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



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.



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.



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.



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



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



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



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.



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.



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



3

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.



4

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)



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



6

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.



7

Figure 3: Distribution of FDI Flows (average 1989-99).


China

27%

Brazil

11%

Mexico

8%

rest

30%

Indonesia

2%

Poland

3%

Korea

3%

Chile

3%

Thailand

3% Malaysia

4%

Argentina

6%



8

Figure 4: Distribution of Portfolio Flows (average 1989-99)


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%



9

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-



10

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



11

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.



12

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.



13

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



14

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



15

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).



16

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



17

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.



18

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 %



19

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.



20

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.



21

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)


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



22

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



23

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,



24

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.



25

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



26

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



27

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).



28

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.



29

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



30

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.



31

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).



32

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.



33

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



34

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.,



35

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



36

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



37

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.



38

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



39

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



40

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.



41

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.



42

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.



43

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



44

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



45

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.



46

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



47

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).



48

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.



49

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.



50

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.



51

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

50

100

150

200

250

300

350

1990 1992 1994 1996 1998 2000 2002

treatiesper

year

0

1000

2000

3000

4000

5000

cumulative

treaties cumulative



52

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



53

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



54

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.



55

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



56

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



57

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



58

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.



59

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



60

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.



61

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.



62

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.



63

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



64

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)



65

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



66

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).



67

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.



68

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.



69

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



70

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



71

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%



72

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



73

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



74

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.



75

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



76

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.



77

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)



78

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.



79

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



80

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.



81

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).



82

financial accounts concentrated in FDI is grounded on the univariate properties of

this flow, and not on its potential interaction with other flow categories.



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



84

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).



85

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.



86

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



87

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.



88

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



89

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



90

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.



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



92

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



93

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



94

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.



95

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.



96

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.



97

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



98

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,



99

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.



100

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.



101

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



102

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.



103

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).



104

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.



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



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).



107

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).



108

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



109

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.



110

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