Author’s Accepted Manuscript
Do countries’ endowments of Non-Renewable
Energy Resources matter for FDI attraction? A
panel data analysis of 125 countries over the period
1995–2012
Aurora A.C. Teixeira, Rosa Forte, Susana
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http://dx.doi.org/10.1016/j.inteco.2016.12.002
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Cite this article as: Aurora A.C. Teixeira, Rosa Forte and Susana Assunção, Do
countries’ endowments of Non-Renewable Energy Resources matter for FDI
attraction? A panel data analysis of 125 countries over the period 1995–2012,
International Economics, http://dx.doi.org/10.1016/j.inteco.2016.12.002
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Do countries’ endowments of Non-Renewable Energy Resources matter for FDI attraction? A panel
data analysis of 125 countries over the period 1995-2012
Aurora A.C. Teixeiraa1, Rosa Forteb2, Susana Assunçãoc
a
Research areas/interests: Human Capital; Economic Growth; Innovation; Bibliometrics
b
c
Research areas/interests: Foreign direct investment (FDI); International Trade; Multinationals
Faculdade de Economia do Porto
ateixeira@fep.up.pt
rforte@fep.up.pt
assuncao.susana@gmail.com
*
Correspondence to: 4200 – 464 Porto. Tel.: +00351225571100.
Abstract
Empirical studies on FDI location determinants have neglected the role of natural resources. Panel data
estimations for 125 host countries over the period between 1995 and 2012 show that a country’s endowment
of Non-Renewable Energy Resources (NRERs) matters for FDI attraction, when measured by the share of
oil, coal and gas exports in total exports but not when measured by oil, coal and gas ‘proven reserves’. Thus,
although to possess a vast amount of proven NRERs is not a sufficient condition for FDI inflows, countries
with low export diversification, highly dependent on the exports of mineral fuel, tend to succeed in attracting
FDI. This evidence supports the content that resource seeking FDI targets mainly economically feeble
countries. Moreover, our results firmly indicate that regardless NRERs endowments, FDI attraction is
fostered when countries make convincing efforts to open up their economies to international trade and
devote resources to the enhancement of their human capital, control of corruption, and have more beneficial
tax rates.
Keywords: FDI, Determinants of FDI; Non-Renewable Energy Resources; Panel data
1
Associate Professor with Habilitation (Agregação) at Faculdade de Economia do Porto (Universidade do
Porto) and researcher at CEF.UP and OBEGEF.
2
Assistant Professor at Faculdade de Economia do Porto (Universidade do Porto) and researcher at CEF.UP.
1
JEL-Codes: F21, C19, O13
1. Introduction
Foreign direct investment (FDI) is regarded as a driving force behind economic growth (Wang, 2009; Aziz
and Mishra, 2016). Many governments see FDI as a way of dealing with stagnation and even the poverty
trap (Brooks et al., 2010; Gohou and Soumaré, 2012).
The growing search for natural resources, in particular for non-renewable energy resources (NRERs),
namely by economies that are growing rapidly, has led to an increase in commodity prices, resulting in a
renewed interest by governments in exploiting energy resources and a redirection of FDI towards the mining
sector (UNCTAD, 2007). Over the last twenty years, economic development policies have tended to neglect
investment in the mining sector (UNCTAD, 2007; Betz et al., 2015). However, in an age when energy
security is a global concern and countries, such as China, are attempting to take positions in mining
companies around the world to ensure future supply and thereby continued economic growth (Moran, 2010;
Henson and Yap, 2016), it is important to understand how far the endowment in NRERs, specifically
‘proven reserves’ (the economically extractable fraction of a resource using current technology – see Grafton
et al., 2004), is a factor that attracts inward FDI.
Despite the enormous amount of literature on FDI (see Faeth, 2009; Mohamed and Sidiropoulos, 2010;
Chanegriha et al., 2016) and non-renewable energy resources (for example, Crawford et al., 1984; Mitchell,
2009; Zhang et al., 2013), considered separately, not many studies have looked at the two topics together,
establishing and appraising a (possible) correlation and causality between these two variables. The few
studies there are in this domain focus on a limited number of regions and countries including Sub-Saharan
Africa (SSA) (Asiedu, 2006; Ezeoha and Cattaneo, 2011), the Middle East and North African (MENA)
countries (Mohamed and Sidiropoulos, 2010; Aziz and Mishra, 2016), African countries (Sanfilippo, 2010),
2
the Economic Community of West African States (ECOWAS) (Ajide and Raheem, 2016), China (Cheung
and Qian, 2009), India (Kumar and Chadha, 2009), Eurasia (Poland, Hungary and the Baltic states)
(Deichmann et al., 2003), the Southern African Development Community (SADC) (Mhlanga et al., 2010),
the nations from the ex-Soviet Union (Ledyaeva, 2009), the BRICS (Brazil, Russia, India, China & South
Africa) (Jadhav, 2012), and a group of developing countries (Asiedu and Lien, 2011; Asiedu, 2013).
Furthermore, except the works of Asiedu (2006), Asiedu and Lien (2011), Ezeoha and Cattaneo (2011) and
Asiedu (2013), such studies neither tend to look specifically at the possible correlation and causality between
FDI and NRERs nor at the particular relevance of the latter as determining the former.
The present study sets out to add evidence to this research area by analysing the role of NRERs in attracting
FDI, controlling for a set of factors that are traditionally regarded as influencing this last macroeconomic
variable (for example, human capital, market size, political stability, openness of the economy) (see Faeth,
2009; Chanegriha et al., 2016).
In order to pursue such endeavour, we resort to econometric panel data techniques involving 125 countries
over the period 1995-2012. The set of countries considered is rather heterogeneous in terms of NRERs:
around one quarter of the countries do not possess ‘proven reserves’ (e.g., Finland, Portugal or Sweden), or
have no/negligible share of NRERs in their total exports (e.g., Botswana, Central African Republic, Haiti),
whereas 18% of the countries hold vast quantities of ‘proven reserves’ (e.g., China, USA, India or
Australia), and about 10% are large NRERs exporters (e.g., Algeria, Nigeria, Yemen, Rep., Kuwait, Saudi
Arabia).
The paper is organised as follows. Section 2 gives a brief overview of the literature on endowments of
NRERs and FDI. The methodology is described in Section 3, with details on the econometric model, the
proxy variables and relevant data sources. The empirical results of the model are presented and discussed in
Section 4. The last section sets out the main contributions, policy implications, limitations of the study, and
put forward future lines of research.
3
2. FDI and natural resource endowments: literature review
A myriad of factors are consider relevant to attract FDI. Among these stand (see Faeth, 2009; Chanegriha et
al., 2016): endowment related factors, most notably, non-renewal natural energy resources (NRERs) and
human capital (Cleeve, 2008; Asiedu, 2006); economic and political factors, which include market size
(Mohamed and Sidiropoulos, 2010; Vijayakumar et al., 2010), market growth, openness of the economy
(Asiedu, 2006; Botrić and Škuflić, 2006), economic (in)stability, production costs, financial and tax
incentives, and infrastructure facilities (Biswas, 2002; Asiedu, 2006); institutional related factors, namely
corruption, political (in)stability (Asiedu, 2006; Mohamed and Sidiropoulos, 2010), and institutional quality.
The importance of each of these factors for attracting FDI is closely related to the motives for investors to
carry out this type of investment. Dunning and Lundan (2008) summarize the four main motives for
companies producing abroad and conduct FDI: ‘market-seeking’, ‘resource-seeking’, ‘efficiency-seeking’,
and ‘asset-seeking’ FDI.
The main purpose of market-seeking FDI is to supply the recipient country or nearby countries. Resourceseeking FDI aims at achieving specific resources of better quality and at lower cost than the investor’s
country of origin. Efficiency-seeking FDI aims at rationalize the structure of the existing investment and that
was motivated by the search for markets or resources. Finally, the main objective of strategic asset-seeking
FDI is to keep or improving investor’s global competitiveness, thus promoting its ‘long-term strategic
objectives’ (Dunning and Lundan, 2008). In this way, as stated by Dunning (1998, p. 9), “[d]ifferent kinds of
investment incentives are needed to attract inbound MNE activity of a natural-resource-seeking, c.f. that of a
market - or efficiency-seeking, kind”.
Table 1 synthetizes the main motives for FDI, their objectives, as well as some host country attractiveness
factors.
4
Regarding the resource-seeking FDI, which is particularly relevant to our work in that the focus is on the
role of natural resources, Sanfilippo (2010) report that recently several African countries have been targeted
as the main destination of Chinese FDI given the growing needs of the country in natural resources, thus
advocating a positive relationship between natural resource endowment and FDI. Indeed, the NRERs are
currently at the centre of the discussion about energy security (Moran, 2010; Wolf, 2014; Capellán-Pérez et
al., 2015). According to some authors (for example, Velthuijsen and Worrel, 1999; Salim et al., 2014),
natural energy resources, especially NRERs, such as oil, coal and natural gas, have been playing a key role
in economic development.
Empirically, existing studies on the relationship between the natural resources endowments and FDI show
mixed results (cf. Table 2).
Most studies (Deichmann et al.., 2003; Asiedu, 2006; Cheung and Qian, 2009; Ledyaeva, 2009; Mohamed
and Sidiropoulos, 2010; Sanfilippo, 2010; Aziz and Mishra, 2016) obtained a positive relation between
natural resources endowments and FDI. For instance, Ledyaeva (2009) looked at the nations from the exUSSR in the period 1995-2005 and noted that the regions richer in natural resources, measured by the oil and
natural gas production index, attract higher amounts of FDI. Controlling for a huge set of factors that may
influence the inflow of FDI to Eurasia countries in the period 1989-1998 (for example, reform measures,
importance of private sector in the economy, GDP and per capita GNP, inflation rate, number of years the
economy is (was) under central planning, rule of law, investment climate; human and social capital),
Deichmann et al. (2003) mention that these countries, rich in oil and natural gas, would not be attractive
were it not for these resources.
Ezeoha and Cattaneo (2011) and Ajide and Raheem (2016) obtained, in some estimated models, a positive
relation between natural resources endowments and FDI whereas in other models results were inconclusive.
Mhlanga et al. (2010) also obtained inconclusive results for the impact of natural resources endowments on
FDI in the SADC countries.
5
Finally, there are studies (Asiedu and Lien, 2011; Jadhav, 2012; Asiedu, 2013) which found a negative
relation between FDI and natural resources endowments. Asiedu and Lien (2011), studying a group of 112
developing countries for a period of 25 years (1982-2007), and analysing the interaction between
democracy, natural resources export intensity and FDI, concluded that natural resources have a negative
direct effect on FDI and “significantly alter the relationship between FDI by reducing the positive effect of
democracy on FDI” (Asiedu and Lien, 2011, p.106). Later, and focusing on BRICS economies, Jadhav
(2012) also evidenced that natural resource availability had significant negative effect on total inward FDI,
suggesting that FDI in these countries were not resource-seeking. In a more recent paper, using data from 99
developing countries in the period 1984-2011, Asiedu (2013) also obtained a negative effect of natural
resources on FDI.3 The author established that the presence of good institutions attenuates the negative
impact of natural resources on FDI but does not completely neutralize it.
All the empirical studies mentioned above use econometric models to gauge the relevance of natural
resources in attracting FDI in various countries. It can be seen that, even though the studies that examine the
relevance of natural resources to attract FDI are unanimous as to the importance of this determinant, most of
them do not look specifically at NRERs. Those that do (for example, Deichmann et al., 2003; Ledyaeva,
2009; Mohamed and Sidiropoulos, 2010) focus on very specific regions of the world (Central and Eastern
3
Asiedu and Lien (2011) explain the existence of a negative relationship based on the following arguments. First, the abundance
of natural resources tends to induce an appreciation of the respective local currency which reduces competitiveness of the
country’s exports, discouraging FDI in other sectors. Second, natural resources, especially oil, “are characterized by booms and
busts”, contributing to a high fluctuation of the exchange rate; furthermore, a high share of exports of fuels and minerals in total
exports indicates low export diversification and consequent increase in the country's exposure to external shocks. These two
factors “generate macroeconomic instability and therefore reduce FDI” (Asiedu and Lien, 2011, p.104). Finally, in countries rich
in natural resources FDI tend to converge to the natural resource sector which although calling for a large initial capital investment
requires small amounts for operations that follow. “Thus, after the initial phase, FDI may be staggered” (Asiedu and Lien, 2011,
p.104).
6
Europe, Central Asia, and the MENA countries). Thus, our study is intended to add empirical evidence on
the special relevance of NRERs and their (possible) correlation with FDI. We use panel data techniques and
look at a large group of countries, including countries having NRERs endowments, with the aim of
establishing a relation between a country’s endowment of such resources and its performance in terms of
FDI attraction.
3. Methodology
3.1. Data and variables
The present study examines the determinants of FDI inflows in 125 countries over the period from 1995 to
2012 (see Table A1 in the Appendix for the list of countries). Table A2, in the appendix, provides
information about the proxies for the relevant variables, namely the sources of data and their definitions.
Following the literature (see Aziz and Mishra, 2016), we categorize the determinants of FDI inflows in three
main categories: endowment (non-renewal energy resources, human capital), economic and policy (market
size, market growth, trade openness, economic instability, production costs, tax rate, infrastructure), and
institutional quality (corruption control, political stability, rule of law).
Foreign direct investment (FDI)
As is standard in the literature (see Biswas, 2002; Asiedu, 2006; Mohamed and Sidiropoulos, 2010; Asiedu
and Lien, 2011), the dependent variable is the net inflows of FDI in percentage of the GDP. It encompasses
new investment inflows less disinvestment regarding the acquisition of a lasting management interest (10
percent or more of voting stock) in an enterprise operating in an economy other than that of the investor.
7
Endowments
Non-renewal natural resources
Several studies (e.g., Deichmann et al., 2003; Mhlanga et al., 2010; Ezeoha and Cattaneo, 2011; Ajide and
Raheem, 2016) use dummy variables as proxy for natural resources endowments. Ezeoha and Cattaneo
(2011) and Ajide and Raheem (2016) chose to use a dummy variable which takes the value one if the
country is classified as abundant in natural resources and zero in the opposite situation, whereas Mhlanga et
al. (2010) used a dummy variable related to investments in mining industry. In turn, Deichmann et al. (2003)
ranked a country’s natural resources endowment at three levels (poor, moderate and high endowment).
Other authors (e.g., Ledyaeva, 2009; Sanfilippo, 2010; Aziz and Mishra, 2016) use production indicators
rather than dummy variables. For instance, the study by Aziz and Mishra (2016) on the location
determinants of foreign direct investment to 16 Arab economies employs the total oil supply as proxy for
natural resources endowments (which includes the production of crude oil, natural gas plant liquids, and
other liquids, and refinery processing gain).
The share of mineral fuel (oil, coal, gas) exports in total exports is the most used proxy (see Asiedu, 2006;
Asiedu and Lien, 2011; Asiedu, 2013). Mohamed and Sidiropoulos (2010), studying the MENA countries,
also used mineral fuels but in levels. Analysing FDI from the investor’s point of view, Cheung and Qian
(2009) use a wider proxy (including, beside mineral fuels, ores and metals) which represents the demand for
sundry raw materials in the various countries.
In this context, and following the scarce literature available, one of the proxies used in the present study to
measure NRERs endowments is the share of fuel minerals (i.e., coal, oil and natural gas) in total exports.
It is important to note that most non-renewable resources are not wholly available for extraction, since only a
small fraction of minerals overcomes the mineralogical barrier, with 'proven reserves' being the
economically extractable part of a resource (Grafton et al.., 2004). Although it is technically possible to
extract resources from beyond the mineralogical barrier, the cost is excessively high and so extraction takes
8
place only up to the barrier. ‘Proven reserves’ are thus confined to a small part of existing resources, which
affects the scarcity of resources and stresses the importance of such reserves to economic development
(Cleveland and Stern, 1999).
A number of countries, such as Cameroon, Chad or Ecuador, although possessing a negligible amount of
‘proven reserves’ of NRERs nonetheless register a high ratio of fuel exports in their total exports. It is
therefore pertinent to use a proxy based on ‘proven reserves’ in addition to the more traditional proxy, the
share of fuel minerals in total exports. To the best of our knowledge no empirical study has yet been
published that examines the effect of holding oil, coal and natural gas ‘proven reserves’ on FDI attraction.
Given that the proven reserves of each of the three resources are expressed in different units (oil in barrels,
coal in tonnes, and natural gas in m3), for comparability purposes they have been converted to TOE (tonnes
of oil equivalent), taking barrels of oil to be American barrels (42 US gallons being approximately 158.9873
litres). Resorting to The International Energy Agency definition of tonne of oil equivalent (TOE), we
considered 1 BOE (barrel of oil equivalent) = 0.14 TOE; 1 TCE (tonne of coal equivalent) = 0.7 TOE; and
103 m3 natural gas = 0.82 TOE (see also Heitor et al., 2000).
Human capital
Several seminal studies (e.g., Lucas, 1990; Dunning, 1998; Noorbakhsh et al., 2001) underline that that lack
of human capital discourages foreign investment in both less-developed and developing countries. Zhang
and Markusen (1999) and Teixeira and Tavares-Lehmann (2014) evidence that the availability of skilled
labour in the host country is a direct requirement of multinationals and affects the volume of FDI inflows.
In line with more recent literature in the area, human capital is measured by the average number of years of
schooling of the working-age population (Teixeira, 2005; Barro and Lee, 2013).
Economic and policy
9
Market size
Market size is seen by the empirical literature as being crucial to attracting FDI (Schneider and Frey, 1985;
Mhlanga et al.., 2010), such that countries with a larger domestic market will be more attractive to investors
because of the greater number of potential consumers. Some authors (Botrić and Škuflić, 2006; Mohamed
and Sidiropoulos, 2010) use the number of inhabitants as a proxy of this determinant. But it is felt that this
indicator does not give a true picture of the attractiveness of the market, especially in a broad sample of
countries which includes underdeveloped, developing and developed nations, as a large population need not
translated into a large number of consumers if they lack purchasing power (Ietto-Gillies, 2005). Based on the
empirical literature, therefore, per capita GDP was deemed a more suitable indicator to measure the
influence of market size.
Market growth
When it comes to potential for market growth, the literature (for example, Mhlanga et al.., 2010; Mohamed
and Sidiropoulos, 2010) generally suggests using the GDP or GNP growth rate as a proxy for this
determinant. A high growth rate for the market should attract more FDI since a growing economy offers
more opportunities for higher profits. So the real GDP growth rate is used as a proxy as it is corrected for the
effect of price variation, thereby giving more credible information on GDP growth.
Trade openness
Openness of the economy is seen in the literature as one of the key determinants of FDI (Vijayakumar et al.,
2010; Chanegriha et al., 2016). A country can increase its attractiveness by adopting a policy that favours
foreign trade, encouraging domestic producers to export, increasing their profitability and attracting foreign
investors (Mohamed and Sidiropoulos, 2010). Based on the empirical literature (Botrić and Škuflić, 2006;
Cleeve, 2008), we decided to use the weight of foreign trade in GDP to measure the degree of openness,
10
such that the greater the ratio the more open the country and the more FDI it would attract (Cleeve, 2008). A
positive relation of this determinant with FDI is thus expected.
Economic instability
Since high or volatile rates of inflation are a clear sign of economic instability (Botrić and Škuflić, 2006), the
rate of inflation was chosen as a proxy for measuring each country’s economic instability. High inflation
rates distort economic activity and reduce investment in productive industries, leading to lower economic
growth (Mohamed and Sidiropoulos, 2010). So we expect that high inflation discourages FDI.
Production costs
Issues of cost reduction and increasing competitiveness often tempt firms to relocate their production
facilities in places where such costs are lower (Dunning and Lundan, 2008), specifically labour costs, with
worker’s wage being the proxy most often referenced in the literature (Schneider and Frey, 1985; Biswas,
2002). We therefore expect that low production costs attract larger FDI inflows. In our study, the large size
of the sample, on the one hand, and the inclusion of countries with scanty statistical information on the other
mean that this indicator could not be used. Two other indicators were chosen instead. The first is the
unemployment rate, which aims at measuring labour market rigidity (see Cockx and Ghirelli, 2016) and thus
an important part of labour related costs. The higher the unemployment rate the more rigid the labour market
and the less attractive it will be for investors. The second indicator relates to the cost of imports (measured in
USD by container). This includes all import costs - administrative charges, the cost of keeping customs
facilities, transport, customs clearance, and other expenses - that can be a determinant in the choice of
location (see Husan, 1996), since this can be a significant cost in raw materials or machinery that has to be
imported.
11
Tax rate
Even though the empirical literature suggests temporary tax exemptions, tax concessions and ease of
repatriation of profits as indicators of financial and tax incentives (Cleeve, 2008), the large size of the
sample meant that none of these data could be obtained for all the countries. So the total tax rate (as
percentage of commercial profits) was chosen instead, since this indicator expresses all the taxes payable by
a firm. According to the literature (see Tavares-Lehmann et al., 2012) it is expected that countries with
lower tax rates will tend to attract greater inward flows of FDI.
Infrastructure
With respect to infrastructure, two proxies were used to measure their quality: the number of phone lines per
100 inhabitants and the losses of electric power transmission and distribution (in percentage of the output).
As the sample includes a range of countries with widely divergent degrees of development – developed and
developing countries – from all over the world, the first proxy should fit the development level of the
developing countries better and the second should identify the different degrees of development among the
developed countries. Bearing in mind the relevant literature, we expect that good infrastructure (expressed
by the high number of phone lines and/or a smaller loss of electric power transmission and distribution)
would be attractive to foreign investors (Biswas, 2002; Asiedu, 2006).
Institutional quality
Corruption control
In the spirit of extant empirical literature (for example, Asiedu, 2006; Cleeve, 2008; Aziz and Mishra, 2016),
we chose the World Bank’s Worldwide Governance Indicator, ‘control of corruption’, as a proxy for a
country’s level of corruption. The higher the ‘control of corruption’ (a standardized measure with a
maximum of, approximately, 2.5, and a minimum of -2.5), the lower the perceptions of citizens that public
12
power is exercised for private gain and/or the "capture" of the state by elites and private interests is
substantial. High ‘control of corruption’ figures are thus linked to higher foreign investment.
Political stability
Some authors, such as Mhlanga et al. (2010), use the risk rating of a country to measure political stability.
But given the difficulty in obtaining this indicator, an alternative was chosen: the political stability and
absence of violence and terrorism measure. This proxy measures the perceived improbability of political
instability and/or politically-motivated violence, including terrorism. It can take values from -2.5 to 2.5 and
the higher the score the greater the stability. It is expected that high levels of political stability, which reflect
low political risk, tend to attract more FDI.
Rule of law
With respect to institutional quality, we followed Asiedu (2006) and took the degree of effectiveness of the
rule of law as a proxy. This indicator measures 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. The closer to 2.5 (maximum) the greater the
confidence. It is thus expected that a high degree of effectiveness of the rule of law should attract investors,
since it offers them greater security.
3.2. Econometric estimation
Panel data estimations have a few advantages over the traditional cross-country models, permitting to study
the dynamics of adjustment and to estimate their effects over a long period of time. On the one hand, panel
data can analyse a set of variables for a great number of countries, which provides more information. Panel
data estimations also assume that countries are heterogeneous, with specific and unobservable
13
characteristics. ‘Cross-section’ and ‘times series’ estimations do not control for this heterogeneity, meaning
the results may be biased (Greene, 2011).
Therefore, to estimate the effects of relevant variables, namely non-renewal energy resources (NRERs), on
FDI inflows we opted for the estimation of panel data, such as in more recent studies which seek to assess
FDI determinants (e.g., Helmy, 2013; Okafor et al., 2015).
According to the literature review, the econometric model specification to estimate is (all variables are
expressed in logs):
FDIit = 1 + 2 NRERit +3 HCit + 4 MSit + 5 MGit + 6 TOit+7 EIit+8 PCit +9 Taxit +
10 INFRit
+11 CCit +12 PSit +13 RLit +ui+εit
where i represents the countries’ index and t represents time.
FDI
FDI inflows as percentage of the GDP
NRER non-renewal energy resources (exports of NRER in total exports; proven reserves)
HC
human capital stock (average years of schooling of the adult population)
MS
market size (GDP per capita)
MG
market growth (real average GDP annual growth)
TO
trade openness (sum of exports and imports in total GDP)
EI
economic instability (inflation rate)
PC
production costs (unemployment rate; cost of imports)
Tax
tax rate (total tax rate in percentage of commercial profits)
INFR infrastructure (fixed telephone subscription per capita and losses, in percentage of output, of electric
power transmission and distribution)
CC
corruption control
PS
political stability
14
RL
rule of law
ui
the unobserved time-invariant fixed effect
εit
the unobserved random coefficient
4. Results
In order to investigate the possibility of non-stationarity in the dataset, it is first necessary to determine the
existence of unit roots in the data series. For this study we have chosen Levin–Lin–Chu (LLC) (2002) and
Harris–Tzavalis (HT) (1999) tests.4 The combined results of the panel unit root tests indicate that all
variables are I(0), showing that the null hypothesis of a panel unit root (non-stationarity) in the level of the
series can be rejected (see Table 3).
The three proxies for institutional quality are (expectedly) highly correlated (see Table A3 in Appendix).
Moreover, one of the proxies for infrastructure (fixed telephone subscriptions) is also highly correlated with
several variables (human capital, control of corruption, rule of law, GDP per capita). Thus, to avoid
multicollinearity, we considered 6 distinct specifications (see Table 4): three considering each institutional
quality proxy in isolation (Models 1: control of corruption; Model 2: Political stability; Models 3: Rule of
law), combined with the two infrastructure proxies (Models 1-3: fixed telephone subscriptions; Models 4-6:
4
The Levin–Lin–Chu (LLC) (2002) test has as the null hypothesis that all the panels contain a unit root. Because the LLC test
requires that the ratio of the number of panels to time periods tend to zero asymptotically, it is not well suited to datasets with a
large number of panels and relatively few time periods. Thus, it is adequate to complement the analysis with the Harris and
Tzavalis (HT) (1999) test. This also has a null of unit root versus an alternative with a single stationary value, but it is designed to
be applied to data sets which are relatively short in T.
15
electric power transmission and distribution losses). Then, we estimate the six specifications including,
separately, each natural resource proxies (Models A – NRER exports; Models B – Proven NRER).5
The fixed effects model was favoured over the random effects model by the Hausman test.
The models estimated present a reasonable fit, with F-stat indicating that all models are overall significant
and R2 with values in line with previous studies using similar estimation methods (e.g., Asiedu, 2006;
Mohamed and Sidiropoulos, 2010).
Our estimates unambiguously evidence that even when controlling for economic, policy and institutional
quality factors, countries whose exports are highly concentrated in oil, coal and gas tend to attract FDI. All
other factors being held constant, if the share of non-renewal energy resources (NRER) exports increase by
one percent, we would expect that, on average, the ratio of FDI in GDP of the countries included in our
sample increases by 0.02-0.03 percent. This result is in line with the studies by Asiedu (2006) or Cheung and
Qian (2009), but contrast with those by Asiedu and Lien (2011), Jadhav (2012), and Asiedu (2013) (all these
studies use the same proxy as we for measuring countries’ natural resources endowments). Thus, as the
evidence gathered for Sub-Saharan Africa (Asiedu, 2006) and China’s outward FDI (Cheung and Qian,
2009), we conclude that FDI is largely driven by natural resources. In short, our results are consistent with
the resource-seeking strategy (Dunning and Lundan, 2008).
Interestingly, however, when we use ‘proven reserves’ (i.e., recoverable and economically profitable
reserves) as proxy for natural resources endowments we find that NRERs fail to emerge as a significant
determinant of FDI.
5
Human capital and GDP pc are also highly correlated. We estimated additional combinations of models considering each of these
variables separately. However, estimations results did not differ from those presented in Table 4. Therefore to keep the
presentation of results simple, we opted to not present the whole combined estimations.
16
Up to the present date, no study used ‘proven reserves’ as proxy for NRER endowments. The closest proxy
might be the production/supply of oil and gas used by Ledyaeva (2009), Sanfilippo (2010), and Aziz and
Mishra (2016). Differently from our results, Ledyaeva (2009) suggests that oil and gas resources are key
determinants of FDI inflows into Russia, whereas Sanfilippo (2010) demonstrates that Chinese investors in
Africa are motivated by the search for long-term access to natural resources, and Aziz and Mishra (2016)
find that the total oil supply positively affects FDI inflows to Arab economies.
Our results seem to entail that being highly endowed in ‘proven’ NRER does not necessarily imply that a
country will attract more FDI. In other words, the impact of natural resource abundance on FDI is highly
contingent on countries’ exports being little diversified and dependent on mineral fuel related resources
(petroleum, coal, or natural gas).
Regarding the remaining FDI determinants, our results follow quite closely extant literature in the area.
Human capital emerges significantly, albeit less strongly as other studies suggest (see Chanegriha et al.,
2016). Such result advises the need for schooling/ skills to be promoted/ developed among adult active
population if a country wishes to attract FDI. Thus, as Aziz and Mishra (2016, p. 343) refer a “well-educated
labour force can be a key element in attracting FDI”.
In our sample, over the period of analysis (1995-2012), both economic and policy, and institutions emerge as
critical factors for attracting FDI. The most significant determinants are ‘rule of law’ (related to the quality
of contract enforcement, property rights), ‘trade openness’, and ‘market growth’. In concrete, a one percent
improvement in the perceptions of quality of contract enforcement or in trade openness leads, on average, to
0.18 percent increase in FDI inflows. The corresponding figure for ‘market growth (measured by real annual
GDP growth) is about 0.13 percent. This suggests, in line with other previous studies (e.g., Felisoni de
Angelo et al., 2010; Helmy, 2013; Okafor et al., 2015), that efforts made to improve the quality of
institutions, fostering of policies that liberalise trade regimes or stimulate internal markets demand do have
an impact on FDI.
17
We further find that other policy and institutional factors, especially the factors that make the investment
environment costly, such as labour market rigidity, tax rate and corruption, have significant influence on FDI
inflows. Specifically, our estimations evidence that a one percent decrease in the unemployment rate or in
total tax rate (in percentage of commercial profits) leads to 0.04 and 0.08 percent increase in FDI,
respectively. Moreover, a one percent improvement in the perceptions of corruption control results in a 0.10
percent increase in FDI inflows.
Market size, economic stability, and infrastructure facility do not seem to help build up the confidence of
foreign investors and increase FDI inflows, which contrast with several extant studies (e.g., Aseidu, 2006;
Xaypanya et al., 2015).
5. Conclusions
The present study analysed the impact of a country’s Non-Renewable Energy Resources (NRERs)
endowments on FDI inflows in a wide sample of countries over almost two decades (1995-2012).
Controlling for a number of factors traditionally believed to influence FDI (e.g., human capital, trade
openness, tax rates, institutional quality), and resorting to fixed effect panel estimations the study contributes
to the scientific literature in the area in three main ways.
First, it contributes to the very scarce literature that specifically addressed the role of NRERs on FDI
inflows. Among the few (13) studies that includes NRERs as a determinant of FDI, only four – Asiedu
(2006; 2013), Ezeoha and Cattaneo (2011), and Asiedu and Lien (2011) – explicitly examine the interaction
between natural resources and FDI.
Second, the present study includes a more updated and wider heterogeneity of countries (125 countries:
developed, developing, less developed, and emergent; located in all continents; rich/poorly endowed in
natural resources), over a longer period (18 years: 1995-2012), than existing studies which explicitly or
implicitly includes NRERs. The vast majority of the latter focuses particular/small set of countries: Arab
18
economies (Aziz and Mishra, 2016); Economic Community of West African States (Ajide and Raheem,
2016); Eurasia (Deichmann et al., 2003); Sub-Saharan Africa countries (Asiedu, 2006; Ezeoha and Cattaneo,
2011); and BRICS (Brazil, Russia, India, China and South Africa) (Jadhav, 2012). The works by Asiedu and
Lien (2011) and Asiedu (2013) encompass a large number of countries (respectively, 112 and 99) but only
consider developing countries. Regarding the time span, although four studies – Mohamed and Sidiropoulos,
2010; Asiedu and Lien, 2011; Asiedu, 2013; Aziz and Mishra, 2016 - cover a longer period than ours, most
of the existing studies focus their analysis on a period of study inferior to fifteen years, with the latest year
analysed being 2008 or earlier.
Third, it is to the best of our knowledge the first empirical study to assess the impact of ‘proven’ (the
economically extractable part of) NRERs on FDI inflows. We plainly demonstrate that the impact of NRERs
endowments on FDI attraction is contingent on how those endowments are measured. Highly resource
endowed countries in terms of ‘proven reserves’ (of oil, coal and gas) do not necessarily attract FDI, whereas
those countries whose mineral fuel exports constitutes a large part of the corresponding total exports tend to
be privileged recipients of FDI. This evidence seems to support Mina’s (2007) content that rich ‘proven’
NRER countries have abundance of financial resources that makes them less reliant on overseas finance or
that resource seeking FDI targets mainly economically shaky countries (Ajide and Raheem, 2016).
Our results, nevertheless, stand in sharp contrast with the recent contribution by Asiedu (2013), who found
that, even after controlling for the quality of institutions and other relevant determinants of FDI, the share of
natural resources in total exports harmfully affects FDI. As referred above, when using the share of mineral
fuel exports in total exports we found a highly significant and positive effect of NRER endowments on FDI.
Moreover, our estimates confidently indicate that when controlling for NRERs endowments, FDI attraction
is fostered once countries make convincing efforts to open up their economies to international trade and
devote resources to the enhancement of their human capital, control corruption, and have more beneficial tax
rates.
19
Such results highlight the prominence of governmental actions over Nature’s idiosyncrasies, in particular the
importance of investing in human capital, improving the quality of institutions (by controlling corruption and
enforcing contracts and property rights), and promoting policies to open/ liberalise the economy (by
adopting export-oriented policies and eliminating/lowering taxes on corporate profits).
Despite the contributions of the present study, several limitations should be itemized, which nevertheless
constitute promising avenues for future research. First, we did not consider the different final uses of the
three types of resource – oil, coal, and natural gas – which may lead to interesting conclusions on the
targeting location of FDI based on the type of fuel. Future research could explore this issue. Second,
although our sample included a wider diversity of countries, such diversity was somehow overlooked. It
would also be interesting to divide the sample in developed and developing countries and assess the extent to
which results would hold for these two groups. Third, further improvements could be accounted for at the
methodological level by using the latest dynamic panel data models which allow for unobserved countryspecific effects, measurement errors, estimator bias due to persistency in time series, and even endogeneity
problems (Bond et al. 2001; Vedia-Jerez and Chasco, 2016).
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Table 1: Summary of the main motives for FDI
Motives for FDI
Market-seeking
Objectives
Host country attractiveness factors
Maintain or protect markets previously supplied
through exports or exploiting or promoting new
markets.
25
Market size and growth;
Psychic distance;
Easy access to adjacent markets.
Resource-seeking
Minimizing costs and ensuring security of supply
sources (e.g. demand for resources such as mineral
fuels, agricultural products, among others);
Reducing the costs of labour (e.g. demand for
abundant supply of cheap unskilled or semi-skilled
labour).
Possession of natural resources such as
mineral fuels;
Abundant endowment of unskilled and cheap
labour;
Access to skilled labour and its cost.
Efficiency-seeking
Re-organize international production
(concentrating production in a few locations from
the which supplies several markets) in order to take
advantage of several factors such as different factor
endowments, institutional frameworks, economic
policies, among others.
Costs of resources such as land, raw materials
or labour “adjusted for productivity of labour
inputs”;
Reduced transport and communication costs
from and within the country;
To be a member of a regional integration
agreement.
Asset-seeking
Increase the investor’s overall portfolio in terms of
physical assets and human skills, which it
considers to contribute to maintain or strengthen its
specific advantages or weaken the ownership
specific advantages of its competitors.
Competition policy, particularly with regard
to mergers and acquisitions;
Several created assets (e.g. technological,
managerial, among others);
Availability of ports, roads and other physical
infrastructure.
Source: Adapted from Dunning and Lundan (2008, p. 325)
Table 2: Factor endowments in natural resources and FDI – summary of empirical studies
FDI targeta
(period of analysis)
Proxy
Method for data
analysis
Effect
Author(s) (year)
Eurasia
(1993-1998)
Dummy variable (=0 if the
country is poorly endowed in
natural resources; =1 if
moderate; =2 if high)
OLS regression
+
Deichmann et al.
(2003)
Dummy variable (=1 if the
country is classified as
resource rich and zero
otherwise)
GLS regression
+/0
Ezeoha and Cattaneo
(2011)
Dynamic panel
data
+/0
Ajide and Raheem
(2016)
Pooled OLS and
Panel data
+/0
Mhlanga et al. (2010)
30 SSA countries
(1995-2008)
15 ECOWAS
(2000-2013)
1,320 projects across 14 SADC
countries
(1994–2005)
Dummy variable (=1 if the
country is classified as
resource rich and zero
otherwise)
Investments in the extractive
industry and utility provision
(water, gas, electricity)
(dummy)
Ex-USSR
(1996-2005)
Production index oil and gas
Spatial
autoregressive
model
+
Ledyaeva (2009)
41 African countries
(1998–2007)
Production of crude oil
Panel data
+
Sanfilippo (2010)
Total oil supply (production of
crude oil, natural gas plant
liquids, and other liquids, and
refinery processing gain)
Dynamic panel
data
+
Aziz and Mishra
(2016)
12 MENA + 24 Developing
countries
(1975-2006)
Exports of mineral fuels (in
log)
Panel data
+
Mohamed and
Sidiropoulos (2010)
50 largest host countries
(1991-2005)
Share of raw material exports
(including fuels, ores and
metals) to its total merchandise
exports
Panel data
+
Cheung and Qian
(2009)
22 SSA countries
Share of mineral fuels in total
Panel data
+
Asiedu (2006)
16 Arab economies
(1984 to 2012)
26
(1984-2000)
exports
112 developing countries
(1982-2007)
BRICS (Brazil, Russia, India,
China and South Africa)
(2000-2009)
99 developing countries
(1984 to 2011)
Dynamic panel
data
-
Asiedu and Lien
(2011)
Panel unit-root test,
and multiple
regressions
-
Jadhav (2012)
Dynamic panel
data
-
Asiedu (2013)
Legend: + positive effect & statistically significant; - negative effect & statistically significant; 0 effect not statistically significant. SSA - Sub-Saharan Africa;
MENA - Middle East and North Africa; SADC - Southern African Development Community; ECOWAS - Economic Community of West African
States.
Notes: a Country is the analysis unit for all studies cited.
Source: Compiled by the authors.
Table 3: Descriptive statistics and unit roots tests of the variables
Group of
determinant
Variable
Proxy
Mean
St.
deviation
Min
Max
FDI
Net FDI inflows
(in % of the GDP)
4.1
6.065
-16.1
87.4
NRER exports (%
of merchandise
exports)
16.3
25.648
0.0
99.7
Proven NRER
35458.0
177226.1
0.0
2812635.0
Human capital
Mean years of
schooling
6.5
1.762
0.6
13.1
Market size
GDP per capita,
PPP (constant
2011 international
$)
14550.1
15749.6
369.8
95751.3
Market growth
GDP per capita
growth (annual %)
2.7
4.802
-18.9
92.4
Trade
openness
Trade (% of GDP)
80.3
49.370
15.6
450.0
16.5
138.697
-27.0
8.6
6.049
1417.7
Natural factors
Endowments
Economic
instability
Economic and
policy
Production
costs
Tax rate
Infrastructure
Institutions
Institutional
quality
LLC
-5.399***
(0.000)
Unit root tests
HT
Time trend
-26.737***
Not included
(0.000)
3.869
(0.999)
-15.704***
(0.000)
Not included
in HT
-63.883***
(0.000)
-20.213***
(0.000)
-1.256
(0.104)
-0.073
(0.4711)
Not included
in LLC
-21.481***
(0.000)
5.754
(1.000)
-6.469***
(0.000)
7.3e+14
(1.000)
-38.693***
(0.000)
-4.374***
(0.000)
5399.5
-8.509***
(0.000)
-35.442***
(0.000)
Not included
0.6
39.3
-1.987**
(0.024)
-6.032***
(0.000)
Not included
966.026
64.4
8525.0
-0.451
(0.326)
-10.904***
(0.000)
Included
50.1
35.354
7.4
339.1
2.9e+13
(1.000)
-2.076**
(0.019)
Not included
in HT(1)
19.0
19.261
0.0
74.8
-15.919***
(0.000)
9.789
(1.000)
Not included
in LLC
15.1
11.757
0.0
98.4
-11.550***
(0.000)
-14.386***
(0.000)
Not included
in HT
Control of
corruption
0.0
1.039
-2.1
2.6
-2.794***
(0.003)
-5.979***
(0.000)
Not included
Political stability
and absence of
violence/terrorism
-0.2
0.941
-3.0
1.7
-17.034***
(0.000)
-5.764***
(0.000)
Not included
in HT
Inflation, GDP
deflator (annual
%)
Unemployment,
total (% of total
labour force)
Cost to import
(US$ per
container)
Total tax rate (%
of commercial
profits)
Fixed telephone
subscriptions (per
100 people)
Electric power
transmission and
distribution losses
(% of output)
Not included
in LLC
Included
Not included
Not included
in HT
-2.316***
-0.717
Not included
in LLC
(0.010)
(0.237)
Note: LLC is Levin–Lin–Chu (adjusted t*), HT is Harris-Tzavalis (z). For LLC and HT, the null hypothesis: panels contain unit root, while the alternative Ha:
panels are stationary. P-values in brackets. *** (**) [*] denotes significance at the 1% (5%) [10%] levels for p-values; (1) no constant and subtracting cross-sectional
Rule of Law
-0.1
0.995
-2.2
27
2.0
means in the case of HT test.
Table 4: Determinants of FDI attraction: fixed effects panel data estimations (dependent variable –FDI/GDP ratio in logs)
Determinan
t
Factor
Endowment endowments
s
Economic
and policy
1
NRER exports
(% of
merchandise
exports)
Proven NRER
0.023
2
**
*
0.023
3
**
*
0.026
Models B
4
**
*
0.023
5
**
*
0.023
6
**
*
0.025
1
2
3
4
5
6
**
*
(0.006) (0.006) (0.002) (0.005) (0.005) (0.002)
0.002
0.002
0.001
0.003
0.002
0.002
(0.632) (0.733) (0.888) (0.554) (0.644) (0.752)
0.075* 0.067
0.059 0.075* 0.066
0.064 0.088** 0.079* 0.072* 0.086** 0.076* 0.075*
(0.089) (0.130) (0.180) (0.078) (0.122) (0.129) (0.047) (0.075) (0.100) (0.045) (0.077) (0.078)
Human
capital
Mean years of
schooling
Market size
GDP per capita,
PPP (constant
2011
international $)
Market
growth
GDP per capita
growth (annual
%)
Trade
openness
Trade (% of
GDP)
Economic
instability
Inflation, GDP
deflator (annual
%)
0.012
0.010
0.016
0.011
0.010
0.016
0.011
0.009
0.015
0.011
0.009
0.014
(0.348) (0.435) (0.199) (0.358) (0.447) (0.212) (0.380) (0.484) (0.243) (0.385) (0.491) (0.252)
Unemployment,
total (% of total
labour force)
0.040** 0.041** 0.044** 0.040** 0.040** 0.044** 0.038** 0.038** 0.041** 0.038** 0.038** 0.041**
(0.021) (0.021) (0.011) (0.022) 0.021) (0.012) (0.031) (0.030) (0.020) (0.031) (0.030) (0.020)
Cost to import
(US$ per
container)
0.005
0.003
0.012
0.003
0.001
0.009
0.008
0.006
0.014
0.006
0.004
0.012
(0.719) (0.840) (0.423) (0.830) (0.955) (0.542) (0.602) (0.715) (0.347) (0.687) (0.807) (0.435)
Production
costs
Tax rate
Infrastructur
e
Institutional
quality
-0.011 -0.006 -0.026 0.001
0.006 -0.009 -0.002 0.004 -0.014 0.007
0.012 -0.001
(0.690) (0.834) (0.342) (0.973) (0.815) (0.722) (0.952) (0.884) (0.613) (0.796) (0.631) (0.969)
0.131** 0.137** 0.137** 0.127**
*
*
*
0.133** 0.133**
*
*
0.125** 0.131**
*
*
0.131**
*
0.122** 0.127**
*
*
0.127**
*
(0.001) (0.000) (0.000) (0.001) (0.000) (0.000) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001)
0.176** 0.176** 0.174** 0.176** 0.175** 0.174** 0.188** 0.188** 0.186** 0.188** 0.187** 0.186**
*
*
*
*
*
*
*
*
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
0.084** 0.089** 0.075** 0.083**
*
0.000)
*
*
*
(0.000) (0.000) (0.000)
0.084** 0.090** 0.076** 0.084** 0.089** 0.075**
**
0.073
*
*
*
*
*
*
*
*
*
*
*
(0.012)
(0.004) (0.002) (0.010) (0.004) (0.002)
(0.004) (0.002) (0.009) (0.004) (0.002) (0.010)
Fixed telephone
subscriptions
(per 100 people)
0.010
0.009
0.018
(0.603) (0.639) (0.342)
Electric power
transmission and
distribution
losses (% of
output)
0.106**
(0.018)
Political stability
and absence of
violence/terroris
m
F-test (p-value)
R square
0.088**
0.004
0.004
0.012
(0.811) (0.840) (0.517)
0.017
0.019
0.019
(0.218) (0.175) (0.166)
0.102**
(0.023)
0.008
(0.632)
Rule of Law
Goodness of
fit
*
Total tax rate (%
of commercial
profits)
Control of
corruption
Institutions
Models A
Proxy
0.016
0.017
0.017
(0.256) (0.209) (0.209)
0.106**
(0.018)
0.007
(0.682)
0.103**
(0.021)
0.006
(0.719)
0.005
(0.766)
0.186**
0.180**
0.173**
*
*
*
0.169**
*
(0.000)
(0.000)
(0.000)
(0.000)
15.39
14.86
16.33
15.51
15.02
16.43
14.66
14.12
15.39
14.78
14.27
15.51
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
0.408
0.442
0.446
0.444
0.442
0.446
0.444
0.440
0.443
0.442
0.440
0.443
Hausman test (p- 29.96
30.31
33.22
29.33
30.86
35.56
29.14
25.53
31.91
28.42
26.05
30.55
value)
(0.002) (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.008) (0.014) (0.003) (0.006) (0.001)
Note: *** (**)[*] estimates are statistically significant at 1% (5%) [10%]. Grey cells highlight the statistical significant estimates. P-values are in brackets. All
variables are in logs. All models include a constant term. Number of observations: 2250 (125 countries*18 years)
Table A1: List of countries in the sample
28
Albania
Dominican Republic
Latvia
Russian Federation
Algeria
Ecuador
Lebanon
Rwanda
Angola
Egypt, Arab Rep.
Lesotho
Saudi Arabia
Argentina
El Salvador
Lithuania
Senegal
Armenia
Ethiopia
Macedonia, FYR
Serbia
Australia
Finland
Madagascar
Sierra Leone
Austria
France
Malawi
Singapore
Azerbaijan
Georgia
Malaysia
Slovak Republic
Bangladesh
Germany
Mali
Slovenia
Belarus
Ghana
Mexico
South Africa
Belgium
Greece
Moldova
Spain
Benin
Guatemala
Mongolia
Sri Lanka
Bolivia
Guinea
Morocco
Sweden
Bosnia and Herzegovina
Haiti
Mozambique
Switzerland
Botswana
Honduras
Nepal
Syrian Arab Republic
Brazil
Hong Kong SAR, China
Netherlands
Tanzania
Bulgaria
Hungary
New Zealand
Thailand
Burkina Faso
India
Nicaragua
Togo
Cameroon
Indonesia
Niger
Tunisia
Canada
Iran, Islamic Rep.
Nigeria
Turkey
Central African Republic Ireland
Norway
Uganda
Chad
Israel
Oman
Ukraine
Chile
Italy
Pakistan
United Kingdom
China
Japan
Panama
United States
Colombia
Jordan
Papua New Guinea
Uruguay
Congo, Dem. Rep.
Kazakhstan
Paraguay
Uzbekistan
Congo, Rep.
Kenya
Peru
Venezuela, RB
Costa Rica
Korea, Rep.
Philippines
Vietnam
Cote d'Ivoire
Kuwait
Poland
Yemen, Rep.
Croatia
Kyrgyz Republic
Portugal
Zambia
Czech Republic
Lao PDR
Romania
Zimbabwe
Denmark
Table A2: Model to be estimated – summary of variables and their proxies
Determinant
Dependent
variable
Natural
Endowments
factors
Variable
Description
Source
Foreign direct
investments (FDI)
net inflows in
percentage of the
GDP
Foreign direct investment are the net inflows
of investment (new investment inflows less
disinvestment) to acquire a lasting
management interest (10 percent or more of
voting stock) in an enterprise operating in an
economy other than that of the investor.
World Bank, World
Development Indicators
[International Monetary Fund,
International Financial Statistics
and Balance of Payments
databases, World Bank,
International Debt Statistics, and
World Bank and OECD GDP
estimates.]
Fuel minerals (coal,
oil and natural gas)
exports (% of
merchandise
exports)
Fuels comprise SITC section 3 (mineral fuels,
lubricants and related materials).
International Trade Centre
Proven reserves
Non-Renewable Energy Resources (Crude Oil
+Natural Gas+ Coal) proved reserves in tonne
of oil equivalent. Proved reserves of crude
oil/Gas/Coal are the estimated quantities
Expected effect on FDI
Positive
29
International Energy Statistics, in
http://www.eia.gov/beta/
Human
capital
Market size
Mean years of
schooling
GDP per capita, PPP
(constant 2011
international $)
which geological and engineering data
demonstrate with reasonable certainty to be
recoverable in future years from reservoirs
under existing economic and operating
conditions.
Average number of years of education
received by people ages 25 and older,
converted from educational attainment levels
using official durations of each level.
GDP per capita based on purchasing power
parity (PPP). PPP GDP is gross domestic
product converted to international dollars
using purchasing power parity rates. Data are
in constant 2011 international dollars.
Barro and Lee (2013), UNESCO
Institute for Statistics
Positive
World Bank, World
Development Indicators [World
Bank, International Comparison
Program database]
Positive
World Bank, World
Development Indicators [World
Bank national accounts data, and
OECD National Accounts data
files]
World Bank, World
Development Indicators [World
Bank national accounts data, and
OECD National Accounts data
files]
Market
growth
GDP per capita
growth (annual %)
Annual percentage growth rate of GDP per
capita based on constant local currency.
Openness of
economy
Trade (% of GDP)
Trade is the sum of exports and imports of
goods and services measured as a share of
gross domestic product.
Inflation, GDP
deflator (annual %)
Inflation as measured by the annual growth
rate of the GDP implicit deflator shows the
rate of price change in the economy as a
whole. The GDP implicit deflator is the ratio
of GDP in current local currency to GDP in
constant local currency.
World Bank, World
Development Indicators [World
Bank national accounts data, and
OECD National Accounts data
files.]
Negative
Unemployment,
total (% of total
labour force)
Unemployment refers to the share of the
labour force that is without work but available
for and seeking employment.
World Bank, World
Development Indicators
[International Labour
Organization, Key Indicators of
the Labour Market database]
Negative
World Bank, World
Development Indicators [World
Bank, Doing Business project]
Negative
World Bank, World
Development Indicators [World
Bank, Doing Business project]
Negative
World Bank, World
Development Indicators
[International
Telecommunication Union,
World Telecommunication/ICT
Development Report and
database.]
Positive
Economic
instability
Economic
and policy
Production
costs
Cost to import (US$
per container)
Tax rate
Total tax rate (% of
commercial profits)
Fixed telephone
subscriptions (per
100 people)
Infrastructure
Cost measures the fees levied on a 20-foot
container in U.S. dollars. All the fees
associated with completing the procedures to
export or import the goods are included.
Total tax rate measures the amount of taxes
and mandatory contributions payable by
businesses after accounting for allowable
deductions and exemptions as a share of
commercial profits.
Fixed telephone subscriptions refers to the
sum of active number of analogue fixed
telephone lines, voice-over-IP (VoIP)
subscriptions, fixed wireless local loop (WLL)
subscriptions, ISDN voice-channel equivalents
and fixed public payphones.
Positive
Positive
Electric power transmission and distribution
losses include losses in transmission between
World Bank, World
sources of supply and points of distribution
Development Indicators [IEA
Negative
and in the distribution to consumers, including
Statistics © OECD/IEA 2014]
pilferage.
Control of Corruption captures perceptions of
the extent to which public power is exercised
Control of
World Bank, Worldwide
for private gain, including both petty and
Positive
corruption*
Governance Indicators
grand forms of corruption, as well as "capture"
of the state by elites and private interests.
Political Stability and Absence of
Political stability
Violence/Terrorism measures perceptions of
World Bank, Worldwide
Institutional
and absence of
the likelihood of political instability and/or
Positive
Institutions
Governance Indicators
*
quality
violence/terrorism
politically-motivated violence, including
terrorism.
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
World Bank, Worldwide
Rule of Law*
Positive
quality of contract enforcement, property
Governance Indicators
rights, the police, and the courts, as well as the
likelihood of crime and violence.
Notes: * Estimate gives the country's score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.
Source: Compiled by the authors.
Electric power
transmission and
distribution losses
(% of output)
30
Table A3: Correlation matrix
FDI
Group of
determin
ant
Proxy
Net FDI
inflows (in
FDI
% of the
GDP)
Fixed
telephone
subscription
s (per 100
people)
Infrastru
Electric
cture
power
transmissio
n and
distribution
losses (% of
output)
Mean years
Human
of
capital
schooling
Inflation,
Economi
GDP
c
deflator
instabilit
(annual %)
y
Unemploy
ment, total
(% of total
labour
Producti
force)
on costs
Cost to
import
(US$ per
container)
Control of
corruption
Political
Institutio stability and
absence of
nal
violence/ter
quality
rorism
Rule of
Law
Total tax
Financial
rate (% of
and tax
incentive commercial
profits)
s
GDP per
capita, PPP
(constant
Market
2011
size
internationa
l $)
GDP per
capita
Market
growth
growth
(annual %)
Openness
Trade (% of
of
GDP)
economy
Net
FDI
inflo
ws
(in
% of
the
GDP
)
Infrastructure
Fixed
telephon
e
subscrip
tions
(per 100
people)
Electric
power
transmis
sion and
distribut
ion
losses
(% of
output)
Hum Econo
an
mic
Production costs
capit instabi
al
lity
Mean
years
of
school
ing
Inflatio
n, GDP
deflator
(annual
%)
Unemploy
ment, total
(% of total
labor
force)
Cost
to
import
(US$
per
contai
ner)
Institutional quality
Contro
l of
corrup
tion
Political
stability and
absence of
violence/ter
rorism
Rule
of
Law
Finan
cial
and
tax
incent
ives
Mar
Marke ket
t size grow
th
Open
ness
of
econo
my
Factor
endowm
ents
Total
tax rate
(% of
comme
rcial
profits)
GDP
per
capita,
PPP
(constan
t 2011
internati
onal $)
Trade
(% of
GDP)
NRER
exports
(% of
merchand
ise
exports)
GDP
per
capita
growt
h
(annu
al %)
1.00
0
0.14
0
1.000
0.04
0
-0.464
1.000
0.19
2
0.806
-0.379
1.000
0.03
1
-0.178
0.103
0.082
1.000
0.04
8
0.191
-0.009
0.256
0.051
1.000
0.05
0
-0.367
0.206
0.303
0.091
0.000
1.000
0.13
5
0.699
-0.518
0.501
-0.317
-0.014
0.400
1.000
0.15
5
0.511
-0.260
0.414
-0.287
0.001
0.242
0.649
1.000
0.14
0
0.711
-0.512
0.549
-0.338
0.013
0.431
0.928
0.698
1.00
0
0.13
0
-0.124
0.057
0.184
0.206
-0.015
0.162
0.223
-0.270
0.29
1
0.13
4
0.897
-0.490
0.760
-0.212
0.089
0.429
0.738
0.514
0.74
-0.226
3
1.000
0.15
1
0.023
0.037
0.057
-0.053
0.032
0.028
0.013
0.071
0.00
-0.018
3
-0.019
1.00
0
0.42
2
0.215
-0.075
0.270
-0.030
0.045
0.183
0.166
0.261
0.18
-0.256
4
0.239
0.08
4
31
1.000
1.000
Factor
endowme
nts
NRER
exports (%
of
merchandis
e exports)
0.02
0
0.102
0.042
0.09 0.335
-0.189
6
Note: Grey cells highlight very high correlations.
Proven
NRER
0.117
0.113
0.098
0.044
0.137
-0.135
0.15
6
0.086
0.213
0.00
9
0.060
1.000
0.327
0.045
0.099
0.111
0.133
0.058
0.17
-0.046
9
0.326
0.03
5
-0.193
0.250
32