The Impact of Corruption on the
Relationship between Internationalization
and Firm Performance. Evidence from U.S.
Oil and Gas Companies.
Combined Thesis for MSc International Financial Management & MSc Finance University of Groningen – Faculty of Economics and Business
Author: Harwin de Vries Studentnumber s2187248
Date: 08-01-16
Supervisor: dr. R.O.S. Zaal
Abstract
This research investigates the moderating effect of corruption on the relationship between internationalization and firm performance for the oil and gas industry. A total of 97 U.S. parents companies are researched for the period 2009 till 2014. Panel data analysis is used to predict the effect of corruption on the relationship between firm performance and internationalization. The results indicate that internationalization negatively influence performance measured in ROA and ROE. Furthermore, the perceived level of corruption in a country has a negative moderating impact on the internationalization-performance relationship. These results add to the existing internationalization literature and increase the understanding of the different underlying theories regarding firm performance and internationalization, as well as the empirical ambiguity of this relationship.
Introduction
A common strategy for firms to grow is internationalization. Internationalization is the supplier’s entry and expansion in multiple markets (Andersen, 1993; Calof and Beamish, 1995). It constitutes the process of adaption, change and development within the fundamental functions, systems and structures of the firm outside its home market (Rask, Strandskov and Hakonsson, 2008). The relationship between internationalization strategies and firm performance has been extensively researched over more than 30 years (Bausch, and Krist, 2007) in various research fields (e.g. international management, economics, marketing, finance, accounting and strategic management). Unfortunately, the effects of internationalization on the performance and value of the firm shows a lack of consensus among researchers (e.g Denis et al., 2002; Morck and Yeung, 1991; Gande et al, 2009; Salama and Putnam, 2013). Previous research at firm level shows that internationalization strategy can have a positive or negative outcome. These outcomes suggest that it is likely that other factors influence the relationship between firm performance and internationalization.
This research will specifically focus on the moderating impact of corruption on the internationalization-performance relationship for oil and gas firms from the U.S. There are several reasons underlying this choice. The first is that due to the worldwide increasing amount of competition and advances in technology, more and more oil and gas firms are setting up foreign subsidiaries (Ernst and Young, 2014; Keogh, Jack, Bower and Crabtree, 1998). Therefore it is an industry characterized by a high level of internationalization. Second, in their pursuit and outside pressure to grow, oil and gas firms start doing business in new countries, they first presumed as too risky, too expensive or too dangerous to do business with, even when there is a high risk of corruption (Ernst and Young, 2014). Key growth markets in the oil and gas industry are countries in Latin America, Africa and the Middle East. Due to instable political systems and a lack of infrastructure these countries are inherently risky. Most of the countries in these areas score low on the Corruption Perception Index, indicating that there is a higher perceived risk of bribery. Third, in the oil and gas industry there is a frequent interaction with public officials since many organizations in the industry are (partly) state-owned. These countries tend to be excessively bureaucratic (Ernst and Young, 2014). These reasons result in many possibilities of demanding a bribe (Al-Kasim et al., 2013). Oil and gas companies often hire a third-party in the form of an agent or a local subsidiary to manage these negotiations. However, under the US Foreign Corrupt Practices Act (FCPA) companies are still liable for their actions, which can therefore still result in severe penalties. Fourth, attention on corrupt practices in international oil and gas investments have been increased recently, which has led to several charges for American oil and gas companies (Olawuyi, 2015). In some cases officials of the companies have even gone to jail for facilitating bribery (Olawuyi, 2015). Fifth, according to the Bribe Payers Index (Transparency International, 2014) firms in the oil and gas sector, are among the firms that are most likely to engage in bribery. Also Al-Kasim et al. (2013) found evidence that corruption is an exacerbating challenge for oil and gas firms. This can result in severe penalties for these companies and a loss of trust, which would inherently lead to a lower firm performance. Finally, according to the Bribe Payers Index (Transparency International, 2008), companies originating from the U.S., together with Spain, and France, have a greater likelihood of resorting to corruption and bribery than other Western countries.
a lower firm performance. However, as far as I am aware, there has not been any research on the impact of corruption on the internationalization-performance relationship. Also Al-Kasim et al. (2013) confirm that empirical evidence into corruption in the oil and gas industry is weak, non-existent or non-available.
In their meta-analysis Bausch and Krist (2007) argue that there is need for further research focusing on other contextual settings such as, industry sector or competition to come to a broader understanding of the effects of internationalization and its consequences on the performance of the firm. This research tries to contribute to the existing research on internationalization by looking at the effects of internationalization on firm performance in a specific industry, namely oil and gas for U.S. based companies. As mentioned before, this industry scores relatively high on corruption levels and given the growing interest and crucial importance of corruption worldwide (Judge, McNatt and Xu, 2011), this research will next to the gap that Bausch and Krist (2007) mention, also look at the moderating influence corruption has on the internationalization-performance relationship. Therefore this research contributes to a broader understanding of the different antecedents and factors that play a role in the internationalization-performance relationship. Furthermore, the outcome of this research also has high practical value for U.S. based oil and gas companies, which are currently internationalized or are planning to be. The research question that this paper tries to answer is the following: What is the impact of the level of corruption in a country on the
relationship between internationalization and firm performance for companies in the oil and gas sector from the United States? The question is answered using panel data regression on 97
U.S. publicly listed oil and gas companies from the period 2009-2014.
The remainder of this thesis is structured in the following way. The next section gives an overview of relevant theories regarding internationalization and firm performance and the possible impact of corruption on this relationship. Previous research will be discussed and hypothesis will be developed. In the third section, the methodology will be discussed. In the fourth section the results will be presented. Finally, the last section discusses the main conclusions of the research and points out the limitations of the research and possibilities for future research.
Literature review
Throughout the years, many different perspectives and theories have been used to explain or address the relationship between internationalization and firm performance. The most important, influential and prevalent theories and models are discussed, followed by previous research that empirically tested the relationship between internationalization and firm performance and the lack of consensus between these researches. Consequently, a hypothesis about internationalization-performance relationship will be developed for the U.S. oil and gas industry. By reviewing previous research on this relationship, it becomes clear that more research is needed on possible factors that influence the relationship between internationalization and firm performance. The main moderating factor that will be empirically tested in this research is corruption. The underlying theories why corruption possibly influences the internationalization-performance relationship are reviewed. As the literature review on corruption will show, corruption can be classified in different categories and can take place in the public and private sector. There are several disadvantages of corruption and bribery, however there can be advantages of corruption as well. These disadvantages and advantages will be reviewed, and the corresponding impact they can have on the internationalization-performance relationship. A hypothesis on the moderating influence of the level of corruption a firm face will be made. The literature review concludes with the conceptual framework of this research.
Theories on Internationalization
Positive Traditional Theories – Learning Theory, the Uppsala Model and the Innovation-Related Internationalization Models
The traditional behavioral perspectives argue firms internationalize in a slow and incremental manner due to high-risk aversion, high (perceived) uncertainty and a lack of knowledge about foreign markets (Madsen and Servais, 1997). Learning theory is most prevalent in the traditional theories. Learning theory views internationalization as an incremental process. The firm gradually adapts to its new environment and therefore slowly increases their international involvement. Firms gradually acquire experience by launching a number of foreign activities (Rask, Strandskov and Håkonsson, 2008). This process fosters knowledge development and organizational learning (Barkema and Vermuelen, 1998). The uncertainty and risks involved in these foreign activities are balanced against the growing economic and organizational involvement. Commitment and experimental learning are, as opposed to a ‘rational plan’, the driving forces behind a further involvement in international operations (Rask, Strandskov and Håkonsson, 2008).
to these foreign markets, the Uppsala model argues, that the internationalized firm will gain a competitive advantage over domestic firms through acquisition, gradual integration, successively increasing commitment to foreign markets and by acquiring, assimilating and exploiting knowledge about foreign markets and operations (Johanson and Vahlne, 1977). If this competitive advantage is exploited, internationalized firms will perform better than purely domestic firms. Knowledge about foreign markets is among the most important sources of a multinational sustainable competitive advantage (Hsu and Pereira, 2008).
The Innovation-Related Internationalization Model (I-Model) provides another perspective of the incremental development of internationalization (Lin, 2012). The I-Model focuses on internationalization as an innovation of the company (Bilkey and Tesar, 1977; Cavusgil, 1980; Reid, 1981; Czinkota, 1982). It views internationalization as a process wherein each next step in the model is an innovation for the firm. The I-Model is partly developed on the basis of the Uppsala Model (Andersen, 1993), it also focuses on the learning sequence. However, experiences, motivation, expectations and attitude of the decision-makers have a significant impact on the internationalization process in the I-Model (Reid, 1981). This is opposed to the Uppsala Model where knowledge was the only explanatory variable. Furthermore, as opposed to the Uppsala Model, the I-Model only focuses on small- and medium-sized firms. The I-model explains how the process of exporting products begins, the role of the management and the factors that influence the decision to internationalize (Collinson and Houlden, 2005). In other words, the decision to internationalize is considered as an innovation for the firm (Andersen, 1992).
The I-Model is empirically tested and supported ((Bilkey and Tesar, 1977; Cavusgil, 1980; Reid, 1981; Czinkota, 1982). Bilkey and Tesar find six different stages in which internationalization takes place for these companies. These stages emphasize the innovation process of internationalization. The I-Model explains how the internationalization process begins, the role of managers, and the factors that influence the decisions (Collinson and Houlden, 2005). Bilkey and Tesar (1977) show that internationalization according to the I-model not only increases the performance of large firms, but also of small- and medium-sized firms. The importance of these findings is that the learning theory, that suggests a positive relationship between internationalization and performance, is also applicable to small- and medium-sized firms.
internationalization process of a firm and its effect on the performance of the firm, there is a growing amount of researchers that questions the generalizability of these models to the increasing globalization and the evolution of the economic environment (Torres, 2004; Gankema, Snuif and Zwart, 2002; Axinn and Metthyssens, 2002). McDougall and Oviatt (1994) argue that an increasing amount of companies are focusing on the international market right from their birth. This is possible due to the network and entrepreneurial skills they have.
Positive Modern Theories – Internationalization of Born Globals
Beamish, 2001; McDougall and Oviatt, 1996; Zahra et al., 2000). However, this has not been tested for specific industries (Fernhaber, McDougall and Oviatt, 2007).
Positive Modern Theories – Tax Advantage Theory
As opposed to the previous mentioned theories on internationalization, the underlying reason for going abroad according to the tax advantage theory is not increasing sales but lower corporate taxes, which consequently lead to a higher profit and therefore a higher firm performance. Recent media releases have shown that many U.S. multinationals (e.g. Starbucks, Apple, and Microsoft) take on aggressive transfer pricing behavior to take advantage of differences in tax laws and tax rates (Taylor, Richardson and Lanis, 2015). Firms with subsidiaries located across countries with variably taxed jurisdiction can shift income (e.g. royalty income, interest, dividend) to subsidiaries with low-tax jurisdictions and can allocate tax-deductible expenses (e.g. interest on debt, R&D expenses) to high-tax jurisdictions (Jacob, 1996). Especially U.S. multinational firms in the technology and pharmaceutical industries are able to shift profits offshore by manipulating the transfer prices of goods and services income can be shifted (Taylor, Richardson and Lanis, 2015). Since the oil and gas sector is a technology intensive industry, this theory is possible also applicable to the oil and gas industry. Research proves that the tax liabilities of U.S. firms with tax haven subsidiaries, are much lower than those U.S. firms without them (Dyreng and Lindsey, 2009). Famous tax havens are the Cayman Islands, Switzerland, Luxembourg, Bermuda, British Virgin Islands, Ilse of Man and Puerto Rico. Semi-tax havens are Ireland and The Netherlands. Tax advantages lead to lower cost, which lead to a higher performance of the firm. Therefore the tax advantage theory suggests a positive relationship between internationalization and firm performance.
Negative Theories
political risks. Examples of political risks are government regulations, trade laws, and currency fluctuations (Sundaram and Black, 1992). Exposure to these risks increases uncertainty and tends to increase costs, which therefore result in a lower firm performance. Foreign markets can also be highly bureaucratic and inefficient, this has also a negative influence on the relationship between internationalization and performance. Agency theory suggests a negative impact of internationalization on the performance of the firm (Hsu and Pereira, 2008) as well. Shareholders and managers have diverging interest, according to agency theory. As a result, managers can pursue strategies that are not in the best interest of the shareholders when the managers are not being monitored closely (Jensen and Meckling, 1976). According to Morck and Yeung (1991), internationalization is a preferred strategy for managers since its increases prestige and it lowers firm-specific risks due to diversification of the firm operations. However, these goals are not necessarily in the best interest of the shareholders. Consequently, this can lead to a lower firm value and a lower firm performance, suggesting a negative relationship between internationalization and performance.
Review of Empirical Research of the Internationalization-Performance Relationship
Contractor, Kundu and Hsu (2003) found that companies in the service sector incur in the beginning stages high initial learning cost when they internationalize. In later stages companies can be over-internationalized and therefore there is a net negative effect on performance (Contractor, Kundu and Hsu, 2003). Also Elango and Sethi (2007) found similar results regarding multinational firms from countries with large economies (U.S., Japan). Reviewing previous research, evidence on the relationship between internationalization and performance is conflicting. This makes it hard to make a distinctive prediction of the relationship between performance and internationalization, especially given the different theories and perspectives that try to explain the relationship between internationalization and performance.
Different theoretical perspectives and previous research suggest a positive relationship between internationalization and firm performance. In summary, I expect the fowling:
Hypothesis 1: Under the learning theory and born globals perspective, internationalization has a positive influence on the performance of U.S. firms in the oil and gas industry.
Clearly, a large amount of theories and previous research suggest a negative relationship between internationalization and firm performance. I label this alternative explanation as the “agency theory and transaction costs” hypothesis.
Hypothesis 2: Under the agency theory and transaction costs theory perspective,
internationalization has a negative influence on the performance of U.S. firms in the oil and gas industry.
Corruption
at the expense of the public good. As a consequence, politicians can maintain their wealth, status or position by manipulating institutions, rules or policies (Transparency International, 2015).
In the private sector corruption creates unfair competition, as a consequence markets can be distorted. When companies pay bribes, they hide these things behind partnerships or subsidiaries. In most cases, oil and gas companies have to buy rights from the local government before starting operations, i.e. extracting oil and gas.
Corruption has several more negative effects. The first is that from an economic point of view, several researchers have proven the negative relationship between a country’s overall GDP and the level of corruption (Husted, 1999; Paldam, 2001; Serra, 2006). Second, Husted (1999), Paldam, (2001) and Serra (2006) furthermore argue that poverty makes individuals more inclined to accept bribes. Therefore a country’s level of economic development may systematically lead to higher corruption (Judge et al., 2011). Furthermore, some authors argue that a high level of corruption has a negative impact on the political structure (Goldsmith, 1999), political stability (Park, 2003), overall government effectiveness (Shleifer and Vishny, 1993) and political openness (Sandholtz and Gray, 2003) of a country. However, there is no consensus on the significance of these topics (Judge et al., 2011).
Apart from the negative effects mentioned above, some researchers argue that there is a positive side of corruption and bribery. De Jong, Tu and Van Es (2012) argue that researchers tend to ‘over-moralize’ corruption and do not take into account the possible positive consequences that corruption might have. A positive effect is that bribery circumvents administrative and regulatory restrictions (Egger and Winner, 2005). This implicates that there has to be a clear distinction between the ‘helping hand’ and the ‘grabbing hand’. The ‘grabbing hand’ increases costs of multinational companies and lowers internationalization, since the company engage in resource-wasting rent seeking activities (Shleifer and Vishny, 1993) and corruption contracts are not enforceable in courts (Boycko et al., 1995). The ‘helping hand’ fosters internationalization by speeding up the bureaucratic process to obtain the legal permissions to start operations (Egger and Winner, 2005).
research originate from the U.S., a country of which its companies are most likely to bribe from the western countries, plus these companies face an increased anti-corruption legislation that increases the chance of severe penalties and a lost of trust. As an effect this will influence the performance of the firm negatively. It is likely that based on the above mentioned negative effects of corruption, there is a negative moderating impact of corruption on the relationship between internationalization and performance of U.S. oil and gas firms. Therefore, I expect the following:
Hypothesis 3: Corruption in the internationalized countries negatively moderates the relationship between internationalization and performance for U.S. oil and gas firms.
Based on reviewing the literature and the developed hypotheses on the internationalization-performance relationship and the moderating impact of corruption on this relationship in this research, I developed the following conceptual framework.
+/-
Figure 1. Conceptual framework
Data and Methodology
To research the impact of corruption on the relationship between internationalization and performance, data is collected using various databases. For firm-specific data, such as ISIN-numbers and financial fundamentals DataStream was used. Data on internationalization, i.e. the number of foreign subsidiaries and the country these foreign subsidiaries operate in are collected using the Orbis Database. Orbis contains information on over 175 million private companies worldwide, while DataStream does not provide data on private companies. Orbis contains detailed company ownership structures including data about (foreign) subsidiaries of the parent company, in Orbis named as the Global Ultimate Owner (GUO). The GUO is the highest parent company, since GUO is a rarely used term parent company is used in the rest of this research. Unfortunately, the Orbis database does not contain specific financial data at subsidiary level. This data is also not available in equivalent databases since firms usually do not provide specific data about their (private) subsidiaries but tend to consolidate the financials figures of all subsidiaries. Therefore this research is constrained to test the hypotheses at the corporate level. To collect data on the corruption level of the parent company, the Corruption Perception Index (CPI) made by Transparency International is used.
Firms have to fulfill several criteria to be included in the sample. First, they have to be an American company in the oil and gas sector with their headquarters in the United States and with at least one foreign subsidiary. Focusing on a single industry is important in controlling for potential confounds such as nature of markets or products (Tihanyi, Ellstrand, Daily and Dalton, 2000). Next, all parent companies have to be active and publicly listed in the United States in the period 2009-2014. This period is chosen since it contains most recent data, while at the same time the influence of the financial crisis of 2008 is limited. These criteria lead to 97 parent companies. However, not every company has all financial data available, this accumulates to a total of 482 observations for these 97 companies over a period of 6 years.
Firm performance measure
𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝐴𝑠𝑠𝑒𝑡𝑠 =𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒. ROE measures in percentages the efficiency of a firm in generating profits from shareholder equity and is measured using: 𝑅𝑒𝑡𝑢𝑟𝑛 𝑜𝑛 𝐸𝑞𝑢𝑖𝑡𝑦 =
!"# !"#$%& !!!"#!!"#$%!! !"#$%&.
Internationalization Measure
Internationalization of the firm is measured in two ways. First, internationalization is measured using the number of foreign countries a firm has subsidiaries in. This method is in line with previous research as shown by the meta-analysis of Bausch and Krist (2007) and is together with foreign sales to total sales the most used approach to measure internationalization. Second, internationalization is measured as the total number of foreign subsidiaries. By using two different ways of measuring internationalization, the validity of the research is higher. There are two underlying reasons for choosing these proxies for internationalization. First, by using foreign sales to total sales it does not become clear in which country the firm operates, which is essential to test for the moderating impact of corruption. Second, there is hardly any public data available on the percentage of foreign sales to total sales.
Country Subsidiaries Country Subsidiaries Country Subsidiaries
Algeria 5 Ghana 4 Papua New Guinea 6
Angola 5 Gibraltar 5 Peru 4
Argentina 30 Greece 1 Philippines 2
Aruba 6 Guinea 1 Poland 14
Australia 173 Guyana 1 Portugal 3
Austria 7 Hong Kong 14 Qatar 2
Azerbaidjan 3 Hungary 4 Republic of Korea 3
Bahamas 57 India 12 Romania 4
Bahrain 1 Indonesia 14 Russian Federation 29
Barbados 10 Iraq 4 Saint Lucia 5
Belarus 2 Ireland 21 Saudi Arabia 4
Belgium 8 Israel 4 Senegal 1
Bermuda 41 Italy 34 Singapore 62
Bolivia 3 Jamaica 1 South Korea 3
Brazil 48 Japan 4 Spain 8
Brunei Darussalam 5 Kazakhstan 9 Sri Lanka 1
Bulgaria 1 Kenya 4 Sweden 2
Cambodia 1 Liberia 7 Switzerland 14
Canada 498 Libya 1 Syria 1
Cayman Islands 263 Luxembourg 70 Tajikstan 2
Chile 13 Malaysia 28 Thailand 13
China 57 Malta 6 Trinidad and Tobago 7
Colombia 10 Marshall Islands 33 Tunisia 1
Curaçao 11 Mauritius 6 Turkey 5
Cyprus 10 Mexico 64 Turkmenistan 1
Czech Republic 3 Mongolia 1 United Arab Emirates 18
Denmark 7 Mozambique 1 United Kingdom 337
Ecuador 1 Namibia 2 United Republic of
Egypt 6 Netherlands 166 Tanzania 1
Equatorial Guinea 7 New Zealand 9 Vanuata 2
Finland 3 Nigeria 29 Venezuela 17
France 29 Norway 63 Virgin Islands (British) 16
Gabon 3 Oman 7 Yemen 1
Germany 57 Panama 10 Grand Total 2.613
Table 1. Countries and the corresponding number of subsidiaries. Corruption Measure
according to Judge et al. (2011). The index constitutes a measure of perceived corruption based on surveys of businesses and experts from each country. The scores ranges from 0 to 100, where 0 represents a completely corrupt state while 100 represents an entirely corrupt-free state (DiRienzo et al., 2007; Pournarakis and Varsekelis, 2004). The score is based on surveys filled out by financial journalists, country experts, and multiple business executives. According to Lancaster and Montiloa (1997) research has demonstrated that CPI is a reliable and valid measure for corruption.
Control Variables
For this research control data was gathered for firm size, leverage and age.
Firm size - The control variable size is included in this research since larger firms tend
to be more internationalized and have more resources to invest (Chatterji et al., 2009). Firm size is also related to firm performance. However, there is conflicting evidence on this effect. Previous research shows that the impact of firm size on profitably can be positive, negative (Ammar et al., 2003) or inconclusive (Buckley, 1978; Singh, 2010). For capturing the size of the firm this research follows the approach of Chatterji et al. (2009) by taking the natural logarithm of total assets. In this way the variable is comparable across firms since it is scaled (Wang, Li and Gsao, 2013).
Leverage – The variable leverage captures any effects caused by differences in capital
structure of the firm (Gande et al., 2009). Recent studies on the relationship between leverage and performance show that leverage has a positive effect on firm performance (Margaritis and Psillaki, 2010). Leverage is controlled as liabilities divided by assets (Gande et al, 2009).
Age - Previous studies on the internationalization-performance relationship generally
also control for age (Bausch and Krist, 2007) since age can theoretically have an influence on the level of internationalization according to the Uppsala model. Next to this, it is also empirically proven to have an influence on the internationalization-performance relationship (Bausch and Krist, 2007).
Estimation Method
of freedom and therefore the power of the test (Brooks, 2008). Furthermore, panel data has the capability to remove the impact of certain forms of the omitted variable bias (Brooks, 2008) and it has the ability to address a broader range of issues (Brooks, 2008).
An unbalanced panel data analysis is conducted to test for the three hypotheses. There are several ways to estimate the effects. The simplest panel data method is a pooled OLS regression and is the first test performed. However, this method has several limitations (Brooks, 2008). A pooled regression assumes that the intercepts are the same for each year and for each company, while this may be inappropriate. In other words, pooled regression assumes no heterogeneity in the data (Brooks, 2008). Furthermore, the pooled regression should also meet several assumptions such as that the variance of the error term is constant and finite over time, better known as homoskedasticity. As Garcia et al. (2010) mention, also endogeneity is possible when using a pooled OLS regression. Endogeneity occurs when there are some unobserved firm specific variables, which could correlate with the dependent variable and the independent variable. As a result, the outcome of the OLS regression could be biased when the omitted variables do correlate with firm performance and internationalization.
Therefore instead also a model with firm fixed effects will be estimated. This allows the regression model to differ cross-sectionally and over time, a Redundant Fixed Effect Test is used to check whether fixed effects are necessary or not. The null hypothesis states that fixed effects are not necessary, therefore if the Redundant Fixed Effect Test is significant (at a 1% significance level), the null hypothesis is rejected and fixed effects are necessary. However, there is also a drawback of the time fixed effect model since variables that have little within-group variation over time will cancel out (Brooks, 2008). This is certainly a problematic drawback in this research given that internationalization level does not varies much overtime and therefore time fixed effects are not possible and only cross-sectional fixed effects are estimated. The reason for this is that the companies in the sample mostly do not start-up or shut down subsidiaries in a new foreign country from year to year, therefore there is only little within-group variation. Setting up a foreign subsidiary is a deliberately choice, that for the oil and gas industry most of the times requires high initial investments.
Next, the model will be estimated using random effects. Under the random effects model the intercepts for each cross-sectional unit are assumed to arise from a common intercept 𝛼 plus a random variable 𝜔!", which varies cross-sectionally, has zero mean, is
are not cancelled out. However, a drawback is that the random effects model assumes that the error term is uncorrelated with the explanatory variables (Brooks, 2008). The Hausman Test is performed to test if random effects are more adequate than fixed effects, the test assesses if the random effects are uncorrelated with the independent variable (Brooks, 2008). According to the null hypothesis, both fixed effects and random effects are appropriate estimators, the alternative hypothesis argues that random effects are not valid while fixed effects are. When the Hausman Test is significant (at a 1% significance level), fixed effects are more appropriate. The results of the Redundant Fixed Effect Tests and the Hausman Tests can be found in table 2. The table shows that fixed effects are always preferred above pooled OLS. Random effects are sometimes preferred over fixed effects and sometime random effects are more appropriate.
To three models that are used: the pooled OLS, fixed effect and random effects models lead to six equations in order to test for the three hypotheses.
The following equation will be estimated to test for the first and second hypothesis using a pooled OLS regression:
𝐹𝑖𝑟𝑚 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛼 + 𝛽!!" ∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 + 𝛽!!"∗ 𝑆𝑖𝑧𝑒 + 𝛽!!"∗ 𝐴𝑔𝑒 + 𝛽!!"∗ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜀!"
Using a pooled OLS model to test for the third hypothesis, about the moderating impact of corruption, the following equation will be estimated:
𝐹𝑖𝑟𝑚 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛼 + 𝛽!!" ∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 + 𝛽!!"∗ 𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 + 𝛽!!"∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 ∗ 𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 + 𝛽!!"∗𝑆𝑖𝑧𝑒 + 𝛽!!"∗ 𝐴𝑔𝑒 +𝛽!!"∗ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜀!" The to test for the first and second hypothesis using fixed effects the following equation will be estimated:
𝐹𝑖𝑟𝑚 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝑋!+ 𝛽!!" ∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 + 𝛽!!"∗ 𝑆𝑖𝑧𝑒 + 𝛽!!"∗ 𝐴𝑔𝑒 + 𝛽!!"∗ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜀!"
To test for the third hypothesis using a fixed effect model, the following equation will be estimated:
The first and second hypothesis are tested by the following equation for the random effects model:
𝐹𝑖𝑟𝑚 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛼 + 𝛽!!" ∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 + 𝛽!!"∗ 𝑆𝑖𝑧𝑒 + 𝛽!!"∗ 𝐴𝑔𝑒 + 𝛽!!"∗ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜔!"
The following equation will be estimated for the third hypothesis using a random effect model:
𝐹𝑖𝑟𝑚 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛼 + 𝛽!!" ∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 + 𝛽!!"∗ 𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 + 𝛽!!"∗ 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 ∗ 𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 + 𝛽!!"∗𝑆𝑖𝑧𝑒 + 𝛽!!"∗ 𝐴𝑔𝑒 +𝛽!!"∗ 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜔!" Where 𝛼, 𝛽, 𝜀, 𝑋! and ω are respectively the intercept term, parameters that explain the independent variable, the error term, the intercept that controls for unobserved firm specific variables and the cross-sectional error term. The subscripts 𝑖, 𝑡 represent the firm and year respectively. In order to test the moderating effect of corruption on the relationship between internationalization and performance the interaction term 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛 ∗ 𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝑖𝑜𝑛 is made. As the hypothesis suggest, I expect that the interaction term have a negative impact on the relation between firm performance and internationalization.
Redundant Fixed Effect Test Hausman Test Cross-section F Cross-section Chi-square Cross Section Random
Measure ROA ROE Tobin’s
Q
ROA ROE Tobin’s
Q
ROA ROE Tobin’s
Results
This section presents and discusses the empirical results of this research, beginning with the descriptive statistics and correlations. These are followed by the regressions analyses that test the hypotheses. In the last part of this section a robustness test is performed.
Descriptive Statistics & Correlations
Mean Median Maximum Minimum Std. Dev. Jarque-Bera Observations ROA 0.81 4.31 21.94 -30.82 11.80 153.14 481 ROE 0.61 7.31 48.78 -59.84 22.89 138.64 481 Internationalization 7.29 3 54 1 9.19 729.97 481 Foreign subsidiaries 28.37 9 337 1 46.76 4447.21 481 Corruption 69.92 72.28 89.00 18.00 15.23 118.17 449
Firm value 23267951 3137170 4.98E+08 30933 66799265 21707.68 467
Firm size 14.41 14.71 19.48 9.94 2.49 17.48 481
Leverage 48.69 49.69 97.22 11.13 17.15 1.89 467
Age 19.55 17 42 0 12.68 35.27 481
Table 3. Descriptive Statistics, ROA, ROE and Leverage are given in percentages.
Table 4 shows the correlation table. There is only a high correlation between ROA and ROE, internationalization and foreign subsidiaries, and firm value, internationalization and foreign subsidiaries. The latter can be explained by the nature of the firm value variable, larger firms tend to have higher firm value than smaller firms and larger firms tend to be more internationalized than smaller firms. The high correlations between the different firm performance measures and different measures of internationalization are not calculated in the same model since they are all proxies for the same variables, therefore these correlations do not cause multicollinearity.
to positively influence the level of internationalization and the performance of the firm. Overall, the correlations between the dependent variable and the control variables are not very high with 0.17 on average. Given all the above, I conclude there is no multicollinearity in the sample. 1 2 3 4 5 6 7 8 9 1.ROA 1 2.ROE 0.91 1 3.Internationalization 0.25 0.23 1 4.Foreign subsidiaries 0.23 0.23 0.89 1 5.Corruption 0.21 0.24 -0.11 -0.01 1 6.Firm value 0.19 0.21 0.50 0.69 0.10 1 7.Firm size 0.46 0.46 0.42 0.51 0.33 0.55 1 8.Leverage -0.11 -0.16 -0.04 -0.03 0.20 0.05 0.25 1 9.Age 0.21 0.17 0.22 0.34 0.08 0.33 0.35 -0.07 1
Table 4. Correlation table
Regression results
smaller firms. Due to some missing data points the total sample consist of 467 observations over the six year period for the 97 firms in the sample.
Table 5
Regression effects of Internationalization on Firm Performance
Dependent Variable Model 1 Pooled OLS Model 2 Fixed Effects Model 3 Random Effects Internationalization 0.0003 (0.602) -0.0001 (-0.005) -0.0013 (-0.07) 0.0003 (0.291) 0.002 (0.049) -0.002 (-0.132) Firm size 0.023***(10.313) 0.014 (1.020) 0.0072 (0.569) 0.047*** (11.034) 0.023 (0.827) 0.055*** (8.198) Age 0.0002 (0.412) 0.0016 (0.607) 0.003 (1.092) 0.0005 (0.708) 0.004 (0.690) -0.0004*** (-0.329) Leverage -0.137*** (-4.915) -0.225*** (-4.928) -0.171***(-5.786) -0.149*** (-3.773) -0.605*** (-6.320) -0.455*** (-6.655) Constant -0.258*** (-8.756) -1.07 (-0.455) -0.057 (-0.272) -0.615*** (-10.936) -0.125 (-0.253) -0.568*** (-6.382) Total Panel of Observations 467 467 467 467 467 467 Adjusted R2 0.266 0.260 0.178 0.263 0.279 0.198
Table 5. Firm performance, the independent variable is measured by ROA and ROE, internationalization is estimated by the number of foreign countries. Firm size is measured as the log of total assets. Leverage is measured as the ratio of liabilities to total assets. The first displayed value per variable represent the regression on ROA, the second displayed value represents ROE. Reported are the coefficients for each of the variables with their corresponding t-values in parentheses. Significance is indicated by ***, **, *, at respectively 1%, 5%, and 10% level.
the highest adjusted R2 of all models. The results of the regression analyses show that in model 2 with fixed effects internationalization has a significant negative effect on the performance of the firm, measured in both ROA and ROE and that this effect is twice as strong for ROE (with a coefficient of -0.097) than for ROA (with a coefficient of -0.046). Leverage is again strongly significant in all models with a large negative coefficient. Firm size is strongly significant in models 1 and 3 but not in the fixed effect model. Age does again have a coefficient close to zero and does not seem to have an influence on the performance of the firm. The fixed effect model in table 6 has the highest R2 of all models in table 5 and 6, therefore this model is therefore likely to best predict the effect of internationalization on firm performance.
Table 6
Regression effects of Internationalization on Firm Performance
Dependent Variable Model 1 Pooled OLS Model 2 Fixed Effects Model 3 Random Effects Foreign Subsidiaries -0.0001 (-0.985) -0.046** (-2.178) -0.0001 (-0.685) -0.0002 (-0.654) -0.097** (-2.080) -0.0002 (-0.496) Firm size 0.027***(11.971) 0.007 (0.539) 0.0288***(8.025) 0.049*** (10.930) -0.002 (-0.087) 0.051***(7.785) Age 0.0004 (1.108) 0.003 (1.257) 0.0006 (0.849) 0.0007 (0.842) 0.009* (1.671) 0.009 (0.781) Leverage -0.093*** (-4.690) -0.169*** (-5.777) -0.131***(-5.579) -0.154*** (-3.893) -0.211** (-3.233) -0.167*** (-3.495) Constant -0.341*** (-11.469) 1.239** (1.994) -0.354*** (-7.455) -0.635*** (-10.712) 2.739** (1.984) -0.666*** (-7.678) Total Panel of Observations 467 467 467 467 467 467 Adjusted R2 0.300 0.607 0.176 0.263 0.485 0.152
Table 6. Firm performance, the independent variable is measured by ROA and ROE, internationalization is estimated by the number of foreign subsidiaries. Firm size is measured as the log of total assets. Leverage is measured as the ratio of liabilities to total assets. The first displayed value per variable represent the regression on ROA, the second displayed value represents ROE. Reported are the coefficients for each of the variables with their corresponding t-values in parentheses. Significance is indicated by ***, **, *, at respectively 1%, 5%, and 10% level.
Moderating effect of corruption
firm performance is negative and significant for ROA and ROE, but the coefficient is with -0.001 and -0.0028 very low.
The leading variable in testing hypothesis 3 is however the interaction variable internationalization*corruption, which measures the moderating effect of corruption on the internationalization-performance relationship. In the random effects model the coefficient is -0.135 for ROA with a 10% significance level, this implies that corruption has a negative moderating effect on the relationship between internationalization and firm performance measured in ROA. Also across all other models the moderator has a negative coefficient, and is furthermore highly significant for pooled OLS.
Table 7
Regression effects of Corruption on the relationship Internationalization-Firm Performance Dependent Variable Model 1 Pooled OLS Model 2 Fixed Effects Model 3 Random Effects Internationalization 0.015***(3.029) 0.003(0.088) 0.015* (1.921) 0.022** (2.204) 0.005 (0.073) 0.022 (1.442) Corruption -0.0012***(-3.146) -0.0007(-0.288) -0.001** (-1.992) -0.0027*** (-3.369) -0.0014(-0.266) -0.0028* (-2.348) Internationalization *Corruption -0.136*** (-2.934) -0.019 (-0.112) -0.135*(-1.889) -0.204** (-2.143) -0.015 (-0.042) -0.203 (-1.416) Firm size 0.025*** (10.056) 0.018 (1.181) 0.026***(6.661) 0.048*** (9.261) 0.029 (0.893) 0.053*** (6.828) Age 0.0003 (0.775) 0.002 (0.540) 0.0003 (0.396) -0.0007 (-0.944) 0.004(0.675) -0.0006 (0.502) Leverage Constant -0.102***(-5.007) -0.407***(-6.889) -0.442***(-6.604) -0.844***(-6.157) -0.231***(-4.801) -0.619*** (-6.138) -0.211(-0.493) -0.332 (-0.369) -0.172***(-4.973) -0.476***(-6.721) -0.479***(-4.736) -0.882***(-4.348) Total Panel of Observations 437 437 437 437 437 437 Adjusted R2 0.275 0.296 0.526 0.519 0.174 0.202
Table 7. Firm performance, the independent variable is measured by ROA and ROE, internationalization is estimated by the number of foreign countries. Corruption is estimated using the CPI index. The interaction variable internationalization*corruption measures the moderating effect of corruption on the relationship between internationalization and firm performance. Firm size is measured as the log of total assets. Leverage is measured as the ratio of liabilities to total assets. The first displayed value per variable represent the regression on ROA, the second displayed value represents ROE. Reported are the coefficients for each of the variables with their corresponding t-values in parentheses. Significance is indicated by ***, **, *, at respectively 1%, 5%, and 10% level.
for the explanatory variables. The interaction term is in table 8, next to ROA (with a coefficient of -0.129), also significant for ROE (with a coefficient of -0.243), both at a 5% level. Control variables do not show substantial different results than in the previous models. While the significance levels of the interaction term are not in all models very high, given the magnitude of the coefficient and that in most models the interaction term is significant, it gives me enough evidence of a negative moderating effect of corruption on the internationalization-performance relationship and therefore hypothesis 3 is accepted.
Table 8
Regression effects of Corruption on the relationship Internationalization-Firm Performance Dependent Variable Model 1 Pooled OLS Model 2 Fixed Effects Model 3 Random Effects Foreign Subsidiaries 0.0023***(3.141) -0.031(-1.396) 0.002** (1.982) 0.005*** (2.777) -0.065 (-1.312) 0.005** (2.013) Corruption -0.001***(-2.722) -0.001(-0.780) -0.001* (-1.824) -0.0023*** (-3.366) -0.004(-1.538) -0.0024** (2.475) Foreign Subsidiaries *Corruption -0.141*** (-3.253) -0.127 (-1.038) -0.129**(-2.105) -0.245*** (-2.802) -0.421 (-1.539) -0.243** (-2.061) Firm size 0.027*** (10.875) 0.011 (0.782) 0.030***(7.756) 0.048*** (9.596) -0.002 (0.052) 0.051*** (7.151) Age 0.0005 (1.431) 0.004 (1.589) 0.0008 (1.107) 0.0009 (1.082) 0.0129**(2.184) 0.0013 (1.016) Leverage Constant -0.111***(-5.500) -0.182***(-4.453) -0.475***(-8.989) -0.901***(-8.469) -0.179***(-5.974) -0.239***(-5.133) 0.689 (1.023) 1.451 (0.960) -0.142***(-5.896) -0.187***(-5.452) -0.503***(-6.273) -0.960***(-6.408) Total Panel of Observations 437 437 437 437 437 437 Adjusted R2 0.308 0.269 0.546 0.472 0.192 0.163
Results Robustness Check
As a robustness check the relationship between internationalization and firm performance is tested using a proxy for firm value, namely Tobin’s Q. Since according to Dale-Olson, Schone and Verner (2013) there is no perfect performance measure, firm performance is also measured using market data. Firm performance will be expressed as firm value using the Tobin’s Q equation: 𝑇𝑜𝑏𝑖𝑛!𝑠 𝑄 = !"#$%& !"#$%"&$'"%$()!!"#$"!"%"&'
!"##"$ !"#$%&!!"#$"!"%!"# . However,
there is a drawback of using firm value as a proxy for firm performance. Generally, large well-established firms have a higher value than small start-ups. The size of the firms in the sample differs from small- and medium sized companies to large multinationals. Since the firm value is therefore not only determined by the performance of the firm, but also by size of the firm. Internationalization is tested using number of foreign countries a firm has investments in and using number of foreign subsidiaries. The results of these tests are presented in Appendix A and B. After performing the Redundant Fixed Effect Test and the Hausman Test, the fixed effect model is most adequate for internationalization and random effects most adequate for foreign subsidiaries. For appendix A, although internationalization is negative, results are not significant. For appendix B, foreign subsidiaries is significant at a 1% level, though the coefficient is with 0.01 very close to 0. Therefore this robustness test does not support the above made conclusions of the relationship between internationalization and firm performance. However, the above-mentioned drawback seems to appear in the sample. Firm size seems to influence firm value considerably more than for ROA and ROE. Furthermore, also age influence the firm value, which can be explained since older firms tend to be larger than younger firms. The correlation between Tobin’s Q and internationalization is almost twice as high (0.38) and more than twice as high for foreign subsidiaries (0.55) compared to the correlation of ROA and ROE with internationalization and foreign subsidiaries. Therefore, the outcomes of the regressions of firm value seem to be biased.
Conclusion
This study answers the following research question: What is the impact of the level of
corruption in a country on the relationship between internationalization and firm performance for U.S. oil and gas companies? It does so by investigating 97 oil and gas firms
with different degrees of internationalization over a six-year period (2009-2014). This paper contributes to previous research on the relationship between internationalization and firm performance by investigating a particular industry, namely the oil and gas industry. Furthermore, it investigates the moderating influence of corruption on this relationship. Over the years many moderating factors have been researched. However, Bausch and Krist (2007) argued that there is need for further research focusing on other contextual settings such as industry sector or competition. This research partly fills this gap by investigating in a particular industry and by adding another moderating factor, namely corruption to previous research.
Findings show that internationalization has a small negative impact on the performance of these firms, i.e. more internationalization leads to a slightly lower firm performance for the companies in the sample. Furthermore, this study finds evidence that the level of corruption has a moderating negative effect on the internationalization-performance relationship. As a result, when a U.S. oil and gas company internationalize to a country that has a high-perceived level of corruption the slightly negative effect on performance is increased and becomes even more negative. To the extent of my knowledge, it is the first research that has investigated and found evidence that corruption has a negative moderating effect on the relationship between internationalization and performance.
corruption has possibilities of high penalties and reputational cost for firms. Furthermore corruption creates distort markets and harms fair competition.
The outcomes lead to several implications for companies and managers operating in the U.S. oil and gas industry. First, internationalizing does not necessary lead to higher firm performance, outcomes show that it is likely that it even lowers the performance of the firm. Managers should take into account the higher complexity involving internationalization and therefore possible higher cost. As a result, when managers are looking for strategic opportunities to increase the performance of the firm, it is likely that internationalization is not always the best option. Furthermore, when a company internationalizes to a country that has a relatively high level of corruption this even magnifies the negative relationship between internationalization and firm performance. Therefore should managers also take into account the level of corruption in a country when they considering to internationalize.
This research is subject to limitations, some of these limitations are opportunities for future research. First, this research focuses on a highly-corruption sensitive industry, therefore results are not generalizable to less corruption sensitive industries since the impact of corruption on the relationship between internationalization and firm performance will be of a different magnitude. Generalizing results to other highly-corruption sensitive industries depends on the character of the industry, for example the mining industry has comparable characteristics as the oil and gas industry. Both industries main activity is extracting natural resources. While for example, the real estate industry does not have much in common with the oil and gas sector. Furthermore, results are also not generalizable to other countries, since results are only tested for U.S. based parent companies. In their meta-analysis Bausch and Krist (2007) find that the internationalization-performance relationship differs per country. Future research could estimate the effect for different industries from companies from different countries.
Third, the size of the subsidiary is not taken into account. A subsidiary with only one employee or very small sales accounts equally to a subsidiary that has thousands of employees and millions of dollars of revenue. This could possibly have a major influence on the outcomes of the tests. While public data on this is currently lacking, a possibility for future research would be to test the relationship in the same way but with subsidiary specific data.
Fourth, as previous research shows, age of the company has a moderating impact on the relationship between internationalization and firm performance. However, in this research age is measured as a control variable against firm performance and is not measured as a control variable for internationalization. This can explain why age does not show significant results.
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