The effect of CER performance on firm performance: the moderating effect of industry-level differences

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effect of industry-level differences

Abstract:

This study investigates the relation between Corporate Environmental Responsibility performance and firm performance for a worldwide sample from 2002 till 2018. Statistical evidence is found for the relationship between CER performance and firm performance, measured by ROA. In addition, the

relationship between CSR performance and firm performance and Emission Reduction performance and firm performance is examined. Both relations are found to be positive and significant. Furthermore, this study examines the moderating effect of pollutive industries on the relationship

between CER and firm performance. The results showed a positive moderating effect of pollutive industries, implying that an increase in CER performance contributes the firm performance to

increase more in pollutive industries relative to non- or less pollutive industries.

Student name: Celeste Joosting Student number: S2484889

Student Email: c.joosting@student.rug.nl

Thesis supervisor: Dr. R.O.S. Zaal MBA Date: 10-01-2020

Field Key words:

CSR, CSR performance, CER performance, Emission Reduction performance, Firm Performance, Industry-level differences, Pollution, Pollutive industries

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Renneboog, 2016). Some argue that corporations can be held responsible for their actions only to shareholders, while others say that corporations are responsible to both society and shareholders (Macintosh, 1999). Corporate social responsibility (from now on referred to as CSR) has therefore been defined in many ways. In 2003, Thorne, Mahoney & Manetti (2003) defined CSR as “an adoption by a corporation of a strategic focus for fulfilling the economic, legal, ethical and philanthropic responsibilities expected by its stakeholders”.

In a more broader sense, CSR can be described as the way in which companies manage their business processes to produce an overall positive impact on society (Garg, 2016). Prior research shows that firms fulfilling corporate social responsibility have a positive external effect, and they could have a great positive impact on the sustainability development of society as a whole (Qiao, Xu & Wu, 2018). But many researches argue that firms that are very committed to CSR simply do so as a sign of managerial agency problems which benefits the firm more than its shareholders (Benabou and Tirole, 2010). Nobel Prize winner Milton Friedman (1962, 1970) states that managers’ primary task is to maximize the value of the enterprise. Managers’ actions are bound by the economic rules and commitment beyond the legal requirements to general social interests contradicts these legal guidelines (Friedman, 1970). Managers can still work toward the improvement of society, but they should do so as private individuals and not as agents of their principals (Friedman, 1970).

Prior literature has acknowledged social responsibility as an important corporate duty (e.g. Edmans, 2011; Deng, Kang and Low, 2013). Because of the significance of corporate social responsibility in corporate decision making, the relationship between a firm’s social and ethical policies or activities and its financial performance is an important topic in literature (Guenster, Bauer, Derwall & Koedijk, 2011). The relationship between corporate social responsibility and measures of firm performance has led to mixed results in previous research, although the positive results do prevail. Proponents of CSR argue that firms act in the best interest of their shareholders when increasing investment in environmental responsibility which as a result increases financial performance (Kim & Statman, 2012). Preston & O’Bannon (1997), for example, found overwhelming evidence of a positive and significant relationship between social performance and financial performance in a sample of US corporations. Falck and Heblich (2007) debate that a strategic practice of CSR will involve a long-term shareholder value approach, which will lead to a profit maximization view as well. They conclude their research paper with the statement: “if a company’s goal is to survive, it better takes a long term view and realizes that if it treats society well, society will return the favor". This paper will focus on the short-term benefits of CSR and the positive effects on firm performance.

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financial, but non-financial measurements. The purpose of the use of these non-financial

performance measures is to interpret managerial incentives with long-term value for shareholders, and to additionally combine the creation of shareholder value with the creation of social value (Chatterji & Levine, 2006).

Not only do financial reasons drive companies to apply CSR, but also moral driven reasons encourage companies to act in a responsible manner (Agudo-Valiente, Garcés-Ayerbe & Salvador-Figueras, 2015). Through globalization, successful firms should be driven at implementing development strategies that are beyond their limits. Expectations are high that environmental issues, such as large and unforeseen environmental liabilities, will be a major and important topic among many firms and transactions (Cuddihy, 2000). Environmental liability is highly significant to all forms of businesses worldwide (Ghoul, Guedhami, Kim, & Park, 2015). Many major environmental as well as financial disasters occurred in the last decades, such as the Deepwater Horizon oil spill in 2010 (Comyns, 2014). Due to different factors such as costly litigation, increased media attention, policies, importance of investors, and social and environmental activists, many corporations are forced to improve environmental performance by strategically and environmentally investments (Ghoul et al., 2015). As a result, society might think that companies invest too little in CSR, while companies themselves believe that they invest enough. This shows that a government has an important role to make sure that firms increase CSR investments to levels that are adequate for society.

Corporate Environmental Responsibility and Emission Reduction

Literature shows extensive evidence on the relationship between CSR and firm performance, however the link between Corporate Environmental Responsibility (from now on referred to as CER) and firm performance is less examined. Already more than fourty years ago Spicer (1978) found evidence from companies in the pulp and paper industry that the ones with better environmental control ratings experience higher profitability. This is similar to the findings of Nehrt (1996), who argued that consequent investment in environmental technologies can have a positive impact on the performance by increasing sales and decreasing procuction costs. One of the main parts of

environmental responsibility is emission reduction. In 1996, Hart and Ahuja found extensive evidence that efforts to reduce emissions and prevent pollution increase short-term firm performance.

Companies with environment-friendly policies will have higher accounting- and market-based

measures of firm performance (Guenster et al., 2011; Russo & Fouts, 1997). As the pollution intensity of industries vary widely, firms operating in the industries with the highest level of environmental pollution might experience the most gain, as some prior studies have suggested a positive

moderating effect of these industry differences (e.g. Nehrt, 1996; Guenster et al., 2011; Russo & Fouts, 1997).

Therefore the following research question is proposed:

How does CER performance influences short-term firm performance and what is the moderating effect of pollutive industries?

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of CSR performance, and more specifically CER performance, on short term firm performance for over 3,000 companies from 68 countries worldwide.

The goal of this study is to test the linkage between CSR and the financial performance of an

organization and even more specific the relationship between CER performance and a firm’s financial performance. In addition, the moderating effect of pollutive industries is examined. These industry effects relate to companies operating in industries that have a relative high environmental impact in contribution to air, water and soil pollution. A lot of research is done on this relationship already and this paper expands to this literature by focusing on a worldwide sample that includes over 65

countries and firms in all different industries.

The remainder of this research is structured as follows: the next section highlights the relevant literature that is required to understand the definition of CSR and its relationship with firm performance. The literature review is followed by the research methodology in which the planned method of this research will be explained. Then the regression results and robustness-test results are presented and finally the findings will be concluded.

2. Literature review and hypotheses development

The first step in understanding the relevant concepts is the clarification, explanation and the importance of the terms CSR, CER and Emission reduction.

2.1 Literature review

2.1.1 Corporate Social Responsibility

Many papers can be found with a definition of CSR and the identification of the roots of CSR. But a brief consideration of the past half century captures most of what is relevant to executives today (Carrol, 2015). As stated before in the introduction Milton Friedman (1970), Nobel Prize-winning economist, once expressed succinctly his viewpoint on CSR in his article as “The Social Responsibility of Business is to Increase Its Profits”. But the founder of Whole Foods, John Mackey, advanced a slightly different point of view when he stated: “The business model that Whole Foods has embraced could represent a new form of capitalism, one that more consciously works for the common good instead of depending solely on the “invisible hand” to generate positive results for society (Mackey, 2005). The ‘invisible hand’ Mackey is referring to, is introduced and explained by Adam Smith in his book “The Wealth of Nations (1776)” as the unobservable market force that helps the demand and supply of goods in a free market to reach equilibrium automatically. Perhaps the most parsimonious definition that encompasses the definition is that CSR represents voluntary firm endeavors which benefit society (Sprinkle & Maines, 2010).

Governance and sustainable development are terms that often come together in prior literature. They emerged in the late 1980’s, with shared characteristics and overlapping potential.

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governance is that sustainable development is an open-ended process and not usefully conceived as a particular specified or specifiable target (Kemp & Parto, 2005).

The debates about CSR continue to grow without a clear consensus on its meaning or value. Lindgreen, Lee & Kim (2009) and Baron, Harjoto & Jo (2011) among others suggest that while definitions of CSR vary, it generally refers to serving people, communities, society, and environment in ways that go above and beyond what is legally required of a firm. Generally speaking, CSR is an extension of a firms’ effort to foster effective corporate governance, which ensures the sustainability of firms via sound business practices that promote accountability, information transparency, and corporate philanthropy (Cai, Jo & Pan, 2012). Based on this paper, this definition fits best with the term CSR and will therefore be used in this thesis.

The total ESG score of a company is the main variable that captures a firm’s overall CSR performance. This score focuses on the Environmental, Social and Governance impacts, which firms have. The ESG score is obtained by the Thomson Reuters ASSET4 database as a measure for CSR performance. ASSET4 ESG data provides over 1000 data items, among others about Environmental scores and Emission Reduction, over 3500 companies worldwide. By also separately analyzing the Environmental component of the ESG score, it allows me to identify potential economic benefits of CER (Yoon, Hwan-Lee & Byun, 2018). In table 1, the three components of ESG are explained and their risks are being treated.

Table 1: ESG Risks and Effects of Risk Materialization for a Company’s Market Valuation

Areas ESG-related risks Effects of risk materialisation

Environment • climate change—extreme

weather, greenhouse effect— greater frequency and severity of natural disasters

• depletion of natural resources—waste of resources, overexploitation • ecosystem degradation, environmental pollution and disposal of hazardous waste

Direct costs: charges for emission, allowances increased cost of raw materials,

materials and energy

elimination of environmental emergencies, social protests, blockades

Indirect costs: loss of reputation ,social ostracism, additional costs of the adverse effects of climate change and environmental pollution, low economic innovation

Society • financial exclusion—

poverty,access to health services and medicines • civilisation diseases— risksassociated with nanotechnology, obesity, pandemics

• human rights and workers’ rights—increased efficiency at the expense of workers, mobbing, unfavourable terms of outsourcing, discrimination

Direct costs: lawsuits, labour disputes, strikes penalties and restrictions imposed by supervisory authorities accidents at work, downtime increased costs of

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in the workplace (especially on grounds of gender)

• ageing population—changes indemographic structure

social ostracism, mismatch of new products to social

expectations, lack of employee loyalty and high staff turnover, higher unemployment

Corporate governance • legal regulations— corruption,inappropriate complaint/appealprocedure, price fixing

• professional ethics rules— unethical contract design, controversial marketing practices, misleading advertising

• equal treatment of stakeholdergroups

• conflicts of interest, structure of management and

supervisory boards, creating value for the different stakeholder groups

• transparency of operations and information policy— communication with the environment

Direct costs: problems in the supply chain penalties restrictions imposed by supervisory authorities, lawsuits difficulties in attracting businesspartners and funding sources Indirect costs: lack/loss of customer loyalty, bad

reputation, costs of conflicts of interest, deterioration of the social and economic

environment in which a company operates—lack of transparency and predictability of activities

Directly sourced from Czerwinska et al. (2015)

2.1.2 Corporate Environmental Responsibility and Emission Reduction

CER is an integral part of CSR and should include two aspects: it should address the range of environmental issues that are affected by business decisions in a way that it actually contributes to and impacts the environmental and ecological deterioration and it must be capable of influencing business policies (DesJardins & Jordan, 2018).The Asset4 database provides a CER pillar that captures a company’s impact on society and environment, such as the air, land and water as well as total ecosystems. This score reflects how well a company is using its management practices to avoid and mitigate its environmental risks and capitalizes on environmental opportunities to generate long-term shareholder value.

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2.2 Hypotheses development

2.2.1 The effect of CSR performance on Firm Performance

The impact of CSR engagement on accounting performance, measured by return on assets, is a longstanding but still unresolved question. According to Margolis and Walsh (2003), over 120 studies between 1971 and 2001 examine the empirical relation between CSR and financial performance, and the results are largely inconclusive. They suggest that, as a recurring reason, previous studies are subject to various imperfections, such as measurement problems related to both CSR and financial performance, a lack of necessary analyses of causality and/or endogeneity, omitted variable problems, a lack of methodological rigor and a lack of theory.

Shah & Arjoon (2015) found that firms implement corporate sustainability initiatives due to being driven by intrinsic self-determined motivation or extrinsic circumstances and pressures. If society in the geographic region where a company operates in is more CSR-conscious, firms might be more likely to act socially responsible for the sake of and due to outside pressures from society (Shah & Arjoon, 2015). Several researchers argue that good corporate governance is associated with better firm performance. Holthausen & Larcker (1999) found evidence that when a company has weaker governance mechanisms, it consequently hase stronger agency problems and thus weaker

performance. Defined by Jensen and Meckling (1976, p. 308): “an agency relationship as a contract

under which one or more persons (the principal(s)) engage another person (the agent) to perform some service on their behalf which involves delegating some decision making authority to the agent”.

When each party’s goal is to maximise its own welfare, the agency problem can arise which increases agency costs and can therefore decrease firm performance (Jensen & Meckling, 1976).

The development of stakeholder and social contract theories is helpful in deeply revealing the impact of CSR on a firm’s economic benefits. Clarkson, Li & Richardson (2011) maintained that a firm is a complex system consisting of different stakeholders. Without stakeholders, it is hard for firms to survive and therefore firms should generate wealth for their stakeholders. Basically, the contract between a firm and society is the contract governing the relationships between the firm and its stakeholders. In 2017 Wickert, Vaccaro & Cornelissen studied cases of multinational corporations acquiring social-oriented enterprises. In his study, he found that multinationals experienced that transactions not only accrued financial benefits, but the firms also benefitted from learning the CSR-related practices.

CSR requirers managers to make multiple trade-offs between shareholders and other stakeholders in the firm. As such, CSR contrasts with the classic injunction that the only responsibility of a business is to make a profit for its shareholders. Some would say that creating long-term value for shareholders may at odds with the ‘softer’ objectives of CSR (Lev, Petrovits & Radhakrishnan, 2010). Previous literature shows positive excess returns among companies with better CSR performance (e.g. Kempf and Osthoff, 2007; Statman and Glushkov, 2009; Flammer, 2015). This study focuses on the effect of CSR performance on firm performance proxied by return on assets. Based on these findings, the first hypothesis is proposed:

H(1a). Corporate Social Responsibility performance is positively related to firm performance.

2.2.2 The effect of CER performance on firm performance

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performance (e.g. Orlitzky, Schmidt & Ryes, 2003; Margolis et al. 2007; Baron et al. 2011). However, there is little systematic evidence that environmental performance influences financial performance and there are even empirical economic studies that show a mild negative relationship between environmental performance and firm competitiveness (Jaffe, Peterson, Portney & Stavins, 1995; Ambec and Barla 2006; Pasurka 2008). Falck and Heblich (2007) argue that firms can “do well by doing good” indicating that they can make a profit and make the world a better place at the same time. Porter and Kramer (2007) describe this as a win-win situation and call it strategic philanthropy; it may contribute to the “sustainability” of a society.

Russo and Fouts (1997) use ratings on environmental compliance and prevention efforts to test the environmental-economic performance relationship for a sample of 243 firms. They find evidence that firms with environment-friendly policies have better economic performance. This is also examined in the paper of Guenster et al. (2011), which results in a positive association between environmental performance and different measures of firm performance for a panel of US firms form 1997 to 2004. Similarly, Kim and Statman (2012) show us that US companies act in shareholders’ interest,

increasing or decreasing environmental responsibility investment as necessary to improve firm performance.

A growing body of literature examines the reasons why companies engage in CER and how this influences corporate performance (e.g. Berchicchi and King, 2007). Because a company’s

environmental choices are a major part of its CSR performance and the importance of CER is still evolving, it will be tested in this thesis whether firms with higher CER performance actually

experience higher firm performance. Although the empirical CSR literature suggests a slightly positive relationship between CSR and firm performance, the impact of CER performance on firm

performance is less explored and has many limitations. Some evidence is found on the CER performance impact, Kim and Statman (2012) claim that companies adjust their CER levels by investing the perfect amount in CER, which is not too much and not too little, to maximize the financial performance. In line with the findings of Kim and Statman’s (2012), I propose the following hypothesis:

H(1b). Corporate Environmental Responsibility performance is positively related to firm performance.

2.2.3 The effect Emission Reduction performance on firm performance

Literature on the performance impact of pollution prevention is determined ambiguous, as some papers found evidence on a positive relationship (e.g. because of cost savings through reducing waste reduction and emissions and therefore enhanced efficiency), but other papers found conflicting evidence, arguing that relative high investment costs do not outweigh the benefits and thus don’t increase a firms’ performance (e.g. Hart and Ahuja, 1996; Cordeiro and Sarkis, 1997; 2001). Schwens and Wagner (2018) found that firms engaging in pollution prevention activities improve their competitiveness which subsequently had positive effects on a firm’s profitability. This thesis focuses on the relationship between CER performance and firm performance in the industries that contribute highly to environmental pollution, included water, air and soil pollution. The Emission Reduction score obtained from Thomson Reuters ASSET4 database captures a company’s

commitment and effectiveness to reducing this environmental emission in its production and operational processes. Therefore Emission Reduction is an important aspect of CER and the association with firm performance to empirically examine the impact in pollution intensive

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H(1c). The level of Emission Reduction commitment is positively related to firm performance.

2.2.4 The moderating effect of pollutive industries

The levels of social and environmental impacts on firm performance vary greatly in different industries (Guthrie & Farneti, 2008). The investigations in CSR reporting go back to 1970 and the majority of examinations focus on these differences across this variety of different industries. It is thus important to examine CSR reporting at the industry specific level. Patten et al. (2014)

documented that firms focus on discussing CSR initiatives and programs, but are not providing performance data in their reports. This suggests that the reports are not about transparency accountability, but about image enhancement and company reputation. This can eventually lead to increased appeal to socially responsible investors.

In contrast to many prior studies and more relevant to this research paper, Guthrie & Farneti (2008) examine CSR disclosure across the Australian food and beverage industry and Patten et al. (2014) focus on the U.S. retail industry-sector. Lately, regulators have realized the importance of bearing in mind the industry and country setting of a company when setting regulations of environmental responsibility and CSR reporting requirements (Guthrie & Farneti, 2008). As reported by the Global Reporting Initiative (GRI 2018, p 106) in their Sustainability Guidelines, “companies are required to explain whether they are subject to any country, regional, or industry regulations and policies for emissions provide examples of such regulations and policies.” The Principles for Defining Report Content guide, which is a part of the Sustainability Guidelines, decision to identify what content the report should cover by considering the organization’s activities and impacts (GRI 2018, p. 8). Despite these general guidelines, only few papers have examined the relationship between social and environmental reponsiblity performance at the industrie level (Guthrie, Petty & Ricceri, 2007). This paper aims to extend this industry-themed analysis by focusing on the social and environmental performance of companies in pollutive industries and the impact on firm performance.

Guthrie (2010) reported evidence that companies in industries with a higher social or environmental impact show higher levels of CSR performance levels compared to their counterparts in other

industries (Guthrie, 2010). However, other studies are showing contradictive results (Campbell, 1996; de Villiers & van Staden, 2006), where companies in industries that have less environmental impact and legitimacy gaps show higher levels of CSR performance. Although these studies find different results, in this thesis the following hypotheses are proposed in line with the paper of Gunthrie: H(2a). There is a moderating industry effect on the relationship between CSR performance and firm

performance, such that this relationship is stronger for firms operating in industries with a high environmental impact.

H(2b). There is a moderating industry effect on the relationship between CER performance and firm

performance, such that this relationship is stronger for firms operating in industries with a high environmental impact.

H(2c). There is a moderating industry effect on the relationship between the level of Emission

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3. Methodology

This research paper contains balanced panel data from companies available in the ASSET4 database and covers a time period of 17 years, from 2002 to 2018. The ASSET4 database is incorporated in Eikon and this is a large and detailed financial database with over 65 years of information on firm level financial numbers. ASSET4 provides objective, relevant and systematic environmental, social and governance (ESG) information based on over 250 key performance indicators and more than 750 data points along with their original data sources and goes back to 2002. To successfully conduct this research and to test what the effects of environmental performance on firm performance are, this study uses regression models with Ordinary Least Squares.

The Hausman-test of random-effects for the panel data is performed and rejects the null hypothesis and therefore the random effects model is not appropriate for models with ROA and Industry Adjusted ROA as dependent variable. Therefore, the regressions performed with OLS will include as well fixed firm effects as fixed year effects. Furthermore, to correct for outliers extreme values of all variables are winsorized at the 1% and 99% level to exclude biased values and because they can cause a non-normal distribution.

In addition, to test for homoscedasticity the Breusch-Pagan test is performed on the before mentioned regression analyses. It tests whether the variance of the errors from a linear regression model is dependent on the values of the independent variables. When the p-value is below the 5% threshold, the null hypothesis of this test is rejected and there is heteroscedasticity. The results of these tests can be found in Appendix A. Every test shows a p-value of 0.0000, which is below the 5% threshold and therefore it can be concluded that there is heteroscedasticity. This issue will be handled with the usage of robust standard errors in STATA.

3.1 Variable measures

3.1.1 CSR, CER and Emission Reduction

CSR initiatives convey inherent information to the partners of a firm and are therefore valuable (Connelly, Shi & Zyung, 2017). As a tool to measure a firms Corporate Social Responsibility the corporate social responsibility Asset4 ESG database of Eikon is used. The dataset contains an overall score for firms, as well as individual scores for four pillars of CSR: Corporate Governance score (CGVSCORE), Economic score (ECNSCORE), Environmental score (ENVSCORE) and Social score (SCOSCORE). Additionally there is an Overall Equal Weighted rating score (A4IR) available. All pillars are separated in various categories whereof the environmental pillar is separated in the following 3 categories; Resource Use, Emissions and Innovation. A firms’ environmental performance means the measure of its impact on living and nonliving ecosystems, such as land, water and air. The emission reduction category measures a company’s management commitment and effectiveness towards reducing environmental emission in the production and operational processes. It reflects a

company’s capacity to reduce air emissions, waste, hazardous waste, water discharges, spills or its impact on biodiversity and to partner with environmental organisations to reduce the environmental impact of the company in the local or broader community (Thomson Reuters).

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Some industries have a major impact on climate change and pollution. Agriculture, for example, plays a fundamental and dual role in human driven climate change and is also a major source of

greenhouse gases to the atmosphere (Rosenzweig & Hillel, 2007). Agriculture also contributes to water pollution, but is not the only cause. Water pollution is the cause of ecological degradation on the planet; it has directly consequences for human water supplies and often seriously affects public health (Deletic & Wang, 2019). This is the cause of a wide range of contaminants, including

chemicals, pathogens and nutrients.

3.1.2 Firm Performance

Many prior studies have examined the relationship between CSR performance and firm performance. In line with many other studies (McGuire, Sundgren & Schneeweis, 1988; Preston & O’Bannon, 1997), the Return On Assets (ROA) as a measure of financial performance is used. The Return On Assets is an indicator of how efficient a company is using its assets to generate earnings, an indicator of a company’s profitability. ROA is calculated as a company’s total net income divided by its total assets and is thus displayed as a percentage. However in the ASSET4 database the ROA measures already available, so further calculations are unnecessary. To check for robustness, the Industry-adjusted Return On Assets is used as an alternative dependent variable. The industry-adjusted ROA is calculated a firm’s ROA minus the industry median ROA, based on the US SIC two digit industry codes. Including the industry-adjusted ROA measures balances the specific industry effects and thus presents a more robust result.

3.1.3 Control variables

The data is gathered from Thomson Reuters Datastream and affects data on company, industry and financial metrics. Other explanatory variables are included in order to decrease omitted variable bias. The firm-level control variable included is firm size (SIZE). This control variable is broadly used in CSR valuation studies and controls for the fact that CSR initiatives are affecting the firm performance variable (Konar and Cohen, 2001: Kim and Park, 2015). Therefore firm size is included as a control variable and is defined as the natural logarithm or a firm’s total assets in US dollars.

SIZE = log(Total Assets)

Many prior studies also included the effect of financial leverage as a control variable as it has a sufficient impact on a firm’s financial performance (Dowell, Hart & Yeung, 2000: Konar & Cohen, 2001: Surroca, Tribo & Waddock, 2010). This ratio is calculated by dividing a firm’s long term debt by its total assets.

LEVERAGE = (Total Debt/Total Assets)

Furthermore, this study controls for a firms’ R&D ratio, as calculated by its R&D expenses divided by total assets. According to Dowell et al. (2000) and Garcia-Castro, Arino & Canela (2010), the R&D ratio is one of the main variables that should be included in research that studied financial

performance. Although R&D activity information is scarcely available, the ratio is still included in this thesis as a control variable.

R&DRATIO = (R&D expenses/Total Assets)

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ROE = Net Income/Total Equity

Finally, year-, country-, and industry-fixed effects are included in this research. Country dummies include all countries worldwide which had available data from the ASSET4 database. The industry dummies are formed by the 2-digit SIC codes, which eventually separated the firms into ten categories. The year dummies include the years 2002 to 2018 and thus covers a time span of 17 years.

3.2 Data and Sample

The initital research sample is retrieved from the ASSET4 database of Thomson Reuters’ Database and includes 8300 companies in a period from 2002-2018. This originally resulted in 141,100 firm-year-observations, but the final sample was cut because of data limitations. The final date sample consists of 51,620 firm-year-observations.

3.3 Regression models

The following regression models are used to examine the proposed hypotheses. The dependent variable is the Return On Assets in every model. To check for robustness of the results the industry-adjusted Return On Assets is included as an alternative dependent variable. Further, lagged variables are included for all independent variables and control variables, as it may take some time for

environmental performances to be realized and thus increase ROA (White, Becker & Savage, 1993: Wood & Jones, 1995).

Hypothesis 1a:

1) ROAi,t = β0 + β1 * CSRi,t-1 + β2 * SIZEi,t-1 + β3 * LEVERAGEi,t-1 + β4 * R&DRATIOi,t-1 + β5 * ROEi,t-1 +

Σ Year_Dummies + Σ Industry_Dummies + Σ Country_Dummies + εi,t

Hypothesis 1b:

2) ROAi,t = β0 + β1 * CERi,t-1 + β2 * SIZEi,t-1 + β3 * LEVERAGEi,t-1 + β4 * R&DRATIOi,t-1 + β5 * ROEi,t-1 +

Σ Year_Dummies + Σ Industry_Dummies + Σ Country_Dummies + εi,t

Hypothesis 1c:

3) ROAi,t = β0 + β1 * EMISSIONi,t-1 + β2 * SIZEi,t-1 + β3 * LEVERAGEi,t-1 + β4 * R&DRATIOi,t-1 + β5 *

ROEi,t-1 + Σ Year_Dummies + Σ Industry_Dummies + Σ Country_Dummies + εi,t

Hypothesis 2a:

4) ROAi,t = β0 + β1 * CSRi,t-1 + (β2 * Pollutioni,t-1 + β3* CSRi,t-1 * Pollutioni,t-1) β4 * SIZEi,t-1 + β5 *

LEVERAGEi,t-1 + β6 * R&DRATIOi,t-1 + β7 * ROEi,t-1 Σ Year_Dummies + Σ Country_Dummies + εi,t

Hypothesis 2b:

5) ROAi,t = β0 + β1 * CERi,t-1 + (β2 * Pollutioni,t-1 + β3* CERi,t-1 * Pollutioni,t-1) β4 * SIZEi,t-1 + β5 *

LEVERAGEi,t-1 + β6 * R&DRATIOi,t-1 + β7 * ROEi,t-1Σ Year_Dummies + Σ Country_Dummies + εi,t

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6) ROAi,t = β0 + β1 * EMISSIONi,t-1 + (β2 * Pollutioni,t-1 + β3* EMISSIONi,t-1 * Pollutioni,t-1) β4 * SIZE i,t-1 + β5 * LEVERAGEi,t-1 + β6 * R&DRATIOi,t-1 + β7 * ROEi,t-1+ Σ Year_Dummies + Σ

Country_Dummies + εi,t

4. Results

4.1 Descriptive statistics and correlations

4.1.1 Descriptive statistics

This section presents the descriptive statistics, correlation matrix and the main results from the OLS regressions performed in Stata. Using a panel data set with data from global firms in the period from 2002 to 2017, the CSR performance, CER performance, Emission Reduction performance and their interaction terms are regressed on the firm performance measure ROA. Table 2 provides the descriptive statistics of all variables included in this thesis. This table gives an overview of the mean, standard deviation, minimum, maximum and number of observations of all these variables. All variables are corrected for outliers and winsorized at 1% on both sides. The CSR, CER and EMISSION scores show very matching numbers.

Table 2: Descriptive Statistics

Table 3 below presents the descriptive statistics by country. The highest number of observations is from the US and the lowest number is from Zimbabwe. Bermuda is the only country with a negative average Return on Assets. The highest score on ROA is from Zimbabwe, but includes only 6

observations. Oman, Slovenia, Kuwait and Bahrain also show a high return on assets. The CSR performance is highest in Finland and lowest in Jersey, but again the latter includes only 27

observations. France scores by far the highest on both the CER performance and Emission Redcution score and ends up fourth in highest CSR performance.

Variable Observations Mean Std. Dev. Min Max

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Table 3: Descriptive Statistics by Country

Country Observations Mean ROA Mean CSR Mean CER Mean EMISSION

United Arab Emirates (AE) 176 1.63 23.88 34.27 32.96

Argentina (AR) 520 1.25 11.13 25.07 24.19 Austria (AT) 412 4.98 50.39 56.38 55.07 Australia (AU) 6,714 0.09 42.32 36.62 39.12 Belgium (BE) 692 5.80 56.73 57.35 57.09 Bahrain (BH) 127 17.84 7.53 13.29 14.99 Bermuda (BM) 28 -2.68 31.91 27.70 42.76 Brazil (BR) 1,535 3.41 54.30 55.91 56.08 Canada (CA) 5,777 1.43 49.37 39.54 42.40 China (CH) 1,463 5.46 59.06 60.17 56.46 Chile (CL) 663 0.01 35.15 45.74 45.86

People’s Republic of China (CN) 4,129 1.36 28.37 37.42 35.71

Colombia (CO) 308 0.22 49.58 51.71 51.57 Cyprus (CY) 11 0.48 37.78 35.97 46.40 Czech Republic (CZ) 74 0.79 48.96 50.77 48.61 Germany (DE) 2,202 6.32 60.45 67.60 64.94 Denmark (DK) 620 1.18 55.29 60.90 58.80 Egypt (EG) 175 1.23 18.52 25.20 25.31 Spain (ES) 972 6.16 69.27 71.78 70.84 Finland (FI) 633 7.73 76.58 77.04 71.46 France (FR) 2,265 5.40 73.91 77.77 75.36 Great-Britain (GB) 7,659 9.88 63.31 58.34 60.59 Greece (GR) 367 6.67 40.04 49.95 52.62 Hong Kong (HK) 3,172 0.95 37.90 40.71 38.06 Hungary (HU) 80 0.02 74.11 75.96 75.29 Indonesia (ID) 636 0.00 49.05 48.48 53.22 Ireland (IE) 262 7.80 49.69 45.49 45.71 Israel (IL) 294 1.69 44.49 48.05 43.33 India (IN) 1,753 0.20 53.01 58.19 56.13 Iceland (IS) 11 0.05 29.07 21.73 18.94 Italy (IT) 1,337 4.69 57.28 58.07 55.89 Jersey (JE) 27 5.73 5.92 12.70 14.24 Jordan (JO) 16 1.80 74.11 77.05 70.99 Japan (JP) 7,358 0.03 41.20 62.05 62.00 Kenya (KE) 15 0.24 33.43 69.30 57.25 Korea, Republic of (KR) 2,044 0.00 47.05 61.01 60.33 Kuwait (KW) 166 17.04 28.05 33.49 35.05

Cayman Islands (KY) 14 2.96 24.33 36.01 31.83

Kazachstan (KZ) 7 0.03 37.08 19.12 21.16 Sri Lanka (LK) 17 0.07 65.84 67.00 47.77 Luxembourg (LU) 98 9.03 51.79 53.42 55.25 Morocco (MA) 46 0.96 32.09 35.88 31.68 Mexico (MX) 719 0.59 38.85 47.91 50.62 Malaysia (MY) 887 2.39 51.05 47.25 50.22 Nigeria (NG) 11 0.03 23.89 16.04 18.04 Netherlands (NL) 808 6.69 76.29 71.18 67.91 Norway (NO) 801 0.87 58.56 55.28 55.42 New Zealand (NZ) 777 4.30 36.97 36.02 38.33 Oman (OM) 91 16.27 22.96 27.80 26.47 Peru (PE) 437 2.93 16.08 27.62 29.51

Philippines, Republic of the (PH) 432 0.15 48.48 44.42 45.41

Pakistan (PK) 74 0.11 9.97 23.59 27.18

Poland (PL) 529 1.91 38.00 41.04 44.50

Portugal (PT) 241 4.22 70.03 69.61 69.80

Qatar (QA) 182 1.98 12.14 16.70 17.96

Romania (RO) 23 0.76 13.30 18.54 29.95

Russion Federation (RU) 601 0.29 46.16 49.48 53.24

Saudi Arabia (SA) 202 1.62 20.74 32.32 30.50

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15 Singapore (SG) 848 4.58 25.29 36.61 58.35 Slovenia (SI) 17 14.43 25.29 36.61 58.35 Thailand (TH) 633 0.26 64.45 56.97 54.90 Turkey (TR) 622 4.26 55.39 59.26 58.71 Taiwan (TW) 2,332 0.24 38.40 53.93 49.80 Uganda (UG) 12 0 . . .

United States (US) 40,438 2.28 47.86 38.96 37.93

Zuid-Afrika (ZA) 2,210 1.32 64.58 56.50 57.71

Zimbabwe (ZW) 6 21.28 30.44 59.18 65.84

The table below, table 4, shows the descriptive statistics by industry. Firms are all classified in groups according to the Standard Industrial Classification (SIC), derived from the ASSET4 database. This table shows the number of observations by industry and the mean of the dependent variable, ROA, and the three main independent variables CSR performance, CER performance and Emission Reduction. Number of observations vary greatly among the different industries, where the agriculture, forestry and fishing category has the lowest number of observations and the manufacturing category has the highest number. Agriculture and transportation are two of the industry categories that contribute highly to environmental pollution and more specifically to air pollution (Abelson, 1992). Agriculture also contributes to water pollution, just like the chemical industry, which is classified under

Manufacturing in the SIC classification. Mining, manufacturing and the oil industry heavily contribute to soil pollution, where the oil industry is classified under Mining, Construction and partly

Transportation in the SIC classification. Therefore, in this thesis, these five industry classifications are marked as pollutive industries and the other four as non- or less pollutive industries. For every independent variable shown in table 4, the average scores of the pollutive industries are above the total average and the average scores of the less pollutive industries are below the total average. The opposite applies to the average return on assets, where the average of ROA for pollutive industries is far below the total average and the average of ROA for less pollutive industries is far above total average.

Table 4: Descriptive Statistics by Industry

SIC Codes Division Observations Mean

ROA Mean CSR Mean CER Mean EMISSION

0100-0999 Agriculture, Forestry and

Fishing 604 2.63 38.78 41.10 46.22 1000-1499 Mining 8,815 -1.11 45.99 38.99 46.13 1500-1799 Construction 3,906 2.60 46.93 52.72 48.48 2000-3999 Manufacturing 38,053 2.28 56.71 61.15 58.56 4000-4999 Transportation, Communications, Electric, Gas and Sanitary service

13,346 3.51 53.91 53.78 56.84

5000-5199 Wholesale Trade 2,723 4.83 45.78 42.05 42.78

5200-5999 Retail Trade 6,294 6.32 50.27 44.18 43.01

6000-6799 Finance, Insurance and Real

Estate

22,659 3.16 43.77 40.31 39.03

7000-8999 Services 14,118 2.26 41.64 34.81 27.17

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4.1.2 Correlations and multicollinearity

Table 5 presents the correlation-matrix for all independent variables observed in this paper. The dependent variable does not exceed the critical level of correlation, which is 0,7, with any

independent variable (Belsey et al. 2005). The correlation between CSR and CER, CSR and EMISSION, CER and EMISSION are exceeding the critical level of 0,7, but these independent variables are all separately examined, so the correlation will not be an issue. The correlation between ROA and ROE is rather high, -0.45, but does not exceed the critical level of 0.7. The assumption of multicollinearity can therefore be neglected.

Table 5: Correlation-Matrix

This table presents the correlation matrix for dependent and independent variables. The final data set consists of 54,175 firm-year-observations from 3,188 firms over the sample period 2002-2018. All variables are winsorized at a 0.01 and 0.99

level to control for outliers. * denote statistical significance at the 1% level.

4.2 Regression Results

This paragraph presents the results of the regression analysis performed in Stata. Three sub paraghraphs, including table 6, table 7 and table 8, show respectively the results of CSR, CER and Emission Reduction performance on firm performance. The original sample included all available information about over 8500 companies in a time period of 17 years, resulting in 141,100 firm-year observations. Due to the limited availability of CSR and CER performance measures, the sample size had to be shortened considerably. After excluding missing values from the sample, the final sample consists of 51,619 firm-year observations.

Unlike most other papers concerning CSR and firm performance, this study uses a sample of

companies all over the world. This includes companies from 68 different countries over a time period of 17 years, from 2002 till 2018. Furthermore, to exclude currency differences, all accounting-based data is collected in US dollars.

4.2.1 CSR performance and firm performance

The following tables present the regression results for all included models. Table 6 provides the results including CSR, table 7 including CER and table 8 including Emission reduction. Model 1 includes all control variables, model 2 shows the regression results including the independent variable CSR, model 3 involves the moderating variable Pollution and finally, model 4 adds the moderation.

ROA CSR CER EMISSION POLLUTION SIZE LEVERAGE LIQUIDITY R&DRATIO

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Table 6: Regression results models (1)-(4)

This table shows the OLS regression results of 4 models including CSR. The Return On Assets is used as dependent variable in all models. Model 1 includes all control variables, model 2 shows the regression results including the independent

variable CSR, model 3 involves the moderating variable Pollution and finally, model 4 adds the moderation. The inclusion of year-fixed effects, industry-fixed effects and country-fixed effects is indicated for every individual model.

Robust standard errors are reported in parentheses and ***, **, * indicate significance at the 1%, 5% and 10% level, respectively.

Model 1 Model 2 Model 3 Model 4

CSR 0.0368*** (0.0017) 0.0368*** (0.0017) 0.0282*** (0.0024) SIZE 1.2204*** (0.0211) -0.1042*** (0.0358) -0.1042*** (0.0358) -0.4627*** (0.0306) LEVERAGE -0.2051*** (0.0312) -0.0727*** (0.0380) -0.0727** (0.0380) -0.1086*** (0.0388) LIQUIDITY 0.0002 (0.0015) 0.0003 (0.0017) 0.0003 (0.0017) 0.0003 (0.0017) R&DRATIO -34.31*** (0.8713) -29.10*** (1.246) -29.10*** (1.246) -26.64*** (1.1463) ROE 0.0025* (0.0014) 0.0027* (0.0016) 0.0027* (0.0016) 0.0035*** (0.0017) Pollution 19.03*** (4.1607) -1.868*** (0.1605) Pollution * CSR 0.0292*** (0.0028) Constant -21.70*** (7.968) 4.762 (9.191) -14.27 (9.986) 10.27 (9.328)

Firm fixed effects Yes Yes Yes Yes

Year fixed effects Yes Yes Yes Yes

Industry fixed effects Yes Yes Yes No

Observations 100,016 51,619 51,619 51.619

Adjusted R-squared 0.1643 0.1692 0.1692 0.1328

Model 1 in table 6 shows the coefficients of the control variables. All control variables are significant except for Liquidity. Size is positively correlated with firm performance, where Leverage shows a negative coefficient, indicating that bigger firms have higher firm performance and firms with higher leverage have lower firm performance. This is in line with the expectations of this thesis. R&D intenstity is also negatively associated with firm performance, implying that when the R&D ratio increases a firm’s performance decreases. Also, ROE has a positive and significant coefficient,

indicating a higher ROA when firms have a higher ROE. This is also in line with the expectations of this thesis. Where Leverage and R&D Ratio remain the same direction in the other models, the Size coefficients become negative in model 2, 3 and 4.

Model 2 includes the main independent variable, CSR performance. The results indicate that firms that increase their CSR performance experience higher firm performance, although the coefficient is rather small (3.68%). This finding, however, is in line with the existing literature, which empirically proved the relationship between CSR performance and firm performance (e.g. Preston &O’Bannon, 1997 Servaes and Tamayo, 2013, Jo & Harjoto, 2011). All control variables are negatively influencing firm performance, except for Liquidity which shows a small and positive effect, but is insignificant. Model 3 includes the dummy variable Pollution, where the coefficient is positive and significant. Firms in pollutive industries have higher firm performance than firms in non or less pollutive

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Model 4 presents the results of the full model, with inclusion of the interaction term. The main independent variable, CSR performance, is smaller than in model 2 and 3, but is still positive and significant which means that firms in non- or less pollutive industries who increase their CSR

performance will also significantly increase firm performance by 2.8%. CSR performance contributes the firm performance to increase more (0.0282+0.0292) in pollutive industries relative to non- or less pollutive industries and the difference in increasing performance for both industries is statistically significant.

These results show strong support for hypotheses 1a and 2a. Hypothesis 1a stated that CSR performance positively affects firm performance, measured by ROA. Hypothesis 2a stated that this relationship was positively moderated by firms operating in pollutive industries. This evidence is found in model 4 of table 6 and therefore both hypotheses can be confirmed.

4.2.2 CER performance and firm performance

The results in table 7 show the regression results to test hypotheses 1b and 2b, which is set up the same as table 6, only the main independent variable in this table is CER performance. The results show a positive and highly significant coefficient for CER performance on ROA. The coefficient in model 2 is smaller than the one of CSR performance in table 6, but still positive, indicating a 1.8% increase in ROA when a firm’s CER performance increases.

In model 4 there is a positive and significant relation between CER performance and firm

performance for firms in non- or less pollutive industries (0.0061). CER performance contributes the firm performance to increase more (0.0061+0.0352) in pollutive industries relative to non- or less pollutive industries and the difference in increasing firm performance for both industries is

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This table shows the OLS regression results of 4 models including CER. The Return On Assets is used as dependent variable in all models. Model 1 includes all control variables, model 2 shows the regression results including the independent

variable CER, model 3 involves the moderating variable Pollution and finally, model 4 adds the moderation. The inclusion of year-fixed effects, industry-fixed effects and country-fixed effects is indicated for every individual model.

Robust standard errors are reported in parentheses and ***, **, * indicate significance at the 1%, 5% and 10% level, respectively.

CER 0.0188*** (0.0017) 0.0188*** (0.0017) 0.0061** (0.0025) SIZE 1.2204*** (0.0211) 0.1154*** (0.0352) 0.1154*** (0.0352) -0.2667*** (0.0302) LEVERAGE -0.2051*** (0.0312) -0.0764** (0.0382) -0.0764** (0.0382) -0.1104*** (0.0389) LIQUIDITY 0.0002 (0.0015) 0.0002 (0.0017) 0.0002 (0.0017) 0.0003 (0.0017) R&DRATIO -34.31*** (0.8713) -28.90*** (1.251) -28.90*** (1.251) -25.82*** (1.154) ROE 0.0025* (0.0014) 0.0027* (0.0016) 0.0027* (0.0016) 0.0036** (0.0017) Pollution 18.99*** (4.175) -1.885*** (0.1559) Pollution * CER 0.0352*** (0.0028) Constant -21.70*** (7.968) -3.513 (6.628) -22.50*** (7.700) 3.250 (6.696)

Observations 100,016 51,620 51,620 51.620

4.2.3 Emission Reduction performance and firm performance

Table 8 presents the results regarding Emission Reduction performances of firms and the impact on ROA. The Emission Reduction coefficients in model 2 and 3 show the lowest numbers up to now, but are still positive and significant. This implies that firms that increase their Emission Reduction commitment will increase firm performance. These results are similar to the results in table 6 and 7, including CSR performance and CER performance respectively, and hypothesis 1c can thereby be confirmed.

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This table shows the OLS regression results of 4 models including EMISSION REDUCTION. The Return On Assets is used as dependent variable in all models. Model 1 includes all control variables, model 2 shows the regression results including the

independent variable EMISSION REDUCTION, model 3 involves the moderating variable Pollution and finally, model 4 adds the moderation. The inclusion of year-fixed effects, industry-fixed effects and country-fixed effects is indicated for every individual model. Robust standard errors are reported in parentheses and ***, **, * indicate significance at the 1%, 5% and

10% level, respectively.

EMISSION 0.0157*** (0.0017) 0.0157*** (0.0017) 0.0032 (0.0026) SIZE 1.2204*** (0.0211) 0.1515*** (0.0349) 0.1515*** (0.0349) -.2110*** (0.0300) LEVERAGE -0.2051*** (0.0312) -0.0773** (0.0382) -0.0773** (0.0382) -0.1187*** (0.0390) LIQUIDITY 0.0002 (0.0015) 0.0003 (0.0017) 0.0003 (0.0017) 0.0003 (0.0018) R&DRATIO -34.31*** (0.8713) -28.70*** (1.251) -28.70*** (1.251) -24.38*** (1.151) ROE 0.0025* (0.0014) 0.0028* (0.0016) 0.0028* (0.0016) 0.0038** (0.0017) Pollution 18.97*** (4.177) -1.484*** (0.1555) Pollution * EMISSION 0.0290*** (0.0029) Constant -21.70*** (7.968) -4.195 (6.630) -23.17*** (7.703) 2.209 (6.707)

Observations 100,016 51,604 51,604 51.604

4.3 Robustness-test

To test the robustness of my previous results, the same OLS regressions were conducted using an alternative dependent variable, the industry-adjusted ROA measures (Cai et al., 2012). According to Campbell (1996) using industry-adjusted ROA measures neutralizes the impact of specific industries on ROA. The industry-adjusted ROA is calculated as the difference between a firm’s ROA and the industry median ROA based on the Standard Industry Classification. The regression models further include the same control variables, country-fixed effects, industry-fixed effects and year-fixed effects in all models. Table 9, 10 and 11 in appendix B report the results of the OLS regression using Industry-Adjusted ROA as the dependent variable.

Consistent with the expectations, the coefficients of CSR, CER and Emission Reduction on firm performance remain positive and hold the same direction. The coefficients of CER and Emission Reduction in model 4 in tables 10 and 11 respectively, become negative and significant. This implies that an increase in CER performance and/or Emission Reduction scores for firms in non- or less pollutive industries will lead to a decrease in firm performance. However, firm performance

significantly increases with CER performance and/or Emission Reduction scores in pollutive industries (-0.0074+0.0361 and -0.0087+0.0333). The difference in increasing or decreasing performance in both industries is statistically significant. Therefore, continued evidence is found that all hypotheses can be confirmed.

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5 Conclusion and limitations

5.1 Conclusion

The results of previous papers that studied the relationship between CER and firm performance have been widely diverse about the exact relationship. Most researchers found a positive and significant relationship between both variables (e.g. Ingram, 1987; Moskowitz, 1975; Anderson & Frankle, 1980) while some others proved the exact opposite or found no evidence and therefore presented an insignificant, negative or no relationship using different proxies for firm performance (Mishra & Suar, 2010; Amaeshi & Amao, 2009). This study contributes to this large body of literature by examining this relationship between social en environmental responsibility performance and the impact on firm performance, measured by ROA.

This study analyzes the impact of CSR performance on firm performance, and more specifically, the impact of the environmental performance on financial firm performance. In addition, the moderating effect of environmental pollutive industries is examined on this relationship. Using a sample of 54,620 firm-year observations from 3,213 firms in 68 countries across the world over the 2002-2018 period, positive and significant coefficients were identified for as well CSR performance, CER

performance and Emission Reduction performance on firm performance, proxied by ROA. From the resource-based perspective, firms that invest more in CSR initiatives and therefore have higher CSR scores will eventually gain more competitive advantage and this will result in higher performance measures.

The research question proposed in this paper is: How does CER performance influences short-term

firm performance and what is the moderating effect of pollutive industries? Therefore, in addition to

the effects of CSR performance on firm performance, the effects of CER performance and emission reduction performance on firm performance are tested as well. To help to answer this research question, a total set of six hypotheses was developed based on the expected effects of social and environmental responsibility on a firm’s performance. Overall this study finds evidence of a positive relationship between CSR and ROA and this is line with other papers (McGuire et al., 1988 Preston & O’Bannon, 1997). Furthermore, hypotheses 1b and 1c, including the effects of CER performance and Emisison Reduction performance, can also be confirmed and therefore firms with better

environmental scores experience higher financial firm performance measured by ROA. Furthermore, the moderating effect of pollutive industries was examined on the three above mentioned relations. The results provided evidence that firm performance significantly increases more after increases in CSR performance, CER performance and Emission Reduction scores for firms active in pollutive industries compared to firms active in non- or less pollutive industries.

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5.2 Limitations

This thesis is subject to several limitations. The dependent variable firm performance is measured by ROA and this is a short-term accounting-based performance measure. The effects of investment in social and environmental responsibility and firms actively reducing emissons may take more time to create financial benefits than the one year that is tested in this research paper. This is because time is required for the cost savings of social and environmental responsibility to be captured as

renegotiation of supply and waste disposal contracts and internal reorganization may be necessary (Hart et al, 1996). However, this thesis examined the short-term effects of CER performance on firm performance and therefore a lag of only one year was taken in the regression models.

Second, CER performance is a very subjective measure and may therefore not always be a accurate reflection of the actual CER score of a company. However, Thomson Reuters ASSET4 database

collects data on a lot of components to develop a score as accurately as possible and these scores are used by many investors as well as researchers and students.

In addition, the measurement of the pollution dummy variable is very subjective and therefore has a higher chance of being biased. In many prior studies all firms in the financial industry (SIC codes 6000-6999) were excluded, because of their regulatory background. However, in this thesis all industries are included and a deliberate distribution is made between pollutive industries and non- or less pollutive industries to examine the moderating effect of these industries.

Following the limitations of this thesis, future research can contribute to the examination of the relationship between CER performance and firm performance by examining the long-term effects on a firm’s performance. Additionally, control variables were limited as only firm-level control variables were included in this thesis. To create more robust results, more country-level characteristics must be taken into account as control variables. However, this thesis did take into account firm-fixed effects to deal with unobserved endogeneity, industry-fixed effects to control for systematic differences in risk and performance across sector types and year-fixed effects to capture the influence of time-series trends.

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