Foreign direct investment
performance and institutional
quality: a French perspective.
Master’s Thesis 30 credits
Department of Business Studies
Uppsala University
Spring Semester of 2020
Date of Submission: 2020-06-03
Enguerrand JOURDIER
Tom VIGUIER
Abstract
The purpose of this research is to provide an understanding of the relationship that may exist between the institutional determinants and the inward flow of FDI in France. Indeed, the French government and various decisions-makers have attributed the unique growth of the inward flow of FDI to the institutional quality of the country. Moreover, to support this assumption, scholars and experts describe France as an institutionally powerful country. Therefore, in order to test this assumption, we have designed an explanatory analysis of the institutional determinants’ indexes from the WGIs over the period from 2005 to 2018 to test their likely relationship with the FDI inflows in France using descriptive, correlation and regression analyses. This study is based on the rich and furnished literature addressing the role of institutional characteristics in attracting FDI. Although our research has been impacted by the Coronavirus pandemic in terms of data collection and analyses, the corroborating evidences from the empirical findings do not validate the raised hypotheses and bring out many practical implications beneficial for national and local policymakers as well as companies’ managers in the worldwide FDI location competition.
Acknowledgement
It was a great pleasure to write a thesis on an exciting and strategically decisive topic concerning the understanding of the investment’s attractiveness of our home country.
The achievement of this research would not have been possible without the backing of many people who helped us to understand this topic in the best way.
We pay a special mention to our supervisor and professor, Christine HOLMSTRÖM LIND, PhD who has been able to advise and support us throughout the research, analyzing and writing process.
We thank all the professors and the MIEX staff at Uppsala University, ICN Business School and Bologna University who helped and advised us during our studies; and mainly we thank James SALLIS, PhD and Olof LINDAHL, PhD from Uppsala University and Kamel MNISRI, PhD from ICN Business School for their support.
We also thank Jiahao SUN, Siri XU and all the members of our master thesis group for their constructive opposition delivered over the writing period.
We thank André-Jacques FRAMENT, Nils BAQUÉ and François OSEMONT for the time they spent to provide us with an efficient proofreading; and mainly we thank Dominique BOULANGER very much for the time she spent to provide us with a very detailed proofreading.
Finally, we also thank our friends and families for their support, encouragement and comments.
France, 3rd June 2020.
Table of Contents 1. Introduction ... 1 1.1. Background ... 1 1.2. Theoretical Gap ... 2 1.3. Research Purpose ... 3 1.4. Research Question ... 4
1.5. Coronavirus pandemic implications ... 4
2. Theories Review and Hypotheses Development ... 5
2.1. Definition of the inward flow of FDI ... 5
2.2. Overview of the FDI location and institutional theories ... 5
2.3. Definitions of governance and institutional determinants ... 7
2.4. Kaufmann’s Worldwide Governance Indicators ... 8
2.4.1. Political Stability ... 10
2.4.2. Government Effectiveness ... 10
2.4.3. Regulatory Quality ... 11
2.4.4. Rule of Law ... 12
2.4.5. Control of Corruption ... 12
2.4.6. Voice and Accountability ... 13
3. Methodology & Data ... 14
3.1. Research Design ... 14
3.2. Data Collection Method ... 15
3.3. Data Sampling... 16
3.4. Specification and Design of the WGIs ... 17
3.5. Study implications of using the WGIs ... 17
3.6. Descriptive Analysis ... 19
3.7. Correlation and Scatter plot Analysis ... 21
3.9. Research Model ... 23
3.10. Ethics ... 24
4. Empirical Results... 26
4.1. Descriptive Statistic Results ... 26
4.2. Correlation Statistic Results ... 28
4.3. Simple Regression Analysis Results ... 29
4.4. Practical Implications ... 31
5. Discussion ... 32
5.1. Limitations ... 37
5.2. Suggestions for future researches ... 37
6. Conclusion ... 39
7. Appendix ... 41
7.1. Dataset ... 41
7.2. WGIs institutional “aggregated” and “underlying” variables building ... 42
7.3. Scatter plots ... 47
7.4. Regression Analysis Results ... 50
Bibliography ... 56
List of Tables Table 1 - Kaufmann Worldwide Governance Indicators ... 9
Table 2 - Data sources ... 16
Table 3 - Simple Regression Analysis Results ... 29
List of Figures Figure 1 - Methodological Framework ... 15
Figure 2 - Descriptive Analysis Results ... 26
Figure 5 - Dataset of the data sampling ... 41
Figure 6 - Aggregated variable of “Political Stability” for France ... 42
Figure 7 - Individual data sources for “Political Stability” in 2005 for France ... 42
Figure 8 - Aggregated variable of “Government Effectiveness” for France ... 43
Figure 9 - Individual data sources for “Government Effectiveness” in 2005 for France ... 43
Figure 10 - Aggregated variable of “Regulatory Quality” for France ... 43
Figure 11 - Individual data sources for “Regulatory Quality” in 2005 for France ... 44
Figure 12 - Aggregated variable of “Rule of Law” for France ... 44
Figure 13 - Individual data sources for “Rule of Law” in 2005 for France ... 44
Figure 14 - Aggregated variable of “Control of Corruption” for France ... 45
Figure 15 - Individual data sources for “Control of Corruption” in 2005 for France ... 45
Figure 16 - Aggregated variable of “Voice and Accountability” for France ... 46
Figure 17 - Individual data sources for “Voice and Accountability” in 2005 for France ... 46
Figure 18 - Simple scatter of FDI by Political Stability variable ... 47
Figure 19 - Simple scatter of FDI by Government Effectiveness variable ... 47
Figure 20 - Simple scatter of FDI by Regulatory Quality variable ... 48
Figure 21 - Simple scatter of FDI by Control of Corruption variable ... 48
Figure 22 - Simple scatter of FDI by Rule of Law variable ... 49
Figure 23 - Simple scatter of FDI by Voice and Accountability variable ... 49
Figure 24 - Simple Regression Results ... 50
List of Acronyms
EEC: European Economic Community FDI: Foreign Direct Investment
FSA: Firm Specific Advantage MNCs: Multinational Companies
OECD: Organization for Economic Cooperation and Development OLS: Ordinary Least Squares
TSCS: Time-Series and Cross-Sectional
1. Introduction
1.1. Background
Since the 1980s, foreign direct investment (FDI) has been at the center of the growth and expansion strategies for multinational companies (MNCs) and it is a crucial indicator of a country’s attractiveness (Mallampally and Sauvant, 1999). At the international level, global FDI was reduced by 35% from $2 trillion in 2015 to $1.3 trillion in 2018. This decline first affected the developed economies which have lost $100 billion of inward flow of FDI (UNCTAD, 2019). At the European level, FDI declined by 4 percent for the first time in six years, mainly as a result of declining FDI in Germany and in the United Kingdom; wherein at the European level, FDI is mainly driven by intra-European investments (EY, 2019).
For the United Kingdom, the unsure and unstable political system of the country related to Brexit may have led to a 13% decline (the lowest level since 2014) in FDI between 2017 and 2018 (EY, 2019). For Germany, the number of FDI projects decreased by 13% in 2018 due to the increased offshoring, the slower economic growth, the limited consumer spending and the weak export growth. Globally, this decline of FDI is a serious reflection of corporate concerns about political uncertainty countrywide characterized by factors such as the difficulty in complying with environmental European standards, the decline of the Chinese demand and the risks of an increase in US customs duties.
France” and “Attractiveness Survey Europe” by EY (2019) argue that the French institutional transformation in terms of administrative simplification, tax competitiveness, labor costs, flexibility of labor law and social dialogue have made the country more attractive in terms of FDI. According to the French member of parliament Marie Lebec (2019), France’s transformation strategy is to develop a “business friendly” framework thanks to government effectiveness and regulatory quality strategies such as the PACTE law (April 2018). In addition, according to Christophe Lecourtier (2019), the CEO of Business France (agency for the economic promotion of France abroad), France attracts foreign investors thanks to its inclusive reforms, its effectiveness and its modern economy and institutions (EY, 2019). According to Emmanuelle Quilès, President and CEO of Janssen France (subsidiary of Johnson & Johnson Group), the French ecosystem favors FDI due to its high-level researchers, its infrastructures, its clinical research, and the research tax credit (EY, 2019). And eventually, according to David Cousquer, the founder of Trendeo, it is also thanks to all the measures taken by France such as the relocation policies and the rise of “Made in France” on a global scale which made France the leading destination for FDI in Europe (EY, 2019). Finally, according to the University of Southern California’s Center for Diplomacy (2019), France is considered as the world’s leading soft power country (e.g., digital, culture, enterprise, education, engagement, government).
In this context, we can wonder whether we can easily attribute to the French institutional quality the merit of attracting FDI and thus make a link between reality and political storytelling.
1.2. Theoretical Gap
still to be explored on the country’s institutional quality as a significant factor in the FDI decision.
Although there are some quantitative studies with significant results about the impact of institutional determinants on FDI inflows nationwide (Horobet and Belascu, 2015; Yimer, 2017), these studies are still unexplored in the existing literature particularly for developed countries like France.
The focus on France as the subject of study is justified by (i) the phenomenon of unique growing of the inward flow of FDI within the first countries in the FDI attractiveness in the European Union (EY, 2019); (ii) the worldwide competition among the developed countries to attract FDI (Jensen, 2003). This study aims to explain nationwide the influence of the institutional determinants on the inward flow of FDI in France as supported by the French political decision-makers, experts and executives.
To fill a gap in the literature, we have specifically investigated the relationship between the institutional determinants of the WGIs and the inward flow of FDI in France. This research contributes in many ways to complete the current knowledge about institutional theory and international trade theory, and contributes to understand and also recognize the role of institutional determinants on the inward flow of FDI in France.
1.3. Research Purpose
1.4. Research Question
This study contributes to theoretical and empirical researches on FDI, representing not only economic factors which have always been at the core of existing studies (Forsgren, 2017), but also taking into consideration institutional factors. Therefore, the research question for this study is as follows:
How and to what extent do institutional determinants attract FDI in France?
To tackle this topic, the study meets the following structure: Section 1, the introduction; Section 2, the theories review and hypotheses development; Section 3, the methodology and data; Section 4, empirical results; Section 5, the discussion; Section 6, conclusions of the study.
Moreover, to provide the most explicit outputs, the authors have avoided the inclusion of personal viewpoints and reflections about observations focusing on the investigation, the analyses, and a detailed presentation of the topic, giving a standpoint as objective and thorough as possible.
1.5. Coronavirus pandemic implications
2. Theories Review and Hypotheses Development
2.1. Definition of the inward flow of FDI
Foreign direct investment is an international transaction of capital from MNCs to create and develop their subsidiaries as well as to manage their businesses in a foreign country. These transactions are divided into the inward and outward flows of investments. The inward flow represents transactions that increase foreign investors’ investments in businesses located in a given host country and new investments from new investors (OECD, 2020).
2.2. Overview of the FDI location and institutional theories
According to Dunning (1977), companies need to meet three factors to reduce or offset the costs and risks associated with international activity in order to be successful: the ownership advantage (O) with determinants like owning production processes, patents, technologies, management skills; the location advantage (L) with determinants like location on protected markets, favorable tax systems, low production, transport costs and reduced risk; and the internalization advantage (I) with determinants like reduced transaction costs, reduced risk of copying technology, quality control. In addition to Dunning’s work, researchers like Markusen (1984) and Helpman (1984) provide different theoretical models to explain the firms’ decision to invest abroad in order to grow and earn more profit by expanding into new markets.
firms’ profitability to invest in FDI abroad. By reviewing the OLI paradigm, Dunning and Lundan (2008) have considered the institutional theory as a necessary condition for countries to remain attractive to FDI since it also affects the firms’ profitability in the host country. According to Dunning and Lundan (2008), the country’s economic system is the macro-organizational mechanism in which resources are allocated (physical environment), while government policies have strategic objectives at both micro and macro levels (institutional environment). In other words, the host country’s economic system, the government policies and effectiveness are directly linked altogether to the host country’s capacities. Moreover, North and Thomas (1973) state that economic factors are not the sole determinants of a country’s development, but the quality of the institutions is also accountable for it. Institutional theory emerges with institutional transitions needed in order to adapt the host country to the market environment. According to Kinoshita and Campos (2006), market size and low labor costs are not enough to determine the factors of the FDI attractiveness. For them, the quality of the institutions and other developments of government efficiency variables are also crucial to attract FDI. According to Coase (1960), the country’s economic growth and the government’s quality depend on a coordination agreement to supply “a positive distribution of the power” between the economic and the political structure to protect and to enhance the country’s development.
To sum up, in addition to the approved economic factors, the most attractive country for FDI can adapt its environment quickly in terms of institutional determinants like government effectiveness and political stability, the property rights and the social and financial system to answer the demand of profitability, protection and sustainability of the FDI decision from firms.
2.3. Definitions of governance and institutional determinants
There are many kinds of governance definitions and institutional determinants used as indexes of reference to compute the countries’ institutional quality. The research still discusses the governance definition and its evaluation process throughout institutional determinants or factors because the definitions and indexes are either too specific or too general. In this sense, there are several types of institutional factors that depend on the topic’s specificity. For instance, in their analyses of the public policies’ impact on the country’s development, de Maillard and Kübler (2016) defined governance as “a set of intermediate variables that account for the way in which the social and political context influences the motives of actors”. Moreover, according to Jobert and Muller (1987), governance is considered as “the State in action” on society thanks to its public policies and government effectiveness. For the World Bank (2020), institutional determinants refer to “rules, enforcement mechanisms, and organizations” that regulate and implement institutional policies. To take into consideration the multiple dimensions of governance, the Worldwide Governance Indicators (WGIs) define governance as “the traditions and institutions by which authority in a country is exercised” and provides the most comprehensive indicators of governance throughout six underlying dimensions.
balanced data to compute such a broad notion as governance and its continuous change over time. Therefore, this research has based its work on the institutional variables described by Kaufmann and al. (2010).
2.4. Kaufmann’s Worldwide Governance Indicators
Since 1996, the WGIs on behalf of the World Bank have published a significant report on global institutional governance about the quality of the institutions in 200 developed and developing countries. The WGIs build and classify six institutional indicators based on about 31 different data sources from companies and individuals (e.g., World Competitiveness Reports, Global Corruption Barometer), from experts like NGOs, Think Tank and governments (e.g., Global Insight, Freedom House, Amnesty International, World Bank) (Kaufmann and al., 2010). The academic research widely uses the WGIs in institutional quality assessment because (i) they provide comprehensive “country coverage about the broad notions of governance they are intended to measure than any individual data source”; (ii) they summarize information “from many different data sources on governance and smooth out the inevitable idiosyncrasies of the individual measures of governance”; and (iii) they mention and consider clear margins of error “that transparently indicate the unavoidable degree of uncertainty associated with measuring governance by any means” (Kaufmann and al., 2007-2010).
Table 1 - Kaufmann Worldwide Governance Indicators
WGIs’ variables WGIs’ variables definition
Political Stability and Absence of Violence/Terrorism
“the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means”
Government Effectiveness
“the quality of public services, the quality of the civil service and the degree of its independence from political pressures”
Regulatory Quality
“the ability of the government to formulate and implement sound policies and regulations that promote private sector
development”
Rule of Law
“the 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”. Control of Corruption “the extent to which public power is
exercised for private gain”
Voice and Accountability
“a country’s citizens are able to participate in selecting their government, freedom of expression, freedom of association, and a
free media.”
2.4.1. Political Stability
According to Globerman and Shapiro (2003), FDI is attracted by stable, legitime, and transparent political institutions. According to Brada and al. (2006), the unstable economies deter the FDI inflow due to the investors’ fear about sudden policy reversals: the host countries must maintain a high level of political stability as important as the level of home countries in order to boost the foreign investors’ confidence. So, according to Schneider and Frey (1985) and Jensen (2003), political instability makes host countries less attractive due to an unpredictable and unsure business environment that might lead to economic disruption and the internalization costs. Nevertheless, according to Sethi and al. (2003), political stability positively and significantly influences the inward flow of FDI because “good governments” provide a legitime market environment and reduce “country-risk”. The power of their public institutions lets MNCs exploit and optimize their home-specific advantage.
Hypothesis 1: Political stability positively influences inward flow of FDI in France.
2.4.2. Government Effectiveness
rights for firms and individuals. According to Francis and al. (2009), the FDI investment decision of MNCs depends on the powerful institutional entities thanks to their ability to promote the quality of the regulations and investment incentives (e.g., international investment agreement). According to Root and Ahmed (1978), the higher the influence of institutions and government policies is, the more the costs decrease and influence the firms’ profitability. According to researchers such as Asiedu (2006), Cleeve (2008) and Mohamed and Sidiropoulos (2010), government effectiveness positively influences the inward flow of FDI.
Hypothesis 2: Government effectiveness positively influences inward flow of FDI in France.
2.4.3. Regulatory Quality
The regulatory quality is the government’s ability to capture and implement qualitative, sound and cost-effective institutional rules as well as norms and regulations to promote economic growth, social welfare, and environmental protection (OECD, 2008). In other words, it is the institution’s capability to provide a “market-friendly framework” that favors FDI by providing national institutional policies, financial management control, and economic standards (Fazio and Talamo, 2008). With the help of politic and economic tools like price controls (e.g., the Central French Bank to manage the State monetary strategy, the national financial stability, and the services to the national economy), government intervention (e.g., French State as a shareholder in MNCs like SNCF or Renault), free movement of capital and simplification of tax and financing (e.g., LME laws in 2008 and PACTE laws in 2019 in France), the developed host countries reduce the investors’ risk-aversion such as the likely hostile foreign acquisition in case of bankruptcy, war price and monopoly. According to Sabir and al. (2019), regulatory quality has a positive and significant influence to attract the inward flow of FDI in developed countries thanks to this “market-friendly framework” which is stronger in developed countries than in developing countries. So, according to Peres and al. (2018), the institutional quality indicator has a positive and significant impact on the inward flow of FDI in developed countries.
2.4.4. Rule of Law
The ability of attracting the inward flow of FDI for a developed country is strengthened by the rule of law, namely an efficient legal system with contract enforcement and strong property rights. Indeed, the rule of law encourages a long-term inward flow of FDI in the host country thanks to its social and legal aspects, its transparent and fairness policies and its individual rights given to foreign investors. According to Gastanaga and al. (1998) and Khoury and Peng (2011), a strong and efficient rule of law reduces the transaction costs significantly and improves MNCs’ profitability because it enables a liberal competition and protects firms from market inequalities and uncertainty. More specifically, according to Gastanaga and al., (1998) and Li and Resnick (2003), the protection of property rights positively influences the inward flow of FDI as well as the high level of consideration, the nearness of law with local institutions and the enforcement of contract which have a positive influence on the inward flow of FDI in developing and developed countries.
Hypothesis 4: Rule of Law positively influences inward flow of FDI in France.
2.4.5. Control of Corruption
is likely to attract FDI insofar as these reforms contribute to the reduction of corruption and offer more transparency and security for the firms. Researchers like Asiedu (2006), Cleeve (2008), and Mohamed and Sidiropoulos (2010) have found that the effect of corruption assessed by the corruption index is statistically negative for the FDI inflow. According to Wei (2000), corruption has a negative impact on FDI flows. So, there is a significant positive relationship between the levels of control of corruption and the inward flow of FDI.
Hypothesis 5: Control of corruption positively influences inward flow of FDI in France.
2.4.6. Voice and Accountability
According to Jensen (2003), voice and accountability plays a core role in pluralism, legitimacy, stability, decentralized power, in other words, a democratic role to develop the quality of the institutions. It can legislate in favor of the rights and duties of the internal and external stakeholders and frame positively the profitability of MNCs. According to Li and Resnick (2003), voice and accountability is an effective element that favors the inward flow of FDI. Indeed, the more decentralized and plural the political system is, the more the political system attracts FDI. Moreover, according to Disdier and Mayer (2004), voice and accountability with a high degree of protection of civil and political freedoms attracts the inward flow of FDI in Western and Eastern Europe. So, voice and accountability favors the FDI attractiveness.
3. Methodology & Data
3.1. Research Design
The aim of this study is to answer our research problem: how and to what extent do institutional determinants attract FDI in France?
In order to carry out this research, we will use a time-series and cross-sectional (TSCS) analysis in order to understand how the six determinants highlighted in the theoretical section might explain and therefore be considered or not as potential determinants of the inward flow of FDI in France. Hence why, a quantitative analysis was deemed more suitable as it was the methodology used in similar researches based on countrywide analyses (Horobet and Belascu, 2015; Yimer, 2017).
In order to carry out the research, we will have a deductive research approach based on quantitative data to make a descriptive analysis as well as a correlation and single regression analyses. In descriptive analysis, the aim is to understand the different features of the various studied populations. In correlation analysis, the aim is to understand how variables are linked to each other’s. Finally, in regression analysis, the aim is to explain why a studied phenomenon occurs by analyzing the relation between independent variables (WGIs indexes) and a dependent variable (inward flow of FDI in France) (Saunders and al., 2012).
In the context of this study, the scope of analysis is France. As for the temporal horizon, the study is longitudinal and uses different data over a 14-year-period from 2005 to 2018. This type of measurement consists in using different data, both temporal and multidimensional, called TSCS data (Hsiao, 2000).
H1: Political Stability positively influences the inward flow of FDI in FRANCE.
H2: Government Effectiveness positively influences the inward flow of FDI in FRANCE. H3: Regulatory Quality positively influences the inward flow of FDI in FRANCE.
H4: Rule of Law positively influences the inward flow of FDI in FRANCE.
H5: Control of Corruption positively influences the inward flow of FDI in FRANCE. H6: Voice and Accountability positively influences the inward flow of FDI in FRANCE.
Figure 1 - Methodological Framework
Source: the authors
3.2. Data Collection Method
The research is based on secondary and quantitative data (taken from the databases of well-known international organizations). The data are collected for the following variables: the inward flow of FDI in France, political stability, government effectiveness, regulatory quality, rule of law, control of corruption, voice and accountability which are drawn from the World Bank dataset and OECD databases. These data are available at a regular one-year interval over the period from 2005 to 2018.
Data as well as their types and sources are gathered in the following table:
Table 2 - Data sources
Data Name Data type Data Sources
Inward flow of FDI in France Secondary OECD
Political Stability Estimate
Index Secondary World Bank
Government effectiveness
Estimate Index Secondary World Bank
Regulatory Quality Estimate
Index Secondary World Bank
Control of Corruption Estimate
Index Secondary World Bank
Rule of Law Estimate Index Secondary World Bank
Voice and accountability
Estimate Index Secondary World Bank
Source: the authors
3.3. Data Sampling
This study is based on the collection of secondary data for France. The data have been selected from the OECD dataset 2020 and from the World Bank WGIs (Kaufman and al., 2010).
To assess the impact of the institutional determinants on the inward flow of FDI in France, the study has used a set of institutional variables developed by Kaufmann and al. (2010). The aggregated governance indicators are constructed on a standard normal random with zero mean and unit standard deviation. The scores indicators are ranging from around -2.5 to +2.5 (weighted value), where “-” means “less” and “+” means “more” regarding the specificity of each of the six institutional indicators (Kaufmann and al., 2010). These indicators are available at an annual frequency.
3.4. Specification and Design of the WGIs
The report issued by the WGIs aggregates several “individual data sources” with their own measures into six institutional indicators (Kaufmann et al. 2010). For instance, the underlying WGIs data source like Transparency International uses its own scale, from 0 to 100, to measure the control of corruption in a country. In comparison, another individual underlying data source can compute the control of corruption on a scale from 1 to 10 (see the full set of the aggregated measures of the WGIs in the Appendix from Figure 6 to Figure 17, p. 42).
To cope with this measurement problem, the WGIs use the statistical method of “Unobserved Components Model” in order to adapt the data coming from the different “individual data sources” to be able to compare them and build the six aggregated indicators of governance for a country as a weighted average of the individual underlying sources (Kaufmann and al., 2010). The advantage of this measurement method is that it is “less sensitive to the extreme values of individual data sources”, given that the different indicators use different choices of units to measure the perception of governance (Marino and al., 2016).
3.5. Study implications of using the WGIs
The first criticism is about the consequences of the margins of error in the WGIs. However, the reports specifies that the error is a “normally-distributed random variable with mean zero and variance one” which is the same for each country considering the studied indicators (Kaufmann and al., 2010). So, the WGIs’ report asserts that there are errors since “individual data sources” are independent of one another. Hence why “the only reason why two [aggregated] sources might be correlated with each other is that they are both measuring the same underlying unobserved governance dimension” (Kaufmann and al., 2010). Moreover, the relationship between the specific concept measured by the “underlying indicator” and the general concept of governance may also be imperfect (Kaufmann and al., 2010).
The second criticism is about the usefulness of the WGIs to conduct an institutional quality assessment over time nationwide and its consequences to capture such a broad topic. However, according to Kaufmann and al. (2007), the WGIs allow to have a global overview of the institutional quality thanks to the diverse dimension of the institutional determinants based on different individual data sources.
The third criticism is about the lack of evidences on the “construct validity”. The lack of “construct validity” of the WGIs is due to the set of different definitions about governance provided by the WGIs. However, according to Kaufmann and al. (2007) and as it is mentioned earlier, there is no consensus about one definition of governance, but there are various nuances. The WGIs have defined indicators and a strict methodology in order to capture and quantify this topic more easily.
Hence why these institutional indicators are used as proxies for the institutional determinants to test the likely relationship between them and the inward flow of FDI in France and to fulfill the purpose of the study.
3.6. Descriptive Analysis
Descriptive statistical analysis methods are used to illustrate the main characteristics of a data set. This study will analyze the following characteristics of the statistical series: median, mean, kurtosis and skewness. In that way, the following variables identified will be analyzed: FDI, POLSTAB, GOVEFF, REGQUA, CONCOR, RULAW, VOAC (defined below in the research model section, p. 23).
The use of descriptive statistics will allow to better understand and present the data, as well as facilitate the understanding of the results of the analysis. As it is mentioned above, several tools can be used to understand the data series, each of these tools provides essential information about the studied statistical population and allows to know if the statistical characteristic of the populations studied can be a source of error or not in the calculation of other results, including correlation and regressions analyses.
Other tools to understand the distribution of the population within the statistical series are skewness and kurtosis.
Skewness evaluates the symmetry of a distribution (Hair and al., 2016). If it is equal to 0, the distribution is symmetrical. If it is positive, the distribution is spread to the right. And if it is negative, the distribution is then spread to the left. There are several key values to understand the skewness of a given series by SPSS. When the skewness is close to 0, the series can be considered fairly symmetrical. The closer the values are to 1 or more / -1 or less, the skewer the distribution is (Pallant, 2001).
The kurtosis corresponds to the dispersion of the “extreme” values by reference to the normal law and so the “weakness” of the distribution (Hair et al., 2016). It is defined as mesokurtic (kurtosis = 3) in the case of a normal distribution. Compared to a normal distribution, a positive flattening indicates that the observations are more gathered in the center and have finer extremities reaching the extreme values of the distribution. The leptokurtic distribution (kurtosis > 3) has thicker extremities than a normal distribution, which means the presence of outliers in the series. Compared to a normal distribution, a negative flattening indicates that the observations are less clustered in the center and have thicker extremities reaching the extreme values of the distribution. The platykurtic distribution (kurtosis < 3) has thinner extremities than a normal distribution and thus the low presence of outliers. In the SPSS software, the results are adjusted so that a mesokurtic type of distribution takes values close to 0. Values referring to -1 denote a platykurtic distribution and values close to +1, a leptokurtic distribution (Pallant, 2001).
3.7. Correlation and Scatter plot Analysis
In the analysis of the TSCS data, the correlation analysis is performed in order to have a better understanding of the various studied variables (Saunders and al., 2012). In this sense, a correlation matrix gives several pieces of information about the relationship that may exist between the different variables under study.
The first information given by this matrix is the significance of the relationship between two variables. In order to determine whether the correlation between the variables is significant, the value of the Sig. must be compared to the significance level. In general, a significance level of 0.05 is a suitable benchmark. A Sig. of 0.05 indicates that the risk of concluding that a correlation exists when there is no correlation is of 5%.
The other essential information given by this matrix is the correlation coefficient. The correlation coefficients are in the range -1.00 to +1.00. The value -1.00 represents a perfect negative correlation, whereas the value +1.00 represents a perfect positive correlation. The value 0.00 represents an absence of correlation (or independence between variables).
There are several types of correlation coefficients, namely the Pearson coefficient (most often employed) and others like the Spearman coefficient for instance. The Spearman coefficient is especially used if the variables explored are indexes. Therefore, in this research, the Spearman coefficient has been used in a non-parametric matrix to examine the importance and the direction of the monotonic relationship between two continuous or ordinal variables. In a monotonic relationship, the variables tend to move in the same relative direction, but not necessarily at a constant speed (Pallant, 2001). In the framework of this research, the Spearman’s coefficient has been used to understand the relations that may exist between the variables.
between two variables. Yet, on the other hand, a heterogeneous scatter plot forming no straight lines shows the potential absence of correlation. Moreover, a scatter plot also allows to visualize the potential outlier or error in the data under study.
3.8. Simple Regression Analysis
In order to better understand and capture as accurately as possible the potential relationship between FDI and potential institutional determinants, it is possible to test the simple linear regression between the FDI used as dependent variables and the WGIs indexes’ values used as independent variables using the OLS (ordinary least square) method.
As with the other statistical analysis performed in this study, having a limited number of data will not allow an absolute result to be drawn about the relationship that may exist between the tested dependent variable and the independent variables. However, just as the use of tools to explore the correlation between the different variables allows to understand trends, the results of the regression analysis give the ability to improve the understanding of the data and their potential relationships.
3.9. Research Model
As written above, this study has used a deductive research approach with quantitative data to make a descriptive analysis as well as correlation and simple regression analyses.
The descriptive analysis will focus on the study of the different statistical populations that will be studied later, focusing on the mean, the median, and the level of skewness and kurtosis.
After drawing conclusions from the descriptive part, there will be a correlation analysis on the following variables (explained below): FDI; POLSTAB; GOVEFF; REGQUA; CONCOR; RULAW and VOAC. This correlation analysis conducted by using a non-parametric correlation matrix will be accompanied by scatter plots of the different pairs of variables studied in order to visualize the relationships that may exist.
Finally, in a third step, the study has used the necessary tools to perform a simple regression analysis between the different independent variables representing the potential institutional determinants and the dependent variable, the FDI flow.
These three types of analyses will allow to draw the best information from our dataset despite the different issues that may arise due to the structure of the data.
In the results, the variables will have the following names:
FDI = Inward flow of FDI in France in millions of USD (OECD, 2020).
POLSTAB = The political stability index measures the “perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means” (World Bank, 2020).
REGQUA = The regulatory quality measures the “perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development” (World Bank, 2020).
CONCOR = The control of corruption measures the “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” (World Bank, 2020).
RULAW = The rule of law measures the “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” (World Bank, 2020).
VOAC = The voice and accountability index measures the “perceptions of the extent to which a country’s citizens can participate in selecting their government, as well as freedom of expression, freedom of association, and a free media” (World Bank, 2020).
3.10. Ethics
Concerning the ethical considerations, quantitative research does not face any problems regarding the ethical obligations of the people involved in answering the research question as this study does not use opinion about the research question. Moreover, confidentiality and consent are not issues since the documents and data used in this study are public, approved, and cannot be altered (Cooper and Dent, 2011).
Furthermore, this study is based on the research model which summarizes how the phenomenon should correspond to the theory by testing the defined hypotheses from the theory review. The research design is addressed through sampling, measurement, and analysis of causal inference. These stages make it possible to represent the phenomenon in correspondence with theory and reality (Shadish and al., 2002).
The methodology of this study is valid since many academic works have approved the WGIs and have found significant results by using them to provide an understanding of the quantitative emphasis of the importance of institutional determinants in the attractiveness of FDI. The study does not seek to define new institutional indicators which would only be applicable to this research. Indeed, the main issue of the replication is the risk to deflect the primary objective of the quantitative research in its understanding of the phenomenon (Gelman, 2015).
4. Empirical Results
4.1. Descriptive Statistic Results
Figure 2 - Descriptive Analysis Results
Source: the authors
With SPSS software, it was possible to have the results presented in the table above. These results allow to describe the different studied populations to better understand the studied data. A general comment can be made about the balance of the data. As it is mentioned in the previous table, these data are perfectly balanced with 14 observations per variable and no missing data. As indicated in the methodology, data balance is an important factor in producing viable statistics. However, having few observations per variable makes the results plausible but cannot, on their own, fully describe the reality of the studied phenomenon.
First, in order to see the degree of distortion of the data compared to a normal distribution, it is important to focus on its skewness. Skewness allows to see where the data are concentrated within the statistical series. The results can be divided into three groups: the first group is the one of the variables being fairly symmetrical with a skewness close to 0, these data concern the series of REGQUA and CONCOR; the second group corresponds to the series moderately to highly skewed between -1 and -0.2 or 0.2 and 1, in this case the variables FDI, POLSTAB, GOVEFF and RULAW can be found; and finally, the third group is the one of the series that are very highly skewed, when the skewness is greater than 1 or less than -1, the VOAC variable is therefore considered highly positively skewed.
A second interesting parameter to consider is kurtosis which makes it possible to realize the presence or not of many outliers in the tails of the statistical series. As with skewness, there are three categories of kurtosis: platykurtic (kurtosis value approaching -1), mesokurtic (kurtosis value close to 0) and leptokurtic (kurtosis value approaching 1). Within the framework of the different studied variables, POLSTAB, GOVEFF and RULAW have a kurtosis value close to 0. This means that the tails are thinner than the normal distribution implying a very low number of outliers in the data. However, the variables FDI, REGQUA, CONCOR and VOAC have kurtosis values close or superior to |1|. This means that the tails are larger than the normal distribution implying the potential presence of outliers in the data.
4.2. Correlation Statistic Results
After describing the statistical environment in which this study was conducted, it is possible to focus on the results of the correlation analysis.
Figure 3 - Nonparametric Results
Source: the authors
First off, it is important to focus on the non-parametric correlation matrix (see Figure 7) generated by these variables. The technique allows to consider in a more precise way the data series. In order to better explain the potential observable relationships between variables, it is also necessary in this study to observe the scatter plots (see the scatter plots, p. 47) generated by SPSS in order to visually represent the interactions between two variables.
structure of data used. However, a clear trend seems to suggest that there is potentially no relationship between the institutional determinants and the inward flow of FDI in France. The scatter plots give a clear visualization of the results that corroborate the estimation that can be made from the results of the non-parametric matrix.
One interesting thing that can be pointed out is that some variables seem to be correlated with each other in a potentially significant way, notably the variables POLSTAB with the variables REGUA, CONCOR as well as RULAW. This correlation, if proven, could be due to the way these indicators are calculated and the sub-criteria taken into consideration (see the section about study implications of using the WGIs, p. 17). Therefore, it would be interesting to carry out a study on the correlations between these different factors in order to see if it would be possible to group them together to make a reliable index encompassing several of these determinants.
4.3. Simple Regression Analysis Results
Table 3 - Simple Regression Analysis Results
Dependent variable Tested Variable Sig. of the relation
FDI POLSTAB 0,953 GOVEFF 0,364 REGQUA 0,181 CONCOR 0,957 RULAW 0,497 VOAC 0,175
Source: the authors
As defined in the methodology section, the significance provides important information on whether a relationship between two variables can be confirmed. The results mentioned above are affected by the same problems as the previous ones due to the low number of observations/variables. However, the very high significance seems to confirm what is suggested by the correlation study. In this sense, the study of simple regressions allows to confirm the tendency that there is no or very little relationship between the institutional determinants tested and the attraction of FDI in France.
To conclude, by using multiple forms of analysis of the data and by using information from previous studies, these corroborating evidences permit to deduce that there is no significant relationship between institutional determinants and the attractiveness of FDI in France. Conducting various analyses and using previous researches give the possibility to mitigate the impact of the lack of observations and thus draw highly probable conclusions. Therefore, with all due caution, the results obtained may not correspond to the expectations put forward from a theoretical point of view and according to the hypotheses. These latter should not be validated regarding the different results of the analyses performed and the likelihood of a non-correlation and causality between the studied variables.
Figure 4 - Empirical Results Framework
Source: the authors
Inward flow of FDI in France (Y) Political Stability (no correlation) Government Effectiveness (no correlation) Regulatory Quality (no correlation)
Rule of Law (no correlation)
Control of Corruption (no correlation)
4.4. Practical Implications
After analyzing the results obtained from the processing of our data, it is essential to consider the practical implications taken from these results. It is important to keep in mind that these recommendations would benefit from further researches on this topic.
The first implication is the potential questioning on the policy making decision processes, particularly at the national level. Indeed, the indexes used for this study concern France as a country and this cannot be generalized to other countries, international structures or other policy strata with a finer granularity. As mentioned above, our results seem to indicate that there is not necessarily a link between the quality of governance (measured by the set of indexes used) and the inward flow of FDI in France. This conclusion calls into question the various statements on the supposed links between the effectiveness of a government’s action and legislative control (measured by the GOVEFF and REGQUA variables) and the attractiveness of inward flow of FDI. Therefore, it seems desirable for the French Government to focus its action on other potential determinants, economic policy for example, in order to support and improve the attraction of FDI in France.
The second implication is that these results also seem to call into question the analyses made by media or political entities. Bearing in mind that the indexes used in this study are not a perfect transcription of the country’s institutional quality, they give however a clear idea of the peculiarities of the country’s governance over time. Therefore, contrary to what has been put forward by others, our results indicate that it does not seem possible to make a link between the inward flow of FDI in France and its institutional quality.
In the discussion section, these trends will be put into perspective through the prism of other researches that have already been conducted on similar topics, so as to better understand the results.
5. Discussion
Institutional theory is defined as a complex, unsure, unpredicted, and conflicting environment in which economic and institutional entities want to operate side by side (North, 1990). From this perspective, our study provides significant interest by addressing the importance of institutional factors in the attractiveness of FDI through the analysis of one country only with its own contextual environment. Our study follows the recommendations of previous researches to analyze the institutional determinants and the attractiveness of FDI relationship by considering a specific contextual environment to explore nationwide and supplement the previous and current literature about the FDI understanding through the institutional prism. Nevertheless, the theory was tested on the data related to FDI inflows in France, between 2005 and 2018, and the statistical results of this study do not show the significant and general relationship between the inward flow of FDI in France and the institutional determinants defined and selected thanks to the theoretical section.
The first point of the discussion is about the ambivalence of the results at the global and national scale analysis.
institutional variables defined by the WGIs as used in our study, the previous empirical studies gathered countries regarding pre-defined features (e.g., developing, developed, transitions, geographic areas, level of development, and incomes) to distinguish the institutional quality specificities. Nevertheless, several previous pieces of research that have fully contributed to the definition and the understanding of the influence of institutional determinants on the attractiveness of FDI lack coherent conclusions about this relationship. For instance, some researchers have argued that a political stability factor is positively and significantly related to the inward flow of FDI (Loree and Guisinger, 1995; Sethi and al., 2003; Bailey, 2018), whereas others find no significant relationship between the political stability factor and the inward flow of FDI (Globerman and Shapiro, 2003; Kobrin, 1976). From that, researchers have argued that it is needed to conduct further researches on the topic through a more specific contextual analysis above the developing or developed level of countries like nationwide, cross-regional or industries analyses (Globerman and Shapiro, 2003; Bailey, 2018; Sabir and al. 2019).
The second point of the discussion is about the strong influence of the level of development within the studied country.
Since our study is about France, it seems coherent to have no significant nationwide results due to the incapability of the indexes to capture the intangible and informal State actions to attract foreign investors. For the case of France, most of the institutional variables do not change over time at the country scale thanks to its stability and its action to provide an efficient and sound framework to protect individuals and firms. According to academic researches, the institutional quality of a country is conditioned by its level of development. In the context of developed countries, there are fewer trade-offs between the institutional quality and the economic development, and then the institutional quality is not fully captured by the commonly used indexes such as the WGIs (Seyoum, 1996; Kumar, 1996; Quinn and Woolley, 2001; Li and Resnick, 2003; Kaufmann and al., 2010). In this sense, Dunning and Lundan (2008) stated that the institutional factors’ effect on the attractiveness of FDI depends on the environmental context of the country, and its influence differs regarding the level of development of the country.
significant way, notably POLSTAB with the variables REGUA, CONCOR, as well as RULAW. According to Kaufman and al (2010), the correlation between some variables is “natural” since each index of the WGIs captures the same dimensions of the institutional quality but under a different angle. So, regarding these results, it is not surprising to have a likely correlation between the variables. Moreover, this correlation gives the indexes a statistical power which allows to see whether these variables impact the inward flow of FDI.
The fourth point of the discussion is about the government’s ability to sell the institutional quality of a country to attract foreign investors.
As mentioned in the introduction, France is considered as the world’s leading soft power country (USC Center on Public Diplomacy, 2019). This position is the evidence of France’s ability to use its soft power as well as its government effectiveness to promote the quality of the public services and to facilitate positive collaborations and FDI by providing government resources and policy tools to help firms and organizations to deal with the current geopolitical uncertainty and instability. Since 2017, France has set active, formal as well as informal “rules of the game” in terms of institutional actions like international agreements and leverages by using its institutional resources to make the country more attractive concerning the investors’ country-risk. In the context of our study, since France can effectively use its soft power to attract FDI, the chosen indexes may not consider these types of soft institutional and sovereignty actions in France. For instance, the “Choose France Summit”, wherein Macron’s Government sells to the most prominent worldwide firms, the French institutional quality and what institutional reforms and rules will be updating and applying in the future. In this perceptive, we may suggest that firms do not consider only the formal institutional determinants to make the FDI decision in France. Indeed, even if they already exist and even if the WGIs thoroughly assess the French institutional quality, someone (like government, political decision-makers) has to sell in an informal way the French institutional quality among firms to attract their investments. According to the academic researchers, the rule of the game, which embodies the formal and informal State actions, is an active tool to express the sovereignty of a host country to interact with foreign investors, to sell them the country’s performance and ambitions as well as to positively influence the inward flow of FDI (Root and Ahmed, 1978; Butler and Joaquin, 1998; Mohamed and Sidiropoulos, 2010; Sabir and al., 2019). Moreover, it is complex to capture properly the informal actions, hence why the quantitative method based on the WGIs may not assess the “selling action” that intends to reduce the institutional distance between countries.
The fifth point of the discussion is about the use of the WGIs as proxies to study the institutional quality in a country.
institution quality. Some indexes use their metrics concerning the institutional variables and can naturally make “inevitable idiosyncrasies measures” (Kaufmann and al., 2007). For instance, Freedom House which computes and publishes an index about the level of democracy within the country, and the famous Transparency International which computes and publishes an index about the level of corruption within the country represent one of the underlying data sources of the WGIs. In contrast with it, in France, Agir Ensemble pour les Droits de l’Homme (AEDH) is known for its nationwide assessment concerning freedom and democracy, but it is not an underlying data source of the WGIs. Moreover, in France, many universities and graduate schools like SciencesPo Paris, have centers of institutional research and provide insights into institutional quality for France and foreign countries. With the variety of nuances and metrics of institutional quality, the previous examples show how hard it is to assess from an objective standpoint a holistic overview of the institutional quality of a country. However, since it is challenging to adequately capture all the institutional dimensions, the WGIs aim to provide the most comprehensive indexes which aggregate all kinds of individual and institutional data sources. The project has been subject to review and criticized by scholars and experts concerning its forces and flaws in the indicators. In the context of our study and as it is mentioned above, although the WGIs are “probably the most carefully constructed governance indicators” (Arndt and Oman 2006), they still seem to fail to take into consideration the intangible and moving actions from a developed country like France.
5.1. Limitations
The exceptional situation (Coronavirus pandemic) in which this study has been led made it impossible to keep contact with the specific French offices possessing more data over a longer period to increase the number of observations and thus the precision of the study (Tabachnick, B. G., Fidell, L. S. 2006; Hair and al., 2014). Indeed, one of the limitations of this study is the lack of observations for each of the institutional variables for France. From the statistical standpoint, the lack of data has hindered us from performing a detailed analysis of the results.
Two further limitations are related to the number of available institutional indicators and the reliability of their proxy. It is important to consider that there is a multitude of variables that can be considered to compute the impact of institutional indicators as a factor of FDI attractiveness in a country like France. Indeed, there are more institutional variables than only those described in the literature review of this study. Moreover, the proxies chosen are close to the determinants under study, but it is difficult to perfectly quantify each determinant.
Finally, another limitation of our study is the absence of control variables. Indeed, one or more control variables would have helped to better understand the relationships that may or may not exist between the FDI and the different variables tested. It is important to note that the presence or absence of a control variable does not call into question the presence or absence of a relationship, but it does allow a better understanding of the parameters characterizing the relationship in a linear regression.
5.2. Suggestions for future researches
Regarding the previous studies and limitations, this study attempts to identify the importance of institutional determinants on the inward flow of FDI in France. Future studies should open to empirical frameworks on FDI according to various other variables and methodologies.
index, women in parliament index); “country risk” indicators (e.g., expropriation risk, special transactions risk, political violence risk); “banking system” indicators (e.g., foreign bank assets, banking system z-scores) or educational, cultural and others indicators (e.g., happiness index, human development index, cultural distance) that are neglected in this study. Further variables will allow to overcome the limited institutional data provided by the WGIs.
Previous studies are strongly focused on the institutional determinants of the flows of FDI in developed and developing countries. However, it is possible to focus the future researches on the main institutional determinants of the inward flow of FDI of a specific country with an additional specific filter like industry (e.g., banking, agriculture, energy) or a cross-regional analysis in France in order to better understand the impact of institutional determinants on the FDI attractiveness at the country scale.
6. Conclusion
Generally speaking, this study has investigated and drawn the initial results about the impact of the institutional determinants on the inward flow of FDI in a developed country. This research conducted at the scale of a country like France completes a missing literature review on this subject and thus provides a more precise understanding of the impact of institutional quality on the attractiveness of FDI. This is even more relevant when studying the phenomenon observed on the only increase of FDI in France accredited to France’s institutional quality by French decision-makers and political experts while the major European countries (UK, Germany) have faced a sharp slowdown in their FDI. Although the results of this research have been altered by the lack of data and by the peculiar situation due to the Coronavirus pandemic, we can conclude that the results obtained do not seem to correspond to the expectations put forward in the theoretical section about the institutional determinants and they do not fully explain the phenomenon studied in France. Indeed, the results seem to show that there is no correlation between the institutional determinants and the attractiveness of FDI in France.
In this sense, in order to answer the research question of this study “How and to what extent do institutional determinants attract FDI in France?”, we can say that corroborating evidences lead to believe that the institutional determinants do not have an impact on the inward flow of FDI in France.
It is essential to mention it again that, although the original initiative of the study was to carry out a quali-quantitative analysis to precisely assess the impact of the institutional determinants on the attractiveness of FDI in France, the Coronavirus pandemic has stopped suddenly all the interviews opportunities. Thus, this research focused only on an explanatory analysis in order to provide an understanding of how and to what extent this unique increase of FDI in France may be credited to the institutional determinants.
the case study of France. This quantitative study does not allow us to understand which factors, when included in the analysis, strengthen or weaken the relationship. Although a correlation analysis was carried out and no significant results were found, partly due to the lack of observations, other quantitative studies show significant results with the analysis of the same institutional determinants and the flow of FDI in other countries with the same limitations.
7. Appendix
7.1. Dataset
7.2. WGIs institutional “aggregated” and “underlying” variables building
Figure 6 - Aggregated variable of “Political Stability” for France
Figure 8 - Aggregated variable of “Government Effectiveness” for France
Figure 9 - Individual data sources for “Government Effectiveness” in 2005 for France
Figure 11 - Individual data sources for “Regulatory Quality” in 2005 for France
Figure 12 - Aggregated variable of “Rule of Law” for France
Figure 14 - Aggregated variable of “Control of Corruption” for France
Figure 16 - Aggregated variable of “Voice and Accountability” for France
7.3. Scatter plots
7.3.1. FDI/POLSTAB
Figure 18 - Simple scatter of FDI by Political Stability variable
7.3.2. FDI/GOVEFF
7.3.3. FDI/REGQUA
Figure 20 - Simple scatter of FDI by Regulatory Quality variable
7.3.4. FDI/CONCOR
Figure 21 - Simple scatter of FDI by Control of Corruption variable
Figure 22 - Simple scatter of FDI by Rule of Law variable
7.3.6. FDI/VOAC
7.4. Regression Analysis Results
7.4.1. Regression POLSTAB
Figure 24 - Simple Regression Results
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate 1 .017a .000 -.083 15189.27125a. Predictors: (Constant), Polstab
ANOVA
aModel
Sum of
Squares df Mean Square F Sig.
1 Regression 823539.825 1 823539.825 .004 .953b Residual 2768567533.60 4 12 230713961.134 Total 2769391073.42 9 13
a. Dependent Variable: FDI b. Predictors: (Constant), Polstab
Coefficients
a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 30751.633 8383.002 3.668 .003 Polstab -1095.512 18336.295 -.017 -.060 .9537.4.2. Regression GOVEFF
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate 1 .263a .069 -.008 14657.29916a. Predictors: (Constant), GovEff
ANOVA
aModel
Sum of
Squares df Mean Square F Sig.
1 Regression 191354049.746 1 191354049.746 .891 .364b Residual 2578037023.68 3 12 214836418.640 Total 2769391073.42 9 13
a. Dependent Variable: FDI b. Predictors: (Constant), GovEff
