2023年12月18日发(作者：夔笑笑)

Assessing the Emerging Global Financial Architecture:

Measuring the Trilemma's Configurations over Time

Joshua Aizenman*

UCSC and NBER

Menzie D. Chinn**

University of Wisconsin and NBER

Hiro Ito†

Portland State University

April 2009

Abstract: We develop a methodology that intuitively characterizes the choices countries have made with respect to

the trilemma during the post Bretton-Woods period. The paper first outlines the new metrics for measuring the

degree of exchange rate flexibility, monetary independence, and capital account openness while taking into account

the recent development of substantial international reserve accumulation. The evolution of our “trilemma indexes”

illustrates that, after the early 1990s, industrialized countries accelerated financial openness, but reduced the extent

of monetary independence while sharply increasing exchange rate stability, all reflecting the introduction of the euro.

In contrast, emerging market countries pursued exchange rate stability as their key priority up to the late 1980s while

non-emerging market developing countries has pursued it throughout the period since 1970. As a stark difference

from the latter group of countries, emerging market countries have converged towards intermediate levels of all

three indexes, characterizing managed flexibility while retaining some degree of monetary autonomy and

accelerating financial openness. This recent trend appears to be sustained by using sizable international reserves as a

buffer. We also confirm that the weighted sum of the three indexes adds up to a constant, validating the notion that a

rise in one trilemma variable should be traded-off with a drop of the weighted sum of the other two. The second part

of the paper deals with normative aspects of the trilemma, relating the policy choices to macroeconomic outcomes

such as the volatility of output growth and inflation, and medium term inflation rates. Some key findings for

developing countries include: (i) greater monetary independence can dampen output volatility while greater

exchange rate stability implies greater output volatility, which can be mitigated by reserve accumulation; (ii) greater

monetary autonomy is associated with a higher level of inflation while greater exchange rate stability and greater

financial openness could lower the inflation level; (iii) a policy pursuit of stable exchange rate while financial

development is at the medium level can increase output volatility, (iv) greater financial openness with a high level of

financial development can reduce output volatility, though greater financial openness with a low level of financial

development can be volatility-increasing; (v) net inflow of portfolio investment and bank lending can increase

output volatility and higher levels of short-term debt or total debt services can increase both the level and the

volatility of inflation.

JEL Classification Nos.: F 15,F 21,F31,F36,F41,O24

Keywords: Impossible trinity; international reserves; financial liberalization; exchange rate; FDI flows.

Acknowledgements: The financial support of faculty research funds of the UCSC and PSU is gratefully

acknowledged. This paper encompasses the results in two shorter papers: “Mundell-Fleming’s Impossible Trinity:

Testing the Stability and Fitness of Trilemma’s Linear Specification” and “The Emerging Global Financial

Architecture – Tracing and Evaluating the New Patterns of the Trilemma’s Configurations”. We would like to thank

Eduardo Borensztein, Eduardo Cavallo, Camilo Tavor, Mathijs van Dijk, and the participants at the BIS-LACEA

2008 Rio meeting and the 4th Tinbergen Conference for their useful comments and suggestions.

_____________________________

*

Aizenman: Department of Economics E2, UCSC, Santa Cruz, CA 95064. Email: jaizen@.

** Chinn: Robert M. La Follette School of Public Affairs; and Department of Economics, University of

Wisconsin, 1180 Observatory Drive, Madison, WI 53706. Email: mchinn@

† Ito: Department of Economics, Portland State University, 1721 SW Broadway, Portland, OR 97201. Tel/Fax:

+1-503-725-3930/3945. Email: ito@

Introduction

A fundamental contribution of the Mundell-Fleming framework is the impossible trinity,

or the trilemma, which states that a country simultaneously may choose any two, but not all, of

the following three goals: monetary independence, exchange rate stability and financial

integration. The trilemma is illustrated in Figure 1; each of the three sides – representing

monetary independence, exchange rate stability, and financial integration – depicts a potentially

desirable goal, yet it is not possible to be simultaneously on all three sides of the triangle. The

top vertex – labeled “closed capital markets” – is, for example, associated with monetary policy

autonomy and a fixed exchange rate regime, but not financial integration, the preferred choice of

most developing countries in the mid to late 1980s.1

Over the last 20 years, most developing countries have opted for increasing financial

integration. The trilemma implies that a country choosing this path must either forego exchange

rate stability if it wishes to preserve a degree of monetary independence, or forego monetary

independence if it wishes to preserve exchange rate stability.

The purpose of this paper is to outline a methodology that will allow us to easily and

characterize in an intuitive manner the choices countries have made with respect to the trilemma

during the post Bretton-Woods period. The first part of our study deals with positive aspects of

the trilemma, outlining new ways of tracing the evolving financial configurations. The second

part deals with normative aspects of the trilemma, relating the policy decisions chosen to

macroeconomic outcomes, such as the volatility of output growth and inflation, and medium

term inflation rates.

We begin by observing that over the last two decades, a growing number of developing

countries, especially emerging market ones, have opted for hybrid exchange rate regimes – e.g.,

managed float buffered by increasing accumulation of international reserves [IR henceforth].

Despite the proliferation of greater exchange rate flexibility, IR/GDP ratios increased

dramatically, especially in the wake of the East Asian crises. Practically, all the increase in

IR/GDP holding has taken place in emerging market countries [see Figure 2]. The magnitude of

the changes during recent years is staggering: global reserves increased from about USD 1

trillion to more than USD 5 trillion between 1990 and 2006.

The dramatic accumulation of international reserves has been uneven: while the IR/GDP

ratio of industrial countries was relatively stable at approximately 4%, the IR/GDP ratio of

developing countries increased from about 5% to about 27%. Today, about three quarters of the

global international reserves are held by developing countries. Most of the accumulation has

been in Asia, where reserves increased from about 5% in 1980 to about 37% in 2006 (32% in

Asia excluding China). The most dramatic changes occurred in China, increasing its IR/GDP

11.

See Obstfeld, Shambaugh, and Taylor (2005) for further discussion and references dealing with the trilemma.

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ratio from about 1% in 1980, to about 41% in 2006 (and approaching 50% by 2008). Empirical

studies suggest several structural changes in the patterns of reserves hoarding (Cheung and Ito,

2007; Obstfeld, et al. 2008). A drastic change occurred in the 1990s in terms of reserve

management among developing countries. The IR/GDP ratios shifted upwards; the ratios

increased dramatically immediately after the East Asian crisis of 1997-98, but subsided by 2000.

Another structural change took place in the early 2000s, mostly driven by an unprecedented

increase in the accumulation of international reserves by China.

The globalization of financial markets is evident in the growing financial integration of

all groups of countries. While the original framing of the trilemma was silent regarding the role

of reserves, recent trends suggest that reserve accumulation may be closely related to changing

patterns of the trilemma for developing countries. The earlier literature focused on the role of

international reserves as a buffer stock critical to the management of an adjustable-peg or

managed-floating exchange-rate regime.2 While useful, the buffer stock model has limited

capacity to account for the recent development in international reserves hoarding – the greater

flexibility of the exchange rates exhibited in recent decades should help reduce reserve

accumulation, in contrast to the trends reported above.

The recent literature has focused on the adverse side effects of deeper financial

integration of developing countries – the increased exposure to volatile short-term inflows of

capital (dubbed “hot money”), subject to frequent sudden stops and reversals (see Calvo, 1998).

The empirical evidence suggests that international reserves can reduce both the probability of a

sudden stop and the depth of the resulting output collapse when the sudden stop occurs.3

Aizenman and Lee (2007) link the large increase in reserves holding to the deepening financial

integration of developing countries and find evidence that international reserves hoarding serves

as a means of self-insurance against exposure to sudden stops. In extensive empirical analysis of

the shifting determinants of international reserve holdings for more than 100 economies over the

1975-2004 period, Cheung and Ito (2007) find that while trade openness is the only factor that is

significant in most of the specifications and samples under consideration, its explanatory power

has been declining over time. In contrast, the explanatory power of financial variables has been

increasing over time.

The increasing importance of financial integration as a determinant for international

reserves hoarding suggests a link between the changing configurations of the trilemma and the

level of international reserves. Indeed, Obstfeld, et al. (2008) find that the size of domestic

financial liabilities that could potentially be converted into foreign currency (M2), financial

Accordingly, optimal reserves balance the macroeconomic adjustment costs incurred in the absence of reserves

with the opportunity cost of holding reserves (Frenkel and Jovanovic, 1981).

3 See Ben-Bassat and Gottlieb (1992), Rodrik and Velasco (1999), and Aizenman and Marion (2004) for papers

viewing international reserves as output and consumption stabilizers.

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openness, the ability to access foreign currency through debt markets, and exchange rate policy

are all significant predictors of international reserve stocks.

We begin by constructing an easy and intuitive way to summarize these trends in the

form of a “Diamond chart.” Applying the methodology outlined in the next section, we construct

for each country a vector of trilemma and IR configurations that measures each country’s

monetary independence, exchange rate stability, international reserves, and financial integration.

These measures are normalized between zero and one. Each country’s configuration at a given

instant is summarized by a “generalized diamond,” whose four vertices measure the three

trilemma dimensions and IR holding (as a ratio to GDP).

Figures 3 and 4 provide a concise summary of the recent history of trilemma

configurations for different income groups and regional groups.4 Figure 3 reveals that, over time,

both industrialized countries and emerging market countries have moved towards deeper

financial integration and losing monetary independence, a stark contrast from non-emerging

market developing countries. Furthermore, emerging market countries have pursued greater

financial integration while non-emerging market developing countries barely have. As of the

2000s, emerging market countries distinctly differ from other groups with its balanced

combination of the three macroeconomic policy goals as well as substantial amount of IR

holding.

In Figure 4, we can see that Latin American economies have liberalized their financial

markets rapidly since the 1990s after some retrenchment during the 1980s. Emerging markets in

Latin America reduced the extent of monetary independence in recent years and maintained a

lower level of exchange rate stability. Emerging Asian economies have achieved comparable

levels of exchange rate stability and financial openness while consistently reducing monetary

independence. This group of economies differ from the other ones the most with their relatively

balanced achievement of the three macroeconomic policy goals and their high levels of

international reserves holding.

Figure 5 presents the development of trilemma indexes for 50 countries (32 of which are

developing countries) during the 1970-2006 period for which we can construct a balanced data

set. Focusing on developing countries, we can observe an interesting trend. Comparing Figure 3b

and 3c reveals the distinctly different trilemma patterns between emerging (EMG) and non-emerging (non-EMG) market countries.5 EMGs moved towards relatively more flexible

exchange rate than Non-EMGs, buffering it by holding much higher IR/GDP, as well as towards

In each diamond chart, the origin is normalized so as to represent zero monetary independence, pure float, zero

international reserves and financial autarky.

5 Table 1 shows that the differences of the Trilemma indexes for monetary independence, exchange rate stability,

and financial openness as well as international reserves holding (as a ratio to GDP) between EMGs and non-EMG

developing countries are statistically significant.

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higher financial integration and lower monetary independence. The figure shows that EMGs

have experienced convergence to some middle ground among all three indexes. In contrast, non-EMGs, on average, have not exhibited such convergence. For both groups, while the degree of

exchange rate stability declined from the early 1970s to the early 1990s, it increased during the

last fifteen years – though one could expect that the present crisis would induce these countries

to move toward higher exchange rate flexibility. Currently, non-EMGs exhibit a greater degree

of exchange rate stability and monetary independence, but a lower degree of financial integration

compared to EMGs.

Despite the cross-country and over-time variations in the trilemma configures, one key

message of the trilemma is instrument scarcity – policy makers face a tradeoff, where increasing

one trilemma variable (such as higher financial integration) would induce a drop in the weighted

average of the other two variables (lower exchange rate stability, or lower monetary

independence, or a combination of the two). Yet, to our knowledge, the validity of this tradeoff

among the three trilemma variables has not been tested properly. A possible concern is that the

trilemma framework does not impose an exact functional restriction on the association between

the three trilemma policy variables.

We conduct a regression analysis to test the validity of the simplest functional

specification for the trilemma: whether the three trilemma policy goals are linearly related. For

this purpose, we also examine and validate that the weighted sum of the three trilemma policy

variables adds up to a constant (see Figure 7). This result confirms the notion that a rise in one

trilemma variable should be traded-off with a drop of a linear weighted sum of the other two.

The regression results also provide another diagnostic tool, allowing a simple description of the

changing ranking among the three trilemma policy goals over time.

In the second half of the paper, we investigate the normative questions pertaining to the

trilemma. More specifically, we examine how the policy choices among the three trilemma

policies affect output growth volatility, inflation rates, and the volatility of inflation, with focus

on developing economies. Given that EMGs collectively have outperformed non-EMGs in terms

of average economic growth rates, it can be the middle ground configuration of the trilemma

policies that have contributed to the recent rapid and better development and high economic

growth among the emerging markets. Yet, without controlling for the macroeconomic

environment, one cannot be definitive about the causality since the middle-ground convergence

may also be the outcome of successful take offs and prolonged growth. Our paper attempts to

verify these issues through regression analyses, looking more systematically at the association

between trilemma choices and economic performance. Upon investigating the link between the

trilemma policy configurations and macroeconomic performance of the countries of our focus,

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we also pay close attention to three other factors, namely, international reserves (IR) holding,

financial development, and external finance.

As has been intensively investigated in the literature, for the last decade since the Asian

crisis of 1997-98, developing countries, especially those in East Asia and the Middle East, are

rapidly increasing the amount of international reserves hoarding. China, the world’s largest

holder of international reserves, currently holds about $2 trillion of reserves, accounting for 30%

of the world’s total. As of the end of 2008, the top 10 biggest holders are all developing countries

except for Japan, and the nine developing countries, including China, Russia, Taiwan, and Korea

hold over 55% of international reserves available in the world. Against this backdrop, it has been

argued that one of the main reasons for the rapid IR accumulation is countries’ desire to stabilize

exchange rate movement. Hypothetically, one could argue that countries hold massive

international reserves to have balanced combinations of exchange rate stability, monetary policy

autonomy, and financial openness. Thus, evidently, one cannot discuss the issue of the trilemma

without incorporating the effect of IR holding, which we will do in this paper.

Secondly, the ongoing crisis has made it clear that financial development can be a

double-edged sword. While it can enable more efficient allocation of capital, it also embraces the

possibility of amplifying shocks to the economy. As a country may incorporate financial

development into its decision-making process for the trilemma configurations, as China has been

alleged to pursue closed financial markets with exchange rate stability as precautionary measures

to protect its underdeveloped financial system, the degree of financial development could affect

the macroeconomic performance of the economy.6 Some also argue that countries with newly

liberalized financial system tend to experience financial fragility (Demirguc-Kent and

Detragiache, 1998). Thus, trilemma policy configurations need to be investigated while

incorporating the level of financial development.

Thirdly, as globalization proceeds with an unprecedented speed, and more countries are

abolishing capital controls, policy makers in countries, especially developing ones, cannot ignore

the effect of capital flows from other countries. As Lane and Milesi-Ferretti (2006) show, the

type, volume, and direction of capital flows has been changing over time, thus policy makers

have to aim at moving targets in their policy decision making. Especially, considering that the

present crisis has shown that the speed and the volume of tsunami of capital flows can be

enormous, we must be abreast of the cost and benefit of trilemma configurations in tandem with

those of external financing such as FDI flows, portfolio flows, and banking lending across

countries.

See Prasad (2008) for the argument that China’s policy of exchange rate stability and closed financial markets is

impairing the country’s macroeconomic management.

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In the remaining of the paper, Section 2 outlines the methodology for the construction of

our “trilemma indexes.” This section also presents summary statistics of the indexes and

examines whether the indexes entail any structural breaks corresponding to major global

economic events. Furthermore, in this section, we test the validity of a linear specification of the

trilemma indexes to examine whether the notion of the trilemma can be considered to be a trade-off and binding. Section 3 conducts more formal analysis on how the policy choices affect output

growth volatility, inflation rates, and the volatility of inflation, with focus on developing

economies. In Section 4, we extend our empirical investigation and examine the impact of other

important economic variables related to the current crisis such as financial development and

various forms of external financing. In Section 5, we make casual observations to see whether

our empirical findings are consistent with the occurrence of the ongoing severe crises in some

countries. We present our concluding remarks in Section 6.

2. Measures of the Trilemma Dimensions

The empirical analysis of the tradeoffs being made requires measures of the policies.

Unfortunately, there is a paucity of good measures; in this paper we attempt to remedy this

deficiency by creating several indices.

2.1 Construction of the Trilemma Measures

Monetary Independence (MI)

The extent of monetary independence is measured as the reciprocal of the annual

correlation of the monthly interest rates between the home country and the base country. Money

market rates are used.7

The index for the extent of monetary independence is defined as:

MI =

1−corr(ii,ij)−(−1)1−(−1)

where i refers to home countries and j to the base country. By construction, the maximum and

minimum values are 1 and 0, respectively. Higher values of the index mean more monetary

policy independence.8,9

The data are extracted from the IMF’s International Financial Statistics (60B..ZF...). For the countries whose

money market rates are unavailable or extremely limited, the money market data are supplemented by those from

the Bloomberg terminal and also by the discount rates (60...ZF...) and the deposit rates (60L..ZF...) series from IFS.

8 The index is smoothed out by applying the three-year moving averages encompassing the preceding, concurrent,

and following years (t – 1, t, t+1) of observations.

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Here, the base country is defined as the country that a home country’s monetary policy is

most closely linked with as in Shambaugh (2004). The base countries are Australia, Belgium,

France, Germany, India, Malaysia, South Africa, the U.K., and the U.S. For the countries and

years for which Shambaugh’s data are available, the base countries from his work are used, and

for the others, the base countries are assigned based on IMF’s Annual Report on Exchange

Arrangements and Exchange Restrictions (AREAER) and CIA Factbook.

Exchange Rate Stability (ERS)

To measure exchange rate stability, annual standard deviations of the monthly exchange

rate between the home country and the base country are calculated and included in the following

formula to normalize the index between zero and one:

ERS=0.01

0.01+stdev(Δ(log(exch_rate))Merely applying this formula can easily create a downward bias in the index, that is, it would

exaggerate the “flexibility” of the exchange rate especially when the rate usually follows a

narrow band, but is de- or revalued infrequently.10 To avoid such downward bias, we also apply

a threshold to the exchange rate movement as has been done in the literature. That is, if the rate

of monthly change in the exchange rate stayed within +/-0.33 percent bands, we consider the

exchange rate is “fixed” and assign the value of one for the ERS index. Furthermore, single year

pegs are dropped because they are quite possibly not intentional ones.11 Higher values of this

We note one important caveat about this index. For some countries and some years, especially early in the sample,

the interest rate used for the calculation of the MI index is often constant throughout a year, making the annual

correlation of the interest rates between the home and base countries (corr(ii, ij) in the formula) undefined. Since we

treat the undefined corr the same as zero, it makes the MI index value 0.5. One might think that the policy interest

rate being constant (regardless of the base country's interest rate) is a sign of monetary independence. However, it

could reflect the possibility that the home country uses other tools to implement monetary policy, rather than

manipulating the interest rates (e.g., manipulation of required reserve ratios and providing window guidance; or

financial repression). To complicate matters, some countries have used reserves manipulation and financial

repression to gain monetary independence while others have used both while strictly following the base country's

monetary policy. The bottom line is that it is impossible to fully account for these issues in the calculation of MI.

Therefore, assigning an MI value of 0.5 for such a case appears to be a reasonable compromise. However, we also

undertake robustness checks on the index.

10 In such a case, the average of the monthly change in the exchange rate would be so small that even small changes

could make the standard deviation big and thereby the ERS value small.

11 The choice of the +/-0.33 percent bands is based on the +/-2% band based on the annual rate, that is often used in

the literature. Also, to prevent breaks in the peg status due to one-time realignments, any exchange rate that had a

percentage change of zero in eleven out of twelve months is considered fixed. When there are two re/devaluations in

three months, then they are considered to be one re/devaluation event, and if the remaining 10 months experience no

exchange rate movement, then that year is considered to be the year of fixed exchange rate. This way of defining the

threshold for the exchange rate is in line with the one adopted by Shambaugh (2004).

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index indicate more stable movement of the exchange rate against the currency of the base

country.

Financial Openness/Integration (KAOPEN)

Without question, it is extremely difficult to measure the extent of capital account

controls.12 Although many measures exist to describe the extent and intensity of capital account

controls, it is generally agreed that such measures fail to capture fully the complexity of real-world capital controls. Nonetheless, for the measure of financial openness, we use the index of

capital account openness, or KAOPEN, by Chinn and Ito (2006, 2008). KAOPEN is based

on

information regarding restrictions in the IMF’s Annual Report on Exchange Arrangements and

Exchange Restrictions (AREAER). Specifically, KAOPEN is the first standardized principal

component of the variables that indicate the presence of multiple exchange rates, restrictions on

current account transactions, on capital account transactions, and the requirement of the

surrender of export proceeds.13 Since KAOPEN is based upon reported restrictions, it is

necessarily a de jure index of capital account openness (in contrast to de facto measures such as

those in Lane and Milesi-Ferretti (2006)). The choice of a de jure measure of capital account

openness is driven by the motivation to look into policy intentions of the countries; de facto

measures are more susceptible to other macroeconomic effects than solely policy decisions with

respect to capital controls.14

The Chinn-Ito index is normalized between zero and one. Higher values of this index

indicate that a country is more open to cross-border capital transactions. The index is originally

available for 181 countries for the period of 1970 through 2006.15 The data set we examine does

not include the United States. The Appendix presents data availability in more details.

2.2 Tracking the Indexes

Variations across Country Groupings

Comparing theses indexes provides some interesting insights into how the international

financial architecture has evolved over time. For this purpose, the “diamond charts” are most

useful. In each diamond chart, the origin is normalized so as to represent zero monetary

See Chinn and Ito (2008), Edison and Warnock (2001), Edwards (2001), Edison et al. (2002), and Kose et al.

(2006) for discussions and comparisons of various measures on capital restrictions.

13 This index is described in greater detail in Chinn and Ito (2008).

14 De jure measures of financial openness also face their own limitations. As Edwards (1999) discusses, it is often

the case that the private sector circumvents capital account restrictions, nullifying the expected effect of regulatory

capital controls. Also, IMF-based variables are too aggregated to capture the subtleties of actual capital controls, that

is, the direction of capital flows (i.e., inflows or outflows) as well as the type of financial transactions targeted.

15 The original dataset covers 181 countries, but data availability is uneven among the three indexes. MI is available

for 172 countries; ERS for 182; and KAOPEN for 178. Both MI and ERS start in 1960 whereas KAOPEN in 1970.

For the data availability of the trilemma indexes, refer to Appendix.

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independence, pure float, zero international reserves and financial autarky. Figure 3 summarizes

the trends for industrialized countries, those excluding the 12 euro countries, emerging market

countries, and non-emerging market developing countries.16

That figure reveals that, over time, while both industrialized countries and emerging

market countries have moved towards deeper financial integration and losing monetary

independence, non-emerging market developing countries have only inched toward financial

integration and have not changed the level of monetary independence. Emerging market

countries, after giving up some exchange rate stability during the 1980s, have not changed their

stance on the exchange rate stability whereas non-emerging market developing countries seem to

be remaining at or slightly oscillating around a relatively high level of exchange rate stability.

The pursuit of greater financial integration is much more pronounced among industrialized

countries than developing countries while emerging market countries have been increasingly

becoming more financial open. Interestingly, emerging market countries stand out from other

groups by achieving a relatively balanced combination of the three macroeconomic goals by the

2000s, i.e., middle-range levels of exchange rate stability and financial integration while not

losing as much of monetary independently as industrialized countries. The recent policy

combination has been matched by a substantial increase in IR/GDP at a level that is not observed

in any other groups.

To confirm the different development paths of the trilemma indexes for the groups of

EMGs and non-EMG developing countries for the last four decades, we conduct mean-equality

tests on the three trilemma indexes and the IR holding ratios between EMGs and non-EMG

developing countries. We report the test results in Table 1 and statistically confirm that the

development path of the trilemma configurations has been different between these two groups of

developing countries.

Figure 4 compares developing countries across different geographical groups.

Developing countries in both Asia and Latin America (LATAM) have moved toward exchange

rate flexibility, but LATAM countries have rapidly increased financial openness while Asian

counterparts haven not. Asian emerging market economies have moved further toward financial

openness on a level comparable with LATAM emerging market countries, yet one key difference

between the two groups is that the former holds much more international reserves than the latter.

More importantly, Asian emerging market countries have achieved a balanced combination of

the three policy goals while the other groups have not, which can easily make one suspect it is

the high volume of IR holding that may have allowed this group of countries to achieve such a

trilemma configuration. We will revisit this issue later on. Lastly, Sub-Saharan African countries

16

The emerging market countries are defined as the countries classified as either emerging or frontier during the

period of 1980-1997 by the International Financial Corporation plus Hong Kong and Singapore.

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appear to have pursued the policy combination of exchange rate stability and monetary

independence while lagging considerably in financial liberalization behind the other regions.

Patterns in a Balanced Panel

Figure 5 again presents the development of trilemma indexes for different subsamples

while focusing on the time dimension of the development, but also restricts the entire sample to

include only the countries for which all three indexes are available for the entire time period. By

balancing the dataset, the number of countries included in the sample reduces to 50 countries out

of which 32 countries are developing countries including 18 emerging market countries. Each

panel presents the full sample (i.e., cross-country) average of the trilemma index of concern and

also its one-standard deviation band. There is a striking differences between industrialized and

developing countries as well as between emerging market and non-emerging market countries.

The top-left panel shows that, between the late 1970s and the late 1980s, the levels of

monetary independence are closer to each other between industrialized countries and developing

ones. However, since the early 1990s, these two groups have been diverging from each other.

While developing countries have been hovering around the medium levels of monetary

independence and slightly deviating from the cross-country average, industrialized countries

have steadily become much less monetary independent and moved farther away from the cross-country average, reflecting the efforts made by the euro member countries.17 In the case of the

exchange rate stability index, after the breakup of the Bretton Woods system, industrialized

countries significantly reduced the extent of exchange rate stability until the early 1980s. After

the 1980s, these countries gradually, but steadily increased the extent of exchange rate stability

to the present – though they experienced some intermittent in the early 1990s due to the EMS

crisis.18 Developing countries, on the other hand, maintained relatively high levels of exchange

rate stability until the 1980s. Although these countries seem to have adopted some exchange rate

flexibility in the early 1980s, they have since maintained constant levels of exchange rate

stability through the early 2000s, which seems to reflect the “fear of floating.” In the last few

years, these countries even gradually increased the level of exchange rate stability. Not

surprisingly, industrialized countries have achieved higher levels of financial openness

throughout the period. The acceleration of financial openness in the mid-1990s remained

significantly high compared to the cross-country average of both the full sample and LDC

subsample. On the other hand, developing countries also accelerated financial openness in the

17

When the euro countries are removed from the IDC sample, the extent of the divergence from the average

becomes less marked although there is still a tendency among the non-euro countries to move toward lower levels of

monetary independence.

18 The ERS index for the non-euro industrialized countries, persistently hovers around the value of .40 throughout

the time period after rapidly dropping in the early 1970s.

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early 1990s after some retrenchment during the 1980s. Overall, LDC countries have been in

parallel with the global trend of financial liberalization throughout the sample period, but the

difference from the industrialized countries has been moderately rising in the last decade.

Broadly speaking, the difference between emerging market countries and non-emerging

market developing countries is smaller than that between IDC and LDC subsamples (shown in

the bottom row of Figure 5). However, the divergence between the two groups seems to be

becoming wider gradually since the mid-1990s. While non-EMG countries have retained

relatively constant levels of monetary independence, EMG countries have become less monetary

independent. As for exchange rate stability, EMG countries are persistently more flexible than

non-emerging ones since 1980 and the difference is wider since the early 1990s. EMG countries

have also become more financially open compared with non-EMG countries since the mid-1990s.

Figure 6 shows the development paths of these indexes altogether, making the differences

between IDCs and LDCs and those between EMGs and non-EMGs appear more clearly. For the

industrialized countries, financial openness accelerated after the beginning of the 1990s and

exchange rate stability rose after the end of the 1990s, reflecting the introduction of the euro in

1999. The extent of monetary independence has experienced a declining trend, especially after

the early 1990s.19

When we look at the group of developing countries, we can see that not only do these

countries differ from industrialized ones, but also they differ between emerging and non-emerging market developing countries. Up to the mid-1980s, exchange rate stability was the

most pervasive policy among the three, though it has been on a declining trend since the early

1970s, followed by monetary independence that has been relatively constant during the period.

Between the mid-1980s and 2000, monetary independence and exchange rate stability became

the most pursued policies while the level of financial openness kept rising rapidly. During the

1990s, the level of monetary independence went up on average while more countries adopted

floating exchange rates and liberalized financial markets. Most interestingly, since 2000, all three

indexes have been converged to the middle ground, which we have already observed as the

balanced achievement of the three policy goals in Figure 4. This result suggests that developing

countries may have been trying to cling to moderate levels of both monetary independence and

financial openness while maintaining higher levels of exchange rate stability – leaning against

the trilemma in other words – which may explain the reason why some of these economies hold

sizable international reserves, potentially to buffer the trade-off arising from the trilemma.

Willett (2003) has called this compulsion by countries with a mediocre level of exchange rate

19 If the euro countries are removed from the sample (not reported), financial openness evolves similarly to the IDC

group that includes the euro countries, but exchange rate stability hovers around the line for monetary independence,

though at a bit higher levels, after the early 1990s. The difference between exchange rate stability and monetary

independence has been slightly diverging after the end of the 1990s.

11

fixity to hoard reserves the “unstable middle” hypothesis (as opposed to the “disappearing

middle” view).

None of these observations are applicable to non-emerging developing market countries.

For this group of countries, exchange rate stability has been the most pervasive policy

throughout the period, though there is some variation, followed by monetary independence.

There is no discernable trend in financial openness for this subsample.

2.3 Identifying Structural Breaks

To shed more light on the evolution of the index values, we investigate whether major

international economic events have been associated with structural breaks in the index series. We

conjecture that major events – such as the breakdown of the Bretton Woods system in 1973, the

Mexican debt crisis of 1982 (indicating the beginning of 1980’s debt crises of developing

countries), and the Asian Crisis of 1997-98 (the onset of sudden stop crises affecting high-performing Asian economies (HPAEs), Russia and other emerging countries) – may have

affected economies in such significant ways that they opted to alter their policy choices.

We identify the years of 1973, 1982, 1997-98, and 2001 as candidate structural breaks,

and test the equality of the group mean of the indexes over the candidate break points for each of

the subsample groups.20 The results are reported in Table 2 (a). The first and second columns of

the top panel indicate that after the breakdown of the Bretton Woods system, the mean of the

exchange rate stability index for the industrialized country group fell statistically significantly

from 0.69 to 0.43, while the mean of financial openness slightly increase from 0.44 to 0.47. Non-emerging market developing countries, however, did not significantly decrease the level of fixity

of their exchange rates over the same time period while they became less monetarily independent

and more financially open. Although the same changes in monetary independence and financial

openness are also observed among emerging market economies, they did move toward more

flexible exchange rates.

Even after the Mexican debt crisis, industrialized countries slightly, but significantly

increased the level of exchange rate stability and significantly increased the level of financial

openness, while holding constant the level of monetary independence. In contrast, the debt crisis

led all developing countries to pursue further exchange rate flexibility, most likely reflecting the

fact that crisis countries could not sustain fixed exchange rate arrangements. However, these

countries also simultaneously pursued more monetary independence. Interestingly, non-emerging

The data for the candidate structural break years are not included in the group means either for pre- or post-structural break years. For the Asian crisis, we assume the years of 1997 and 1998 are the break years and therefore

remove observations for these two years.

20

12

developing market countries tightened capital controls as a result of the debt crisis while

emerging market countries did not follow the suit.

The Asian crisis also appears to be a significant event in the evolution of the trilemma

indexes. The level of industrialized countries’ monetary independence dropped significantly

while their exchange rates became much more stable and their efforts of capital account

liberalization continued, all reflecting the European countries’ movement toward economic and

monetary union. Non-emerging market developing countries on the other hand increased the

level of all three indexes. Emerging market countries also started liberalizing financial markets

but much more significantly, though they lost monetary independence and slightly gained

exchange rate stability.

Several other major events are candidates for inducing structural breaks identified. For

example, anecdotal accounts date globalization at the beginning of the 1990s, when many

developing countries began to liberalize financial markets. Also, China’s entry to the World

Trade Organization in 2001 was, in retrospect, the beginning of the country’s rise as the world’s

manufacturer. Because the effect of these events may have often been conflated with that of the

Asian crisis we also test whether the years of 1990 and 2001 can be structural breaks.

The results are reported in Table 2 (b); the first two columns show the results of the mean

equality test for the trilemma indexes with the year of 1990 as the candidate structural break

whereas the last two columns report those with the year of 2001 as the structural break. The top

panel shows that for industrialized countries, 1990 can be a structural break for all three indexes.

However, when we compare the statistical magnitude of the change in the index for monetary

independence across different candidate structural breaks (i.e., compare the t-statistics for

monetary independence in column 4 of Table 2 (a), in column 2 of Table 2 (b), and in column 4

of Table 2 (b)), the mean equality test is most strongly rejected for the no structural break of

1997-98 hypothesis. We obtain the same result for exchange rate stability, though for financial

openness, the structural break of 1990 rejects the null hypothesis the most significantly.21 For the

group of non-emerging market developing countries, the structural break of 1990 is the most

significant for monetary independence and financial openness while it is the year of 2001 for

exchange rate stability. For emerging market countries, however, the most significant structural

break is found to have occurred in 2001 for monetary independence and exchange rate stability,

and in 1997-98 for financial openness.

21

The finding that both monetary independence and exchange rate stability entail structural breaks around the Asian

crisis can be driven merely by the countries that adopted the euro in 1999. We repeat the same exercise using the

industrial countries sample without the euro countries, and find that the structural breaks for monetary independence

and financial opens remain the same as in the full IDC sample (1997-98 and 1990, respectively), but that the

exchange rate stability series is found to have a structural break in 2001. Also, the change in the exchange rate

stability series is negative (i.e., further exchange rate flexibility) in both 1990 and 2001.

13

Lastly, we compare the t-statistics across different structural breaks for each of the

indexes and subsamples. Given that the balanced dataset is used in this exercise, the largest t-statistics should indicate the most significant structural break for the series. For example,

industrial countries’ monetary independence and exchange rate stability series have the largest t-statistics when the structural break of 1997-98 is tested.22 For financial openness, however, the

year of 1990 is identified with the largest structural break. The results for other variables and

subsamples are shown in Table 2 (c). For non-emerging LDC and EMG countries, the debt crisis

is found to be the most significant structural break for exchange rate stability. The year of 1990

is the most significant structural break for monetary independence and financial openness for

non-emerging developing market countries, whereas the year of 2001 and the Asian crisis of

1997-98 are, respectively, for emerging market countries.

2.4 The Linear Relationships between the Trilemma Indexes

While the preceding analyses are quite informative on the evolution of international

macroeconomic policy orientation, we have not shown whether these three macroeconomic

policy goals are “binding” in the context of the impossible trinity. That is, it is important for us to

confirm that countries have faced the trade-offs based on the trilemma. A challenge facing a full

test of the trilemma tradeoff is that the trilemma framework does not impose any obvious

functional form on the nature of the tradeoffs between the three trilemma variables. To illustrate

this concern, we note that the instrument scarcity associated with the trilemma implies that

increasing one trilemma variable, say higher financial integration, should induce lower exchange

rate stability, or lower monetary independence, or a combination of these two policy

adjustments.23 Yet, the nature of the trade-off is not specified. Hence, we test the validity of a

simplest possible trilemma specification – a linear tradeoff. Specifically, we test that the

weighted sum of the three trilemma policy variables adds up to a constant. This reduces to

examining the goodness of fit of this linear regression:

1=ajMIi,t+bjERSi,t+cjKAOPENi,t +εt where j can be either IDC, ERM, or LDC. (1)

Because we have shown that different subsample groups of countries have experienced different

development paths, we allow the coefficients on all the variables to vary across different groups

22 When the sample is restricted to non-euro IDCs, the most significant structural break for exchange rate stability is

found to be 1973, the year when the Bretton Woods system collapsed, while those for monetary independence and

financial openness are unchanged.

23 More generally, increasing of one Trilemma variable should induce a drop of the second Trilemma variable, or a

drop in the third Trilemma variable, or a combination of the two.

14

of countries – industrialized countries, the countries that have been in the European Exchange

Rate Mechanism (ERM), and developing countries – allowing for interactions between the

explanatory variables and the dummies for these subsamples.24 The regression is run for the full

sample period as well as the subsample periods identified in the preceding subsection. The

results are reported in Table 3.

The rationale behind this exercise is that policy makers of an economy must choose a

weighted average of the three policies in order to achieve a best combination of the two. Hence,

if we can find the goodness of fit for the above regression model is high, it would suggest a

linear specification is rich enough to explain the trade off among the three policy dimensions. In

other words, the lower the goodness of fit, the weaker the support for the existence of the trade-off, suggesting either that the theory of the trilemma is wrong, or that the relationship is non-linear.

Secondly, the estimated coefficients in the above regression model should give us some

approximate estimates of the weights countries put on the three policy goals. However, the

estimated coefficients alone will not provide sufficient information about “how much of” the

policy choice countries have actually implemented. Hence, looking into the predictions using the

ˆERS, and

ˆMI,

bestimated coefficients and the actual values for the variables (such as

aˆKAOPEN) will be more informative.

cThirdly, by comparing the predicted values based on the above regression, i.e.,

ˆERS+cˆMI+bˆKAOPEN, over a time horizon, we can obtain some inferences regarding how

a“binding” the trilemma is. If the trilemma is found to be linear, the predicted values should hover

around the value of 1, and the prediction errors should indicate how much of the three policy

choices have been “not fully used” or to what extent the trilemma is “not binding.”

Table 3 presents the regression results. The results from the regression with the full

sample data are reported in the first column, and the others for different subsample periods are in

the following columns. First of all, the adjusted R-squared for the full sample model as well as

for the subsample periods is found to be above 94%, which indicates that the three policy goals

are linearly related to each other, that is, countries face the trade-off among the three policy

options. Across different time periods, the estimated coefficients vary, suggesting that countries

alter over time the weights on the three policy goals.

Figure 7 illustrates the goodness of fit from a different angle. In the top panels, the solid

ˆERS+cˆMI+bˆKAOPEN) based on the full

lines show the means of the predicted values (i.e.,

asample model in the first column of Table 3 for the groups of industrial countries (left) and

24

The dummy for ERM countries is assigned for the countries and years that corresponds to participation in the

ERM (i.e., Belgium, Denmark, Germany, France, Ireland, and Italy from 1979 on, Spain from 1989, U.K. only for

1990-91, Portugal from 1992, Austria from 1995, Finland from 1996, and Greece from 1999).

15

developing countries (right).25 To incorporate the time variation of the predictions, the subsample

mean of the prediction values as well as their 95% confidence intervals (that are shown as the

shaded areas) are calculated using five-year rolling windows.26 The panels also display the

rolling means of the predictions using the coefficients and actual values of only two of the three

ˆERS (brown line with diamond nodes),

aˆMI+cˆKAOPEN (green line

ˆMI+btrilemma terms –

aˆERS+cˆKAOPEN (orange line with “x”).

with circles),

bFrom these panels of figures, we can first see that the predicted values based on the

model hover around the value of one closely for both subsamples. For the group of industrial

countries, the prediction average is statistically below the value of one in the late 1970s through

the beginning of the 1990s. However, since then, one cannot reject the null hypothesis that the

mean of the prediction values is one, indicating that the trilemma is “binding” for industrialized

countries. For developing countries, the model is under-predicting from the end of the 1970s

through the mid-1990s. However, unlike the IDC group, the mean of the predictions has become

statistically smaller than one since 2000. At the very least, for both subsamples, the mean of the

predictions never rises above the value of one in statistical sense, implying that, despite some

years when the trilemma is not binding, the three macroeconomic policies are linearly related

with each other.27

The top panels also show that, among industrialized countries, the policy combination of

increasing exchange rate stability and more financial openness rapidly became prevalent after the

beginning of the mid-1990s. Among developing countries, the policy combinations of monetary

independence and exchange rate stability has been quite dominant throughout the sample period

while the policy combination of exchange rate stability and financial openness has been the least

prevalent over, most probably reflecting the bitter experiences of currency crises.

2526 For this exercise, predictions also incorporate the interactions with the dummy variables shown in Table 3.

Both the mean and the standard errors of the predicted values are calculated using the rolling five-year windows.

ˆ∑∑xti=1t−4nitThe formula for the mean and the standard errors can be shown as

xt|t−4=ˆ∑∑(xti=1t−4nitn×5 and

−xt|t−4)2ˆ)=SE(xn×5−1, respectively, where n refers to the number of countries in a subsample (i.e., IDC and

n×5ˆit to the prediction values, and

xt|t−4 to the mean ofxˆit in the rolling five-year window.

LDC),

x27Because of the use of rolling five-year windows, the lines in the figures only start in 1974.

One may question the uniqueness of this regression exercise by pointing at the left-hand side variable being an

identity scalar. As a robustness check, we ran a regression of MIi,t on ERSi,t and KAOPENi,t, recovered the estimated

coefficients for aj, bj, and equation (1), and recreated panels of figures comparable to those in Figure 7. These

alternative figures appeared to be very much comparable to Figure 7 and therefore confirmed our conclusions about

the linearity of the trilemma indexes as well as the development of the subsample mean of prediction values based

on equation (1).

16

In the lower panels, we can observe the contributions of each policy orientation (i.e.,

ˆERS, and

cˆMI,

bˆKAOPEN) for the IDC and LDC groups.28 While less developed countries

amaintained high, though fluctuating, levels of monetary independence, both exchange rate

stability and financial integration remained at much lower levels throughout the period with the

former moderately declining and the latter slightly increasing. In the last decade or so, while

monetary independence is on a declining trend, the gap between the predictions based on

exchange rate stability and financial openness has been somewhat shrinking. This may indicate

that more countries tend to try to achieve certain levels of exchange rate stability and financial

openness together while maintaining high levels of monetary independence. This kind of effort

can be done only when the countries accumulate high levels of international reserves that allow

them to intervene in foreign exchange markets, consistent with the fact that many developing

countries increased international reserves in the aftermath of the Asian crisis of 1997-98.

However, as the concept of the trilemma predicts, this sort of environment must involve a rise in

the costs of sterilized intervention especially when the actual volume of cross-border transactions

of financial assets increase and when there is no reversal in the three policies.29 This seems to

explain the drop in the level of monetary independence after 2000 for this group of countries.30

The experience of the industrialized countries casts a stark contrast. Although monetary

independence was also IDC’s top priority until the 1990s, it yielded to financial integration in the

late 1990s and to exchange rate stability in the early 2000s. The trend of financial liberalization

and exchange rate stability correspond to declines in the level of monetary independence, which

persistently kept falling and became the lowest priority in the 2000s. Such changes in the relative

weights of the three policy goals do not require the countries to accumulate international reserves

as was the case with developing countries.31

3. Regression Analyses

Although the above characterization of the trilemma indexes allows us to observe the

development of policy orientation among countries, it fails to identify countries’ motivations for

policy changes. Hence, we examine econometrically how various choices regarding the three

They are again the means based on five-year rolling windows.

Refer to Aizenman and Glick (2008) and Glick and Hutchison (2008) for more analysis on the limit of sterilized

intervention.

30 When this exercise is repeated for both the emerging market country (EMG) group and the non-emerging market

developing country group (Non-EMG LDC), the results remain about the same, only except for that the financial

liberalization is more evident for the EMG group; the drop in the level of monetary independence is larger; and the

gap between the predictions based on exchange rate stability and financial openness has been shrinking further.

31 We also repeat the exercise using the regression models (whose results shown in Table 3) for each of the

subsample period (excluding the break years). The results (not reported) are qualitatively the same as in Figure 7.

2928

17

policies affect final policy goals, namely, output growth stability, low inflation, and inflation

stability.

The basic model we estimate is given by:

yit=α0+α1TLMit+α2TRit+α3(TLMit×TRit)+XitΒ+ZtΓ+DiΦ+εit (2)

yit is the measure for macro policy performance for country i in year t. More specifically, yit is

either output volatility measured as the five-year standard deviations of the growth rate of per

capita real output (using Penn World Table 6.2); inflation volatility as the five-year standard

deviations of the monthly rate of inflation; or the five-year average of the monthly rate of

inflation. TLMit is a vector of any two of the three trilemma indexes, namely, MI, ERS, and

KAOPEN.32 TRit is the level of international reserves (excluding gold) as a ratio to GDP, and

(TLMit x TRit) is an interaction term between the trilemma indexes and the level of international

reserves. We are particularly interested in the effect of the interaction terms because we suspect

that international reserves may complement or substitute for other policy stances.

Xit is a vector of macroeconomic control variables that include the variables most used in

the literature, namely, relative income (to the U.S. based on PWT per capita real income); its

quadratic term; trade openness (=(EX+IM)/GDP); the TOT shock as defined as the five-year

standard deviation of trade openness times TOT growth; fiscal procyclicality (as the correlations

between HP-detrended government spending series and HP-detrended real GDP series); M2

growth volatility (as five-year standard deviations of M2 growth); private credit creation as a

ratio to GDP as a measure of financial development; the inflation rate; and inflation volatility. Zt

is a vector of global shocks that includes change in U.S. real interest rate; world output gap; and

relative oil price shocks (measured as the log of the ratio of oil price index to the world’s CPI).

Di is a set of characteristic dummies that includes a dummy for oil exporting countries and

regional dummies. Explanatory variables that persistently appear to be statistically insignificant

are dropped from the estimation.

εit is an i.i.d. error term.

The data set is organized into five-year panels of 1972-1976, 1977-81, 1982-1986, 1987-91, 1992-96, 1997-2001, 2002-06. All time-varying variables are included as five-year averages.

The full sample is divided into the groups of industrialized countries (IDC) and developing

countries (LDC) which also includes a subgroup of commodity exporters (COMMOD-LDC), i.e.,

developing countries that are either exporters of fuel or those of non-fuel primary products as

defined by the World Bank, and a subgroup of emerging market countries (EMG). We report the

results only for the last three groups, i.e., only subsamples related to developing countries.

In Table 3, we have shown that these three measures of the trilemma are linearly related. Therefore, it is most

reasonable to include two of the indexes concurrently, not just individually nor all three collectively.

32

18

Since inflation volatility turned out to be a significant explanatory variable for the

regressions for output volatility and the level of inflation, and also the inflation level for the

regressions for inflation volatility, we need to implement an estimation method that handles

outliers properly. Hence, we decide to use the robust regression method which downweights

outliers.33 Also, we remove the observations if their values of inflation volatility are greater than

a value of 30 or the rate of inflation (as an explanatory variable) is greater than 100%.

Furthermore, for comparison purposes, the same set of explanatory variables is used for the three

subsamples except for the regional dummies.

3.1 Estimation of the Basic Model

3.1.1 Output Volatility

The regression results for the estimation on output volatility are shown in Tables 4-1

through 4-3 for the three subsamples of developing countries, i.e., developing countries,

developing commodity exporters, and emerging market countries. Different specifications are

tested using different combinations of the trilemma indexes as well as their interaction terms.

The results are presented in columns 1 through 6 in each table.34 The variables that consistently

appear to be statistically insignificant are dropped from the estimations.

The model explains well the output volatility for the developing countries subsample

(Table 4-1). Across different model specifications, the following is true for the group of

developing countries: The higher the level of income is (relative to the U.S.), the more reduced

output volatility is, though the effect is nonlinear. The bigger change occurs on U.S. real interest

rate, the higher output volatility of developing countries may become, indicating that the U.S.

real interest rate may represent the debt payment burden on these countries. The higher TOT

shock there is, the higher output volatility countries experience, consistent with Rodrik (1998)

and Easterly, Islam and Stiglitz (2001) who argue that volatility in world goods through trade

openness can raise output volatility.35 Countries with procyclical fiscal policy tend to experience

more output volatility while oil exporters also experience more output volatility.36

The robust regression procedure conducts iterative weighted least squares regressions while downweighting

observations that have larger residuals until the coefficients converge.

34 The dummies for “East Asia and Pacific” and “Sub-Saharan Africa” are included in the model for developing

countries, but not reported to conserve space.

35 The effect of trade openness is found to have insignificant effects for all subgroups of countries and is therefore

dropped from the estimations. This finding reflects the debate in the literature, in which both positive (i.e., volatility

enhancing) and negative (i.e., volatility reducing) effects of trade openness has been evidenced. The volatility

enhancing effect in the sense of Easterly et al. (2001) and Rodrik (1998) is captured by the term for (TOT*Trade

Openness) volatility. For the volatility reducing effect of trade openness, refer to Calvo et al. (2004), Cavallo (2005,

2007), and Cavallo and Frankel (2004). The impact of trade openness on output volatility also depends on the type

of trade, i.e., whether it is inter-industry trade (Krugman, 1993) or intra-industry trade (Razin and Rose,1994).

36 Countries in East Asia and Pacific as well as in Sub Sahara Africa tend to experience more output volatility

(results not reported).

33

19

Countries with more developed financial markets tend to experience lower output

volatility, a result consistent with the theoretical predictions by Aghion, et al. (1999) and

Caballero and Krishnamurthy (2001) as well as past empirical findings such as Blankenau, et al.

(2001) and Kose et al. (2003). This result indicates that economies armed with more developed

financial markets are able to mitigate output volatility, perhaps by allocating capital more

efficiently, lowering the cost of capital, and/or ameliorating information asymmetries (King and

Levine, 1993, Rajan and Zingales, 1998, Wurgler, 2000). We will revisit this issue later on.

Among the trilemma indexes, only the monetary independence variable is found to have a

significant effect on output volatility; the greater monetary independence one embraces, the less

output volatility the country tends to experience. This finding is no surprise, considering that

stabilization measures should reduce output volatility, especially more so under higher degree of

monetary independence.37 Mishkin and Schmidt-Hebbel (2007) find that countries that adopt

inflation targeting – one form of increasing monetary independence – are found to reduce output

volatility, and that the effect is bigger among emerging market countries.38 This volatility

reducing effect of monetary independence may explain the tendency that developing countries,

especially, non-emerging market ones, try not to reduce the extent of monetary independence

over years.

Like other developing countries, less developed commodity exporting countries are also

susceptible to changes in U.S. real interest rates and TOT shocks, but other variables do not

exhibit the same effects (Table 4-2). Again, countries with greater monetary independence tend

to experience lower output volatility. Interestingly, more exchange rate stability per se does not

have any significant impact on output volatility, but if it is coupled with higher levels of

international reserves holding, then countries can reduce output volatility, which may help

explain the recent buildup of international reserves by developing, especially oil exporting,

countries. Additionally, more financially open commodity exporters seem able to reduce output

volatility, though, interestingly, the coefficient on the interaction term between KAOPEN and

international reserve holding is significantly positive in one of the models. This result indicates

that countries with higher levels of reserves holding than 27% of GDP can experience more

output volatility. This result is somewhat counterintuitive.

37 This finding can be surprising to some if the concept of monetary independence is taken synonymously to central

bank independence because many authors, most typically Alesina and Summers (1993), have found more

independent central banks would have no or little at most impact on output variability. However, in this literature,

the extent of central bank independence is usually measured by the legal definition of the central bankers and/or the

turnover ratios of bank governors, which can bring about different inferences compared to our measure of monetary

independence.

38 The link is not always predicted to be negative theoretically. When monetary authorities react to negative supply

shocks, that can amplify the shocks and exacerbate output volatility. Cechetti and Ehrmann (1999) find the positive

association between adoption of inflation targeting and output volatility.

20

While emerging market countries share many of the same traits in macroeconomic

variables as those in the LDC sample, the results on the trilemma indexes are a little different.

Countries with more stable exchange rate tend to experience higher output volatility, which

conversely implies that countries with more flexible exchange rates will experience lower levels

of output volatility, as was found in Edwards and Levy-Yeyati (2005) and Haruka (2007).

However, the interaction term is found to have a statistically negative effect, suggesting that

countries holding high levels of international reserves are able to reduce output volatility. The

threshold level of international reserves holding is 21-24% of GDP. Singapore, a country with a

middle level of exchange rate stability (0.50 in the 2002-06 period) and a very high level of

international reserves holding (100% as a ratio of GDP), is able to reduce the output volatility by

2.65-3.2 percentage points.39 China, whose exchange rate stability index is as high as 0.97 and

whose ratio of reserves holding to GDP is 40% in 2006, is able to reduce volatility by 1.1-1.5

percentage points. The estimation results on the trilemma variables are summarized in Table 7.40

Figure 8 graphically shows the marginal interactive effects between ERS and IR based on

the estimates from Column 2 of Table 4-3. For presentation purposes, in the figure, the EMG

group of countries is divided into (a) the Asian group, (b) the Latin American group, and (c) the

other EMG countries. In all the panels of figures, the contours are drawn to present different

levels of the effect of ERS on output volatility conditional on the level of IR. Also, the solid

horizontal line refers to the threshold of IR at 21% of GDP, above which higher levels of ERS

will have a negative impact on output volatility.41 For example, the solid line of contour above

the threshold shows the combinations of ERS and IR that leads to a one percentage point

reduction in the output volatility. In the figure, we can see that the further toward the northeast

corner in the panel, i.e., the higher level of ERS and IR a country pursues, the more negative

impact it can have on output volatility. Below the threshold, however, it is true that the further

toward the southeast corner, i.e., the higher level of ERS and the lower level of IR a country

pursues, the more positive impact it can have on output volatility. In each of the panels, the

scatter diagrams of ERS and IR are superimposed. The black circles indicate ERS and IR for the

period of 2002-06 and the red “x’s” for the 1992-96 period.

See Moreno and Spiegel (1997) for earlier study of trilemma configurations in Singapore.

Following Acemoglu (2003), we also suspect institutional development plays a role in reducing output volatility.

To measure the level of institutional development, we use the variable LEGAL, which is the first principal

component of law and order (LAO), anti-corruption measures (CORRUPT), and bureaucracy quality (BQ). However,

it turns out that the LEGAL variable is statistically insignificant and sometimes with the wrong sign (not reported).

Given small variations in the time series of the variable, this result is not surprising.

41 We also note that the estimated coefficient on IR (level) is significantly positive in Columns (2) and (6) of Table

4-3, which indicates that, while a higher level of IR holding can lessen the positive effect of ERS, a higher level of

IR holding itself is volatility-enhancing. This is not captured in Figure 8 since we focused on the effect of ERS and

how it changes depending on the level of IR.

4039

21

Using these diagrams, we can make several interesting observations. First, between the

1992-96 and 2002-06 periods, a period which encompasses the last wave of global crises, i.e., the

Asian crisis of 1997-98, the Russian crisis of 1998, and the Argentina crisis of 2001-02, many

countries, especially those in East Asia and Eastern Europe, increased their IR holding above the

threshold. Secondly, the movement is not necessarily toward the northeast direction. Rather, it is

around the threshold level where the effect of ERS is neutral (i.e., zero percentage point impact),

unless they move much higher toward output volatility-reducing territory (such as China and

Bulgaria). Thirdly, while we observe a moderately positive association between ERS and IR,

none of these observations are applicable to Latin American countries. Lastly, there are not many

countries that have achieved combinations of ERS and IR to reduce output volatility significantly.

Countries such as Botswana, China, Hong Kong, Malaysia, Jordan, and Singapore are more of

exceptions. However, at the very least, these estimation results should explain why many

countries, especially those with the intention of pursuing greater exchange rate stability, are

motivated to hold a massive amount of international reserves.

3.1.2 Inflation Volatility

We repeat the exercise for inflation volatility. The results for subsamples of developing

countries are reported in Tables 5-1 through 5-3 and summarized in Table 7.

Across different subsamples, countries with higher relative income

tend to experience

lower inflation volatility, and naturally, those with higher levels of inflation are expected to

experience higher inflation volatility. The TOT shock is found to increase inflation volatility.

Furthermore, for commodity exporters, oil price increases would lead to higher inflation

volatility.

The performance of the trilemma indexes appears to be the weakest for this group of

estimations overall. Monetary independence is found to be an inflation volatility decreasing

factor for commodity exporters. However, given that it is also an output volatility decreasing

factor for this group of countries, this finding is somewhat counterintuitive.

Emerging market countries, on the other hand, tend to experience higher inflation

volatility if they are more open to capital account transactions. This significantly positive effect

of financial openness may be capturing financial turbulence that can arise as a result of financial

liberalization policy. In fact, when we include the interaction term between the crisis dummy and

the financial openness variable, the statistical significance of the financial openness variable

declines while the interaction term enters the estimation marginally significantly.42

42 The currency crisis dummy variable is derived from the conventional exchange rate market pressure (EMP) index

pioneered by Eichengreen et al. (1996). The EMP index is defined as a weighted average of monthly changes in the

nominal exchange rate, the international reserve loss in percentage, and the nominal interest rate. The weights are

inversely related to the pooled variance of changes in each component over the sample countries, and adjustment is

22

3.1.3. Medium-run Level of Inflation

Tables 6-1 through 6-3 show the results for the regressions on the level of inflation.

These three tables report that countries with higher inflation volatility, M2 growth volatility, and

oil price shocks tend to experience higher output volatility. Also, when the world economy is

experiencing a boom, developing countries tend to experience higher inflation, which

presumably reflects strong demand for goods produced and exported by developing countries.

Countries with more monetary autonomy tend to experience higher inflation. From the

perspective that greater monetary independence should be synonymous with a more independent

central bank, most typically exemplified by the literature of time-inconsistency in monetary

policy, a country with greater monetary independence should be able to lower inflation.43 One

possible explanation would be that countries with higher levels of monetary independence

attempt to monetize their debt and cause higher inflation. Such countries may be better off if they

are not monetarily independent and just import monetary policy from other countries through

fixed exchange rate arrangements.

As a matter of fact, in all three subsamples, higher exchange rate stability is found to lead

countries to experience lower inflation, a result consistent with the literature (such as Ghosh et

al., 1997). This finding and the previously found positive association between exchange rate

stability and output volatility are in line with the theoretical prediction that establishing stable

exchange rates is a trade-off issue for policy makers; it will help the country to achieve lower

inflation by showing a higher level of credibility and commitment, but at the same time, the

efforts of maintaining stable exchange rates will rid the policy makers of an important

adjustment mechanism through fluctuating exchange rates – which would explain the negative

coefficient on monetary independence in the output volatility regressions.

Furthermore, for the LDC group, the interaction term between ERS and international

reserves holding is found to have a positive impact on the rate of inflation. Models 2 and 6 in

Table 6-1 show that if the ratio of reserves holding to GDP is greater than 53% or 65%,

respectively, the efforts of pursuing exchange rate stability can help increase the level of

inflation. Although these levels of reserves holding are very high, this means that countries with

excess levels of reserves holding will eventually face the limit in the efforts of fully sterilizing

made for the countries that experienced hyperinflation following Kaminsky and Reinhart (1999).

For countries

without data to compute the EMP index, the currency crisis classifications in Glick and Hutchison (2001) and

Kaminsky and Reinhart (1999) are used.

43 In other words, more independent central bankers should be able to remove the inflation bias (Kydland and

Prescott, 1977 and Barro and Gordon, 1983).

23

foreign exchange intervention to maintain exchange rate stability, thereby experiencing higher

inflation.44

Lastly, models (3) through (6) in all subsamples show that the more financially open a

developing country is, the lower inflation it will experience. Interestingly, the more open to trade

a country is, the more likely it is to experience lower inflation, though this effect is weakly

significant only for the LDC group.

As globalization became actively debated, the negative association between “openness”

and inflation was more frequently remarked upon.45 Romer (1993), extending the Barro-Gordon

(1983) model, theorized and empirically verified that the more open to trade a country becomes,

the less motivated its monetary authorities are to inflate, suggesting a negative link between trade

openness and inflation. Razin and Binyamini (2007) predicted that both trade and financial

liberalization will flatten the Phillips curve, so that policy makers will become less responsive to

output gaps and more aggressive in fighting inflation.46 Here, across different subsamples of

developing countries, we present evidence consistent with the negative openness-inflation

relationship.

3.2. How Does a Policy Orientation Affect Macroeconomic Performance?

Composite Indexes for Policy Orientation

As we have already seen, decisions on which two of the three policy goals – monetary

independence, exchange rate stability, and financial integration – to retain, or which one to give

up, characterizes the international financial regime a country decides to implement. For example,

currency unions such as the Euro countries and the Gulf Cooperation Council (GCC) or countries

with currency boards like Argentina before 2001 require member countries to abandon monetary

independence, but retain exchange rate stability and financial openness. The Bretton Woods

system kept countries financially closed, but let them exercise an independent monetary policy

and to stabilize their currency values. Thus, measures constructed by two of the above three

indexes can allow one to summarize the policy orientations of countries. In other words,

measures composed of two of the three indexes should be able to show how close countries are

to the “vertex” of the trilemma triangle.

For this purpose, we construct composite indexes based on two of the above three

measures. The principal component of MI and ERS measures how close countries (MI_ERS) are

44 Aizenman and Glick (2008) and Glick and Hutchison (2008) show that China, whose ratio of reserves holding to

GDP is estimated to be 50%, has started facing more inflationary pressure in 2007 as a result of intensive market

interventions to sustain exchange rate stability (though the onset of global crisis has reversed these trends).

45 Rogoff (2003) argues that globalization contributes to dwindling mark-ups, and thereby, disinflation.

46 Loungani et al. (2001) provides empirical evidence that countries with greater restrictions on capital mobility face

steeper Phillips curves.

24

toward the vertex of “closed economy” whereas that of ERS and KAOPEN (ERS_KAO) refers to

the vertex of currency union or currency board, and that of MI and KAOPEN (MI_KAO) to

“floating exchange rate.” Again, all three indexes are normalized between zero and one. Higher

values indicate a country is closer toward the vertex of the trilemma triangle.

Estimation with Composite Indexes

Columns 7 though 12 in Tables 4-1 through 6-3 show the estimation results for different

models each of which include one composite index and its interaction with reserves holding.

Tables 4-1 and 4-2 show that countries with higher MI_KAO, i.e., countries with more flexible

exchange rates, tend to experience lower output volatility, which is in line with the oft-argued

automatic stabilizing role of flexible exchange rates. For developing countries, the more

financially closed an economy is (the higher its MI_ERS is), output volatility tends to be lower.

Given that monetary independence is found to have a volatility reducing effect in the estimations

with individual trilemma indexes, it is monetary independence that leads to lower output

volatility whether financially closed economies with more stable exchange rates or financially

open but with more flexible exchange rates. Emerging market economies (Table 4-3), on the

other hand, seem to follow different dynamics. Economies with higher MI_ERS, i.e., more

closed financial markets, are able to reduce output volatility only when they hold ample reserves.

In Tables 5-1 through 5-3, we see that developing countries or emerging market

economies with higher exchange rate stability and more financial openness (ERS and KAO), or

those with weaker monetary independence, tend to experience higher inflation volatility.

Commodity exporters that pursue greater monetary independence and financial openness (MI

and KAO) tend to experience less inflation volatility (Table 5-2).

The level of inflation can be lowered if a developing or commodity-exporting country

pursues greater monetary independence and more stable exchange rates (Columns 7 and 8 in

Tables 6-1 and 6-2). Or, if developing countries, whether commodity-exporting or emerging

market ones, pursue a policy combination of greater exchange rate stability and more financial

openness, these economies should be able to lower the level of inflation. This finding can be

disappointing news for monetary authorities because it implies that, to implement disinflationary

policy, policy makers should yield monetary policy making to another country and invite more

policy discipline by opening financial markets.

4. Further Analyses of the Trilemma Configurations on Macro-Performance

While the above analysis sheds important light on how the trilemma configurations affect

macroeconomic performance of the economies, other important questions, especially those

which have emerged out of the ongoing financial crisis, are not directly addressed. In this section,

25

we further investigate the following two more issues. First, we take a closer look at the effect of

financial development on output volatility. Secondly, we examine the impacts of external

financing on output volatility, inflation volatility, and the medium-term level of inflation.

4.1 Interactions Between the Trilemma Configurations and Financial Development

The ongoing global financial crisis has illustrated that financial development can be a

double-edged sword. While further financial development may enhance output growth and

stability by ameliorating information asymmetry, enabling more efficient capital allocation, and

allowing for further risk sharing, it can also expose economies to high-risk, high-return financial

instruments, thereby involving the possibility of amplifying real shocks and/or falling into the

boom-burst cycles. Naturally, the effect of financial development deserves further investigation,

which we are about to conduct.

In Tables 4-1 through 4-3, we have seen that more financial development can lead to less

output volatility, but its effect is significant only for the LDC subsample. One may also wonder

how trilemma configurations can interact with the level of financial development. There is no

question that monetary policy with high levels of authorities’ independence, which is found to be

volatility-reducing, should work better with more developed financial markets. Exchange rate

stability, which can lead to higher output volatility, may be less disturbing if financial markets

handle capital allocation more efficiently. Financial liberalization can easily be expected to work

hand in hand with financial development to reduce economic volatility.

With these assumptions, we test to see if there is any interaction between the trilemma

indexes and financial development which we measure using private credit creation as a ratio to

GDP (PCGDP). The results turn out to be simply futile; when the previous output volatility

regressions from Tables 4-1 through 4-3 are repeated, including interaction terms between the

trilemma indexes and PCGDP, none of the interaction terms turn out to be significant (not

reported). These results are not surprising or discouraging, because, as we already mentioned, we

suspect that the effect of financial development can be ambiguous.

The weakness of using interaction terms is that we must assume that the effect of

PCGDP on the link between the trilemma indexes and output volatility is monotonic; a higher

level of PCGDP must either enhance, have no impact on, or lessen the link. Given the

insignificance of the interaction terms from the initial investigation, we suspect the nonlinearity

of PCGDP. As such, we decide to use the dummies for different level groups of PCGDP.47 That

is, PCGDP_HI is assigned a value of one for a country if the country’s PCGDP is above the 75th

percentile in the distribution of five-year averages of PCGDP within a five-year window, and

This investigation is motivated by Hnatkovska and Loayza (2005), who examines the nonlinear effect of structural

variables, including financial development, on the output volatility-growth link.

47

26

zero, otherwise. PCGDP_LO takes a value of one if the country’s PCGDP is below the 25th

percentile, and zero, otherwise. PCGDP_MD takes a value of one if the country’s PCGDP lies

between the 25th and 75th percentiles in a five-year period. We interact these level category

dummies with the trilemma indexes and include the interaction terms in the output volatility

regressions, hoping to capture the nonlinear effect of financial development on the link between

the trilemma configurations and output volatility.

Table 8 reports the estimation results only for the PCGDP variable and the interaction

terms for the developing countries subsample (Columns 1-3) and the emerging market countries

subsample (Columns 4-6) in order to conserve space. At the bottom of the tables, we also report

the Wald test statistics for the tests on the differences in the estimated coefficients of the

interaction terms between the trilemma indexes and different PCGDP groups.

In Columns 1-3, we can see that this analysis does not yield any significant results for the

group of developing countries. Exchange rate stability may contribute to higher output volatility

if the country is equipped with medium (or higher) levels of financial development while the low

level of financial development may contribute to reducing output volatility, though none of the

estimated coefficients are significant.

Among EMGs (Columns 4-6), we see more interesting results. The estimated coefficient

on the term “ERS x Medium PCGDP” is significant in Columns 4 and 5. In Column 5, the

coefficient on “ERS x High PCGDP” is also significant, and both “ERS x Medium PCGDP” and

“ERS x High PCGDP” are greater than “ERS x Low PCGDP” in the estimates’ magnitude

although they are not statistically significantly different. At least, we can surmise that for

countries with underdeveloped financial markets, higher levels of exchange rate stability do not

lead to higher output volatility. Those with medium levels of financial development do seem to

experience higher output volatility when they pursue a more stable exchange rate, suggesting that

countries with newly developed financial markets can be volatile when they pursue greater

exchange rate stability. Furthermore, in both Columns 4 and 5, the estimated coefficients on the

interaction term between ERS and IR are found to be significantly negative. Using the estimates,

we can estimate that to cancel or lessen the volatility-enhancing effect of ERS, EMGs with

medium (or higher) levels of financial development need to hold at least 22-25% of GDP of

international reserves. However, this rule is not applicable to those with underdeveloped

financial markets.

Financial development and financial openness seem to have interesting interactive effects

on output volatility as well. While those EMGs with medium or higher levels of financial

development tend to experience less output volatility when they decide to pursue more stable

exchange rates, those with underdeveloped financial markets are expected to experience greater

output volatility if they pursue greater financial openness. When the coefficient of “KAOPEN x

27

Medium PCGDP” and “KAOPEN x High PCGDP” are compared to that of “KAOPEN x Low

PCGDP,” the difference is found to be statistically significant. These results indicate that

emerging market economies need to be equipped with highly developed financial markets if they

want to reap the benefit of financial liberalization on their output volatility.

These findings suggest that a policy management leaning more toward exchange rate

stability is most likely to exacerbate output volatility when the economy is equipped with

medium levels of financial development. Having a higher level of financial openness and

financial development can yield a synergistic impact to dampen output volatility, presumably by

facilitating allocation of capital, ameliorating information asymmetry, and thereby reducing the

cost of capital.48 The worst and more significant case is that a country with underdeveloped

financial markets can exacerbate output volatility caused by financial liberalization.

4.2 The Effects of External Financing

Financial liberalization has increased its pace over the last two decades. This, however,

does not mean that countries suddenly became more financially linked with others. In the 1980s,

developing countries received external financing in the form of sovereign debt, but the debt crisis

experience spurred many of these countries to shy away from sovereign debt. After the 1990s,

the role of FDI became more important and more recent waves of financial liberalization have

contributed to a rise in portfolio flows across borders as well. As Lane and Milesi-Ferretti (2006)

note, the type, volume, and direction of capital flows have changed over time.

4.2.1 Incorporation of External Financing

Against this backdrop, we extend our investigation by incorporating the effect of external

financing. More specifically, we include the variables that capture net FDI inflows, net portfolio

inflows, net ‘other’ inflows (which mostly include bank lending in IFS), short-term debt, and

total debt service. For net capital flows, we use the IFS data and define them as external

liabilities (= capital inflows with a positive sign) minus assets (= capital inflows with a negative

sign) for each type of flows – negative values mean that a country experiences a net outflow

capital of the type of concern. Short-term debt is included as the ratio of total external debt and

total debt service as is that of Gross National Income (GNI). Both variables are retrieved from

WDI. Because the debt-related variables are limited, we only deal with one subsample that is

composed of developing countries for which the debt-related variables are available. Also, to

48 See Bekaert et al., (2000, 2001), Henry (2000), Stultz (1999) among others for the link between financial

liberalization and the cost of capital. Chinn and Ito (2006) show that financial openness can exogenously lead to

more financial development.

28

isolate the effect of external financing from currency crises, we include a dummy for currency

crises.

The results are reported in Table 9 for all three dependent variables, output volatility in

columns 1 through 3, inflation volatility in columns 4 through 6, and inflation level in columns 7

through 9. We present the estimated coefficients only for the variables of interest.49 Table 9

shows that the more ‘other’ capital inflows, i.e., banking lending or more net portfolio inflows, a

country receives, the more likely it is to experience higher output volatility, reflecting the fact

that countries that experience macroeconomic turmoil often experience an increase in inflows of

banking lending or “hot money” such as portfolio investment. FDI inflows appear to contribute

to lowering inflation volatility, which is somewhat counterintuitive. One possible explanation is

that countries tend to stabilize inflation movement to attract FDI, and this may also explain the

negative, but less significant, coefficients on the net FDI inflow variables in the inflation level

regressions. Other types of capital flows do not seem to matter for either inflation volatility or

inflation levels.

Both short-term debt and total debt service are positive and significant contributors to

both inflation volatility and inflation level, supporting our previous argument that countries do

tend to monetize their debt especially when their monetary authorities embrace more

independence – the estimated coefficient on monetary independence continues to be significantly

positive in the inflation level regressions.

Among the trilemma indexes, greater monetary independence continues to be a negative

contributor to output volatility though it is also a positive contributor to the level of inflation.

More financial openness is now a negative contributor to output volatility for this sample of

countries while its negative impact on the level of inflation remains. Higher exchange rate

stability continues to dampen the level of inflation, but holding too much of international

reserves (more than 45% of GDP) can cancel the negative effect and contribute to higher

inflation.

4.2.2 External Financing and Policy Orientation

Given that the combination of two out of three policy stances is what matters to the

macro outcomes, when we estimate the effect of external financing, it is important to condition

on what kind of policy combination is being pursued by the recipient countries.50 The best way

for us to do that is to examine the interactive effect between the type of external financing and

that of the policy combination. For that purpose, we create dummy variables for the types of

49 Overall, other macroeconomic variables retain the characteristics found in the previous regressions, though they

tend to be less statistically significant.

50 See IMF (2007) for an examination of the relationship between how countries manage capital inflows and

subsequent macroeconomic outcomes.

29

policy orientation using the composite trilemma indexes we have been using. That is, if the

composite index MI_ERS turns out to be the highest compared to the other two, MI_KAO and

ERS_KAO, then a value of one is assigned for D_MI_ERS and zero for the other two,

D_MI_KAO and D_ERS_KAO. In the results shown in Table 10, the external financing

variables are interacted with the dummy for one particular type of policy combination. For

example, in columns 1 and 2 of Table 10 we use in the estimation of output volatility the dummy

for the policy orientation of greater monetary independence and exchange rate stability (MI_ERS;

or “financially closed” policy option) and interact it with the external financing variables.

Columns 3 and 4 use the dummy for the policy orientation of greater monetary independence and

further financial opening (“more flexible exchange rate” policy), and columns 5 and 6 use that of

greater exchange rate stability and further financial opening (“currency union” or currency

board). The following six columns report the results for the estimation of inflation volatility and

the next six for the level of inflation.

For output volatility, we find different types of external financing can have different

impacts on output volatility depending on the policy regime in place. Net FDI inflows, for

example, tend to dampen output volatility in general, but it can enhance the volatility in a regime

that has pursued greater monetary independence and more stable exchange rates (i.e., less

financial openness). Net portfolio inflows seem to have a positive impact on output volatility, but

its volatility increasing impact is especially higher for the countries with the ERS-KAO

(“currency union”) regimes, in line with what has been found in the crisis literature. Countries

with more flexible exchange rates (or monetary independence and financial openness), on the

other hand, may be able to dampen the volatility-increasing effect, though its effect for this

policy orientation is not found to be statistically significant. Positive net inflows of bank lending

can be volatility increasing, but that effect can be dampened, though only marginally

significantly, if the country adopts the policy combination of exchange rate stability and financial

openness.

The greater the debt service is, the more likely a country is to experience higher levels of

output volatility, especially when the country pursues a combination of greater exchange rate

stability and financial openness. This result appears to be consistent with the “original sin”

argument; countries that are indebted in a foreign currency and that try to maintain both

exchange rate stability and capital account openness often experience sudden capital flow

reversal and consequently higher output volatility.

In the inflation volatility regressions, it seems that net inflow of FDI contributes to lower

inflation volatility across different policy regimes in general. However, the volatility-reducing

effect is even higher for countries with flexible exchange rates. The table also shows that, for

countries with flexible exchange systems, portfolio inflows can lower inflation volatility. These

30

results imply that if a country is considering to allow more influx of FDI or portfolio flows while

wanting to lower inflation volatility, it would be best to adopt a flexible exchange rate system or

keep the overall level of financial openness at low levels. Lastly, total debt services can make

countries with monetary independence and financial openness experience higher inflation

volatility while financially closed regimes would experience a slight drop in inflation volatility.

This may be because rapid currency depreciation could enlarge the size of total debt which could

encourage countries to monetize away the debt.

Different types of policy combinations seem to matter only for ‘other’ (i.e., bank lending)

inflows in the estimation for the level of inflation; a net recipient of bank lending flows tends to

experience lower inflation if it adopts a policy combination of monetary independence and

financial openness, but it could experience higher inflation if it adopts a financially closed

system. One merit of a country with currency union-like regime is that it can dampen the

inflation pressure of total debt services. A country with closed financial markets on the other

hand may experience higher output volatility as a result of higher levels of debt services.

Implications for the Current Crisis

International Reserve Holdings: Is the Trilemma Still Binding?

It has been argued that one of the main causes of the financial crisis of 2008-09 is the

ample liquidity provided by the global imbalances; current account surplus countries hoard

international reserves in an attempt to stabilize their exchange rates, export liquidity to the global

markets, and finance profligacy in the advanced countries, especially the United States.51 In

5.

5.1

Figure 8, we have seen that some, but not many, countries pursue higher levels of ERS and IR

concurrently. Figure 9 updates Figure 8 by using the updated Trilemma indexes and IR data for

2007 and compare with the data from the 2002-06 period. In the panels of figures, we can

observe that countries’ positions do not change much. The only noticeable change would be that

countries continue to increase their IR holding, but they are not necessarily moving toward the

northeast corner. Why do these countries continue to increase their IR holding?

One possible conjecture is that countries holding a massive amount of foreign reserves

might allow the relaxation of the trilemma, i.e., achieve all three goals at the same time. Figure

10 displays a scatter diagram for EMG countries’ ERS and MI_KAO (composite index of MI

and KAOPEN), which the concept of the trilemma predicts should be negatively correlated.

There are two groups of country-years shown in the diagram; one is a group of country-years

with the IR holding greater than 21% of GDP, the threshold above which ERS can have output

volatility-reducing effect as shown in Figures 8 and 9, and the other is those with the IR holding

less than 21% of GDP. If the above speculation is right, the (green) triangles – country-years

51 See Roubini (2008) as one example.

31

with >21% IR – in the diagram should be scattered above the circles – country-years with <21%

IR.

Theoretically, these two variables should be negatively correlated – the higher level of

ERS a country pursues, the lower level of MI-KAO, which is a proxy to the weighted average of

MI and KAO it has to choose as we formally confirmed in Section 2.4. In the figure, however,

the fitted lines for both groups are barely negatively sloped – the estimated coefficients for both

are statistically insignificantly negative. We test whether the slopes and intercepts of these two

fitted lines are statistically different. If the conjecture that higher levels of IR holding could relax

the trilemma, a country should be able to pursue higher levels of MI-KAO with the same level of

ERS, which would either make the slope flatter or raise the intercept, i.e., the conditional mean

of MI-KAO. Simple coefficient equality tests reveal that the slopes of the two fitted lines are not

statistically different from each other, but that the intercept for the fitted line for the country-years with >21% IR is significantly higher than that for the <21% IR group. This is in line with

the conjecture that higher levels of IR holding can allow a country to pursue a higher weighted

average of MI and KAOPEN, i.e., relax the trilemma.

Given the findings from the output volatility regressions in Table 4, for the EMG

countries, having greater monetary independence could lead a country to reduce output volatility.

If a country holds a higher level of IR than 21% of its GDP, it may be able to relax the trilemma,

so that it may decide to pursue greater monetary independence and financial openness while

maintaining exchange rate stability. One easy candidate that fits this category is China. Figure 11

shows the trilemma configurations and IR holding for emerging market countries in East Asia

and China. We can observe that while it does not give up its exchange rate stability and monetary

independence, China’s IR holding has been increasing and financial openness has inched up.

Although we have not tested formally, we find evidence consistent with the view that countries’

efforts to “relax the trilemma” can involve an increase in IR holding, which may have

contributed to the global expansion of liquidity prior to the financial crisis of 2008-09. We leave

testing this argument as one of our future research agendas.

5.2 Is the Current Crisis Consistent with Our Models?

As the IMF has revised the GDP estimates downward for many developing countries

several times since the fall of 2008, it has become clear that the ongoing crisis is not just an

American problem or the one in the industrial world, but a major challenge for the global

economy. In other words, the concept of “de-coupling” is no longer applicable.

Given that we can identify the countries that are experiencing more severe economic

situations than others as the time of this writing, we examine whether the current crisis situations

are consistent with what we have found from our previous findings. That is, we use the data from

32

2007 for the variables upon which we have focused in this paper and see whether the conditions

of these variables as of the eve of the crisis present any signals for the ongoing crisis. For this

purpose, Table 11 presents the variables of our focus for a group of emerging market countries.

Namely, the table reports PCGDP, IR (both as% of GDP), the three trilemma indexes, and the

external finance variables. dX refers to the change of the variable X compared to the 2002-06

period.52 In the table, we also report swap lines provided by the U.S. Federal Reserve and rescue

loans provided by the IMF (as of March 2009). The swap lines and rescue loans are reported to

identify which countries are experiencing more severe situations than others although countries

without these arrangements can be also experiencing dire situations.

Before making observations of these countries, it is noteworthy to point out that the size

of the swap lines or the IMF rescue loans is not so big for most of the countries. For Brazil,

Mexico, and Korea, it is about 2-3% of GDP and 7% for Pakistan. It is only for Singapore and

Hungary that the size of the additionally available IR is relatively substantial, around 18% of

GDP. Based on what we found in Figures 8 and 9, we can see that, except for Singapore and

Hungary, the effect of these swap lines or IMF rescue loans can be quite minimal at most to

reduce output volatility. Obstfeld et al. (2009) also mention the smallness of the additional IR

provided for developing countries, especially compared to industrialized countries, and argue

that these additional reserves would merely have signaling effects, unlike industrial countries’

that can have real effects to relax liquidity constraints.53 Our results are consistent with their

observation.

Let us now make observations about the conditions pertaining to trilemma configurations

and both internal and external financing of the concerned countries. Among the countries with

the swap or rescue loan arrangements, Hungary, Korea, and Pakistan experienced a relatively

rapid increase in net inflows of bank lending (‘Other’). In Table 9, we see that countries with

positive net inflow of ‘other’ investment tend to experience higher output volatility. Among the

three countries, Hungary appears to have pursued the combination of MI and KAOPEN whereas

Pakistan did that of MI and ERS. Both combinations, MI-KAO or MI-ERS, are found to lead

bank lending flows to have a bigger impact on output volatility (Table 10). The Pakistani

economy is also subject to higher output volatility because its financial development level is not

high although it pursues greater exchange rate stability. Interestingly, several other East

European countries, such as Lithuania, Poland, and Slovak Republic, and Russia also

experienced large increases in net inflow of bank lending, which suggest that these economies

5253 PCGDP is as of 2006 (or 2005 if the figure for 2006 is unavailable) because it is unavailable for 2007.

They also argue that the fact that a more substantial amount of rescue reserves can be readily available for

industrialized countries should be the reason why industrialized countries do not (have to) hold a massive amount of

IR.

33

can be subject to higher output volatility.54 In Table 9, we also found that the higher level of net

inflow of portfolio investment it receives, the greater output volatility a country would have to

face. The impact can be greater especially when the country pursues a policy combination of

ERS and KAO. Both Brazil and Argentina experienced a rapid increase in net inflow of portfolio

investment although neither of them pursued the policy combination of ERS and KAO. The table

also shows that Venezuela may be exposed to higher output volatility; it pursued fixed exchange

rate though its IR fell significantly while portfolio inflow increased. Thus, our casual

observations confirm that the inferences we obtained from our estimations seem to be consistent

with the economic conditions that led to severe crisis situations.

6. Concluding Remarks

Our paper outlined a methodology to trace the changing patterns of the trilemma

configurations. Taking a longer-run view, it reveals striking differences between the choices of

industrialized and developing countries during 1970-2006. The recent trend suggests that among

emerging market countries, the three dimensions of the trilemma configurations: monetary

independence, exchange rate stability, and financial openness, are converging towards a “middle

ground” with managed exchange rate flexibility, which they attempted to buffer by holding

sizable international reserves, while maintaining medium levels of monetary independence and

financial integration. Industrialized countries, on the other hand, have been experiencing

divergence of the three dimensions of the trilemma and moved toward the configuration of high

exchange rate stability and financial openness and low monetary independence as most

distinctively exemplified by the euro countries’ experience.

This configuration of the three macroeconomic policies is an outcome of the evolution of

different system arrangements. Over years, external shocks have affected the policy arrangement

across countries. In this regard, we have shown that major crises in the last four decades, namely,

the collapse of the Bretton Woods system, the debt crisis of 1982, and the Asian crisis of 1997-98, caused structural breaks in the trilemma configurations. For both industrialized and

developing countries, the major events in the last decade, such as the emergence of rapid

globalization and the rise of China, have also impacted the policy arrangements significantly.

With these results, we can safely expect that the present turbulence in the global financial

markets could challenge the stability of the current trilemma configuration.

We also tested whether the three macroeconomic policy goals are “binding” in the

context of the impossible trinity. That is, we attempted to provide evidence that countries have

faced the trade-offs based on the trilemma. Because there is no specific functional form of the

Latvia, though not categorized as an EMG country in the dataset, also experienced an influx of bank lending in

this year and is experiencing a severe economic crisis in 2008-09.

54

34

trade-offs or the linkage of these three policy goals, we tested a simplest linear specification for

the three trilemma indexes and examined whether the weighted sum of the three trilemma policy

variables adds up to a constant. Our results confirmed that countries do face the binding trilemma.

That is, a change in one of the trilemma variables would induce a change with the opposite sign

in the weighted average of the other two.

While external forces could impact countries’ decisions on the trilemma configurations,

policy makers decide on the specifics of the combination of the three policies depending on the

goals they would like to achieve. Hence, we also tested how each one of the three policy choices

as well as the combination of the two could affect the economic outcomes policy makers pay

close attention to, such as output volatility, inflation volatility, and medium-term inflation rates,

with a particular focus on developing countries.

We found countries with higher levels of monetary independence tend to experience

lower output volatility. When we restrict our sample to emerging market economies, we also

found that countries with higher levels of exchange rate fixity tend to experience higher output

volatility. However, this effect can be mitigated by holding international reserves if the level of

international reserves is higher than 19-22% of GDP. This result motivates the reason why so

many emerging market countries want to hold massive amounts of international reserves.

We also found that countries with more monetary autonomy tend to experience higher

inflation, which may reflect countries’ motives to monetize their debt. Countries with higher

exchange rate stability tend to experience lower inflation as has been found in the literature.

Furthermore, financial openness helps a country to experience lower inflation, possibly

indicating that globalization gives more discipline than monetary autonomy to a country’s

macroeconomic management.

We also extended our estimation model to investigate the following two questions

relevant to the current crisis: 1) Can financial development affect the link between trilemma

policy configurations and output volatility?; and 2) How can external financing affect

macroeconomic performances interactively with the trilemma configurations?

Regarding the effect of financial development on the link between the trilemma

configurations and output volatility, we found a nonlinear effect among emerging market

economies that medium-levels of financial development can raise the volatility-enhancing impact

of exchange rate stability. Highly developed financial markets can help financial liberalization

policy to reduce output volatility while underdeveloped financial markets could exacerbate

output volatility, signifying the synergistic effects between financial development and financial

opening.

In the estimations with the variables for external financing, we find the following: net

recipients of cross-border bank lending or portfolio flows – or the “hot money” – tend to

35

experience higher output volatility, a result consistent with the literature. We also took a closer

look at the effect of policy orientations on the effect of external financing and found that the

effect of different types of external financing can depend upon the policy regime adopted by a

country. First, net FDI inflows tend to dampen output volatility in general, but it can increase the

volatility in a “financially closed” regime, i.e., one with greater monetary independence and

more stable exchange rates. Net portfolio inflows can be volatility-increasing, and its effect is

greater for the countries with currency union or alike regimes. This type of regimes, however,

can dampen the volatility-enhancing effect of bank lending. Among the variables related to

sovereignty debt, the greater the debt service is, the more likely a country could experience

higher levels of output volatility, especially when combined with greater exchange rate stability

and financial openness, a result consistent with the “original sin” literature.

Our results also help answer why many countries have been hoarding massive amount of

IR, which has been claimed to be one of the causes of the current global financial crisis. A

motive for countries to hold massive IR is its desire to relax the trilemma; voluminous IR

holding allows countries to pursue both a higher level of exchange rate stability and a higher

weighted average of the other two trilemma policies through active foreign exchange

interventions. Given our finding that holding a higher level of IR than 21-24% of GDP can

dampen or even reverse the volatility-increasing effect of exchange rate stability, this finding is

plausible.

Lastly, our empirical findings are consistent with the conditions of the countries that are

currently experiencing macroeconomic turmoil; countries in turmoil do seem to be the ones with

the trilemma variables and those related to both internal and external financing at the levels that

lead to higher output volatility. In other words, our model could predict higher output volatility

for countries experiencing or at the brink of financial crises. This bolsters the validity of our

empirical analyses.

36

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40

Appendix: Data Availability of the Trilemma measures

Country

code

(cn)

Country Name

Base Country

Monetary

Independence

(MI)

Exchange rate

stability

(ERS)

KA Openness

(KAOPEN)

1 512 Afghanistan (C)

2 914 Albania (C)

3 612 Algeria (C)

4 614 Angola (C)

5 311 Antigua and Barbuda

6 213 Argentina (E) (C)

7 911 Armenia

8 314 Aruba

9 193 Australia

10 122 Austria

11 912 Azerbaijan

12 313 Bahamas, The

13 419 Bahrain (C)

14 513 Bangladesh (E)

15 316 Barbados

16 913 Belarus

17 124 Belgium

18 339 Belize

19 638 Benin

20 514 Bhutan

21 218 Bolivia (C)

22 616 Botswana (E) (C)

23 223 Brazil (E)

24 918 Bulgaria (E)

25 748 Burkina Faso

26 618 Burundi (C)

27 662 Cote d’Ivoire (E) (C)

28 522 Cambodia

29 622 Cameroon

30 156 Canada

31 624 Cape Verde

32 626 Central African Rep.

33 628 Chad (C)

34 228 Chile (E) (C)

35 924 China (E)

36 233 Colombia (E)

37 632 Comoros

38 636 Congo, Dem. Rep. (C)

39 634 Congo, Rep. (C)

40 238 Costa Rica

41 960 Croatia

42 423 Cyprus

43 935 Czech Republic (E)

44 128 Denmark

45 611 Djibouti

46 321 Dominica

47 243 Dominican Republic

48 248 Ecuador (E)

49 469 Egypt, Arab Rep. (E)

50 253 El Salvador

51 642 Equatorial Guinea (C)

52 643 Eritrea

53 939 Estonia

54 644 Ethiopia (C)

55 819 Fiji

56 172 Finland

57 132 France

58 646 Gabon (C)

59 648 Gambia, The

60 915 Georgia

61 134 Germany

62 652 Ghana (E) (C)

63 174 Greece

64 328 Grenada

U.S.

U.S.

France

U.S.

U.S.

U.S.

U.S.

U.S.

U.S.

Germany

U.S.

U.S.

U.S.

U.S.

1960-74 U.K.; 1975-U.S.

U.S.

Germany

U.S.

France

Rupee

U.S.

South Africa

U.S.

Germany

France

1960-70 Belgium; 1971-U.S.

France

U.S.

France

U.S.

Germany

France

France

U.S.

U.S.

U.S.

France

U.S.

France

U.S.

Germany

Germany

Germany

Germany

U.S.

U.S.

U.S.

U.S.

U.S.

U.S.

France

U.S.

Germany

U.S.

U.S.

Germany

Germany

France

U.K.

U.S.

U.S.

U.S.

1960-80 U.S.; 1981-Germany

U.S.

(172) (181) (178)

- - 1961 2005 1970 2004

1992 2006 1993 2006 1996 2006

1974 2006 1961 2006 1970 2006

1995 2006 1961 2006 1993 2006

1981 2006 1961 2006 1985 2006

1977 2006 1961 2006 1970 2006

1995 2006 1993 2006 1996 2006

1986 2006 1987 2006 1992 2006

1969 2006 1961 2006 1970 2006

1960 2006 1961 2006 1970 2006

1993 2006 1993 2006 2000 2006

1970 2006 1961 2006 1977 2006

1975 2006 1967 2006 1976 2006

1972 2006 1972 2006 1976 2006

1967 2006 1961 2006 1974 2006

1993 2006 1993 2006 1996 2006

1960 2006 1961 2006 1970 2006

1979 2006 1961 2006 1985 2006

1964 2006 1961 2006 1970 2006

1982 2006 1961 2006 1985 2006

1960 2006 1961 2006 1970 2006

1976 2006 1961 2006 1972 2006

1964 2006 1965 2006 1970 2006

1991 2006 1961 2006 1996 2006

1964 2006 1961 2006 1970 2006

1977 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1994 2006 1961 2006 1973 2006

1968 2006 1961 2006 1970 2006

1960 2006 1961 2006 1970 2006

1985 2006 1961 2006 1982 2006

1968 2006 1961 2006 1970 2006

1968 2006 1961 2006 1970 2006

1977 2006 1961 2006 1970 2006

1980 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1983 2006 1961 2006 1981 2006

1982 2003 1961 2006 1970 2000

1968 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1992 2006 1993 2006 1998 2006

1969 2006 1961 2006 1970 2006

1993 2006 1994 2006 1998 2006

1960 2006 1961 2006 1970 2006

1996 2006 1961 2006 1982 2006

1981 2006 1961 2006 1982 2006

1995 2006 1961 2006 1970 2006

1970 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1983 2005 1961 2006 1970 2006

1985 2006 1961 2006 1973 2006

- - 1961 2006 1998 2006

1993 2006 1993 2006 1998 2006

1985 2006 1961 2006 1970 2006

1974 2006 1961 2006 1975 2006

1960 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1968 2006 1961 2006 1970 2006

1977 2006 1961 2006 1971 2006

1995 2006 1996 2006 1998 2006

1960 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1960 2006 1961 2006 1970 2006

1981 2006 1961 2006 1979 2006

41

Country

Code

(cn)

Country Name

Base Country

Monetary

Independence (MI)

Exchange rate

stability (ERS)

KA Openness

(KAOPEN)

65 258 Guatemala (C)

66 656 Guinea (C)

67 654 Guinea-Bissau (C)

68 336 Guyana (C)

69 263 Haiti

70 268 Honduras (C)

71 532 Hong Kong, China (E)

72 944 Hungary (E)

73 176 Iceland (C)

74 534 India (E)

75 536 Indonesia (E)

76 429 Iran, Islamic Rep. (C)

77 433 Iraq (C)

78 178 Ireland

79 436 Israel (E)

80 136 Italy

81 343 Jamaica (E)

82 158 Japan

83 439 Jordan (E)

84 916 Kazakhstan

85 664 Kenya (E)

86 826 Kiribati

87 542 Korea, Rep. (E)

88 443 Kuwait

89 917 Kyrgyz Republic

90 544 Lao PDR

91 941 Latvia

92 446 Lebanon

93 666 Lesotho

94 668 Liberia (C)

95 672 Libya (C)

96 946 Lithuania (E)

97 137 Luxembourg

98 674 Madagascar (C)

99 676 Malawi (C)

100 548 Malaysia (E)

101 556 Maldives

102 678 Mali (C)

103 181 Malta

104 682 Mauritania (C)

105 684 Mauritius (E)

106 273 Mexico (E)

107 868 Micronesia, Fed. Sts.

108 921 Moldova

109 948 Mongolia (C)

110 686 Morocco (E)

111 688 Mozambique

112 518 Myanmar (C)

113 728 Namibia (C)

114 558 Nepal

115 138 Netherlands

116 353 Netherlands Antilles

117 196 New Zealand (C)

118 278 Nicaragua (C)

119 692 Niger (C)

120 694 Nigeria (E) (C)

121 142 Norway

122 449 Oman (C)

123 564 Pakistan (E)

124 283 Panama

125 853 Papua New Guinea (C)

126 288 Paraguay (C)

127 293 Peru (E) (C)

128 566 Philippines (E)

129 964 Poland (E)

130 182 Portugal

131 453 Qatar (C)

132 968 Romania

U.S.

1960-73 France; 1974-U.S.

U.S.

1960-75 U.K.; 1976-U.S.

U.S.

U.S.

U.S.

1960-91 U.S.; 1992-Germany

1960-90 U.S.; 1991-Germany

1960-79 U.K.; 1980-U.S.

U.S.

U.S.

U.S.

1960-78 U.K.; 1979-Germany

U.S.

Germany

U.S.

U.S.

U.S.

U.S.

U.S.

Australia

U.S.

U.S.

U.S.

U.S.

Germany

U.S.

South Africa

U.S.

U.S.

Germany

1960-78 Belgium; 1979- Germany

France

U.S.

U.S.

U.S.

France

France

1960-73 France; 1974-U.S.

U.K.

U.S.

U.S.

U.S.

U.S.

France

U.S.

U.S.

South Africa

1960-82 U.S.; 1983-India

Germany

U.S.

Australia

U.S.

France

U.S.

Germany

U.S.

U.S.

U.S.

1960-85 Australia; 1986-U.S.

U.S.

U.S.

U.S.

Germany

Germany

U.S.

U.S.

1960

1986

1975

1966

1994

1979

1982

1971

1964

1964

1983

1960

-

1964

1982

1964

1961

1960

1966

1994

1967

-

1964

1975

1993

1979

1993

1964

1980

1981

1963

1994

1985

1970

1963

1966

1978

1964

1969

1964

1967

1976

1996

1995

1993

1969

1994

1975

1991

1974

1960

1980

1969

1990

1964

1964

1964

1980

1964

1986

1974

1990

1960

1964

1991

1960

1980

1994

2006 1961 2006 1970 2006

2006 1961 2005 1970 2006

2006 1961 2006 1981 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1969 2006 1998 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1968 2006 1970 2006

2006 1961 2006 1970 2006

- 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1994 2006 1998 2006

2006 1961 2006 1970 2006

- 1961 2006 1990 2005

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1994 2006 1998 2006

2006 1961 2006 1970 2006

2006 1993 2006 1998 2006

2006 1961 2006 1970 2006

2006 1961 2006 1972 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1993 2006 1998 2006

2006 1961 2006 -

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1982 2006

2006 1961 2006 1970 2006

2006 1961 2006 1972 2006

2006 1961 2005 1970 1964

2006 1961 2006 1972 1967

2006 1961 2006 1970 1976

2006 1961 2006 1996 1996

2006 1992 2006 1998 1995

2006 1991 2006 1998 1993

2006 1961 2006 1970 1969

2006 1961 2006 1988 1994

2006 1961 2006 1970 1975

2006 1962 2006 1994 1991

2006 1961 2006 1970 1974

2006 1961 2006 1970 1960

2006 1961 2006 1970 1980

2006 1961 2006 1970 1969

2006 1961 2006 1970 1990

2006 1961 2006 1970 1964

2005 1961 2006 1970 1964

2006 1961 2006 1970 1964

2006 1961 2006 1977 1980

2006 1961 2006 1970 1964

2006 1961 2006 1970 1986

2006 1961 2006 1979 1974

2006 1961 2006 1970 1990

2006 1961 2006 1970 1960

2006 1961 2006 1970 1964

2006 1961 2006 1990 1991

2006 1961 2006 1970 1960

2006 1967 2006 1976 1980

2006 1961 2006 1976 1994

42

Country

Code

(cn)

Country Name

Base Country

Monetary

Independence (MI)

Exchange rate

stability (ERS)

KA Openness

(KAOPEN)

133 922 Russian Federation (E)

134 714 Rwanda (C)

135 716 Sao Tome & Principe (C)

136 862 Samoa

137 135 San Marino

138 456 Saudi Arabia (C)

139 722 Senegal

140 718 Seychelles

141 724 Sierra Leone

142 576 Singapore (E)

143 936 Slovak Republic (E)

144 961 Slovenia (E)

145 813 Solomon Islands (C)

146 726 Somalia (C)

147 199 South Africa (E)

148 184 Spain

149 524 Sri Lanka (E)

150 361 St. Kitts and Nevis

151 362 St. Lucia

152 364 St. Vinc. & the Gren. (C)

153 732 Sudan (C)

154 366 Suriname (C)

155 734 Swaziland (C)

156 144 Sweden

157 146 Switzerland

158 463 Syrian Arab Republic

159 528 Taiwan (E)

160 923 Tajikistan

161 738 Tanzania (C)

162 578 Thailand (E)

163 742 Togo (C)

164 866 Tonga

165 369 Trinidad & Tobago (E) (C)

166 744 Tunisia (E)

167 186 Turkey (E)

168 925 Turkmenistan (C)

169 746 Uganda (C)

170 926 Ukraine

171 466 United Arab Emirates (C)

172 112 United Kingdom

173 298 Uruguay

174 846 Vanuatu

175 299 Venezuela, RB (E) (C)

176 582 Vietnam (C)

177 474 Yemen, Rep.

178 754 Zambia (C)

179 698 Zimbabwe (E) (C)

U.S.

1960-73 Belgium; 1974-U.S.

U.S.

Australia

Germany

U.S.

France

U.S.

1960-77 U.K.; 1978-U.S.

Malaysia

Germany

Germany

1960-85 Australia; 1986-U.S.

U.S.

U.S.

Germany

1960-92 U.S.; 1993-India

U.S.

U.S.

U.S.

1960-71 U.K.; 1972-U.S.

U.S.

South Africa

Germany

Germany

U.S.

U.S.

U.S.

U.S.

U.S.

France

Australia

1960-75 U.K.; 1976-U.S.

France

U.S.

U.S.

U.S.

U.S.

U.S.

Germany

U.S.

U.S.

1960-89 France; 1990-U.S.

U.S.

U.S.

U.S.

U.S.

1995

1966

1989

1983

-

1997

1964

1979

1966

1972

1993

1993

1981

-

1960

1964

1964

1981

1981

1981

1978

1991

1974

1960

1964

2003

1985

1997

1973

1977

1964

1981

1965

1964

1964

-

1980

1992

-

1960

1976

1981

1964

1996

1996

1965

1965

2006 1993 2006 1998 2006

2006 1961 2006 1970 2006

2006 1961 2006 1981 2006

2006 1961 2006 1975 2006

- 1961 2006 1996 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1981 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1994 2006 1998 2006

2006 1992 2006 1998 2006

2006 1961 2006 1982 2006

- 1961 1989 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1988 2006

2006 1961 2006 1983 2006

2006 1961 2006 1983 2006

1984 1961 2006 1970 2005

2006 1961 2006 1970 2006

2006 1961 2006 1973 2006

2006 1961 2006 1970 2006

2006 1961 2006 1996 2006

2006 1961 2006 1970 2006

2006 1983 2006 - -

2006 1993 2006 1998 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1989 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

- 1994 2001 1998 2006

2006 1961 2006 1970 2006

2006 1993 2006 1998 2006

- 1967 2006 1976 2006

2006 1961 2006 1970 2006

2006 1965 2006 1970 2006

2006 1961 2006 1985 2000

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1991 2006 1995 2006

2006 1961 2006 1970 2006

2005 1961 2005 1984 2006

Notes: The base countries are primarily based on Shambaugh (QJE) and complemented by information from

IMF’s Annual Report on Exchange Arrangement and Exchange Restrictions and CIA Factbook

43

Table 1: Mean-Equality Tests of the Trilemma Indexes between Emerging Market

Countries (EMG) and Non-Emerging Market Developing Countries (Non-EMG LDC)

1971 – 1980 1981 – 1990 1991 – 2000 2001-2006

Non-EMG LDC .4495 .4510 .4748 .4427

Monetary EMG .4784 .4772 .4941 .3847

Independence (MI)

Difference .02883 .0262 .0193 -.0579

t-statistics 2.86*** 2.71*** 2.07** 4.31***

Non-EMG LDC .7941 .7228 .6508 .7266

Exchange Rate EMG .6703 .4983 .4901 .5364

Stability (ERS)

Difference -.1238 -.2245 -.1607 -.1902

t-statistics 6.70*** 11.04*** 8.47*** 8.68***

Non-EMG LDC .3511 .3138 .3785 .4177

Financial

EMG .2803 .2522 .4014 .5498

Openness

Difference -.0708 -

.0616 .0230 .1320

(KAOPEN)

t-statistics 3.42*** 3.08*** 1.1912% 5.09***

Non-EMG LDC .1013 .1093 .1331 .1772

International

EMG .1109 .1104 .1697 .2322

Reserves Holding

Difference .0095 .0011 .0366 .0550

(% of GDP; IR)

t-statistics 1.31* 0.12 4.25*** 4.67***

44

Table 2 (a): Tests for Structural Breaks in the Trilemma Indexes

Monetary Independence

1970-72 1974-81 1983-96 1999-2006

Mean 0.376 0.407 0.389 0.139

Change +0.031 -0.018 -0.250

t-stats (p-value) 1.31 (0.11) 0.85 (0.20) 11.91 (0.00)***

Mean 0.688 0.429 0.476 0.702

Change -0.259 +0.047 +0.226

t-stats (p-value) 6.64 (0.00)*** 2.41 (0.01)** 12.45 (0.00)***

Mean 0.439 0.469 0.688 0.955

Change +0.030 +0.219 +0.266

t-stats (p-value) 1.62 (0.07)* 4.34 (0.00)*** 5.27 (0.00)***

1970-72 1974-81 1983-96 1999-2006

Mean 0.500 0.399 0.457 0.534

Change -0.101 +0.058 +0.077

t-stats (p-value) 1.68 (0.06)* 1.84 (0.04)** 3.55 (0.00)***

Mean 0.786 0.780 0.635 0.742

Change -0.006 -0.145 +0.107

t-stats (p-value) 0.10 (0.46) 5.26 (0.00)*** 3.76 (0.00)***

Mean 0.267 0.365 0.326 0.391

Change +0.098 -0.040 +0.065

t-stats (p-value) 5.73 (0.01)*** 2.25 (0.02)** 3.93 (0.00)***

1970-72 1974-81 1983-96 1999-2006

Mean 0.526 0.474 0.508 0.407

Change -0.052 +0.034 -0.100

t-stats (p-value) 2.16 (0.03)** 1.42 (0.09)* 3.81 (0.00)***

Mean 0.818 0.715 0.517 0.579

Change -0.103 -0.198 +0.63

t-stats (p-value) 3.38 (0.00)*** 9.55 (0.00)*** 2.71 (0.01)***

Mean 0.210 0.229 0.240 0.474

Change +0.020 +0.010 +0.234

t-stats (p-value) 5.03 (0.00)*** 0.40 (0.35) 8.88 (0.00)***

Industrial

Countries (18)

Exchange Rate Stability

Financial Openness

Monetary Independence

Non-Emerging

Developing

Countries

(32)

Exchange Rate Stability

Financial Openness

Monetary Independence

Emerging

Market

Countries

(18)

Exchange Rate Stability

Financial Openness

Note: * significant at 10%; ** significant at 5%; *** significant at 1%

45

Table 2(b): Tests for Structural Breaks in the Trilemma Indexes

Monetary Independence

1983-89 1991-2006 1983-2000 2002-2006

Mean 0.396 0.246 0.355 0.126

Change -0.150 -0.229

t-stats (p-value) 3.17 (0.00)*** 5.82 (0.00)***

Mean 0.476 0.599 0.511 0.715

Change +0.124 +0.204

t-stat (p-value) 2.64 (0.01)*** 5.33 (0.00)***

Mean 0.578 0.905 0.748 0.949

Change +0.327 +0.201

t-stats (p-value) 9.22 (0.00)*** 2.62 (0.01)**

1983-89 1991-2006 1983-2000 2002-2006

Mean 0.421 0.522 0.483 0.517

Change +0.100 +0.034

t-stats (p-value) 4.80 (0.00)*** 1.05 (0.15)

Mean 0.633 0.699 0.643 0.778

Change +0.066 +0.135

t-stats (p-value) 2.01 (0.03)** 4.73 (0.00)***

Mean 0.296 0.376 0.336 0.400

Change +0.080 +0.064

t-stats (p-value) 5.94 (0.00)*** 3.20 (0.00)***

1983-89 1991-2006 1983-2000 2002-2006

Mean 0.471 0.469 0.508 0.385

Change -0.002 -0.123

t-stats (p-value) 0.08 (0.47) 4.52 (0.00)***

Mean 0.537 0.532 0.515 0.608

Change -0.005 +0.093

t-stats (p-value) 0.19 (0.43) 3.95 (0.00)***

Mean 0.188 0.403 0.282 0.482

Change +0.215 +0.200

t-stats (p-value) 6.27 (0.00)*** 4.23 (0.00)***

Industrial

Countries (18)

Exchange Rate Stability

Financial Openness

Monetary Independence

Non-Emerging

Developing

Countries

(32)

Exchange Rate Stability

Financial Openness

Monetary Independence

Emerging

Market

Countries

(18)

Exchange Rate Stability

Financial Openness

Note: * significant at 10%; ** significant at 5%; *** significant at 1%

46

Table 2(c): Summary of the Structural Breaks Tests

Structural Breaks

Monetary Independence

Exchange Rate Stability

Financial Openness

1997-98

1997-98

(1973 for non-Euro Countries)

1990

Industrial

Countries

(IDC)

Non-Emerging

Developing

Countries

(NOEMG)

Monetary Independence

Exchange Rate Stability

Financial Openness

1990

1982

1990

Emerging

Market

Countries

(EMG)

Monetary Independence

Exchange Rate Stability

Financial Openness

2001

1982

1997-98

47

Table 3: Regression for the Linear Relationship between the Trilemma Indexes:

1=ajMIi,t+bjERSi,t+cjKAOPENi,t +εt

(1) (2) (3) (4) (5) (6) (7) (8) (9)

FULL 1970-72 1974-81 1983-96 1999-2006 1983-89 1991-20061983-20002002-20061.084

0.946

1.339

0.99

0.336

1.065

0.558

0.931

0.522

Monetary Independence

[0.039]***

[0.127]***[0.069]***[0.057]***[0.109]***

[0.066]***[0.077]***[0.057]***[0.101]***

0.611

0.665

0.597

0.647

0.223

0.613

0.633

0.66

0.448

Exch. Rate Stability

[0.032]***

[0.076]***[0.090]***[0.051]***[0.181]

[0.061]***[0.100]***[0.050]***[0.249]*

0.437

0.369

0.29

0.448

0.869

0.439

0.632

0.468

0.733

KA Openness

[0.021]***

[0.050]***[0.063]***[0.031]***[0.072]***

[0.045]***[0.042]***[0.029]***[0.091]***

-0.166

–

0.375

-0.287

0.159

-0.43

-0.059

-0.104

-0.022

ERM x MI

[0.072]**

–

[0.299]

[0.111]***[0.119]

[0.286]

[0.103]

[0.086]

[0.126]

-0.026

–

0.254

0.073

-0.115

0.218

-0.398

-0.105

-0.338

ERM x ERS

[0.055]

–

[0.165]

[0.073]

[0.183]

[0.104]**

[0.108]***[0.067]

[0.251]

-0.005

–

-0.273

-0.009

0.039

0.09

0.137

-0.012

0.177

ERM x KAOPEN

[0.052]

–

[0.128]**

[0.054]

[0.075]

[0.122]

[0.059]**

[0.054]

[0.097]*

0.148

0.389

-0.175

0.299

0.78

0.214

0.675

0.365

0.567

LDC x MI

[0.045]***

[0.164]**

[0.097]*

[0.065]***[0.119]***

[0.078]***[0.083]***[0.064]***[0.120]***

-0.193

-0.371

-0.118

-0.21

0.211

-0.134

-0.244

-0.24

0.001

LDC x ERS

[0.035]***

[0.094]***[0.097]

[0.055]***[0.184]

[0.067]**

[0.103]**

[0.054]***[0.252]

-0.158

-0.136

-0.043

-0.176

-0.536

-0.009

-0.362

-0.257

-0.378

LDC x KAOPEN

[0.030]***

[0.079]*

[0.081]

[0.051]***[0.080]***

[0.069]

[0.052]***[0.045]***[0.100]***

1850

150

400

700

400

350

800

900

250

Observations

0.95

0.98

0.94

0.96

0.95

0.96

0.96

0.95

0.95

Adjusted R-squared

Robust standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

NOTES: ERM is a dummy for the countries and years that correspond to participation in ERM (i.e., Belgium, Denmark, Germany, France, Ireland,

and Italy from 1979, Spain from 1989, U.K. only for 1990-91, Portugal from 1992, Austria from 1995, Finland from 1996, and Greece from 1999)

48

2023年12月18日发(作者：夔笑笑)

Assessing the Emerging Global Financial Architecture:

Measuring the Trilemma's Configurations over Time

Joshua Aizenman*

UCSC and NBER

Menzie D. Chinn**

University of Wisconsin and NBER

Hiro Ito†

Portland State University

April 2009

Abstract: We develop a methodology that intuitively characterizes the choices countries have made with respect to

the trilemma during the post Bretton-Woods period. The paper first outlines the new metrics for measuring the

degree of exchange rate flexibility, monetary independence, and capital account openness while taking into account

the recent development of substantial international reserve accumulation. The evolution of our “trilemma indexes”

illustrates that, after the early 1990s, industrialized countries accelerated financial openness, but reduced the extent

of monetary independence while sharply increasing exchange rate stability, all reflecting the introduction of the euro.

In contrast, emerging market countries pursued exchange rate stability as their key priority up to the late 1980s while

non-emerging market developing countries has pursued it throughout the period since 1970. As a stark difference

from the latter group of countries, emerging market countries have converged towards intermediate levels of all

three indexes, characterizing managed flexibility while retaining some degree of monetary autonomy and

accelerating financial openness. This recent trend appears to be sustained by using sizable international reserves as a

buffer. We also confirm that the weighted sum of the three indexes adds up to a constant, validating the notion that a

rise in one trilemma variable should be traded-off with a drop of the weighted sum of the other two. The second part

of the paper deals with normative aspects of the trilemma, relating the policy choices to macroeconomic outcomes

such as the volatility of output growth and inflation, and medium term inflation rates. Some key findings for

developing countries include: (i) greater monetary independence can dampen output volatility while greater

exchange rate stability implies greater output volatility, which can be mitigated by reserve accumulation; (ii) greater

monetary autonomy is associated with a higher level of inflation while greater exchange rate stability and greater

financial openness could lower the inflation level; (iii) a policy pursuit of stable exchange rate while financial

development is at the medium level can increase output volatility, (iv) greater financial openness with a high level of

financial development can reduce output volatility, though greater financial openness with a low level of financial

development can be volatility-increasing; (v) net inflow of portfolio investment and bank lending can increase

output volatility and higher levels of short-term debt or total debt services can increase both the level and the

volatility of inflation.

JEL Classification Nos.: F 15,F 21,F31,F36,F41,O24

Keywords: Impossible trinity; international reserves; financial liberalization; exchange rate; FDI flows.

Acknowledgements: The financial support of faculty research funds of the UCSC and PSU is gratefully

acknowledged. This paper encompasses the results in two shorter papers: “Mundell-Fleming’s Impossible Trinity:

Testing the Stability and Fitness of Trilemma’s Linear Specification” and “The Emerging Global Financial

Architecture – Tracing and Evaluating the New Patterns of the Trilemma’s Configurations”. We would like to thank

Eduardo Borensztein, Eduardo Cavallo, Camilo Tavor, Mathijs van Dijk, and the participants at the BIS-LACEA

2008 Rio meeting and the 4th Tinbergen Conference for their useful comments and suggestions.

_____________________________

*

Aizenman: Department of Economics E2, UCSC, Santa Cruz, CA 95064. Email: jaizen@.

** Chinn: Robert M. La Follette School of Public Affairs; and Department of Economics, University of

Wisconsin, 1180 Observatory Drive, Madison, WI 53706. Email: mchinn@

† Ito: Department of Economics, Portland State University, 1721 SW Broadway, Portland, OR 97201. Tel/Fax:

+1-503-725-3930/3945. Email: ito@

Introduction

A fundamental contribution of the Mundell-Fleming framework is the impossible trinity,

or the trilemma, which states that a country simultaneously may choose any two, but not all, of

the following three goals: monetary independence, exchange rate stability and financial

integration. The trilemma is illustrated in Figure 1; each of the three sides – representing

monetary independence, exchange rate stability, and financial integration – depicts a potentially

desirable goal, yet it is not possible to be simultaneously on all three sides of the triangle. The

top vertex – labeled “closed capital markets” – is, for example, associated with monetary policy

autonomy and a fixed exchange rate regime, but not financial integration, the preferred choice of

most developing countries in the mid to late 1980s.1

Over the last 20 years, most developing countries have opted for increasing financial

integration. The trilemma implies that a country choosing this path must either forego exchange

rate stability if it wishes to preserve a degree of monetary independence, or forego monetary

independence if it wishes to preserve exchange rate stability.

The purpose of this paper is to outline a methodology that will allow us to easily and

characterize in an intuitive manner the choices countries have made with respect to the trilemma

during the post Bretton-Woods period. The first part of our study deals with positive aspects of

the trilemma, outlining new ways of tracing the evolving financial configurations. The second

part deals with normative aspects of the trilemma, relating the policy decisions chosen to

macroeconomic outcomes, such as the volatility of output growth and inflation, and medium

term inflation rates.

We begin by observing that over the last two decades, a growing number of developing

countries, especially emerging market ones, have opted for hybrid exchange rate regimes – e.g.,

managed float buffered by increasing accumulation of international reserves [IR henceforth].

Despite the proliferation of greater exchange rate flexibility, IR/GDP ratios increased

dramatically, especially in the wake of the East Asian crises. Practically, all the increase in

IR/GDP holding has taken place in emerging market countries [see Figure 2]. The magnitude of

the changes during recent years is staggering: global reserves increased from about USD 1

trillion to more than USD 5 trillion between 1990 and 2006.

The dramatic accumulation of international reserves has been uneven: while the IR/GDP

ratio of industrial countries was relatively stable at approximately 4%, the IR/GDP ratio of

developing countries increased from about 5% to about 27%. Today, about three quarters of the

global international reserves are held by developing countries. Most of the accumulation has

been in Asia, where reserves increased from about 5% in 1980 to about 37% in 2006 (32% in

Asia excluding China). The most dramatic changes occurred in China, increasing its IR/GDP

11.

See Obstfeld, Shambaugh, and Taylor (2005) for further discussion and references dealing with the trilemma.

1

ratio from about 1% in 1980, to about 41% in 2006 (and approaching 50% by 2008). Empirical

studies suggest several structural changes in the patterns of reserves hoarding (Cheung and Ito,

2007; Obstfeld, et al. 2008). A drastic change occurred in the 1990s in terms of reserve

management among developing countries. The IR/GDP ratios shifted upwards; the ratios

increased dramatically immediately after the East Asian crisis of 1997-98, but subsided by 2000.

Another structural change took place in the early 2000s, mostly driven by an unprecedented

increase in the accumulation of international reserves by China.

The globalization of financial markets is evident in the growing financial integration of

all groups of countries. While the original framing of the trilemma was silent regarding the role

of reserves, recent trends suggest that reserve accumulation may be closely related to changing

patterns of the trilemma for developing countries. The earlier literature focused on the role of

international reserves as a buffer stock critical to the management of an adjustable-peg or

managed-floating exchange-rate regime.2 While useful, the buffer stock model has limited

capacity to account for the recent development in international reserves hoarding – the greater

flexibility of the exchange rates exhibited in recent decades should help reduce reserve

accumulation, in contrast to the trends reported above.

The recent literature has focused on the adverse side effects of deeper financial

integration of developing countries – the increased exposure to volatile short-term inflows of

capital (dubbed “hot money”), subject to frequent sudden stops and reversals (see Calvo, 1998).

The empirical evidence suggests that international reserves can reduce both the probability of a

sudden stop and the depth of the resulting output collapse when the sudden stop occurs.3

Aizenman and Lee (2007) link the large increase in reserves holding to the deepening financial

integration of developing countries and find evidence that international reserves hoarding serves

as a means of self-insurance against exposure to sudden stops. In extensive empirical analysis of

the shifting determinants of international reserve holdings for more than 100 economies over the

1975-2004 period, Cheung and Ito (2007) find that while trade openness is the only factor that is

significant in most of the specifications and samples under consideration, its explanatory power

has been declining over time. In contrast, the explanatory power of financial variables has been

increasing over time.

The increasing importance of financial integration as a determinant for international

reserves hoarding suggests a link between the changing configurations of the trilemma and the

level of international reserves. Indeed, Obstfeld, et al. (2008) find that the size of domestic

financial liabilities that could potentially be converted into foreign currency (M2), financial

Accordingly, optimal reserves balance the macroeconomic adjustment costs incurred in the absence of reserves

with the opportunity cost of holding reserves (Frenkel and Jovanovic, 1981).

3 See Ben-Bassat and Gottlieb (1992), Rodrik and Velasco (1999), and Aizenman and Marion (2004) for papers

viewing international reserves as output and consumption stabilizers.

2

2

openness, the ability to access foreign currency through debt markets, and exchange rate policy

are all significant predictors of international reserve stocks.

We begin by constructing an easy and intuitive way to summarize these trends in the

form of a “Diamond chart.” Applying the methodology outlined in the next section, we construct

for each country a vector of trilemma and IR configurations that measures each country’s

monetary independence, exchange rate stability, international reserves, and financial integration.

These measures are normalized between zero and one. Each country’s configuration at a given

instant is summarized by a “generalized diamond,” whose four vertices measure the three

trilemma dimensions and IR holding (as a ratio to GDP).

Figures 3 and 4 provide a concise summary of the recent history of trilemma

configurations for different income groups and regional groups.4 Figure 3 reveals that, over time,

both industrialized countries and emerging market countries have moved towards deeper

financial integration and losing monetary independence, a stark contrast from non-emerging

market developing countries. Furthermore, emerging market countries have pursued greater

financial integration while non-emerging market developing countries barely have. As of the

2000s, emerging market countries distinctly differ from other groups with its balanced

combination of the three macroeconomic policy goals as well as substantial amount of IR

holding.

In Figure 4, we can see that Latin American economies have liberalized their financial

markets rapidly since the 1990s after some retrenchment during the 1980s. Emerging markets in

Latin America reduced the extent of monetary independence in recent years and maintained a

lower level of exchange rate stability. Emerging Asian economies have achieved comparable

levels of exchange rate stability and financial openness while consistently reducing monetary

independence. This group of economies differ from the other ones the most with their relatively

balanced achievement of the three macroeconomic policy goals and their high levels of

international reserves holding.

Figure 5 presents the development of trilemma indexes for 50 countries (32 of which are

developing countries) during the 1970-2006 period for which we can construct a balanced data

set. Focusing on developing countries, we can observe an interesting trend. Comparing Figure 3b

and 3c reveals the distinctly different trilemma patterns between emerging (EMG) and non-emerging (non-EMG) market countries.5 EMGs moved towards relatively more flexible

exchange rate than Non-EMGs, buffering it by holding much higher IR/GDP, as well as towards

In each diamond chart, the origin is normalized so as to represent zero monetary independence, pure float, zero

international reserves and financial autarky.

5 Table 1 shows that the differences of the Trilemma indexes for monetary independence, exchange rate stability,

and financial openness as well as international reserves holding (as a ratio to GDP) between EMGs and non-EMG

developing countries are statistically significant.

4

3

higher financial integration and lower monetary independence. The figure shows that EMGs

have experienced convergence to some middle ground among all three indexes. In contrast, non-EMGs, on average, have not exhibited such convergence. For both groups, while the degree of

exchange rate stability declined from the early 1970s to the early 1990s, it increased during the

last fifteen years – though one could expect that the present crisis would induce these countries

to move toward higher exchange rate flexibility. Currently, non-EMGs exhibit a greater degree

of exchange rate stability and monetary independence, but a lower degree of financial integration

compared to EMGs.

Despite the cross-country and over-time variations in the trilemma configures, one key

message of the trilemma is instrument scarcity – policy makers face a tradeoff, where increasing

one trilemma variable (such as higher financial integration) would induce a drop in the weighted

average of the other two variables (lower exchange rate stability, or lower monetary

independence, or a combination of the two). Yet, to our knowledge, the validity of this tradeoff

among the three trilemma variables has not been tested properly. A possible concern is that the

trilemma framework does not impose an exact functional restriction on the association between

the three trilemma policy variables.

We conduct a regression analysis to test the validity of the simplest functional

specification for the trilemma: whether the three trilemma policy goals are linearly related. For

this purpose, we also examine and validate that the weighted sum of the three trilemma policy

variables adds up to a constant (see Figure 7). This result confirms the notion that a rise in one

trilemma variable should be traded-off with a drop of a linear weighted sum of the other two.

The regression results also provide another diagnostic tool, allowing a simple description of the

changing ranking among the three trilemma policy goals over time.

In the second half of the paper, we investigate the normative questions pertaining to the

trilemma. More specifically, we examine how the policy choices among the three trilemma

policies affect output growth volatility, inflation rates, and the volatility of inflation, with focus

on developing economies. Given that EMGs collectively have outperformed non-EMGs in terms

of average economic growth rates, it can be the middle ground configuration of the trilemma

policies that have contributed to the recent rapid and better development and high economic

growth among the emerging markets. Yet, without controlling for the macroeconomic

environment, one cannot be definitive about the causality since the middle-ground convergence

may also be the outcome of successful take offs and prolonged growth. Our paper attempts to

verify these issues through regression analyses, looking more systematically at the association

between trilemma choices and economic performance. Upon investigating the link between the

trilemma policy configurations and macroeconomic performance of the countries of our focus,

4

we also pay close attention to three other factors, namely, international reserves (IR) holding,

financial development, and external finance.

As has been intensively investigated in the literature, for the last decade since the Asian

crisis of 1997-98, developing countries, especially those in East Asia and the Middle East, are

rapidly increasing the amount of international reserves hoarding. China, the world’s largest

holder of international reserves, currently holds about $2 trillion of reserves, accounting for 30%

of the world’s total. As of the end of 2008, the top 10 biggest holders are all developing countries

except for Japan, and the nine developing countries, including China, Russia, Taiwan, and Korea

hold over 55% of international reserves available in the world. Against this backdrop, it has been

argued that one of the main reasons for the rapid IR accumulation is countries’ desire to stabilize

exchange rate movement. Hypothetically, one could argue that countries hold massive

international reserves to have balanced combinations of exchange rate stability, monetary policy

autonomy, and financial openness. Thus, evidently, one cannot discuss the issue of the trilemma

without incorporating the effect of IR holding, which we will do in this paper.

Secondly, the ongoing crisis has made it clear that financial development can be a

double-edged sword. While it can enable more efficient allocation of capital, it also embraces the

possibility of amplifying shocks to the economy. As a country may incorporate financial

development into its decision-making process for the trilemma configurations, as China has been

alleged to pursue closed financial markets with exchange rate stability as precautionary measures

to protect its underdeveloped financial system, the degree of financial development could affect

the macroeconomic performance of the economy.6 Some also argue that countries with newly

liberalized financial system tend to experience financial fragility (Demirguc-Kent and

Detragiache, 1998). Thus, trilemma policy configurations need to be investigated while

incorporating the level of financial development.

Thirdly, as globalization proceeds with an unprecedented speed, and more countries are

abolishing capital controls, policy makers in countries, especially developing ones, cannot ignore

the effect of capital flows from other countries. As Lane and Milesi-Ferretti (2006) show, the

type, volume, and direction of capital flows has been changing over time, thus policy makers

have to aim at moving targets in their policy decision making. Especially, considering that the

present crisis has shown that the speed and the volume of tsunami of capital flows can be

enormous, we must be abreast of the cost and benefit of trilemma configurations in tandem with

those of external financing such as FDI flows, portfolio flows, and banking lending across

countries.

See Prasad (2008) for the argument that China’s policy of exchange rate stability and closed financial markets is

impairing the country’s macroeconomic management.

6

5

In the remaining of the paper, Section 2 outlines the methodology for the construction of

our “trilemma indexes.” This section also presents summary statistics of the indexes and

examines whether the indexes entail any structural breaks corresponding to major global

economic events. Furthermore, in this section, we test the validity of a linear specification of the

trilemma indexes to examine whether the notion of the trilemma can be considered to be a trade-off and binding. Section 3 conducts more formal analysis on how the policy choices affect output

growth volatility, inflation rates, and the volatility of inflation, with focus on developing

economies. In Section 4, we extend our empirical investigation and examine the impact of other

important economic variables related to the current crisis such as financial development and

various forms of external financing. In Section 5, we make casual observations to see whether

our empirical findings are consistent with the occurrence of the ongoing severe crises in some

countries. We present our concluding remarks in Section 6.

2. Measures of the Trilemma Dimensions

The empirical analysis of the tradeoffs being made requires measures of the policies.

Unfortunately, there is a paucity of good measures; in this paper we attempt to remedy this

deficiency by creating several indices.

2.1 Construction of the Trilemma Measures

Monetary Independence (MI)

The extent of monetary independence is measured as the reciprocal of the annual

correlation of the monthly interest rates between the home country and the base country. Money

market rates are used.7

The index for the extent of monetary independence is defined as:

MI =

1−corr(ii,ij)−(−1)1−(−1)

where i refers to home countries and j to the base country. By construction, the maximum and

minimum values are 1 and 0, respectively. Higher values of the index mean more monetary

policy independence.8,9

The data are extracted from the IMF’s International Financial Statistics (60B..ZF...). For the countries whose

money market rates are unavailable or extremely limited, the money market data are supplemented by those from

the Bloomberg terminal and also by the discount rates (60...ZF...) and the deposit rates (60L..ZF...) series from IFS.

8 The index is smoothed out by applying the three-year moving averages encompassing the preceding, concurrent,

and following years (t – 1, t, t+1) of observations.

7

6

Here, the base country is defined as the country that a home country’s monetary policy is

most closely linked with as in Shambaugh (2004). The base countries are Australia, Belgium,

France, Germany, India, Malaysia, South Africa, the U.K., and the U.S. For the countries and

years for which Shambaugh’s data are available, the base countries from his work are used, and

for the others, the base countries are assigned based on IMF’s Annual Report on Exchange

Arrangements and Exchange Restrictions (AREAER) and CIA Factbook.

Exchange Rate Stability (ERS)

To measure exchange rate stability, annual standard deviations of the monthly exchange

rate between the home country and the base country are calculated and included in the following

formula to normalize the index between zero and one:

ERS=0.01

0.01+stdev(Δ(log(exch_rate))Merely applying this formula can easily create a downward bias in the index, that is, it would

exaggerate the “flexibility” of the exchange rate especially when the rate usually follows a

narrow band, but is de- or revalued infrequently.10 To avoid such downward bias, we also apply

a threshold to the exchange rate movement as has been done in the literature. That is, if the rate

of monthly change in the exchange rate stayed within +/-0.33 percent bands, we consider the

exchange rate is “fixed” and assign the value of one for the ERS index. Furthermore, single year

pegs are dropped because they are quite possibly not intentional ones.11 Higher values of this

We note one important caveat about this index. For some countries and some years, especially early in the sample,

the interest rate used for the calculation of the MI index is often constant throughout a year, making the annual

correlation of the interest rates between the home and base countries (corr(ii, ij) in the formula) undefined. Since we

treat the undefined corr the same as zero, it makes the MI index value 0.5. One might think that the policy interest

rate being constant (regardless of the base country's interest rate) is a sign of monetary independence. However, it

could reflect the possibility that the home country uses other tools to implement monetary policy, rather than

manipulating the interest rates (e.g., manipulation of required reserve ratios and providing window guidance; or

financial repression). To complicate matters, some countries have used reserves manipulation and financial

repression to gain monetary independence while others have used both while strictly following the base country's

monetary policy. The bottom line is that it is impossible to fully account for these issues in the calculation of MI.

Therefore, assigning an MI value of 0.5 for such a case appears to be a reasonable compromise. However, we also

undertake robustness checks on the index.

10 In such a case, the average of the monthly change in the exchange rate would be so small that even small changes

could make the standard deviation big and thereby the ERS value small.

11 The choice of the +/-0.33 percent bands is based on the +/-2% band based on the annual rate, that is often used in

the literature. Also, to prevent breaks in the peg status due to one-time realignments, any exchange rate that had a

percentage change of zero in eleven out of twelve months is considered fixed. When there are two re/devaluations in

three months, then they are considered to be one re/devaluation event, and if the remaining 10 months experience no

exchange rate movement, then that year is considered to be the year of fixed exchange rate. This way of defining the

threshold for the exchange rate is in line with the one adopted by Shambaugh (2004).

9

7

index indicate more stable movement of the exchange rate against the currency of the base

country.

Financial Openness/Integration (KAOPEN)

Without question, it is extremely difficult to measure the extent of capital account

controls.12 Although many measures exist to describe the extent and intensity of capital account

controls, it is generally agreed that such measures fail to capture fully the complexity of real-world capital controls. Nonetheless, for the measure of financial openness, we use the index of

capital account openness, or KAOPEN, by Chinn and Ito (2006, 2008). KAOPEN is based

on

information regarding restrictions in the IMF’s Annual Report on Exchange Arrangements and

Exchange Restrictions (AREAER). Specifically, KAOPEN is the first standardized principal

component of the variables that indicate the presence of multiple exchange rates, restrictions on

current account transactions, on capital account transactions, and the requirement of the

surrender of export proceeds.13 Since KAOPEN is based upon reported restrictions, it is

necessarily a de jure index of capital account openness (in contrast to de facto measures such as

those in Lane and Milesi-Ferretti (2006)). The choice of a de jure measure of capital account

openness is driven by the motivation to look into policy intentions of the countries; de facto

measures are more susceptible to other macroeconomic effects than solely policy decisions with

respect to capital controls.14

The Chinn-Ito index is normalized between zero and one. Higher values of this index

indicate that a country is more open to cross-border capital transactions. The index is originally

available for 181 countries for the period of 1970 through 2006.15 The data set we examine does

not include the United States. The Appendix presents data availability in more details.

2.2 Tracking the Indexes

Variations across Country Groupings

Comparing theses indexes provides some interesting insights into how the international

financial architecture has evolved over time. For this purpose, the “diamond charts” are most

useful. In each diamond chart, the origin is normalized so as to represent zero monetary

See Chinn and Ito (2008), Edison and Warnock (2001), Edwards (2001), Edison et al. (2002), and Kose et al.

(2006) for discussions and comparisons of various measures on capital restrictions.

13 This index is described in greater detail in Chinn and Ito (2008).

14 De jure measures of financial openness also face their own limitations. As Edwards (1999) discusses, it is often

the case that the private sector circumvents capital account restrictions, nullifying the expected effect of regulatory

capital controls. Also, IMF-based variables are too aggregated to capture the subtleties of actual capital controls, that

is, the direction of capital flows (i.e., inflows or outflows) as well as the type of financial transactions targeted.

15 The original dataset covers 181 countries, but data availability is uneven among the three indexes. MI is available

for 172 countries; ERS for 182; and KAOPEN for 178. Both MI and ERS start in 1960 whereas KAOPEN in 1970.

For the data availability of the trilemma indexes, refer to Appendix.

12

8

independence, pure float, zero international reserves and financial autarky. Figure 3 summarizes

the trends for industrialized countries, those excluding the 12 euro countries, emerging market

countries, and non-emerging market developing countries.16

That figure reveals that, over time, while both industrialized countries and emerging

market countries have moved towards deeper financial integration and losing monetary

independence, non-emerging market developing countries have only inched toward financial

integration and have not changed the level of monetary independence. Emerging market

countries, after giving up some exchange rate stability during the 1980s, have not changed their

stance on the exchange rate stability whereas non-emerging market developing countries seem to

be remaining at or slightly oscillating around a relatively high level of exchange rate stability.

The pursuit of greater financial integration is much more pronounced among industrialized

countries than developing countries while emerging market countries have been increasingly

becoming more financial open. Interestingly, emerging market countries stand out from other

groups by achieving a relatively balanced combination of the three macroeconomic goals by the

2000s, i.e., middle-range levels of exchange rate stability and financial integration while not

losing as much of monetary independently as industrialized countries. The recent policy

combination has been matched by a substantial increase in IR/GDP at a level that is not observed

in any other groups.

To confirm the different development paths of the trilemma indexes for the groups of

EMGs and non-EMG developing countries for the last four decades, we conduct mean-equality

tests on the three trilemma indexes and the IR holding ratios between EMGs and non-EMG

developing countries. We report the test results in Table 1 and statistically confirm that the

development path of the trilemma configurations has been different between these two groups of

developing countries.

Figure 4 compares developing countries across different geographical groups.

Developing countries in both Asia and Latin America (LATAM) have moved toward exchange

rate flexibility, but LATAM countries have rapidly increased financial openness while Asian

counterparts haven not. Asian emerging market economies have moved further toward financial

openness on a level comparable with LATAM emerging market countries, yet one key difference

between the two groups is that the former holds much more international reserves than the latter.

More importantly, Asian emerging market countries have achieved a balanced combination of

the three policy goals while the other groups have not, which can easily make one suspect it is

the high volume of IR holding that may have allowed this group of countries to achieve such a

trilemma configuration. We will revisit this issue later on. Lastly, Sub-Saharan African countries

16

The emerging market countries are defined as the countries classified as either emerging or frontier during the

period of 1980-1997 by the International Financial Corporation plus Hong Kong and Singapore.

9

appear to have pursued the policy combination of exchange rate stability and monetary

independence while lagging considerably in financial liberalization behind the other regions.

Patterns in a Balanced Panel

Figure 5 again presents the development of trilemma indexes for different subsamples

while focusing on the time dimension of the development, but also restricts the entire sample to

include only the countries for which all three indexes are available for the entire time period. By

balancing the dataset, the number of countries included in the sample reduces to 50 countries out

of which 32 countries are developing countries including 18 emerging market countries. Each

panel presents the full sample (i.e., cross-country) average of the trilemma index of concern and

also its one-standard deviation band. There is a striking differences between industrialized and

developing countries as well as between emerging market and non-emerging market countries.

The top-left panel shows that, between the late 1970s and the late 1980s, the levels of

monetary independence are closer to each other between industrialized countries and developing

ones. However, since the early 1990s, these two groups have been diverging from each other.

While developing countries have been hovering around the medium levels of monetary

independence and slightly deviating from the cross-country average, industrialized countries

have steadily become much less monetary independent and moved farther away from the cross-country average, reflecting the efforts made by the euro member countries.17 In the case of the

exchange rate stability index, after the breakup of the Bretton Woods system, industrialized

countries significantly reduced the extent of exchange rate stability until the early 1980s. After

the 1980s, these countries gradually, but steadily increased the extent of exchange rate stability

to the present – though they experienced some intermittent in the early 1990s due to the EMS

crisis.18 Developing countries, on the other hand, maintained relatively high levels of exchange

rate stability until the 1980s. Although these countries seem to have adopted some exchange rate

flexibility in the early 1980s, they have since maintained constant levels of exchange rate

stability through the early 2000s, which seems to reflect the “fear of floating.” In the last few

years, these countries even gradually increased the level of exchange rate stability. Not

surprisingly, industrialized countries have achieved higher levels of financial openness

throughout the period. The acceleration of financial openness in the mid-1990s remained

significantly high compared to the cross-country average of both the full sample and LDC

subsample. On the other hand, developing countries also accelerated financial openness in the

17

When the euro countries are removed from the IDC sample, the extent of the divergence from the average

becomes less marked although there is still a tendency among the non-euro countries to move toward lower levels of

monetary independence.

18 The ERS index for the non-euro industrialized countries, persistently hovers around the value of .40 throughout

the time period after rapidly dropping in the early 1970s.

10

early 1990s after some retrenchment during the 1980s. Overall, LDC countries have been in

parallel with the global trend of financial liberalization throughout the sample period, but the

difference from the industrialized countries has been moderately rising in the last decade.

Broadly speaking, the difference between emerging market countries and non-emerging

market developing countries is smaller than that between IDC and LDC subsamples (shown in

the bottom row of Figure 5). However, the divergence between the two groups seems to be

becoming wider gradually since the mid-1990s. While non-EMG countries have retained

relatively constant levels of monetary independence, EMG countries have become less monetary

independent. As for exchange rate stability, EMG countries are persistently more flexible than

non-emerging ones since 1980 and the difference is wider since the early 1990s. EMG countries

have also become more financially open compared with non-EMG countries since the mid-1990s.

Figure 6 shows the development paths of these indexes altogether, making the differences

between IDCs and LDCs and those between EMGs and non-EMGs appear more clearly. For the

industrialized countries, financial openness accelerated after the beginning of the 1990s and

exchange rate stability rose after the end of the 1990s, reflecting the introduction of the euro in

1999. The extent of monetary independence has experienced a declining trend, especially after

the early 1990s.19

When we look at the group of developing countries, we can see that not only do these

countries differ from industrialized ones, but also they differ between emerging and non-emerging market developing countries. Up to the mid-1980s, exchange rate stability was the

most pervasive policy among the three, though it has been on a declining trend since the early

1970s, followed by monetary independence that has been relatively constant during the period.

Between the mid-1980s and 2000, monetary independence and exchange rate stability became

the most pursued policies while the level of financial openness kept rising rapidly. During the

1990s, the level of monetary independence went up on average while more countries adopted

floating exchange rates and liberalized financial markets. Most interestingly, since 2000, all three

indexes have been converged to the middle ground, which we have already observed as the

balanced achievement of the three policy goals in Figure 4. This result suggests that developing

countries may have been trying to cling to moderate levels of both monetary independence and

financial openness while maintaining higher levels of exchange rate stability – leaning against

the trilemma in other words – which may explain the reason why some of these economies hold

sizable international reserves, potentially to buffer the trade-off arising from the trilemma.

Willett (2003) has called this compulsion by countries with a mediocre level of exchange rate

19 If the euro countries are removed from the sample (not reported), financial openness evolves similarly to the IDC

group that includes the euro countries, but exchange rate stability hovers around the line for monetary independence,

though at a bit higher levels, after the early 1990s. The difference between exchange rate stability and monetary

independence has been slightly diverging after the end of the 1990s.

11

fixity to hoard reserves the “unstable middle” hypothesis (as opposed to the “disappearing

middle” view).

None of these observations are applicable to non-emerging developing market countries.

For this group of countries, exchange rate stability has been the most pervasive policy

throughout the period, though there is some variation, followed by monetary independence.

There is no discernable trend in financial openness for this subsample.

2.3 Identifying Structural Breaks

To shed more light on the evolution of the index values, we investigate whether major

international economic events have been associated with structural breaks in the index series. We

conjecture that major events – such as the breakdown of the Bretton Woods system in 1973, the

Mexican debt crisis of 1982 (indicating the beginning of 1980’s debt crises of developing

countries), and the Asian Crisis of 1997-98 (the onset of sudden stop crises affecting high-performing Asian economies (HPAEs), Russia and other emerging countries) – may have

affected economies in such significant ways that they opted to alter their policy choices.

We identify the years of 1973, 1982, 1997-98, and 2001 as candidate structural breaks,

and test the equality of the group mean of the indexes over the candidate break points for each of

the subsample groups.20 The results are reported in Table 2 (a). The first and second columns of

the top panel indicate that after the breakdown of the Bretton Woods system, the mean of the

exchange rate stability index for the industrialized country group fell statistically significantly

from 0.69 to 0.43, while the mean of financial openness slightly increase from 0.44 to 0.47. Non-emerging market developing countries, however, did not significantly decrease the level of fixity

of their exchange rates over the same time period while they became less monetarily independent

and more financially open. Although the same changes in monetary independence and financial

openness are also observed among emerging market economies, they did move toward more

flexible exchange rates.

Even after the Mexican debt crisis, industrialized countries slightly, but significantly

increased the level of exchange rate stability and significantly increased the level of financial

openness, while holding constant the level of monetary independence. In contrast, the debt crisis

led all developing countries to pursue further exchange rate flexibility, most likely reflecting the

fact that crisis countries could not sustain fixed exchange rate arrangements. However, these

countries also simultaneously pursued more monetary independence. Interestingly, non-emerging

The data for the candidate structural break years are not included in the group means either for pre- or post-structural break years. For the Asian crisis, we assume the years of 1997 and 1998 are the break years and therefore

remove observations for these two years.

20

12

developing market countries tightened capital controls as a result of the debt crisis while

emerging market countries did not follow the suit.

The Asian crisis also appears to be a significant event in the evolution of the trilemma

indexes. The level of industrialized countries’ monetary independence dropped significantly

while their exchange rates became much more stable and their efforts of capital account

liberalization continued, all reflecting the European countries’ movement toward economic and

monetary union. Non-emerging market developing countries on the other hand increased the

level of all three indexes. Emerging market countries also started liberalizing financial markets

but much more significantly, though they lost monetary independence and slightly gained

exchange rate stability.

Several other major events are candidates for inducing structural breaks identified. For

example, anecdotal accounts date globalization at the beginning of the 1990s, when many

developing countries began to liberalize financial markets. Also, China’s entry to the World

Trade Organization in 2001 was, in retrospect, the beginning of the country’s rise as the world’s

manufacturer. Because the effect of these events may have often been conflated with that of the

Asian crisis we also test whether the years of 1990 and 2001 can be structural breaks.

The results are reported in Table 2 (b); the first two columns show the results of the mean

equality test for the trilemma indexes with the year of 1990 as the candidate structural break

whereas the last two columns report those with the year of 2001 as the structural break. The top

panel shows that for industrialized countries, 1990 can be a structural break for all three indexes.

However, when we compare the statistical magnitude of the change in the index for monetary

independence across different candidate structural breaks (i.e., compare the t-statistics for

monetary independence in column 4 of Table 2 (a), in column 2 of Table 2 (b), and in column 4

of Table 2 (b)), the mean equality test is most strongly rejected for the no structural break of

1997-98 hypothesis. We obtain the same result for exchange rate stability, though for financial

openness, the structural break of 1990 rejects the null hypothesis the most significantly.21 For the

group of non-emerging market developing countries, the structural break of 1990 is the most

significant for monetary independence and financial openness while it is the year of 2001 for

exchange rate stability. For emerging market countries, however, the most significant structural

break is found to have occurred in 2001 for monetary independence and exchange rate stability,

and in 1997-98 for financial openness.

21

The finding that both monetary independence and exchange rate stability entail structural breaks around the Asian

crisis can be driven merely by the countries that adopted the euro in 1999. We repeat the same exercise using the

industrial countries sample without the euro countries, and find that the structural breaks for monetary independence

and financial opens remain the same as in the full IDC sample (1997-98 and 1990, respectively), but that the

exchange rate stability series is found to have a structural break in 2001. Also, the change in the exchange rate

stability series is negative (i.e., further exchange rate flexibility) in both 1990 and 2001.

13

Lastly, we compare the t-statistics across different structural breaks for each of the

indexes and subsamples. Given that the balanced dataset is used in this exercise, the largest t-statistics should indicate the most significant structural break for the series. For example,

industrial countries’ monetary independence and exchange rate stability series have the largest t-statistics when the structural break of 1997-98 is tested.22 For financial openness, however, the

year of 1990 is identified with the largest structural break. The results for other variables and

subsamples are shown in Table 2 (c). For non-emerging LDC and EMG countries, the debt crisis

is found to be the most significant structural break for exchange rate stability. The year of 1990

is the most significant structural break for monetary independence and financial openness for

non-emerging developing market countries, whereas the year of 2001 and the Asian crisis of

1997-98 are, respectively, for emerging market countries.

2.4 The Linear Relationships between the Trilemma Indexes

While the preceding analyses are quite informative on the evolution of international

macroeconomic policy orientation, we have not shown whether these three macroeconomic

policy goals are “binding” in the context of the impossible trinity. That is, it is important for us to

confirm that countries have faced the trade-offs based on the trilemma. A challenge facing a full

test of the trilemma tradeoff is that the trilemma framework does not impose any obvious

functional form on the nature of the tradeoffs between the three trilemma variables. To illustrate

this concern, we note that the instrument scarcity associated with the trilemma implies that

increasing one trilemma variable, say higher financial integration, should induce lower exchange

rate stability, or lower monetary independence, or a combination of these two policy

adjustments.23 Yet, the nature of the trade-off is not specified. Hence, we test the validity of a

simplest possible trilemma specification – a linear tradeoff. Specifically, we test that the

weighted sum of the three trilemma policy variables adds up to a constant. This reduces to

examining the goodness of fit of this linear regression:

1=ajMIi,t+bjERSi,t+cjKAOPENi,t +εt where j can be either IDC, ERM, or LDC. (1)

Because we have shown that different subsample groups of countries have experienced different

development paths, we allow the coefficients on all the variables to vary across different groups

22 When the sample is restricted to non-euro IDCs, the most significant structural break for exchange rate stability is

found to be 1973, the year when the Bretton Woods system collapsed, while those for monetary independence and

financial openness are unchanged.

23 More generally, increasing of one Trilemma variable should induce a drop of the second Trilemma variable, or a

drop in the third Trilemma variable, or a combination of the two.

14

of countries – industrialized countries, the countries that have been in the European Exchange

Rate Mechanism (ERM), and developing countries – allowing for interactions between the

explanatory variables and the dummies for these subsamples.24 The regression is run for the full

sample period as well as the subsample periods identified in the preceding subsection. The

results are reported in Table 3.

The rationale behind this exercise is that policy makers of an economy must choose a

weighted average of the three policies in order to achieve a best combination of the two. Hence,

if we can find the goodness of fit for the above regression model is high, it would suggest a

linear specification is rich enough to explain the trade off among the three policy dimensions. In

other words, the lower the goodness of fit, the weaker the support for the existence of the trade-off, suggesting either that the theory of the trilemma is wrong, or that the relationship is non-linear.

Secondly, the estimated coefficients in the above regression model should give us some

approximate estimates of the weights countries put on the three policy goals. However, the

estimated coefficients alone will not provide sufficient information about “how much of” the

policy choice countries have actually implemented. Hence, looking into the predictions using the

ˆERS, and

ˆMI,

bestimated coefficients and the actual values for the variables (such as

aˆKAOPEN) will be more informative.

cThirdly, by comparing the predicted values based on the above regression, i.e.,

ˆERS+cˆMI+bˆKAOPEN, over a time horizon, we can obtain some inferences regarding how

a“binding” the trilemma is. If the trilemma is found to be linear, the predicted values should hover

around the value of 1, and the prediction errors should indicate how much of the three policy

choices have been “not fully used” or to what extent the trilemma is “not binding.”

Table 3 presents the regression results. The results from the regression with the full

sample data are reported in the first column, and the others for different subsample periods are in

the following columns. First of all, the adjusted R-squared for the full sample model as well as

for the subsample periods is found to be above 94%, which indicates that the three policy goals

are linearly related to each other, that is, countries face the trade-off among the three policy

options. Across different time periods, the estimated coefficients vary, suggesting that countries

alter over time the weights on the three policy goals.

Figure 7 illustrates the goodness of fit from a different angle. In the top panels, the solid

ˆERS+cˆMI+bˆKAOPEN) based on the full

lines show the means of the predicted values (i.e.,

asample model in the first column of Table 3 for the groups of industrial countries (left) and

24

The dummy for ERM countries is assigned for the countries and years that corresponds to participation in the

ERM (i.e., Belgium, Denmark, Germany, France, Ireland, and Italy from 1979 on, Spain from 1989, U.K. only for

1990-91, Portugal from 1992, Austria from 1995, Finland from 1996, and Greece from 1999).

15

developing countries (right).25 To incorporate the time variation of the predictions, the subsample

mean of the prediction values as well as their 95% confidence intervals (that are shown as the

shaded areas) are calculated using five-year rolling windows.26 The panels also display the

rolling means of the predictions using the coefficients and actual values of only two of the three

ˆERS (brown line with diamond nodes),

aˆMI+cˆKAOPEN (green line

ˆMI+btrilemma terms –

aˆERS+cˆKAOPEN (orange line with “x”).

with circles),

bFrom these panels of figures, we can first see that the predicted values based on the

model hover around the value of one closely for both subsamples. For the group of industrial

countries, the prediction average is statistically below the value of one in the late 1970s through

the beginning of the 1990s. However, since then, one cannot reject the null hypothesis that the

mean of the prediction values is one, indicating that the trilemma is “binding” for industrialized

countries. For developing countries, the model is under-predicting from the end of the 1970s

through the mid-1990s. However, unlike the IDC group, the mean of the predictions has become

statistically smaller than one since 2000. At the very least, for both subsamples, the mean of the

predictions never rises above the value of one in statistical sense, implying that, despite some

years when the trilemma is not binding, the three macroeconomic policies are linearly related

with each other.27

The top panels also show that, among industrialized countries, the policy combination of

increasing exchange rate stability and more financial openness rapidly became prevalent after the

beginning of the mid-1990s. Among developing countries, the policy combinations of monetary

independence and exchange rate stability has been quite dominant throughout the sample period

while the policy combination of exchange rate stability and financial openness has been the least

prevalent over, most probably reflecting the bitter experiences of currency crises.

2526 For this exercise, predictions also incorporate the interactions with the dummy variables shown in Table 3.

Both the mean and the standard errors of the predicted values are calculated using the rolling five-year windows.

ˆ∑∑xti=1t−4nitThe formula for the mean and the standard errors can be shown as

xt|t−4=ˆ∑∑(xti=1t−4nitn×5 and

−xt|t−4)2ˆ)=SE(xn×5−1, respectively, where n refers to the number of countries in a subsample (i.e., IDC and

n×5ˆit to the prediction values, and

xt|t−4 to the mean ofxˆit in the rolling five-year window.

LDC),

x27Because of the use of rolling five-year windows, the lines in the figures only start in 1974.

One may question the uniqueness of this regression exercise by pointing at the left-hand side variable being an

identity scalar. As a robustness check, we ran a regression of MIi,t on ERSi,t and KAOPENi,t, recovered the estimated

coefficients for aj, bj, and equation (1), and recreated panels of figures comparable to those in Figure 7. These

alternative figures appeared to be very much comparable to Figure 7 and therefore confirmed our conclusions about

the linearity of the trilemma indexes as well as the development of the subsample mean of prediction values based

on equation (1).

16

In the lower panels, we can observe the contributions of each policy orientation (i.e.,

ˆERS, and

cˆMI,

bˆKAOPEN) for the IDC and LDC groups.28 While less developed countries

amaintained high, though fluctuating, levels of monetary independence, both exchange rate

stability and financial integration remained at much lower levels throughout the period with the

former moderately declining and the latter slightly increasing. In the last decade or so, while

monetary independence is on a declining trend, the gap between the predictions based on

exchange rate stability and financial openness has been somewhat shrinking. This may indicate

that more countries tend to try to achieve certain levels of exchange rate stability and financial

openness together while maintaining high levels of monetary independence. This kind of effort

can be done only when the countries accumulate high levels of international reserves that allow

them to intervene in foreign exchange markets, consistent with the fact that many developing

countries increased international reserves in the aftermath of the Asian crisis of 1997-98.

However, as the concept of the trilemma predicts, this sort of environment must involve a rise in

the costs of sterilized intervention especially when the actual volume of cross-border transactions

of financial assets increase and when there is no reversal in the three policies.29 This seems to

explain the drop in the level of monetary independence after 2000 for this group of countries.30

The experience of the industrialized countries casts a stark contrast. Although monetary

independence was also IDC’s top priority until the 1990s, it yielded to financial integration in the

late 1990s and to exchange rate stability in the early 2000s. The trend of financial liberalization

and exchange rate stability correspond to declines in the level of monetary independence, which

persistently kept falling and became the lowest priority in the 2000s. Such changes in the relative

weights of the three policy goals do not require the countries to accumulate international reserves

as was the case with developing countries.31

3. Regression Analyses

Although the above characterization of the trilemma indexes allows us to observe the

development of policy orientation among countries, it fails to identify countries’ motivations for

policy changes. Hence, we examine econometrically how various choices regarding the three

They are again the means based on five-year rolling windows.

Refer to Aizenman and Glick (2008) and Glick and Hutchison (2008) for more analysis on the limit of sterilized

intervention.

30 When this exercise is repeated for both the emerging market country (EMG) group and the non-emerging market

developing country group (Non-EMG LDC), the results remain about the same, only except for that the financial

liberalization is more evident for the EMG group; the drop in the level of monetary independence is larger; and the

gap between the predictions based on exchange rate stability and financial openness has been shrinking further.

31 We also repeat the exercise using the regression models (whose results shown in Table 3) for each of the

subsample period (excluding the break years). The results (not reported) are qualitatively the same as in Figure 7.

2928

17

policies affect final policy goals, namely, output growth stability, low inflation, and inflation

stability.

The basic model we estimate is given by:

yit=α0+α1TLMit+α2TRit+α3(TLMit×TRit)+XitΒ+ZtΓ+DiΦ+εit (2)

yit is the measure for macro policy performance for country i in year t. More specifically, yit is

either output volatility measured as the five-year standard deviations of the growth rate of per

capita real output (using Penn World Table 6.2); inflation volatility as the five-year standard

deviations of the monthly rate of inflation; or the five-year average of the monthly rate of

inflation. TLMit is a vector of any two of the three trilemma indexes, namely, MI, ERS, and

KAOPEN.32 TRit is the level of international reserves (excluding gold) as a ratio to GDP, and

(TLMit x TRit) is an interaction term between the trilemma indexes and the level of international

reserves. We are particularly interested in the effect of the interaction terms because we suspect

that international reserves may complement or substitute for other policy stances.

Xit is a vector of macroeconomic control variables that include the variables most used in

the literature, namely, relative income (to the U.S. based on PWT per capita real income); its

quadratic term; trade openness (=(EX+IM)/GDP); the TOT shock as defined as the five-year

standard deviation of trade openness times TOT growth; fiscal procyclicality (as the correlations

between HP-detrended government spending series and HP-detrended real GDP series); M2

growth volatility (as five-year standard deviations of M2 growth); private credit creation as a

ratio to GDP as a measure of financial development; the inflation rate; and inflation volatility. Zt

is a vector of global shocks that includes change in U.S. real interest rate; world output gap; and

relative oil price shocks (measured as the log of the ratio of oil price index to the world’s CPI).

Di is a set of characteristic dummies that includes a dummy for oil exporting countries and

regional dummies. Explanatory variables that persistently appear to be statistically insignificant

are dropped from the estimation.

εit is an i.i.d. error term.

The data set is organized into five-year panels of 1972-1976, 1977-81, 1982-1986, 1987-91, 1992-96, 1997-2001, 2002-06. All time-varying variables are included as five-year averages.

The full sample is divided into the groups of industrialized countries (IDC) and developing

countries (LDC) which also includes a subgroup of commodity exporters (COMMOD-LDC), i.e.,

developing countries that are either exporters of fuel or those of non-fuel primary products as

defined by the World Bank, and a subgroup of emerging market countries (EMG). We report the

results only for the last three groups, i.e., only subsamples related to developing countries.

In Table 3, we have shown that these three measures of the trilemma are linearly related. Therefore, it is most

reasonable to include two of the indexes concurrently, not just individually nor all three collectively.

32

18

Since inflation volatility turned out to be a significant explanatory variable for the

regressions for output volatility and the level of inflation, and also the inflation level for the

regressions for inflation volatility, we need to implement an estimation method that handles

outliers properly. Hence, we decide to use the robust regression method which downweights

outliers.33 Also, we remove the observations if their values of inflation volatility are greater than

a value of 30 or the rate of inflation (as an explanatory variable) is greater than 100%.

Furthermore, for comparison purposes, the same set of explanatory variables is used for the three

subsamples except for the regional dummies.

3.1 Estimation of the Basic Model

3.1.1 Output Volatility

The regression results for the estimation on output volatility are shown in Tables 4-1

through 4-3 for the three subsamples of developing countries, i.e., developing countries,

developing commodity exporters, and emerging market countries. Different specifications are

tested using different combinations of the trilemma indexes as well as their interaction terms.

The results are presented in columns 1 through 6 in each table.34 The variables that consistently

appear to be statistically insignificant are dropped from the estimations.

The model explains well the output volatility for the developing countries subsample

(Table 4-1). Across different model specifications, the following is true for the group of

developing countries: The higher the level of income is (relative to the U.S.), the more reduced

output volatility is, though the effect is nonlinear. The bigger change occurs on U.S. real interest

rate, the higher output volatility of developing countries may become, indicating that the U.S.

real interest rate may represent the debt payment burden on these countries. The higher TOT

shock there is, the higher output volatility countries experience, consistent with Rodrik (1998)

and Easterly, Islam and Stiglitz (2001) who argue that volatility in world goods through trade

openness can raise output volatility.35 Countries with procyclical fiscal policy tend to experience

more output volatility while oil exporters also experience more output volatility.36

The robust regression procedure conducts iterative weighted least squares regressions while downweighting

observations that have larger residuals until the coefficients converge.

34 The dummies for “East Asia and Pacific” and “Sub-Saharan Africa” are included in the model for developing

countries, but not reported to conserve space.

35 The effect of trade openness is found to have insignificant effects for all subgroups of countries and is therefore

dropped from the estimations. This finding reflects the debate in the literature, in which both positive (i.e., volatility

enhancing) and negative (i.e., volatility reducing) effects of trade openness has been evidenced. The volatility

enhancing effect in the sense of Easterly et al. (2001) and Rodrik (1998) is captured by the term for (TOT*Trade

Openness) volatility. For the volatility reducing effect of trade openness, refer to Calvo et al. (2004), Cavallo (2005,

2007), and Cavallo and Frankel (2004). The impact of trade openness on output volatility also depends on the type

of trade, i.e., whether it is inter-industry trade (Krugman, 1993) or intra-industry trade (Razin and Rose,1994).

36 Countries in East Asia and Pacific as well as in Sub Sahara Africa tend to experience more output volatility

(results not reported).

33

19

Countries with more developed financial markets tend to experience lower output

volatility, a result consistent with the theoretical predictions by Aghion, et al. (1999) and

Caballero and Krishnamurthy (2001) as well as past empirical findings such as Blankenau, et al.

(2001) and Kose et al. (2003). This result indicates that economies armed with more developed

financial markets are able to mitigate output volatility, perhaps by allocating capital more

efficiently, lowering the cost of capital, and/or ameliorating information asymmetries (King and

Levine, 1993, Rajan and Zingales, 1998, Wurgler, 2000). We will revisit this issue later on.

Among the trilemma indexes, only the monetary independence variable is found to have a

significant effect on output volatility; the greater monetary independence one embraces, the less

output volatility the country tends to experience. This finding is no surprise, considering that

stabilization measures should reduce output volatility, especially more so under higher degree of

monetary independence.37 Mishkin and Schmidt-Hebbel (2007) find that countries that adopt

inflation targeting – one form of increasing monetary independence – are found to reduce output

volatility, and that the effect is bigger among emerging market countries.38 This volatility

reducing effect of monetary independence may explain the tendency that developing countries,

especially, non-emerging market ones, try not to reduce the extent of monetary independence

over years.

Like other developing countries, less developed commodity exporting countries are also

susceptible to changes in U.S. real interest rates and TOT shocks, but other variables do not

exhibit the same effects (Table 4-2). Again, countries with greater monetary independence tend

to experience lower output volatility. Interestingly, more exchange rate stability per se does not

have any significant impact on output volatility, but if it is coupled with higher levels of

international reserves holding, then countries can reduce output volatility, which may help

explain the recent buildup of international reserves by developing, especially oil exporting,

countries. Additionally, more financially open commodity exporters seem able to reduce output

volatility, though, interestingly, the coefficient on the interaction term between KAOPEN and

international reserve holding is significantly positive in one of the models. This result indicates

that countries with higher levels of reserves holding than 27% of GDP can experience more

output volatility. This result is somewhat counterintuitive.

37 This finding can be surprising to some if the concept of monetary independence is taken synonymously to central

bank independence because many authors, most typically Alesina and Summers (1993), have found more

independent central banks would have no or little at most impact on output variability. However, in this literature,

the extent of central bank independence is usually measured by the legal definition of the central bankers and/or the

turnover ratios of bank governors, which can bring about different inferences compared to our measure of monetary

independence.

38 The link is not always predicted to be negative theoretically. When monetary authorities react to negative supply

shocks, that can amplify the shocks and exacerbate output volatility. Cechetti and Ehrmann (1999) find the positive

association between adoption of inflation targeting and output volatility.

20

While emerging market countries share many of the same traits in macroeconomic

variables as those in the LDC sample, the results on the trilemma indexes are a little different.

Countries with more stable exchange rate tend to experience higher output volatility, which

conversely implies that countries with more flexible exchange rates will experience lower levels

of output volatility, as was found in Edwards and Levy-Yeyati (2005) and Haruka (2007).

However, the interaction term is found to have a statistically negative effect, suggesting that

countries holding high levels of international reserves are able to reduce output volatility. The

threshold level of international reserves holding is 21-24% of GDP. Singapore, a country with a

middle level of exchange rate stability (0.50 in the 2002-06 period) and a very high level of

international reserves holding (100% as a ratio of GDP), is able to reduce the output volatility by

2.65-3.2 percentage points.39 China, whose exchange rate stability index is as high as 0.97 and

whose ratio of reserves holding to GDP is 40% in 2006, is able to reduce volatility by 1.1-1.5

percentage points. The estimation results on the trilemma variables are summarized in Table 7.40

Figure 8 graphically shows the marginal interactive effects between ERS and IR based on

the estimates from Column 2 of Table 4-3. For presentation purposes, in the figure, the EMG

group of countries is divided into (a) the Asian group, (b) the Latin American group, and (c) the

other EMG countries. In all the panels of figures, the contours are drawn to present different

levels of the effect of ERS on output volatility conditional on the level of IR. Also, the solid

horizontal line refers to the threshold of IR at 21% of GDP, above which higher levels of ERS

will have a negative impact on output volatility.41 For example, the solid line of contour above

the threshold shows the combinations of ERS and IR that leads to a one percentage point

reduction in the output volatility. In the figure, we can see that the further toward the northeast

corner in the panel, i.e., the higher level of ERS and IR a country pursues, the more negative

impact it can have on output volatility. Below the threshold, however, it is true that the further

toward the southeast corner, i.e., the higher level of ERS and the lower level of IR a country

pursues, the more positive impact it can have on output volatility. In each of the panels, the

scatter diagrams of ERS and IR are superimposed. The black circles indicate ERS and IR for the

period of 2002-06 and the red “x’s” for the 1992-96 period.

See Moreno and Spiegel (1997) for earlier study of trilemma configurations in Singapore.

Following Acemoglu (2003), we also suspect institutional development plays a role in reducing output volatility.

To measure the level of institutional development, we use the variable LEGAL, which is the first principal

component of law and order (LAO), anti-corruption measures (CORRUPT), and bureaucracy quality (BQ). However,

it turns out that the LEGAL variable is statistically insignificant and sometimes with the wrong sign (not reported).

Given small variations in the time series of the variable, this result is not surprising.

41 We also note that the estimated coefficient on IR (level) is significantly positive in Columns (2) and (6) of Table

4-3, which indicates that, while a higher level of IR holding can lessen the positive effect of ERS, a higher level of

IR holding itself is volatility-enhancing. This is not captured in Figure 8 since we focused on the effect of ERS and

how it changes depending on the level of IR.

4039

21

Using these diagrams, we can make several interesting observations. First, between the

1992-96 and 2002-06 periods, a period which encompasses the last wave of global crises, i.e., the

Asian crisis of 1997-98, the Russian crisis of 1998, and the Argentina crisis of 2001-02, many

countries, especially those in East Asia and Eastern Europe, increased their IR holding above the

threshold. Secondly, the movement is not necessarily toward the northeast direction. Rather, it is

around the threshold level where the effect of ERS is neutral (i.e., zero percentage point impact),

unless they move much higher toward output volatility-reducing territory (such as China and

Bulgaria). Thirdly, while we observe a moderately positive association between ERS and IR,

none of these observations are applicable to Latin American countries. Lastly, there are not many

countries that have achieved combinations of ERS and IR to reduce output volatility significantly.

Countries such as Botswana, China, Hong Kong, Malaysia, Jordan, and Singapore are more of

exceptions. However, at the very least, these estimation results should explain why many

countries, especially those with the intention of pursuing greater exchange rate stability, are

motivated to hold a massive amount of international reserves.

3.1.2 Inflation Volatility

We repeat the exercise for inflation volatility. The results for subsamples of developing

countries are reported in Tables 5-1 through 5-3 and summarized in Table 7.

Across different subsamples, countries with higher relative income

tend to experience

lower inflation volatility, and naturally, those with higher levels of inflation are expected to

experience higher inflation volatility. The TOT shock is found to increase inflation volatility.

Furthermore, for commodity exporters, oil price increases would lead to higher inflation

volatility.

The performance of the trilemma indexes appears to be the weakest for this group of

estimations overall. Monetary independence is found to be an inflation volatility decreasing

factor for commodity exporters. However, given that it is also an output volatility decreasing

factor for this group of countries, this finding is somewhat counterintuitive.

Emerging market countries, on the other hand, tend to experience higher inflation

volatility if they are more open to capital account transactions. This significantly positive effect

of financial openness may be capturing financial turbulence that can arise as a result of financial

liberalization policy. In fact, when we include the interaction term between the crisis dummy and

the financial openness variable, the statistical significance of the financial openness variable

declines while the interaction term enters the estimation marginally significantly.42

42 The currency crisis dummy variable is derived from the conventional exchange rate market pressure (EMP) index

pioneered by Eichengreen et al. (1996). The EMP index is defined as a weighted average of monthly changes in the

nominal exchange rate, the international reserve loss in percentage, and the nominal interest rate. The weights are

inversely related to the pooled variance of changes in each component over the sample countries, and adjustment is

22

3.1.3. Medium-run Level of Inflation

Tables 6-1 through 6-3 show the results for the regressions on the level of inflation.

These three tables report that countries with higher inflation volatility, M2 growth volatility, and

oil price shocks tend to experience higher output volatility. Also, when the world economy is

experiencing a boom, developing countries tend to experience higher inflation, which

presumably reflects strong demand for goods produced and exported by developing countries.

Countries with more monetary autonomy tend to experience higher inflation. From the

perspective that greater monetary independence should be synonymous with a more independent

central bank, most typically exemplified by the literature of time-inconsistency in monetary

policy, a country with greater monetary independence should be able to lower inflation.43 One

possible explanation would be that countries with higher levels of monetary independence

attempt to monetize their debt and cause higher inflation. Such countries may be better off if they

are not monetarily independent and just import monetary policy from other countries through

fixed exchange rate arrangements.

As a matter of fact, in all three subsamples, higher exchange rate stability is found to lead

countries to experience lower inflation, a result consistent with the literature (such as Ghosh et

al., 1997). This finding and the previously found positive association between exchange rate

stability and output volatility are in line with the theoretical prediction that establishing stable

exchange rates is a trade-off issue for policy makers; it will help the country to achieve lower

inflation by showing a higher level of credibility and commitment, but at the same time, the

efforts of maintaining stable exchange rates will rid the policy makers of an important

adjustment mechanism through fluctuating exchange rates – which would explain the negative

coefficient on monetary independence in the output volatility regressions.

Furthermore, for the LDC group, the interaction term between ERS and international

reserves holding is found to have a positive impact on the rate of inflation. Models 2 and 6 in

Table 6-1 show that if the ratio of reserves holding to GDP is greater than 53% or 65%,

respectively, the efforts of pursuing exchange rate stability can help increase the level of

inflation. Although these levels of reserves holding are very high, this means that countries with

excess levels of reserves holding will eventually face the limit in the efforts of fully sterilizing

made for the countries that experienced hyperinflation following Kaminsky and Reinhart (1999).

For countries

without data to compute the EMP index, the currency crisis classifications in Glick and Hutchison (2001) and

Kaminsky and Reinhart (1999) are used.

43 In other words, more independent central bankers should be able to remove the inflation bias (Kydland and

Prescott, 1977 and Barro and Gordon, 1983).

23

foreign exchange intervention to maintain exchange rate stability, thereby experiencing higher

inflation.44

Lastly, models (3) through (6) in all subsamples show that the more financially open a

developing country is, the lower inflation it will experience. Interestingly, the more open to trade

a country is, the more likely it is to experience lower inflation, though this effect is weakly

significant only for the LDC group.

As globalization became actively debated, the negative association between “openness”

and inflation was more frequently remarked upon.45 Romer (1993), extending the Barro-Gordon

(1983) model, theorized and empirically verified that the more open to trade a country becomes,

the less motivated its monetary authorities are to inflate, suggesting a negative link between trade

openness and inflation. Razin and Binyamini (2007) predicted that both trade and financial

liberalization will flatten the Phillips curve, so that policy makers will become less responsive to

output gaps and more aggressive in fighting inflation.46 Here, across different subsamples of

developing countries, we present evidence consistent with the negative openness-inflation

relationship.

3.2. How Does a Policy Orientation Affect Macroeconomic Performance?

Composite Indexes for Policy Orientation

As we have already seen, decisions on which two of the three policy goals – monetary

independence, exchange rate stability, and financial integration – to retain, or which one to give

up, characterizes the international financial regime a country decides to implement. For example,

currency unions such as the Euro countries and the Gulf Cooperation Council (GCC) or countries

with currency boards like Argentina before 2001 require member countries to abandon monetary

independence, but retain exchange rate stability and financial openness. The Bretton Woods

system kept countries financially closed, but let them exercise an independent monetary policy

and to stabilize their currency values. Thus, measures constructed by two of the above three

indexes can allow one to summarize the policy orientations of countries. In other words,

measures composed of two of the three indexes should be able to show how close countries are

to the “vertex” of the trilemma triangle.

For this purpose, we construct composite indexes based on two of the above three

measures. The principal component of MI and ERS measures how close countries (MI_ERS) are

44 Aizenman and Glick (2008) and Glick and Hutchison (2008) show that China, whose ratio of reserves holding to

GDP is estimated to be 50%, has started facing more inflationary pressure in 2007 as a result of intensive market

interventions to sustain exchange rate stability (though the onset of global crisis has reversed these trends).

45 Rogoff (2003) argues that globalization contributes to dwindling mark-ups, and thereby, disinflation.

46 Loungani et al. (2001) provides empirical evidence that countries with greater restrictions on capital mobility face

steeper Phillips curves.

24

toward the vertex of “closed economy” whereas that of ERS and KAOPEN (ERS_KAO) refers to

the vertex of currency union or currency board, and that of MI and KAOPEN (MI_KAO) to

“floating exchange rate.” Again, all three indexes are normalized between zero and one. Higher

values indicate a country is closer toward the vertex of the trilemma triangle.

Estimation with Composite Indexes

Columns 7 though 12 in Tables 4-1 through 6-3 show the estimation results for different

models each of which include one composite index and its interaction with reserves holding.

Tables 4-1 and 4-2 show that countries with higher MI_KAO, i.e., countries with more flexible

exchange rates, tend to experience lower output volatility, which is in line with the oft-argued

automatic stabilizing role of flexible exchange rates. For developing countries, the more

financially closed an economy is (the higher its MI_ERS is), output volatility tends to be lower.

Given that monetary independence is found to have a volatility reducing effect in the estimations

with individual trilemma indexes, it is monetary independence that leads to lower output

volatility whether financially closed economies with more stable exchange rates or financially

open but with more flexible exchange rates. Emerging market economies (Table 4-3), on the

other hand, seem to follow different dynamics. Economies with higher MI_ERS, i.e., more

closed financial markets, are able to reduce output volatility only when they hold ample reserves.

In Tables 5-1 through 5-3, we see that developing countries or emerging market

economies with higher exchange rate stability and more financial openness (ERS and KAO), or

those with weaker monetary independence, tend to experience higher inflation volatility.

Commodity exporters that pursue greater monetary independence and financial openness (MI

and KAO) tend to experience less inflation volatility (Table 5-2).

The level of inflation can be lowered if a developing or commodity-exporting country

pursues greater monetary independence and more stable exchange rates (Columns 7 and 8 in

Tables 6-1 and 6-2). Or, if developing countries, whether commodity-exporting or emerging

market ones, pursue a policy combination of greater exchange rate stability and more financial

openness, these economies should be able to lower the level of inflation. This finding can be

disappointing news for monetary authorities because it implies that, to implement disinflationary

policy, policy makers should yield monetary policy making to another country and invite more

policy discipline by opening financial markets.

4. Further Analyses of the Trilemma Configurations on Macro-Performance

While the above analysis sheds important light on how the trilemma configurations affect

macroeconomic performance of the economies, other important questions, especially those

which have emerged out of the ongoing financial crisis, are not directly addressed. In this section,

25

we further investigate the following two more issues. First, we take a closer look at the effect of

financial development on output volatility. Secondly, we examine the impacts of external

financing on output volatility, inflation volatility, and the medium-term level of inflation.

4.1 Interactions Between the Trilemma Configurations and Financial Development

The ongoing global financial crisis has illustrated that financial development can be a

double-edged sword. While further financial development may enhance output growth and

stability by ameliorating information asymmetry, enabling more efficient capital allocation, and

allowing for further risk sharing, it can also expose economies to high-risk, high-return financial

instruments, thereby involving the possibility of amplifying real shocks and/or falling into the

boom-burst cycles. Naturally, the effect of financial development deserves further investigation,

which we are about to conduct.

In Tables 4-1 through 4-3, we have seen that more financial development can lead to less

output volatility, but its effect is significant only for the LDC subsample. One may also wonder

how trilemma configurations can interact with the level of financial development. There is no

question that monetary policy with high levels of authorities’ independence, which is found to be

volatility-reducing, should work better with more developed financial markets. Exchange rate

stability, which can lead to higher output volatility, may be less disturbing if financial markets

handle capital allocation more efficiently. Financial liberalization can easily be expected to work

hand in hand with financial development to reduce economic volatility.

With these assumptions, we test to see if there is any interaction between the trilemma

indexes and financial development which we measure using private credit creation as a ratio to

GDP (PCGDP). The results turn out to be simply futile; when the previous output volatility

regressions from Tables 4-1 through 4-3 are repeated, including interaction terms between the

trilemma indexes and PCGDP, none of the interaction terms turn out to be significant (not

reported). These results are not surprising or discouraging, because, as we already mentioned, we

suspect that the effect of financial development can be ambiguous.

The weakness of using interaction terms is that we must assume that the effect of

PCGDP on the link between the trilemma indexes and output volatility is monotonic; a higher

level of PCGDP must either enhance, have no impact on, or lessen the link. Given the

insignificance of the interaction terms from the initial investigation, we suspect the nonlinearity

of PCGDP. As such, we decide to use the dummies for different level groups of PCGDP.47 That

is, PCGDP_HI is assigned a value of one for a country if the country’s PCGDP is above the 75th

percentile in the distribution of five-year averages of PCGDP within a five-year window, and

This investigation is motivated by Hnatkovska and Loayza (2005), who examines the nonlinear effect of structural

variables, including financial development, on the output volatility-growth link.

47

26

zero, otherwise. PCGDP_LO takes a value of one if the country’s PCGDP is below the 25th

percentile, and zero, otherwise. PCGDP_MD takes a value of one if the country’s PCGDP lies

between the 25th and 75th percentiles in a five-year period. We interact these level category

dummies with the trilemma indexes and include the interaction terms in the output volatility

regressions, hoping to capture the nonlinear effect of financial development on the link between

the trilemma configurations and output volatility.

Table 8 reports the estimation results only for the PCGDP variable and the interaction

terms for the developing countries subsample (Columns 1-3) and the emerging market countries

subsample (Columns 4-6) in order to conserve space. At the bottom of the tables, we also report

the Wald test statistics for the tests on the differences in the estimated coefficients of the

interaction terms between the trilemma indexes and different PCGDP groups.

In Columns 1-3, we can see that this analysis does not yield any significant results for the

group of developing countries. Exchange rate stability may contribute to higher output volatility

if the country is equipped with medium (or higher) levels of financial development while the low

level of financial development may contribute to reducing output volatility, though none of the

estimated coefficients are significant.

Among EMGs (Columns 4-6), we see more interesting results. The estimated coefficient

on the term “ERS x Medium PCGDP” is significant in Columns 4 and 5. In Column 5, the

coefficient on “ERS x High PCGDP” is also significant, and both “ERS x Medium PCGDP” and

“ERS x High PCGDP” are greater than “ERS x Low PCGDP” in the estimates’ magnitude

although they are not statistically significantly different. At least, we can surmise that for

countries with underdeveloped financial markets, higher levels of exchange rate stability do not

lead to higher output volatility. Those with medium levels of financial development do seem to

experience higher output volatility when they pursue a more stable exchange rate, suggesting that

countries with newly developed financial markets can be volatile when they pursue greater

exchange rate stability. Furthermore, in both Columns 4 and 5, the estimated coefficients on the

interaction term between ERS and IR are found to be significantly negative. Using the estimates,

we can estimate that to cancel or lessen the volatility-enhancing effect of ERS, EMGs with

medium (or higher) levels of financial development need to hold at least 22-25% of GDP of

international reserves. However, this rule is not applicable to those with underdeveloped

financial markets.

Financial development and financial openness seem to have interesting interactive effects

on output volatility as well. While those EMGs with medium or higher levels of financial

development tend to experience less output volatility when they decide to pursue more stable

exchange rates, those with underdeveloped financial markets are expected to experience greater

output volatility if they pursue greater financial openness. When the coefficient of “KAOPEN x

27

Medium PCGDP” and “KAOPEN x High PCGDP” are compared to that of “KAOPEN x Low

PCGDP,” the difference is found to be statistically significant. These results indicate that

emerging market economies need to be equipped with highly developed financial markets if they

want to reap the benefit of financial liberalization on their output volatility.

These findings suggest that a policy management leaning more toward exchange rate

stability is most likely to exacerbate output volatility when the economy is equipped with

medium levels of financial development. Having a higher level of financial openness and

financial development can yield a synergistic impact to dampen output volatility, presumably by

facilitating allocation of capital, ameliorating information asymmetry, and thereby reducing the

cost of capital.48 The worst and more significant case is that a country with underdeveloped

financial markets can exacerbate output volatility caused by financial liberalization.

4.2 The Effects of External Financing

Financial liberalization has increased its pace over the last two decades. This, however,

does not mean that countries suddenly became more financially linked with others. In the 1980s,

developing countries received external financing in the form of sovereign debt, but the debt crisis

experience spurred many of these countries to shy away from sovereign debt. After the 1990s,

the role of FDI became more important and more recent waves of financial liberalization have

contributed to a rise in portfolio flows across borders as well. As Lane and Milesi-Ferretti (2006)

note, the type, volume, and direction of capital flows have changed over time.

4.2.1 Incorporation of External Financing

Against this backdrop, we extend our investigation by incorporating the effect of external

financing. More specifically, we include the variables that capture net FDI inflows, net portfolio

inflows, net ‘other’ inflows (which mostly include bank lending in IFS), short-term debt, and

total debt service. For net capital flows, we use the IFS data and define them as external

liabilities (= capital inflows with a positive sign) minus assets (= capital inflows with a negative

sign) for each type of flows – negative values mean that a country experiences a net outflow

capital of the type of concern. Short-term debt is included as the ratio of total external debt and

total debt service as is that of Gross National Income (GNI). Both variables are retrieved from

WDI. Because the debt-related variables are limited, we only deal with one subsample that is

composed of developing countries for which the debt-related variables are available. Also, to

48 See Bekaert et al., (2000, 2001), Henry (2000), Stultz (1999) among others for the link between financial

liberalization and the cost of capital. Chinn and Ito (2006) show that financial openness can exogenously lead to

more financial development.

28

isolate the effect of external financing from currency crises, we include a dummy for currency

crises.

The results are reported in Table 9 for all three dependent variables, output volatility in

columns 1 through 3, inflation volatility in columns 4 through 6, and inflation level in columns 7

through 9. We present the estimated coefficients only for the variables of interest.49 Table 9

shows that the more ‘other’ capital inflows, i.e., banking lending or more net portfolio inflows, a

country receives, the more likely it is to experience higher output volatility, reflecting the fact

that countries that experience macroeconomic turmoil often experience an increase in inflows of

banking lending or “hot money” such as portfolio investment. FDI inflows appear to contribute

to lowering inflation volatility, which is somewhat counterintuitive. One possible explanation is

that countries tend to stabilize inflation movement to attract FDI, and this may also explain the

negative, but less significant, coefficients on the net FDI inflow variables in the inflation level

regressions. Other types of capital flows do not seem to matter for either inflation volatility or

inflation levels.

Both short-term debt and total debt service are positive and significant contributors to

both inflation volatility and inflation level, supporting our previous argument that countries do

tend to monetize their debt especially when their monetary authorities embrace more

independence – the estimated coefficient on monetary independence continues to be significantly

positive in the inflation level regressions.

Among the trilemma indexes, greater monetary independence continues to be a negative

contributor to output volatility though it is also a positive contributor to the level of inflation.

More financial openness is now a negative contributor to output volatility for this sample of

countries while its negative impact on the level of inflation remains. Higher exchange rate

stability continues to dampen the level of inflation, but holding too much of international

reserves (more than 45% of GDP) can cancel the negative effect and contribute to higher

inflation.

4.2.2 External Financing and Policy Orientation

Given that the combination of two out of three policy stances is what matters to the

macro outcomes, when we estimate the effect of external financing, it is important to condition

on what kind of policy combination is being pursued by the recipient countries.50 The best way

for us to do that is to examine the interactive effect between the type of external financing and

that of the policy combination. For that purpose, we create dummy variables for the types of

49 Overall, other macroeconomic variables retain the characteristics found in the previous regressions, though they

tend to be less statistically significant.

50 See IMF (2007) for an examination of the relationship between how countries manage capital inflows and

subsequent macroeconomic outcomes.

29

policy orientation using the composite trilemma indexes we have been using. That is, if the

composite index MI_ERS turns out to be the highest compared to the other two, MI_KAO and

ERS_KAO, then a value of one is assigned for D_MI_ERS and zero for the other two,

D_MI_KAO and D_ERS_KAO. In the results shown in Table 10, the external financing

variables are interacted with the dummy for one particular type of policy combination. For

example, in columns 1 and 2 of Table 10 we use in the estimation of output volatility the dummy

for the policy orientation of greater monetary independence and exchange rate stability (MI_ERS;

or “financially closed” policy option) and interact it with the external financing variables.

Columns 3 and 4 use the dummy for the policy orientation of greater monetary independence and

further financial opening (“more flexible exchange rate” policy), and columns 5 and 6 use that of

greater exchange rate stability and further financial opening (“currency union” or currency

board). The following six columns report the results for the estimation of inflation volatility and

the next six for the level of inflation.

For output volatility, we find different types of external financing can have different

impacts on output volatility depending on the policy regime in place. Net FDI inflows, for

example, tend to dampen output volatility in general, but it can enhance the volatility in a regime

that has pursued greater monetary independence and more stable exchange rates (i.e., less

financial openness). Net portfolio inflows seem to have a positive impact on output volatility, but

its volatility increasing impact is especially higher for the countries with the ERS-KAO

(“currency union”) regimes, in line with what has been found in the crisis literature. Countries

with more flexible exchange rates (or monetary independence and financial openness), on the

other hand, may be able to dampen the volatility-increasing effect, though its effect for this

policy orientation is not found to be statistically significant. Positive net inflows of bank lending

can be volatility increasing, but that effect can be dampened, though only marginally

significantly, if the country adopts the policy combination of exchange rate stability and financial

openness.

The greater the debt service is, the more likely a country is to experience higher levels of

output volatility, especially when the country pursues a combination of greater exchange rate

stability and financial openness. This result appears to be consistent with the “original sin”

argument; countries that are indebted in a foreign currency and that try to maintain both

exchange rate stability and capital account openness often experience sudden capital flow

reversal and consequently higher output volatility.

In the inflation volatility regressions, it seems that net inflow of FDI contributes to lower

inflation volatility across different policy regimes in general. However, the volatility-reducing

effect is even higher for countries with flexible exchange rates. The table also shows that, for

countries with flexible exchange systems, portfolio inflows can lower inflation volatility. These

30

results imply that if a country is considering to allow more influx of FDI or portfolio flows while

wanting to lower inflation volatility, it would be best to adopt a flexible exchange rate system or

keep the overall level of financial openness at low levels. Lastly, total debt services can make

countries with monetary independence and financial openness experience higher inflation

volatility while financially closed regimes would experience a slight drop in inflation volatility.

This may be because rapid currency depreciation could enlarge the size of total debt which could

encourage countries to monetize away the debt.

Different types of policy combinations seem to matter only for ‘other’ (i.e., bank lending)

inflows in the estimation for the level of inflation; a net recipient of bank lending flows tends to

experience lower inflation if it adopts a policy combination of monetary independence and

financial openness, but it could experience higher inflation if it adopts a financially closed

system. One merit of a country with currency union-like regime is that it can dampen the

inflation pressure of total debt services. A country with closed financial markets on the other

hand may experience higher output volatility as a result of higher levels of debt services.

Implications for the Current Crisis

International Reserve Holdings: Is the Trilemma Still Binding?

It has been argued that one of the main causes of the financial crisis of 2008-09 is the

ample liquidity provided by the global imbalances; current account surplus countries hoard

international reserves in an attempt to stabilize their exchange rates, export liquidity to the global

markets, and finance profligacy in the advanced countries, especially the United States.51 In

5.

5.1

Figure 8, we have seen that some, but not many, countries pursue higher levels of ERS and IR

concurrently. Figure 9 updates Figure 8 by using the updated Trilemma indexes and IR data for

2007 and compare with the data from the 2002-06 period. In the panels of figures, we can

observe that countries’ positions do not change much. The only noticeable change would be that

countries continue to increase their IR holding, but they are not necessarily moving toward the

northeast corner. Why do these countries continue to increase their IR holding?

One possible conjecture is that countries holding a massive amount of foreign reserves

might allow the relaxation of the trilemma, i.e., achieve all three goals at the same time. Figure

10 displays a scatter diagram for EMG countries’ ERS and MI_KAO (composite index of MI

and KAOPEN), which the concept of the trilemma predicts should be negatively correlated.

There are two groups of country-years shown in the diagram; one is a group of country-years

with the IR holding greater than 21% of GDP, the threshold above which ERS can have output

volatility-reducing effect as shown in Figures 8 and 9, and the other is those with the IR holding

less than 21% of GDP. If the above speculation is right, the (green) triangles – country-years

51 See Roubini (2008) as one example.

31

with >21% IR – in the diagram should be scattered above the circles – country-years with <21%

IR.

Theoretically, these two variables should be negatively correlated – the higher level of

ERS a country pursues, the lower level of MI-KAO, which is a proxy to the weighted average of

MI and KAO it has to choose as we formally confirmed in Section 2.4. In the figure, however,

the fitted lines for both groups are barely negatively sloped – the estimated coefficients for both

are statistically insignificantly negative. We test whether the slopes and intercepts of these two

fitted lines are statistically different. If the conjecture that higher levels of IR holding could relax

the trilemma, a country should be able to pursue higher levels of MI-KAO with the same level of

ERS, which would either make the slope flatter or raise the intercept, i.e., the conditional mean

of MI-KAO. Simple coefficient equality tests reveal that the slopes of the two fitted lines are not

statistically different from each other, but that the intercept for the fitted line for the country-years with >21% IR is significantly higher than that for the <21% IR group. This is in line with

the conjecture that higher levels of IR holding can allow a country to pursue a higher weighted

average of MI and KAOPEN, i.e., relax the trilemma.

Given the findings from the output volatility regressions in Table 4, for the EMG

countries, having greater monetary independence could lead a country to reduce output volatility.

If a country holds a higher level of IR than 21% of its GDP, it may be able to relax the trilemma,

so that it may decide to pursue greater monetary independence and financial openness while

maintaining exchange rate stability. One easy candidate that fits this category is China. Figure 11

shows the trilemma configurations and IR holding for emerging market countries in East Asia

and China. We can observe that while it does not give up its exchange rate stability and monetary

independence, China’s IR holding has been increasing and financial openness has inched up.

Although we have not tested formally, we find evidence consistent with the view that countries’

efforts to “relax the trilemma” can involve an increase in IR holding, which may have

contributed to the global expansion of liquidity prior to the financial crisis of 2008-09. We leave

testing this argument as one of our future research agendas.

5.2 Is the Current Crisis Consistent with Our Models?

As the IMF has revised the GDP estimates downward for many developing countries

several times since the fall of 2008, it has become clear that the ongoing crisis is not just an

American problem or the one in the industrial world, but a major challenge for the global

economy. In other words, the concept of “de-coupling” is no longer applicable.

Given that we can identify the countries that are experiencing more severe economic

situations than others as the time of this writing, we examine whether the current crisis situations

are consistent with what we have found from our previous findings. That is, we use the data from

32

2007 for the variables upon which we have focused in this paper and see whether the conditions

of these variables as of the eve of the crisis present any signals for the ongoing crisis. For this

purpose, Table 11 presents the variables of our focus for a group of emerging market countries.

Namely, the table reports PCGDP, IR (both as% of GDP), the three trilemma indexes, and the

external finance variables. dX refers to the change of the variable X compared to the 2002-06

period.52 In the table, we also report swap lines provided by the U.S. Federal Reserve and rescue

loans provided by the IMF (as of March 2009). The swap lines and rescue loans are reported to

identify which countries are experiencing more severe situations than others although countries

without these arrangements can be also experiencing dire situations.

Before making observations of these countries, it is noteworthy to point out that the size

of the swap lines or the IMF rescue loans is not so big for most of the countries. For Brazil,

Mexico, and Korea, it is about 2-3% of GDP and 7% for Pakistan. It is only for Singapore and

Hungary that the size of the additionally available IR is relatively substantial, around 18% of

GDP. Based on what we found in Figures 8 and 9, we can see that, except for Singapore and

Hungary, the effect of these swap lines or IMF rescue loans can be quite minimal at most to

reduce output volatility. Obstfeld et al. (2009) also mention the smallness of the additional IR

provided for developing countries, especially compared to industrialized countries, and argue

that these additional reserves would merely have signaling effects, unlike industrial countries’

that can have real effects to relax liquidity constraints.53 Our results are consistent with their

observation.

Let us now make observations about the conditions pertaining to trilemma configurations

and both internal and external financing of the concerned countries. Among the countries with

the swap or rescue loan arrangements, Hungary, Korea, and Pakistan experienced a relatively

rapid increase in net inflows of bank lending (‘Other’). In Table 9, we see that countries with

positive net inflow of ‘other’ investment tend to experience higher output volatility. Among the

three countries, Hungary appears to have pursued the combination of MI and KAOPEN whereas

Pakistan did that of MI and ERS. Both combinations, MI-KAO or MI-ERS, are found to lead

bank lending flows to have a bigger impact on output volatility (Table 10). The Pakistani

economy is also subject to higher output volatility because its financial development level is not

high although it pursues greater exchange rate stability. Interestingly, several other East

European countries, such as Lithuania, Poland, and Slovak Republic, and Russia also

experienced large increases in net inflow of bank lending, which suggest that these economies

5253 PCGDP is as of 2006 (or 2005 if the figure for 2006 is unavailable) because it is unavailable for 2007.

They also argue that the fact that a more substantial amount of rescue reserves can be readily available for

industrialized countries should be the reason why industrialized countries do not (have to) hold a massive amount of

IR.

33

can be subject to higher output volatility.54 In Table 9, we also found that the higher level of net

inflow of portfolio investment it receives, the greater output volatility a country would have to

face. The impact can be greater especially when the country pursues a policy combination of

ERS and KAO. Both Brazil and Argentina experienced a rapid increase in net inflow of portfolio

investment although neither of them pursued the policy combination of ERS and KAO. The table

also shows that Venezuela may be exposed to higher output volatility; it pursued fixed exchange

rate though its IR fell significantly while portfolio inflow increased. Thus, our casual

observations confirm that the inferences we obtained from our estimations seem to be consistent

with the economic conditions that led to severe crisis situations.

6. Concluding Remarks

Our paper outlined a methodology to trace the changing patterns of the trilemma

configurations. Taking a longer-run view, it reveals striking differences between the choices of

industrialized and developing countries during 1970-2006. The recent trend suggests that among

emerging market countries, the three dimensions of the trilemma configurations: monetary

independence, exchange rate stability, and financial openness, are converging towards a “middle

ground” with managed exchange rate flexibility, which they attempted to buffer by holding

sizable international reserves, while maintaining medium levels of monetary independence and

financial integration. Industrialized countries, on the other hand, have been experiencing

divergence of the three dimensions of the trilemma and moved toward the configuration of high

exchange rate stability and financial openness and low monetary independence as most

distinctively exemplified by the euro countries’ experience.

This configuration of the three macroeconomic policies is an outcome of the evolution of

different system arrangements. Over years, external shocks have affected the policy arrangement

across countries. In this regard, we have shown that major crises in the last four decades, namely,

the collapse of the Bretton Woods system, the debt crisis of 1982, and the Asian crisis of 1997-98, caused structural breaks in the trilemma configurations. For both industrialized and

developing countries, the major events in the last decade, such as the emergence of rapid

globalization and the rise of China, have also impacted the policy arrangements significantly.

With these results, we can safely expect that the present turbulence in the global financial

markets could challenge the stability of the current trilemma configuration.

We also tested whether the three macroeconomic policy goals are “binding” in the

context of the impossible trinity. That is, we attempted to provide evidence that countries have

faced the trade-offs based on the trilemma. Because there is no specific functional form of the

Latvia, though not categorized as an EMG country in the dataset, also experienced an influx of bank lending in

this year and is experiencing a severe economic crisis in 2008-09.

54

34

trade-offs or the linkage of these three policy goals, we tested a simplest linear specification for

the three trilemma indexes and examined whether the weighted sum of the three trilemma policy

variables adds up to a constant. Our results confirmed that countries do face the binding trilemma.

That is, a change in one of the trilemma variables would induce a change with the opposite sign

in the weighted average of the other two.

While external forces could impact countries’ decisions on the trilemma configurations,

policy makers decide on the specifics of the combination of the three policies depending on the

goals they would like to achieve. Hence, we also tested how each one of the three policy choices

as well as the combination of the two could affect the economic outcomes policy makers pay

close attention to, such as output volatility, inflation volatility, and medium-term inflation rates,

with a particular focus on developing countries.

We found countries with higher levels of monetary independence tend to experience

lower output volatility. When we restrict our sample to emerging market economies, we also

found that countries with higher levels of exchange rate fixity tend to experience higher output

volatility. However, this effect can be mitigated by holding international reserves if the level of

international reserves is higher than 19-22% of GDP. This result motivates the reason why so

many emerging market countries want to hold massive amounts of international reserves.

We also found that countries with more monetary autonomy tend to experience higher

inflation, which may reflect countries’ motives to monetize their debt. Countries with higher

exchange rate stability tend to experience lower inflation as has been found in the literature.

Furthermore, financial openness helps a country to experience lower inflation, possibly

indicating that globalization gives more discipline than monetary autonomy to a country’s

macroeconomic management.

We also extended our estimation model to investigate the following two questions

relevant to the current crisis: 1) Can financial development affect the link between trilemma

policy configurations and output volatility?; and 2) How can external financing affect

macroeconomic performances interactively with the trilemma configurations?

Regarding the effect of financial development on the link between the trilemma

configurations and output volatility, we found a nonlinear effect among emerging market

economies that medium-levels of financial development can raise the volatility-enhancing impact

of exchange rate stability. Highly developed financial markets can help financial liberalization

policy to reduce output volatility while underdeveloped financial markets could exacerbate

output volatility, signifying the synergistic effects between financial development and financial

opening.

In the estimations with the variables for external financing, we find the following: net

recipients of cross-border bank lending or portfolio flows – or the “hot money” – tend to

35

experience higher output volatility, a result consistent with the literature. We also took a closer

look at the effect of policy orientations on the effect of external financing and found that the

effect of different types of external financing can depend upon the policy regime adopted by a

country. First, net FDI inflows tend to dampen output volatility in general, but it can increase the

volatility in a “financially closed” regime, i.e., one with greater monetary independence and

more stable exchange rates. Net portfolio inflows can be volatility-increasing, and its effect is

greater for the countries with currency union or alike regimes. This type of regimes, however,

can dampen the volatility-enhancing effect of bank lending. Among the variables related to

sovereignty debt, the greater the debt service is, the more likely a country could experience

higher levels of output volatility, especially when combined with greater exchange rate stability

and financial openness, a result consistent with the “original sin” literature.

Our results also help answer why many countries have been hoarding massive amount of

IR, which has been claimed to be one of the causes of the current global financial crisis. A

motive for countries to hold massive IR is its desire to relax the trilemma; voluminous IR

holding allows countries to pursue both a higher level of exchange rate stability and a higher

weighted average of the other two trilemma policies through active foreign exchange

interventions. Given our finding that holding a higher level of IR than 21-24% of GDP can

dampen or even reverse the volatility-increasing effect of exchange rate stability, this finding is

plausible.

Lastly, our empirical findings are consistent with the conditions of the countries that are

currently experiencing macroeconomic turmoil; countries in turmoil do seem to be the ones with

the trilemma variables and those related to both internal and external financing at the levels that

lead to higher output volatility. In other words, our model could predict higher output volatility

for countries experiencing or at the brink of financial crises. This bolsters the validity of our

empirical analyses.

36

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40

Appendix: Data Availability of the Trilemma measures

Country

code

(cn)

Country Name

Base Country

Monetary

Independence

(MI)

Exchange rate

stability

(ERS)

KA Openness

(KAOPEN)

1 512 Afghanistan (C)

2 914 Albania (C)

3 612 Algeria (C)

4 614 Angola (C)

5 311 Antigua and Barbuda

6 213 Argentina (E) (C)

7 911 Armenia

8 314 Aruba

9 193 Australia

10 122 Austria

11 912 Azerbaijan

12 313 Bahamas, The

13 419 Bahrain (C)

14 513 Bangladesh (E)

15 316 Barbados

16 913 Belarus

17 124 Belgium

18 339 Belize

19 638 Benin

20 514 Bhutan

21 218 Bolivia (C)

22 616 Botswana (E) (C)

23 223 Brazil (E)

24 918 Bulgaria (E)

25 748 Burkina Faso

26 618 Burundi (C)

27 662 Cote d’Ivoire (E) (C)

28 522 Cambodia

29 622 Cameroon

30 156 Canada

31 624 Cape Verde

32 626 Central African Rep.

33 628 Chad (C)

34 228 Chile (E) (C)

35 924 China (E)

36 233 Colombia (E)

37 632 Comoros

38 636 Congo, Dem. Rep. (C)

39 634 Congo, Rep. (C)

40 238 Costa Rica

41 960 Croatia

42 423 Cyprus

43 935 Czech Republic (E)

44 128 Denmark

45 611 Djibouti

46 321 Dominica

47 243 Dominican Republic

48 248 Ecuador (E)

49 469 Egypt, Arab Rep. (E)

50 253 El Salvador

51 642 Equatorial Guinea (C)

52 643 Eritrea

53 939 Estonia

54 644 Ethiopia (C)

55 819 Fiji

56 172 Finland

57 132 France

58 646 Gabon (C)

59 648 Gambia, The

60 915 Georgia

61 134 Germany

62 652 Ghana (E) (C)

63 174 Greece

64 328 Grenada

U.S.

U.S.

France

U.S.

U.S.

U.S.

U.S.

U.S.

U.S.

Germany

U.S.

U.S.

U.S.

U.S.

1960-74 U.K.; 1975-U.S.

U.S.

Germany

U.S.

France

Rupee

U.S.

South Africa

U.S.

Germany

France

1960-70 Belgium; 1971-U.S.

France

U.S.

France

U.S.

Germany

France

France

U.S.

U.S.

U.S.

France

U.S.

France

U.S.

Germany

Germany

Germany

Germany

U.S.

U.S.

U.S.

U.S.

U.S.

U.S.

France

U.S.

Germany

U.S.

U.S.

Germany

Germany

France

U.K.

U.S.

U.S.

U.S.

1960-80 U.S.; 1981-Germany

U.S.

(172) (181) (178)

- - 1961 2005 1970 2004

1992 2006 1993 2006 1996 2006

1974 2006 1961 2006 1970 2006

1995 2006 1961 2006 1993 2006

1981 2006 1961 2006 1985 2006

1977 2006 1961 2006 1970 2006

1995 2006 1993 2006 1996 2006

1986 2006 1987 2006 1992 2006

1969 2006 1961 2006 1970 2006

1960 2006 1961 2006 1970 2006

1993 2006 1993 2006 2000 2006

1970 2006 1961 2006 1977 2006

1975 2006 1967 2006 1976 2006

1972 2006 1972 2006 1976 2006

1967 2006 1961 2006 1974 2006

1993 2006 1993 2006 1996 2006

1960 2006 1961 2006 1970 2006

1979 2006 1961 2006 1985 2006

1964 2006 1961 2006 1970 2006

1982 2006 1961 2006 1985 2006

1960 2006 1961 2006 1970 2006

1976 2006 1961 2006 1972 2006

1964 2006 1965 2006 1970 2006

1991 2006 1961 2006 1996 2006

1964 2006 1961 2006 1970 2006

1977 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1994 2006 1961 2006 1973 2006

1968 2006 1961 2006 1970 2006

1960 2006 1961 2006 1970 2006

1985 2006 1961 2006 1982 2006

1968 2006 1961 2006 1970 2006

1968 2006 1961 2006 1970 2006

1977 2006 1961 2006 1970 2006

1980 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1983 2006 1961 2006 1981 2006

1982 2003 1961 2006 1970 2000

1968 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1992 2006 1993 2006 1998 2006

1969 2006 1961 2006 1970 2006

1993 2006 1994 2006 1998 2006

1960 2006 1961 2006 1970 2006

1996 2006 1961 2006 1982 2006

1981 2006 1961 2006 1982 2006

1995 2006 1961 2006 1970 2006

1970 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1983 2005 1961 2006 1970 2006

1985 2006 1961 2006 1973 2006

- - 1961 2006 1998 2006

1993 2006 1993 2006 1998 2006

1985 2006 1961 2006 1970 2006

1974 2006 1961 2006 1975 2006

1960 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1968 2006 1961 2006 1970 2006

1977 2006 1961 2006 1971 2006

1995 2006 1996 2006 1998 2006

1960 2006 1961 2006 1970 2006

1964 2006 1961 2006 1970 2006

1960 2006 1961 2006 1970 2006

1981 2006 1961 2006 1979 2006

41

Country

Code

(cn)

Country Name

Base Country

Monetary

Independence (MI)

Exchange rate

stability (ERS)

KA Openness

(KAOPEN)

65 258 Guatemala (C)

66 656 Guinea (C)

67 654 Guinea-Bissau (C)

68 336 Guyana (C)

69 263 Haiti

70 268 Honduras (C)

71 532 Hong Kong, China (E)

72 944 Hungary (E)

73 176 Iceland (C)

74 534 India (E)

75 536 Indonesia (E)

76 429 Iran, Islamic Rep. (C)

77 433 Iraq (C)

78 178 Ireland

79 436 Israel (E)

80 136 Italy

81 343 Jamaica (E)

82 158 Japan

83 439 Jordan (E)

84 916 Kazakhstan

85 664 Kenya (E)

86 826 Kiribati

87 542 Korea, Rep. (E)

88 443 Kuwait

89 917 Kyrgyz Republic

90 544 Lao PDR

91 941 Latvia

92 446 Lebanon

93 666 Lesotho

94 668 Liberia (C)

95 672 Libya (C)

96 946 Lithuania (E)

97 137 Luxembourg

98 674 Madagascar (C)

99 676 Malawi (C)

100 548 Malaysia (E)

101 556 Maldives

102 678 Mali (C)

103 181 Malta

104 682 Mauritania (C)

105 684 Mauritius (E)

106 273 Mexico (E)

107 868 Micronesia, Fed. Sts.

108 921 Moldova

109 948 Mongolia (C)

110 686 Morocco (E)

111 688 Mozambique

112 518 Myanmar (C)

113 728 Namibia (C)

114 558 Nepal

115 138 Netherlands

116 353 Netherlands Antilles

117 196 New Zealand (C)

118 278 Nicaragua (C)

119 692 Niger (C)

120 694 Nigeria (E) (C)

121 142 Norway

122 449 Oman (C)

123 564 Pakistan (E)

124 283 Panama

125 853 Papua New Guinea (C)

126 288 Paraguay (C)

127 293 Peru (E) (C)

128 566 Philippines (E)

129 964 Poland (E)

130 182 Portugal

131 453 Qatar (C)

132 968 Romania

U.S.

1960-73 France; 1974-U.S.

U.S.

1960-75 U.K.; 1976-U.S.

U.S.

U.S.

U.S.

1960-91 U.S.; 1992-Germany

1960-90 U.S.; 1991-Germany

1960-79 U.K.; 1980-U.S.

U.S.

U.S.

U.S.

1960-78 U.K.; 1979-Germany

U.S.

Germany

U.S.

U.S.

U.S.

U.S.

U.S.

Australia

U.S.

U.S.

U.S.

U.S.

Germany

U.S.

South Africa

U.S.

U.S.

Germany

1960-78 Belgium; 1979- Germany

France

U.S.

U.S.

U.S.

France

France

1960-73 France; 1974-U.S.

U.K.

U.S.

U.S.

U.S.

U.S.

France

U.S.

U.S.

South Africa

1960-82 U.S.; 1983-India

Germany

U.S.

Australia

U.S.

France

U.S.

Germany

U.S.

U.S.

U.S.

1960-85 Australia; 1986-U.S.

U.S.

U.S.

U.S.

Germany

Germany

U.S.

U.S.

1960

1986

1975

1966

1994

1979

1982

1971

1964

1964

1983

1960

-

1964

1982

1964

1961

1960

1966

1994

1967

-

1964

1975

1993

1979

1993

1964

1980

1981

1963

1994

1985

1970

1963

1966

1978

1964

1969

1964

1967

1976

1996

1995

1993

1969

1994

1975

1991

1974

1960

1980

1969

1990

1964

1964

1964

1980

1964

1986

1974

1990

1960

1964

1991

1960

1980

1994

2006 1961 2006 1970 2006

2006 1961 2005 1970 2006

2006 1961 2006 1981 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1969 2006 1998 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1968 2006 1970 2006

2006 1961 2006 1970 2006

- 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1994 2006 1998 2006

2006 1961 2006 1970 2006

- 1961 2006 1990 2005

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1994 2006 1998 2006

2006 1961 2006 1970 2006

2006 1993 2006 1998 2006

2006 1961 2006 1970 2006

2006 1961 2006 1972 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1993 2006 1998 2006

2006 1961 2006 -

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1982 2006

2006 1961 2006 1970 2006

2006 1961 2006 1972 2006

2006 1961 2005 1970 1964

2006 1961 2006 1972 1967

2006 1961 2006 1970 1976

2006 1961 2006 1996 1996

2006 1992 2006 1998 1995

2006 1991 2006 1998 1993

2006 1961 2006 1970 1969

2006 1961 2006 1988 1994

2006 1961 2006 1970 1975

2006 1962 2006 1994 1991

2006 1961 2006 1970 1974

2006 1961 2006 1970 1960

2006 1961 2006 1970 1980

2006 1961 2006 1970 1969

2006 1961 2006 1970 1990

2006 1961 2006 1970 1964

2005 1961 2006 1970 1964

2006 1961 2006 1970 1964

2006 1961 2006 1977 1980

2006 1961 2006 1970 1964

2006 1961 2006 1970 1986

2006 1961 2006 1979 1974

2006 1961 2006 1970 1990

2006 1961 2006 1970 1960

2006 1961 2006 1970 1964

2006 1961 2006 1990 1991

2006 1961 2006 1970 1960

2006 1967 2006 1976 1980

2006 1961 2006 1976 1994

42

Country

Code

(cn)

Country Name

Base Country

Monetary

Independence (MI)

Exchange rate

stability (ERS)

KA Openness

(KAOPEN)

133 922 Russian Federation (E)

134 714 Rwanda (C)

135 716 Sao Tome & Principe (C)

136 862 Samoa

137 135 San Marino

138 456 Saudi Arabia (C)

139 722 Senegal

140 718 Seychelles

141 724 Sierra Leone

142 576 Singapore (E)

143 936 Slovak Republic (E)

144 961 Slovenia (E)

145 813 Solomon Islands (C)

146 726 Somalia (C)

147 199 South Africa (E)

148 184 Spain

149 524 Sri Lanka (E)

150 361 St. Kitts and Nevis

151 362 St. Lucia

152 364 St. Vinc. & the Gren. (C)

153 732 Sudan (C)

154 366 Suriname (C)

155 734 Swaziland (C)

156 144 Sweden

157 146 Switzerland

158 463 Syrian Arab Republic

159 528 Taiwan (E)

160 923 Tajikistan

161 738 Tanzania (C)

162 578 Thailand (E)

163 742 Togo (C)

164 866 Tonga

165 369 Trinidad & Tobago (E) (C)

166 744 Tunisia (E)

167 186 Turkey (E)

168 925 Turkmenistan (C)

169 746 Uganda (C)

170 926 Ukraine

171 466 United Arab Emirates (C)

172 112 United Kingdom

173 298 Uruguay

174 846 Vanuatu

175 299 Venezuela, RB (E) (C)

176 582 Vietnam (C)

177 474 Yemen, Rep.

178 754 Zambia (C)

179 698 Zimbabwe (E) (C)

U.S.

1960-73 Belgium; 1974-U.S.

U.S.

Australia

Germany

U.S.

France

U.S.

1960-77 U.K.; 1978-U.S.

Malaysia

Germany

Germany

1960-85 Australia; 1986-U.S.

U.S.

U.S.

Germany

1960-92 U.S.; 1993-India

U.S.

U.S.

U.S.

1960-71 U.K.; 1972-U.S.

U.S.

South Africa

Germany

Germany

U.S.

U.S.

U.S.

U.S.

U.S.

France

Australia

1960-75 U.K.; 1976-U.S.

France

U.S.

U.S.

U.S.

U.S.

U.S.

Germany

U.S.

U.S.

1960-89 France; 1990-U.S.

U.S.

U.S.

U.S.

U.S.

1995

1966

1989

1983

-

1997

1964

1979

1966

1972

1993

1993

1981

-

1960

1964

1964

1981

1981

1981

1978

1991

1974

1960

1964

2003

1985

1997

1973

1977

1964

1981

1965

1964

1964

-

1980

1992

-

1960

1976

1981

1964

1996

1996

1965

1965

2006 1993 2006 1998 2006

2006 1961 2006 1970 2006

2006 1961 2006 1981 2006

2006 1961 2006 1975 2006

- 1961 2006 1996 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1981 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1994 2006 1998 2006

2006 1992 2006 1998 2006

2006 1961 2006 1982 2006

- 1961 1989 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1988 2006

2006 1961 2006 1983 2006

2006 1961 2006 1983 2006

1984 1961 2006 1970 2005

2006 1961 2006 1970 2006

2006 1961 2006 1973 2006

2006 1961 2006 1970 2006

2006 1961 2006 1996 2006

2006 1961 2006 1970 2006

2006 1983 2006 - -

2006 1993 2006 1998 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1989 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

- 1994 2001 1998 2006

2006 1961 2006 1970 2006

2006 1993 2006 1998 2006

- 1967 2006 1976 2006

2006 1961 2006 1970 2006

2006 1965 2006 1970 2006

2006 1961 2006 1985 2000

2006 1961 2006 1970 2006

2006 1961 2006 1970 2006

2006 1991 2006 1995 2006

2006 1961 2006 1970 2006

2005 1961 2005 1984 2006

Notes: The base countries are primarily based on Shambaugh (QJE) and complemented by information from

IMF’s Annual Report on Exchange Arrangement and Exchange Restrictions and CIA Factbook

43

Table 1: Mean-Equality Tests of the Trilemma Indexes between Emerging Market

Countries (EMG) and Non-Emerging Market Developing Countries (Non-EMG LDC)

1971 – 1980 1981 – 1990 1991 – 2000 2001-2006

Non-EMG LDC .4495 .4510 .4748 .4427

Monetary EMG .4784 .4772 .4941 .3847

Independence (MI)

Difference .02883 .0262 .0193 -.0579

t-statistics 2.86*** 2.71*** 2.07** 4.31***

Non-EMG LDC .7941 .7228 .6508 .7266

Exchange Rate EMG .6703 .4983 .4901 .5364

Stability (ERS)

Difference -.1238 -.2245 -.1607 -.1902

t-statistics 6.70*** 11.04*** 8.47*** 8.68***

Non-EMG LDC .3511 .3138 .3785 .4177

Financial

EMG .2803 .2522 .4014 .5498

Openness

Difference -.0708 -

.0616 .0230 .1320

(KAOPEN)

t-statistics 3.42*** 3.08*** 1.1912% 5.09***

Non-EMG LDC .1013 .1093 .1331 .1772

International

EMG .1109 .1104 .1697 .2322

Reserves Holding

Difference .0095 .0011 .0366 .0550

(% of GDP; IR)

t-statistics 1.31* 0.12 4.25*** 4.67***

44

Table 2 (a): Tests for Structural Breaks in the Trilemma Indexes

Monetary Independence

1970-72 1974-81 1983-96 1999-2006

Mean 0.376 0.407 0.389 0.139

Change +0.031 -0.018 -0.250

t-stats (p-value) 1.31 (0.11) 0.85 (0.20) 11.91 (0.00)***

Mean 0.688 0.429 0.476 0.702

Change -0.259 +0.047 +0.226

t-stats (p-value) 6.64 (0.00)*** 2.41 (0.01)** 12.45 (0.00)***

Mean 0.439 0.469 0.688 0.955

Change +0.030 +0.219 +0.266

t-stats (p-value) 1.62 (0.07)* 4.34 (0.00)*** 5.27 (0.00)***

1970-72 1974-81 1983-96 1999-2006

Mean 0.500 0.399 0.457 0.534

Change -0.101 +0.058 +0.077

t-stats (p-value) 1.68 (0.06)* 1.84 (0.04)** 3.55 (0.00)***

Mean 0.786 0.780 0.635 0.742

Change -0.006 -0.145 +0.107

t-stats (p-value) 0.10 (0.46) 5.26 (0.00)*** 3.76 (0.00)***

Mean 0.267 0.365 0.326 0.391

Change +0.098 -0.040 +0.065

t-stats (p-value) 5.73 (0.01)*** 2.25 (0.02)** 3.93 (0.00)***

1970-72 1974-81 1983-96 1999-2006

Mean 0.526 0.474 0.508 0.407

Change -0.052 +0.034 -0.100

t-stats (p-value) 2.16 (0.03)** 1.42 (0.09)* 3.81 (0.00)***

Mean 0.818 0.715 0.517 0.579

Change -0.103 -0.198 +0.63

t-stats (p-value) 3.38 (0.00)*** 9.55 (0.00)*** 2.71 (0.01)***

Mean 0.210 0.229 0.240 0.474

Change +0.020 +0.010 +0.234

t-stats (p-value) 5.03 (0.00)*** 0.40 (0.35) 8.88 (0.00)***

Industrial

Countries (18)

Exchange Rate Stability

Financial Openness

Monetary Independence

Non-Emerging

Developing

Countries

(32)

Exchange Rate Stability

Financial Openness

Monetary Independence

Emerging

Market

Countries

(18)

Exchange Rate Stability

Financial Openness

Note: * significant at 10%; ** significant at 5%; *** significant at 1%

45

Table 2(b): Tests for Structural Breaks in the Trilemma Indexes

Monetary Independence

1983-89 1991-2006 1983-2000 2002-2006

Mean 0.396 0.246 0.355 0.126

Change -0.150 -0.229

t-stats (p-value) 3.17 (0.00)*** 5.82 (0.00)***

Mean 0.476 0.599 0.511 0.715

Change +0.124 +0.204

t-stat (p-value) 2.64 (0.01)*** 5.33 (0.00)***

Mean 0.578 0.905 0.748 0.949

Change +0.327 +0.201

t-stats (p-value) 9.22 (0.00)*** 2.62 (0.01)**

1983-89 1991-2006 1983-2000 2002-2006

Mean 0.421 0.522 0.483 0.517

Change +0.100 +0.034

t-stats (p-value) 4.80 (0.00)*** 1.05 (0.15)

Mean 0.633 0.699 0.643 0.778

Change +0.066 +0.135

t-stats (p-value) 2.01 (0.03)** 4.73 (0.00)***

Mean 0.296 0.376 0.336 0.400

Change +0.080 +0.064

t-stats (p-value) 5.94 (0.00)*** 3.20 (0.00)***

1983-89 1991-2006 1983-2000 2002-2006

Mean 0.471 0.469 0.508 0.385

Change -0.002 -0.123

t-stats (p-value) 0.08 (0.47) 4.52 (0.00)***

Mean 0.537 0.532 0.515 0.608

Change -0.005 +0.093

t-stats (p-value) 0.19 (0.43) 3.95 (0.00)***

Mean 0.188 0.403 0.282 0.482

Change +0.215 +0.200

t-stats (p-value) 6.27 (0.00)*** 4.23 (0.00)***

Industrial

Countries (18)

Exchange Rate Stability

Financial Openness

Monetary Independence

Non-Emerging

Developing

Countries

(32)

Exchange Rate Stability

Financial Openness

Monetary Independence

Emerging

Market

Countries

(18)

Exchange Rate Stability

Financial Openness

Note: * significant at 10%; ** significant at 5%; *** significant at 1%

46

Table 2(c): Summary of the Structural Breaks Tests

Structural Breaks

Monetary Independence

Exchange Rate Stability

Financial Openness

1997-98

1997-98

(1973 for non-Euro Countries)

1990

Industrial

Countries

(IDC)

Non-Emerging

Developing

Countries

(NOEMG)

Monetary Independence

Exchange Rate Stability

Financial Openness

1990

1982

1990

Emerging

Market

Countries

(EMG)

Monetary Independence

Exchange Rate Stability

Financial Openness

2001

1982

1997-98

47

Table 3: Regression for the Linear Relationship between the Trilemma Indexes:

1=ajMIi,t+bjERSi,t+cjKAOPENi,t +εt

(1) (2) (3) (4) (5) (6) (7) (8) (9)

FULL 1970-72 1974-81 1983-96 1999-2006 1983-89 1991-20061983-20002002-20061.084

0.946

1.339

0.99

0.336

1.065

0.558

0.931

0.522

Monetary Independence

[0.039]***

[0.127]***[0.069]***[0.057]***[0.109]***

[0.066]***[0.077]***[0.057]***[0.101]***

0.611

0.665

0.597

0.647

0.223

0.613

0.633

0.66

0.448

Exch. Rate Stability

[0.032]***

[0.076]***[0.090]***[0.051]***[0.181]

[0.061]***[0.100]***[0.050]***[0.249]*

0.437

0.369

0.29

0.448

0.869

0.439

0.632

0.468

0.733

KA Openness

[0.021]***

[0.050]***[0.063]***[0.031]***[0.072]***

[0.045]***[0.042]***[0.029]***[0.091]***

-0.166

–

0.375

-0.287

0.159

-0.43

-0.059

-0.104

-0.022

ERM x MI

[0.072]**

–

[0.299]

[0.111]***[0.119]

[0.286]

[0.103]

[0.086]

[0.126]

-0.026

–

0.254

0.073

-0.115

0.218

-0.398

-0.105

-0.338

ERM x ERS

[0.055]

–

[0.165]

[0.073]

[0.183]

[0.104]**

[0.108]***[0.067]

[0.251]

-0.005

–

-0.273

-0.009

0.039

0.09

0.137

-0.012

0.177

ERM x KAOPEN

[0.052]

–

[0.128]**

[0.054]

[0.075]

[0.122]

[0.059]**

[0.054]

[0.097]*

0.148

0.389

-0.175

0.299

0.78

0.214

0.675

0.365

0.567

LDC x MI

[0.045]***

[0.164]**

[0.097]*

[0.065]***[0.119]***

[0.078]***[0.083]***[0.064]***[0.120]***

-0.193

-0.371

-0.118

-0.21

0.211

-0.134

-0.244

-0.24

0.001

LDC x ERS

[0.035]***

[0.094]***[0.097]

[0.055]***[0.184]

[0.067]**

[0.103]**

[0.054]***[0.252]

-0.158

-0.136

-0.043

-0.176

-0.536

-0.009

-0.362

-0.257

-0.378

LDC x KAOPEN

[0.030]***

[0.079]*

[0.081]

[0.051]***[0.080]***

[0.069]

[0.052]***[0.045]***[0.100]***

1850

150

400

700

400

350

800

900

250

Observations

0.95

0.98

0.94

0.96

0.95

0.96

0.96

0.95

0.95

Adjusted R-squared

Robust standard errors in brackets * significant at 10%; ** significant at 5%; *** significant at 1%

NOTES: ERM is a dummy for the countries and years that correspond to participation in ERM (i.e., Belgium, Denmark, Germany, France, Ireland,

and Italy from 1979, Spain from 1989, U.K. only for 1990-91, Portugal from 1992, Austria from 1995, Finland from 1996, and Greece from 1999)

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