OSIPP Discussion Paper : DP-2019-E-011

Capital Flow Dynamics in Emerging Market Economies
Masahiro Enya
Associate Professor, Faculty of Economics and Management
Institute of Human and Social Sciences, Kanazawa University, Japan
Akira Kohsaka
Professor Emeritus, Osaka School of International Public Policy
Osaka University, Japan
Kimiko Sugimoto
Professor, Hirao School of Management, Konan University, Japan
November 20, 2019
[Keywords] gross capital flows, emerging market economies (EMs), types of capital flows, Asian
Financial Crisis, Global Financial Crisis[JEL code] F3, F4, F6[Abstract] This paper analyzes the dynamics of gross capital flows since the 1990s across three
regions, i.e. East Asia, Europe and Latin America, and across types of capital flows, i.e. foreign
direct investment, portfolio equity flows, portfolio debt flows and other investment. First, wedemonstrate distinct features of gross capital inflows and outflows with selected EMs across thethree regions by types of capital flows. Then, using panel data regression in the period of 20002015, we show how both domestic and global factors contribute to the dynamics of these grosscapital flows. We confirm that both global factors such as expected growth and international
investors’ risk perception in AEs, and domestic factors such as exchange rate regimes, andfinancial deepening in EMs, contribute to the dynamics. Furthermore, we detect significant
regional diversities in relative importance and sensitivities in the roles of these factors.
Corresponding author: Akira Kohsaka, E-mail: kohsaka@osipp.osaka-u.ac.jp. We gratefully
acknowledge financial support from Grant-in-Aid for Scientific Research (B) (16H03627). We arealso grateful to the attendees at the 16th International Convention of the East Asian Economic
Association, Taipei, Taiwan, October 27-28, 2018 and the 87th International Atlantic Economic
Conference, Athens, March 27-30, 2019 for their useful comments.

1. Introduction
International capital flows to emerging market economies (EMs) concern many policymakers. In
fact, the 1997-98 Asian Financial Crisis (AFC) was a nightmare to those in East Asia, when surging
capital inflows and their reversals trapped their economies with plummeting exchange rate devaluation
into a serious economic downturn accompanying prolonged investment slump.
The Global Financial Crisis (GFC) in 2008 shows us that the sudden stop of capital flows is not
limited to EMs. The surge and reversal of capital flows occurred in advanced economies (AEs) andspilled over to emerging economies. Even greater surge than that before the AFC stopped suddenlywith the GFC. This time, however, economic downturn in EMs was rather short-lived than before aswell as in AEs.
In the past, sudden stops and/or reversals of capital inflows after rapid expansions have been
associated with costly economic crises in EMs, occasionally triggered by shifts of monetary policy inAEs. This history matters. In fact, we witnessed significant irreversible structural changes in theinternational macro-financial economic arena since the 1980s, which include EMs’ increasing
integration into the global financial markets and higher share in global output.
International gross capital flows have behaved like a roller-coaster in recent years (IMF, 2011).
After a surge toward the global financial crisis (GFC), gross inflows dropped sharply, and recovered
their momentum slowly over a couple of years thereafter (Figure 1). Capital flows rose exponentiallybefore the GFC in the 2000s, then dropped sharply during the GFC, and recovered to the level of the
beginning of the 2000s by 2010. The fluctuations in gross inflows were much larger for advanced
economies (AEs), where capital flows rose from 10% (2002) to 25% (2007) of GDP, then dropped to-10% and recovered to 10% again. In emerging market economies (EMs), gross inflows rose from
2.5% of GDP to 12.5%, fell to 0%, and recovered to 5% of GDP.
>>Figure 1: gross capital inflows to AEs and EMs
In contrast, the fluctuations in net capital flows, i.e. gross inflows minus gross outflows, were
sharper for EMs rather than AEs (Figure 1), suggesting that gross outflows offset gross inflows morein AEs than in EMs. Net inflows fluctuated more sharply in EMs, rising from 1% to almost 4%,
dropping to -2.5%, and recovered to 3%, while in AEs, they remained between 0% and 2% of GDP
during the period. Note, however, that gross inflows and net inflows started to show less comovements in the 2000s in EMs as has been the case in AEs.
International capital flows can help finance domestic demand, but can also exacerbate economic
and/or financial boom-bust cycles. Way back in the early 1980s, Diaz-Alejandro (1983) warned therisk of capital flows driven by financial liberalization to lead to financial crash through the Chili’s
experience in the 1970s. Despite this, the lost decade of Latin America economies in the 1980s, and
the AFC in the 1990s, however, policy advisors had kept advising to remove barriers against capital
inflows to EMs as part of financial as well as market liberalization. Only after the GFC, they eventually1admitted that international capital flows can be a destructive force, increasing volatility and starting
suffering crises, rather than promoting growth and diversifying risk by increasing investment
opportunities. These days, intervention to reduce the volatility of capital inflows, using capital controls
and foreign exchange market intervention has been supported by the IMF (IMF, 2013, WEO October,p. 113).
With this background in mind, this paper analyses the dynamics of gross capital flows since the1990s across three regions of EMs as well as across types of capital flows. Most prior studies in thefield have discussed EMs as one group (for example, as reviewed by Koepke, 2015), which conceals
potential differences in the dynamics of capital flows across the regions as well as across types ofcapital flows. We are interested in the possible regional diversities in capital flow dynamics as well as
in roles played by both domestic and global factors that potentially contribute to the dynamics.
First, we demonstrate and confirm distinct features of gross capital inflows and outflows withselected EMs across three regions, i.e. East Asia, Europe and Latin America by types of capital flows,i.e. foreign direct investment (FDI), portfolio equity flows, portfolio debt flows and other investment.
FDI has played the major role in inflow trend throughout the three regions and in inflow swing inEuropean EMs. Other investment including bank loans have played varying roles over time and acrossthe regions, i.e. losing importance in East Asia, cyclically important in Europe and least important in
Latin America, reflecting regional historical developments, while portfolio equity and debt flows havebeen relatively less important as against popular views extended in policy advisers in international
financial institutions..
Then, using panel data regression for these EMs in the period of 2000-2015, we show how both
domestic and global factors contribute to the dynamics of these gross capital flows across the regions
and the capital flow types. We confirm that both global factors such as expected growth and policy
interest rate differentials, international investors’ risk perception and monetary policy shifts in AEs,and domestic factors such as institutions of policy making, capital flow restrictions, foreign exchange
reserves, exchange rate regimes, and financial deepening in EMs, contribute to capital flow dynamics
in EMs. Furthermore, we detect significant regional diversities in relative importance as well as inrelative sensitivities in the roles of these domestic and global factors.
Finally, we scrutinize further on the reasons why East Asian EMs has shown distinct structures aswell as dynamics in capital flows as compared with other EM regions. Particularly, we go deeper into
policy frameworks and financial development, which would condition the size, the role and the impact
of capital flows with EMs. The last section concludes the paper with some comments on potential
roles of capital flows with EMs and on the history of policy debates on the capital flows with EMs.

2. Capital inflows and outflows in East Asia and other regions
Literature on capital flows have focused on either net flows or gross flows. Gross capital flows in2

EMs consist of both gross capital inflows to EMs by non-residents and gross capital outflows fromEMs by residents, while net capital inflows are the difference between gross inflows and outflows.
As is shown in Figure 1, gross and net capital inflows to EMs were not much different until the early
1990s, but not anymore in more recent period.
In fact, such aggregate measures as net capital inflows conceal offsetting movements of various
components within the measures across not only direction of flow, but type of flow and recipient
regions (IMF, 2016). For example, Figure 2 shows that even net inflows are diverse between three
representative regions, i.e. East Asia, Eastern Europe and Latin America (Panel a). Net inflows to EMs
as a whole have not seen any strong trend during the period, reflecting those of East Asia withincreasing importance in global output, while those to Eastern Europe have demonstrated visible surge
toward the GFC and bust in the post-GFC period.
>>Figure 2: diverse capital inflows to EMs
Also, even gross capital inflows to EMs have concealed offsetting movements of theircomponents across type of flow (Figure 2, Panel b). FDI and the other three types of capital flows
have shown some asymmetry not only in change of direction, but in volatility.
This section reviews changes in gross capital flows and their components for EMs in East Asiaand other regions. We focus on the flows since the 1990s, which include several crisis episodes suchas the Asian Financial Crisis (AFC) and the Global Financial Crisis (GFC). We select 17 EMs in 3
regions, i.e. 7 countries from East Asia (EA7), 5 from Latin America (LA5) and 5 from Eastern Europe(EE5)1. To cover the period of financial globalization, this section examines annual data for the yearsof 1990-2015 based on IMF’s International Financial Statistics. We will observe distinct regional
differences in capital flows with respect to size and composition during the period.
Total capital inflows and outflows
Capital inflows to EMs show large swings at more or less different timing across three regions
during the sample period as shown in Figure 3. Capital inflows to EA7 dropped sharply at the AFC as
well as at the GFC. They dropped from 6% of GDP to 2% during 1996-98, and from 8% to 2% during
2007-2008. Their recovery was slow at the AFC, being hit again by the dotcom bubbles, while rather
swift recovery at the GFC.
>>Figure 3: capital inflows and outflows by regions
Capital inflows to LA5 dropped more modestly for 1993-1995 (the 1994 Mexican crisis), 20012002 (the dotcom bubbles), and 2007-2008 (GFC), by 2-3% of GDP. Being belatedly integrated to theinternational financial markets, EE5 depend heavily on external finance from the start and experienced
a sharp drop of capital inflows for the first time at the GFC from more than 15% to 6% to GDP.
Capital outflows excluding foreign reserves from EMs have become non-negligible gradually1

EA7: China, Indonesia, India, Korea, Malaysia, Philippines, Thailand, EE5: Czech, Hungary,
Poland, Slovak, Turkey, LA5: Argentina, Brazil, Chile, Colombia, Mexico.
3

(Figure 3). Particularly in EA7, they show some steady increases from less than 1% of GDP in theearly 1990s to recent 3-4%. LA5 also show some increases in their capital outflows, though modest
from 1% to 2% of GDP throughout the period. EE5 witnessed non-negligible size of capital outflows,
but an upward trend is hard to detect until now. Capital outflows sometimes positively correlated tocapital inflows as in the case of EA7 and EE5, while their fluctuations are smaller. The positive
correlation tends to mitigate the fluctuations in net capital inflows to EMs. Little correlation between
inflows and outflows can be found in LA5.
The size of capital inflows relative to GDP is distinctively larger in EE5 than in EA7 and LA5,
while the size of capital outflows more or less similar across the regions. Consequently, the size of net
inflows is larger in EE5 than the other two regions (Figure 3). In fact, EA7 generated negative net
inflows in 2015-2016, which, unlike in the AFC, is not a sign of economic crisis this time. Actually,
we see huge compile of foreign exchange reserves toward the GFC as well as for a few years after that.
Capital inflows by type and region
To detect the role of key capital flow components, we next decompose capital flows by type offlow. Capital flows consist of four types in the balance of payments data: foreign direct investment(FDI), portfolio equity, portfolio debt, and other investments including bank flows. The large swings
in capital inflows to EMs are mostly parallel to those in other investment flows in all the three regions
and magnified by similar movements of portfolio debt as well as of portfolio equity, even though thelatter is small in size (Figure 4).
>>Figure 4: capital inflows by type and region
Figure 4 demonstrates quite distinct time profiles for the four asset types across the regions. While
FDI is the largest gross inflow component across the regions, its time profile is quite contrasting acrossthe regions. In EA7, FDI has been stable at 2-3% of GDP and played a stabilizing role against otherthree components which have commoved more or less, creating inflow swings together. In LA5, FDI
has replaced and dominated other investment by the 2000s, and remained rather stable at 3-4% of GDP,
while portfolio debt is the strongest component creating inflow swings, followed by other investment.
By contrast, in EE5, FDI is the major component which has generated inflow swings, with otherinvestment magnifying the swing.2 Portfolio equity inflows are relatively small in size and its procyclical role is limited more or less across the regions.
As in the case of inflows, FDI is the largest outflow component across the three regions, but its
role is distinct between EA7 and the other two regions (Figure 5). FDI has contributed to trend
increases in outflows in EA7 and LA5, but not in EE5, while FDI played a pro-cyclical role to outflows
in EE5, but not in EA7 and LA5. Both portfolio debt and other investment contributed to cyclicalmovements in outflows across the regions, while portfolio equity is also pro-cyclical to a lesser degree
2

In EE5, portfolio debt inflows have been counter-cyclical to total inflows, which may reflect some
sort of government intervention.
4

than other components in LA5 and EE5.
>>Figure 5: capital outflows by type and region
In sum, we can see distinctive sizes and time profiles of gross capital inflows and outflows
across emerging market regions such as EA7, EE5 and LA5. As a result, sizes and time profiles ofnet capital inflows are also distinctive across the regions, while the net inflows commove with grossinflows to a greater extent than in AEs. Looking further into the components of capital flows, we see
diverse sizes and time profiles of inflows and outflows by type of capital flows as well as by theregions. Some underpin trend movements and others contribute to cyclical patterns. Some become
dominant and others retrench. Noting that these heterogeneous components’ heterogeneous
movements across region constituted and/or were aggregated into gross capital flows, it is not only
far from surprising to see these distinctive sizes and time profiles of capital flows between EA7, EE5
and LA5, but also unwise to assume EMs as a mass of representative EMs.

3. External assets and liabilities in East Asia and other regions
Capital flows to and from EMs affect their external assets and liabilities positions and structures,and they also affect capital flows in turn. In fact, continued and sometimes surging capital flows
resulted in accumulation of external assets and liabilities in EMs. Now they have more engaged in
global asset trade, so that getting increasingly integrated to global financial markets.
Based on the database by Lane and Milesi-Ferretti (2007, 2017), this section reviews changes inexternal assets and liabilities and their components for the same country groups (i.e. EA7, LA5, andEE5) during the period of 1980-2015. As a result of the distinct regional differences in capital flows,
we will also observe distinct differences in external assets and liabilities with respect to size andcomposition across the regions during the period.
The accumulation in total external assets and liabilities of EMs exhibits very distinct time profiles
across the three regions as shown in Figure 6. Total external liabilities of EA7 has remained generally
stable around 50% of GDP. There was some hiccups downward at the AFC as well as at the GFC, but
the level of total external liabilities regained previous levels and showed no sign of any strong upward
trend since the GFC.
>>Figure 6: external assets and liabilities by type and region
By contrast, total external liabilities of EE5 showed a strong upward trend since the mid-1990s,
which started from around 50% of GDP, went beyond 100% before the GFC and stayed at nearly 120%of GDP in 2015, which is more than twice as large as those of EA7 relative to GDP. Total externalliabilities of LA5 showed some upward trend until around the early 2000s, but stayed at around 70%
of GDP until 2014.
The apparent stability of total external liabilities of EA7, however, contains offsetting changes in
its components (Figure 6). FDI has steadily increased from less than 10% of GDP to nearly 25% during
5the sample period, and portfolio equity has also increased from around 5% to the present 10% withsome short-lived surge before the GFC. The offsetting component is other investment, which gradually
fell from more than 30% of GDP before the AFC to a little more than 10% before the GFC andremained mostly stable there. In other words, in EA7, we see explicit shifts from debt (portfolio debt
and other investment) to non-debt liabilities (FDI and portfolio equity) in the composition of externalliabilities particularly after the AFC.
Total external assets (including foreign exchange reserves) of EA7 showed a steady increase from
10% of GDP in the early 1990s to more than 50% by the GFC, and stayed there. In the meantime,
foreign exchange market interventions for exchange rate stability helped accumulating foreignexchange reserves beyond 30% of GDP. As a result, EA7 has become a net external creditor as theregion as a whole since around the GFC. The second largest contributor to this increase in external
assets is FDI, which monotonously increased from 3% of GDP to more than 10% during the period.
Other investment is a large component, but its increase has been very moderate, while portfolio equity
increased from negligible to more than 2% of GDP.
The strong upward trend of total external liabilities in EE5 has been underpinned by an explosive
growth of FDI from 30% of GDP in the mid-2000s to more than 50%, and the second largest
component has remained to be other investment at around 30% of GDP during the same period.
Portfolio equity has played a minor role in the liability accumulation. EE5 showed a modest increase
in total external assets relative to its increase in external liabilities in contrast to the case of EA7. The
increase in EE5 is mostly supported by FDI. Consequently, the net external debtor position has beenmagnified in the recent period in EE5 from 30% (2001) to 50% (2015).
The development of total external liabilities in LA5 has been a compromise of those of its four
capital components during the sample period. The basic long-run upward trend has been supported by
FDI. Portfolio equity climbed up until the GFC, but stagnated thereafter, and other investments are
swinging as, upward in the early 2000s, downward toward the GFC, and slowly upward again
thereafter. Upward trend of external assets in LA5 is slower than in the other two regions, which has
been supported by FDI, foreign exchange reserves and portfolio equity at a far from dramatic pace. As
a result, LA5 has remain a net external debtor, but not more nor less in the recent period.
All in all, the structure of external assets and liabilities of the three regions after the GFC can besummarized as in Table 1, which shows that EA7 is conspicuous with respect to the size of grossexternal liabilities, the share of nondebt liabilities in total external liabilities, and the size/sign of net
external liabilities. Namely, gross external liabilities are as small as 50% of GDP as against 114% in
EE5, the share of nondebt liabilities is as small as 33% as against 58% in EE5, and net externalliabilities are as small as -4% of GDP as against 66% in EE5. The Table appears to suggest that EA7
is successful in attaining more resilient external portfolio structure against global financial risks thanthe other regions.
6

>>Table 1: External liabilities structure

4. Drivers of capital flows: empirical estimation
How and why some EMs are more resilient to capital flow fluctuations? Are they policies,
institutions and/or the degree of financial integration which matter? Macro-financial linkages between
AEs and EMs consist of common global factors that generate similar effects across all EMs on one
hand, and individual country-specific factors that differ between individual EMs on the other. Global
factors are related to commodity prices, global output, global interest rates and global asset prices aswell as global shifts in market sentiment or risk aversion. The role of such global factors reflects
financial globalization or the increasing financial integration of EMs in the recent decades.
In fact, total external liabilities (and then assets) of EMs grew rapidly relative to GDP particularly
since the 1990s. As seen in the previous sections, the growth reflected particularly those in foreign
direct investment and portfolio equity flows, while other investment was large, but rather stable, andportfolio bond flows were small. Financial integration has increased unevenly across regions, though.
EMs in east Asia kept their exposure more or less stable relative to GDP since the AFC. In contrast,
EMs in Europe increased their total external liabilities from 50% in the 1990s to more than 100% of
GDP and EMs in Latin America from 50% to 75% by the GFC. As to relative contributions ofcomponents of capital inflows to the growth of external liabilities, both FDI and other investment
flows contributed less in EMs in East Asia than in those in Europe and Latin America
Estimation strategies
We have witnessed such diverse pattern as well as composition of capital inflows to EMs acrossregions as above. We see no reason why we can assume common financial linkages nor homogenous
financial adjustments across regions as well as individual EMs there. Since total capital flows consistof four types of capital, which show very distinct dynamic features over time, it would be moremeaningful to examine what drive these dynamics by separating by types rather than by aggregating
across types. This section links changes in four types of capital flows to EMs to a set of potential
contributing factors and to examine how structural characteristics, policy choices and other factors of
EMs as well as of the global economy affect the dynamics of capital flows and then how EMs aredifferent from one another in these contexts.
Generally, international capital flows result from decisions by residents and nonresidents to allocate
investments across countries. Other things being equal, investors allocate more investments in aparticular country, the higher the risk-adjusted returns relative to those from investments in othercountries. Expected returns from investing in a country is related to such local factors as its expected
growth and interest rate differentials and the quality of domestic policy making and institutions on one
hand, and such global factors as the risk appetite of investors, global expected growth and interest rate
differentials between AEs and EMs, commodity prices and policy choices of AEs.
7

Estimation methodology
In this section, the sample of 17 countries, constituting EA7, LA5 and EE5, is covered on thequarterly basis for the first quarter of 2000 through the fourth quarter of 2016. We focus on the period
after the AFC when EA7 were forced to seriously restructure their macro-financial policy frameworks.
After all, our empirics are free from econometric problems associated with structural breaks in capitalflow dynamics during the 1980s and the 1990s.
We follow a panel data specification used in IMF (2016) that relates EMs’ country-specific capital
flows to domestic factors such as country-specific expected growth and policy interest rate and country
characteristics as the quality of institution and the degree of capital restrictions on one hand, and to
global factors such as VIX and/or corporate bond spread for investors’ risk perception, AEs’ expected
growth and policy interest rate, oil price changes and long-term to short-term yield gap for monetary
policy in US (for AE) on the other hand. Namely, our regression equation is as follows:3
Capital flows t = β0 + β1 expected domestic growth t + β2 domestic policy interest rate t +β3 domestic
institution t + β4 domestic capital flow restriction t + β5 global risk perception t + β6 AE corporatebond spread t + β7 expected AE growth t + β8 AE policy interest rate t +β9 oil price changes t +β10
AE yield gap t + u t
Basic results for inflows
Basic results of estimation without considering regional differences are shown in Table 2. For
total inflows as a reference (the first column), we see positive and statistically significant coefficientson domestic expected growth, institution, and capital restrictions, and negative and statisticallysignificant coefficients on global risk perceptions (corporate bond spreads as well as VIX, the latter ofwhich is not reported here), and expected global growth. These results are generally consistent withprior studies such as Ahmed and Zlate (2013) and NSM (2014), except for the positive coefficient on
capital restrictions, which suggests a possible reverse causality where capital inflows trigger capital
restrictions. Coefficients on domestic policy interest rate and on global factors such as US policy
interest rate, oil price changes and US yield gaps are not statistically significant4.
>>Table 2: drivers of capital inflows to EMs: basic results of estimation
Next, we will see that the four types of capital flows show distinct features (the other columns ofTable 2), which are combined to generate the above result for total inflows. For FDI inflows, we see
positive and statistically significant coefficients on institution and negative coefficient on expected3
4Definitions of dependent as well as explanatory variables are given in the Appendix,
IMF (2016) shows a significantly positive coefficient on domestic growth differential, though.
8

global growth, but coefficients on expected domestic growth, capital restrictions and global risk
perceptions are not significant any more as against the case of the total inflows.
For portfolio equity inflows, we see negative and significant coefficients on global risk
perceptions, but no significant coefficients on any domestic factors and on expected global growth asagainst the case of total inflows. Additionally, we see positive and significant coefficient on US yieldgap. For portfolio debt flows, we see positive and significant coefficients on domestic institution andcapital restrictions, and negative and significant coefficient on global risk-perceptions and expected
global growth, but no significant coefficient on expected domestic growth as against the case of total
inflows.
Finally, other investment inflows find positive and significant coefficients on expected domestic
growth, institutions and capital restrictions and on global expected global growth, and negative andsignificant coefficients on global risk-perceptions and expected global growth as in the case of total
inflows). They also respond positively to US yield gap.
To sum, the result suggests that, among domestic factors, total capital inflows’ positive responses
to expected domestic growth comes from other investment, but not from the other components, andtheir positive responses to institution come from FDI, portfolio debt and other investment, but notfrom portfolio equity. Meanwhile their positive correlations with capital restrictions come from
portfolio debt and other investment, but not from FDI and portfolio equity. Among global factors, theresult suggests that total inflows’ negative responses to global risk perceptions come from portfolio
equity and debt flows as well as other investment but not from FDI, and those to expected global
growth come from FDI, portfolio debt and other investment but not from portfolio equity5.
Basic results for outflows
Turning to capital outflows, for total outflows, we see negative and statistically significant
coefficients on global risk perceptions and expected global growth (Table 3). Coefficients on domestic
factors such as domestic expected growth and interest rate, and on global factors such as expected
global interest rate differentials, oil price changes and US yield gaps are not statistically significant.
>>Table 3: drivers of capital outflows to EMs: basic results of estimation
For FDI outflows, we see negative and statistically significant coefficient on expected global
growth, but the coefficient on global risk perceptions is not significant as against the case of the total
inflows. We see positive and significant coefficients on domestic institution and capital outflow
restrictions for FDI outflows. For portfolio equity outflows, we see negative and significant
coefficients on global risk perceptions and expected global growth as in the case of total outflows. In
5Negative responses to global risk perceptions were confirmed by prior studies such as Ahmad andZlate (2013), F (2012) and Mirres-Ferretti and Tille (2011) for portfolio equity and debt and as
Bruno and Shin (2013) and Mirres-Ferretti and Tille (2011) for other investment. Positive responses
to global growth differentials were also confirmed by studies such as Ahmed and Zlate (2013) for
portfolio debt and as Bruno and Shin (2013) for other investment.
9

addition, we see negative and significant coefficients on domestic capital outflow restrictions and on
oil price changes, and positive coefficient on US yield gap. For portfolio debt outflows, we see
negative and significant coefficients on global risk perceptions, but the coefficient on expected global
growth is not significant as against the case of total outflows. In addition, we see negative andsignificant coefficients on domestic institution and capital outflow restrictions. Finally, otherinvestment inflows have negative and significant coefficient on global risk perceptions, but not on
expected global growth. In addition we see negative and significant coefficient on oil price changes,
but no significant coefficient on any other factors.
To sum, the result suggests that total capital outflows’ negative response to global risk perceptions
come from non-FDI outflows and that to expected global growth come from FDI. It seems that
institutions and global growth differences work as push factors to FDI outflows in EMs, while capitaloutflow restrictions effectively constrain portfolio outflows and domestic portfolio investors as wellas other investment outflows share global risk perceptions.
Regional differences in drivers of capital flows
We now examine how capital flows to EMs respond to global factors differently from those to
the other regions, by multiplying each explanatory variable by regional dummies. Table 4 shows theresult.
>>Table 4: drivers of capital flows to EMs: results of estimation with regional dummies
We see rather robust negative responses to risk perceptions of total inflows as well as theircomponent flows except FDI almost without regional differences (Table 4),6. We also detect robust
negative responses to expected global growth of portfolio debt and other investment without regional
differences. Note that positive responses to US yield gap of portfolio equity and other investment
become insignificant when using regional dummies. If this is truly the case, it seems to suggest thatshifts in monetary policy stance in US will not much affect portfolio equity and other investment
inflows as well as total capital inflows to EMs.
For some global factors, some of the significant responses of capital inflows we have got so far
turn out to be sorts of fallacies of composition. For example, positive responses of FDI to expected
global growth differentials are those from EE5, but not generally hold, while portfolio debt and otherinvestment remain to respond positively to expected global growth across the regions. Also, negative

6

We rely on corporate bond spreads for global risk perceptions rather than VIX. Although

VIX and corporate bond spread for global risk perception are highly correlated, coefficientson VIX lost statistical significance in some cases, when using regional dummies. Actually, we
see negative coefficients on portfolio equity and other investment, but they are notsignificant any more, which results in negative, but not significant coefficient on VIX in total
inflows.
10

responses of FDI to US yield gaps come from EE5 and not generally hold in the other regions.
Estimated responses of capital flows to global factors are summarized in Table 5, where signs show
those of significant coefficients.
>>Table 5: responses of capital flows to domestic and global factors: a summary
As to outflows (not reported here), we can see robust negative responses to global risk perceptionsof portfolio equity and debt outflows even with significant regional dummies, but negative responses
of total outflows as well as other investment to global risk perceptions become insignificant with
regional dummies. We also see for portfolio equity outflows to see robust negative responses to
expected global growth and oil price changes and positive ones to US yield gaps even with regionaldummies. But, positive responses to expected global growth of total outflows become insignificant,when using regional dummies, because they reflect strong positive responses of FDI specific to EE5.
In sum, our basic results on the responses to global factors of total inflows and outflows showthat they are sometimes various combinations of the responses of various types of capital flows, butother times they reflect some extraordinary changes specific to some regions. Our next move will be
to examine where these regional or other heterogeneities come from.

5. Domestic structures and policy frameworks
This section tries to find out why EA7 is different from the other EM regions in terms ofcomposition of capital flows and their dynamics. We start from some changes in the structuralcharacteristics and policies of EMs since the AFC. They are increasing integration to global financial
markets, changing structure of external assets and liabilities, and shifts in macroeconomic policy in
EMs.
Integration to global financial markets
Gross capital flows to and from EMs affect their external portfolio positions and compositions,and they in turn affect their capital flows. Each time they experience capital flow surges, they
accumulated external assets and liabilities and became integrated to global financial markets. How
they have been integrated differs across regions.
While all the three regions see more or less increases in both external assets (excluding foreignexchange reserves) and liabilities relative to GDP, EA7 shows the most modest increase in externalliabilities from slightly less than 50% to 50% during the period of 2000-2015 and some increase inexternal assets from 4*% to more than 50%, becoming a net creditor in terms of external positions.
The other two regions have seen higher increases in external liabilities and lower increases inexternal assets, strengthening their net borrower positions further than before. Particularly, EE5 see
most conspicuous increase in external liabilities from 50% to more than 100% of GDP, worsening
their external positions significantly.

11Structure of external assets and debts
Along with these accumulations of external assets and liabilities, their compositions havechanged to a great extent. First, as to external liabilities, FDI has become the major component
throughout the three regions, the degree of which is the most conspicuous in EE5 (increasing from
25% of GDP in 2000 to more than 50% in 2015), followed by LA5 and EA7 (40% and 25% each in2015).
While portfolio equity has remained modest and stable around 10% of GDP across the three
regions since the GFC, portfolio debt has been very modest and stable in EA7 at 5% of GDP, while
LA5 and EE5 have shown more or less upward trend up to 15% and 20% of GDP, respectively. BothEA7 and LA5 have retained some stable levels of other investment at 10-15% of GDP after its sharp
drop around the AFC, while EE5 appeared to sustain the plateau of 25-30%.
To sum up, EA7 is distinct from the other two regions in that non-debt liabilities such as FDI and
portfolio equity have increased their share to replace debt liabilities such as portfolio debt and otherinvestment. While both LA5 and EE5 have increased their FDI shares in external liabilities, LA5 hasrelied on portfolio debt instead of other investment, and EE5 have kept fairly heavily relied on otherinvestment (loans).
Shifts in policy
Ten years before the GFC, the policy authorities in East Asia witnessed boom-bust cycles withmassive international capital flows, forced to face problems on how to deal with these flows.
According to conventional wisdom, they were advised not to control the flows, but to make exchange
rate flexible and to discipline monetary and fiscal policy. They followed these advices at first, at least.
Actually, they leant that virtual fixed exchange rate to the US dollar was not sustainable, so that there
was no choice but to move to freer exchange rates.
The authorities have believed that exchange rate stability and maintaining international
competitiveness remain to be their indispensable macroeconomic policy objectives, on the other hand.
They started macroeconomic adjustments with slower economic growth, lower investment and current
account surplus forced by net capital outflows. Even after net capital inflows resumed in the early
2000s, they maintained exchange rate stability and international competitiveness, resulting inaccumulation of foreign exchange reserves, probably as a by-product.
In order to maintain exchange rate stability as well as financial stability, the policy authorities in
East Asia have used a constellation of policy tools, such as monetary policy, exchange rate policy,
prudential policy, and capital controls. Monetary policy can help controlling booms stimulated bycapital inflows, foreign exchange market intervention can help containing currency appreciation whichmight threaten international competitiveness, prudential policy can help reducing excessive credit
growth and containing financial-stability risks, and capital controls can help reducing excessive capital

12

flows and changing their compositions to more desirable directions7.
The result is what we have seen in the above as some conspicuous configuration of financialfeatures as foreign exchange accumulation, more reliance on non-debt external liabilities and moremanaged and less flexible exchange rates, which characterized East Asia even since before the GFC.
Domestic financial development
The reliance on international capital flows can be assessed in view of both intertemporal tradeand international asset trade. In the former context, saving-investment gaps imply that EE5 withpersistently negative gaps (or current account deficits in the balance of payments) are most reliant on
net capital inflows, while EA7 are least reliant on net capital inflows as net international lenders withpersistently positive saving-investment gaps and LA5 are in-between the two regions.
In the context of international asset trade, the reliance on capital flows depend on how deep
domestic financial developments are. Domestic financial systems are expected to intermediate
financial resources between sectors, thereby helping external financing of deficit sectors, particularlyin the private sector. External financing has three channels, i.e. the banking sector, the stock market
and the bond market. In emerging market economies with relatively underdeveloped financial systems,
the banking sector tends to play the major role in domestic financial intermediation, while the stockand the bond market to play limited roles. Global financial development database by the World Bank
shows direct measures of financial intermediation to the private sector through private credit by thebanking sector and private bonds outstanding, separately from public bond outstanding for public
sector financing (Figure 7).
>>Figure 7: financial intermediation, 1995-2015
Figure 7, covering three points in time during 1995-2015, reveals several aspects worth
mentioning. First, before the GFC, China, Korea, Malaysia and Thailand have attained developed
domestic credit outstanding as large as more than 100% of GDP, which is comparable to those inadvanced economies. While India, Indonesia and Philippines in EA7 remain underdeveloped in thisrespect, only Chile showed comparable financial intermediation in LA5 and none did in EE5.
Second, these EMs with deep domestic credit outstanding also developed private bond markets
to some degree. Third, with or without developments in private financial intermediation, public sector
financing has seen significant expansions through both domestic and international bond markets acrossthe EM regions.
Now what can we say about the interplay between domestic financial development andinternational capital flows? First, as far as banking flows are concerned, the surges and stops of7

Ghosh, Ostry and Qureshi 2017 found: first, EMs respond to capital inflows using a combinationof conventional macroeconomic and other less orthodox policy instruments, second, the policy
response differs across the types of capital flows, and third, policies are, generally, more likely torespond during inflow surges than in more normal times.
13

banking flows remain to have significant impacts on domestic credit supply in EMs except for somefinancially developed EMs in EA7 (and Chile in LA5). In other word, only EA7 have become more
resilient against banking flows among EM regions. Second, the vulnerability to volatile portfolio debt
flows are rather concentrated to public sectors than to private financing in EMs. Third, the volatilityof portfolio equity flows may impact on domestic equity markets, but may not on domestic financial
intermediation because of their limited roles there.
Regional integration in investment positions
We have focused on the heterogeneity of EMs, particularly working on regional differences infinancial integration to the global financial market, domestic policy frameworks and domestic
financial developments. As a matter of fact, however, the global financial market as well as global
investors in AEs are far from homogeneous. Furthermore, they tend to show an intrinsic regional bias.
In other words, the way EMs become integrated to the global financial market is necessarily unevenand regionally biased.
If we compare the size (as % of EMs’ GDP) and composition of financial linkages across EMs,
the early 2000s have witnessed that bank liabilities to European AEs grew rapidly in EMs in Europe,
while stagnated in EMs in Asia after the AFC. Since portfolio equity flows increased in Asia and Latin
America rapidly, EMs in Europe were exposed to banking crises, whereas EMs in Asia and Latin
America exposed to securities markets turmoil.
In view of investor countries, while North America, i.e. Canada and US, are the main portfolio
equity investors across the three EM regions, Europe, i.e. France, Germany, Italy and UK, are the main
portfolio debt investors in EE5 and compete with North America in LA5 (Figure 9). Figure 9 showsthat, until the GFC, European banks were the main source of banking flows not only in EMs in EE5,
but also in EA7. East Asia, i.e. Hong Kong, Japan and Singapore, were the main investors to EA7 in
FDI and portfolio debt, though the latter was relatively small in size. In portfolio equity and otherinvestment, we see almost no regional bias in investor countries in EA7.
>>Figure 8: regional financial linkages between AEs and EMs
In the post-GFC period, European banks’ presence decreased in EA7 and LA5, replaced by East
Asian and North America banks, respectively. While we can find generally significant regional bias ininvestment positions in all capital flow types, it is particularly the case with FDI and banking claims,
which have become most dominant capital flow types to EMs for now. Investors in East Asia, Europe
and North America tend to prefer EA7, EE5 and LA5, respectively, to the other regions.

6. Concluding remarks
In theory, international capital flows can improve national welfare through intertemporal trade as
well as through international asset trade. In intertemporal trade, net capital flows exchange assets inreturn for goods and services to help financing higher-return domestic investment with lower-return14

foreign saving, thereby enhancing global efficiency in resource allocation and to help smoothing
domestic consumption, thereby improving intertemporal welfare. In international asset trade, grosscapital flows exchange domestic assets in return for foreign assets to help diversifying risks, thereby
enhancing global efficiency in resource allocation and to help reducing state-dependent variability ofincome and consumption.
In practice, however, net capital inflows do not necessarily finance higher-return domestic
investment. Rather, volatile net flows rarely contribute to domestic consumption smoothing, butsometimes contribute to consumption crash. Likewise, gross capital inflows do not necessarily
diversify investment risks. Rather, they often times exacerbate risks through their pro-cyclical nature,
which likely generates overexpansion, resulting in boom and bust cycles and again economic crash.
Since the 1980s, developing economies have been advised to deregulate financial markets andopen up capital accounts to accept capital flows. Capital flows were regarded as beneficial and capital
controls were harmful and ineffective. At the AFC in 1997, not financial liberalization but virtual fixedexchange rate regimes and crony capitalism appeared to take responsibilities. The policy authorities
have muddled through the post-crisis period, learning lessons on how to manage exchange rates aswell as capital flows.
International advisors admit the dangers of excessive capital flows (surges) and the need to
manage the flows as late as after the GFC (Ostry et al. 2010). They still believe that capital flows arebasically beneficial and only their surges may cause problems, though. In their macro-fundamental
policy priorities in EMs, flexible exchange rates, low public debt, and macro-prudence always comebefore selective measures for capital flows such as foreign exchange reserves and capital market
restrictions.
Strengthening macroeconomic fundamentals in EMs is always desirable. If this goal is attained,
it is not EM anymore, though. We should note that, as far as an economy is one of EMs or international
investors regard it so, capital flows are intrinsically volatile, so that dangerous, particularly to EMs.
Policy authorities in EMs, particularly those in East Asia, know this very well. They have made welluse of euphemism as prudential instead of capital controls to restrict instruments or channels favoredby foreigners, which is understandably rational to cope with international business professionals withvested interests and academic professionals and policy advisors with market fundamentalism.

15

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17

Figure 1: capital flows to AEs and EMs(% of GDP)

26
24
22
20
18
16
14
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
-12
-14
-16

1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008H1
2008H2
2009H1
2009H2
2010H1

a. Advanced Economies

Derivatives

Other Bank and Private

Other Government

Portfolio Debt

Portfolio Equity

FDI

Total gross inflows]

Total net inflows

26
24
22
20
18
16
14
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
-12
-14
-16

1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008H1
2008H2
2009H1
2009H2
2010H1b. Emerging Market Economies

Derivatives

Other Bank and Private

Other Government

Portfolio Debt

Portfolio Equity

FDI

Total gross inflows

Total net inflows

Source: IMF, World Economic Outlook, April 2016, adapted from Figure 4.1.

18

Figure 2: Net and gross capital inflows to EMs by region, 2000-2015(% of GDP)a. net inflows by region
12

10
8
6
4

2
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
-2
Latin America

East Asia

Eastern Europe

b. gross inflows by type
5
4

3
2
1

0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
-1
FDI

portfolio equity

portfolio debt

other investment

Source: IMF, World Economic Outlook, April 2016, adapted from Figures 2.2 – 2.5.

19

Figure 3: Total capital inflows and outflows by regions (ratio to GDP)

Data source: IMF, International Financial Statistics.

20

Figure 4: Capital inflows by type and region (ratio to GDP)

Data source: IMF, International Financial Statistics.

21

Figure 5: capital outflows by type and region (ratio to GDP)

Data source: IMF, International Financial Statistics.

22

Figure 6: External assets and liabilities by region(ratio to GDP)
Total assets:EA7

Total liabilities:EA7

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

0
1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013
EQUITY

FDI

DEBT

LOAN

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013

RESERVES

EquityTotal assets:EE5

Total liabilities:EE5

1.2

1.2

1.1

1.1

1

1

0.9

0.9

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

FDI

DEBT

LOAN

0

0

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 20131980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013
EQUITY

FDI

DEBT

LOAN

EquityRESERVES

Total assets:LA5

Total liabilities:LA5

0.8

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013
EQUITY

FDI

DEBT

LOAN

RESERVES

23

DEBT

LOAN

0
1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013
EquityData source: Lane and Milesi-Ferretti, 2017.

FDI

FDI

DEBT

LOAN

Figure 7: Financial intermediation(% of GDP)
0

50

100

150

200

250

300

chi na 1995

2005
2015

i ndi a 1995
2005

2011
indonesi a 1995

2005
2015

kore a 1995
2005
2015
malaysi a 1995
2005
2015
phil ippi ne s1995
2005
2015
thai land 1995
2005
2015

czech 1995
2005

2015
hungary 1995

2005
2015

pol and 1996
2005

2011
sl ovak 1995

2005
2011
turkey 1995
2005
2015

argenti na 1995
2005
2014
braz il 1995
2005
2014
chi le 1995

2005
2015

col ombi a 1995
2005

2015
mexi co 1995

2005
2015

dc to pr iv ate

priv ate bo nd

intl private bon d

pu blic b ond

Data source: World Bank, Global financial development database, June 2017.
24

intl public bon dFigure 8: regional financial linkages between AEs and EMs (% of GDP)

Data source: IMF, Coordinated Portfolio Investment Survey (CPIS), IMF, Coordinated Direct Investment Survey (CDIS), and BIS,
Locational Banking Statistics.
25

Table 1: External wealth' structure
EA7% of GDP

EE5

LA5

G7

2007

2015

2007

2015

2007

2015

2007

2015

Gross liabilities

55

50

108

114

64

79

175

183

Debt liabilities

19

16

46

56

20

31

116

112

Nondebt liabilities

36

34

61

58

44

47

59

70

Gross assets

56

54

54

59

36

52

175

171

Debt assets

15

13

18

15

13

16

96

82

Nondebt assets

7

14

21

26

13

20

75

84

Foreign reserves

33

27

14

18

11

16

4

5

Net liabilities

-1

-4

54

55

28

27

0

12

Gross liabilities and assets

110

104

161

173

100

131

350

353

Source: Updated version of the External Wealth of Nations Mark II database (see Lane and Milesi-Ferretti (2007, 2017)).
Note: Each value shows the weighted average of capital-to-GDP ratio. Debt assets or liabilities include portfolio debt investments (Debt) and other investments(Loan), while nondebt assets or liabilities include direct investments (FDI) and portfolio equity investments (Equity) in each group.

26

Table 2: Drivers of capital inflows to EMs: basic results
VARIABLES

Total

FDI

EquityBondLoang_domestic

0.808**

0.282

0.016

0.097

0.354***(0.349)(0.205)(0.044)(0.093)(0.118)-0.047-0.018-0.002-0.021-0.003(0.053)(0.031)(0.007)(0.014)(0.018)0.055**

0.046***

0.003

0.025***

0.009(0.023)(0.014)(0.003)(0.006)(0.008)0.044*

0.008

0.003

0.014**

0.021***(0.024)(0.014)(0.003)(0.006)(0.008)-5.157***-0.855-0.528*** -1.232*** -2.013***(0.924)(0.536)(0.117)(0.245)-2.873*** -0.678**-0.018-0.458*** -1.250***(0.541)(0.317)(0.068)(0.143)(0.183)-0.464

0.062

0.045

-0.146-0.028(0.338)(0.196)(0.043)(0.090)(0.114)

0.004

0.005

-0.004***-0.0000.002(0.011)(0.007)(0.001)(0.003)(0.004)-0.009-0.396

0.267***

0.222

0.408*(0.646)(0.374)(0.081)(0.170)(0.218)0.205*** 0.059***

0.012**

0.034*** 0.056***(0.037)(0.022)(0.005)(0.010)(0.013)0.008-0.001

0.002

0.011***

0.003(0.010)(0.006)(0.001)(0.003)(0.003)-0.008-0.008-0.0010.001

0.008**(0.010)(0.006)(0.001)(0.003)(0.003)-0.009-0.004-0.001

0.002

0.002

(0.010)(0.006)(0.001)(0.003)(0.003)0.049

0.038**

0.001-0.0040.012(0.031)(0.018)(0.004)(0.008)(0.010)Observations

1,020

1,051

1,016

996

1,020

R-squared

0.067

0.038

0.064

0.103

0.096

i_domestic
institution
capitalrestrictions_inflow
B_Corporatebondspread
g_us
i_us
oilpricechange
USyieldgap
Constant
Q1
Q2
Q3
GFC

(0.312)

Number of A
16
16
16
16
16
Note: Standard errors in parentheses. ***, ** and * denote the 1%, 5% and 10% significance level,
respectively. All the results are obtained by the fixed effect model. See Appendix Table for thedefinitions of variables and their data sources.
27

Table 3: Drivers of capital outflows from EMs: basic results
VARIABLES

Total

FDI

EquityBondLoang_domestic

0.182

0.273

0.017

0.103

0.362***(0.311)(0.205)(0.044)(0.093)(0.118)0.004-0.008-0.002-0.0190.001(0.048)(0.032)(0.007)(0.014)(0.018)0.006

0.046***

0.003

0.024***

0.008(0.021)(0.014)(0.003)(0.006)(0.008)0.006

0.030**

0.003

0.011*

0.018**(0.020)(0.013)(0.003)(0.006)(0.008)-3.455***-0.883*(0.818)(0.533)(0.116)-2.142*** -0.687**-0.018i_domestic
institution
capitalrestrictions_outflow
B_Corporatebondspread
g_us-0.522*** -1.202*** -1.971***(0.244)(0.311)-0.459*** -1.251***(0.482)(0.316)(0.068)(0.143)(0.183)-0.500*

0.086

0.047

-0.138-0.015(0.302)(0.196)(0.043)(0.090)(0.114)-0.0070.005-0.004***-0.001

0.002(0.010)(0.006)(0.001)(0.003)(0.004)-0.734-0.3610.267***

0.222

0.409*(0.577)(0.373)(0.081)(0.170)(0.218)0.189***

0.045**

0.012**

0.034*** 0.055***(0.034)(0.022)(0.005)(0.010)(0.013)-0.001-0.001

0.002

0.011***

0.003(0.009)(0.006)(0.001)(0.003)(0.003)-0.012-0.008-0.0020.001

0.008**(0.009)(0.006)(0.001)(0.003)(0.003)-0.012-0.004-0.001

0.002

0.002

(0.009)(0.006)(0.001)(0.003)(0.003)0.028

0.039**

0.001-0.0050.011(0.027)(0.018)(0.004)(0.008)(0.010)Observations

1,024

1,051

1,016

996

1,020

R-squared

0.034

0.043

0.064

0.102

0.095

Number of A

16

16

16

16

16

i_us
oilpricechange
USyieldgap
Constant
Q1
Q2
Q3
GFC

Note: Standard errors in parentheses. ***, ** and * denote the 1%, 5% and 10% significance level,
respectively. All the results are obtained by the fixed effect model. See Appendix Table for thedefinitions of variables and their data sources.

28

Table 4: Drivers of capital inflows to EMs: results with regional dummies
VARIABLES

Total

FDI

Equity

Bond

Other

g_domestic

0.406(0.609)0.645(0.843)0.724(1.046)-0.071(0.110)0.094(0.400)-0.045(0.128)0.063(0.040)-0.099*(0.057)0.119**(0.060)0.048(0.031)-0.069(0.089)-0.077(0.057)-3.666***(1.323)-1.850(1.642)-1.554(1.847)-2.266**(0.906)0.568(1.246)-2.359(1.449)-0.506(0.587)0.425(0.821)-0.149(0.911)-0.013(0.019)-0.006(0.026)0.070**(0.029)-0.241(1.080)2.050(1.444)-2.750*(1.601)-0.135(0.360)0.666(0.495)0.176(0.619)-0.063(0.065)-0.053(0.227)0.006(0.075)0.032(0.024)-0.019(0.033)0.100***(0.036)0.007(0.018)-0.017(0.050)-0.039(0.034)-0.469(0.780)-0.490(0.955)0.180(1.093)-0.150(0.535)-0.078(0.725)-1.212(0.859)-0.032(0.347)0.146(0.468)0.633(0.541)0.001(0.012)-0.004(0.015)0.022(0.017)-0.252(0.638)0.592(0.837)-1.748*(0.947)0.107(0.076)-0.153(0.105)-0.174(0.131)0.004(0.014)-0.098*(0.050)-0.008(0.016)0.001(0.005)0.007(0.007)-0.005(0.008)0.001(0.004)0.035***(0.011)-0.010(0.007)-0.358**(0.166)-0.378*(0.206)0.178(0.231)-0.245**(0.113)0.385**(0.156)0.324*(0.181)-0.146**(0.073)0.411***(0.104)0.188*(0.114)-0.003(0.002)-0.006*(0.003)0.005(0.004)-0.052(0.135)0.572***(0.181)
0.285(0.200)0.327**(0.159)-0.155(0.223)-0.343(0.273)0.006(0.029)0.090(0.106)-0.053(0.033)0.012(0.010)-0.019(0.015)0.054***(0.016)0.019**(0.008)-0.057**(0.023)-0.024(0.015)-1.073***(0.345)-0.047(0.431)-0.041(0.481)-0.812***(0.236)0.385(0.328)1.035***(0.378)-0.405***(0.153)0.349(0.218)0.405*(0.237)-0.010*(0.005)0.005(0.007)0.025***(0.007)-0.298(0.282)0.680*(0.379)0.593(0.417)0.110(0.201)
0.292(0.277)0.245(0.345)-0.019(0.036)0.142(0.132)-0.032(0.042)0.017(0.013)-0.070***(0.019)0.078***(0.020)0.021**(0.010)-0.031(0.029)-0.025(0.019)-1.619***(0.435)-0.880(0.540)0.346(0.608)-1.076***(0.298)-0.068(0.410)-0.166(0.477)0.059(0.193)-0.464*(0.270)0.474(0.300)-0.002(0.006)0.001(0.009)0.016*(0.009)0.300(0.356)0.188(0.475)-0.313(0.527)

1,020
0.119
16

1,051
0.087
16

1,016
0.127
16

996
0.162
16

1,020
0.186
16

g_domestic × EA7
g_domestic × EE5
i_domestic
i_domestic × EA7
i_domestic × EE5
institution
institution × EA7
institution × EE5
capitalrestrictions_inflow
capitalrestrictions_inflow × EA7
capitalrestrictions_inflow × EE5
B_Corporatebondspread
B_Corporatebondspread × EA7
B_Corporatebondspread × EE5
g_us
g_us × EA7
g_us × EE5
i_us
i_us × EA7
i_us × EE5
oilpricechange
oilpricechange × EA7
oilpricechange × EE5
USyieldgap
USyieldgap × EA7
USyieldgap × EE5

Observations
R-squared
Number of A

Note: Standard errors in parentheses. ***, ** and * denote the 1%, 5% and 10% significance level,
respectively. All the results are obtained by the fixed effect model. See Appendix Table for thedefinitions of variables and their data sources.
29

Table 5: Drivers of capital inflows to EMs: summary with regional dummies

g_domestic
Domestic i_domestic
factor institution
capitalrestrictions_inflow

B_Corporatebondspread
g_us
Globali_us
factoroilpricechange
USyieldgap

Inflows_Total
EA7 EE5 LA5
+
+
+
+
－
－

－

－

+
+
－
－

Inflows_FDI
EA7 EE5 LA5++Inflows_Equity
EA7 EE5 LA5

+

－

－
－

+

－
+

Note: See Appendix Table for the definitions of variables and their data sources.

30

+
－
+
+
－
+

－
－
－
－

Inflows_Bond
EA7 EE5 LA5
+
+
+
－
－

+
－
+

+
+
－
－
－

Inflows_Loan
EA7 EE5 LA5
+
+
－
+
－
－

+
－
－

－
+
+

+
+

+

+

Appendix Table: Variable definitions and data source
Variable

Definition

Data Source

Inflows_Total, it

Gross Total Inflows (FDI, PI, OI) in the ratio to GDP (quarterly GDP,
converted to USD based on average exchange rate)

IFS, IMF

Outflows_Total, it

Gross Total Outflows (FDI, PI, OI) in the ratio to GDP (quarterly GDP,
converted to USD based on average exchange rate)

IFS, IMF

Inflows_FDI, it

Gross Direct Investment Inflows in the ratio to GDP (quarterly GDP, converted
IFS, IMF
to USD based on average exchange rate)

Inflows_Equity, it

Gross Equity Investment Inflows in the ratio to GDP (quarterly GDP, converted
IFS, IMF
to USD based on average exchange rate)

Inflows_Bond, it

Gross Bond Investment Inflows in the ratio to GDP (quarterly GDP, converted
IFS, IMF
to USD based on average exchange rate)

Inflows_Loan, it

Gross Other Investment Debt Inflows in the ratio to GDP (quarterly GDP,
converted to USD based on average exchange rate)

IFS, IMF

Outflows_FDI, it

Gross Direct Investment Outflows in the ratio to GDP (quarterly GDP,
converted to USD based on average exchange rate)

IFS, IMF

Outflows_Equity, it

Gross Equity Investment Outflows in the ratio to GDP (quarterly GDP,
converted to USD based on average exchange rate)

IFS, IMF

Outflows_Bond, it

Gross Bond Investment Outflows in the ratio to GDP (quarterly GDP,
converted to USD based on average exchange rate)

IFS, IMF

Outflows_Loan, it

Gross Other Investment Debt Outflows in the ratio to GDP (quarterly GDP,
converted to USD based on average exchange rate)

IFS, IMF

g_domestic

real GDP growth rate forecast in country i (one-year ahead) (spring→Q1, Q2,
WEO, IMF
fall→Q3,Q4), semiannual)

i_domestic

real interest rate in country i (policy rate, deflated by forecast inflation(one-year
IFS, IMF; WEO, IMF
ahead), semiannual)

institution, it

institutional quality, Rule of law (annual )World Governance Indicators

capitalrestrictions_inflow, it

Overall capital inflow restrictions index, 0-1

Fernandez et al (2015)

capitalrestrictions_outflow, it

Overall capital outflow restrictions index, 0-1

Fernandez et al (2015)

lvix, tlogarithm of the Chicago Board Options Exchange's Volatility Index (VIX)

Chicago Board Options Exchange

g_us

real GDP growth rate forecast in US (one-year ahead) (spring→Q1, Q2, fall→
IFS, IMF; WEO, IMF
Q3,Q4), semiannual)

i_us

real interest rate in US

IFS, IMF; WEO, IMF

B_Corporatebondspread, t

Moody's Seasoned Baa Corporate Bond Yield Relative to Yield on 10-Year
Treasury Constant Maturity, rate, Monthly, not Seasonally Adjusted

Federal Reserve Economic Dataoilpricechange, t

the growth rate of oil price

IFS, IMF

USyieldgap, t

10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant
Maturity, rate, Monthly, Not Seasonally Adjusted

Federal Reserve Economic Data

EA7, iDummy variable, 1 for East Asian economies, 0 otherwise

LA5, iDummy variable, 1 for Latin American economies, 0 otherwise

EE5, iDummy variable, 1 for Emerging European economies, 0 otherwise

GFC

Dummy variable, 1 for 2008Q4 and 2009Q1 0 otherwise31