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The influence of arbitrary and pervasive corruption on FDI inflows and the moderating effect of corruption distance: evidence from Latin America/O impacto da corrupcao arbitraria e generalizada nos influxos de IDE e o efeito moderador da distancia de corrupcao: Evidencias da America Latina.

1. INTRODUCTION

The influence of corruption on countries' ability to attract foreign direct investment (FDI) has been extensively analyzed in the literature, but with inconclusive results (Habib & Zurawicki, 2002; Cuervo-Cazurra, 2006, 2008; Godinez & Liu, 2015; Ferreira et al., 2016). The dominant argument is that corruption has a negative impact on ability to attract FDI by increasing uncertainty and the costs of FDI (Smarzynska & Wei, 2000; Wei, 1998, 2000; Habib & Zurawicki, 2002; Voyer & Beamish, 2004; Egger & Winner, 2005). However, another side of the argument is that corruption facilitates FDI by oiling the wheels of transactions, which become more expeditious, and by bypassing institutional inefficiencies (Huntington, 1968; Bardhan, 1997; Cuervo-Cazurra, 2008). The benefits of avoiding inefficient institutions through corruption could offset additional costs and uncertainty (Cuervo-Cazurra, 2008), although other authors have found no relationship between corruption and FDI (e.g., Hines, 1995). However, although the literature on the influence of corruption in FDI is vast, it has neglected two dimensions. On the one hand, distinguishing the type of corruption (exceptions in Rodriguez et al., 2005; Cuervo-Cazurra, 2006; Ferreira et al., 2016) and on the other hand, albeit less frequently, considering the corruption distance between the home country and host country (Habib & Zurawicki, 2002; Godinez & Liu, 2015).

Corruption, as an element of the institutional quality of countries, is an indicator of the ability to attract FDI (Kinoshita & Campos, 2004; Egger & Winner, 2005; Wernick et al., 2009; Zeghni & Fabry, 2009). More developed institutional environments, with consequent lower corruption levels, may encourage FDI by reducing risks, uncertainty and transaction costs (Ali et al., 2010; Chao & Kumar, 2010). Institutional insufficiencies like the lack of monitoring of legal systems (Lapalombara, 1994; Jeong & Weiner, 2012) or the existence of excessive or inefficient bureaucracy (Leff, 1964; LaPalombara, 1994; Cuervo-Cazurra, 2006) encourage resorting to abuse of public power to obtain private benefits.

In internationalization, the greater the differences in corruption levels, the less likely multinational enterprises (MNEs) will know how to handle social pressures and the less likely they will be to make FDI (Habib & Zurawicki, 2002). Nevertheless, some studies have shown that corruption distance, and perhaps its direction, differentiates the impact of corruption on FDI. Godinez and Liu (2015), for example, found that MNEs based in countries with relatively low corruption levels are not familiar with the formal and informal institutions associated with corruption, thus perceiving greater risk and uncertainty in FDI. On the other hand, companies based in countries with high corruption levels are not dissuaded by high corruption levels in host countries. In other words, on the one hand, there is the possibility of MNEs from low corruption countries finding it difficult to understand the values and norms of the market in which they wish to operate and the organizational legitimacy they should achieve when faced with corruption. On the other hand, MNEs from high corruption countries could view the situation differently, as they may have developed abilities to deal with corruption in their countries of origin (Godinez & Liu, 2015; Ferreira et al., 2016).

In this study, we analyzed the influence of arbitrary corruption and pervasive corruption on FDI flows, gauging the moderating role of corruption distance between home and host country. Specifically, we evaluated whether the relationship between arbitrary and pervasive corruption and FDI flows is altered depending on the distance between the corruption levels in the home and host countries. We empirically observed FDI flows in Latin American countries from 2010 to 2014. With a sample of 473 investments from 27 countries in 17 Latin American countries, we used Tobit regression models. We concluded that pervasive corruption has a negative impact on FDI flows and that corruption distance moderates the effect of arbitrary corruption on FDI flows.

This study makes two contributions to the field. First, it extends our knowledge on the impact of corruption in the host country regarding its ability to attract FDI. Due to the contradictory results of other studies, further empirical demonstrations of the ability to discourage or stimulate FDI are justified. Latin American countries provide the context for the empirical testing. However, this study includes a special analysis of the type of corruption in FDI flows. Arbitrary corruption and pervasive corruption imply different configurations, predictably with different influences on FDI flows (Cuervo-Cazurra, 2008).

The study also contributes to the still incipient research on the moderating role of corruption distance between the home and host countries, underlining the importance of institutional quality of the home country in the context of foreign investment. Corruption distance has been seen as dissuasive to FDI in that institutional differences increase the effort of multinationals to gather, interpret and organize the information required for FDI (Godinez & Liu, 2015). However, the direction in which the investment flows is a determining factor due to the possible competences acquired by MNEs based on their experience with dealing with corruption in their domestic environments.

2. LITERATURE REVIEW AND HYPOTHESES

The increasing volume of FDI since the nineties has aroused growing interest in the academic community on the possible determinants of FDI flows (Daude & Stein, 2007; Ali, Fiess & Macdonald, 2010). Economic factors have traditionally been viewed as fundamental determinants of FDI flows (Mudambi & Navarra, 2002), including aspects such as the dimension and growth of the market, access to natural resources, the cost of labor, and inflation levels (Habib & Zurawicki, 2002). However, differences in institutional structures in countries were either ignored or viewed as a secondary factor (Mudambi & Navarra, 2002). Nevertheless, institutions are critical elements in companies' decisions on FDI, and developed institutional environments attract FDI because they reduce transaction costs (Mudambi & Navarra, 2002; Gelbuda et al., 2008). The institutional quality of countries is fundamental for their capacity to attract FDI IDE (Bevan et al., 2004; Benassy-Quere et al., 2007; Wernick et al., 2009). Institutional quality encompasses element ranging from governmental stability, lack of social tension, compliance with the law, economic development, sophistication of education and incorporation of knowledge (Peng & Heath, 1996; Peng, 2003; Ferreira et al., 2016).

Corruption is a face of institutionally less developed environments. Corruption has a negative effect on company performance (Doh et al., 2003; Uhlenbruck et al., 2006; Cuervo-Cazurra, 2008). It may be the result of institutional insufficiencies (Ferreira et al., 2016). A common example of how institutional gaps give rise to corruption is a flaw in legal systems when it comes to monitoring laws, compliance with the law and punishment for offenses (Karnani, 2007). The lack of monitoring and a flawed institutional system are incentives for corruption (Jeong & Weiner, 2012). On the other hand, sophisticated legal and political institutions act as barriers to corruption, formalizing the costs associated with corruption in the form of fines (Galang, 2012) and legal proceedings. Therefore, institutionally sophisticated frameworks make it possible to attract MNEs and investments, while the existence of institutional gaps and inefficiencies discourage companies from investing.

Despite the argument and current conviction that corruption harms development, the effect of corruption on attracting FDI is unclear. Stereotypically, countries with high corruption levels tend to attract less FDI (Wei, 1998). This is because corrupt countries do not offer the same conditions of access to markets as all their competitors, preferring those who are willing to pay bribes (Wei, 1998). However, another perspective proposes that corruption facilitates business, making transactions less expeditious by overcoming institutional deficiencies (Bardhan, 1997; Huntington, 1968). In these cases, corruption can effectively facilitate FDI (Cuervo-Cazurra, 2008). For example, bribing civil agents to obtain licenses triggers market mechanisms and incentives for better functions of bureaucratic systems (Lui, 1985). In other words, corruption could constitute an advantage against the risks of the political system, as it helps to mitigate risks such as expropriation (Leff, 1964). Thus, a positive effect of corruption in attracting FDI will occur especially in countries with less developed institutions and excessive or inefficient regulations (Cuervo-Cazurra, 2006; Leff, 1964). In countries with excessive regulations and other administrative constraints, corruption can function as a "friendly hand" to encourage FDI (Egger & Winner, 2005). In short, the benefits of bypassing deficient institutions through corruption can offset additional costs and uncertainties.

2.1. Arbitrary and pervasive corruption in the host country and FDI flows

Corruption can divided into two types: arbitrary and pervasive. Arbitrary corruption has to do with the uncertainty associated with corrupt acts (Rodriguez et al., 2005; Cuervo-Cazurra, 2008), a counterpoint to pervasive corruption, which reflects the degree of institutionalization of corruption, i.e., its transversal nature in the public sector (Rodriguez et al., 2005) or even society. Arbitrary corruption represents the uncertainty associated with corruption. When MNEs are entering foreign markets, they do not know whether they will be asked to pay bribes to achieve their goals, such as public contracts or licensing (Cuervo-Cazurra, 2008). Wei (1998) showed that uncertainties related to corruption have a greater influence on the ability to attract FDI than the level of corruption, when analyzed separately. Indeed, the difficulties involved in MNEs complying with legal requirements, norms and regulations will be greater when arbitrary corruption levels in host countries are high (Rodriguez et al., 2005).

Arbitrary corruption in the host country will have a negative effect on decisions to make FDI. Arbitrary corruption hinders MNEs when it comes to estimating, for example, the likelihood of their being asked to pay bribes or the cost of these bribes. There is also the possibility that different elements of political, legal and bureaucratic agents will ask for bribes independent of one another (Cuervo-Cazurra, 2008). Therefore, arbitrary corruption increases the difficulty of planning the exact costs of a new FDI operation (Rodriguez et al., 2005). This is why arbitrary corruption is often referred to using the vulgar term "disorganized corruption" (Shleifer & Vishny, 1993) due to the uncertain possibility of bribery and the form it will take, the possibility of independent bribes and uncertainty regarding the effective outcome of a bribe (Doh et al., 2003; Rodriguez et al., 2005). When there is less arbitrary corruption, there will be less uncertainty associated with the operation. Therefore, a decision will be made to invest in this territory (Wei, 1998).

Hypothesis 1: Higher levels of arbitrary corruption in the host country are negatively related to the ability to attract FDI.

Pervasive corruption, on the other hand, can be understood as the known cost of corruption. In other words, the expectable and predictable cost of a certain operation or transaction (Cuervo-Cazurra, 2008). This kind of corruption is based on the idea that a bribe, once paid, ensures the delivery of the goods, or the effectiveness of the agreed transaction, also known as organized corruption (Elliot, 1997).

When investing in countries with high pervasive corruption, companies already expect that they will be obliged to pay bribes either to public agents who handle administrative processes to lighten the burden of bureaucracy or to government agents to obtain public contracts and licenses (Cuervo-Cazurra, 2008). In any case, these costs or charges can be expected and estimated beforehand. Therefore, we understand pervasive corruption as a measurement of external institutional pressures on companies to practice acts of corruption (Uhlenbruck et al., 2006). Although pervasive corruption reflects the known cost of corruption (Cuervo-Cazurra, 2008), recurring payments increase costs (Meyer, 2001) and reduce the expected return on investments by MNEs, discouraging foreign investment.

Hypothesis 2: Higher pervasive corruption levels in the host country are negatively related to the ability to attract FDI.

2.2 Corruption distance

Institutional differences between countries make it more difficult for MNEs to gather, interpret and organize information required for FDI. These differences discourage FDI and conflicts can even emerge between the need for local adaptation and the internal structure of MNEs (Benassy-Quere et al., 2007). Moreover, the differences between corruption levels in the home and host country can increase the risk and costs of FDI. Conceptually, we can refer to these differences between countries as distance. The concept of distance has bene used to refer to how cultures differ between countries (e.g., Kogut & Singh, 1988) and how institutions differ (Hernandez & Nieto, 2015). We refer here to corruption distance as the difference between the level of corruption in the public sector between FDI home and host countries, in accordance with Eden and Miller (2004).

Specific characteristics of home countries can also influence the cost and willingness of MNEs to indulge in corrupt practices (Godinez & Liu, 2015). The corruption level of home countries at least partly determines the sensitivity of MNEs regarding corruption in host countries (Cuervo-Cazurra, 2006). For instance, MNEs from countries with laws against corruption abroad might tend to avoid investing in countries with high corruption levels, while MNEs with headquarters in corrupt countries will not be reticent about investing in countries with similar levels of corruption (Cuervo-Cazurra, 2006). Likewise, MNEs located in low corruption countries will probably avoid investing in countries with high corruption rates (Habib & Zurawicki, 2001) due to their limited knowledge concerning how to deal with corruption. In these cases, companies have not learned how to deal with corruption in their own domestic markets (Pajunen, 2008). This means that corruption is an unfamiliar phenomenon to them and leads them to decide not to invest (Driffield et al., 2013). In other words, companies from less corrupt countries have not developed a corruption capability (Ferreira et al., 2016).

If institutional differences and different levels of corruption between the host and home countries discourage FDI, it is important to understand whether there are additional effects of corruption distance between countries. Countries can be characterized by different levels of arbitrary corruption, a phenomenon that leads to high levels of uncertainty (Ferreira et al., 2016). Therefore, arbitrary corruption alone dissuades investment (Cuervo-Cazurra, 2008), with no expected growth of uncertainty due to a greater corruption distance. Arbitrary corruption involves, for example, corrupting members of government or other political elites, judges and bureaucrats who use their power of discretion to exploit legal gaps. These forms of corruption do not seem to have an impact on the decision to invest because foreign investors are aware of the problem, even if they cannot measure it. In other words, the growing uncertainty or risk that could be predictable by greater corruption distance does not effectively generate further reductions in investments. Companies from corrupt countries could actually gain an advantage in operations in other highly corrupt countries as they may have the knowledge to deal with arbitrary corruption dimensions.

In short, if the level of uncertainty associated with the existence of arbitrary corruption, added to the uncertainty created by the potential emerging nature or transition of markets is not sufficient to dissuade investment, it is not expected that corruption distance will potentiate this dissuasive effect and, on the contrary, attenuate any possible negative effect of arbitrary corruption. An explanation by Godinez and Liu (2015) is that in conditions of great uncertainty, companies tend to use entry modes with greater control on operations abroad, which mean higher investments.

Hypothesis 3: Corruption distance between home and host country attenuates the negative relationship between arbitrary corruption and FDI flow.

As stated above, pervasive corruption is institutionalized in a set of practices and norms that have to be adapted to customs, including acts of bribery (Uhlenbruck et al., 2006). Therefore, MNEs need to incur costs to adapt to informal corruption practices (Egger & Winner, 2005). However, adapting to norms and practices requires specific capabilities and knowledge of the host country (Cuervo-Cazurra, 2008). Nevertheless, there is less knowledge when the corruption distance is greater and MNEs are less capable of dealing with the pressures of operating in a corrupt domain (Uhlenbruck et al., 2006). Investors intending to operate in pervasive corruption countries with high levels of corruption distance face higher costs and risks. Thus, greater corruption distance reduces an MNE's ability to adapt to the norms and practices of the host country, reducing the likelihood of FDI.

Hypothesis 4: The corruption distance between the home and host countries intensifies the negative relationship between pervasive corruption and FDI flow.

3. METHOD

Corruption in Latin America is considered widespread and deeply rooted (Godinez & Liu, 2015). Despite high corruption, significant FDI have been made in the region from diverse sources, predominantly from countries with relatively lower corruption levels, meaning more developed countries. FDI flows into Latin America have increased not only because of the attractive market (with a population of over six hundred million and a growing middle class, albeit in different ways), but also because Latin American countries are undergoing a period of pro-market transformations (Cuervo-Cazurra & Dau, 2009). In other words, their institutional systems have improved, with more formality and efficiency. Even so, great heterogeneity persists among Latin American companies and different levels of economic and institutional development.

3.1 Sample

The sample used in the study included FDI flows into 17 Latin American countries from 2010 to 2014 from 27 home countries. The host countries are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Trinidad and Tobago and Uruguay. The home countries are South Africa, Germany, Argentina, Belgium, Bermuda, Brazil, Canada, China, Colombia, France, Spain, the USA, Guatemala, the Netherlands, India, Italy, Japan, Luxemburg, Mexico, Norway, Panama, Peru, the UK, Russia, Switzerland, Uruguay and Venezuela. Although the number of host countries is limited, the number of observations totaled 473 pairs of home/host countries. Of these 473 observations, 405 are for FDI flows from less corrupt to more corrupt countries, and 68 FDI flows are from more corrupt to less corrupt countries.

3.2. Variables

The dependent variable of our study is the natural logarithm of the value of FDI flows, measured in millions of dollars, adding a unit, considering cases in which reported net flow was equal to zero. Net flows of FDI in Latin American Countries from 2010 and 2014 were used. These flows were taken from the publication of the Commission for Latin America and the Caribbean (ECLAC) for 2016.

The two independent variables in our study are arbitrary corruption in the host country and pervasive corruption. The arbitrary corruption variable in the FDI host country was measured using the standard deviation of the corruption perception index (CPI) of Transparency International. The standard deviation of this indicator represents the variation in the scores attributed to a given country. This variation reflects the uncertainty regarding the perceived corruption level, serving as a proxy for arbitrariness (see Ferreira et al., 2016; Bogmans & Jong, 2011).

Pervasive corruption was operationalized by the measurement proposed by Uhlenbruck et al. (2006), using data from the Executive Opinion Survey of the World Economic Forum, available in the Global Competitiveness Report for 2010 and 2015. This measurement is based on the average responses to the five components of the following question: In your country, how common is it for firms to make undocumented extra payments or bribes in connection with (1) imports and exports; (2) public utilities; (3) annual tax payments; (4) awarding of public contracts and licenses; (5) obtaining favorable judicial decisions? Although in the original report the scale of responses varies from 1 (very common) to 7 (never occurs), in this study, we have inverted the scale for better interpretation, with the lower values meaning less likelihood of requests for bribes.

Corruption distance between home and host country is the difference between the value of the Corruption Perception Index (CPI) of the home country and the value of the CPI of the host country, using data from International Transparency. A similar measurement was used by Hernandez and Nieto (2015), but applied to institutional distance.

Control variables

We also included a set of control variables with regard to the country. These variables are generally considered in gravitational models, which have proved useful when it comes to explaining FDI flows (Wei, 2000; Bevan et al., 2004; Cuervo-Cazurra, 2008). The bureaucracy of the host country is a reflection of institutional insufficiency, as a bloated bureaucracy is a barrier to FDI (Benassy-Quere et al., 2007; Godinez & Liu, 2015). The bureaucracy of the host country was measured using the value, in days, of the time required to create a new business, with data collected from the Doing Business Project of the World Bank, available in the Global Competitiveness Report.

The geographical distance between home and host country facilitates FDI measurement, as the greater the distance, the higher the cost of transport and consequently more incentive for FDI (Wei, 2000). In this study, we used physical distance in kilometers between the capitals of the pairs of home and host countries (see Ferreira et al., 2016), available in the Geodist database of the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII).

The Gross Domestic Product (GDP) of the host country is a reflection of the dimension of the market and its attractiveness (Globerman & Shapiro, 2003; Buckley et al., 2007). In this study, we used the natural GDP logarithm of the host countries, available in the World Development Indicators database of the World Bank. A market can also be more attractive if other MNEs have invested in it, as this investment reduces uncertainty regarding investments in these countries (Campos & Kinoshita, 2003). The presence of FDI in the host country variable was operationalized with the weighted average of responses to the question "In your country, how predominant is the ownership of companies by foreign capital?", available in the Executive Opinion Survey of the World Economic Forum, collected from the Global Competitiveness Report. The scale of responses ranges from 1 (extremely rare) to 7 (highly predominant). Finally, the attractiveness of a host country for FDI can also be influenced by the tax burden (Slamrod, 1990). The tax burden of the host country was operationalized through a combination of the percentage of tax on profit, percentage of tax on work and other taxes, using data from the Doing Business Project of the World Bank, available in the Global Competitiveness Report.

3.3. Analysis procedures

To gauge the impact of arbitrary and pervasive corruption on FDI and the moderating effect of corruption distance, we used the Tobit statistical regression model. It is adequate because the dependent variable, the natural FDI logarithm, has non-negative values, i.e., it is censored to the left (Cuervo-Cazurra, 2008). Given the existence of logarithmic variables on both sides of the equation, this model is referred to as being double-log (Wei, 2000).

4. RESULTS

Table 2 shows the descriptive statistics, median and standard deviation of the variables and their correlations. The Variance Inflation Factors (VIF) were tested to gauge possible problems of multicollinearity of the variables. The tests did not have significant results.

Table 3 shows the results of the regression to test the hypotheses. Model 1 includes only the control variables. Models 2 to 5 test the hypotheses and Model 6 is the full model. Model 2 presents the results for the test of Hypothesis 1, suggesting that arbitrary corruption in host countries would negatively influence FDI flows. The resulting coefficient is not statistically significant, and H1 was not confirmed. This result does not corroborate the position in the literature regarding the importance of arbitrary corruption (Cuervo-Cazurra, 2008), affirming that arbitrary corruption has a negative impact on attracting FDI. Investors may not manage to plan their costs beforehand or the probability of being asked for bribes in countries where they intend to invest. This could at least partly explain the nonconfirmation of the hypothesis.

Hypothesis 2 is tested in Model 3, proposing a negative influence of pervasive corruption on FDI flows. The hypothesis was confirmed with a negative and statistically significant coefficient (P=-0.233, p<0.01) for FDI flows. Thus, the higher the levels of pervasive corruption, the lower the FDI flow. It may be that the expected cost of additional undocumented payments for a transaction and the certainty of their recurrence makes MNEs hesitant about making investments (Ferreira et al., 2016; Cuervo-Cazurra, 2008).

Model 4 tests H3, which proposes a moderating effect of corruption distance between the home and host country with regard to arbitrary corruption and FDI flows. The coefficient is statistically significant (P=0.083, p<0.05), confirming the hypothesis. The result of the interaction term indicates that the negative impact of corruption is attenuated by corruption distance. In other words, the greater the corruption distance, the less the sensitivity of FDI will be to arbitrary corruption.

Finally, Model 5 tests the moderating effect of corruption distance between the home and host countries regarding the relationship between pervasive corruption and FDI. The result is not statistically significant to confirm the proposed hypothesis. Corruption distance would imply costs and effort in addition to those already known for pervasive corruption. This would make the operation riskier and more uncertain. However, in this study, it was not possible to verify the idea that the difference between the corruption level of home and host countries would accentuate the already negative relationship between FDI flows and pervasive corruption, corroborating Habib and Zurawicki (2002).

5. DISCUSSION AND FINAL COMMENTS

This study contributes to the literature on the impact of corruption on FDI flows along two lines. On the one hand, it distinguishes between arbitrary and pervasive corruption. On the other hand, it scrutinizes the corruption distance between home and host countries. In other words, we propose that to understand how corruption levels can affect the attractiveness of countries and MNEs' decisions to invest in them, we also need to look at corruption distance. The statistical study was conducted at the national level, with data on Latin American countries. This study therefore expands the extant literature on types of corruption (e.g., Uhlenbruck et. al., 2006; Cuervo-Cazurra, 2008) and studies on corruption distance (Godinez & Liu, 2015).

Most existing research has concluded that corruption effectively discourages foreign investment (Judge et al., 2011). However, our study does not fully corroborate the outcomes of previous studies. The diverging results may be at least partly due to our considering corruption not as a single dimension but because we distinguish between kinds of corruption. The results on FDI flows into Latin America from 27 countries showed that higher pervasive corruption levels reduce the ability to attract FDI. This is in keeping with most research on the theme (see, for example, Rodriguez et al., 2005). Nevertheless, we did not identify a significant effect between arbitrary corruption and FDI flows. This is in opposition to the proposals of Uhlenbruck et. al. (2006) and Cuervo-Cazurra (2008). Apparently, firms that invest in Latin American countries are not dissuaded by arbitrary corruption. A possible explanation for this is that this type of corruption is already expected by companies that do not succeed in estimating the real impact of this kind of corruption on their operations, precisely due to its random and discriminative nature. In other words, in the Latin American context, it may be more difficult to evaluate, understand and identify arbitrary corruption compared with pervasive corruption.

However, our study also shows that we need to analyze corruption distance. When investigating the moderating effect corruption distance in the relationship between FDI flows and arbitrary and pervasive corruption, we found different results for each type of corruption. If, on the one hand, there is a statistically significant effect in the relationship between pervasive corruption and FDI flows, in the relationship between arbitrary corruption and FDI, corruption distance acts as a mitigating effect. This means that although investors, in situations of corruption distance, continue to fear the effects of corruption at the heart of their organizations, they will not be deterred by arbitrary corruption in host countries. This result is in keeping with and strengthens the notion regarding the analysis of the impact of arbitrary corruption on FDI in isolation. In other words, countries that invest in Latin America will apparently not be dissuaded by arbitrary corruption. This reduced fear is due to the fact that scarce institutional familiarity in host countries, in comparison with home countries, does not add uncertainty to an already uncertain market (Cuervo-Cazurra, 2008). Another possible explanation is that the attractiveness of and the profit expected from international operations justify the risk of corruption (Bardhan, 1997; Huntington, 1968). Meanwhile, investment in situations of pervasive corruption does not appear to be mitigated or potentiated by corruption distance. It may be that the dissuasive force of pervasive corruption justifies the absence of significant moderation.

A dimension that may be relevant to the study of the effects of corruption, possibly like other institutional inefficiencies, is the direction of corruption (Godinez & Liu, 2015) and the possibility of companies developing what could be called a corruption capability. In other words, companies could develop a capability for dealing with corruption in their domestic markets. Cuervo-Cazurra and Genc (2008) call this, in the typology of Dunning's OLI, an ownership advantage. In this measurement, it would be interesting to understand how companies from more corrupt countries managed to use this capability in their international expansion.

5.1 Limitations and suggestions for future studies

This study has limitations. First, given the complex and multifaceted nature of corruption, it is difficult to measure it precisely as it is difficult to persuade managers to participate in the study. Thus, the use of secondary and general data on the perception of corruption does not allow the identification of the different facets that corruption might assume, nor is it possible to gauge the actual corruption levels that exist. Future studies, using qualitative methodologies and, especially, based on questionnaires, could shed further light on the phenomenon of corruption.

The use of secondary data, albeit from reliable sources, imposes further limitations. The number of observations was based on a relatively small group of host countries, making it necessary to cover a period of five consecutive years. However, it is important to highlight that we did not conduct longitudinal analyses given that the hypotheses did not forecast effects over time. Even so, it could be important to understand how improvements in the institutional environment, where corruption is found, might positively influence countries' ability to attract foreign investments.

It is important to note that most FDI flows into Latin America are from countries that are more institutionally developed, with lower corruption levels. In other words, the analysis might have a bias towards investments from less corrupt to more corrupt countries. However, it would be interesting to observe the opposite effect in greater detail when home countries have higher corruption levels than host countries, i.e., when home countries are more corrupt than the host countries that receive foreign direct investments.

Although there is already substantial knowledge on the effects of corruption, much research remains to be done. For instance, future studies could analyze the economic consequences of corruption, such as its effect on the composition of FDI (Smarzynska & Wei, 2000). Companies with market orientation (which Dunning called market seeking) may be subject to different corruption levels than companies that use strategic asset seeking. These analyses also involve scrutinizing the trade-offs of the cost and benefits of becoming involved in corruption (Ferreira et al., 2016).

To conclude, institutional factors are important to MNEs' investment decisions. This importance is greater when we analyze developing and Latin American countries, as institutional immaturity increases transaction costs and risk levels (Ali et al., 2010). Corruption stems from institutional insufficiencies and causes uncertainty in internationalization. Our analysis of FDI flows to Latin American countries led to the conclusion that corruption distance mitigates the negative effect of arbitrary corruption of the host country in FDI flows. On the other hand, regardless of the direction of the investment, the pervasive corruption of host countries alone has a negative impact on FDI, suggesting that organized and recurring (or pervasive) corruption dampens FDI flows.

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DOI: 10.18568/1980-4865.13150-62

ARTICLE DETAILS

Article history:

Received: August 08, 2017

Reviewed: September 11, 2017

Accepted December 07, 2017

Available online April 10, 2018

Double Blind Review System

Scientifc Editor

Raquel Moutinho

DETALHES DO ARTIGO

Historico do Artigo:

Recebido: 08 de Agosto de 2017

Revisado: 11 de Setembro de 2017

Aceito: 07 de Dezembro de 2017

Disponivel online: 10 de Abril de 2018

Sistema de revisao "Double blind review"

Editor Cientifico

Raquel Moutinho

* Daniel Mendes Pessegueiro holds Master in International business. Researcher at the globADVANTAGE. Consultant in management and information systems. Research interest in international business environments and their influence on the internationalization of firms. E-mail: dpessegueiro@hotmail.com

* Manuel Anibal Silva Portugal Vasconcelos Ferreira is Professor of Strategy and International Business. Ph.D in Management by the University of Utah, USA. Post doctorate by the University of Sao Paulo. His research interests lie on an institutional perspective on emerging economies, including the role of the government, business groups and the strategies of emerging multinationals, especially from Latin America. E-mail: manuel.portugal.ferreira@gmail.com

* Nuno Rosa Reis is Professor of Strategy and International Business. Ph.D in Management by the University of Coimbra. His research interests include the impact of institutional differences on the decisions and performance of multinational enterprises. E-mail: nuno.m.reis@ipleiria.pt

* Claudia Sofia Frias Pinto is Professor in the Master and Doctoral Program in Management at Unoesc. She teaches courses in the areas of Strategy and International Business. PhD in Management by the Fundacao Getulio Vargas--FGV/EAESP. Her research focuses on the strategies and behavior of multinational firms from emerging countries and cross-border acquisitions. E- mail: claudia.frias.pinto@gmail.com

Daniel Mendes Pessegueiro (1AB), Manuel Anibal Silva Portugal Vasconcelos Ferreira (ABC), Nuno Rosa Reis (AB), and Claudia Sofia Frias Pinto (C)

(A) Escola Superior de Tecnologia e Gestao, Instituto Politecnico de Leiria, ESTG-IPL, Leiria, Portugal

(B) Centre of Applied Research in Management and Economics, CARME, Leiria, Portugal (C) Universidade Nove de Julho, PPGA, UNINOVE, Sao Paulo, SP, Brazil

(D) Universidade do Oeste de Santa Catarina, PPGA, UNOESC, Chapeco, SC, Brazil

(1) Corresponding author: dpessegueiro@hotmail.com

Daniel Mendes Pessegueiro (AB), Manuel Anibal Silva Portugal Vasconcelos Ferreira (ABC), Nuno Rosa Reis (AB) e Claudia Sofia Frias Pinto (C)

(A) Escola Superior de Tecnologia e Gestao, Instituto Politecnico de Leiria, ESTG-IPL, Leiria, Portugal

(B) Centre of Applied Research in Management and Economics, CARME, Leiria, Portugal

(C) Universidade Nove de Julho, PPGA, UNINOVE, Sao Paulo, SP, Brasil

(D) Universidade do Oeste de Santa Catarina, PPGA, UNOESC, Chapeco, SC, Brasil
Tab. 1
Description of variables

Variables                Mensuration

Influxo de IDE           Logaritmo natural dos influxos de IDE no pais
                         receptor, em milhoes de dolares

Independent Variables

Arbitrary corruption     Standard deviation of Corruption Perception
in host country          Index (CPI)

Pervasive corruption     Average response to the five components of
of host country          the following question: "In your country, how
                         common is it for firms to make undocumented
                         extra payments or bribes in connection with
                         (a) imports and exports; (b) public
                         utilities; (c) annual tax payments; (d)
                         awarding of public contracts and licenses;
                         (e) obtaining favorable judicial decisions?"
                         Scale varies from (very common) to 7 (never
                         occurs). The values were inverted for better
                         interpretation.

Corruption distance      Dichotomous variable with a value of 1 when
                         corruption levels in the home country are
                         lower than in the host country, and 0 when
                         corruption levels in the home country are
                         higher than in the host country.

Controls Variables

Bureaucracy of host      Time needed to create a new business, in
country                  days.

Geographical distance    Physical distance in kilometers between
from home to host        capitals of pair of home and host countries
country

GDP host country         Natural logarithm of the value of GDP in
                         millions of dollars

Presence of FDI in       Weighted average of responses to the
host country             following question: "In your country, how
                         predominant is the ownership of companies by
                         foreign capital?" The scale varies from 1
                         (extremely rare) to 7 (highly predominant).

Tax burden of host       Combination of percentage of tax on profit,
country                  percentage of tax on work and other taxes.

Variables                Source

Influxo de IDE           Commission for Latin America and the
                         Caribbean (ECLAC, 2016)

Independent Variables

Arbitrary corruption     Transparency International (2010-14)
in host country

Pervasive corruption     Global Competitiveness Report (2010-14)
of host country

Corruption distance      Transparency International (2010-2014)

Controls Variables

Bureaucracy of host      Global Competitiveness Report (2010-14)
country

Geographical distance    Centre d'Etudes Prospectives et
from home to host        d'Informations Internationales (CEPII)
country

GDP host country         World Development Indicators, do Banco
                         Mundial (2010-14)

Presence of FDI in       Global Competitiveness Report (2010-14)
host country

Tax burden of host       Global Competitiveness Report (2010-14)
country

Source: Prepared by the authors

Tab. 2
Descriptive statistics and correlation matrix

Variable                                    Mean          SD

1. FDI flow                                2.312        0.882
2. Arbitrary corruption host country       3.258        1.732
3. Pervasive corruption host country       6.230        0.928
4. Corruption distance                     0.850        0.359
5. Bureaucracy of host country             32.069       28.835
6. Geographic distance between home       6903.504     4174.965
   and host countries
7. GDP host country                        10.996       0.696
8. Presence of FDI in host country         4.860        0.745
9. Tax burden of host country              54.270       21.935

Variable                                     1            2

1. FDI flow                                1.000
2. Arbitrary corruption host country       -0.085       1.000
3. Pervasive corruption host country     -0.143 **      0.031
4. Corruption distance                    .108 *        -0.061
5. Bureaucracy of host country            0.121 **     0.412 **
6. Geographic distance between home       0.100 *       -0.056
   and host countries
7. GDP host country                       0.645 **      -0.043
8. Presence of FDI in host country         0.078       0.104 *
9. Tax burden of host country             0.315 **      0.074

Variable                                     3            4

1. FDI flow
2. Arbitrary corruption host country
3. Pervasive corruption host country       1.000
4. Corruption distance                    0.359 **      1.000
5. Bureaucracy of host country            0.103 *      0.098 *
6. Geographic distance between home      -0.133 **     0.205 **
   and host countries
7. GDP host country                       -0.114 *     0.170 **
8. Presence of FDI in host country       -0.650 **    -0.307 **
9. Tax burden of host country             0.393 **     0.165 **

Variable                                     5            6

1. FDI flow
2. Arbitrary corruption host country
3. Pervasive corruption host country
4. Corruption distance
5. Bureaucracy of host country             1.000
6. Geographic distance between home       0.182 **      1.000
   and host countries
7. GDP host country                       0.303 **     0.379 **
8. Presence of FDI in host country       -0.263 **      -0.012
9. Tax burden of host country             0.178 **      0.080

Variable                                     7            8

1. FDI flow
2. Arbitrary corruption host country
3. Pervasive corruption host country
4. Corruption distance
5. Bureaucracy of host country
6. Geographic distance between home
   and host countries
7. GDP host country                        1.000
8. Presence of FDI in host country         0.071        1.000
9. Tax burden of host country             0.411 **    -0.251 **

* p <0.1; ** p< 0.05; *** p<0.01.

Source: Prepared by the authors

Tab. 3
Results of the regression

                                          Model 1        Model 2

Arbitrary corruption in host country                      -0.028
Pervasive corruption in host country
Corruption distance
Arbitrary corruption host country
   * corruption distance
Pervasive corruption host country
   * corruption distance
Bureaucracy of host country              -0.002 ***       0.001
Geographic distance between home and     -3.425 ***     -3.488 ***
   host countries
GDP host country (log)                   0.881 ***      0.861 ***
Presence of FDI in host country            0.042          0.064
Tax burden in host country                0.003 *        0.003 **
N                                           473            473
[chi square]                            377.561 ***    381.069 ***
Log. Likelihood                           -483.948       -483.026
Akaike Criterion                          981.896        982.052

                                          Model 3        Model 4

Arbitrary corruption in host country                    -0.096 **
Pervasive corruption in host country     -0.233 ***
Corruption distance                                       -0.212
Arbitrary corruption host country                       0.0863 **
   * corruption distance
Pervasive corruption host country
   * corruption distance
Bureaucracy of host country              -4.957 ***       -0.001
Geographic distance between home and     -3.768 ***     -3.704 ***
   host countries
GDP host country (log)                   0.866 ***      0.845 ***
Presence of FDI in host country          -0.153 **        0.092
Tax burden in host country                0.005***       0.003 **
N                                           473            473
[chi square]                            406.413 ***    390.227 ***
Log. Likelihood                           -476.159       -480.559
Akaike Criterion                          968.319        981.117

                                          Model 5        Model 6

Arbitrary corruption in host country                      -0.065
Pervasive corruption in host country     -0.258 ***     -0.215 **
Corruption distance                        0.350          0.320
Arbitrary corruption host country                         0.057
   * corruption distance
Pervasive corruption host country          -0.024         -0.054
   * corruption distance
Bureaucracy of host country              -0.003 ***       -0.002
Geographic distance between home and     -4.095 ***     -4.169 ***
   host countries
GDP host country (log)                   0.843 ***      0.824 ***
Presence of FDI in host country           -0.145*         -0.107
Tax burden in host country               0.006 ***      0.006 ***
N                                           473            473
[chi square]                            415.797 ***    421.118 ***
Log. Likelihood                           -473.743       -472.389
Akaike Criterion                          967.486        968.779

Dependent variable: Log IDE pais receptor

Note: * p <0.1; ** p< 0.05; *** p<0.01.

Source: Prepared by the authors
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Author:Pessegueiro, Daniel Mendes; Ferreira, Manuel Anibal Silva Portugal Vasconcelos; Reis, Nuno Rosa; Pin
Publication:InternexT: Revista Eletronica de Negocios Internacionais da ESPM
Date:Jan 1, 2018
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