Are Capital Flows Fickle? Increasingly? And
Does the Answer Still Depend on Type?
Barry Eichengreen
Department of Economics
Universität von Kalifornien
Berkeley, CA 94720, USA
eichengr@berkeley.edu
Poonam Gupta
World Bank
Washington, Gleichstrom
pgupta5@worldbank.org
Oliver Masetti
World Bank
Washington, Gleichstrom
omasetti@worldbank.org
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Abstrakt
According to conventional wisdom, capital flows are fickle. Focusing on emerging markets, we ask
whether this conventional wisdom still holds in our contemporary world. Our results show that, despite
recent structural and regulatory changes, much of it survives. Foreign direct investment (FDI) inflows
are more stable than non-FDI inflows. Within non-FDI inflows, portfolio debt and bank-intermediated
flows remain the most volatile. Whereas FDI inflows are driven mainly by pull factors, portfolio debt and
equity are driven mainly by push factors; bank-intermediated flows are driven a combination of push
and pull factors. Capital outflows from emerging markets behave differently, Jedoch. FDI outflows from
emerging markets have grown and become significantly more volatile. There is similarly an increase in
the volatility of bank-intermediated capital outflows from emerging markets. Our findings underscore
that outflows from emerging markets, both FDI and bank-related flows, have come to play a growing
role and warrant greater attention from analysts and policymakers.
1. Einführung
According to conventional wisdom, capital flows are fickle (sehen, z.B., Bluedorn et al. 2013;
Sussangkarn 2017). They are fickle more or less independent of time and place. Haben
reached this conclusion, analysts then go on and rank different capital flows according to
their volatility. Here the consensus is that foreign direct investment (FDI)-related flows are
least volatile and bank-intermediated flows are most volatile. Other portfolio capital flows
rank somewhere in between; within this intermediate category debt flows are generally
considered to be more volatile than equity-based flows.
Asian Economic Papers 17:1
© 2018 by the Asian Economic Panel and the Massachusetts Institute of
Technologie
doi:10.1162/ASEP_a_00583
Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
This conventional wisdom is a distillation of the experience of earlier decades (sehen
Becker and Noone [2008] for a survey of the literature in which this experience is re-
viewed). Yet the structure and regulation of international financial markets continue
to change, especially recently. Chinese outward FDI has risen dramatically relative to
other sources of FDI, Zum Beispiel, raising the question of whether FDI is equally sta-
ble regardless of source. South–South FDI flows have risen more generally, again rais-
ing the question of whether they behave in the same relatively stable manner as other
FDI flows. Bank-intermediated flows have fallen, as large global banks have delever-
aged and curtailed their cross-border operations in response to tighter regulatory over-
sight, although there is also the question of whether banks in emerging markets have
stepped into this market space. Asian bond markets have grown relative to bond mar-
kets in other regions, pointing to the question of whether flows into and out of the bond
markets of different regions are equally stable. Corporate bond markets have grown rel-
ative to sovereign bond markets. International investors have become active in equity
markets worldwide.
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All this raises the question of whether the conventional wisdom still holds in our contem-
porary world. Some authors suggest that it may not. Blanchard and Acalin (2016), zum Beispiel-
reichlich, argue that FDI is now as volatile as portfolio capital flows.
In this paper we revisit these questions, focusing on emerging markets. We ask: How do
the magnitude and volatility of various capital flows compare? How have they evolved
im Laufe der Zeit? What are the observable empirical correlates of different flows?
We analyze trends in capital flows since the 1990s, including in the post–Global Fi-
nancial Crisis era. Whereas a majority of previous studies have utilized annual data
largely for reasons of availability and convenience, we work here with quarterly
Daten. This allows us to analyze capital flows at business cycle frequencies and around
country-specific sudden stops and global stops—events that are hard to pinpoint using
annual data.
In terms of inflows into emerging markets, our results suggest that the patterns identified
in earlier work persist despite recent structural and regulatory changes, and that much of
the conventional wisdom survives. FDI inflows remain more stable than non-FDI inflows:
FDI inflows have lower volatility; are more persistent; and decline by smaller amounts in
both country-specific sudden stop and global stop episodes. Within non-FDI inflows, port-
folio debt and bank-intermediated flows remain the most volatile. Bank-intermediated
flows, which rose in the mid 2000s, are especially volatile. They decline most sharply dur-
ing country-specific sudden stops and global stop episodes. These results may not be sur-
prising, but their constancy is surprising. Empirical regularities in international finance
that stand the test of time are the exception, not the rule.
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
But outflows from emerging markets behave differently. In contrast to the findings for in-
flows, we document important changes since the turn of the century and in the most recent
Jahrzehnt, insbesondere, in the behavior of outflows. FDI outflows from emerging markets
have grown and become significantly more volatile. Ähnlich, there is a significant increase
in the volatility of “other” (mainly bank-intermediated) capital outflows from emerging
markets since the turn of the century. Those other outflows are twice as volatile again as
FDI outflows, as measured by the coefficient of variation of gross flows scaled by GDP, In
the most recent period, 2011-15. In terms of shocks to the capital account of the balance of
payments, our findings underscore that outflows from emerging markets, both FDI and
bank-related flows, have come to play a growing role and deserve greater attention from
analysts and policymakers.
2. Magnitude, persistence, and volatility of capital flows
We use quarterly data from the IMF’s Balance of Payments Statistics between 1990:Q1 and
2015:Q4 for 34 emerging countries.1 The data are patchy for the earlier years; coverage
improves over time, yielding an unbalanced panel. The capital flow data are in U.S. dollars.
We scale them by annual trend GDP for purposes of analysis.
We analyze inflows and outflows separately. Data are available separately for FDI and
non-FDI flows. The latter are further decomposed into portfolio flows (and into portfolio
equity and portfolio debt), versus what are labeled “other” flows. The “other” category in-
cludes flows through the banking sector (loans, deposits, and banking capital), loans raised
by the private sector, trade credits, official government flows, and other smaller residual
components. We exclude flows to the general government and monetary authorities, Re-
taining only private flows. The largest share of (private) other flows is made up of flows
through the banking sector. Hence some researchers simply refer to them as “bank flows.”2
Figuren 1 Zu 4 und Tisch 1 show that the average FDI and non-FDI inflows are roughly
equal in magnitude. Median average annual flows are 2.6 percent and 2.4 percent of GDP
annually.3 Within non-FDI flows, andere (bank) flows are the largest, followed by portfolio
debt. Portfolio equity flows remain relatively small, averaging 0.2 percent of GDP over the
entire period and just 0.16 percent a year in the last five years. Outflows are smaller than in-
flows on average (these being emerging markets). Figur 2, as we read it, confirms that the
relative magnitude of other flows has declined and that portfolio debt has increased since
the 2008–09 global financial crisis.
1 The same set of countries included in Eichengreen and Gupta (2016).
2 Sehen, z.B., Bluedorn et al. (2013).
3 These are unweighted averages for the 34 sample countries.
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Figur 1. FDI and non-FDI capital inflows
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Figur 2. Components of non-FDI capital inflows
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We measure volatility by the standard deviation and coefficient of variation. By these
Maßnahmen, non-FDI flows are relatively volatile. Portfolio debt flows and banking flows
are among the most volatile. Non-FDI flows are more volatile than FDI flows and
less persistent.
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Figur 3. FDI and non-FDI capital outflows
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Figur 4. Components of non-FDI capital outflows
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In Table 2 we compare consecutive five-year periods. Portfolio debt inflows increased in
2006–10 and again in 2011–15. Less widely appreciated, FDI outflows from emerging mar-
kets rose strongly in 2006–10. Other flows also increased in 2006–10.
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Tisch 1. Magnitude, volatility, and persistence of capital inflows and outflows
Median
quarterly
average
Median
standard
deviation
Median
coefficient
of variation
Persistence
FDI
non-FDI
Portfolio equity
Portfolio debt
Other flows
inflows
outflows
inflows
outflows
inflows
outflows
inflows
outflows
inflows
outflows
0.65
0.14
0.61
0.32
0.05
0.02
0.24
0.06
0.32
0.20
0.63
0.28
1.31
0.94
0.22
0.08
0.60
0.22
1.08
0.81
0.96
1.64
2.12
1.93
3.15
3.01
3.17
3.13
2.90
3.34
0.53
0.26
0.40
0.21
0.31
0.3
0.14
0.13
0.43
0.18
Notiz: Mean, standard deviation and coefficient of variation are the median across all countries in the sample.
Coefficient of variation is the standard deviation divided by the mean. Persistence is the AR(1) coefficient of a
fixed-effects panel regression for respective capital flows. Non-FDI flows are the sum of portfolio equity, portfolio
debt, and private other flows. Data are quarterly from 1990:Q1 to 2015:Q4. All capital flows are expressed as
percent of annual trend GDP.
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Tisch 2. Trends in the magnitude and volatility of capital inflows and outflows
1991–1995
1996–2000
2001–2005
2006–2010
2011–2015
FDI
FDI
inflows Mean (quarterly average)
Standard deviation
Coeff. of variation
outflows Mean (quarterly average)
Standard deviation
Coeff. of variation
Portfolio equity
inflows Mean (quarterly average)
Standard deviation
Coeff. of variation
Portfolio equity
outflows Mean (quarterly average)
Portfolio debt
inflows Mean (quarterly average)
Standard deviation
Coeff. of variation
Standard deviation
Coeff. of variation
Portfolio debt
outflows Mean (quarterly average)
Standard deviation
Coeff. of variation
Other flows
inflows Mean (quarterly average)
Other flows
outflows Mean (quarterly average)
Standard deviation
Coeff. of variation
Standard deviation
Coeff. of variation
0.23
0.15
0.61
0.01
0.02
0.93
0.06
0.10
1.35
0.00
0.00
1.91
0.03
0.23
1.52
0.01
0.07
1.95
0.22
0.97
1.26
0.10
0.65
1.64
0.76
0.50
0.71
0.04
0.07
1.25
0.05
0.12
1.56
0.00
0.02
2.44
0.11
0.39
1.72
0.03
0.09
2.08
0.32
0.79
1.41
0.24
0.66
1.56
0.55
0.38
0.70
0.07
0.14
1.49
0.03
0.09
2.21
0.01
0.04
2.19
0.10
0.40
1.58
0.04
0.14
1.85
0.20
0.59
0.92
0.17
0.63
2.11
0.92
0.59
0.57
0.29
0.30
1.11
0.05
0.21
1.99
0.04
0.12
1.80
0.20
0.63
2.64
0.05
0.24
2.38
0.56
1.09
1.65
0.31
1.08
2.42
0.69
0.41
0.56
0.20
0.26
1.17
0.04
0.14
2.79
0.01
0.03
1.68
0.38
0.63
1.97
0.02
0.17
1.44
0.17
0.67
1.30
0.19
0.66
2.29
Notiz: Mean, standard deviation and coefficient of variation are the median across all countries in the sample during respective time period.
Coefficient of variation is standard deviation divided by mean. Data are quarterly from 1990:Q1 to 2015:Q4. All capital flows are expressed as
percent of annual trend GDP.
We are interested in whether the volatility of flows, as measured by the coefficient of vari-
ation (adjusting the standard deviation by their mean in the same period) has risen sig-
nificantly. In Tables 3 Und 4 we therefore regress the coefficients of variation for a pooled
sample of five-year periods on dummy variables for those five-year periods. We include a
constant term, exclude the first five-year period and add country fixed effects.
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Tisch 3. Coefficient of variation of capital inflows
1996–2000
2001–05
2006–10
2011-15
Country fixed effects
Beobachtungen
R2
NEIN. of countries
FDI
0.119
[0.83]
0.101
[0.52]
−0.012
[0.10]
0.068
[0.46]
Ja
165
Portfolio
Eigenkapital
−0.186
[0.31]
0.862
[1.30]
0.591
[0.82]
0.428
[0.54]
Ja
140
0.008
34
0.019
33
Portfolio
debt
Other
flows
0.704
[0.93]
−0.908
[0.80]
1.336*,^^
[1.76]
1.096
[1.29]
Ja
142
0.077
34
0.126
[0.14]
−0.520
[0.61]
0.255
[0.31]
−0.155
[0.14]
Ja
147
0.012
34
Notiz: The dependent variable is the coefficient of variation of capital flows of
type i in country c in period p. The coefficients of variation are regressed on time
dummies indicating the different periods, where the first period (1991–95) Ist
excluded. The interpretation of the coefficient is thus in relation to this first period.
We exclude observations where the coefficient of variation exceeds a value of +10
or is below −10. Robust t-statistics are displayed in brackets. ***, **, *indicate
significance at the 1 Prozent, 5 Prozent, Und 10 percent level. Zusätzlich, tests are
conducted for whether the coefficients are significantly different from the previous
Zeitraum; ^, ^^, ^^^indicate significant differences at the 1 Prozent, 5 Prozent, Und 10
percent level.
Tisch 4. Coefficient of variation of capital outflows
FDI
Portfolio
Eigenkapital
1996–2000
2001–05
2006–10
2011-15
0.285
[0.46]
0.931**
[2.43]
0.542*
[1.86]
1.49***,^^
[2.96]
Country fixed effects
Beobachtungen
NEIN. of countries
R2
Ja
157
34
0.072
Notiz: See notes to Table 3.
0.475
[0.42]
0.358
[0.50]
−0.164
[0.20]
−0.136
[0.14]
Ja
132
32
0.010
Portfolio
debt
−0.666
[0.84]
−1.219
[1.31]
−0.667
[0.53]
−1.054
[0.85]
Ja
132
32
0.011
Other
flows
0.699
[1.23]
1.427*
[1.97]
1.589**
[2.29]
0.598
[0.77]
Ja
133
34
0.089
The results indicate few changes on the inflow side. Portfolio debt inflows are significantly
higher in 2006–10 than in 1990–95, but there are no other changes. This is evidence of stabil-
ity in the volatility of inflows over time.
Im Gegensatz, there are significant increases in the volatility of FDI outflows from emerging
markets in 2001–10 and again in 2011–15. Zusätzlich, we see significant increases in the
volatility of “other” (bank-related) outflows after the turn of the century. We obtain the
same results at even higher levels of precision (significance) when we regress the coeffi-
cients of variation on time trends (t = 1 in 1990–95, t = 2 in 1996–2000, usw.).
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
This is a striking answer to our question about trends in volatility. Capital inflows into
emerging markets are volatile but not increasingly so. What is new is the growing volatility
of outflows from emerging markets, bank-related outflows after the turn of the century,
and FDI outflows after 2005 and especially after 2010. That FDI outflows are a growing
source of capital account volatility in emerging markets is not adequately appreciated in
the literature, in our view.
Which countries are mainly responsible for this increase in the level and volatility of FDI
outflows from emerging markets? Some readers will suspect that China is driving the re-
Ergebnisse. But recall that all such flows in our analysis are scaled by country-specific trend GDP.
The countries with the highest share of outward FDI in GDP in the most recent five-year
period are Chile, Malaysia, Ungarn, and Russia—not China. In 2015 the countries with
the largest such ratio were Chile, Israel, Malaysia, and Thailand. China figures, auf dem anderen
Hand, when one focuses instead on the growth of the FDI-to-GDP ratio. The countries with
the largest annual increase in FDI outflows relative to GDP in 2011–15, in declining or-
der, were Hungary, Südafrika, Chile, and China. The countries with the largest annual
increase in 2014–15 so measured, again in descending order, are Hungary, Chile, Israel,
Poland, and China.4
Readers may also worry that the increase in the volatility of capital outflows from emerg-
ing markets (both FDI and bank-related outflows) is driven by a few outliers, bei dem die
average outflow is small so that a limited increase in the variance can produce a large in-
crease in the coefficient of variation. We therefore made the same statistical comparisons
dropping the top and bottom 2 percent of the observations. Reassuringly, the broad pat-
terns remained the same.
3. Capital flows in sudden stops and capital flight episodes
Following Eichengreen and Gupta (2016), we classify an episode as a sudden stop when total
capital inflows (FDI, portfolio equity and debt, and other inflows by nonresidents) Abfall
below the average in the previous 20 quarters by at least one standard deviation, when the
decline lasts for more than one quarter, and when flows are two standard deviations be-
low their prior average in at least one quarter.5 The sudden-stop episode then ends when
flows recover to at least the prior mean minus one standard deviation. Analogously, Wir
define an episode of capital flight as a sharp increase in gross outflows by residents. Specif-
isch, a period qualifies when total capital outflows (FDI, portfolio equity and debt, Und
4 Note in addition that China is not included in most of our analysis because data on the composi-
tion of capital flows are incomplete.
5 One difference is that here we define sudden stops in terms of the behavior of total capital flows—
FDI and non-FDI alike—whereas in Eichengreen and Gupta (2016) we defined sudden stops in
terms of the behavior of non-FDI flows only.
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Tisch 5. Capital inflows in sudden stops
Stopct
Country fixed effects
Country-specific trends
Beobachtungen
NEIN. of countries
R2
FDI
−0.346**
[2.37]
Ja
Ja
2,401
34
0.098
Portfolio
Eigenkapital
−0.735***
[3.33]
Ja
Ja
2,373
33
0.053
Portfolio
debt
−1.064***
[7.22]
Ja
Ja
2,401
34
0.077
Other
flows
−1.540***
[9.40]
Ja
Ja
2,401
34
0.128
Notiz: The dependent variables are capital flows of the respective type as a percentage of
trend GDP. They are standardized by subtracting the country-specific mean and dividing
by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q5.
Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1 Prozent,
5 Prozent, Und 10 percent level.
other outflows by residents) exceed the average in the previous 20 quarters by at least one
Standardabweichung, when the increase lasts for more than one quarter, and when outflows
are two standard deviations above their prior average in at least in one quarter. Capital
flight episodes then end when capital outflows decline below the prior mean plus one
Standardabweichung.
We summarize the behavior of capital flows around country-specific stops and flights by
estimating the panel regression,
Yict
= βSSct
+ θ
C
+ tct
+ ε
ict
,
(1)
where i refers to specific capital flows, c to the country and t to quarter-year: We regress
capital flows of type i, denoted Yict, on a dummy variable for the country-specific sudden
stop (or flight) SSct, country-fixed effects θ
C, and country-specific time trends tct. For ease
of comparison, we normalize Yict by subtracting from each observation its country-specific
mean and dividing it by the country-specific standard deviation.
We see in Table 5 that portfolio equity, portfolio debt and other inflows all turn negative
during sudden stops. The decline in inflows is sharpest for other flows and smallest for
FDI. Zusätzlich, portfolio equity and debt outflows and especially other outflows drop sig-
nificantly below their average in sudden stops (Tisch 6). This suggests that resident flows
are stabilizing. Looking at the scale of outflows in Figures 5 Und 6, Jedoch, it is evident
that the decline in outflows during sudden stops is smaller than the decline in inflows. Also
even if the decline in outflows by residents partially offsets the decline in inflows by non-
residents, this stabilizing impact is only partial, and net inflows still decline.
Endlich, during periods of capital flight all categories of capital outflow increase (Tisch 7).
The increase is again largest for other flows, followed by debt outflows. It is smallest
for FDI.
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Tisch 6. Capital outflows in sudden stops
FDI
−0.165
[1.07]
Ja
Ja
2,397
34
0.143
Portfolio
Eigenkapital
−0.328***
[2.90]
Ja
Ja
2,120
32
0.062
Portfolio
debt
−0.334*
[1.81]
Ja
Ja
2,280
32
0.032
Other
flows
−0.353**
[2.70]
Ja
Ja
2,398
34
0.045
Stopct
Country fixed effects
Country-specific trends
Beobachtungen
NEIN. of countries
R2
Notiz: See notes to Table 5.
The panels of Figure 5 document these points further. They show that although FDI in-
flows decline, that decline is small relative to other types of flows, and FDI inflows remain
positive during sudden stops. Im Gegensatz, average portfolio equity and debt inflows turn
negative in sudden stop periods (Tisch 8). Although the drop at t = 0 is sharp, inflows re-
cover and are back to pre-crisis levels within four quarters of the start of the episode. Other
flows also turn negative at t = 0, and in addition recover very slowly, much more slowly
than in the case of portfolio equity and debt flows. Other flows still remain negative four
quarters after the beginning of the sudden stop episode.
These patterns are summarized in panel regressions:
=
Yict
j=4(cid:2)
j=−4
β
jSSct+ j
+ θ
C
+ tct
+ ε
ict
,
(2)
where we regress capital flows (normalized by country-specific mean and standard devia-
tion) of type i, for country c in time period t, Yict, on dummy variables for different quarters
Vor, during and after country-specific sudden stops, on country-fixed effects θ
C, und weiter
country-specific time trends tct.
The estimated coefficients indicate that all types of inflows drop significantly at the start
of a sudden stop period. The coefficient is largest for other inflows and portfolio debt in-
flows. The impact lasts longer for portfolio debt flows and other flows, with the coefficient
remaining significantly negative for three and four quarters, jeweils, after the start of
the sudden stop episode. These results, and the sharp drop in other flows in particular, Sind
consistent with what Levchenko and Mauro (2007) found in their earlier study.
4. Capital flows during global stops
We define a global stop as a period when three conditions are met: median capital inflows
decline by at least one standard deviation below their mean in the preceding 20 quarters;
the drop lasts for at least two quarters; and the drop exceeds the mean by two standard
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Figur 5. Capital inflows around country-specific sudden stops
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Notiz: This figure shows behavior of respective types of capital inflows, as percent of trend GDP, around stop periods. t = 0 is the first quarter of
a stop period. For each period (t − 4 to t + 4) first the mean is calculated for different sudden stops for a given country. Solid line is the median
of the country means, and broken line is the mean of the country means.
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Tisch 7. Capital outflows in capital flight episodes
FDI
0.488***
[3.48]
Ja
Ja
2,040
33
0.150
Portfolio
Eigenkapital
Portfolio
debt
Other
flows
0.627***
[4.36]
Ja
Ja
1,920
31
0.085
0.528***
[5.70]
Ja
Ja
2,036
32
0.050
1.043***
[9.83]
Ja
Ja
2,040
33
0.104
Flightct
Country fixed effects
Country-specific trends
Beobachtungen
NEIN. of countries
R2
Notiz: See notes to Table 5.
Tisch 8. Capital inflows during sudden stops
Stop –4
Stop –3
Stop –2
Stop –1
Stop
Stop +1
Stop +2
Stop +3
Stop +4
Country fixed effects
Country-specific trend
Beobachtungen
NEIN. of countries
R2
FDI
0.226
[1.22]
0.465*
[1.86]
0.331
[1.59]
0.332
[1.46]
−0.381*
[−2.01]
−0.255
[−1.49]
−0.039
[−0.13]
−0.140
[−1.04]
−0.176
[−0.92]
Ja
Ja
2,401
34
0.103
Portfolio
Eigenkapital
Portfolio
debt
Other
flows
0.190
[0.60]
−0.396*
[−1.90]
0.131
[0.67]
−0.437*
[−1.73]
−1.336***
[−3.93]
−0.741**
[−2.64]
−0.581***
[−2.82]
−0.198
[−1.57]
0.070
[0.61]
Ja
Ja
2,373
33
0.068
0.396*
[1.99]
0.444*
[1.90]
0.081
[0.42]
0.021
[0.10]
−1.089***
[−4.98]
−1.065***
[−5.06]
−0.404*
[−2.03]
−0.464***
[−3.38]
−0.074
[−0.38]
Ja
Ja
2,401
34
0.073
0.426**
[2.62]
0.243
[1.40]
0.358
[1.68]
0.264
[1.08]
−1.102***
[−3.98]
−2.029***
[−6.77]
−1.179***
[−5.70]
−1.410***
[−5.28]
−0.860***
[−3.31]
Ja
Ja
2,401
34
0.152
Notiz: The dependent variables are capital inflows of the respective type as percent of
trend GDP. Variables are standardized by subtracting the country-specific mean and
dividing by the country-specific standard deviation. Capital flows are regressed on
country-specific sudden stops and dummies indicating 1 Zu 4 quarters before, the quarter
when the sudden stop starts, Und 1 Zu 4 quarters after the start of a sudden stop period.
The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets.
***, **, * indicate significance at the 1 Prozent, 5 Prozent, Und 10 percent level.
deviation in at least one quarter.6 The global stop ends when capital inflows are no longer
at least one standard deviation below their earlier mean. This approach identifies 1998:Q3–
1998:Q4 and 2008:Q4–2009:Q1 as global stop periods.
6 Median capital inflows are calculated as the sum of FDI, portfolio equity, portfolio debt, and other
flows as a percentage of trend GDP.
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Tisch 9. Capital inflows around global stops
Global Stopt
Country fixed effects
Country-specific trends
Beobachtungen
NEIN. of countries
R2
FDI
0.262**
[2.35]
Ja
Ja
3,237
34
0.11
Portfolio
Eigenkapital
−0.536***
[−4.59]
Ja
Ja
2,962
33
0.05
Portfolio
debt
−0.712***
[−7.65]
Ja
Ja
3,084
34
0.07
Other
flows
−0.761***
[−5.59]
Ja
Ja
3,213
34
0.07
Notiz: The dependent variables are capital flows of the respective type as a percentage of
trend GDP. They are standardized by subtracting the country-specific mean and dividing
by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4.
Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1 Prozent,
5 Prozent, Und 10 percent level.
Tisch 10. Capital outflows around global stops
Global Stopt
Country fixed effects
Country-specific trends
Beobachtungen
NEIN. of countries
R2
FDI
0.006
[0.06]
Ja
Ja
3,201
34
0.176
Portfolio
Eigenkapital
−0.028
[−0.24]
Ja
Ja
2,709
32
0.071
Portfolio
debt
−0.342**
[−2.61]
Ja
Ja
2,871
32
0.038
Other
flows
−0.473***
[−4.21]
Ja
Ja
3,209
34
0.042
Notiz: The dependent variables are capital flows of the respective type as a percentage
of trend GDP. They are standardized by subtracting the country-specific mean and
dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to
2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at
Die 1 Prozent, 5 Prozent, Und 10 percent level.
We again estimate a panel regression of the form
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Yict
= βGSt
+ θ
C
+ tct
+ ε
ict
,
(3)
where i refers to specific capital flows, c to the country and t to quarter-year. We regress
Yict, capital flows of type i (normalized by subtracting from each observation its country-
specific mean and dividing it by the country-specific standard deviation) on a dummy for
the global stop, country-fixed effects θ
C
, and country-specific time trends tct.
Results are in Tables 9 Und 10. While portfolio equity, portfolio debt, and other inflows all
decline in global stops, FDI inflows do not, suggesting that they are heavily influenced by
other factors. Auffallend, FDI inflows behave “countercyclically,” rising significantly during
global stops. Andererseits, resident outflows decline around global stops only in the
case of portfolio debt and other flows (the change in outward FDI and portfolio equity
outflows is essentially zero). To the extent that there is stabilizing behavior during episodes
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
of global stops, it comes through rising inward FDI by nonresidents and declining portfolio
debt and other financial outflows by nonresidents and residents alike.
5. Correlates of capital inflows
To analyze the drivers of capital flows, we estimate regressions in the form of equation
(4), where the dependent variable Yict is capital flows of type i, in country c, in quarter t.
As before, flows are normalized by subtracting from each observation the country-specific
mean and dividing by the country-specific standard deviation.
= β
Yict
1Fed Fund Ratet
+ β
2 ln (VIXt ) + β
3Zct−1
+ θ
C
+ (cid:5)
ct
.
(4)
Capital flows are regressed on global factors: the federal funds rate and the Chicago Board
Options Exchange volatility index (VIX) (converted to log scale).7 We also include a vector
of country-specific variables, Zct−1. Domestic variables include quarterly real GDP growth,
capital account openness (the Chinn-Ito index); financial sector depth (stock market cap-
italization or bank assets as percent of GDP); and proxies for the business environment
(the ICRG rating of investment risk, which is an index ranging from 0 Zu 12; a score of 12
points equates to very low risk and a score of 0 points to very high risk).8 We lag these by
one quarter (or one year for the variables that are available at annual frequency).9
Regressions are estimated with country-fixed effects and robust standard errors. Weil
some of the structural variables are slow-moving, the fixed effects estimates may not be
very precise, therefore we also run the same equations excluding the fixed effects. The re-
sults turn out to be very similar, hence we do not report them to save space.
The first four columns of Tables 11 durch 14 suggest that FDI is driven mainly by pull
factors, but that portfolio flows seem to be driven mainly by push factors, and so-called
other flows are driven both by push and pull factors.
Most inflows are not strongly correlated with the federal funds rate, with the prominent
exception of portfolio debt inflows (an increase in the U.S. policy rate predictably dampens
debt flows). Higher global risk aversion as measured by VIX reduces portfolio capital in-
flows but not FDI inflows (the coefficient of VIX is negative and significant for all non-FDI
7 In variations of these regressions we include the 10-year U.S. bond yield as an indicator of the U.S.
monetary policy. Results are similar to those obtained with the federal funds rate.
8 As an alternative to GDP growth, we included one-year ahead growth forecast from the World
Economic Outlook database in the regressions. Its coefficient is insignificant in all regressions.
9 To limit multicollinearity, we estimate our regressions with a parsimonious set of control
Variablen.
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Tisch 11. Correlates of FDI flows
Federal funds rate
Log(VIX)
GDP growth
Investment
Umfeld
Chinn-Ito index of
capital account
openness
Bank assets, Prozent
of GDP
Country fixed effects
Beobachtungen
R2
NEIN. of countries
FDI inflows
(1)
(2)
(3)
(4)
0.008
[0.39]
0.020
[0.21]
0.035***
[3.72]
0.038**
[2.12]
−0.082
[0.88]
0.025***
[3.10]
0.168***
[6.09]
0.022
[0.97]
0.023
[0.25]
0.034***
[3.67]
0.191***
[4.52]
Ja
2,256
0.023
29
Ja
2,234
0.071
29
Ja
2,148
0.049
29
0.044
[1.50]
−0.023
[0.23]
0.040***
[3.84]
0.023***
[5.02]
Ja
2,001
0.094
29
FDI outflows
(5)
−0.090***
[4.35]
−0.142
[1.63]
0.019***
[2.83]
(6)
−0.061***
[3.02]
−0.215**
[2.58]
0.012**
[2.19]
0.138***
[5.91]
(7)
−0.080***
[3.38]
−0.152*
[1.78]
0.017**
[2.41]
0.115**
[2.15]
Ja
2,256
0.039
29
Ja
2,234
0.069
29
Ja
2,148
0.049
29
(8)
−0.063*
[1.90]
−0.155
[1.61]
0.024***
[3.23]
0.016***
[3.07]
Ja
2,001
0.086
29
Notiz: The dependent variable is FDI inflows as percent of trend GDP, in columns (1)–(4) and FDI outflows as percent of trend GDP in
columns (5)–(8). The dependent variables are standardized by subtracting the country-specific mean and dividing by the country-specific stan-
dard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1
Prozent, 5 Prozent, Und 10 percent level.
Tisch 12. Correlates of portfolio equity flows
Portfolio equity inflows
(3)
(2)
(1)
Federal funds rate
Log(VIX)
GDP growth
Investment environment
Chinn-Ito index of capital
account openness
Bank assets, Prozent
of GDP
Country fixed effects
Beobachtungen
R2
NEIN. of countries
0.018
[1.06]
−0.561***
[8.17]
0.006
[1.23]
0.014
[0.69]
−0.562***
[8.10]
0.007
[1.20]
−0.017
[0.59]
Ja
2,197
0.039
29
Ja
2,175
0.040
29
0.007
[0.38]
−0.582***
[7.82]
0.006
[1.16]
−0.046
[1.45]
Ja
2,093
0.042
29
(4)
0.015
[0.71]
−0.614***
[8.13]
0.004
[0.73]
0.001
[0.16]
Ja
1,945
0.044
29
Portfolio equity outflows
(5)
(7)
(6)
−0.007
[0.38]
−0.341***
[2.87]
−0.010
[1.46]
0.011
[0.58]
−0.412***
[3.43]
−0.016**
[2.29]
0.102***
[4.16]
0.003
[0.15]
−0.324***
[2.83]
−0.013*
[1.93]
0.100**
[2.45]
Ja
1,945
0.012
27
Ja
1,923
0.030
27
Ja
1,853
0.020
27
(8)
0.011
[0.41]
−0.301**
[2.55]
−0.006
[0.66]
0.007*
[1.76]
Ja
1,702
0.019
27
Notiz: The dependent variable is portfolio equity inflows as percent of trend GDP in columns (1)–(4) and portfolio equity outflows as percent
of trend GDP in columns (5)–(8). The dependent variables are standardized by subtracting the country-specific mean and dividing by the
country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate
significance at the 1 Prozent, 5 Prozent, Und 10 percent level.
flows and largest for portfolio debt and portfolio equity flows). FDI seems to be affected
more by domestic than external factors (Zum Beispiel, GDP growth appears to act as a pull
factor for FDI). A better investment climate is associated with larger FDI inflows, as we
have come to expect. Im Gegensatz, growth and the investment climate do not appear to act as
pull factors for portfolio flows.
36
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Tisch 13. Correlates of portfolio debt flows
Federal funds rate
Log(VIX)
GDP growth
Investment environment
Chinn-Ito index of capital
account openness
Bank assets, Prozent
of GDP
Country fixed effects
Beobachtungen
R2
NEIN. of countries
Portfolio debt inflows
(1)
−0.062***
[3.50]
−0.590***
[9.54]
−0.002
[0.31]
(2)
−0.063***
[3.40]
−0.584***
[8.54]
−0.002
[0.24]
−0.008
[0.27]
Ja
2,173
0.047
29
Ja
2,151
0.046
29
(3)
−0.071***
[3.99]
−0.641***
[10.13]
−0.005
[0.66]
(4)
−0.067***
[3.63]
−0.664***
[8.90]
−0.001
[0.09]
0.019
[0.69]
Ja
2,069
0.060
29
0.002
[0.90]
Ja
1,921
0.062
29
Portfolio debt outflows
(6)
(5)
(7)
0.001
[0.08]
−0.453***
[3.65]
−0.006
[1.06]
0.009
[0.76]
−0.483***
[3.83]
−0.009
[1.56]
0.051**
[2.43]
Ja
2,055
0.021
28
Ja
2,033
0.025
28
0.006
[0.62]
−0.446***
[3.65]
−0.006
[1.10]
0.040
[1.29]
Ja
1,957
0.023
28
(8)
−0.001
[0.09]
−0.441***
[3.54]
−0.010*
[1.83]
0.000
[0.01]
Ja
1,807
0.021
28
Notiz: The dependent variable is portfolio debt inflows as percent of trend GDP in columns (1)–(4) and portfolio debt outflows as percent of trend
GDP in columns (5)–(8). The dependent variables are standardized by subtracting the country-specific mean and dividing by the country-specific
Standardabweichung. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the
1 Prozent, 5 Prozent, Und 10 percent level.
Tisch 14. Correlates of other flows
Other inflows
(2)
(1)
(3)
(4)
Other outflows
(5)
(6)
Federal funds rate
Log(VIX)
GDP growth
Investment environment
Chinn-Ito index of capital
account openness
Bank assets, Prozent
of GDP
Country fixed effects
Beobachtungen
R2
NEIN. of countries
0.037*
[2.01]
−0.415***
[4.02]
0.063***
[5.69]
0.053**
[2.68]
−0.455***
[4.46]
0.058***
[5.89]
0.078**
[2.53]
Ja
2,232
0.123
29
Ja
2,210
0.134
29
0.037*
[1.84]
−0.428***
[4.18]
0.062***
[5.74]
0.031
[1.28]
−0.506***
[4.86]
0.063***
[4.97]
0.023
[1.51]
−0.306***
[3.00]
0.016**
[2.67]
0.033*
[1.91]
–0.328***
[3.06]
0.013**
[2.09]
0.048**
[2.29]
0.044
[1.05]
Ja
2,128
0.126
29
0.007*
[1.73]
Ja
1,980
0.124
29
Ja
2,229
0.022
29
Ja
2,207
0.026
29
(7)
(8)
0.026
[1.51]
–0.334***
[3.34]
0.012**
[2.37]
0.034*
[1.87]
–0.362***
[3.15]
0.019***
[2.95]
0.056
[1.60]
Ye
2,125
0.025
29
0.008**
[2.61]
Ja
1,977
0.035
29
Notiz: The dependent variable is “other” inflows as percent of trend GDP in columns (1)–(4) and other outflows as percent of trend GDP in
columns (5)–(8). The dependent variables are standardized by subtracting the country-specific mean and dividing by the country-specific stan-
dard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1
Prozent, 5 Prozent, Und 10 percent level.
As a measure of the co-movement of capital flows across emerging countries, we include
median flows to all other emerging countries or to all other emerging markets within the
region.10 Global capital flows are highly significant for all types of flows, but the effect
10 We calculate these global or regional median flows for total capital flows, as well for specific types
of capital flows, and include them separately in the regressions. These results are available from
the authors on request.
37
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is strongest for the specific subcategory of capital flows under consideration. Wieder, Das
points to factors other than country-specific growth and the country-specific investment
climate in driving capital flows. Global flows are also more influential than regional flows.
Including global or regional median capital flows also reduces the impact of VIX, Weil
all of these variables capture global risk appetite to some extent.
Endlich, we ask whether the effects of these variables have changed in recent years, us-
ing 2003 as the year when the estimated relationship may have changed (consistent with
Eichengreen and Gupta 2016). For this we construct a time dummy for the post-2003 pe-
Riod, and interact it with the variables included in the regressions. We do not find much
evidence of a change in the coefficients after 2003. Dummies for different periods—before
and after 2000, 2008, Und 2010, respectively—similarly do not yield significant interactions
with the explanatory variables.
6. The behavior of outflows
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We analyzed the correlates of outflows analogously. Some of the patterns for outflows
are broadly similar to those for inflows. Non-FDI outflows are higher during periods of
lower risk aversion. Zusätzlich, global risk aversion as a measured by the VIX is also a
significant determinant of FDI outflows from emerging markets (in contrast to FDI inflows
to emerging markets, where the VIX was not significant as noted above). Both FDI and
non-FDI outflows are strongly correlated with median global and regional outflows.
One of our key findings is that capital outflows from emerging markets, FDI and bank-
related outflows in particular, have grown not just larger but also more volatile. We can
use these regression results to ask which of the significant determinants of these outflows
have themselves grown more volatile over time. The one determinant of outflows that is
robustly significant and also has become more variable over time is the VIX. The coefficient
of variation of the VIX rises by more than half between 1990–2000 and 2001–10; although
it comes down slightly in 2011–15, it is still significantly higher than in the earlier 1990–
2000 Zeitraum. There is also an increase in the volatility of GDP growth, which translates in to
more volatile capital outflows, in the 2006–10 period relative to other years, although this
change is not statistically significant relative to other periods.
These results thus point to variations in global risk aversion as a factor in the growing
volatility of FDI and bank-related outflows from emerging markets, although they beg
the question of why those variations in global risk appetite do not have a similar effect in
raising the volatility of FDI inflows into those same markets. Econometrically, the answer
is that the VIX has a smaller coefficient and is less significant for FDI inflows than FDI out-
flows. Zusätzlich, any impact of an increasingly variable VIX in amplifying the volatility
of FDI inflows into emerging markets in the recent period is offset at least partially by a less
38
Asian Economic Papers
Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
volatile investment climate in emerging markets.11 Economically, we do not have a good
answer for why FDI to emerging markets is less sensitive to global risk appetite than FDI
from emerging markets.
7. Conclusions
According to conventional wisdom as distilled in the literature and from past experience,
capital flows are volatile. They are volatile independent of time and place. But different
capital flows exhibit different degrees of volatility: FDI-related flows are least volatile,
and bank-intermediated flows are most volatile. Other portfolio capital flows rank in be-
zwischen, and within this intermediate category debt flows are generally considered to be
more volatile than equity-based flows.
In this paper we revisit this conventional wisdom, focusing on emerging markets. We ask
how much of the conventional view survives recent changes in market structure and reg-
ulation. We investigate how the magnitude and volatility of various kinds of capital flows
compare and how they have evolved over time. We analyze the empirical correlates of
different flows.
In terms of inflows into emerging markets, our results suggest that most of the patterns
identified in earlier work persist despite structural and regulatory changes and that much
of the conventional wisdom survives. FDI inflows into emerging markets remain more
stable than non-FDI inflows: FDI inflows have lower volatility; are more persistent; and de-
cline by smaller amounts in country-specific sudden stop and global stop episodes. Within
non-FDI inflows, bank-intermediated flows, which rose in the mid 2000s, are most volatile,
least persistent, and decline most sharply during country-specific sudden stop and global
stop episodes.
But outflows from emerging markets, which are increasingly important, behave differ-
ently. In contrast to inflows, we document important changes since the turn of the century,
and in the most recent decade in particular, in the behavior of outflows. FDI outflows from
emerging markets have grown and become significantly more volatile. Ähnlich, there is a
significant increase in the volatility of bank-intermediated capital outflows from emerging
markets since the turn of the century. In terms of shocks to the capital account of the bal-
ance of payments, our findings underscore that outflows from emerging markets, both FDI
and bank-related flows, have come to play a growing role and deserve greater attention
from emerging-market analysts and policymakers.
11 Our measure of the investment climate improved sharply in some emerging markets in the 1990s
while deteriorating in others, before settling down (generally at improved levels) after the turn of
the century.
39
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Verweise
Becker, Chris, and Clare Noone. 2008. Volatility and Persistence of Capital Flows. In: Regional Finan-
cial Integration in Asia: Present and Future, BIS Papers No. 42, S. 159–180. Basel: Bank for International
Settlements.
Blanchard, Oliver, and Julien Acalin. 2016. What Does Measured FDI Actually Measure? Policy Brief
NEIN. PB16-17. Washington, Gleichstrom: Peterson Institute of International Economics.
Bluedorn, John, Rupa Duttagupta, Jaime Guajardo, and Petia Topolova. 2013. Capital Flows Are
Fickle: Anytime, Anywhere. Working Paper No. 13/183, Washington, Gleichstrom: International Monetary
Fund.
Eichengreen, Barry, and Poonam Gupta. 2016. Managing Sudden Stops. Paper presented to the Bank
of Chile Annual Research Conference, Santiago, 9–10 November.
Levchenko, Andrei, and Paulo Mauro. 2007. Do Some Forms of Financial Flows Help Protect From
Sudden Stops? World Bank Economic Review 21:389–411.
Sussangkarn, Chalongphob. 2017. Managing Economic Stability Under Volatile Capital Flows: Ost
Asia Perspectives. Asian Economic Papers 16(1):174–192.
Appendix A: Data
Table A.1 Variables used in the analysis
Variable/set of variables
Capital flows (FDI, portfolio
Eigenkapital, portfolio debt, andere
flows)
Quelle
Haver
Details
Original source is IFS. Old series BPM5 and
new series BPM6 are spliced in the first
year when the new series is available for
each country; quarterly frequency
Nominal GDP
World Development Indicators,
In US$ (at market exchange rates); annual Trend GDP (USD) Federal funds rate (FFR) VIX index GDP growth World Bank Generated Haver DataStream Haver Investment profile International Country Risk Guide (ICRG), PRS Group Chinn-Ito index Authors’ Web site frequency Estimated using Hodrick-Prescott filter over annual GDP in US$
UNS. policy rate; quarterly frequency
CBOE volatility index; quarterly frequency
Real GDP in local currency seasonally
adjusted, year-on-year growth in percent;
quarterly frequency
Index of a country’s investment risk profile
ranging from 0 Zu 12: a score of 12 points
equates to very low risk and a score of 0
points to very high risk
Index of capital account liberalization ranging
from −1.89 to 2.39. Higher values indicate
higher capital account openness; annual
frequency
Bank assets
Global Financial Development
Percent of GDP; annual frequency
Datenbank (GFDD), World Bank
40
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Are Capital Flows Fickle? Increasingly? And Does the Answer Still Depend on Type?
Table A.2 Countries and periods included in the sample
Argentina
Armenien
Belarus
Brasilien
Bulgaria
Chile
Kolumbien
Croatia
Czech Republic
Guatemala
Ungarn
Indien
Indonesien
Israel
Jordanien
Kasachstan
Latvia
Lithuania
Malaysia
Mexiko
Pakistan
Peru
Philippinen
Poland
Republik Korea
Romania
Russland
Südafrika
Sri Lanka
Thailand
Truthahn
Ukraine
Venezuela
Vietnam
Start
1990:Q1
1993:Q1
1996:Q1
1990:Q1
1991:Q1
1991:Q1
1996:Q1
1993:Q1
1995:Q1
1990:Q1
1990:Q1
1990:Q1
1990:Q1
1990:Q1
1990:Q1
1996:Q1
1993:Q1
1995:Q1
1999:Q1
1990:Q1
1990:Q1
1991:Q1
1990:Q1
1990:Q1
1990:Q1
1991:Q1
1994:Q1
1990:Q1
1990:Q1
1990:Q1
1990:Q1
1994:Q1
1994:Q1
1996:Q1
End
2015:Q4
2015:Q4
2015:Q4
2014:Q4
2015:Q2
2015:Q4
2014:Q4
2015:Q4
2015:Q4
2015:Q4
2015:Q4
2015:Q2
2015:Q3
2015:Q4
2015:Q4
2015:Q1
2015:Q4
2015:Q4
2015:Q4
2014:Q4
2015:Q4
2015:Q4
2015:Q4
2015:Q4
2015:Q4
2015:Q4
2015:Q4
2014:Q4
2014:Q4
2015:Q4
2015:Q4
2015:Q4
2014:Q4
2015:Q3
NEIN. von
observations
104
92
80
104
98
100
80
92
84
104
104
102
103
104
104
77
92
84
68
104
104
100
104
86
104
100
88
104
104
104
104
88
88
79
Notiz: The table displays the maximum number of observations. Observations for
specific flows in some countries are slightly lower than reported here.
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