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Rev Dev Econ. 2020;00:1–29.
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1
wileyonlinelibrary.com/journal/rode
Received: 1 February 2018
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Revised: 15 June 2020
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Accepted: 4 August 2020
DOI: 10.1111/rode.12720
REGULAR ARTICLE
Democracy in the neighborhood and foreign direct
investment
MehmetPinar1
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ThanasisStengos2
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction
in any medium, provided the original work is properly cited.
© 2020 The Authors. Review of Development Economics published by John Wiley & Sons Ltd
1Business School, Edge Hill University,
Ormskirk, United Kingdom
2Department of Economics and Finance,
University of Guelph, Guelph, Ontario,
Canada
Correspondence
Mehmet Pinar, Business School, Edge Hill
University, St Helens Road, Ormskirk L39
4QP, United Kingdom.
Email: mehmet.pinar@edgehill.ac.uk
Funding information
Research Investment Fund of Edge
Hill University; Natural Sciences and
Engineering Research Council of
Canada, Grant/Award Number: 401004;
British Academy, Grant/Award Number:
AF140068
Abstract
The determinants of foreign direct investment (FDI) have
been extensively studied. Even though there is extensive
research in the area, most of it is based on analyzing the
effects of host country characteristics on FDI flows, and
yet there is little research on how neighboring country
characteristics play a role in facilitating FDI flows to host
countries. This paper analyzes the association between the
democracy level in neighboring countries and FDI flows to
host countries. Using bilateral FDI flows from the OECD
countries, with a large host country sample, we find that
countries surrounded by democratic countries attract higher
FDI flows. Furthermore, we find evidence that countries
that are surrounded by neighboring countries with good in-
stitutions tend themselves to have better institutions, experi-
ence lower civil conflict, and have higher political stability
and hence indirectly attract higher FDI flows. Our findings
suggest that if neighboring countries act in such way as
to become more democratic, FDI flows to these countries
would be higher since not only does improving the quality
of democracy attract more FDI inflows, but also being sur-
rounded by neighboring advanced democratic countries will
also lead to higher FDI flows to them.
KEYWORDS
democracy, democracy in the neighborhood, foreign direct
investment, institutions, neighborhood characteristics
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PINAR ANd STENGOS
1
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INTRODUCTION
The determinants of foreign direct investment (FDI) have been extensively studied in the literature
(see, for example, Blonigen & Piger, 2014, for an extensive list of determinants). Even though there is
extensive research on the determinants of FDI flows, most of this research is based on the source, and
the effect of host countries’ characteristics on FDI flows, and there is little research on how neighbor-
ing country characteristics play a role in FDI flows to host countries.
The linkages between neighboring country characteristics, however, have been examined in the
literature. For instance, Easterly and Levine (2000) find that growth levels of countries are positively
associated with the growth levels in their neighboring countries, and policies in neighboring coun-
tries are contagious (see also Kelejian, Murrell, & Shepotylo, 2013, where governance in neighbor-
ing countries is positively associated with countries’ own governance levels). Similarly, Bosker and
Garretsen (2009) find that the institutional quality of countries and of their neighbors has a direct
effect on their long-term economic development. Recently, Qureshi (2013) finds that conflicts in
neighboring countries reduce bilateral trade. To our knowledge, recent literature has only consid-
ered the relevance of host country characteristics in attracting FDI flows. For instance, in a seminal
paper, Alfaro, Kalemli-Ozkan, and Volosovych (2008) find that the institutional quality of the host
country explains the Lucas paradox (Lucas, 1990) suggesting that the reason why rich countries do
not invest in poor countries is due to poor institutions in the latter (see also Pinar & Volkan, 2018).
Similarly, Jensen (2003) finds that democratic countries attract relatively higher FDI flows compared
to authoritarian regimes—see also Jakobsen and de Soysa (2006) and Asiedu and Lien (2011), where
democratization in most countries leads to higher FDI flows. One potential reason for this is that the
democratization process contributes to greater FDI openness, leading to higher FDI flows (see, for
example, Pandya, 2014). In a recent paper, Bekaert, Harvey, Lundblad, and Siegel (2014) show that
the reduction in political risk, which is captured by various institutional quality proxies of the host
country, leads to a significant increase in FDI levels (see also Economou, Hassapis, Philippas, &
Tsionas, 2017 where institutional factors are determinants of FDI both for the OECD and developing
countries).1 Even though the political, democratic, and institutional setting in the host country is ex-
tensively investigated, the effect of democratization of neighboring countries on FDI flows to the host
country has not yet been examined in the literature, and this paper intends to fill this gap.
There are many channels that can affect FDI in a country through different characteristics of its
neighboring countries (see Section2 for a detailed discussion on some of these channels). An obvious
one is for FDI to depend on the present and expected future performance of a given country, which in
turn depends on the level of competition between the respective country and its neighboring countries.
As discussed above, countries that have better institutional settings receive relatively higher capital
flows, and neighboring countries would compete to improve their institutional quality setting to attract
higher capital flows. In that respect, it is not surprising to expect that neighbors’ institutional setting
matters for FDI where the diffusion of information and institutional framework is stronger (see, for
example, Easterly & Levine, 2000; Kelejian et al., 2013; Ward & Dorussen, 2015). In this paper we
argue that neighboring countries that compete to attain a better institutional environment to attract
FDI create positive spillover effects on each other, suggesting that institutional agglomerations tend
to attract more investment.
JEL CLASSIFICATION
F21; F23; F3
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We use gravity models and dynamic panel data estimation techniques and find that countries sur-
rounded by good democracies attract higher FDI flows. For robustness, we control for different neigh-
boring country characteristics such as the rule of law, political stability, civil conflict, and the market
size, and our results remain robust. Furthermore, the results are also robust to the use of different
samples and estimation techniques.
The paper is organized as follows. In Section2 we discuss briefly how neighboring country char-
acteristics play a role in countries’ economic, social and political outcomes and discuss how these
factors might also play a role in attracting FDI flows to host countries. Section3 discusses the data
and variables used in this paper, and also provides details on how neighboring country characteristics
are obtained. Section4 presents the estimation technique, and Section5 offers the results. Section6
concludes.
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NEIGHBORING COUNTRY CHARACTERISTICS AND
THEIR EFFECT ON OTHER COUNTRIES
In this section we discuss the effects of neighboring country characteristics on other countries and
discuss how these factors might play direct and/or indirect roles in attracting higher FDI flows to host
countries.
It is a long-established finding that countries with higher market access (MA) and/or market po-
tential (MP), which is measured by the closeness of a country to other high-income countries, tend to
have a higher income (see, for example, Crafts & Venables, 2003; Redding & Venables, 2004; Liu &
Meissner, 2015), with access to the export market found to be one of the main factors behind this rela-
tionship (see Bosker & Garretsen, 2012; de Sousa, Mayer, & Zignago, 2012). Even though the above
papers examined the relationship between MA and economic development of a country, recent studies
also examined the link between FDI and MA. For instance, Blanco (2012) finds that the surrounding
market potential has a significant positive effect on net FDI flows to Latin American countries. At a
regional level, multinational firms in the German border region show a significant preference to invest
in the neighboring Czech regions (Schäffler, Hecht, & Moritz, 2017). Similar spillover effects on
neighboring regions have been found in Polish counties, with those counties identified as special eco-
nomic zones having a strong positive employment impact in the host county as well as in neighboring
counties (Cizkowicz, Cizkowicz-Pekala, Pekala, & Rzonca, 2017).
Another effect across neighboring countries is the spillover of social, economic, and political pol-
icies and outcomes. In particular, policies are found to be contagious when countries surrounded by
other countries with better institutions also have a better institutional setting. For example, Kelejian
et al. (2013) find that governance in neighboring countries is positively related to the countries’ own
governance levels. Similarly, Ward and Dorussen (2015) find that public knowledge of the impor-
tance of governance leads to higher diffusion of good governance to neighboring countries. One of
the practical implications of this is the establishment and enlargement of the European Union. In
order to join the EU, countries need to follow the Copenhagen criteria that require ‘stable conditions
guaranteeing democracy and the rule of law’, something that led to the diffusion of democracy and in-
stitutional quality within Europe. Furthermore, institutional quality in neighboring countries is found
to be important in affecting economic growth and development. For instance, Easterly and Levine
(2000) show that the economic performance of a country is good if its neighbors have relatively higher
economic growth. On the other hand, Bosker and Garretsen (2009) find that not only does countries’
own institutional quality matter for long-term economic development but also the institutional quality
of neighboring countries directly explains development levels. In other words, being surrounded by
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countries with better institutional quality not only leads to better governance but also increases gross
domestic product (GDP) per capita. In this paper we examine whether these indirect linkages between
neighboring and host country affect FDI flows.
The other channel that is found to be important in affecting a country’s income level is political
unrest or instability in neighboring countries. For instance, Ades and Chua (1997) find that political
instability in a neighboring country may lead to lower economic performance by decreasing the mag-
nitude of trade and expenditure on education and increasing the expenditure on the military. Qureshi
(2013) finds that conflict in neighboring countries reduces bilateral trade. Furthermore, conflict in
neighboring countries may lead to inflows of refugees (Moore & Shellman, 2007), resulting in an in-
creased probability of violence and civil war (Salehyan & Gleditsch, 2006; Blattman & Miguel, 2010),
as well as an adverse effect on health and education of refugee-hosting economies (Baez, 2011).
Bandyopadhyay, Sandler, and Younas (2014) find that both domestic and transnational terrorism in
the host country have a negative effect on FDI (see also Gaibulloev & Younas, 2016, who show that
higher conflict levels lead to lower levels of domestic bank lending). In a more recent paper, Filer and
Stanišić (2016) show that terror incidents not only lower FDI flows to host countries but also lead to
lower investment to neighboring countries. However, Hegre (2014) finds that the democracies have
less conflict than semi-democracies, suggesting that the establishment of long-lasting democracies
has some mitigating effect on conflict.
Based on the previous literature, we expect that democratic institutions in neighboring countries
are key in affecting the allocation of FDI. If a country is surrounded by countries that have strong
democratic institutions, these neighboring countries are less likely to experience conflict (see, for
example, Hegre, 2014), which would then decrease the likelihood of the host country experiencing
conflict (Salehyan & Gleditsch, 2006; Blattman & Miguel, 2010). Furthermore, if a host country is
surrounded by countries that have good institutions, this would also improve the economic outcomes
of the host country (Bosker & Garretsen, 2009), and would lead to improvements in its institutional
quality (Kelejian et al., 2013; Ward & Dorussen, 2015). Overall, being surrounded by countries that
have well-developed institutions would increase the likelihood of countries improving their economic
and institutional outcomes and decrease the likelihood of them experiencing conflict. Therefore, if a
host country is surrounded by countries that have better institutions, this would make the host country
a relatively safer and socioeconomically more desirable place in which to invest compared to other
host countries that are surrounded by countries that have worse institutions. As a result, we expect that
host countries surrounded by countries that have good institutions are likely to attract more FDI flows.
In Section5 we will examine whether this theoretical expectation holds empirically. This proposition
has been analyzed by the contagion effect concerning financial flows (see, for example, Kaminsky &
Reinhart, 2000; Corsetti, Pericoli, & Sbracia, 2005; Forbes & Warnock, 2012; Dell'Erba & Reinhardt,
2015); however, whether neighboring characteristics directly affect FDI flows has not yet been exam-
ined, something that we intend to do in this paper.
Given the above discussion on potential direct and indirect links between neighboring country
characteristics and FDI to a host country, we will examine whether these characteristics play a direct
or indirect role in FDI flows to host countries. In particular, we will examine two channels by which
the quality of institutions in the neighboring countries can affect FDI flows to a country. Firstly, we
will examine whether investing countries consider regional institutional quality to play a direct role
in attracting FDI. Secondly, we will analyze the indirect relationship of institutional quality in neigh-
boring countries and other factors in the host country, such as the effects of neighboring institutional
quality on other factors in the host country.
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PINAR ANd STENGOS
3
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DATA AND VARIABLES
Our paper uses panel data of FDI flows from 34 OECD countries to 143 host countries between 1993
and 2012 as the dependent variable (see the list of the countries used in Table A1 in Appendix A).
The OECD provides FDI flows from OECD countries to host countries between 1985 and 2013;
however, most of the FDI flow data before the 1993 period and for 2013 had many missing values and
therefore are excluded from the analysis.2 The reason for the exclusion of the period before 1993 was
our attempt to include as many OECD countries as possible in our analysis since some of the OECD
countries became independent after 1991, and they did not have data for FDI flows.3 Still, our data set
covers a period of analysis that is longer than those used by the recent studies—for example, Mishra
and Jena (2019) used the period 2001–12, Belgibayeva and Plekhanov (2019) used bilateral FDI flows
for the period 2008–12, Xu (2019) considered the period 2001–12, and Donaubauer, Neumayer, and
Nunnenkamp (2020) cover the period 2001–12.
3.1
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Institutional quality
There are many measures of institutional quality, but one of the most commonly used in the literature
is the democracy index from the Polity IV project (i.e. Polity 2). This index captures three elements
that are directly related to the protection of property rights and other aspects that are important to at-
tract FDI. These three essential elements are described as follows:
One is the presence of institutions and procedures through which citizens can express
effective preferences about alternative policies and leaders. Second is the existence of
institutionalized constraints on the exercise of power by the executive. The third is the
guarantee of civil liberties to all citizens in their daily lives and acts of political partici-
pation. (Marshall, Gurr, & Jaggers, 2016, p. 14).
Knutsen (2011) finds that the relatively democratic countries have higher protection of property rights.
Hence, we use a democracy proxy as our primary institutional quality measure, which consists of yearly
data between 1993 and 2012 for 167 countries. We do not use small countries with little or no flows of
FDI, and we ended up having data for 143 host countries.
We also use some other institutional quality proxies that are commonly used in the literature as
controls in our robustness analysis, such as the rule of law measure from the World Governance
Indicators constructed by the World Bank (Kaufmann, Kraay, & Mastruzzi, 2013). The effects of the
rule of law on economic growth have been extensively examined through the mitigation of violence,
the protection of property rights, institutional checks on government and control of private capture and
corruption (see Haggard & Tiede, 2011, for theoretical and empirical analysis of the effect of the rule
of law on aspects mentioned above).
3.2
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Neighboring country characteristics
For a host country i, we proceed to calculate the institutional quality of its neighboring countries by
taking the average of the institutional quality values of the neighboring countries. For a given time t,
we obtain the neighboring country institutional quality proxy as
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PINAR ANd STENGOS
where
N_INS𝑖𝑡
is the average institutional quality of neighboring countries of country i at time t, and
INSt
is an
n×1
vector of observations on institutional quality levels for n countries. Finally, W is a
n×n
weighting matrix defined as
where m is the total number of neighbors of country i.4 Here, our main neighboring institutional quality
measure is the democracy score; however, we have neighboring institutional quality proxies by using the
rule of law, political stability, and absence-of-violence components from the World Governance Indicators
as an additional set of control variables.
We also construct conflict and market size measures of neighboring countries similarly (see
Section2 for details of their importance). It is also possible that a given country might not be sur-
rounded by larger markets, but located to be close to high-income countries. Therefore, we also con-
struct a market potential (MP) measure at a given time t similarly to the Redding and Venables (2004)
market potential measure as follows:
where
MP𝑖𝑡
is the market potential of country i at time t and
Dist𝑖𝑗
is the great circle distance between
capital cities of countries i and j.
3.3
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Other control variables
In line with the literature on the determinants of FDI and gravity models, we also control for source
and host country characteristics such as the market size of the source and host countries (i.e. GDP
of source and host countries, respectively), population and land areas of source and host countries.
Another important factor that has been considered in gravity models while examining bilateral
trade and FDI flows is regional trade agreements (e.g. Baier & Bergstrand, 2007, Balgati, Egger, &
Pfaffermayr, 2008; Ullah & Inaba, 2012; Thangavelu & Narjoko, 2014; Chenaf-Nicet and Rougier,
2016; Martínez-San Román, Bengoa, & Sánchez-Robles, 2016; Cherif & Dreger, 2018), which we
will also include in our estimations. Furthermore, we control for civil conflict and institutional quality
of the host country. The definitions and sources of the variables used are presented in Table A2, and
summary statistics are provided in Table A3.
4
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METHODOLOGY
In general, it is hard to account for all the factors that might affect FDI flows since capital flows
are potentially affected by hard-to-measure country-specific factors such as culture, trust, and social
N_INS𝑖𝑡 =WINSt
w
𝑖𝑗 =
⎧
⎪
⎨
⎪
⎩
1
mif iand j
are neighbors
0 otherwise,
MP𝑖𝑡 =ij ≠
∑GDP𝑗𝑡
Dist
𝑖𝑗
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PINAR ANd STENGOS
capital. This could potentially lead to an omitted variable bias. Furthermore, a positive relationship
between (countries’ own and neighboring countries’) institutions and FDI flows might be due to
reverse causality. To account for the first concern (omitted variables and unobserved country hetero-
geneity), we use the gravity model with fixed effects. Although the gravity model is known for its ap-
plication in the trade flows—see, for example, Anderson and van Wincoop (2003) and see Head and
Mayer (2014) for an overview of the use of gravity models in trade flows—gravity models have also
been used to examine bilateral FDI flows (Head & Ries, 2008; Bergstrand & Egger, 2011; Petri, 2012;
Bellos & Subasat, 2012; Thangavelu & Narjoko, 2014; Mishra & Jena, 2019; Xu, 2019; Belgibayeva
& Plekhanov, 2019; Donaubauer et al., 2020). Therefore, our first estimation method is to use the fol-
lowing gravity model estimation specification:
where
Fi,j,t
is the FDI flows from country i (source) to j (host) at time period t. X includes the control vari-
ables that account for the size of the source and host economies, such as income, population, land area,
and other host country characteristics such as conflict and institutional quality. Furthermore, we include
the distance between the source and host country (Distancei,j) to account for information asymmetries and
transaction costs. Languagei,j and Colonyi,j are dummy variables taking the value 1 if countries share a
common language and have had a colonial link, respectively, and 0 otherwise.
RTA𝑖𝑗𝑡
is a dummy variable
taking the value 1 if countries i (source) and j (host) have a regional trade agreement at time period t 0 zero
otherwise. Finally, we include
NXj,t
as the characteristics of the neighboring countries that might affect the
FDI flows to host countries such as the quality of institutions, market potential of the host country, market
size of the neighboring countries, and civil conflict.
Since the unobserved variables might be correlated with country-pair characteristics, we also con-
trol for country-pair fixed effects (Baier & Bergstrand, 2007). Finally, even though the gravity model
accounts for time-invariant country effects and fixed time effects, the results obtained may be affected
by endogeneity. The system generalized method of moments (GMM) has been one of the most popular
methods to account for potential endogeneity while examining the determinants of FDI flows (see, for
example, Asiedu & Lien, 2011; Bandyopadhyay et al., 2014; Aziz, 2018; Saini & Singhania, 2018;
Neanidis, 2019, for the use of GMM). As such, we apply the system GMM estimator suggested by
Blundell and Bond (1998) where the lagged levels and first differences are used as instruments for the
endogenous variables. In sum, this paper uses two popular methods in examining the determinant of
the FDI flows to ensure the robustness of our findings.
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EMPIRICAL ANALYSIS
5.1
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Gravity model estimates
We begin our analysis with the estimation of the gravity model. Table1 presents the results when
we control for year and source-country fixed effects (YSFE) and year and country-pair fixed effects
(YCPFE) when we use democracy as a proxy for institutional quality. In columns 1–4 we control for
the year and source-country fixed effects and use a different set of control variables. In column 1 we
use the lagged FDI flows and democracy in the host and neighboring countries of the host country.
We find that not only do democratic countries receive relatively higher FDI flows but also countries
(1)
ln (
Fi,j,t
)
=𝛽0+ln
(
Fi,j,t−1
)
+X
�
j,t−1𝛽j+X
�
i,t−1𝛽i+𝛿1ln(𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒)𝑖𝑗 +𝛿2(𝐿𝑎𝑛𝑔𝑢𝑎𝑔𝑒)
𝑖𝑗
+
𝛿
3
(𝐶𝑜𝑙𝑜𝑛𝑦)
𝑖𝑗
+𝛿
4
(𝑅𝑇 𝐴)
𝑖𝑗𝑡
+𝜸
(
NX
j,t−1)
+𝛼
j
+𝛼
t
+𝜇
i,j,t
,
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PINAR ANd STENGOS
TABLE 1 Gravity model estimations
Variables
(1) (2) (3) (4) (5)
YSFE (1) YSFE (2) YSFE (3) YSFE (4) YCPFE
ln(FDI flows)i,j,t–1 0.834*** (0.00431) 0.820*** (0.00458) 0.759*** (0.00571) 0.735*** (0.00615) 0.747*** (0.00401)
Democracyj,t–1 0.0444*** (0.00456) 0.0333*** (0.00457) 0.0204*** (0.00457) 0.0190*** (0.00462) 0.0254*** (0.00527)
N_Democracyj,t–1 0.0186*** (0.00517) 0.00893* (0.00518) 0.0121** (0.00521) 0.00959* (0.00522) 0.0135** (0.00592)
ln(GDP)i,t–1 1.174*** (0.0477)
ln(GDP)j,t–1 0.556*** (0.0209) 0.527*** (0.0209) 0.637*** (0.0208)
ln(Population)i,t–1 −0.560*** (0.0472)
ln(Population)j,t–1 −0.0969*** (0.0249) −0.0428* (0.0249) −0.121*** (0.0295)
ln(Area)i,t–1 −0.241*** (0.0217)
ln(Area)j,t–1 −0.102*** (0.0159) −0.0538*** (0.0163) −0.110*** (0.0197)
RTAi,j,t0.852*** (0.0590) 0.246*** (0.0714)
Languagei,j0.475*** (0.0742)
Colonyi,j0.758*** (0.120)
Distancei,j−0.376*** (0.0319)
Observations 28,872 28,872 28,272 28,272 28,272
Number of pairs 3,905
R-squared .836 .837 .843 .845 .838
R-squared (within) .113
R-squared (between) .943
Notes: The dependent variable is the natural logarithm of the foreign direct investment flows from source country i to host country j at time t. Democracy and N_Democracy are the institutional quality
proxies for the host country and neighboring countries of the host country, respectively. Languagei,j and Colonyi,j takes the value 1 if source (country i) and host (country j) share a common language
and have had a colonial link, respectively. Distancei,j is the distance between the capital cities of the source and host countries. RTAi,j,t takes the value 1 if source (country i) and host (country j) have a
regional trade agreement at time t, and 0 otherwise. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Robust standard errors are reported in parentheses.
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PINAR ANd STENGOS
that are surrounded by relatively democratic countries also receive higher FDI flows where the coef-
ficients on host country democracy and democracy level of neighboring countries are positive and
statistically significant at the 1% level. On the other hand, the coefficient of lagged FDI inflows is
positive, suggesting that the current FDI flows are positively correlated with the FDI inflows in the
previous year. Column 2 of Table1 gives the estimation results when we include the RTA dummy
variable as an additional control variable, and in line with the previous literature, we find that host
countries receive relatively higher FDI flows if they have regional trade agreements with source coun-
tries. In column 3 we include additional controls for host country characteristics, such as the market
size (i.e. GDP), population, and area. In line with the empirical literature, we find that countries with
larger market sizes receive relatively higher FDI flows, whereas countries with larger populations and
areas receive relatively lower FDI flows. In column 4 we also include language and colony dummies,
which take the value 1 if source and host share a common language and have had a colonial link, re-
spectively. We also control for the geographical distance between the capital cities of the source and
host countries. All estimates of the other variables are in agreement with the existing literature, as we
find that if investing and host countries share a common language and have had a colonial link, host
countries tend to receive higher FDI flows. Finally, if the source and host countries are further away
from each other, there are lower levels of FDI flows. Finally, column 5 introduces year and country-
pair fixed effects (where we drop the dummy variables as these variables are captured by the country-
pair fixed effect and do not vary over time). Similarly to the previous cases, we find that countries that
have a relatively larger market size invest more and also receive more investment. The relationship
between the level of democracy in the host country and FDI flows is positive and significant at the
1% level. Furthermore, the coefficient on the democracy level of neighboring countries is positive and
significant at the 5% level.
Overall, results from columns 1–5 control for the year, source, and country-pair fixed effects, and
suggest that the OECD countries invest relatively more in countries that are democratic and also if
host countries are surrounded by other democratic countries. Regarding the other variables that we
controlled for, coefficient estimates have the expected signs. The results for all specifications confirm
that the democracy level of neighboring countries positively affects FDI flows. In particular, a one
standard deviation increase in the democracy level of neighboring countries (i.e., 5.585) leads to a
7.54% increase in FDI flows to host countries (based on the coefficient of the democracy level of
neighboring countries in column 5). For a country that receives average FDI flows of $461 million,
a one standard deviation increase in the democracy level of neighboring countries leads to a $43.75
million increase in FDI flows.
5.2
|
Estimation results: Controlling for other neighboring country
characteristics
It is possible that the association between institutional quality in neighboring countries and FDI flows to
host countries might be due to omitted variables relating to neighboring country characteristics. Table2
presents the results when we control for other neighboring country characteristics such as civil conflict in
the host and civil conflict in the neighboring country, market size of the neighboring countries (measured
by the average GDP levels of neighboring countries), and the market potential of the host country when
we include the year and country-pair fixed effects as in the specification used in column 5 of Table1. In all
these specifications, besides the additional neighboring country characteristics, we include the lagged FDI
flows to examine the dynamic aspect of FDI flows, democracy in the host and neighboring countries, and
the market size of the source and host countries. In column 1 of Table2 we first control for civil conflict
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TABLE 2 Controlling for other neighboring characteristics
Variables (1) (2) (3) (4)
ln(FDI flows)i,j,t–1 0.774*** (0.00730) 0.773*** (0.00736) 0.769*** (0.00739) 0.767*** (0.00749)
Democracyj,t–1 0.0300*** (0.00548) 0.0333*** (0.00543) 0.0324*** (0.00555) 0.0341*** (0.00566)
N_Democracyj,t–1 0.0115* (0.00633) 0.0118* (0.00639) 0.0179*** (0.00638) 0.0131* (0.00685)
ln(GDP)i,t–1 0.466*** (0.0227) 0.466*** (0.0228) 0.476*** (0.0228) 0.477*** (0.0229)
ln(GDP)j,t–1 0.491*** (0.0210) 0.467*** (0.0214) 0.511*** (0.0213) 0.498*** (0.0222)
Conflictj,t–1 −0.0696*** (0.0180) −0.0740*** (0.0182) −0.0629*** (0.0181) −0.0645*** (0.0183)
N_Conflictj,t–1 −0.0122* (0.00661) −0.0131* (0.00671) −0.0120* (0.00691) −0.0121* (0.00701)
ln(N_GDP)j,t–1 0.0502*** (0.0161) 0.0469*** (0.0161)
ln(Market potential)j,t–1 1.172*** (0.130) 1.154*** (0.132)
Observations 28,272 28,007 28,272 28,007
Number of pairs 3,905 3,874 3,905 3,874
R-squared .8357 .8359 .8361 .8364
Notes: The dependent variable is the natural logarithm of the foreign direct investment flows from source country i to host country j at time t. Democracy and N_Democracy are the institutional
quality proxies for the host country and neighboring countries of the host country, respectively. Conflict and N_Conflict are the conflict levels in the host and neighboring countries of the host country,
respectively. N_GDP represents the average GDP of the neighboring countries of the host country. All estimations are obtained after controlling for the year and country-pair fixed effects. *, **, and ***
denote significance at the 10%, 5%, and 1% level, respectively. Robust standard errors are reported in parentheses.
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PINAR ANd STENGOS
in the host country and its neighboring countries. Both coefficients on civil conflict in the host country and
its neighboring countries enter the regressions with the expected negative sign and both are statistically
significant. In column 2 of Table2 we control for the average market size of the neighboring countries,
and we find that an increase in the average market size of neighbors leads to a rise in the FDI flows to
the host country. In column 3 we also control for the market potential of the host country as neighboring
countries might not directly be relatively rich themselves, but the host country may still be close enough
to richer countries. We find that countries with higher market potential (i.e. countries that are close to
high-income countries) also receive relatively higher FDI flows. Finally, in column 4 we control for all
these neighboring characteristics. When we control for all factors, we find that all of the neighboring char-
acteristics enter the regressions with the expected signs. Civil conflict in the host country and neighboring
countries depresses FDI flows to host countries. If the host country is surrounded by relatively larger
markets and also has higher market potential, it also tends to receive higher FDI flows. Finally, even after
controlling for additional neighboring country characteristics, the coefficient on the democracy level of
neighboring countries is still significant at the 10% level.
In Table2 we controlled for the year and country-pair fixed effects. However, some studies analyze
how FDI might affect the institutional quality of the host country (e.g. Demir, 2016) while others in-
vestigate the importance of the institutional setting in attracting FDI (e.g. Alfaro et al., 2008) resulting
in endogeneity. To address this issue, we use the system GMM estimator proposed by Blundell and
Bond (1998).
Table3 controls for the neighboring country characteristics with the system GMM estimator, where
both institutional quality proxies and conflict in the host and neighboring countries are considered as
endogenous variables.5 In our regressions, we utilized 3–5 lags of endogenous variables as instrumen-
tal variables.6 In all cases, the Hansen test of overidentifying restrictions suggests that we cannot reject
the null hypothesis of instrument validity. Even after accounting for the potential endogeneity, our
main finding remains the same, with democratic countries and countries that are surrounded by rela-
tively better democracies receiving relatively higher FDI flows. Compared to Table2, once we control
for potential endogeneity, we find that the coefficients on civil conflict in neighboring countries and
the average market size of the neighboring countries still have the expected signs, but they now be-
come insignificant.7 Countries with higher market potential still receive relatively higher FDI flows,
but the coefficient drops from 1.154 to 0.466 (see column 4 of Tables2 and 3, respectively). The co-
efficient of democracy of neighboring countries with the system GMM estimator is nearly three times
as large as in the corresponding country-pair fixed effects estimation (0.0390 versus 0.0131 from
column 4 of Tables2 and 3, respectively). The coefficient on the democracy level of the host is also
positive and significant at the 5% level when we control for all neighboring country characteristics.
Furthermore, coefficients on the remaining control variables (i.e. lagged FDI flows, the market size of
the source and host countries) are positive and significant at the 1% level.
Our findings suggest that a unit increase in democracy score in the host and neighboring countries
would lead to a rise in FDI flows to host countries of roughly 3.5% and 4%, respectively. A one stan-
dard deviation increase in the democracy level of neighboring countries (i.e. 5.585) leads to a 24.33%
increase in FDI flows to host countries (based on the coefficient of the democracy level of neighboring
countries in column 4 of Table3). For a country that receives average FDI flows of $461 million, a one
standard deviation increase in the democracy level of neighboring countries leads to a $112.16 million
increase in FDI flows. On the other hand, the estimated coefficient of lagged FDI flows is 0.819 in
column 4 where we control for all neighboring country characteristics, which would suggest that the
long-run effect of a unit increase in the democracy level of neighboring countries is roughly 24%.8
Overall, our main finding (i.e. the quality of institutions in neighboring countries is positively
associated with FDI flows) is robust after accounting for various fixed effects (results obtained with
12
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PINAR ANd STENGOS
the gravity model), other neighboring country characteristics such as the market size and potential
and conflict in neighboring countries, and even after tackling potential endogeneity problem (results
obtained with system GMM estimations).
5.3
|
Results with the use of different samples
We carry out robustness checks of our findings when we use different samples to test whether our
findings are driven by some particular set of countries, and the results are presented in Table4. We
use the same set of neighboring country characteristics in our analysis to compare our findings with
the baseline finding, which is in column 4 of Table3 using the full sample case.
In column 1 of Table4 we exclude the islands from our analysis to examine whether the defini-
tion of closest neighbor for island countries affect our results or not. We find that the effect of the
democracy level in neighboring countries increases when compared to the baseline sample. This is
something that was expected as the effect of contagious countries on each other would be larger when
compared to islands that are relatively secluded from each other.
In column 2 we exclude the European Union countries from the list to examine whether our results
might be driven by the membership of the EU, since entry and membership of the EU require conver-
gence in democratic institutions through EU legislation as well as economic convergence through the
TABLE 3 Controlling for other neighboring characteristics with system GMM
Variables (1) (2) (3) (4)
ln(FDI flows)i,j,t–1 0.820*** (0.00671) 0.820*** (0.00671) 0.819*** (0.00680) 0.819*** (0.00678)
Democracyj,t–1 0.0361** (0.0173) 0.0365** (0.0175) 0.0342** (0.0172) 0.0342** (0.0174)
N_Democracyj,t–1 0.0325** (0.0166) 0.0343** (0.0165) 0.0370** (0.0182) 0.0390** (0.0193)
ln(GDP)i,t–1 0.392*** (0.0202) 0.391*** (0.0202) 0.401*** (0.0206) 0.400*** (0.0206)
ln(GDP)j,t–1 0.369*** (0.0212) 0.365*** (0.0218) 0.382*** (0.0206) 0.377*** (0.0213)
Conflictj,t–1 −0.0240 (0.0430) −0.0205 (0.0436) −0.0191 (0.0433) −0.0152 (0.0440)
N_Conflictj,t–1 −0.0148 (0.0151) −0.0148 (0.0152) −0.0143 (0.0152) −0.0141 (0.0153)
ln(N_GDP)j,t–1 0.0107 (0.0164) 0.00838 (0.0165)
ln(Market
potential)j,t–1
0.452*** (0.112) 0.466*** (0.119)
Observations 28,272 28,007 28,272 28,007
Number of pairs 3,905 3,874 3,905 3,874
AR(3) (p-value)a .984 .993 .984 .994
Overidentification
test (p-value)b
.097 .110 .103 .119
Notes: The dependent variable is the natural logarithm of the foreign direct investment flows from source country i to host country
j at time t. Democracy and N_Democracy are the institutional quality proxies for the host country and neighboring countries of the
host country, respectively. Conflict and N_Conflict are the conflict levels in the host and neighboring countries of the host country,
respectively. N_GDP represents the average GDP of the neighboring countries of host country. All estimations utilize the system
GMM estimation technique and use 3–5 lags for the endogenous variables. *, **, and *** denote significance at the 10%, 5%, and 1%
level, respectively. Robust standard errors are reported in parentheses.
aArellano–Bond test that the third-order autocorrelation in residuals is 0. First- second-order autocorrelations are not reported as they
are rejected at the 10% significance level.
bHansen J-test for overidentification of restrictions in GMM estimation.
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PINAR ANd STENGOS
TABLE 4 System GMM estimations with different samples
Variables
(1) (2) (3) (4) (5) (6)
No islands No EU No euro zone No OECD No SSA No high income
ln(FDI flows)i,j,t–1 0.813*** (0.00724) 0.808*** (0.00750) 0.816*** (0.00717) 0.817*** (0.00781) 0.822*** (0.00719) 0.805*** (0.00850)
Democracyj,t–1 0.0372** (0.0175) 0.0379** (0.0169) 0.0391** (0.0178) 0.0337** (0.0165) 0.0368** (0.0179) 0.0366** (0.0176)
N_Democracyj,t–1 0.0529** (0.0220) 0.0514** (0.0211) 0.0433** (0.0215) 0.0454** (0.0217) 0.0468** (0.0219) 0.0511** (0.0229)
ln(GDP)i,t–1 0.406*** (0.0215) 0.475*** (0.0244) 0.428*** (0.0222) 0.462*** (0.0261) 0.394*** (0.0234) 0.484*** (0.0271)
ln(GDP)j,t–1 0.390*** (0.0225) 0.414*** (0.0241) 0.392*** (0.0228) 0.422*** (0.0270) 0.348*** (0.0236) 0.428*** (0.0292)
Conflictj,t–1 −0.0218 (0.0449) −0.0128 (0.0440) −0.0234 (0.0458) −0.0122 (0.0459) 0.0322 (0.0481) −0.0145 (0.0449)
N_Conflictj,t–1 −0.00498 (0.0161) −0.0110 (0.0153) −0.00673 (0.0161) −0.00895 (0.0156) 0.0106 (0.0154) −0.00351 (0.0171)
ln(N_GDP)j,t–1 −0.00181 (0.0179) −0.000729 (0.0167) 0.00415 (0.0162) 0.0149 (0.0181) −0.0227 (0.0218) 0.00100 (0.0194)
ln(Market
potential)j,t–1
0.346*** (0.125) 0.647*** (0.191) 0.560*** (0.153) 0.571*** (0.202) 0.662*** (0.125) 0.576*** (0.208)
Observations 26,133 22,808 25,503 19,754 21,865 18,204
Number of pairs 3,615 3,341 3,668 2,889 2,945 2,611
AR(3) (p-value)a .936 .868 .790 .849 .652 .802
Over-identification
test (p-value)b
.169 .180 .106 .175 .323 .262
Notes: The dependent variable is the natural logarithm of the foreign direct investment flows from source country i to host country j at time t. Democracy and N_Democracy are the institutional
quality proxies for the host country and neighboring countries of the host country, respectively. Conflict and N_Conflict are the conflict levels in the host and neighboring countries of the host country,
respectively. N_GDP represents the average GDP of the neighboring countries of the host country. All estimations utilize the system GMM estimation technique and use 3–5 lags for the endogenous
variables. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Robust standard errors are reported in parentheses.
aArellano–Bond test that the third-order autocorrelation in residuals is 0. First- second-order autocorrelations are not reported as they are rejected at the 10% significance level.
bHansen J-test for overidentification of restrictions in GMM estimation.
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PINAR ANd STENGOS
European Structural and Investment Funds.9 When the EU countries are excluded from our sample,
the coefficients of the democracy level of the host countries and neighboring countries are still posi-
tive and statistically significant at the 5% level. In column 3 we exclude the subgroup of EU countries
from our sample which are also part of the euro zone. In that case, the democracy levels in the host
and neighboring countries remain positive and significant at the 5% level. In column 4 we exclude the
OECD countries from the host country group, and the results of our main variable of interest (neigh-
boring democratic institutions) remain significant.
In column 5 we exclude the countries in sub-Saharan Africa (SSA) from our analysis since the
neighboring country characteristics have been found to be important for this region (Easterly &
Levine, 2000). Furthermore, factors that attract FDI to this region are found to be different compared
to other developing countries (Asiedu, 2002), and improvements in institutional quality in this region
have fallen behind other developing regions (Asiedu, 2004), even though market access to this region
has had a positive effect in recent years (Bosker & Garretsen, 2012). The positive effect of neighbor-
ing institutional quality on FDI flows to host countries remains significant after excluding the SSA
countries from our analysis.
We exclude the high-income countries from our analysis in column 6.10 Similarly to the EU case,
institutional quality in both host and neighboring countries remains a significant factor, with relatively
higher coefficients estimates compared to the baseline ones.
Finally, irrespective of the sample used, coefficients on the lagged FDI, the market size of the
source and host country, and host country market potential remain positive and significant at the 1%
level.
5.4
|
Controlling for additional host country characteristics
We also control for some other characteristics of the host country since they could be correlated with
neighbor country characteristics, and their omission could lead to omitted variable bias. For instance,
to be part of the EU, countries need to follow the Copenhagen criteria that require ‘stable condi-
tions guaranteeing democracy [and] the rule of law’. This enables countries to trade freely, hence
increasing trade, financial integration, and infrastructure integration through the European Structural
and Investment Funds Regulations. In other words, the initial conditions to be part of the EU (i.e.
institutional, political, and economic requirements) provided a way to promote trade, financial, and
structural integration. To control whether the diffusion of institutional quality (i.e. institutional qual-
ity levels in the neighboring countries) does not have an effect on FDI flows through these factors,
we control for capital account openness, trade openness, and infrastructure levels in the host country.
Furthermore, Asiedu and Lien (2011) find that democracy promotes higher FDI flows if and only
if a host country has a natural resource rent that is less than a given threshold, and as such, we also
control for oil rents of the host country. Table5 presents the results when we control for capital open-
ness (measured as the updated capital account openness measure of Chinn & Ito, 2006, 2008), trade
openness (measured as the total value of exports and imports as a percentage of GDP), infrastructure
(measured as the number of telephone lines per 100 people) and oil rents (measured as the value of
oil rents as a percentage of GDP) of the host country, which are obtained from World Development
Indicators of the World Bank, respectively. Columns 1–4 report the results when we include capital
account openness, trade openness, infrastructure level, and oil rents of the host country in the analysis
one at a time, respectively. We find that countries that trade more and with better infrastructure also
receive relatively higher FDI flows. Finally, column 5 of Table5 controls for all these factors; now
capital openness becomes negative and significant. More importantly, after accounting for additional
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TABLE 5 System GMM results with additional host country characteristics
Variables (1) (2) (3) (4) (5)
ln(FDI flows)i,j,t–1 0.820*** (0.00678) 0.814*** (0.00693) 0.816*** (0.00670) 0.818*** (0.00659) 0.816*** (0.00666)
Democracyj,t–1 0.0390** (0.0190) 0.0427** (0.0202) 0.0367** (0.0176) 0.0487** (0.0202) 0.0395* (0.0202)
N_Democracyj,t–1 0.0379* (0.0212) 0.0469** (0.0227) 0.0406* (0.0211) 0.0508** (0.0225) 0.0494** (0.0225)
ln(GDP)i,t–1 0.397*** (0.0206) 0.406*** (0.0208) 0.403*** (0.0206) 0.402*** (0.0209) 0.401*** (0.0207)
ln(GDP)j,t–1 0.380*** (0.0221) 0.404*** (0.0214) 0.335*** (0.0280) 0.359*** (0.0237) 0.361*** (0.0296)
Conflictj,t–1 −0.0214 (0.0464) 0.0260 (0.0566) 0.00872 (0.0494) 0.00260 (0.0563) 0.0880 (0.0586)
N_Conflictj,t –1 −0.00211 (0.0161) 0.00506 (0.0185) 0.00529 (0.0172) −0.00214 (0.0177) 0.00399 (0.0185)
ln(N_GDP)j,t–1 0.00581 (0.0169) −0.0168 (0.0172) −0.0212 (0.0185) 0.00452 (0.0168) −0.0322* (0.0190)
ln(Market potential)j,t–1 0.472*** (0.123) 0.407*** (0.117) 0.420*** (0.127) 0.516*** (0.109) 0.360*** (0.109)
Capital opennessj,t–1 −0.0825 (0.126) −0.267*** (0.0952)
Trade opennessj,t–1 0.00483*** (0.000561) 0.00436*** (0.000625)
ln(Phones per 100)j,t–1 0.148*** (0.0570) 0.144*** (0.0550)
Oil rentsj,t–1 0.00412 (0.00427) 0.00161 (0.00403)
Observations 27,515 27,679 27,851 27,634 26,675
Number of pairs 3,826 3,848 3,873 3,837 3,759
AR(3) (p-value)a .740 .934 .949 .961 .796
Overidentification test
(p-value)b
.131 .148 .139 .135 .131
Notes: The dependent variable is the natural logarithm of the foreign direct investment flows from source country i to host country j at time t. Democracy and N_Democracy are the institutional
quality proxies for the host country and neighboring countries of the host country, respectively. Conflict and N_Conflict are the conflict levels in the host and neighboring countries of the host country,
respectively. N_GDP represents the average GDP of the neighboring countries of host country. All estimations utilize the system GMM estimation technique and uses 3–5 lags for the endogenous
variables. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Robust standard errors are reported in parentheses.
aArellano–Bond test that the third-order autocorrelation in residuals is 0. First- second-order autocorrelations are not reported as they are rejected at the 10% significance level.
bHansen J-test for overidentification of restrictions in GMM estimation.
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PINAR ANd STENGOS
host country characteristics, the effects of host country and neighboring countries’ institutional set-
tings on FDI flows remain significant.
5.5
|
Controlling for alternative institutional quality measures
We have chosen the democracy measure as our main institutional quality measure as it captures various
aspects of the institutional setting (see Section3 for discussion). To control whether our results are robust
to additional measures of institutional quality, we further control for, on the one hand, the rule of law
and, on the other hand, political stability and absence-of-violence measures from the World Governance
Indicators. The former is one of the most common measures used to assess institutional quality, whereas
the latter can be considered as an additional control for political stability. Similarly to the previous cases,
we use the institutional quality measures (democracy and the rule of law) and civil conflict measures
as endogenous variables in our estimations. Table6 summarizes the results when we include the rule
of law in the host country and the rule of law in the neighboring countries in our analysis. We find that
the rule of law in the host country does matter for FDI flows as countries with better-functioning legal
systems attract relatively higher FDI flows, yet the effect of the rule of law in the neighboring countries
is negative and statistically insignificant. We repeat our analysis by including political stability and the
absence-of-violence proxy. Our findings for the political stability proxy are similar to those for the rule
of law, where higher levels of political stability in the host country attract higher capital flows, yet the
political stability proxy in neighboring countries is not significant. The remaining factors are in line with
the findings of the previous tables. Moreover, most importantly, the democracy level in both the host
country and neighboring countries positively and significantly affects the levels of FDI flows.
5.6
|
Results with the use of alternative democracy proxy
We used the democracy index from the Polity IV project as our proxy to measure the quality of demo-
cratic institutions of the host and the neighboring countries of the host country. To control whether our
results are robust to an alternative proxy of democratic institutions, we use the Varieties of Democracy
(V-DEM) indicator of political corruption index. The index is arrived at by taking the average of the
public sector corruption index, the executive corruption index, the indicator for legislative corruption
and the indicator for judicial corruption (for details, see Coppedge et al., 2020; Pemstein et al., 2020),
which could be considered as a measure of political accountability (for examinations of the impact of
democratic accountability on FDI flows, see Kolstad & Villanger, 2008; Doytch & Eren, 2012; Bailey,
2018). The political corruption index ranges between 0 and 1, and higher values represent a higher pres-
ence of corruption in executive, legislative, and judicial levels. To be consistent with the democracy
index of Polity IV, we reverse the measure so that higher values represent lower political corruption.
Table7 presents the results when we re-estimate the equations of Tables2 and 3 with the use of
the political corruption variable as a proxy for democratic accountability instead of the democracy
index from Polity IV. The results reported in columns 1–4 and 5–8 of Table7 are obtained with the
gravity and system GMM models, respectively. With either estimation method, we find that political
corruption in the host and the neighboring countries is a significant determinant of the FDI flows to
the host country. Furthermore, in all cases, the Hansen test of overidentifying restrictions suggests
that we cannot reject the null hypothesis of instrument validity. Overall, we find that the quality of the
democratic institutions in the neighboring countries of the host country is a significant determinant
of the FDI flows to the host country even after using an alternative proxy for democratic institutions.
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PINAR ANd STENGOS
TABLE 6 System GMM estimations with additional institutional quality measures
Lags used
Rule of law Political stability
3–5 3+ 3–5 3+
Variables (1) (2) (3) (4)
ln(FDI flows)i,j,t–1 0.823*** (0.00697) 0.823*** (0.00679) 0.823*** (0.00675) 0.823*** (0.00670)
Democracyj,t–1 0.0435*** (0.0157) 0.0328** (0.0135) 0.0273** (0.0136) 0.0254** (0.0124)
N_Democracyj,t–1 0.0462** (0.0201) 0.0396** (0.0178) 0.0372** (0.0179) 0.0297* (0.0163)
ln(GDP)i,t–1 0.389*** (0.0205) 0.386*** (0.0202) 0.385*** (0.0202) 0.384*** (0.0201)
ln(GDP)j,t–1 0.337*** (0.0297) 0.318*** (0.0268) 0.347*** (0.0229) 0.342*** (0.0219)
Conflictj,t–1 −0.0226 (0.0430) 0.00408 (0.0366) 0.0615 (0.0480) 0.0377 (0.0412)
N_Conflictj,t–1 0.00119 (0.0149) 0.000182 (0.0132) 0.000538 (0.0144) 0.00744 (0.0133)
ln(N_GDP)j,t–1 0.0158 (0.0238) 0.00458 (0.0211) −0.0125 (0.0170) −0.0126 (0.0163)
ln(Market potential)j,t–1 0.798*** (0.136) 0.709*** (0.129) 0.695*** (0.122) 0.659*** (0.117)
Rule of lawj,t–1 0.220* (0.115) 0.291*** (0.102)
N_Rule of lawj,t–1 −0.178 (0.178) −0.122 (0.150)
Political stabilityj,t–1 0.344*** (0.0973) 0.312*** (0.0828)
N_Political stabilityj,t–1 −0.0722 (0.126) 0.0263 (0.109)
Observations 26,120 26,120 26,120 26,120
Number of pairs 3,867 3,867 3,867 3,867
AR(3) (p-value)a .999 .999 .997 .997
Over-identification test (p-value)b .060 .120 .101 .189
Notes: The dependent variable is the natural logarithm of the foreign direct investment flows from source country i to host country j at time t. Democracy and N_Democracy are the institutional
quality proxies for the host country and neighboring countries of the host country, respectively. Conflict and N_Conflict are the conflict levels in the host and neighboring countries of the host country,
respectively. N_GDP represents the average GDP of the neighboring countries of host country. Rule of law and N_Rule of law represent the quality of rule of law in host country and neighboring
countries of the host country, respectively. Political stability and N_Political stability represent the quality of political stability in host country and neighboring countries of the host country,
respectively. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Robust standard errors are reported in parentheses.
aArellano–Bond test that the third-order autocorrelation in residuals is 0. First- second-order autocorrelations are not reported as they are rejected at the 10% significance level.
bHansen J-test for overidentification of restrictions in GMM estimation.
18
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PINAR ANd STENGOS
TABLE 7 Gravity and system GMM estimations with the use of the political corruption variable as a democratic accountability proxy
Variables
Gravity model System GMM
(1) (2) (3) (4) (5) (6) (7) (8)
ln(FDI flows)i,j,t–1 0.772***
(0.00725)
0.769***
(0.00732)
0.768***
(0.00727)
0.767***
(0.00735)
0.824***
(0.00615)
0.824***
(0.00618)
0.824***
(0.00629)
0.823***
(0.00635)
Political corruptionj,t–1 0.753***
(0.126)
0.860***
(0.126)
0.824***
(0.124)
0.774***
(0.128)
0.615***
(0.122)
0.684***
(0.109)
0.666***
(0.112)
0.623***
(0.125)
N_Political corruptionj,t–1 0.353**
(0.154)
0.308**
(0.156)
0.335**
(0.148)
0.274*
(0.157)
0.299**
(0.132)
0.472*
(0.245)
0.331**
(0.136)
0.278*
(0.146)
ln(GDP)i,t–1 0.470***
(0.0225)
0.475***
(0.0227)
0.479***
(0.0225)
0.477***
(0.0227)
0.385***
(0.0194)
0.383***
(0.0194)
0.387***
(0.0244)
0.389***
(0.0249)
ln(GDP)j,t–1 0.450***
(0.0216)
0.446***
(0.0214)
0.465***
(0.0211)
0.476***
(0.0227)
0.352***
(0.0212)
0.362***
(0.0263)
0.347***
(0.0297)
0.370***
(0.0361)
Conflictj,t–1 −0.0363**
(0.0178)
−0.0373**
(0.0180)
−0.0351
(0.0612)
−0.0334
(0.0640)
N_Conflictj,t–1 −0.0140**
(0.00652)
−0.0207***
(0.00678)
−0.00354
(0.00682)
−0.00440
(0.00684)
ln(N_GDP)j,t–1 0.0190
(0.0168)
0.0262
(0.0166)
−0.0674
(0.0797)
−0.0287
(0.0862)
ln(Market potential)j,t–1 0.895***
(0.128)
0.912***
(0.129)
0.158
(0.794)
0.245
(0.824)
Observations 28,578 28,531 28,796 28,313 28,578 28,313 28,578 28,313
Number of pairs 3,935 3,904 3,935 3,904 3,935 3,904 3,935 3,904
R-squared .8355 .8345 .8346 .8360
AR(3) (p-value)a .928 .847 .868 .907
Over-identification test (p-value)b .233 .207 .180 .211
Note: The dependent variable is the natural logarithm of the foreign direct investment flows from source country i to host country j at time t. Political corruption and N_Political corruption are the
political corruption proxies for the host country and neighboring countries of the host country, respectively. Conflict and N_Conflict are the conflict levels in the host and neighboring countries of the
host country, respectively. N_GDP represents the average GDP of the neighboring countries of the host country. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Robust
standard errors are reported in parentheses.
aArellano–Bond test that the third-order autocorrelation in residuals is 0. First- second-order autocorrelations are not reported as they are rejected at the 10% significance level.
bHansen J-test for overidentification of restrictions in GMM estimation.
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PINAR ANd STENGOS
TABLE 8 Possible indirect links between neighboring institutional quality and host country characteristics
Host country characteristics
Democracy (Polity IV) in
neighboring countries
Democracy (Political corruption) in
neighboring countries
Rule of law in neighboring
countries
Political stability in
neighboring countries
Democracy (Polity IV) 0.719*** (0.00328) 11.22*** (0.0561) 3.319*** (0.0167) 3.672*** (0.0225)
Democracy (Political
corruption)
0.0227*** (0.000194) 0.868*** (0.00233) 0.257*** (0.000779) 0.259*** (0.00106)
Rule of law 0.0706*** (0.000717) 2.956*** (0.00856) 0.902*** (0.00246) 0.894*** (0.00362)
Political stability 0.0707*** (0.000677) 2.330*** (0.00981) 0.676*** (0.00294) 0.784*** (0.00365)
Conflict −0.0510*** (0.00082) −1.030*** (0.0154) −0.278*** (0.00467) −0.328*** (0.00672)
ln(GDP) 0.101*** (0.00115) 4.052*** (0.0206) 1.279*** (0.00621) 1.210*** (0.00753)
ln(GDP per capita) 0.106*** (0.00109) 4.330*** (0.0126) 1.350*** (0.00396) 1.319*** (0.00553)
Notes: Different host country characteristics are used as dependent variables. *, **, and *** denote significance at the 10%, 5%, and 1% level, respectively. Robust standard errors are reported in
parentheses.
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PINAR ANd STENGOS
5.7
|
Possible indirect channels of neighboring institutional quality
We have examined how institutional quality in neighboring countries directly affects FDI flows to
host countries. We now examine briefly how neighboring institutional quality might also affect other
factors in the host country that are also found to be important in attracting FDI flows to these countries.
Table8 suggests some possible ways in which institutional quality in neighboring countries (i.e.
two proxies of democracy, the rule of law, and political stability in the neighboring countries) might
influence some other characteristics of the host country. This table presents regression results that
summarize the association between host country characteristics and the institutional quality of the
neighboring countries after controlling for year effects.
The significant associations between neighboring institutions and the characteristics of the host
country are in line with the existing literature. We find that countries that are surrounded by other
countries with good institutions will also have a good institutional setting (see, for example, Kelejian
et al., 2013, who show that the rule of law in neighboring countries has a direct effect on the rule
of law of the home country after accounting for a variety of control variables). Even though the rule of
law and political stability in neighboring countries are not significant in our results, improvements of
these in neighboring countries can affect the rule of law and political stability in the host countries,
something that, in turn, was found to be important in attracting higher FDI flows. In other words,
better rule of law and higher political stability in neighboring countries leads to higher capital flows
to host countries via their effect on the host country’s institutional setting. Similarly, countries that
are surrounded by better democracies tend to be more democratic themselves, suggesting that the
democratic neighbors have not only a direct but also an indirect effect on FDI flows to host countries.
Moreover, being surrounded by countries with good institutions leads not only to higher institutional
quality for a given host country but also to higher levels of economic development—see, for exam-
ple, seminal papers by Acemoglu, Johnson, and Robinson (2001) and Rodrik et al. (2004) which
show that institutional quality is one of the main determinants of long-term economic development.
Furthermore, these countries achieve higher development levels since there is a significant correlation
between market size (i.e. GDP) and standard of living (i.e. GDP per capita) with neighboring institu-
tions (see, for example, Bosker & Garretsen, 2009, who found that the neighboring institutions have
a direct effect on economic development of countries). Furthermore, countries that are surrounded by
good institutions experience lower civil conflict and have higher political stability.
Overall, Table8 summarizes some channels by which the institutional quality of neighboring
countries and as well the institutional quality of the host country might play a role in attracting higher
levels of FDI. In our analysis, we provided the direct link that democratic institutions of the neigh-
boring countries do indeed matter for the FDI flows to host countries, yet these channels need to be
further explored to disentangle how and why institutions matter for attracting capital.
6
|
CONCLUSIONS AND DISCUSSION
In this paper we examine whether democratic institutions in neighboring countries matter for FDI
flows to host countries. We find that investing countries not only look at the democratic institutions
of the host country but also evaluate the democratic institutions in neighboring countries when they
decide to invest. In particular, we find that countries that are democratic and surrounded by other
democratic countries receive relatively higher FDI flows. Our results were robust to the choice of
different estimation techniques (gravity model and system GMM), different samples, and another set
of neighboring country characteristics such as the conflict in neighboring countries, market size of
|
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PINAR ANd STENGOS
the neighboring countries, and market potential of the host country. We also controlled for alternative
measures of institutional quality settings and used an alternative proxy for democratic institutions,
and we find that democratic institutions in neighboring countries still matter for FDI flows to host
countries. Furthermore, in some specifications, we find that civil conflict in neighboring countries
also leads to lower levels of FDI flows to host countries, and countries that are close to relatively
larger markets also receive more FDI flows. In all specifications, we also find that host countries that
have higher market potential (i.e. if a host country is relatively closer to high-income countries) also
receive relatively higher FDI flows. Furthermore, we briefly investigated potential indirect links of
neighboring country institutional settings with other factors in the host country. For instance, we find
that countries that are surrounded by neighbors that have better institutions tend to have a better in-
stitutional setting themselves. In other words, improvements in neighboring institutions also increase
the FDI flows to host countries through their effect on a better institutional setting of the host country.
Considering both the direct and indirect effects of the democracy level of neighboring countries, its
impact on FDI flows is significant in both the short and long term.
Our study provides empirical support for the importance of neighboring country characteristics on
the FDI location choice. Even though the institutional theory is one of the most utilized frameworks in
this research, its use in empirical work is relatively small (see, for example, Blonigen & Piger, 2014;
Nielsen et al., 2017). In this paper our empirical findings show that it is not only the institutional
setting of the host countries that matters for the location choice of the FDI but also the institutional
setting of the neighboring countries. Therefore, future studies should incorporate neighboring country
characteristics into their analysis.
Our results suggest that if neighboring countries act together to improve their institutional quality,
FDI flows to these countries will be much higher since not only does improving their institutional
quality attracts more FDI inflows but also being surrounded by neighboring countries that have better
institutional quality will also lead to higher FDI flows. In other words, regions that are clustered with
relatively low institutional quality (or with lower democratic institutions) can attract higher levels of
FDI flows by acting together and improving their institutional settings.
We should note that we analyzed FDI flows from the OECD countries to an extensive list of host
countries. We found that the OECD countries give importance to the institutional quality setting of the
host country and also whether countries are surrounded by other countries with good institutions when
investing. However, investments made by emerging and developing countries have become important
in recent years. For instance, it has been found that emerging economies have a better willingness to
operate in countries that have relatively poor institutions (see, for example, Aleksynska & Havrylchyk,
2013). For example, Chinese outward investments go to countries that have relatively poor institutions
(see, for example, Kolstad & Wiig, 2012). Hence, even though a better institutional setting in neigh-
boring countries matters for the OECD countries, it might not do so in the case of emerging economies
investing in other countries. As a result, countries that are surrounded by countries with relatively poor
institutions might receive relatively lower FDI flows from the OECD countries, but might receive
relatively higher investment from emerging markets. This can then lead to divergence in the evolution
of institutional settings, depending on the origins of the FDI flows. This is potentially a new research
area, which needs to be investigated in the future.
ACKNOWLEDGMENTS
Mehmet Pinar is grateful to the financial support from the British Academy's Advanced Newton
Fellowship (AF140068) and the Research Investment Fund of Edge Hill University. Thanasis Stengos
acknowledges the financial support from the Natural Sciences and Engineering Research Council
of Canada (401004). The authors are grateful to the participants of the 4th International Conference
22
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PINAR ANd STENGOS
on Applied Theory, Macro and Empirical Finance (2018) and two anonymous reviewers for their
comments.
DATA AVAILABILITY STATEMENT
The data used in this paper is derived from public domain resources. The detailed data sources are
given in the Appendix.
ORCID
Mehmet Pinar https://orcid.org/0000-0001-5518-188X
Thanasis Stengos https://orcid.org/0000-0002-7771-1121
ENDNOTES
1 Note that we discuss only some of the papers that analyze the importance of democracy, political risk, and institu-
tional setting in the host countries, among many other factors that are found to be important, such as the financial
openness of a country, institutional distance between source and host country, trade openness, tax levels, and differ-
ences in wage levels in two countries—see Blonigen and Piger (2014) and Nielsen, Asmussen, and Weatherall (2017)
for a review of literature on determinants of FDI.
2 The detailed bilateral FDI flows from OECD countries to partner countries can be obtained from https://stats.oecd.
org/index.aspx?DataS etCod e=FDI_FLOW_PARTNER.
3 For instance, Estonia and Slovenia gained their independence in 1991 and both the Czech Republic and Slovakia
became independent nations in 1993 after the dissolution of Czechoslovakia.
4 When a country is an island, we consider the closest country in terms of distance between capital cities of countries.
Note that we also repeated our analysis by excluding islands from the sample, and the results are presented in Section
5.
5 The choice of endogenous variables is in line the recent literature analyzing similar relationships. For instance,
Asiedu and Lien (2011) use a similar estimation technique considering democracy as an endogenous variable,
whereas, Qureshi (2013) considers conflict in the host and neighboring countries as endogenous variables.
6 The first two orders of autocorrelation are rejected at the 10% significance level. However, in all cases, the Arellano–
Bond test that the third-order autocorrelation in residuals is 0 is not rejected and we used third or higher lags as
instrumental variables.
7 We also included the average GDP per capita levels of neighboring countries in our regressions and found that the co-
efficient on average GDP per capita of neighboring countries is not significant after controlling for other neighboring
country characteristics.
8 Note that the coefficient estimates are obtained with log-level regressions and the estimated percentage change is
calculated as
100 (
e
0.0390∕(1−0.819)
−1
)
.
9 We exclude the EU countries from the year that they became a member. We also redo our analysis by only excluding
those countries from the sample which were members during the whole period and the results are similar to those
reported here; these results are available upon request from the authors.
10 We use the World Bank classification to exclude the high-income countries from the analysis.
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SUPPORTING INFORMATION
Additional Supporting Information may be found online in the Supporting Information section.
How to cite this article: Pinar M, Stengos T. Democracy in the neighborhood and foreign
direct investment. Rev Dev Econ. 2020;00:1–29. https://doi.org/10.1111/rode.12720
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APPENDIX A
TABLE A1 Countries used in the analysis
Afghanistan Estonia* Lebanon Saudi Arabia
Albania Ethiopia Liberia Senegal
Algeria Finland* Libya Sierra Leone
Angola France* Lithuania Singapore
Argentina Gabon Luxembourg* Slovakia*
Armenia Gambia Madagascar Slovenia*
Australia* Georgia Malawi Somalia
Austria* Germany* Malaysia South Africa
Azerbaijan Ghana Mali Spain*
Bangladesh Greece* Mexico* Sri Lanka
Belarus Guatemala Moldova Sudan
Belgium* Guinea Mongolia Swaziland
Bolivia Guinea-Bissau Morocco Sweden*
Botswana Guyana Mozambique Switzerland*
Brazil Haiti Myanmar Syria
Bulgaria Honduras Namibia Tajikistan
Burkina Faso Hungary* Nepal Tanzania
Cameroon Iceland* Netherlands* Thailand
Canada* India New Zealand* Togo
Central Afr. Rep. Indonesia Nicaragua Trinidad &
Tobago
Chad Iran Niger Tunisia
Chile* Iraq Nigeria Turkey*
China Ireland* Norway* Turkmenistan
Colombia Israel* Oman Uganda
Congo, Dem. Rep. Italy* Pakistan Ukraine
Congo, Rep. Jamaica Panama United Arab
Emirates
Costa Rica Japan* Papua New Guinea United
Kingdom*
Côte d'Ivoire Jordan Paraguay United States*
Croatia Kazakhstan Peru Uruguay
Cyprus Kenya Philippines Uzbekistan
Czech Rep.* Korea, Dem. Rep. Poland* Venezuela
Denmark* Korea, Rep.* Portugal* Vietnam
Dominican Rep. Kuwait Qatar Yemen
Ecuador Kyrgyzstan Romania Zambia
Egypt Laos Russia Zimbabwe
El Salvador Latvia Rwanda
Notes: Countries with * are the OECD countries.
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TABLE A2 Variables, definitions and sources
Variable Definition Source
Aggregate FDI inflows Aggregate FDI inflows (measured in U.S.
dollars)
World Bank – World
development Indicators (WDI)
FDI inflows Bilateral FDI flows from OECD countries to
host countries (measured in U.S. dollars)
OECD. Available via: https://data.
oecd.org/
GDP Gross Domestic Product (in constant 2010
U.S. dollars)
World Bank – WDI
GDP per capita GDP divided by the population World Bank – WDI
Population Country’s total population World Bank – WDI
Land area Country's total area (measured in km square) World Bank – WDI
Language Dummy variable indicating if a language is
spoken by at least 9% of the population in
source and host countries
Mayer and Zignago (2011)
Colony Dummy variable indicating whether source
and host countries have ever had a colonial
link
Mayer and Zignago (2011)
Distance Distance between most important cities/
agglomerations between source and host
countries
Mayer and Zignago (2011)
Regional trade agreements Dummy variable indicating whether a source
and host country has a regional trade
agreement in a given period or not.
World Trade Organisation.
Available via: https://rtais.
wto.org/UI/Publi cMain tainR
TAHome.aspx
Democracy Polity 2 measure ranges between −10
and+10, which suggests full autocracy and
democracy, respectively.
Polity IV Project, Political
Regime Characteristics and
Transitions, 1800–2015,
Available via: http://www.syste
micpe ace.org/inscr data.html
Rule of law Rule of law captures perceptions of the extent
to which agents have confidence in and abide
by the rules of society, and in particular the
quality of contract enforcement, property
rights, the police, and the courts, as well as
the likelihood of crime and violence, which
ranges between −2.5 and+2.5, where higher
score represents better rule of law.
Rule of law component of the
World Governance Indicators
(Kaufmann et al. 2013)
Available via: http://info.world
bank.org/gover nance/ wgi/#home
Political stability and
Absence of Violence
Political Stability and Absence of Violence/
Terrorism measures perceptions of the
likelihood of political instability and/or
politically motivated violence, including
terrorism. This measure ranges between −2.5
and+2.5, where higher score represents
higher political stability.
Political stability and absence
of violence component of the
World Governance Indicators
(Kaufmann et al. 2013)
Available via: http://info.world
bank.org/gover nance/ wgi/#home
(Continues)
28
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PINAR ANd STENGOS
Variable Definition Source
Conflict Magnitude score of episode(s) of civil
violence and warfare, ethnic violence and
warfare involving that state in that year.
Center for Systemic Peace (CSP)
Major Episodes of Political
Violence, 1946–2015, URL:
www.syste micpe ace.org/warli
st.htm
Conflict in neighboring
countries
Sum of all societal (civil and ethnic) MEPV
magnitude scores for all neighboring
countries
Center for Systemic Peace (CSP)
Major Episodes of Political
Violence, 1946–2015, URL:
www.syste micpe ace.org/warli
st.htm
Capital account openness A measure of capital account liberalization
where the index ranges between 0 and 1,
where higher score represents more open
capital account. This index is based on IMF's
Annual Report on Exchange Arrangements
and Exchange Restrictions.
Chinn and Ito (2006, 2008).
Updated data available via:
http://web.pdx.edu/~ito/Chinn
-Ito_websi te.htm
Trade openness Total imports and exports of goods divided by
GDP, in %
World Bank – WDI
Phone Fixed telephone subscriptions (per 100
people)
World Bank – WDI
Oil rents/GDP Oil rents divided by GDP, in % World Bank – WDI
TABLE A2 (Contined)
|
29
PINAR ANd STENGOS
TABLE A3 Summary statistics
Variable Mean
Std.
Dev. Min Max Obs
ln (FDI flows) 9.46 8.91 0 25.87 38,891
Democracy in host country 4.38 6.27 −10 10 38,198
Democracy in neighbouring countries 3.83 5.44 −10 10 38,599
Rule of law in host country −0.01 1.07 −2.67 2.00 36,366
Rule of law in neighbouring countries −0.11 0.89 −1.91 1.98 36,366
Political stability of law in host country −0.15 1.01 −3.32 1.66 36,366
Political stability in neighbouring countries −0.22 0.77 −2.13 1.69 36,366
ln(Source country GDP) 26.80 1.57 22.76 30.37 38,891
ln(Host country GDP) 25.12 2.05 19.30 30.37 38,094
ln (Source country population) 16.48 1.55 12.48 19.57 38,891
ln (Host country population) 16.51 1.51 12.48 21.02 38,888
ln (Source land area) 12.05 1.58 7.86 16.03 38,891
ln (Host land area) 12.45 1.79 6.51 16.61 38,891
Common language dummy variable 0.09 0.29 0 1 38,891
Colony dummy variable 0.04 0.20 0 1 38,891
ln (Distance) 8.42 0.99 2.95 9.88 38,891
RTA dummy variable 0.29 0.46 0 1 38,891
Civil conflict in host country 0.51 1.41 0 10 38,599
Civil conflict in neighbouring countries 2.18 3.77 0 29 38,599
ln (Average GDP of neighbouring countries) 25.90 1.78 19.97 30.37 38,526
ln (Market Potential) 17.52 0.25 16.63 17.93 38,891
