Supervisor: Heather Congdon Fors 
Master Degree Project No. 2016:89 
Graduate School 
 
 
 
 
 
 
 
Master Degree Project in Economics 
 
 
 
 
 
 
Foreign Direct Investments and Institutional 
Quality: a Panel Analysis of Non-OECD Countries 
 
 
 
 
 
Emil Biström 
Foreign Direct Investments and Institutional
Quality: a panel analysis of non-OECD countries
Emil Bistro¨m
Supervisor: Heather Congdon Fors
June 2, 2016
Abstract
Foreign Direct Investments (FDI) have been increasing as a share of world
GDP during the last decades and constitutes 40 percent of the external de-
velopment finance to developing and transition economies. This study aims
to contribute to the understanding of the allocation of FDI across countries;
why some countries see high levels of inflow and others see less. A panel of
non-OECD countries from 1996 to 2014 is studied in order to investigate the
relationship between FDI levels and several aspects of institutional quality.
Previous literature and theory suggests that low institutional quality could
be a impediment for FDI inflow. The results in this study support this view
and find a positive association between FDI inflow per capita and institutional
quality. Furthermore, institutional quality seems to have a persistent effect
on the FDI inflows. In support of recent literature on the Lucas Paradox, in-
vestors seems to take more aspects of institutional quality into account when
investing in poor countries.
Keywords: Foreign Direct Investments, Institutional Quality, non-OECD
countries, panel data
1
Contents
1 Introduction 3
2 Literature Review 6
2.1 Economic determinants of FDI . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 FDI and the role of institutional factors . . . . . . . . . . . . . . . . . . . 7
3 Theoretical Background 9
3.1 Capital flows and the Lucas Paradox . . . . . . . . . . . . . . . . . . . . . 9
3.2 Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4 Hypothesis 12
5 Data, definitions and Descriptive Statistics 13
5.1 Dependent Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5.2 Variables on Institutional Quality . . . . . . . . . . . . . . . . . . . . . . 14
5.3 Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5.4 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6 Empirical Strategy 20
6.1 Estimation of fixed effects and random effects . . . . . . . . . . . . . . . . 21
6.2 The Hausman test - to choose model of estimation . . . . . . . . . . . . . 22
6.3 Further considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
7 Results 25
7.1 Baseline Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
7.2 Robustness of the Baseline Results . . . . . . . . . . . . . . . . . . . . . . 26
7.3 Evaluation of the WGIs . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
7.4 Further analysis and robustness . . . . . . . . . . . . . . . . . . . . . . . 30
8 Conclusion 33
References 36
Appendix I 40
Appendix II 42
Appendix III 45
Appendix IV 46
Appendix V 49
Appendix VI 50
Appendix VII 51
2
1 Introduction
After several debt crises during the 1980s, several developing countries underwent
policy adjustments and eased restrictions on capital flows. Together with an intensi-
fied globalization, foreign direct investments (FDI), as a component of international
capital flows, has increased significantly (World Bank, 1997; Carkovic and Levine,
2005). While the role of FDI in economic development is still controversial, FDI
accounts for more than 40 percent of external development finance to developing
and transition economies (UNCTAD, 2015). Consequently, the importance of FDI
in economic development was a central topic during the Third International Con-
ference on Financing for Development in Addis Ababa, Ethiopia, in July 2015.
Globally, FDI levels in the year of 2014 are six times higher compared to 1990 as
illustrated in Figure 1 below. Even if the FDI levels have decreased since the burst
of the financial crisis, recovery is expected in the upcoming years (UNCTAD, 2015).
Over time, FDI has also been increasingly important as a component of world GDP.
In the beginning of 1990s FDI constituted well below one percent, yet increased to
over two percent in the last ten years. This progress is illustrated in comparison to
the global remittances and global aid in Figure 2 below. The composition of global
FDI in terms of world GDP has been indeed volatile and sensitive to financial crises,
but it has still increased in largest terms compared to the other financial flows.
Not surprisingly, the FDI flows are far from equally distributed among the world
economies. In fact, there is a huge dispersion in the sources of foreign capital from
country to country. For example, in the poorest countries of the world, aid is still
the largest source of foreign capital. To get a sense of how important these capital
sources are in different income groups of countries, Appendix I pictures graphs on
the sources of foreign capital for Low Income, Lower Middle Income and Upper
Middle Income groups, respectively. However, the importance of FDI in terms of
GDP has increased in all three of these income groups during the last 25 years, also
pictured in Appendix I.
Because FDI has increased its share of GDP in all income groups internation-
ally, yet varied in its degree across countries, it is important to study the potential
determinants of variation. Additionally, some countries attract more FDI inflows
than others, which motivates further investigation. In this study, the aim is to look
into determinants associated with variations in FDI levels, focusing on the factors
related to institutional quality in the different countries. Closely related, Glober-
man and Shapiro (2002) and Buchanan et al (2012) use aggregated measures of the
3
Worldwide Governance Indicators. The former focuses the late 1990s and the latter
the period of 1996-2006. Busse and Hefeker (2007) and Goswami and Haider (2014)
defines several aspects of institutional quality and other risk factors and evaluate
their importance from 1984-2003 and 1984-2009. All of them find the institutional
factors significant when explaining FDI.
In this study, a standardized average of the Worldwide Governance Indicators
(WGI) and the Quality of Government from the International Country Risk Guide
(ICRG) are used to explain the variation in FDI levels across countries. In order to
find what aspects of institutional quality that could be associated with these vari-
ations, an evaluation of the WGIs will be performed, inspired by Daude and Stein
(2007b). Furthermore, Buchanan et al (2012) explicitly demand a study covering the
financial crisis in 2008 and its aftermath. This paper will cover the critical periods
of the burst of the IT-bubble in the early 2000s and the financial crisis of 2008 as
the panel in this study considers data from 1996 to 2014. As can be seen in Figure
1, both of these events have been critical to the development of global FDI levels.
Moreover, inspired by Alfaro et al (2008) and Papaioannou (2009), this study
will contribute to the literature on the Lucas Paradox – why capital flows do not fol-
low neoclassical economic theory and flow to poorer countries to a larger extent. In
order to explore the relevance of the Lucas Paradox, the sample will be constituted
by non-OECD countries. Hence, “rich” countries are excluded. In this context, low
institutional quality will be regarded as a risk factor when studying the allocation of
international capital across countries. My contribution is also to divide the data into
sub-samples constituted by the group of income level in order to see if institutional
factors may explain FDI levels, unconditional on the level of income.
The main result in this paper is that FDI inflows per capita is positively asso-
ciated with institutional quality, which is supported by previous literature. More
specifically, Regulatory Quality seems to be the most important variable for the full
sample. The lagged effect of institutional quality is also significant, which indicates
support for a hypothesis of causality. When analyzing the sub-samples conditional
on income levels, the results support recent explanations to the Lucas Paradox of
why less capital is directed to poor countries. This follows from the findings that
institutional quality factors in the Low Income group are highly significant, while
the higher groups are less so. In the High Income group, all institutional quality
factors are insignificant.
This paper is structured as follows; a literature review is presented in section
2 and the theoretical background in section 3. Based on these two sections, the
4
hypothesis follows in section 4. In section 5, the data, definitions and descriptive
statistics are described. Section 6 presents the empirical strategy and some further
considerations. The results are presented in section 7 and section 8 provides the
conclusion.
Figure 1: Global FDI 1990-2014. Billion of dollars (World Bank, 2016a).
Figure 2: Global financial flows as share of GDP 1990-2014 (World Bank, 2016a).
5
2 Literature Review
A large share of the previous research on FDI focuses on its relationship to eco-
nomic growth. The results in the existing literature are quite conflicting, as the
research on causal channels for economic growth in general. Much of the literature
in the FDI-Growth nexus springs from studies on effects on the productivity in an
economy. Caves (1974) found positive externalities of FDI, in the form of spillovers,
from multinational enterprises to workers in the domestic firms within the same
sector. Later, Findlay (1978) modeled the “contagion” effect of FDI on the rate of
technological progress in the host country; driven by improvements in technology,
management practices, etc. used by foreign firms.
By this reasoning can FDI can be viewed as a bundle of capital stocks, knowledge,
and technology, but the relationship between FDI and growth is highly sensitive to
country-specific factors. FDI can affect growth endogenously via spillover effects,
but these are absorbed with varying efficiency by the host economies (de Mello,
1997). While FDI seems to be an important vehicle for international transfer of
technology, an improvement in productivity of FDI only holds when the host coun-
try has a minimum threshold stock of, for example, human capital (Borensztein et
al, 1998). Other evaluated country-specific factors that could be relevant are insti-
tutional (de Mello, 1999; Alguacil et al, 2012), the local financial market (Alfaro et
al, 2004), the capital intensity and level of technology in the local sector (Cipollina
et al, 2012).
After all, the positive relationship between FDI and growth seems to be sup-
ported in the literature, while the channels of causation seem to be hard to ad-
dress. The evidence of differences in absorptive capacities between countries, due
to country-specific characteristics, are vital in order to understand how the spillover
effects from FDI can differ in their intensity when affecting the host country. Like-
wise, it is possible that sound economic policies may spur both growth and FDI
(Carkovic and Levine, 2005).
2.1 Economic determinants of FDI
There are a number of variables that are commonly used in the literature when trying
to identify the potential determinants of FDI inflows. Generally, when describing
FDI flows, there is a core set of economic variables that has been identified to be
decisive. The most commonly used, either as an explanation or as a control is the
6
Market Size of the economy of interest. The Market Size is in this context commonly
proxied by the GDP (Globerman and Shapiro, 2002). In a large market, it is possible
to utilize resources more efficiently and exploit economies of scale to a larger extent.
In the literature, Market Size is widely accepted as a significant determinant of FDI
flows (Wheeler and Mody, 1992; Chakrabarti, 2001; Globerman and Shapiro, 2002;
Li and Resnick, 2003; Bevan and Estrin, 2004; Asiedu, 2006; Be´nassy-Que´re´ et al,
2007; Busse and Hefeker, 2007; Goswami and Haider, 2014).
However, out of the remaining economic variables commonly used in the liter-
ature, it has been harder to find a consensus on the effects on FDI. The effects of
these variables on FDI have been widely discussed with different hypotheses for each
and every variable. Their effects seem to be highly dependent of the context. The
direction and magnitude of their impact are of wide variation due to differences in
theoretical perspectives, methodologies, sample-selection, type of data and analyti-
cal tools (Chakrabarti, 2001). Commonly used variables to explain flows of FDI are
Growth Rate (Gastanaga et al, 1998; Li and Resnick, 2003; Busse and Hefeker, 2007;
Goswami and Haider, 2014), Openness to Trade (Asiedu, 2002; Busse and Hefeker,
2007; Buchanan et al, 2012; Goswami and Haider, 2014), Macroeconomic Stability
in the form of Inflation (Asiedu, 2002; Busse and Hefeker, 2007) or of the Exchange
Rate (Be´nassy-Que´re´ et al, 2001; Li and Resnick, 2003).
More controversial are variables like Labor Cost (Wheeler and Mody, 1992; Ran-
jan and Agrawal, 2011), Human Capital (Globerman and Shapiro, 2002; Asiedu,
2006; Goswami and Haider, 2014) and Tax Rate (Gastanaga et al, 1998; Wei, 2000).
In the context of the developing world there are also significant findings of im-
portance of effects from the channels of Infrastructure (Asiedu, 2002; Goswami
and Haider, 2014) and Natural Resources (Gastanaga et al, 1998; Asiedu, 2006).
Some additional variables are borrowed from the theory of international trade, such
as Time zone differences (Daude and Stein, 2007a), Membership of International
Organizations (Dreher et al, 2015) and Gravity factors (Bevan and Estrin, 2004;
Be´nassy-Que´re´ et al, 2007).
2.2 FDI and the role of institutional factors
Recent literature has begun to consider institutional factors as potential determi-
nants of FDI. De Mello (1997) points out the policy regime and institutional features
of the host economy as potential determinants of FDI. Institutional features include
the degree of political stability, government intervention, the bureaucratic proce-
7
dures and the existence of property rights legislation (de Mello, 1997). There are
several reasons to why institutional quality could affect FDI inflows. One broadly
accepted view is that ineffective institutions bring additional costs to the invest-
ment projects, for example in the form of corruption (Wei, 2000). Another broadly
accepted view is to the fear of sunk costs as investment decisions are vulnerable to
uncertainty. In the context of institutional quality, there are uncertainties due to
government inefficiency and weak enforcement of property rights and laws (Be´nassy-
Que´re´ et al, 2007).
In recent literature, the role of the recipient country’s institutional quality has
been explored in order to explain FDI flows. Institutional quality is measured in
different ways along with the different studies performed on the topic. The most
commonly used measures of institutional quality related to FDI flows are defined
in an aggregated way, like Institutional Quality (Be´nassy-Que´re´ et al, 2007; Daude
and Stein, 2007b; Alfaro et al, 2008) or Governance (Globerman and Shapiro, 2002;
Buchanan et al, 2012). These two measures do, loosely speaking, illustrate the same
institutional features. Both Daude and Stein (2007b) and Buchanan et al (2012)
evaluate the same institutional setup of variables; the Worldwide Governance Indi-
cators (WGI) including Voice and Accountability, Political Stability and Absence of
Violence, Government Effectiveness, Regulatory Quality, Rule of Law and Control of
Corruption. Nonetheless, Daude and Stein (2007b) aggregately define them as the
Quality of Institutions, while Buchanan et al (2012) address them as Governance –
a proxy for institutional quality. However, when Globerman and Shapiro (2002) use
the measure of Governance, they, on a disaggregated level, include the variables of
Political Instability, Rule of Law, Graft Regulatory Burden, Voice and Political Free-
dom and Government Effectiveness. This ambiguity is an indication and somewhat
an illustration on the lack of consensus of how institutional quality should be cor-
rectly defined and measured. Nevertheless, there is a consensus that such variables
are indeed central to explain FDI variation. The quality of institutions is positively
associated to FDI levels (Globerman and Shapiro, 2002; Be´nassy-Que´re´ et al, 2007;
Daude and Stein, 2007b; Buchanan et al, 2012).
Daude and Stein (2007b) and Globerman and Shapiro (2002) evaluate the dis-
aggregated quality indicators to find out which are significant. Daude and Stein
(2007b) conclude that Government Effectiveness, Regulatory Quality and Rule of
Law are the most important in this context. These results are in line with Glober-
man and Shapiro (2002) who also find that legal and regulatory variables, as well as
effective delivery of Government services are most important to attract FDI. In the
8
context of sub-Saharan African countries, Asiedu (2002) find Degree of Corruption
and Rule of Law as significant. Interestingly, there are some works on other types
of institutional variables that explain FDI, for example Human Rights (Blanton and
Blanton, 2007) and Level of Democracy (Li and Resnick, 2003; Doces, 2010).
More recently, Goswami and Haider (2014) have performed a study on several
risk factors including both OECD and non-OECD countries between 1984 and 2009.
In their study, they use the concept of Political Risk divided into three disaggregated
measures; risk of Governance failure, Cultural conflict and Partners’ attitude. In
their analysis, they find support for significant impact of all three aspects. In another
panel study, performed by Busse and Hefeker (2007) on developing countries between
1984 and 2003, FDI flows are explained by both institutional factors and political risk
factors. They find Government stability, factors of internal and external Conflict,
Law and order and Quality of Bureaucracy as significant when explaining FDI.
Furthermore, Papaioannou (2009) have found that both institutional quality and
other risk factors are important when describing international bank flows (including
FDI) between investing and host countries. After evaluating aggregated measures
of Institutional Quality, Political Risk, Economic Risk and Financial Risk, he finds
that poorly performing institutions, such as weak protection of property rights, legal
inefficiency and a high risk of expropriation are major impediments when attracting
foreign capital.
3 Theoretical Background
3.1 Capital flows and the Lucas Paradox
In the neoclassical economic models of Solow (1956) and Swan (1956) type, capital
flows follow from the standard assumptions of technology in the economy. Lucas
(1990) gives a simple illustrative example that can be summarized as follows; two
countries produce the same good (Y) with the same production function
Y = AF (K,L);
∂Y
∂K
> 0,
∂Y
∂L
> 0;
∂2Y
∂K2
< 0,
∂2Y
∂L2
< 0 (1)
where (A) is a constant and capital (K) and labor (L) are homogenous inputs. The
production per worker only differs if they have different capital per worker. By
the Law of Diminishing Returns, marginal product of capital is higher in the less
productive economy. This means that the returns in the less productive economy
9
(rL) are assumed to be higher than in the high productive economy (rH), or more
formally:
∂YL
∂K
(= rL) >
∂YH
∂K
(= rH). (2)
If trade in the capital good is free and competitive, new investment will occur only in
the less productive economy, since it is greater unexploited investment opportunities
there. This behavior will continue until the returns are equalized between the two
countries (Lucas, 1990).
As the marginal product of capital should be higher in less developed countries
in the world, they should attract more capital. This should at least be true under
the assumption that world capital markets are somewhere close to being free and
complete. Hence, as it is not the case, Lucas (1990) concludes that the neoclassical
models do not explain capital flows in the real world. This contradiction has later
been known as the Lucas Paradox. Lucas (1990) himself draws up three explanations
of why this is the case.
The first two explanations are related to human capital and they are indeed
relevant and interesting, but not in the scope of this paper. In the third explanation,
he explores the possibility of imperfections in the capital market as a limiting force
on capital flows between countries. This market failure may be due to problems of
asymmetric information which are inherent in the international capital markets. He
identifies mistrust, uncertainty, fear of losing invested capital and lack of political
arrangements between countries as possible channels of what he call “political risk”.
This can be illustrated as structural differences in the constant (A) between the high
productive and low productive economies, and more formally:
AL < AH . (3)
In order to get less developed countries more attractive for foreign investors, he
concludes that they need to be more open “to foreign investment on competitive
terms”. The structural differences between economies, in terms of ”political risk”,
must be equalized in order to be attractive to investors.
The variety of structural problems that can be found in these countries decreases
the risk-adjusted returns of the investment and could therefore explain why capital
does not flow to these countries in the quantities one would expect, when looking at
the neoclassical economic theory. The risk-adjusted returns that foreign investors get
from investing in the developing countries may be lower than the rate of returns that
are predicted by neoclassical economic theory (Prasad et al, 2007). The risk-adjusted
10
returns are higher in the high productive economy (r∗H) than the less productive
economy (r∗L), more formally:
r∗L < r
∗
H . (4)
Using this reasoning, there are some theoretical explanations to the paradox of
capital flows and why these flows cannot be explained by the neoclassical economic
theory. The “political risk” deters investors and they change their expectations after
a risk-adjusted prediction of returns of their investments.
3.2 Institutions
Theoretically, the role of institutions in the economy can be illustrated by the defini-
tion used by North (1991), who defines institutions as “humanly devised constraints
that structure political, economic and social interaction”. Institutions consist of
formal constraints, like constitutions, laws and property rights. They also consist
of informal constraints such as sanctions, taboos, customs, traditions and codes of
conduct. Moreover, these institutions have been devised to create societal order
and to reduce uncertainty in the society. Therefore, effective institutions reduce
transaction costs and raise the benefits of economic activity (North 1991).
In general, the institutional theories are mainly explored in relation to economic
performance and development over time. North (1991) argues that institutions
provide the incentive structure of an economy that over time shapes economic change
towards growth, stagnation or decline. In support of this institutional hypothesis,
Acemoglu, Johnson and Robinson (2005) find institutional factors outperforming
other common explanations of economic development, such as the hypotheses of
geography (Diamond, 1997) or culture/religion (Weber, 1905).
As an example, North and South Korea shared the same history, cultural roots
and geography before the separation in the aftermath of the Second World War.
Since then, the institutional setups of North and South Korea have drifted apart and
created two different nations with totally different economic development (Acemoglu
and Robinson, 2012). Other illustrative examples with supporting evidence for
the institutional theory is the case of Nogales - a city on the border of the U.S.
and Mexico (Acemoglu and Robinson, 2012), the “colonial experience” (Acemoglu,
Johnson and Robinson, 2001) and the effects of the “Mita” in today’s Peru and
Bolivia (Dell, 2010). In all of these cases, the institutional differences have been
found to be a significant determinant for differences in economic outcome.
When having a look at non-case studies, as above, Rodrik et al (2004) and Hall
11
and Jones (1999) show that institutional quality influences income levels and output
per worker. More recently, Alfaro et al (2008) and Papaioannou (2009) have shown
that low institutional quality in the host countries was the leading explanation to
the Lucas Paradox and the allocation of foreign capital during the period of 1970 to
2000 and 1984 to 2002. This motivates the role of institutions when foreign investors
are attracted to the different economies around the world.
Following the findings of Alfaro et al (2008) and Papaioannou (2009) and the
existing literature on determinants of FDI flows, the function of FDI inflow can be
described as
FDIi = f(X i, Inst i) (5)
where FDIi, the FDI inflow in country i, is a function of X i, a vector of conventional
economic variables and Inst i, a vector of institutional quality factors in country i.
The vector of institutional quality is of main interest in this study.
4 Hypothesis
Theoretically, the reasoning from Lucas (1990) and Prasad et al (2007) helps to
understand why capital flows do not follow the patterns assumed by neoclassical
economic models. The focus here will be on Lucas’s (1990) explanation on “political
risk” which is assumed to be a deterring factor when FDI is allocated across the
world economies. Following the findings of Alfaro et al (2008) and Papaioannou
(2009), the institutional quality in the host countries will be in focus in order to
understand the risk factors that international investors are facing when making
their risk-adjusted investment decisions.
The hypothesis relies on the reasoning from the previous sections in which the
factors that may determine the location of FDI across countries were reviewed.
However, there is no consensus on how to measure institutional quality. Therefore
the set of Worldwide Governance Indicators (WGI), together with the Quality of
Governance from International Country Risk Guide will be used as measures of
institutional quality. Furthermore, the six dimensions of the WGIs will be evaluated
in order to find evidence of a positive association between institutional quality and
FDI variations across countries.
Hence, the hypothesis in this paper is that the effect of better institutional
quality is associated with higher levels of FDI inflow. The logic of this hypothesis
can be found in the above reasoning of Alfaro et al (2008) and Papaioannou (2009)
12
– higher institutional quality is associated with less risk, on average, and therefore
better investment possibilities. In order to test the hypothesis, an econometric model
is formulated, in its basic form
ln
(
FDIit
capitait
)
= Institβ + X itδ + uit, (6)
where FDIit is the net inflow of foreign direct investments in country i in time t
which is divided by the size if the population in country i at time t. The logarithm of
this term is used in the purpose of simplifying interpretations as percentage changes,
and to reduce problems of outliers and eventual skewnesses in the distribution. Inst it
is a factor of institutional quality and X it is a vector of controls for country i at time
t. uit is the error term. The main parameter of interest is β which illustrates the
effect of institutional quality on the investment levels. If significant, this parameter
is expected to be positive following the reasoning above.
The model is inspired by studies covered in the literature review. Using FDI in
levels is not unusual, but previous studies have partly been interested in FDI as a
share of GDP (or % of GDP). My interest is to look on FDI levels to get a sense of
the magnitudes.
5 Data, definitions and Descriptive Statistics
The data used in this study is mainly obtained from the World Development In-
dicators (WDI) database which is compiled from officially-recognized international
sources and presents the most current and accurate global development data avail-
able (World Bank, 2016a). The data on institutional quality has been obtained from
the Quality of Government Standard Dataset (2016). The final dataset in this study
cover 127 countries over the period 1996-2014 and the total number of country-year
observations is 2413. OECD countries affiliated before 1994, including South Ko-
rea and smaller western economies like Andorra and Monaco are excluded from the
study. Unfortunately, some countries lack data for important series; Pakistan, Syria,
Ethiopia, among others, are excluded for this reason. A list of included countries,
respective defined income group and data availability can be found in Appendix II.
5.1 Dependent Variable
The dependent variable of FDI per capita is constructed by two separate series from
the WDI database. The data on FDI is obtained from the Foreign direct investment,
13
net inflows (BoP, current US$) and accounts for the net inflow in current US dollars
of foreign investment to acquire a lasting management interest (10 percent or more
of voting stock) in a domestic enterprise. To get the final data serie of FDI inflow
per capita as dependent variable, the FDI series is divided by the WDI series of
Population, total (World Bank, 2016). Some countries in the dataset do face a
negative FDI value. This is probably due to disinvestment, i.e. more foreign capital
has left the country than entered during a specific time period t. As the natural
logarithm of FDI inflow per capita is used, the negative FDI values of disinvestment
will go lost in the analysis. Luckily, disinvestment is a very rare phenomenon. Out
of the 2413 country-year observations on the FDI, only 77 are negative.
5.2 Variables on Institutional Quality
In the evaluation of institutional quality factors, main focus will be on the World-
wide Governance Indicators (WGI) defined with support from the World Bank.
When interest in Governance was growing during the 1990s, the six dimensions of
Governance was defined and data on these six dimensions stretches back to 1996
(World Bank, 2015). As they are widely used as indicators of institutional quality
in previous literature and most of the included variables in this study are extracted
from the World Bank, it is reasonable to pick also these variables from their source.
In the WGI project, Governance is defined as “the traditions and institutions
by which authority in a country is exercised” and this includes (World Bank, 2015):
• the process by which governments are selected, monitored and replaced,
• the capacity of the government to effectively formulate and implement sound
policies,
• and the respect of citizens and the state for the institutions that govern eco-
nomic and social interactions among them.
The indicators are based on several hundred individual variables measuring percep-
tions of governance from over 30 data sources constructed by 25 different organi-
zations. Below, each definition of these six dimensions is presented (World Bank,
2015):
1. Voice and Accountability captures perceptions of the extent to which a coun-
try’s citizens are able to participate in selecting their government. Also free-
dom of expression, freedom of association, and freedom of media is included.
14
2. Political stability and Absence of Violence is a measure of the perceptions
of the likelihood of political instability and/or politically motivated violence,
including terrorism.
3. Government Effectiveness captures the 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 captures perceptions of the ability of the government to
formulate and implement sound policies and regulations that permit and pro-
mote private sector development.
5. Rule of Law captures perceptions of the extent to which agents have confidence
in and abide by the rules of society, and particular the quality of contract
enforcement, property rights, the police, and the courts, and likelihood of
crime and violence.
6. Control of Corruption captures perceptions of the extent to which public power
is exercised for private gain, including both petty and grand forms of corrup-
tion, and “capture” of the state by elites and private interests.
The six indicators vary between roughly -2.5 and 2.5 with a mean of zero and
standard deviation of one in each year of observation. In order to get a scale from 0
to 10, they are standardized by adding each observation by 2.5 and then multiplying
each by 2. The higher a value on an indicator is the better is the relative institutional
quality. Moreover, an average WGI-index will be used as Average Institutional
Quality in this study. This series is constructed as a mean of the sum of all six
governance dimensions for each i at each t. No weighting of the aspects is done.
Despite that the WGIs are widely accepted as reliable indicators of institutional
quality, some criticism that has been raised against them is worthy to mention.
First of all, the indicators are based on expert’s and organization’s perceptions;
hence, not on realities. This is important to remember as there has been findings of
differences between, for example, perceptions of corruption and corruption in reality
(Olken, 2009). In this sense, there might be lack of construct validity, i.e. it is
questionable if the WGIs measure what they intend to. For example, if an indicator
value changes from one year to another; is it actually a real change or a perceived
change of the indicator that is observed? Following the construction of WGIs, with a
15
mean of zero and a standard deviation of one; global governance will never improve
on average (Thomas, 2010). Consequently, a decrease in a value of an indicator
from one year to another for country i do not imply that the country is worse off
in reality. Rather, it might be the case that other countries have increased in their
relative position. These measurement problems are of course serious, but as long
as one is aware of the fact that these indicators are constructed measures, these
problems do not constitute a threat to the purpose and reliability of this study. In
this case, a relative and perceived measure might be appropriate as investors are
determining investment opportunities by comparing relative expected returns across
countries before making decisions.
The robustness check on institutional quality will be done on the basis of the
PRS Group’s (2016) series Quality of Government from the International Country
Risk Guide (ICRG). Their measure on Quality of Government ranges between 0 and
1 and is constructed by a weighted mean of the IRCG-variables of Corruption, Law
and Order and Bureaucracy Quality. The higher value, the better is the Quality of
Government. In order to compare IRCG’s Quality of Government with the Average
Institutional Quality contructed by WGIs, this series is multiplied by 10 so it ranges
from 0 to 10 as well. This series is used as robustness as it contains data from 31
countries less than the series from the WGI. Moreover, the WGIs are more suitable as
in the purpose of evaluating the aspects of institutional quality and to be consistent
by using World Bank data. Along with the list of countries in Appendix II, the data
series available for each country may be found. In general can the critique against
the WGIs, discussed above, be valid also in the context of the variable from ICRG.
5.3 Control Variables
For the inclusion of control variables in this study, the most common hypotheses
and findings from previous literature is used to investigate the variations in FDI
levels. All variables that are used as controls can be found as indicators at the
WDI webpage (World Bank, 2016a). The control variables included are Market
Size, Growth Rate, Openness to Trade, Macroeconomic Stability, Infrastructure and
Natural Resources. Due to delimitations and lack of data, some potential controls
are not included in the model. Labor Cost, Human Capital and Tax Rate are not
included and these delimitations may of course cause a problem for my model.
However, it is reasonable that e.g. factors that are correlated with Labor Cost, such
as poor labor regulations, might be correlated with some of the institutional factors
16
and will therefore be included in the analysis anyway. The same argument could be
valid for factors associated to Human Capital, such as low literacy levels and poor
education, and eventual uncertainties in Tax Rate.
All in all, the most commonly used variables in previous literature are included
in the model in order to describe FDI variations across countries. The definitions
and the using of proxy variables do also follow previous reasoning.
Market Size is proxied by the natural logarithm of GDP and the series orig-
inates from the WDI of “GDP PPP (current international US$)”. It has been
established that a larger market attracts FDI (Wheeler and Mody, 1992; Glober-
man and Shapiro, 2002). The logarithm transformation of this variable is used as
it is heavily skewed to the left, but also to get more intuitive interpretations. After
the transformation, it is almost normal distributed.
Growth Rate is the annual GDP growth and originates from the WDI of “GDP
growth (annual %)”. Higher levels of Growth Rate are expected to attract higher
levels of FDI as the return of capital may be higher in a growing economy (Gastanaga
et al, 1998; Busse and Hefeker, 2007).
Openness to Trade is proxied from the WDI series of “Trade (% of GDP)”, which
is the share of GDP that is constituted by the sum of exports and imports of goods
and services. If an economy is more open to the world in terms of less trade barriers,
restrictions, etc, it is reasonable to assume that it will attract more FDI (Asiedu,
2002; Buchanan et al, 2012).
Macroeconomic stability is proxied by the annual inflation rate (Asiedu, 2006)
and this series originates from the WDI of “Inflation, consumer prices (annual %)”.
Inflation is expected to be negatively associated with FDI as it may constitute a
source of uncertainty for the investors.
Infrastructure is proxied by the natural logarithm of the WDI series of “Fixed
telephone subscriptions (per 100 people)” as commonly done (Asiedu, 2002). It has
been significantly proved that low levels of infrastructure deter foreign investors
(Goswami and Haider, 2014). Therefore, it is reasonable to assume a positive rela-
tionship between Infrastructure and FDI inflow. The logarithm transformation of
this variable is used as it is very skewed to the left, but also to get more intuitive
interpretations. After the transformation, it is closer to normal distributed.
Natural Resources originate from the WDI series of “Total natural resources rents
(% of GDP)”. In previous literature, it has been evidence of a positive relationship
between the existence of natural resources and FDI levels (Asiedu, 2006).
17
5.4 Descriptive Statistics
The basic descriptive statistics are presented in Table 1 below. In total there are 2413
country-year observations. All the included variables suffer from some missing values
due to seemingly random causes. Hence, there is no need to worry about attrition.
It is worthy to mention that there are no observations for the WGI-variables at
the years of 1997, 1999 and 2001. Moreover, for the Quality of Government series
from ICRG, 31 of the included countries are completely missing data for this series.
Nevertheless, when running regressions, the panel is “strongly balanced”.
Table 1: Descriptive Statistics
Variables Observations Mean Std. Dev. Min Max
ln(FDI per capita) 2311 4.119 2.002 -4.494 9.421
Avg Intitutional Quality 2032 4.463 1.307 1.521 8.185
Quality of Government 1793 4.848 1.353 1.389 9.167
Voice and Accountability 2032 4.358 1.594 0.556 7.946
Political Stability 2032 4.562 1.618 0.220 7.799
Government Effectiveness 2032 4.504 1.548 0.938 9.859
Regulatory Quality 2032 4.611 1.520 0.480 9.495
Rule of Law 2032 4.349 1.455 0.856 8.789
Control of Corruption 2032 4.393 1.432 1.327 9.833
ln(Market Size) 2356 24.389 1.941 19.788 30.522
Growth Rate 2378 4.567 6.463 -62.076 149.973
Openness to Trade 2373 89.120 48.502 15.580 531.737
Macroeconomic Stability 2101 10.183 94.310 -18.109 4145.108
ln(Infrastructure) 2403 1.792 1.532 -2.487 3.969
Natural Resources 2192 12.210 16.123 0.002 89.329
Mean natural log of FDI per capita during the period is slightly above 4 with a
standard deviation of 2; varying between almost negative 4.5 and slightly above
9.4. The mean of the standardized Average Institutional Quality is 4.46, the mean
of the disaggregated WGIs, respectively, are close to this value; ranging between
4.35 and 4.61. The standard deviation is also quite similar across the WGIs, with
Control of Corruption as varying the least and Political Stability varying the most.
Nonetheless, Political Stability has the lowest minimum value and it also has the
lowest maximum value. Control of Corruption has the highest minimum value and
Government Effectiveness has the highest maximum value among the WGIs. For
the Quality of Government from ICRG, there is a slightly higher mean and variation.
Among the control variables, it is easy to find examples of events illustrating
variations and heterogeneities. There are economies with yearly observations of
Growth Rate higher than 100 percent in a year; Equatorial Guinea in 1997 after
oil findings in 1995, and Libya in 2012 after the civil war in 2011. The negative
Growth Rate of 62 percent is accounted by Libya in 2011. However, these events are
extreme. The heterogeneity among the included countries can also be illustrated by
18
the variation in Market Size; very large economies like China and Brazil are included,
but also very small economies like the Comoros and Guinea-Bissau. Highest values
in Openness to Trade are accounted by expected economies, like Singapore and
Malaysia, but again, also Equatorial Guinea the years after the oil findings. At the
Macroeconomic Stability, the inflation rate has varied considerably across countries
over the period; deflation rates as low as at 18 percent and an inflation rate as high
as 4145 percent in Angola in 1996. However, when looking at the mean of the control
variables, there are not many unexpected values or other surprises. The pairwise
correlations of the institutional quality variables and control variables are presented
in Appendix III, respectively.
To get a sense of the relationship between the key variables, graphics are useful
tools. Figure 3 illustrates a scatterplot between the average of natural logarithm of
FDI per capita and Average Institutional Quality, over the period of interest.
Figure 3: Average relationship between Average Institutional Quality and ln of FDI
per capita over the period 1996-2014.
In Figure 3, the positive relationship between the two variables is quite obvious.
On average during this period, higher institutional quality is associated with higher
inflow of FDI per capita. The “Best Fit”-line is positively sloped with an R-squared
just above 0.40, while the correlation is almost 0.64. In the plot, there are some
extremes; Singapore up to the right, Equatorial Guinea down to the right and Nepal
far to the left. In Appendix IV, you may find the graphics of each dimension of the
19
WGIs and their average relationship with natural logarithm of FDI per capita during
1996-2014. All relationships are positive.
In Figure 4 below, the average relationship between natural logarithm of FDI
per capita and Quality of Government from the ICRG is presented. Also in Figure
4 there is a clear positive relationship between institutional quality and FDI per
capita. The R-squared is just below 0.40 and the correlation is approximately 0.62,
almost identical to above.
Figure 4: Average relationship between Quality of Government and ln of FDI per
capita over the period 1996-2014.
6 Empirical Strategy
The data used in this study is of panel form, i.e. it is measured over two dimen-
sions. The included countries constitute the cross-section dimension and the yearly
observations of these countries add the time dimension. Error terms of such models
are likely to display certain types of dependence which should be taken into account
in the analysis. Consider the following linear regression model:
yit = X itβ + uit, i = 1, ...,m, t = 1, ..., T, (7)
where X it is a 1 × k vector of observations on the explanatory variables and uit is
the error term. There are m cross-sectional units and T time periods, for a total of
20
N = m× T observations. The assumption of uit being independent and identically
distributed is, however, not likely to hold in the panel data framework. Therefore,
the most common panel data models are so called error-components models. In
such setups, the error term uit is modeled as two or three separate shocks that are
independent from each other:
uit = et + υi + εit (8)
where et is a shock that is unique over the observations at time period t, υi is a
shock that is affecting all observations for the cross-sectional unit i and εit only
affect observation it (Davidson and MacKinnon, 2004). Theoretically, this model is
identical to the model that was presented in the hypothesis section above and this
type of model will be used in this study.
Panel data sets have several advantages over conventional cross-section data sets
or time series data sets. The estimates in panel data analysis are more efficient
as there are more observations, which in turn increase the degrees of freedom and
reduce the collinearity among the explanatory variables. Moreover, some economic
questions that cannot be addressed using cross-sectional or time series data sets
can be analyzed with panel data analysis. Using panel data models, it is possible
to resolve or at least reduce the magnitude of biases from time invariant omitted
variables, either if measured wrongly or unobserved. Instead of a “snapshot” from
a particular moment in time that would be more or less accurate as in cross-section
studies; it is possible to explore the dynamics in panel data in order to control for
omitted factors that may bias the model (Hsiao, 2014). In this way, as omitted
factors are controlled for, identification problems might be reduced (Verbeek, 2012).
In error-components models, the shocks of et are the same over the cross-section
at time period t, but different across the periods in time. The et can be modeled
as a trend factor or as T-1 time dummies (Stock and Watson, 2012). As shown in
Figure 1 above, the FDI levels are not sloped linearly positive, and therefore it is
favorable to use T-1 dummies to control for the time effects of et.
6.1 Estimation of fixed effects and random effects
When it comes to the time invariant and individual specific factors modeled as υi
above, two main estimation techniques can be used in order to treat these effects.
Basically, the choice of model relies on the assumption of the relationship between
the time invariant unobserved heterogeneity υi and the explanatory variables xit
21
that are included in the model (Verbeek, 2012).
If correlation between υi and xit is allowed, the fixed effects model is suitable to
use. In the fixed effects model, the υi effects are eliminated by the so called within
transformation (Verbeek, 2012). The time invariant unobserved heterogeneity of
υi is controlled for by using the time variation in the dependent and indenpendent
variables within each cross-sectional unit (Wooldridge, 2014). See Verbeek (2012)
or Wooldridge (2014) for formal illustrations of the within transformation.
Phenomenon that can be controlled for by this transformation is, for example,
firm fixed effects like the management quality (e.g. in Veerbek, 2012), state fixed
effects like cultural attitudes (e.g. in Stock and Watson, 2012) and country fixed
effects like technical efficiency (e.g. in Hsiao, 2014). In the context of this study,
the unobserved heterogeneity could be present in the form of country-specific char-
acteristics that are constant over the time period in country i but still affecting
the model. In case of this study, it could be cultural or religious attitudes, deep
institutional and historical factors, geography, among others.
However, in theory, a more efficient estimator may exist than the within estima-
tor. This is obtained by using the random effects model. In this model, a crucial
restriction is imposed. The time invariant unobserved heterogeneity in country i is
required to be independent from the explanatory variables. More formally, υi and
xit must be uncorrelated in order for the random effects model to be consistent. The
estimator from this model is often referred to as the feasible GLS estimator which
is a matrix-weighted average combination of the within estimator from the fixed
effects model and the between-groups estimator, which reflects the changes between
the cross sectional subjects (Davidson and MacKinnon, 2004).
To summarize, if it is possible that the assumption of independence between
υi and xit is likely to hold, then the estimator from the random effects model is
consistent and more efficient than the estimator from the fixed effects model and
the former should preferably be used (Wooldridge, 2014).
6.2 The Hausman test - to choose model of estimation
To treat the time invariant unobserved heterogeneity υi as fixed or random is not
easy to tell without formal investigation. The differences in the parameter estimates
between the fixed effects model and the random effects model might be large, espe-
cially if T is small and m is large. Generally, the fixed effects model is favorable
if the individuals are like countries, firms or industries, i.e. an explicit “one of a
22
kind” character. In any case, choosing between the models is not a straightforward
process (Verbeek, 2012).
However, it exist a formal test to choose between them (Hausman, 1978). The
idea is to compare the two estimators in order to discover the relationship between υi
and xit – and if the estimators are significantly different. Under the null hypothesis
in this test, υi and xit are uncorrelated. If one fails to reject the null hypothesis, the
random effects model is preferred. If one can reject the null hypothesis, then the
fixed effects model is preferred by the reasoning above. The fixed effects model is
consistent in both cases, but the estimator from the random effects model is more
efficient if the assumption holds. If the null hypothesis can be rejected, the random
effects model is both inconsistent and inefficient, due to the problems discussed. In
other words, the Hausman test is not only a test to choose between the models, it
also determines the trade-off between efficiency and consistency in the estimation
process (Verbeek, 2012).
In order to model the time invariant unobserved heterogeneity accurately, as ran-
dom or fixed effects; the Hausman test is performed. In all the different estimations
in this study, the indication is clear; the null hypothesis of orthogonality between υi
and xit is not likely to hold and is therefore rejected in all the different specifications.
Therefore the fixed effects model will be used in this study.
6.3 Further considerations
In the econometric estimations performed in this study, robust standard errors will
be used in order to handle eventual heteroskedasticity. In the estimations of the fixed
effects model, cluster effects on country level will be allowed. This means that the
assumption of independence between the observations within the cluster is relaxed.
This is possible to do without serious problems of potential autocorrelation as the T
is accounted yearly, hence fairly long periods, and as the dummies for each year are
included and accounts for the yearly effects. The strength of including the cluster
effects is that correlation between clusters is not allowed. Furthermore, compared
to time series data with few cross sectional m and a large number of time periods
T – also with shorter periods (monthly, daily, etc), this panel have a large m and a
relatively small T. The regular problems that make time series models suffer from
serial correlation are not considered to be a serious problem in this study due to the
advantageous structure of the data (Verbeek, 2012).
In the model specified in this paper, there are reasons to suspect some multi-
23
collinearity between the explanatory variables – as in many cases in country level
analysis. For example, it is reasonable that to suspect that the institutional envi-
ronment in a country is somewhat correlated with the Market Size, but also factors
related to macroeconomic stability or infrastructural features. In order to get a sense
of the extent of the multi-collinearity in the model, the Variance Inflation Factor
(VIF) is inspected. The VIF is an index over the severity of the multi-collnearity
for each explanatory variable, respectively. However, a high VIF or signs of high
multi-collinearity do not necessarily imply the exclusion of any of the explanatory
variables. Rather, the results should be interpreted with caution. Luckily the stan-
dard errors are biased in a way that the estimation results are less significant, i.e.
bias the significance downwards. Hence, the significance of the results will eventu-
ally be more conservative than the results from a model without multi-collinearity.
An exclusion of an important explanatory variable on the other hand, might bias
the estimates – a bigger problem. High VIFs do not by themselves discount the
results of the analysis (O’Brien, 2007). When inspecting the multi-collinearity by
the corresponding VIFs in the specifications below, both Market Size and the insti-
tutional quality variables have high VIF values throughout the analysis. This is not
surprising as discussed above, but it is important to note that the standard errors
for these variables are somewhat inflated. The other variables are not suffering from
“too high” VIFs.
Another very interesting and severe issue related to analyses on cross-country
level is the simultaneity problem in disentangling the cause and effect. This issue
has been deeply discussed in studies related to economic growth (e.g. Mankiw et
al, 1995), but it is also relevant in the context of this study. Interpreting the semi-
elasticities between natural logarithm of FDI inflow per capita and the institutional
quality variables as causal effects should not be done without caution. The variables
of institutional quality are constructed measures of experts’ perceptions; while the
true institutional quality is unknown. Therefore, it is not reasonable to seek causal
effects in the data. However, the channels in which the institutional quality might
attract FDI could be causal. But with a cynical read, one could imagine that the
government in country i at time t could change institutions in order to attract
foreign capital. Hence, the causality could go both ways. Therefore, the focus in
this study will lie on the institutional quality and its association with variations in
FDI levels.
In general, the most effective way to establish causality is by using an Instru-
mental Variable (IV) strategy. In lack of potential IVs, a possible way to explore the
24
causality is to use lags. Therefore, lagged institutional quality will be used in this
purpose and to check robustness of the results. When lagging institutional quality,
the eventual problems of reverse causality at time t might be reduced. At time t,
causality could arguably go both ways. It is, however, less likely that institutional
quality at time t-1 is affected by FDI at time t. Furthermore, it is reasonable that
some investment decisions are exposed to some rigidity. Even if investors are as-
sumed to collect information efficiently and to be sensitive to changes considering
risks, the investment decisions might be determined on past data (last month or
even last year). Therefore, it is not unrealistic to consider the lagged information
on institutional quality.
7 Results
7.1 Baseline Results
The baseline results are presented in Table 2 below in which the Average Insitutional
Quality is used as a factor of institutional quality.
Table 2: Baseline Results.
Variables Coefficients
ln(FDI per capita)t (1) (2) (3) (4) (5)
Avg Institutional Qualityt 0.438*** 0.207* 0.197 0.187
(0.032) (0.124) (0.131) (0.129)
Avg Institutional Qualityt−1 0.299**
(0.132)
ln(Market Size)t -0.022 1.381*** 1.377*** 0.999** 1.046**
(0.018) (0.286) (0.374) (0.386) (0.485)
Growth Ratet 0.0160** 0.0251*** 0.0192** 0.0169** 0.0214**
(0.0075) (0.0064) (0.0076) (0.0072) (0.0097)
Openness to Tradet 0.0092*** 0.0055*** 0.0063*** 0.0053** 0.0060**
(0.0009) (0.0017) (0.0020) (0.0021) (0.0024)
Macroeconomic Stabilityt -0.0024 -0.0002 -0.0042 -0.0001
(0.0031) (0.0041) (0.0039) (0.0003)
ln(Infrastructure)t 0.535*** 0.126 0.131 0.147
(0.0269) (0.117) (0.115) (0.113)
Natural Resourcest 0.0281*** 0.0159** 0.0091 0.0087
(0.0023) (0.0080) (0.0083) (0.0092)
Constant -210.6*** -85.22** -85.44* -22.44** -24.04**
(13.27) (34.86) (46.25) (9.195) (11.42)
Fixed Effects No Yes Yes Yes Yes
Time Trend Yes Yes Yes No No
Time Dummies No No No Yes Yes
Cluster Effects No Yes Yes Yes Yes
Observations 1 512 1 891 1 512 1 512 1 416
R-Squared 0.643 0.384 0.413 0.445 0.453
Nr of Countries - 125 114 114 114
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
25
The regression in column (1) is a Pooled OLS estimation using robust standard
errors, including the standardized average of the WGIs as the institutional qual-
ity variable, the control variables and a time trend, but without allowing for time
invariant unobserved heterogeneity. Therefore, the results in column (1) can be con-
sidered as biased and column (2) to (5) more interesting. Moving from column (1) to
column (2) to (5) shows the importance of modeling the time invariant unobserved
heterogeneity, in this case as fixed effects. An estimation using fixed effects, clus-
ters, robust standard errors and including the three most commonly used controls
in previous literature is presented in column (2). When adding the remaining three
controls in column (3), the magnitudes and significance levels of the controls are
quite similar – a good sign of robustness. The variable of institutional quality, is
however, insignificant.
When expanding the precision in the model even further and modeling the time
effects as time dummies, the magnitudes and significance levels keeps somewhat
consistent also in column (4). When moving from column (4) to (5), the Average
Institutional Quality is modeled with a lag. In column (2) to (5), the controls of
Market Size, Growth Rate and Openness to Trade are consistently significant with
expected signs and small variation in magnitude and level of significance. This
indicates robustness of the baseline results.
Column (4) and (5) is of main focus as the yearly effects are modeled in the most
accurate way, for this case - as dummies. This implies, for example, that a ceteris
paribus increase in Market Size by 1 percent is associated with an on average increase
of FDI inflow per capita by approximately 1 percent. An increase in Growth Rate
by 1 percent is on average associated with an increase in FDI inflows per capita by
1.7 to 2.1 percent. Also Openness to Trade is posetively associated with FDI inflows
per capita.
The Average Institutional Quality, is insignificant in column (3) and (4), but
the magnitude of the estimate is slightly the same in column (2) to (4), varying in
the interval of approximately 0.19 to 0.21. Important to remember is that the high
VIFs may indicate inflated standard errors making these results a bit conservative
in the level of significance. In order to explore the persistent effect of the variable
of interest, Average Institutional Quality is lagged in column (5). When lagged,
the magnitude increases and get significant at the 5 percent level. Hence, a ceteris
paribus increase by 1 rank (scale from 0 to 10) in the lagged value of Average
Institutional Quality is on average associated with an increase in FDI inflows per
capita by approximately 23 percent.
26
7.2 Robustness of the Baseline Results
In order to explore the robustness of the results in Table 2, the institutional quality
indicator is changed to the Quality of Government from the ICRG in Table 3 below.
Table 3: Robustness of the Baseline Results
Variables Coefficients
ln(FDI per capita)t (1) (2) (3) (4) (5)
Quality of Governmentt 0.228*** 0.185* 0.187* 0.167*
(0.0304) (0.0953) (0.0993) (0.0993)
Quality of Governmentt−1 0.230**
(0.090)
ln(Market Size)t -0.106*** 1.582*** 1.840*** 1.429*** 1.464***
(0.0206) (0.385) (0.456) (0.475) (0.509)
Growth Ratet 0.0260** 0.0277*** 0.0179** 0.0171** 0.0180**
(0.0123) (0.0083) (0.0083) (0.0079) (0.0082)
Openness to Tradet 0.0074*** 0.0042 0.0066* 0.0059* 0.0053*
(0.0011) (0.0028) (0.0034) (0.0031) (0.0031)
Macroeconomic Stabilityt -0.0009 -0.0006** -0.0009** -0.0008**
(0.0007) (0.0003) (0.00035) (0.0003)
ln(Infrastructure)t 0.682*** 0.238 0.212 0.234
(0.027) (0.155) (0.160) (0.164)
Natural Resourcest 0.0190*** 0.0150 0.0073 0.0078
(0.002) (0.010) (0.0112) (0.0113)
Constant -232.4*** -78.15* -45.06 -33.72*** -34.70***
(13.03) (45.86) (52.14) (11.51) (12.34)
Fixed Effects No Yes Yes Yes Yes
Time Trend Yes Yes Yes No No
Time Dummies No No No Yes Yes
Cluster Effects No Yes Yes Yes Yes
Observations 1 409 1 668 1 409 1 409 1 337
R-Squared 0.630 0.394 0.435 0.463 0.457
Nr of Countries - 94 90 90 90
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
After changing the model specification, the signs, magnitudes and significance levels
are generally consistent across the controls, respectively. The magnitude of Market
Size increases to about 1.5, while Growth Rate and Openness to Trade not change
much compared to Table 2. The only critical change from the previous model is
that Macroeconomic Stability turns significantly negative.
More interesting for the purpose of this paper, Quality of Government is consis-
tently significant throughout the columns. This supports the hypothesis of a positive
association between institutional quality and FDI. The magnitude of the coefficient
estimate varies between approximately 0.17 and 0.23 in column (2) to (5). Hence,
a ceteris paribus increase by 1 rank (scale from 0 to 10) in Quality of Government
is on average associated by an increase in FDI inflows per capita of around 20 per-
cent. Again, when lagging the effect of institutional quality factor, the magnitude
increases a little. This confirms the indication of a persistent effect of institutional
27
quality on FDI, which is supporting a hypothesis of eventual causality.
As the Average Institutional Quality in Table 2 was standardized by myself, it
is not surprising that those results are more inconsistent than the counterparts in
Table 3. The Quality of Government measure from the ICRG on the other hand,
was constructed by experts who have weighted the different aspects in a more careful
way. The Quality of Government from the ICRG could therefore be regarded as a
more reliable measure of institutional quality in practice. In the estimations using
either the Average Institutional Quality or the Quality of Government, it is notable
that the magnitude of the coefficient estimate – or the economic significance – is
quite the same. However, the standard errors of the Average Institutional Quality
are consistently larger.
Overall, the analysis above gives us support in the hypothesis of inclusion of insti-
tutional quality factors when analyzing the variation of FDI levels across countries.
Furthermore, as the lagged effects of the institutional quality are consistently signif-
icant in Table 2 and Table 3, there is support of a persistent effect of institutional
quality on FDI, and hence, some support for the hypothesis of causality.
When it comes to the question of making a choice in accuracy between the
models, there are reasons to believe that the models from column (4) and (5) are
preferable, as argued above regarding the modeling of the time effects.
7.3 Evaluation of the WGIs
In Table 4, the results from the separate regressions of the WGIs are presented. The
significance levels and coefficients of the control variables are generally stable. Even
if this is the case, some of omitted variable bias might be present in all six columns
as it is reasonable that some factors are omitted due to the strategy of including the
WGIs one by one.
Looking at the results in Table 4, the controls are somewhat consistent in their
magnitude, sign and significance - a sign of robustness. Interesting for the sake of
robustness is also that the controls behave very close to how they do in Table 2; the
magnitudes are increasing just a little.
When it comes to the variables of interest, the WGIs, it is important to remember
that the VIFs are consistently high and the standard errors are somewhat inflated.
Therefore, as previously mentioned, the significance levels are conservatively esti-
mated, but the magnitudes of the estimates are assumed to be correct.
28
Table 4: Evaluation of WGIs
Variables Coefficients
ln(FDI per capita)t (1) (2) (3) (4) (5) (6)
Voice and Accountabilityt -0.0091
(0.0961)
Political Stabilityt 0.0304
(0.0584)
Government Effectivenesst 0.0972
(0.0859)
Regulatory Qualityt 0.277***
(0.0987)
Rule of Lawt 0.129
(0.0974)
Control of Corruptiont 0.0533
(0.102)
ln(Market Size)t 1.121*** 1.090*** 1.075*** 0.852** 1.025*** 1.091***
(0.384) (0.381) (0.383) (0.392) (0.387) (0.386)
Growth Ratet 0.0174** 0.0174** 0.0171** 0.0172** 0.0171** 0.0172**
(0.0073) (0.0072) (0.0072) (0.0072) (0.0074) (0.0073)
Openness to Tradet 0.0057*** 0.0056*** 0.0055*** 0.0051** 0.0054** 0.0055***
(0.0021) (0.0020) (0.0021) (0.0021) (0.0021) (0.0021)
Macroeconomic Stabilityt -0.0054 -0.0050 -0.0049 -0.0041 -0.0046 -0.0049
(0.0039) (0.0040) (0.0039) (0.0039) (0.0039) (0.0039)
ln(Infrastructure)t 0.115 0.121 0.122 0.123 0.121 0.121
(0.113) (0.112) (0.115) (0.116) (0.115) (0.117)
Natural Resourcest 0.0067 0.0071 0.0082 0.0089 0.0091 0.0076
(0.0085) (0.0084) (0.0083) (0.0080) (0.0081) (0.0086)
Constant -24.45*** -23.89** -23.84** -19.39** -22.77** -24.00**
(9.176) (9.131) (9.174) (9.336) (9.223) (9.200)
Fixed Effects Yes Yes Yes Yes Yes Yes
Time Trend No No No No No No
Time Dummies Yes Yes Yes Yes Yes Yes
Cluster Effects Yes Yes Yes Yes Yes Yes
Observations 1 512 1 512 1 512 1 512 1 512 1 512
R-Squared 0.443 0.443 0.444 0.452 0.444 0.443
Nr of Countries 114 114 114 114 114 114
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The results from Table 4 indicate which of the dimensions of the WGIs that matter
for foreign investors. As it seems, Regulatory Quality is the only significant one, on
the 1 percent level. Hence, a ceteris paribus increase in the rank by 1 (scale from 0
to 10) of Regulatory Quality is on average associated with an increase in FDI inflows
per capita by almost 28 percent. Regulatory Quality is also the WGI with highest
magnitude on its coefficient estimate.
In Appendix V, the persistent effects of the WGIs may be found in Table 5.
When lagging the WGIs, two more variables are significant – Rule of Law on the 5
percent level and Government Effectiveness on the 10 percent level. When lagged,
Regulatory Quality is decreasing in both magnitude and significance level. Hence,
a ceteris paribus increase in the rank by 1 of the lagged values of Rule of Law or
Regulatory Quality is on average associated with an increase in FDI inflows per
capita by around 21 percent. These results are significant on the 5 percent level.
29
A ceteris paribus increase in the rank by 1 of the lagged value of Government
Effectiveness is on average associated with an increase in FDI inflows per capita by
almost 15 percent.
The results above are quite striking. For the full sample of non-OECD countries
in this study, only Regulatory Quality is significant at time t. As it is significant
also when lagged, it seems like the most important institutional factor for foreign
investment decisions are aspects related to ease of doing business, regulatory bur-
den and tax inconsistencies, among others. This variable can, loosely speaking,
be regarded as including factors related to business and investment climate. By
the same reasoning, market unfriendly policies, like price controls or uncertainties
in regulations seem to deter investors. As Government Effectiveness and Rule of
Law are significant when lagged, there are indications of their importance for FDI.
These results are supporting the main points from previous literature (Globerman
and Shapiro, 2002; Daude and Stein, 2007b). However, the support for Regulatory
Quality is more robust than the support of Government Effectiveness and Rule of
Law. As the lagged values are significant, it gives some additional support for the
hypothesis of causality. Factors related to political aspects and corruption do not
seem significant in its association with variation in FDI levels for the full sample,
which is indeed an interesting finding as well.
7.4 Further analysis and robustness
In order to explore the validity of the theoy related to the Lucas Paradox and to
check robustness of the results, the sample will now be divided into four sub-samples
depending on the income level. The classification of the income groups are taken
from the World Bank Atlas method for Low Income, Lower Middle Income, Upper
Middle Income and High Income countries from the calender year 2013 (World Bank,
2016b). This definition can be found together with the list of countries in Appendix
II. Descriptive statistics on the institutional quality factors for each income group
can be found in Table 6 in Appendix VI. In the following analysis, the regression
tables associated to Table 7, 8, 9 and 10 can be found in Appendix VII, respectively.
In the robustness analysis below, the institutional quality factors will be analyzed
at time t and no lagged variables will be used. Firstly, as the sub-samples below
are smaller and the strategy of lagging is consuming observations, it is reasonable
to keep as many observations as possible. Second, as investors are assumed to be
rational, forward looking and sensitive to real time changes, it is not unreasonable
30
to analyze models without lags. Hence, the focus in this section will be on the
how institutional quality factors are associated with variations in FDI levels in each
income group.
In Table 7 in Appendix VII, the results from the sample of countries in the Low
Income group are presented. When looking at only low income countries, it is clear
that the institutional quality indeed is important when analyzing the variation of
FDI levels in these countries. The institutional factors of Average Institutional Qual-
ity, Quality of Government, Government Effectiveness, Regulatory Quality, Rule of
Law and Control of Corruption are all significant, respectively. If any of these fac-
tors are ceteris paribus increased by 1 rank (scale 0 to 10), it is on average associated
with an increase in FDI inflows per capita by almost 36 to almost 49 percent. Hence,
in low income countries, the institutional factors are highly associated to the vari-
ation in FDI levels. The only control variable that is significant throughout this
analysis is Openness to Trade. Moreover, it is interesting to note that the R-squared
of approximately 0.60 in the analysis of the Low Income countries is substantially
higher than in the analyses of the other income groups below. To summarize, for
low income countries at time t, more aspects of institutional quality are important
than in the full sample as both of the aggregated measures are significant and not
only Regulatory Quality among the WGIs. These results are in line with previous
literature (Asiedu, 2002; Daude and Stein, 2007b) and supporting recent literature
on the Lucas Paradox (Alfaro et al, 2008; Papaioannou, 2009).
In Table 8 in Appendix VII, the results from the sample of countries in the Lower
Middle Income group are presented. In the results from this sample, only Quality of
Government is significant among the institutional quality factors, and only Market
Size among the controls. A ceteris paribus increase in Quality of Government is
associated with an on average increase in FDI inflows per capita by approximately
21 percent at the 10 percent significance level. Furthermore, an increase in Mar-
ket Size by 1 percent is associated with an increase in FDI inflows per capita by
approximately 1.5 to 1.9 percent. The institutional factors seem to be somewhat
important in this income group, but not as important as in the low income group.
Again, it is important to remember that the high VIFs inflate the standard errors,
and making the significance levels more conservative than they may be in reality.
In Table 9 in Appendix VII, the results from the sample of countries in the Upper
Middle Income group are presented. The results from this sample also have only
one significant institutional factor; Regulatory Quality - which has almost the same
magnitude as in the analysis of the low income countries. A ceteris paribus increase
31
in Regulatory Quality by 1 percent is associated with an on average increase in FDI
inflows per capita by almost 50 percent. Hence, in the group of higher middle income
countries, the business and investment climate seems to be the most important
institutional factor when explaining the variation of FDI levels across countries.
Among the controls, Market Size is almost consistently significant and Openness to
Trade is somewhat (3 out of 8 regressions) significant at the 10 percent level. The
consistency in the different specifications of the institutional quality factors are not
as stable as in results from the other income groups presented in this section.
In Table 10 in Appendix VII, the results from the sample of countries from the
High Income group are presented. The significant factors in this income group are
Growth Rate and Infrastructure; no institutional factors are significant. If Growth
Rate is ceteris paribus increased by 1 percent, it is on average associated with an
increase in FDI inflows per capita by at least 6 percent. If Infrastructure is ce-
teris paribus increased by 1 percent it is on average associated with an increase in
FDI inflows per capita by 1.3 to 1.4 percent. A reasonable explanation to these re-
sults is that high income countries generally have higher institutional quality, larger
size of the market, etc. However, the growth rates and infrastructure might vary
across countries in this income group. These results indicate that when countries
reach a threshold of a certain income level, the factors that attract foreign investors
are related to the growth level and the level of the infrastructure and not to the
institutional quality. While growth influencing investment choices is somewhat in-
tuitive, infrastructure is less so. Plausible explanations could be that investors are
attracted by high-tech countries, or when there are high infrastructural levels, then
transactional costs associated with investments might be lower.
The results from the robustness checks above, focusing on different income seg-
ments, give some indications of a non-linearity in the relationship between institu-
tional quality and FDI levels. As it seems, institutional factors matter for investment
decision from abroad up to a certain threshold of the income level. For very poor
countries, different aspects of the institutional quality are significantly important
when explaining FDI levels. These results supports the previous literature (Asiedu,
2002; Daude and Stein 2007b). For middle income countries, some aspects of insti-
tutional quality are still significant, but less so. Finally, for high income countries,
institutional factors are insignificant. Instead, more investment-rational aspects,
such as the growth rate, become significant with a high magnitude.
To understand why a threshold of the income level in the economy might exist in
the context of FDI levels and institutional quality, and where to find this threshold
32
is difficult to pinpoint. But the above results indicate that the higher the income
levels in an economy, the less the aspects of the institutional quality influences the
investment decisions. Some aspects, like Regulatory Quality, seem to be relevant
for investors even at higher income-levels and therefore the thresholds seem vary
for different aspects of institutional quality. As Regulatory Quality is significant
also for the upper middle income group, it seems that the factors related to the
business and investment climate have a higher threshold than the other WGI aspects.
Nonetheless, the institutional quality does not seem to influence the FDI levels in
high income countries. A possible explanation to this, may be that higher income
levels in turn are associated with higher levels of institutional quality and after
some degree of institutional quality, it is “safe” to invest in the country, even if the
institutional quality is not the highest observed.
The findings that have been discussed here supports the explanation of Lucas
Paradox that is related to uncertainty and risks. When investors have risk-adjusted
their expectations of returns; risks and uncertainties related to institutional quality
factors make countries with low institutional attributes less attractive to invest in.
In poor countries or countries in lower income groups below the critical thresholds,
low institutional quality may deter investors. This, in turn, might be a reason to
why these countries do not see high inflows of capital from abroad and why FDI
levels are lower than capital flows of for example, remittances and aid. Hence, the
findings from Alfaro et al (2008) and Papaioannou (2009) are highly supported for
the low income countries. However, when the income level is above the thresholds
of income level, the institutional quality is not associated with the variations in FDI
levels. Hence, the relationship between institutional quality and FDI levels is related
to the income level in a country. This might be due to the explanation given above;
when the income level is high in an economy, it is on average associated to higher
levels of institutional quality.
8 Conclusion
Foreign Direct Investments (FDI), as a share of world GDP, has increased rapidly
during the last decades. FDI levels in 2014 are six times higher than in 1990 and
constitute more than 40 percent of the external development finance to developing
and transition economies. Moreover, some countries do not see much of the inflow of
FDI. As FDI levels are predicted to be continually increasing globally over time and
the variation in levels of FDI is high, the purpose of this paper was to investigate
33
the determinants of FDI. More specifically, previous literature and the Lucas Para-
dox stimulated interest in the role of risk factors and institutional quality and its
association with variations in levels of FDI. Furthermore, measures from the World-
wide Governance Indicators are used as proxies for institutional quality in the main
specifications and a measure from the International Country Risk Guide is used to
check robustness.
Using a sample of non-OECD countries, the baseline results in section 7.1 and
7.2 supports previous literature on the importance of including institutional quality
when describing FDI levels. As measures of institutional quality, Average Insti-
tutional Quality is significant at time t-1 and Quality of Government at time t
and time t-1, for the full sample in this study. While Quality of Government is a
weighted measure and Average Institutional Quality is a non-weighted average; the
significance of Quality of Government gives more credible results as an aggregated
measure. These results support the main hypothesis in this paper, FDI levels are
positively associated with institutional quality.
In order to evaluate the different aspects of institutional quality, the Worldwide
Governance Indicators were included one by one in section 7.3. At time t, Regulatory
Quality is the only significant aspect. While at time t-1, Government Effectiveness
and Rule of Law are also significant. There are two things to conclude from this.
First, when studying the association between institutional quality and FDI levels
for the full sample of non-OECD countries over the period of 1996 to 2014, the most
important aspect seems to be Regulatory Quality. A ceteris paribus increase in Reg-
ulatory Quality of 1 rank (scale from 0 to 10) is associated with an increase in FDI
per capita by almost 30 percent. This result gives an indication of the importance
of a good business and investment climate in a country in order to attract foreign
investors. The indications of the importance of Government Effectiveness and Rule
of Law gives further support for previous literature (Globerman and Shapiro, 2002;
Daude and Stein, 2007b). In the future it might be possible to find even better
measures of the aspects of institutional quality in order to explore the true chan-
nels. Another idea for future research possibilities could be to investigate FDI from
the investors perspective using similar variables; what is determining e.g. U.S. or
Chinese FDI allocations and explore eventual differences.
Second, the persistent effect of institutional quality is significant in sections 7.1,
7.2 and 7.3 indicates some support of the hypothesis of causality. The strategy of
lagging the institutional quality should reduce the bias of reverse causality. However,
it is not reasonable to draw a conclusion of causality from the data, as the measures
34
of institutional quality are constructed from perceptions. The results rather give
support to the hypothesis of causality within the underlying channels of some aspects
of institutional quality on FDI levels. In order to address the causality in a more
proper way, future researchers could try to find an IV-strategy or use relevant case
studies to disentangle cause and effect. Furthermore, as the lagged institutional
quality factors are significant at time t-1, future researchers could investigate the
dynamics of international investment decisions as the results from this study may
indicates some rigidity.
In order to investigate the validity of the Lucas Paradox and in the purpose
of robustness, the sample was divided into four separate sub-samples by their in-
come group in section 7.4. The results give an indication of non-linearity in the
relationship between institutional quality and FDI inflows per capita, conditional
on the income level. Almost all aspects of institutional quality are significant for
Low Income countries while just a few are significant for the higher income groups.
These results supports previous literature (Alfaro et al, 2008; Papaioannou, 2009)
on the Lucas Paradox ; low institutional quality is associated with lower levels of
FDI inflows, and this might be a sign of a deterring factor for investors. As this
result is especially evident in poorer countries, it gives further support for the expla-
nation of the Lucas Paradox, i.e. why poorer countries see lower inflows of capital.
In the High Income group on the other hand, institutional quality is not significant
at all – while the factor of e.g. Growth Rate is important. As the results in this
study indicates non-linearity in the relationship between institutional quality and
FDI inflows per capita, future researchers could investigate the thresholds of income
level where institutional quality factors not affect the investment decisions.
The results in this paper have shown the importance of taking factors of institu-
tional quality into account when analyzing variations in FDI levels across countries.
These findings are interesting for policymakers in global development institutions
and in developing countries as FDI have been increasing in levels and in its share
of world GDP during the last decades, as well as constituting a large part of the
external development finance. Improving institutional quality in developing coun-
tries is indeed a win-win strategy as it is associated with higher levels of FDI inflows
and proved to determine a better economic outcome (e.g. Acemoglu and Robinson,
2012). As discussed in the literature review, higher institutional quality does also in-
crease host countries absorptive capacity in order to take advantage of the practices
and spillovers of foreign firms. In turn, this emphasizes the importance of sustainable
investment strategies in developing countries by multinational enterprises.
35
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39
Appendix I
Figure 5: Financial flows as share of GDP 1990-2014 (World Bank, 2016a). Low Income
Countries.
Figure 6: Financial flows as share of GDP 1990-2014 (World Bank, 2016a). Lower Middle
Income Countries.
40
Figure 7: Financial flows as share of GDP 1990-2014 (World Bank, 2016a). Upper Middle
Income Countries.
Figure 8: FDI inflow as a share of GDP 1990-2014 (World Bank, 2016a). Comparison
between income groups.
41
Appendix II
Atlas Method Classification for calender year 2013 (World Bank, 2016b):
Income Group Definition
Low Income <= 1 045 GNI per capita (US$)
Lower Middle Income 1 046 to 4 125 GNI per capita (US$)
Upper Middle Income 4 126 to 12 745 GNI per capita (US$)
High Income > 12 746 GNI per capita (US$)
List of Countries:
Nr Country Name Income Segment WGI data ICRG data
1 Albania Upper Middle Yes Yes
2 Algeria Upper Middle Yes Yes
3 Angola Upper Middle Yes Yes
4 Antigua and Barbuda High Yes -
5 Argentina High Yes Yes
6 Armenia Lower Middle Yes Yes
7 Azerbaijan Upper Middle Yes Yes
8 Bahamas High Yes Yes
9 Bahrain High Yes Yes
10 Bangladesh Low Yes Yes
11 Barbados High Yes -
12 Bhutan Lower Middle Yes -
13 Belarus Upper Middle Yes Yes
14 Belize Upper Middle Yes -
15 Benin Low Yes -
16 Bolivia Lower Middle Yes Yes
17 Bosnia and Herzegovina Upper Middle Yes -
18 Botswana Upper Middle Yes Yes
19 Brazil Upper Middle Yes Yes
20 Brunei High Yes Yes
21 Bulgaria Upper Middle Yes Yes
22 Burkina Faso Low Yes Yes
23 Cambodia Low Yes -
24 Cameroon Lower Middle Yes Yes
25 Chad Low Yes -
26 Chile High Yes Yes
27 China Upper Middle Yes Yes
28 Colombia Upper Middle Yes Yes
29 Comoros Low Yes -
30 Congo Lower Middle Yes Yes
31 Cote d’Ivoire Lower Middle Yes Yes
32 Costa Rica Upper Middle Yes Yes
33 Croatia High Yes Yes
34 Czech Republic High Yes Yes
35 Djibouti Lower Middle Yes -
36 Dominica Upper Middle Yes -
37 Dominican Republic Upper Middle Yes Yes
38 Ecuador Upper Middle Yes Yes
39 Egypt Lower Middle Yes Yes
40 El Salvador Lower Middle Yes Yes
42
41 Equatorial Guinea Upper Middle Yes -
42 Eritrea Low Yes -
43 Estonia High Yes Yes
44 Fiji Upper Middle Yes -
45 Gabon Upper Middle Yes Yes
46 Gambia Low Yes Yes
47 Georgia Lower Middle Yes -
48 Ghana Lower Middle Yes Yes
49 Grenada Upper Middle Yes -
50 Guatemala Lower Middle Yes Yes
51 Guinea Low Yes Yes
52 Guinea-Bissau Low Yes Yes
53 Guyana Lower Middle Yes Yes
54 Haiti Low Yes Yes
55 Honduras Lower Middle Yes Yes
56 India Lower Middle Yes Yes
57 Indonesia Lower Middle Yes Yes
58 Iran Upper Middle Yes Yes
59 Israel High Yes Yes
60 Jamaica Upper Middle Yes Yes
61 Kazakhstan Upper Middle Yes Yes
62 Jordan Upper Middle Yes Yes
63 Kenya Lower Middle Yes Yes
64 Kuwait High Yes Yes
65 Kyrgyzstan Lower Middle Yes -
66 Laos Lower Middle Yes -
67 Latvia High Yes Yes
68 Lebanon Upper Middle Yes Yes
69 Lesotho Lower Middle Yes -
70 Libya Upper Middle Yes Yes
71 Lithuania High Yes Yes
72 Macedonia Upper Middle Yes -
73 Madagascar Low Yes Yes
74 Malawi Low Yes Yes
75 Malaysia Upper Middle Yes Yes
76 Maldives Upper Middle Yes -
77 Mali Low Yes Yes
78 Mauritania Lower Middle Yes -
79 Mauritius Upper Middle Yes -
80 Mexico Upper Middle Yes Yes
81 Mongolia Upper Middle Yes Yes
82 Moldova Lower Middle Yes Yes
83 Morocco Lower Middle Yes Yes
84 Mozambique Low Yes Yes
85 Namibia Upper Middle Yes Yes
86 Nepal Low Yes -
87 Nicaragua Lower Middle Yes Yes
88 Niger Low Yes Yes
89 Nigeria Low Yes Yes
90 Oman High Yes Yes
91 Panama Upper Middle Yes Yes
92 Paraguay Upper Middle Yes Yes
93 Peru Upper Middle Yes Yes
94 Philippines Lower Middle Yes Yes
43
95 Poland High Yes Yes
96 Qatar High Yes Yes
97 Romania Upper Middle Yes Yes
98 Russia High Yes Yes
99 Rwanda Low Yes -
100 Saudi Arabia High Yes Yes
101 Samoa Lower Middle Yes -
102 Senegal Low Yes Yes
103 Seychelles High Yes -
104 Sierra Leone Low Yes Yes
105 Singapore High Yes Yes
106 Slovakia High Yes Yes
107 Slovenia High Yes Yes
108 South Africa Upper Middle Yes Yes
109 Sri Lanka Lower Middle Yes Yes
110 Swaziland Lower Middle Yes -
111 Tajikistan Low Yes -
112 Tanzania Low Yes Yes
113 Thailand Upper Middle Yes Yes
114 Togo Low Yes Yes
115 Trinidad and Tobago High Yes Yes
116 Tunisia Upper Middle Yes Yes
117 Turkey Upper Middle Yes Yes
118 Turkmenistan Upper Middle Yes -
119 Uganda Low Yes Yes
120 Ukraine Lower Middle Yes Yes
121 United Arab Emirates High Yes Yes
122 Uruguay High Yes Yes
123 Uzbekistan Lower Middle Yes -
124 Venezuela Upper Middle Yes Yes
125 Vietnam Lower Middle Yes Yes
126 Zambia Lower Middle Yes Yes
127 Zimbabwe Low Yes Yes
44
Appendix III
Table 1: Pairwise correlations of the Institutional Quality aspects.
AQI QOG VA PS GE RQ RL CC
AQI 1.0000
QOG 0.7627 1.0000
VA 0.7778 0.3992 1.0000
PS 0.7462 0.5061 0.4588 1.0000
GE 0.9285 0.8122 0.6351 0.5754 1.0000
RQ 0.8975 0.6838 0.6938 0.5124 0.8727 1.0000
RL 0.9507 0.7976 0.6482 0.6747 0.9047 0.8401 1.0000
CC 0.9099 0.7648 0.5903 0.6346 0.8711 0.7665 0.9001 1.0000
AQI = Average Institutional Quality (Average of the WGI aspects)
QOG = Quality of Government
VA = Voice and Accountability
PS = Political Stability
GE = Government Effectiveness
RQ = Regulatory Quality
RL = Rule of Law
CC = Control of Corruption
Table 2: Pairwise correlations of the control variables.
AQI QOG MSE GR OT MSY INF NR
AQI 1.0000
QOG 0.7627 1.0000
MSE 0.0054 0.1881 1.0000
GR -0.0701 -0.0025 0.0183 1.0000
OT 0.2814 0.3604 -0.1670 0.1758 1.0000
MSY -0.0726 -0.0014 0.0355 0.0143 0.0202 1.0000
INF 0.5728 0.5266 0.2722 -0954 0.2424 -0.0458 1.0000
NR -0.2819 -0.0929 0.1795 0.1400 0.1009 0.0676 -0.0983 1.0000
AQI = Average Institutional Quality (Average of the WGI aspects)
QOG = Quality of Government
MSE = ln(Market Size)
GR = Growth Rate
OT = Openness to Trade
MSY = Macroeconomic Stability
INF = ln(Infrastructure)
NR = Natural Resources
45
Appendix IV
Figure 9: Average relationship between Voice and Accountability and ln of FDI per
capita over the period 1996-2014. Correlation = 0.39.
Figure 10: Average relationship between Political Stability and ln of FDI per capita
over the period 1996-2014. Correlation = 0.56.
46
Figure 11: Average relationship between Government Effectiveness and ln of FDI
per capita over the period 1996-2014. Correlation = 0.65.
Figure 12: Average relationship between Regulatory Quality and ln of FDI per capita
over the period 1996-2014. Correlation = 0.63.
47
Figure 13: Average relationship between Rule of Law and ln of FDI per capita over
the period 1996-2014. Correlation = 0.59.
Figure 14: Average relationship between Control of Corruption and ln of FDI per
capita over the period 1996-2014. Correlation = 0.57.
48
Appendix V
Table 5: Evaluation of lagged WGIs
Variables Coefficients
ln(FDI per capita)t (1) (2) (3) (4) (5) (6)
Voice and Accountabilityt−1 0.145
(0.0945)
Political Stabilityt−1 0.0666
(0.0544)
Government Effectivenesst−1 0.147*
(0.0886)
Regulatory Qualityt−1 0.206**
(0.0955)
Rule of Lawt−1 0.214**
(0.102)
Control of Corruptiont−1 0.108
(0.0985)
ln(Market Size)t 1.258*** 1.207** 1.184** 1.072** 1.113** 1.206**
(0.474) (0.476) (0.486) (0.493) (0.488) (0.484)
Growth Ratet 0.0203** 0.0216** 0.0209** 0.0215** 0.0218** 0.0208**
(0.0099) (0.0097) (0.0099) (0.0101) (0.0100) (0.0099)
Openness to Tradet 0.0059** 0.0062*** 0.0063*** 0.0062** 0.0060** 0.0061**
(0.0024) (0.0024) (0.0024) (0.0024) (0.0023) (0.0023)
Macroeconomic Stabilityt -0.0003 -0.0003 -0.0002 -0.00006 -0.0002 -0.0002
(0.00035) (0.00035) (0.00036) (0.00038) (0.00036) (0.00034)
ln(Infrastructure)t 0.138 0.130 0.129 0.127 0.128 0.132
(0.114) (0.112) (0.113) (0.111) (0.111) (0.113)
Natural Resourcest 0.0064 0.0058 0.0073 0.0067 0.0086 0.0070
(0.0097) (0.0094) (0.0094) (0.0093) (0.0093) (0.0095)
Constant -28.36** -26.80** -26.63** -24.25** -25.21** -26.98**
(11.25) (11.33) (11.50) (11.67) (11.53) (11.50)
Fixed Effects Yes Yes Yes Yes Yes Yes
Time Trend No No No No No No
Time Dummies Yes Yes Yes Yes Yes Yes
Cluster Effects Yes Yes Yes Yes Yes Yes
Observations 1 416 1 416 1 416 1 416 1 416 1 416
R-Squared 0.450 0.448 0.449 0.452 0.452 0.448
Nr of Countries 114 114 114 114 114 114
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
49
Appendix VI
Table 6: Descriptive Statistics of Institutional Quality for each income group.
Variables Mean Std. Dev. Min Max
Low Income
Avg Institutional Quality 3.485 0.793 1.682 5.057
Quality of Government 3.772 1.068 1.389 6.574
Voice and Accountability 3.581 1.126 0.648 5.750
Political Stability 3.722 1.368 0.251 6.920
Government Effectiveness 3.249 0.829 0.938 5.149
Regulatory Quality 3.497 0.998 0.480 5.499
Rule of Law 3.340 0.930 0.856 5.327
Control of Corruption 3.520 0.837 1.368 6.703
Lower Middle Income
Avg Institutional Quality 3.956 0.790 1.887 6.158
Quality of Government 4.436 1.024 2.222 6.944
Voice and Accountability 3.928 1.220 0.803 6.531
Political Stability 3.992 1.411 0.395 7.615
Government Effectiveness 4.040 0.843 2.321 6.555
Regulatory Quality 4.101 0.886 0.702 6.851
Rule of Law 3.825 1.018 1.585 7.167
Control of Corruption 3.851 0.886 2.222 7.550
Upper Middle Income
Avg Institutional Quality 4.456 1.184 1.521 6.735
Quality of Government 4.740 1.011 1.944 8.750
Voice and Accountability 4.415 1.677 0.556 7.335
Political Stability 4.595 1.533 0.220 7.419
Government Effectiveness 4.560 1.258 1.556 7.495
Regulatory Quality 4.590 1.446 0.619 7.004
Rule of Law 4.289 1.260 1.221 7.113
Control of Corruption 4.289 1.240 1.327 7.499
High Income
Avg Institutional Quality 6.063 1.012 3.273 8.185
Quality of Government 6.304 1.103 3.056 9.167
Voice and Accountability 5.482 1.670 1.276 7.946
Political Stability 6.004 1.335 1.754 7.799
Government Effectiveness 6.308 1.103 3.468 9.859
Regulatory Quality 6.326 1.227 2.842 9.495
Rule of Law 6.139 1.122 2.747 8.789
Control of Corruption 6.122 1.402 2.825 9.833
50
Appendix VII
Table 7: Robustness on Low Income countries.
Variables Coefficients
ln(FDI per capita)t (1) (2) (3) (4) (5) (6) (7) (8)
Avg Institutional Qualityt 0.450*
(0.244)
Quality of Governmentt 0.463**
(0.203)
Voice and Accountabilityt 0.0522
(0.188)
Political Stabilityt -0.0275
(0.133)
Government Effectivenesst 0.453**
(0.187)
Regulatory Qualityt 0.488**
(0.180)
Rule of Lawt 0.460**
(0.177)
Control of Corruptiont 0.356**
(0.159)
ln(Market Size)t -1.316 -0.428 -1.172 -1.091 -0.680 -1.276 -1.015 -0.803
(0.835) (0.796) (0.796) (0.766) (0.843) (0.789) (0.729) (0.956)
Growth Ratet -0.0076 0.0061 -0.0038 -0.0059 -0.0005 -0.0050 -0.0047 -0.0023
(0.0078) (0.0140) (0.0070) (0.0069) (0.0098) (0.0088) (0.0089) (0.0090)
Openness to Tradet 0.0307*** 0.0308*** 0.0306*** 0.0312*** 0.0303*** 0.0286*** 0.0314*** 0.0304**
(0.0101) (0.0084) (0.0100) (0.0100) (0.0105) (0.0099) (0.0092) (0.0107)
Macroeconomic Stabilityt -0.0043 -0.0065 -0.0054 -0.0068 -0.0072 -0.0013 -0.0073 -0.0067
(0.0083) (0.0100) (0.0079) (0.0083) (0.0083) (0.0084) (0.0072) (0.0071)
ln(Infrastructure)t 0.171 0.330 0.155 0.139 0.069 0.118 0.129 0.093
(0.161) (0.259) (0.159) (0.157) (0.136) (0.165) (0.156) (0.165)
Natural Resourcest 0.0254 0.0418 0.0214 0.0225 0.0067 0.0261 0.0094 0.0103
(0.0183) (0.0311) (0.0158) (0.0151) (0.0196) (0.0167) (0.0137) (0.0187)
Constant 27.64 6.813 24.66 22.59 14.97 26.77 20.71 17.47
(18.76) (17.83) (17.88) (17.26) (19.15) (17.76) (16.25) (21.65)
Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes
Time Trend No No No No No No No No
Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes
Cluster Effects Yes Yes Yes Yes Yes Yes Yes Yes
Observations 279 253 279 279 279 279 279 279
R-Squared 0.609 0.628 0.612 0.6146 0.596 0.618 0.615 0.596
Nr of Countries 21 16 21 21 21 21 21 21
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
51
Table 8: Robustness on Lower Middle Income countries.
Variables Coefficients
ln(FDI per capita)t (1) (2) (3) (4) (5) (6) (7) (8)
Avg Institutional Qualityt 0.112
(0.240)
Quality of Governmentt 0.213*
(0.123)
Voice and Accountabilityt -0.0788
(0.186)
Political Stabilityt 0.0664
(0.0823)
Government Effectivenesst 0.0664
(0.142)
Regulatory Qualityt 0.251
(0.186)
Rule of Lawt 0.0677
(0.201)
Control of Corruptiont -0.109
(0.192)
ln(Market Size)t 1.731*** 1.756** 1.917*** 1.762*** 1.765*** 1.779*** 1.552** 1.878***
(0.631) (0.665) (0.653) (0.608) (0.589) (0.591) (0.667) (0.590)
Growth Ratet 0.0170 0.0078 0.0171 0.0170 0.0162 0.0173 0.0198 0.0175
(0.0136) (0.0116) (0.0131) (0.0136) (0.0136) (0.0136) (0.0134) (0.0129)
Openness to Tradet 0.0075 0.0016 0.0073 0.0076 0.0075 0.0075 0.0076 0.0075
(0.0047) (0.0054) (0.0046) (0.0047) (0.0047) (0.0046) (0.0048) (0.0046)
Macroeconomic Stabilityt -0.0037 -0.0106 -0.0041 -0.0042 -0.0033 -0.0038 -0.0030 -0.0041
(0.0040) (0.0066) (0.0046) (0.0042) (0.0042) (0.0039) (0.0043) (0.00434)
ln(Infrastructure)t -0.060 -0.042 -0.058 -0.063 -0.058 -0.063 -0.084 -0.068
(0.155) (0.179) (0.159) (0.158) (0.154) (0.156) (0.154) (0.160)
Natural Resourcest 0.0061 -0.0124 0.0031 0.0055 0.0055 0.0055 0.0099 0.0042
(0.0132) (0.0241) (0.0136) (0.0132) (0.0130) (0.0129) (0.0115) (0.0132)
Constant -39.80** -41.39** -43.28*** -40.34*** -40.42*** -40.73*** -36.17** -42.49***
(14.61) (15.92) (15.12) (14.26) (13.90) (13.93) (15.49) (13.94)
Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes
Time Trend No No No No No No No No
Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes
Cluster Effects Yes Yes Yes Yes Yes Yes Yes Yes
Observations 435 385 435 435 435 435 435 435
R-Squared 0.476 0.522 0.477 0.476 0.477 0.476 0.484 0.476
Nr of Countries 32 23 32 32 32 32 32 32
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
52
Table 9: Robustness on Upper Middle Income countries.
Variables Coefficients
ln(FDI per capita)t (1) (2) (3) (4) (5) (6) (7) (8)
Avg Institutional Qualityt 0.242
(0.222)
Quality of Governmentt -0.0291
(0.165)
Voice and Accountabilityt -0.0596
(0.136)
Political Stabilityt 0.0415
(0.0745)
Government Effectivenesst 0.0201
(0.147)
Regulatory Qualityt 0.495***
(0.169)
Rule of Lawt 0.0510
(0.206)
Control of Corruptiont 0.113
(0.183)
ln(Market Size)t 1.245* 3.290*** 1.309* 1.373* 1.366* 1.350* 0.869 1.400*
(0.709) (0.699) (0.687) (0.720) (0.726) (0.697) (0.740) (0.700)
Growth Ratet 0.0145 0.0104* 0.0147 0.0145 0.0150* 0.0143 0.0136 0.0150
(0.0089) (0.0053) (0.0091) (0.0091) (0.0089) (0.0091) (0.0083) (0.0092)
Openness to Tradet 0.0047 0.0063* 0.0048* 0.0052 0.0054 0.0052 0.0038 0.00548*
(0.0031) (0.0031) (0.0027) (0.0032) (0.0032) (0.0031) (0.0034) (0.0032)
Macroeconomic Stabilityt -0.0034 -0.0007** -0.0042 -0.0053 -0.0050 -0.0052 -0.0025 -0.0059
(0.0066) (0.0003) (0.0062) (0.0067) (0.0066) (0.0069) (0.0066) (0.0066)
ln(Infrastructure)t 0.441 0.288 0.463 0.442 0.451 0.441 0.371 0.447
(0.363) (0.437) (0.350) (0.355) (0.347) (0.358) (0.342) (0.339)
Natural Resourcest 0.0112 0.0213* 0.0113 0.0105 0.0094 0.0107 0.0122 0.0095
(0.0146) (0.0121) (0.0144) (0.0139) (0.0147) (0.0139) (0.0145) (0.0140)
Constant -29.54 -80.24*** -30.50* -31.62* -31.57* -31.20* -21.47 -31.90*
(17.55) (17.52) (16.95) (17.48) (17.58) (17.10) (17.93) (17.06)
Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes
Time Trend No No No No No No No No
Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes
Cluster Effects Yes Yes Yes Yes Yes Yes Yes Yes
Observations 499 467 499 499 499 499 499 499
R-Squared 0.479 0.530 0.477 0.476 0.476 0.476 0.505 0.476
Nr of Countries 38 30 38 38 38 38 38 38
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
53
Table 10: Robustness on High Income countries.
Variables Coefficients
ln(FDI per capita)t (1) (2) (3) (4) (5) (6) (7) (8)
Avg Institutional Qualityt -0.171
(0.340)
Quality of Governmentt 0.233
(0.171)
Voice and Accountabilityt 0.0427
(0.273)
Political Stabilityt 0.0785
(0.134)
Government Effectivenesst -0.00432
(0.198)
Regulatory Qualityt -0.275
(0.228)
Rule of Lawt 0.0253
(0.117)
Control of Corruptiont -0.187
(0.248)
ln(Market Size)t -0.314 -0.0253 -0.328 -0.381 -0.391 -0.405 -0.0443 -0.356
(0.797) (0.787) (0.756) (0.808) (0.755) (0.817) (0.794) (0.802)
Growth Ratet 0.0623*** 0.0606*** 0.0654*** 0.0615*** 0.0610*** 0.0616*** 0.0627*** 0.0615***
(0.0204) (0.0183) (0.0204) (0.0206) (0.0207) (0.0204) (0.0202) (0.0205)
Openness to Tradet -0.0002 0.006 -0.0004 -0.0003 -0.0003 -0.0003 0.0003 -0.0004
(0.0065) (0.0077) (0.0065) (0.0064) (0.0061) (0.0064) (0.00630 ) (0.0062)
Macroeconomic Stabilityt 0.0046 -0.0033 0.0044 0.0059 0.0072 0.0060 0.0048 0.0061
(0.014) (0.0096) (0.0149) (0.0141) (0.0142) (0.0145) (0.0144) (0.0146)
ln(Infrastructure)t 1.318* 1.235* 1.380** 1.381** 1.425** 1.395** 1.229* 1.400**
(0.667) (0.676) (0.563) (0.630) (0.648) (0.641) (0.614) (0.635)
Natural Resourcest -0.0166 -0.0089 -0.0191 -0.0145 -0.0140 -0.0139 -0.0157 -0.0143
(0.0156) (0.0178) (0.0137) (0.0169) (0.0169) (0.0169) (0.0168) (0.0166)
Constant 9.426 -0.396 9.709 9.816 9.430 10.190 3.816 8.885
(18.58) (19.09) (17.80) (18.89) (18.37) (19.19) (18.55) (20.05)
Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes
Time Trend No No No No No No No No
Time Dummies Yes Yes Yes Yes Yes Yes Yes Yes
Cluster Effects Yes Yes Yes Yes Yes Yes Yes Yes
Observations 299 304 299 299 299 299 299 299
R-Squared 0.459 0.456 0.464 0.458 0.459 0.458 0.465 0.458
Nr of Countries 23 21 23 23 23 23 23 23
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
54