MSc Thesis 2017
University of Groningen, Faculty of Economics and Business
The effect of industrial and international diversification on firm performance in the Western European Energy Industry
Abstract:
It is helpful for management to know whether to diversify internationally and/ or industrially and in what way the different business strategies influence performance. The goal of this paper is to contribute to this debate by analyzing the Western European energy market, a dynamic and interesting industry. The sample consists of 129 firms, with observations from 2009 till 2015. A separation is made between conventional and renewable energy firms and between Great Britain and the rest of Western Europe. The results show that there is a negative relation between international and industrial diversification on firm performance.
Keywords: international diversification, industrial diversification, financial performance, business strategies, renewable energy firms, conventional energy firms, Western Europe
Name: Marijn Achtereekte
Student number: s2059452 Study Programme: IFM
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Table of contents
1. Introduction ... 3 2. Literature ... 4 2.1. Energy market ... 5 2.2. International diversification ... 6 2.3. Industrial diversification ... 8 3. Methodology ... 9 3.1. Data ... 9 3.2. Measures ... 10 3.3. Control variables ... 11 3.4. Methodology ... 11 3.5. Descriptive statistics ... 13 4. Results ... 154.1 Energy industry Western Europe ... 15
4.1.1. ROA ... 15
4.1.2. Tobin’s Q ... 18
4.2 Conventional energy firms ... 18
4.3 Renewable energy firms ... 20
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1.
Introduction
The energy industry deals with a continually changing environment. More firms tend to address these changes by opening up markets in renewable energy. On the other hand, conventional energy companies need to adapt their business strategies and anticipate to a continually changing environment. A possible example can be given by the enormous decrease of oil prices in 2008 and 2014. Also, the Paris Agreement, of the 5th of October 2016 is a premonition that governments and so indirectly firms will have to introduce new measures to achieve the requirements set out in the Paris Agreement (United Nations, 2015). Different strategies can be applied by firms to counter negative effects and play alongside the new rules and regulations. Moreover, firms generally use different business strategies to increase their corporate performance. However the literature does not agree on the influence of business strategies on the performance of firms.
There has been extensive research in the international business literature on diversification strategies, like diversifying a firm’s operations across more than one business line (industrial diversification) and diversifying across more than one national market (international diversification). Companies tend to diversify internationally in their search for (more) competitive advantage and profit (Porter, 1990). This international diversification may result in positive impacts on firms (Geringer, Beamish and DaCosta, 1989), but also comes with a price (Tallman and Li, 1996). The literature cannot draw unambiguous conclusions as a result of different operationalization of variables and the use of different industries and countries. However, most of the research was done for manufacturing firms. A gap in the literature is the influence of diversification on performance in the energy sector. A recent article by Li, Wang, Lou, Cheng and Yang (2016) investigates this relation on energy firms listed in the Chinese market. They found that industrial diversification has a negative relation with corporate performance and that international diversification relates to a positive change in performance of renewable energy firms, and negatively to conventional energy firms. This paper finds its inspiration on the research executed by Li, Wang, Lou, Cheng and Yang (2016), but distinguishes itself through applying the same research principle in the Western European market, with a deepened understanding of the theory involved in this research area.
4 region is one of the pioneers in the global energy industry aiming to reduce its dependency on imports from other countries and to meet the targets in the battle against global warming. It is also interesting to identify differences between conventional energy firms and renewable energy firms in their application of diversification strategies and its relation to firm performance. Moreover, it can be of importance to analyze possible differences between countries in Western Europe, and especially between the United Kingdom and other Western European countries. The United Kingdom differs on several levels compared to the rest of Western Europe, for instance that the United Kingdom uses a common law legal system and the other Western European countries apply a code law system. Next to that, the United Kingdom has a shareholder corporate governance model, which is more timely and conservative, while the other countries in Western Europe use a stakeholder corporate governance model, which is less timely and less conservative (Doupnik and Perera, 2011). When the United Kingdom is compared to the rest of Western Europe there is a difference in their approach on the energy industry. The United Kingdom holds on a very liberal attitude, focused on deregulation, which is beneficial for the competition in the sector. A result of this liberal approach is that the prices paid by consumers in the United Kingdom are much lower than in other countries in Western Europe.
The first section will provide the main theoretical background of the business strategies, the opposing views and results, and some general information about the energy sector in Western Europe. Resulting from this literature review, different hypotheses are developed. Hereafter, the methodology section describes the sample, the operationalization of all variables, the method for analysis and the descriptive statistics. In the fourth section I will present the results of the different models and the section followed will consist of an extensive elaboration on the findings presented before. Finally, I will present the conclusions and give recommendations for further research.
2.
Literature
5 be described on the basis of previous literature. First a general description of the energy market in Western Europe will be given. This research investigates two specific business strategies that involve diversification, which are international diversification and industrial diversification and their relation with economic firm performance. Hypotheses are formulated alongside the literature research.
2.1. Energy market
The energy market is a difficult market, but also a very attractive one with a lot of potential. This is not only the case for firms competing in this industry, but also for governments, looking at the large employment possibilities and the amount of capital that goes with the industry. Also, it has an extremely important function in the role of supporting other industries successfully completing their manufacturing and business. A lot is at stake, so it is difficult for governments whether they want to intervene, or simply set boundaries in between firms can freely move. Moreover, since the early 2000s firms and governments not only deal with economic interests, but they also have to address the environmental and security issues.
An article by the Van den Heuvel, De Jong and Van der Linde (2010) concludes that because energy companies have to deal with a dynamic and uncertain environment, they continuously need to work on and improve their strategies for growth. It can be said that the European market has matured, as growth rates have started to fall. There are two business strategies firms in the energy industry can pursue, which are international diversification, from being a domestic firm to becoming an international firm, and industrial diversification, to grow as a company by using new products. Between 2000 and 2007, a lot of the large energy companies in Western Europe used economies of scope and became active in not only the gas but also the power business (Van den Heuvel, De Jong and Van der Linde, 2010).
A recent research on sustainable development and the value creation with energy firms showed that firms that are more focused on sustainability keep better control over their costs and also tend to obtain more profit than conventional-driven energy firms (Pätäri, Jantunen, Kyläheiko and Sandström, 2012). In other words, the sustainable firms have outperformed the conventional firms between the years 2000 and 2009.
6 The amount of renewable energy in the total energy mix has increased through more backing from public authorities. The renewable energy sector captivated serious investments through different forms of financial packages; incentives, long term feed-in tariffs (guaranteeing a fixed income) and green certificates. Germany supports their renewable energy industry with subsidies of nearly € 20 billion per year (2012) and € 240 per citizen in 2014 (Deloitte, 2015a). France’s investment, in 2006 - 2011, in the support of their renewable energy sector turned out to be € 14.3 billion and for the period 2012 - 2020 it is predicted that France will need € 40.5 billion to stimulate this sector (Deloitte, 2015b). These huge investments of course increase consumer prices in a great deal of countries. For instance, in Germany the consumer prices rose with +/- 32% in the years between 2008 and 2013. The transition to more renewable sources of energy requires the acceptance of the citizens and for that reason the consumer prices for electricity need to stay fairly priced (International Energy Agency, 2013).
Each country in the European Union has set a goal for 2020 regarding the total share of renewable energy in gross final energy consumption (Table Appendix.1). The United Kingdom needs to change their approach on renewable energy because they are behind in their process of reaching their 2020 goal. France and The Netherlands also need to invest a lot to successfully reach their aimed share of renewables in 2020. This implies that the authorities and the firms in those countries will try and need to focus more on renewables and this may possibly lead to new regulations and or subsidies. Norway, Finland, Sweden and Austria are forerunners, already having a + 30% share of renewable energy in the total energy mix.
2.2. International diversification
When thinking about a firm’s globalization and internationalization intentions, it can be said that internationalization and the continuous expansion drift of multinational corporations (MNCs) has changed the scene of the global economy for some years now (Helpman, 1984). Firms have to look broader than just their home market when they want to stay competitive with their rivals (Porter, 1986). There are different definitions of a MNC, one article by Kogut and Zander (1993) specifies MNCs as economic organizations that expand from its domestic origin to countries abroad, while Guisinger (1973) defines it as a firm with any operations in two or more countries.
7 diversification strategy. This view holds that firms need to have heterogeneous resources by developing unique resources and capabilities that are difficult to copy, in order to perform well (Barney, 1991). MNCs are contemplated as social communities whose productive knowledge defines a comparative advantage; once these firms succeed in having more productive knowledge, this will result in a comparative advantage compared to others (Nelson and Winter, 1982). Furthermore, international business theory suggests that foreign direct investment (FDI) determines a firm’s desire to exploit firm specific assets such as technological advantages, management skills, and geographical advantages (Hymer, 1976). Of course, when a firm has a competitive advantage it is beneficial to exploit these advantages in other countries. Especially when industrial diversification comes with high opportunity costs.
The transaction cost theory on the other hand, implies problems that go with international diversification. Examples of reasons that restrain a positive impact of international diversification are opportunistic behavior of partners, liability of foreignness, less flexibility and different requirements to enter or leave markets (Hitt, Hoskisson, and Kim, 1997). Moreover, it is argued that international diversification comes with higher monitoring costs, varying legal systems and higher auditing costs (Burgman, 1996). These will result in more agency costs. An opposing view to the transaction cost theory is that firms can use the traits, rules and regulations of other countries to their advantage. For instance tax policies or product prices might be beneficial when compared to the base country and more profit may be generated.
So, a lot of conflicting sights have been portrayed in the literature regarding international diversification. Authors argue that international diversification is a way to reduce the firm’s risk exposure (Agmon and Lessard, 1977). However, a more recent school of thought has indicated that diversification increases a firm’s risk due to fluctuation in exchange rates, agency theories and institutional risks (Reeb, Kwok and Baek, 1998). As for investors, they consistently favor portfolios that are international and diverse (Balcılar, Demirer and Hammoudeh, 2015). A different study shows that more global diversification has a negative impact on excess value and less global diversification positively impacts excess value (Berger and Ofek, 1995).
8 empirical findings differ between studies. As the literature is inconclusive about international diversification and the direction of its relation to firm performance, I will mainly look at the specific energy industry when formulating a hypothesis on international diversification. The energy market in Western Europe is matured and firms need to look across borders to increase their performance. Also, the European Union makes it easy to cross borders, as there are no difficult regulations involved. Therefore, the following hypothesis is formulated:
Hypothesis 2: There is a positive relation between international diversification and firm performance in the Energy industry in Western Europe.
2.3. Industrial diversification
A lot has been written about industrial diversification. Most of the literature shows industrial diversification has a negative impact on firm performance, because of agency effects (Aggarwal and Samwick, 2003), internal capital markets (Shin and Stulz, 1998) and market microstructures (Habib, Johnsen and Naik, 1997). According to Aggarwal et al (2003) agency theory implies that because managers have no corporate claims, they make decisions best suited for them and in most cases this is not in the best interest to the value of the firm. Regarding industrial diversification, they contemplate the agency theory with two different explanations. Firstly, managers tend to diversify because they want to mitigate their unsystematic risk. Secondly, agency theory claims that managers are always on the lookout for private benefits (Stulz, 1990). Leading a more diversified firm may result in more status, more power, more money, more interesting future career possibilities, or the feeling of being valuable and irreplaceable (Jensen and Murphy, 1990; Aggarwal and Samwick, 2003).
Considering the internal capital market, some literature claims that industrial diversification leads to the inefficiency of allocating resources, because of information asymmetry (Martin and Sayrak, 2003). This can be explained through two different reasons given by Shin and Stulz (1998). A wish for power leads to a biased allocation of resources in the internal capital market. Moreover, a diversified company lacks responsiveness for, and is less sensitive to investment opportunities.
9 asymmetry, in contrast to managers of diversified firms that receive biased information that in the end may negatively impact the firm’s value (Habib and Johnsen, 1997).
On the other hand, some studies show a positive relation of industrial diversification on firm performance, because of the efficient use of internal capital markets (Gertner, Scharfstein, and Stein, 1994). Through reallocation, the money can be invested in projects with the highest present value and profit. Next to the internal capital market, firms have the possibility to obtain a diversification discount (Mansi and Reeb, 2002; Campa and Kedia, 2002). So, some studies find a positive relation between industrial diversification and a firm’s value (Campa and Kedia, 2002).
There are different forms of industrial diversification and a major difference is the relatedness of a firm’s industry. Markides and Williamson (1996) argue that related diversification only improves a firm’s performance when it gets competitive advantage by gaining access to strategic assets. Moreover, it can be said that in general, firms that have a related diversification strategy perform better than firms that have an unrelated diversification strategy (Bettis, 1981; Rumelt, 1982). The energy industry is an unusual industry for research on industrial diversification. For that reason I will formulate the following hypothesis based on the research by Li et al (2016). They find a significant and negative relation between industrial diversification and firm performance in the energy industry in China. Therefore, the following hypothesis is formulated:
Hypothesis 3: Firms that do not use an industrial diversification strategy perform better than firms that do make use of an industry diversification strategy.
3.
Methodology
3.1. Data
10 REV 2 industrial classification in Orbis. The companies are separated into the renewable (3511, 3512, 3514) or conventional (0610, 0620, 1920) classification based on their primary NACE REV 2 code. Due to limited data availability and missing data for multiple variables the original sample is reduced from 193 to a total of 129 energy firms situated in Western Europe. Moreover, firms that went bankrupt during our sample period were also excluded from the sample. The set of variables of the firms are gathered over a 7-year period, from 2009 until 2015. This dataset of 129 firms still dealt with some severe outliers. To not further reduce the sample size by removing firms with outliers, the data is winsorized at 1% at both tails. This adjustment changes the values of bottom and top 1% to the values at 1% and 99%. By removing outliers, winsorizing also reduces the skewness of the variable.
3.2. Measures
The independent variable industrial diversification will be measured by a factor variable with a value of 0 when they do not make use of industrial diversification, 1 when they operate in an unrelated industry, and a value of 2 if they (also) operate in a related industry. Another measure for industrial diversification is the number of segments a company is operating in, based on the NACE REV 2 code. These are split up in four different groups; group 1: one or two segments, group 2: three or four segments, group 3: five or six segments and group 4: more than six segments. A broadly accepted definition of international diversification is: “a strategy through which a firm expands the sales of its products or services across the borders of global regions and countries into different geographic locations or markets” (Hitt, Ireland and Hoskisson, 2007, pp 251). Measures for concentration cannot be used in this study, because markets cannot be clearly defined as the sample contains only listed companies and a lot of different countries. Therefore, in this research, the degree of foreign sales to total sales is used as a proxy for international diversification. An overview of previous literature on regional diversification and firm performance shows that the degree of foreign sales to total sales is one commonly used and accepted across literature (Qian, Li, Li and Qian, 2008).
11 often used as a proxy for performance when measuring the influence of industrial diversification (Berger and Ofek, 1995).
3.3. Control variables
I use three variables to control the model on a firm level. To control for the size of a firm the natural logarithm of the total assets is used. The reason for this is that large firms tend to be more steady than smaller firms, and therefore are less likely to default (Harris and Raviv, 1991). Furthermore, I control for the profitability of a firm (EBIT/ SALES) and for capital expenditures (CAPEX/ SALES), which reflects the investments a firm does. Another control variable that is used is the leverage of firm (TOTAL DEBT/ CAPITAL). Some literature also controls for research and development expenditures and advertising expenditures. However, a research on diversification by (Denis, Denis and Yost, 2002) shows that including these variables in the analysis does not have an impact on the results. For that reason both variables will not be used in this analysis. As the total sample contains many different countries, I will control for two country level variables. Countries in Western Europe tend to be similar on some levels, but can be distinguished on the basis of inflation and real GDP Growth. Both variables are used as a control variable in this analysis.
3.4. Methodology
12 errors, also known as the Huber/white estimators of variance and cluster for company. This will give more trustworthy results.
Six different regressions are performed on two different dependent variables, namely ROA and Tobin’s Q, covering the complete market for listed energy firms in Western Europe:
(1) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒!= ∝ +𝛽! 𝐹𝑆𝑇𝑆 + 𝛽! ln 𝑡𝑜𝑡𝑎𝑠𝑠 + 𝛽! 𝐶𝑎𝑝𝐸𝑥!"#$% + 𝛽! 𝐸𝐵𝐼𝑇!"#$% + 𝛽! 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽! 𝐼𝑁𝐹𝐿 + 𝛽!𝐺𝐷𝑃 + εi (2) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒!= ∝ +𝛽! 𝐹𝑆𝑇𝑆 + 𝛽! ln 𝑡𝑜𝑡𝑎𝑠𝑠 + 𝛽! 𝐶𝑎𝑝𝐸𝑥!"#$% + 𝛽! 𝐸𝐵𝐼𝑇!"#$% + 𝛽! 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽! 𝐼𝑁𝐹𝐿 + 𝛽!𝐺𝐷𝑃 + 𝛽!(𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦) + εi (3) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒!= ∝ +𝛽! 𝐹𝑆𝑇𝑆 + 𝛽! ln 𝑡𝑜𝑡𝑎𝑠𝑠 + 𝛽! 𝐶𝑎𝑝𝐸𝑥!"#$% + 𝛽! 𝐸𝐵𝐼𝑇!"#$% + 𝛽! 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽! 𝐼𝑁𝐹𝐿 + 𝛽!𝐺𝐷𝑃 + 𝛽! 𝐺𝐵 + 𝜀𝑖 (4) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒!= ∝ +𝛽! 𝐹𝑆𝑇𝑆 + 𝛽! ln 𝑡𝑜𝑡𝑎𝑠𝑠 + 𝛽! 𝐶𝑎𝑝𝐸𝑥!"#$% + 𝛽! 𝐸𝐵𝐼𝑇!"#$% + 𝛽! 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽! 𝐼𝑁𝐹𝐿 + 𝛽!𝐺𝐷𝑃 + 𝛽!" 𝐼𝑁𝐷𝑈𝑆!"#$%&"'"()*"+, + 𝜀𝑖 (5) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒!=∝ +𝛽! 𝐹𝑆𝑇𝑆 + 𝛽! ln 𝑡𝑜𝑡𝑎𝑠𝑠 + 𝛽! 𝐶𝑎𝑝𝐸𝑥!"#$% + 𝛽! 𝐸𝐵𝐼𝑇!"#$% + 𝛽! 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽! 𝐼𝑁𝐹𝐿 + 𝛽!𝐺𝐷𝑃 + 𝛽!! 𝐼𝑁𝐷𝑈𝑆!"#$"%&! + 𝜀𝑖 (6) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒!= ∝ +𝛽! 𝐹𝑆𝑇𝑆 + 𝛽! ln 𝑡𝑜𝑡𝑎𝑠𝑠 + 𝛽! 𝐶𝑎𝑝𝐸𝑥!"#$% + 𝛽! 𝐸𝐵𝐼𝑇!"#$% + 𝛽! 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽! 𝐼𝑁𝐹𝐿 + 𝛽!𝐺𝐷𝑃 +𝛽! 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝛽! 𝐺𝐵 + 𝛽!" 𝐼𝑁𝐷𝑈𝑆!"#$%&"'"()*"+, + 𝛽!! 𝐼𝑁𝐷𝑈𝑆!"#$"%&! + 𝜀𝑖
With the following meanings:
Firm Performance = Financial firm performance, measured by ROA and Tobin’s Q
i = 129
LN(totass) = Natural Logarithm total assets EBIT_sales = Ebit/ sales
CapEx_sales = Capital expenditures/ sales Leverage = Total Debt/ total equity INFL = Inflation p/y p/country GDP = Economic growth p/y p/country
FSTS = International diversification (Foreign sales/ total sales) Industry = Industry (dummy variable, conventional vs renewable firms)
13 INDUSdiversification = Industrial diversification and relatedness (factor variable)
INDUSsegments = Industrial diversification, # of segments (factor variable) ε = error term
This covers the Western European energy industry. After these results, split sample tests generate results for both the conventional energy industry, as well as for the renewable energy industry.
3.5. Descriptive statistics
Table 1 shows the pair-wise correlation matrix. It can be seen that the independent, and control variables are almost all significant on a 5% level with the dependent variable ROA, except for leverage. Moreover, this also goes for the Tobin’s Q, the dependent variable in the other model. There is a high positive correlation between the size of a firm (ln total assets) and the dependent variable ROA, .423. However, when looking at the Tobin’s Q, there is a negative correlation with the size of a firm, -.377. This latter measure of performance is based on the total market value of a firm and the total asset value, expected future value, which explains the difference with ROA. Another high correlation is the one between Capex/ Sales and Ebit/ Sales, -.477. This can be justified for the reason that cash can either be used as a capital expenditure, or just as an earning. Although there is a lot of correlation between the variables this does not automatically mean there is causality.
Table 1: Pair-wise correlation matrix (*, significant on a 5% level)
Variables 1. 2. 3. 4. 5. 6. 7. 1. ROA 1 2. FSTS -.159* 1 3. Total assets (ln) .423* .027 1 4. Capex / TS -.213* .072* -.226* 1 5. Ebit / TS -.453* -.073* .332* -.477* 1 6. Leverage .033 -.151* .244* -.070* .196* 1 7. Tobin’s Q -.213* .096* -.377* .130* -.330* -.407* 1
14 conventional firms than for renewable firms, on average. Depicting Tobin’s Q over the years for renewable and conventional energy firms illustrates that the Tobin’s Q for conventional energy firms falls over the years (Graph A2) and for renewable energy firms it remains even (Graph A3). This also applies to the ROA, but less remarkable (Graph A4 and Graph A5). This downward trend implies that investors have less confidence in the future and as a result the market value of the firm drops.
Conventional energy firms tend to have significantly more foreign sales to total sales (48.284%), compared to renewable energy firms (27.122%). This means that conventional energy firms tend to internationally diversify themselves more often (Graph A6 and Graph A7). This might be explained by the fact that renewable energy firms are younger and there are still enough domestic possibilities. Also, conventional energy firms have more capital expenditures to total sales (61.342 over 49.154). Table 2 illustrates that conventional energy firms have a more negative Ebit/ sales than do renewable energy firms. I will deal with an explanation for this in the discussion part.
Table 2: Descriptive statistics (y=7)
Renewable (n=77) Conventional (n=52) Total (n=129)
Variables O Mean Median O Mean Median O Mean Median
1.ROA 496 1.17 3.295 357 -3.277 2.13 853 -.689 3.03 2.Tobin’s Q 514 .504 .359 361 .792 .525 875 .623 .428 3. FSTS 478 27.122 2.275 340 48.284 50.365 818 35.918 26.015 3.Total assets (ln) 514 14.616 14.786 361 14.208 14.210 875 14.448 14.689 4. Capex / TS 505 49.246 11.29 341 53.367 9.52 846 49.116 10.52 5. Ebit / TS 514 -.239 .104 345 -1.491 .040 859 -.742 .078 6. Leverage 514 47.404 51.375 360 34.922 34.055 874 42.263 43.3
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4.
Results
4.1 Energy industry Western Europe
4.1.1. ROA
These models show that the degree of internationalization has a significant negative impact on the ROA. The complete model (6) in table 3 depicts that with 99% certainty, it can be said that when the ratio of foreign sales increases with 1, it negatively impacts the ROA with 0.057. These results show that international diversification negatively influences firm performance. In other words, when a firm has 100% foreign sales, its ROA is 5.7 lower. In all six models it can also be seen that, with 99% certainty, the size of a firm positively impacts the ROA, the bigger the firm, the more ROA is generated. Of course this is only when holding all other variables constant. Moreover, in these regressions, the Ebit/ sales has a significant positive impact on the ROA, on a 1% significance level, meaning a higher ratio of Ebit/ sales will result in a higher ROA. With 95% certainty, the leverage has a negative impact on ROA (-.086): the more debt to equity a firm has, the lower ROA.
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Table 3: Regression models (dependent variable: ROA)
Model: 1 2 3 4 5 6 Variables Intercept -20.71*** -21.18*** -19.22*** -20.93*** -21.55*** -20.44*** (6.185) (6.448) (6.665) (6.181) (6.209) (7.438) FSTS -.0612*** -.058*** -.058*** -.062*** -.059*** -.057*** (.016) (.016) (.017) (.016) (.017) (.017) Total Assets (ln) 1.791*** 1.787*** 1.712*** 1.818*** 1.819*** 1.761*** (.387) (.383) (.408) (.395) (.382) (.435) Capex/ TS .006 .006 0.007 .006 .006 .006 (.007) 1.310*** (.007) (.007) (.007) (.007) (.007) (.007) Ebit/ TS 1.310*** 1.301*** 1.297*** 1.304*** 1.291*** 1.276*** (.387) (.390) (.388) (.388) (.389) (.390) Leverage -.076** -.079** -.080** -.076* -.085** -.086** (.037) (.039) (.038) (.038) (.039) (0.04) Renew. Industry .940 -.221 (1.448) (1.883) Great Britain -1.631 -1.236 (1.829) (2.137) 1.Unrelated div. .312 1.013 (1.818) (2.998) 2.Related div. -1.476 -2.061 (1.283) (2.235) 1.Segments (3-4) 1.317 1.528 (1.586) (1.711) 2.Segments (5-6) 1.63 2.453 (1.687) (3.171) 3.Segments (7+) -3.988** -3.963 (1.971) (3.790) GDPGrowth -.005 -.011 -.009 -.008 .009 .0171 (.151) (.152) (.150) (.151) (.154) (.153) Inflation .193 .190 .210 .178 .182 .197 (.213) (.213) (.206) (.220) (.220) (.214) N 789 789 789 789 789 789 F-Statistic 21.99 18.73 18.98 17.01 15.72 11.11 adj r2 0.3389 0.3390 0.3398 0.3384 0.3422 0.3425
17
Table 4: Regression models (dependent variable: Tobin’s Q) Model: (Tobin's) Q) 7 8 9 10 11 12 Variables Intercept 1.826*** 1.897*** 1.710*** 1.811*** 1.808*** 1.752*** (.266) (.276) (.273) (.260) (.255) (.285) FSTS .001 .000 .000 .001 .000 .000 (.001) (.001) (.001) (.001) (.001) (.001) Total Assets (ln) -.063*** .063*** -.057*** -.061*** -.054*** -.051*** (.014) (.014) (.014) (.014) (.016) (.017) Capex/ TS -.000 -.000 -.000 -.000 -.000 -.000 (.000) (.000) (.000) (.000) (.000) (.000) Ebit/ TS -.028* -.026* -.027* -.028* -.025 -.025) (.015) (.015) (.015) (.015) (.016) (.016) Leverage -.008*** -.007*** -.007*** -.008*** -.007*** -.007*** (.002) (.002) (.002) (.002) (.002) (.002) Renew. Industry -.139* -.056 (.073) (.077) Great Britain .125 .095 (.100) (.111) 1.Unrelated div. -.055 -.106 (.111) (.175) 2.Related div. -.036 -.068 (.070) (.091) 1.Segments (3-4) -.181* -.131 (.010) (.110) 2.Segments (5-6) -.125 -.003 (.120) (.176) 3.Segments (7+) -.183 -.074 (.166) (.214) GDPGrowth .013 .014 .012 .013 .013 .012 (.011) (.011) (.012) (.010) (.011) (.012) Inflation -.006 -.006 -.007 -.007 -.006 -.008 (.013) (.012) (.012) (.013) (.013) (.013) N 804 804 804 804 804 804 F-Statistic 8.30 7.76 7.39 6.69 6.03 4.71 adj. R2 .2859 .2950 .2901 .2848 .2971 .2990
18 4.1.2. Tobin’s Q
The regressions (7-12) in table 4 are based on a different dependent variable, namely the Tobin’s Q. As said before, in literature, the Tobin’s Q is used often to measure the influence of industrial diversification on performance (Berger and Ofek, 1995). The models in table 4 imply that there is no relation between industrial diversification and Tobin’s Q whatsoever. Compared to the models based on the ROA (table 3), these regressions show a negative impact of the size of a firm on Tobin’s Q, with a 99% certainty level, ceteris paribus. In other words, when a firm is larger, the Tobin’s Q will drop. The results of model (8) depict a difference between the renewable and conventional energy firms. With 90% certainty it can be said that renewable energy firms have a value of Tobin’s Q that is 0.139 lower than conventional energy firms, with a standard error of .073, ceteris paribus. Nonetheless, the complete model (12) indicates that this relation is no longer significant. Model (11) shows that firms operating in three or four segments have a lower (-.181) Tobin’s Q than firms operating in only one or two segments. This significant relation is also lost when all factor and dummy variables are added in model (12). Nothing can be said about the possible effects of industry diversification based on regression model (12). Model 12 presents an adjusted R squared of 29.90%, thereby assuming 29.90% of the variance of Tobin’s q is explained by this model.
Both, table 3 and table 4, show no significant difference between conventional and renewable energy firms regarding firm’s performance. According to these models, hypothesis 1, claiming that firms in the renewable energy industry outperform conventional energy firms, must be rejected.
4.2 Conventional energy firms
19
Table 5: Regression models for conventional energy firms
Model: 13 14 15 16
Variables (ROA) (ROA) (Tobin's Q) (Tobin's Q)
Intercept -33.700*** -42.36*** 1.879*** 1.623*** (10.6) (14.91) (.422) (.412) FSTS -.053* -.047 .000 .000 (.031) (.034) (.002) (.002) Total Assets (ln) 2.621*** 3.467*** -.059*** -.031 (.670) (1.05) (.018) (.028) Capex/ TS .001 -.001 -.000 -.000 (.010) (.010) (.001 (.001) Ebit/ TS .795* .728 -.022 -.024 (.464) (.466) (.022) (.022) Leverage -.118 -.168* -.009** -.010** (.086) (.096) (.004) (.005) Great Britain -.899 -.035 (3.595) (.176) 1.Unrelated div. 6.475* .007 (3.698) (.234) 2.Related div. 2.888 -.016 (4.926) (.135) 1.Segments (3-4) -6.586** -.292 (3.197) (.196) 2.Segments (5-6) -8.965 -.190 (.981) (0.26) 3.Segments (7+) -18.25** -.255 (8.314) (.307) GDPGrowth -.381* -.405* -.000 -.002 (.209) (.220) (.012) (.012) Inflation .839* .944** .015 .017 (.466) (.443) (.028) (.024) N 332 332 334 334 F-Statistic 66.4 33.5 5.91 4.15 adj. R2 .3297 .3494 .2445 .2494
*, ** and *** Correlation is significant at the 0.10, 0.05 & 0.01 level (2 tailed), respectively. For all models the standard error is adjusted for 52 clusters (company), resulting in robust standard errors and significance levels. Variables are winsorized at 1% at each tail.
20 the variance of ROA and model (16) describes 24.94% of the Tobin’s Q, both for conventional energy firms.
4.3 Renewable energy firms
Just as for the complete sample, table 6 also shows a significant and negative relation between the degree of foreign sales and ROA for renewable energy firms (models 17 & 18). Again, model (18) cannot generate conclusions between international diversification and the Tobin’s Q, as no significance is detected. A remarkable difference between conventional energy firms and renewable energy firms can be seen in the relation between the number of segments and ROA. Model (18) illustrates that renewable energy firms with five or six segments perform better than firms with only one or two segments (8.160), ceteris paribus and with 90% certainty. Model (18) explains 47.10% of the variation of ROA for renewable energy firms.
21
Table 6: Regression models for renewable energy firms
Model: 17 18 19 20
Variables (ROA) (ROA) (Tobin's Q) (Tobin's Q)
Intercept -7.101 -9.128 1.691*** 1.590*** (6.616) (7.237) (.373) (.377) FSTS -.049** -.051** .000 .000 (.021) (.020) (.001) (.001) Total assets (ln) .915** .891** -.060*** -.054** (.437) (.364) (.022) (.021) Capex/ TS .007 .007 -.000) -.000 (.007) (.007) (.000) (.000) Ebit/ TS 2.330*** 2.254*** -.041* -.038 (.460) (.551) (.022) (.028) Leverage -.071 -.077* -.006*** -.006*** (.043) (.043) (.002) (.002) Great Britain -.113 .245* (2.172) (.138) 1.Unrelated div. -3.495 -.254** (2.989) (.110) 2.Related div. -4.081 -.125* (2.987) (.069) 1.Segments (3-4) 3.084 -.025 (3.386) (.164) 2.Segments (5-6) 8.160* .237 (4.104) (.178) 3.Segments (7-8) 1.963 .174 (3.856) (.205) GDPGrowth .116 .144 .021 .019 (.179) (.182) (.013) (.014) Inflation -.173 -.139 -.016 -.015 (.179) (.198) (.011) (.012) N 457 457 470 470 F-Statistic 18.58 17.9 13.85 15.92 adj. R2 0.4598 0.4710 0.2986 0.3182
22
4.4 Robustness tests
Multiple robustness tests are performed on the general model (6). When ROA is replaced with Return on Equity (ROE) model (21), the coefficients are not extremely different. They move in the same direction, but are more extreme. The international diversification proxy is still negative (-.155) on a 1% significance level. The firm size has a larger impact on ROA as well (5.127) compared to (1.761) in the original model. No conclusions regarding industrial diversification can be drawn from this model (21) with ROE as a dependent variable. This was also the case for the general model (6) with ROA as dependent variable. With 90% certainty it can be said that firms with more than seven segments have a lower ROE than firms with only one or two segments. Less of the dependent variable is explained through this model (21). This model only describes 24.93% of the variation of ROE, compared to 34.25% in model (6).
23
Table 7: Robustness Tests
Models: 6 21 22 23
Variables (ROA) (ROE) (ROA) (ROA)
(2010-2015) (2009-2014) Intercept -20.44*** -51.01*** -17.51*** -21.89*** (7.438) (14.52) (6.18) (8.30) FSTS -.057*** -.155*** -.057*** -.062*** (.017) (.04) (.02) (.02) Total assets (ln) 1.761*** 5.127*** 1.614*** 1.837*** (.435) (1.11) (.39) (.48) Capex/ TS .006 .014 .008 .003 (.007) (.01) (.01) (.01) Ebit/ TS 1.276*** 1.821*** 1.273*** 1.206*** (.390) (.64) (0.39) (.40) Leverage -.086** -.504*** -.090** -.0693 (0.04) (.13) (.04) (.04) Renew. Industry -.221 -.0324 -.718 -.832 (1.883) (4.34) (1.91) (2.05) Great Britain -1.236 2.766 -1.701 .496 (2.137) (4.85) (2.05) (2.32) 1.Unrelated div. 1.013 3.217 .307 1.762 (2.998) (9.10) (2.94) (3.12) 2.Related div. -2.061 -3.855 -2.688 -1.775 (2.235) (5.42) (2.27) (2.03) 1.Segments (3-4) 1.528 4.345 1.624 1.565 (1.711) (5.64) (1.78) (1.95) 2.Segments (5-6) 2.453 6.445 3.15 1.791 (3.171) (9.49) (3.22) (3.39) 3.Segments (7+) -3.963 -19.41* -3.257 -4.084 (3.790) (11.63) (3.75) (4.18) GDPGrowth .0171 -.988** -.040 -.099 (.153) (.40) (.26) (.12) Inflation .197 -.313 .106 .183 (.214) (.70) (.21) (.24) N 789 780 680 677 F-Statistic 11.11 6.49 8.88 11.29 Adj. R2 0.3425 0.2493 0.3418 0.3447
24
5.
Discussion
A possible explanation for the difference in the results between ROA and Tobin’s Q as a dependent variable can be given by the fact that Tobin’s Q is also dependent on corporate governance. A research by Bertrand and Mullainathan (2003) concludes that entrenched managers have the possibility to layback, underinvest and take no risks. However, if the firm’s net present value is reduced, underinvestment causes Tobin’s Q to increase (Dybvig and Warachka, 2012). This makes them less vulnerable and keeps them safer from being fired. This can make sense in our situation as we use the years 2009 till 2015, the years following the financial crisis. Firms, also in the energy sector, had a tough time and were not eager to invest.
Next to a possible difference in corporate governance, the difference between ROA and Tobin’s Q as performance measure is most easily explained by the fact that Tobin’s Q describes the market value of the company, suggesting future firm performance, as described in the literature review. In the analysis on the results during this discussion Tobin’s Q will therefore be treated as the expected future firm performance.
5.1 International diversification
Results show a significant negative relation between the degree of foreign sales to firm performance (ROA). However, there is no significant relation between international diversification and Tobin’s Q. The split sample analysis illustrates that only for renewable energy firms the ROA drops when there are more foreign sales. This is opposite of the hypothesis formulated in the literature review, stating a positive relation between international diversification and firm performance. Several reasons can be discussed that might have lead to this negative relation.
25 theory, referred to in the literature review, is that firms can use traits, rules and regulations of other countries to their own advantage. An example is Enel (Italy), they switched their attention by looking at new markets, especially in developing countries. There is more potential, a growing domestic market and high subsidies for replacing conventional energy (Scott, 2012). The choices made for going abroad therefore depend on corporate governance
Moreover, countries possibly favor their own companies and energy market through subsidies and regulations. The Western European energy market is difficult as company and customers not only deal with the government, but also with the European Union. Sometimes the free market principle is neglected, which is an example of the transaction cost theory as not every firm has the same possibilities on the market. For example, for some years (since 2013) the European Union has been warning Germany for possible illegal price discrimination (Barker and Basager, 2014). In Germany consumers pay a premium on both domestic as well as foreign electricity. Then, this money is only invested in domestic energy firms, which results in an unfair market system. As renewable energy firms are more dependent on external finance (see descriptives) and subsidies, abroad activities involve more risk and lower return.
Concerning conventional energy firms, our model shows no significant relation between international diversification and firm performance. Firms can easily export their oil and gas and the ROA of a firm does not significantly depend on the amount they have sold in foreign countries. However, the supply and demand ratio is not balanced and oil prices have continuously dropped from 2015 onwards. This price does not change a lot between countries and regions, nor does the homogeneous product and for that reason the resource based view does not hold up in the case for conventional energy firms. This also explains the non-significance in the relation.
5.2 Industrial diversification
26 Renewable energy firms find a significant negative relation between Tobin’s Q and industrial diversification regarding the other proxy for industrial diversification. Firms with a related industrial diversification strategy have a lower market value than firms that do not use an industrial diversification strategy, in line with the expectations. Firms that use an unrelated industrial diversification strategy have the lowest market value. According to this model it can be concluded that industrial diversification has a negative relation to firm performance, both for renewable and conventional energy firms.
Next to possible causes lined out in the literature review, a more direct and feasible impact for the lower performance of industrially diversified firms is the international financial crisis. Data shows that larger conventional energy firms perform better when operating in as few as possible segments, during the period 2009-2015. This can be related to the agency theory described in the literature review. Managers tend to make decisions to benefit themselves, also in crises, which may not be in the best interest of the firm. During crises this impacts the firm, as managers want to remain in position and secure their jobs. Especially in the conventional energy industry where firms are older and it is more difficult to change and respond to new circumstances.
Two arguments can be described to explain the better performance in related industrial diversification compared to unrelated industrial diversification strategy. First, economies of scope give related diversification an advantage; knowledge and resources can more easily be used in the related industry as well. Moreover, costs can be saved on management as they need less time and possess more relevant information already (Li et al., 2016). Also, as stated in the literature, a company’s market value is better described when it is a specialized firm, as there is no information asymmetry, opposed to managers in diversified firms that receive biased information (Habib and Johnsen, 1997).
5.3 Great Britain
27 Next to that, Great Britain still needs to speed up their transition to renewables. The positive Tobin’s Q (future value) for renewable energy firms can be explained by the fact that overall energy consumption in Great Britain today, is largely based on conventional energy. In 2012 the share of renewable energy was lower (4.1%) than in other member states (European Commission, 2014). This means that there are a lot of chances for renewable energy firms in the near future as Great Britain has set targets through the climate change act 2008 and the EU has set energy targets for 2020 for each country.
5.4 Managerial implications
Some management implications can be generated from the results discussed in the previous section. Managers for renewable energy firms should try to limit industrial diversification and focus on their core business, as the performance is negatively impacted by industrial diversification. The same holds for conventional energy firms, only they await more challenges in the near future as oil prices still remain low and citizens, companies and countries more and more demand cleaner energy sources. Furthermore, goals set by the European Union make it even more difficult for these companies to profit on the long term. So, conventional energy management may inaccurately interpret the results in the previous section, as a lot of near future challenges are not taken into account.
Management of renewable energy firms should be keen on comparing the possibilities between different countries. Especially when we are approaching the year 2020, in which goals are set by the European Union on the share of renewable energy. Countries might enhance a larger share for renewable energy in the energy mix by promotion and subsidies, which we have seen over the recent years for some countries. Graph a1 depicts a low share of renewables for Great Britain, France, Luxembourg, Belgium and The Netherlands. Highest chances for public support can be found in these countries. Next to subsidies, other characteristics like taxes, knowledge and resources are interesting things for management to keep in mind when considering business strategies. They should look at the best fit between their company strengths and a country’s opportunities.
28 their advantage. The process to a 100% renewable energy market is slow and will take a long time.
The expected action of Great Britain to leave the European Union is also something that has to be dealt with by the different stakeholders associated with the energy industry in Great Britain. It might be more difficult for firms to export energy to the European continent, depending on the negotiations between the EU and Great Britain. However, they have always been very liberal and in favor of a free energy market. Moreover, they have a lot of possibilities for renewable energy; especially as to wind energy. This is a very difficult situation for management as they are dependent on a lot of other stakeholders. They can lobby for an open and transparent market at the European Union. On the other hand, the EU does not feel like making it easy for Great Britain, tough negotiations are expected that will definitely take several years.
So, managers in the energy industry have a lot of difficult decisions to make and they need to have a look at their business strategy on the short- and long-term. The industry is continuously changing and the opinion of all stakeholders should be taken into account when making decisions.
6.
Conclusion
This study examines the influence of industrial and international diversification on firm performance, measured by Tobin’s Q and ROA, in the Western European energy industry. The literature review elucidates those different business strategies by means of an overview of previous research in the field of international business. A sample of 129 firms is analyzed over six years, 2009-2015.
Results show that industrial diversification and international diversification have a significant negative relation with firm performance. A split sample analysis showed a significant negative relation between international diversification and performance for renewable energy firms only. Both for renewable energy, as well as for conventional energy firms, a negative relation was found between firm performance and industrial diversification.
29 situations for companies and countries in the discussion of the current situation, results and managerial implications. Managers of firms in the energy industry need to deal with a lot of different factors in their pursue for the ideal business strategy. Management should not make decisions solely based on the findings in this paper, as they might send them in the wrong direction. For instance, firms should also base their strategy on the characteristics of the firm, the strategy, international and/ or industrial diversification risks and opportunities and country traits. Examples to consider as a manager are the recent low oil prices that result in more expensive renewables, the Paris Agreement, possible subsidies by countries for renewables, public and private pressure for renewable energy and Great Britain leaving the European Union.
30
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8. Appendix
Graph A1: Share of Renewable energy sources (per country)
34
Graph A2: Tobin’s Q (conventional energy firms) Graph A3: Tobin’s Q (renewable energy firms)
Graph a4: ROA (conventional energy firms) Graph a5: ROA (renewable energy firms)
Graph A6: FSTS (conventional energy firms) Graph A7: FSTS (renewable energy firms)
0 1 2 3 4 2008 2010 2012 2014 2016 Year
TobinsQ Fitted values
-8 0 -6 0 -4 0 -2 0 0 20 2008 2010 2012 2014 2016 Year ROA Fitted values
0 10 20 30 40 50 Pe rce n t 0 20 40 60 80 100 FSTS 0 10 20 30 Pe rce n t 0 20 40 60 80 100 FSTS -8 0 -6 0 -4 0 -2 0 0 20 2008 2010 2012 2014 2016 Year ROA Fitted values
0 1 2 3 4 2008 2010 2012 2014 2016 Year
35
Table A1: Normal OLS Regressions
Models: a1. total market a2. total market a3. conventional a4. Renewable a5. conventional a6. Renewable
Variables (ROA) (Tobin's Q) (ROA) (ROA) (Tobin's Q) (Tobin's Q)
