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Do contagion effects exist in capital flow volatility?+
Hyun-Hoon LEE++
Kangwon National University, Korea
Cyn-Young PARK
Asian Development Bank, Philippines
Hyung-suk BYUN Kangwon National University, Korea
April 2013
Volatility of capital flows; emerging countries, contagion Keywords
F21, F36, G15 JEL Classification
+ An earlier version of this paper was prepared as a background paper for the theme section of Asian Development Bank’s annual publication “Asia Capital Markets Monitor,” in its August 2011 issue. This paper was presented at APEA 2011 Conference held at Pusan National University, Korea in June 2011 and at the 10th Annual Conference on Korea and the World Economy held at Claremont McKenna College in August 2011. The authors are grateful to the conference participants, Aitor Erce, Rebecca Neumann, and Won Joong Kim for comments and suggestions. The authors are particularly indebted to the reviewer of the Journal for his/her useful suggestions. The authors would also like to thank Lea Ortega for her excellent research assistance. ++ Corresponding author: Professor Hyun-Hoon Lee, Department of International Trade and Business, Kangwon National University, Chuncheon, 200-701, South Korea. Phone: +82-33-250-6186; Fax: +82-33-256-4088; Email: [email protected]
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Abstract The cross-border transmission of a financial shock has been a subject of rich literature. While
a large number of studies have focused on the phenomenon of strong co-movements of asset
prices and capital flows in the event of financial stress, very few discussed the contagion or
spillover effects in terms of capital flow volatility. This paper is one of the first attempts to
assess, empirically, whether or not there is a global and regional spillover effect in the
volatility of capital flows to emerging and developing countries. Based on the sample of 49
emerging and developing countries for the period 1980-2009, the empirical results suggest
strong and significant contagion effects from global and regional volatilities in the volatility
of capital flows to individual economies. The magnitudes of contagion vary depending on the
type of capital flows, whether it is foreign direct investment (FDI) or portfolio and other
investment (mostly bank lending). The findings also suggest the volatility dynamics of gross
flows is different from that of net flows. The volatility of net inflows is more exposed to
intra-regional contagion compared to that of gross inflows.
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1. Introduction
Volatility of international capital flows and the limited ability of developing economies to
deal with it continue to be a major international policy concern. Multiple episodes of
financial crisis in the 1990s highlighted the disruptive potential of capital flow volatility
beyond a national border, prompting the global talk about reforming the international
monetary and financial system. Fischer (1998) noted two important reasons for the reform.
First, international capital flows to emerging markets tend to be too large and volatile,
frequently disrupting their economic activity. Second, international financial systems are
often subject to contagion. As the global financial crisis of 2008/09 has renewed interest in
the reform of the international monetary system, there is a great sense of déjà vu.
The cross-border transmission of a financial shock has been a subject of rich literature. The
world witnessed a series of financial crisis in the 1980s and 1990s, which emanated from one
country and spread quickly to others. Beginning with the October 1987 stock market crash in
the U.S. and the 1992 Exchange Rate Mechanism (ERM) crisis in Europe, the spillover or
contagion effect caught the attention of both policy and academic circles. But the term,
financial contagion, became more widely accepted to the public when the Mexican
devaluation in December 1994 brought an abrupt end to capital flows to many Latin
American economies and triggered speculative attacks on their currencies—dubbed as the
“tequila effect” afterwards. The next crisis that hit many Asian economies in 1997 spread
beyond the regional boundary, triggering a debt default and ruble depreciation by Russia and
pushing the U.S. hedge fund Long-Term Capital Management to the brink of bankruptcy. The
spillover or contagion effect culminated in the global financial crisis of 2008/09, which
rattled financial markets worldwide and pushed the world economy into the worst recession
since World War II.
Contagion refers to the cross-border transmission of financial shocks, through co-movements
of asset prices and capital flows.1
1 Throughout this paper, the terms “contagion” and “spillover” are used interchangeably in a broad sense of “co-movements” as defined here.
The causes of contagion fall into broadly two categories:
similar fundamentals and herding behaviors of financial agents (Calvo and Reinhart, 1996;
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and Dornbusch et al., 2000). First, co-movements may arise when economies share similar
fundamentals and have strong macroeconomic interdependence through trade and financial
linkages. Asset prices and capital flows may respond similarly to a shock given the similar
fundamentals while the shock can be transmitted rapidly through trade and investment
channels. It may be natural to expect that these similar fundamentals lead to strong co-
movements in asset prices and capital flows. Second, co-movements may result from the
behaviors of financial agents regardless of economic fundamentals. A crisis in one country
may prompt investors to withdraw from other countries without properly examining the
fundamental linkage. Eichengreen et al. (1997) also find that the odd of a speculative attack
in one country increases when there was a speculative attack elsewhere in the world,
suggesting the existence of the herding or bandwagon effects in currency crises. Behaviors of
indiscriminating investors and speculators may seem “irrational.” However, seemingly
irrational behaviors, driven by herding, panics, a loss of confidence, and swings in investor
sentiment can still result from individually rational choices. Many speculated on the
connection between herding/contagion and volatility of capital flows (Calvo and Reinhart,
1996; and Dornbusch et al., 2000). Karolyi (2003) surveys the various definitions and
taxonomies of international financial contagion and suggests that the empirical evidence of
contagion is at best controversial depending on the arbitrary definition of the purely
contagious effects.
Empirical studies in search for the evidence of contagion have largely focused on co-
movements of asset prices and volatilities, with some extensions to examine excess or
excessive co-movements during times of financial distress. King and Wadhwani (1990) first
examined the correlations across the US, UK and Japanese stock markets during the US stock
market crash in 1987. Since their seminar paper, many studies have investigated the
contagion effect by analyzing co-movements of international asset market returns and
volatilities.2
Indeed, the analysis of volatility spillovers across markets has been established
as a fairly standard empirical approach to test and estimate the international market links.
Another area of interest has been cross-border capital flows. Ebbs and flows of international See, among others, Diebold and Yilmaz (2009, 2011), Edwards (1998), Edwards and Susmel (2001, 2011), Hamao et al (1990), Koutmos and Booth (1995), Sumel and Engle (1994), Ng (2000), and Yilmarz, (2010).
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capital flows attracted much attention in the context of contagion.3
With increased capital
mobility, studies also proliferated to investigate why capital flows, especially to developing
economies, are volatile and what determines the level of cross-border flows. Becker and
Noone (2008) noted some stylized facts about the volatility of capital flows to developing
countries in this regard. First, when measured in percent of GDP, the volatility of capital
flows to developing countries is significantly higher than that of capital flows to developed
countries. Second, emerging and developing economies tend to have more frequent episodes
of volatility hikes than developed countries, reflecting their vulnerability to crises. Third,
different types of capital flows have different volatilities.
The behavior of capital flow volatility may yield additional insights for financial contagion.
Financial theory suggests volatility arise in the exchange of information, with many
theoretical frameworks developed to capture the information content of volatility in the
analysis of asset (option) pricing.4
Bachetta and van Wincoop (1998) suggest that incomplete
information may also be a source of capital flow volatility, which can explain why volatility
could be high during the process of financial liberalization in emerging and developing
countries. The information content of volatility can be transmitted across borders. For
example, a reversal in capital flows to an emerging economy may prompt international
investors to reassess their portfolio exposure, not only to the affected economy only, but also
to other emerging economies perceived to be at similar risks, and to retreat from these
markets altogether.
Given its disruptive nature and potential contagion, policy makers around the world are often
concerned about the volatility of capital flows. Only recently, however, some empirical
studies have begun to try to identify the sources of capital flow volatility and examine its
determinants. Broner and Rigobon (2006) and Alfaro et al. (2007) try to explain why foreign
capital flow is more volatile in emerging economies than in advanced economies. Broner and
Rigobon (2006) find that the higher volatility in developing countries can be explained by
their relatively high exposures to occasional crises, contagion, and more persistent shocks to
capital flows compared with developed economies, rather than by the volatility of their 3 See Forbes and Warnock (2012) for a survey. 4 The application of theoretical frameworks for the information content of volatility has been largely limited to asset pricing models.
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economic fundamentals. Alfaro et al. (2007) emphasize the importance of institutional quality
and the soundness of macroeconomic policies in explaining these volatility differences by
focusing on total equity flows (FDI and portfolio flows).
Recent studies also note the different patterns of volatility exhibited by different types of
capital flows and explore the factors behind the volatility dynamics. Neumann et al. (2009)
examine how different types of capital flows respond to the opening of financial market.
Specifically, they show that a further opening of financial markets tends to increase the
volatility of FDI in emerging economies, while it does not lead to any meaningful change in
the volatility of portfolio investment flows. This, however, stands in contrast to the findings
of the IMF’s 2007 Global Financial Stability Report that financial market openness and
institutional quality are negatively associated with the volatility of capital flows in both
emerging and developed economies.
Similarly, Broner et al. (2011) focus on the different patterns of gross capital flows vis-à-vis
net flows and find that gross capital flows are more volatile than net capital flows. They argue
that this is because there is a positive correlation between gross capital inflows and gross
capital outflows driven by foreign and domestic agents respectively; when foreigners invest
more in a country, domestic agents also invest more abroad, and vice versa. Among the three
types of gross capital inflows and outflows, they show that other investments are most
volatile.
Broto et al. (2011) suggest global conditions have differential impacts on FDI, portfolio
investment, and other investment inflows, where capital inflows are defined as purchases by
non-residents of domestic assets minus their sales of such assets. Based on Engle and
Gonzalo Rangel (2008), they estimate conditional volatilities of different types of capital
flows to investigate the impact of various domestic and global factors on volatility. Their
results show that global factors have become increasingly significant relative to country
specific factors since 2000 underlining the volatility of portfolio and other investment flows
and that the institutional framework has important implications for capital flow volatility.
In a similar fashion, Mercado and Park (2011) investigate the impact of a set of domestic and
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global factors on the level and volatility of different types of capital flows to developing
economies, using the standard deviation of these flows (in % of GDP) in 5-year rolling
windows as the volatility estimates. Their findings suggest that better institutional quality is
important for attracting more and stable capital inflows while “pull” factors in general seem
more relevant than “push” factors for capital flow volatility. They also report that a regional
factor plays an important role in determining the volatility of capital inflows to emerging
Europe and emerging Latin America.
None of the above studies, however, explicitly deal with the spillover phenomenon of capital
flow volatilities. There is rich literature on the volatility spillovers between international asset
markets for evidence of contagion. Various methodologies including the use of the GARCH
family have been employed to provide rigorous estimates for volatility and its dynamics. In
contrast, not enough studies focused on the dynamics of capital flow volatility and
international interdependence in its movements, let alone to try to improve the volatility
measures. This paper aims to fill the gap in the literature by adopting a new measure of
capital flow volatility and assessing the spillover or contagion effect in the volatility of
capital flows to developing countries.
First, we propose to use a new measure of the volatility which captures the true magnitude of
variation in capital flows. While adopting the most commonly used measure of volatility –
standard deviation of capital flows in a moving window, we used a more rigorous
normalization procedure to minimize the effect of the size of capital flows on the magnitude
of volatility. Without a proper normalization, the standard deviation would be affected by the
size of capital flows and the volatility spillover effect would also be over- or under-estimated.
Acknowledging that the analysis of both gross and net capital flows is important for
understanding the underlying sources of volatility (Broner et al., 2011), we measure two
separate volatilities using gross and net capital flows respectively for three different types of
capital flows (FDI, portfolio investment, and other investment inflows).
We then estimate the effects of spillover or contagion more specifically, by regressing the
volatility of capital inflows to individual economies against the volatility of capital inflows to
all developing economies, their regional neighbors, and all the other developing countries
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outside the region, alternatively. This is in addition to a battery of domestic factors drawing
on the previous studies.
Based on the sample of 49 emerging and developing countries for the period 1980-2009, this
paper presents evidence for strong and significant contagion effects from global and regional
volatilities on the volatility of capital flows in different types to individual economies. The
volatility of FDI flows is the lowest and it is least susceptible to intra-regional contagion,
relative to those of portfolio investment or other investment (mostly bank lending). The
volatilities of portfolio investment and other investment behave in a roughly similar manner
in terms of their magnitudes and exposure to cross-border contagion. It is also found that
gross capital inflows and net capital inflows are similar in terms of the size of their volatilities,
the volatility of net capital flows responds to intra-regional contagion than that of gross
inflows.
The remainder of this paper is organized as follows. After a brief discussion on the data and
accurate measure of volatility of capital flows, Section 2 presents the trend of volatilities for
different types of capital flows in different country groups. In Section 3, we describe the
empirical framework and the key variables. In Section 4, we report and discuss our main
results. Section 5 offers a summary and some policy implications.
2. Descriptive Analysis
2.1. Data on capital flows
Earlier studies point to the relatively high volatility of short-term capital flows such as bank
lending and portfolio investments compared with long-term flows such as foreign direct
investment (FDI). For example, Broner et al. (2010) show that among the three types of gross
capital inflows and outflows, other investments (mostly bank lending) are most volatile. Wei
and Wu (2001), Ju and Wei (2006), Levchenko and Mauro (2007), and Tong and Wei (2009)
also find that the economy is more vulnerable to a financial crisis if the composition of
capital flows is skewed toward short-term flows that are more likely to be reversed than FDI
in times of financial stress.
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For this paper, we collected annual data5
on capital inflows and outflows for 49 emerging
and developing economies during the period 1990-2009 from the IMF’s International
Financial Statistics (IFS) online database. In order to evaluate the differences in the contagion
effects of volatility by different types of capital inflows, our data is divided into foreign direct
investment (FDI), portfolio investment, and other investment flows based on IFS.
FDI includes equity capital, reinvested earnings, other capital, and financial derivatives
associated with various intercompany transactions between affiliated enterprises. Portfolio
investment includes transactions with nonresidents in financial securities of any maturity
(such as corporate securities, bonds, notes, and money market instruments) other than those
included in direct investment, exceptional financing, and reserve assets. Other investment
includes all financial transactions not covered in direct investment, portfolio investment,
financial derivatives, or reserve assets. Major categories are transactions in currency and
deposits, loans, and trade credits.
For each type of capital inflows, we are concerned with the behaviors of both gross and net
capital flows. The gross capital inflow represents non-residents’ purchases minus non-
residents’ sale of domestic assets. While the gross capital inflow in the reporting economy is
generally shown with a positive figure, reflecting an increase in net inward investment by
non-residents, it can also have a negative figure if non-residents’ sale of domestic assets is
greater than non-residents’ purchases. The net capital inflow is defined as gross inflows
minus gross outflows. A positive value implies a net increase in liabilities on the rest of the
world, while a negative value implies a net increase in credit on the rest of the world.
It is in fact one of our main objectives to assess whether gross capital flows are more volatile
than net capital flows or vice versa and whether the volatility of gross capital flows has a
larger spillover effect than the volatility of net capital flows or vice versa. As Broner et al.
(2011) noted, the previous literature concentrated on studying net capital flows, while less is
known about the behavior of gross capital flows (p.1). Broner et al. (2011) argue that the
volatility of gross capital flows (both inflows and outflows) is higher than that of net flows
5 For many countries in the sample, quarterly series of capital flows were not available.
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because during expansions, foreigners invest more domestically and domestic agents invest
more abroad, while during crises or contractions, total gross flows collapse or retrench (p.3).
2.2. Measurement for volatility of capital flows
Measuring volatility of capital flows is not straightforward (Broto et al., 2011). The most
commonly used measure of volatility in the literature is standard deviation of capital flows in
a moving window (eg., Broner et al., 2011; Broto et al., 2011; Mercado and Park, 2011; and
Neumann et al., 2009). In this paper, we also use the standard deviation in a rolling window
to estimate time-varying volatilities. But recognizing some important drawbacks of the
standard deviation, we adopt a standardization methodology similar to the concept of
coefficient of variation over different sub-periods to derive more accurate measure of
volatility.
As in Broto et al. (2011), the standard deviation of capital flows in a rolling window for
country i in year t, σit, is given by the following expression:
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2
( 1)
1 ( )t
it ikk t n
flown
σ µ= − −
= −
∑
Where ( 1)
1 t
ikk t n
flown
µ= − −
= ∑ , and flowik denotes capital inflows (or capital inflows relative to
country i’s GDP) to country i and in period k.
While the standard deviation is a popular measure of volatility, this measure has some
important drawbacks. First, technically, the standard deviation shows dispersion from the
mean, so different means will generate different dimensions to the standard deviation. That is,
the standard deviation will give very different estimates of volatility depending on the means
(in this case, the size of capital flows) and does not necessarily correspond to the actual
magnitude of variation. For instance, let us consider two hypothetical cases of capital inflows
to two different countries during the period of 2005-2009, which are (2, 4, 1, 3, 5) for country
i and (20, 40, 10, 30, 50) for country j. While the variation of capital inflows appears to be the
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same for both countries, the standard deviation for country i is 1.58 and it is 15.8 for country j.
With steady increases in capital flows to developing economies since 1990s, the use of the
standard deviation may well exaggerate the size of volatility over time. Recognizing such
drawback, this paper normalizes the size of capital flows in each rolling window. That is, we
set the largest capital flows in absolute terms at 100 and adjust the rest of capital flows series
accordingly. In the above example, for country i, 5 will be set to 100 and others will be
adjusted accordingly to generate a normalized capital flow series (40, 80, 20, 60, 100). The
appropriate normalization for country j, with 50 set to 100 would generate the same set. The
“normalized” standard deviation is now 31.6 in both cases.6
Second, the choice of the length of a rolling window is arbitrary. Recent studies including
IMF (2007), Neumann et al. (2009), and Mercado and Park (2011) used 5-year rolling
windows to calculate the standard deviation of capital inflows. In this paper, we also use the
five-year moving window.7
We also tried to better match the volatility to each year. For
example, we use the standard deviation of capital flows during a 5-year rolling window for
the volatility value in the mid-year of this window. That is, the above equation is not
considered as the standard deviation of time t, but of time t-(n-1)/2. Therefore, in the above
example, 31.6 will be considered as the volatility of capital inflows in 2007.
Third, the standard deviation of a rolling window is strongly persistent. The standard
deviation of each rolling window is highly related to the standard deviations of the previous
and next rolling windows to the extent that these windows overlap. Therefore when the
standard deviation of capital flows in a rolling widow is used as the dependent variable in a
regression analysis, the error term is serially correlated by construction. In order to overcome
6 One may wish to employ coefficient of variation to get “dimensionless” volatility by dividing standard deviation by the mean of each window. However, when the mean value is less than 1.0, the coefficient of variation becomes very large and sensitive to small changes in the mean value. For example, consider another two series of capital flows of (20, 40, 5, -20, -40) and (20, 40, 2.5, -20, -40). In these two series, the only difference is 5 and 2.5 at each midpoint and hence their volatilities should be similar. Indeed, our preferred method of normalization would produce the volatility of 79.254 and 79.068 for each series respectively. But the coefficient of variation will produce very different volatilities for these two series at 63.285 and 126.992 respectively. 7 We also used the three-year moving window as a robustness check and found a similar result. A difference is that using the 3-year moving window gives more variability in the volatility trend, as the smoothing effect of a longer window is reduced.
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this drawback, this study utilizes the system GMM estimator for dynamic panel data models
of Blundell and Bond (1998), which combine moment conditions for the model in first
differences with moment conditions for the model in levels.
2.3. Trend of volatility by different types of capital flows
This section compares the relative size of volatilities of gross and net inflow, respectively, by
different types of capital, measured by “normalized” standard deviation of 5-year moving
window for the period of 1980 to 2009. Figure 1A shows the trend of volatility of gross
capital inflows by three different types of capital inflows (relative to GDP) into all
developing countries included in the sample of this study: FDI, portfolio investment, and
other investment. It is worthwhile to note that FDI has the lowest volatility and portfolio
investment and other investment have similar volatilities. This finding is consistent with the
previous studies that FDI is relatively more stable and resilient to shocks compared with other
types of capital flows. Figure 1B illustrates the corresponding trend for net capital inflows.
We find that the degree and the pattern of the volatility of net inflows are very similar to
those of gross inflows. This is at odds with Broner, et al (2011) who find that the volatility of
gross capital is more volatile than net capital. This is because our “normalized” measure of
volatility would not be affected by the absolute size of capital flows and it will only reflect
the relative fluctuations of capital flows.8
Figures 2A-2C depict the trend of volatility of three different types of capital flows (gross
and net, respectively) into three different regional groups of developing countries: (A) East
Asian developing countries; (B) Latin American developing countries; and (C) Eastern
European developing countries.9
8 The volatility measure of standard deviation depends highly on the average size of flows. In Broner et al. (2011, Table 1), the volatility of net capital (CIF – COD) is about half of that of either gross capital inflows (CIF) or gross outflows (COD) , while the volatility of total capital flows (CIF + COD) is about twice of that of either CIF or COD.
As seen in the Figures, the volatility of FDI flows is the
lowest in all three different regions, while portfolio and other investment flows show similar
9 The whole sample also includes the developing countries located in Africa, Middle East, South Asia, and the Pacific, but the average volatility of capital inflows to these countries is not included as these countries do not reveal regional identity as compared with Asia, Latin America and Eastern Europe. See Appendix 1 for the list of the countries in each regional group.
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levels of volatility, no matter whether capital inflows are measure as gross inflows or net
inflows. The figures also show that volatilities of all three types of capital flows (both gross
and net) to Asian and Eastern European developing countries picked up during the period of
recent global financial crisis of 2008-2009, while those to Latin America appear to have
remained stable during the same period. Given that our data coverage is limited until 2009,
the full crisis impact may not have been captured. Nonetheless, the results suggest that the
crisis effects might have been delayed in Latin American developing countries. It is also
interesting to note that in the regions of Latin America and Eastern Europe, the volatility of
portfolio investment appears to have increased in recent years
Tables in Appendix 2 provide the estimated volatilities for different types of capital inflows
(gross and net) to the 49 individual countries included in the sample of the study.
3. Empirical Specification
3.1. The Model
This paper constructs a panel data set using the volatility measures obtained in the previous
section as the dependent variable. Explanatory variables are grouped in three categories:
external variables, policy variables and control variables. The equation to be estimated is
VMit = β0 + EVit β1 + PVit-1 β2 + CVit-1 β3 + εit.
Where VM is a volatility measure of capital inflows, EV is a vector of external variables, PV
is a vector of policy variables, and CV is a vector of control variables. β is a vector of
unknown coefficients and εit is an error term. As will be explained below, except for external
variables, all other explanatory variables are one-year lagged to minimize endogeneity
problem.
3.2. Dependent variable
We use volatility of capital inflows measured by standard deviation of capital flows relative
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to GDP. Thus, the dependent variable is the volatility of one of the following three types of
capital flows, measured with gross inflows or net inflows:
- VFDI : 5-year standard deviation of FDI inflows in % of GDP
- VFPI: 5-year standard deviation of portfolio investment inflows in % of GDP
- VOI: 5-year standard deviation of other investment inflows in % of GDP
3.3. Explanatory variables
As the main purpose of this paper as asserted in the introduction is to assess the effects of
spillover or contagion in the volatility of capital flows to emerging economies, we construct
two separate variables to evaluate the impact of regional and global volatilities on the
volatility of capital flows to individual economies. First, intra-regional volatility is
constructed as the simple average volatility of capital flows to the neighboring countries in
the same region when an individual economy belongs to each of three regional groups – East
Asia, Latin America and Eastern Europe. Second, global volatility is constructed as the
simple average of volatilities of capital flows to all emerging countries in the sample. Global
volatility can be further refined to extra-regional volatilities of capital flows to emerging
countries. That is, extra-regional volatility is constructed as the simple average volatility of
capital flows to all other emerging countries outside each of the three regional groups.
External variables:
It is noted that when the dependent variable is the volatility of gross (or net) capital inflows,
the global and regional spillover variables are also measured in terms of gross (or net) capital
flows.
a. Global volatility
ALL: Simple average of the world-wide volatilities of (gross or net) capital inflows to all
emerging countries
b. Intra-regional volatility
East_Asia_Intra: Simple average volatility of (gross or net) capital inflows to other
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neighboring countries in East Asia
Latin_America_Intra: Simple average volatility of (gross or net) capital inflows to other
neighboring countries in Latin America
East_Europe_Intra: Simple average volatility of (gross or net) capital inflows to other
neighboring countries in Eastern Europe
c. Extra-regional volatility
East_Asia_Extra: Simple average volatility of (gross or net) capital inflows into other
emerging countries outside East Asia
Latin_America_Extra: Simple average volatility of (gross or net) capital inflows into other
emerging countries outside Latin America
East_Europe_Extra: Simple average volatility of (gross or net) capital inflows into other
emerging countries outside Eastern Europe
As contagion can occur in very short-term, the above global and regional volatility variables
enter in regression contemporaneously.
We include four policy variables in the regression analysis: financial market openness,
institutional quality, macroeconomic stability, and accumulation of foreign exchange reserves.
Policy variables:
Financial openness is measured as Chin and Ito’s financial openness index (KOPEN), which
is the extensity of capital controls based on the information from the IMF’s Annual Report on
Exchange Arrangements and Exchange Restrictions. Institutional quality (INSTITUTION) is
measured as World Bank’s Worldwide Governance Indicators developed by Kaufmann, Kraay,
and Mastruzzi.10
This includes measures for voice and accountability, political stability and
absence of violence, government effectiveness, regulatory quality, rule of law; and control of
corruption. It is expected that volatility of capital flows is negatively related with institutional
quality.
On the other hand, macroeconomic stability is approximated by CPI-based inflation rate
10 See Kaufmann, Kraay, and Mastruzzi (2010) for a detailed explanation of methodology.
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(INFLATION) and countries with high inflation rate are expected to have higher volatility of
capital flows. Broto et al (2011) argue that investors might view domestic inflation as a signal
of macroeconomic mismanagement in emerging economies, hence inducing an increase in
the volatility of gross capital flows. Finally, the stock of foreign exchange reserves in months
of imports (RESIMP) is to measure vulnerability to balance of payment crises. But, as Broto
et al (2011) note, the relationship is not straightforward: low stock of foreign exchange
reserves may lead to liquidity crises and therefore, higher volatility, while larger foreign
reserves may reflect countries’ need to self-insure and therefore, higher volatility. Countries
also build up foreign exchange reserves as a cushion for abrupt outflows of foreign capital.
- KOPEN (+)11
- INSTITUTION (-): Institutional quality
: Chin and Ito’s financial openness index
- INF (+): CPI-based inflation rate
- RESIMP (+ or -): Stock of foreign reserves in months of imports
To minimize problems of endogeneity, all of the above variables are one-year lagged.
We include as control variables GDP per capita (GDP_PC) and real GDP growth rates
(PGDP). GDP per capita (constant in 2000 US dollars) is to capture the level of economic
development. The relationship between GDP per capita and volatility of capital inflow may
be positive or negative. The relationship may be positive as the least developed countries are
likely to display lower levels of volatility because they rely primarily on official development
assistance flows. The relationship may be negative as income rises among emerging market
economies, financial flows become more stable similar to the experience of advanced
economies, hence yielding lower levels of volatility.
Control variables:
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GDP growth rate is expected to show a negative relationship with the volatility of capital
inflows as it is the proxy for the dynamism of the recipient countries. These variables are all
taken from the World Bank’s World Development Indicators Online database. 11 Expected signs are in parentheses. 12 Following Broto et al (2011), we also included the quadratic term of the log of GDP per capita in a different regression but did not find a consistent result.
17
- GDP_PC (+ OR -): log of GDP per capita (constant in 2000US$)
- PGDP (-): Change in GDP_PC (i.e. growth rate):
Similarly to the policy variables, the control variables are also one-year lagged.
3.4. Specification
As mentioned in the previous section, the standard deviation of a moving window is strongly
persistent as it leans on previous periods. That is, the standard deviations using overlapping
rolling widows are highly correlated by construction. Therefore, when the standard deviation
of capital flows over a rolling widow is used as the dependent variable in a regression
analysis, the error term is serially correlated by construction. In order to overcome this
drawback, this study utilizes the system GMM estimator for dynamic panel data models of
Blundell and Bond (1998).
4. Results
4.1. Global contagion effects
Table 1 reports the results for the contagion effects of all other emerging countries, the impact
of the policies, and the country specific condition. The sample covers the period of 1980 –
2009. Note that the dependant variable is the standard deviation of inflows of different types
of capital as ratio of GDP over the five-year rolling windows. Columns (1) – (3) report the
results when the volatilities are measured using the gross capital inflows of FDI, portfolio
investment and other investment, respectively, while Columns (4) – (6) report the
corresponding results when the volatilities are measured using the net capital inflows. The
estimated results are described here by the type of variables.
Before turning to the contagion effect variable, let us first consider the estimates for GDP per
capita in Table 1. GPD per capita in its level (GDP_PC) yields statistically significant
Control variables
18
positive estimates in all columns but Column (3), which is the equation for the volatility of
gross inflows of other investment. Therefore, in our sample, lowest income groups have
lower volatilities of capital inflows relative to the higher income groups. This may be due to
the fact that least developed countries rely primarily on official development assistance flows,
as noted in the previous section.
GDP growth rate (PGDP) reveals negative and significant results in all three equations for
gross inflows, suggesting that countries with fast economic growth tend to incur lower capital
inflow volatilities, irrespective of types of capital inflows. However, in the case of net inflows,
it enters with negative and significant coefficient only for FDI, while it enters with positive
and significant coefficients for portfolio investment and other investment, which is rather
puzzling.
We find strong contagion effects. The estimates for ALL, or the average value of capital flow
volatilities of all emerging countries in the world show positive and significant coefficients in
all equations. That is, capital flows to a developing country become more volatile when
capital flows to other developing countries become more volatile. Among the three types of
capital flows, FDI appears to have the greatest spillover effect of its volatilities. This is
somewhat surprising as the FDI flow is most stable among the three types of capital flows.
When the results for gross inflows (Columns 1 -3) are compared with those for net inflows
(Columns 4-6), the spillover effect appears slightly greater in gross inflows than in net
inflows for equations for FDI and portfolio investment, while it is the opposite for other
investment. Overall, the strong spillover effects found in all equations suggests two things.
First, a developing country can fall into a difficult situation when other developing countries
are experiencing financial turmoil. Second, therefore, there is a need for more coordinated
policies among developing countries to respond collectively to a financial turmoil and contain
the contagion effect.
External variables
As for the policy variables, we find that greater financial market opening (KOPEN) does not
lead to a consistent impact on the volatilities of different types of capital inflows to
Policy variables
19
developing countries. Specifically, in the case of other investment flows (both gross and net),
countries with greater degree of financial market openness appear to suffer from greater
degree of capital flow volatility, while in the case of portfolio investment inflows (gross) and
FDI inflows (net), countries with greater degree of financial market openness tend to enjoy
lower degree of volatilities of inflows. This finding is similar to earlier findings [Neumann et
al. (2009) and Mercado and Park (2011)] that financial openness affects different types of
capital flows differently. However, these studies find that financial openness reduces
volatility of portfolio investment flows, while it may increase the volatility of FDI flows.
When the volatilities are measured using gross inflows, institutional quality (INSTITUTION)
appears to have significant and negative effects on all three types of gross capital flows (FDI,
portfolio and other investments). Consistent with earlier studies [Broner and Rigobon (2005),
IMF (2007a), Wei (2011), and Mercado and Park (2011)], this result suggests that better
institutional quality is associated with more stable flows. However, when the volatilities are
measured in terms of net inflows, only the equation for portfolio investment yields a
significant negative coefficient, while the FDI equation yields a positive and significant
coefficient for institutional quality.
The estimates for inflation rates (INF) are positive and highly significant in the equation for
other investment (both gross and net), while they are negative and significant in the equations
of FDI (both gross and net) and portfolio investment (gross), which is at odds with some
earlier findings. In contrast, Broto et al (2011) and Mercado and Park (2011) find no
significant effect of inflation on the size and volatility of gross capital inflows. In any case,
this result is puzzling and deserves further investigation in the future studies.
Lastly, the stock of foreign exchange reserves in months of imports (RESIMP) enters with
positive and significant coefficients in the case of FDI (both gross and net), but with negative
and significant coefficients in the case of portfolio investment (both gross and net). An
increase in the stock of foreign exchange to some extent would signal an improvement in
macro-financial stability, which should help stabilize capital flows (low volatility), although
it is not clear if this variable would matter once it exceeds certain levels. It is rather puzzling
to see an increase in volatility in case of FDI.
20
4.2. Intra-regional vs. extra-regional contagion effects
We divided the contagion effect into intra-regional contagion effect and extra-regional
contagion effect. Table 2 reports these contagion effects for the volatilities of the three types
of gross capital inflows to the regions of East Asia, Latin America and Eastern Europe. Table
3 also reports the corresponding results for net capital inflows.
Let us first focus on the results for gross capital inflows in Table 2. Among the three types of
capital inflows, portfolio investment and other investment have positive and highly
significant (1%) coefficients for all of the intra-regional dummy variables for the three
regions, while FDI has a positive and significant (5%) coefficient only for the East Asian
regional dummy but a negative and significant (10%) coefficient for the Latin American
regional dummy. On the other hand, extra-regional contagion effects are observed only for
portfolio investment flows to East Asian and Latin American countries and for other
investment flows to Eastern Europe.
In the case of net capital inflows as reported in Table 3, intra-regional contagion effects are
observed for all three types of capital flows in all three tree regions. Indeed, the size of the
coefficients for the intra-regional contagion effects is greater in all equations for net capital
inflows than for gross capital flows. On the other hand, extra-regional dummies enter with
positive and significant coefficients only in some regions for certain types of capital flows.
The findings suggest that the large global contagion effects observed in Table 1 are largely
due to strong intra-regional contagion effects in all three types of capital flows. Such a strong
intra-regional contagion effects are particularly evident in net capital flows. Strong intra-
regional contagion effects may reflect the investors’ herding behavior perhaps due to their
perception of similarities of these economies and therefore, regional policy coordination may
be an important element in designing a policy framework to manage capital inflows.
4.3. Robustness checks
21
Many scholars have observed that the volatility of capital flows is highly correlated with the
level of capital flows in most emerging countries. Therefore, one may suspect that the
spillover effect of capital flow volatility may become smaller when we control the spillover
effects of the level of capital flows.
Table 4 reports the results when the world’s average capital flow, defined as the total flows of
each type of capital divided by the world GDP, is added in the equations reported in Table 1.
Note that as net capital flows should add to zero globally, the same variable is also included
in the equations for the net flows.
The level of world average carries positive and significant coefficients only in some
equations: “gross” FDI and portfolio investment inflows and “net” other investment flows.
This is not surprising because our measure of volatility is, unlike other studies, the
normalized standard deviation which is free from the size of capital flows.
The results for the global contagion effects and other control and policy variables remain
almost the same as those without the global level of capital flows in the regression equation.
A similar finding is also observed in the equations for the intra- and extra-regional contagion
effects and hence the results are not reported.
Second, our sample period includes the recent period of global financial crisis which may
have been dominated by a global impact on capital flows and hence it may be interesting to
assess whether our observed intra-regional contagion remains strong even when the recent
period is excluded. Table 5 summarizes the results with the sub sample period of 1980-2007
(i.e., the years of 2008 and 2009 are excluded). For the comparison, the summary results with
the whole sample period of 1980-2009, as reported in Tables 1-3, are also presented in the
upper panel.
When the recent episode of global crisis is excluded, the size of the coefficients for intra-
regional spillover effects appears to decrease to some extent in most equations for gross
inflows, but remains roughly similar in most equations for net inflows. Thus, we can
conclude that our findings of strong regional contagion are not due to the recent episode of
22
global crisis, especially in the case of net capital inflows.
Lastly, as a robustness check, we also used the volatility measure calculated using window of
three years instead of five years. The results are not reported here but our key results remain
valid: global and intra-regional spillover effects are strong.
5. Summary and conclusions
The purpose of this paper was to assess how the volatility of different types of capital flows
to emerging countries is affected by the volatility of capital flows elsewhere as well as other
economic and policy factors. For this purpose, this paper suggests adopting a more rigorous
normalization procedure to the usual standard deviations to provide more accurate measures
of capital flow volatility than past studies. The paper estimates volatilities of not only gross
capital flows but also net capital flows in three different forms (FDI, foreign portfolio
investment and other investment) to individual, regional, and global economies. Then, the
volatilities in individual economies have been regressed against the global and regional
volatilities as well as a battery of domestic factors.
The empirical results suggest strong and significant contagion effects from intra-regional
volatilities in different types of capital flows to emerging economies. Such intra-regional
contagion effects are stronger for portfolio investment and other investment, relative to FDI.
It is also found that intra-regional contagion effects are stronger when capital inflows are
measured in net inflows than in gross inflows.
The evidence of contagion from regional volatilities implies that emerging market economies
need to exercise more vigilance in monitoring the dynamics of cross-border capital flows and
strengthen efforts to contain the spillover or contagion effects to maintain stable capital flows
and hence promote financial stability. Among the domestic policy variables, institutional
quality appears to have significant and negative effects on all three types of gross capital
flows, suggesting that better institutional quality is associated with more stable flows.
However, other policy variables have differential effects on volatility depending on different
types of capital inflow, presenting policy dilemma and challenge to producing coordinated
23
efforts by global and regional policy makers.
24
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Figure 1: Volatilities of Different Types of Capital Inflows to All Developing Countries in the World
(A) Gross Capital Inflow
(B) Net Capital Inflow
Note: FDI, FPI, and IOI refer to the 5 year standard deviation of foreign direct investment, portfolio investment, and other flows (mostly bank lending) in % of GDP, respectively. Source: Calculated by the authors using the IMF’s International Financial Statistics (IFS) and World Bank’s World Development Indicators-Global Development Finance (WDI-GDF) online database.
28
Figure 3: Volatilities of FDI, Portfolio Investment and Other Investment to Different Country Groups
(A-1) Asian Developing Countries: Gross Inflows
(A-2) Asian Developing Countries: Net Inflows
29
(B-1) Latin American Developing Countries: Gross Inflows
(B-2) Latin American Developing Countries: Net Inflows
30
(C-1) Eastern European Developing Countries: Gross Inflows
(C-1) Eastern European Developing Countries: Gross Inflows
Note: FDI, FPI, and IOI refer to the 5year standard deviation of foreign direct investment, portfolio investment, and other flows (mostly bank lending) in % of GDP, respectively. Source: Calculated by the authors using the IMF’s International Financial Statistics (IFS) online database.
31
Table 1: Determinants of Volatility of Gross Capital Inflows to Emerging Countries
VFDI VFPI VOI VFDI VFPI VOI(1) (2) (3) (4) (5) (6)
Lag of Dep Variable 0.917*** 0.834*** 0.835*** 0.754*** 0.860*** 0.863***
(0.011) (0.011) (0.007) (0.018) (0.012) (0.014)
Volatility of World Average 0.831*** 0.742*** 0.483*** 0.747*** 0.490*** 0.601***
(0.058) (0.043) (0.038) (0.046) (0.019) (0.043)
KOPEN 0.205 -0.854*** 1.296*** -0.914*** 0.544 0.856***
(0.172) (0.203) (0.283) (0.231) (0.339) (0.326)
INSTITUTION -1.735*** -10.916*** -6.262*** 2.606** -10.120*** 2.437
(0.539) (1.745) (1.351) (1.255) (1.065) (1.482)
INF -0.004*** -0.001** 0.003*** -0.002*** -0.000 0.006***
(0.000) (0.001) (0.001) (0.000) (0.001) (0.001)
RESIMP 0.280*** -0.285** 0.063 0.255** -0.550*** -0.212
(0.102) (0.114) (0.082) (0.121) (0.112) (0.242)
GDP_PC 1.129*** 4.798*** 0.165 2.959*** 2.220*** 3.217***
(0.319) (0.411) (0.664) (0.828) (0.450) (1.161)
PGDP -0.938*** -0.351*** -0.995*** -1.118*** 0.275*** 0.317***
(0.036) (0.067) (0.045) (0.083) (0.062) (0.075)
Constant -29.267*** -60.166*** -13.098*** -37.517*** -32.544*** -45.991***
(2.018) (3.939) (4.360) (6.769) (3.643) (9.809)
Number of observations 955 718 986 970 804 975
Number of countries 49 45 49 49 48 49
Arellano-Bond test
AR 1 (p-value) 0.000 0.000 0.000 0.000 0.000 0.000
AR 2 (p-value) 0.519 0.052 0.122 0.951 0.171 0.900
Sargen test (p-value) 0.571 0.807 0.651 0.695 0.746 0.732
Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standard errors.***, **, and * denote one, five, and ten percent level of significance, respectively.
Gross Inflow Net InflowSample period: 1980-2009
32
Table 2: Regional Contagion Effects on Volatility of Gross Capital Inflows
(1) (2) (3) (4) (5) (6)Lag of Dep Variable 0.998*** 0.986*** 0.851*** 0.848*** 0.822*** 0.845***
(0.022) (0.024) (0.024) (0.012) (0.015) (0.012)
East_Asia_Intra 0.092** 0.220*** 0.147***
(0.041) (0.025) (0.045)
Latin_America_Intra -0.113* 0.143*** 0.235***
(0.062) (0.032) (0.029)
East_Europe_Intra 0.042 0.157*** 0.301***
(0.026) (0.034) (0.038)
East_Asia_Extra 0.032 0.124*** -0.052
(0.043) (0.036) (0.054)
Latin_America_Extra -0.240*** 0.093*** 0.059
(0.038) (0.025) (0.051)
East_Europe_Extra -0.017 -0.045 0.103***
(0.046) (0.031) (0.040)
KOPEN -0.393 -0.070 -0.928*** -0.700*** 1.685*** 1.211***
(0.405) (0.163) (0.287) (0.207) (0.279) (0.388)
INSTITUTION -1.299*** -1.316 -10.682*** -11.338*** -2.864** -5.836***
(0.454) (1.099) (1.114) (2.294) (1.354) (1.147)
INF -0.002*** -0.001 0.000 -0.003*** 0.002*** 0.002*
(0.001) (0.001) (0.002) (0.001) (0.001) (0.001)
RESIMP 0.060 0.020 -0.337 -0.076 0.306* 0.192*
(0.125) (0.107) (0.311) (0.193) (0.175) (0.108)
GDP_PC 2.278*** 2.378*** 3.359*** 3.802*** -0.652 1.014
(0.778) (0.413) (0.746) (0.903) (1.206) (1.099)
PGDP -0.813*** -0.827*** -0.570*** -0.562*** -0.982*** -1.097***
(0.069) (0.069) (0.163) (0.114) (0.074) (0.066)
Constant -15.290*** -14.473*** -20.272*** -21.245*** 8.273 0.648
(4.702) (2.884) (6.079) (6.597) (8.690) (9.015)
Number of observations 955 955 712 718 986 986
Number of countries 49 49 45 45 49 49
Arellano-Bond test
AR 1 (p-value) 0.000 0.000 0.000 0.000 0.000 0.000
AR 2 (p-value) 0.473 0.450 0.059 0.062 0.089 0.116
Sargen test (p-value) 0.769 0.818 0.783 0.827 0.795 0.736
VFDI VFPI VOI
Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standarderrors. ***, **, and * denote one, five, and ten percent level of significance, respectively.
Sample period: 1980-2009
33
Table 3: Regional Contagion Effects on Volatility of Net Capital Inflows
(1) (2) (3) (4) (5) (6)Lag of Dep Variable 0.790*** 0.797*** 0.840*** 0.864*** 0.766*** 0.889***
(0.026) (0.017) (0.019) (0.014) (0.037) (0.028)
East_Asia_Intra 0.343*** 0.327*** 0.643***
(0.041) (0.043) (0.049)
Latin_America_Intra 0.357*** 0.295*** 0.496***
(0.031) (0.034) (0.067)
East_Europe_Intra 0.237*** 0.450*** 0.818***
(0.054) (0.075) (0.101)
East_Asia_Extra 0.148*** -0.013 0.063
(0.046) (0.030) (0.039)
Latin_America_Extra 0.171*** 0.106* 0.103*
(0.027) (0.056) (0.059)
East_Europe_Extra 0.040 0.165*** -0.073
(0.057) (0.063) (0.087)
KOPEN -1.442*** -1.018*** 1.008 1.443*** 2.688*** 0.526
(0.418) (0.236) (0.904) (0.424) (0.445) (0.703)
INSTITUTION 0.585 2.103** -15.376*** -9.585*** -4.528 2.137
(1.353) (1.051) (2.568) (1.176) (3.641) (2.781)
INF -0.001** -0.002*** -0.003*** -0.002 0.002* 0.005***
(0.001) (0.000) (0.001) (0.002) (0.001) (0.001)
RESIMP 0.298 0.360* -0.281 -0.298*** -0.889 0.195
(0.270) (0.208) (0.272) (0.102) (0.751) (0.551)
GDP_PC 4.089*** 4.329*** 1.931 0.770 -3.727 3.991
(0.642) (0.896) (1.574) (0.734) (3.449) (2.599)
PGDP -0.673*** -0.860*** -0.105 0.150* 0.658*** 0.137
(0.093) (0.068) (0.167) (0.080) (0.235) (0.135)
Constant -30.097*** -27.860*** -17.309 -0.586 22.014 -27.499
(4.672) (6.542) (12.494) (6.085) (23.648) (17.807)
Number of observations 970 970 804 804 975 975
Number of countries 49 49 48 48 49 49
Arellano-Bond test
AR 1 (p-value) 0.000 0.000 0.000 0.000 0.000 0.000
AR 2 (p-value) 0.851 0.884 0.162 0.162 0.867 0.618
Sargen test (p-value) 0.774 0.720 0.702 0.853 0.973 0.701
Sample period: 1980-2009VFDI VFPI VOI
Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standarderrors. ***, **, and * denote one, five, and ten percent level of significance, respectively.
34
Table 4: Determinants of Volatility of Capital Inflows to Emerging Countries: With the world average level of flows included
VFDI VFPI VOI VFDI VFPI VOI(1) (2) (3) (1) (2) (3)
Lag of Dep Variable 0.974*** 0.862*** 0.833*** 0.747*** 0.864*** 0.858***
(0.013) (0.015) (0.008) (0.020) (0.012) (0.013)
Volatility of World Average 1.087*** 0.860*** 0.454*** 0.760*** 0.701*** 0.554***
(0.065) (0.047) (0.036) (0.047) (0.061) (0.046)
Level of World Average 1.449*** 2.971*** -0.080 -0.203 -1.012*** 0.673***
(0.135) (0.460) (0.065) (0.192) (0.317) (0.233)
KOPEN 0.017 -1.130*** 1.189*** -0.851*** 1.301** 0.490
(0.155) (0.169) (0.249) (0.264) (0.518) (0.341)
INSTITUTION -1.369** -11.758*** -4.916*** 2.455* -11.934*** 5.773***
(0.695) (2.073) (1.497) (1.448) (1.266) (1.850)
INF -0.005*** -0.001 0.002** -0.002*** 0.002 0.006***
(0.001) (0.001) (0.001) (0.000) (0.001) (0.001)
RESIMP -0.045 -0.676** 0.015 0.297 -0.366*** -0.410**
(0.155) (0.269) (0.123) (0.182) (0.140) (0.209)
GDP_PC 0.242 4.573*** 0.456 3.273*** 3.198*** 1.968*
(0.441) (0.668) (0.757) (0.841) (0.608) (1.096)
PGDP -1.107*** -0.528*** -0.822*** -1.110*** 0.555*** 0.275***
(0.062) (0.108) (0.103) (0.084) (0.118) (0.075)
Constant -33.939*** -65.569*** -14.089*** -39.680*** -49.910*** -35.007***
(3.312) (6.581) (5.433) (6.983) (6.832) (9.591)
Number of observations 955 718 986 970 804 975
Number of countries 49 45 49 49 48 49
Arellano-Bond test
AR 1 (p-value) 0.000 0.000 0.000 0.000 0.000 0.000
AR 2 (p-value) 0.520 0.058 0.123 0.954 0.177 0.925
Sargen test (p-value) 0.648 0.802 0.684 0.716 0.807 0.830
Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standard errors.***, **, and * denote one, five, and ten percent level of significance, respectively.
Net InflowSample period: 1980-2009
Gross Inflow
35
Table 5: Summary of Global and Regional Spillover Effects of Capital Flows
VFDI VFPI VOI VFDI VFPI VOI(1) (2) (3) (4) (5) (6)
Average of World Volatilities 0.831*** 0.742*** 0.483*** 0.747*** 0.490*** 0.601***
(0.058) (0.043) (0.038) (0.046) (0.019) (0.043)
East_Asia_Intra 0.092** 0.220*** 0.147*** 0.343*** 0.327*** 0.643***
(0.041) (0.025) (0.045) (0.041) (0.043) (0.049)
Latin_America_Intra -0.113* 0.143*** 0.235*** 0.357*** 0.295*** 0.496***
(0.062) (0.032) (0.029) (0.031) (0.034) (0.067)
East_Europe_Intra 0.042 0.157*** 0.301*** 0.237*** 0.450*** 0.818***
(0.026) (0.034) (0.038) (0.054) (0.075) (0.101)
Average of World Volatilities 0.863*** 0.420*** 0.429*** 0.890*** 0.397*** 0.467***
(0.043) (0.023) (0.036) (0.049) (0.018) (0.029)
East_Asia_Intra 0.058* 0.147*** 0.055 0.321*** 0.392*** 0.510***
(0.031) (0.020) (0.059) (0.024) (0.066) (0.025)
Latin_America_Intra -0.091** -0.067 0.178*** 0.378*** 0.172*** 0.560***
(0.037) (0.050) (0.037) (0.014) (0.043) (0.042)
East_Europe_Intra -0.002 0.135*** 0.191*** 0.169*** 0.668*** 0.582***
(0.037) (0.038) (0.032) (0.059) (0.114) (0.073)
1980-2009
1980-2007
Notes: Estimates are made with Blundell-Bond (1998)'s two-step system GMM. Shown in parentheses are standard errors. ***, **, and *denote one, five, and ten percent level of significance, respectively. The results for the period 1980-2009 are taken from Tables 1, 2, and 3,while those for the period 1980-2007 are the corresponding results when the global crisis years of 2008 and 2009 are excluded from thesample.
Gross Inflow Net InflowSampleperiod
36
Appendix 1: List of Countries in Each Regional Group
HKG Hong Kong ARG Argentina BUL Bulgaria BOS Botswana
INO Indonesia BRA Brazil CRO Croatia COTE Cote d'IvoireKOR Korea, Rep. CHI Chile CZE Czech Republic GEO GeorgiaMAL Malaysia COL Colombia EST Estonia GHA GhanaPHI Philippines ECU Ecuador HUN Hungary IND IndiaPRC China MEX Mexico LAT Latvia ISR IsraelSIN Singapore PAN Panama LIT Lithuania JOR JordanTHA Thailand PER Peru POL Poland KAZ KazakhstanVIE Vietnam URU Uruguay RUS Russian Federation KEN Kenya
VEN Venezuela SLK Slovak Republic NIG NigeriaSLV Slovenia OMA OmanTUR Turkey PAK PakistanUKR Ukraine PNG Papua New Guinea
SAF South AfricaSAU Saudi ArabiaSLV SloveniaSRI Sri Lanka
East Asia Latin America Eastern Europe Others
37
Appendix 2: Volatilities of Different Types of Capital Inflows to Developing Countries: 5-year Moving Windows
(A) Volatility of Foreign Direct Investment Inflowss
Source: 5-year standard deviation of foreign direct investment flows (gross vs. net) calculated by the authors using the IMF’s International Financial Statistics (IFS) online database.
1980-1984
1985-1989
1990-1994
1995-1999
2000-2004
2005-2009
1980-1984
1985-1989
1990-1994
1995-1999
2000-2004
2005-2009
ARG 24.6 39.7 15.4 31.4 31.0 23.2 39.0 39.1 15.7 34.7 35.7 23.2
BAN 37.7 47.1 36.9 18.5 38.5 47.4 36.9 18.4
BOS 37.4 39.8 50.6 26.0 46.8 27.7 35.6 36.0 49.4 33.3 65.8 29.8
BRA 14.0 34.0 28.4 35.8 24.5 19.3 12.0 38.5 40.6 35.8 32.6 58.8
BUL 37.9 38.7 22.1 26.8 35.6 37.5 22.7 27.5
CHI 29.3 36.2 24.2 24.8 18.1 17.8 30.3 36.2 24.4 24.4 28.5 17.9
COL 31.0 32.3 18.5 31.2 12.3 20.6 36.3 33.5 24.1 32.0 35.7 52.3
COTE 25.7 31.4 59.2 17.7 20.1 7.7 19.1 34.5 67.1 18.5 15.9 40.5
CRO 34.6 24.0 25.7 26.5 24.9 52.9
CZE 32.3 32.8 32.9 35.3 32.2 48.5
ECU 15.9 32.4 31.6 19.6 43.0 32.7 18.9 24.8 31.2 19.6 43.1 32.8
EST 25.7 23.0 25.5 31.5 26.4 37.1
GEO 28.4 28.2 28.9 26.3
GHA 38.0 28.7 41.3 23.8 26.4 36.1 44.7 30.2 41.1 23.8 26.4 33.2
HKG 33.4 12.2 83.5 72.4
HUN 15.6 24.5 42.7 16.3 33.1 61.9
IND 15.9 16.9 26.7 16.7 21.9 31.2
INO 25.3 10.1 65.1 52.5 25.8 38.0 24.0 13.1 67.5 43.2 31.1
ISR 30.2 17.7 28.3 27.5 32.3 30.4 55.9 32.2 44.3 36.1 47.8 45.9
JOR 31.2 40.4 72.1 43.4 32.5 21.6 32.9 69.7 49.1 38.7 33.3 22.6
KAZ 18.0 19.7 29.5 20.0 22.3 23.7
KEN 35.7 35.8 41.1 29.4 37.2 40.0 40.8 39.8 41.0 34.0 38.3 41.9
KOR 35.2 20.8 23.4 37.6 32.0 31.3 75.4 58.2 31.4 56.9 47.3 43.5
LAT 21.9 28.8 39.4 20.7 28.4 35.3
LIT 33.8 29.2 33.9 34.6 31.6 36.9
MAL 22.1 26.9 16.7 17.3 34.2 31.9 22.1 26.9 17.0 20.5 34.2 51.0
MEX 15.2 17.6 25.4 6.9 18.0 16.3 14.9 17.1 25.4 6.9 19.0 27.4
NIG 76.2 35.7 36.8 14.2 12.9 14.5 79.4 35.7 33.1 14.9 13.1 19.8
OMA 23.3 29.7 22.3 24.7 41.6 20.1 23.4 29.1 22.3 24.6 62.1 24.7
PAK 27.1 16.2 11.1 17.3 30.0 27.1 13.3 11.4 20.4 30.2 40.7
PAN 56.5 54.0 26.0 33.2 34.8 23.2 56.5 54.0 26.0 33.2 34.8 22.5
PER 69.0 34.1 43.6 20.7 22.3 17.6 69.2 36.7 43.6 21.9 22.8 16.5
PHI 64.1 38.0 34.2 23.1 37.9 25.4 64.1 38.0 29.1 24.9 42.5 45.4
PNG 21.5 15.3 32.0 36.6 39.2 43.4 21.2 19.7 31.9 41.9 38.2 43.7
POL 23.1 10.7 42.9 15.8 29.4 24.3 65.6 42.7 15.6 29.0 23.7
PRC 12.7 37.1 10.7 7.6 23.0 24.1 42.5 10.4 6.4 38.3
RUS 25.4 28.2 25.1 15.5 63.4 57.9
SAF 44.7 53.9 33.6 40.0 37.7 40.5 41.7 74.4 42.9 56.4
SAU 45.7 87.7 43.8 62.5 38.9 24.3 45.7 71.2 43.8 62.5 38.9 22.4
SIN 20.3 24.1 27.1 21.5 25.8 32.2 20.1 28.6 35.0 31.9 61.1 43.0
SLK 25.4 36.7 32.5 54.0
SLV 26.7 36.2 53.0 29.8 42.7 48.9
SRI 23.5 26.3 31.1 32.3 6.2 23.1 23.8 26.9 31.4 32.3 1.5 23.5
TAP 23.1 22.3 33.9 39.9 35.4
THA 19.8 36.1 24.9 36.0 16.6 26.5 20.1 38.3 28.8 39.3 15.2 40.0
TUR 32.9 34.2 13.9 9.5 31.8 25.9 32.8 34.5 13.0 32.7 36.4 27.5
UKR 22.8 17.8 20.7 26.6 17.7 19.7
URU 17.8 27.5 17.4 43.5 52.9 49.1 19.9 27.4 19.0
VEN 37.3 38.4 33.2 32.4 31.9 56.8 40.5 47.9 46.6 32.7 44.5 78.3
VIE 6.2 30.0 5.8 30.1
Gross flows Net flows
38
(B) Volatility of Foreign Portfolio Investment Inflows
1980-1984
1985-1989
1990-1994
1995-1999
2000-2004
2005-2009
1980-1984
1985-1989
1990-1994
1995-1999
2000-2004
2005-2009
ARG 36.5 26.2 40.5 57.7 37.9 69.2 33.6 27.6 42.9 68.4 34.9 67.2
BAN 42.6 42.1 77.2 37.1 42.7 42.1 82.1 45.1
BOS 69.1 55.8 47.1 76.7 67.1 32.9 56.5 65.3
BRA 73.6 20.8 39.1 31.3 72.6 43.8 78.9 17.8 38.9 31.0 88.0 45.2
BUL 73.6 45.3 61.9 69.6 46.5 82.2
CHI 29.7 40.9 40.5 46.5 27.2 64.1 61.9 29.1
COL 28.4 26.2 47.4 48.0 28.4 45.4 39.0 62.8 66.3
COTE 22.3 47.5 37.6 66.2 42.9 65.2 65.9 49.4
CRO 50.9 31.7 68.3 39.3 76.9 79.9
CZE 27.8 32.9 39.2 72.1 85.3 79.8
ECU 41.6 44.8 49.6 41.6 44.3 50.3
EST 40.7 35.8 48.4 47.3 39.6 46.0
GEO 40.7 51.0 39.8
GHA 38.9 26.5 35.5 34.6 33.5 47.2 37.0 25.7 35.3 34.6 33.5 53.7
HKG 44.2 44.2 68.5 49.7
HUN 66.0 37.1 54.7 67.3 40.6 73.2
IND 40.7 37.5 52.1 41.1 36.7 54.0
INO 61.6 46.2 90.6 67.0 27.9 47.8 60.9 46.2 90.7 64.0 29.9
ISR 46.3 44.4 50.9 28.5 52.3 23.1 50.2 48.8 35.0 46.8 54.5 45.8
JOR 32.1 41.1 23.5 59.3
KAZ 45.9 53.5 48.9 46.6 46.0 56.7
KEN 36.3 71.6 39.5 34.7 40.6 21.9
KOR 44.7 50.4 35.1 35.2 26.3 53.4 44.7 53.9 36.0 46.4 39.3 57.7
LAT 41.3 44.5 51.4 61.4 59.5
LIT 41.5 28.0 38.5 43.2 38.4 54.3
MAL 37.1 47.7 46.5 48.4 52.1 62.8 37.1 47.7 46.5 46.9 54.4 51.2
MEX 85.5 71.8 33.1 72.1 61.2 39.2 88.0 54.0 47.0 71.4 34.0 67.3
NIG 46.5 25.9 33.7 47.1 42.9 39.5 59.1
OMA 66.0 66.0 50.4 63.2
PAK 36.4 38.7 54.7 58.7 44.7 35.7 38.7 54.8 59.0 51.8
PAN 75.0 26.5 37.3 67.4 42.2 39.6 47.4 55.4 36.6 64.0 48.4 54.0
PER 68.7 43.5 45.5 48.7 73.7 56.1 65.9
PHI 40.2 47.0 47.8 46.1 56.6 54.2 41.0 54.1 49.3 81.9 68.9
PNG 63.9 38.1 36.4
POL 39.3 31.9 50.6 39.9 33.5 62.3
PRC 34.3 43.2 40.7 32.5 38.4 53.5 64.1 69.7 53.7
RUS 48.4 49.1 69.6 49.3 46.1 69.9
SAF 52.9 35.9 33.5 63.2 48.9 47.8 41.4 31.5 52.4 62.4
SAU 69.8 48.4 68.0 65.6 55.0 42.3
SIN 38.4 55.9 44.3 42.9 57.9 68.1 41.0 57.2 56.9 17.7 30.1 39.5
SLK 41.3 44.8 45.4 80.7
SLV 37.5 48.0 39.4 39.5 54.6 62.1
SRI 18.6 27.7 11.5 63.5 58.3 51.5
TAP 48.3 42.2 41.1 30.4 57.3
THA 25.7 43.1 42.2 45.2 66.9 47.1 25.7 43.1 42.2 45.3 64.4 82.8
TUR 37.4 71.5 65.8 47.5 35.5 60.0 69.6 49.2
UKR 70.1 40.6 62.2 70.1 40.6 62.7
URU 44.9 27.2 32.9 27.4 32.5 46.2 45.0 27.8 32.9 31.9 86.7 54.7
VEN 43.6 57.8 50.6 80.4 43.5 63.6 54.0 58.5
VIE 42.5 43.0
Gross flows Net flows
39
Source: 5-year standard deviation of foreign portfolio investment flows (gross vs. net) calculated by the authors using the IMF’s International Financial Statistics (IFS) online database.
40
(C) Volatility of Other Inflows (OI) - Gross
Source: 5-year standard deviation of other investment flows (gross vs. net) calculated by the authors using the IMF’s International Financial Statistics (IFS) online database
1980-1984
1985-1989
1990-1994
1995-1999
2000-2004
2005-2009
1980-1984
1985-1989
1990-1994
1995-1999
2000-2004
2005-2009
ARG 60.9 46.0 56.6 37.8 40.5 66.2 65.0 46.2 44.7 64.1 39.6 28.2
BAN 16.0 16.9 21.3 13.8 25.3 30.0 13.8 21.5 24.9 59.5 59.3 54.9
BOS 18.9 36.8 29.7 23.3 62.8 48.4 28.4 62.0 43.5 42.1 17.3 16.0
BRA 68.7 16.8 38.6 69.9 40.6 44.3 85.0 14.6 36.4 63.6 32.4 74.0
BUL 37.8 70.5 65.4 39.7 40.8 34.2 44.5 65.6 49.7 50.5 47.3
CHI 71.2 34.2 64.6 34.0 80.6 71.7 71.1 34.9 37.7 60.1 38.3 30.7
COL 22.1 48.0 76.4 49.7 46.0 44.3 34.4 53.0 79.9 73.1 63.9 45.1
COTE 52.3 34.0 35.9 37.5 39.0 77.5 80.6 49.7 37.6 16.5 32.0 36.1
CRO 30.4 36.4 20.4 24.1 34.1 34.5
CZE 40.5 59.2 54.9 58.9 45.9 73.3
ECU 62.3 25.6 64.3 52.9 51.9 59.2 64.7 61.0 61.0 44.8 56.6 41.1
EST 33.3 29.9 48.5 44.9 45.9 34.0
GEO 57.7 34.8 35.2 61.0
GHA 37.1 34.5 21.0 24.6 51.9 56.3 26.9 32.9 23.9 24.6 51.9 70.3
HKG 76.9 45.5 80.4 61.3
HUN 28.2 38.1 69.7 66.1 34.5 34.7 45.9 43.2 80.3 44.4
IND 29.4 16.9 21.9 33.9 35.5 29.2 36.1 19.2 14.5 38.1 33.6 32.6
INO 28.2 54.3 44.9 28.7 68.9 40.8 28.9 54.3 44.9 26.8 31.6
ISR 30.7 51.3 27.8 29.4 48.0 60.0 30.2 52.4 26.2 32.7 36.3 83.8
JOR 21.5 31.0 53.6 75.1 70.9 75.3 36.5 33.9 44.7 60.3 70.3 55.2
KAZ 29.6 41.3 46.3 52.0 44.0 49.5
KEN 22.1 32.3 53.6 43.2 43.7 20.3 29.1 38.6 75.7 67.1 58.9 28.2
KOR 32.9 46.9 41.8 82.9 55.6 55.4 32.9 40.7 77.7 60.6 72.4 61.7
LAT 28.6 24.6 47.6 27.1 20.3 58.8
LIT 29.1 39.5 57.8 35.8 38.2 71.4
MAL 35.8 54.1 48.6 44.3 49.2 53.4 39.0 57.8 52.7 64.5 21.0 38.4
MEX 44.7 49.2 42.8 69.1 43.0 58.6 53.9 43.7 47.3 71.6 56.7 57.9
NIG 69.0 29.4 36.3 70.0 24.7 49.9 62.1 29.1 30.6 46.5 17.1 37.6
OMA 38.0 46.7 76.2 40.6 66.0 41.5 76.0 48.5 67.9 44.1 53.5 53.5
PAK 17.4 28.5 21.7 94.4 28.4 24.1 29.8 24.5 84.0 33.5 35.6
PAN 71.9 46.1 31.8 75.6 47.0 35.0 63.2 50.4 49.2 67.4 70.9 78.0
PER 56.7 20.3 59.2 50.1 49.9 50.6 56.8 33.4 63.7 47.9 61.5 50.6
PHI 49.8 77.1 15.0 51.6 28.7 80.0 47.7 77.0 15.0 58.5 67.1 42.1
PNG 36.0 25.4 54.7 35.4 55.9 68.5 39.7 54.0 61.9
POL 68.1 35.5 43.5 65.8 59.0 42.2 35.4 42.3 40.2 47.7 51.1
PRC 29.5 65.2 61.6 46.8 44.5 28.4 61.5 46.9 63.3 69.2
RUS 72.7 55.9 47.1 33.6 46.2 66.9
SAF 46.2 46.4 60.4 72.5 48.9 42.8 32.4 55.8 58.4 39.4
SAU 62.8 30.1 59.6 62.8 66.9 53.5 52.6 44.3 36.9 73.4 40.8 41.8
SIN 30.3 40.7 56.1 46.2 39.6 37.5 28.2 37.4 61.6 57.7 47.2 58.9
SLK 59.6 59.2 35.2 66.2
SLV 30.4 25.2 50.2 56.3 19.6 70.7
SRI 22.2 21.6 17.6 32.9 79.2 36.7 21.8 22.5 21.0 36.8 76.4 49.9
TAP 49.3 34.1 50.0 41.1 54.0
THA 20.0 42.4 17.8 76.7 28.4 59.4 21.5 46.9 27.0 70.0 32.2 70.2
TUR 26.4 73.7 64.2 19.8 64.9 49.2 70.6 80.3 65.5 31.1 57.0 34.2
UKR 26.5 47.9 44.7 31.9 30.9 42.1
URU 38.2 54.5 44.8 43.2 54.9 71.0 30.7 43.3 52.2 61.1 44.7 76.5
VEN 61.2 58.1 47.8 59.3 66.5 61.0 25.4 47.3 46.1 23.1 25.5 25.1
VIE 34.4 28.2 79.4 46.7
Gross flows Net flows
