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8/2/2019 An Evaluation of the Conventional Wisdom on Capital Flow Volatility_ FDI Inter-flow Correlation and Financial Account
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AN EVALUATION OF THE CONVENTIONAL WISDOM
ON CAPITAL FLOW VOLATILITY: FDI INTER-FLOW CORRELATION
AND FINANCIAL ACCOUNT VOLATILITY
by
José Ramón Perea
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOLUNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of theRequirements for the Degree
MASTER OF ARTS(ECONOMICS)
August 2006
Copyright 2006 José Ramón Perea
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UMI Number: 1438533
1438533
2007
UMI Microform
Copyright
All rights reserved. This microform edition is protected againstunauthorized copying under Title 17, United States Code.
ProQuest Information and Learning Company300 North Zeeb Road
P.O. Box 1346Ann Arbor, MI 48106-1346
by ProQuest Information and Learning Company.
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ii DEDICATION
To my mother and sisters, for their unconditional love and support.
And to Alex, Icíar, and Martín, for the future lies in them.
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iii ACKNOWLEDGEMENTS
I thank my advisor Dr. Jeffrey Nugent for inspiring this research, and for his
constant encouragement and guidance. Working with him has truly been one of the
most rewarding experiences of my life.
I also thank Dr. Peter Rosendorff and Dr. Aris Protopapadakis, for their enriching
feedback and suggestions.
I would also like to thank Dr. Carol Wise, for her mentoring and help during these
years, which has facilitated enormously the progress of my dissertation, and my life
as a graduate student.
Finally, I thank family and friends for being the source of my motivation throughout
this process.
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iv CONTENTS
DEDICATION ii
ACKNOWLEDGEMENTS iii
LIST OF TABLES v
LIST OF FIGURES vi
ABSTRACT vii
1. Introduction 1
2. Stylized Facts of Capital Flows to Developing Nations 2
3. The Impact of FDI over the Host Economy 13
4. Why the Concern? The Consequences of Volatile Capital Flows 22
4.1. Growth Effects of Macroeconomic Volatility 25
5. Building the Conventional Wisdom on Capital Flow Volatility 30
5.1. Empirical Literature 31
5.2. Theoretical Reasons for FDI Stability 335.3. Counterexamples 38
5.4. Policy Implications 48
6. Study Scope 52
6.1. Data Sources and Variables 556.2. Measures of Volatility 63
7. Empirical Analysis 67
7.1. Estimation Results 777.2. Robustness 86
8. Conclusion 107
BIBLIOGRAPHY 110
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v LIST OF TABLES
Table 1: Main Variables 59
Table 2: Panel Estimation 83
Table 3: Heteroscedasticity-consistent Estimation and FGLS Estimation 91
Table 4: Endogeneity Tests 95
Table 5: Two-Stage Least Squares Estimation 97
Table 6: Variance Inflation Factors 100
Table 7: Model 3 Centered Variables Regressions 101
Table 8: Regressions with Additional Institutional Proxies 105
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vi LIST OF FIGURES
Figure 1: World Exports vs. Private Capital Flows (% of GDP) 4
Figure 2: Aggregate Net Resource Flows to Developing Nations 5
Figure 3: Distribution of FDI Flows (average 1989-99) 7
Figure 4: Distribution of Portfolio Flows (average 1989-99) 8
Figure 5: Capital Inflows to Developing Nations (%of GNP, period average) 10
Figure 6: Growth vs. FDI Net Inflows (annual averages, 1970-99) 18
Figure 7: Change in Private Capital Flows (selected countries) 21
Figure 8: Bilateral Investment and Double Taxation Treaties (1990-2002) 51
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vii ABSTRACT
This thesis investigates whether one of the main challenges to the conventional
wisdom on capital flows volatility, based on the possibility of negative correlations
between different types of flows, is empirically relevant for the case of Foreign
Direct Investment (FDI). This claim has been suggested as a possible limitation of
the view of FDI as the most desirable flow for financing purposes, but we know of
no attempt to study its relevance empirically. Our analysis fails to prove a
systematic presence of these interactions between flows. Instead, and in line with
the predictions of the traditional literature on capital flows volatility, we show that
a large share of FDI in total capital flows is a significant predictor of a stable financial
account.
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1
1. Introduction
The literature on capital flows has recently benefited from intense interest in the
volatility of different types of flows, and its potential effects over the receiving
economy. With some exceptions, there is a consensus in this literature that Foreign
Direct Investment (FDI) is the most stable flow of capital. This has added to the
view that this flow is the most beneficial source of external finance for host
economies.
In a desire to contribute to this debate, this study differs from the tradition in the
volatility literature, which has almost invariably focused on individual flow
volatilities, to the volatility of the financial account as a whole. Through this
modification, we can investigate whether or not there is sufficient substitution1
between types of capital flows to cast doubts on the stabilizing effect of FDI.
Moreover, although we concentrate on the prospects of substitution effects
specifically for FDI, our results permit us to conclude on their likelihood for other
types of flows as well.
1 This term will be defined in detail in later sections of the study.
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2
With this research purpose, our study proceeds as follows: section 2 illustrates the
recent developments in international capital flows, which have contributed for the
enhanced view of FDI. The latter is nevertheless a consequence of certain beneficial
effects that the flow allegedly possesses, among which its stability is the most
recently recognized advantage. These effects are briefly reviewed on section 3.
Section 4 directly tackles the issue of volatility, by enumerating some of the reasons
to favor capital flows stability. This leads to an investigation of the specific record
achieved by FDI in volatility studies (section 5). While in general the balance of the
literature portrays FDI as a stable and benign flow, there are important conceptual
and empirical counterexamples. In all, this dialogue has identified some of the
possible limitations of the existing research on capital flow volatility, which in turn
provide the basis for our empirical design. This design is presented in section 6.
Section 7 discusses the estimation results and a set of robustness checks, and
section 8 concludes.
2. Stylized Facts of Capital Flows to Developing Nations
The end of the XX Century has witnessed one of the most important increases in
the level of transactions transcending national boundaries, leaving countries at any
level of development more integrated within the world economy. To some
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scholars, the current wave of Globalization actually falls short from former episodes
of more intense economic interaction between countries (O’Rourke and
Williamson, 1999). But leaving aside these historical considerations, an evaluation of
recent trends in the international economy helps to illustrate some characteristics
specific to the contemporary economic internationalization. Figure 1 compares the
evolution of world’s total exports with private capital flows during the last quarter
of the XX Century. While exports as a percentage of GDP have followed a steady,
but fairly slow upward trend, from about 14% to 25%, the share of private capital
flows increased much more rapidly, from 5% of GDP to about 25%, catching up
with the relative importance of exports. If there is one single feature that
differentiates the current Globalization wave from other apparently similar
historical instances, it is the surge in international capital flows.
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Figure 1: World Exports vs. Private Capital Flows (% of GDP).
Source: World Development Indicators
If we take a circumscribed view to the Developing countries, the focus of the
present study, the performance of capital flows is even more remarkable, especially
during the 1990s; aggregate net resource flows2, an approximate measure of the net
external capital that a country receives, experienced a significant increase in the
developing world during the first half of the decade of the nineties. Although there
were also some downturns, especially the global reversal in financial flows to
developing nations after 1997, the overall growth rate experienced over the
2 World Bank (2001) defines aggregate net resource flows as the sum of net flows on long-term debt(excluding IMF) plus net direct foreign investment, portfolio equity flows and official grants(excluding technical cooperation).
0
5
10
15
20
25
30
1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001
Exports of goods and services (% of GDP) Gross private capital flows (% of GDP)
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5
nineties was dramatic, moving from 43.5$ billion in 1990, to 225.8$ billion in 2000
(Peñalver, 2002).
Figure 2: Aggregate Net Resource Flows to Developing Nations
0%
10%
20%
30%
40%
50%
60%
70%
80%90%
100%
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
years
%
largest 6 receivers other ldcs
Source: Global Development Finance
Trends in aggregate indicators nevertheless mask critical differences, both in terms
of the relative success of countries attracting external funds, and in the evolution of
each of the individual flows composing the financial account. The first claim is
illustrated in Figure 2, which shows the intense concentration of aggregate net
resource flows among developing countries, with the six largest receivers reaping
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more than half of the total funds available to developing nations as a whole during
the last decade. This concentration of aggregate funds is just as impressive when we
consider some flows individually, especially those based on private equity. Figure 3
and 4 show the distribution of FDI and Portfolio investment among developing
nations during the 1989-99 period. During this time, the largest 10 receivers of FDI
and portfolio investment reaped averages of 68% and 79% respectively of the total
flows. Thus, this pattern of concentration seems to have intensified in recent years:
Relying on data for 2002, Dodd (2004) remarks that 61% of FDI in developing
economies accrued to only four countries, and as much as 96% of portfolio equity
investment to just six countries. The counterpart of this trend is obvious, as many
developing nations have been almost entirely unsuccessful in attracting equity funds
from abroad. Such is the case of Sub-Saharan countries, which for the same year
were able to attract only 4.9% of the global amount of FDI to developing nations
and an even smaller fraction of portfolio flows.
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Figure 3: Distribution of FDI Flows (average 1989-99).
Source: Global Development Finance
China
27%
Brazil
11%
Mexico
8%
rest
30%
Indonesia
2%
Poland
3%
Korea
3%
Chile
3%
Thailand
3% Malaysia
4%
Argentina
6%
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Figure 4: Distribution of Portfolio Flows (average 1989-99)
Source: Global Development Finance
Concentration is much less acute in the case of commercial debt (proxied by long-
term debt flows), and particularly Official Development Assistance (ODA). In the
latter case, no country has received more than 9% of the total funds available, a
figure that contrasts drastically with those of FDI or portfolio investment. Why is
ODA much less concentrated among countries? Although there are several forces
at hand, foreign aid, at least partially, is allocated following a set of non-economic
criteria that many developing countries are able to satisfy. Examples are
humanitarian needs, or political or colonial links with donors. On the contrary, the
prerequisites for attracting FDI or portfolio investment are much more difficult to
Korea
13%
China
10%
Brazil
10%
rest
21%
Mexico
13%
South Africa
8%India
6%
Indonesia
6%
Malaysia 5%
Thailand
4%
Argentina
4%
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fulfill, leading these flows to be more asymmetrically distributed. For instance,
portfolio investment requires the development of a domestic financial market that
does not exist at all in many third world countries. Similarly, the importance of host
market size as the most relevant locational determinant for FDI, poses
insurmountable challenges in attracting FDI for small nations (a majority among the
developing world).
The concentration of private equity would not have had such severe consequences,
if the flows that are more disseminated across countries (e.g., ODA) had
experienced the same kind of growth as private equity. But another identifiable
trend for the last two decades has been the continuous increase in private
investment as the most important element in the financial account of developing
nations, which increasingly has tended to substitute for external financing based on
foreign aid and bank lending. Figure 5 illustrates this transition, which traces its
origin back to the eighties, when debt crises in several Latin American nations led
to a curtailment of external commercial lending to developing countries. Before
that, however, debt was the most important external fund to developing nations,
partly due to the huge pool of capital available after the oil shocks of the seventies.
As the supply of funds temporarily exceeded the possibilities of investment in the
industrial world, some of these funds were recycled through a lax –if not negligent-
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credit policy that allowed third world nations to have a fairly autonomous
developmental strategy.
Figure 5: Capital Inflows to Developing Nations (% of GNP, periodaverage)
0
1
2
3
4
5
6
1975-82 1983-89 1990-98
%
Official FDI Portfolio Bank credit
Source: Global Development Finance.
As the importance of debt faded, so did ODA (Griffith-Jones and Ocampo, 2000).
Much of the latter decrease has been blamed on “aid fatigue”, as many ODA
recipients have not been able to eradicate the problems that initiated the
concessionary help. At the same time, the end of the Cold War in 1989 reduced
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the incentive for donors to allocate politically-based foreign aid, as the threat of
realignment with the opposite bloc was no longer possible3.
In all, the international market for capital flows has faced two major
transformations: one is the entrance of former pariahs into the market for
international flows, who by becoming successful in attracting private investment,
have distorted the geographical paths that North-South flows had followed in
previous decades. Recent patterns of FDI illustrate the importance of these new
players, especially China and some of the Eastern European economies (e.g., Poland,
Czech Republic) as magnets for direct investment in merely a decade. As Figure 3
illustrates, China has received 27% of the total FDI flows accruing to developing
nations between 1989 and 1999. Although not included in the figure, but based on
the same calculations, the former communist economies account for 11% of that
sum. In other words, almost half of the FDI that has been raised during the nineties
has been channeled to countries that were not politically feasible hosts during
previous decades.
Another major change has been the major rearrangement of the types of flows that
contribute to the external financing of developing nations. As private equity
3 On this regard, Boschini and Olofsgard (2003) show that the reduction in military expenditures inthe Eastern bloc after 1989 explains the sharp reduction in foreign aid.
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investment has gained unprecedented importance in satisfying the needs of
developing nations, ODA and commercial debt have become increasingly marginal.
In this process, the countries that lack the market fundamentals to attract private
investment have faced the greatest challenge to secure a stable source of external
financing. Ironically, most of these underperformers attracting private investment
are countries at the lowest levels of development, and the ones in greatest need of
new sources of finance. This has configured an allocation of global capital flows that
in the developing world has unambiguously favored higher income countries; and in
doing so, it has trapped the least developed nations in a sort of vicious cycle in
which they are precluded from accessing external financing due to their lack of a
minimum threshold of development, perpetuating their backwardness.
So far, the picture that we have depicted implicitly assumes at best a passive role
for host economies in the allocation of international funds, implying that these are
entirely on the side of the investors, creditors, or donors. However, an important
consequence of the increased importance of private capital flows as a source of
external finance for developing nations has been the change in regulatory policies, a
process that is particularly relevant for the case of foreign direct investment:
Whereas in previous decades, FDI activities were either strictly controlled or even
nationalized, today FDI is almost universally encouraged by governments.
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Why do governments have such a strong interest in gaining the favor of
international direct investors? To some extent, the relative scarcity of bank credit
and development assistance could explain this process. But since other types of
private flows (i.e., portfolio investment) have not received such interest from host
governments, we must look for other forces specific to the case of foreign direct
investment. Here, the main justification comes from the large literature on the
impact of FDI on host economies, which has strongly supported the new regulatory
attitude towards this type of flow, and eventually the fierce pattern of inter-state
competition to attract FDI. In the following section, and before illustrating some of
these policy measures, we will review the main theoretical and empirical work that
has supported the preferential treatment of FDI, with particular emphasis on
volatility, which is the central focus in this study.
3. The Impact of FDI over the Host Economy
The developmental role of FDI is a frequently studied topic, and one for which the
empirical evidence remains mixed. The earliest attempts (MacDougal, 1960) to
study the effects of international investment on host economies were based on the
Hecksher-Ohlin model, the standard model for the study of International Trade.
Within this rigorous framework, there is no distinction between different types of
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international capital (i.e., FDI and portfolio), and the main analytical conclusion
works in the same way as that of the standard Hecksher-Ohlin model. Namely, the
idea that the influx of foreign capital to capital-scarce countries would increase the
marginal product of labor, while it would reduce the marginal product of capital.
There are several reasons for the inability of that paradigm to provide an effective
account of the potential effects of FDI. The fact that it does not distinguish between
different types of international investment essentially equates the effect of FDI with
that of portfolio investment on the host economy, a premise that is unanimously
rejected in more contemporary research. Also, while the assumption of perfect
competition may facilitate a parsimonious theoretical model, this comes at the price
of compromising its factual relevance, given the oligopolist structure of the
industries that are more prone to engage in FDI.
In light of these limitations, the eventual obsolescence of the Hecksher-Ohlin model
to explain patterns of international investment led the way to another, grounded on
the theory of industrial organization, which could specifically address the potential
impact of FDI. The pioneering effort in this regard is the work of Stephen Hymer,
which sharply departs from the perfectly competitive HOS model, and stresses the
existence of scale economies, differential access to credit markets, and
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informational barriers to market entry as some of the characteristics that shatter
the assumption of homogeneous production functions across firms. These
differences in firms’ abilities to operate allow some corporations to be endowed
with a set of advantages (e.g., a more efficient production function, access to
cheaper inputs, or ownership of a differentiated product) that will outweigh the
inherent disadvantages of operating in an alien market, where domestic firms
possess better information4. In other words, it is the existence of market
imperfections what allows for the development of firm-specific advantages, and
ultimately, for the ability of firms to surpass their own frontiers.
Blomstrom et al. (1996) remark that acknowledging the importance of these firm’s
abilities is especially necessary for the analysis of FDI in developing nations, where
domestic enterprises are generally smaller and less competitive than their foreign
competitors. In this uneven environment, the entry of foreign firms may have either
positive or negative consequences, the latter being very different from those arising
from North-North flows, where host country firms are likely to enjoy a more level
playing field with source country firms.
4 This is the essence of the Hymer-Kindleberger hypothesis, initially raised by Hymer and developedby Kindleberger (1969).
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The above line of research has proved to be much more useful in identifying the
potential impact of FDI over the host economy, partly due to a pertinent focal
change from aggregate to firm-level data, where the gains from FDI can be identified
more accurately. Here, the effects derived from FDI can be analyzed across several
dimensions: the transfer of technology to domestic firms, through backward or
forward linkages (Aitken and Harrison, 1999); improving the domestic labor pool
through the dissemination of know-how and more sophisticated managerial
techniques through labor turnover (Gerschenberg, 1987). In the case of export-
oriented FDI, benefits may also arise from improving the current account of the
host economy, particularly for countries with small or undiversified exports. Thus,
foreign presence in the export sector can deepen and accelerate the opening of the
economy to international markets, a crucial policy objective for countries
undergoing structural adjustment programs (Dunning, 1993).
While the above is not an exhaustive list of the theoretical benefits that FDI may
bring, the empirical evidence offers a much muddier picture of these effects. For
instance, Aitken and Harrison (1999) use a sample of foreign and domestic firms in
Venezuela, to find that FDI has a negative effect on the productivity of upstream
domestic firms, as foreign firms tend to redirect demand from domestic to
imported inputs. Similarly, Lall and Streeten (1977) raise doubts about the
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improvement of the Balance of Payments via FDI, showing that for a sample of
developing nations the net external transactions of FDI operations resulted in a net
deficit, mainly due to profit repatriation. Thus, there are other negative effects that
need to be taken into account: for instance, the possibility that domestic investment
can be crowded-out by FDI, if the technological or managerial expertise of
international investors is superior enough so as to suffocate domestic competition
(Bosworth and Collins, 1999). FDI can also cause important geographical
dislocations to the host country, if those investments arise in the context of
agglomeration economies5. China, with 90% of its FDI stock concentrated in the
coastal regions (Global Development Finance, 2002), is an example of the non-
economic, yet far-reaching implications (e.g., internal migration, urbanization, etc.)
of FDI over the host country.
5 Agglomeration economies arise when firms obtain benefits from locating near each other. Thesebenefits may arise from multiplicity of suppliers and customers, or reductions in transportationcosts.
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Figure 6: Growth vs. FDI Net Inflows (annual averages, 1970-99)
Source: Global Development Finance.
But despite the possible negative effects, economic or otherwise, the cross-
sectional evidence confirms a significant correlation between FDI presence and
economic growth (De Mello, 1996). This slightly positive relationship is illustrated
by Figure 6, which juxtaposes the average ratio of inward FDI/GDP, with the
average growth rate for the 1970-99 period. Yet the looseness of this relation
suggests that Growth is by no means a sure outcome from FDI. On the contrary,
the literature states that the ability for the host country to experience growth out
of the entry of foreign investors is highly dependent on the characteristics of both
the host country and the industry in which the foreign endeavor unfolds. Along
-2
0
2
4
6
8
10
-10 -5 0 5 10 15
FDI/GDP %
GDP Growth %
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these lines, Borensztein et al (1998) conclude that there is a threshold level of
income and human capital per capita that the host needs to surpass in order for FDI
to make a significant contribution to economic growth. In a similar fashion,
Blomstrom et al (1994) agree on the existence of a positive effect only for higher-
income developing countries, which have the ability to assimilate the technology
brought by foreign firms.
Overall, the line of research we have outlined in the previous section has provided
mild support for the idea (largely endorsed by policy makers), that FDI is the most
attractive source of external finance for the developmental purposes of the host
economy. Leaving aside these effects, recent events in the international economy
have delivered yet another point of reference to enhance this debate: the decade of
the nineties was plagued by financial crises (e.g., European Monetary System 1993,
Mexico 1994, East Asia 1997, Brazil 1999, Russia 1999), that were particularly
virulent in developing nations. In all these crises, it is difficult to pinpoint a single
culprit; but one that appears consistently as an aggravating factor is the sudden and
substantial withdrawal of international capital flows from the countries affected by
the turmoil.
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In order to see this contrasting behavior of international capital before and after
crises, figure 7 shows the rate of change in total private flows for a selected number
of countries that were especially affected. Interestingly enough, many of the
countries most affected by these crises had been -at least on the surface- examples
of orthodoxy in the opening of their economies to foreign goods and capital. Not
surprisingly therefore, in most cases we can observe a pre-crisis period of very high
rates of growth of private capital flows, which quickly sink into negative rates the
year the crisis appears. In some cases (i.e., Argentina in 2002), there is also a speedy
return of capital flows soon after the crisis has taken place.
While illustrating the vulnerability of private flows to sudden withdrawals, this figure
does not explain the relative sensitivity of the various components of the financial
account to these financial crises. Nevertheless, this is precisely the foundation for
the most recent reason for the superiority of FDI from a developmental point of
view. In most instances, the massive withdrawals of other capital flows have
contrasted with a relatively stable FDI, which has pictured this flow as relatively
invulnerable to financial and currency crises (World Bank 1997; UNCTAD 1998). In
the following section, we will build on two points to justify this new argument in
favor of FDI: first, we will highlight the consequences of volatile capital flows over
the receiving economy, focusing on macroeconomic volatility and economic growth.
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Secondly, and in light of the detrimental effects of erratic capital flows, we will
review the evidence that has tended to portray FDI, not without counterexamples,
as the most resilient source of external financing.
Figure 7: Change in Private Capital Flows (selected countries)
Source: Global Development Finance
Argentina
-200%
-150%
-100%
-50%
0%
50%
100%
94 95 96 97 98 99 00 01
Brazil
-80%
-60%
-40%
-20%
20%
40%
60%80%
100%
95 96 97 98 99 00 01 02
Malaysia
-100%
-50%
0%
50%
100%
150%
200%
92 93 94 95 96 97 98 99 00 01
Indonesia
-500%
500%
1000%
1500%
91 92 93 94 95 96 97 98 99
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4. Why the Concern? The Consequences of Volatile Capital Flows
As a result of the central role that the variability of capital flows has played during
the recent crises, reactions quickly followed among policymakers and scholars alike.
In the first case, some nations (e.g., Malaysia 1998) backtracked from previous
commitments to capital liberalization, and imposed controls with the objective of
reducing crisis-induced capital outflows. And just as the crises shook the policy
world, they also invigorated a research agenda that aimed at elucidating the
macroeconomic effects of capital volatility on host economies.
One of the first investigations on the issue is provided in Gavin and Hausman
(1996), which finds that capital flow volatility in Latin America bears substantial
responsibility for the overall macroeconomic volatility of the region during last
decade. In a subsequent investigation, Easterly et al (2000) provide the intuition for
the mechanisms through which the financial account, or its time series behavior, can
be a conduit for macroeconomic fluctuations: on one hand it allows private firms to
overcome under-developed financial markets, into a larger pool of funds; thus, it
also grants the policymaker an effective tool for smoothing domestic economic
shocks via capital borrowing. Indeed, there is evidence that the access to
international capital might act as an efficient means of softening domestic shocks, as
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shown in Bekaert et al (2004). Notably however, these results are not replicated
for the case of emerging markets; where arguably the most intense changes in
financial account liberalization have taken place. For these economies, the study is
only able to conclude that financial liberalization has not further increased the
already high existing macroeconomic variability, a finding that is interpreted as an
indication that financial liberalization might not be able to deter output volatility in
countries that are institutionally or financially backward.
Factors other than institutional characteristics can also reduce the potential benefits
of financial liberalization. The opening to the international capital markets also
makes the supply of funds dependent, at least in part, on conditions unrelated to
the national economy. This in turn expands the set of factors that can eventually
lead to credit rationing (e.g., a reversal of foreign investor confidence in the
economy), and makes financially integrated economies more vulnerable to external
shocks.
Along these lines, Rodrik (2001) provides a more categorical conclusion about the
paths through which capital flows volatility influences macroeconomic volatility:
relying on a group of Latin American and Caribbean countries, he considers the
volatility of several socio-economic indicators (i.e., terms of trade, monetary policy,
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etc.), finding that capital flow volatility is the greatest correlate of the overall
volatility of GNP. Moreover, this relationship has grown so strong during the last
decade, that a one point increase in the standard deviation of gross private capital
flows as a percentage of GDP is linked to a more-than-half percentage point
increase in the standard deviation of GDP. The fact that regression analysis is the
analytical choice for disentangling this relationship leads the same author to suggest
that the causal link might as well go from GDP volatility to capital flow volatility,
since international investors might be responding to market fundamentals. But even
if this is the case and capital flow volatility is not an originating factor of
macroeconomic volatility, the author concludes that it can be interpreted as a
magnifying factor of these imbalances. In this way, flow volatility effects become
causal in a dynamic analysis of GDP volatility. Finally, and although at a very
preliminary level, World Bank (2002) shows that this link with capital flow volatility
is also maintained if, instead of GDP volatility, we consider the volatility of the rate
of economic growth6.
6 The study makes use of a correlogram between the standard deviation of capital inflows, and thestandard deviation of GDP growth for a sample of 90 developing nations.
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4.1. Growth Effects of Macroeconomic Volatility
At this point, we could argue that the mere recognition of capital flow volatility as a
determinant of macroeconomic volatility is not enough for one to conclude on the
need to tackle the former, if there were no consequences over the average rate of
growth that the economy would achieve in the long run. But this does not seem to
be the case if we reflect on the large empirical evidence that has corroborated a
direct association between macroeconomic volatility and lower rates of economic
growth. One of the earliest analyses to configure GDP volatility as a factor
influencing economic growth appears is Kormendi and Meguire (1985), which for a
sample of 47 countries over the 1950-77 period, includes the standard deviation of
real output growth among a comprehensive set of potential determinants of
economic growth. This early consideration of volatility as a factor influencing
growth is grounded on a hypothesis raised by Black (1979), who envisioned a trade-
off between the risk and return a technology faces. In this fashion, agents tend to
select riskier technologies only if these have a larger expected return. When we
take these individual decisions at the aggregate level, riskier technology (proxied by
output growth volatility) should be associated with greater economic growth. This
positive relationship between growth volatility and economic growth is ratified in
the empirical analysis, with countries enjoying an approximately 1% greater
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economic growth in return for an increase of 2% in the standard deviation of
economic growth rate.
The above link between macroeconomic volatility and economic growth has faced
insurmountable problems to be maintained, and subsequent research has gradually
depicted a negative relationship between the two indicators. An initial departure
came from Grier and Tullock (1989): drawing on some of the same variables used
by Kormendi and Meguire (1985), they find that such specification is more suited
for analyzing growth on OECD nations. However, when complemented by some
growth determinants relevant to developing nations (e.g., population growth, oil
wealth, political infrastructure), some important modifications arise. Particularly for
the case of growth volatility, the authors find that a positive relationship with
economic growth unambiguously holds only for the case of advanced countries; but
in fact becomes negative for subsamples of Latin American and Asian countries.
The findings reached by Grier and Tullock (1989) suggested critical differences in
the role of growth volatility across various levels of development. At the theoretical
level, one of the strongest justifications for this turnaround came from the idea that
characteristics inherent to most investment expenses (e.g., sunk costs), lead firms
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to face investment irreversibilities7 , which in turn prevent firms from fully recovering
their initial investment outlay if market conditions deteriorate. Investment
irreversibility becomes a critical issue for firms desiring to operate in uncertain
economic environments, since the firm’s expected behavior is to delay investment
expenditures until more accurate information about the future is obtained. This
combination of irreversible investments and unstable economic markets makes FDI
less likely (Rivoli and Salorio, 1996), allowing irreversibility to bridge
macroeconomic volatility into lower rates of investment, and ultimately of growth.
Aizenman and Marion (1993) provide a compelling example on the responsibility of
investment in linking economic volatility and growth. In a later study (Aizenman and
Marion, 1996), they narrow the previous link to private investment alone, while
public investment appears to increase with higher macroeconomic volatility. The
opposing paths that public and private investment follow in relation to volatility lead
to two important conclusions: first, different types of investment may be
determined by deeply distinct behavioral factors. For the case of public investment,
the authors suggest that its positive correlation with GDP volatility may reflect
increases in public investment aimed to compensate for declines in private
investment at times of increased volatility. Secondly, the effects of public and private
7 An in-depth review of the notion of irreversibility can be found in Dixit (1992). See also Pyndick (1991).
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investment tend to nullify each other, which makes the use of aggregate data on
investment (i.e, public and private) unsuitable for identifying significant association
with GDP volatility.
Further research has brought the possibility for new intermediate causal factors.
Ramey and Ramey (1995) challenge the validity of investment-based explanations,
after finding that the share of investment in GDP has no influence on the relation
between GDP volatility and growth. Instead, the authors blame economic
uncertainty, proxied by the standard deviation of the residuals of a forecast
equation in which GDP growth is the dependent variable, for the link between
macroeconomic volatility and lower growth. This negative association between
economic uncertainty and growth appears to be robust to alternative proxies for
uncertainty, at least for the case of uncertainty about inflation (Zarnowitz and
Lambros 1987), about economic policy (Aizenman and Marion 1993); or to the use
of volatility measures other than the standard deviation8.
Even in the presence of competing causal factors or indicators, the most important
finding for our purposes is that the negative relationship between macroeconomic
volatility and growth is often sustained. And by doing so, it provides conclusive
8 Recently, Ranciere et al (2005) find that the skewness of the distribution of credit growth has arobust negative effect on GDP growth.
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evidence on the detrimental effects that pronounced business cycles can have on
the long run average growth of the economy, in dramatic contrast with those claims
that have pictured economic growth as being independent of business cycles
fluctuations (Lucas, 1987).
Summing up, the notion that shocks or disturbances on GDP have a permanent
effect over the ultimate path of economic growth, makes minimizing those
fluctuations an important developmental objective. Thus, in light of the evidence
linking capital flow volatility with growth or GDP volatility, policies aiming to
achieve a stable financial account appear to be critical for successfully integrating
into the international markets in a way that is reconcilable with stable patterns of
economic growth. Despite this realization, the crises that have affected the
developing world throughout the last two decades have not been accompanied by
policy initiatives aimed at controlling the fluctuations on the financial account. With
some exceptions (e.g., Chile in 1992, Malaysia in 1997), the behavior of the financial
account was seen by host governments as a dictate of the international markets,
and initiatives towards controlling the massive entrance or exit of flows have
generally not been implemented. This lack of agency over their external sector is
arguably behind the focus on the research on capital flow behavior, which has been
concentrated on the identification of those elements of the financial account that
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are more stable. In what follows, we will review the record of this strand of
research, highlighting some of the limitations in the literature that motivate the
present study.
5. Building the Conventional Wisdom on Capital Flow Volatility
Being to a large extent a byproduct of the financial turmoil of last decade, the work
on the time series behavior of capital flows is a research agenda whose inception
dates from the time of these crises. As we stated earlier, the main objective of this
line of work has been to identify which capital flow has the lowest volatility, and
allegedly therefore is more conducive to a stable financial account. Aside from the
empirical work on the issue, some theoretical papers have helped to solidify the
superiority of FDI among capital flows. After reviewing these two strands of the
literature, we will highlight their most important shortcomings, and the relatively
modest echo that these shortcomings and counterexamples have had in the policy
realm.
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5.1. Empirical Literature
In many of the studies, a preliminary taxonomy with respect to the relative volatility
of capital flows has been to distinguish them by their maturity structures, whether
explicit or not. In doing so, the researcher has been able to draw a fundamental
distinction between short and long-term flows, even if some of them are not bound
by strict maturity dates. Turner (1991) is an early example of this type of analysis,
finding that short-term band lending is the most volatile flow, with long-term bank
lending the most stable. FDI, a flow that does not have an explicit expiration date, is
found to be in the middle of both. Findings of this sort suggest the idea that the
maturity term of flows provide valuable information about the actual volatility
patterns of the flow; those flows with short maturities being “hot”, or volatile and
speculative in nature.
Turner’s study offered an initial view on the relative volatility of different capital
flows, but its inception before the financial crises of the nineties prevented it from
evaluating how different types of capital flows performed during those turbulent
times. The baton of this research was passed to other studies, which increasingly
tended to conclude on the relative stability of FDI9. World Bank (1999) shows that
9 Some additional examples are provided in UNCTAD (1999) and Nunnemkamp (2001).
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the share of non-FDI private capital flows in GDP have exhibited more volatility
than FDI shares throughout the last quarter of the XX century. A more
disaggregated comparison of private flows is provided in UNCTAD (1998), which
finds that annual FDI flows to developing countries during the 1992-1997 have
exhibited a lower coefficient of variation10 than portfolio investment or commercial
bank loans. World Bank (1999) also offers a slightly different inquiry, as it
discriminates between the behavior of flows before and after the emergence of a
crisis. Selecting the major instances of capital flows into 21 developing nations, the
study shows that the coefficient of variation of private non-FDI flows is higher than
for FDI flows in two thirds of the sample. Thus, when the time horizon is expanded
and the post-surge period is included, this gap in volatility measures is still
maintained. Taken together, the findings suggest that FDI tends to be the most
stable flow, irrespective of the stage of the capital flow cycle in which the economy
stands.
With this large evidence concluding on the resilience of FDI, other studies have
moved towards the use of more sophisticated proxies of volatility, to see whether
these results could be replicated. Sarno and Taylor (1999) employ maximum
likelihood Kalman filtering techniques and variance ratio statistics to distinguish
10 The coefficient of variation is a measure of relative variability, and is computed as the quotient of the standard deviation divided by the mean of the series.
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between the temporary and permanent components of such flows. The purpose for
this identification is that, if much of the flow variation arises from temporary
components, the flow could be potentially reversible, and should therefore be more
volatile. With this empirical design, the authors analyze four types of capital flows
showing that portfolio investments and official flows are largely temporary in
nature, and therefore subject to reversibility. On the other hand, FDI, followed by
commercial lending, displays the largest permanent component, an indication that it
is more bound to long-term considerations of profitability, and hence more stable
than the other components of the financial account.
5.2. Theoretical Reasons for FDI Stability
In the search for the reasons that may make FDI to be the most stable flow, its own
definition provides a good starting point to think about the influencing factors.
Following OECD’s Benchmark Definition of Foreign Direct Investment, FDI is defined as
“an international investment by a resident entity in one economy in an enterprise
resident in another economy with the objective of obtaining a lasting interest”. This
concept of lasting interest suggests that direct investors are associated with the
object of investment by a long-term relationship that is generally not present for
other kind of international capital flows (notably portfolio investment). If the
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concept of lasting interest has provided some intuitive foundation for the greater
stability of FDI, additional support has also come from the idea that investments in
physical capital, which are central to FDI activities, are not as easily reversed as
cross-border share-trading, or debt instruments (Persaud, 2001). As we will see in
the brief literature review that follows, even though in most instances these
preconceived notions about the relative resilience of FDI have been empirically
confirmed, there are a handful of counterexamples that shed some doubt about
uncritically embracing this claim about FDI.
World Bank (1997) adopts this comparative approach to offer some of the factors
behind the relative stability of FDI. The study lists three main factors that account
for the volatility of flows in emerging markets.
The first of these are changes in interest rate differentials, or in the stock market
returns between emerging and industrial countries. Arguably, this factor should only
affect portfolio investment, since it is originally determined by the existence of
higher interest or stock returns in the host economy. On the other hand, FDI flows
are not affected as much by swings in international interest rates, since they are
determined to a larger extent by long-term considerations of the host market (e.g.,
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consumer base), features that do not change as frequently as interest rates or stock
returns.
Second, herding and contagion have been identified as two other factors that have
exacerbated the volatility of portfolio investments. While each of these reflects
different dimensions of investor behavior, they are deeply intertwined, and based on
the existence of asymmetric information in the financial markets, a problem that
FDI seems to successfully circumvent: herding occurs when investors, especially in
the presence of incomplete information, tend to imitate each others’ decisions,
effectively de-linking investment moves from market fundamentals.
Incomplete information has certainly been at the core of much of the patterns of
investment for middle-income countries. Even the coining of the term “emerging
markets” is illustrative of this scenario of incomplete information, where countries
that differed greatly in their market fundamentals were nonetheless conjoined
together. Not surprisingly, contagion, or the transmission of crises from one
country to another, has been a likely consequence of this process of market
homogenization. But just as in the case of interest rate differentials or herding,
contagion appears to be specific to portfolio investment. FDI decisions, on the
other hand, are immune to these processes of investment imitation, mainly due to
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the greater information in the hands of direct investors (Goldstein and Razin, 2002;
Sarno and Taylor, 1999).
But if the lack of knowledge about the investment environment influences the
greater resilience of FDI, the same about the business activity of direct investors
also appears as a factor. Aware of the extent to which intangible assets affect FDI
operations, Albuquerque (2003) constructs a model in which the explanation for
the lower volatility of FDI arises from the inability of host agents to conduct the
business operations in the absence of foreign presence. Intangible assets (i.e.,
brands, R&D expenditures, patents, etc.) are for the most part inalienable, and
without the involvement of the foreign investor, the value of the investment for the
host economy is small. On the other hand, portfolio investment or commercial
debt are largely appropriable. In all, and in an scenario where there are no
international enforcement mechanisms to guarantee contracts between foreign
investors/creditors and the host nation, the inalienability of FDI makes this flow
relatively immune to expropriation by the host executive. This not only would
explain the relative stability of FDI when signals about the domestic economy turn
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negative, but also the higher proportion of FDI in the financial account in countries
at the bottom of sovereign credit rankings11.
The third and final factor that that accounts for the higher stability of FDI flows has
to do with the different degrees to which investors can disinvest between the two
types of flows. On one hand, technological developments in international financial
markets, along with a relaxation of host market regulations towards capital flows
have made it easier than ever for portfolio investment flows to return to the home
country. Regulatory changes have also facilitated direct investment flow
repatriations to the headquarters. But in contrast to portfolio investment, FDI ends
up on the ownership of physical facilities, an attachment that limits a speedy resale
in two ways: first, in general the price of the asset underlying the direct investment
is not publicly known (World Bank, 1997). This poses an information asymmetry
problem that generally lengthens the negotiation time between agents, and it does
not guarantee a fair price for the seller. All these factors combined make direct
investments have a longer resale time than stocks or bonds. And accordingly, it
slows the ability of direct investment to be reversed, forcing foreign investors to
have a profit horizon that is inherently long-term (Lipsey, 2001).
11 Investment ratings like the Euromoney’s country rating and the Institutional Investor’s countrycredit rating show a significant and negative relationship with the share of FDI in gross capital flows.
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The above impediments for direct investments to change ownership not only make
them as more stable, but also more efficient in avoiding certain temporary shocks: a
case in point is currency crises, temporary shocks that generally do not embrace a
change in the fundamentals of the economy. Confirming this immunity to currency
crises, Nitithanprapas and Willett (2000) show, for a sample of 26 economies
during the crises of 1994 and 1997, that low levels of FDI, along with the current
account deficits and a distorted exchange rate are strong predictors of a country’s
proclivity to suffer these financial imbalances.
5.3. Counterexamples
Despite the development of theoretical models explaining the factors behind the
resilience of FDI, and the extensive evidence in its support, the literature has raised
a few, but relevant counterexamples. The earliest one is in Claessens et al (1995),
which adopts a sample of 10 industrial and developing nations to actively react
against the conventional wisdom on capital flows volatility. Instead, the authors
argue that the term maturity is simply not enough to categorize a flow as volatile or
stable. One intuitive reason for this mismatch is that there are instruments that can
transform a short-term flow into long-term one, or vice versa. For instance, debt
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roll-over may allow the maturity of a short term debt flow to be extended to one
more typical of long-term flows.
But going beyond developments in international financial markets, a more emphatic
point raised by the authors is that the interaction between different flows may also
help to blur the connection between the flow labels and their time series
properties. The feature of capital flows that leads to this dynamic is the degree of
substitution, in particular the existence of inter-flow negative correlation. When
two flows exhibit a strong degree of substitution, their time fluctuations would tend
to offset each other, and the resulting volatility of the financial account would
essentially remain unaffected. Therefore, studies that do not take into account the
possible interactions between the elements of the financial account, and rely
exclusively on the univariate properties of the flow to decide on their relative
stability, might be unable to reflect the volatility that is actually transmitted into the
financial account and the economy. Accordingly, the authors call for the need to
focus on the effects on the financial account in order to draw definite conclusions
on the relative stability of the flows.
With all these caveats at hand, their empirical study looks at several dimensions of
the time series behavior of the flows, to raise doubts about the existence of a flow
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systematically more stable. To assess the importance of substitution effects, inter-
flow correlations are calculated, and even though the authors do not identify any
systematic behavior, they remark that this feature is large enough so as to disqualify
analyses based on the univariate properties of the flow. In order to analyze the
relative volatility of the flows, they compute standard deviations and coefficient of
variations of the flows, similarly yielding no systematic pattern of volatility. To the
contrary, FDI and long term debt turn out to be the most volatile flows in four
countries, and portfolio investment in two cases. Surprisingly, and despite its
accounting label, short-term flows appear to be the least volatile in seven of the ten
cases analyzed.
A complementary notion to volatility is the idea of persistence, measured by the
flow’s degree of autocorrelation, with the idea that flow series that are positively
autocorrelated (negatively) would be relatively persistent (volatile). Here, the
conventional wisdom is partially ratified, as FDI exhibits positive autocorrelation,
while short-term flows have negative autocorrelation. The evidence on flow
persistence is complemented by calculating half-lives from impulse responses12,
namely the number of quarters required for a shock to the series to lose half of its
value. A priori shocks in highly and positively autocorrelated series would propagate
12 Half-lives are computed by estimating a univariate AR(4) model for each flow.
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themselves for longer time than negatively correlated ones. And again, at this point
of the analysis no systematic patterns are found, as most of the half-lives tend to be
one quarter, irrespective of the flow to which they belong.
To further challenge the conventional conclusions drawn in the literature, the
authors ask about the extent to which the composition of the financial account
determines its forecasting ability. The underlying idea is that, if flows behave
according to the conventional wisdom, financial accounts that are concentrated in
presumably stable flows would be predicted more accurately. To do so, a forecast
of the overall financial account is constructed based on its past information, as well
as on the contemporaneous share of individual flows. The latter turn out to
improve little the forecasting ability of the financial account. This serves the purpose
to conclude that movements in the overall financial account are not very influenced
by the type of capital flow, presumably because of inter-flow substitution. In all, this
is the final idea that allows the authors to conclude that the separate analysis of
time series flows is not adequate for conveying definite conclusions over which flow
is the most stable. Rather, it is the financial account balance that should take a
central role in this analysis, as it would implicitly account for possible interaction
between the flows.
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The previous work opened the path for later research to consider the potential
role of substitution between flows, albeit with some modifications. Chuhan et al
(1993) adopts the same basic distinction across the life span of the flow (i.e., short
and long term) that Turner (1991) initiated, to examine the differences that may lie
behind short term investment and FDI. A crucial assumption here is that FDI is
more connected to the outlay of physical capital, configuring it as a long-term flow,
and therefore more stable. To validate or contest this assumption, they aim to
investigate whether there are major differences in the stationarity of the flows, a
result that would support the notion that the categories of flows are useful in
distinguishing between stable and volatile flows.
Possible differences across flows are also investigated through the relative
persistence of a disturbance to an autorregresive model for each of the flows.
However, and despite that initial assumption, the authors find that stationarity tends
to be rejected in most series, independently of the flow to which they belong. And
regarding the persistence of the series, shocks to FDI series tend to have a more
lasting effect than shocks to short term investment. This battery of univariate
techniques is unable to find major differences in the time series properties of the
flows, and only the results on flow persistence yield marginal support for the notion
that FDI is more stable.
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Significant differences arise however when certain flow interactions are considered.
In particular, the authors employ Granger causality techniques to show that short
term investment responds, not only to changes in other flows, but also to changes
in short term flows in other countries. These dependencies are not enough to
decide on the greater volatility of short term flows, but they do suggest that these
flows are more sensitive to factors outside their own fundamentals, and to
contagion effects from crises in other countries. These last differences, although
counterbalanced by the results of the stationarity analysis, lead the authors to
conclude that the different categories of inflows do offer a meaningful distinction to
label a flow as “hot” or “cold”, and to advocate that some level of disaggregation
should be maintained in research on capital flows. In sum, through these findings the
authors highlight the greater susceptibility of portfolio investment to other flow
movements. Following on this argument, Bosworth and Collins (1999) fail to find
any significant correlation, positive or negative, across the various types of flows to
developing nations.
The debate on capital flow volatility has benefited from more recent work that has
aimed to challenge the conventional wisdom with the help of sophisticated data on
industrial countries, generally not available for most of the existing literature, which
has predominantly focus on the developing world. Persaud (2001) for instance relies
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on a high-frequency dataset including observations on FDI flows arising from
mergers and acquisitions in Europe and the United States, to show that the
standard deviation of monthly changes for this type of FDI is higher than the same
indicator for both debt and equity portfolio investment. Moreover, the author
introduces in the literature the use of two proxies –skewness and kurtosis- that,
while not explaining the usual concept of volatility of a series, provide information
about the way it is distributed. Of these two, kurtosis measures the relative size of
the distribution tails, with higher value implying fatter tails. In other words, a high
kurtosis entails a greater tendency for the distribution to have extreme
observations, which for the case of the series considered is equivalent to flow
surges and reversals.
With these analytical considerations, the striking finding is that the kurtosis of FDI
based on mergers and acquisitions turns to be higher than for both types of
portfolio investment (i.e., debt and equity), disqualifying statements that have
uncritically portrayed FDI as unable to reach large surges or reversals (e.g., Lipsey,
2001). On the contrary, the study shows that at least those FDI operations based
on merger and acquisitions have the potential to exhibit both massive inflows and
outflows, even more so than portfolio flows. Thus, while the study focuses on US-
Eurozone cross-border flows, in principle the same conclusion could be applicable
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to patterns of FDI in developing nations during last decade, if we recall the
importance that privatization programs have had in attracting direct investments. In
these instances, the acquisition of former public companies led to one-time large
flows at the time of the purchase, a mode of entry that contrasts with Greenfield 13
FDI operations, which tend to disseminate their inflows more evenly throughout
time, reducing the possibility for capital rush. In fact this seems to be behind the
larger volatility found for FDI vis-à-vis other flows in some countries and years (e.g.,
Brazil in 1997), where privatization schemes were a central element in the strategy
to lure international investors (ECLAC, 1999; UNCTAD, 1997). In all, Persaud’s
findings suggest that certain specific FDI features can drastically alter the vision of
this flow as a resilient source of external financing for the host economy.
We can think of additional factors that can potentially erode the usual view of FDI.
As already stated, privatization programs have acted as a catalyst for the arrival of
massive FDI flows to developing nations throughout the last part of the XX
Century. This way of setting business operations in the host economy has
substantial differences with the historically more frequent “Greenfield” FDI. Here,
the critical difference in time series behavior is that privatization-led FDI flows are
13 Greenfield FDI occurs when international investors set up their business operation without thepurchase of an existing company in the host economy, and rather by setting up a new physicalfacility.
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concentrated at the moment of the purchase, with much smaller flows accruing to
the host economy in subsequent years. One consequence of this asymmetrical
sequence of flows is obviously the difficulty to maintain those high levels of FDI
flows in countries actively engaged in privatization (Griffith-Jones and Leape, 2001),
once there are few remaining state owned enterprises susceptible to be privatized.
Another outcome, even more related to our issue of interest, is that the
distribution of FDI flows would tend to be more prone to outliers, a factor that can
accelerate the overall volatility of aggregate FDI series.
To a certain extent, the above factor can be considered circumstantial, given that
privatization programs cannot be sustained once state retreat from the economy is
complete. However, other factors that can influence the volatility of direct
investment flows are inherent to the array of instruments employed by
multinational corporations to conduct the operations of their subsidiaries. For
instance, the ability to hedge between home and host country risk through debt is a
basic measure to reduce the exposure of international direct investors to a
particular economic market (Bird and Rajan, 2002). Through this process, direct
investors present the facilities of the subsidiary as collateral to borrow domestically
in the host nation, to then repatriate the loan funds to the parent firm. In this way,
the exposure to a worsening economic scenario is reduced, despite the short-term
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impossibility to divest through physical down-sizing. A similar point could be made
of an increase in profit remittances from the subsidiary to the parent firm, which
would also act to reduce the exposure of direct investors to the host economy.
The financial engineering at the disposal of the multinational firm is not the only
factor that is generally overlooked by the conventional view on capital flows, and
which can alter the typical conclusions on the stability of FDI. Another reason for
the resilience of FDI is grounded on the fact that some of its most important
locational determinants are variables that cannot vary much in the short run14. This
nevertheless does not preclude other short-term, more volatile variables, from
affecting FDI decisions. Among these, the role of exchange rates in aggravating FDI
volatility has been specifically addressed in the literature. World Bank (1999)
highlights the exchange rate as one variable that, being susceptible to acute
fluctuations in the short run, can alter FDI flows abruptly.
The evidence on this issue is however not conclusive. On one hand, a solid finding
of the literature is that the likelihood of exchange rate appreciation in the host
nation deters subsequent FDI inflows15. But the role of the variability of the
14 A consistent result of the empirical literature on FDI finds market size, proxied by host countryGDP levels, as the most important location determinant. For a review, see Singh and Jun (1995).15 Evidence of this relationship is found in Cushman (1985); also in Barrell and Pain (1996).
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exchange rate over direct investment flows is a much more contested question:
early investigations on the issue (e.g., Cushman 1985) found the standard deviation
of the change of the exchange rate to have a positive impact over FDI. Later studies
however portray a negative association between exchange rate volatility and FDI:
Campa (1993) shows that exchange rate volatility deters the entry of foreign firms
into the wholesale industries in the United States. Similarly, Benassy-Quere et al.
(2000) find a detrimental effect of exchange rate instability on the FDI flowing from
OECD countries to developing nations. In all, this ambiguous relationship reflects
the existence of counterweighing forces16, which in turn leave no clear indication on
how FDI flows react to an unstable exchange rate.
5.4. Policy Implications
Notwithstanding the existence of factors that can accelerate FDI volatility, this
literature review clearly concludes that this flow is the most stable source of
external financing. This view, also prevailing in the policy realm, has justified the
preference for a financial account geared towards this type of flow, an objective that
has been carried through various measures. One has been the establishment of
16 While a negative relationship between exchange rate volatility and FDI can be accommodatedwithin the general idea that economic uncertainty (over which exchange rate volatility is onepossible dimension) deters FDI activity, Cushman justifies the positive link he finds on the idea thatFDI provides a better safeguard than trade to exchange rate fluctuations, giving firms an incentive tointernalize their business operations in order to reduce their exposure to terms of trade shocks.
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investment promotion agencies by economies eager to attract FDI. The number of
agencies, whose general objective is to improve the investment environment of the
host economy, has skyrocketed during recent years. UNCTAD (2001) reports a
record number by the end of the decade of the nineties, with 164 investment
promotion agencies at the national level, and more than 250 at the sub-national.
The array of services undertaken by these agencies is comprehensive, but at a
minimum level it involves two basic tasks. One is the diffusion of information about
the host economy, with the purpose of reducing informational asymmetries that
may have a discouraging effect on potential foreign investors. A second, more
proactive duty is the assistance on the bureaucratic and legal requirements that the
foreign investor needs to fulfill upon its entrance in the host economy. In addition
to these, and more controversially, incentives have also included benefits like tax
breaks, land grants, or other special regulatory treatment. Such an ardent policy
move to lure international investors has understandably found opposition, as it has
eventually raised doubts about the overall welfare gains that the host economy
receives from investments induced through excessively generous and costly
incentive programs17.
17 Blomstrom and Kokko (2003) state that the use of incentives is generally an inefficient way toraise national welfare, if it is not accompanied by a complementary set of policies designed toimprove the competitiveness of local firms.
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A complementary measure in the hands of national executives to promote FDI has
been the establishment of bilateral investment treaties. These inter-country
arrangements almost tripled in recent years, moving from 1,639 treaties in 1990, to
4,436 in 2002 (see Figure 8). Just as in the case of investment promotion programs,
bilateral investment treaties aim to facilitate international investment. But as
opposed to incentive schemes, which may entail some discriminatory measure in
favor of foreign investors, bilateral investment treaties do not go beyond
establishing a level playing field between domestic and foreign competitors. This is
accomplished through the eradication of double taxation, compensation for
investment expropriation, or other measures that in general aim to guarantee fair
treatment to foreign investors in the host market18.
18 While the general evidence on the effectiveness of incentive programs in attracting FDI isfragmentary, the effectiveness of Double Taxation Treaties is even less satisfactory: Blonigen andDavies (2000) find that these schemes not only fail to promote FDI flows, but actually have a short-lived negative effect. This surprising result is consequence of the uncertainty that the new regulatoryenvironment delivers, which may lead some international investors to “wait and see” beforeinvestment decisions are taken.
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Figure 8: Bilateral Investment and Double Taxation Treaties (1990-2002)
Source: UNCTAD
A final element leading toward a regulatory environment favoring FDI over other
private flows has been the recent financial crises. In some cases, the gravity of these
imbalances resulted in the imposition of capital controls, whose main purpose was
to deter short-term capital outflows. Given the presumed long-term orientation of
FDI, this flow should be relatively unaffected by measures that aim to tackle
speculative short-term flows. In addition, this relative freedom of FDI from capital
control measures arises also from the paths available to a foreign subsidiary to
repatriate funds to the parent company. Desai et al. (2004) on this matter report
that, at least in the case of U.S. multinationals, capital controls have been effectively
0
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1990 1992 1994 1996 1998 2000 2002
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evaded through profit reallocations, or variations in intra-firm trade, which have no
counterpart in the case of portfolio investment.
Summing up, the political economy of capital flows has unmistakably headed
towards measures facilitating FDI inflows in contrast to the treatment to other
flows (notably, portfolio investment), which have not enjoyed such benign
treatment. In contrast, there are several episodes in which general trade and capital
liberalization has been accompanied by tighter control of certain capital flows, and
in some cases blunt discouragement. Our motivation for this study is sustained
precisely on these policy ramifications, assuming that the research that has
enthroned FDI as the most stable flow is at least partly responsible for this policy
response. In what follows, we will proceed to highlight some of the limitations and
unaddressed questions of this literature, which in view of its relevance at the policy
level, calls for further investigation on the issue of FDI volatility.
6. Study Scope
A defining characteristic of the literature on capital flows behavior is that studies on
capital flow volatility, especially those that tend to ratify the view of FDI as the most
stable source, have relied on separate analyses of each of the flows to reach its
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conclusions. It is from this analysis of univariate properties that the literature has
concluded on the desirability of FDI for the host economy. This approach however
does not take into account the volatility of the financial account, even though this
should be the element whose time series properties are of ultimate concern for the
policymaker. Arguably, if we set aside those beneficial effects of capital flows that
are independent of their time series behavior (e.g., FDI spillovers), the main policy
objective regarding capital flows would be the achievement of a sure source of
external financing of the current account. It is not stable capital flows per se, but
rather a stable financial account that really matters for policymakers. Surprisingly
however, there are very few studies, which we proceed to detail below, that have
made reference to how flows influence the volatility of the financial account.
Bringing the volatility of the financial account to the center of the analysis is a
necessary step to disentangle a fundamental objective of this study: recalling the
substitution effect portrayed in Claessens et al. (1995), potentially one of the most
significant conceptual challenges to the prevailing views on capital flow volatility, we
are interested in investigating whether FDI participates in these interactions among
flows. A method to show this possibility is the inclusion of cross-correlation flows,
a point that was specifically addressed in Claessens et al. (1995), and Ramos (2002).
But, while illustrative, the simple observance of negative correlation coefficients
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cannot tell us whether they are large enough to drastically alter the final volatility of
the financial account.
Our interest on this last question, and specifically for the case of FDI, lead us to
adopt a different approach, based on an econometric approach that first investigates
the possibility of inter-flow substitution a-la-Claessens, with an econometric
specification in which the volatility of the financial account is the dependent variable.
This allows us to examine whether potential substitution effects are large
enough to impede the transmission of the volatility of FDI into that of
the financial account. Thus, a second, related purpose of the analysis is to check
whether or not greater FDI presence compared to other capital flows
results into greater stability of the financial account, a finding that would
reinforce the conventional wisdom on capital flows volatility. In what follows, we
provide a description of the variables and data sources utilized, a justification of our
choice for the measure of volatility, a more detailed specification of the
econometric model and an explanation of how this one can address our research
questions.
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6.1. Data Sources and Variables
We focus our analysis exclusively on the case of developing nations, a restriction
that is grounded on the following considerations: First, capital flows, as well as
most of other macroeconomic series, have generally been more volatile in the
developing world. More advanced nations have been better able to avoid capital
flow crises, as they are not affected so much by the factors that create them in the
first place: in general, developed nations possess a good set of economic
fundamentals, and are relatively immune to financial contagion. Ultimately, they are
in a better position to provide domestic financing buffers to sudden withdrawals of
foreign flows.
Second, the relatively smaller size of developing economies makes them more
sensitive to fluctuations in capital flows. This should certainly be the case for small,
open developing nations, which are more exposed to international trade and
finance, flows thereof which can be huge relative to the small size of their domestic
market, have joined forces to aggravate the volatility of other economic variables
(Easterly and Kraay, 1999). Large developing nations however have not been
immune to fluctuations in capital inflows, as many of them experienced drastic
surges after liberalizing the financial account, or undertaking other economic
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reforms. In all, volatility of economic aggregates in the developing world has affected
small and large developing economies alike, while leaving the most advanced nations
relatively protected.
Within the developing world, we take a comprehensive approach that contrasts
with the more confined scope of the existing studies on capital flow volatility. Some
of the work that has delivered the most important challenges to the conventional
wisdom on capital flows volatility has relied on fairly small samples. The empirical
analysis on Claessens et al. (1995), for instance, covers merely 10 cross-sections,
equally divided across industrial and developing nations. A fairly similar sample is
adopted in Chuhan et al. (1993), which covers 7 industrial countries and 8 emerging
economies. Just as the cross-sectional dimension, sometimes the time dimension is
also constrained. Chuhan et al. (1993) covers the 1985-1994 period, almost the
same as in Sarno and Taylor (1999), whose sample ranges from 1988 to 1997. An
expected tradeoff in some of the previous examples is that these limited spans are
generally rewarded with higher frequency in the data used. This is the case in both
Claessens et al. (1995) and Chuhan et al. (1993), which use quarterly data on four
types of capital flows. Unfortunately, such a rich dataset is not available in our case,
since for some of the countries that we cover, particularly least developed nations,
quarterly time series are virtually non-existent. The use of annual observations
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therefore has the advantage of allowing us to include these countries, for which FDI
is usually the most important external private fund. Given the data limitations
constraining our study, we use several datasets to build our sample, among which
IMF’s International Financial Statistics (hereafter, IFS), and World Bank’s World
Development Indicators (WDI) database constitute the backbone. Favoring breadth
over frequency, and depending on the specific stage of our analysis, we were able to
include up to 104 developing countries in our empirical study.
Our focus on the effects of FDI volatility on net financing requires us to obtain data
on the balance of the financial account, as this is the element that comprises the
capital flows that the national economy receives, and in doing so reflects the extent
to which the international economy finances the consumption and investment
expenditures of the host economy. For our set of regressors, we compiled
observations on net flows of FDI, computed as the difference between the assets
and liabilities sides, a measure that includes both inflows and outflows of FDI, and
that accordingly can reflect greater fluctuations compared to other FDI proxies (i.e.,
FDI stocks, FDI gross flows). Persaud (2001) in this regard remarks that part of the
support to the notion that FDI is the most stable flow has come from the idea,
intuitively sound, that investments in physical capital, central to FDI activities are
not as easily reversed as cross-border share-trading, or interest rates in debt
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instruments. Including the balance on net flows, however, provides a better
estimate of all the ways through which multinational corporations can affect the
actual flow of funds to the host economy.
Regarding the data sources, our model19 required two variables based on FDI for
our analysis: the first one is the net inflow of FDI to GDP ratio, computed from the
World Development Indicators database; another FDI-related variable that we
adopt is the share of FDI in total flows. As this variable is not readily available for
our sample of countries, we compute it by dividing our first variable (FDI/GDP)
over the ratio of total flows to GDP20. Moving away from the balance of payments,
WDI also supplied observations for series on GDP, financial development, and
trade openness. Table 1 summarizes the main variables used in this study.
19 At several stages of the analysis we added further explanatory variables whose source is notdetailed in this section, as they were not kept in the final econometric specification. These sourceswill nevertheless be identified in our discussion of the empirical results.20 A similar procedure is used by Hausmann and Fernández-Arias (2000) to arrive at the same typeof variable.
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Table 1: Main Variables
Variable Proxies for… Definition Source
FAVFinancial Account
VolatilityStd. dev. of Financial account as % of
GDPIFS
FDIV FDI volatilityStd. dev. of ratio of FDI net flows to
GDPWDI
FDITKShare of FDI in total
capitalGross FDI inflows as a % of total
capital inflowsIFS
FDIVTK Interaction term FDIV x FDITK N/A
FDIV2 Quadratic term FDIV squared N/AGDP Development levels Real GDP per capita WDI
M2GDP Financial Development Broad money as % of GDP WDI
OPEN Trade Openness Exports plus imports as % of GDP WDI
We note that dividing both the financial account and FDI by GDP levels is a
necessary step prior to the calculation of their respective volatilities. This approach
is precisely the one followed in Rodrik (2001). It attempts to eradicate the biases
that would arise from using the unweighted data, which would tend to place as
most volatile those flows accruing to middle income countries, given that these are
the largest receivers of external flows. Normalizing the series therefore eliminates
the influence of the “economic size” of the country over volatility measures.
It has been long recognized that data on international capital flows, particularly that
on FDI, is filled with considerable inaccuracies. Several multilateral organizations
(i.e., IMF, OECD, UNCTAD) have increasingly devoted attention to the extent to
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which measurement problems affect the existing statistics, as well as their
underlying reasons. One of the main causal factors is the two dimensions that
define the way FDI data is gathered. The first distinguishes between individual or
aggregate transaction reporting21. The second allows the data to be collected by a
statistical agency, or directly from the transactor. In practice, the choice for data
collection is non-trivial, as it defines which operations fall under the FDI category
and which do not. For instance, individual transaction reporting from banks
provides a fairly accurate estimate of FDI operations associated with cash flows. It is
however much less effective for recording operations that do not have a flow of
cash associated, as it is the case of certain intra-firm transactions (i.e., the transfer
of proprietary technology from the parent to the subsidiary).
Other sources of measurement error in these statistics include the use of different
country standards for categorizing a transaction as FDI. While widely accepted, the
use of the 10% equity threshold is not universally endorsed. Some countries (e.g.,
France, Germany) have established a higher percentage threshold, while others
completely disregard its use, and instead classify FDI operations on a case-by-case
basis (IMF, 1992). Adding to this variation in national practices, we find countries
21 Individual reporting methods record every transaction pertaining to FDI, usually from banks.Aggregate reporting on the other hand compiles the total amount of transactions during specificreporting periods, and it is therefore more likely to come through enterprise surveys.
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that do not accurately record some transactions associated with FDI. Such is the
case of reinvested earnings, or certain types of FDI disinvestments22. In all, most of
the measurement discrepancies across nations are based either on the incapacity of
some nations to collect some types of FDI operations, or on their departure from
the common standards for compiling FDI data.
Moving beyond their causal factors, can we quantify the measurement bias of FDI
data? One of the earliest and most relevant efforts to assess the measurement bias
on FDI statistics has been IMF’s Report on the Measurement of International
Capital Flows. Using the recorded difference between global outward and inward
direct investment, the study finds a substantial discrepancy between the two figures,
which in the late eighties averages $16.5 billion, and approximately 10% of the
global outward flow of FDI. The same report also concludes that a large share of
the discrepancy arises from the entry of reinvested earnings, which emerges as the
leading cause for measurement error.
A related study, albeit having a more restricted focus, is Patterson (1992). It
calculates the statistical discrepancy in FDI data by analyzing the geographical
22 The sale of a foreign company in the host country would theoretically be recorded as a capitaloutflow in the FDI account of the Balance of Payments. But in practice, reporting this operation maydepend entirely on the seller’s will, especially if it is not done through financial intermediaries. Somecountries (e.g., United States) circumvent this limitation by requiring a compulsory filing directlyfrom the foreign investor, but others lack the capability to correctly register such disinvestments.
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breakdown of outward and inward flows of seven major FDI players (Australia,
Canada, Germany, Japan, Netherlands, United Kingdom, and United States). One of
its main findings is a tendency for offsetting positive and negative bilateral
inconsistencies, which yields an estimated annual discrepancy of $3.5 billion for the
seven countries during the 1986-88 period. In this way, the study implicitly suggests
the possible inadequacy of concluding on the importance of the statistical
discrepancy on FDI data by simply looking at the entry errors and omissions23.
When we shift the focus of the measurement problem to the balance on the
Financial Account, the biases inherited from the FDI account have to be added to
those that are specific to the other elements of the Financial Account. Within these,
the same IMF report has also recognized some reasons for the statistical
discrepancies of the other major categories of financial flows (i.e., Portfolio
Investment and Other Investment).
The identification of these distorting forces has served the basis for subsequent
adjustments in the data that have aimed to reduce the size of these biases in more
recent years. Despite these improvements, any study relying on these statistics may
23 Theoretically, since data on capital flows is recorded on a double-entry basis, analyzing the errorsand omissions entry could provide a good estimate of the relevance of measurement problems.Unfortunately, this strategy does not take into account the possibility that positive and negativeerrors may offset each other, which can render the errors and omissions entry ineffective tocorrectly identify the size of the measurement error.
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suffer from measurement error bias. Our empirical analysis, however, incorporates
a feature that may reduce the actual damage of this bias, if present. Specifically, our
variables based on FDI flows or financial account balances are constructed as
volatility or average indicators for four-year periods. This in turn should reduce the
annual fluctuations from measurement error in a particular year (Wei and Wu,
2001).
6.2. Measures of Volatility
A defining characteristic in the literature of capital flow behavior has been the lack
of consensus in choosing a measure of volatility. In principle, the traditional measure
of volatility has been the standard deviation of changes in the series (Persaud, 2001);
however an also typical even if “unscientific” view of volatility has aimed to identify
drastic changes in the direction of the flow. This approach, while not necessarily
delivering the most accurate measure of the volatility of a series, has some appeal in
so far as it is able to identify drastic surges or reversals in the flow, generally
unexpected by the policymaker, and arguably the most destabilizing for the
economy. Lipsey (1999) for instance adopts a notion of volatility based on the
number of times the series changes sign, with the objective to reflect the frequency
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in which inflows turn into outflows and vice versa. In this fashion, sign changes
provide a notion of the instability of a capital flow in contributing to net financing.
Nevertheless, sign changes fall short of offering an accurate notion of volatility.
Ramos (2002) on this matter warns that the sole use of sign changes may lead to
fallacious inferences about the instability of the series, since flows with large but few
sign changes would generally be more volatile, compared to series with many sign
changes that are relatively small in size. Persaud (2001) also adopts an indicator to
pinpoint extreme inflows or outflows, through the kurtosis of the distribution.
Being a measure of the flatness of the tails of a distribution, the kurtosis gives a
notion of the importance of outliers in the distribution. Particularly for the case of
capital flows series, higher kurtosis would reflect a relatively larger occurrence of
drastic surges and reversals of flows.
We can find a more accurate focus on the “unexpected” volatility of a flow in Osei
et al. (2002), which in addition to more standard indicators of volatility (e.g.,
coefficient of variation), include the standard deviation around a forecast trend
based on adaptive expectations. In other words, the forecast should capture the
trend value of the series that would have been predicted using the past values of
the series24. A related example is provided in Claessens et al. (1995), through a
24 A similar measure can be found in Lensink and Morrisey (2001)
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forecast autoregressive model used to test the predictability of the series.
Notwithstanding the above exceptions, the present study follows the approach of
the majority of the existing literature, which has aimed to identify the “actual”
volatility of capital flows, in turn disregarding the role of agents’ expectations. This
focus has generally favored the use of two indicators, either the standard deviation
of the series (Rodrik, 2001; Easterly et al., 2000); Easterly and Kraay, 1999), or the
coefficient of variation (Claessens et al., 1995).
The use of the coefficient of variation has some advantages over the standard
deviation. Probably the most evident is the ability of the coefficient of variation to
correct for trends in the series. The standard deviation on the other hand, is more
susceptible to increase at times of rising capital flows (Nunnemkamp, 2001). This
nevertheless is a more necessary feature when the data used for the construction
of the volatility indicator is taken “as is”, and not as a proportion to GDP (as in our
case). When dealing with normalized data, however, sustained periods of rising
capital flows would be weighted by an accordingly increased economic activity, and
therefore of GDP levels.
But if the reliance on variable ratios diminishes the advantages of using the
coefficient of variation as the volatility measure, our focus on net financing makes
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the use of this indicator quite illegitimate. The use of flow series can yield both
positive and negative values, which at times can lead to a very small mean. This in
turn would artificially increase the resulting coefficient of variation, even if the series
have not been particularly volatile. Moreover, series whose volatility patterns are
essentially similar may end up with radically different coefficient of variations, if the
resulting means for them differ in sign. In all, these serious caveats justify the use of
the standard deviation over the deflated series as the most adequate volatility
indicator for our analysis.
With these points supporting our choice for a volatility measure, we construct
standard deviations of the variable ratios based on non-overlapping consecutive
four-year periods. We feel this time frame is long enough not to intensify the
heterogeneity that would arise from standard deviations with shorter memory,
which would be more affected by idiosyncratic shocks. Yet, it is not so large to
excessively decrease the total number of periods for our study, which would result
in a severe loss of degrees of freedom. Other studies on the volatility of flows
overtime have also adopted similar time horizons (Sarisoy-Guerin, 2003).
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7. Empirical Analysis
The goal of the present study is to examine whether the supremacy of FDI time
series properties can be contested when we center the analysis on the volatility of
the financial account. Specifically, we are interested in checking whether financial
accounts that are more concentrated in FDI are more stable. Secondly, but deeply
intertwined, whether the possibility of negative inter-flow correlations, originally
raised in Claessens et al. (1995), are large enough so as to offset the effect of FDI
volatility over the financial account. To address these questions, the following
empirical model is initially considered, where i and t represent respectively the
cross-sectional and time dimensions of the sample.
it kit
K
k
k it it it it it it it X FDITK FDIV xFDITK FDIV FDITK FDIV FAV ε δ β β β β β ++++++= ∑
=1
2
5
2
4321)()()(
Let us elaborate on the variables that comprise the model: our dependent variable
is financial account volatility (FAV), measured as the standard deviation of its real
balance. The explanatory variables on the other hand, can be divided into a set of
general control measures25 integrated in the summation term, and regressors
25 Initially, these are variables on GDP levels, financial development, and trade openness. At severalsteps of the analysis, these are complemented by a set of institutional variables.
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whose inclusion responds directly to the questions we wish to address. Such is the
case of FDI volatility (FDIV), the share of FDI in total gross private capital flows26
(FDITK), and an interaction between these two (FDIVTK). In addition, we also
include a quadratic term on FDIV (FDIV2) that seeks to qualify further the
relationship between FDI and financial account volatility.
The inclusion of FDITK responds to our desire to identify whether financial
accounts that are more concentrated in FDI tend to be less volatile. A priori, it may
seem more in accordance with our study to construct this variable as a share based
on the financial account. But since this is a net figure based on double entry system,
its resulting balance can be either positive or negative, which makes the financial
account unfit to be used as a denominator in a variable ratio. On the contrary,
gross measures of FDI and private flows are not affected by this problem, and
reflect a more accurate picture of the importance of FDI vis-à-vis the rest of the
components of the financial account.
To account for a potential negative correlation between FDI and other flows, which
is substantial enough to offset the effect of FDI volatility over the financial account,
we make use of two other regressors: FDI volatility (measured in the same way as
26 Data to compute this ratio was obtained from IFS.
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our dependent variable), and an interaction term between this variable and FDITK.
Arguably, if the degree of co-movement between FDI and other flows is not
important, FDI volatility should be transmitted into financial account volatility
irrespective of the prevailing share of FDI in the financial account, and we would
expect a positive and significant coefficient on FDI volatility. Alternatively, a negative
or non-significant sign on this variable would indicate that FDI volatility is not
passed on to financial account volatility, either due to its limited presence in the
financial account compared to other flows, or because of the existence of negative
correlations between FDI and other flows.
To distinguish between these last two possible paths, an interaction term between
the previous explanatory variables is added to further clarify which of the two is in
effect. A negative correlation between FDI and other flows that reduces financial
account volatility would be reflected by a negative and significant coefficient in the
interaction term. This outcome would suggest that as the share of FDI increases in
total flows, its volatility is less important for that of the financial account. But, with
FDI being an element of the financial account, the simultaneous reduction in the
transmission of its volatility to the financial account, along with an increased share
of the flow in the financial account could only arise from substantial negative inter-
flow correlation. If this were the case, we would agree with the traditional
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literature on the stabilizing properties of FDI, but because of a very different causal
argument. In particular, this beneficial time behavior would not arise from FDI being
more resilient, but from its degree of co-movement with other capital flows.
Any other result on the interaction term would cast severe doubts on the
existence of substitution effects for FDI flows. A non-significant interaction term,
for instance, would imply that the relationship between FDI volatility and financial
account volatility is the same at all ranges of FDITK. Furthermore, the simultaneous
attainment of a non-significant interaction term and a significantly positive
coefficient on FDIV would contribute the strongest refutation of the substitution
effects for FDI, as the coefficient on FDIV would also confirm that the transmission
of FDI volatility is actually transferred to financial account volatility. Similarly,
positive and significant coefficients for both FDIV and the interaction term would
suggest that the effect of FDI volatility over the volatility of the financial account
would be greater, a result that would also lead us to reject the hypothesis of
mutually offsetting correlations between FDI and other flows.
All samples would not be suitable to the use of an interaction term of this sort. For
example, any existing counterbalancing flow volatilities would be irrelevant if a
single flow gathers the large majority of the financial account. In this instance, an
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interaction term like ours would be insignificant, not because of the absence of
negative correlations, but because the financial account would be comprised almost
entirely by one flow type.
A glance at the univariate properties of the share of FDI in total private flows
however confirms that this is not the case for our sample. We find that 90% of
FDITK observations have a value below 50%27, enabling in principle that
ameliorating effect of interflow correlation over the financial account across the
almost totality of our sample. Finally, at several stages of the analysis we add
quadratic terms for FDIV and FDITK, in order to disentangle possible non-linear
behaviors, and also to clarify further the interpretation of the interaction term.
Summing up, there are two alternative arguments that motivate this stage of our
research. On the one hand, the conventional wisdom on capital flow volatility,
blatantly unconcerned with financial account volatilities and inter-flow correlations,
views FDI as being the most desirable flow based on its time series properties.
Specifically for our model, this line of thinking would be most ratified through
coefficients that would be significant and positive for FDI volatility, negative and
27 The 90th percentile is 47%, with an overall mean of 22%
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significant for FDITK, and non-significant (or positive and significant) for the
interaction term.
On the other hand, there is the alternative claim, and the one we are particularly
interested to investigate, from Claessens et al. (1995) that individual flow volatilities
are mutually offsetting and thus that FDI may not be so desirable and stabilizing in
itself. This hypothesis would be corroborated in our specification through non-
significant (or even negative) coefficients on FDI volatility, but especially a negative
and significant interaction term. Such a finding would imply that increases in FDI
volatility are unable to affect financial account volatility (or that they actually reduce
it). Moreover, the negative interaction term would convey the idea that this inability
to transfer FDI volatility into greater financial account volatility is more acute the
stronger the presence of FDI in total flows, a result that could only occur in the
case of FDI being strongly and negatively correlated with other flows. In this
fashion, FDI would have the most desirable time series properties for the host
economy, this time through a causal argument radically different from the studies
based on the univariate properties of capital flows.
If the above combination of coefficient signs for our variables is the strongest
possible validation of the argument advanced in Claessens et al. (1995), it is
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certainly not the only outcome in which the conventional wisdom on capital flows
would be disputed. For instance, simply finding a positive and significant coefficient
for the share of FDI in the financial account would pose a strong challenge to the
conventional view on capital flows, as it would imply that financial accounts biased
towards FDI are more unstable. While there are obviously additional combinations
of signs, these three cases constitute important alternative possibilities.
Before closing this discussion, we feel is necessary to note what may have already
been obvious to the reader. Namely, that although we refer to the potential role of
correlation among flows, we do not explicitly model correlation variables in our
analysis. To our knowledge, the few studies that have considered the role of inter-
flow correlation have focused on a country-by-country calculation of correlation
coefficients, with the purpose of identifying what pairs of flows tend to be most
negatively correlated. While this approach is able to illustrate useful regularities in
the correlation of a flow with others, it does not identify whether such correlation
is strong enough to offset individual volatilities. These are however the necessary
considerations for our analysis. On the other hand, a unified reading of our
explanatory variables can identify whether the correlation between FDI and other
flows is capable of reducing the transmission of FDI volatility over the financial
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account, making dispensable the inclusion of a variable on inter-flow correlation in
our specification.
As stated before, while the previous regressors are the critical ones to address our
research questions, we also incorporate a set of control variables that were
included across all specifications, all in four-year averages. As a gross approximation
to the state of development of the host country, we include per capita GDP (GDP).
More specific aspects of the developmental stage of the economy are accounted for
through financial development (M2GDP), measured by the ratio of money and
quasi-money to GDP; and the typical measure of openness, the ratio of exports plus
imports to GDP (OPEN)28.
There are ample reasons to presume that financial development affects the volatility
of the financial account. Possibly the most appealing one in our case is that a
developed financial market is usually a technical requirement for attracting certain
capital flows that have traditionally been considered to be highly unstable. This is
the certainly the case with portfolio investment flows, but also some types of FDI
investments, such as those based on international mergers and acquisitions, that in
some studies have been proven to exhibit greater volatility than other types of
28 Data on these variables is compiled from the World Development Indicators database.
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direct investments (Persaud, 2001). In this way, the development of domestic
financial markets has increasingly eased the mobility of FDI across the globe, as
multinationals enjoy an expanding set of financial instruments and practices (e.g.,
derivatives, hedging), that a priori could compensate for their relatively immobile
investments in physical capital (South Centre, 1997). A counterargument however,
would favor an alleviating influence of financial development over financial account
volatility, as it expands the range of capital flows that the host economy can attract,
possibly reducing the volatility of the financial account through its diversification.
We also find reasons for the addition of trade openness to our set of explanatory
variables. But, as opposed to the case of financial development, the link between
trade openness and the financial account does not automatically bring possible
implications over the volatility of the former. This is in part a consequence that
exports and imports refer to the accounting counterpart of our dependent variable,
the current account. But if not about its volatility, the literature does offer strong
links between openness and the size and composition of capital flows. Hausmann
and Fernández-Arias (2000), for instance, finds empirical evidence that relatively
open economies tend to attract more foreign capital; and in addition, although in a
not so strong bond, it also leads to a composition of capital flows that is less
skewed towards FDI. These aspects about financial account size and composition
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may therefore be important enough to yield volatility patterns, justifying the
inclusion of such a measure in our study. For instance, it is reasonable to assume
that open economies may face greater financial account volatility associated with
the larger scale of capital flows; a volatility that would not necessarily be accounted
for by general measures of development.
In another paper, the same authors highlight that trade openness is positively
associated with the likelihood of current account crises, a term associated with
drastic reversals of funds, and therefore with increased volatility. Arguably,
increased volatility in the current account might transcend to the financial account,
especially in developing nations, where the buffer provided by international reserves
is not large enough to accommodate these swings in funds. Razin et al. (2002)
conveys a closer hypothetical link between openness and financial account volatility,
as it blames trade openness for the occurrence of boom-bust cycles of investments,
a phenomenon that should closely be associated with higher volatility of the
financial account if those cyclical fluctuations are also applicable to foreign
investments.
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7.1. Estimation Results
With the variables at hand, and after computing 4-year volatility or average
measures, we are able to construct an unbalanced panel of 104 countries, with
periods ranging from 2 to 5. Ours is therefore a typical unbalanced panel sample,
with a relatively large number of countries for relatively few periods. There are
numerous advantages of undertaking an empirical study based on panel data, some
of the most cited being the increased degrees of freedom from exploiting both
cross-section and time dimensions, or the reduction in the collinearity of the
explanatory variables29. But besides these factors, a key benefit of panel data
estimation is that it allows much greater flexibility in the way the heterogeneity
among cross-sections is treated, as it permits several estimation methods depending
on these country differences. A general expression for a panel data model can be
articulated as follows:
it k itk
K
k
it u X Y +=∑
=
β 1
it t iit u ε λ α ++=
The above encompasses two sources of heterogeneity, alpha and lambda, which
reflect country and time-specific effects. In this fashion, the intercept varies across
29 For a more detailed review see Hsiao (2003)
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both dimensions of the panel sample, granting each observation with a unique
intercept, but constant slopes30. In dealing with this type of models, there are
essentially two initial estimation methods: The first procedure is Fixed Effects, also
called Least Squares Dummy Variable, since it is essentially equivalent to inserting a
vector of i-1 countries and t-1 time dummies in a basic OLS specification. A major
benefit from estimating a panel sample through Fixed Effects is that it treats the
country specific effects as fixed parameters that can be correlated with the
explanatory variables. An alternative estimation, Random Effects, sees the country
or time effects as random observations from the population, and not as constant
parameters. And in addition, it assumes the explanatory variables to be strictly
exogenous. As a result, the unobserved effects are assumed not to be correlated
with the included regressors.
Choosing between both estimation methods is not a relevant matter when the time
dimension of the panel is large, as both lead to the same estimator. But given the
“short T” of our panel, whether to treat the specific effects as fixed or random is a
decisive point in the analysis. If the specific and independent variables are
30 Other specifications permit regressor coefficients to vary across country and/or time as well. Butas Yaffee (2003) remarks, these models would require country and time dummies, but alsointeraction terms between these and the rest of the explanatory variables. And with thisskyrocketing set of regressors, the loss of degrees of freedom could be so large that it wouldeliminate much of the advantages of pooling the data, or even render the model impossible toestimate.
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uncorrelated, both procedures yield consistent estimates; but in this case random
effects models would be preferable because they are also efficient, while Fixed
Effects are not. If on the other hand they are correlated, the estimates of random
effects models would no longer be consistent, given that it is grounded on the
orthogonality of specific effects and regressors. In these circumstances, it would be
better to use Fixed effects, since it does not require this assumption to deliver
consistent estimators. To decide which estimation method fits our data best, we
perform F tests on the significance of group tests, and a Hausman test to decide
between Fixed or Random Effects, both included in table 2.
The first test asks about the poolability of our data. If specific effects are not
statistically significant, there would not be any need to include group dummies in
the model. Hence, OLS would in principle yield BLUE estimators, along with a
substantial gain in degrees of freedom. This significance test is analytically similar to
an F test, where the R-square of the LSDV model is compared to that of the pooled
OLS. Thus, we reject the null hypothesis if the R-square from the LSDV model is
substantially larger than the pooled OLS, which confirms a significant improvement
in explanatory power from accounting for country heterogeneity. Applying this test
to our model yields significant statistics in all specifications, rejecting therefore the
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null hypothesis31 of insignificant differences across countries, and the use of OLS as
an adequate estimation procedure.
Once we confirm the significant group heterogeneity, the Hausman test helps us to
choose between Fixed and Random Effects estimation. Given that the fundamental
difference in assumptions between both procedures was the correlation, or lack
thereof, between unobserved heterogeneity and explanatory variables, the test
precisely checks for the existence of such correlation, through a comparison of the
covariance matrixes of the LSDV and the Random Effects model. If there are no
significant differences between the two, the null hypothesis of no correlation
between the unobserved effects and explanatory variables is validated, and Random
Effects can be chosen. But, a rejection of the null hypothesis favors instead the use
of Fixed Effects estimators, as these remain consistent in the presence of the
referred correlation. The test statistic is given by the following expression, which
under the null hypothesis would follow a chi-square whose degrees of freedom are
the number of regressors minus one.
2
1
1'
)ˆˆ
()]ˆ
var()ˆ
[var()ˆˆ
( −
−→−−−=
k RE LSDV RE LSDV RE LSDV
W χ β β β β β β
31 The statistic is compared to a critical F of (109, 390) degrees of freedom, whose value at the 95%level is 1.31.
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When applied to our models, Hausman tests rejected the null hypothesis in all
specifications but one32. A priori, the conclusions of the test are in accordance with
the intuition of our study, which aims to cover most of the developing nations that
effectively receive North-South FDI flows. Thus, to the extent that our cross-
sectional units come close to representing the entire population under study, Fixed
Effects would be a more pertinent course of action33.
The same table includes the results of the several specifications we estimate. We
start with a basic model in which FDIV and FDITK are accompanied by their
interaction term, and a reduced set of general control variables (Model 1). Here,
the significance and signs of FDI volatility (positive) and FDITK (negative), initially
suggest that FDI volatility is transmitted into the financial account, and that an
increasing importance of FDI in external financing reduces financial account
volatility. Finally, the insignificance of the interaction term helps to assure that this
last conclusion is not due to offsetting volatilities between FDI and other flows. In
all, the unified reading of these results delivers a picture that is in close agreement
with the conventional wisdom on capital flows, where the greater stability of
32 The exception is the model with a quadratic term on FDIV, and without interaction. Nevertheless,the use of Random or Fixed effects did not yield differences in the coefficient signs or statisticalsignificance. To illustrate these similar results, we include both estimation procedures for thisspecification.33 For justifications of the use of Fixed Effects based on the comprehensive character of the samplesee Greene (2002) and Pirtilla (2000).
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financial accounts concentrated in FDI is grounded on the univariate properties of
this flow, and not on its potential interaction with other flow categories.
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Table 2: Panel Estimation
Model 1 Model 2
Fixed effects Fixed effects Random effects Variables coeff t coeff t coeff t co
FDIV 0.787 3.76*** 0.621 5.16*** 0.73 7.36*** 0
FDITK -4.45 -3.96*** -3.171 -2.84*** -2.8 -3.38*** -3
FDIVTK 0.199 0.64 -0
FDIV2 0.0055 2.81*** 0.0038 2.23** 0.0
GDP -0.0004 -2.47*** -0.0005 -2.58** -0.00017 -2.3** -0.0
M2GDP 0.047 2.33** 0.0463 2.3** 0.024 2.67*** 0.0
OPEN 0.026 2.08** 0.024 1.88* 0.023 4.47*** 0.
Adj. R-square 0.57 0.58 0.52
F test (poolability) 1.37** 1.48***
Hausman 14.63** 9.6
d.freedom 390 390 499
cross-sections 104
Heteroscedasticity(LM test)
8.11*** 6.59***
Panel Autocorrelation(Wooldridge)
0.23 0.23
For all regressions: The dependent variable is financial account volatility, calculated as the standard deviation of the real balaare estimated on non-overlapping 4-year-periods.
***, **, * : significant at the 99%, 95%, and 90% levelModel 2 also includes Random effects, given the results of the Hausman test.Heteroscedasticity: significant values reject the null hypothesis of homoscedasticityPanel Autocorrelation: significant values reject the null hypothesis of no autocorrelation
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Regarding the additional control variables, the results corroborate most of our
previous discussion on the expected relationship with the dependent variable. Both
financial development and openness enter the equation with positive and significant
signs. Once we account for these more specific aspects of the economy, our
specification also identifies a negative relationship between per capita GDP and
financial account volatility. In the absence of more specific control regressors, such
a result could have been surprising since the middle-income countries, at the top of
our sample in terms of per capita GDP34, have been the ones ravaged by financial
crises during the last two decades.
With the above results serving as a preliminary reference, we add an additional set
of specifications to scrutinize in more detail the relationship between our
interaction term and the dependent variable. This would seem wise in view of the
caveats raised in some of the empirical work using interaction variables. Particularly,
the idea that the relevance of an interaction term can be distorted if there are
significant, albeit unaccounted, non-linear effects of the variables that compose the
interaction term35. To assure that potential non-linearities are not affecting our
interaction coefficient, we added specifications in which quadratic terms for both
FDIV and FDITK were included without the interaction term, one at a time. From
34 We remind the reader that our dataset only includes developing nations.35 For an example, see Hansen and Tarp (2001).
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these, we only found the existence of significant increasing returns to FDIV36 (Model
2). Thus, this is the only instance in which the Hausman test yields a non-significant
statistic, suggesting the use of Random Effects estimation.
We estimate model 3 in order to check the extent to which potential non-
linearities on FDI alter the significance of our interaction term. Therefore, we
include in this specification both quadratic and interaction variables. There are
nevertheless no major changes in our prior conclusions about individual coefficients,
and, while the significance of the non-linear behavior on FDI volatility is maintained,
the interaction term remains insignificant. The inclusion of the quadratic term
however does change the sign of the interaction term, which becomes negative.
Besides this change, there are no critical departures from the signs and significance
for the rest of the variables.
Summarizing, our introductory investigation finds no ability for FDI to reduce the
transmission of its volatility to the financial account through inter-flow correlations.
On the contrary, FDI volatility has a positive and significant effect on financial
account volatility, in a relationship that seems to exhibit increasing returns. This
however does not negate the well-established conclusion that FDI has the most
36 We do not include the results for the model with the quadratic term on FDITK, as this was notsignificant.
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attractive time series properties among the various categories of flows, as we still
find evidence that increasing shares of FDI in total flows are associated with more
stable financial accounts.
7.2. Robustness
To further examine the preceding results, which strongly endorse the conventional
view on capital flows volatility, we subjected them to a battery of tests and
estimation methods with the purpose of identifying and correcting for several
possible econometric biases. One of these, that is particularly prominent when
dealing with longitudinal data, is the existence of heteroscedasticity.
Heteroscedastic disturbances usually arise in panel samples when the scale of the
dependent variable tends to vary across cross-sectional units. This could be
particularly relevant in our case, which pools cross sections that differ greatly even
after the variables have been normalized. In addition, our reliance on an unbalanced
panel may also lead to heteroscedasticity, due to the varying size of cross-sections
of the sample (Greene, 2002). This presumption of heteroscedastic disturbances
was indeed confirmed using a Lagrange Multiplier (LM) test, whose null hypothesis
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of homoscedasticity was rejected for all specifications at the 99% confidence level37
(see table 2).
If the presence of heteroscedasticity can be seen as a consequence of exploiting the
cross-sections of our panel, the time series dimension also creates specification
problems of their own. In particular, time series are generally affected by
autocorrelation in the error term, with series displaying memory between present
and past observations. For longitudinal data in particular, autocorrelation may
emerge if there is a systematic variation in the omitted variables over time, an event
that would not be detected by an error term that is assumed to be independently
distributed across time periods (Hsiao, 2003). But in spite of the general tendency
of panel data models to be autocorrelated, our method for constructing the
variables might avoid the presence of autocorrelated disturbances, in so far as
variables created through averages or volatility measures of non-overlapping
periods should exhibit less autocorrelation than the original data series.
This is precisely what seems to be at work in our sample. We checked for the
existence of autocorrelation through a test specific for panel data, developed in
Wooldridge (2002). The test checks for the existence of autocorrelation through
the first-differenced model. Here, the results give some credibility to the
37 The LM test follows a chi-square whose degrees of freedom are the number of slope parameters.
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presumption that averaging significantly reduces the presence of autocorrelation, as
the test is unable to reject the null hypothesis of no autocorrelation for any of our
models (see table 2).
Having a heteroscedastic error term allows Least Squares Dummy Variable
estimates to remain unbiased, but inconsistent, invalidating the inferences we can
draw from significance tests. To correct for this bias, we re-estimate our model
using two alternate methods: A first option is the use of heteroscedasticity-
consistent errors, a method grounded on the seminal paper by White (1980). This
approach tackles directly the inconsistency of the estimates by correcting their
standard errors through their new computation, but this time using a covariance
matrix that corrects the heteroscedastic bias. In doing so, the resulting standard
errors are consistent and able to deliver accurate inferences on the coefficients,
which can still be estimated through LSDV.
Table 3 presents the estimates resulting from adopting White heteroscedastic-
consistent standard errors, which in essence maintains the results obtained in the
standard LSDV estimation, despite an increase in the standard errors for some
regressors. The variable on trade openness is the most affected on this regard,
ceasing to be significant in any of the specifications. Among the central variables of
this study, FDI volatility exhibits also a drop in statistical significance (particularly in
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model X), although it always remains within acceptable boundaries of significance
(i.e., p-values below 0.1). And more importantly, there are no sign changes to
report in our variables, maintaining the same relationships with the dependent
variable from previous regressions.
Besides preserving our original estimation procedure intact, and in contrast to
other heteroscedasticity correction methods (e.g., Weighted Least Squares),
another advantage of White’s errors is that the researcher is not obliged to have
any previous knowledge about the functional form of the heteroscedasticity. But
there are also some caveats, in so far White’s covariance matrix tends to be
underestimated in finite samples (McKinnon and White, 1985), which in turn could
leave the t-ratios to be relatively large; and ultimately, lead us to erroneously
conclude in favor of the significance of the coefficients. To further scrutinize these
results, we also corrected for heteroscedasticity using Feasible Generalized Least
Squares (FGLS). Just as in the case of White’s heteroscedasticity-consistent errors,
the use of FGLS delivers consistent estimates without requiring prior information of
the type of heteroscedasticity, and without departures from the estimation
procedure used originally (i.e., LSDV). The method works in a sequential way,
whose initial step involves extracting the residuals from an OLS regression over our
model. This leads to a second regression in which the natural log of the squared
residuals is regressed against the explanatory variables. Taking the predicted values
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of this last regression permits us to get an estimate of the standard deviation of the
error term, which becomes a weight in a transformed version of our structural
equation.
Once again, the reliance on FGLS does not much change our conclusions on the
coefficients, especially for our main variables of interest. Both FDI volatility and the
share of FDI in total flows retain their original signs and significance. The most
noticeable change that FGLS estimation introduces is that the interaction term is
negative and significant in model 1. This significance nevertheless disappears when
we move to the larger model with the quadratic term on FDIV. On the other hand,
the statistical significance of our general purpose regressors proves to be very
sensitive to the estimation method, with GDP and M2GDP becoming insignificant
when we depart from White’s errors estimation to FGLS, being the opposite case
for OPEN.
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Table 3: Heteroscedasticity-consistent Estimation and FGLS Estimation
White’s heteroscedasticity consistent errors Feasible Generalized Least Squ
Specification Model 1 Model 2 Model 3 Model 1 Model 2
Variables coeff t coeff t coeff t coeff z coeff z
FDIV 0.755 2.08** 0.618 2.66** 0.647 1.66* 1.14 8.38*** 0.687 9.94***
FDITK -4.56 -3.45*** -3.15 -2.32** -3.07 -2.11** -1.97 -5.75*** -1.752 -9.57***
FDIVTK 0.249 0.49 -0.0555 -0.11 -0.347 -1.75*
FDIV2 0.0057 1.78* 0.0057 1.87* 0.0044 3.97***
GDP -0.00026 -2.34** -0.0002 -2.39** -0.00027 -2.39** -0.00002 -0.7 -0.00001 -0.49
M2GDP 0.055 1.97** 0.055 1.97** 0.055 1.97** 0.0042 0.89 0.0046 1.01
OPEN 0.025 1.55 0.0226 1.49 0.0225 1.45 0.022 8.84*** 0.025 11.12***
adj R-sq 0.6 0.61 0.61 0.4 0.4
N 521
Log-Likelihood -1085.8 -1084.8
Wald chi2(6) 810.95 2857.25
prob>chi2 0 0
For all regressions: The dependent variable is financial account volatility, calculated as the standard deviation of the real b
are estimated on non-overlapping 4-year-periods. ***, **, * : significant at the 99%, 95%, and 90% level
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Moving on to other possible econometric caveats, it is highly probable that some of
the regressors used in our econometric model turn out to be endogenous with
respect to the dependent variable. This is especially the case for some of the
variables that were included for “general” control purposes, and for which we can
find bibliographical references suggesting feedback effects with the financial account.
For instance, the causal or consequential nature of financial development with
regards to financial account volatility is not resolved. On one hand, let us recall the
causal avenues identified in earlier sections of this chapter, through which financial
development could affect financial account volatility. Briefly, these were grounded
on the likely diversification of flows arising from more sophisticated financial
intermediaries; or the ability to change the relative mobility of some flows (FDI in
particular) through novel financial instruments. But on the other hand, a
simultaneous claim sees financial depth partially determined by the absence of
shocks, as this allows agents to devise their economic plans in closer association
with financial markets (Easterly et al., 2000).
Openness offers a parallel case in point. While in our previous discussion we
highlighted some of the ways in which openness may lead to capital flow volatility,
Cavallo and Frankel (2004) raise the possibility that trade could be partially
determined by a variable that closely resembles financial account volatility, namely
the occurrence of sudden stops on capital flows. Amid the several paths that the
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authors delimit, one that is suitable for our discussion is that the occurrence of
crises, manifested through immediate stops in capital flows, act as a powerful
incentive to engage in economic reforms, among which sweeping liberalization of
the trade account (and consequently, openness) is generally one of its defining
features.
Regarding the variables based on FDI flows, there are no intuitive reasons from
where to presume the existence of endogeneity for the case of FDI volatility. Given
that this flow is a component of the financial account, the link between both
volatility rates, if existing, should go from FDI to the financial account, and not the
other way around. Endogeneity, on the other hand, might be a legitimate concern in
the case of the share of FDI in total flows, if we assume that the various types of
capital flows do not react in a similar way to incidents of macroeconomic volatility,
a behavior that could ultimately alter the relative composition of the external
financing of the country. While again, this relates to a somewhat general sense of
economic volatility, and not specifically in our dependent variable, more formal
testing for endogeneity would seem justified in this latter case.
The standard procedure for concluding on the endogeneity of regressors is the
Wu-Hausman test, a method that requires an initial selection of instruments. We
therefore devised a set of instruments that in most cases combine lags of the
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instrumented regressor, as well as variables for which we could expect a significant
degree of correlation. Examples of the latter were an index on life expectancy
extracted from WDI (LIFE), which is a plausible instrument for both income per
capita and financial development. For the case of trade openness, we relied on an
empirical regularity identified in Easterly and Kraay (1999), which identifies a
relatively greater trade openness of small states. We therefore include the same
dummy variable for small country these authors use (MICRO)38. For other cases in
which bibliographical references were not available to sustain our choice of
instruments (i.e., FDITK), this was grounded on the de facto degree of correlation
that they exhibited with the variable subjected to the endogeneity test.
Before turning on to the results of the Hausman test, we should remark that its
validity is in great way dependent on the selection of the instruments. This is a
critical matter particularly for those regressors for which references were largely
absent. We therefore decided to formally inspect our choices through the Sargan
test for the validity of instruments. The test follows a chi-square of m-k degrees of
freedom, with m being the number of instruments and k the regressors being
instrumented. Thus, for test statistics above the corresponding table value, the null
hypothesis of valid instruments would be rejected.
38 The variable is extracted from WDI, and takes the value of 1 for countries with population lowerthan 1 million.
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Table 4: Endogeneity TestsVariable Instruments Hausman test Sargan test
FDITK
1st and 2nd lags
(l1sh, l2sh)Small state dummy
(MICRO)
h = 0.15 s ≈ 0
GDP
1st and 2nd lags(l1gdp, l2gdp)
Life expectancy(LIFE)
h= 1.28 s ≈ 0
OPEN
1st and 2nd lags(l1open, l2open)
Small state dummy(MICRO)
h = 4.13***(endogenous)
s ≈ 0
M2GDP
1st and 2nd lags
(l1m2, l2m2)
h = 2.08**
(endogenous) s≈
0***, **, * values significant at the 99%, 95%, and 90% levelHausman test: significant values reject the null hypothesis of exogeneity.Sargan test: non-significant values accept the null hypothesis of valid instrument selection. Thistest is compared to a chi-square with 2 degrees of freedom (table value at the 95% level is0.1) for all variables but M2GDP, which follows a chi-square with 1 d.f. (table value is 0.004).
Using the Sargan test as the criteria to decide on which instruments are adopted,
table 4 summarizes the final selection of instruments and their results for both the
Hausman and Sargan test39. The final choice of instruments appears to be valid,
with the Sargan null hypothesis not being rejected for any combination of
instrumental variables. Thus, the Hausman test recognizes both financial
development and trade openness to be endogenous with respect to financial
account volatility, which in turn would render our regression estimates to be
inconsistent. We therefore estimate our panel model using two-stage least squares,
through a framework that allows accounting for both the cross-sectional
39 Although we tested several combinations of instruments, for the sake of simplicity we do notinclude the ones that failed the Sargan test.
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heterogeneity of the sample, heteroscedasticty, and the endogeneity bias of our
regressors.
To implement two-stage least squares estimation (2SLS), we use the same matrix of
instrumental variables considered for the detection of endogeneity for financial
development and openness, since the Sargan test proved the instruments to be
acceptable. As its name indicates, 2SLS arrives at consistent estimates through two
sequential steps: In the first one, the endogenous variables are estimated against
instrumental and exogenous regressors, in an auxiliary specification that allows
obtaining predicted variables for the endogenous variables. In the final stage, the
predicted values for both financial development and openness substitute for their
actual observations in our structural equation, in a final regression that delivers
heteroscedasticity-consistent estimators.
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Table 5: Two-Stage Least Squares Estimation
Specification Model 1 Model 2 Model 3
Variables coeff t coeff t coeff t
FDIV 1.168 3.48*** 0.38 1.14 0.66 1.73*
FDITK -4.32 -2.26** -4.28 -2.35** -2.69 -1.7*
FDIVTK -0.432 -0.9 -0.84 -1.62
FDIV2 0.0372 1.69* 0.053 2.23**
GDP -0.00018 0.5 -0.00014 -0.52 -0.00012 0.66
M2GDP 0.0722 1.17 0.078 1.28 0.0748 1.25
OPEN -0.053 -1.45 -0.044 -1.27 -0.049 -1.38
N 328
Cross-sections 91Adj. R-square 0.61 0.62 0.62
For all regressions: The dependent variable is financial account volatility (FAV), calculated asthe standard deviation of the real balances. Regressions are estimated on non-overlapping
4-year-periods.***, **, * : significant at the 99%, 95%, and 90% level
M2GDP and OPEN are instrumented through first and second lags, LIFE, and MICRO.
The results from 2SLS estimation are reported in table 5. Even though the new
estimation alters some of previous findings, the most important of our earlier
conclusions are sustained. First, instrumenting for our endogenous variables takes
away their statistical significance, and even the sign for the case of trade openness,
which becomes negative. But for the central variables in our study, essentially the
same pattern of behavior is observed: the interaction term remains non-significant
for all specifications, and in combination with this result, FDI volatility and the share
of this flow in total flows mostly retain their original signs and significance. Only for
the case of the most comprehensive specification (model 3), there is a noticeable
drop in statistical significance for both FDIV and FDITK, although they remain
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within satisfactory levels (90% level). Additionally, the results for models 2 and 3
confirm the importance of the non-linear behavior of FDI volatility, just as it was
identified in the previous regressions. Altogether a picture emerges that conflicts
with any endorsement of possible counterbalancing interactions between FDI and
other flows that could alleviate financial account volatility.
Another robustness check we address is multicollinearity, a problem that can be
especially important in regressions that include interaction or quadratic terms
(Aiken and West, 1991), themselves composed out of other explanatory variables.
The identification of multicollinearity however is in itself problematic, in light of the
absence of a formal test that could categorically accept or reject its presence.
Rather, the available methods at our disposal offer an indication of the degree of
multicollinearity. Among these, one of the most widely used is the calculation of
Variance Inflation Factors (VIF). In the event of a variable affected by
multicollinearity, the VIF indicates how much larger the standard error of its
coefficient estimate is, compared to what it would have been had there been no
collinear relation with other variables. In general, a widely accepted rule of thumb is
that VIFs larger than 10 indicate that multicollinearity is a problem, since the
corresponding standard error would be more than three times as large as in the
case of a VIF of no correlation among regressors. Under these circumstances, it
would be obviously more likely to conclude on the non-significance of the variable,
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even though what really lies beneath is a collinear relation, rather than an irrelevant
regressor.
Table 6 presents the VIFs for our explanatory variables, which show evident signs of
multicollinearity between FDIV and the interaction term. Hence, taking into
consideration the importance that the latter has on the course of our analysis, we
proceed to re-estimate our specification with a method that can eradicate the
collinear relation between both variables. This is accomplished through centering
the variables that intervene in the collinear interaction term (FDIV and FDITK), and
creating a new interaction coefficient as a product of this new “mean centered”
variables40. As the same table illustrates, once this transformation on the variables is
implemented, multicollinearity ceases to be a problem for our variables, all of them
displaying low levels of VIF.
40 Aiken and West (1991) offer a detailed exposition of the implementation and advantages of centering variables in specifications with interaction terms.
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Table 6: Variance Inflation Factors
Variables Non-centered Centered
FDIV 17.87 1.51
FDITK 1.94 1.16
FDIVTK 18.52 2.22
FDIV2 5.55 2.62
GDP 1.1 1.11
M2GDP 1.2 1.22
OPEN 1.6 1.26
A point worth mentioning is that centering slightly changes the interpretation of the
coefficients of the two variables that create our interaction term. In the uncentered
regressions, the estimate for FDIV would be interpreted as the slope of a
regression of FAV on FDIV when FDITK is zero. After centering, the FDIV
coefficient equates the same slope, but evaluated at the average of FDITK41. With
these newly centered variables, we offer a final set of regressions (Table 7) on the
most inclusive model, where both interaction and quadratic terms are added to our
usual set of variables.. These are estimated using alternatively White’s
heteroscedasticity-consistent estimation, feasible generalized least squares, and two-
stage least squares42. These results are the final confirmation of the absence of
interflow correlations involving foreign investment: once again, FDI volatility has a
positive and significant effect over financial account volatility, while the share of FDI
in total flows retains its negative relation with the dependent variable. On the other
41 For our sample, this value is 22%.42 We use in this case the same instruments as in the uncentered regressions.
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hand, removing the multicollinearity bias of the interaction term does not bring its
explanatory power to significant levels. This is in accordance with the results we
have reported throughout this empirical study. Besides these results on our
variables of interest, there are no changes to remark on our additional control
variables, since they remain uncentered in this part of the analysis.
Table 7: Model 3 Centered Variables Regressions
Estimation White errors FGLS 2SLS
Variables coeff t coeff z coeff z
FDIV 0.6817 3.84*** 0.3927 4.74*** 0.59 2.09***
FDITK -3.2746 -2.52** -2.0509 -3.3*** -4.498 -2.13**
FDIVTK -0.67598 -1.19 -0.0704 -0.23 -1.56 -1.49
FDIV2 0.0208 1.91* 0.0324 7.3*** 0.094 2.04**
GDP -0.00027 -2.43** -0.000042 0.23 -0.0001 -0.61
M2GDP 0.0564 2.03** 0.0084 1.5 0.078 1.06
OPEN 0.02253 1.47 0.0299 10.72*** -0.028 -0.42
N 506 506 298
Cross-sections 104 104 90
Adj R-sq 0.589 0.35 0.19
For all regressions: The dependent variable is financial account volatility, calculated as thestandard deviation of the real balances. Regressions are estimated on non-overlapping 4-year-
periods.***, **, * : significant at the 99%, 95%, and 90% level
Acting as a final sensitivity analysis, table 8 includes different combinations of
additional control variables, which are estimated using both heteroscedastic robust
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errors, and FGLS. Among this new set of explanatory variables, one that we feel
particularly inclined to consider is exchange rate stability, after reflecting on our
previous discussion on its effects over FDI. Thus, there are many indications that
point to the idea that other capital flows are also affected by the same variable.
Bachetta and Van Wincoop (1998) for instance, find that the level of capital flows
tends to be higher under a fixed exchange rate regime. Similar conclusions are also
reached in Lopez-Mejia (1999). We therefore include a measure on exchange rate
volatility extracted from ICRG, and calculated as the annual percentage change in
the exchange rate of the national currency against the US dollar (ERV). Still within
the macroeconomic realm, we also added an ICRG measure of country risk for the
current account43 (RCA), the counterpart of the financial account in the national
accounting system. Besides the link delimited through the Balance of Payments
identity, there is a wide theoretical literature that has set the ground for a close
relationship between current and financial account. Calvo et al. (1996), for instance,
associate large inflows of capital with current account deficits. At an empirical level,
Sarisoy (2003) warns that the causal connection between the volatility of capital
flows and current account might be a particular feature of the macroeconomy of
developing nations. All told, we find a substantial basis for assuming that an
43 The index ranges from 0 to 15 points, with higher points indicating lower country risk emanatingfrom the current account.
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uncertain or risky evolution of the current account would be associated with more
unstable performance of capital flows.
A final variable that we include at this stage relates to the country’s degree of
openness of the capital account (KAOPEN). In many instances, tightening the
mobility of capital flows has been a basic policy measure to avoid volatility in capital
flows. To account for this possibility, we make use of the index developed in Chinn
and Ito (2005), itself based on the IMF’s Annual Report on Exchange Arrangements
and Exchange Restrictions (AREAER). The latter envisions four types of capital
account restrictions (multiple exchange rates, restrictions on current account
transactions, on capital account transactions, and requirement of the surrender of
export proceeds), and a set of corresponding binary variables, that take the value of
1 if the specific restriction is a feature of the country. With this reference as guide,
Chinn and Ito build their index by associating higher values with greater capital
account openness44.
Among the newly added variables, exchange rate volatility is the one that presents
the most consistent behavior, as a greater degree of exchange rate volatility is
significantly associated with greater financial account volatility in both estimation
44 This is done by reversing the original AREAER dummies, so that they take the value of one whenthere are no restrictions in the capital account. For a more detailed explanation of how this variableis constructed, see Chinn and Ito (2005).
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methods. The results on RCA and KAOPEN on the other hand preclude us from
any categorical conclusion on their effect over financial account volatility, with the
first variable swinging sign between regressions, and the second being insignificant.
But more importantly for our discussion, we find no change on the variables related
to the behavior of FDI: the volatility and the relatively share of FDI consistently
maintain the sign and significance we observe in previous regressions. Also as in
previous regressions, the interaction term remains insignificant throughout all
specifications and estimations.
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Table 8: Regressions with Additional Institutional Proxies
White’s heteroscedasticity consistent errors Feasible Generalized Least Squ
Specification Exchange RateVolatility Current AccountStability Capital AcctOpeness Exchange RateVolatility Current AccountStability
Variables coeff t coeff t coeff t coeff z coeff z
FDIV 0.76 2.1** 1.53 2.5** 0.47 2.04** 1.22 9.2*** 0.99 5.08***
FDITK -4.24 -3.1*** -3.4 -1.7* -6.14 -3.4*** -1.5 -3.9*** -1.09 -2.1**
FDIVTK 0.23 0.65 -1.5 -1.8 1 1.2 -0.49 -1.2 -0.5 -1.4
GDP -0.0002 -2.27** -0.0002 -1.4 -0.0002 -1.86* -0.00008 -2.6*** -0.0008 -0.27
M2GDP 0.057 2.05** 0.06 1.97* 0.044 1.44 0.008 1.88* 0.006 1.31
OPEN 0.024 1.5 0.041 1.4 0.05 2.83*** 0.02 1.6 0.02 10.9***
ERV 1.04 1.69* 1.5 5.5***
RCA -0.4 -1.87* 3.25 7.59**
KAOPEN -0.14 -0.7
adj R-sq 0.6 0.37 0.38
N 520 297 412 520 297
Log-Likelihood -1076.8 -610.5
Wald chi2(7) 755.1 397.6
prob>chi2 0 0 For all regressions: The dependent variable is financial account volatility, calculated as the standard deviation of the real
are estimated on non-overlapping 4-year-periods. ***, **, * : significant at the 99%, 95%, and 90% lev
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106
Besides extending the range of macroeconomic variables into the right hand side of
our equation, a second course of action in variable selection was to incorporate
proxies for more general institutional features of the country. So far unaccounted
for in our model, some non-macroeconomic aspects of the nation may have an
important role in the attraction of international capital. A very recent example on
this link is offered by Alfaro et al. (2003), who find in the notion of “institutional
quality”, a gross measure of various institutional indices, the fundamental variable to
explain the Lucas paradox (i.e., the absence of a substantial North-south capital flow
despite large capital return differentials).
To explore this possibility, we added one at a time the risk indexes built by ICRG
on political risk, government stability and corruption within the political system;
thus, we also included a general index on political constraints, extracted from
Witold Henisz’s Polcon database. While we do not provide tables for the sake of
simplicity, we found that none of these additions proved to be significant. The
general irrelevance of the institutional variables can be partially traced to the little
variation that some of these indexes experience overtime45, which can be translated
into a diminished explanatory power when they are applied to panel data studies.
45 As an illustrative note, we computed descriptive statistics for all these new measures, finding thatthe only non-index variable (the annual percentage change in exchange rate) had a coefficient of variation of -171.4. This result contrasts with those obtained for the index proxies, whosecoefficients of variation range from 12.6 (political risk) to 32.2 (risk for exchange rate stability).
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Another problematic aspect of their inclusion is that, especially for our sample of
developing nations, their availability tends to be limited with respect to length of
time coverage. Our regressions with these institutional variables resulted in drastic
losses in degrees of freedom, falling from approximately 506 observations to as low
as 250. Despite this fall in observations, particularly problematic in the case of fixed
effects estimation, the results on the effects of our variables on the behavior of FDI
in the financial account remained largely unchanged.
8. Conclusion
The view that FDI is the most stable flow is well established in the literature on
capital flows volatility. In fact, this research body that has become the latest
contributor to the notion that FDI is the most beneficial capital flow for the
receiving economy. Such a conclusion has led to important changes in policies,
which during the last decade have increasingly tilted towards measures to attract
FDI, along with exerting tighter control over flows deemed speculative or volatile46.
One of the most serious challenges to the above claim has been originally raised by
Claessens et al. (1995). Among the points raised by the authors to challenge the
46 For a review of some of the innovations in financial account policies during the decade of thenineties, see Ocampo (2001).
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conventional wisdom, and although not explicitly investigated, they state that the
volatility of a flow may be irrelevant in the presence of negative correlation with
other flows, as the respective flow volatilities would tend to offset each other, with
no effect over the volatility of the financial account. Accordingly, they advocate that
the financial account and not the flow must be the focal point for volatility studies,
in order to effectively account for these interactions.
Aware of this possibility, we have designed an empirical model whose ultimate
purpose was to identify whether the beneficial time series properties of FDI are
maintained when the volatility of the financial account is brought to the center of
the analysis; and more specifically, whether the diffusion of FDI volatility over the
financial account is restrained due to negative correlations between FDI and other
flows. Our results nevertheless raise doubts about the likelihood that FDI
substitutes for other flows: both the positive and significant coefficient on FDI
volatility and the irrelevance of our interaction term suggests that the transmission
of FDI volatility over the financial account is not diminished by counterbalancing
interflow correlations. Had that been the case, we should have expected a negative
and significant interaction term showing that, as the importance of FDI in the
financial account increases, the transmission of its volatility over the financial
account is reduced. On the contrary, across all the specifications we estimate, the
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interaction term is never significant, while the coefficient on FDI volatility remains
positive and significant.
In the absence of substitution effects between FDI and other capital flows, shifting
the locus of study from the individual flow to the financial account could
compromise the notion of FDI as the flow with the soundest time series properties,
especially if other flows are benefited by mutually compensating correlations. But
we find no indication that an interaction of this sort between non-FDI flows might
be partially determining the volatility of the financial account: our regressions
deliver a negative and significant effect of FDITK, confirming that financial accounts
more heavily concentrated in FDI tend to be less volatile. This not only leads us to
conclude that FDI is indeed the flow most conducive to a stable financial account;
but also that the existence of a significant counterbalancing correlation between
flows other than FDI should be anecdotal if existing at all.
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