MSc Thesis 2019-2020
Does environmental performance pay off in industrial transportation
businesses? Moderating effects of internationalisation and country choice
Name: Lili Lyubenova Student number: S3967557
Study Programme: MSc International Financial Management Supervisor: Dr. R.O.S. Zaal MBA
Co-Assessor: Dr. S. Homroy Date of submission: 05-06-2020
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-Abstract-
This thesis investigates the impact of environmental performance on firm accounting and market performances for companies in the industrial transportation sector over the period 2008 - 2018. Additionally, moderating effects of firm degree of internationalisation and country governance effectiveness, measured by the World Bank's Worldwide governance indicators (WGI), are examined. The results reveal a statistically significant positive relationship between environmental performance and firm performances, yet more robust to the accounting-based proxy. Against the expectations, the empirical analyses provide no evidence for the moderating effect of the degree of internationalisation, but only a direct negative correlation to firm performances. Contrary to the hypothesis that country's strong governance effectiveness strengthens the relationship between environmental performance and firm performances, analyses demonstrate that companies experience positive returns only when embedded in states with weaker governance effectiveness.
Key words: Corporate environmental performance; Financial performance; Internationalisation
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1. Introduction
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Nowadays, having more and more companies operating cross-borders, it is interesting to examine the internationalisation effect on environmental performance and financial performance relationship. The globalisation process has reached such a stage where physical boundaries are no longer an obstacle neither for humans nor enterprises. Enterprises that operate in foreign markets face some entry barriers and need to comply with local regulations, which could result in the development or adjustment of their environmental strategy. Moreover, firms extending their market reach abroad, face the liabilities of foreignness (Zaheer, 1995). In the international context, companies are exposed to different cultures and stakeholders' priorities and need to gain legitimacy to reach new partners (Dowling and Pfeffer, 1975). For instance, what is considered as standard in Switzerland, the country with the highest EPI (environmental performance index) in 2018 based on a report produced by Yale and Columbia universities in collaboration with the World Economic Forum, is extraordinary in some less developed countries. Therefore, it is relevant to study whether reaching to new foreign markets, referred to as internationalisation, moderate somehow the impact of firms' environmental performance on financial outcomes. This paper is doing so by having a firm-level continuous moderator, namely the degree of internationalisation conducted by the portion of foreign sales.
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impact of countries' characteristic on firm ESG performance (Baldini, Dal Maso, Liberatore, Mazzi, and Terzani, 2018; Cai, Pan, and Statman, 2016). In this study, the focus is on the moderating effect that country governance index has on the relation between firm environmental performance and financial outcomes. Similar to the work of De Villiers and Marques (2016) such comparison is carried by looking at the country worldwide governance indicators (hereafter: WGI) provided by the World Bank Group and adds to the literature of countries influence over environmental performance economic values.
Although the industrial transportation sector is highly related to environmental issues, due to the produced emissions, there is a deficit of empirical studies on the connection between the financial and environment performance of those companies. Therefore, with a globally diversified sample, this study addresses the question of whether engagement in environmental performance brings economic value to the firms in the industrial transportation sector. It contributes to the continuous academic debate upon sustainable companies' added value from their eco-friendly policies. By focusing on the internationalisation level of the firm, this study aims to capture the effect of having sales in foreign countries, referring to the legitimacy theory. Both developed and developing countries are included in the sample, allowing to examine the influence of the country choice. Hence, the study empirically contributes to the literature on the question of how country governance affects the environmental score influence on financial performance.
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2. Theoretical background and hypotheses development
Khan, Zhang, Anees, Golpîra, Lahmar, and Qianli (2018) point out that nowadays, society is seeking sustainable goods and lean production. In consequence, governments worldwide promote and amend already existing environmental policies. This process started back in the second half of 20 century highly influenced by the intensive after-war industrial development. In 2014 the European Union passed a law requiring public member-states companies with over 500 employees to report their social and environmental performance (Directive 2014/95/EU). Such mandatory regulations promote transparency and have a significant impact on the development of a culture of non-financial disclosures. According to the UN Environmental report from the beginning of 2019, 176 countries in the world have passed environmental framework laws (United Nations Environment Programme, 2019). However, the report raises concerns about the practical implementation of those laws and emphasise on the importance of regulatory bodies.
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review of major incidents from the second half of the 20th century (e.g. Nuclear reactor explosion, Chernobyl in 1986; Exxon Valdez oil spill, US in 1989) that resulted in increased social pressure and raised the awareness mainly towards environmental matters (Kolk, 2016).
2.1. Environmental performance and its impact on financial performance
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connection with environmental studies contributing the most. Another meta-analysis from Endrikat, Guenther, and Hoppe (2014) concludes a positive relationship between corporate environmental performance and financial outcomes. A succeeding meta-study conducted by Hang, Geyer‐Klingeberg, and Rathgeber (2019) provides evidence for a long-term trade-off between environmental performance and financial performance. There are still papers on the topic that find non-significant results neither for the combined ESG score not for its separate components (Atan, Alam, Said, and Zamri, 2018). However, the authors have a sample of only Malaysian listed firms, and the results are not to be generalised. Another paper by Jacobs, Singhal, and Subramanian (2010) provides mixed results with some of the environmental practices being non-significant, while others positively or negatively correlated to the stock market value of the firms in question.
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financial outcomes favourably in the long run through sales increase. That is in line with the notion that customers seek eco-friendly corporations and green products. Such reasoning is also relevant in business to business (B2B) relations, namely firms looking for third-party logistics service providers are willing to pay more to such companies recognised as sustainable. This way, the former can associate themselves with the excellent company image of the latter and gain dividends indirectly via the partnership. As a result, logistic firms can enjoy easier access not only to financing but also to new customers and even markets. There are also indications of how environmental dedication can strengthen financial performance internally. Previous researches find that employers adhered to CSP and in particular environment performance drawn the attention of the young and educated individuals (Behrend, Baker, and Thompson, 2009; Lin, Tsai, Joe, and Chiu, 2012; Bohlmann, Krumbholz, and Zacher, 2018). Klimkiewicz and Oltra (2017) further argue that it is remarkably accurate for millennials, who are more "green" orientated. Therefore, sustainability can also be seen as a managerial strategy to make the industrial transportation industry more attractive for highly skilled job seekers for the higher the commitment of the employees, the better the bottom line.
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Blancard and Petit, 2019). Hence, the risk of disclosing untruthful information may not be worth taking.
Based on the above arguments and the overall positive findings for value-adding environmental performance both internally and externally, the following hypothesis is proposed:
Hypothesis 1: Companies in the industrial transportation sector that score higher on the environmental pillar have better financial performance.
2.2. Moderating effect of internationalisation level
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for more environmental disclosures to gain legitimacy. Few papers in the extant literature study the level of internationalisation and its influence on sustainable performance and financial results. Duque-Grisales and Aguilera-Caracuel (2019) hypothesise that the environmental score for multinational companies is even more influential for financial performance. The authors study the moderating effect of geographic diversification for Latin America multinationals and conclude that global presence significantly and positively impacts the connection between their financial performance proxy - ROA and both overall ESG score, as well as environmental and governance scores separately. Controversially, Isaksson and Woodside (2016) fail to support a hypothesis that internationalisation level is a condition related to subsequent high CSP and financial performance. However, their sample consists of only Swedish multinationals, so no comparison is made with fully domestic companies, and the authors point out that this limitation could drive the results. In both papers, the authors implement the level of internationalisation in the context of purely multinational companies. Ellimäki, Gómez-Bolaños, Hurtado-Torres, and Aragón-Correa (2019) study the energy and utility sectors and find that firms disclose more environmental reports when internationalising their business. However, in their study disclosures are not followed by increased environmental performance. Upon these reasonings and the connection with legitimacy theory, the relationship between environmental score and financial performance is expected to be more pronounced with the increasing degree of internationalisation. Subsequently, the following hypothesis is proposed:
Hypothesis 2a: The relationship between environmental score and financial performance is strengthened by a higher degree of internationalisation.
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2019). The term decoupling in the sense of corporate social performance refers to a gap between the company's policies and practices (Graafland and Smid, 2019; Meyer and Rowan, 1977). Decoupling is also defined as "window dressing" (Weaver, Trevino, and Cochran, 1999) and cast doubts about firms' actual engagement in environmentally friendly processes. In their study Aragón-Correa, Marcus, and Hurtado-Torres (2016) find that multinationals can simultaneously be more environmentally responsible on paper, but in reality to have even worse environmental performance than their less international counterparts. Tashman, Marano, and Kostova (2019) arrive at the conclusion that reports manipulating influences negatively the legitimacy of multinationals. In a situation where investors have suspicions about the truthfulness of the disclosures, or the company has a history of CSP related scandals, the additional disclosures could be seen as management manoeuvres and, therefore, have even negative effect on the overall firm performance. Such misleading practices are also known in the literature as "greenwashing", first introduced by the American environmentalist Jay Westerveld in 1986 (Koh, Ghazoul, Butler, Laurance, Sodhi, Mateo-Vega, and Bradshaw, 2010) and nowadays used as a term for false disclosures (Becker-Olsen and Potucek, 2013). Furthermore, environmental disclosure is voluntarily done, but due to multinationals' complexity and diversification (Aabo, Pantzalis, and Park, 2015), it is often hard to be connected to real implementation (Ellimäki, Gómez-Bolaños, Hurtado-Torres, and Aragón-Correa, 2019). This opacity could result in limiting the overall positive effects of environmental performance on firm legitimacy and therefore, financial performance. Correspondingly, an alternative hypothesis about the moderating effect of the degree of internationalisation on the main relationship is proposed:
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2.3. Moderating effect on countries characteristics
This paper studies the effect of specific country characteristics on the link between firm environmental score and financial performance. Whether and how country differences can moderate this connection is vital as it influences companies' management and political decision making. For this purpose, the study utilises governance ratings provided by the World Bank Group. The evaluation of the country's governance strength is divided into six groups called worldwide governance indicators (hereafter: WGI) - control of corruption, political stability and absence of violence, governance effectiveness, regulatory quality, rule of law, and voice and accountability. Extending the work of De Villiers and Marques (2016), who examine three out of the six WGIs, this thesis focuses on the whole six-dimension model by generating an average WGI index per country. The contribution to this stream of the literature is by providing cross-country examination and further analyse the country moderating effects on the firm environmental score and financial performance, specifically for the industrial transportation sector.
Scholars provide evidence of how different countries aspects impact firms' CSP engagement (Baldini, Dal Maso, Liberatore, Mazzi, and Terzani, 2018; Cai, Pan, and Statman, 2016; Di Giuli and Kostovetsky, 2014; Ioannou and Serafeim, 2012). Cai, Pan, and Statman (2016) conclude that country factors are much more critical in explaining variation in corporate social performance than firm characteristics. Therefore, it is interesting to examine if the country governance index also moderates the relationship between firms' environmental and financial performances. Few articles previously focused on WGI and other countries' variables to test their moderating effects upon the relationship between companies' sustainable performance and a proxy for financial performance, and respective findings are discussed further in this section.
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Control of corruption dimension deals with the measurements implemented by the government to fight against the misuse of power. These include bribery and suspicious transactions among other corrupt practices in public and private sectors for individual gains. In states where it is cheaper to falsify disclosures in order to build a green corporate image, people and business partners would be suspicious about companies' ethical intentions. Extensive corruption practices will raise concerns, and stakeholders will not reward environmentally friendly business because of their doubts. Alternatively, a higher level of corruption control should mitigate common prejudices and strengthen the economic benefits to enterprises in the transportation sector. Cai, Pan, and Statman (2016) find that managers manipulate CSR reports instead of putting them into practice in countries where corruption is widespread. Likewise, Ioannou and Serafeim (2012) argue that the level of corruption is the most influential variable explaining corporate social performance. The authors observe higher sustainable performance in countries with less corruption, also in financial terms, firms profit less from their ethical practices in more corrupted states.
2.3.2. Political stability and absence of violence/terrorism
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that strong political stability foster the environmental performance of the respective country. Political volatility increases the possibility of nationalisation, which, on the other hand, is associated with higher investment risk. The latter El Ghoul, Guedhami, and Kim (2017) argue that may cause the forgo of investments. Since green investments are often considered as not essential, it is likely that managers abstain them in more politically unstable states. Another article by Cai, Pan, and Statman (2016) find that environmental performance is weaker in countries with tenuous political rights.
2.3.3. Government effectiveness
Worldwide governance indicators provide a ranking of government effectiveness based on public institutional quality and society satisfaction with the overall standard of living. In highly effective governments, it is more likely that respective institutions perform constant scrutinising on companies' reports (Campbell, 2007). In contrast, if there is a high level of bureaucracy, such investigations are hardly performed on time. Regular screening would ensure stakeholders that the disclosed information is accurate. The latter is in line with Sun and Zhang (2019), who claim that government effectiveness is crucial in the fight against greenwashing. What is more, individuals in less governmentally developed countries would be more concerned with the quality of vital public services, such as the healthcare system, education and so for, but not with a sustainable way of doing business. De Villiers and Marques (2016) study the moderating effect of government effectiveness on the relationship between CSP disclosures and the firm's share price. The authors provide evidence than state's government quality enhances the economic benefits from sustainability.
2.3.4. Regulatory quality
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quality indicates that there is more freedom for private business and fewer entry barriers for investors. These economic conditions imply that the government permits peer's competition, which urges companies to seek unique resources via CSP in order to gain competitive advantages (El Ghoul, Guedhami, and Kim, 2017). This is in line with RBV theory, where unique resources are valuable and main drivers of company performance. A more robust environmental performance, especially in a non-green industry such as the logistics, could be outstanding and provide the corresponding company with better financial performance. Comparable to the government effectiveness results, De Villiers and Marques (2016) conclude that a high level of regulatory quality moderate positively the correlation between CSP disclosure and share price. Further, Sun and Zhang (2019) provide evidence that rigorous government policies incentivise companies to act responsibly.
2.3.5. Rule of law
The rule of law captures the enforceability of contracts, efficiency of the legal system and the overall degree of security. Again from RBV theory perspective, it is reasonable to argue that if the government ensures the protection of intellectual property rights, enterprises will be more disposed to invest in environmentally friendly know-how and employ such practices in business as usual. Economically speaking, in states with better scoring financing is more accessible since creditors are protected, which further reduces the cost of capital for the business (El Ghoul, Guedhami, and Kim, 2017). Managers are willing to undertake new partnerships as the probability of expropriation is weaker due to the sound judicial system and property rights protection. Besides, De Villiers and Marques (2016) observe that companies extend their CSP reporting in states with higher investor protection and further experience higher financial returns.
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Voice and accountability indicator accounts the level of democracy in the country measured by civil liberties, press freedom and respect of human rights, among others. In states with more robust civil liberties, individuals have a tribune to express their concerns about the environment and rise demands towards the government and the business. De Villiers and Marques (2016) argue that democratic countries enhance the positive financial outcome of sustainability in line with the view that democratic rights will increase stakeholders' demand for improved quality of life. As a result, society will recognise environmental engagement, notably for harmful industries. Cai, Pan, and Statman (2016) support the notion that states with robust civil liberties encourage companies to act in an environmentally responsible way. A study conducted by Di Giuli and Kostovetsky (2014) focuses on US public firms and show that companies score higher at CSP when the CEO is democratic, or they are incorporated in a democratic state. The authors claim that if the company is embedded in a democratically orientated environment, stakeholders will demand more sustainable performance, yet, in the end, firms experience a decline in the value. Controversially, Aouadu and Marsat (2018) in their cross-country analysis, find that the positive effect of CSP on Tobin's Q is more pronounced in countries with higher press freedom.
Based on the above discussion and relying on the RBV, the expectations are for all six dimensions to moderate positively the relationship between environmental performance and financial performance positively. Therefore, the following hypothesis is proposed:
Hypothesis 3a: The relationship between environmental score and financial performance is strengthened by a higher level of WGI_index.
3. Data and methodology
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World Bank Group for countries' WGI. Thomson Reuters' DataStream provides financial information for a variety of companies worldwide nicely grouped in various business sectors. That allowed the sample of this study to include only firms classified under the industrial transportation sector, which was chosen because of its sensitivity to environmental issues. The sector constitutes of five subsectors, namely delivery services, marine transportation, railroads, transportation services, and trucking. The source of environmental performance data is Thomson Reuters' Asset 4. Even though the database is one of the most comprehensive (Shaukat, Qiu, and Trojanowski, 2016), many observations had to be excluded due to missing environmental score. Fortunately, this was not a problem with the county variable, since the World Bank Group provides governance indicators (WGI) for over 200 countries in the world. After adjusting the original dataset according to the data limitations, the final sample consists of 652 unique companies in 34 different countries. The panel data is unbalanced and in the time frame 2008-2018. Further, all countries with less than five observation were excluded, which let to 4399 firm-year observations.
The remaining of this chapter gives more precise information about all used variables, details about the empirical model and data analyses.
3.1. Dependent variables
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However, some researchers argue that accounting ratios do not capture the actual financial performance because of their historical nature and propensity to manipulation by the management (Agarwal and Taffler, 2008). Some of the market-based proxies for financial performance are stock return, market-to-book ratio, and Tobin's Q (Aouadi and Marsat, 2018; Hasan, Kobeissi, Liu, and Wang, 2018; Angulo-Ruiz, Donthu, Prior, and Rialp, 2018). These variables focus more on long-term horizons but are often criticised for being impacted by factors independent of firms' activities (Platonova, Asutay, Dixon, and Mohammad, 2018). So, advocates of accounting and market methods have contradicting views on which is the best way to assess the performance. Because the sample includes firms from different countries, I need to consider the potential discrepancies arising from different legal systems and financing methods that make one method or the other more suitable for the measurement. Therefore, following Cornett, Erhemjamts, and Tehranian (2016) and Zhao and Murrell (2016), who replicate one of the most cited studies in the filed by Waddock and Graves (1997), I will examine simultaneously one accounting and one market variable. The dependent variables are firms' return on assets (hereafter: ROA) and Tobin's Q. This approach will make it possible to distinguish if there are any differences in correlations and magnitudes of the two dependent variables. ROA (DataStream code: WC08326) is calculated as
ROA = Net income/Book value of assets, (1) where the results are presented in percentage term.
Tobin`s Q is calculates as
Tobin's Q= (Total assets – Book value of equity + Market capitalization)/Total assets, (2)
where the respective DataStream codes are as follows: Total assets: WC02999, Book value of equity: WC3501, Market capitalization: WC08001.
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There is no universal way to measure neither firm responsibility level nor financial performance. Researchers use rating agency indexes such as Kinder, Lydenberg, Domini, and Company (KLD) social index (Petrenko, Aime, Ridge, and Hill, 2016; Chen, Guo, Hsiao, and Chen, 2018; Col and Patel, 2019), succeeded by MSCI ESG STATS (Burke, Hoitash, and Hoitash, 2019; Bouslah, Kryzanowski, and M'Zali, 2018; Cai, Pan, and Statman, 2016), Thomson Reuters' Asset4 environmental, social, and governance (hereafter: ESG) score (Duque‑Grisales and Aguilera‑Caracuel, 2019; Aouadi and Marsat, 2018), or carry own CSP analysis (Platonova, Asutay, Dixon, and Mohammad, 2018) for the studies. Shaukat, Qiu, and Trojanowski (2016) argue that Thomson Reuters Asset4 dataset presents extensive information for enterprises environmental, social and governance (ESG) business behaviour. After Thomson Reuters acquired the former Swiss Asset4 agency in 2009, the scope of markets is continuously extended. The methodology of ranking provides transparent and complete data, collected and analysed from various sources such as companies' CSR reports, companies and NGO's websites, and the news, among others (Thomson Reuters Eikon, 2020). The database provides an environmental score pillar derived from 61 measures clustered in three sub-categories: resource use (35% weight), emission (35% weight) and innovation (29% weight). The ranking between 0 and 100 captures to what extent the firm is orientated towards eco-friendly resources and operations to reduce its environmental burden. Companies with a lower score have weaker environmental performance and induce a more negative effect on the ecosystem. Observations with no environmental score data were excluded from the sample.
ENV= 0.35 x Resource use + 0.35 x Emission + 0.29 x Innovation (3) 3.3. Moderating variables
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total reported sales (DataStream code: WC08731). Therefore, genuinely domestic companies have 0% foreign sales, whereas firms operating entirely abroad have 100% foreign sales.
INT = Foreign sales/Total sales (%) (4)
Worldwide governance indicators. The country-level characteristic that this study considers is the state government development. For the purpose, the World Bank database is used, which presents the governance in six dimensions, discussed in Chapter 2.3. Each dimension varies between -2.5 and 2.5, presenting respectively weak and strong governance performance. The research dataset is produced by Daniel Kaufmann and Aart Kraay. The aggregate indicators are summarised based on numerous data sources - surveys in the public and private sectors, NGOs, public organisations, market information providers and others (Kaufmann, Kraay,and Mastruzzi, 2011). This broad input assures that the data is comprehensive and not manipulated by the states for some political gains. Following Pinkowitz, Stulz, and Williamson (2016), I construct an aggregate governance index taking the equal-weighted average value of all six indicators for the particular year and country. Therefore, WGI_index variable is calculated as
WGI_index = (Voice and accountability + Political stability + Government effectiveness + Regulatory quality + Rule of law + Control of corruption)/6 (5)
3.4. Control variables
Control variables are essential in order for the empirical model to have higher explanatory power and to lower the impact of unobserved variables. This thesis includes some control variables, which are found relevant in the extant literature.
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with a larger size (Duque-Grisales and Aguilera-Caracuel, 2019; Reverte, Gomez-Melero, and Cegarra-Navarro, 2016), which positively affects firm profitability. However, empirical studies that support both sides can be found. Following El Ghoul, Guedhami, and Kim (2017) and many others, the natural logarithm of total assets in millions of USD is used to control for firm size.
SIZE = Log(Total assets in Mil $) (6)
The capital structure of the company is included in the empirical model, as it is a measure for financial risk (Callan and Thomas, 2009). Highly leveraged firms are more likely to face financial distress and bear from its direct and indirect costs (Hackbarth, 2009; Myers, 1977). Lee, Cin, and Lee (2016) claim that debt repayments force the firms to forgo new investments, which further harms the bottom line. Many articles have found a negative relationship between leverage and firm financial performance (Aouadi and Marsat, 2018; El Ghoul, Guedhami, and Kim, 2017; Lee, Cin, and Lee, 2016; Jiao, 2010). On the other hand, the debt level is found to be a determinant of the CSR performance (Baldini, Dal Maso, Liberatore, Mazzi, and Terzani, 2018; Cai, Pan, Statman, 2016), as leveraged firm tend to disclose more to reduce the agency cost (Harjoto and Jo, 2011; Reverte, 2009; Michael and William, 1976). Therefore, my expectations are leverage to be positively correlated with environmental performance and negatively correlated with firm performance. Leverage is calculated as the ratio of total debt (DataStream code: WC03255) to total assets in percentage term.
LEV = Total debt/Total assets (%) (7)
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upcoming year. Capital expenditure (CAPEX; DataStream code: WC08416) shows expenses on equipment as per cent of total assets.
CAPEX = Capital expenditures/Total Assets (%) (8)
Table1 summarizes the variables that are used in the model with their description and
corresponding sources.
Table 1. Variables description and respective sources.
Variables Description Sources
Tobin's Q (Total assets – Book value of equity + Market capitalization)/Total assets
Thomson Reuters' DataStream
ROA Net income/Book value of assets (%) Thomson Reuters' DataStream ENV Environmental performance (0-100) Thomson Reuters' Asset 4 SIZE Natural logarithm of total assets in Mil $ Thomson Reuters' DataStream LEV Total debt (%) of total assets Thomson Reuters' DataStream CAPEX Capital expenditures (%) of total assets Thomson Reuters' DataStream INT Foreign sales to total sales ratio (%) Thomson Reuters' DataStream WGI_index Average score of all 6 governance indicators The World bank group
3.5. Empirical model
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extreme values. Equation (9) represents the full model, however, I perform a step-wise approach where moderating variables and interaction variables are added incrementally. Following the standard practice in empirical financial studies, I use one year lagged (t-1) independent variables to control for endogeneity issues (Nuber, Velte, and Hörisch, 2020; Stellner, Klein, and Zwergel, 2015).
𝑅𝑂𝐴𝑖𝑡(𝑇𝑜𝑏𝑖𝑛′𝑠 𝑄𝑖𝑡) = 𝛽0+ 𝛽1𝐸𝑁𝑉𝑖𝑡−1+ 𝛽2𝑆𝐼𝑍𝐸𝑖𝑡−1+ 𝛽3𝐿𝐸𝑉𝑖𝑡−1+ 𝛽4𝐶𝐴𝑃𝐸𝑋𝑖𝑡−1+ 𝛽5𝐼𝑁𝑇𝑖𝑡−1+ 𝛽6𝐼𝑁𝑇𝑖𝑡−1∗ 𝐸𝑁𝑉𝑖𝑡−1+ 𝛽7𝑊𝐺𝐼_𝑖𝑛𝑑𝑒𝑥𝑐𝑡+ 𝛽8𝑊𝐺𝐼_𝑖𝑛𝑑𝑒𝑥𝑐𝑡∗ 𝐸𝑁𝑉𝑖𝑡−1+
∑ 𝛽𝑐𝐹𝐸 + ∑ 𝛽𝑡𝐹𝐸 + 𝜀𝑖𝑡, (9)
Where i, t, and c denote company, year, and country, respectively; ROA (Tobin's Q) is a proxy for firm performance for company i in year t; ENV is environmental score for company i in year t-1; SIZE is total assets of company i in year t-1; LEV is s proxy for liquidity risk for company i in year t-1; CAPEX represents capital intensity of company i in year t-1; INT captures the internationalisation degree of company i in year t-1; the interaction between INT and ENV clusters international companies with high environmental performance; WGI_index is the combined index of all six dimension provided by The World Bank Group for country c in year t; the interaction between WGI_index and ENV clusters companies with high environmental performance incorporated in countries with high governance indicators; ∑ 𝛽𝑐𝐹𝐸 𝑎𝑛𝑑 ∑ 𝛽𝑡𝐹𝐸 control for country and year fixed effects, respectively; and 𝜀 is an error
term.
3.6. Summary statistics
Table 2 reports the number of observations per country, excluding those with less than five
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countries have positive indicators, meaning that overall, their governance indexes are above the average. The country with the highest index is New Zealand (1.825), followed by Switzerland (1.762) and Norway (1.772). Whereas, the state with the lowest WGI index is China (-0.455), driven by its government nature. It can be observed that the sample is not evenly distributed across countries, with The United States accounting for roughly 17.4% of all observations. Table 2. Summary statistics. This table present the mean of country-level variables WGI_index across countries and the respective number of observations. WGI_index is the average WGI score for a country computed as an average of all six governance indicators provided by The World Bank Group for the period 2008-2018. The index varies in the range (-2.5 - (-2.5).
Country Mean
WGI_index Obs. Country
Mean
WGI_index Obs.
Australia 1.584 215 Mexico -0.287 61
Austria 1.509 85 Netherlands 1.675 99
Belgium 1.254 54 New Zealand 1.825 71
Brazil -0.069 47 Norway 1.772 61
Canada 1.634 182 Philippines -0.304 14
Chile 0.969 7 Portugal 0.998 8
China -0.455 385 Singapore 1.560 196
Denmark 1.749 194 South Africa 0.189 59
France 1.165 137 South Korea 0.778 86
Germany 1.480 155 Spain 0.844 167
Hong Kong 1.448 344 Switzerland 1.762 65
India -0.212 17 Taiwan 1.052 48
Indonesia -0.240 20 Thailand -0.293 31
Italy 0.507 50 Turkey -0.183 9
Japan 1.315 603 United Arab Emirates 0.585 40
Kuwait -0.164 9 United Kingdom 1.400 91
Malaysia 0.356 25 United States 1.253 764
3.7. Descriptive statistics
Table 3 provides descriptive statistics of the variables used in the model. What can be
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company is undervalued and oppositely - a ratio above 1 shows that the market value of the company is higher than its book value of assets. The mean of Tobin's Q is 1.478, meaning that on average firms in the sample are overvalued by the market. The main variable of interest, environmental performance (ENV), is distributed from 0 to 94, out of 100. However, the median of 39.63 shows that most of the companies have a score below the average of 50. SIZE represents the book value of assets in millions $. Leverage (LEV) and capital expenditures (CAPEX) are presented as a per cent of firms' total assets and are on average, 34% and 6%, respectively. Variable INT captures the degree of internationalisation, and as seen from Table
3, the sample consists of both entirely domestic and purely international companies.
WGI_index, as discussed in the previous subsection, shows that a greater part of the sample presents companies incorporated in states with higher than average governance indicators. Table 3. Descriptive statistics. This table provides descriptive statistics of the variables used in the model over the sample period of 2008 to 2018. Abbreviations are as followed: ROA (return on assets) is net income as a % of total assets; Tobin's Q is calculated as (Total assets – Book value of equity + Market capitalization)/Total assets; ENV is a firm’s environmental performance score; SIZE is total assets in million $; LEV is the total debt as a % of total assets; CAPEX is the capital expenditure as a % of total assets; INT is the foreign sales as a % of total sales; WGI_index is the average WGI score for a country computed as an average of all six governance indicators. Firm-level independent variables (ENV, SIZE, LEV, CAPEX, and INT) are lagged by one year, to control for endogeneity.
N Mean Median min Max St.Dev
ROA 4399 5.230 5.080 -11.700 19.700 5.084
Tobin's Q 4399 1.478 1.240 0.458 4.034 0.704
ENV 4399 38.916 39.630 0 94.233 27.712
SIZE (in mil $) 4399 14.293 7.734 0.172 73.237 16.120
LEV 4399 33.865 32.327 0 91.286 17.843
CAPEX 4399 6.285 4.980 0 27.230 5.263
INT 4399 31.047 21.620 0 100 33.168
WGI_index 4399 1.110 1.315 -0.576 1.862 0.658
3.8. Correlation matrix
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ENV, is also positively correlated to both. However, the correlation is indicated as significant only for the accounting measurement (ROA). The highest correlation (0.513) is observed within the control variable Log(SIZE) and ENV, however, this is in line with previous findings on the topic. Firm-level moderating variable, INT, is positively correlated to ENV but negatively to both Tobin's Q and ROA. These relations are indicators that firms which internalise their business simultaneously engage more in environmental performance but experience a negative impact on financial performance. Interestingly, WGI_index is significantly positively correlated with the accounting proxy but not significantly correlated with Tobin's Q. Therefore, the assumptions are for the strength of governance to have a more robust impact on ROA. Table 4. Correlation matrix. This table shows the correlation between variables used in the model over the sample period of 2008 to 2018. Abbreviations are as followed: ROA (return on assets) is net income as a % of total assets; Tobin's Q is calculated as (Total assets – Book value of equity + Market capitalization)/Total assets; ENV is a firm’s environmental performance score; SIZE is total assets in million $; LEV is the total debt as a % of total assets; CAPEX is the capital expenditure as a % of total assets; INT is the foreign sales as a % of total sales; WGI_index is the average WGI score for a country computed as an average of all six governance indicators. Firm-level independent variables (ENV, SIZE, LEV, CAPEX, and INT) are lagged by one year, to control for endogeneity. * shows significance at the .01 level.
Variables (1) (2) (3) (4) (5) (6) (7) (8) (1) Tobin's Q 1.000 (2) ROA 0.647* 1.000 (3) ENV 0.016 0.042* 1.000 (4) Log(SIZE) -0.196* -0.132* 0.513* 1.000 (5) LEV -0.338* -0.321* 0.080* 0.269* 1.000 (6) CAPEX -0.032 -0.066* -0.112* -0.023 0.015 1.000 (7) INT -0.124* -0.132* 0.041* 0.114* -0.052* -0.060* 1.000 (8) WGI_index 0.016 0.055* 0.124* -0.041* -0.192* 0.050* 0.033 1.000 4. Findings
29
for the calculation of leverage, were winsorised at 1 and 99%., while total assets, capital expenditures and Tobin's Q were winsorised only at 99%. Then, all continuous independent firm-level variables were lagged by one year to control for endogeneity issues. Further, I conducted a Hausman test to analyse whether random effects or fixed effects method is more efficient for empirical testing. The results are presented in Appendix A and show a significant difference, so the null hypothesis stating that the random effects estimator is a more appropriate method was rejected. Hence, I accepted the alternative hypothesis that the fixed effect estimator should be used. Further, the Breusch-Pagan test for heteroskedasticity was conducted. Results are presented in Appendix B and show that the same variance assumption does not hold. Therefore, to manage heteroskedasticity and autocorrelation, I imply robust standard errors clustered at a company level.
Table 5 reports the results with dependent variable ROA. Model (1) shows outcomes from
30
(Evangelista, Colicchia, and Creazza, 2017), which results in superior financial performance (Flammer, 2013).
Table 5. Regression analysis using ROA. This table displays the results of the pooled OLS regressions with dependent variable ROA (return on assets). Abbreviations are as followed: ROA (return on assets) is net income as a % of total assets; ENV is a firm’s environmental performance score; SIZE is total assets in million $; LEV is the total debt as a % of total assets; CAPEX is the capital expenditure as a % of total assets; INT is the foreign sales as a % of total sales; WGI_index is the average WGI score for a country computed as an average of all six governance indicators. Firm-level independent variables (ENV, SIZE, LEV, CAPEX, and INT) are lagged by one year to control for endogeneity. Extreme variables are winsorized to mitigate the effects of outliers. Models (2), (3), (4), (5) and (6) report results with year and country fixed effects. Robust standard errors clustered at firm-level are reported in parentheses. ***, **, and * show significance at the .01, .05, and 0.10 level, respectively.
Dependent variable: ROA
(1) (2) (3) (4) (5) (6) ENV 0.022*** 0.002 0.009* 0.014* 0.039*** 0.042*** [0.006] [0.005] [0.005] [0.007] [0.009] [0.010] SIZE -0.483*** -0.304* -0.204 -0.233 -0.138 [0.144] [0.174] [0.168] [0.172] [0.167] LEV -0.085*** -0.072*** -0.075*** -0.073*** -0.077*** [0.008] [0.008] [0.008] [0.008] [0.008] CAPEX -0.049* -0.061** -0.066** -0.056** -0.061** [0.028] [0.027] [0.027] [0.027] [0.027] INT -0.016** -0.016** [0.007] [0.006] INT*ENV -0.000 -0.000 [0.000] [0.000] WGI_index 4.661*** 4.095*** [1.211] [1.209] WGI_index*ENV -0.026*** -0.025*** [0.007] [0.006] Constant 8.573*** 5.023*** 8.241*** 8.535*** 1.000 2.187 [0.544] [0.563] [0.777] [0.766] [2.197] [2.149]
Year F.E. No Yes Yes Yes Yes Yes
Country F.E. No Yes Yes Yes Yes Yes
R-squared 0.119 0.242 0.297 0.311 0.304 0.317
Adj. R-squared 0.119 0.235 0.290 0.303 0.296 0.309
Observations 4399 4399 4399 4399 4399 4399
31
interaction term of international companies with strong environmental performance is not significant. Hence, no conclusions can be made about its impact on the accounting-based measurement for financial performance. Exploring the country constructed variable WGI_index shows that the state's level of governance effectiveness is indeed a determinant of logistics firms' profitability. The coefficient of WGI_index (β7) in Models (5) and (6) indicates positive and highly statistically significant results at 1% level. Surprisingly, the interaction term of the country's governance index and level of environmental performance is with a negative sign. The overall impact of both ENV and WGI_index on ROA is still positive. However, higher environmental performance in states with strong governance weakens the positive correlations to ROA. So, companies operating in countries with weaker WGI_index (β1 from columns 5 and
6) benefit more from their environmental politics as opposed to firms in countries with strong governance index (β8 from columns 5 and 6). Those findings are in contradiction with
Hypothesis 3, so the influence of country-level moderator is further investigated in the robustness test chapter.
32
performance of industrial transportation companies is not strong enough, and no conclusions can be mode upon these results. Therefore, hypotheses 2a/b and 3 are nor supported.
Table 6. Regression analysis using Tobin’s Q. This table displays the results of the pooled OLS regressions with dependent variable Tobin's Q. Abbreviations are as followed: Tobin's Q is calculated as (Total assets – Book value of equity + Market capitalization)/Total assets; ENV is a firm’s environmental performance score; SIZE is total assets in million $; LEV is the total debt as a % of total assets; CAPEX is the capital expenditure as a % of total assets; INT is the foreign sales as a % of total sales; WGI_index is the average WGI score for a country computed as an average of all six governance indicators. Firm-level independent variables (ENV, SIZE, LEV, CAPEX, and INT) are lagged by one year to control for endogeneity. Extreme variables are winsorized to mitigate the effects of outliers. Models (2), (3), (4), (5) and (6) report results with year and country fixed effects. Robust standard errors clustered at firm-level are reported in parentheses. ***, **, and * show significance at the .01, .05, and 0.10 level, respectively.
Dependent variable: Tobin's Q
(1) (2) (3) (4) (5) (6) ENV 0.003*** -0.000 0.003*** 0.003** 0.002 0.002 [0.001] [0.001] [0.001] [0.001] [0.001] [0.002] SIZE -0.109*** -0.115*** -0.114*** -0.117*** -0.116*** [0.023] [0.026] [0.027] [0.027] [0.027] LEV -0.012*** -0.008*** -0.008*** -0.008*** -0.008*** [0.001] [0.001] [0.001] [0.001] [0.001] CAPEX -0.002 -0.007 -0.007 -0.008 -0.008 [0.005] [0.005] [0.005] [0.005] [0.005] INT 0.000 0.000 [0.001] [0.001] INT*ENV -0.000 -0.000 [0.000] [0.000] WGI_index -0.012 -0.013 [0.146] [0.152] WGI_index*ENV 0.001 0.001 [0.001] [0.001] Constant 1.983*** 1.169*** 1.606*** 1.596*** 1.619*** 1.610*** [0.103] [0.062] [0.119] [0.118] [0.260] [0.268]
Year F.E. No Yes Yes Yes Yes Yes
Country F.E. No Yes Yes Yes Yes Yes
R-squared 0.139 0.362 0.422 0.422 0.422 0.422
Adj. R-squared 0.139 0.355 0.416 0.416 0.416 0.416
Observations 4399 4399 4399 4399 4399 4399
4.1. Robustness check
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financial performance, namely ROE (return on equity), turns out to be more than 50% positively correlated to both ROA and Tobin's Q in my sample. It is for this reason that instead of using substitutes, I further investigate the influence of country governance index in more detail. I divide the sample into two subsamples based on the median value of WGI_index variable. Hence, the sample with values below (above) the median is representative for countries of my sample with weaker (stronger) governance indicators.
Table 7 provides some new insights on the question whether country characteristics influence
on the connection between environmental and firm performances. While the coefficient of ENV remains positive and statistically significant for countries with below the median WGI_index, results are with a reversed sign for the sample with high governance index. Said otherwise, companies operating in states with relatively weaker governance protection enjoy positive returns from their environmental performance. On the other hand, firms from countries with higher WGI_index experience decrease or no effect on the next year ROA. One explanation for these outcomes is that in states with strong civil rights, it is anything but unusual companies to behave sustainably. Hence, firms' environmental enforcement is essential and presumably required by the social norms and the government. Referring to RBV theory, firms' competitive and strategic resources are those that are VRIN - valuable, rare, inimitable, and non-substitutable (Peteraf, 1993; Barney, 1991). Nevertheless, where most of the peers on the market perform in an excellent environmental manner, the latter is no longer outstanding and that valuable. Similarly, Ellimäki, Gómez-Bolaños, Hurtado-Torres, and Aragón-Correa (2019) conclude that firms focus on environmental performance as a means to gain legitimacy when countries' governance is week.
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ROA in all models, but the results are significant at 1% level only for firms embedded in less efficient states in terms of WGI_index. Those findings support the view that liabilities of foreignness hurt firms' profitability (Zaheer, 1995). Interaction between internationalization degree and environmental performance shows no statistically different results and hence no support for hypotheses 2a/b.
Table 7. Robustness test using ROA. This table displays the results of the pooled OLS regressions with dependent variable ROA (return on assets). Observations are divided by the median value of WGI_index into two subsamples – “Low” captures countries below median value and “High” captures countries with index above the median value. Abbreviations are as followed: ROA (return on assets) is net income as a % of total assets; ENV is a firm’s environmental performance score; SIZE is total assets in million $; LEV is the total debt as a % of total assets; CAPEX is the capital expenditure as a % of total assets; INT is the foreign sales as a % of total sales; WGI_index is the average WGI score for a country computed as an average of all six governance indicators. Firm-level independent variables (ENV, SIZE, LEV, CAPEX, and INT) are lagged by one year to control for endogeneity. Extreme variables are winsorized to mitigate the effects of outliers. All columns report findings with controls for year and country fixed effects. Robust standard errors clustered at firm-level are reported in parentheses. ***, **, and * show significance at the .01, .05, and 0.10 level, respectively.
Dependent variable: ROA
(1) (2) (3) (4)
Low High Low High Low High Low High
ENV 0.021*** -0.017*** 0.034*** -0.013** 0.033*** -0.013** 0.033*** -0.008 [0.008] [0.006] [0.009] [0.006] [0.008] [0.006] [0.008] [0.009] SIZE -0.520* -0.16 -0.448* -0.104 -0.449* -0.086 [0.280] [0.216] [0.246] [0.192] [0.247] [0.191] LEV -0.083*** -0.058*** -0.083*** -0.062*** -0.083*** -0.063*** [0.011] [0.013] [0.011] [0.013] [0.011] [0.014] CAPEX -0.032 -0.101*** -0.029 -0.106*** -0.029 -0.106*** [0.039] [0.034] [0.038] [0.034] [0.038] [0.034] WGI_index 6.550*** 3.008 6.567*** 2.884 [1.512] [3.028] [1.529] [3.109] INT -0.039*** -0.010 -0.039*** -0.005 [0.008] [0.007] [0.012] [0.007] INT*ENV -0.000 -0.000 [0.000] [0.000] Constant 8.126*** 5.163*** 9.876*** 8.356*** 1.835 3.924 1.801 3.866 [1.385] [0.712] [1.161] [1.016] [2.184] [5.015] [2.211] [5.004]
Year F.E. Yes Yes Yes Yes Yes Yes Yes Yes
Country F.E. Yes Yes Yes Yes Yes Yes Yes Yes R-squared 0.300 0.212 0.375 0.252 0.414 0.255 0.414 0.256 Adj.
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Another distinction between the two samples is the explanatory power of the models. For instance, in the first column of Model (4), the adjusted R-squared indicates that independent variables account for 40% of the variation in ROA. Whereas, the adjusted R-squared for the subsample of high WGI_index countries is only 25%. This remarkable difference shows that
Eq. (9) has a better fit with companies in countries with relatively lower governance index.
Outcomes from regression with the market-based proxy for financial performance Tobin's Q are presented in Table 8. Coefficients of β1 environmental performance are comparable with
the findings in Table 7. It can be observed that ENV is highly positively correlated at the 1% level to Tobin's Q in the sample with countries having below-median WGI_index. Thus, greater engagement in sustainable performance is an indicator of future growth opportunities and better market valuation (Hasan, Kobeissi, Liu, and Wang, 2018). Whereas, ENV results from the high WGI_index sample are not statistically significant.
36
Lewin, 2007). Considering Tobin's Q is a market-based indicator, it is highly sensitive upon investors' perception and anticipation for the respective company. So, shareholders' assumption for international operations incited by more mediocre regulations in the host country can frustrate investors with high sustainable expectations and result in short-selling of their shares. Table 8. Robustness test using Tobin’s Q. This table displays the results of the pooled OLS regressions with dependent variable Tobin's Q. Observations are divided by the median value of WGI_index into two subsamples – “Low” captures countries below median value and “High” captures countries with index above the median value. Abbreviations are as followed: Tobin's Q is calculated as (Total assets – Book value of equity + Market capitalization)/Total assets; ENV is a firm’s environmental performance score; SIZE is total assets in million $; LEV is the total debt as a % of total assets; CAPEX is the capital expenditure as a % of total assets; INT is the foreign sales as a % of total sales; WGI_index is the average WGI score for a country computed as an average of all six governance indicators. Firm-level independent variables (ENV, SIZE, LEV, CAPEX, and INT) are lagged by one year to control for endogeneity. Extreme variables are winsorized to mitigate the effects of outliers. All columns report findings with controls for year and country fixed effects. Robust standard errors clustered at firm-level are reported in parentheses. ***, **, and * show significance at the .01, .05, and 0.10 level, respectively.
Dependent variable: Tobin's Q
(1) (2) (3) (4)
Low High Low High Low High Low High
ENV 0.002 -0.002** 0.005*** 0.001 0.005*** 0.000 0.006*** -0.001 [0.002] [0.001] [0.002] [0.001] [0.002] [0.001] [0.002] [0.001] SIZE -0.128*** -0.115*** -0.129*** -0.104*** -0.135*** -0.108*** [0.046] [0.030] [0.047] [0.029] [0.047] [0.029] LEV -0.010*** -0.005*** -0.010*** -0.005*** -0.011*** -0.005*** [0.002] [0.002] [0.002] [0.002] [0.002] [0.002] CAPEX -0.005 -0.009** -0.005 -0.009** -0.005 -0.010** [0.007] [0.004] [0.007] [0.004] [0.007] [0.004] WGI_index 0.248 -0.674** 0.344 -0.648** [0.209] [0.303] [0.218] [0.304] INT 0.002 -0.002** 0.004** -0.003*** [0.001] [0.001] [0.002] [0.000] INT*ENV -0.000* 0.000 [0.000] [0.000] Constant 1.543*** 1.087*** 1.760*** 1.444*** 1.452*** 2.558*** 1.265*** 2.571*** [0.224] [0.063] [0.198] [0.108] [0.263] [0.532] [0.280] [0.527]
Year F.E. Yes Yes Yes Yes Yes Yes Yes Yes
Country F.E. Yes Yes Yes Yes Yes Yes Yes Yes R-squared 0.360 0.368 0.426 0.424 0.429 0.430 0.431 0.433 Adj.
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5. Conclusion and discussion
38
39
5.1. Managerial and political implications
This thesis highlights some critical aspects which are to be considered by the managers. Overall findings indicate that industrial transportation companies benefit from their adherence to sustainable environmental performance. Hence, managers and CEOs should not underestimate its importance and implement the environmental strategy into the company's vision and mission. Moreover, it should be considered that firms from states with relatively weaker governance index are the ones that value more economically. Whereas in markets where sustainable performance is a joint practice, environmental performance does not catch the same attention. Hence, it is not recognised as a competitive resource, which brings added value to the company. It is for this reason that managers should not expect higher financial returns only based on environmental performance. In contrast, they should seek other strategic resources to gain legitimacy and outstand among their competitors. This study highlights the importance of strategic management in both developed and developing markets.
Analysing the Worldwide governance indicators provides valuable information for policymakers. Results from this study show that companies in the industrial transportation business in highly regulated states experience non-positive financial outcomes from the environmental performance. Thus, it is reasonable to assume that firms in countries with above-median WGI index act sustainably to comply with the regulations, but not to make any profits. Therefore, governments could provide some additional incentives for firms to encourage them to be eco-friendly indeed. That could further mitigate the likelihood that greenwashing activities occur and would promote a healthy business environment.
5.2. Limitations
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