Download Patterns, Paradoxes, and Puzzles of International Capital Flows

Patterns, Paradoxes, and Puzzles of International Capital Flows
Revisited: The Role of Diversification
Laura Alfaro
Harvard Business School and NBER
Sebnem Kalemli-Ozcan
University of Houston, ECB and NBER
Vadym Volosovych
Florida Atlantic University
April 2008
Abstract
We argue that the common practice of using the (negative of) current account, that is
the financial account, to test the predictions of the neoclassical model regarding the patterns
of international capital flows can lead to misleading findings. This is especially true in the
1990s that is characterized by a massive increase in gross flows as oppose to net flows. The
current account balance reflects a number of non-market activities, while the neoclassical model’s
predictions are about private-market behavior. Data limitations for the developing countries
magnifies the mis-interpretation of the evidence. Taking our cue from the “new” models of
current account with optimal portfolio choice, we investigate the patterns of capital mobility in
the last decade focusing both on net and gross flows. Our results that focus on long run changes
suggest that there is a positive relation between persistent productivity shocks, proxied by
decade growth, and net capital flows once we condition on the extent of portfolio diversification.
The magnitude of such flows is much smaller than what is implied by the neoclassical benchmark
with full diversification of capital income though. Focusing on the levels of inflows and outflows
we find a strong effect of risk sharing and institutional quality both on capital inflows and
outflows, whereas when we focus on equity inflows the main determinant of foreign investment
turns out to be institutional quality.
JEL Classification: F21, F41, O1
Keywords: current account, capital inflows, capital outflows, growth, institutions, risk sharing, portfolio diversification.
1
Introduction
The surge in international capital flows in the 1990s has renewed interest in understanding the forces
driving them. Although the predictions of the neoclassical theory regarding the determinants of
international capital mobility are well established, the data has not been willing to support these
predictions for some time. Indeed, the gap between theory and the data has led to many puzzles such
as the lack of capital flows from rich to poor countries (the Lucas paradox), the high correlation
between savings and investment in OECD countries (the Feldstein-Horioka puzzle); the lack of
investment in foreign capital markets by home country residents (the home bias puzzle); the low
correlations of consumption growth across countries (the lack of risk sharing puzzle). All of these
puzzles directly or indirectly related to the small size and the opposite direction of capital flows
relative to the theoretical benchmarks.1
Along with the explosion of international asset trade, new models that emphasize portfolio choice
have increasingly taken center stage in the current debate.2 Starting with the partial equilibrium
approach of Kraay and Ventura (2000), this literature highlights the importance of countries’ net
external positions in the determination of current account balances. According to Kraay and
Ventura (2000), capital flows are caused by portfolio growth through changes in wealth, where
countries invest the marginal unit of wealth as the average unit. In other words, portfolio shares
are constant. The advantage of this approach is that it can be carried directly to the data since
empirically the theory boils down to an accounting identity, where current account is equal to
savings times the portfolio shares. On the other hand, more recent papers, such as Devereux and
Sutherland (2006), Evans and Hnatkovska (2005) and Tille and van Wincoop (2008a), focus on
general equilibrium effects and show that international capital flows can be broken down into a
portfolio growth component that is associated with savings and a portfolio reallocation component
associated with changes in expected risk and returns.3 Tille and van Wincoop (2008b) show that
1
See Obstfeld and Rogoff (2000) for an overview of the major puzzles in international economies.
There is also a separate but related literature that attempts to reconcile the counterfactual predictions of neoclassical models focusing on developing countries. See Gourinchas and Jeanne (2007) and Prasad, Subramanian, and
Zingales (2007).
3
Kraay and Ventura (2003) also talk about these two components but their “new” rule shows that the changes in
current account primarily reflect portfolio growth (i.e. changes in the size of the country portfolio without systematic
changes in its composition). They also argue that the empirical evidence for the “new” rule for OECD countries
2
1
the so-called new rule (that countries hold a constant ratio of domestic to foreign capital) of Kraay
and Ventura holds in steady state of any model, which creates an obvious problem when trying to
identify competing models from the data.
The contribution of this paper is twofold. First, we show that the common practice of using the
(negative of) current account to test the predictions of the neoclassical model regarding the patterns
of international capital flows can lead to misleading findings. The current account balance reflects
a number of non-market activities, while the neoclassical model’s predictions are about privatemarket behavior. Data limitations for the developing countries magnifies the mis-interpretation of
the evidence. Second, in the light of the new portfolio models, we argue that one must focus on
inflows and outflows together with net flows in order to understand the patterns of international
capital flows. The policy implications of such an exercise will be crucial given the importance of
the distinction between net and gross flows for the emerging market crisis as emphasized by Henry
(2006).
We study the recent globalization period of 1990–2004 excluding the debt crisis period as we
believe the traditional models are not well suited to explain this event (data issues also limit the
analysis of this period). We also focus on developing and emerging market countries in most of
the analysis given their increasingly important role in global financial markets. Our results that
focus on long run changes suggest that there is a positive relation between persistent productivity
shocks, proxied by decade growth, and net capital flows once we condition on the extent of portfolio
diversification. The magnitude of such flows is much smaller than what is implied by the neoclassical
benchmark with full diversification of capital income though. Focusing on the levels of inflows and
outflows we find a strong effect of risk sharing and institutional quality both on capital inflows
and outflows, whereas when we focus on equity inflows the main determinant of foreign investment
turns out to be institutional quality. We do not find evidence for the “new” rule of current account
for the developing countries.
In the next section, we present a conceptual framework that serves the purpose of clarifying
the theoretical relationship between capital flows, returns and productivity as established in the
in long horizons reflects the fact that the composition of country portfolios has been stable. In the short run, the
authors argue that increases in savings might lead to portfolio rebalancing (i.e. systematic changes in the composition
of country portfolio) but this is undone in the long run.
2
literature. This section also discusses the current stage of the related empirical evidence. Section 3
takes a first look at the data by analyzing simple correlations. Section 4 undertakes the empirical
exercise and section 5 concludes the paper.
2
Conceptual Framework: Capital Flows, Returns and Productivity
Textbook treatment of international capital mobility is very clear on three main functions of globally
integrated and efficient world capital markets.4 These are: 1) asset-price arbitrage ensures that
agents in different countries face same prices for a given asset; 2) people in different countries can
share risks to their consumption; 3) investors allocate new saving regardless of its origin towards
the world’s most productive opportunities. All of these can be used as indicators of free capital
mobility and any of these can break down in a world of uncertainty and incomplete markets.
We evaluate international capital mobility by investigating the determinants of the quantity of
capital flows, an observable, within an econometric framework.5 We, as the extensive literature that
precedes us, chose to do so since as argued by Obstfeld (1995) it is impossible to find internationally
comparable measures of after tax returns to capital. Although the most direct approach is to
compare the marginal products of capital across countries as argued by Obstfeld, comparing capitaloutput ratios is deeply flawed since marginal product of capital need not be the same everywhere in
the absence of an internationally common aggregate production function and hence the extensive
cross-country variation in total factor productivity (TFP).6 The comparisons of marginal products
of capital across countries also requires the equality of returns within a country, an assumption
that is not supported in the data of many developing countries as shown by Banerjee and Duflo
(2002).7
4
See Obstfeld (1993), Obstfeld and Rogoff (1995) and Obstfeld and Taylor (2004).
An alternative is to observe the prices such as bond yield differentials or stock market premiums.
6
Lucas (1990) and King and Rebelo (1993) indeed show wide variation in marginal product of capital across
countries.
7
A recent attempt by Caselli and Feyrer (2007) shows that adjusting the capital-output ratios by relative price
of capital to output significantly decreases the cross-country variation in these ratios. However their calculations of
MPKs requires the equalization of returns within the countries, which contradicts the empirical evidence.
5
3
MPK
Figure 1: Equilibrium capital stock as a function of productivity
α-1
MPKi= Ai(Ki/Li) -δ
(A2=1.5 A1)
R=0.06
K2
K1
K
Although standard neoclassical models predict that capital will move to countries where the
marginal product of capital is higher, in the presence of uncertainty, risk-adjusted returns to investment may not be as high as the one suggested by low capital-labor ratios, as argued by Obstfeld
(1995). Indeed the recent research confirms this indirectly by showing that low capital-labor ratio
countries have also low productivity due to weak institutions (Alfaro, Kalemli-Ozcan, Volosovych
(2007)), and governments that repeatedly default (Reinhart and Rogoff (2004)).
The canonical approach to capital flows, returns and productivity can be summarized in figure 1.
The figure illustrates the relationship between capital stock and its marginal product (MPK) and
4
how this relationship depends on the level of productivity within a small open economy framework.
Let K be the country’s capital stock and A be its productivity level. The MPK schedule shows
how marginal product varies as the capital stock increases. For a given labor force, L, productivity,
A, and depreciation, δ, increases in capital stock, K, reduces its marginal product due to law of
diminishing returns, where α is assumed to be 1/3. Due to the small economy assumption the world
interest rate is constant (assumed to be 0.06). Domestic capital stock is determined by the equation
M P K = R, such as the level of capital stock K1 . Holding the capital-labor ratio constant the
equilibrium level of capital stock is higher in countries with higher productivity, which can be seen
by comparing the levels of capital stock K1 to K2 . The equilibrium capital stock K2 corresponds to
the MPK schedule given by the dashed line where productivity is higher (assumed to be 1.5 times
higher than the productivity level corresponding to the solid MPK line, i.e., A2 = 1.5A1 ).
The other side of this analysis is the canonical intertemporal approach to the current account
with one asset-bond, where the current account is just a consumption smoothing tool. When a
small open economy is hit by a temporary income shock, all of it is absorbed by savings. Hence,
the current account response will be equal to the savings invested abroad, i.e., CAt = St . It is well
known that this “old” rule is inconsistent with empirical facts manifested as the Feldstein-Horioka
puzzle.
Kraay and Ventura (2000) consider a partial equilibrium small open economy with exogenous
returns on investment where the benchmark equilibrium will look like the one depicted in figure
1. The authors consider three assets: bonds, domestic capital and foreign capital. Further, they
assume that diminishing returns to capital are not as important as investment risk (which will
tweak the slopes in figure 1). This set up delivers the “new” rule which says that when a country
is hit by a temporary productivity shock, it invests the additional savings according to its given
portfolio shares, i.e., CAt = (N F A/W )St . The direct implication of the “new” rule is that debtor
countries run current account deficits and creditor countries run current account surpluses following
transitory shocks. Kraay and Ventura (2000, 2003) provide empirical evidence that the “new” rule
holds for OECD countries in the long-run when one runs a cross-sectional regression with averaged
data. This evidence on the “new” rule is consistent with Feldstein-Horioka puzzle. However, the
fact that the rule is about the dynamic response of the current account to transitory shocks and
that the evidence is such that the rule cannot explain short run response but only the long run
5
current account behavior is problematic.8
Kraay and Ventura (2003) explain the difference between the short run failure and the long
run success of the “new” rule with adjustment costs. But, there is the additional concern that
the “new” rule is about transitory income shocks and the evidence provided by Glick and Rogoff
(1995) is such that TFP shocks are persistent for the G7 countries.9 Glick and Rogoff (1995)
argue that the intertemporal approach to the current account implies that following a persistent
country-specific shock, there will be a current account deficit in excess of the corresponding rise
in investment. This is because permanent income rises more than current income as a result of
the shock leading to a fall in savings. Hence, given the random walk nature of country-specific
shocks for OECD countries, they argue that the finding of larger responses of investments than
current accounts constitutes a puzzle. They argue that the response of savings can be justified by
productivity shocks that are slowly mean reverting.10
In spite of these empirical issues, the work of Kraay and Ventura (2000, 2003) highlight the
importance of viewing international capital flows from a portfolio choice perspective. Many recent
papers have distinguished between portfolio growth and portfolio reallocation. The recent work
by Tille and van Wincoop (2008b) show that in a general equilibrium set up the “new” rule of
Kraay and Ventura emerges in steady state of many models.11 The authors show how the steady
state current account of a country is equal to its savings times the ratio of net foreign assets to
the country’s wealth, which is line with the empirical evidence of Kraay and Ventura (2003). Tille
and van Wincoop (2008b) assume transitory shocks though and emphasize that the “‘new” rule
should be about the dynamic response of the current account to a transitory change in savings
that is driven by a transitory income shock. The authors emphasize that in the partial equilibrium
set up of Kraay and Ventura (2000), there is no differentiation between net and gross flows and in
the general equilibrium set up with two-way capital flows, the ratio of net foreign assets to wealth
8
There might also be a concern about the empirical relevance of the weak diminishing returns and strong investment
risk assumption. The existing evidence does not seem to be in favor of diminishing returns as capital flows from
South to North which was first pointed out by Lucas (1990).
9
One can think of policy shocks as transitory income shocks of course.
10
Permanent shocks, i.e., random walks are not mean reverting, but time series tests can not separate random
walks from, say, mean reverting AR(1) processes with a coefficient to lagged productivity very close to unity.
11
See also Devereux and Sutherland (2007), Devereux and Saito (2007) and Engel and Matsumoto (2008) for these
class of models.
6
depends on portfolio shares chosen by investors from both countries as well as the relative wealth
of the two countries. These results underscore the need to analyze the movements of inflows and
outflows, not only net flows.12 The authors show that there will be positive outflows and negative
inflows for a country that has experienced a positive transitory shock as a result of the allocation
of new savings to foreign assets exactly as in the case of the “traditional” rule. And as we have
discussed this result is at odds with the data. Hence, we are left with some empirical support
for the “new‘”rule of Kraay and Ventura and none yet for the general equilibrium framework of
the “new” models. None of these arguments are backed by robust and systematic evidence so far,
which is our goal in this paper.13
The main problem is that the “new” models cannot be easily carried to the data. Hence, we
motivate our empirical exercise with a simpler model that can be tested directly in the data and
has similar predictions to the work of Tille and van Wincoop (2008b) as far as mean reverting
portfolio shares are concerned.
Kalemli-Ozcan, Reshef, Sorensen, and Yosha (2008) show that under the assumption of full
diversification of capital income capital flows to high growth regions and hence high output regions
run current account deficits and hold negative net asset positions. In their model, because of
full diversification and hence no risk premia, relative investment will be determined by relative
productivity with no role for savings rates. Hence, both debtor and creditor countries can attract
capital on net and run current account deficits if they are hit by positive persistent productivity
shocks, of which the evidence is provided by Glick and Rogoff (1995). On the other hand, poorer
regions might be in the “catch-up growth” phase, which implies that we should observe low output
regions have relatively higher growth and are attracting capital from other regions.
Kalemli-Ozcan, Reshef, Sorensen, and Yosha (2008) assume that each region’s output is determined by a Cobb-Douglas production function where the level of “productivity” differs across
12
Note that the effect of the transitory shock on expected excess return and volatility of returns will be important
in driving the change in portfolio shares. They claim although each of these terms might be small on its own dividing
first by second can create a first order effect. Curcuru, Dvorak and Warnock (2007) find small expected excess returns
on U.S. assets and liabilities. These authors pay particular attention to data issues in the way U.S. collects current
account and stock data and find a negligible difference on net returns for the U.S.
13
Guo and Jin (2008) analyze the role of omitted variables in driving the results of Kraay and Ventura (2000) and
also show that for OECD coutnries portfolio shares measured as NFA/W follows a mean reverting process which will
be inconsistent with constant portfolio shares assumption of Kraay and Ventura (2000).
7
regions and subject to random shocks that last for a number of years. Such shocks can be purely
technological but can also be regulatory shocks or relative price shocks. Under the assumption
that capital adjusts to the equilibrium level within one period (that corresponds to a decade in the
data) following persistent productivity shocks, their model predicts a positive relationship between
capital flows and output growth, where the predicted proportionality equals to the capital’s share in
the production function. The mechanism is simply that productivity shocks leads to higher growth
and to a higher return to capital. Capital therefore flows in until the equilibrium is reached where
returns are equalized in all regions. Under the assumption of full diversification of ownership across
regions, capital is mainly owned by residents of other regions (assuming that each region is small
compared to the total area under consideration). A simple consequence of the above is that if all
regions start out at about the same level of output, then the regions that are subject to relatively
large increases in productivity will become regions that will be debtors and at the same time will
have a relatively high level of output due to persistent shocks. In other words, we expect states
and countries—if the phase of “catch-up growth” has come to an end—with relatively high output
to be debtors.14
These predictions of the model can be tested empirically and they do so using state level
data from the U.S. to give the best chance to their model given the fully financially integrated
50 small open economies of the U.S. Using simulations and regressions they find support for the
following implications of the model: 1) income increases less than output in high growth states,
2) net dividends converge to zero in the absence of growth shocks, and 3) high output states tend
to pay net dividends. They conclude that the main explanation for the small size and “wrong”
direction of international capital flows is more likely due to “frictions” associated with national
borders—making international financial markets de facto incomplete—rather than to deficiencies
in the simple neoclassical model.15
As a result, in this paper we would like to focus on the response of net flows, inflows and outflows
14
Note that Gourinchas and Jeanne (2007) and Prasad et al. (2007) find exactly the opposite in a developing
country context; i.e., they find capital goes to less productive countries and a positive correlation between current
account and growth, respectively.
15
Examples of frictions associated with borders are explicit barriers to investment or factors affecting investors
ex post returns such as bad institutions (corruption and rule of law), and sovereign risk; see, for example Alfaro,
Kalemli-Ozcan, and Volosovych (2003) and Reinhart and Rogoff (2004).
8
to persistent growth shocks in the light of the models reviewed above. We will specifically focus on
the globalization period of the last decade and on the developing countries given the substantial
variation among growth rates across these countries over time.
3
A Simple Look at the Data
3.1
Net flows, Inflows, and Outflows
We first look at the simple correlations of the data since we would like to underscore the importance
of data-quality issues when dealing with developing countries as well as the substantial variation
in current accounts over time and across countries. Take two extreme examples: Madagascar and
South Korea. Madagascar is a poor country.16 Consumption is close to 90 percent of the income
and investment is only 4 percent of GDP.17 The country has experienced recurrent political and
economic crises. After independence from France in 1947, the country adopted a socialist system
nationalizing much of the economy. Continuous decline and balance of payment problems led the
country to seek the International Monetary Fund (IMF) assistance in the early 1980s. Slowly, the
country adopted market reforms; most of the liberalization efforts, however, did not start before the
mid-1990s. In 1980, PPP-adjusted output per worker was 2,366 U.S. dollars. By 2000 it was only
1,584 U.S. dollars. The current account has been in deficit throughout this whole period, averaging
close to 7.5 percent of GDP. Average net inflows of foreign direct investment (FDI) amounted to
only 0.46 percent of GDP and official development assistance and official aid has averaged more
than 10 percent of GDP during the period (never below 5 percent). In 2000, the World Bank
and the IMF agreed to support a comprehensive debt reduction package for Madagascar under the
enhanced Heavily Indebted Poor Countries (HIPC) Initiative.18
South Korea’s experience, on the other hand, is very different.19 After the war, the economy
16
In 2005, its GDP was close to 5 billion U.S. dollars with population of more than 18 million. 80 percent of the
people live in the countryside and are engaged in subsistence farming. Life expectancy at birth is 56 years.
17
Between 1980–2000 the average consumption as percentage of GDP was 88.8 and average investment 3.8.
18
Total service relief from all of Madagascar’s creditors is worth about 1.5 billion U.S. dollars (which is equivalent
to about 40 percent of total debt outstanding).
19
In 2005, the Korea’s GDP was close to 787.6 billion U.S. dollars with a population of close to 48 million (85
percent of which are urban population). Investment through at this period was close to 36 percent and consumption
close to 52 percent of GDP.
9
followed what has been characterized as an outward-oriented industry-led strategy (or export-led
industrialization) with the government favoring investments in heavy machinery and chemicals.
Between 1980 and 2000 output per worker grew at rate of 5.4 percent per year. During this period
the country experienced only two years of negative growth: in 1980 in the wake of the second oil
shock and the international debt crisis and the assassination of the President Park Chung-Hee; and
1998 in the mist of the Asian financial crisis were the country was forced to seek a record 58 billion
U.S. dollars emergency rescue funding from the IMF and the World Bank. Between 1980 and 1990,
Korea net inflows of capital were close to zero. However, this number masks huge variations with
current account deficits turning into surpluses following both crisis periods and a history of heavy
restrictions by the governments. “Over several decades, South Korea experienced rapid sustained
growth in the presence of capital controls.[...] Comprehensive capital controls were used to insulate
the domestic financial market from the global market.”20 Noland (2007) describes in detail the
different measures adopted by the country.21
Figures 2 and 3 depict average over 1991–2004 of the total inflows and outflows of capital (from
the financial account) and (the negative of) the current account balance for Korea and Madagascar,
normalized by GDP and population respectively.22 While Madagascar reports negative 1.2 percent
of GDP of foreign inflows, reported outflows are virtually zero (Figure 2). In most cases the zero
foreign assets for low-income countries may also mean misreporting of the asset position. Relatively
large positive net flows of capital of 7.4 percent of GDP largely consist of aid flows.23 For South
Korea, the current account balance of 1.2 percent of GDP represents the gross flows of two to four
times larger.24 Figure 3 demonstrates even more drastic differences when we normalize the flows
20
Taken from Noland (2007).
“Inwards FDI was discouraged by permitting entry only into a limited range of sectors, imposing minority
ownership requirements, requiring technology transfer (in the absence of any intellectual property rights enforcement),
and imposing strict export requirement. And while there were modest relaxation in the late-1970s, actual FDI inflows
remained minuscule until a wide ranging liberalization was undertaken in response to the 1997 crisis.[...] Stock
market investment by nonresidents was prohibited until 1992 and then subject to stringent quantitative ceilings.
[...] Investment by nonresidents in domestic bonds was prohibited until 1996, and then subject to quantitative
limitations.[...] For much of this period outbound investment was similarly restricted. Domestic residents were not
permitted to open foreign accounts or purchase foreign securities [.....].”
22
See Appendix for details of the data. Capital flows data correspond to flows of FDI, equity and debt from the
financial account.
23
The inflows and outflows of capital consist of equity investment and debt flows that do not include aid flows and
grants but may contain some public flows.
24
Korea experienced a large reversal in the current account balance from about –9 billion US dollars over 1991–97
21
10
Figure 2: International capital flows for Korea and Madagascar, 1991–2004 (per GDP)
percentage of GDP
8
7.4
6
4
2
3.7
2.2
0
−1.2
−2
−1.2
Korea
Madagascar
(average 1991−2004)
Total Capital Inflows
Total Capital Outflows, including E/O
Negative of Current Account Balance
11
’000 of 2000 U.S. dollars per capita
Figure 3: International capital flows for Korea and Madagascar, 1991–2004 (per capita)
0.43
.4
.3
0.26
.2
.1
0.02
0
−0.00
−.1
−0.10
Korea
Madagascar
(average 1991−2004)
Total Capital Inflows
Total Capital Outflows, including E/O
Negative of Current Account Balance
12
with population. The figure stresses the minuscule size of capital flows for populous Madagascar.
The bottomline is that relatively small current account balances in two countries hide substantial
differences in gross flows of capital.
These two examples highlight the important role of government policies and institutions in the
determination of current account balances. As Ventura (2003) notes, any sound explanation of why
Madagascar and Korea have such different experiences should be based on a detailed comparison
of institutions, policies, and histories of both countries. Similarly, any satisfactory account of the
reversals of Korea’s current account deficits into surpluses must be based on a thorough analysis
of the world economic events that took place around those dates, and of Madagascar’s persistent
current account deficits—of the role of aid by international donors. Our objective in this paper
is to search for broad patterns and explanations that are common to many countries and dates.
As the previous two examples underscore, such a task is particulary difficult among developing
countries characterized by government interventions, world shocks, capital controls, sovereign risk,
boom-bust cycles, aid flows, poverty, subsistence consumption, among others.
3.2
Net Capital Flows and Growth
Figure 4 presents the scatter plot of net capital flows on contemporaneous growth with averaged
data over the 1990s for the largest sample of developing countries with available data.25 The
figure reveals no robust relation between net flows and growth for the largest sample of developing
countries (black solid line for 106 countries). This result is consistent with Chinn and Prasad
(2003) who find no robust relation between net flows and growth for a sample of 18 industrial
and 71 developing countries for the period 1971-1995. However the result is not consistent with
Gourinchas and Jeanne (2007) who finds a negative relation between growth and capital flows.
Indeed we also find a negative relation once we concentrate on the most commonly used 67 sample
of developing countries as shown by red dashed line.26 Comparing these two samples of 106 versus 67
developing countries we realize that high growth-low saving and hence high current account deficit
to 18.7 billion US dollars over 1998–2004 after East Asian financial crisis.
25
The sample includes 106 countries listed in the Appendix. The sample does not include outliers. Indeed,
throughout the paper we pay particular attention that our results are not driven by outliers.
26
This is the sample used by Gourinchas and Jeanne (2007).
13
countries in Eastern Europe and ex-Soviet Union–shown by blue long-dash line—are excluded from
the 67 country sample. The main reason for this is the fact that data for these countries did not
exist before 1990s. Excluding those countries puts a larger weight on high growth-high savings East
Asian countries whose savings patterns remains to be well understood in the literature.27 If we
exclude the high savings East Asian countries the negative relationship between flows and growth
disappears or if we exclude the Eastern European countries from the large sample of 106 countries
then the negative relation appears (shown by green dash-dot line). Hence, although it is true that
China runs a surplus and the United States a deficit, these countries are not representative of a
broader sample.
Figure 5 presents a similar scatter plot for net flows and domestic investment for the same
period with the same qualitative result.
The bottomline is that the positive relation between savings and growth plaques the relationship
between productivity and capital flows, especially in a developing country context where there are
both high saving and low saving countries with similar medium-run growth rates. Thus we would
like to test, in the light of the models summarized above, once we control for savings—or its proxy
determinants— will it be the case that capital flows to high productive countries? Also will the
magnitude of such flows be consistent with what is implied by a simple neoclassical model as shown
in Kalemli-Ozcan, Reshef, Sorensen, and Yosha (2007)?28 We turn to a regression framework to
answer these questions.
4
Empirical Evidence
4.1
Data
Calculation of Capital Flows
The data on annual capital flows come from International Financial Statistics (IFS) issued
by the IMF. Although there are other data sources, the IMF provides the most comprehensive
27
Domestic savings is often highly correlated with growth. See Carroll and Weil (1994). Loayza et al. (2000) find
similar effect on a cross country analysis and Kraay (2002) and Horioka and Wan (2006) in China.
28
Note that in spite of the evidence confirming the model’s predictions for the U.S. states, Ekinci, Kalemli-Ozcan
and Sorensen (2007) did not find the same evidence for European countries.
14
Figure 4: Net capital flows and growth in developing countries, the 1990s–2000s.
LBN
20
AZE
MOZ
Negative CA / GDP (%)
BIH
ZMB
TKM
10
COG
0
ARM
LAO
KGZ
TZA
TGO
GEO
MDA MWI MLI
MDG HND
BEN
NER
EST
UGALTU
SDN
HUN
SEN
MKD ALBBFA
GIN MLT
ROM
GHA
HRV
RWA
TCD
NPL
GTM
KHM
JAM
BOL
SVK
PAN
PER
LVA
TUN
CRICYP
LKA
FJI CZE
POL
BGRMEX
ECU
SLV
KAZ
PHL
ISR
CIV CMR
CHL
YEM
JOR
DOM
VNM
PAK
BRA
COL
ETH
HTI
TJK KEN
PRY
TUR
ARG
URY
SWZ
SAU
ZAF BGD IND
MUS
MAR
SVN
IDNTHA KOR
TTO
EGY
SYR
UKR
MYS
IRN
NGA PNG
VEN
DZA NAM
GAB
HKG
RUS
BWA
BLR
CHN
-10
SGP
-20
-5
0
5
10
GDP per capita growth (%)
Developing samples; the 1991-2004 period
Black: 106 DEV; Red: 67 DEV*
Blue long-dash line: 39 DEV = 106 DEV - 67 DEV*
Green dash-dot line: 106 DEV - CEECs & exUSSR
Notes: All the variables are sample averages over stated the period 1991–2004. See definitions of samples in Appendix B. Net capital flows is the negative of the Current Account Balance (IMF’s IFS line 78ald) consists of the sum
of the balance on goods, services and income, plus current transfers, average over stated period. IMF IFS data for
Current Account is net of Exceptional Financing (IFS line 79ded), which includes any other transactions undertaken
by the authorities to finance the “overall balance,” as an alternative to, or in conjunction with, the use of reserve
assets and the use of the IMF credit and loans from the IMF. Current Account in current US dollars is expressed as
percentage of GDP in current US dollars. GDP per capita growth is at PPP in 2000 International US dollars from
Penn World Table, ver. 6.2. Growth rate is calculated as log-difference of the GDP per capita levels from the same
source.
15
Figure 5: Net capital flows and domestic investment in developing countries, the 1990s–
2000s.
LBN
Negative CA / GDP (%)
20
AZE
MOZ
BIH
ZMB
10
0
ARM
TKM
LAO
KGZ
COG
TGO TZA
MLI
MWI GEO
MDA
MDG BEN
NER
LTU
ESTHND
UGA
SDN
SEN
ALB
BFA HUN
MKD
MLT
GIN
ROM
GHA
HRV
RWA
TCD
NPL
GTM
KHM
JAM
BOL
SVK
PAN
PER
CZE
LVA
CRI
FJI
CYPLKATUNBLR
POL MEX
BGR
ECU
SLV
KAZ
PHL
ISR
CIV
CMR
CHL
YEM JOR VNM
DOM
PAK
BRA
COL
ETH
HTI
TJK
PRY
KEN
TUR
ARG SAU
URYZAF
SWZ
IND MUS
BGD
MAR
SVN IDN
THAKOR
EGYTTOSYR
CHN
UKR
MYS
PNG
IRN
NGA
VEN
NAM
DZA
HKG
RUS GAB
BWA
-10
SGP
-20
10
20
30
40
Domestic Investment / GDP (%)
Developing samples; the 1991-2004 period
Black: 106 DEV; Red: 67 DEV*
Blue long-dash line: 39 DEV = 106 DEV - 67 DEV*
Green dash-dot line: 106 DEV - CEECs & exUSSR
Notes: All the variables are sample averages over the period 1991–2004. See definitions of samples in Appendix B.
Net capital flows is the negative of the Current Account Balance (IMF’s IFS line 78ald) consists of the sum of the
balance on goods, services and income, plus current transfers, average over stated period. IMF IFS data for Current
Account is net of Exceptional Financing (IFS line 79ded), which includes any other transactions undertaken by the
authorities to finance the “overall balance,” as an alternative to, or in conjunction with, the use of reserve assets and
the use of the IMF credit and loans from the IMF. Current Account in current US dollars is expressed as percentage
of GDP in current US dollars. Investment-to-GDP ratio is calculated from the raw Gross capital formation (outlays
on additions to the fixed assets of the economy plus net changes in the level of inventories) and GDP data from the
World Bank, both in nominal US dollars.
16
and comparable data on capital flows. We use data on the (negative) current account balance to
proxy net capital flows in the countries. We construct the measures of net capital flows, inflows and
outflows of capital from the IFS data (the appendix provides the details of data calculation). Inflows
of capital correspond to net flows of foreign claims on domestic capital (change in liabilities). The
main categories of capital flows we consider are flows of foreign direct investment (FDI), portfolio
equity investment (equity), and debt. We measure outflows of capital as the net flows of domestic
claims on foreign assets (change in assets). Similarly to Lane and Milesi-Ferretti (2001), we treat
net errors and omissions as unrecorded capital outflows and add them as a part debt assets. We
also use Lane and Milesi-Ferretti data set to investigate the role of valuation effects. We explore
the capital flows pattern with the typical GDP normalization employed in other studies.29
Issues with Capital Flows Data
Although the IMF provides the most comprehensive data coverage, there are several issues
behind the compilation of the balance-of-payments (BOP) statistics, as discussed in greater detail
by Lane and Milesi-Ferretti (2001).30 There are issues related to measurement of the change in
the assets (or outflows) in the balance of payments statistics. There are substantial missing data
and misreporting for many countries, in particular developing countries. Also, some countries do
not report data for all forms of capital flows. Unfortunately, it is hard to verify whether the data
are really missing as opposed to simply being zero. For example, portfolio equity data for most
countries are negligible until recently but at the same time several developing countries tend to
report data for liabilities only and no data for assets. This is especially the case for foreign direct
investment flows.
Outflows are generally captured in the “errors and omissions” account. Frankel (2001), for
example, argues that data collection is much better for capital flowing in a country than capital
flowing out.31 It is also hard to disentangle how much of the outflows are related to FDI and
29
In the current version of the paper we focus on capital flows normalized by GDP in order to compare our results
to the literature’s. We believe that data expressed as real dollars per capita are more in line with the neoclassical
theory. In previous work we used data normalized by population. Regression analysis using flows normalized with
GDP as a dependent variable and GDP as control variable will pick up the business cycle even when using initial
GDP. Indeed, we find important differences when using per GDP flows versus per capital flows (which we argue is
less subject to this bias). These results are available upon request.
30
These issues are documented in detail in Alfaro, Kalemli-Ozcan, and Volosovych (2007).
31
Frankel (2001) gives the example that no comprehensive survey of the U.S. residents holdings of foreign securities
17
portfolio equity and how much related to debt. The division changes by country and period of
study. For the debt data, there are additional issues. Consequent to the debt crisis there are several
measurement problems related to different methodologies of recording non-payments, rescheduling,
debt forgiveness and reductions.32
When studying the question of whether international capital mobility are efficiently allocated
internationally, the researcher should consider private flows (that is, those that at least theoretically
respond to the market incentives assumptions underlying the neoclassical model). Considering the
whole financial account or the current account (with the sign reversed) will include other types
of flows, such as aid flows, official development assistance flows and government borrowing, which
are not driven by market considerations.33 Hence we look at a sample of all countries and a subsample of developing ones, and a sample of countries excluding countries with high aid flows (HIPC
countries).
4.2
Econometric Model and the Preliminary Results
Descriptive Statistics
Tables 1 and 2 presents summary statistics for different samples for net capital flows per GDP,
real per capita GDP growth, savings and investment to GDP, log the GDP per capita and net
foreign asset position to GDP. It becomes evident from the table that there are important differences across samples.34 Table 3 shows the correlations. Some interesting patterns emerge. In the
sample of 106 countries, we find that both savings and investment are negatively correlated with
capital flows and positively correlated with growth. However upon exclusion of HIPC countries,
had been conducted since World War II, until one was conducted in 1994. See Curcuru, Dvorak and Warnock (2007)
for a detailed analysis of the methodology and issues behind the collection of U.S. balance of payments and net asset
position data.
32
As noted by Lane and Milessi-Feretti (2001) these issues create large discrepancies between debt data reported
by different agencies.
33
During the 70s and 80s, and important part of debt flows to many developing countries were to government and
government related related activities (via directly or via implicit guarantees.
34
Reduction in sample size may be due to availability of additional data in many cases in particular the net foreign
asset position of countries. But as highlighted by tables, this may change results significantly.
18
the correlation between current account and investment is not significant.35
Level Regressions for Net Flows
Our country level regressions are motivated by the several models outlined before. Hence, in
its essence, the reduced-form empirical exercise tries to evaluate the relative importance of various
models. We start by running standard long-run net capital flows regressions with averaged data for
countries over the decade. There are two reasons why we chose this approach: First we would like to
filter the data from noise caused by business cycle fluctuations as much as we can, and second given
the fact that Kraay-Ventura rule holds only in the long-run cross-sectional regressions, we would
like to test the alternatives in this set up too. The disadvantage of this approach will be about
econometric identification since country fixed effects and reverse causality might create endogeneity
problems. To deal with this issue we will run change regressions with averaged lagged data next
which will have a diff-in-diff character.
Table 4 shows the results from the OLS regression of net capital flows on growth, savings, and
investment. Hence this regression has the form:
−CAi /GDPi = β0 + β1 Si /GDPi + β2 Ii /GDPi + β3 ∆ log GDPi + i
Our main specification is the one that is shown in columns (5) and (6), however given the high
correlation between growth, saving, and investment we present the results by adding variables one
by one as shown in columns (1) to (5). The sample “DEV” used here is the broadest sample of
developing countries. The regressions in this table basically confirm the finding of the scatter plots
shown in the previous section. There is a strong negative role of savings for net capital flows. Once
we control for savings, investment turns out to be positively and significantly related to net capital
flows. The same is also true for growth but the coefficient on growth is statistically insignificant.
35
Chinn and Prasad (2003) analyze the role of aid flows and other transfers from abroad. The authors re-estimated
their regression analysis for developing countries using aid and other official grants as a percentage of GDP. The
authors find a negative coefficient (although not significant for non-developing Africa which includes the bulk of
HIPC countries).
19
We obtain similar results when we expand the sample to include industrialized countries (IND) and
world in general (WW) as seen in columns (6) and (7). Columns (4) to (7) also control for the level
of initial GDP per capita to deal with the possibility that the decade growth is only proxying for
the initial level of development and the fact that this variable is omitted might cause a downward
bias in the growth variable.36 In every specification we do not find a robust relation between GDP
growth and net flows.37
Table 5 repeats the exercise for the commonly used sample of developing countries. Cross-section
studies with long-run averages typically use this smaller sample due to unavailability of data for
eastern Europe before 1990s. Column (1) shows growth per capita to be negatively associated with
net capital flows, and column (2) shows a similar negative association with investment. However
these results are not robust to controlling for savings as shown in columns (3) and (5). We also
investigate the role of HIPC countries. HIPCs, characterized by low productivity, are usually net
recipients of foreign resources mostly in the form of aid flows and grants. Current account data
includes the official aid flows.38 In columns (6) to (8) of Table 5 we try to reduce the effect of aid
flows by excluding the countries with dominant aid flows (HIPC countries; listed in the appendix).39
Once we control for savings the coefficient on growth turns out to be positive and significant. But
remember that these regressions mainly posit contemporaneous correlations and hence we do not
want to attach a causal interpretation to the positive significant coefficient on growth. The results
of this table also confirms the findings of the previous figures.
These results should not come as a surprise. As surveys by Obsfeld and Rogoff (1995, 1996),
and Obstfeld (1995, 1986), have noted, “The current account, savings, and investment are jointly
36
Column (7) suggest that there is no Lucas Paradox once we consider the sub-sample of industrial countries. Note
also that the GDP per capita variable is not significant in the WW sample that includes both industrialized and
the developing countries in the last 10 years as oppose to the last 30 years as shown by Alfaro, Kalemli-Ozcan, and
Volosovych (2007).
37
In the case of industrialized countries, average growth rate of GDP is positive but not significant at conventional
levels. Similarly, Chinn and Prasad (2003) found a positive cross-sectional correlation between average output growth
and current account balances only for industrial countries.
38
See Arslanalp and Henry (2005). More generally, as highlighted by the HIPC countries, important attention must
given to the role of private versus public flows, although the data do not allow many times for clear distinctions as
explained later. Simply excluding countries that have received aid flows as a percentage of GDP above some threshold
may bias results; the analysis requires excluding countries for which aid flows were the main source of foreign capital
or more generally, cases where flows have not responded to private incentives.
39
Alternative is to use private flows which we did for robustness obtaining similar results.
20
determined endogenous variables that respond to common exogenous shocks, it may be misleading
to identify a specific ex-post investment or saving shift as a cause of a current account change.”40
Systematic current account targeting by government would, if successful, produce a strong savings
and investment correlation, even with high capital mobility. In addition, many developing countries
control capital flows, and recently, many countries have engaged in substantial reserve accumulation
which has been linked to exchange rate management which affect the dynamics of the current
account.41
Chinn and Prasad (2003) provide a broad empirical characterization of the determinants of
current account balances for a 18 industrials and 71 developing countries for the period 1971-1995.
In many ways, we re-establish many of their results in the current period. The variables that are
significant in their study and here turn out to be the proxy determinants of savings as shown in
Table 6. We experiment with many variables and the ones that turn out to be significant across
many specifications are government balance, financial deepening, and net foreign assets. All these
variables, as proxy determinants of savings, turn out to be negatively significant. We present results
for the broad sample of developing countries in column (1). The number of countries diminishes to
84 due to net foreign asset position data availability. Column (2) presents results for industrialized
countries and column (3) for a world sample.42
Kraay and Ventura (2000) argue that pooling the data of debtor and creditor countries will
lead to misleading findings since the response of current account to a shock will differ among these
countries. Hence we investigate the “new” rule proposed by Kraay and Ventura in Table 7 by
running the following regression:
−CAi /GDPi = β0 + β1 xi ∗ Si /GDPi + β2 ∆ log GDPi + i
where x̄i = N F Ai /W ealthi .
40
Obstfeld and Rogoff (1996) p.17. Some of the factors that affect both savings and investment are for example
demographic ones.
41
As Obstfeld (1995) notes, an open economy faces and intertemporal budget constraint relating the differences
between its saving and investment–the current account–to the change in its net external assets. Under some economic
conditions this constraint alone implies that savings and investment ratios averaged over sufficiently long periods must
to close despite capital mobility.
42
We also controlled for age dependency which turns out to be significant only in certain specifications.
21
In fact Kraay and Ventura run pooled, within-country and between-country regressions, the
latter shown by the above equation. They control for the productivity shocks as we do. The
authors find supporting evidence for the between regression and not the others.
We use the broad sample of developing countries, we also exclude HIPC countries and compare
results to industrial countries. Following Kraay and Ventura (2000) we use wealth estimates calculated in a similar way in columns (1), (2), and (5). This estimation basically assumes W=NFA+K
and hence using these wealth estimates reduce the sample size drastically due to unavailability of
data for developing countries. That is why we use NFA/GDP in columns (3), (4) and (6). Although
NFA/GDP is not the portfolio share given the correlation between NFA/GDP and NFA/W that is
over 80 percent the same cross-sectional variation will be captured by NFA/GDP. But we should
note that the normalization by GDP may cause a bias against the finding of a coefficient of 1.
For developing countries we never find the evidence of the “new” rule. Let alone finding a
coefficient of 1, we do not find a significant effect of the interaction term. Even when we do find
a significant negative effect as shown in columns (1) and (4), it is way below one and it looses
the significant once we control savings and/or NFA suggesting that for developing countries the
interaction term proxies the level of savings.43
For industrial countries we do find the evidence of the “new” rule with a coefficient that is
similar to the one estimated in other studies as well as in Kraay-Ventura as shown in column (5).
However this 0.87 coefficient goes down to 0.65 once we consider the longer sample until 2004 instead of the Kraay-Ventura sample of 1991–1997, as shown in column (6).
Change Regressions for Net Capital Flows
In this section we motivate the regressions with the models of Glick and Rogoff (1995) and
Kalemli-Ozcan, Reshef, Sorensen, and Yosha (2008), where we will investigate the changes in
capital flows. This framework will give us the econometric advantage of dealing with country fixed
effects and reverse causality, albeit in a crude way.
43
These results are available upon request.
22
The reduced form regression that is based on diversification of capital income, out of these
models can be written as follows:
∆(−CA)i = β0 + β1 ∆ log GDPi + ei
where ∆(−CA)i = (−CA)i,avg(1998−2004) −(−CA)i,avg(1991−1997) and ∆ log GDPi = log GDPi,1997 −
log GDPi,1991 .
The sample for growth and for the capital flows are non-overlapping to prevent measurement
errors in output to enter on both sides of the equality sign because that would create a spurious
correlation between the left- and right-hand sides. At the same time, for our empirical strategy
to produce significant empirical results TFP shocks need to be persistent so the averaging done to
eliminate the business cycle will not average out relative productivity shocks. As mentioned, Glick
and Rogoff (1995) provide direct evidence of high persistence of TFP shocks at the country-level.
Also in our sample, the growth over the period 1991–1997 varies considerably allowing us to identify
the β1 coefficient.
If ownership of capital is fully diversified, we expect to find an estimated significant β1 of about
0.33. If we find a coefficient insignificant or significant but smaller than this, we may ask if some
countries are better integrated than others or if during certain periods the financial integration is
higher. For example, are capital flows for the countries with higher levels of portfolio diversification
or NFA positions respond to growth shocks differently? To test this question we run the following
regression:
∆(−CA)i = β0 + β1 ∆ log GDPi + γ (Zi − Z) ∆ log GDPi + ei ,
where Zi refers to an “interaction” variable that measures the average level of portfolio diversification or the change in portfolio diversification.44 If γ is positive and significant we interpret this as
showing that capital markets are more integrated between countries with higher degrees of portfolio
diversification, since the more positive relation between the capital flows and growth indicates a
44
The interaction variable Z is demeaned in order to keep the interpretation of the γ coefficient unchanged as
explained by Ozer-Balli and Sorensen (2007).
23
higher degree of capital market integration. We include the non-interacted effect of Z because the
non-interacted effect might have a direct effect if the Z-term is left out this could spuriously be
captured by the interaction term.
We find no evidence of a pure growth effect. The we tried many candidate “Z”s that will serve as
a proxy for the extent of portfolio diversification. A variable of particular interest is the extent of risk
sharing defined as the percentage of idiosyncratic risk to a country i’s GDP insured through factor
g − gni
g + εit .
git = νi + βi gdp
income flows. It is estimated by the individual-country regression gdp
it
it
g represents the idiosyncratic part of GDP calculated as GDP per capita growth
The variable gdp
it
g ≡ ∆ log gdpit −∆ log gdpWorld . The
rate of country-i in period t minus the world GDP growth: gdp
it
t
git is defined similarly using GNI data. We need to subtract the aggregate component
variable gni
from the growth rates because the world fluctuations (or systematic risk) cannot be insured. Full
risk sharing implies that idiosyncratic shocks to GDP and GNI are uncorrelated and βbi = 100
percent.
Table 8 shows the results that once we interacted the growth with the change in risk sharing
where the change is defined in a similar way to the change in capital flows we find a significant
effect, as shown for all three samples. The magnitude of this effect is such that for the mean level
of the change in risk sharing the response of capital flows will be 0.0035 times the growth for the
developing country sample and 0.0033 for the world sample. Hence it is 100 times smaller than
the predicted effects by the small open economy model with full diversification. Of course, this
result can entirely be driven by reverse causality or the measurement error. Hence we run the same
regressions using the lagged values in the RHS as shown in table 9. Now the implied magnitudes are
0.0023, 0.0013, and 0.0002 for the developing, developing minus HIPC and for the world samples
respectively. Hence they are even smaller as expected.
The risk sharing variable is an estimated regressor and hence there will be attenuation bias.
Thus, we investigate the role of change in NFA next as shown in tables 10 and 11, where table
11 uses lagged values. Here the sign is negative since a negative NFA multiplied by a negative
coefficient will imply a positive effect of growth on capital flows. Thus, in a similar way to the
original Kraay-Ventura rule, debtor countries respond positively to productivity shocks and hence
receive more flows and run current account deficits. The implied net growth effects from table 10
24
is 0.02, 0.15, and 0.01 for the three samples respectively and 0.01, 0.1, and 0.01 from table 11 that
uses lagged values for the three samples respectively. Although these values are still below 0.3 that
is predicted by the model, nevertheless they are getting close and hence indicate the important role
of portfolio diversification in the response of capital flows to productivity shocks.45
Finally, table 12 investigates the determinants of inflows and outflows. We run many regressions
and report only the significant results along with other variables that are found significant elsewhere
but not here. We define inflows to include inflows equity (foreign direct investment an portfolio
flow) and debt flows. Capital outflows include equity and debt flows and net errors and omissions,
which in many developing countries represent unrecorded outflows. We present result for the broad
sample of developing countries and the world sample and also for only equity inflows and equity
outflows in the last two columns. Overall we find that in terms of foreign investments, and in
particular equity inflows, institutional quality is the main driver, which re-establishes our results
from Alfaro, Kalemli-Ozcan, and Volosovych (2007, 2008). Risk sharing turns out to be important
both for inflows and outflows but not for the equity component.
5
Conclusion
In this paper we analyzed patterns of international investments across countries. Earlier attempts
to fit neoclassical models have had limited success. Data limitations, in particular for developing
countries, and the use of negative current account—plagued by government behavior—may account
for this failure.
In addition, the current account balance hides substantial differences in gross flows. The surge of
inflows and outflows has renewed interest in models that emphasize portfolio choice. However these
general equilibrium models with many different predictions are hard to carry to the data. In this
paper, we tried to infer and test the reduced form predictions of these models guiding our empirical
exercise also with the partial equilibrium models with similar reduced form predictions. Data issues
notwithstanding, our results that focus on the long run changes suggest that there is a positive
relation between persistent productivity shocks, proxied by growth, and net capital flows once we
45
Note that these values are also close to what is found for Europe by Ekinci, Kalemli-Ozcan and Sorensen (2008),
that is a value of 0.15.
25
control the extent of portfolio diversification. The magnitude of such flows, however, is much smaller
than what is implied by the neoclassical benchmark with full diversification of capital income.
Focusing on the levels of inflows and outflows we find a strong association between both institutional
quality and international portfolio diversification and capital inflows and outflows. Once we focus
only on the equity flows though, as in our previous work, we find that main determinant of inflows
of private foreign capital is institutional quality.
26
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Noland, M. 2007. “South Korea’s Experience with International Capital Flows,” in S. Edwards,
editor, Capital Controls and Capital Flows in Emerging Economies: Policies, Practices and
Consequences, forthcoming.
Obstfeld, M., 2004. “External Adjustment,” Bernhard Harms Lecture delivered at the Kiel Institute for World Economics.
Obstfeld, M., 2005. “International Capital Mobility in the 1990s,” in Peter B. Kenen, ed. Understanding Interdependence: The Macroeconomics of the Open Economy, NJ: Princeton University Press, 1995.
Obstfeld, M., 1986. “Capital Mobility in the World Economy: Theory and Measurement,” CarnegieRochester Conference Series on Public Policy 24, 55–103.
Obstfeld, M. and Kenneth R., 2000, “The Six Major Puzzles in International Macroeconomics: Is
There a Common Cause?” in NBER Macroeconomics Annual 2000, editors: B. Bernanke
and K. Rogoff. The MIT Press, Cambridge.
Obstfeld, M. and K. Rogoff, 1996. Foundations of International Macroeconomics. Cambridge, Mass.:
MIT Press.
Obstfeld, M. and K. Rogoff, 1995. “The Intertemporal Approach to the Current,” in Gene Grossman and Kenneth Rogoff, eds. Handbook of International Economics 3, Amsterdam: NorthHolland, Elsevior Press.
Obstfeld, M. and A. M. Taylor, 2004. Global Capital Markets Integration, Crisis, and Growth. Cambridge, Cambridge University Press.
Portes, R. and H. Rey, 2005. “The Determinants of Cross-Border Equity Transaction Flows,” Journal of International Economics 65, 269-296.
Prasad, E., R. G. Rajan and A. Subramanian, 2007. “Patterns of International Capital Flows and
Their Implications for Economic Development,” forthcoming.
Reinhart, C. and K. Rogoff, 2004. “Serial Default and the “Paradox” of Rich to Poor Capital
Flows,” American Economic Review Papers and Proceedings 94, 52–58.
Tille, C. and Van Wincoop, E., 2007. “International Capital Flows,” NBER Working Paper 12856.
Tille, C. and Van Wincoop, E., 2008. “New Perspective on ‘The New Rule’ of the Current Account,” mimeo.
Tornell, A. and A. Velasco, 1992. “Why Does Capital Flow from Poor to Rich Countries? The
Tragedy of the Commons and Economic Growth,” Journal of Political Economy 100, 1208–
1231.
29
Ventura, J. 2003. “Towards a Theory of Current Accounts,” The World Economy, April.
Wei, S.-J., 2000. “Local Corruption and Global Capital Flows,”Brookings Papers in Economic Activity 2, 303–346.
Wei, S.-J. and Y. Wu, 2002. “Negative Alchemy? Corruption, Composition of Capital Flows, and
Currency Crises,” in Preventing Currency Crises in Emerging Markets, editors: S. Edwards
and J. Frankel. The University of Chicago Press, Chicago.
30
A
Data Description
Dependent Variables
All dependent variables expressed as percentage of GDP are calculated by dividing the IFS
flows data in current U.S. dollars by the GDP data in current U.S. dollars from the World Bank’s
WDI, on-line edition.
Negative of the Current Account Balance (IFS line 78ald) consists of the sum of the balance
on goods, services and income, plus current transfers, average over stated period. The IFS data
for current account is net of exceptional financing (IFS line 79ded), which includes any other
transactions undertaken by the authorities to finance the “overall balance,” as an alternative to,
or in conjunction with, the use of reserve assets and the use of the IMF credit and loans from the
IMF.
Total inflows of capital per capita is the sum of inflows of direct (IFS line 78bed), portfolio
investment (IFS line 78bgd), and other investment (IFS line 78bid), which represent flows of capital
into the reporting economy. Portfolio investment flows include transactions with nonresidents in
financial securities of any maturity (such as corporate securities, bonds, notes, and money market
instruments); they consist of equity securities and debt securities. Because a lot of problematic
data for this category, an assumption is made that the missing data represents zero flows. Major
categories of other investment are transactions in currency and deposits, loans, and trade credits—
the flows of debt type.
Total outflows of capital is the sum of outflows of direct (IFS line 78bdd), portfolio investment
(IFS line 78bfd), and other investment (IFS line 78bhd), which represent flows of capital into the
reporting economy. Portfolio investment flows include transactions with nonresidents in financial
securities of any maturity (such as corporate securities, bonds, notes, and money market instruments); they consist of equity securities and debt securities. Because a lot of problematic data for
this category, an assumption is made that the missing data represents zero flows. Similarly to Lane
and Milesi-Ferretti (2001), net errors and omissions are assumed to capture unrecorded capital
flows and reflect changes in the stock of debt assets held abroad (outflows) by domestic residents.
Major categories of other investment are debt-like transactions in currency and deposits, loans, and
31
trade credits.
Explanatory Variables
GDP per capita (level and growth) is in 2000 International dollars at PPP from Penn World
Table, ver.6.2. Initial period is in the year immediately preceding the period over which the
dependent variable is calculated. Growth rate is calculated as log-difference of the GDP per capita
levels from the same source.
Investment (percent of GDP) is calculated from the raw Gross capital formation (outlays on
additions to the fixed assets of the economy plus net changes in the level of inventories) and GDP
data from the World Bank, both in nominal US dollars.
Saving (percent of GDP) is calculated from Gross saving (gross national income less total consumption, plus net transfers) and nominal GDP from the World Bank, both in nominal US dollars.
Government budget balance (percent of GDP) is revenue (including grants) minus expense,
minus net acquisition of nonfinancial assets from the World Bank WDI online database (2008).
Financial deepening is domestic credit to private sector (percent of GDP) through loans, purchases of non-equity securities, and trade credits and other accounts receivable, that establish a
claim for repayment from the World Bank WDI online database (2008).
Net Foreign Assets (NFA) (percent of GDP) ratio is the net external position stock from Lane
and Milesi-Ferretti, Mark II relative to GDP. NFA (positive for creditor countries, negative for
debtor countries) is the total assets minus total liabilities. The categories of assets and liabilities included are direct and portfolio equity, debt (portfolio and other investment), and financial
derivatives. Assets also include Total reserves minus gold.
Foreign equity in portfolio and Foreign debt security in portfolio (percent) are estimates for
industrialized OECD countries from Sorensen et al. (2007).
Average Institutional Quality (the index ranging from 0 to 10) is the sum of all the rating
components from International Country Risk Guide, averaged over the relevant sample period.
The components are investment profile, government stability, internal conflict, external conflict,
no-corruption index, non-militarized politics, protection from religious tensions, law and order,
protection from ethnic tensions, democratic accountability, quality of bureaucracy. A higher score
32
means lower risk. The data comes from the PRS Group (2005).
Average income risk sharing (percent) quantifies the percentage of idiosyncratic risk to a country
i’s GDP insured through factor income flows. It is estimated by the individual-country regression
g − gni
g + εit . The variable gdp
g represents the idiosyncratic part of GDP
git = νi + βi gdp
gdp
it
it
it
calculated as GDP per capita growth rate of country-i in period t minus the world GDP growth:
g ≡ ∆ log gdpit − ∆ log gdpWorld . The variable gni
git is defined similarly using GNI data. We
gdp
it
t
need to subtract the aggregate component from the growth rates because the world fluctuations (or
systematic risk) cannot be insured.46 Full risk sharing implies that idiosyncratic shocks to GDP
and GNI are uncorrelated and βbi = 100 percent.
46
In empirical study it is impossible to calculate the true whole world total GDP and GNI aggregates. The empirical
notion of the world includes 23 high-income OECD countries by the World Bank classification that on average account
for 80% of total GDP of my largest empirical sample.
33
B
Samples
129WW
Albania
Algeria
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Benin
Bolivia
Bosnia & Herzegovina
Botswana
Brazil
Bulgaria
Burkina Faso
Cambodia
Cameroon
Canada
Chad
Chile
China
Colombia
Congo, Rep.
Costa Rica
Cote d’Ivoire
Croatia
Cyprus
Czech Republic
Denmark
Dominican Republic
Ecuador
Egypt, Arab Rep.
El Salvador
Estonia
Ethiopia
Fiji
Finland
France
Gabon
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Haiti
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Rep.
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Rep.
Kyrgyz Republic
Lao PDR
Latvia
Lebanon
Lithuania
Luxembourg
Macedonia, FYR
Madagascar
Malawi
Malaysia
Mali
Malta
Mauritius
Mexico
106DEV
x
x
x
x
79DEV
(106DEV
-HIPC)
x
x
x
x
x
x
x
x
x
x
67DEV*
x
45DEV*
(67DEV*
-HIPC)
22IND
x
84DEVb
x
x
x
x
69DEVb
(84DEVb
-HIPC)
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
51DEVb *
40DEVb *
(51DEVb *
-HIPC)
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
106WWb
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Continued on the next page
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Samples (continued)
129WW
Moldova
Morocco
Mozambique
Namibia
Nepal
Netherlands
New Zealand
Niger
Nigeria
Norway
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Romania
Russian Federation
Rwanda
Saudi Arabia
Senegal
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
Sudan
Swaziland
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Uganda
Ukraine
United Kingdom
United States
Uruguay
Venezuela, RB
Vietnam
Yemen, Rep.
Zambia
106DEV
x
x
x
x
x
79DEV
(106DEV
-HIPC)
x
x
67DEV*
45DEV*
(67DEV*
-HIPC)
x
x
x
22IND
x
84DEVb
x
x
69DEVb
(84DEVb
-HIPC)
x
x
x
x
51DEVb *
40DEVb *
(51DEVb *
-HIPC)
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
106WWb
x
x
x
x
Notes: The table presents main regression samples without outliers. Outliers are described in notes to corresponding regression
tables.
In all the samples we keep the largest number of countries with available data the main variables of interest. They are average
negative of current account balance, average gross domestic investment, average gross saving, and net foreign assets (NFA) (all as
a percentage of GDP), GDP p.c. growth, and the logarithm of the GDP p.c. level. See the definitions of variables in Appendix A.
The following notation is used in this table and throughout this paper. WW is the whole world countries sample. IND is
industrialized OECD countries sample without Luxembourg. DEV is the developing countries sample. DEV-HIPC is DEV
without Heavily Indebted Poor Countries (HIPC) according to the World Bank as of October 2006 (see Arslanalp and Henry,
2005), see the list below. The corresponding samples with the asterisk (*) exclude a subsample of mostly emerging-market
countries in Eastern Europe and some other countries (see the table). The samples with the superscript “b” are “base” samples
including all countries with available data for the main explanatory variables and also for government budget balance and
domestic credit to private sector (both as a percentage of GDP).The samples with asterisk (*) and the superscript “b” are the
common samples formed from the latter two subsamples.
Heavily Indebted Poor Countries (23 counties) are Bolivia, Burkina Faso, Cameroon, Congo Rep., Cote d’Ivoire, Ethiopia,
Gambia, Ghana, Guinea, Guinea-Bissau, Honduras, Madagascar, Malawi, Mali, Mozambique, Niger, Senegal, Sierra Leone,
Sudan, Tanzania, Togo, Uganda, Zambia.
35
x
x
Table 1: Descriptive statistics of the regression variables in the main samples
Period 1991–2004
106DEV
-CA/y
ygr
svg/y
i/y
logyi
nfa/y
mean
3.3
1.6
18.7
22.5
8.2
-49.3
sd
5.5
2.1
9.3
5.2
0.9
52.6
min
-17.0
-4.8
-0.6
10.6
6.0
248.0
max
22.5
8.3
48.2
39.2
10.0
129.6
79DEV
-CA/y
ygr
svg/y
i/y
logyi
nfa/y
mean
2.2
1.9
21.4
23.6
8.5
-35.0
sd
5.6
2.1
8.6
5.0
0.8
44.3
min
-17.0
-2.5
-0.6
14.3
6.0
155.0
max
22.5
8.3
48.2
39.2
10.0
129.6
67DEV*
-CA/y
ygr
svg/y
i/y
logyi
nfa/y
mean
2.1
1.7
19.5
22.0
8.0
-51.7
sd
4.8
2.0
9.7
5.6
0.9
53.8
min
-17.0
-4.8
-0.6
10.6
6.1
248.0
max
17.0
8.3
48.2
39.2
10.0
129.6
45DEV*
-CA/y
ygr
svg/y
i/y
logyi
nfa/y
mean
0.1
2.0
23.2
23.3
8.5
-36.0
sd
4.0
1.8
8.6
5.5
0.7
48.2
min
-17.0
-2.5
11.5
14.3
7.0
105.9
max
4.7
8.3
48.2
39.2
10.0
129.6
129WW
-CA/y
ygr
svg/y
i/y
logyi
nfa/y
mean
2.6
1.7
19.4
22.3
8.5
-42.1
sd
5.5
1.9
8.8
4.9
1.1
53.5
min
-17.0
-4.8
-0.6
10.6
6.0
248.0
max
22.5
8.3
48.2
39.2
10.4
129.6
22IND
-CA/y
ygr
svg/y
i/y
logyi
nfa/y
mean
-0.3
1.9
21.8
21.3
9.9
-13.6
sd
3.9
0.9
4.4
2.5
0.2
40.1
min
-9.1
0.5
15.1
17.0
9.4
-91.5
max
5.3
5.5
31.6
26.5
10.2
110.5
min
-6.8
-2.2
-0.6
15.2
6.0
207.6
max
22.5
7.2
33.5
36.8
9.8
44.4
Memorandum
106DEV–67DEV*
-CA/y
ygr
svg/y
i/y
logyi
nfa/y
mean
5.5
1.6
17.5
23.2
8.4
-45.1
sd
6.0
2.3
8.5
4.5
0.9
50.9
Notes: The following notation is used to define the samples. The list of countries is in Appendix B. In all the samples we
keep the largest number of countries with available data for negative of current account balance -CA/y, GDP p.c. growth ygr,
logarithm of the GDP p.c. level logyi, average gross domestic investment i/y, average gross saving svg/y, and NFA nfa/y. All the
variables are percentages of GDP (except for GDP level and growth) and are sample averages over stated time period (GDP per
capita level is period initial, the year of 1990). DEV is the developing countries sample; DEV-HIPC is DEV without Heavily
Indebted Poor Countries (HIPC) according to the World Bank as of October 2006 (see Arslanalp and Henry, 2005), see the list
below. The corresponding samples with the asterisk (*) exclude a subsample of emerging-market countries in Eastern Europe
and some other countries. WW is the whole world sample. IND is industrialized OECD countries sample without Luxembourg.
See Appendix A for the definitions of variables.
36
Table 2: Descriptive statistics of the regression variables in the main samples
Period 1991–2004
84DEVb
-CA/y
ygr
govb/y
dcred/y
nfa/y
mean
3.0
1.9
-2.3
35.2
-46.6
sd
5.4
2.1
3.9
32.7
48.8
min
-17.0
-4.8
-17.5
3.7
248.0
max
22.5
8.3
13.3
163.4
117.0
69DEVb
-CA/y
ygr
govb/y
dcred/y
nfa/y
mean
2.3
2.1
-1.9
39.4
-34.9
sd
5.5
2.0
3.7
33.9
36.6
min
-17.0
-2.2
-15.2
5.1
105.4
max
22.5
8.3
13.3
163.4
117.0
51DEV*b
-CA/y
ygr
govb/y
dcred/y
nfa/y
mean
1.4
1.9
-2.1
43.2
-47.7
sd
4.1
2.0
4.2
36.1
50.1
min
-17.0
-4.8
-17.5
5.6
248.0
max
9.8
8.3
13.3
163.4
117.0
40DEV*b
-CA/y
ygr
govb/y
dcred/y
nfa/y
mean
0.5
2.2
-1.4
49.8
-36.3
sd
4.0
1.8
3.7
37.3
40.5
min
-17.0
-0.8
-7.0
13.9
105.4
max
4.7
8.3
13.3
163.4
117.0
106WWb
-CA/y
ygr
govb/y
dcred/y
nfa/y
mean
2.2
1.9
-1.8
47.6
-39.1
sd
5.4
1.9
3.9
39.9
50.2
min
-17.0
-4.8
-17.5
3.7
248.0
max
22.5
8.3
13.3
163.4
117.0
22IND-Jap
-CA/y
ygr
govb/y
dcred/y
nfa/y
mean
-0.2
1.9
-0.2
94.6
-15.3
sd
4.0
0.9
3.6
28.6
40.2
min
-9.1
0.5
-5.8
45.7
-91.5
max
5.3
5.5
11.9
161.3
110.5
Notes: The following notation is used to define the samples. The list of countries is in Appendix B. In all the samples we
keep the largest number of countries with available data for negative of current account balance -CA/y, GDP p.c. growth ygr,
logarithm of the GDP p.c. level logyi, average gross domestic investment i/y, average gross saving svg/y, and NFA nfa/y. All
the variables are percentages of GDP (except for GDP level and growth) and are sample averages over stated time period
(GDP per capita level is period initial, the year of 1990). The following notation is used. DEV is the developing countries
sample; DEV-HIPC is DEV without Heavily Indebted Poor Countries (HIPC) according to the World Bank as of October 2006
(see Arslanalp and Henry, 2005), see the list below. The corresponding samples with the asterisk (*) exclude a subsample of
emerging-market countries in Eastern Europe and some other countries. The samples with the superscript “b” are the “base”
samples including all countries with available data for the main explanatory variables listed before and also for government
budget balance govb/y and domestic credit to private sector dcred/y.WW is the whole world sample. IND is industrialized
OECD countries sample without Luxembourg. See Appendix A for the definitions of variables.
37
Table 3: Correlations of the regression variables in the main samples
Period 1991–2004
106DEV
ygr
svg/y
i/y
logyi
nfa/y
-CA/y
-0.0426
-0.7139*
-0.1790*
-0.3809*
-0.5066*
79DEV
ygr
svg/y
i/y
logyi
nfa/y
-CA/y
0.0897
-0.6924*
-0.1021
-0.2250*
-0.3796*
67DEV*
ygr
svg/y
i/y
logyi
nfa/y
-CA/y
-0.2530*
-0.7924*
-0.4103*
-0.5792*
-0.6704*
45DEV*
ygr
svg/y
i/y
logyi
nfa/y
-CA/y
-0.1398
-0.7543*
-0.4824*
-0.2654*
-0.5673*
129WW
ygr
svg/y
i/y
logyi
nfa/y
-CA/y
-0.0541
-0.7279*
-0.1358
-0.4618*
-0.5796*
22IND
ygr
svg/y
i/y
logyi
nfa/y
-CA/y
0.1947
-0.8082*
0.1272
-0.5257*
-0.7251*
ygr
svg/y
i/y
logyi
0.2028*
0.3220*
0.0190
0.2980*
0.7325*
0.5137*
0.5641*
0.3403*
0.3138*
0.5714*
ygr
svg/y
i/y
logyi
0.0870
0.2892*
-0.1353
0.0810
0.7116*
0.2626*
0.4333*
0.1455
0.2359*
0.5210*
ygr
svg/y
i/y
logyi
0.4282*
0.4729*
0.0906
0.3889*
0.8233*
0.5434*
0.5809*
0.3402*
0.3057*
0.5095*
ygr
svg/y
i/y
logyi
0.4617*
0.5542*
-0.0310
0.1550
0.8838*
0.1694
0.4838*
0.1141
0.3072*
0.4738*
ygr
svg/y
i/y
logyi
0.2012*
0.3020*
0.0473
0.2711*
0.6972*
0.4909*
0.5899*
0.2063*
0.2643*
0.5942*
ygr
svg/y
i/y
logyi
-0.1470
-0.0636
-0.5137*
-0.2881
0.4654*
0.2216
0.6706*
-0.3170
0.0880
0.4326*
Memorandum
106DEV–67DEV*
ygr
svg/y
i/y
logyi
nfa/y
-CA/y
0.2241
-0.6285*
0.0914
-0.3037*
-0.3866*
ygr
svg/y
i/y
logyi
-0.1884
0.0532
-0.0836
0.1565
0.5814*
0.5481*
0.5634*
0.3077*
0.3216*
0.6836*
84DEVb
ygr
govb/y
dcred/y
nfa/y
-CA/y
-0.0392
-0.5465*
-0.2590*
-0.5085*
ygr
govb/y
dcred/ys
0.1033
0.3283*
0.4099*
0.1352
0.2806*
0.3105*
69DEVb
ygr
govb/y
dcred/y
nfa/y
-CA/y
0.1219
-0.6260*
-0.1864
-0.4529*
ygr
govb/y
dcred/ys
0.0876
0.2804*
0.1392
0.1094
0.4065*
0.1884
51DEV*b
ygr
govb/y
dcred/y
nfa/y
-CA/y
-0.4230*
-0.6520*
-0.3612*
-0.7262*
ygr
govb/y
dcred/ys
0.2216
0.4272*
0.4887*
0.2623*
0.4097*
0.3055*
40DEV*b
ygr
govb/y
dcred/y
nfa/y
-CA/y
-0.2876*
-0.7461*
-0.2363
-0.7085*
ygr
govb/y
dcred/ys
0.2486
0.4002*
0.1485
0.2223
0.5719*
0.1613
106WWb
ygr
govb/y
dcred/y
nfa/y
-CA/y
-0.0430
-0.5670*
-0.3548*
-0.5901*
ygr
govb/y
dcred/ys
0.1208
0.2364*
0.3500*
0.2163*
0.2964*
0.4280*
22IND-Jpn
ygr
govb/y
dcred/y
nfa/y
-CA/y
0.1708
-0.4596*
-0.1663
-0.7198*
ygr
govb/y
dcred/ys
0.2253
-0.2526
-0.2527
-0.1218
-0.0324
0.4553*
Notes: Asterisk * next to correlation coefficients denotes significance at 10% level. The following notation is used to define
the samples. The list of countries is in Appendix B. In all the samples we keep the largest number of countries with available
data for negative of current account balance -CA/y, GDP p.c. growth ygr, logarithm of the GDP p.c. level logyi, average gross
domestic investment i/y, average gross saving svg/y, and NFA nfa/y. All the variables are percentages of GDP (except for GDP
level and growth) and are sample averages over stated time period (GDP per capita level is period initial, the year of 1990).
The following notation is used. DEV is the developing countries sample; DEV-HIPC is DEV without Heavily Indebted Poor
Countries (HIPC) according to the World Bank as of October 2006 (see Arslanalp and Henry, 2005), see the list below. The
corresponding samples with the asterisk (*) exclude a subsample of emerging-market countries in Eastern Europe and some
other countries. The samples with the superscript “b” are the “base” samples including all countries with available data for
the main explanatory variables listed before and also for government budget balance govb/y and domestic credit to private
sector dcred/yWW is the whole world countries sample. IND is industrialized OECD countries sample without Luxembourg.
See Appendix A for the definitions of variables.
Table 4: Net capital flows, the 1990s–2000s: Benchmark Samples
Dependent variable: Negative of the Current Account (average over 1991–2004)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Sample
DEV
DEV
DEV
DEV
DEV
WW
IND
Average GDP
p.c. growth
–0.11
(0.30)
0.04
(0.33)
0.28
(0.23)
–0.09
(0.28)
–0.08
(0.15)
–0.08
(0.13)
0.14+
(0.09)
0.79***
(0.11)
0.83***
(0.09)
0.92***
(0.05)
–0.76***
(0.06)
–0.78***
(0.05)
–0.94***
(0.04)
–2.24***
(0.54)
0.08
(0.30)
–0.05
(0.21)
–1.54**
(0.60)
0.15
106
0.77
106
0.80
129
0.99
22
–0.19+
(0.13)
Average
investment
Average saving
–0.44***
(0.06)
Log GDP p.c.,
1990
R2
Countries
0.00
106
0.03
106
0.52
106
Notes: The following notation is used to define the samples. The list of countries is in Appendix B. DEV is the
developing countries sample DEV106. WW is the whole world sample, 129WW. IND is industrialized OECD countries
sample without Luxembourg, 22IND. In all the samples we keep the largest number of countries with available data
for negative of current account balance, GDP p.c. growth, logarithm of the GDP p.c. level, average gross domestic
investment, average gross saving, and NFA except for the following outliers in terms of high net flows in DEV sample
Equatorial Guinea (41%) and Nicaragua (25%), if present. All the variables are percentages of GDP (except for GDP
level and growth) and are sample averages over stated time period (GDP per capita level is period initial, the year
of 1990). See Appendix A for the definitions of variables. All regressions include a constant and are estimated by
OLS with White’s correction of heteroskedasticity. Standard errors are in parentheses. *** , **, *, and + denote
significance at 1%, 5%, 10%, and 15% under null that the coefficient is zero. All the variables are sample averages over
stated time period unless stated otherwise. Negative of the Current Account Balance (IMF’s IFS line 78ald) consists
of the sum of the balance on goods, services and income, plus current transfers, average over stated period. IMF IFS
data for Current Account is net of Exceptional Financing (IFS line 79ded), which includes any other transactions
undertaken by the authorities to finance the “overall balance,” as an alternative to, or in conjunction with, the use
of reserve assets and the use of the IMF credit and loans from the IMF. Current Account in current US dollars is
expressed as percentage of GDP in current US dollars. Investment-to-GDP ratio is calculated from the raw Gross
capital formation (outlays on additions to the fixed assets of the economy plus net changes in the level of inventories)
and GDP data from the World Bank, both in nominal US dollars. Saving-to-GDP ratio is calculated from Gross
saving (gross national income less total consumption, plus net transfers) and nominal GDP from the World Bank,
both in nominal US dollars. GDP per capita (level and growth) is at PPP in 2000 International US dollars from
Penn World Table, ver. 6.2. Growth rate is calculated as log-difference of the GDP per capita levels from the same
source.
39
Table 5: Net capital flows, the 1990s–2000s: Commonly Used “Developing” Sub-Sample
Dependent variable: Negative of the Current Account
(1)
(2)
Sample
Average GDP
p.c. growth
(3)
(5)
(6)
DEV
–0.61**
(0.29)
Average
investment
–0.18
(0.41)
0.26
(0.25)
–0.49**
(0.22)
Log GDP p.c.,
1990
0.17
67
–0.05
(0.11)
–0.31
(0.32)
0.58***
(0.21)
0.62***
(0.08)
–0.42***
(0.06)
0.06
67
(7)
0.64
67
(8)
DEV-HIPC
–0.32**
(0.14)
Average saving
R2
Countries
(4)
0.55***
(0.14)
–0.66***
(0.07)
–2.92***
(0.68)
–0.55+
(0.36)
0.38
67
0.82
67
0.24*
(0.13)
–0.41***
(0.07)
–0.68***
(0.10)
–0.59
(0.63)
0.02
45
0.62
45
0.74
45
Notes: The following notation is used to define the samples. The list of countries is in Appendix B. DEV is the
developing countries sample 67DEV*; DEV-HIPC is DEV without Heavily Indebted Poor Countries (HIPC) according
to the World Bank as of October 2006 (see Arslanalp and Henry, 2005), 45DEV*. In all the samples we keep the
largest number of countries with available data for negative of current account balance, GDP p.c. growth, logarithm
of the GDP p.c. level, average gross domestic investment, average gross saving, and NFA. All the variables are
percentages of GDP (except for GDP level and growth) and are sample averages over 1991–2004 in contemporaneous
specification and over 1991–1997 in lagged specification (negative CA is over 1998–2004 in lagged specification). GDP
per capita level is period initial, the year of 1990 everywhere. See Appendix A for the definitions of variables. All
regressions include a constant and are estimated by OLS with White’s correction of heteroskedasticity. Standard
errors are in parentheses. *** , **, *, and + denote significance at 1%, 5%, 10%, and 15% under null that the
coefficient is zero. All the variables are sample averages over stated time period unless stated otherwise. Negative
of the Current Account Balance (IMF’s IFS line 78ald) consists of the sum of the balance on goods, services and
income, plus current transfers, average over stated period. IMF IFS data for Current Account is net of Exceptional
Financing (IFS line 79ded), which includes any other transactions undertaken by the authorities to finance the “overall
balance,” as an alternative to, or in conjunction with, the use of reserve assets and the use of the IMF credit and
loans from the IMF. Current Account in current US dollars is expressed as percentage of GDP in current US dollars.
Investment is investment-to-GDP ratio, calculated from the raw Gross capital formation (outlays on additions to the
fixed assets of the economy plus net changes in the level of inventories) and GDP data from the World Bank, both
in nominal US dollars. Saving is saving-to-GDP ratio, calculated from Gross saving (gross national income less total
consumption, plus net transfers) and nominal GDP from the World Bank, both in nominal US dollars. GDP per
capita (level and growth) is at PPP in 2000 International US dollars from Penn World Table, ver. 6.2. Growth rate
is calculated as log-difference of the GDP per capita levels from the same source.
40
Table 6: Net capital flows, the 1990s–2000s: Proxies for Saving, Benchmark Samples
Dependent variable: Negative of the Current Account
(average over 1991–2004)
(1)
(2)
(3)
Sample
DEV
IND
WW
Average GDP p.c.
growth
0.59**
(0.29)
0.50
(0.41)
0.57**
(0.27)
–0.59***
(0.17)
–0.55***
(0.14)
–0.58***
(0.13)
–0.02*
(0.01)
0.02*
(0.01)
–0.01+
(0.01)
–0.05***
(0.01)
–0.08***
(0.01)
–0.05***
(0.01)
0.48
84
0.83
22
0.56
106
Average government
budget balance
Average financial
deepening
Average NFA/GDP
(percent)
R2
Countries
Notes: The following notation is used to define the samples. The list of countries is in Appendix B. DEV in column
is the developing countries sample 84DEVb ; WW is whole world sample, 106WWb ; IND is industrialized countries
sample without Luxembourg and Japan (missing data for budget balance in the source used), 22IND-Jpn. In all the
samples we keep the largest number of countries with available data for negative of current account balance, GDP p.c.
growth, logarithm of the GDP p.c. level, average gross domestic investment, average gross saving, and NFA except
for the following outliers in terms of high net flows in DEV sample Equatorial Guinea (41%) and Nicaragua (25%), if
present. All the variables are percentages of GDP (except for GDP level and growth) and are sample averages over
stated time period (GDP per capita level is period initial, the year of 1990). See Appendix A for the definitions of
variables. All regressions include a constant and are estimated by OLS with White’s correction of heteroskedasticity.
Standard errors are in parentheses. *** , **, *, and + denote significance at 1%, 5%, 10%, and 15% under null that the
coefficient is zero. All the variables are sample averages over stated time period unless stated otherwise. Negative of
the Current Account Balance (IMF’s IFS line 78ald) consists of the sum of the balance on goods, services and income,
plus current transfers, average over stated period. IMF IFS data for Current Account is net of Exceptional Financing
(IFS line 79ded), which includes any other transactions undertaken by the authorities to finance the “overall balance,”
as an alternative to, or in conjunction with, the use of reserve assets and the use of the IMF credit and loans from the
IMF. Current Account in current US dollars is expressed as percentage of GDP in current US dollars. GDP per capita
growth is at PPP in 2000 International US dollars from Penn World Table, ver. 6.2. Growth rate is calculated as
log-difference of the GDP per capita levels. Government budget balance, financial deepening, and dependency ratio
are from the World Bank WDI online database (2008). Government budget balance is revenue (including grants)
minus expense, minus net acquisition of nonfinancial assets. Financial deepening is domestic credit to private sector
through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a
claim for repayment. NFA to GDP ratio is the net external position stock from Lane and Milesi-Ferretti, Mark II
relative to GDP. NFA (positive for creditor countries, negative for debtor countries) is the total assets minus total
liabilities. The categories of assets and liabilities included are direct and portfolio equity, debt (portfolio and other
investment), and financial derivatives. Assets also include Total reserves minus gold.
41
Table 7: Net capital flows, the 1990s–2000s: Kraay-Ventura Rule, Benchmarks Samples
Dependent variable: Negative of the Current Account
(1)
(2)
(3)
(4)
(5)
(6)
DEV
1991-97
DEV-HIPC
1991-97
DEV
1991-04
DEV-HIPC
1991-04
IND
1991-97
IND
1991-04
Average GDP p.c. growth
0.25
(0.29)
0.25
(0.32)
–0.11
(0.31)
0.42
(0.37)
–0.30
(0.29)
0.25
(0.71)
NFA/Total Wealthkv ×
Gross saving/GDP
–0.36*
(0.21)
–0.19
(0.23)
Sample
Time period
NFA/GDPa ×
Gross saving/GDP
–0.87***
(0.11)
–0.18
(0.14)
–0.39***
(0.08)
0.05
106
0.17
79
–0.65**
(0.25)
Average gross saving/GDP
(percent)
Average NFA/GDP
(percent)
R2
Countries
0.09
33
0.05
29
0.67
21
0.21
21
Notes: The following notation is used to define the samples. The list of countries is in Appendix B. DEV is
the developing countries sample 106DEV; DEV-HIPC is DEV without Heavily Indebted Poor Countries (HIPC)
according to the World Bank as of October 2006 (see Arslanalp and Henry, 2005), 79DEV. IND is industrialized
OECD countries sample without Luxembourg, 22IND, and also without Iceland with missing NFA/Total Wealthkv .
In all the samples we keep the largest number of countries with available data for variables in this table and the
negative of current account balance, GDP p.c. growth, logarithm of the GDP p.c. level, average gross domestic
investment, average gross saving, and NFA except for the following outliers in terms of high net flows in DEV sample
Equatorial Guinea (41%) and Nicaragua (25%), if present. In addition, the following countries are outliers in columns
(1) and (2): Congo (with high net flows and large negative NFA/Wealth) and Singapore (with large saving/GDP).
All the variables are sample averages calculated over the time period indicated in the table. See Appendix A for
the definitions of variables. All regressions include a constant and are estimated by OLS with White’s correction of
heteroskedasticity. Standard errors are in parentheses. *** , **, *, and + denote significance at 1%, 5%, 10%, and
15% under null that the coefficient is zero. All the variables are sample averages over stated time period unless stated
otherwise. Negative of the Current Account Balance (IMF’s IFS line 78ald) consists of the sum of the balance on
goods, services and income, plus current transfers, average over stated period. IMF IFS data for Current Account is
net of Exceptional Financing (IFS line 79ded), which includes any other transactions undertaken by the authorities
to finance the “overall balance,” as an alternative to, or in conjunction with, the use of reserve assets and the use of
the IMF credit and loans from the IMF. Current Account in current US dollars is expressed as percentage of GDP
in current US dollars.GDP per capita growth is at PPP in 2000 International US dollars from Penn World Table,
ver. 6.2. Growth rate is calculated as log-difference of the GDP per capita levels. Net Foreign Assets (NFA)/Total
Wealthkv over 1991–97 is calculated based on the data from Kraay, Loayza, Serven and Ventura (2000) (KLSV). NFA
is Domestic claims on foreign capital minus Foreign claims on domestic capital plus lending by domestic residents to
foreigners minus Borrowing by domestic residents from foreigners; components of NFA are in constant 1990 US dollars
from Balance of Payments statistics by the IMF. Wealth is the sum of NFA and Domestic capital stock (includes gold
reserves) in constant 1990 US dollars at PPP (based on Penn World Table (PWT), Version 5.6 data). NFA/GDPa is
authors own calculations. NFA is the stock of Total foreign assets minus stock of Total foreign liabilities from Lane
and Milesi-Ferretti (2007) “Mark II” database in constant 2000 US dollars. Saving-to-GDP ratio is calculated from
Gross saving (gross national income less total consumption, plus net transfers) and nominal GDP from the World
Bank, both in nominal US dollars.
†
Variables in the interaction term are cross-sectionally demeaned; the coefficient is multiplied by 100.
42
Table 8: Changes in Net capital flows, the 1990s–2000s:
Dependent variable: Change in negative of the Current Account
between 1998–2004 and 1991–1997
(1)
(2)
(3)
(4)
(5)
(6)
Sample
DEV
DEV-HIPC
WW
DEV
DEV-HIPC
WW
Average GDP p.c.
growth
0.46
(0.51)
0.15
(0.34)
0.49
(0.49)
0.53
(0.44)
0.20
(0.44)
0.53
(0.41)
Change in average risk
sharing
0.01
(0.02)
0.02
(0.02)
0.01
(0.01)
0.01
(0.02)
0.02
(0.02)
0.01
(0.01)
2.70*
(1.54)
0.50
(1.46)
2.77**
(1.51)
0.14
72
0.02
55
0.13
91
GDP p.c. growth ×
change in avg. risk sharing†
R2
Countries
0.03
72
0.01
55
0.03
91
Notes: In all the samples we keep the largest number of countries with available data for the variables in this table except for
the following outliers, if present, in terms of high estimated risk sharing Armenia, Burkina Faso, low estimated risk sharing
Swaziland, large negative NFA/GDP Congo, Niger (also high risk sharing). The following notation is used to define the
samples. The list of countries is in Appendix B. DEV is the developing countries sample 106DEV; DEV-HIPC is DEV without
Heavily Indebted Poor Countries (HIPC) according to the World Bank as of October 2006 (see Arslanalp and Henry, 2005),
79DEV. WW is the whole world sample, 129WW. All the variables are sample averages over the time period 1991–2004. See
Appendix A for the definitions of variables. All regressions include a constant and are estimated by OLS with White’s correction
of heteroskedasticity. *** , **, *, and + denote significance at 1%, 5%, 10%, and 15% under null that the coefficient is zero.
All the variables are sample averages over stated time period unless stated otherwise. Negative of the Current Account Balance
(IMF’s IFS line 78ald) consists of the sum of the balance on goods, services and income, plus current transfers, average over
stated period. IMF IFS data for Current Account is net of Exceptional Financing (IFS line 79ded), which includes any other
transactions undertaken by the authorities to finance the “overall balance,” as an alternative to, or in conjunction with, the
use of reserve assets and the use of the IMF credit and loans from the IMF. Current Account in current US dollars is expressed
as percentage of GDP in current US dollars.GDP per capita growth is at PPP in 2000 International US dollars from Penn
World Table, ver. 6.2. Growth rate is calculated as log-difference of the GDP per capita levels. Risk sharing is the individual
country’s percentage of the idiosyncratic risk to GDP smoothed through factor income flows estimated by country regressions
as described in Appendix A.
† Variables in the interaction term are cross-sectionally demeaned; the coefficient is multiplied by 100.
43
Table 9: Changes in Net capital flows, the 1990s–2000s:
Dependent variable: Change in negative of the Current Account
between 1998–2004 and 1991–1997
(1)
(2)
(3)
(4)
(5)
(6)
Sample
DEV
DEV-HIPC
WW
DEV
DEV-HIPC
WW
Lagged average GDP p.c.
growth
–0.10
(0.20)
–0.14
(0.20)
–0.08
(0.20)
–0.12
(0.18)
–0.17
(0.17)
–0.13
(0.17)
Change in average
risk sharing
0.01
(0.02)
0.01
(0.02)
0.01
(0.01)
0.01
(0.02)
0.01
(0.02)
0.01
(0.03)
1.72***
(0.62)
1.07**
(0.50)
1.77***
(0.60)
0.10
71
0.07
54
0.09
90
Lagged GDP p.c. growth ×
change in avg. risk sharing†
R2
Countries
0.01
71
0.02
54
0.01
90
Notes: In all the samples we keep the largest number of countries with available data for the variables in this table except
for the following outliers, if present, in terms of low estimated risk sharing Congo (also large negative change in –CA/GDP),
Russia. The following notation is used to define the samples. The list of countries is in Appendix B. DEV is the developing
countries sample 106DEV; DEV-HIPC is DEV without Heavily Indebted Poor Countries (HIPC) according to the World Bank
as of October 2006 (see Arslanalp and Henry, 2005), 79DEV. WW is the whole world sample, 129WW. All the variables are
sample averages over the time period 1991–1997. See Appendix A for the definitions of variables. All regressions include a
constant and are estimated by OLS with White’s correction of heteroskedasticity. *** , **, *, and + denote significance at
1%, 5%, 10%, and 15% under null that the coefficient is zero. All the variables are sample averages over stated time period
unless stated otherwise. Negative of the Current Account Balance (IMF’s IFS line 78ald) consists of the sum of the balance
on goods, services and income, plus current transfers, average over stated period. IMF IFS data for Current Account is net
of Exceptional Financing (IFS line 79ded), which includes any other transactions undertaken by the authorities to finance the
“overall balance,” as an alternative to, or in conjunction with, the use of reserve assets and the use of the IMF credit and loans
from the IMF. Current Account in current US dollars is expressed as percentage of GDP in current US dollars.GDP per capita
growth is at PPP in 2000 International US dollars from Penn World Table, ver. 6.2. Growth rate is calculated as log-difference
of the GDP per capita levels. Risk sharing is the individual country’s percentage of the idiosyncratic risk to GDP smoothed
through factor income flows estimated by country regressions as described in Appendix A.
† Variables in the interaction term are lagged and cross-sectionally demeaned; the coefficient is multiplied by 100.
44
Table 10: Changes in Net capital flows, the 1990s–2000s:
Dependent variable: Change in negative of the Current Account
between 1998–2004 and 1991–1997
(1)
(2)
(3)
(4)
(5)
(6)
Sample
DEV
DEV-HIPC
WW
DEV
DEV-HIPC
WW
Average GDP p.c.
growth
0.42
(0.45)
0.02
(0.27)
0.46
(0.43)
0.34
(0.33)
0.12
(0.29)
0.39
(0.32)
Change in NFA/Total Wealtha
–0.00
(0.01)
0.01*
(0.01)
–0.00
(0.01)
0.00
(0.01)
–0.02
(0.02)
0.00
(0.01)
–1.20**
(0.51)
–3.03
(2.23)
–1.21**
(0.51)
0.16
93
0.03
70
0.15
114
GDP p.c. growth ×
Change NFA/Total Wealtha†
R2
Countries
0.03
93
0.01
70
0.03
114
Notes: In all the samples we keep the largest number of countries with available data for the variables in this table except for
the following outliers, if present, (in Panel A) in terms of high estimated risk sharing Armenia, Burkina Faso, low estimated
risk sharing Swaziland, large negative NFA/GDP Congo, Niger (also high risk sharing); (in Panel D) large negative change in
NFA/Wealth in Uganda, Zambia. The following notation is used to define the samples. The list of countries is in Appendix B.
DEV is the developing countries sample 106DEV; DEV-HIPC is DEV without Heavily Indebted Poor Countries (HIPC) according to the World Bank as of October 2006 (see Arslanalp and Henry, 2005), 79DEV. WW is the whole world sample,
129WW. All the variables are sample averages over the time period 1991–2004. See Appendix A for the definitions of variables.
All regressions include a constant and are estimated by OLS with White’s correction of heteroskedasticity. *** , **, *, and
+ denote significance at 1%, 5%, 10%, and 15% under null that the coefficient is zero. All the variables are sample averages
over stated time period unless stated otherwise. Negative of the Current Account Balance (IMF’s IFS line 78ald) consists of
the sum of the balance on goods, services and income, plus current transfers, average over stated period. IMF IFS data for
Current Account is net of Exceptional Financing (IFS line 79ded), which includes any other transactions undertaken by the
authorities to finance the “overall balance,” as an alternative to, or in conjunction with, the use of reserve assets and the use of
the IMF credit and loans from the IMF. Current Account in current US dollars is expressed as percentage of GDP in current
US dollars.GDP per capita growth is at PPP in 2000 International US dollars from Penn World Table, ver. 6.2. Growth rate is
calculated as log-difference of the GDP per capita levels. Risk sharing is the individual country’s percentage of the idiosyncratic
risk to GDP smoothed through factor income flows estimated by country regressions as described in Appendix A. Net Foreign
Assets (NFA)/Total Wealthkv over 1991–97 is calculated based on the data from Kraay, Loayza, Serven and Ventura (2000)
(KLSV). NFA is Domestic claims on foreign capital minus Foreign claims on domestic capital plus lending by domestic residents
to foreigners minus Borrowing by domestic residents from foreigners; components of NFA are in constant 1990 US dollars from
Balance of Payments statistics by the IMF. Wealth is the sum of NFA and Domestic capital stock (includes gold reserves) in
constant 1990 US dollars at PPP (based on Penn World Table (PWT), Version 5.6 data).
† Variables in the interaction term are cross-sectionally demeaned; the coefficient is multiplied by 100.
45
Table 11: Changes in Net capital flows, the 1990s–2000s:
Dependent variable: Change in negative of the Current Account
between 1998–2004 and 1991–1997
(1)
(2)
(3)
(4)
(5)
(6)
Sample
DEV
DEV-HIPC
WW
DEV
DEV-HIPC
WW
Lagged average GDP p.c.
growth
–0.12
(0.17)
–0.18
(0.16)
–0.08
(0.16)
–0.11
(0.16)
–0.08
(0.16)
–0.08
(0.16)
Change in NFA/Total Wealtha
–0.00
(0.01)
0.01**
(0.00)
–0.00
(0.01)
0.00
(0.01)
–0.01
(0.01)
0.00
(0.01)
–0.50**
(0.22)
–1.86*
(0.95)
–0.51**
(0.21)
0.07
93
0.06
70
0.06
114
Lagged GDP p.c. growth ×
Change NFA/Total Wealtha†
R2
Countries
0.01
93
0.03
70
0.00
114
Notes: In all the samples we keep the largest number of countries with available data for the variables in this table except
for the following outliers, if present, (in Panel A) in terms of low estimated risk sharing Congo (also large negative change
in –CA/GDP), Russia; (in Panel B) low lagged growth and large positive change in risk sharing in Ukraine; (in Panel D)
large negative change in NFA/Wealth in Uganda, Zambia. The following notation is used to define the samples. The list of
countries is in Appendix B. DEV is the developing countries sample 106DEV; DEV-HIPC is DEV without Heavily Indebted
Poor Countries (HIPC) according to the World Bank as of October 2006 (see Arslanalp and Henry, 2005), 79DEV. WW is the
whole world sample, 129WW. Lagged variables are sample averages over the time period 1991–1997; changes are from 1991–97
to 1998–2004. See Appendix A for the definitions of variables. All regressions include a constant and are estimated by OLS
with White’s correction of heteroskedasticity. *** , **, *, and + denote significance at 1%, 5%, 10%, and 15% under null that
the coefficient is zero. All the variables are sample averages over stated time period unless stated otherwise. Negative of the
Current Account Balance (IMF’s IFS line 78ald) consists of the sum of the balance on goods, services and income, plus current
transfers, average over stated period. IMF IFS data for Current Account is net of Exceptional Financing (IFS line 79ded),
which includes any other transactions undertaken by the authorities to finance the “overall balance,” as an alternative to, or in
conjunction with, the use of reserve assets and the use of the IMF credit and loans from the IMF. Current Account in current
US dollars is expressed as percentage of GDP in current US dollars.GDP per capita growth is at PPP in 2000 International
US dollars from Penn World Table, ver. 6.2. Growth rate is calculated as log-difference of the GDP per capita levels. Risk
sharing is the individual country’s percentage of the idiosyncratic risk to GDP smoothed through factor income flows estimated
by country regressions as described in Appendix A. Net Foreign Assets (NFA)/Total Wealthkv over 1991–97 is calculated
based on the data from Kraay, Loayza, Serven and Ventura (2000) (KLSV). NFA is Domestic claims on foreign capital minus
Foreign claims on domestic capital plus lending by domestic residents to foreigners minus Borrowing by domestic residents from
foreigners; components of NFA are in constant 1990 US dollars from Balance of Payments statistics by the IMF. Wealth is the
sum of NFA and Domestic capital stock (includes gold reserves) in constant 1990 US dollars at PPP (based on Penn World
Table (PWT), Version 5.6 data). Net Foreign Assets (NFA)/Total Wealtha is authors own calculations. NFA is the stock of
Total foreign assets minus stock of Total foreign liabilities from Lane and Milesi-Ferretti (2007) “Mark II” database in constant
2000 US dollars. Total Wealth is Domestic capital stock plus NFA from from Lane and Milesi-Ferretti (2007). Domestic capital
stock is estimated by perpetual inventory method as in Caselli (1995) based on the most recent PWT, Version 6.2 assuming
depreciation of 6%. Capital stock in 2000 International dollars at PPP is recalculated to constant 2000 US dollars using Price
Level of Investment from PWT6.2.
† Variables in the interaction term are lagged and cross-sectionally demeaned; the coefficient is multiplied by 100.
46
Table 12: Total capital inflows and outflows, the 1990s–2000s: Benchmark Samples
Dependent variable: As percentage of GDP (average over 1998–2004)
(1)
(2)
(3)
(4)
(5)
(6)
Inflows
Inflows
Outflows
Outflows
Equity
Inflows
Equity
Outflows
Sample
DEV
WW
DEV
WW
DEV
DEV
Lagged Average GDP p.c.
growth
–0.21
(0.24)
–0.29
(0.25)
–0.08
(0.20)
–0.16
(0.21)
–0.15
(0.11)
–0.06
(0.12)
Lagged Average NFA/GDP
0.05***
(0.02)
0.05***
(0.02)
0.02
(0.03)
0.02
(0.03)
0.01
(0.01)
0.01
(0.01)
Lagged Average institutional
quality (index)
1.34+
(0.83)
2.67***
(0.63)
1.72**
(0.84)
3.15***
(0.70)
1.21***
(0.27)
0.51*
(0.30)
Lagged Average
risk sharing
0.09**
(0.04)
0.08**
(0.04)
0.10**
(0.05)
0.08*
(0.05)
0.01
(0.03)
0.03+
(0.02)
0.26
72
0.36
91
0.22
53
0.33
72
0.15
72
0.20
53
Dependent variable
R2
Countries
Notes: In all the samples we keep the largest number of countries with available data for the variables in this table and for
negative of current account balance, GDP p.c. growth, logarithm of the GDP p.c. level, average gross domestic investment,
average gross saving, and NFA except for the following outliers in terms of high net flows in DEV sample Equatorial Guinea
(41%) and Nicaragua (25%), in terms of high total (equity) inflows Ireland, Belgium, Azerbaijan (but low lagged growth), in
terms of high total (equity) outflows Ireland, Belgium, Hong Kong, and Sudan with very poor inst. quality. The following
notation is used to define the samples. The list of countries is in Appendix B. DEV is the developing countries sample DEV106
with non-missing data for all variables in his table and without Ethiopia with missing inflows data. WW is whole world sample
129WW with non-missing data for all variables in his table and without Ethiopia with missing inflows data. All the explanatory
variables are sample averages over 1991–97 time period. See Appendix A for the definitions of variables. All regressions include
a constant and are estimated by OLS with White’s correction of heteroskedasticity. Standard errors are in parentheses. ***
, **, *, and + denote significance at 1%, 5%, , 10%, and 15% under null that the coefficient is zero. All the variables are
sample averages over stated time period unless stated otherwise. Total inflows of capital is the sum of inflows of direct (IFS
line 78bed), portfolio investment (IFS line 78bgd), and other investment (IFS line 78bid), which represent flows of capital into
the reporting economy. Portfolio investment flows include transactions with nonresidents in financial securities of any maturity
(such as corporate securities, bonds, notes, and money market instruments); they consist of equity securities and debt securities.
Because a lot of problematic data for this category, an assumption is made that the missing data represents zero flows. Major
categories of other investment are transactions in currency and deposits, loans, and trade credits—the flows of debt type.
Inflows in current U.S. dollars are expressed as percentage of GDP in current U.S. dollars. GDP per capita growth is at PPP
in 2000 International US dollars from Penn World Table, ver. 6.2. Growth rate is calculated as log-difference of the GDP per
capita levels. NFA to GDP ratio is the net external position stock from Lane and Milesi-Ferretti, Mark II relative to GDP.
Institutional quality index (composite) from International Country Risk Guide, PRS Group (2005). Income risk sharing is the
individual country’s percentage of the idiosyncratic risk to GDP smoothed through factor income flows estimated by country
regressions as described in Appendix A.
47