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Keywords:

  • Corporate governance;
  • location behaviour;
  • corporate social responsibility;
  • multinational enterprises

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References

This paper analyses international location decisions of corporations based on corporate governance considerations. Using firm level data on 540 Multinational Enterprises (MNEs) with 44,149 subsidiaries in 188 countries, we test whether firms with relatively good governance standards are more often located in countries with a weak governance system. We find empirical support for this hypothesis, especially for those corporations present in low-income countries.


1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References

This paper analyses whether corporate international location decisions are driven by differences in national governance systems. For example, do multinational firms look for direct investment opportunities in countries with weaker governance than at home? A firm that is confronted with high pressure from stakeholders at home might find it attractive to shift production to an economy with less strict governance codes. In this way, domestic corporate governance institutions might impact international location choices in a firm-specific way. For instance, environmental and human rights pressure groups' actions with respect to Royal Dutch and Nike have affected their corporate governance codes as well as their international production and location decisions. Firms that have weak corporate governance codes might consider it more profitable to produce with capital intensive technologies in a country with a well-structured governance system. In general, economic theory on international location decisions argues that these decisions depend on the one hand on a number of standard factors (a comprehensive theoretical discussion is given by Billington, 1999), but on the other hand also on institutions, like the quality of environment, political, legal, and social factors (Boddewyn, 1988).

In order to improve our understanding of the role of institutions in general in Multinational Enterprises' (MNEs) location decisions, we need insight into the impact of governance institutions on location decisions. Firms that e.g. are inclined to be relieved from shareholder pressure may want to start or continue business in economies with less strict codes. Conversely, firms with rather poor governance standards that want to start a project, may be interested in locating in countries with well-developed governance standards. It remains an empirical issue to identify these two views.

This paper starts with a brief review of the literature on international location activity, the candidate role for governance institutions and the corporate performance-governance nexus in section 2. As we show, there is relatively little attention for the role of institutions like governance in this literature so far. In section 3 we argue that in order to establish an active role for governance, we need to know about the interaction between corporate performance and governance. Next, we describe in section 3 the data and discuss the descriptive statistics. In section 4 we present our methodology. Section 5 gives the analysis and results. Finally, in section 6, we summarise and conclude.

2. International location activity, governance, and firm performance

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References

In this section, we review the literature on international location decisions, the candidate role of institutions in general and governance in particular, and the impact of governance on firm performance.

International location decisions by MNEs are complex corporate decisions. Two strands of literature describe the main determinants of location choice: the traditional theory on Foreign Direct Investment (FDI) (see e.g. Markusen, 1995) and the so-called new economic geography (see e.g. Brakman et al., 2001). The “traditional” theory focuses e.g. on the role of local costs, access to production factors, transportation costs, relative size and market power. It is commonly believed that so-called horizontal investments are more likely in the case of large markets and high transportation costs, while vertical investments arise when local costs are relatively low. Besides these rather standard arguments, public policy of course also affects location decisions. The second strand of literature, the new economic geography, focuses on spatial imperfections that affect location choice and performance. A firm might want to be in a region where competitors run a successful business, but increased competition reduces this attractiveness.

According to Blonigen (2005), the growth of MNE activity in the form of foreign FDI has grown faster than most other international transactions in the last decades. Therefore it is important to know what the determinants of MNEs' location decisions are. Empirical research on factors that determine FDI patterns and the impact of MNEs on parent and host countries is in its developing stage. As Blonigen (2005) argues, the literature has shown that it cannot simply be concluded that factors such as exchange rates or tax policies have an unambiguous general impact on FDI patterns. Blonigen discusses that firm-specific characteristics, like the availability of intangible assets, such as technologies or managerial skills, are important, but typically hard to measure, and so are problematic in empirical studies. Using empirical proxies, like R&D and advertising, reveals that firms that lack innovation, as compared to their peers, typically are engaged more in FDI (see Blonigen, 1997).

One can distinguish partial and general equilibrium approaches to FDI and location decisions.1 In partial equilibrium models, exchange rate effects, taxes, and tariffs are used as determinants. For instance, if a currency appreciates, the price of a local project will decrease for a foreign investor, but the nominal return on the project's probably will not. Tax issues are most complicated and empirical evidence of the impact of changes in corporate and indirect tax rates is mixed at least (see e.g. Desai et al., 2004). Trade protection might be another determinant of international MNE location decisions. If a firm can avoid tariffs by substituting production for exports, this would be a high-potential candidate determinant of location decisions. Belderbos (1997) indeed finds evidence of this so-called tariff-jumping FDI.

Blonigen (2005) points at the potentially valuable role of institutions as a determinant of FDI, particularly for less-developed economies. For example, poor legal protection of assets increases the chances of expropriation of corporate assets, reducing the probability of FDI. Poor quality of institutions also lowers expected profitability and, therefore, reduces the probability of successful FDI. Blonigen further argues that it is hard to find good empirical proxies for institutions, since these are typically hard to measure. Most measures are composite indices developed from survey responses from government officials and businessmen familiar with the country involved. This troubles cross-country comparability, because the sampling of respondents might differ per country. An exception is the direct measurement of institutions like legal standards (see La Porta et al., 1997), which have been extensively used in the literature on finance and development. Legal institutions can be measured directly from legal codes, but still are prone to interpretation issues if it comes to details (like the measurement of shareholder protection). Another problem in the empirical literature that tries to estimate the impact of the role of institutions on economic variables is the fact that institutions tend to change very slowly. This troubles our country time series analysis and favours a cross-sectoral approach (see for instance Levine and Zervos, 1998, for an example in the law and finance literature). One of the institutional variables used in some of the FDI literature is corruption: Wei (2000) for instance finds that FDI is negatively related to corruption, but Wheeler and Mody (1992) do not find support for the negative role of corruption.

Other examples of empirical partial location studies are e.g. Basile et al. (2003) and Yamawaki (2006) on FDI in the EU. These studies estimate (conditional) choice models of location decisions. The econometric specification is mostly a (nested) logit model of the binary choices to be represented in a specific country. Boddewyn (1988) points out that the non-market environment to a large extent affects the efficiency of MNE performance. The literature acknowledges that politics is of great importance to MNEs (see Ring et al., 1990; Brewer, 1992; Sundaram and Black, 1992; Murtha and Lenway 1994), but that other actors are not well covered (Baron, 2001). Dunning (1993) argues that any theory of MNE activity that does not seek to understand and explain the role of governments and other stakeholders as just another variable impacting upon firm behaviour is bound to be deficient. There also is a literature on FDI location decisions and political country risk. The empirical literature finds mixed results. Oneal (1994) finds that authoritarian regimes provide higher returns to investors. Li and Resnick (2003) show that non-democratic institutions attract higher levels of FDI. Busse (2004), on the other hand, finds that democratic institutions lead to a higher inflow of FDI. Jensen (2003) finds that MNEs pay lower premiums for political risk insurance coverage in democratic regimes.

We focus on governance in international location decisions. Firms having footage in an economy with strict governance codes may find it attractive to shift operations to countries with less strict governance codes. The latter type of behaviour can be compared with the so-called pollution haven hypothesis that holds in environmental economics: a polluting firm looks for a county that allows more pollution than its home country (Letchumanan and Kodama, 2000). But the evidence might also turn to the opposite: firms benefit from well-developed governance and can freely expand corporate activity. Then, our null hypothesis is that the governance standard of the firm is not related to its international location decision. A null hypothesis also is that the governance standard of the country of destination is not related to its attractiveness with respect to FDI. The alternative hypothesis in both cases is that governance does matter. Furthermore, we explicitly test whether firms with relatively low (high) governance standards are more often locating in countries with relatively low (high) governance standards. Among others, we will also condition for wealth of the host country.

A precondition for an effective role of governance though is that governance affects corporate performance. Therefore, it is important to shortly review corporate governance and the different views on the performance-governance nexus. Governance is defined as the set of informal arrangements that are used in handling the consequences of these unforeseen states of the world. Since the work of Shleifer and Vishny (1997), control rights of financiers are considered to be the key elements in governance, especially if ownership is widely spread (see Dyck and Zingales, 2004). Nowadays listed firms are required to satisfy ingenious governance codes, which basically enable financiers to get a more direct influence on corporate decisions. There is a fundamental debate on the relation between governance and performance though. Agency costs that arise from the differences in interest of managers and financiers can lead to either over- or underinvestment. The most famous strand of the literature is that on overinvestment, as initiated by Jensen and Meckling (1976): Managers try to benefit from the fact that they have access to information that is undisclosed to outsiders, especially if objectives and/or risk attitudes of managers and banks are not aligned due to pay-off structures or intrinsic motivations of the managers (see also Tirole, 2006). For instance, managers may try to enjoy fringe benefits, set up “small empires”, or engage in risky projects – activities which are partly unobservable to the bank and not necessarily performance enhancing.

Information asymmetries can also lead to underinvestment. First, when creditors have less information about a firm and its investment prospects, they will demand a premium for supplying more finance. Consequently, the cost of external funds will exceed that for internal funds (see e.g. Bernanke and Gertler, 1989, for a theoretical exposition and Whited, 1992, for an empirical support). Second, underinvestment may also arise because of managerial shirking. In the model of Aggarwal and Samwick (1999), managers forego profitable projects that have net private costs to them. These costs are generated by increased oversight due to the new investment project. Third, the “Managerial Myopia” model argues that e.g. equity markets may not allocate capital efficiently because of the absence of dedicated investors with a long-term horizon (see, for example von Thadden, 1995; Tirole 2006, p. 300).

Thus a more strict governance code might lead to a better or worse performance of the firm. Therefore, it remains an empirical issue whether firms like to operate in more strict governance environments. It is likely that firms that want to be freed from local pressure are trying to locate firm activity in countries with less strict governance codes and ways of conduct. We test our main hypothesis whether firms with relatively low governance standards are more often located in countries with poor governance related regulations, i.e. a weak business environment, conditioning for “normal” determinants of location choices. It appears that firms with a relatively high quality of corporate governance do not avoid countries with poor governance standards. Next, we test whether these results hold especially for low-income countries, because these countries in general have poorer governance structures. Our data support this second hypothesis as well. We will focus explicitly on the governance of corruption, of business ethics, and corporate social responsibility (driven by data availability). These indicators are, of course, closely correlated at the firm level with other indicators of corporate governance, like the composition of the board of directors, compensation, ownership, etc.

3. Data

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References

Data on firm responsibility is taken from the Ethical Investment Research Service (EIRIS). EIRIS has composed a cross-sectional dataset which covers 2685 MNEs, located across the globe, and contains information on company policy, corporate reporting as well as on breaches by or convictions of the MNE. The topics that are included are environmental issues, stakeholder issues, governance, business ethics, and genetic engineering. Ratings between −1 and 3 are assigned. The details on Corporate Social Responsibility-scoring are in Table B of the Appendix. We use four indicators of governance quality: Governance of bribery and corruption, Governance code of ethics, Governance code of ethics systems and Governance of business principles. This choice is mainly driven by data availability. Indicators of the quality of governance of corruption, business ethics, and corporate social responsibility, of course are correlated to other indicators of the quality of shareholder influence, like ownership, board composition, etc. We consider these indicators to have an underlying latent variable that measures the quality of a firm's governance. We apply factor analysis on the four indicators to generate a single factor, named “Governance”, and use this factor in our econometric analysis.

Data on the international location of firms is extracted from the reported subsidiaries of European firms. To this extent, we use AMADEUS, a database that contains accounting information for a large number of European firms. Note that a subsidiary can have subsidiaries itself. Accordingly, AMADEUS classifies subsidiaries at different accounting levels, where each subsidiary level is divided into sublevels. We look at the subsidiaries at the highest reported level and use information on the country location of the subsidiary and the most recent information on sales and assets of the subsidiary (2004–2005). Our starting point is the Dow Jones Stoxx 600 selection list of largest European companies. After omitting financials and banks, we created a pooled and balanced cross-section data set of the 540 companies. Table 1 gives an overview of the number of MNEs in our dataset, classified by the country in which the company is chartered and by the industry the company is in. Overall, most MNEs are based in the UK and a ranking of the number of MNEs in each country is in accordance with what one would expect given countries' population sizes. An exception is Switzerland, which is relatively overrepresented. For each company we have information on its presence in 233 countries, yielding a vector of 125,820 observations. Surely, not each individual firm has operations in every country. Impressively however, in 188 of the 233 countries, at least one multinational is present. Table 2 gives an overview of the average number of countries an MNE is operating in by region and industry. We also visualise the global presence of MNEs in Figure 1. On average, an MNE is active in 17 countries. Firms producing basic materials are on average active in more countries than firms in other industries, mainly because they have a large share of activities in the European market. It appears that the oil and gas industry is most evenly scattered across the globe. The utilities industry scores the lowest on international presence. Moreover, most MNEs are active in the U.S. and Canada. The Eastern Asian, European and North American markets are by far the most attractive in absolute as well as in relative numbers.

Table 1. Number of MNEs by industry and home country (by charter)
CountryBasic MaterialsConsumer GoodsConsumer ServicesHealth CareIndustrialsOil & GasTechnologyTelecommunicationsUtilitiesAll
Austria1000200115
Belgium21211002110
Switzerland541810021031
Germany597714031248
Denmark02053001011
Spain021018322634
Finland32202121013
France21214213281256
United Kingdom153273961719612234
Greece0120110218
Italy04704213627
Luxembourg0000100001
Netherlands25414451026
Norway2100111107
Portugal0010100114
Sweden34239112025
All40791253713522442632540
Table 2. Average number of countries in which MNEs are operating by industry and region
Industry
Region (Total #Countries)Basic MaterialsConsumer GoodsConsumer ServicesHealth CareIndustrialsOil & GasTechnologyTelecommunicationUtilitiesAverage MNE
  1. The table entries are industry averages of the number of countries an MNE is operating in per region. Total number of countries per region is in parentheses. A list of countries included is in Table A in the Appendix. The column Average MNE is a sample average irrespective of industry and the row World is a sample average irrespective of Region.

Africa (58)2.12.10.70.81.53.40.50.50.31.3
Antarctica (4)0.00.00.00.00.00.00.00.00.00.0
Caribbean and Bahamas (21)0.40.50.10.20.31.00.20.70.10.3
Central and North America (13)2.62.20.92.41.82.51.71.21.01.7
Eastern Asia (25)4.33.61.23.92.42.12.60.80.42.4
Europe (45)12.011.26.212.09.38.08.37.44.68.7
Middle East (15)0.60.80.30.50.61.00.30.30.20.5
Oceania (29)1.00.80.31.00.60.60.40.10.20.6
South America (13)2.82.10.71.71.62.31.10.91.31.5
Western Asia (10)0.20.20.10.10.10.30.10.00.00.1
World (233)25.823.410.522.618.321.115.312.08.017.0
image

Figure 1. Global presence of multinational enterprises

Download figure to PowerPoint

We also extracted firm specific control variables from the AMADEUS database. Here, we aim at variables that give a description of the structure of the firm, as the literature treats the location and the governance of the firm as structural characteristics too. The firm-specific variables are Age of the MNE in years, number of Employees, Leverage, as measured by debt divided by total assets, and Liquidity. Furthermore, we extracted Market Capitalisation in billions of euros from the Dow Jones Stoxx 600 selection list. As such, we include both accounting (i.e. book) and financial market data to describe the structure of the firm. An overview of the descriptive statistics of the variables is in Table 3. We need to control for the individual conditioning drivers of location decisions. For example, firm size, measured by the number of employees or age of the firm, the cash position, measured by liquidity, and borrowing capacity, measured by leverage, all can have an impact on location decisions.

Table 3. Descriptive statistics of multinational enterprises
Correlations*
 MinMaxMeanStandard DeviationMedianAgeEmployeesMarket Capitali-sationLiquidityLeverageGovernance
  • * 

    In these correlations and all subsequent calculations, natural logarithms have been taken of Age, Employees, Market Cap. and Liquidity to account for the skewed distribution.

  • ** 

    Factor scores of the four Social Responsibility indicators on Governance listed in Table B in the Appendix.

  • For variable Definitions see Table B in the Appendix.

  • Sources: AMADEUS, EIRIS.

Age in Years01714640311.00     
Employees35419,20035,04860,98512,8540.261.00    
Market Cap.0.13155.896.3714.751.950.150.611.00   
Liquidity0.0816.721.31.221.01−0.04−0.08−0.021.00  
Leverage0.051.510.620.180.630.030.260.03−0.231.00 
Governance**−1.951.22010.360.030.270.36−0.020.031.00

If one compares the median to the mean values of the variables Age, Employees, Leverage and Liquidity in Table 3, it becomes clear that these variables are characterised by a heavily skewed distribution. For example, the average MNE has 35,048 employees, whereas the median MNE has 12,854 employees. To control for the statistical consequences of these skewed distributions we use the natural logarithm of the variables in estimation. As expected, Employees and Market Capitalisation are highly correlated (correlation coefficient equals 0.61; see the right hand side of Table 3). Larger firms require both more capital and more labour. Age shows a significant correlation with Employees as well as with Market Capitalisation. One can argue that growth of MNEs is initially high, but as a certain level of size is reached, the additional years will not matter. Liquidity and Leverage do not show significant correlations with other firm characteristics. Furthermore, Table 3 reveals that the quality of corporate governance – as represented by our variable Governance– does not show a significant correlation with any other variable.

To measure a country's governance standard we use the Kaufmann et al. (2005) dataset. This dataset presents estimates of six dimensions of governance covering 209 countries and territories for five time periods: 1996, 1998, 2000, 2002 and 2004. The dimensions are Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. Higher levels indicate better regulation in a country and/or less uncertainty in the business environment. We provide detailed variable definitions in Table B of the Appendix.

4. Methodology

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References

A commonly used econometric modelling technique in the literature of location choice, is the conditional logit model (see McFadden, 1974). We stay close to the literature and adopt this binary location choice model. We assume that the choice of the subsidiary location is the dependent variable. For each MNE we explain the choice of whether or not to be present in a country. We construct a binary variable Yij which is equal to 1 if company i has at least one subsidiary in country j. We assume that the benefits (i.e. the profitability) Bij to MNE i (i = 1, … , N) of locating in country j (i = 1, … , J) is a latent variable:

  • image

Here, Dij is the deterministic part andεij the error term. Dij is related to country characteristics zj and parent-level firm group characteristics xkj in the following way:

  • image

Here, we put a subscript j in the term xkj, since we do not a priori exclude possible interaction between parent-level firm group characteristics and country characteristics. The MNE chooses the location if the benefits are large enough, say larger than a threshold B*, and we only observe this outcome. The probability of observing MNE i choosing location j is:

  • image

The actual outcome, given Dij, eventually depends on the distribution of the error terms εij. We want to test whether there is a significant interaction effect between a firm's corporate governance scores and a country's business environment and therefore delegate any direct country effect to the country fixed effects. Unfortunately, this means that we cannot observe which country characteristics drive its attractiveness to MNEs besides governance issues. As such, we ensure that we fully control for observable and unobservable country characteristics. We add the following firm level control variables: Age, Employees, Leverage (as measured by debt divided by total assets), Liquidity, and Market Capitalisation. Due to skewness of the distribution of most of the variables, we take the logarithms, except for Leverage. Furthermore, we create a “home” dummy, which is equal to one if we consider subsidiaries located in the same country as where the MNE is based. We omit the observations for which this dummy is equal to one. There has been some debate whether cultural distance is an important determinant in international diversification, e.g. a meta-analysis by Tihanyi et al. (2005) indicates that these differences do not seem to matter, particularly for MNEs based outside the US. Nonetheless, we add a colonial dummy variable, which is equal to one if the country where the subsidiary is located is a former colony of the country where the MNE is headquartered. Please note that cultural variables are all well approximated by this dummy (see also Tihanyi et al., 2005).

5. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References

We estimate the following equation:

  • image

Yij = 1, if MNE i is present in country j.Λ is the logistic function. We estimate a conditional logit function and condition on Country fixed effects. Firmi are the reported firm characteristics, Industry are industry dummies, and FormerColony is a dummy which indicates if a country is a former colony of the country where the MNE is based. For Business Environment, we use Control of Corruption, Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law and Voice and Accountability. Higher values of Governance indicate better Corporate Governance, higher values of Business Environment indicate better regulation and/or lower levels of uncertainty. To account for potential clustering, we calculate t-values using the Huber-White robust standard errors. The estimation results are presented in Table 4.

Table 4. Country presence of MNEs and corporate governance
Model:123456
Control of CorruptionGovernment EffectivenessPolitical StabilityRegulatory QualityRule of LawVoice and Accountability
  1. The estimated logit model is: Presence = E[Yij] = Λ(αj Countryj + βk Industry + η FormerColony + γiFirmδ(Governancei ×Business Environmentj)). Yij = 1 if MNE i is present in country j.Λ is the logistic function, conditional on Country fixed effects. FormerColony = 1 if the country is a former colony of the country where the MNE is based. Firmi are the reported firm characteristics. Industry are industry dummies. For Business Environment we use Control of Corruption, Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law and Voice and Accountability respectively. Higher values of Governance indicate better Corporate Governance, higher values of Business Environment indicate better regulation and/or lower levels of uncertainty. For brevity sake, the country and industry fixed effects are not reported. Definitions of the variables are in Table B of the appendix. The t-values are calculated using the Huber-White robust standard errors. * indicates significance at ten, ** at five, and *** at one per cent, respectively.

VariableCoefficient     
(t-value)     
Log MarketCapitalisation0.236***0.235***0.237***0.236***0.235***0.237***
(13.77)(13.73)(13.79)(13.75)(13.72)(13.85)
Log Age0.303***0.303***0.303***0.303***0.303***0.303***
(19.83)(19.85)(19.87)(19.85)(19.83)(19.85)
Log Liquidity0.254***0.254***0.253***0.254***0.254***0.253***
(9.42)(9.43)(9.42)(9.42)(9.43)(9.39)
Leverage−0.304***−0.304***−0.303***−0.303***−0.304***−0.303***
(−2.67)(−2.67)(−2.66)(−2.66)(−2.67)(−2.66)
Log employees0.411***0.411***0.411***0.411***0.411***0.411***
(22.68)(22.69)(22.66)(22.69)(22.69)(22.65)
Former Colony0.671***0.672***0.665***0.667***0.673***0.655***
(5.23)(5.23)(5.17)(5.21)(5.23)(5.09)
Governance0.068***0.081***0.045**0.075***0.069***0.053***
(3.40)(3.74)(2.30)(3.39)(3.33)(2.70)
Governance×Control of Corruption−0.086***     
(−5.69)     
Governance×Government Effectiveness −0.096***    
 (−5.53)    
Governance×Political Stability  −0.105***   
  (−5.69)   
Governance×Regulatory Quality   −0.104***  
   (−5.67)  
Governance×Rule of Law    −0.097*** 
    (−5.91) 
Governance×Voice and Accountability     −0.084***
     (−4.58)
Pseudo R20.1480.1480.1480.1480.1480.148
Number of Observations81,59782,05882,05881,59782,05882,058

Note that in Table 4 we do not report country and industry fixed effects. We see that in all five equations, the parameter estimates of the number of Employees, the Market Capitalisation and Age are significant. Clearly, larger and older firms operate in more countries, since they have had the time to grow and the resources to expand. With respect to our financial indicators, we find that the impact of Liquidity is significantly positive and that of Leverage is significantly negative. Having enough liquid assets within the firm seems to be a requirement for international expansion. Furthermore, highly leveraged firms seem to be hindered with respect to international expansion. In all our estimations, the estimated parameter of FormerColony is positive and significant, indicating that colonial history remains to be important for current business affairs. Recall that the FormerColony dummy is almost identical to a common language dummy, so we cannot determine whether the observed positive effect is purely due to historical ties, or due to a comparative advantage in communication.

The quality of corporate governance – as reflected by our variable Governance– has a positive influence on international location decisions. We also find a significant and robust relation between the quality of corporate governance and country business environment. The parameter estimates of the interaction between on the one hand Governance and on the other hand Control of Corruption, Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law and Voice and Accountability are all negative and significant. From this result, it appears that firms with relatively better corporate governance do not avoid countries with poor governance standards: these firms are relatively more often located in those countries. This was to be expected, as this type of location choice behaviour can only be advocated successfully to the stakeholders of the company when corporate governance standards are high. In this case, various stakeholders trust the MNE to deal with unforeseen events in the appropriate way. Apparently, only companies that have strong corporate governance can deal with uncertainties that are associated with operating in countries with poor governance.

Additionally, we split our sample in three groups of countries, high-income, middle-income, and least-developed countries2 to assess whether the level of economic development has an impact on the location decision-governance nexus. Table 5 provides the results of the estimations for the sub sample of Low Income Countries (non-OECD countries and other non-high income countries as classified by the World Bank). Table 5 shows virtually the same results as in Table 4. However, if we estimate the same model for the high-income countries,3 we find no significant interaction effect between firm governance and country governance indicators. This indicates that especially when it comes to location decisions in developing countries, governance does matter.

Table 5. Country presence in low and middle income countries of MNEs and corporate governance
Model:123456
Control of CorruptionGovernment EffectivenessPolitical StabilityRegulatory QualityRule of LawVoice and Accountability
  1. The estimated logit model is: Presence = E[Yij] = Λ(αj Countryj βk Industry +η FormerColony +γiFirm + δ(Governancei ×Business Environmentj)). Yij = 1 if MNE i is present in country j.Λ is the logistic function, conditional on Country fixed effects. FormerColony = 1 if the country is a former colony of the country where the MNE is based. Firmi are the reported firm characteristics. Industry are industry dummies. For Business Environment we use Control of Corruption, Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law and Voice and Accountability respectively. Higher values of Governance indicate better Corporate Governance, higher values of Business Environment indicate better regulation and/or lower levels of uncertainty. For brevity sake, the country and industry fixed effects are not reported. Definitions of the variables are in Table B of the appendix. The t-values are calculated using the Huber-White robust standard errors. * indicates significance at ten, ** at five, and *** at one per cent, respectively.

VariableCoefficient     
(t-value)     
Log Market Capitalisation0.253***0.253***0.254***0.253***0.253***0.254***
(10.69)(10.61)(10.69)(10.66)(10.67)(10.72)
Log Age0.333***0.333***0.333***0.333***0.333***0.333***
(16.02)(16.05)(16.05)(16.02)(16.01)(16.02)
Log Liquidity0.181***0.181***0.181***0.180***0.181***0.180***
(5.59)(5.61)(5.59)(5.58)(5.60)(5.58)
Leverage−0.837***−0.837***−0.838***−0.838***−0.838***−0.838***
(−5.14)(−5.14)(−5.15)(−5.14)(−5.14)(−5.15)
Log employees0.492***0.492***0.492***0.492***0.492***0.492***
(24.14)(24.16)(24.14)(24.14)(24.16)(24.14)
Former Colony0.799***0.805***0.800***0.797***0.805***0.798***
(4.37)(4.39)(4.33)(4.33)(4.40)(4.33)
Governance0.0320.051**0.0350.051**0.0350.053**
(1.45)(2.13)(1.60)(2.25)(1.60)(2.39)
Governance×Control of Corruption−0.102***     
(−2.95)     
Governance×Government Effectiveness −0.115***    
 (−3.26)    
Governance×Political Stability  −0.072***   
  (−2.66)   
Governance×Regulatory Quality   −0.075***  
   (−2.66)  
Governance×Rule of Law    −0.095*** 
    (−3.09) 
Governance×Voice and Accountability     −0.044*
     (−1.78)
Pseudo R20.1710.1710.1710.1710.1710.170
Number of Observations61,31361,31361,31361,31361,31361,313

To illustrate the differences between high-income, middle-income, and least-developed countries we present the estimation results for one of the country governance indicators, Control of Corruption. These results (presented in columns 1–4 of Table 6) are representative for the results of each individual indicator.

Table 6. Differences in MNE presence for various country samples
Sub Sample:123456
All countriesHigh Income Countries (OECD and non-OECD)Low and Middle Income CountriesLow Income CountriesHigh Governance CountriesLow Governance Countries
  1. The estimated logit model is: Presence = E[Yij] = Λ(αj Countryj + βk Industry + η FormerColony + γiFirm + δ(Governancei × Business Environmentj)). Yij = 1 if MNE i is present in country j.Λ is the logistic function, conditional on Country fixed effects. FormerColony = 1 if the country is a former colony of the country where the MNE is based. Firmi are the reported firm characteristics. Industry are industry dummies. For Business Environment we use one combined factor score based on the six Business Environment indicators. Higher values of Governance indicate better Corporate Governance, higher values of Business Environment indicate better regulation and/or lower levels of uncertainty. In the High Governance and Low Governance Countries estimations, we dropped the interaction term. For brevity sake, the country and industry fixed effects are not reported. Definitions of the variables are in Table B of the appendix. The t-values are calculated using the Huber-White robust standard errors. * indicates significance at ten, ** at five, and *** at one per cent, respectively.

Number of countries15238114877181
VariableCoefficient     
(t-value)     
Log Market Capitalisation0.236***0.207***0.253***0.272***0.210***0.286***
(13.74)(8.63)(10.67)(8.54)(11.05)(8.13)
Log Age0.303***0.266***0.333***0.366***0.277***0.361***
(19.82)(11.87)(16.03)(14.46)(14.43)(14.80)
Log Liquidity0.254***0.327***0.181***0.134***0.311***0.112***
(9.42)(7.93)(5.59)(3.51)(9.34)(3.14)
Leverage−0.303***0.189−0.838***−1.016***−0.011−1.049***
(−2.66)(1.41)(−5.14)(−4.52)(−0.09)(−5.21)
Log employees0.411***0.340***0.492***0.480***0.378***0.503***
(22.68)(14.06)(24.14)(17.41)(17.20)(20.45)
Former Colony0.669***0.432***0.800***0.716***0.577***0.739***
(5.20)(3.29)(4.35)(3.53)(3.59)(3.90)
Governance0.072***−0.0080.042*0.024−0.0140.064**
(3.44)(−0.13)(1.91)(0.65)(−0.63)(2.01)
Governance×Control of Corruption−0.106***−0.014−0.096***−0.135**  
(−5.83)(−0.30)(−3.05)(−2.25)  
Pseudo R20.1480.1320.1710.1770.1370.180
Number of Observations81,59720,28461,31346,56138,26345,178

On the basis of Table 6, we establish that the interaction effect that we find for the entire sample is not significant when we focus on the high-income countries, but it is significant at the 1 per cent significance level for the low- and middle-income countries and at the 5 per cent significance level for the lowest income countries. Apparently, corporate governance standards are more important for location decisions when it comes to governance differences in developing countries. In short, only MNEs with high governance standards choose to locate in countries with poor business environments, and this is especially true for low- income countries. Table 6 also reveals that the financial position of the firm as indicated by leverage is not relevant to location decisions in high-income countries, but it is so in middle- and lower-income countries.

The results can in fact be interpreted in two ways. The evidence suggests that there are opportunities for good corporate governance companies to locate their subsidiaries in countries with less strict governance systems. Second, it could be that there are incentives for bad corporate governance companies to shift some operations to countries with stronger governance rules. Whether the negative sign of the interaction between company and country governance indicators is due to the first hypothesis or the second is unclear so far. We therefore split up our sample in subsets of high-governance and low-governance-countries and estimate our model, omitting the now obsolete interaction effect. The estimation results are in columns 5–6 of Table 6. Column 6 of Table 6 reveals that for the subset of low-governance countries, the coefficient of the firm governance indicator is positive and significant, implying that good-governance companies are relatively more often present in these countries. We therefore conclude that we should interpret the negative sign as evidence that there are incentives for good corporate governance companies to locate their subsidiaries in countries with less strict governance systems.

6. Conclusion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References

This paper analyses the international location behaviour of multinational enterprises by explicitly taking account of governance. We analyse governance from the perspective of the firm (corporate governance), as well as from the national perspective (business environment). To this extent, we use a binary location choice model that describes corporate international location decisions. We investigate the internationalisation patterns of 540 European multinationals with more than 40 thousand subsidiaries in 188 countries. We explicitly test whether firms with relatively low governance standards are more often locating in countries with low governance related regulations, i.e. a weak business environment, conditioning for “normal” determinants of location choices. It appears that firms with a relatively high quality of corporate governance do not avoid to locate in countries with poor governance standards. This is based on the observation that these firms are relatively more often located in countries with poor domestic governance codes. A possible explanation is that this type of location choice behaviour can only be advocated successfully to the stakeholders of the company when corporate governance standards are high indeed. In this case, the various stakeholders might trust the internationally operating firm to deal with unforeseen events in an appropriate manner. Apparently, only companies that have strong corporate governance are able to deal with the uncertainties that are associated with low standards of governance in the destination country. In addition, we find that our results are stronger for low-income countries, implying that governance standards are specifically important here. These findings are very much in line with other studies that focus on institutional determinants of international location behaviour (e.g. Basile, 2003; Li and Resnick, 2003; Blonigen, 2005; Yamawaki, 2006). In all, we conclude that corporate governance does play a significant role too in international location decisions of MNEs.

Acknowledgements

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References

We are grateful to useful comments by various participants of the “4th International conference on corporate governance” held at the Birmingham Business School, July 3rd 2006. We thank two anonymous referees for constructive comments. We thank NWO for financial support.

Notes
  • 1

    We skip the discussion on general equilibrium models here, since this discussion has little direct relevance to our paper, and refer to Blonigen (2005).

  • 2

    The distinction is based on the convention suggested by the World Bank. Thus, high-income countries have an average GDP per capita of $10,066 in 2004 (54 countries), middle-income countries have an average per capita income between $826 and $10,065 in 2004 (93 countries), and the least-developed countries have an average per capita GDP below $ 825 in 2004 (61 countries).

  • 3

    These results are not reported but available on request.

Appendix

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References
Table A. List of included countries
Country NameRegion
AfghanistanWestern Asia
AlbaniaEurope
AlgeriaAfrica
American SamoaOceania
AndorraEurope
AngolaAfrica
AnguillaCaribbean and Bahamas
AntarcticaAntarctica
Antigua and BarbudaCaribbean and Bahamas
ArgentinaSouth America
ArmeniaWestern Asia
ArubaCaribbean and Bahamas
AustraliaOceania
AustriaEurope
AzerbaijanWestern Asia
BahamasCaribbean and Bahamas
BahrainMiddle East
BangladeshEastern Asia
BarbadosCaribbean and Bahamas
BelarusEurope
BelgiumEurope
BelizeCentral and North America
BeninAfrica
BermudaCentral and North America
BhutanEastern Asia
BoliviaSouth America
Bosnia and HerzegovinaEurope
BotswanaAfrica
Bouvet IslandAntarctica
BrazilSouth America
British Indian Ocean TerritoryAfrica
Brunei DarussalamEastern Asia
BulgariaEurope
Burkina FasoAfrica
BurundiAfrica
CambodiaEastern Asia
CameroonAfrica
CanadaCentral and North America
Cape VerdeAfrica
Cayman IslandsCaribbean and Bahamas
Central African RepublicAfrica
CeutaAfrica
ChadAfrica
ChileSouth America
China, People's Republic ofEastern Asia
Christmas IslandsOceania
Cocos Islands (or Keeling Islands)Oceania
ColombiaSouth America
ComorosAfrica
CongoAfrica
Congo (Democratic Republic of)Africa
Cook IslandsOceania
Costa RicaCentral and North America
Côte d'IvoireAfrica
CroatiaEurope
CubaCaribbean and Bahamas
CyprusEurope
Czech RepublicEurope
DenmarkEurope
DjiboutiAfrica
DominicaCaribbean and Bahamas
Dominican RepublicCaribbean and Bahamas
EcuadorSouth America
EgyptAfrica
El SalvadorCentral and North America
Equatorial GuineaAfrica
EritreaAfrica
EstoniaEurope
EthiopiaAfrica
Falkland IslandsSouth America
Faroe IslandsEurope
FijiOceania
FinlandEurope
Former Yugoslav Republic of MacedoniaEurope
FranceEurope
French PolynesiaOceania
French Southern TerritoriesAntarctica
GabonAfrica
GambiaAfrica
GeorgiaWestern Asia
GermanyEurope
GhanaAfrica
GibraltarEurope
GreeceEurope
GreenlandCentral and North America
GrenadaCaribbean and Bahamas
GuamOceania
GuatemalaCentral and North America
GuineaAfrica
Guinea-BissauAfrica
GuyanaSouth America
HaitiCaribbean and Bahamas
Heard Island and McDonald IslandsOceania
Holy SeeEurope
HondurasCentral and North America
Hong KongEastern Asia
HungaryEurope
IcelandEurope
IndiaEastern Asia
IndonesiaEastern Asia
Iran (Islamic Republic of)Middle East
IraqMiddle East
IrelandEurope
IsraelMiddle East
ItalyEurope
JamaicaCaribbean and Bahamas
JapanEastern Asia
JordanMiddle East
KazakhstanWestern Asia
KenyaAfrica
KiribatiOceania
Korea, Democratic People's Republic ofEastern Asia
Korea, Republic ofEastern Asia
KuwaitMiddle East
KyrgyzstanWestern Asia
Lao People's Democratic RepublicEastern Asia
LatviaEurope
LebanonMiddle East
LesothoAfrica
LiberiaAfrica
Libyan Arab JamahiriyaAfrica
LiechtensteinEurope
LithuaniaEurope
LuxembourgEurope
MacaoEastern Asia
MadagascarAfrica
MalawiAfrica
MalaysiaEastern Asia
MaldivesEastern Asia
MaliAfrica
MaltaEurope
Marshall IslandsOceania
MauritaniaAfrica
MauritiusAfrica
MayotteAfrica
MelillaAfrica
MexicoCentral and North America
Micronesia (Federated States of)Oceania
Moldova (Republic of)Europe
MongoliaEastern Asia
MontserratCaribbean and Bahamas
MoroccoAfrica
MozambiqueAfrica
MyanmarEastern Asia
NamibiaAfrica
NauruOceania
NepalEastern Asia
NetherlandsEurope
Netherlands AntillesCaribbean and Bahamas
New CaledoniaOceania
New ZealandOceania
NicaraguaCentral and North America
NigerAfrica
NigeriaAfrica
NiueOceania
Norfolk IslandOceania
Northern Mariana IslandsOceania
NorwayEurope
Occupied Palestinian TerritoryMiddle East
OmanMiddle East
PakistanWestern Asia
PalauOceania
PanamaCentral and North America
Papua New GuineaOceania
ParaguaySouth America
PeruSouth America
PhilippinesEastern Asia
PitcairnOceania
PolandEurope
Porto RicoCaribbean and Bahamas
PortugalEurope
QatarMiddle East
RomaniaEurope
Russian FederationEurope
RwandaAfrica
Saint HelenaAfrica
SamoaemenOceania
San MarinoEurope
Sao Tome and PrincipeAfrica
Saudi ArabiaMiddle East
SenegalAfrica
Serbia and MontenegroEurope
SeychellesAfrica
Sierra LeoneAfrica
SingaporeEastern Asia
SlovakiaEurope
SloveniaEurope
Solomon IslandsOceania
SomaliaAfrica
South AfricaAfrica
South Georgia and South Sandwich IslandsAntarctica
SpainEurope
Sri LankaEastern Asia
St Kitts and NevisCaribbean and Bahamas
St LuciaCaribbean and Bahamas
St Pierre and MiquelonCentral and North America
St Vincent and GrenadinesCaribbean and Bahamas
SudanAfrica
SurinameSouth America
SwazilandAfrica
SwedenEurope
SwitzerlandEurope
Syrian Arab RepublicMiddle East
TaiwanEastern Asia
TajikistanWestern Asia
Tanzania (United Republic of)Africa
ThailandEastern Asia
Timor-LesteEastern Asia
TogoAfrica
TokelauOceania
TongaOceania
Trinidad and TobagoCaribbean and Bahamas
TunisiaAfrica
TurkeyMiddle East
TurkmenistanWestern Asia
Turks and Caicos IslandsCaribbean and Bahamas
TuvaluOceania
UgandaAfrica
UkraineEurope
United Arab EmiratesMiddle East
United KingdomEurope
United StatesCentral and North America
United States Minor Outlying IslandsOceania
UruguaySouth America
UzbekistanWestern Asia
VanuatuOceania
VenezuelaSouth America
Viet-NamEastern Asia
Virgin Islands (British)Caribbean and Bahamas
Virgin Islands (US)Caribbean and Bahamas
Wallis and FutunaOceania
YemenMiddle East
ZambiaAfrica
ZimbabweAfrica
Table B. Variables and sources
VariableDefinitionSource
Governance Bribery and Corruption“Does the company have policies and procedures on bribery and corruption?” (No policy disclosed = 1 Has adopted a policy = 2, Has clear policy and procedures = 3)EIRIS
Governance Code of Ethics“Does the Company have a code of ethics and, if so, how comprehensive is it?” (No = −1, Limited = 0, Basic = 1, Intermediate = 2, Advanced = 3)EIRIS
Governance Code of Ethics Systems“Does the Company have a system for implementing a code of ethics and, if so, how comprehensive is it?” (No = −1, Limited = 0, Basic = 1, Intermediate = 2, Advanced = 3)EIRIS
Governance Business Principles“Has the company adopted a code of ethics or business principles which it communicates to all employees?” ( No evidence of = 1, Has adopted = 2, Clearly communicates = 3)EIRIS
GovernanceFactor Scores based on a factor analysis of the above four Corporate Governance indicators.Own Calculations
Total AssetsReported total assets as of 2004 in thousands of U.S. dollars.Amadeus
LeverageRatio of: (current liabilities + non-current liabilities)/total assets as of 2004.Amadeus
AgeAge in years of the company as of 2004, based on the reported date of incorporationAmadeus
EmployeesNumber of reported employees as of 2004Amadeus
LiquidityReported Liquidity ratio (%) as of 2004Amadeus
Market CapFree Float Market Capitalisation (in billion EUR) as of 03.01.2005Dow Jones Stoxx
Control of corruption Voice and accountability Rule of Law Government effectiveness Regulatory quality Political stabilityBased on several hundred indicators, drawn from 37 separate data sources constructed by 31 different organisations. Compiled using an unobserved component technique by Kaufmann et al. (2005).Worldbank, see Kaufmann et al. (2005)

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  2. Abstract
  3. 1. Introduction
  4. 2. International location activity, governance, and firm performance
  5. 3. Data
  6. 4. Methodology
  7. 5. Results
  8. 6. Conclusion
  9. Acknowledgements
  10. Appendix
  11. References
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Lammertjan Dam is a PhD student at the University of Groningen. He expects to defend his thesis about corporate social responsibility and financial markets in February 2008. He teaches corporate governance and asset pricing.

Bert Scholtens is Associate Professor of Finance at the University of Groningen. He teaches credit risk analysis and international financial management. His research focus is on socially responsible finance. He has published in journals like Sustainable Development, Journal of Banking and Finance, World Development, and Energy Journal.

Elmer Sterken is Professor of Monetary Economics at the University of Groningen. He has published papers on monetary policy, real options theory, investment, information problems, board composition, business cycles, financial systems, and sports economics. His current research interests are in financial imperfections, monetary policy rules, corporate behaviour, and sports. Sterken was a visitor/visiting professor to the Dutch Central Planning Bureau, Yale University, Kobe University, Emory University, Osaka University and is a member of CESifo and the Euro Area Business Cycle Network.