Valuation determinants of joint ventures in the
energy industry
Master thesis, International Financial Management Faculty of Business and Economics, University of Groningen
13 June 2016
2 Abstract
This paper empirically investigates the determinants of firm value of joint ventures that are active in the energy business. The focus of the study lies with the European Union Emissions Trading Scheme (EU ETS) and the influence of oil prices in its different phases, as well as the equity ownership structures of joint ventures from different locations and with a focus on different energy sources. Based on a dataset with 1,213 observations from 2005 to 2015, evidence is found that the impact of oil prices on firm value changes in the different phases of the EU ETS. Moreover, the results of the study show that there is an inverted U-shaped relationship between equity ownership and firm value for European firms, which has a sharper peak for renewable energy joint ventures.
Key Words: Joint Venture; Energy; Valuation; EU ETS; Equity; Ownership
3 1. Introduction
Joint ventures are a form of interfirm cooperation, where two or more partner firms work together to compete with firms outside their relationship (Hitt, Dacin, Levitas, Arregle, & Borza, 2000). The energy sector is one of the industries in which joint ventures are often formed (Meschi & Riccio, 2008). For the firms involved, working in joint ventures enables participation in a complex globalising market by building competitive advantages, through which value can be created (Chen & Chen, 2003; Mindruta, Moeen, & Agarwal, 2016). One example of an international joint venture with at least one European firm is the Malampaya project in the Philippines, a project for Offshore Natural Gas and the first of its kind in the Philippines1. It is co-owned by Shell (Operator), Chevron and the Philippine government. Active participation of all partners is required to solve technological, logistical and financial issues2.
Though there is a trend where the use of joint ventures as the Malampaya project has increased in popularity, only a small number of those joint ventures are considered a success. This happens because actual outcomes often do not live up to the expectations firms had beforehand (Hitt et al., 2000; Gomes, Barnes, & Mahmood, 2016). It is important to determine what factors truly cause a change in the value of the firm, so that firms can evaluate their strengths and possible pitfalls when entering into and managing joint ventures. This is especially important for the energy industry, where projects are often large, complicated and expensive. Using joint ventures, risks and expenses can be spread, while there is an increase in resources and capabilities (Dann, 2011).
Previous research has focused on either joint ventures in general, without focusing on a specific industry or sector (e.g. Chen & Chen, 2003), or with a focus on the automobile industry (e.g. Dyer & Nobeoka, 2000; Wilhelm, 2011). Previous research focusing on the energy industry relates to case studies and is especially project-based, where single joint ventures are researched (e.g. Raineri & Contreras, 2010). These case studies are difficult to generalise and not suited for theory testing (Karlsson, 2009). The increase in the use of joint ventures and their failure rate has made way for a growing concern amongst scholars (Gomes et al., 2016). However, to the best of the author’s knowledge, there is no comprehensive research on the performance of joint ventures in the energy industry.
1
http://www.shell.com/about-us/major-projects/malampaya-phases-two-and-three.html
2
4 Therefore, this study will strive to answer the following research question: “What are the determinants of firm value of joint ventures in the energy industry?” It will do so by means of a quantitative study using a multiple regression analysis, where the effects of several determinants on firm value will be established. The focus of these determinants lies with the European Union Emissions Trading Scheme and the influence of oil prices in its different phases, as well as the equity ownership structures of joint ventures from different locations and with a focus on different energy sources. With this research, the literature on industry-specific factors that relate to joint ventures will be enhanced. Empirical research is needed on this topic as there remains a high level of ambiguity on the causal mechanisms suggested in previous literature (Christoffersen, 2013). Moreover, this study has real-world relevance for practitioners, as there remains a high failure rate amongst joint ventures. The inclusion of different schemes based on the industry a firm resigns in could therefore be of aid when making strategic decisions.
The rest of this paper is structured as follows. Section 2 holds an overview of previous literature as well as the determinants researched and hypotheses, after which section 3 contains the methodology applied. Section 4 holds the results of the research and section 5 provides the discussion. Finally, a conclusion with contributions and suggestions for further research is given in section 6.
2. Literature review
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2.1 Joint ventures
Joint ventures are a form of strategic alliances. In this paper, strategic alliances are defined as cooperative agreements between two or more firms, who work together to compete with firms outside their relationship by co-developing, sharing or exchanging technologies, products or services (Gulati, 1998; Hitt et al., 2000; Yang, Zheng, & Zhao, 2014). Within the realm of strategic alliances, joint ventures involve the creation of an independent but jointly owned equity firm outside of the partners’ own firms (Gulati, 1995).
Dyer and Singh (1998) argued that joint ventures, rather than contractual alliances that do not involve the creation of an independent business, are the most competent when it comes to incentive alignment: knowledge transfer is promoted and higher value sharing can be achieved. However, though the firms are committed, joint ventures are a distinct organisation managed by multiple partners. By its very nature, tensions occur due to changes in partners’ behaviour, their willingness to share information, and the omnipresent challenge of loyalty versus opportunism (Meschi & Riccio, 2008). Still, joint ventures signal partner commitment due to the level of capital invested, which enhances cooperation between firms and increases the odds of success (Beamish & Lupton, 2009).
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2.2 Sources of Energy
Within the energy industry, a distinction can be made between different sources of energy. The basic distinction is between non-renewable energy and renewable energy. Non-renewable energy sources include hard coal, crude oil and natural gas: these are fossil mineral fuels whose quantities are finite and limited. Renewable energy sources include solar, wind, water and biomass: the exploitation of these resources does not deplete the resource in itself (Pach-Gurgul, 2014).
Due to global warming issues, it is important to reduce the scale of fossil fuel consumption. Substitutes for the traditional fossil fuels are essential and can be realised through an increase in the use of renewable energy sources (Von Eije, Von Eije, & Westerman, 2013). This has been solidified in the 2015 Paris Agreement, where the energy market needs to be redesigned to be more renewable (European Commission, 2015). Though this is the first truly global initiative, with 196 countries involved, the official signing of the agreement and thus its adherence starts in 2016 and 20173. Before this, several regions, countries or blocs of countries have already adopted schemes to reduce emissions. The largest Emissions Trading Scheme (ETS) was created by the European Union (EU), with the aim of managing the reduction of firms’ carbon emissions (Ortas & Álvarez, 2016). The scheme is based on a cap-and-trade system, or allowance trading, where there is a limit on the total amount of pollution allowed (Charitou, 2015). The total amount allowed is divided into small pieces and allocated to different firms. A carbon market is created where firms can trade emission allowances if they are below their threshold, and where firms need to buy allowances or pay a fine if they cross their threshold (Jeffrey & Perkins, 2015). As the total amount of pollution allowed is decreased over time, the EU ETS scheme has resulted in a significant reduction of carbon dioxide emissions (Ortas & Álvarez, 2016). There are three phases in the EU ETS, which are shown in table 1.
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Table 1. Phases of the European Union Emission Trading Scheme
Phase Years Actions Penalty
1 2005-2007 ‘Learning by doing’: setting prices, testing the system 100% of allowances given free of charge
€40 per tonne
2 2008-2012 Scope of the system widened: different emissions included Influence of the crisis created a drop in carbon prices 90% of allowances given free of charge
€100 per tonne
3 2013-2020 Scope of the system widened: different emissions included Harmonisation of rules
40% of allowances given through auction: share will rise yearly
€100 per tonne
Source: adapted from http://ec.europa.eu/clima/policies/ets/index_en.htm
Due to the EU ETS, there is a willingness to switch to energy sources that are less carbon-based. This occurs as it is more expensive to use polluting activities, which creates incentives for firms to apply processes that are environmentally-friendly (Ortas & Álvarez, 2016). The energy industry (e.g. oil refinement) is one of four main industries covered by the EU ETS, and therefore is required to uphold the rules set by the EU ETS (Anger & Oberndorfer, 2008).
With the necessity and thus emergence of new environmentally-friendly technologies, the focus of firms will shift from economies of scale to knowledge-based resources. This can occur through the use of joint ventures as there is an increasing need to understand resources and technologies that go beyond the capabilities of a single firm: opportunities lie beyond the boundaries of the firm (Pätäri & Westerman, 2011). It would therefore seem that renewable energy sources increase firm value. However, research on general non-specific growth has found a negative relation between the use of renewable energy and GDP growth (Von Eije et al., 2013).
8 difficulties gathering funds if their risks are too high, slowing down the momentum of the renewable energy industry (Erzurumlu, Davies, & Joglekar, 2014).
One researched determinant for the development of the renewable energy industry is the oil price. In general, research has found the relationship between oil prices and stock prices to be negative (e.g. Kilian & Park, 2009). However, the renewable energy industry might benefit from an increase in oil prices as alternative energy sources become more interesting and viable (Managi & Okimoto, 2013). Kumar, Managi and Matsuda (2012) find that past movements in oil prices determine variation in renewable energy stocks, where renewable energy sources can serve as substitutes when oil prices are rising. Reboredo (2015) adds to this by arguing that incentives to promote the development of renewable energy are encouraged when oil prices are high as there is dependence between the returns of oil and renewable energy stocks.
The following hypothesis can then be formed, which will be researched in each of the three phases of the EU ETS, as the influence of the oil price could change in the different phases. Moreover, changes occurred in 2008 due to the financial crisis, after which a surplus of carbon allowances occurred, which coincides with phase 2 of the EU ETS (Managi & Okimoto, 2013; Dirix, Peeters, & Sterckx, 2015; Inchauspe, Ripple, & Trück, 2015):
Hypothesis 1a: Oil prices are negatively related to the firm value of non-renewable energy joint ventures.
Hypothesis 1b: Oil prices are positively related to renewable energy firms’ stock returns, which in turn are positively related to the firm value of renewable energy joint ventures.
2.3 Control
9 Steinhubl, 1997). It is thus important to focus on the right partner for each joint venture as well as the right terms of the partnership and the right region.
One of the key determinants of joint venture performance is control, a subject that has received attention constantly in joint venture literature (Meschi & Riccio, 2008). There are multiple aspects related to the control of joint ventures, of which the focus in this study is related to strategic control as based on the research by Child (2001). Strategic control relates to management over a joint venture based on the proportion of equity ownership a firm has, which determines the firm’s power over the strategic direction of the joint venture.
To determine the preferred equity ownership strategy of international joint ventures, previous research has investigated the relationship between different equity strategies and joint venture performance. Three general equity strategies are possible: majority equity ownership share, fifty-percent equity ownership share and minority equity ownership share, though most literature is focused on either a majority equity ownership share or fifty-percent equity ownership share. Previous research has found mixed results, where evidence on firm performance, measured in different ways, was found positive, negative or even insignificant for all different equity structures.
Regarding the majority equity ownership share, it is viewed as potentially advantageous in reducing managerial complexity as well as in reducing challenges related to coordination, thereby alleviating potential conflicts between the joint venture partners (Merchant, 2002). Related to this, a reduction in complexity reduces the time necessary to make decisions, a timely effort when joint decisions must always be made (Zeira & Parker, 1995). The positive aspects for the majority equity ownership share are particularly evident when the partner that holds the majority possesses more advanced technology and skills (Calantone & Zhao, 2001). This partner will also have a higher incentive to invest in the joint venture. However, this could decrease the investment and cooperation incentives from the partner with a minority equity ownership share (Mantecon, Liu, & Gao, 2012). If the dominant partner exploits their powerful situation, the minority partner can get frustrated and conflicts can arise (Christoffersen, 2013).
10 interests are harmonised (Luo, 2001). This occurs as there is more monitoring between the partners as well as exchange of information, thereby reducing the incentive to make value-destroying and self-interested decisions (Mantecon et al., 2012). Furthermore, as trust is important in the maintaining of a stable joint venture where both partners contribute valuable resources, this is most viable in a partnership without dominant partner (Christoffersen, 2013). However, coordinating activities has a high cost in a fifty-percent equity ownership share structure (Desai, Fritz, & Hines, 2004). Also, it is an inefficient organisational structure, because the joint decision making can lead to conflicts and slow decision making speeds due to permanent negotiation (Mantecon et al., 2012; Meschi & Riccio, 2008).
In a recent study, Li, Zhou and Zajac (2009) research a possible inverted U-shaped relationship between equity ownership structure and joint venture productivity. The authors argue that a focus on either control benefits or collaboration benefits alone suggests different linear patterns. Combining the two would provide an inverted U-shaped relationship where an increase in foreign equity ownership increases partner commitment, but an excessively dominant foreign ownership reduces commitment and knowledge transfer from the minority partner. Significant quantitative evidence for the inverted U-shaped relationship is found for international joint ventures based in China.
11 Hypothesis 2a: Equity ownership in joint ventures has an inverted U-shaped relationship with the firm value of joint ventures in the energy industry.
Combining the subjects of renewable energy and control, it is expected that the inverted U-shaped relationship between equity ownership share and firm value changes for different energy sources. This could occur due to changing tensions based on dominating joint venture management and the circulation versus retention of knowledge (Meschi & Riccio, 2008). Whether firms are willing to cooperate and share their knowledge depends on the equity ownership share of the firm itself and its partner.
In the renewable energy industry, a knowledge base is required that exceeds the possibilities of a single firm in order to create scientific discoveries and equity is required for the financing of Research and Development (R&D) (Baldi, Peri, & Vandone, 2014; Yang et al., 2014). Due to the unique coordination challenges that joint ventures with a high R&D intensity face, as those in the renewable energy industry, it is important to sustain incentives to share tacit knowledge even with moral hazards present (Sampson, 2007). Zhang, Li, Hitt and Cui (2007) studied whether R&D intensity combined with a majority equity ownership share leads to increased performance, as this would lower moral hazards of local partners. However, the results were insignificant and thus there is no support of a positive relationship between majority equity ownership share and performance. This would lead to the expectation of increased benefits of fifty-percent equity ownership share for joint ventures that deal with renewable energy, as these provide high levels of cooperation while at the same time minimise moral hazards due to increased monitoring (Mantecon et al., 2012). This leads to the anticipation that the valuation effects are more prominent for renewable energy joint ventures, with a higher peak in terms of benefits around the fifty-percent equity ownership share, after which a sharper decline follows for increasing majority equity ownership share. The following hypothesis can then be formed:
Hypothesis 2b: The inverted U-shaped relationship between equity ownership and firm value is more prominent for renewable energy joint ventures.
12 2008). Joint ventures are one way of cooperation between firms to collaborate on pollution diminishing innovations (Chiou & Hu, 2001). This would then be especially relevant in firms involved in renewable energy joint ventures. Due to the regulations present, finding solutions to incorporate renewable energy might be more important, which would lead to a higher peak for renewable energy. To incorporate the possible differences in the European firms and thus regional aspects of joint ventures as well as the influence of the EU ETS, the hypotheses regarding joint venture control will also be researched for the European dataset. These hypotheses are as follows:
Hypothesis 2c: For European firms, equity ownership in joint ventures has an inverted U-shaped relationship with the firm value of joint ventures in the energy industry.
Hypothesis 2d: For European firms, the inverted U-shaped relationship between equity ownership and firm value is more prominent for renewable energy joint ventures.
2.5 Measuring performance
There has been a growing interest within academic literature on the topic of joint venture performance as there is a high level of competition combined with a high failure rate (Gomes et al., 2016). In the energy industry, only 20 percent of oil companies are skilled in matching their objectives with their joint venture approaches (Ernst & Steinhubl, 1997).
13 When a firm considers forming a joint venture, it is often wary of the impact of the joint venture on the valuation of the firm itself (Beamish & Lupton, 2009). There are subjective and objective measures to assess the performance of joint ventures, where subjective measures relate to goals obtained while objective measures relate to profit maximising figures (Park & Kim, 1997).
First, subjective measures reflect the goals of the partner firms and whether those goals are met. Therefore, they are argued to reflect the advantages of joint ventures as sought by the partner firms (Anderson, 1990). However, there is sensitivity to the results, where only the initial goals are taken into account and the outcome might be based on the personal view of employees (Christoffersen, 2013). For example, it is possible that the joint venture has not achieved its initial objectives due to premature termination, while the performance of the joint venture itself is satisfactory (Beamish & Lupton, 2009). On the other hand, objective measures relate to accounting measures, which have several advantages. Accounting measures have a higher reliability in their interpretation over survival measures, as their results are always related to performance, whereas for survival this depends on the context. It also has advantages over subjective measures, which have problems related to subjectivity due to same-source variance (Christoffersen, 2013). As there remains ambiguity related to the performance of joint ventures, it is important to use objective and empirical methods (Beamish & Lupton, 2009).
14 3. Methodology
3.1 Data collection and analysis
For the joint ventures in the energy industry, the main source of information was the Zephyr database by Bureau van Dijk. This database has information on joint ventures based on deals made between firms that include the announcement date, deal value, description and often the equity ownership structure. The search for joint ventures was limited to announcements between 2000 and 2015 and only joint ventures with a positive deal value were included. Moreover, rumoured joint ventures were excluded so that only officially announced joint ventures were incorporated. No specific country was chosen as energy joint ventures occur globally, though one of the partner firms had to be from the European Union as to ensure compliance with the EU ETS.
The search for joint ventures was done manually in two phases. First, a search for non-renewable energy joint ventures was based on industry classification (SIC) codes related to the energy business. Second, as there is no specific SIC code for renewable energy, keywords were used based on an extensive list of types of renewable energy. These joint ventures had to include ‘energy’ in their description to ensure that they were involved in the energy business and not solely e.g. ocean shipping instead of ocean energy from tidal waves. Table 2 shows the SIC codes and keywords used.
Table 2. SIC codes and keywords used to search for joint ventures
Non-renewable energy - SIC Codes Renewable energy - Keywords
12 Coal mining renewable biofuel (s)
13 Oil and gas extraction solar biopower
291 Petroleum refining photovoltaic bioproducts
299 Miscellaneous products of petroleum and coal water hydropower
wind ocean
geothermal hydrogen
15 The searches provided an initial amount of 386 firms, of which 331 unique, involved in 161 joint ventures. The joint ventures were then researched to determine their lifespan or if they were still in operation under the same owners. This data was manually collected based on annual reports and other digital sources, such as firm announcements where one firm was bought out and joint ventures became subsidiaries. Subsequently, DataStream was used to gather data on those joint ventures active anytime between 2005 and 2015. The raw dataset included 1,227 observations of unbalanced panel data. Data points were considered outliers if they were more than three standard deviations away from the mean in either direction (Osborne & Overbay, 2004). Observations were removed if this occurred for firm value, otherwise only the value of the variable itself was removed, resulting in a dataset of 1,213 observations.
3.2 Dependent and independent variables
Firm value, for the purpose of this study, is measured as the market-to-book ratio of equity as discussed in the literature review. The interpretation of the outcome is then valued in relation to the independent variables, which are discussed below.
Regarding the energy source, a distinction is made between renewable energy and non-renewable energy based on both SIC-coding and the purpose of the joint venture in question. A dummy variable labelled ‘Energy Source’ was then created, where a joint venture involved with renewable energy has a value of “1”. Joint ventures working with non-renewable energy have a value of “0”.
16 Regarding the equity ownership structure of the joint ventures, a dummy variable was created to incorporate the difference between equal, majority and minority equity shares. Joint ventures with a fifty-percent equity ownership share structure or other equally shared equity ownership percentages assumed a value of “0”. A joint venture partner firm took the value of “1” if the partner had a majority equity ownership share (Merchant, 2002), whereas the value of “2” was given to partners with a minority equity ownership share. Moreover, a dichotomous dummy variable was included to include the effects for the European firms. The variable took the value of “1” if the parent firm was from a European country and the value of “0” otherwise. To determine the non-linear relationship between the equity ownership percentage and firm value, a variable was created to incorporate the exact equity ownership percentages of the firms behind the joint venture. The equity ownership structure was not always given. In these cases, the registered capital of the firm to the joint venture was divided by the total registered capital of the joint venture, which created an equity ownership percentage (Li et al., 2009).
3.3 Control variables
Several control variables were implemented to determine the relative impact of the newly tested independent variables. The following control variables were implemented in the current study:
Size: the study controls for the size of the firms as this is related to firm-specific resources and the ability of firms to create value (Merchant, 2002). A negative relationship with firm value is expected, as previous research related to joint venture longevity, a different performance measure, found that this decreased for firm size (Delios & Beamish, 2001). It is measured as the logarithm of total assets (Salamaa & Putnamb, 2013).
Leverage: this relates to the debt-to-equity ratio of the firm and has been found to have an impact on tax shields and thereby the value of a firm (Wang & Zhang, 2015). A higher debt-to-equity ratio is expected to have a positive relationship with firm value. It is measured by dividing the total debt by total equity.
17 Culturally embedded opportunism: cultural differences between the joint venture partners and thus the culture of the firms behind the joint venture could impact the performance of the joint venture (Merchant, 2002). Culturally embedded opportunism is measured using the individualism-collectivism scores from Hofstede (1983). As there were often more than two partners behind the joint venture, the absolute value of the individualism-collectivism score was used instead of the absolute difference in scores between firms as in Merchant (2002). The scores were taken from the Hofstede Centre4.
Interest rate level: this control variable relates to risk factors of the equity market. It is measured both on an annual basis and as a five-year average level. The interest rate level a firm has impacts its firm value and especially so if hedging activities are undertaken, whereby firm value increases even as the uncertainty of the interest rate level increases (French, 1985; Panaretou, 2014). It is unclear what the relationship will be, because the relationship between interest rate levels and firm value differs per industry (Lesseig & Stock, 1998).
Number of partners: if there are more partners involved in the joint venture management, this can increase conflicts and complexity of the decision-making. This could lead to termination of the joint venture and is therefore expected to decrease firm value (Meschi & Riccio, 2008). It is measured by manually counting the number of partners that are present in each joint venture.
3.4 Data analysis
Regression analyses are conducted with the use of SPSS and with firm value as the dependent variable. To investigate the influence of stock returns as a mediator between oil prices and firm value, several steps need to be undertaken. First, the direct effects of oil prices on firm value and oil prices on stock returns need to be significant. Then, a multiple regression analysis is required including the effect of both the independent variable oil prices and the mediating variable stock returns on firm value. A mediation effect is present if the oil prices are no longer significant while the stock returns remain significant (MacKinnon, Fairchild, & Fritz, 2007).
Regarding the expected inverted U-shaped relationship between the equity ownership percentage and firm value, a curve estimation regression is undertaken. This analysis includes the quadratic
4
18 function necessary to create and test the (inverted) U-shaped model. The regression equations are given in table 3. Only the main independent variables are included in the equations.
Table 3. Regression equations
Variable Regression equation(s)
Oil Prices Firm value = ₀ + ₁*Oil prices + Mediation Effect 1. Firm value = ₀ + ₁*Oil prices +
2. Stock returns = ₀ + ₁*Oil prices +
3. Firm value = ₀ + ₁*Oil prices + 2*Stock returns + Equity Ownership Firm value = ₀ + ₁*Equity percentage2
+
4. Results
4.1 Descriptive statistics
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Table 4. Descriptive statistics of the full sample for the period 2005 to 2015
Variable N Mean Median Std. Dev Min Max Valid Missing Firm value 1,107 106 1.84 1.55 1.15 -2.31 8.70 Stock returns 1,128 85 8,419.22 1,037.19 14,618.21 .00 60,074.81 Oil price 1,213 0 85.76 96.94 21.90 52.32 111.63 Equity % 1,158 55 40.25 49.00 17.89 0.80 90.00 Interest rate 1,114 99 4.82 4.13 4.18 0 79 Interest rate 5 year average 1,063 150 5.19 4.64 3.44 0 43 INDIV 1,192 21 61.56 68.00 23.87 13 91 GDP PC 1,115 98 34,257.82 38,364.94 20,827.17 729.00 102,832.26 # Partners 1,213 0 2.85 2.00 1.32 2 7 Size 1,126 87 17.46 17.69 2.55 8.76 24.54 Leverage 1,125 88 .94 .50 1.60 -4.35 12.71 4.2 Correlations
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4.3 Univariate analysis
Table 5 shows the univariate analyses of the independent variables on the dependent variable firm value. The entire dataset is incorporated as well as the separate results for non-renewable and renewable energy. The tables for the separate datasets and including the control variables can be found in appendix D, tables D1, D2 and D3.
Table 5. Univariate analyses for each separate independent variable
Firm value
Variable R R Square Beta Significance
Stock returns Total .027 .001 .027 .378
Non-renewable .022 .001 .022 .538
Renewable .072 .005 .072 .194
Oil price Total .158 .025 -.158 .000***
Non-renewable .210 .044 -.210 .000***
Renewable .077 .006 -.077 .160
Equity % Total .096 .009 -.096 .002***
Non-renewable .136 .018 -.136 .000***
Renewable .059 .003 -.059 .291
*** Indicates significance at the 1% level ** Indicates significance at the 5% level * Indicates significance at the 10% level
21 The equity ownership percentage again has a negative beta for all three univariate analyses (-.096; -.136; and -.059). It is significant at the 1% level for both the total dataset and the non-renewable energy dataset, but not statistically significant for the non-renewable energy dataset with a p-value of .291. The univariate analysis is linear and therefore does not include a possible curve yet, though it gives a first indication that a majority equity ownership share is not always beneficial, since an increase in equity ownership percentage is paired with a decrease in firm value. For the control variables, all variables aside from the number of partners are significant for both the total dataset and the non-renewable energy dataset. However, for the renewable energy dataset, only the interest rate level, culturally embedded opportunism and GDP per capita are significant.
4.4 Regression analyses
In order to determine the control variables to be included in the final model, a first regression was run with the control variables only (Wang & Zhang, 2015). Appendix E, table E1 shows the regression analyses of the control variables. All control variables were statistically significant aside from the interest rate level and culturally embedded opportunism. These were removed from the regression. Apart from the firm’s size, all of the betas and significance levels increased. The interest rate five year average went from 10% significance to 1% significance. Therefore, the following control variables are included in the final model: interest rate five year average, GDP per capita, number of partners, size and leverage.
4.4.1 Oil prices
22 4.4.2 Mediation effect
There are three steps for the mediation effect, as discussed in section 3.4. First, the direct effect of oil prices to firm value has a beta of -.077, which is negative as well as insignificant with a p-value of .160. Oil prices to stock return has a beta of -.019 and is insignificant with a p-p-value of .722. When both variables are entered into the model, the p-value of oil prices increases to .180 while that of stock returns decreases to .205. Moreover, the beta of stock returns changes to positive with .070. However, the coefficients remain statistically insignificant. The results can be found in appendix G, table G1. As all relations are insignificant, there is no proof that supports hypothesis 1b, where it was predicted that stock returns acts as a mediator between oil prices and firm value for renewable energy joint ventures.
4.4.3 Temporal effects
To determine the effect of the EU ETS on the firm value of energy joint ventures, the dataset was divided into three groups based on the phases of the EU ETS: 2005-2007; 2008-2012 and 2013-2015. For 2005-2007, the oil price has a positive and significant effect on firm value for all three datasets. For the total dataset as well as when only renewable energy is included the significance level is 1%, for non-renewable energy this is 5%. This would imply a rise in firm value if oil prices are high, even for the non-renewable energy joint ventures, which is unlike expected.
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Table 6. Regression analyses for the different phases of the EU ETS
Firm value
Variable All years 2005-2007 2008-2012 2013-2015
Oil price Total -.008***
[.000] .036*** [.000] .003 [.311] -.023 [.102] Non-renewable -.010*** [.000] .025** [.012] -.001 [.715] -.020 [.145] Renewable .001 [.880] .066*** [.000] .011* [.052] -.013 [.704] *** Indicates significance at the 1% level
** Indicates significance at the 5% level * Indicates significance at the 10% level
4.4.4 Curve estimation
Hypothesis 2a predicts an inverted U-shaped relationship between equity ownership percentage and firm value. The curve estimation regression includes a quadratic function to accommodate the possible (inverted) U-shape. The model has a beta of -.008 but is not significant with a p-value of .944.
Hypothesis 2b predicts that the inverted U-shaped relationship is more prominent for joint ventures that focus on renewable energy. The model has a beta of -.265, but it is not statistically significant with a p-value of .133. Figure 1 has the relationships between equity ownership percentage and firm value, where the inverted U-shaped relationship seems more prominent for renewable energy joint ventures, even though it is not significant. The R2 is quite low at .009 for the total dataset and .010 for the renewable dataset.
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Figure 1. Firm value of the entire dataset (top left), the renewable energy dataset (top right), the entire European dataset (bottom left) and the European renewable energy dataset (bottom right).
5. Discussion
5.1 Oil prices
25 cost because of high oil prices has the ability to reduce corporate earnings and affect economic activities in a negative way (Managi & Okimoto, 2013; Wan & Kao, 2015).
However, renewable energy has the potential to benefit from rising oil prices as it can be seen as a substitute for crude oil (Omri, Daly, & Nguyen, 2015). As previous research has found a link between oil prices and the stock prices of renewable energy firms, this was tested and expanded to confirm or reject the impact for the firm value of joint ventures. Contrary to previous research, the effects for the total time span of the dataset are all insignificant. The hypothesized relationship is not supported and there is no mediating effect of stock returns on the relationship between oil prices and firm value for renewable energy joint ventures. When comparing this to research by Omri, Daly and Nguyen (2015), they conclude that the impact of oil prices is only minor, whereas economic policies may be more efficient, though they look at the consumption of renewable energy and not the effects for the firms themselves.
The current study adds to this in that it takes the EU ETS into account. Aside from the regressions of the entire time period, the different phases of the EU ETS and therefore its possible effects have been included. When the three phases of the EU ETS are taken into consideration, the relationship between oil prices and stock returns remains insignificant. The relationship between oil prices and firm value is significant in phase 1 and 3 for the univariate analysis in all datasets, though the relationship switches from positive to negative. When the final control variables are included, the regressions in phase 3 cease to be significant. The question then remains why and when there is a switch in the direction of the relationship and what causes the drop in significance in the second phase as well as the third phase when controls are included.
26 and an R2 of .168. Appendix I tables I1, I2 and I3 show the results of the oil price in different phases as well as the changes in oil prices.
Figure 2. Changes in oil prices from 2005 to 2015
Phase 1 of the EU ETS is known as ‘learning by doing’. The general criticism of this phase relates to the prices of the carbon emission allowances, which dropped from approximately €30 to €5, most probable due to an unlikely high emission baseline on which the carbon emission allowances and prices were based. For joint ventures involved in non-renewable energy, this did not create an incentive to lower levels of CO2 emissions (Segura, Ferruz, Gargallo, & Salvador, 2014). On the other hand, rising energy prices and an intended increase in R&D incentives for renewable energy would have prompted the non-renewable energy joint ventures to lower prices while at the same time increasing the quantity of their export (Curuk & Sen, 2015). Both of these aspects could have led to an increased firm value even though oil prices rose and the EU ETS had started. Still, the positive effect on firm value is stronger for those joint ventures focused on renewable energy. This is in line with previous literature focusing on the influence of rising oil prices on renewable energy firms (e.g. Managi & Okimoto, 2013). However, the impact of the EU ETS in this first phase is minimal, because firms would only undertake actions when the costs of reducing emissions is lower than the costs of buying the carbon emission allowances (Anderson & Di Maria, 2011). With low allowance prices, the environmental policy implemented
27 has failed to encourage environmental innovation in the first phase and it is likely that the positive firm value is only attributed to the oil prices.
Although during phase 1 elements of the emission market were established and improved to provide smooth sailing for phase 2, the second phase of the EU ETS was mostly characterised by the financial crisis. Due to the economic downturn, an oversupply in carbon emission allowances was created (Koch, Fuss, Grosjean, & Edenhofer, 2014). Previous research by Brouwers, Schoubben, Van Hulle and Van Uytbergen (2016) found that the market value of firms participating in the EU ETS was only influenced when the price of carbon emission allowances was high in combination with anticipated allowance scarcity. This was not the case in the second phase of the EU ETS. The external circumstances can be found in the dataset where the change in oil prices is not significant for the non-renewable energy dataset taken over the period 2008-2012. Still, the oil price and its changes had a significant positive influence on the firm value of renewable energy joint ventures, though only marginal. This could have occurred due to overlapping policies to stimulate wind and solar energy (Koch et al., 2014).
28 5.1.2 Control variables
A negative relationship between size and firm value was hypothesized and found. This confirms previous research by Delios and Beamish (2001). For leverage, a positive relationship between the debt-to-equity ratio of a firm and its firm value was expected. The results verify this expectation, confirming the tax shield effect (Wang & Zhang, 2015). As for the interest rate level, only the five year average was taken into account in the final model. There were no clear expectations regarding the effect it would have on firm value. The results show a positive relationship. This adds clarity to conflicting previous literature on the effect of interest rate levels of industrial firms on firm value.
For GDP, the expectation was a positive relationship with firm value due to possibilities of spending on publicly funded research. However, the results show a significant negative relationship between GDP and firm value. When compared to previous research, results on GDP itself are mixed. A study focused on the USA found a negative relationship, while a study focused on a large panel of countries found a positive relationship (Baccini & Urpelainen, 2012; Omri et al., 2015). Though this study focuses on a panel of countries, the results do not match the previous research, which might be due to the different time span, since Omri et al. (2015) have data from 1990 to 2011. Finally, regarding the number of partners, a negative relationship was expected due to complexity of decision-making. However, a positive relationship was found. This is unlike previous research, which shows a consistent negative relationship (e.g. Gong, Shenkar, Luo, & Nyaw, 2007). This difference could occur due to the industry of the joint ventures. The energy industry is characterised by joint ventures to work on large and complex issues or sites and therefore lends itself to the possibility of multiple partners.
5.2 Curve estimations
29 Although a visual representation shows the expected relationship, it is not significant. When performing a linear regression, the equity ownership percentage squared remains insignificant.
The same estimations were undertaken for the European dataset. Here, the results show a different story where both the total and renewable energy datasets follow an inverted U-shaped relationship and renewable joint ventures follow a more leptokurtic curve. As a robustness check, a linear regression with the squared value of equity ownership percentage was again undertaken. Here, the results and significance levels are exactly the same, confirming the outcome of the curve estimation.
This provides partial evidence, in limited datasets, of the inverted U-shaped relationship between equity ownership percentage and firm value. For the European dataset, the previous research by Li et al. (2009) is confirmed. Whereas they base their findings on international joint ventures based in China, the current research corroborates the theory for European firms that enter into energy joint ventures. Though Li et al. (2009) find a peak at 57% ownership; the current study has a lower peak at 41%, which could occur due to the differences in dataset, time period and industry focus. As a robustness check, the curve estimation was also performed for the firms outside Europe, though the results were not significant. Instead, a negative linear relationship was found. This relates to previous research by Park and Kim (1997), Pangarkar and Lee (2001) and Meschi and Cheng (2007). These three studies all focused on areas outside of Europe: USA, Singapore and China, respectively. This further emphasises the different effects that equity ownership could have based on firm and country characteristics.
30 Reserve (USA). In all of these cases, the partner firm from the EU had 50 per cent equity ownership or less.
So why is the inverted U-shaped relationship only significant for European firms? The regional economic integration and the institutional environment within Europe have caused a thriving environment for joint ventures. Here, the aim of joint ventures is often related to technology transfer and learning while at the same time not losing control and protecting intellectual property (Oxley, 1999). To foster this, a joint venture in which one of the firms has dominant control through a high level of equity ownership is not desirable. Moreover, the time period in this research coincides with the periods of the EU ETS. In order to oblige to the EU ETS rules and regulations, innovations are necessary to diminish carbon emissions in order to avoid fines. This would have been especially important during the later time period of the EU ETS. Joint ventures can be used to create new cutting edge innovations by cooperating with other firms. The knowledge of multiple firms is then combined and the necessary collaboration thrives in cooperative joint ventures.
This is in line with, and adds to, the general literature on joint ventures regarding cooperation between firms to create synergies, in opposition to the acquisition of knowledge to win in a ‘race to learn’ (Grant & Baden-Fuller, 2004). In the energy industry, and especially where renewable energy is concerned, cooperation is important to create sustainable solutions. The projects undertaken are of a large scale and are often too complex for a firm to undertake on their own. Therefore, though there will always be tension between the firms, it is important to increase gains through the synergies and new technologies created, whether they are innovative measures to extract gas from a near-empty field or a cutting edge technology for new photovoltaic cells.
6. Conclusion
31 When the entire time period is concerned, oil prices are negatively and significantly related to firm value for non-renewable energy, thereby confirming previous research on the subject. However, no evidence of the effect on firm value is found when renewable energy is concerned. When the three phases of the EU ETS are taken into account in combination with changes in the oil price, it can be seen that changes in the oil price have a positive relationship with firm value, though this is not significant in all phases. The increasing oil price of phase 1 in combination with a still not optimally working EU ETS led to a change in strategy for non-renewable energy joint ventures to focus on discounts and quantity. Renewable energy joint ventures were able to benefit from rising oil prices, though without the scarce input of the EU ETS. The second phases is characterised by the financial crisis and an oversupply of carbon emission allowances. Oil prices only had a small influence on the firm value of renewable energy joint ventures, which is most likely due to overlapping policies to stimulate wind and solar energy. Finally, it can be said that the EU ETS started working properly in its third phase. Changes were made to improve the efficiency of the carbon market and the influence of decreasing oil prices is not significant anymore. This is especially important for renewable energy joint ventures, which were expected to decrease in value. This shows that there is an increasing emphasis put on the importance of renewable energy.
32 venture. Taking those two aspects together, an internal equity ownership structure can then be chosen to combine both internal goals and external regulations such as the EU ETS.
This paper contributes to the existing literature on the performance of joint ventures. As there is a high failure rate amongst joint ventures as well as ambiguity amongst previous research, it is important to establish good practices. The conflicting evidence found in previous research might be caused due to taking a general approach, rather than focusing on a single industry. This paper forms the context for a single industry and even for subdivisions within that industry, to create a well-defined context that can aid both researchers and practitioners. With regards to oil prices, several macroeconomic variables had been researched without providing a general consensus. Due to its focus on a single industry and a sub focus on the differences between non-renewable and renewable energy, this study has uncovered how the impact of oil prices can differ. This is intertwined with the influence of the EU ETS, which has shown its importance especially in its later phase, where the determining power of oil prices was undermined by the policies of the EU ETS. Moreover, the current study contributes to the international business literature by enhancing the available knowledge on equity ownership structure, an important aspect of joint ventures that has previously received mixed results. This research shows that it is not equity ownership on its own that matters, but that it needs to be combined with other factors such as an institutional environment, the European Union in this case, by providing significant empirical relationships that differ between European and non-European firms.
The results of this study are useful for policy makers too. It is important to realise the dynamics between oil prices and the renewable energy industry, due to a focus of policies on the decreasing reliance on fossil fuels and in turn an increase in renewable energy. The right incentives are needed for firms to cooperate and to invest in the development of renewable energy. The EU ETS has set the first steps for this as a large and harmonised carbon emission market, though not without its struggles. It even diminished the effect of oil prices on firm value in phase 3, which adds to a growing, though still small, literature that shows a weakening importance of oil prices. The positive aspects of the EU ETS can be used by policy makers to alter new policies around the world, such as the Paris Agreement.
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40 Appendices
Appendix A. Variables and codes
Table A1. Variables and their codes
Variable Code
Firm value Firm value
Stock returns Stock returns
Oil price Oil price
Percentage of equity ownership Equity %
Interest rate level Interest rate
Interest rate level five year average Interest rate 5 year average Culturally embedded opportunism INDIV
Gross Domestic Product Per Capita GDP PC
Number of partners # Partners
Size Size
41 Appendix B. Skewness, Kurtosis and Normality
Table B1. Skewness and Kurtosis of the variables
Variable N Skewness Std. Dev Kurtosis Std. Dev Valid Missing Firm value 1,107 106 1.395 .074 3.658 .147 Stock returns 1,128 85 1.941 .073 2.548 .146 Oil price 1,213 0 -.172 .070 -1.546 .140 Equity % 1,158 55 -.150 .072 -.261 .144 Interest rate 1,114 99 7.464 .073 102.142 .146 Interest rate 5 year average 1,063 150 5.251 .075 46.546 .150 INDIV 1,192 21 -.608 .071 -.819 .142 GDP PC 1,115 98 .332 .073 .495 .146 # Partners 1,213 0 1.911 .070 3.117 .140 Size 1,126 87 -.434 .073 .201 .146 Leverage 1,125 88 3.633 .073 16.765 .146
Table B2. Normality tests of the variables
Variable Kolmogorov-Smirnov Shapiro-Wilk
Statistic Sig. Statistic Sig.
Firm value .115 .000*** .913 .000***
Stock returns .289 .000*** .630 .000***
Oil price .202 .000*** .860 .000***
Equity % .200 .000*** .934 .000***
Interest rate .164 .000*** .546 .000***
Interest rate 5 year average .130 .000*** .780 .000*** INDIV .146 .000*** .879 .000*** GDP PC .129 .000*** .908 .000*** # Partners .305 .000*** .655 .000*** Size .084 .000*** .982 .000*** Leverage .263 .000*** .564 .000***
The Kolmogorov-Smimov test of normality has been corrected for the Lilliefors Significance
42 Appendix C. Correlations Table C1. Correlations 1 2 3 4 5 6 7 8 9 10 1 1 2 -.032 1 3 -.118** .039 1 4 -.123** -.067* -.024 1 5 -.131** -.113** -.050 .516** 1 6 .353** .006 -.057 -.017 .036 1 7 .189** .086** -.132** -.021 .075* .697** 1 8 .129** -.016 -.646** .106** .099** .139** .288** 1 9 .241** -.010 -.087** -.206** -.223** -.107** -.018 .055 1 10 -.178** .028 .007 -.180** -.201** -.047 .004 -.094** .042 1 1 is Stock returns, 2 is Oil price, 3 is Equity %, 4 is Interest rate, 5 is Interest rate 5 year average. 6 is INDIV, 7 is GDP PC, 8 is # Partners, 9 is Size and 10 is Leverage
Table C2. VIF statistics
Variable Tolerance VIF
Stock returns .682 1.465
Oil price .927 1.079
Equity % .558 1.792
Interest rate .643 1.556
Interest rate 5 year average .577 1.734
INDIV .361 2.773
GDP PC .365 2.741
# Partners .461 2.171
Size .859 1.164
