Master Thesis. Tilburg University School of Economics and Management Department of Economics

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1 Master Thesis Tilburg University School of Economics and Management Department of Economics Does social performance lead to better financial performance? Solving the issue of reverse causality. Abstract: We address the endogeneity problem that has plagued previous studies on the relation between corporate social responsibility (CSR) and firm value. This study will show that the 2004 Presidential election outcomes in counties close to corporate headquarters make powerful instruments for identifying the impact of CSR on the valuation of U.S. companies. We find that doing good results in doing better: when accounting for endogeneity, better corporate social performance results in higher firm value, consistent with the business case for CSR. Author: Tias van Moorsel Administration number: s Study program: Economics Study track: Resources, development and growth Graduation date: Supervisor: prof. dr. D.P. van Soest Second reader: dr. J.M.M. Derwall 1

2 Acknowledgements I have been studying at Tilburg University for six-and-a-half years and with this thesis as final project I hope to finish my master in Economics. It was a great period in which I learned a lot and also had a lot of fun and for that I want to thank some people. First, I would like to thank prof. dr. van Soest. for supervising me on my master thesis. Especially the huge amounts of time he made available for discussion are much appreciated. Without his help this thesis would not have had the quality I believe it has now. Furthermore I would like to thank dr. Derwall for feedback on my thesis and for being the second reader. Also I would like to thank my family, especially my parents, who have always supported me. Without them I was not able to achieve all things I did and enjoy life to the fullest. In addition to my family I want to thank my girlfriend Simone, who always supported me, especially in times when I did not see my thesis succeeding, but also for her useful comments on this thesis. Furthermore I want to thank Remco for being one of my closest friends during these six-and-a-half years, Lucas for all the moments we spend together in the library, Marcel for being a great roommate and all other members of De Ronde Tafel; Matthijs, Harm, Kim, Tom, Casper, Lawrence and Wouter for the great time we spend together at the library, Mensa, pubs and on the soccer field. 2

3 1. Introduction Managers always have to decide where to allocate their firm s scarce resources, but besides traditional concerns of strategic management also social issues start playing an important role in the decision process (Prahalad & Hamel, 1994). This emerging influence of social issues is a result of different stakeholder expectations (Freeman, 1984) and is important because satisfaction of various stakeholder groups is instrumental for corporate financial performance (Jones, 1995). Firms spending more money on social issues such as employee well-being, community welfare and pro-active environmental management will reap higher economic benefits in the future as it will encourage employee loyalty and firm goodwill, attract investors, promote community goodwill, improve relations with regulators and gain trust and favor of customers (Portney, 2008; Lyon & Maxwell, 2008). However, general criticism on stakeholder theory, based on Friedman s (1970) statement that managers only responsibility is to increase shareholders wealth, argues otherwise. These, but other reasons as well (e.g. Preston & O Bannon, 1997), have been analyzed extensively over the past forty years (Orlitzky, Schmidt & Rynes, 2003; Allouche & Laroche, 2005; Margolis, Elfenbein & Walsh, 2009) but results are somewhat ambiguous concerning the relation between corporate social performance (CSP) and corporate financial performance (CFP). Although researchers increasingly observe that firms with better social and environmental performance all else equal are valued at a premium (Dowell, Hart & Yeung, 2000; Baron 2007), the literature has yet to establish whether the relationship between CSP and CFP is causal, or not. Two endogeneity issues have typically plagued this literature. The first is that the positive correlation between CSP and CFP might be the result of agency problems between the firm s shareholders and its executive management (Preston & O Bannon, 1997); the higher the profits, the more opportunities firm s decision makers have to pursue their private objectives. The second is that the relationship between corporate social performance and firm value may not be causal, but that both are driven by unobserved firm-specific factors. For example, managers may have specific styles, values and cultural backgrounds, which are difficult to measure, but could affect both their financial performance and their decision to invest in socially desirable activities. Although some progress has been made to address the issue of endogeneity (Harjoto & Jo, 2011; Garcia-Castro, Arino & Canelo, 2011; Jiao, 2010; Schreck, 2011), it is still an open question whether the firm s degree of social responsibility causally affects its financial performance. Two-stage least squares (2SLS) regression has been suggested to solve the issue of causality. Instruments, which are variables that are correlated with the firm s corporates social performance, but that are not correlated with the firm s financial performance, need to be used in 2SLS to estimate social performance of a firm. Once CSP is estimated by the use of these instruments, it can explain the influence on CFP without being troubled with issues of causality. Several instruments have been proposed thus far: lagged values of CSP or CFP, equity ownership by activists public pension funds, executive compensation, transparency of a firm and a 3

4 firm s age (Harjoto & Jo 2011, Garcia-Castro et al. 2011, Jiao 2010, Schreck 2011). However, these instruments all contain limitations and therefore we propose alternative candidate variables as instruments. This study will make use of voting behavior variables, which capture the political view of citizens living in the jurisdiction in which the firm s headquarters is located, as instruments. Rubin (2008) and Di Giuli & Kostovetsky (2011) link political views of American citizens, reflected by the share of Republican (Democratic) votes in the presidential election of 2004, to be less (more) in line with principles of CSR. Since customers, employees, suppliers and investors tend to reside around firm s headquarters (Porter, 2000; Coval & Moskowitz, 1999); the political view of these stakeholders, at least partly, reflects the social preference of some of the firm s most important stakeholders. We show that voting results in counties around corporate headquarters, during the 2004 presidential elections in the U.S., make a powerful instrument for identifying the impact of CSR on the valuation of U.S. companies. In particular, county-level voting outcomes, weighted by the distance from corporate headquarters, significantly explain corporate social performance without being correlated to firm value. Thus when endogeneity is accounted for, we find that corporate social performance positively affects the firm s financial performance as measured by the ratio of its market value to its book value, known as Tobin s q. The increase in Tobin s q associated with a one standard deviation increase in corporate social performance is about 0.1 standard deviation according to OLS estimates and about 0.5 standard deviations under an instrumental variable approach. This finding, that better social performance causes higher firm value, strengthens the business case for CSR. Our results are robust to a range of control variables and alternative specifications. This study proceeds as follows. Section 2 outlines the rationale and methodology behind the use of voting behavior as instruments in our empirical analyses. Section 3 will discuss the data on CSP, CFP and the control variables used. Section 4 presents and discusses ordinary least squares, twostage least squares and three-stage least squares results and in section 5 we will conclude, discuss the limitations of this study and mention possibilities for further research. 2. The issue of causality 2.1 Two-stage Least Squares Until this day the literature has not agreed whether firms do socially good to do financially well 1 or whether doing financially well enables a firm to do socially good 2 (Seifert, Morris & Bartkus, 2004). This issue of causality, which is in the literature known as the reverse causality issue, entails problems 1 The argument that doing socially good to perform financially well, is based on the notion that good management of social responsibilities and stakeholders increase firm performance (Preston & O Bannon, 1997; Waddock & Graves, 1997). 2 The argument that doing financially well enables a firm to do socially good, is based on the notion that profitability generates slack resources which can be devoted to CSP (Preston & O Bannon, 1997; Waddock & Graves, 1997). 4

5 of endogeniety in statistical results (Schreck, 2011) and may have been one of the reasons that thus far results have been ambiguous (Ruf, Muralidhar, Brown, Janney & Paul, 2001). Therefore, any empirical study investigating the link between CFP and CSP needs to employ the adequate research methodology to correct for this potential estimation bias (Makni, Francouer & Bellavance, 2009). By making use of ordinary least squares (OLS) (Waddock & Graves, 1997; Hillman & Keim, 2001) or the Granger causality test (Nelling & Webb, 2009; Makni et al., 2009), it has been tried to show in which direction causality runs, but results are ambiguous and the power of these tests are limited. First of all, OLS is not capable of eliminating reverse causality issues (Stock & Watson, 2007) as it only captures correlations. Second, since many measures of CSP tend to vary only very little over time (serial correlation) 3, making use of lagged variables of CSP in the OLS regressions (e.g. Waddock & Graves, 1997;Hillman & Keim, 2001), does not help to overcome the issue of reverse causality. Third, the Granger causality test, which only differs from OLS regression by including a lagged dependent variable as regressor, is also unable to identify in which way causality runs, because it does not get rid of the issue of serial correlation either. Therefore the explanatory power of the Granger causality test, just as with OLS, tends to be limited. Taking these limitations into account, this study follows Jiao (2010), Schreck (2011) and others, by using instrumental variable regressions to obtain a consistent estimator of the coefficient of CSP. Instrumental variable regressions require at least one instrumental variable that is related to CSP but not related to CFP. If an instrument satisfies these conditions, the coefficient for CSP can be estimated using an instrument estimator named two-stage least squares (2SLS): (1) (2) In equation (1) is the dependent variable indicating corporate social performance of firm, indicates the instrument, is a set of control variables (for more details see paragraph 3.2.3), and is the error term. and are the estimated coefficients of the first stage and is a constant. In equation (2), is the financial performance of firm, is the predicted value of corporate social performance of firm estimated in equation (1), is a set of control variables and is the error term. and are the estimated coefficients of the second stage and is a constant In the first stage, an OLS has been performed on equation (1) to get an estimate of obtain results in the second stage, the from the first stage, is inserted in equation (2). This results in a consistent coefficient that enables us to give a valid interpretation on the influence of CSP on CFP. 3 The CSP scores in our sample have a positive (0.9995) and significant (p < 0.01) correlation for the years 2004 and These years seem suitable to compare, since there were no events, such as a change in rating criteria/assessment by KLD or a financial crisis, which could drastically change the firm s CSP score. 5. To

6 2.2 Previously used instruments Coming up with a proper instrument may be difficult and perhaps therefore not many studies have made use of 2SLS. Jiao (2010) makes use of two different instruments. The first instrument is an indicator for positive earnings in the previous year, with a value one if earnings were positive in the previous fiscal year and zero otherwise. However as we expect the sign of earnings to vary little over time, this instrument may also suffer from issues of serial correlation. Furthermore, an indicator for positive earnings may also directly influence CFP. This indicator can be perceived as a signal that a firm is performing well, thereby influencing for example analysts, who evaluate the firm s performance. The second instrument used by Jiao (2010) is equity ownership by activist public pension funds in the previous years. Empirical findings by Woidtke (2002) suggest a negative relation between the valuation of a firm and ownership by activist public pension funds. This is because actions undertaken by these funds may be motivated more by political or social beliefs than by firm performance. The negative influence on CFP may however also occur via CSP, because public pension funds are capable of successfully installing changes in firms, thereby influencing CSP (Smith, 1996; Del Guerio & Hawkings, 1999). Just as this study, Woidtke (2002) makes use of 2SLS to control for the problem that unobserved factors such as CSP may bias the influence of ownership by activist public pension funds on corporate financial performance. Therefore, the negative relation between the valuation of a firm and ownership by activist public pension funds found by Woidtke (2002) properly indicates that the instrument does not satisfy the criteria of being a suitable instrument. 4 Schreck (2011) uses lagged values of the CSP variable as instrumental variable. The first is that previous year s CSP score may still have an influence on current CFP. 5 For example Makni et al. (2009), who use a lagged variable for CSP, show that a higher overall corporate social performance Granger causes a lower market return (p. 417). A second critique concerns serial correlation in the CSP score of a firm. If a firm s CSP score is almost similar between years, it is of no use to use a lagged variable of CSP as one will get similar results. Third, Schreck (2011) does not report any results that test the suitability of the proposed instruments, thus besides theoretical limitations it lacks empirical support as well. Due to the lack of instruments used in this field of research, also studies are reviewed that made use of instrumental variables whose only focus was to overcome the problem of omitted variables. For example Harjoto & Jo (2011) use the age of a firm, and Garcia-Castro et al. (2010) use industry dummies, corporate governance indicators and firm s visibility as instruments. Although test 4 Although both instruments may have theoretical limitations, tests indicating suitability of the proposed instrument (e.g. Sargan-Hansen and F-test of the first stage regression) say otherwise and validate the use of these two instruments. 5 It is noted that Schreck (2011) makes use of an approximately 3-year lag, from which he reasons that this time lapse is too long for past CSP to have an influence on CFP. This reasoning might hold, although we are not familiar with any research looking into this issue, and therefore we would like to refer to this as a general limitation of using lagged variables. 6

7 statistics from Garcia-Castro et al. (2010) show that, except for visibility, the used instruments are valid; we have some doubts concerning these instruments. That is because the age of a firm has a negative effect on CFP (Konar & Cohen, 2001; Rao, Agarwal & Dahlhoff, 2004) and industry dummies also influence CFP (Schreck, 2011; McWilliams & Siegel, 2000). 6 For corporate governance, Garcia-Castro et al. (2010) use three different constructs as an instrument: limited executive compensation, ownership strength and transparency. All three variables are directly linked to CFP; executive compensation influences CFP positively (Murphy, 1985), ownership strength has a positive effect on CFP (Morck, Shleifer & Vishny, 1988) besides, Garcia-Castro et al. (2010) show that ownership strength not significantly influence the CSP score 7 and Lang, Lins & Maffett (2011) argue that greater transparency increases Tobin s q. The last instrument proposed by Garcia-Castro et al. (2010) is firm s visibility, but when a firm is more visible, it will perform better as it has better exposure to investors, media and consumers (van der Laan et al., 2008), besides, test results also indicate visibility is not a suitable instrument (Garcia-Castro et al., 2010). 2.3 Voting behavior Although some test results (cf. Harjoto & Jo, 2011; Garcia-Castro et al., 2011; Jiao, 2010) may indicate instruments to be suitable, we have raised some concerns about the use of these instruments. Therefore a new instrument will be constructed that satisfies the criteria of having no correlation with CFP, but is correlated to CSP. The instrument that we are going to use is voting behavior. Rubin (2008) and Di Giuli & Kostovetsky (2011) empirically show that political views in the United States are correlated with CSP. Republican values are less in line with principles of CSR when comparing to Democrats who embrace concepts of CSR more often (Rubin, 2008). This is in line with the results of his study, that show that a one standard deviation increase in the percentage of Republican votes (which corresponds to a 7.38% increase in Republican votes), corresponds to a 2% increase in the probability that a firm will be indicated as socially irresponsible. Also results from Di Giuli & Kostovetsky (2011) indicate a significant difference in CSR embracement between Democrats and Republicans in states/counties. A one standard deviation increase in the percentage of Democratic votes is associated with a 0.1 standard deviation improvement in CSP. Besides this, Di Giuli & Kostovetsky (2011) show that the political environment is largely uncorrelated (ρ = -0.01) with excess returns after the elections, indicating that no relation exists between CFP and voting behavior. This reasoning has convinced us that voting behavior may be a proper instrument, but to be sure this has been tested and the results can be found in paragraph 4.2. To address voting behavior the share of votes for the Republican candidate in the 2004 presidential election (SRV) will be used. Two variables have been constructed to indicate voting 6 Garcia-Castro et al. (2010) redefine their measure of CFP and, therefore, industry dummy may be a suitable instrument, but not in the context of this study, as we use it as a control variable. 7 The first stage results from Garcia-Castro et al. (2010) indicate that ownership strength does not have a significant influence (p > 0.1) on CSP. 7

8 behavior; (1) share of Republican votes in the county where a firm s headquarter is situated and (2) share of Republican votes in the state where a firm s headquarter is situated. Share of Republican votes in a county ( ) are a share of total votes cast in a county. Share of Republican votes in a state ( ) is the sum of Republican votes cast in all counties that make up the particular state divided by the sum of total votes cast in all counties that make up the particular state. However, it is not directly clear that these are proper instruments, for two reasons. First, both may suffer from weak instrument bias. The variable could be a poor predictor of CSP because the county in which a firm resides may be too small to contain all (or even a representative subset of the) relevant stakeholders and may not properly reflect stakeholder views for firms whose headquarters are located close to the state s boundary. Because, the political views of people living in neighboring counties in adjacent states may be much more important in determining CSP scores than those of people living elsewhere in the state in which the firm s headquarters are located. Second, voting outcomes in the 2004 Presidential elections may not just properly reflect stakeholder views, but they may also affect the firm s profitability via the political process. Jurisdictions with a Republican majority may have different laws and regulations than jurisdictions in which the majority votes for the Democratic party, and this is especially likely at the state level. If this is the case, and may be correlated with the firm s value, and the exclusion restriction would be violated. For these two reasons, also additional instruments have been constructed that use information on voting outcomes not only in the state and county where the corporate headquarters are located, but also in surrounding counties. That is because we expect that political views of stakeholders, who assumingly live closer by, have a bigger influence on CSR policy than the political views of stakeholders farther away. Tobler (1970) argues that everything is related to everything else, but near things are more related than distant things (p. 236). Besides that, Coval & Moskowitz (1999) found that investors have a bias to invest in firms that are located near their own residence, management tends to reside near their own headquarters (Rubin, 2008) and from a more organizational point of view Porter (2000) argues that workers and suppliers, but also customers, tend to be close to a firm s headquarters as those are essential needs for a firm to survive. The chance of one being a stakeholder increases if one lives closer by, which may indicate that they are more concerned with the role of the firm in addressing societal issues. To address this issue of distance, we will also construct two spatial variables that represent the share of Republican votes. 8

9 We will address the issue of distance by making use of a spatial lag variable, which is a weighted average of the observations on the variable over neighboring units (Drukker, Peng, Prucha & Raciborski, 2011). The spatial lag for county can be calculated using the following model: (3) where denotes the number of Republican votes of counties surrounding county, denotes the voting result of county and are nonstochastic weights. This is actually multiplying a matrix with voting results per county ( ) with a matrix containing the weights ( ). The most frequently used matrices for nonstochastic weights are contiguity and inverse-distance spatial-weight matrices, from now on respectively contiguity matrix and inverse-distance matrix. A contiguity matrix assigns weights of one to counties that have adjacent borders and assigns weights of zero otherwise. An inverse-distance matrix is a bit different from a contiguity matrix as it does not only take into account the counties with adjacent borders, but also assigns weights to the other counties that are in prespecified proximity. 8 Making use of a contiguity and inverse-distance matrix we construct two new spatially explicit variables using county voting outcomes. The first is an adjusted contiguity measure 9 ; using and to enumerate the total number of counties (=3109 counties) in the 48 states 10, the adjusted contiguity variable is constructed as follows: (4) where has a value one if either or and have adjacent borders, and zero otherwise. The numerator of this measure sums up, the number of votes for the Republican candidate ( ) in county as well as those in all other counties that share a common border with county, and the denominator does the same but then for the total number of votes ( ) cast in the own and contiguous counties. That means that captures the political views of the citizens not just in the firm s county, but in the somewhat wider vicinity. The second is a distance weighted measure, and is constructed as follows: (5) 8 Counties that are closer to each other will be assigned higher weights. 9 A contiguity matrix would assign a weight of one for the adjacent counties, and zero for the own county and counties who do not have adjacent borders. Our adjusted contiguity matrix also assigns a weight of one to the own county and therefore differs from regular contiguity matrices. 10 Following spatial econometrics literature we have excluded Alaska and Hawaii as they do not share borders with any other U.S. states. 9

10 where is the distance, for all, between the centroids of counties and (as measured in K kilometers) and is an indicator function that has value one if and zero otherwise. Hence, the numerator of this measure assigns a weight of to the number of votes for the Republican candidate ( ) cast in county, and adds up all the inverse-distance weighted vote counts for all counties whose centroids are within a circle of kilometers from the center of county. Thus Republican votes farther away receive less weight than votes closer by, and the denominator is constructed in a similar fashion (but then for the inverse-distance weighted number of votes cast in total). We follow the spatial economics literature by calculating this variable using two different cutoff distances, that is K = 100 kilometers and K =250 kilometers. 3. Data sources, variables and summary statistics 3.1 Data sources Before we continue discussing the other variables of interest, we first briefly address from which sources we have gathered the data. The only variable discussed thus far, voting behavior, has been constructed using voting statistics from To construct the spatial lag variables we followed the instructions from Drukker et al. (2011) and the accompanying data they used from the US Census Bureau. 11 Data regarding CSP is from Kinder, Lydenberg, and Domini (KLD) which is an independent rating agency that specializes in assessing corporate social responsibility. Financial data such as Tobin s q, assets and capital expenditure are from Compustat, which is a database of financial, statistical and market information on active and inactive global companies throughout the world. Firm age is derived from the Center for Research in Security Prices (CRSP), a database that maintains a comprehensive collection of security price, return, and volume data for the NYSE, AMEX and NASDAQ stock markets. Our sample consists of firms that are covered by KLD, which is for the year 2004 the S&P3000 index. The S&P3000 captures the largest 3,000 firms in the United States listed on the stock market. The year 2004 has been chosen since that year s presidential elections results are especially suitable to use, because it represents two dichotomous political viewpoints (Rubin, 2008). After eliminating firms with missing data and excluding financial firms 12, our final sample consists of 1984 firms. 3.2 Variables The following three paragraphs will discuss the other variables of interest for this study. These are corporate social performance (CSP), corporate financial performance (CFP) and a set of control variables. 11 We downloaded tl_2008_us_county00.zip from 12 Following Yermack (1996), financial firms are excluded from the sample because of accounting issues and regulation. 10

11 3.2.1 Corporate Social Performance Corporate social responsibility can and has been defined in many different ways, as is shown by Dahlsrud (2008), who analyzed 37 definitions of CSR. The most frequent used definition is the one from the Commission of the European Communities (as cited in Dahlsrud, 2008): A concept whereby companies integrate social and environmental concerns in their business operations and in their interaction with their stakeholders on a voluntary basis (p. 7). Although many definitions of CSR exist, the confusion and discussion around CSR is not so much around the definition, but is about the way corporate social responsibility is measured. Addressing CSR by means of corporate social performance, which is a multidimensional construct, with behaviors ranging across a wide variety of inputs, internal behaviors or processes and outputs (Waddock & Graves, 1997, p. 304) is the standard in this field of research (Marom, 2006; Margolis, Elfenbein & Walsh, 2009). A significant amount of different CSP constructs have been developed the last forty years but all have their limitations (for an extensive overview of both constructs and limitations, see Waddock & Graves, 1997). To handle these limitations, Ullman (1985) suggests developing a reputational index which lists firms exhibiting especially good or bad social performance, from which a proper variable can be constructed to measure CSP. 13 Van der Laan, van Ees & van Witteloostuijn (2008) indicate that two sources of reputation indexes have become dominant in this field of research; the Fortune Corporate Reputation Index and the index constructed by the Kinder, Lydenberg, Domini corporation also known as the KLD index. Of these two, KLD is the de facto standard at the moment (Waddock, 2003; Schreck, 2011). Besides, KLD has several advantages beyond those of the Fortune Corporate Reputation Index. First, KLD covers the largest number of firms (van der Laan et al., 2008). Second, each company is not rated on a single criterion, but on multiple criteria that are relevant for the CSP scores (Waddock, 1997). Third, for the assessment of the criteria, KLD uses a mixture of company reports, published reports, court decisions and reports, governmental reports and investigative journalism (Waddock, 2003) while the Fortune Index only makes use of data given by managers outside the investigated firm and that data is not validated (Scharfman, 1996). Fourth, the same set of criteria is used by KLD to evaluate related firms (Waddock, 1997) which does not happen in the Fortune Index. Because the KLD index is clearly the better of the two, it is the KLD index that will be used to construct a variable that measures CSP. As indicated, CSR operates on different levels within and outside the firm and KLD covers this by assessing each firm along six criteria (Community involvement, Diversity, Employee relations, Environmental performance, Product characteristics and Others) with several strength and concerns. Here, the Community dimension includes amongst others support for housing (strength), support for education (strength), investment controversies (concern) and negative economic impact (concern). The Diversity dimension includes women and minority contracting (strength), employment of disabled 13 Recent studies, such as Schreck (2011) indicate that there are also limitations to the use of a reputation index. This will be discusses in paragraph 5.2, which addresses the limitations of this study. 11

12 (strength) and non-representation of minorities (concern). The Employee dimension includes employee involvement (strength), health and safety (strength), workforce reductions (concern) and labor union issues (concern). The Environmental dimension includes recycling (strength), clean energy (strength), hazardous waste (concern) and ozone depleting chemicals (concern). The Product dimension includes quality of the product (strength), R&D and innovation (strength), product safety (concern) and marketing/contracting issues (concern). The Other dimension includes limited compensation (strength), transparency (strength), ownership issues (concern), gambling (concern) and firearms (concern). 14 The Other dimension, following Jiao (2010), is a dimension in which several separate ratings, such as alcohol, gambling and corporate governance are pooled together for brevity. 15 KLD also assesses firms on a seventh criterion, human rights, but this has been excluded from the construct because the scores have too little cross-sectional variation. For the assessment of the six criteria KLD makes use of a binary system in which one indicates the presence of a strength or concern and zero the absence of a strength or concern. Studies using KLD data (Waddock & Graves, 1997; Garcia-Castro et al., 2010; Jiao, 2010; Di Giuli & Kostovetsky, 2011; Schreck, 2011) aggregate the dummy variables into one score variable, since including every single strength and concern into econometric models would be methodologically problematic (Schreck, 2011). This study constructs a score variable per KLD category and an overall CSP score variable. The score variable per KLD category is created as follows; the strength and concerns per category are summed up separately and then concerns are subtracted from the strengths. The total score for CSP is constructed by adding up all the six category scores. However, there may be some concerns about the inclusion of the Other category when constructing the CSP variable, that is because this pooled category includes six negative categories. This entails that a firm is involved in one the following controversial business areas: alcohol, firearms, nuclear, tobacco, military and gambling. Rubin (2008) argues that firms that do business in one of these controversial business areas cannot be labeled as socially responsible and should therefore be excluded from any sample using a CSP construct. However, the focus of this study is if acting socially responsible has an influence on a firm s financial performance and therefore it should not take into consideration if a firm is doing controversial business. Van der Laan et al. (2008) and Callan & Thomas (2009) argue differently; including negative categories may bias regression results because they can only have a neutral or negative influence on a firm s CSP score. Therefore in paragraph 4.2 we will exclude firms that are active in one of the controversial business areas to see if our results are still robust. 14 Appendix A provides a detailed breakdown of all the criteria that are used to assess the six KLD categories. 15 In 2002, KLD renamed their other category into corporate governance in order to better communicate their rating criteria. We continue referring to the other category, because the criteria bear little resemblance with those that are conventional for assessing corporate governance along the lines of Shleifer and Vishny (1997) or Gompers, Ishii and Metrick (2003). 12

13 3.2.2 Corporate Financial Performance Measuring CFP seems easier than it actually is, since the literature does not agree on which kind of measurement to use for CFP. Margolis, Elfenbein & Walsh (2009), show in their meta-analysis that 75 studies make use of accounting-based measures of performance and 125 studies make use of marketbased measures. Using either a market-based measure or an accounting-based measure of financial performance carries, according to Allouche & Laroche (2005), theoretical and empirical implications even if both measures may be suited to answer the questions about the CSP-CFP interaction. Accounting-based measures are subject to bias from managerial manipulation and differences in accounting procedures (Brilloff, 1976) and capture only historical aspects of CFP (McGuire, Schneeweis & Hill, 1986). To overcome the problems with accounting-based measures, McGuire, Sundgren & Schneeweis (1988) suggest to use market-based measures because they are less susceptible to differential accounting procedures and managerial manipulation and represent investors evaluations of a firm s ability to generate future economic earnings rather than past performance (p.859). Especially if we want to know if CSP has an effect on CFP, the best way to measure this would be using market-based measures, which takes into account all relevant information about a firm when assessing its value (Schreck, 2011). A frequently used market-based measure of CFP is Tobin s q (cf. Morck, Schleifer & Vishny, 1988; Dowell et al., 2000; Callan & Thomas, 2009; Jiao, 2010; Schreck, 2011; Garcia-Castro et al., 2011), which is the ratio of market value to the replacement cost of total assets (Tobin & Brainard, 1977; Tobin, 1978). Tobin s q has been based on an argument made by Keynes (1936): The daily revaluations of the Stock Exchange, though they are primarily made to facilitate transfers of old investments between one individual and another, inevitable exert a decisive influence on the rate of current investment. For there is no sense in building up a new enterprise at a cost greater than that at which a similar existing enterprise can be purchased; whilst there is an inducement to spend on a new project what may seem an extravagant sum, if it can be floated off on the Stock Exchange at an immediate profit. (p. 151) Tobin s intent of the quotient was to look at the causal relationship between Tobin s q and investment, as he argued that if Tobin s q exceeds unity, shareholders would have an incentive to invest, since the value of their new investment would exceed its costs. However, if a firm s Tobin s q is greater than unity, the market value of the firm is in excess of its replacement cost and thus can Tobin s q also be used to evaluate the firm s performance (Lindenberg & Ross, 1982). The final question that remains is which construct of Tobin s q to use, as there are many different ways to estimate Tobin s q (Perfect & Wiles, 1994). We will make use of a simple estimator (cf. Kaplan & Zingales, 1997; Gompers et al., 2003; Malmendier, Tate & Yan, 2005; Jiao, 2010: Schreck, 2011), which is based on book value data. It is called the simple estimator of Tobin s q 13

14 because the data that is needed is easily available and the methods used to calculate other estimators of Tobin s q are so complex and cumbersome that it is highly unlikely that even the most dedicated of analysts would ever attempt to undertake them (Chung & Pruitt, 1994, p. 70). Although this simple estimator does not produce results comparable to other estimators of Tobin s q, it provides acceptable estimates (Perfect & Wiles, 1994; Chung & Pruitt, 1994)). Besides, given that this study uses Tobin s q as the dependent variable, Jiao (2010) argues that the measurement error in Tobin s q, if it exists, does not seriously confound the results. The simple estimator of Tobin s q is equal to the market value of assets divided by the book value of assets and is constructed as follows: (6) ( ) where is the number of common stock outstanding at firm and is the share price of the stock, are total assets of firm, is total stockholder s equity and are deferred taxes The numerator, market value of assets, is the sum of values of ownership claims on the firms (Lindenberg & Ross, 1981), in which the ownership claims come from common stockholders and debtors. 18 Common stockholders claims are valued as times and claims by debtors are proxied by minus 19. are added to the equation because it is akin to debt, it represents a future cash outflow which is not captured by the proxy for debt (Arcelus, Mitra &, Srinivasan, 2005). For the denominator, the book value of assets is used instead of the replacement cost of assets, because the later requires many assumptions and is therefore difficult to measure (Kaplan & Zingales, 1997). To avoid non-normality of the distribution, the natural logarithm of Tobin s q is used throughout this study Control variables To avoid omitted-variable bias one can make use of control variables to overcome the problem. We follow the literature (cf. McWilliams & Siegel, 2000; Konar & Cohen, 2001; Harjoto & Jo, 2011; Garcia-Castro et al., 2011; Jiao, 2010; Schreck, 2011) when employing a set of control variables 20, which are: 16 Deferred taxes are taken as a balance sheet item. 17 The abbreviations also correspond to the variables that are derived from Compustat to compute Tobin s q. 18 Holders of preferred stock are also claimants of ownership of a firm, but are not taken into account in this study. That is because the Compustat does not provide the liquidating values of preferred stock for most firms, meaning it would reduce our sample drastically. This implies we make use of a more conservative estimator of Tobin s q. 19 The market value of debt is rather difficult to obtain (cf. Lindenberg & Ross, 1981) and is therefore proxied by minus since - = total liabilities (debt) (Kaplan & Zingales, 1997). 20 One might argue that not all proposed control variables are exogenous, but rather decision variables. For example, capital expenditures, which is a commonly known control variable, is the result of managerial 14

15 Advertising intensity: the ratio of advertising expense to total sales. Unfortunately Compustat does not report advertising expenses for all firms, as firms are not obliged to disclose them. To overcome the problem of a quickly reducing sample size and to control for the possibility that nonreporting firms are different from reporting firms, this study follows Jiao (2010) in creating a dummy equal to one when the relevant data is missing and zero otherwise. Advertising expenses are expected to have a positive effect on CFP because they improve sales figures (McWilliams & Siegel, 2000; Morgan & Rego, 2006; Rao et al., 2004). Firm s age: the natural logarithm of the number of months since the stock of the firm appears in the database of the Center for Research in Security Prices (CRSP) up until the cutoff date. The cutoff date is Age is expected to influence CFP negatively, because when assets, technology and equipment get older, they become less efficient and hence not as profitable as a firm with newly acquired assets (Konar & Cohen, 2001; Rao et al., 2004). Capital expenditure: this is the ratio of capital expenditures to total assets. It is anticipated that capital expenditures have a positive relation with Tobin s q, because a high Tobin s q indicates one should invest in a firm (Tobin & Brainard, 1977; Chappell & Cheng, 1982). Second, newly acquired capital is assumed to be more efficient than already acquired capital, thereby positively influencing CFP (Konar & Cohen, 2001). Delaware dummy: will be measured by a dummy that equals one if the firm is incorporated in the state of Delaware and zero otherwise. When a firm is incorporated in Delaware it is significantly worth more than similar firms incorporated elsewhere (Daines, 2001). This is because Delaware s revenue is highly dependent on incorporation fees and it therefore produces corporate law that firms demand (Romano, 1985). Dividend pay-out: is constructed by dividing cash dividends by market capitalization. Cash dividends paid are not disclosed for the majority of firms in Compustat, therefore the sum of common dividends and preferred dividend is taken. Market capitalization is calculated by multiplying the number of shares outstanding with the stock price. Findings by Lie (2004) suggest an ambiguous relation between dividend pay-outs and CFP. That is because dividend pay-out may signal positive financial performance, but on the other hand it lowers the amount of resources available to invest thereby decreasing future economic benefits, which are captured by Tobin s q (Lie, 2004). Industry dummy: Industry dummies are included to control for differences in average industry financial performance (Schreck, 2011; Chand, 2006), competition intensity (McWilliams & Siegel, 2000) and varying degree of regulation (Griffin & Mahon, 1997). Industry dummies are based on the two-digit Sectoral Industry Clarification (SIC) code. 21 Following Yermack (1996) financial firms are decisions. Therefore also results will be presented in paragraph 4.2, in which all firm-specific control variables are excluded from the regression. 21 The following SIC categories are used: 1. agriculture, forestry and fishing, 2. mining, 3. construction, 4. manufacturing, 5. transportation, communication, electric, gas and sanitary services, 6. wholesale trade, 7. retail 15

16 excluded from our sample as they might have distorted measures of Tobin s q due to accounting and regulation issues. Leverage: is the ratio of long-term debt to total assets. The anticipated influence of leverage on CFP is negative, because high levels of debt increase the probability of default which may lead investors to value a firm as less valuable (McWilliams & Siegel, 2000; van der Laan et al., 2008) R&D intensity: the ratio of R&D expenditure to total sales. This variable has the same problem as advertising expenditure, since there is no obligation for firms to disclose their R&D expenses,. To overcome the problem of a quickly reducing sample size and to control for the possibility that nonreporting firms are different from reporting firms, this study follows Jiao (2010) in creating a dummy equal to one when the relevant data is missing and zero otherwise. McWilliams & Siegel (2000) expect a positive relation between R&D expenditure and CFP. Sales growth: the ratio of current year s total sales by last year s total sales. It is expected that sales growth positively influences CFP (Rao et al., 2004). Firm size: we have taken the natural logarithm of total assets to control for firm size. The expected influence of size on CFP is ambiguous according to Wu (2006), Fang, Noe & Tice (2009). Larger firms may have more resources available and enjoy economies of scale, but are also more visible and thus more vulnerable to shifts in public opinion (Waddock & Graves, 1997; van der Laan et al., 2008; Jiao, 2010) Location dummy: due to unobserved heterogeneity in the environment in which a firm s headquarters is located, we need to control for location. State regulation may be different, as is the case in Delaware, and influence CFP. But also local supply and demand conditions materially influence stock prices and financial performance (Hong, Kubik & Stein, 2008). This is controlled for by using either economic regions, as suggested by Hong et al. (2008) or by the use of dummies for all the 48 states. Following Hong et al. (2008), the states are clustered into nine economic regions: New England, Middle Atlantic, Midwest, Plains, Atlantic Coast, Deep South, Southern Plains and Mountain. 22 When we make use of the 48 state dummies we still include the dummy variable for Delaware, because the dummy for Delaware controls for incorporation of the firm, while this is not necessarily the same as firm s headquarters location. Both dummies will be used to see which one is more efficient, but we prefer to use the 48 state dummies as those are better capable of controlling for unobserved heterogeneity in the environment. trade, 8. finance, insurance and real estate, 9. services, 10. public administration, 11. non-classifiable establishments. 22 Appendix B provides a detailed overview of which states correspond to a specific economic region. 16

17 3.3 Summary statistics Table 1 indicates that the firms in our sample have on average a negative total CSP score of 0.57 and range between -10 and 11. A negative average total CSP score is a striking result, as we would expect that firms act more socially responsible nowadays. Besides the negative total score, four out of six prespecified KLD criteria have a negative average. Table 1: Summary statistics of total CSP score and the individual criteria that KLD assesses. Variable N Mean Median Standard Minimum Maximum deviation Total CSP score Community Diversity Employee Environment Product Others In Table 2 the descriptive statistics of the financial and voting variables can be found. We see that on average firms in our sample have a Tobin s q of 2.30 with a maximum of Few other firms also report remarkably high Tobin s q, but these findings are in line with findings from Jiao (2010). However, one might argue that these remarkably high Tobin s q may bias the results, but the results, when removing the lower and upper 1 percentile of Tobin s q from our sample, do not change drastically and are significant at the 10% level. 23 The age of firms differs a lot, from 1 month up till 948 months, with an average age of about 19 years and 64.46% of all firms in our sample are incorporated in the state of Delaware. Firms spend on average 1.21% and 56.03% of sales on respectively advertising and R&D. The high average of R&D spending is mainly caused by pharmaceutical companies, who invest heavily in products that might pay-off in the future (Schrerer, 2001). Next to that firms spend on average about 5% on new capital, pay out 0.82% dividends, have an average leverage of 17.92% and sales grew in 2004 on average with 26.45%. With respect to voting behavior, the instrument in this study, it might be surprising to see that all different measures of the share of Republican votes are below 50%, while the Republican candidate won the presidential election. However, due to the set-up of the presidential elections in the U.S. it is possible to receive less votes nationwide and still become president of the U.S.. Besides that, about 60% of the firms in our sample are located in states that had a larger share of Democratic voters We also make use of the natural logarithm of Tobin s q in our regressions, which results in a better normal distribution, thus controlling for the firms with a higher Tobin s q. 24 Appendix C provides the number of firms per state and corresponding voting behavior. 17

18 Table 2: Summary statistics of Tobin s q, control variables and voting variables. Variable N Mean Median Std. Dev. Min Max Natural log of Tobin s q Firm s age (in months) Firm size , , , Delaware Advertising intensity (%) Capital expenditure (%) Dividend pay-out (%) Leverage (%) R&D intensity (%) Sales growth (%) (%) (%) (%) (%) (%) In Table 3 we provide the correlations between the main variables. The positive correlation between CFP and CSP, although not significant at 1% but significant at 7%, is consistent with results from Jiao (2010), Garcia-Castro et al. (2010) and Nelling & Webb (2008), but does not have any causal inferences. Besides, the positive correlation between CFP and CSP may be caused by the control variables, because both CSP and CFP are significantly correlated with some of control variables. The correlations between CFP and the control variable are consistent with results from Jiao (2010), Garcia- Castro et al. (2010) and Nelling & Webb (2008). As expected there is no significant correlation between firm value, addressed by Tobin s q, and the voting variables. Considering that the correlations between a firm s CSP score and the different voting variables are significant (p<0.01), it indicates that voting variables may be suitable instruments to use. 18