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African
Develop
ment Ba
nk Grou
p
Working
Pape
r Serie
s
n°285
Septe
mber 2
017
Owen Nyang`oro
Capital Inflows and EconomicGrowth in Sub-Sahara Africa
Working Paper No 285
Abstract This study analyzes the effect of capital flows on economic growth in sub-Saharan Africa, using a system of generalized methods of moment (GMM) model. It tests the extent to which the level and volatility of capital inflows, both disaggregated and total, affect economic growth. The study finds that portfolio equity has a positive effect on economic growth while private equity and debt are inversely related to growth. However, volatility of portfolio equity and private equity has no impact on economic growth, pointing to low levels of financial integration in these countries. Total capital inflows, both gross and net inflows, have a negative effect on growth, while volatility of total gross capital inflows
has a positive effect, and that of total net capital inflows is positively related to growth. The effect of total capital inflows is possibly influenced by the overall effect of debt in these economies. The findings suggest that concerns on capital inflows should mainly be addressed through the debt market, and that the growth benefits of capital inflows can be achieved by improving financial markets, ensuring macroeconomic stability, and having in
place good institutions. .
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Correct citation: Nyang`oro, O. (2017), Capital Inflows and Economic Growth in Sub-Saharan African Countries, Working Paper Series N° 285, African Development Bank, Abidjan, Côte d’Ivoire.
1
Capital Inflows and Economic Growth in Sub-Saharan Africa
Owen Nyang`oro1
JEL Codes: C33, F21, F32
Keywords: Capital inflows, FDI, portfolio flows, economic growth, SSA
1 School of Economics, University of Nairobi, Kenya. The paper was developed while the author was a Visiting Research Fellow at the Development Research Department (EDRE), African Development Bank. The author is grateful to African Economic Research Consortium (AERC) and the African Development Bank (AfDB) for that opportunity. The work benefited from comments and suggestions from Dr. Amadou Boly and Prof. John Ayanwu, Development Research Department, African Development Bank. Comments of seminar participants at the African Development Bank seminar are also appreciated.
2
1. Introduction
The level of capital inflows to sub-Saharan African (SSA) countries has increased in recent
periods, with the volume of external financial flows to SSA increasing from $20 billion in 1990 to
above $120 billion in 2012, driven mainly by private capital flows and remittances (Sy and
Rakotondrazaka 2015). The rise in inflows is attributed to cyclical and structural factors,
suggesting that the flows are likely to be sustained over the long term (Pradhan et al. 2011). The
composition of capital flow has also shifted away from debt instruments to equity instruments,
both direct and portfolio, but with a substantial increase in portfolio flows (Fernandez-Arias and
Montiel 1996; Sy and Rakotondrazaka 2015).
Capital inflows provide a major source of financing and, hence, investment in the recipient
countries, thereby supporting growth (Fernandez-Arias and Montiel 1996; de Mello, Jr. 1999;
Calderón and Nguyen 2015), facilitating transfer of managerial and technological know-how, and
improving performance of domestic financial markets (Borensztein, De Gregorio, and Lee 1998;
Kose et al. 2010; Calderón and Nguyen 2015). Despite the beneficial effects of capital flows, they
also have some negative effects (Singh 2003; Glick and Hutchison 2009; Cardarelli, Elekdag, and
Kose 2010; Pradhan et al. 2011; Combes, Kinda, and Plane 2012), including policy challenges
such as the exchange rate policy to pursue and whether to put in place controls (Elbadawi and Soto
1994; Bosworth, Collins, and Reinhart 1999). Capital flows may generate overheating, excessive
credit creation and asset price bubbles, loss of competitiveness due to currency appreciation, and
increased vulnerability to crisis (Bosworth, Collins, and Reinhart 1999; Bacchetta and van
Wincoop 2000; Bekaert and Harvey 2000; Cardarelli, Elekdag, and Kose 2010; Pradhan et al.
2011; Forbes and Warnock 2012).
Capital flows may cause instabilities in the capital account due to its transitory nature that
makes it volatile (Fernandez-Arias and Montiel 1996; Singh 2003). For instance, Singh (2003)
asserted that while foreign direct investment (FDI) may be less volatile than other capital flows,
its implications on a country’s balance of payments by creating foreign exchange liabilities in the
form of dividend payments or profits repatriation, should be considered as foreign exchange
liabilities may result into a liquidity crisis even in the short run.2 However, FDI flows may also
2Non-FDI capital inflows are considered “hot money” that could potentially switch direction within a short horizon (Alfaro, Kalemli-Ozcan, and Volosovych 2007; Glick and Hutchison 2009). Singh (2003) noted that FDI may not be
3
surge, leading to exchange rate appreciation and reducing competitiveness (Singh 2003). Thus
resurgence of capital flows has led to challenges in macroeconomic management and pressures in
asset markets in many emerging markets (Pradhan et al. 2011). Hence, financial integration,
though beneficial, may also lead to macroeconomic instabilities resulting from exposure of
domestic markets to external volatility, making the impact of integration ambiguous (Calderón and
Schmidt-Hebbel 2008).
Studies on capital flows have focused on the determinants of capital flows (Agenor 1998;
Ahmed and Zlate 2014; Brafu-Insaidoo and Biekpe 2014), the effectiveness of capital controls
(Forbes and Warnock 2012; Davis and Presno 2014), and the macroeconomic implications of
capital flow surges and episodes (Rangarajan and Prasad 2008; Cardarelli, Elekdag, and Kose
2010; Burger and Ianchovichina 2014). Other studies have addressed the relationship between
capital flows and growth, or between capital flows and demand, specifically, investment and
consumption (Federico, Vegh, and Vuletin 2013; Calderón and Nguyen 2015). Studies have not
conclusively established the relationship between capital flows and growth, however, some
empirical work has shown positive effects while others show negative effects.
Empirical evidence has focused on the effect of financial integration and, hence, capital
flows on economic growth, investment, and savings, with little focus having gone toward
establishing the contribution of capital flow and its volatility on economic growth in SSA.
Increased capital inflows to SSA countries have made these economies more susceptible to
instabilities in the international financial system; yet, there is no conclusive evidence on the effect
of such flows. This study seeks to establish the effects and extent to which the level and volatility
of capital inflows impact economic growth. Gross capital inflows and net capital inflows are
considered in disaggregate and aggregate form. A distinction is made between private equity
inflows and debt inflows, to establish whether there are differential impacts by type of capital
inflows, and the results are compared to those of total capital inflows.
2. Background Information
The volume of capital flows to SSA countries has increased, especially after the financial crisis of
2008. The increase is attributed to improved macroeconomic performance and implementation of
more stable than other capital flows, as it is hard to distinguish between FDI and portfolio flows, and that its calculation in the IMF balance of payments consists of retained earnings, which are affected by business cycles.
4
the Multilateral Debt Relief Initiative by some countries, which coincided with increases in global
liquidity and higher oil and commodity prices (Deléchat et al. 2009). Most of this increase arises
from increasing levels of FDI.
Figure 1 shows the trends of FDI and portfolio equity inflows to SSA, as a share of the
gross domestic product (GDP). Before the 1990s, the shares of both flows were low, averaging
less than 1 percent of GDP. Acceleration of both inflows started in the 1990s, a period
characterized by financial liberalization in most SSA countries. The trend, however, changed after
1998. The share of portfolio equity inflows declined drastically from 2.7 percent in 1999 to –0.3
percent in 2001, while the share of FDI fell from 2.7 percent in 1999 to 1.9 percent in 2000, but
picked up the following year to record the highest proportion, of 4.5 percent. This increase
coincided with global trade developments, in a period when China joined the World Trade
Organization (WTO). Inflows of portfolio equity have been fluctuating since then, while that of
FDI has generally increased. In 2014, SSA recorded net FDI inflows of US$46.1 billion and net
portfolio equity inflows of US$5.3 billion.
Figure 1. Foreign direct investment and portfolio inflows to SSA countries
Source: World Bank (2017b).
Over the same period, SSA countries recorded improved real GDP growth. The recent
growth rates have averaged about 5.5 percent since 2000, almost reaching the growth rates
recorded in early 1980s (Figure 2). This has been accompanied by high levels of investment as a
proportion of GDP, of about 20 percent for the entire period. Savings have also taken a slightly
upward trend from the early 1980s. While these developments cannot be directly attributed to
capital inflows, the trends show that there is some relationship between them; however, this may
vary by country.
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Foreign direct investment, net inflows(% of GDP)Portfolio equity, net inflows (%GDP)
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Figure 2. Trends of real GDP growth, investment, and savings in SSA
Source: IMF (2017b).
The current account balance has been negative, however, for most of the period, with
adverse situations recorded in early 1980s, late 1990s, and early 2000s (Figure 3). In 1981, the
current account balance share of GDP was –5.9 percent, but this improved later, recording a
positive value of 0.9 percent in 1985. Current account balance is related to movements in capital
flows through changes in exchange rate resulting from capital flows; however, there are also other
factors that affect current account balance.
Figure 3. Current account balance, percentage of GDP
Source: IMF (2017b)
The rest of the paper is organized as follows. In Section 2, relevant literature on capital flows
is reviewed. The methodology and data are discussed in Section 3; findings are presented in
Section 4, while conclusions are presented in the last section.
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Investment (%GDP)GDP, constant prices (% change)Gross national savings (%GDP)
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6
3. Literature Review
3.1. Channels of macroeconomic effects of capital flows
Capital flows affect economic growth either directly, through its effect on savings, cost of capital,
technology transfer, and development of financial sector, or indirectly, through increased product
specialization and improvements in macroeconomic policies and institutions due to competitive
pressures (Prasad et al. 2003, 2007). Capital flows are transmitted through three channels: an
overvaluation channel resulting from currency appreciation; a savings and investment channel
through domestic savings; and an institutional development channel where capital flows carry
indirect benefits via development of domestic financial sector institutions (Deléchat et al. 2009).
The direct effects are captured by the savings channel, and the efficiency and productivity
channel (Edwards 2001). The savings channel considers the effect of capital inflows on volume of
foreign savings through its ability to finance a current account deficit. This has a positive impact
on growth for credit-deficient countries and is indeterminate in effect in countries with low
investment opportunities due to real exchange rate appreciation, resulting from capital inflows
(Rodrik and Subramanian 2009). The efficiency and productivity growth channel considers
efficient allocation of international capital from capital-abundant countries to capital-scarce
countries with high return to capital, leading to a reduction in the cost of capital and, hence,
increase in investment and growth (Eichengreen and Leblang 2003; Henry 2007). A more open
capital account leads to better performance, as elimination of capital controls reduces distortions
and results in higher return on investment and productivity (Eichengreen and Leblang 2003;
Edwards 2001; Henry 2007).3 The savings channel addresses financing needs, while the efficiency
and productivity channel meets production needs and covers aspects, such as knowledge spillovers
and technology transfers.4 In the savings channel, capital inflow leads to rapid monetary
expansion; creates excess rise in domestic demand as domestic absorption increases, leading to
inflationary pressures and appreciation of the real exchange rate if demand is on non-traded goods,
or to current account deficits if demand is on traded goods (Berument and Dincer 2004; Combes,
Kinda, and Plane 2012).
3Stiglitz (2000) however noted that some interventions are welfare-enhancing. 4The difference is that the savings channel helps in bridging the financing gap, while the productivity channel mainly focuses on technological skills, know-how, and production capability. For instance, a recent study by Hoxhaj, Marchal, and Seric (2016), using firm-level data, found that capital flows, specifically FDI flows, are complementary to employment of foreign skilled works in SSA.
7
3.2. Empirical literature review
Empirical studies on capital flows have considered several aspects of its relation to growth,
exchange rate, credit, and other macroeconomic factors. The impact on various macroeconomic
factors is channeled through shocks resulting from capital inflows and depends on the allocative
efficiency of the domestic economy, the causes of the inflow, the domestic macroeconomic
context, and factors that determine the sustainability of inflows (Fernandez-Arias and Montiel
1996). This was emphasized by Choong, Yusop, and Law (2010) who established that FDI
promotes economic growth via an efficiency effect, while portfolio investment stimulates
economic growth via an investment effect. Borensztein, De Gregorio, and Lee (1998) found that
FDI is important for technology transfer and contributes relatively more to growth than domestic
investment and, hence, supports a crowding-in effect to domestic investment. Productivity of FDI,
however, is based on the absorptive capability of a country, as FDI and portfolio equity flows
promote economic growth more in countries with developed domestic financial markets and
broader institutional frameworks (Durham 2004; Driffield and Jones 2013).
Variations in capital flows to developing countries are mostly explained by shocks to real
variables of economic activity, such as foreign output and domestic productivity (De Vita and
Kyaw 2008). The impact of capital flows also depends on the level of financial development of a
country. Countries at low levels of financial development experience a negative effect on
performance, in the case of a more open capital account (Edwards 2001). Choong et al. (2010)
found private capital flows to positively impact growth in countries with well-developed financial
sectors but have negative effects in situations of poor financial sector development. The level of
capital flows, however, depends on the degree of market integration, which is measured by
differences in rates of return across countries (Frankel 1992). As Mohan and Kapur (2010) found,
the increasing volume of private capital flows to emerging market economies depends on, among
other factors, their growing degree of financial openness over time, growth in overall profitability
of firms, positive interest differentials in favor of these economies, and the expectation of
continuing currency appreciation.
The link between capital flows and growth has also been studied by testing their causal
relationship. Studies on the relationship between capital flows and growth, however, are
inconclusive in their findings.5 Examples are Cho and Tien (2014); Calderón and Nguyen (2015);
5 See Almfraji and Almsafir (2014) for a review of studies of the link between FDI and economic growth.
8
Tsai (1994); and Omri and Kahouli (2014), who found a positive relationship, while MacDonald
(2015) and Gourinchas and Jeanne (2013) found a negative relationship. Cho and Tien (2014)
examined the sources of growth of 32 countries in SSA and found that growth is largely associated
with an increase in the share of working-age population, capital accumulation, and total factor
productivity. This is supported by findings of Calderón and Nguyen (2015) that aid and FDI
inflows positively affect growth, while sovereign debt inflows do not. Tsai (1994) and Omri and
Kahouli (2014), in separate studies, established two-way linkages between FDI and growth.
Using cointegration and panel Granger causality tests, Abbes et al. (2015) found economic
growth and FDI to be cointegrated in the long run, in a sample of 65 countries from 1980–2010.
However, they established a unidirectional causality from FDI to GDP. De Mello, Jr. (1999), in a
study involving a sample of Organisation for Economic Co-operation and Development (OECD)
and non-OECD countries from 1970–1990, found positive effects of FDI on economic growth,
which they concluded was either through capital accumulation or knowledge transfer by
augmenting existing stock of knowledge (de Mello, Jr. 1999). Albulescu (2015) found both direct
and portfolio investments exerted an influence on long-term economic growth; when equity and
investment funds instruments were considered, Aizenman, Jinjarak, and Park (2011) found a
positive effect of FDI on growth but no effect from portfolio inflows and equity investment, while
short-term debt has no effect before a crisis period and a negative effect during the crisis. These
results are supported by the work of Quinn and Toyoda (2008), who used both de jure and de facto
measures of financial openness to demonstrate that capital account liberalization positively affects
growth in both developed and emerging market nations. Other studies that found a positive
relationship between capital flows and growth are Berument and Dincer (2004); Cardarelli,
Elekdag, and Kose (2010); and Federico, Vegh, and Vuletin (2013).
A negative correlation between net capital inflows and productivity was found by
MacDonald (2015), showing it to be caused by the most liquid assets of a country, including
foreign exchange reserve purchases. Gourinchas and Jeanne (2013) also found that capital does
not flow more to countries that invest and grow more, but is driven by national savings and that
capital flows are related to the pattern of international reserves. Other macroeconomic variables
are also affected by capital flows, such as exchange rate, asset prices, lending rates, and bank
lending (Elbadawi and Soto 1994; Brooks et al. 2001; Jansen 2003). The effects are different,
however, for periods before and after a crisis (Kandil and Trabelsi 2015).
9
Studies have also considered the role of global factors and, hence, surges and stops to
explain the link between capital flows and economic performance. Studies along these lines have
found global factors and their contagion to play significant roles in reducing capital flow volatility
(Forbes and Warnock 2012). Growth, interest rate differentials, and global risk are established to
determine net capital inflows to emerging markets (Ahmed and Zlate 2014). Sharp surges in capital
inflows pose policy challenges, since the inflow surges might run counter to the objectives of
domestic macroeconomic policies (Pradhan et al. 2011). Capital controls have been suggested in
some studies as a way of managing capital inflow surges; however, there have been mixed results
in their effectiveness (Forbes and Warnock 2012; Ahmed and Zlate 2014; Davis and Presno 2014).
Due to this ambiguity, it is suggested that countries should pursue policies that strengthen their
ability to withstand capital flow volatility rather than trying to reduce it (Forbes and Warnock
2012), because macro prudential and structural measures that strengthen the capacity of the
economy to absorb capital inflows may not be adequate in addressing capital flow challenges if
used alone (Balakrishnan et al. 2012). Literature aligned to the savings channel of capital flows
has established a positive relationship between capital flows and credit, both at the macroeconomy
level and at the firm level (see, for example, Harrison, Love, and McMillan [2004]; Furceri,
Guichard, and Rusticelli [2012]).
Among SSA countries, specific studies in this area include Deléchat et al. (2009) who
found a strong positive correlation between private capital flows and real GDP growth in a study
of 44 countries in SSA from 2000–2007. They also established that savings and investment are
positively and significantly related to capital inflows, which they interpreted to imply that the
countries are not necessarily constrained by investment. Their study is for a shorter period and
relies only on ordinary least squares (OLS) to derive the relationship. In a separate study, Alley
(2015) used panel cointegration to study 14 SSA countries and found private capital flows to
positively affect economic output and growth. The effects of private capital flow shocks were
negative, which they attributed to the poor response of the region’s economic performance to
inflows of private capital.
In another study, Alley and Poloamina (2015) found that shocks to FDI per capita increase
GDP per capita, while shocks to portfolio investment per capita and bank lending per capita lead
to a reduction in GDP per capita. Egbetunde and Akinlo (2015), using a panel cointegration
approach, found that financial globalization led to deterioration of economic growth in the long
10
run in 21 SSA countries, which they attributed to weak institutions. The impact varied with the
type of flow, as FDI decreases growth while debt improves growth. Another study by Mougani
(2012), using OLS, found financial integration positively and significantly affects growth;
however, the effect was not evident using generalized methods of moment (GMM). From a
graphical analysis, they found that instability of capital flows was more severe in countries that
were more open to capital flows and more pronounced for portfolio investment flows than for FDI.
The study captured volatility in capital flows and output growth using an autoregressive process
but did not estimate the impact of the volatility measures on growth, as they limited their analysis
to grouping countries in terms of level of volatility.
The literature points to the various macroeconomic variables that are affected by capital
inflows. The effects are not standard across countries, as they depend mainly on the type of capital
flowing to a country, the level of financial development, and the macroeconomic state of a country.
Despite empirical studies on the link between capital flows and growth, limited attempts have been
made to establish the growth effects of the level and volatility of capital flows. The empirical
evidence also does not conclusively support either positive or negative impacts of capital flows on
growth (Prasad et al. 2003; Aizenman, Jinjarak, and Park 2011). Such effects are possibly easy to
discern by studying the relationship between capital flows and economic growth. The focus of the
literature has also been more toward developed and emerging countries, with few empirical studies
on SSA, as discussed previously. This study establishes the relationship between the level and
volatility of capital inflows and economic growth. Capital inflows, measured using either gross
inflows or net inflows, are disaggregated into private equity inflows and debt inflows to test their
differential effects on macroeconomic variables.
4. Methodology and Data
4.1. Theoretical framework
The study draws from the neoclassical growth theory to establish the relationship between capital
flows and economic growth. From a simple neoclassical growth model, output depends on capital
and labor and total factor productivity or a measure of technological progress. In this case,
international capital enters the model directly through the capital stock. From extant literature,
capital inflows augment capital in low-capital economies where return to capital is higher than the
return to capital in high-capital economies. Hence, capital will flow from high-capital countries to
11
low-capital countries. Within this framework, capital inflows complement savings in capital-poor
countries and reduce the cost of capital and, hence, increase domestic investment (Kose et al.
2010). Private equity flows also leads to transfer of technology, knowledge, and managerial skills
(Borensztein, De Gregorio, and Lee 1998; Kose et al. 2010; Calderón and Nguyen 2015). Other
indirect benefits of capital inflows that drive growth include development of the domestic financial
sector, improvement in institutions, and better macroeconomic policies (Kose et al. 2009, 2010).
The indirect benefits are linked to the channels of capital flow transmission to the economy and
enhance growth through their effect on total factor productivity (Kose et al. 2009, 2010).
Capital inflows may lead to improvement in the financial sector to reduce vulnerability to
crisis, and have a positive impact on macroeconomic stability in financially open economies (Kose
et al. 2010). As the financial sector becomes more developed, the growth benefits of capital flows
will improve. Bekaert and Harvey (2000), for instance, found that with increased capital flows,
risk is reduced, equity returns become highly correlated with the world market, per capita GDP
increases marginally, and inflation and foreign exchange rate volatility are lowered.
Macroeconomic stability has implications for the volume and composition of capital flows, as
developed financial markets moderate the effects of shocks and helps reduce macroeconomic
volatility (Kose et al. 2010). Macroeconomic implications of financial globalization are
experienced through the effects on economic growth and growth volatility (Kose et al. 2010). Thus,
capital inflows enhance growth more in countries with strong macroeconomic policies and where
there is macroeconomic stability (Eichengreen 2000). Policy also plays a significant role in
explaining changes in the level of inflows and their volatility (Alfaro, Kalemli-Ozcan, and
Volosovych 2007).
The quality of institutions affects the composition and level of capital flows to developing
countries and are more important for financially open economies (Kose et al. 2010). Alfaro,
Kalemli-Ozcan, and Volosovych (2007) emphasized the role of institutions in shaping long-term
capital flows among a cross-section of countries. They found that both low institutional quality
and bad policies have played a role in the long-run volatility of capital flows during 1970–2000.
Institutions create an incentive structure of an economy and affect economic performance through
their effect on investment decisions, by protecting property rights of entrepreneurs (Alfaro,
Kalemli-Ozcan, and Volosovych 2007). Other than the indirect benefits, the level of trade
openness is also important, as it improves growth and stability benefits of integration by reducing
12
the probability of crises associated with sudden stops and current account reversals (Kose et al.
2010).
4.2. Empirical specification
The empirical model is based on Eichengreen and Leblang (2003), relating economic growth to its
determinants. The model is modified by including capital inflow variables. Drawing from the
theoretical framework and from growth literature, the general panel model is specified as:
, (1)
where is real GDP per capita growth in country i at time t, is a measure of capital flows,
Zit is a set of control variables that are determinants of real output per capita growth, and is an
error term. In most empirical work, the measure of capital flows is used as a de facto measure of
financial integration (see Kose et al. [2010]; Lane and Milesi-Ferretti [2007]; and Slesman,
Baharumsha, and Wohar [2015]). This measure provides a better picture of the extent of a
country’s integration into global financial markets (Kose et al. 2010). The error term can be
decomposed into country effects, capturing unobserved characteristics and an error term, such that
Equation (1) can be expressed as:
(2)
Capital inflow measures considered here are total capital inflows, or disintegrated into
specific components as private equity inflows (comprised of FDI and portfolio investment
inflows), and debt inflows as shares of GDP. Both gross capital inflows and net capital inflows are
used separately in the study. The initial income level, measured as the logarithm of real GDP per
capita at the beginning of a 5-year non-overlapping period, is included to capture growth
convergence. Other variables controlled for include: investment share of GDP; trade as a ratio of
GDP to measure openness (Eichengreen and Leblang 2003); annual inflation measured by using
percentage change in the consumer price index to capture macroeconomic stability; government
expenditure to GDP as a fiscal indicator (Slesman, Baharumshah, and Wohar 2015); private sector
credit as a ratio of GDP to measure the level of domestic financial depth; Polity2 indicator to
capture institutional quality or governance indicator; and a dummy taking the value of 1 if a
country is rich with resources and 0, otherwise. In a robustness check, the level of liquidity,
n
lititlliitiitiit uZKyy
1,21101
ity itK
itu
n
litiitlliitiitiit ZKyy
1,21101
13
measured by broad money to GDP, and a political rights and civil liberties indicator from Freedom
House, are used to capture domestic financial depth and institutional quality, respectively.
In line with growth literature, the data is averaged over 5-year, non-overlapping periods
from 1980–2011.6,7 The dependent variable is the average growth rate over a 5-year window. The
independent variables are also averaged over 5-year periods, except for initial income, which is
captured by the logarithm of real GDP per capita at the beginning of a 5-year period, and volatility
measures, which are standard deviations of the respective capital inflow variable over the 5-year
period.8
Given that Equation (2) is specified in a dynamic form, the lagged dependent variable is
correlated with the error terms; thus, estimation of the model, using OLS, will lead to biased
estimates. This problem was solved by Arellano and Bond (1991), using a generalized methods of
moments (GMM) estimator.9
(3)
Equation (3) still suffers from possible endogeneity due to feedback between economic growth
and its determinants, such as the capital flow measure, or due to omitted variable bias. This is
addressed using a system GMM estimator (Blundell and Bond 1998), which instruments for the
effects. The one-step system GMM estimator is used to estimate the model in Equation (3). GMM
takes first-difference from the dynamic model regression equation in order to remove the
unobserved time-invariant, country-specific effects and instrument the right-hand side variables in
first-differenced equations, using levels of the series and their lagged values. Many variants of the
model are estimated, considering gross capital flows or net capital flows. Disaggregated capital
inflow variables and aggregate capital inflows are also used separately in the same model, resulting
in different results, as presented in Section 5. To test whether lagged values of the explanatory
variables are valid instruments, the Sargan test of overidentifying restrictions, which considers the
sample analogy of the moment conditions used in the estimation process, is performed.
6Averages are used to remove cycle patterns in the data. For more details, see Islam (1995). 7 Due to the period covered in the study, the last period is an aggregation of 7 years, not 5 years. 8Standard deviations are used in growth studies to capture volatility of variables. See, for instance, Easterly, Islam, and Stiglitz (2001). 9Aggregating the data over 5-year periods results in a panel where the cross-sectional units are larger than the time units, making the use of GMM applicable.
n
lititlliitiitiit ZKyy
1,21101
14
4.3. Data source and description
Annual data covering 26 SSA countries from 1980 to 2011 (list of countries is presented in
Appendix Table A.1) is used. The sample size and the period covered is restricted by availability
of data, especially data on capital flows. Data on capital flow variables are obtained from the
database of Alfaro, Kalemli-Ozcan, and Volosovych (2014). The variables used from the database
are from Lane and Milesi-Ferretti (2007),10 who reported consistent estimates of foreign assets and
liabilities and their subcomponents, paying attention to their valuation effects. The database
provides estimates of annual capital flows based on the International Financial Statistics database
(IFS) issued by the International Monetary Fund (IMF 2017a), the Global Development Finance
database (GDF) by the World Bank (2017a), and the Development Assistance Committee database
(DAC) by the OECD’s Development Co-operation Directorate (OECD 2017). Annual capital
flows are calculated as annual flows of foreign liabilities less the flows of foreign assets. The data
used from the database are direct investment inflows, portfolio equity inflows, and debt inflows.
Debt inflows consist of both private and publicly guaranteed debt received by an economy. Private
equity inflows are calculated by summing up FDI and portfolio investment inflows, while total
capital inflows are obtained by summing together private equity inflows and private debt inflows.
These provide the gross capital inflow values. Net capital inflows are obtained by deducting assets
(outflows) from liabilities (inflows) of the respective type of capital inflows.
Data on real GDP per capita growth, inflation, trade, domestic credit, and broad money are
from World Development Indicators (WDI) (World Bank 2017b) and International Financial
Statistics (IFS) (IMF 2017a) databases. Data on government expenditures are sourced from Penn
World Table version 8.1 (Feenstra, Inklaar, and Timmer 2015). Polity2 data is obtained from Polity
IV Project database (Polity IV 2014). It captures political and regime types and the score range
from –10 for a fully institutionalized autocracy to +10 for a fully institutionalized democracy. The
political rights and civil liberties indicator is from Freedom House (2017), with the score ranging
from 0 (lowest score) to 7 (highest score). The indicator is constructed by taking the mean of the
two scores. The dummy capturing countries as either resource rich or resource poor is constructed
based on African Development Report 2007 (AfDB 2007) and classifications of countries
10 This database has been used widely to measure de facto openness of the financial sector. See, for example, Bhattacharya, Patnaik, and Pundit (2013) and Slesman, Baharumshah, and Wohar (2015).
15
according to natural resource availability in Lundgren, Thomas, and York (2013). The summary
statistics are provided in Appendix Table A.2.
5. Results and Discussion
The estimation results are presented in Table 1. Model 1 presents the estimation of growth equation
with no capital inflow variables, but includes lagged real GDP per capita growth and initial real
GDP per capita. The growth equation is then estimated by including separately, the gross capital
inflow variables (left side panel) and net capital inflow variables (right side panel) and their impact
evaluated. In each case, volatility of capital inflows is included to establish the effect of capital
inflow volatility on economic growth. The results show that the lagged real GDP per capita growth
is negative and insignificant in all the models, showing no momentum effect of past growth on
current growth levels. The initial real GDP per capita has a negative and significant coefficient in
all the models, showing convergence in growth, as expected from growth literature (see, Sachs and
Warner [1992]; Islam [1995]). Increase in inflation leads to a decline in the real value of fixed
assets and also affects consumption as purchasing power falls; thus, it is expected to lead to a
decline in economic growth. From the results, a percentage increase in inflation leads to growth
declining by 0.07 percent, while a percentage increase in government expenditure leads to growth
falling by 0.14 percent in the baseline model. The investment ratio has a expected positive sign,
though is not significant in all the models. The dummy that captures countries that are resource
rich has a positive, but insignificant coefficient in the baseline model.
In Models 2–4, capital inflow variables are included in the growth equation estimated in
Model 1. Gross portfolio equity inflows, gross FDI inflows, and gross debt inflows are included
in the growth equation (Model 2); Model 3 has gross private equity inflows, gross debt inflows,
and their volatilities, and Model 4 has gross total capital inflows and volatility of gross total capital
inflows included. In all the models, the coefficients of initial real GDP per capita growth, trade
openness, government expenditure, institutional measure (Polity2), and resource dummy maintain
the same signs and significance as in Model 1.
16
Table 1. GMM estimation results—Dependent variable is real GDP per capita growth (RGDPG)
Gross capital inflows Net capital inflows
Variable (1) (2) (3) (4) (5) (6) (7)
Lagged
RGDPG -0.101 -0.108 -0.107 -0.125 -0.103 -0.114 -0.121
[-1.29] [-1.38] [-1.36] [-1.62] [-1.33] [-1.49] [-1.57]
Initial real
GDP per
capita -1.332** -1.412** -1.926*** -1.534** -1.974*** -1.669*** -1.690***
[-2.19] [-2.60] [-3.20] [-2.58] [-3.25] [-2.93] [-2.92]
Inflation -0.069*** -0.042* -0.055** -0.061*** -0.042* -0.048** -0.055**
[-3.04] [-1.81] [-2.37] [-2.74] [-1.90] [-2.06] [-2.43]
Investment 0.052 0.038 0.009 0.028 0.087* 0.052 0.049
[0.96] [0.74] [0.20] [0.56] [1.79] [1.08] [1.00]
Private
sector credit 0.043** 0.005 0.026 0.045** 0.029* 0.026* 0.045***
[2.56] [0.26] [1.66] [2.60] [1.85] [1.73] [2.67]
Openness 0.059*** 0.051*** 0.064*** 0.070*** 0.048*** 0.047*** 0.068***
[3.56] [3.60] [4.21] [4.47] [3.63] [3.67] [4.17]
Government
expenditure -0.142*** -0.088** -0.089** -0.132*** -0.101** -0.097** -0.131***
[-3.37] [-2.14] [-2.23] [-3.07] [-2.51] [-2.42] [-3.05]
Polity2 0.075* 0.091** 0.096** 0.074* 0.077* 0.080** 0.072*
[1.82] [2.26] [2.37] [1.84] [1.93] [2.04] [1.77]
Resource
rich 0.896 1.052* 1.776** 1.158* 1.881** 1.460** 1.333**
[1.37] [1.68] [2.45] [1.67] [2.53] [2.14] [1.99]
Portfolio
equity flows
0.124
-0.019
[1.57]
[-1.13]
FDI flows
-0.012
0.001
[-1.31]
[0.04]
Debt flows
-0.016* -0.014*
-0.012* -0.012*
[-1.97] [-1.91]
[-1.74] [-1.68]
17
Private
equity flows
-0.026*
-0.006
[-1.96]
[-1.39]
Total capital
flows
-0.009
-0.007
[-1.25]
[-1.46]
Vol. of
portfolio
equity
-0.013
[-0.53]
Vol. of FDI
-0.067
[-1.13]
Vol. of debt
-0.029
-0.040 -0.049**
[-0.89]
[-1.59] [-2.16]
Vol. of
private
equity
0.035**
-0.007
[2.45]
[-0.52]
Vol. of total
flows
0.006
-0.023*
[0.81]
[-1.67]
Constant 6.204** 8.433*** 11.290*** 7.608** 10.936*** 9.834*** 8.326***
[2.04] [2.81] [3.45] [2.30] [3.37] [3.14] [2.67]
Observations 130 130 130 130 130 130 130
Sargan 28.52(0.24) 38.80(0.22) 47.24(0.20) 34.60(0.35) 53.60(0.27) 41.47(0.21) 33.23(0.41)
F test 5.67(0.00) 4.28(0.00) 4.87(0.00) 5.20(0.00) 4.04(0.00) 4.49(0.00) 5.14(0.00)
Autocorr.1 -3.96(0.00) -4.13(0.00) -3.74(0.00) -3.78(0.00) -3.84(0.00) -3.98(0.00) -3.95(0.00)
Autocorr.2 -0.01(0.99) -0.13(0.90) -0.01(0.99) -0.34(0.74) -0.06(0.95) -0.04(0.97) -0.20(0.85)
Notes: t-statistics are in square brackets while probabilities are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
18
Private sector credit also has the same sign, but is now significant all through, except in
Models 2 and 3, where it has the right sign, but is not significant. While Easterly, Islam, and Stiglitz
(2001) noted that the financial sector depth, measured as ratio of credit to GDP, increases the
likelihood of a downturn and, thus, may negatively affect growth. Private sector credit growth
reflects increased access to finance in credit-constrained countries, such as SSA countries, and
thus, has a positive effect on growth.
Government expenditure is negatively related to economic growth, leading to a fall in
growth following an increase in government expenditure.11 The measure of trade openness is
highly significant and positive to growth in all the cases, showing that open economies will benefit
more from growth. The coefficient of the institutional measure, Polity2, has the same range of
magnitude with inclusion of capital inflow variables. Investment ratio is still not significant in all
the models, though positively related to growth. The dummy capturing resource-rich countries is
now significant and has a positive coefficient in all the models with a coefficient greater than 1. It
shows that on average, resource-rich countries will tend to grow faster than resource-poor
countries.
Turning to the variables of interest and capital inflow variables, it is established from
Model 2 that gross portfolio equity inflows have a positive coefficient, while gross FDI inflows
and gross debt inflows have negative coefficients. However, it is only gross debt inflows that have
a significant effect on growth. When gross private equity inflows (sum of gross portfolio inflows
and gross FDI inflows) and gross debt inflows are considered, both gross private equity inflows
and gross debt inflows are significant and negatively affect growth. The volatility of private equity
has a positive coefficient and is significant, while that of debt is negative but not significant.
Consideration of gross total capital inflows (Model 4) shows that gross total capital inflows and
its volatility are insignificant to growth.
The results do not change much when net capital inflow variables are considered, instead
of gross capital inflow variables (right side panel of Table 1). In this case, net debt inflows have a
negative and significant coefficient, while net portfolio equity inflows, net FDI inflows, and net
private equity inflows are insignificant. The volatility of net debt inflows are also significant and
have a negative sign, implying that debt volatility negatively affects growth. When net total capital
11 Barro (1991) found an inverse relationship between the share of government consumption in GDP and growth and attributed this to the distortionary effects of taxes or government expenditure programs, which lower savings and growth.
19
inflows are used instead of the disaggregated net capital inflows (Model 7), the coefficients of net
total of capital inflows and its volatility are negative, though it is only the volatility of total capital
inflows that is significantly related to growth.
From the variables of interest, that is capital inflow measures and their volatilities, it is
established that both private equity inflows and debt inflows lead to a significant decline in
economic growth. The private capital inflow measure consists of FDI inflows and portfolio equity
inflows. The sign and significance of the coefficient, therefore, support the findings of Choong,
Yusop, and Law (2010); Gourinchas and Jeanne (2013); and MacDonald (2015) that capital
inflows are negatively correlated to growth. In SSA country studies, similar results are established
by Alley (2015); Alley and Poloamina (2015); and Egbetunde and Akinlo (2015). These studies
found that FDI inflows resulted in a decline in growth, and that shocks to capital flows negatively
affect growth. The finding that debt is negatively related to economic growth is in line with a vast
literature on debt and economic growth (e.g., Eberhardt and Presbitero [2015]).
In all the estimations, overidentification of the instruments is tested using the Sargan test,
and the null that overidentifying restrictions are valid is rejected. The F test also shows that the
models are well specified. Autocorrelation of orders 1 and 2 is tested, and it is established that
while autocorrelation of order 1 is present, given the relationship between growth and its lagged
value, autocorrelation of order 2 is not present.
5.1. Robustness check
To check whether the results are robust to different specifications, the model is estimated by using
broad money to GDP,12 as a measure of financial development instead of private sector credit, and
the Freedom House indicator constructed from political rights and civil liberty scores instead of
Polity2, as an institutional indicator. A 3-year-averaged, non-overlapping panel covering the
period 1980–201113 is also used to check for validity of the results. The results are reported in
Table 2 and Table 5, respectively. In each case, the results for the different capital flow measures
with their volatilities are presented. In both cases, the instruments are found not to be
overidentified, and there is no second order autocorrelation.
12Broad money to GDP and private sector credit to GDP are both used in the literature as indicators of financial intermediation (see Pagano [1993]). 13 Taking 3-year, nonoverlapping averages results in the last period having an average of 5 years from 2007–2011.
20
When other control variables are used, the coefficient of lagged real GDP per capita growth
is still negative and insignificant (Table 2). Initial real GDP per capita, inflation, trade openness,
government consumption, and dummy for resource-rich countries are all significant as before, with
the expected signs. Investment ratio is still not significant, although it has the correct sign. Of the
control variables that have been introduced, the broad money-to-GDP ratio has a positive
coefficient but is weakly significant in Models 2, 5 and 7. This shows that improved liquidity leads
to improvements in growth, as high liquidity may imply credit availability to the private sector and
thus support growth. The institutional variable, Freedom, is significant and positively related to
real GDP per capita growth in all the models, thus emphasizing the role of institutions in growth
regressions, as established in the earlier models.
21
Table 2. Estimation using other control variables —Dependent variable is real GDP per capita growth (RGDPG)
Gross capital inflows Net capital inflows
Variables (1) (2) (3) (4) (5) (6) (7)
Lagged (RGDPG) -0.106 -0.121 -0.124 -0.091 -0.094 -0.120 -0.125
[-1.39] [-1.60] [-1.63] [-1.18] [-1.23] [-1.60] [-1.65]
Initial real GDP per
capita -1.823*** -2.371*** -1.557*** -2.162*** -2.328*** -2.157*** -1.923***
[-3.29] [-4.06] [-2.78] [-3.61] [-4.12] [-3.99] [-3.44]
Inflation -0.060** -0.055** -0.065*** -0.070*** -0.043* -0.054** -0.063***
[-2.61] [-2.32] [-2.87] [-2.84] [-1.87] [-2.27] [-2.79]
Investment 0.036 0.010 0.014 0.028 0.030 0.025 0.026
[0.71] [0.20] [0.27] [0.58] [0.60] [0.51] [0.52]
Broad money 0.015 0.050* 0.036 0.036 0.057* 0.050 0.058*
[0.45] [1.69] [1.17] [1.19] [1.97] [1.60] [1.83]
Openness 0.054*** 0.058*** 0.062*** 0.059*** 0.049*** 0.054*** 0.064***
[3.98] [4.14] [4.41] [4.16] [3.88] [4.24] [4.49]
Government
expenditure -0.097** -0.084** -0.116*** -0.100** -0.079** -0.090** -0.119***
[-2.41] [-2.15] [-2.78] [-2.51] [-2.00] [-2.31] [-2.87]
Freedom 0.519*** 0.542*** 0.462** 0.583*** 0.426** 0.480*** 0.434**
[2.91] [3.09] [2.48] [3.22] [2.49] [2.74] [2.34]
Resource rich 1.570** 2.459*** 1.505** 2.186*** 2.455*** 2.235*** 1.914***
[2.23] [3.18] [2.09] [2.79] [3.20] [3.08] [2.65]
Portfolio equity
flows 0.098
0.001 -0.010
[1.43]
[0.57] [-0.62]
FDI flows -0.010
-0.021* -0.007
[-1.23]
[-1.81] [-0.44]
Debt flows -0.010 -0.011
-0.006 -0.013* -0.011
[-1.31] [-1.39]
[-0.76] [-1.88] [-1.46]
Private equity
flows
-0.025*
-0.007*
[-1.95]
[-1.67]
22
Total capital flows
-0.009
-0.005
[-1.22]
[-1.46]
Vol. of portfolio
equity
-0.012
[-0.51]
Vol. of FDI
-0.023
[-0.39]
Vol. of debt
-0.056**
-0.052** -0.053**
[-2.24]
[-2.04] [-2.37]
Vol. of private
equity
0.036***
-0.014
[2.90]
[-1.06]
Vol. of total flows
0.006
-0.021*
[0.96]
[-1.97]
Constant 8.491*** 11.678*** 6.553** 9.471*** 11.407*** 10.279*** 7.928***
[2.96] [3.80] [2.16] [3.16] [3.86] [3.63] [2.74]
Observations 130 130 130 130 130 130 130
No. of countries 26 26 26 26 26 26 26
Sargan 33.46(0.45) 37.31(0.36) 36.29(0.28) 30.79(0.72) 51.64(0.33) 35.59(0.44) 29.97(0.57)
F test 5.04(0.00) 5.36(0.00) 5.51(0.00) 5.14(0.00) 4.21(0.00) 5.19(0.00) 5.80(0.00)
Autocorr.1 -4.05(0.00) -3.92(0.00) -3.85(0.00) -4.15(0.00) -4.18(0.00) -4.13(0.00) -3.86(0.00)
Autocorr.2 -0.08(0.94) -0.13(0.89) -0.34(0.74) 0.03(0.97) -0.02(0.98) -0.16(0.88) -0.23(0.82)
Notes: t-statistics are in square brackets; probabilities are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
23
Considering the coefficients of capital inflow variables, only private equity inflows (both
gross and net) and net debt inflows are significant and negatively related to growth. The measures
of volatility of both gross private equity inflows and gross debt inflows are also significant and
have positive and negative coefficients, respectively. Gross total of capital inflows and its volatility
are insignificant, while net total of capital inflows is insignificant but its volatility is significant
and has a negative coefficient. This shows that volatility of net capital inflows reduce growth. The
results, therefore, largely stand as before, despite use of other control variables.
Next, a 3-year-average, non-overlapping period from 1980–2011 is used to test whether
the findings still hold. In this case, the initial real GDP per capita is measured as the logarithm of
real GDP per capita at the beginning of a 3-year, non-overlapping period. The results are reported
in Table 3. As in the previous section, real GDP per capita growth is negatively related to its lagged
value, while the initial level of GDP per capita is negatively related to real GDP per capita growth,
showing convergence. The lagged value of real GDP per capita growth is still not significant as
before. Trade openness and the institutional variable (Polity2) lead to improvement in per capita
growth. Inflation has the correct sign, but is only significant when total gross capital inflows are
considered. Investment also has the expected sign and is only significant when net capital inflows
are considered. The dummy for resource-rich countries is also positive and significant.
Government expenditure significantly leads to a reduction in growth, with an absolute magnitude
of between 0.14–0.19. Consideration of the capital inflow variables shows that gross portfolio
equity inflows have a positive and significant effect on per capita growth, while gross FDI inflows
and gross debt inflows have a negative and significant effect on per capita growth. Net portfolio
equity inflows are positive but not significant, while net FDI inflows and net debt inflows both
have negative coefficients and are significantly related to growth. Volatility of debt inflows is also
negative and significant, irrespective of the way capital inflows are measured. Private equity
inflows, both gross and net, have negative and significant impacts on growth. Volatility of gross
private equity inflows, however, has a positive and significant coefficient, while volatility of net
private equity inflows has a negative and significant coefficient. Both total gross capital inflows
and total net capital inflows have negative and significant coefficients, and their volatilities are
significant but with positive and negative signs, respectively.
24
Table 3. Estimation using 3-year averaged panel—Dependent variable is real GDP per capita
growth (RGDPG)
Gross capital inflows Net capital inflows
Variables (1) (2) (3) (4) (5) (6) (7)
Lagged (RGDPG) -0.044 -0.039 -0.046 -0.028 -0.025 -0.026 -0.031
[-0.69] [-0.64] [-0.73] [-0.45] [-0.40] [-0.42] [-0.48]
Initial real GDP per
capita -0.015** -0.018*** -0.014** -0.019*** -0.018*** -0.017** -0.018**
[-2.48] [-2.77] [-2.09] [-2.85] [-2.74] [-2.44] [-2.56]
Inflation -0.023 -0.028 -0.034* -0.029 -0.025 -0.027 -0.025
[-1.18] [-1.55] [-1.77] [-1.47] [-1.40] [-1.47] [-1.33]
Investment 0.048 0.046 0.074 0.062 0.079* 0.082* 0.102**
[1.13] [1.07] [1.59] [1.45] [1.89] [1.92] [2.25]
Private sector
credit -0.016 0.013 0.032* 0.021 0.014 0.020 0.037*
[-0.80] [0.82] [1.72] [1.31] [0.87] [1.22] [1.96]
Openness 0.064*** 0.074*** 0.080*** 0.071*** 0.064*** 0.062*** 0.077***
[4.38] [4.56] [4.78] [4.39] [4.05] [4.11] [4.54]
Government
expenditure -0.127*** -0.140*** -0.189*** -0.144*** -0.148*** -0.149*** -0.186***
[-3.50] [-3.94] [-4.87] [-3.95] [-4.09] [-4.02] [-4.66]
Polity2 0.001*** 0.001*** 0.001** 0.001*** 0.001*** 0.001*** 0.001**
[2.87] [3.39] [2.25] [3.18] [3.35] [3.19] [2.41]
Resource rich 0.014** 0.020*** 0.013* 0.021*** 0.019** 0.018** 0.017**
[2.14] [2.65] [1.90] [2.69] [2.56] [2.34] [2.24]
Portfolio equity
flows 0.194**
0.002 -0.009
[2.49]
[0.97] [-0.67]
FDI flows -0.015*
-0.029** -0.026**
[-1.70]
[-2.55] [-2.24]
Debt flows -0.024*** -0.022***
-0.019*** -0.019*** -0.016**
[-3.32] [-2.93]
[-2.79] [-2.71] [-2.28]
Private equity
flows
-0.032**
-0.012**
25
[-2.40]
[-2.28]
Total capital flows
-0.014**
-0.008*
[-1.99]
[-1.77]
Vol. of portfolio
equity
-0.028
[-0.82]
Vol. of FDI
0.010
[0.36]
Vol. of debt
-0.044*
-0.039 -0.044*
[-1.68]
[-1.49] [-1.80]
Vol. of private
equity
0.054***
-0.038**
[3.19]
[-2.03]
Vol. of total flows
0.015*
-0.029**
[1.78]
[-1.99]
Constant 0.091*** 0.108*** 0.064* 0.103*** 0.101*** 0.093** 0.079**
[2.66] [2.94] [1.75] [2.80] [2.84] [2.43] [2.11]
Observations 234 234 234 234 234 234 234
Sargan 86.51(0.17) 93.76(0.20) 77.90(0.19) 86.79(0.17) 101.80(0.40) 89.26(0.30) 74.65(0.24)
F test 6.77(0.00) 6.89(0.00) 7.51(0.00) 6.55(0.00) 5.64(0.00) 5.94(0.00) 7.09(0.00)
Autocorr.1 -5.37(0.00) -5.75(0.00) -4.93(0.00) -5.42(0.00) -6.83(0.00) -5.50(0.00) -5.10(0.00)
Autocorr.2 0.36(0.72) 0.12(0.90) 0.53(0.60) 0.42(0.68) 0.09(0.93) 0.04(0.97) 0.26(0.80)
Notes: t-statistics are in square brackets; probabilities are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
26
These results largely support the findings from the main estimations, even with inclusion
of other control variables or when 3-year averages are taken. The main results suggest that portfolio
equity inflows improve real per capita growth while debt inflows lead to a reduction in real per
capita growth. FDI inflows, however, lead to a reduction in growth. Volatility of private equity
inflows improves growth, while that of debt inflows negatively affects growth. In aggregated form,
total gross capital inflows lead to reduction in per capita growth while the volatility improves
growth. However, total net capital inflows and its volatility lead to a reduction in growth. One of
the reasons for these results may be that the level of capital inflows (especially portfolio equity
and FDI inflows) to SSA countries is still low (see UNCTAD [2015]); hence, the volatility of such
inflows does not have much effect on these economies when aggregate data is used, but may have
considerable impacts in the short run, as they affect other macroeconomic variables. Another factor
may be the absorptive capacity of SSA countries as negative effects of capital inflows on growth
is experienced in countries with low levels of financial development (Edwards2001) and better
institutions (Alfaro, Kalemli-Ozcan, and Volosovych 2007).
The results provide clarity on different findings in the literature with respect to the link
between capital inflows and growth. In situations where capital inflow variables are aggregated, a
negative effect of capital inflows on per capita growth is established. For instance, MacDonald
(2015) found a negative relationship between net capital flows and growth, while Cardelli,
Elekdag, and Kose (2010) found a positive relationship, though with a different sample and period.
As pointed out by Albulescu (2015), the impact of capital flows is well established when the
various instruments are considered separately. In studies that have considered such a
disaggregation, private capital inflows have been found to have a positive impact on growth (e.g.,
Deléchat et al. [2009]; Driffield and Jones [2013]; Alley [2015]; Alley and Poloamina 2015). The
results show that various components of total capital inflows have different effects on growth.
Portfolio equity inflows have a positive effect on growth, while private equity inflows and debt
inflows have negative effects on growth. In SSA countries, the level of portfolio inflow has grown
in recent times, especially for countries with relatively advanced stock markets. This has led to
availability of much needed capital that has supported firm activities such as financing of working
capital and investment and, hence, contributing directly to economic expansion. Growth in
portfolio flows has been accompanied by volatility in portfolio flows, given the nature of such
flows. Volatilities of such flows have had negative effects on growth, as they create instabilities.
27
The role of financial market, however, is important for such a relationship to be established
(Durham 2004; Choong, Yusop, and Law 2010).
6. Conclusions
The study provides evidence on the link between capital inflows and economic growth by using a
dynamic model to estimate these effects. Capital inflows, both gross inflows and net inflows, are
considered both in disaggregated and in aggregate form. The findings reveal that the effect of
capital inflows in an economy is captured well when capital inflows are considered in its
disaggregated form. Portfolio equity inflows promote growth, while private equity inflows and
debt inflows reduce growth. The effect of debt inflows, however, is prominent, given that it leads
to a negative effect on the economy. The difference in the effect of the various measures of capital
inflows may be due to their different natures. Private equity involves having some ownership
interests and, hence, such inflows will likely be experienced where they are most likely to earn a
return. Private equity is also dominated by FDI, compared to portfolio flows, which has a negative
effect on growth. Debt inflows on the other hand result in an obligation that must be paid back
and, thus, may increase risk in the market. Volatility of capital inflows is established as leading to
a reduction in growth, irrespective of the type of flows in most of the cases. It is largely expected
that volatility of capital inflows will create macroeconomic disturbances and reduce growth; the
effect in this case, however, is small, which may be attributed to low levels of financial openness,
so that only a small proportion of capital flows into these economies. This may be due to
uncertainty resulting from volatile inflows, thus negatively affecting economic activities.
Among the control variables, inflation, which captures macroeconomic stability, and
government expenditure, a measure of fiscal policy, are shown to have significantly negative
effects on growth. The implication is that ensuring macroeconomic stability by ensuring low
inflation rates and fiscal discipline will lead to improvements in economic growth. Private sector
credit, openness, and the institutional indicator have positive and significant effects on real GDP
per capita growth. One fact in developing countries, such as SSA countries, is that the credit market
is not well developed, thus, limiting access. Hence, improvements in credit availability and access
to the private sector will encourage private sector investment and enhance growth. One role of
capital inflows is to bridge the savings–investment gap and provide financing for investment. In
this case, capital inflows will provide the necessary financing to meet investment needs. Trade
28
openness, which has positive effects on real per capita GDP growth, thus encouraging more open
trade regimes by reducing barriers to trade, is beneficial to the SSA countries. As established in
the review, trade openness also helps in enhancing the growth benefits of capital flow. Strong and
reliable institutional and governance structures are necessary to achieve higher growth. While
capital inflows lead to improved institutions, capital will only flow to countries with better
institutions; thus, institutions are important in determining the composition and volume of capital
flows, while at the same time enhancing growth benefits of capital flows. The implication is that
well developed institutional structures will benefit the SSA economies more in realizing their
growth objectives.
The conclusion derived from the study is that capital flows, a de facto measure of financial
integration, are important for growth but their effects vary by the type of capital flows being
considered. Concerns about capital inflows in an economy should therefore be addressed by
considering the respective capital inflow components. SSA economies should focus on improving
the development of financial markets by putting into place measures for foreign participation, thus
allowing for improvement in foreign capital inflows, while at the same time supporting access to
credit. These measures will improve domestic investment and spur growth. These actions should
be supported by developing strong institutions that support property rights, with a focus on
channeling foreign capital, especially publicly guaranteed debt, to development expenditures,
given the negative effect of debt inflows on growth.
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Appendix
Table A.1. List of sub-Saharan African countries covered in the study
Benin Botswana Burkina Faso Burundi Cameroon Central African Republic Chad Congo, Republic of Côte d’Ivoire Gabon Gambia, The Ghana Kenya
Lesotho Madagascar Malawi Mali Mauritius Niger Nigeria Rwanda Senegal South Africa Swaziland Togo Zambia
Table A.2. Summary statistics of the variables
Variable Mean Minimum Maximum Standard
Deviation Real GDP per capita growth 0.70 -8.18 11.04 3.15 Initial real GDP per capita (log) 6.54 4.95 9.04 1.00 Inflation 10.74 -2.41 122.17 13.36 Investment/GDP 19.56 3.17 63.15 8.69 Private sector credit/GDP 19.36 1.92 147.15 21.00 Broad money/GDP 25.05 9.76 99.47 14.13 Openness 71.37 12.88 191.09 36.27 Government expenditure 16.38 4.01 49.95 7.21 Polity2 -0.82 -10 10 6.12 Freedom 3.39 1 6.6 1.51 Resource rich (dummy) 0.46 0 1 0.50 Gross portfolio equity inflows 1.80 0.00 120.32 10.10 Net portfolio equity inflows -8.65 -1150.15 6.54 93.81 Gross FDI inflows 29.85 1.17 919.45 76.46 Net FDI inflows 21.38 -46.47 178.92 27.34 Gross private equity inflows 31.64 1.17 1039.77 85.57 Net private equity inflows 12.72 -1196.62 178.88 102.94 Gross debt inflows 70.22 7.44 285.28 48.67 Net debt inflows 54.61 -69.34 223.04 46.75 Gross total capital inflows 101.87 10.75 1325.04 114.71 Net total capital inflows 67.33 -1232.06 288.91 122.09 Notes: This is a total sample of 26 countries from 1980–2011. The statistics represent 5-year, non-overlapping averages, except for the last period, which is averaged to 7 years (i.e., 2005–2011).
