0.030 Advertising 0.256 0.143 0.023 -0.116 0.113 -0.116 1.000 -0.080 R&D_Expense 0.237 0.173 0.247 -0.206 0.097 0.001 0.083 1.000

Data cover an 18 year period ending in 2008. Variables are: Community, the normalized KLD CSR measure for community relations indicators;

Diversity, the normalized KLD CSR measure for diversity indicators; Employee, the normalized KLD CSR measure for employee relations indicators; Environment, the normalized KLD CSR measure for environmental indicators; Human Rights, the normalized KLD CSR measure for human rights indicators; Product, the normalized KLD CSR measure for product indicators; Advertising, the advertising expenditure per share, and; R&D, the expenditure on research and development expenditure per share.

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Table 3: Summary Return Statistics for High, Low and Long-Short CSR Portfolios

Panel A: All Industry Portfolios

Variable No. Obs Low % Return per month, Value Wtd High % Return per month, Value Wtd LS % Return per month, Value Wtd Low % Return per month, Equally Wtd High % Return per month, Equally Wtd LS % Return per month, Equally Wtd Community 216 0.50 0.52 0.02 0.70 0.85 0.15 (4.52) (4.22) (2.19) (5.35) (4.72) (2.06) Diversity 216 0.52 0.38 -0.13 0.70 0.72 0.02 (4.92) (4.32) (2.43) (5.44) (5.05) (1.73) Employee 216 0.49 0.57 0.08 0.74 0.77 0.03 (4.19) (5.15) (2.84) (5.33) (5.34) (1.60) Environment 216 0.47 0.49 0.02 0.74 0.71 -0.03 (4.25) (4.92) (2.30) (5.28) (5.00) (1.75) Human 168 0.40 0.52 0.13 0.66 0.26 -0.40 (4.67) (8.13) (6.88) (5.79) (8.81) (6.10) Product 216 0.49 0.42 -0.07 0.71 0.81 0.10 (4.22) (5.15) (2.50) (5.23) (5.34) (1.72)

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Panel B: Industry-neutral Portfolios

Variable No. Obs Low % Return per month, Value Wtd High % Return per month, Value Wtd LS % Return per month, Value Wtd Low % Return per month, Equally Wtd High % Return per month, Equally Wtd LS % Return per month, Equally Wtd Community 216 0.47 0.47 0.00 0.70 0.71 0.02 (4.49) (4.53) (1.84) (5.39) (4.69) (1.77) Diversity 216 0.53 0.49 -0.03 0.75 0.75 -0.01 (4.85) (4.31) (2.01) (5.44) (5.05) (1.66) Employee 216 0.50 0.54 0.05 0.75 0.68 -0.07 (4.48) (4.46) (1.52) (5.38) (5.09) (1.41) Environment 216 0.47 0.49 0.02 0.71 0.65 -0.06 (4.36) (5.03) (2.26) (5.35) (5.32) (2.05) Human 168 0.23 0.06 -0.18 0.57 0.01 -0.56 (4.71) (7.91) (5.85) (5.62) (9.63) (7.36) Product 216 0.48 0.41 -0.08 0.71 0.71 0.01 (4.41) (4.59) (1.95) (5.25) (5.20) (1.86)

The Table shows the mean percentage value-weighted and equally-weighted return per month from portfolios formed on the following CSR indicators: Community, the normalized KLD CSR measure for community relations indicators; Diversity, the normalized KLD CSR measure for diversity indicators; Employee, the normalized KLD CSR measure for employee relations indicators; Environment, the normalized KLD CSR measure for environmental indicators; Human, the normalized KLD CSR measure for human rights indicators; and Product, the normalized KLD CSR measure for product indicators. Panel A show the returns for all-industry portfolios, whilst Panel B shows the returns for portfolios formed within industry groups (“best” in industry class), which are then value-weighted across industries. Figures in parentheses are standard deviations. Portfolios are formed at KLD year ends and returns are over the period 1992-2009.

Figure

Figure 1. Illustrative example of “Community” normalized score for 1991
Table 1.  Summary Statistics
Table 2 Correlations between CSR indicators
Table 3: Summary Return Statistics for High, Low and Long-Short CSR Portfolios  Panel A: All Industry Portfolios
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References

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