The impact of the degree of conflict on the FDI inflows

(1)

The impact of the degree of conflict on the FDI inflows

by Jincheng Lu

University of Groningen Faculty of Economics and Business

P.O. Box 800, 9700 AV Groningen, The Netherlands

Master Thesis - International Business and Management

June, 2015

Tel: 0639121358 Email: J.Lu.6@student.rug.nl

(2)

ABSTRACT

The purpose of this exploratory study is to examine how political risk particularly conflicts affect foreign direct investment (FDI) inflows. Using an econometric analysis for a data sample of 170 countries and the period 1998 to 2002, we identify those indicators that matter most for the activities of multinational enterprises. Overall, 6 different indicators for political risk and governance are employed in the empirical analysis. The results indicate that VA (Voice and Accountability), and RQ (Regulatory Quality) are highly significant determinants of foreign investment inflows. Using a novel data set, we also study the impact of political risk on FDI inflows at the sectoral level. We find that political risk does not have a significant impact on primary and manufacturing sector but that Regulatory Quality matters for FDI inflows in services.

Key Words: Political risk, conflicts, FDI inflows, primary sector, manufacturing

(3)

1. INTRODUCTION

Foreign direct investment (FDI) has been viewed through several theoretical lenses, with researchers taking different snapshots of the phenomenon. There is certain amount of scholars have attempted to address limitations of international trade theories under the rubric of FDI. Market imperfections theory emphasized the firm‘s decision to invest overseas is explained as a strategy to capitalize on certain capabilities not shared by competitors in foreign countries (Hymer, 1970). The international production theory suggested the propensity of a firm to initiate foreign production will depend on the specific attractions of its home country compared with resource implications and advantages of locating in another country (Morgan and Katsikeas, 1997). This theory shows that the host country‘s economic fundamentals may not be sufficient for inward FDI, foreign government actions also significantly influence the piecemeal attractiveness and entry conditions for firms.

(5)

economic exchange and political hostilities. In this study, we seek to bridge the gap between international business strategy and conflicts by investigating how FDI inflows were influenced by political risk.

Based on the literatures, we argue that conflicts and international business strategy have a huge impact on each other. Both areas need to be analyzed in order to open up the possibility to ease the decision making process of risky investment and outsourcing decisions of business managers. Thus, the main specific research question of interest is: To what extent do political risk particularly conflicts affect the FDI

inflows? We choose Foreign Direct Investment (FDI) as one of the most important

international business strategies most MNEs will consider and select. Since international production has grown to become the most salient aspect of the global economy. There has been also a rapid and steady growth in global FDI flows since the late 1980s. Aggregate net inflows of FDI (in current US dollars) increased nearly six times from $ 53 billion in 1985 to $ 315 billion in 1996 (Chakrabarti, 2001). World foreign direct investment (FDI) inflows reached $865 billion in 1999, about 14% of global gross domestic capital formation as compared with 2% 20 years ago (UNCTAD, 2000: xvi). From 1980 to 2002, foreign direct investment (FDI) stock increased tenfold, with a particularly drastic rise in developing countries in the 1990s, when the growth of FDI stock exceeded the growth of world exports (UNCTAD, 1995, 2003). The number of multinational enterprise (MNE) parent firms also increased by reaching 63,000 in 2000 and 79,000 in 2007, associated with 690,000 and 790,000 foreign affiliates, respectively (UNCTAD, 2000: xv; 2008: xvi). Foreign affiliates worldwide now hire some 82 million employees (UNCTAD, 2007: xvi). Even for developing countries, the inward FDI stock rose from about 13% of their GDP in 1980 to about a third in 2002. (Li and Vashchilko, 2010; Bussmann, 2010). From these figures, we can conclude that MNEs and FDI have played a more vital role in the field of international business.

(6)

to conflicts and how FDI is determined by political conflicts. Some international business literatures have pay attention to the importance of country-specific differences in political and institutional factors as determinants of FDI flows. Therefore, we are inspired by the literature and decide to use the FDI inflows as dependent variables to analyze the effect on those with conflicts as well. Conflict is a wide topic to grasp, but based on previous literatures we mainly focus on the measurement of political risk. In this study, we use political risk as independent variables to investigate how it affects FDI inflows. Hence, we choose the six governance indicators introduced by Kaufmann et al. (2011) as explanatory variables in order to conduct our research. We are most concerned with whether those indicators of the governance can affect the investment decisions of MNEs. The six indicators are: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption. Rule of Law and Control of Corruption are supposed to specify good governance, but can be used as a reversed version as well for ―bad governance‖. Rotberg (2007) stated that poor governance is prone to undergo civil wars and conflict. Consequently, we use the six indicators as the measurement of political risk in order to determine the degree of conflict in a country.

(7)

analysis results to find whether conflicts prone countries affect their investment outcomes. For example, some MNEs from specific industries have invested in countries with high level of instability and decreased FDI inflows may withdraw capital. But FDI inflows in some countries of specific industries may not be affected by highly political instability. Investments in these countries would be still a reasonable strategy for MNEs because our results show that other factors such as market size (GDP) and trade openness can also be the key determinants for creating stable profits.

(8)

2. LITERATURE REVIEW AND HYPOTHESES

2.1 Foreign direct investment theory

Nowadays the issue of foreign direct investments is attracting more attention. For many developing countries, FDI is an engine and mechanism of employment, productivity improvements, technology transfer (Qian and Baek, 2011). The local firms would acquire advanced technologies and managerial skills from spillovers. When regarding to MNEs, FDI is one of the most important business strategies to enter into foreign markets. When multinational corporations enter different foreign markets, it is market failures that attract FDI and give them the advantage in those markets. Foreign investors consider that their superior technology and knowledge will give them the opportunity to obtain market share (Denisia, 2010).

For MNEs, FDI refers to the purchase of physical assets or a significant amount of the ownership (stock) of a company in another country to gain a measure of management control (Li and Vashchilko, 2010). A firm invests abroad when its ownership advantages over tangible and intangible assets, together with the host-country attributes such as resource endowments or government policies, make production abroad profitable, and when it prefers direct hierarchical control of production over other alternative modes such as licensing or trade to satisfy the demand for its products (Dunning, 1993).

(9)

ownership (O) specific characteristics. Besides that, he also acknowledged that one also had to explain why such firms opted to generate and/or exploit their O specific advantages internally, rather than to acquire and/or sell these, or their rights, through the open market. Such advantages he referred to as internalization (I) advantages (Dunning, 2001). Such investments could be: (natural) resource seeking, market seeking, efficiency-seeking or strategic asset-seeking. Rivoli and Salorio (1996) concluded the OLI framework implicitly gives rise to a normative decision rule: when ownership and location advantages make foreign productive activities profitable, and internalization advantages make hierarchical exchanges the best way to exploit the opportunity, then a firm should engage in FDI. OLI advantages-based framework analyzes why, and where, MNEs would invest abroad. The Uppsala model (Johanson and Vahlne, 1977) posits that MNEs engage in FDI incrementally and finally larger investments are made into countries that were further away in psychic distance terms. It shows the psychic distance also influences the FDI decisions.

After introducing the fundamental theories regard to the determinants of selecting FDI strategy by MNEs to exploit the opportunity and obtain market share, the rest of the literature will focus on how degree of conflicts in one country will influence its FDI inflows and provide some hypotheses related to the research question.

2.2 Political risk discourages the FDI inflows

(10)

violence tend to discourage FDI flows. In the past, research on political risk focused on other risks such as expropriation and, more recently, the risk posed by policy uncertainty in the host country (Oetzel et al., 2007). For example, Li and Vashchilko (2010) offered a review of literature related to the impacts of military conflict and security alliances on FDI in international business and political science. In that article, they provided a lot of research to show how other scholars have devoted much attention to studying aggregate indicators of political risk or stability.

To understand how political risk affects the FDI strategy made by MNEs, we firstly pay attention to the definitions of political risk. Hayakawa et al. (2013) defined political risk refers to the quality of institutional environment. That is, political risk is the risk that the returns to investment may suffer as a result of low institutional quality and political instability. Vadlamannati (2012) thought political risk can be defined as the risks faced by firms regarding unexpected alternations in legal regulations by the host country government guiding FDI policy. There are many reasons to believe that sound institutional quality and low political instability (and hence low political risk) should attract more FDI. According to Chase et al. (1988), a readily accepted notion today is that risk aversion underlies all foreign investment activity. Risk aversion suggests a positive risk return trade-off which builds the basis for valuing and comparing expected cash flows from the foreign investment. They explained corporations seem to be willing to pay a price for and employ such political risk information in the investment decision.

(11)

economic process and has a negative effect on FDI flows. In a cross-sectional analysis of FDI flows to 36 countries for 1977 and 1982, Loree and Guisinger (1995) found that political stability significantly promotes FDI inflows in 1982, but not in 1977. Woodward and Rolfe (1993) used data for all reported manufacturing plant openings from 1984 to 1987 in the Caribbean Basin to test which variables have a statistically significant effect on investments choice, the results showed that political stability increases the probability of a country being selected as an investment location. Jun and Singh (1996) regressed an aggregated indicator for political risk, based on a number of sub-components, and several control variables on the value of foreign direct investment inflows. For their data sample of 31 developing countries, the political risk index is statistically significant and the coefficient implies that countries with higher political risk attract less FDI. However, Sethi et al. (2003) found that political and economic stability, measured by a composite variable on a 100-point scale, does not influence US FDI flows to 17 West European and 11 Asian countries from 1981 to 2000. Globerman and Shapiro (2003) conducted a two-stage analysis of US FDI flows to 143 countries from 1994 to 1997, in which the first stage investigates the causal factors of the probability that a country is an FDI recipient, and the second stage examines the determinants of the amount of FDI received. They figured out that an index of political instability and violence, measures armed conflict, social unrest, ethnic tensions, terrorist threats, etc. The results indicate that governance infrastructure also plays a critical role in the determination of the volume of US FDI flows across countries. Recently, Vadlamannati (2012) made use of aggregate data on U.S. firms‘ investment activities in 101 developing countries during the period 1997–2007 to reassess the impact of political risk on FDI at firm-level. The results showed that lower political risk is associated with an increase in U.S. firms with an equity stake of 51% or above followed by a higher proportion of investment in fixed assets and also an increase in their return on investments, controlling for a host of relevant factors.

(12)

volatility in the host countries that the investors face. Domestic instability, civil war and international conflicts will reduce the profitability of investing in the host country because domestic sales and exports-imports are inefficient, production splits and the facility is damaged or destroyed. The political volatility also affects the value of the host country‘s currency, thus reducing the value of the assets invested in the host country as well as of the future profits generated by the investment. Gastanaga et al. (1998) examined the link between various political variables and foreign investment inflows. They concluded that lower corruption and nationalization risk levels, and better contract enforcement are resulting in higher FDI inflows. But their findings do not always hold up, which may be due to the relatively small country sample of 22 developing countries. Li and Resnick (2003) found that democratic rights lead can improve property rights protection, which in turn boosts foreign investment. Apart from this indirect impact on FDI, increases in democracy may reduce FDI. These literatures all provide that political risk helps explain FDI inflows because the increase of political risk really reduces FDI.

(13)

profitability of investing thus help host countries to attract more FDI inflows. Based on the literature discussed above, we identify the following hypothesis:

Hypothesis 1: political risk in a country is negatively associated with FDI inflows.

2.3 International conflicts

Nowadays, violent conflicts are not rare occurrences. According to Oetzel and Getz (2012), many of these conflicts occurred in countries that are important locations for business, such as Russia, India, Nigeria, Thailand, and Mexico. The large body of literature has dealt with the question of whether international conflicts result in the decrease of FDI inflows in one country. Therefore, one of the important issues is to find the definition of conflicts and how these conflicts related to FDI inflows.

(14)

businesses and to investor expectations of political risk.

According to Driffield et al., (2013), the total stock of FDI in conflict countries was US$ 169 billion in 2009 based on UNCTAD data. In the last 10 years more than 500 multinational firms have been involved in this. It demonstrates foreign investment could be an important factor in promoting peace. Conflict prone countries might avoid violent conflict in order not to deter foreign investors. From country level, suspending trade and investment would decrease the income for many industries and reduce economic growth. So it is not in a country's interest to go to war with a country with which its private economic agents maintain an extensive exchange of goods and capital (Russett & Oneal, 2001). From the perspective of MNEs, invest in a foreign country is a strategy help them earn higher profits. If the costs associated with this risk are higher than the expected benefits, corporations might decide against the investment, unless the country is affected by conflict to a small extent so that the risk for investors is minor or non-existent (Bussmann, 2010).

(15)

al., 2007). Based on the theoretical discussions above, we identify the following testable hypothesis:

Hypothesis 2: political conflicts in a country are negatively associated with FDI inflows.

2.4 FDI inflows in different industries

Since investments of MNEs are not from only one specific sector, it is interesting to explore how political risks and conflicts affect FDI inflows of three main industry sectors in different ways. Although it seems natural most literatures argue that FDI can convey great advantages to host countries, such gains might differ across primary, manufacturing, and services sectors. The UNCTAD report suggested prospects for FDI vary significantly by industry. The outlook for the services sector will continue to be more positive than for the manufacturing or primary sectors. The industries expected to be at the forefront of FDI growth are computing and ICT, public utilities, transportation and tourism-related services in the services sector; electrical and electronic products, machinery and metals in the manufacturing sector; and mining and petroleum in the primary sector (UNCTAD report).

(16)

to be medium- or long-term in nature with at least three- to five-year time horizons and some as long as thirty years. Given the long-term nature of most investments and the fact that ratings agencies are unable to predict risk events, MNEs need strategies for managing risk on an ongoing basis (Oetzel et al., 2007).

Some researchers focus on the FDI inflow composition of different sectors in specific countries. According to Madem et al. (2014), the major sectors attracting FDI inflows in India have been services and electrical & electronics amounting to US$ 30,421millions or 32 % of total FDI. They observed that services sector, telecom, software, housing and real estate and construction have witnessed more than 5% increment of FDI during 2000 and 2012. Remaining all the sectors have achieved less than 5% increment of FDI. Liu (2011) explained the FDI in manufacturing industry is very important to China, FDI in manufacturing industry represents 63.2 per cent of total utilized FDI in China during the period 1997- 2008 and reached US$ 49.89 billion in 2008.

(17)

Spar (1998), in the semiconductor industry, Intel similarly eschewed joint ventures or partnerships in its assembly and test facilities in Costa Rica, China, and the Philippines, just as in its wafer fabrication plants in Ireland and Israel.

In Ernst (2005)‘s article, sectoral distribution of FDI in Argentina, Brazil and Mexico was illustrated. Argentina is the only country with sizeable investments in primary resources, with a share of 37 per cent of accumulated FDI flows in 1990-2002. The main reason for that is the creation of a special regime for that sector, its deregulation and privatization, and recent oil discoveries. In Argentina and Brazil, the largest share of FDI went to services, not because FDI in manufacturing declined, it also saw a boom, but because services FDI increased more rapidly, mainly as a result of deregulation and the privatization of State-run companies. In Mexico, the secondary sector is still the most important, but only slightly, in terms of FDI flows, with a share of 50 per cent (compared with the tertiary sector‘s share of 49 per cent).

(18)

predictions of how political risk impacts FDI inflows differently in different sectors. For these reasons, we suggest the following hypothesis:

Hypothesis 3: The impact of political risk on FDI inflows is different for different industry sectors.

2.5 Market size (GDP) influence FDI inflows

(19)

Market size hypothesis has appeared as an explanatory variable in most empirical studies on the determinants of FDI. For example, Schneider and Frey (1985) analyzed a single equation politico-economic model using aggregate data on 54 less developed economies for 1976, 1979, and 1980 and confirm that real per capita GNP is the most significant economic determinant of per-capita FDI. Wheeler and Mody (1992) obtained results that market-size is an important determinant in determining multinational investor response and that it plays an even more significant role in the developing countries than in the industrial countries. Besides that, an overwhelming majority of empirical studies confirm the importance of the link between income levels and FDI inflows as well. But AKIN (2009) found evidence that GDP per capita is a poor indicator for the market seeking FDI activities in developing countries, both population and GDP are crucial. He suggested that FDI is taken into account the size of market in developing countries not in per capita basis but rather in aggregate size. More precisely, FDI will more likely focus on regional areas with relatively higher purchasing power rather than an expansion through the country. According to Busse and Hefeker (2007), the size of a particular market may indicate the attractiveness of a specific location for the investment, in the case that the multinational corporation aims to produce for the local market (horizontal or market-seeking FDI). Therefore, we formulate the last hypothesis:

(20)

3. METHODS

3.1 Data collection

In order to test four hypotheses, we collect data from different databases. Our functional form includes dependent variables, independent variables and control variables. In this study, we use FDI inflows as dependent variables, six indicators of political risk and market size (GDP) as independent variables, trade openness as control variables. The source and measurement of all variables are summarized in Table 1 (Page 25). We were able to measure most variables for a cross-section of more than 170 countries. The six governance indicator variables were available for 188 countries from the Worldwide Governance Indicators (WGI) project database. The total FDI inflows, GDP and trade openness data were available from World Bank datasets.

Our data analysis comprises five models. The model one is made up by 170 countries during the period from1998 to 2002 to analyze the impact of governance indicators and GDP on total FDI inflows. We choose these countries and years mainly due to the data limitations. The model only contains the countries which have data of all six governance indicators, GDP, trade openness and FDI inflows.

(21)

measures of FDI data by sector for a broad cross-section of countries over several decades, particularly for developing countries (Alfaro, 2003). In order to overcome the difficulties, we following Ali et al., (2010), to combine three comparable data sources on sectoral FDI: OECD International Direct Investment Statistics (database), OECD‘s International Direct Investment Statistics Yearbook (2003) and UNCTAD‘s World Investment Directory. Detailed information on FDI inflows by sector for OECD countries is available in OECD International Direct Investment Statistics (database) and International Direct Investment Statistics Yearbook. Data on inflows by sector are available for OECD countries, in most cases, ranging from the late-1990s to the very early 2010s.

In this study, we combine the data of International Direct Investment Statistics Yearbook (2003) and International Direct Investment Statistics (database) to analyze the FDI inflows of OECD countries from 1990s until 2002. For the rest of the countries in the sample, we complemented the OECD data with information obtained from the World Investment Directory: Volume IX Latin America and the Caribbean 2004 published by UNCTAD, this volume contains FDI information for countries from Latin America, and the Caribbean. The main difficulty with these data is that their availability and presentation evolve from country volume. The latest country volume contains information comparable in level of detail to the OECD data that cover the period from 1992 to 2002.

3.2 Measures

3.2.1 Dependent variables: FDI inflows

(22)

zero values. Therefore, we exclude the countries with negative average FDI inflows in the final analysis. Traditional FDI is made from fixed assets such as machines, equipments or estates. However, FDI inflows are generally defined as the measure of the net inflows of the investment needed to acquire a lasting management interest (10 percent or more of the voting stock) in an enterprise operating in an economy other than that of the investor, the investor's purpose being an effective voice in the management of the enterprise (Alfaro, 2003). The FDI data refer only to a part of resources invested by the multinationals in the host country. The investments financed through debt or equity are not taken into account, fact that may underestimate the size of investment done by multinationals abroad (Schneider and Matei, 2010). Following Globerman and Shapiro (2002), we find the use of a single year‘s data on FDI flows can be misleading, particularly for small countries, where a single transaction in a given year can create temporary and possible large changes in recorded FDI flows, including negative values. In order to minimize this possibility, we chose to average the FDI data over 1998–2002.

3.2.2 Independent variables

Political risk

(23)

Corruption (Kaufmann et al., 2011). These six indicators are defined as follows: 1. Voice and Accountability (VA) – capturing perceptions of the extent to which a

country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.

2. Political Stability and Absence of Violence/Terrorism (PV) – capturing perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism.

3. Government Effectiveness (GE) – capturing perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.

4. Regulatory Quality (RQ) – capturing perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.

5. Rule of Law (RL) – capturing perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.

6. Control of Corruption (CC) – capturing perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests.

(24)

territories). However, a disadvantage is that the indicators are estimated, and thus subject to measurement error.

Each of six variables was measured by using their standard normal units, ranging from approximately -2.5 to 2.5, with higher values corresponding to better governance outcomes. The higher values indicate one country has better political governance and less political risk. And the less political risk results in the lower potential of conflicts. The World Bank data base provides percentile rank data of six indicators separately from 1998, 2000, 2002. Therefore, we chose to average the data of governance indicators over these three years to measure the political risk during this period. To avoid the negative values in our regression model, we add 2.5 to each original value. Thus, the values we use are ranging from approximately 0 to 5.

Market size (GDP)

With the background from literatures, we add the following independent variable in the regressions: GDP in current U.S. dollars. GDP is defined as the standard measure of the value of final goods and services produced by a country during a period minus the value of imports (OECD data). The variable was obtained from archival data from the World Bank datasets. The GDP variable is also measured in millions of constant US dollars and log-transformed. The GDP variable is expected to be positively associated with FDI inflows.

3.2.3 Control variables

Trade openness

(25)

2007). Chakrabarti (2001) thought the maintained hypothesis is: given that most investment projects are directed towards the tradable sector, a country‘s degree of openness to international trade should be a relevant factor in the decision. According to Globerman and Shapiro (2002), the openness of an economy, measured by trade flows as a ratio of GDP, is likely related to a host country‘s legal and political framework that, in turn, is supportive of business investment and attracts FDI. Building on these findings, and on the fact that most empirical studies suggest trade openness likely to be positive correlated with FDI inflows, we also include trade openness in our model specification. Trade openness is defined as the sum of exports and imports of goods and services measured as a share of gross domestic product from World Bank data. We add the ratio of imports and exports to GDP as control variable. It will be log-transformed in the model and expected to be positively associated with FDI inflows.

Table 1. Variables, definitions and data sources

Variable Definition Source

FDI FDI inflows in $US, averaged 1998–2002 World Bank data GDP GDP in current $US, averaged 1998–2002 World Bank data Trade Ratio Trade (% of GDP) in current $US, averaged 1998–2002 World Bank data VA Voice and Accountability Index, measures civil liberties, political

rights, free press, fairness of legal system etc.

Kaufmann et al., (2011)

PV Political Instability and Violence Index, measures armed conflict, social unrest, ethnic tensions, terrorist threats etc.

GE Government Effectiveness Index, measures red tape and bureaucracy, waste in government, public infrastructure etc.

RQ Regulatory Quality Index, measures government intervention, trade policy, capital restrictions etc.

RL Rule of Law Index, measures contract enforcement, property rights, theft and crime, etc.

CC Control of Corruption Index, measures corruption among public and private officials, extent of bribery etc.

(26)

4. ANALYSIS AND RESULTS

4.1 Models and Analysis

Following the introduction of the variables, we now turn to the empirical linkages between political risk and FDI flows in different sectors. Given the purpose of examining which conflict indicators determining FDI inflows and whether political risk have different effects on FDI inflows in the primary, manufacturing, and services sectors, the empirical analysis is constructed by regressing FDI inflows of each industry of selected countries on a set of conflict indicators. The correlation between the dependent variable FDI inflows and the six indicators–embodying explanatory variables for degree of conflict–is measured in a cross-section analysis. The regressions are conducted in the form of ordinary least squares (OLS) analysis.

Similar to most studies in the empirical literature on FDI flows, the logarithm for investment flows and the independent variables is used. The functional form is written as follows:

log FDIi = a0+ a1 log GDPi + a2 log Tradei + a3 Political Riski +ei.

The acronym FDI represents the dependent variable foreign direct investment inflows. Here aj are the estimated parameters and the Political Riskstands for one of the six

(27)

term.

To analyze the relation between FDI inflows and political risk, we provide 5 models. Model 1 had the FDI inflows as the dependent variable. Observations were included averages for the period 1998 to 2002 from 170 countries which have positive average FDI inflows. Model 2 had a sample of 49 countries (33 OECD and 16 Latin America and the Caribbean countries). These 2 models analyze how political risk and GDP influence the total FDI inflows. Models 3-5 had FDI inflows of primary, secondary and tertiary industry sector as the dependent variable separately. Model 3 includes an available sample of 41 countries include 25 OECD and 16 Latin America and the Caribbean countries. We exclude the countries with negative average FDI inflows in primary sector. Model 4 has an available sample of 45 countries: 30 OECD and 15 Latin America and the Caribbean countries. Model 5 includes 46 available countries comprise 30 OECD and 16 Latin America and the Caribbean countries. These 3 models analyze how political risk and GDP influence the FDI inflows of each industry sector differently.

4.2 Results

(28)

Table 2. Descriptive statistics and correlation matrix n=170 Variables Mean s.d. 1 2 3 4 5 6 7 8 9 1.FDI Inflows 2.LogGDP 2.52 10.11 1.11 0.99 1.00 0.87 1.00 3.LogTrade 1.87 0.23 -0.05 -0.27 1.00 4.VA 2.46 0.96 0.45 0.37 0.11 1.00 5.PV 2.44 0.96 0.31 0.22 0.26 0.76 1.00 6.GE 2.53 0.99 0.58 0.54 0.19 0.82 0.80 1.00 7.RQ 2.53 0.95 0.56 0.51 0.16 0.82 0.76 0.93 1.00 8.RL 2.46 0.98 0.49 0.48 0.19 0.81 0.84 0.97 0.90 1.00 9.CC 2.51 1.01 0.49 0.48 0.17 0.76 0.78 0.95 0.87 0.96 1.00 Note: All correlations reported relate to averages for the period 1998 to 2002

Variables are defined in Table 1.

From the Table 2, we can find all measures are quite highly correlated, but the within six individual measures of political risk values are typically higher than those between GDP, trade and six political risk indices. In particular, the political risk indices are highly correlated with its six individual component measures. Therefore, to avoid the problem of multicollinearity, the 6 political risk indicators will be singly added to the benchmark regression.

Table 3. Model1, Average 1998-2002(All countries)

Political risk variable

Variable: Independent

Variable

Dependent variable: log FDI

(29)

n 170 170 170 170 170 170 170

Notes: t-values reported in parentheses; multicollinearity has been tested by the creation of variance inflation factors (VIF);*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Table 4. Model2, Average 1998-2002(49 countries)

Variable

Dependent variable: log FDI

VA PV GE RQ RL CC (1) (2) (3) (4) (5) (6) (7) Log GDP Log Trade Political Risk Constant R2 n 1.046*** 1.033*** 1.054*** 1.012*** 0.977*** 1.070*** 1.033*** (16.387) (12.266) (13.012) (10.447) (11.235) (11.492) (11.538) 0.273*** 0.260*** 0.281*** 0.247*** 0.215** 0.291*** 0.263*** (4.271) (3.178) (3.459) (2.907) (2.657) (3.551) (3.319) 0.018 -0.012 0.039 0.088 -0.029 0.015 (0.238) (-0.165) (0.467) (1.157) (-0.365) (0.198) 9.165*** -9.025*** -9.267*** -8.752*** -8.430*** -9.467*** -9.013*** (-9.568) (-7.971) (-8.074) (-6.679) (-7.350) (-7.446) (-7.294) 0.87 0.87 0.87 0.87 0.87 0.87 0.87 49 49 49 49 49 49 49 Notes: t-values reported in parentheses; multicollinearity has been tested by the creation of variance inflation factors (VIF);*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

(30)

regression, log GDP and control variable trade openness have the expected sign and are significant at the 1 percent level: countries with larger markets, higher degrees of openness, and lower political risk received more FDI inflows. This model produces a surprisingly high level of explanation for cross-section estimation (R2= 0.80), which suggests that GDP and trade openness acts as a control variable for a variety of economic factors. In the next 6 columns, the indicators of governance and political risk have been added in addition to GDP and trade openness. The results show that VA (Voice and Accountability), and RQ (Regulatory Quality) has a positive impact on FDI inflows, as the coefficients of both indicators are positive and statistically significant at the 1 or 5 percent level. It shows that countries with a lower political risk and better institutions related to Voice and Accountability, Regulatory Quality received more FDI inflows in the period 1998 to 2002.

With respect to Hypothesis 1, we can see from Table 3 VA (Voice and Accountability), RQ (Regulatory Quality) was positively and significantly associated with FDI inflows. Results suggest that FDI inflows are positively associated with the higher values of Voice and Accountability and Regulatory Quality. Since the higher value of each indicator indicates one country has better political governance and less political risk, Hypothesis 1 was partially supported. Political risk in terms of lower Voice and Accountability and Regulatory Quality lead to higher political risk. Higher political risk in a country is negatively associated with FDI inflows. When regarding Hypothesis 2, we find that PV (Political Stability and Absence of Violence/Terrorism) is not significantly associated with FDI inflows. Thus, Hypothesis 2 was not supported. We cannot provide evidence to show how political conflicts in a country are associated with FDI inflows.

(31)

5, we can see how the independent variables influence the FDI inflows in the primary sector. It shows trade openness is the only variable that is negatively and statistically significant at the 5 or 10 percent level associated with FDI inflows. GDP and political risk was not significantly related to primary sector FDI inflows. Therefore, the lower trade openness is much more relevant for attracting primary sector FDI than higher GDP and lower political risk in our sample countries. Table 6 and Table 7 present the results of manufacturing and services sector separately. All regressions reported in Table 6 find GDP and trade openness has a positively and statistically significant impact on manufacturing sector FDI inflows. But the influence of political risk is not statistically significant. From Table 7, we can see GDP is positively and statistically significant at 1 percent level associated with services sector FDI inflows. Trade openness only has positive but significant at 5 or 10 percent level related to FDI inflows in some columns. Finally, column (5) shows one indicator of political risk RQ (Regulatory Quality) has positive and statistically significant at 5 percent level impact on services sector FDI inflows. Therefore, Hypothesis 3 was supported by only one indicator in services sector. From our sample countries, political risk in terms of lower Regulatory Quality is negatively associated with services sector FDI inflows. But the level of political risk did not influence the primary sector and manufacturing sector FDI inflows.

(32)

5. CONCLUSION AND LIMITATION

5.1 Conclusions

The purpose of this study is to assess whether and to what extent the political risk and conflicts in host countries influence FDI flows. In addition, we examine whether the impacts of political risk the same for investments in the three main industry sectors and how the role of market size as determinants of FDI. This study therefore focuses on a broad set of indices such as six political risk indicators, market size and trade openness. These indicators measure for a relatively large sample of 170 countries for the total FDI inflows and more than 40 countries for the each industry sector FDI inflows. The time period to measure was from 1998 to 2002. The main results of our paper can be summarized as following:

(33)

Second, regarding to the impact of political risk on FDI inflows by different sectors, we did not find a lot of conclusions. It appears that political risk does not influence much for FDI inflows into the primary and manufacturing sector. Limited sample of countries and the data from more than 10 years ago are the two main reasons lead to the non-significant difference results at sectoral level. However, political risk in terms of RQ (Regulatory Quality) is negatively associated with services sector FDI inflows. In this regard, our findings reinforce similar conclusions drawn in Ali et al., (2010) and Ernst (2005). We confirm that the stability of political institutions in services industries would attract more FDI inflows, and this increase is mainly as a result of deregulation and the privatization of State-run companies in many countries. The deregulation and privatization in services sector offer more opportunity for FDI investments. Thus, countries with better Regulatory Quality will attract more FDI by providing sound policies and regulations that permit and promote private sector development.

Third, our study obtains results of interest besides the role of political risk. Regarding Hypothesis 3, we confirm the market size (GDP) is positively related to FDI inflows. This result is consistent with earlier studies that market size is statistically the most important predictor of whether a country will receive FDI and, if so, the amount (Globerman and Shapiro, 2003). We also find countries with high trade openness are likely to attract more FDI inflows.

(34)

targeted by the host country government. Once the host country‘s industrial policies and regulations changes, MNEs may face increased political risks suddenly. In addition, managers should consider more carefully the importance of the political voice, transparent legal and regulatory regimes in host countries. Managers of MNEs in services sector should consider more sound policies and regulations that permit and promote private sector development when they invest OECD, Latin America and the Caribbean countries. When MNEs invest OECD countries, we find from literatures that FDI inflows into manufacturing are almost completely free, aside from economy-wide restrictions such as notification or screening requirements. Within non-manufacturing, MNEs from finance, electricity, transport and telecommunications are facing the most constrained restrictions. Managers may use our findings to choose a better FDI strategy based on their industries and to consider lobbying the host government in an effort to shape the country‘s policies and regulations.

5.2 Limitations and suggestions for future studies

As with all empirical research, some certain limitations of this study should be acknowledged. First, the aim of our study is to investigate how conflict influences the FDI inflows. As we mentioned before, conflicts can be narrowed down to two parts – violent conflicts and political conflicts. But in this study, we mainly use six indicators of political risk to focus on the relationship between political risk, political conflicts and FDI inflows. We did not analyze the fluctuations of FDI inflows in countries with violent conflicts. Future studies should also include violent conflicts to examine how violent affect FDI inflows.

(35)

will explain the impacts of political conflicts with more reasonable results. It may also have econometric problem relates to the fact that time-series regression analysis may involve autocorrelation of the disturbances (Busse and Hefeker, 2007). Future study should employ lagged FDI flows and change the econometric specification to a dynamic panel.

(36)

References

AKIN, M. S. 2009. How Is the Market Size Relevant as a Determinant of FDI in Developing Countries? A Research on Population and the Cohort Size.

Alfaro, L. 2003. Foreign direct investment and growth: Does the sector matter. Harvard Business School, 1-31.

Ali, F. A., Fiess, N., & MacDonald, R. 2010. Do institutions matter for foreign direct investment?. Open Economies Review, 21(2): 201-219.

Banga, R. 2003. Impact of government policies and investment agreements on FDI inflows. Indian council for research on international economic relations, 1-43.

Barolli, B., Takahashi, K., & Tomizawa, T. 2009. The impact of political volatility on foreign direct investment: evidences from the Western Balkan countries. Bulletin of

Yamagata University, 40(1): 65-78.

Busse, M., & Hefeker, C. 2007. Political risk, institutions and foreign direct investment. European journal of political economy, 23(2): 397-415.

Bussmann, M. 2010. Foreign direct investment and militarized international conflict. Journal of Peace Research, 47(2): 143-153.

Chakrabarti, A. 2001. The determinants of foreign direct investments: Sensitivity analyses of cross‐country regressions. Kyklos, 54(1): 89-114.

Chase, C. D., Kuhle, J. L., & Walther, C. H. 1988. The relevance of political risk in direct foreign investment. Management International Review, 31-38.

Denisia, V. 2010. Foreign direct investment theories: An overview of the main FDI theories. European Journal of Interdisciplinary Studies, (3).

Driffield, N., Jones, C., & Crotty, J. 2013. International business research and risky investments, an analysis of FDI in conflict zones. International Business

Review, 22(1): 140-155.

Dunning, J. H. 2001. The eclectic (OLI) paradigm of international production: past, present and future. International journal of the economics of business, 8(2): 173-190.

Dunning, J. H. 1993. Multinational enterprises and the global economy. New York:

(37)

Ernst, C. 2005. The FDI–employment link in a globalizing world: The case of Argentina, Brazil and Mexico. International Labour Office, Employment Strategy

Paper, 17.

Globerman, S., & Shapiro, D. 2002. Global foreign direct investment flows: The role of governance infrastructure. World development, 30(11): 1899-1919.

Globerman, S., & Shapiro, D. 2003. Governance infrastructure and US foreign direct investment. Journal of International Business Studies, 34(1): 19–40.

Hayakawa, K., Kimura, F., & Lee, H. H. 2013. How does country risk matter for foreign direct investment?. The Developing Economies, 51(1): 60-78.

Hymer, S. 1970. ―The efficiency (contradictions) of multinational corporations‖,

American Economic Review, 60: 441-448.

Johanson, J. and Vahlne, J. E. 1977 ‗The internationalization process of the firm – a model of knowledge development and increasing market commitments‘, Journal of

International Business Studies，8: 23–32.

Jun, K., & Singh, H. 1996. The determinants of foreign direct investment in developing countries. Transnational Corporations, 5(2): 67-105

Kaufmann, D., Kraay, A., & Mastruzzi, M. 2011. The worldwide governance indicators: methodology and analytical issues. Hague Journal on the Rule of Law, 3(02): 220-246.

Krifa-Schneider, H., & Matei, I. 2010. Business climate, political risk and FDI in developing countries: Evidence from panel data. International Journal of Economics

and Finance, 2(5): 54.

Li, Q., & Resnick, A. 2003, Reversal of Fortunes: Democratic Institutions and Foreign Direct Investment Inflows to Developing Countries. International

Organization, 57(1): 175-211.

Li, Q., & Vashchilko, T. 2010. Dyadic military conflict, security alliances, and bilateral FDI flows. Journal of International Business Studies, 41(5): 765-782.

Liu, K., & Daly, K. 2011. Foreign Direct Investment in China Manufacturing

Industry–Transformation from a Low Tech to High Tech

Manufacturing.International Journal of Business and Management, 6(7).

(38)

281–299.

Moran, T. H. 2005. How does FDI affect host country development? Using industry case studies to make reliable generalizations. Does foreign direct investment promote

development, 281-313.

Morgan, R. E., & Katsikeas, C. S. 1997. Theories of international trade, foreign direct investment and firm internationalization: a critique. Management Decision, 35(1): 68-78.

Oetzel, J., & Getz, K. 2012. Why and how might firms respond strategically to violent conflict&quest. Journal of International Business Studies, 43(2): 166-186.

Oetzel, J., Getz, K. & Ladek, S. 2007. The Role of Multinational Enterprises in Responding to Violent Conflict: A Conceptual Model and Framework for Research. American Business Law Journal, 44(2): 331-358.

Polachek, S., Seiglie, C., & Xiang, J. 2005. Globalization and international conflict: Can FDI increase peace?. Department of Economics. Working Paper. Rutgers

University, Newark, New Jersey.

Qian, X., & Baek, K. 2011. An Analysis on Political Risks and the Flow of Foreign Direct Investment in Developing and Industrialized Economies. Economics,

Management, and Financial Markets, (4): 60-91.

Rivoli, P., & Salorio, E. 1996. Foreign direct investment and investment under uncertainty. Journal of International Business Studies, 335-357.

Rotberg, R. I., & West, D. L. 2004. The good governance problem: doing something

about it (Vol. 39). World Peace Foundation.

Russett, B. & Oneal, J. R. 2001. Triangulating peace: Democracy, interdependence,

and international organizations. New York: Norton.

Schneider, F., & Frey, B. S. 1985. Economic and political determinants of foreign direct investment. World development, 13(2): 161-175.

Sethi, D., Guisinger, S. E., Phelan, S. E., & Berg, D. M. 2003. Trends in foreign direct investment flows: A theoretical and empirical analysis. Journal of International

Business Studies, 34(4): 315–326.

(39)

Spar, D. L. 1998. Attracting high technology investment: Intel's Costa Rican

plant (Vol. 11), World Bank Publications.

UNCTAD Prospects for Foreign Direct Investment and the Strategies of Transnational Corporations, 2005-2008

Vadlamannati, K. C. 2012. Impact of political risk on FDI revisited—An aggregate firm-level analysis. International Interactions, 38(1): 111-139.

Wheeler D. and A. Mody 1992. International Investment Location Decisions: The Case for U.S. Firms, Journal of International Economics, 33: 57–76.

Woodward, D., & Rolfe, R. 1993. The location of exportoriented foreign direct investment in the Caribbean Basin. Journal of International Business Studies, 24(1): 121–144.

World Investment Directory (Volume IX) Latin America and the Caribbean, 2004

World Bank indicator, data base:

http://info.worldbank.org/governance/wgi/index.aspx#report

http://data.worldbank.org/indicator/NY.GDP.MKTP.CD?page=3

OECD (2014), "Foreign direct investment: flows by industry", OECD International Direct Investment Statistics (database).DOI: http://dx.doi.org/10.1787/data-00334-en

(40)

Appendix A: Models for sector analysis

Variable

Dependent variable: log FDI in Primary sector

VA PV GE RQ RL CC (1) (2) (3) (4) (5) (6) (7) Log GDP Log Trade Political Risk Constant R2 n 0.191 0.091 0.240 0.001 0.118 0.071 0.023 (1.090) (0.346) (0.962) (0.004) (0.437) (0.230) (0.077) -0.368** -0.451* -0.327 -0.491* -0.419* -0.446* -0.475** (-2.098) (-1.878) (-1.417) (-1.988) (-1.847) (-1.863) (-2.063) 0.110 -0.056 0.182 0.078 0.118 0.169 (0.511) (-0.278) (0.711) (0.360) (0.482) (0.723) 2.772 4.202 -2.008 5.619 3.739 4.598 5.344 (0.821) (0.953) (0.458) (1.070) (0.860) (0.901) (1.086) 0.26 0.26 0.26 0.27 0.26 0.26 0.27 41 41 41 41 41 41 41 Notes: t-values reported in parentheses; multicollinearity has been tested by the creation of variance inflation factors (VIF);*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Variable

Dependent variable: log FDI in manufacturing sector

(41)

Log GDP Log Trade Political Risk Constant R2 n 1.037*** 1.038*** 1.029*** 1.029*** 0.986*** 1.079*** 1.050*** (14.619) (10.564) (10.991) (10.991) (9.657) (9.901) (10.109) 0.267*** 0.268*** 0.259*** 0.259*** 0.229** 0.294*** 0.275*** (3.759) (2.905) (2.888) (2.888) (2.574) (3.271) (3.161) -0.002 0.011 0.011 0.061 -0.047 -0.016 (-0.022) (0.140) (0.140) (0.700) (-0.510) (-0.176) -11.889*** -11.908*** -11.767*** -11.767*** -11.276*** -12.473*** -12.075*** (-9.415) (-7.750) (-7.625) (-7.625) (-7.308) (-7.279) (-7.286) 0.85 0.85 0.85 0.85 0.85 0.85 0.85 45 45 45 45 45 45 45 Notes: t-values reported in parentheses; multicollinearity has been tested by the creation of variance inflation factors (VIF);*** significant at 1% level; ** significant at 5% level; * significant at 10% level

Variable

Dependent variable: log FDI in services sector

(42)

inflation factors (VIF);*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

Appendix B:

Country Sample (Year = 1998-2002)

(43)

Primary sector

Argentina, Australia, Austria, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Czech Republic, Denmark, Dominican Republic, Ecuador, El Salvador, Estonia, France, Germany, Greece, Guyana, Honduras, Hungary, Italy, Jamaica, Japan, Korea, Mexico, Netherlands, Nicaragua, Norway, Paraguay, Peru, Poland, Portugal, Slovak Republic, Spain, Sweden, Trinidad and Tobago, Turkey, United Kingdom, United States, Venezuela

Manufacturing sector

Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Czech Republic, Denmark, Dominican Republic, Ecuador, El Salvador, Estonia, Finland, France, Germany, Greece, Guyana, Honduras, Hungary, Iceland, Ireland, Italy, Jamaica, Japan, Korea, Mexico, Netherlands, Nicaragua Norway, Paraguay, Peru, Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland, Trinidad and Tobago, Turkey, United Kingdom, United States

Services sector

The impact of the degree of conflict on the FDI inflows