Are Corporate Social Responsibility and Advertising Complements or Substitutes in Producing Firm Reputation?

Transcription

1 Are Corporate Social Responsibility and Advertising Complements or Substitutes in Producing Firm Reputation? Patrick Lloyd-Smith* and Henry An** December 12, 2016 ABSTRACT Management researchers have studied how corporate social responsibility (CSR) and advertising influence firm reputation, but existing empirical research on the relationship between these two activities is limited and the focus has been too narrow. We adopt a signaling perspective, which emphasizes the strategic roles of market actions, investments, and communications firms send to stakeholders, and empirically investigate this relationship using techniques from production economics. Using a unique panel dataset of US-listed companies between 2005 and 2014, we estimate flexible production functions to generate output elasticities and elasticities of substitution. Our results show that advertising, own-firm CSR activities, and industry-level CSR spillovers contribute positively to firm reputation. We also find evidence consistent with advertising and CSR being substitutes toward the production of firm reputation. Lastly, we find that the elasticity results vary according to which firm reputation measure - general public, business executives, or CSR experts - is used in the analysis. Keywords: advertising; elasticities; production functions; reputation; social responsibility. * Corresponding author. Department of Resource Economics & Environmental Sociology, University of Alberta, 515 General Services Building, T6G 2H1, Edmonton, Alberta, Canada; T: ; E: lloydsmi@ualberta.ca; Fax: ** Department of Resource Economics & Environmental Sociology, University of Alberta, 515 General Services Building, T6G 2H1, Edmonton, Alberta, Canada; T: ; E: henry.an@ualberta.ca; Fax:

2 INTRODUCTION Corporate reputation and other intangible assets are increasingly being recognized as key drivers of firm profitability and success (Fombrun & Shanley, 1990; Brown & Perry, 1994; Black et al., 2000; Deephouse, 2000; Roberts & Dowling, 2002). While this large literature has demonstrated the positive relationship between a company s reputation and its financial performance, research into how firms build their reputations is more limited (Rindova et al., 2005; Basdeo et al., 2006). Part of the reason for this lack of understanding on how corporate reputations are formed is that reputation has been conceptualized in multiple ways and is often understood as a multidimensional construct (Lange et al., 2011). Walker (2010) differentiates between institutional theories of reputation formation that focus on how firms gain legitimacy within their institutional context and signaling theories that focus on how strategic communications are used by firms to build, maintain and defend their reputation. Basdeo et al. (2006) suggest that market actions, investments and communications can all be broadly understood as strategic signals firms send to stakeholders and stakeholders use these signals to form perceptions of these firms reputations. In this paper we propose to view reputation formation as a production process in which firms use different signaling inputs to produce reputation. By explicitly adopting a reputation production function, we can develop and test theories about reputation formation while accommodating more complex nonlinear relationships between inputs and using more theoretically sound measures of how inputs substitute or complement for one another. There are many actions that firms can take to influence their reputation (Rindova et al., 2005; Basdeo et al., 2006; Melo & Garrido-Morgado, 2012; Zavyalova et al., 2012). In this paper, we focus on CSR performance, evaluated using third-party ratings, and advertising levels, measured using expenditures. We focus on CSR and advertising because they have a well-established association with reputation both theoretically (McWilliams & Siegel, 2001; Du et al., 2010) and empirically (Fombrun & Shanley, 1990; Melo & Garrido-Morgado, 2012) and thus provide a useful illustration of the production economics approach to investigating the interactions between actions. 2

3 Furthermore, based on Lange s (2011) conceptualization of reputation as being known for something, these two actions are especially relevant given our signalling perspective. Activities that fall under the general rubric of CSR, such as implementing projects that reduce carbon dioxide emissions or making donations to community groups, have become more important as consumers, employees, and other stakeholders have placed more attention on the social activities of firms. 1 Furthermore, firms spend significant amounts of money on CSR activities and the most commonly stated business benefit from CSR is to have a better brand and reputation (Economist, 2008). Advertising is another important channel firms use to raise the company s public profile and influence stakeholder perceptions by presenting the firm in a favorable light, and is commonly included in economic models of reputation formation (Fombrun & Shanley, 1990; Melo & Garrido-Morgado, 2012). While there is substantial evidence that suggests CSR and advertising both affect a firm s reputation (Fombrun & Shanley, 1990; McWilliams & Siegel, 2001; Melo & Garrido-Morgado, 2012), a priori, it is not clear how CSR activities and advertising relate to one another in the production of firm reputation. Firms with high levels of CSR may want to advertise more heavily to signal to consumers their goodwill credentials and increase their overall reputation (McWilliams & Siegel, 2001). In this interpretation, CSR activities and advertising are complementary activities (Servaes & Tamayo, 2013). Alternatively, firms with low levels of CSR could increase their advertising levels to compensate for their lack of goodwill (Parguel et al., 2011). In this case, the trade-off between investing in advertising versus investing in CSR suggests that these two inputs should be viewed as substitutes. The relationship between CSR and advertising is important because whether they are complements or substitutes has different implications for how firms use these activities to improve their reputation (Du et al., 2010). In addition, the relationship has an impact on how regulators view the effectiveness of CSR to improve social and environmental performance of companies (Lyon & Maxwell, 2008; Lynch-Wood et al., 2009). The main proposition of this paper is to extend the signaling perspective on reputation formation and use economic theory derived measures of elasticity of substitution to address this lack of research on the interactions between CSR and 3

4 advertising in producing firm reputation. Because of the central role for stakeholders in the signaling theory of reputation formation, a secondary motivation of this paper is to examine whether the use of different stakeholder ratings of firm reputation matters (Walker, 2010). The existing literature almost exclusively uses business executive ratings of firm reputation as the key reputation variable even though firms may be more interested in influencing their reputation in the eyes of general public or specific experts. To these ends, our empirical analysis is based on a panel dataset of US-listed companies between 2005 and 2014, and uses corporate reputation measures from surveys of three different stakeholder groups, various CSR metrics and advertising expenditure amounts, and other firm-level controls collected from several sources. We estimate several production models that deal with potential endogeneity concerns and are flexible regarding the degree of substitutability. The analysis shows that advertising, own-firm CSR, and industry-level CSR are generally positively correlated with reputation. We find evidence consistent with advertising and CSR-related activities being net substitutes toward the production of firm reputation. Specifically, when we consider the production of general public reputation in our preferred fixed effects models, the elasticity of substitution between advertising and CSR ranges between 1.2 and 3.5, which suggests that on average the firms in our sample treat CSR-related activities and advertising as substitutes. We also find that the extent to which advertising and CSR are substitutes depends on which reputation measure (i.e., public, business executives, or CSR experts) is used in the analysis. This paper contributes to the reputation literature in four distinct ways by overcoming some of the limitations of earlier work. The first contribution of this study is that we adopt an explicit production economics approach to the issue which allows use to derive elasticity measures consistent with economic theory. Studies to date share the common characteristic of using linear reduced form modeling (McGuire et al., 1988; Fombrun & Shanley, 1990; Turban & Greening, 1997; Black et al., 2000; Melo & Garrido-Morgado, 2012). These studies implicitly adopt a linear production function of reputation and may include a simple interaction term between advertising and CSR to infer the relationship between two inputs based on the sign of the coefficient of the interaction term: positive coefficients indicate complements and negative coefficients 4

5 indicate substitutes. However, these interaction terms represent partial correlations and are not appropriate measures of whether the two inputs are substitutes or complements. We specify a flexible production function and use the more rigorous Morishima elasticity of substitution (Blackorby & Russell, 1989) to investigate the relationship between CSR and advertising on reputation. Our empirical strategy allows us to estimate the extent to which advertising and CSR are substitutes or complements in addition to accommodating more complex nonlinear relationships between the explanatory variable. The second contribution of this paper is that we investigate spillover effects on a firm from the average level CSR performance of the other firms in the industry. Taking into account the fact that a firm s reputation depends on the action of its industry peers has been investigated in other types of market action (Clark & Montgomery, 1998; Rindova & Fombrun, 1999; Basdeo et al., 2006), but has not been previously studied in the CSR context. However, these issues are especially pertinent in the CSR context because stakeholders could have a difficult time differentiating between the CSR activities of specific firms and the broader level of CSR performance at the industry level. Stakeholders may not keep track of the CSR performance of each company, but rather group like companies together as they form their reputation perceptions (Yu et al., 2008; Zavyalova et al., 2012). Thus, industry-level CSR performance may impact the firm reputation production process because of these additional signals that stakeholders receive about the relative performance of a specific firm. These industry-csr spillover effects can be negative or positive and we empirically investigate these issues in this study. The third contribution of this paper is the consideration of different target audiences. If firms behave strategically to shape how consumers perceive them, then it would be more appropriate to consider consumer reputation rankings of firms. One of the limitations of the existing literature is the reliance on a limited set of data, and almost all of the empirical papers use the same reputation rankings from Fortune magazine s Most Admired Companies list, the same CSR measures from KLD (Kinder, Lydenberg & Domini), and the same advertising data from COMPUSTAT. Each of these data sources and metrics have their limitations (Wartick, 2002; Chatterji et al., 2009). In terms of reputation, the Fortune reputation ranking data is collected from a select group of business executives, directors, and financial analysis and thus has been criticized as representing 5

6 only a narrow segment of potential stakeholders (Wartick, 2002). In this paper, we consider reputation ranking data from a survey of the general public who are most likely the target of most of the advertising efforts. In addition, we also consider reputation ranking data from a survey of CSR experts who are most familiar with the CSR activities of specific companies. Our fourth contribution is the use of alternative datasets for CSR performance and advertising to ensure the robustness and validity of results. In our review of the literature, the majority of the studies that investigate CSR performance among firms use data from KLD. 2 The main reasons the KLD measure of CSR is widely used is that it was one of the earliest standardized CSR metrics, dating back to 1991, and includes a large number of firms. However, recent studies have suggested that there may be advantages to considering different sources of data. Chatterji et al. (2009) conduct an empirical analysis to assess the external validity of KLD ratings and raise some important issues regarding the simplifying aggregation assumptions used in constructing these metrics. Furthermore, Chatterji et al. (2014) find a surprising lack of convergence amongst various CSR metrics and recommend the use of multiple ratings schemes in empirical studies. We complement the KLD data with two alternative CSR rankings assess the consistency and validity of results across various measures. Similarly, much of the existing literature use the same advertising data from the COMPUSTAT database. 3 In the COMPUSTAT advertising data a large portion (around 40 percent) of firms do not report any advertising expenditures, as firms do not need to report expenditures that are immaterial. 4 Furthermore, the COMPUSTAT advertising data includes expenditures outside the United States. To address these concerns, we also use data from Ad$pender, which tracks advertising expenditures by firms for millions of products across all media types in the United States. To the best of our knowledge, this is the first study in which disparate data sources have been merged together and used concurrently in an empirical analysis. THEORY AND RESEARCH HYPOTHESES A strong reputation improves firm profitability in several ways. Customers make purchasing decisions based on the reputation of a company (Costa & Vasconcelos, 2010); 6

7 suppliers are more committed to relationships with highly reputable firms (Bennett & Gabriel, 2001); and employees have lower turnover or accept lower wages in exchange for working for a reputable company (Helm, 2013). On a more strategic level, the seemingly intangible nature of reputation makes it more difficult for competing firms to replicate, which can also lead to sustained firm returns (Drejer, 2000; Roberts & Dowling, 2002). The value of corporate reputation has also been demonstrated in empirical work linking reputation measures with metrics of firm performance (Fombrun & Shanley, 1990; Brown & Perry, 1994; Black et al., 2000; Roberts & Dowling, 2002). Reputation research has turned to the question of how reputations are formed and what actions firms can take to build a reputation (Rindova et al., 2005; Basdeo et al., 2006; Zavyalova et al., 2012). Inputs and the reputation production process Using the conceptual framework of Basdeo et al. (2006), market actions provide visible signals upon which stakeholders form their perceptions of a firm s reputation. Integrating these concepts into a formal production economics approach, these visible signals can be viewed as inputs into the reputation production function. This perspective assumes that firms play an active role in producing and managing their reputation (Rindova et al., 2005; Basdeo et al., 2006) rather than acting as passive recipients of their public profile. There are a wide range of signals that firms use as inputs to generate reputation including general market actions (Basdeo et al., 2006) as well as quality of productive resources, certifications from institutional intermediaries, and affiliation with high-status actors (Rindova et al., 2005). Two actions that have a well-established role in reputation formation are activities geared toward improving CSR performance and advertising. Firms spend significant amounts of money on CSR, with many large companies spending in excess of $100 million per year on CSR-related activities (Singer & Tonello, 2012). There are multiple reasons firms engage in CSR: to attract investors, to win over customers, to acquire social license or promote community goodwill, to gain employee and supplier loyalty, to improve relationships with regulators, and to improve the bottom line (Portney, 2008). Most of these reasons relate at least indirectly to the reputation of the firm. Siegel & Vitaliano (2007) assess whether firms use CSR as a type of product 7

8 differentiation. They find evidence consistent with the strategic use of CSR by firms that sell upscale goods and services where product quality attributes are more important. A large related literature has assessed CSR s impact on firm performance. Several studies have investigated whether CSR provides insurance-like effects on the financial performance of firms in the face of negative events (Godfrey et al., 2009; Minor & Morgan, 2012; Flammer, 2013). Godfrey et al. (2009) find that institutional CSR activities aimed at the firm s stakeholders and society at large do provide insurance-like benefits against negative legal and regulatory actions toward the firm. Similarly, Minor & Morgan (2012) find that the decline in stock prices following a product recall is significantly less for firms that engage in CSR compared with firms that do not. Flammer (2013) finds similar results but also that eco-harmful behavior has had an increasingly negative impact on a firm s stock performance over time whereas the positive stock premium from ecofriendly behavior has been decreasing over time. Overall, results are generally mixed with many studies finding a negative relationship between CSR and firm performance suggesting that the costs of CSR might outweigh the benefits (Melo & Garrido-Morgado, 2012). A much smaller empirical literature has attempted to directly model the drivers of firm reputation. 5 Melo & Garrido-Morgado (2012) conduct an empirical analysis of USlisted companies and assess whether CSR is a key driver of corporate reputation. They find that CSR performance has a positive impact on firm reputation and these effects are heterogeneous across industries suggesting that industry context is important. CSR, however, is not the only strategy available to firms to increase their reputation profile. Firms in the United States spent approximately $170 billion on advertising in 2013 (EMarketer, 2013). Similar to CSR activities, firms have many reasons for spending money on advertising, such as to increase sales, to allow firms to charge a higher price, to project a certain image, and to increase brand loyalty. Bagwell (2007) considers three alternative views economists use to interpret advertising. Advertising can be persuasive by altering consumers tastes and creating illusory product differentiation and brand loyalty; informative by responding to potential imperfect consumer information on product; and complementary to the advertised product by increasing the social prestige of consuming a particular good or service. At least implicitly, all three views allow a role 8

9 for reputation building as an objective of advertising. Melo & Garrido-Morgado (2012) find that advertising levels has a positive, but not significant, impact on reputation. This result may be an artifact of their data as advertising is largely targeted to the consumer market while the reputation ratings used in the study are from a survey of business executives. Industry-level CSR spillovers While the firm s own activities can be expected to influence its reputation, actions by rival firms in the same industry may also influence the perceptions stakeholders hold of a specific firm (Basdeo et al., 2006). For example, Melo & Garrido-Morgado (2012) find that industry-level effects are important determinants of firm reputation, but they do not assess whether industry average CSR levels impact firm reputation. Research into how rival firms actions impact a company s reputation has focused on general market actions such as pricing and marketing actions, new product introductions and licensing activities (Young et al., 1996; Basdeo et al., 2006). These spillover effects can be positive or negative depending on how the relative performance of firms is viewed by stakeholders. While Young et al. (1996) find a negative effect on reputation of rivals actions on a firm, Basdeo et al. (2006) do not find evidence for negative spillover effects but do find evidence of positive spillover effects of rival actions. These issues are relevant for CSR activities because stakeholder may have a hard time discerning firm specific CSR signals from the average CSR level in the firm s industry. For example, suppose a firm has a low level of CSR performance in an industry with generally high levels of CSR. How do stakeholders view the reputation of this particular firm? If stakeholders assess CSR levels at the general industry level and pay less attention to amount of own-firm CSR activities, then this firm would benefit from a positive spillover effect. The presence of positive spillover effects leads to the possibility of firms free-riding off the CSR investments of its industry competitors. Alternatively, stakeholders could view the company with low levels of CSR as a laggard relative to its industry peers and the spillover effects can be negative. Taken together, these arguments suggest that industry-level CSR, defined as the average level of other firms CSR in a 9

10 specific firm s industry, can have a positive or negative effect on a firm s reputation leading to two competing hypotheses: reputation. Hypothesis 1. Industry-level CSR performance has a positive spillover effect on firm reputation. Hypothesis 2. Industry-level CSR performance has a negative spillover effect on firm Substitutes or complements? But what about the complements versus substitutes question posed in the title of this paper? The link between advertising, CSR and reputation has been an active area of research in the management literature, yet no study has directly addressed the interactions between advertising and CSR using appropriate metrics. The complements viewpoint is grounded in an early theoretical contribution on firm behavior provided in McWilliams & Siegel (2001). They hypothesize that potential positive synergies may exist between CSR and advertising. The intuition is that potential customers must be aware of CSR activities and advertising can play a role in raising the awareness of individuals who would like to purchase goods and services with CSR attributes (Du et al., 2010). These theoretical insights suggest that advertising and CSR should be viewed as complements. There is also some supporting empirical evidence for the complements viewpoint from the literature on CSR, advertising, and firm performance. Servaes & Tamayo (2013) find that CSR and firm value are positively related for firms with high levels of advertising but generally negative or insignificant for firms with low levels of advertising. This suggests that CSR and advertising may be complements in producing firm value above some threshold level of advertising. However, the paper focuses on firm 10

11 performance, not reputation, and the simple linear interaction terms used are not true measures of elasticities. A second interpretation of CSR and advertising hypothesizes these inputs to be substitutes in producing firm reputation. Gardberg & Fombrun (2006) argue that CSR should be viewed as a strategic investment along the lines of advertising because it creates intangible assets for companies by building reputational and social capital. Similarly, in a dynamic framework, Lundgren (2011) develops a model of firm goodwill through investments in pollution abatement. In this framing, companies face a trade-off between investing in CSR activities or advertising and should invest in whatever activity creates a higher return. Empirical evidence for the substitute interpretation is provided by Barrage et al. (2014) who investigate the effect of green advertising by BP leading up to the Deepwater Horizon oil spill on retail prices and demand at BP gasoline stations after the spill. Using local-level Ad$pender data, they find that BP margins and volumes declined significantly after the spill, but pre-spill exposure to BP s environmental advertising softened the impact of the spill on BP s operations. Most relevant for this current study, Barrage et al. (2014) also find that BP s reputational decline was significantly less severe in areas with highest levels of BP advertising prior to the spill. Thus, this study provides some limited support for the use of advertising as an effective avenue to offset the negative financial and reputational impacts of adverse environmental events. Part of the aim of the current paper is to investigate whether these results derived from only examining BP generalize to a wider range of firms. At least part of the conflicting empirical evidence on the relationship between CSR and advertising in generating firm reputation is due to the lack of a rigorous and flexible measure of substitution elasticity. The previous literature has relied on linear interaction terms between CSR and advertising that shed light on partial correlations, but do not represent appropriate metrics of substitution elasticity. In this paper we use the Morishima elasticity of substitution measure favored by production economists. On a theoretical level, the Morishima elasticity of substitution is generally considered a more valid measure of the degree of substitution between inputs than other metrics because it is better grounded in economic theory (Blackorby & Russell, 1989). Furthermore, a key empirical 11

12 benefit of using the Morishima elasticity of substitution is that it does not impose symmetry in the degree of substitution between two inputs. 6 Allowing for asymmetry is important because CSR may be more easily substitutable than advertising, or vice versa. In light of conflicting theoretical and empirical evidence, we consider two competing hypotheses. In support of Gardberg & Fombrun (2006), we hypothesize that: Hypothesis 3. CSR and advertising are substitutes in producing firm reputation (i.e. elasticity of substitution is positive). Alternatively, based on the conceptual arguments by McWilliams & Siegel (2001), we hypothesize that: Hypothesis 4. CSR and advertising are complements in producing firm reputation (i.e. elasticity of substitution is negative). Different stakeholders One of two main findings of a recent review of the corporate reputation literature is that various stakeholder groups may have different perceptions of corporate reputations (Walker, 2010). Stakeholders will interpret signals produced by firms differently because they have diverse concerns, interests, and goals, leading to multiple reputation assessments (Dukerich & Carter, 2000). Consequently, we propose that the impacts of CSR and advertising on reputation, and the relationship between the two, will depend on which type of stakeholder is assessing the firm s reputation. We collect reputation measures from three groups of stakeholders - the general public, business executives, and CSR experts - to assess how our results are sensitive to who is rating the company s reputation. The general public is the target of most advertising and thus we expect advertising to have the greatest impact on the general public s reputation perception. Because of this targeting, we also expect the elasticity of substitution between advertising and CSR to be highest for the general public reputation. Furthermore, the general public may have a difficult time discerning between own-firm and industry-level CSR activities. 12

13 Therefore, we expect the spillover effects from industry-level CSR to be the greatest for this stakeholder group. Business executives will likely focus more on the financial aspects of firm performance and be less influenced by advertising and CSR activities. Thus, we expect that CSR and advertising will have the smallest effect on this group. Lastly, CSR experts are likely most capable of focusing on own-firm CSR levels and be less influenced by advertising and industry-level CSR activities. Therefore, we expect ownfirm CSR to have a positive and significant effect on CSR experts reputation perceptions, while industry spillover and advertising effects are likely to be minimal. These arguments and the insights on differences in reputation perceptions across stakeholders from (Walker, 2010) lead to the following hypotheses: Hypothesis 5. Advertising and CSR activity have a larger effect on how the general public perceives reputation compared with business executives. Hypothesis 6. Advertising and industry-level CSR activity have a larger effect on how the general public perceives reputation compared with CSR experts. DATA In this study, we focus on US listed companies during the period 2005 to Using various sources, we combine four main types of data: reputation, CSR, advertising, and controls. 7 The exact number of firms and years covered depends on the subset of variables used in the different models. We review each of these variables in turn and Table 1 provides the summary statistics Insert table 1 around here Dependent variable 13

14 Company reputation. A firm s reputation is most commonly measured through surveys of stakeholders where respondents are either asked to provide an absolute or industry-relative firm ranking on a rating scale (Wartick, 2002). 8 We consider three sets of reputation variables that elicit firm rankings from different stakeholder groups - the general public, business executives, and CSR experts. The first reputation variable is derived from the RepTrak survey, an annual survey of the general public in the US conducted by the Reputation Institute. The RepTrak Pulse metric provides an overall assessment of the company s reputation by asking questions along seven dimensions that measure the degree of admiration, trust, good feeling, and overall esteem that the general public hold about firms. 9 Respondents rate up to 5 companies with which they are somewhat or very familiar. Only companies that are engaged in general public facing commercial activities and/or have a general amount of familiarity with the public are included in the survey. As we are primarily interested in the relationship between advertising and CSR activities on the general reputation of the firm, this PUBLIC reputation variable is our main reputation variable of interest. 10 The second set of reputation data is collected from Fortune magazine s list of America s Most Admired Companies. Fortune s reputation measurement is the most commonly used metric of reputation (Walker, 2010). Fortune magazine administers an annual survey to executives, directors, and analysts and asks them to rate companies on nine criteria relating to reputation relative to other companies in their industry. 11 The nine criteria are averaged to yield an overall reputation score. 12 The third set of reputation data is taken from Newsweek s Green Rankings data set. This reputation variable, denoted by EXPERT, is based on an opinion survey of CSR professionals, academics, and other environmental experts. Respondents are asked to rate a random sample of 15 companies on a scale from 1 (laggard) to 100 (leader) along environmental performance, commitment, and communication dimensions. 13 We can expect the impacts of advertising and CSR to differ across the reputation measures due to both the different dimensions of reputation assessed by the specific questions of each survey as well as the different stakeholders surveyed. The presence of both of these differences complicates the interpretation of any results. In general, the reputation questions used in each survey are specifically targeted to each stakeholder 14

15 group. For example, the RepTrak survey asks about reputation dimensions that the general public care about, the Fortune survey of executives has a more financial focus, and the Newsweek survey of CSR experts focuses more on environmental performance and commitment. Independent variables Corporate social responsibility (CSR). CSR is a multidimensional and difficult activity to measure (Melo & Garrido-Morgado, 2012). There are many different CSR metrics that rate and compare companies across these different aspects of CSR and in this study we use CSR measures from three different organizations primarily to assess the robustness of the results. 14 The first CSR measure we consider is an aggregate metric of environmental and social performance of a company obtained from the Thomson Reuters Datastream database. This measure uses the ASSET4 ESG framework and rates over 1,200 companies in North America each year on 250 key performance indicators. These performance indicators are subsequently aggregated into four pillars: environmental performance, social performance, economic performance, and corporate governance performance. We exclude corporate governance and economic performance because there is debate on whether these indicators should be part of CSR as they focus on shareholders rather than stakeholders (Servaes & Tamayo, 2013). 15 The environmental performance metric combines 70 performance indicators covering areas including use of resources such as water, energy and materials, emission reductions, and product innovations. The social performance metric uses data on 88 performance indicators aggregated into areas of employment quality, health and safety, training and development, diversity, human rights, community, and product responsibility. The two pillars are aggregated using equal weights and z-scoring all underlying data points by comparing a company s rating to all other companies on a yearly basis. The resulting scale is between 0 and 100 and reflects the relative measure of environmental and social performance. The second CSR measure aggregates environmental impact and green policies scores obtained from Newsweek s Green Rankings into a single metric. The 15

16 environmental impact score is derived from data compiled by Trucost and represents a comprehensive, quantitative, and standardized measurement of the total environmental impacts of a firm using data on over 700 metrics. The green policies score attempts to measure how a company manages its environmental footprint and is based on data from MSCI ESG Research. Both these metrics use a scale from 0 to 100 and a simple average of the two scores is calculated. 16 The third CSR measure is taken from the KLD database which dates back to In large part due to its longevity, the KLD database is the most frequently cited source of CSR performance in the academic literature. The KLD approach uses a numerical assessment of the strengths and concerns of a firm across 80 different indicators in the following categories: community, corporate governance, diversity of workforce, employee relations, environment, human rights, and product issues. Each indicator uses a simple count measure of 0, 1 or 2 for strengths and concerns in each CSR sub-category. Because the number of qualitative issues within each CSR sub-category varies from year to year, it is difficult to directly compare strengths and concerns across years. We follow the approach of Servaes & Tamayo (2013) to appropriately scale the sub-categories by dividing the number of strengths and concerns for each sub-category by the maximum possible number of assessed issues strengths and concerns for each year. However, we do not construct a net CSR measure as described by Servaes & Tamayo (2013) because we are interested in the differing impacts of strengths and concerns on reputation. Instead, we follow Kotchen & Moon (2012) and construct a CSR index of strengths as the sum of all strength indicators across all categories and a similar method is used to construct a separate corporate social irresponsibility (CSI) index of concerns. 17 In these aggregate CSR and CSI indices, we follow Servaes & Tamayo (2013) and exclude the corporate governance and product categories. 18 It is difficult to know if these different CSR variables will have varying impacts on reputation and their relationship with advertising. Thus, the main purpose of using various CSR measures is to test the robustness of our results. These robustness checks are especially important given the finding by Chatterji et al. (2014) regarding the lack of convergence amongst various CSR metrics. However, one key difference between the measures is that the Asset4 and Newsweek CSR variables use an aggregate net CSR 16

17 metric while the KLD data differentiate between CSR and CSI. Consequently, the KLD data allow us to assess the relationship between advertising and CSR, between advertising and CSI, as well as between CSR and CSI. This difference could be important as firms may use advertising with CSR and CSI differently. Lastly, we are also interested in whether there are any positive or negative spillover benefits from industry-level CSR improvements. Specifically, we are interested in how an increase in the CSR levels of other firms in the same industry impact the reputation of a given firm. Thus, we create an industry-level CSR variable, INDCSR, which is unique for each firm. This variable captures the average level of CSR of all other firms in the same industry as the firm. Advertising. For the advertising data, we use information from two sources. First, the COMPUSTAT accounting database provides self-reported firm expenditures on advertising by North American firms. The advertising expenses variable from COMPUSTAT includes the cost of advertising media (i.e., radio, television and print) and promotional expenses but excludes selling and marketing expenses. One potential concern with the COMPUSTAT advertising variable is that firms self-report their advertising expenses and thus may have different interpretations of what counts as advertising expenses versus other types of expenses. As noted earlier, one issue with this data source is a large portion (around 40 percent) of firms do not report any advertising expenditures and we do not know if these firms truly do not spend any money on advertising or just a small amount of money. Consistent with the prior literature, we set advertising expenditures to zero if they are missing in the database (Fee et al., 2009; Servaes & Tamayo, 2013). The second source of advertising data is obtained from the Ad$pender database maintained by Kantar Media. The company monitors US expenditure information for over 3 million brands across 18 media types (e.g., television, newspaper, internet, etc.) and the information can be aggregated at the firm level. Although data is provided on both the units of advertising and the amount spent on the different media types, we use aggregate advertising expenditures in our analysis instead of number of units because it is difficult to aggregate units of advertising across different media sources in a meaningful way. 19 We set advertising expenditures to zero for firms with missing data. 17

18 As is common in the literature, for both advertising variables we compute advertising intensity as advertising expenditures divided by total assets of the firm. Almost all previous empirical studies use the COMPUSTAT advertising data source because it is already linked with other firm control variables and because access to the Ad$pender database is more limited. Table 1 shows that the COMPUSTAT advertising intensity level is around 3 times higher than the Ad$pender levels. The main reason for this difference is that the Ad$pender amounts only include what the firm spends on buying the actual advertising spots in the various media, while the COMPUSTAT amounts also include all marketing and promotional expenses, including what the firm spends on developing the advertisements. The correlation coefficient between these two advertising variable is 0.59 which suggests that there are material differences between these two variables that could affect our results. The Ad$pender advertising data may be preferred to the COMPUSTAT data because it is collected by an independent firm that applies the same methodology regarding what constitutes advertising while the COMPUSTAT data is self-reported by each company, and companies do not have to report annual advertising expenses if they are under $5 million. However, the COMPUSTAT data have better coverage both in terms of breadth (number of firms per year) and depth (the number of years). We use model specifications with each variable to assess the robustness of the results. Controls. In terms of control variables, we use insights from the literature on the important factors to include in the analysis and the accounting data from the COMPUSTAT database. The majority of reputation and CSR research use measures of the size of the company as key control variables. We use both the total assets of a company (AT) as well as the number of employees (EMP). A measure of risk is another control variable that is often included in reputation and CSR models (Melo & Garrido- Morgado, 2012). We calculate the ratio of total debt to total assets (RISK) and include this metric in the analysis. Another important control variable to include is past financial performance to remove the financial halo effect on reputation (Brown & Perry, 1994). We include both return on assets (ROA) and market-to-book ratio (MTB) as control variables to account for any financial halo effect on reputation. 18

19 EMPIRICAL APPROACH The empirical strategy is grounded in production economics, and contributes to the existing literature by estimating the contribution of advertising and CSR to firm reputation and more importantly investigating the relationship between advertising and CSR in the production of firm reputation. We use the richness of our dataset which we compiled using several disparate sources to examine whether the effects of advertising and CSR on firm reputation vary with the group forming the reputation (i.e., PUBLIC vs. EXECUTIVE vs. EXPERT). Our use of multiple data sources also allows us to consider different measures of advertising and CSR to investigate the robustness of our results. We use lagged-one year input levels to address the possibility of reverse causality and fixedeffects models to address endogeneity concerns due to omitted (time-invariant) variable bias. 20 We specify the production function for reputation of firm i as: REPit = f (ADSit 1,CSRit 1, INDCSRit 1, Xit 1) (1) where REPit is firm i s reputation at time t, ADSit 1 is advertising level at time t 1, CSRit 1 is the firm s own CSR level, INDCSRit 1 is the average CSR level for all other firms in firm i s industry, and Xit 1 is a vector of control variables. The next important choice in the analysis is the specification of the explicit functional form of Eq. (1) to be estimated. A pure translog function is inappropriate because the advertising intensity variable has a substantial portion of observations with zero input levels. Knowing that simply adding a small number or one to this variable would bias our estimates of the elasticity of substitution, we decide to include the advertising variable in levels without taking the natural logarithm. A semi-translog function is selected for the ASSET4 CSR and Newsweek CSR variables because of the absence of zeros, but a simple quadratic functional form is chosen for the KLD CSR and CSI measures because these variables contain firms with zero scores. Two of the benefits of using a translog function instead of a quadratic function are that the translog is a 19

20 second-order approximation to the unknown production function, Eq. (1), and working with the natural logarithms of data reduces the impact of outliers. For each model specification, we estimate two types of models. First, we estimate a basic pooled OLS model using industry fixed-effects. 21 Second, we estimate models with firm fixed-effects (FE) to capture unobserved time-invariant own-firm effects. 22 Both sets of models include year fixed effects and standard errors are adjusted for heteroskedasticity and the correlation of data from the same firm using clustered standard errors. The FE semi-translog production function to be estimated can be represented as lnrep it = α 0 + β 1 ADS it 1 + β (ADS it 1ADS it 1 ) + β 2 lncsr it 1 + β (lncsr it 1lnCSR it 1 ) + β 3 lnindcsr it 1 + β (lnindcsr it 1lnINDCSR it 1 ) + β 12 ADS it 1 lncsr it 1 T 1 + β 23 lncsr it 1 lnindcsr it 1 + γlnx it 1 + T t + D i + θ it where the notation is the same as Eq. (1) and Tt are year fixed-effects, Di are firm fixed effects, and θ it is the error term. 23 The semi-translog production function specification is used with the ASSET4 and Newsweek CSR data. The FE quadratic production function used in estimation with KLD data is t=1 N 1 i=1 REP it = α 0 + β 1 ADS it 1 + β (ADS it 1ADS it 1 ) + β 2 CSR it 1 + β (CSR it 1CSR it 1 ) + β 3 CSI it 1 + β (CSI it 1CSI it 1 ) + β 12 ADS it 1 CSR it 1 + β 13 ADS it 1 CSI it 1 + β 23 CSR it 1 CSI it 1 T 1 + γlnx it 1 + T t + D i + θ it t=1 N 1 i=1 where CSI is the index of corporate social irresponsibility of a firm (Kotchen & Moon, 2012). 20

21 For all of these models we are primarily interested in deriving two traditional production metrics. First, we calculate elasticities of output (Ei) to assess the magnitude and direction of the relationship between the inputs and reputation. For the semi-translog function, the first derivatives of the semi-translog function with respect to the logged inputs yields the output elasticities (CSR and INDCSR) and the first derivatives with respect to the non-logged inputs multiplied by the input level yields the output elasticities for the non-logged input (ADS). We calculate the output elasticities using the model coefficients and the mean values of the variables. Second, we calculate elasticities of substitution between the various inputs to determine whether inputs are substitutes or complements using a flexible measure, the Morishima elasticity of substitution (σij M ). Inputs are considered complements if σij M < 0 and substitutes if σij M > 0. Following Chambers (1988), the Morishima elasticity of substitution can be represented as where Fij is the i,j cofactor and F is the determinant of F, the Hessian matrix of the production function. 24 As with the output elasticities, we calculate the elasticities of substitution at the sample means. RESULTS We estimate a total of 8 production function specifications using both pooled OLS and FE models. Models 1 to 6 all use the PUBLIC reputation metric from the Reputation Institute as the dependent variable, but differ in their use of the CSR measure and the advertising metric. 25 The first three models use the COMPUSTAT advertising data and the Asset4 CSR measure (Model 1), the Newsweek CSR variable (Model 2), and the KLD CSR measures (Model 3). Models 4, 5, and 6 use the same CSR measures as the first three models, but use the Ad$pender advertising data. The last two models use the Asset4 CSR data and the COMPUSTAT advertising data, but differ in their reputation variable. Model 21

22 7 uses the EXECUTIVE reputation ranking from Fortune magazine and Model 8 uses the EXPERT reputation rating from Newsweek. Table 2 presents the pooled OLS and FE model results using the three different CSR measures, the PUBLIC reputation variable and the COMPUSTAT advertising data. 26 The specific coefficients of a semi-translog model are difficult to directly interpret because of the many higher order and interaction terms, and many of the coefficients are insignificant. However, we can note that the industry fixed-effects are significant in the pooled OLS models suggesting that the specific industry of the firm has an important impact on public reputation. Furthermore, firm fixed-effects are significant and there is a noticeable difference in the adjusted R 2 statistic suggesting that controlling for time invariant firm-specific omitted variables is important. Elasticity of output Insert table 2 around here Table 3 presents the output elasticities for the three input variables and the first six model specifications. Examining the ASSET4 CSR measure results first (top panel), ownfirm CSR activities has a positive and significant impact on firm reputation in both the pooled OLS and FE models. Specifically, increasing own-firm CSR activity levels by 1 percent is associated with a 0.06 (FE) to 0.07 (Pooled OLS) percent increase in firm reputation held by the general public. However, industry-level CSR activities are generally positive and providing some evidence in support of Hypothesis 1 compared to Hypothesis 2. Interestingly, in Model 1 with COMPUSTAT advertising data, an increase in industry-level CSR activities leads to a larger increase in firm reputation than an increase in a firm s own CSR activities. This finding can be interpreted as suggesting that there are positive reputation spillover benefits from industry-level CSR activities for firms in the same industry. 27 Advertising intensity is positive and significant except for the FE specification of Model

23 Insert table 3 around here The second panel in Table 3 shows the results using the Newsweek CSR variable. The estimated output elasticities are similar, but generally smaller than the first panel results and range from 0 to 0.02 percent for advertising intensity, 0 to 0.07 percent for own firm CSR activities, and 0 to 0.06 percent for industry-level CSR activities. The third panel in Table 3 presents the output elasticities using the models with the KLD CSR and CSI variables. In comparison to the previous models, the novel result in this context is that while own-firm CSR is positive and significant across all models, own-firm CSI has a negative and significant impact on firm reputation for most model specifications. Furthermore, a one percentage increase in own-firm CSR levels has a larger impact on reputation than an equal increase in own-firm CSI levels. One of the reasons for the differences in results using the different advertising and CSR variables is that the samples change. For example, the Ad$pender data begins in 2008 while the COMPUSTAT advertising data includes early years back to Similarly, the Newsweek CSR variable only includes data for the years 2009 to 2012 whereas the other two CSR variables cover the years 2005 to The changing sample makes direct comparison between the different models difficult, but the overall results seem consistent across model specifications. 28 Elasticity of factor substitution Table 4 presents the Morishima elasticity of substitution estimates between the various sets of inputs and a t-test of whether the elasticities of substitution are significantly different from zero. Examining the relationship between advertising and own-firm CSR, the large share of positive estimated elasticities suggest that these two inputs are substitutes, in support of Hypothesis 3, with the exception of the pooled OLS model using the ASSET4 CSR variable and the Ad$pender data. The empirical evidence does not support Hypothesis 4 that these two inputs are complements. 23