PRELIMINARY

Rounding the Corners of the Policy Trilemma:
Sources of monetary policy autonomy
Michael W. Klein
Fletcher School, Tufts University, and NBER
andJay C. Shambaugh
George Washington University and NBER

June 30, 2013
Abstract: A central result in international macroeconomics is that a government cannot simultaneously
opt for open financial markets, fixed exchange rates, and monetary autonomy; rather, it is constrained tochoosing two of these three. In the wake of the Great Recession, however, there has been an effort toaddress macroeconomic challenges through intermediate measures, such as narrowly targeted capital
controls or limited exchange rate flexibility. This paper addresses the question of whether theseintermediate policies, which round the corners of the triangle representing the policy trilemma, afford afull measure of monetary policy autonomy. Our results confirm that extensive capital controls or
floating exchange rates enable a country to have monetary autonomy, as suggested by the trilemma. .
Partial capital controls, however, do not generally enable a country to have greater monetary control
than is the case with open capital accounts unless they are quite extensive. In contrast, a moderateamount of exchange rate flexibility does allow for some degree of monetary autonomy, especially inmany emerging and developing economies.

I.

Introduction:1
Policy makers cannot have it all, at least in the sphere of international macroeconomics. This is

the idea behind the policy trilemma, a central principle of international macroeconomics that maintains
that a country can maintain only two of three policies; a fixed exchange rate, open capital markets, anddomestic monetary autonomy. The reason is fairly straightforward. If a country has a fixed exchange
rate and open financial markets, its interest rate must follow that of the base country, which implies
sacrificing monetary autonomy. Otherwise, for example, an increase in the base country interest rate notmatched by the domestic country would lead to investors shifting funds to assets denominated in thehigher interest rate currency, which generates a depreciation of the exchange rate. Thus, monetary
autonomy requires that a country must either allow the exchange rate to change or shut down the flow offinance across borders.
The policy trilemma is often depicted using the diagram presented in Figure 1. Each of thecorners of the triangle represents one of the three policy choices. A government can choose a position
represented by one of the sides of the triangle: a floating exchange rate with monetary autonomy andcapital mobility (side A); a pegged exchange rate with capital mobility but no monetary autonomy (sideB); or monetary autonomy and a pegged exchange rate, but with capital controls (point C). While astrict interpretation of the trilemma means that countries are forced to one of these sides, a moretempered view is that the trilemma simply highlights tradeoffs; if financial markets are open, moreautonomy requires more exchange rate flexibility or, conversely, if the exchange rate is to remain stable,
more autonomy would require a more closed capital market.
The policy trilemma has been subjected to extensive empirical testing over the last decade. Early
results (Shambaugh (2004), Obstfeld et al (2005)) established the validity of the framework. These
results countered the notion that countries broadly lacked monetary autonomy, or that there was little
difference in monetary autonomy that could be obtained under different exchange rate regimes.
Subsequent work supports the relevance of the trilemma. Bluedorn and Bowdler (2010) demonstratethat it holds for identified monetary policy shocks in the base country, not just for actual interest rate
movements. Miniane and Rogers (2007) confirm that interest rates in countries that peg follow theinterest rate of their base country more closely than do the interest rates of countries that float, but they1

We thank Maury Obstfeld, Linda Goldberg, Olivier Jeanne, and participants at seminars at American University, Johns
Hopkins University, London Business School, Queens College (Belfast), Trinity University (Dublin), and ETH (Zurich) for
comments. We also thank the organizers, participants, and in particular our discussant Keen Meng Choy at the Bank ofJapan’s International Monetary and Economic Studies conference. We thank Anthony Trifero and Patrick O’Halloran forresearch assistance and Hiro Ito, Menzie Chinn, and Bilge Erten for sharing data.

1

do not find significant differences across their capital control regime categories. Aizenmann et al (2010)
show that, in this context, movements in one policy requires corresponding changes in another policy;
for example, more restricted capital flows enable a country to have either more monetary autonomy orgreater control over its exchange rate.
A common result in the literature, one that we find as well, is that open pegs broadly follow thebase country interest rate and closed non-pegs do not. But the focus of this paper is to go beyond thispoint by analyzing the extent to which monetary policy autonomy can be achieved through limited
capital market intervention, or through allowing the exchange rate to fluctuate within a narrow band,two sets of policies that have gained greater attention since the onset of the global financial andeconomic crisis. .
The international policy making community had been somewhat skeptical about the wisdom ofusing capital controls to generate autonomy in the pre-crisis period. In the wake of the crisis, however,there has been a re-evaluation of the desirability of capital controls, especially those restrictions that are
imposed episodically at times of incipient inflow surges. In a notable reversal from their earlier stance,
the IMF has suggested capital controls may be warranted under certain conditions (see Ostry et al,2012). Theoretical work offers a view that targeted capital controls, used in a flexible manner, could
serve a prudential role and reduce financial fragility (Jeane (2011, 2012), Jeanne and Korinek (2010),
Korinek (2011), Bianchi (2011)) or, for pegged countries could substitute for monetary policy autonomy
and provide a pegged country some control over macro policy management (Farhi and Werning (2012)
and Schmitt-Grohe and Uribe (2012)).
One facet of this paper is to empirically investigate whether limited capital controls temper theloss of monetary policy autonomy that accompanies pegged exchange rates. An important point is thepossible distinction between episodic, targeted capital controls and long-standing controls over a wide
range of assets. Another facet of this paper is to study whether an intermediate exchange rate regime, a“soft” peg in which an exchange rate is allowed to fluctuate within relatively wide bands, affords moremonetary autonomy than a peg in which the domestic currency is more tightly aligned with the value ofthe currency of a base country.
We consider each of these two facets of middle-ground policies, and the possibility of rounding
the corners of the policy trilemma, along the dimensions of time and policy intensity. Middle-groundpolicies arise in the time dimension through blended behavior across years, such as opening and closing

2the financial account, or flipping back and forth across exchange rate regimes. Policy middle grounds ina particular year are represented by loose pegs and limited capital controls.
These two ways in which the corners of the policy trilemma can be rounded, by switchingpolicies over time and by mid-range policies, are relevant for both exchange rate and capital control
regimes. While there are some cases of movement towards “bipoloar” exchange rate regimes, assuggested by Fischer more than a decade ago and as exemplified by the move from the EMS bands to asingle currency in the euro-zone, there remains an important and empirically relevant middle groundbetween long-standing pure pegs and floats. One aspect of this is the continued presence of “soft” pegs.
Another is the flipping back and forth between pegs and floats (Klein and Shambaugh (2008).
Likewise, there are empirically relevant middle ground policies with respect to capital controls. Whilesome countries have long-standing, pervasive capital controls, there is a substantial subset of countries
that use limited controls on an episodic basis. Klein (2012) calls these capital control regimes “walls”
and “gates,” respectively, and shows that Klein (2012)walls are more effective than gates in limiting
asset price booms and swings in the value of the real exchange rate.
In this paper, we also find differences in the effects of gates and walls, and that the sharp
trilemma corner representing capital account openness cannot be rounded by using temporary capital
controls unless they are quite extensive. Similarly, flipping exchange rate regimes does not change thegeneral nature of the trilemma. When countries are pegged, they move with the base, when not pegged,
they do not. Thus a temporary float does seem to generate autonomy and a temporary peg sacrifices it.
However, soft pegs do seem to generate more monetary policy autonomy than tight pegs, even though
they do not afford as much autonomy as floating exchange rates.
On net, then, the results presented in this paper confirm the basic message of the trilemma; pegs
and open countries have less monetary autonomy, and efforts to gain autonomy through middle range
policies may be difficult. The most simple and most sure way to get monetary autonomy, without
strongly closing off the capital account, is to allow more flexibility in the exchange rate.
II. Methodology
The key question in this paper is how a country can achieve monetary autonomy, in particular
the extent to which the trade-offs represented by the policy trilemma are materially altered by allowing
some exchange rate flexibility, or by using capital controls episodically. An answer to this question
sharpens our understanding of the policy trilemma. It is not the case, however, that the trilemma is3

refuted by results showing a degree of monetary autonomy is obtained with soft pegs rather than justwith pegs, or with gates rather than walls. The trilemma does not require that countries only occupy
corner solutions, rather it is an expression of the trade-offs faced by policy-makers. To answer thisquestion we follow the techniques used in Shambaugh (2004) and Obstfeld et al. (2005). The core
methodology in this paper is straightforward. It begins from the simple interest parity equation(1) Rit = Rbit + %ΔEe + ρ
where R represents the nominal interest rate, Rb is the interest rate of the base country, %ΔEe is the
expected change in the exchange rate and ρ represents the risk premium on the asset in question.
Under a purely credible peg with a perfectly fixed exchange rate, the expected change in theexchange rate is zero. If the assets in question have similar risk (and they are either short term money
markets or short term government bonds which should have relatively low risk premia), then the local
interest rate must equal the base-country interest rate. In the case of nonpegs, however, the local interest
rate could differ from the base-country rate.
In addition, the equation should only hold if money can flow across borders to enforce it. If
capital controls are tight,there is no reason Rit must equal Rbit regardless of %ΔEe, Alternatively, we
could think about capital controls as a wedge between Rit and Rbit . For example, if there were a tax on
foreign borrowing and there were net inflows (so the marginal capital came from abroad) we could
rewrite (1) as:(2) Rit = Rbit + %ΔEe + ρ+ τ
where τ represents the tax. Thus, by raising or lowering τ, the government could have Rit movedifferently from Rbit even if %ΔEe is equal to zero, effectively giving the country monetary autonomy.
This, in principle, is the monetary autonomy Farhi and Werning (2012) or Schmitt Grohe and Uribe(2012) have in mind.2
One complication is that we do not directly observe the expected change in the exchange rate or
the risk premium. Interest rates may not be equal, even in the case of pegged exchange rates, because of
movements in these variables. Furthermore, there is extensive research showing that this basic equation
does not hold for nonpegs when one uses the realized ex post exchange rate change as a proxy for the ex2

Schmitt-Grohe and Uribe state: “As a result, the government can indirectly affect employment in the nontraded sector bymanipulating the intertermporal price of tradables (the interest rate) via capital controls.” It should be noted, Schmitt-Groheand Uribe do not suggest this is a first best policy. They suggest allowing the exchange rate to change is preferable, but study
the question of what should be done if that tool is not available. Farhi and Werning, argue: “In response to transitory shocks,
however, capital controls now play a more important countercyclical role” when prices are sticky, and “This discussion
underscores the fact that capital controls allow the country to regain some monetary autonomy and, with it, some control overthe intertemporal allocation of spending.”

4

ante expected change. But, as long as %ΔEe + ρ do not move systematically with the base interest rate
(which would be true, for example if the peg was fully credible and the risk premium was constant), theestimates should show that the change in the local interest rate moves with the change in the baseinterest rate. Therefore, one set of our empirical tests centers on(3) ΔRit = α + βΔRbit + µit
The coefficient of interest in this equation is β. If the trilemma held perfectly, we would expect β to beequal to 1 for purely open pegs. Put differently, if equation (1) holds, ρ and %ΔEe are constant, and thecountry does not try to move Rit on its own, then β will equal 1. Thus, we will use the estimated β as ameasure for countries’ monetary autonomy. If ρ and %ΔEe are not constant, then β will equal

(4) 1

, %

Thus, if Rbit and %ΔEe or ρ are negatively correlated, then β will be less than one, and if positively
correlated, it will be less than one, even if there is no autonomy. On the other hand, transaction costs andsmall barriers to arbitrage may allow for slight changes in the interest rates such that β is less than 1.
Obstfeld et al (2005) also demonstrate through simulations that as long as a country is allowed a small
amount of exchange rate flexibility within a band, the estimated β in such an equation could be closer to
0.5. We can also learn about the extent of autonomy in a particular sample of countries by the Rsquared in such an equation. A high R-squared shows that little else drives the local interest rate otherthan the base interest rate. A low R-squared shows that many other factors may drive the local interest
rate. Again, Obstfeld et al (2005) provide a benchmark for the R-squared suggesting that even a country
following the base country quite closely in a credible peg with tight bands will still likely generate an Rsquared of only 0.1 or 0.2.
The specification that uses first differences, (3), has the additional advantage of accounting forthe fact that nominal interest rates exhibit substantial persistence and may be best treated as very close toa unit root. Shambaugh (2004) discusses the time series properties of the data in detail.3 Our tests use
annual data, which enables us to abstract from slightly different adjustment speeds across countries so

3

One can estimate levels relationships and test the long run relationship and speed of adjustment and use critical values thatvary depending on the unit root properties of the data (see Shambaugh (2004) and Frankel et al (2005)), but doing so requires
fairly long samples with the same properties. As we want to focus on shifts across exchange rate and capital controls
regimes, it is difficult to find long enough episodes to use these techniques.

5we can pool data. Accordingly, both the exchange rate regime and capital control regime data are also
coded at the annual frequency.
In practice, we will run separate regressions for subsamples of the data, corresponding todifferent combinations of exchange rate regime and capital controls, and compare βs and R² across thesecategories. For example, if we divided the sample along the dimensions of floats vs. pegs and open
capital markets vs. closed capital markets, we would have four categories; floats with open capital
markets, pegs with open capital markets, floats with closed capital markets and pegs with closed capital
markets. In this case, we expect to have the larger βs and higher R²s for the set of pegs as compared tothe set of floats, and for the set of open capital accounts as compared to the set of closed capital
accounts.
We can test for the statistical significance across these samples by pooling the data and using a
regression that interacts the change in the base interest rate with the exchange rate regime and an
indicator of capital account controls. In the case of peg vs. nonpeg (P = 1 for peg, P = 0 for nonpeg), theequation would be:(5) ΔRit = α + βRΔRbit + βRPPitΔRbit µit .
In this case, the effect of a change in the base country interest rate on the local interest rate is βR for anonpegged country and, βR + βRP for a peg. We can test βRP to see if there is a statistically significant
gap in the β across pegs and nonpegs. We expect βRP to be positive (the domestic interest rate follows
the base interest rate more closely when the country pegs). This equation can be augmented with otherinteractions – in particular measures of a country’s capital control regime. It is straightforward to extend
this specification to three categories of capital controls (e.g. walls, gates and open) and/or three
categories of exchange rate regimes (peg, soft peg, and float).
One disadvantage of the interaction technique is that if βR is well estimated in one sub-sample(e.g. pegs) but is noisily estimated in the other (floats), when data is pooled, βRP may be estimated withlarge errors. In this particular circumstance, we have a strong prediction for what β is for the open
pegged countries, but in theory, there is no constraint on the monetary policy of the closed or floating
countries. If some countries move their interest rates sharply, and sometimes in the same direction of
the base country, pooling the data may show a fair bit of volatility.
At a conceptual level, the danger in such tests is that countries are moving their interest rates
together for reasons other than the constraints of the trilemma. If all countries are the same, comparing
pegs and floats would still be an effective method. But, if for some reason, pegs are more apt to make6

the same interest rate changes as the base country, our methodology will overstate the influence ofpegging. Shambaugh (2004) provides a number of tests looking at trade shares and other reasons a
country may follow the base and finds that the core results still hold. The capital control results are insome ways insulated from this bias because it biases against the results. That is, if a country imposes
capital controls to avoid having to move with the base when there is pressure to do so, we would expect
it to appear to still follow the base to some extent relative to a non-capital control observation that faced
no such pressure. Given that the result is that capital controls insulate the local country, this potential
issue does not appear to be too problematic.
A number of other controls could be included in the regression. For example, country fixed
effects could be included, but these are effectively meaningless as to be anything other than zero, would
suggest a country is constantly raising its interest rate (if positive) or lowering it (if negative). One
could also include year fixed effects, and this would be a way to remove any global shock to interest
rates. Many papers that examine the trilemma do not do so because they use only one base country
which would be collinear with a time fixed effect. If there are multiple bases, though, and if the basecountry interest rates are not correlated, this would provide the necessary variation. However, thisapproach also removes a fair bit of information. To the extent that many countries follow the US dollar,
many observations are tied to the U.S. interest rates. In that case, a time fixed effect – estimated acrossthe average of the sample – will remove much of the movement from the dollar interest rate. Thus, in asmall sample, with a limited number of bases, time fixed effects may be problematic. Throughout the
paper, we report the version without year fixed effects, but we also occasionally note the year fixed
effect results (and include some tables with year fixed effects in the appendix). The core results of thepaper are not altered by year fixed effects, but it does make interpreting R-squared difficult acrosssamples.

III. Data
A crucial aspect of any test of the trilemma is identifying the indicators of monetary policy,
exchange rate regime and capital controls that can be used to test how closely the interest rate of acountry follows the interest rate of the base country. In this section we discuss the indicators we use,and present some statistics.

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The core measure of a fixed exchange rate is derived from Shambaugh (2004) and is extended
here to a longer sample.4 This measure codes a country as pegged if its exchange rate stays within a +/2% band over the course of a year against its base country. The choice of the band width is based on thefact that over time, ranging from the arbitrage bands of the gold standard to Bretton Woods, to the EMS,
bands of roughly 2% have often been allowed in fixed exchange rate systems. The base country is
identified based on the declared intent of the country, a country’s history, and by testing against avariety of base countries. Countries are also considered pegged if their exchange rate is constant in 11
of twelve months but has a discrete devaluation or revaluation in the other month of the year. To avoid
misclassifying a country that simply has low volatility in a given year, single year pegs are not included
as pegs. Inspection of the observations of pegs shows no examples of countries that appear to be
spuriously coded as pegged due to lack of volatility.
We do not use the simple declaration by countries as to whether they are pegged or not since, as
is well documented in the literature, countries often do not accurately declare their exchange rate
regimes. Other classifications are available as well, but as we would like to isolate different aspects ofthe policy trilemma, we prefer this measure which focuses solely on the exchange rate behavior. Klein
and Shambaugh (2010) document that this classification scheme has a reasonably high correlation withboth de jure and other de facto classifications.5 In addition to these pegs, we also consider a category of“soft”pegs, as in Obstfeld et al. (2010). An exchange rate is classified as a soft peg if it is not a peg andif the bilateral exchange rate with the base country stays within a range of up to +/-5% in a given year or
has no month where the exchange rate changed by more than 2% up or down (as a practical matter,almost no countries qualify for the second criterion that fail to meet the first). As with the peg
classification, countries may not be considered a float in both the year prior to and following theobservation for the coding to count it as a soft peg.6 This is intended to reduce the odds of spuriously
coding countries as soft pegs. For example, the United States would never be coded as a soft peg
against Germany, as it never stays within a +/-5% band for more than one year at a time. Henceforth,4

See Klein and Shambaugh (2010) for an extensive discussion of the coding of exchange rate regimes, and the different typesof classifications that have been used.
5
Regarding two other popular choices, the Reinhart Rogoff classification codes countries as pegged if the black market
exchange rate is stable, but that in some sense mixes two aspects of the trilemma – financial controls and exchange ratestability. Thus, for the purposes of examining the trilemma, it seems a pure focus on the exchange rate is more appropriate.
Similarly, Levy Yeyati and Sturzennegar use data on reserves volatility to see if a country is intervening to maintain its peg.
But, the index subsequently must add other pegs that do not intervene but that are clearly low volatility options. Furthermore
due to the greater data needs, the index is available for a smaller sample of observations.
6Countries do not have to be a soft peg for two years in a row, since a country could move from a peg to a soft peg or from asoft peg to a peg. Only soft pegs that are bordered on either side by a float are considered spurious.

8

we will use the phrase peg to refer to a peg within 2% bands, a soft peg as one within 5% up or downbands, and a float as a country that is neither. When looking simply at the binary coding we will refer topegs and nonpegs where nonpegs are both floats and soft pegs.
Table 1A presents the statistics on the exchange rate regime for the two data sets used in theanalyses in this paper, the 1973 – 2011 data and the shorter 1995 – 2011 data (The data sets correspondto the two different capital control measures discussed below. The shorter data set covers fewer
countries than the longer data set.) In the Gates and Walls data set, there is a roughly even divisionacross the three exchange rate regime categories of peg, soft peg, and floating. The longer data set has arelatively higher proportion of pegs, a relatively lower proportion of soft pegs, and a slightly smaller
proportion of floating exchange rate observations. But, this difference is not one related to time period
but country coverage. There is almost no difference in the peg / soft peg / float division in the fullsample or in the post 1995 sample. Thus, the middle ground appears sufficiently occupied to warrant
investigation.7
Classifying capital control regimes is more difficult than classifying exchange rate regime. One
can choose either a de jure or de facto means of classifying exchange rate regimes but capital controls
can only be gauged using de jure measures since there is a general absence of data on gross capital flows
and, even if these data were available, it is not clear what the level of gross flows would be in the case of
free capital mobility. It would be difficult to determine the extent of de facto controls by considering
whether rates of return are equalized across countries since this assumes efficient markets, knowledge ofinvestors’ expectations of the future value of the exchange rate, information on investor preferences andcorrelations of returns with other measures of risk. Thus, researchers tend to use de jure measures of thecountries’ laws and regulations concerning financial flows. A disadvantage of any coding that relies onlaws or administrative rules as opposed to their application is it will be unable to measure intensity ofrules or of their implementation (for example, a 1% tax is a tax just as a 20% tax is and either would be
coded as a control).
The most consistent source of cross-country data on capital controls is the Annual Report on
Exchange Arrangements and Exchange Restrictions (AREAER) published by the IMF. Up until 1995,
this yearbook presented binary codes across a limited number of fields (controls on current account7

See also Popper et al (2012) and Williamson (2000) among other studies for a discussion of the fact that the middle ground
does appear occupied. It is worth noting that even conventional pegs differ from the hard pegs (e.g. currency unions andcurrency boards) that Fischer suggested would be the logical endpoints. Roughly half of the pegs are not purepegs but havesome movement within the 2% bands.

9transactions, capital account transactions, multiple exchange rates, etc.). The level of detail was
expanded dramatically in the 1995 yearbook which began to report separate indicators of controls onmoney market instruments (debt instruments with a maturity of less than 1 year), bonds (debt
instruments with a maturity of greater than one year), financial investments (which includes bankborrowing and lending), collective investments, equities, and direct investment. Also, for the first time,
separate indicators were included for regulations on inflows and outflows for each of these categories.8
We use two sets of indicators of capital controls in this paper, both of which draw on the
AREAER. The Chinn-Ito index (created in Chinn and Ito (2006)) takes the first principal component of
the IMF AREAER yearbook coding of controls relating to current or capital account transactions, theexistence of multiple exchange rates, and the requirements of surrendering export proceeds. This index
covers a wide set of countries over a long time period, allowing for tests on a large sample of data. The
annual Chinn-Ito data we use in this paper covers the 1973 to 2011 period. This classification allows fora country to be in different categories across time. As shown in Figure 2, these data are close to trimodal, so we use these data to create three dummy variables, one for open capital accounts, one forclosed capital accounts, and a third for an intermediate level of openness.9
The second set of capital control indicators use information from the post-1995 volumes of the
AREAER in a manner that builds on Schindler (2009).10 We follow the strategy of Klein (2012) who
distinguishes between countries that persistently had open capital accounts (“open”), countries thatpersistently had closed capital accounts (“walls”), and countries that used capital controls episodically(“gates”); thus, unlike the Chinn-Ito data, in this scheme a country does not change categories acrosstime.11 This division of countries into the three categories is based on the information from the AREAER
for five categories of financial flows (all but direct investment) for both inflows and outflows. As
discussed in Klein (2012), there is a relatively straightforward separation of countries into these three8

We focus on the ten indicators signifying controls on inflows or outflows in the categories other than direct investment.
Direct investment controls can be associated with intellectual property rules, national security, or many other features
separate from controlling the flow of finance. Because the indicators are highly correlated (especially for walls countries that
effectively have controls on all indicators), we focus on the gates / walls distinction, not fine variation across the different
indicators.
9Comparing these cuts in the index to the old binary coding of the IMF yearbook (for the years where available), we foundthat the open countries were always open according to the yearbook, the closed countries were always closed, and those thatare in the middle ground based on the Chinn-Ito index were sometimes coded as open and sometimes coded as closed.
10
We thank Bilge Erten, who extended the country coverage of the Klein (2012) data set and kindly shared her data with us.
11
In the three way coding based on the Chinn Ito index, 87 of the 134 countries change capital control status with 66 making
moves in the direction of more openness, and 56 making moves in the direction of being more closed. Some countries
moved back and forth, and some countries moved more than once (for example, in two steps from completely closed tocompletely open) generating a total of 159 moves in capital account status based on our three way coding.

10

categories since capital controls were imposed for long duration and over a majority of categories for“walls” countries, while there were almost entirely absent for the countries in the open category. For thewalls countries, the average value of the ten categories (where 1 signifies a restriction and 0 represents
no restriction) is .93. That is, these countries are almost entirely closed in every year. Conversely, theopen countries have an average value of .02, suggesting most never have even a single restriction noted.
For the countries in the gates category, the average value for the 0/1 indicators is .43. We
distinguish between years in which the capital account was closed in any way and those in which it was
open. Open gates are those that have no restrictions (and hence an average value of zero); closed arethose that have any restriction at all (average value of .54). Thus, while the open gates look very much
like the open countries in general (in that there are no restrictions), closed gates are somewhat different
from walls. In addition to not being long lasting, they are not as extensive even when closed. We
sometimes divide the closed gates into “narrow” gates, which equals 1 in years in which represents theaverage of the number of closed categories was greater than zero and equal to or less than 0.49 (that is,
fewer than half of the 10 categories are closed, average value .26), and “broad” gates, which equals 1 inin years in which this average is greater than 0.49 (meaning at least half of the categories show a
restriction, average value .77). These broad gates are more similar to walls in their coverage of assets,though given their temporary nature, they still may be more permeable.
Table 1B reports the number of observations in each category for each classification scheme.
The statistics in this panel show that the Chinn-Ito (CI) classification allows for a country to be indifferent categories across time. This classification puts 38% of the 1973 – 2011 observations in theclosed category, 31% in the mid-range of capital controls category, and 31% in the open category. The
Gates-Walls-Open (GWO) classification divides the data by country, not by country-year observation.
It places the most observations in the gates category (48%), the next most in the open category (29%),
with the remaining 23% in the closed category. Of those 610 gates country year observations, 123
represent years in which there are no controls in place and 487 have some type of controls on asset
trade.. These closed gates observations are roughly evenly split between broad gates and narrow gates(272 broad and 215 narrow).
Table 1C compares the Chinn-Ito (CI) and Gates/Walls/Open (GWO) indicators. This tableshows that the vast majority of observations coded as open in GWO are also coded as open in CI. Mostof the GWO walls observations are in the closed CI category (with some in the mid-range). Effectively
all the GWO open gates are coded as open by CI, and the closed gates are spread across all three11

categories. The narrowly targeted closed gates are predominantly open and middle (though some areclosed) and the broad closed gates are predominantly middle and closed (though some are open).
These statistics show that there is an empirically relevant number of observations that are not thepolar cases of float and peg, or fully open and fully closed capital accounts. We next turn to thequestion of whether these middle ground policies allow for more monetary autonomy than a peg, or thana fully open capital account.12

IV. Results
This section presents results for both a data set using the Chinn-Ito capital control data, which
covers the period 1973 – 2011, and one using the gates, walls, and open division of countries, which
covers the period 1995 – 2011.
IV.1 Core evidence on the Trilemma
Table 2 presents subsample regressions using the longer (Chinn-Ito) data set running regressions
with the specification presented in equation 3. In the first binary split of the data, we isolate pegs fromall other countries (grouping soft pegs and floats together) and isolate open countries from those withany controls (middle closed or closed capital markets). Each of the four panels of table 2 represents oneof the archetypal options that the trilemma presents: open pegs, open nonpegs, closed pegs, and closed
nonpegs. Each panel shows the β coefficient (that on the base interest rate) as well as the standard error,
the number of observations and the R2.
For open financial market pegs sample, the coefficient is 0.68 (and the base interest rate
movements explain 28% of local country interest rate movements).13 Alternatively, the coefficient in
12

The other data used in the analysis (interest rates, reserves, etc) are from standard sources and are described in the data
appendix. The data set is an unbalanced panel based on data availability for exchange rates, interest rates, and capital control
data. Regressions using the Chinn-Ito data set are limited to 3126 observations across 134 countries. The only limitation
other than data availability is that the United States is not included as it has no logical base country (any correlation between
the US and another country’s interest rate is presumed to be the other country following the US). In addition, large swings inlocal interest rates are excluded since, in a handful of extreme cases, rates change by over 900 percentage points in a year andthese observations would dominate the results. Leaving out changes of 50 percentage points or more represents roughly 1%of the observations, leaving out changes of 9 percentage points represents roughly 5% of the observations. In small samples,large but not extreme changes (between 9 and 50 percentage points) can also be large enough for one observation to dominate
the estimates. We thus use the 9 percentage point cutoff for our baseline results. All main results are consistent if using the
50 percentage point change cutoff. Two other possible restrictions – dropping very small countries or dropping countries
with clearly non market interest rates (ones that do not change for multiple years) – tend to strengthen the results, but theseresults are not reported.
13
Standard errors are clustered at the country level so as to allow for an unstructured covariance matrix within a set ofobservations for a country (controlling for serial correlation in the panel as well as the possibility of different size errors

12the closed pegs sample is 0.40, also highly statistically significantly different from zero, but the Rsquared is only half the size. Thus, despite the closed financial markets, these pegs still move inconjunction with the base interest rate. Interest rates in open float countries are also not completely
uncorrelated with those of the base countries. The coefficient in this group is 0.23 and is statisticallysignificantly different from zero.14 Finally, interest rates in the closed nonpegs countries show almost
no relationship with the base countries’ interest rate. The coefficient is close to zero (.09), though it isstatistically different from zero at the 90 percent confidence level. Figure 3 (panels a-d) highlight theseresults. It shows scatter plots of these subsamples. Open pegs appear to move quite closely with thebase country while closed pegs and open nonpegs move somewhat with the base. The picture for theclosed nonpegs shows almost no relationship at all.
One way of seeing if pegs truly follow the base or are just more likely to move with majorcountry interest rates is to look at how they respond to interest rate changes other than that of their basecountry. If one looks at the non-dollar based sample, pegs and nonpegs respond quite differently to theirbase interest rate – with pegs far more correlated with the base – but they respond nearly identically tochanges in the US dollar interest rate.15 To make comparisons across groups easier, we use thespecification in equation (5) and include interaction terms. These results are reported in the marginalcolumn and row of Table 2. The coefficient on pegs versus nonpegs is .33 and the coefficient on open
versus closed is .27. Both are statistically significantly different from zero at the 99 percent confidence
level.16 These results are quite similar to those in Shambaugh (2004) or Obstfeld et al (2005), eventhough these results include ten more years of data.
One notable result is that while the trilemma predicts that an open financial market float orclosed financial market peg have autonomy -- only an open peg does not – the results show significant
coefficients for both of the off diagonals of the grid. Both being open and pegging seems to costmonetary autonomy and giving up either one does not in and of itself gain pure autonomy. These results
are consistent with those in Obstfeld et al (2005) and Bluedorn and Bowdler (2010) but suggest that aacross countries). One could alternatively control for correlation within a given year across countries, the standard errors arenearly identical across the two choices. Both clustering methods yield standard errors considerably larger than a simple
heteroskedasticity correction or uncorrected errors.
14
It is worth noting that incipient pegs do not drive the positive coefficient. Dropping countries that are floating but will peg
sometime soon does not change the coefficient or the significance level.
15
Within the non-dollar based sample, pegs and nonpegs both have coefficients near .2 or .3 on changes in the dollar rate.
Nonpegs have the same coefficient on changes in their own base rate. Pegs, though, have a coefficient of .67 on changes intheir own base rate.
16
Crises do not explain the low coefficient for nonpegs. Eliminating countries that pegged the year before or two years
before does not change the result.

13

more fine analysis of these policy regimes is needed to understand what a country must do to achieve
more monetary autonomy.
Appendix table 2A replicates these results including year effects. To the extent that the basecountries’ interest rates are correlated, or that particular subsamples have one dominantly used basecountry, the results will be pushed towards zero as much of the variation in the base rate will be capturedby the fixed effects. Additionally, if a country is not really following the base country, but simply
moving together when the whole world moves, we would also see the β moved towards zero. But theresults in Table 2a show that there is little change in the coefficients in the open pegs and closed pegs
subsamples, although both move towards zero, suggesting that the nonzero β may in fact be simply
driven by common shocks.
IV.2 Rounding the Corners
One reason open nonpegs and closed pegs may show a correlation with the base country, as
represented by the results in table 2, is suggested by Calvo and Reinhart (2002) – perhaps the floats do
not purely float – and by the variety of coding of capital flows – perhaps not all “closed” countries arereally closed. As to the latter, this is by default true as our binary coding is generated from a continuous
index. Thus we can test further by examining more fine gradations of policy choices. To do so, we
separate soft pegs from the nonpeg group leaving a more pure float category and split the middle closed
countries from the truly closed. In a sense, the argument is that while the trilemma would suggest theclosed pegs and open nonpegs should have zero β coefficients, if there are soft pegs in the open nonpegs
and if there are countries with leaky controls in the closed pegs, it makes sense that the coefficients arenonzero. Splitting our results into a 3x3 grid should provide more clarity.
The basic idea of the trilemma is demonstrated by looking across or up and down the grid. For
open countries, pegs have a higher coefficient than soft pegs which have a higher coefficient than floats.
Similarly, across the peg categories, the middle open countries tend to have lower coefficients than the
open countries and the closed countries lower than that. A notable result is that all types of floating
countries now have coefficients that are close to zero and not statistically different from zero (far rightcolumn). Once the soft pegs have been split out of the nonpeg sample, the remaining floating countries
appear to have no connection to the base country. The same is not true for the “true” closed countries(the bottom row). There is still some correlation with the base country for both the pegged and soft
pegged countries, though it is notably lower than that of the middle open or open countries. Soft pegged
14

countries do appear to be in a middle ground between pegs and floats (middle column), the gaps are lessclear for the mid-open countries (middle row).
Once again, we can test the statistical significance of the difference across the regimes using theinteraction approach. The coefficients reported at the bottom of the table let us test the significance ofthe differences across exchange rate regimes. Pegs are quite distinct from floats with a coefficient on
the difference of 0.41. Soft pegs lie in the middle, and are statistically distinct from both extreme
exchange rate regimes. The difference between the coefficients on pegs and soft pegs is .19 (statisticallydistinct from zero at the 99 percent level) and the gap between soft pegs and floats is .22 (statisticallydistinct from zero at the 99 percent level). This suggests that soft pegs in many ways accomplish theirgoal. They gain monetary autonomy relative to pegged countries while maintaining some exchange ratestability. They do not, though, gain as much autonomy as floats.
Looking at financial openness (reported on the far right of the table) we see that open countries
are clearly distinct from closed countries (coefficient on the gap is .29, statistically significant at the
99% level), but open countries are not distinct from mid-open countries. Also, mid open countries arealso clearly distinct from closed countries. The results thus suggest that middle levels of capital controls
do not seem to insulate a country from base country interest rate movements. Only the more extremely
closed capital accounts have generated policy autonomy. This can also be seen by simply examining thecells. Mid-open pegs have a coefficient of 0.54, fairly close to the 0.68 of the open pegs and quite a bit
higher than the more insulated 0.25 of the closed countries. Mid-open soft pegs and open soft pegs are
statistically indistinguishable. These gaps across softpegs and other regimes or mid-open countries andother regimes are quite similar and retain the same level of significance if year effects are included (seeappendix table 3A)Considering the question posed in the introduction, it seems that rounding the corners of thetrilemma has not appeared successful with capital controls relative to allowing more exchange rate
flexibility. Soft pegs still move with the base considerably, but not as much as pegs. A partial closingof the financial market appears even less useful in terms of generating autonomy. As long as markets
are not mostly closed, pegs and soft pegs move with the base, and floats do not.
An important caveat arises when investigating the difference between advanced and nonadvanced economies (not shown here, but discussed more in section V). Pegged and soft pegged
advanced economies have higher coefficients than those in emerging or developing economies (evencontrolling for financial openness). For advanced countries, the gap between pegged and soft pegged is15smaller than for non-advanced countries, and is not statistically significant. In many cases, these arecountries in the EMS making slightly larger changes in the exchange rate than the 2% band cutoff, or arecountries like the UK or Switzerland who track both the German exchange rate and interest rate fairly
closely, or Canada (which does the same with regards to the US) or New Zealand (which follows
Australian interest rates fairly closely and whose exchange rate stays within 5% bounds in many years).
Conversely, in the non-advanced sample, the gap between soft pegs and pegs is slightly larger.
One can reject that the coefficients are the same in a pooled specification. In particular, the soft pegs
have virtually no relationship to the base country interest rate in the most open capital accountsubsample. This subsample includes a large group of countries across Africa, Latin America, Emerging
Europe, and Asia whose exchange rate is relatively stable, and yet who seem to have enough room tomove within the band to maintain some semblance of monetary autonomy. For these countries,
rounding the corner of the trilemma has been successful. They have maintained a more stable exchange
rate than a pure float, and yet do not move their interest rate in lock step with the base country.

IV.3 A Temporal Exchange Rate Middle Ground: Flippers
Soft pegs are not the only manner in which there is a middle ground in exchange rate regimes.
There is also the tendency of countries to shift back and forth between hard pegs and float, which sets up
three groups of exchange rate regimes; peggers, floaters, and flippers (Klein and Shambaugh (2008)). In
this section we examine whether there are differences in the extent of monetary autonomy across pegged
categories that take this temporal behavior into account.
Previous work has suggested that the impact of a peg may grow over time; for example, pegs inplace for a long time matter more for trade than do pegs that are relatively new. This difference may
reflect the effect of the current length of a peg on the expectation that it will continue. Klein andShambaugh (2008) show that the conditional likelihood that a peg will last one more year rises with thelength of time on the peg. Does the impact of this durability translate to the extent of monetary
autonomy? One may expect this to be the case if the durability of the peg affects the unobserved
expected change in the exchange rate. Conversely, to the extent that the limits on monetary autonomy
are effectively an arbitrage condition, and changes in the base interest rate transmit to changes in thedomestic interest rate immediately, there may not be a temporal middle ground between long-lived pegs
and long-lived floats.
16

We investigate this question in Table 4. This table presents regressions of the form of equation(2) for different categories of exchange rate regime over the 1973 – 2011 period (but without
distinguishing across capital control categories). Column 1 presents the estimates for the peg subsample
as a whole. Columns 2 and 3 include observations for either long pegs (the pegs last for at least 5 years)
or short pegs (less than 5 years). There appears to be no difference with regards to monetary autonomy
across this division with regards to the coefficient on the base rate.17 The R-squared on the much smaller
short peg samples are lower, perhaps because there is more volatility in the local rate in the early yearsof a peg as it is establishing credibility. Columns 4 and 5 present results for the current length of a peg,regardless of the eventual duration of the full peg episode. The sample in Column 4 includes pegs inexistence for 5 years or more while the sample in Column 5 includes pegs in existence for 4 years or
less. The coefficients on the change in the base interest rate are virtually the same across these two
subsamples. The coefficients are also very close to these values if we limit the new pegs to those inplace for only 2 years or less (Column 6) or peg episodes lasting two years or less (Column 7). These
results suggest that the “temporal” middle ground is not distinct from the general fact that a country pegs
its currency, without regard for the length of time the peg has been in place or for the eventual durationof the peg. The monetary policy of a country with a pegged exchange rate significantly follows that of
the base country.
Klein and Shambaugh (2008) show that nonpegs can be just as ephemeral as pegs, with
nonpegged episodes often lasting just 2 or 3 years as a country flips back and forth from pegging and not
pegging. Using the same categorizations for old and new and short or long nonpeg episodes, thecoefficient on the base interest rate is consistently between .1 and .2 (results not shown), and thedifferences across groups are not significant. This suggests that temporarily floating can in fact generatemonetary autonomy, something of a contrast to the result that many gates are ineffective (shown below).
It may be that the parallel to the temporary float is a very broad gate in that one does not float only overa small subset of assets.
Perhaps most surprisingly, even temporary soft pegs that are sandwiched around pegging show
considerably lower correlation with the base rate than pegs. For the nearly 200 observations where acountry is a soft peg but either pegs in the year before or after the soft peg (or soft pegs in a series of two

17

Note that there are far more long pegs observations than short peg observations. As noted by Klein and Shambaugh(2008), this is because, with annual data, longer pegs are sampled far more often than shorter pegs, not because there are
more episodes of long pegs than episodes of short pegs.

17

years and pegs at the start or finish of that period), the coefficient on the base rate is close to zero andnot statistically significantly different from zero.18
IV.4 Gates and Walls
The results with the Chin-Ito data set do not support the idea that limited capital market
interventions provide monetary autonomy. To further test the importance of different types of capital
controls, we turn to the gates-walls dataset. These data enable us to distinguish between countries thathave longstanding controls on a wide category of assets (walls) and those that use controls episodically(gates). We can also distinguish, among the gate countries, whether the presence of temporary extensive
controls (broad gates) differs from temporary controls on a more limited set of assets (narrow gates).
The sample includes 73 countries over the 1995 – 2011 period, with 34 countries in the gates category,
16 in the wall category, and 23 in the open category.
The first results using these data are presented in Table 5 which uses a subsample specification
along the lines of equation (5) in which the regressors include the change in the base interest rate, a peg
dummy variable, and the interaction of the peg dummy and the change in the base country interest rate.
The table reports the coefficient on the change in the base interest rate (which represents monetary
autonomy for countries that do not peg), the linear combination of this coefficient and the interactions(which represents monetary autonomy for countries that peg) and the coefficient on the interaction(which represents the difference in monetary autonomy between peggers and non-peggers).
The first column of Table 5 shows that pegging significantly limits monetary autonomy for theset of countries with open capital accounts, and that there is a significant difference between peggers andnon-peggers for this subset; the coefficient for peggers is 0.71 and is statistically significantly different
from zero at 99%, the coefficient for nonpegs is about 0.11 and has a t-statistic of about 0.5, and thedifference in the coefficients between pegs and nonpegs is significantly different from zero at better
than the 99% level of confidence. At the other polar extreme, the coefficients for walls countries(shown in the last column) demonstrate no statistically significant correlation with the base countries’interest rates, even for the pegged countries. This is a slightly different result from that of tables 2 and 3
where closed pegs do follow the base to some degree. One reason for the distinction may be the smaller
18

This is one result that definitely could result from the bias of not being able to observe changes in risk or devaluation
expectations. If in the face of a crisis a country allows slightly more exchange rate movement and moves its interest rate
sharply in response to shocks, it may appear to be unconnected with the base, but may not truly have autonomy. The next
section begins to address this issue.

18

sample with the gates-walls data set, but another reason may be that the walls categorization better
captures the difference between these countries and the rest of the sample than the categorization
obtained by splitting the Chinn-Ito data into three categories. If this latter explanation is correct, thensystematic capital controls that are left in place for long periods of time are truly effective at isolating
countries from the base rate regardless of exchange rate regime.
The middle four columns of Table 5 present results for the gates countries. The first two of thesecolumns presents the estimates for closed gates and open gates. The coefficients are 0.61 and 0.70,
respectively, and in both cases the total effect is statistically significantly different from zero at a highlevel of significance. This suggests that closed gates, in general, provide almost no monetary autonomy
and, in this way, are indistinguishable from open gates. The narrow gates and broad gates columns
present results for subsets of the closed gate category in which fewer than half of the asset categories are
restricted (narrow gates) or half or more of the categories are restricted (broad gates).

The narrowgates observations show a strong relationship between the base country and the local country interest
rates for pegged countries, implying that a limited imposition of capital controls does not afford
monetary autonomy for pegged countries. The broad gates countries show less relationship with thebase country though the coefficient is still large and statistically different from zero.
Tables 5A and 5B present estimates of the differences in the coefficients across gates-walls-opencategories based on a pooled regression with interaction terms (an expanded version of equation (5)).
The upper diagonal elements of these tables represent differences across coefficients for peg
observations and the lower diagonal represents differences across coefficients for non-peg observations.
The results in the upper right cell of Table 5A shows that there are statistically significant differences,when countries have pegged exchange rates, in the correlation between the changes in the domestic andbase interest rates between open and wall countries, but not open countries and gates countries,
regardless of whether the gates are open or closed. The estimates presented in Table 5B show thatnarrow gates are also statistically significantly different from walls countries, whereas broad gates –whose coefficient is in between open and walls countries – is not statistically different from either. Thissuggests that if a country imposes controls across a wide set of asset classes, it may be able to generatesome monetary autonomy, but the point estimates are somewhat noisy and it is difficult to say if broadgates are truly distinct from open countries or narrowly targeted gates.
Does this suggest countries can round the corners of the trilemma? In a sense, no. They cancertainly make tradeoffs and generate monetary autonomy by closing the financial account, but that19

autonomy only seems to come with a wide use of measures. As noted above, using administrative
measures means we cannot test for the intensity with which these controls are used. It may be that broadgates can be used without shutting down the capital markets, but the results suggests that limited
controls on a small set of assets will not be effective at regaining autonomy. Given the political
economy difficulties often associated with making changes to capital control regimes, the necessity tomake those changes across a wide set of assets to try to replicate monetary autonomy through thetightening and loosening of capital controls seems to be a difficult process. This is not to say that it
could not be done, but rather, the average experience of countries in our sample is that autonomy is notregained unless controls are widespread.

V. Autonomy to do what?
Throughout this paper, we have argued that a failure to follow the base country interest rate
represents realized or potential monetary autonomy. It is of course possible that spikes in risk or
expectations are leading to changes in the local interest rate that are different from the base, but acountry really still has no control over its interest rate. We have tried to remove high volatility
outcomes that may not represent actual policy autonomy by eliminating outlier values of the change in
the interest rate. We next consider how monetary autonomy is used.
Equation 6 shows a basic Taylor monetary policy rule where the policy interest rate is a function of aconstant, the output gap, and the gap between the current inflation rate and the preferred inflation rate.
(6) Rit = α + γ(Y-Y*) + σ(π– π*)Thus, assuming no changes in the optimal inflation rate or Y*, the change in the policy rate should be:(7) ΔRit = γ (ΔY) + σ(Δπ)
Given that Y* is likely to be growing as well, the actual reaction function likely includes (ΔY-ΔY*), but
as long as ΔY* is a constant, we can separate it out and assume that the change in the base interest rateshould be a function of the growth rate in the economy and the change in the inflation rate. Due to bothlags in the central bank’s information set and the fact that the policy rate will influence growth andinflation, we focus on the lagged GDP growth rate and lagged change in the inflation rate. The
predictions, then, are fairly simple and intuitive. A country that has fast GDP growth rate or rising
inflation in the previous year should have a higher interest rate this year than last year (that is, theyshould be increasing their policy rate), so that the change in the interest rate from last year to this year
20

would be positive. One can take a fairly agnostic view about what significant coefficients on thesevariables mean. A reaction to lagged growth may not be intended to stabilize output or employment, but
may simply be a result of the fact that lagged growth is used to predict future inflation and the central
bank targets future inflation. We are not concerned with this issue, however, but our focus is on whether
interest rates respond to local conditions. The simplest form of the trilemma suggests that this would not
be the case for a country with an open capital market and a pegged exchange rate.
We test whether countries use autonomy in a way consistent with this rule by including lagged
GDP growth and the lagged change in the inflation rate along with the change in the base country
interest rate for subsamples that distinguish among peg, soft peg and float, and between open, middle
level closed and closed capital accounts, using the Chinn Ito data set.19 If a country has no autonomy
and simply follows the base, the local conditions should have zero coefficients. If the country has
autonomy and can respond to its own shocks, the base interest rate should have a zero coefficient andthe local conditions should explain the interest rate change.20 It should be noted that the familiar
problem of identifying the effect of monetary policy holds here in some regard. In the classic story, if acentral bank is able to move rates in a way that perfectly stabilizes the economy, it will appear that it has
had no effects because the interest rate is changing but there is no response in the economy. In this case,if the central bank is able to keep inflation perfectly at its optimum and keep output at full employment,
it will be changing rates to respond to shocks, but the inflation and GDP growth variables will be
constants, so it will appear that the central bank is changing rates, but not in response to these variables.
With more detailed data focused on a smaller set of countries, Clarida et al (1998) test a forward looking
rule that examines if six advanced country central banks are responding to changes in forecasts ofinflation.21
One concern is that advanced and emerging market and developing countries may have very
different policy rules that respond to shocks in quite different manners. For this reason, we split the
sample across advanced and non-advanced countries. Also, there is a concern that the 1970s in

19

Clarida et al (1998) produce a test similar in spirit to examine the extent to which the U.S., Germany, and Japan follow an
inflation targeting like rule and to what extent the UK, Italy, and France follow that rule or are influenced by Germany’s
monetary policy. They find the EMS countries operate differently and that the constraint of Germany’s interest rate has
meaningful effects. They use a forward looking rule and allow for individual country policy rules.
20
The sample changes for two reasons from the earlier results. First, some countries lack inflation or real GDP growth data
(in the WDI database). Second, once again, large outliers have been excluded – we trim 1% outliers on either side for bothchanges in inflation and GDP growth. Since the large outliers in interest rate changes are already excluded, many of the mostvolatile economic outcomes are already excluded from the sample.
21
Future work on a country by country basis will explore these issues in more detail.

21

particular, but also to some extent the 1980s may have had different policy rules prior to the “Great
Moderation”. We thus limit our sample to 1990- 2011 (many of the basic results appear similar in thefull sample) and do not report estimates for subsamples with fewer than 20 observations. The tables
report, for each subsample with a sufficient number of observations, the coefficients on the base interest
rate changes, lagged GDP growth and lagged changes in inflation as well as an F-test for the joint
significance of the domestic variables.
Table 6A shows the results for the advanced country subsamples. Pegged countries do notrespond to either lagged changes in inflation or lagged growth (and the coefficient on lagged growth isthe opposite of what is expected, implying an increase in interest rates during a recession). For soft
pegged advanced countries, in both open and mid-open observations, there is a positive and statisticallysignificant (at the 90% level) response to lagged output, implying some use of monetary policy tostabilize the domestic economy. For open floats, those coefficients are larger and statistically significant
from zero at the 99% level. The open floats still show a positive correlation with the base country, but
are able to respond to their own economy as well. When year fixed effects are included (not shown in
the table), the coefficient on own GDP growth remains statistically significant, but that on the base rate
moves towards zero and is not statistically different from zero.
Table 6B reports a similar set of tests for the non-advanced sample. Open pegs respond to thebase rate strongly, but also respond to their own economy (though this coefficient is not statisticallysignificant when year fixed effects are included). Neither open soft pegs nor open floats show
statistically significant coefficients on domestic variables. The coefficients on own GDP are positive andthe sample is relatively small with a fair bit of volatility. So, we cannot rule out that they are using
countercyclical monetary policy, but we cannot say definitively that they are. For mid-open countries,
the pegged countries have a coefficient around 0.5 on the base rate and statistically insignificant
coefficients on the other variables. The soft pegs and floats, though, show a very strong reaction to local
growth. The middle open soft pegged non-advanced sample is an interesting one in that there is asignificant response to the base interest rate (with a smaller coefficient than in the peg category) andboth the domestic inflation and growth variables. This is some sense is the ultimate middle ground of
the policy trilemma and may represent policy rules that include inflation and growth as well as theexchange rate. For closed countries, only floating countries have a connection to the local growth rate,
while both soft pegs and floats have a statistically significant coefficient on lagged inflation.

22

In general, the results in table 6 seem supportive of the notion that low βs can be interpreted asproviding room for monetary autonomy. Despite using only lagged annual data to test for a reaction to
the domestic economy and despite enforcing the coefficients be the same across country and time, we
frequently find soft pegs and floats showing more reaction to the local economy than pegs while pegs
have a higher coefficient on the base country interest rate.
VI: Additional considerations:
An additional way that countries could try to pursue autonomy is to use reserves to stabilize theexchange rate but use the interest rate to stabilize the domestic economy (see Aizenmann (2011) for
further discussion). Such a policy effectively circumvents the trilemma. If sterilized intervention isineffective without some use of capital controls, such a policy would not be helpful. If though, some
limited capital controls could interact with reserves interventions in a way to make them more effective,
one might see reserves as an additional tool to round the edges of the trilemma. In limited work thus far,
we have not found significant impacts of reserves on the extent of monetary autonomy, though it
remains an area of further research.

VI: Conclusion
Concerns with international capital flows have once again raised the topic of whether capital
controls offer an attractive policy option. An important aspect of that debate is whether targeted,temporary capital controls offer some degree of monetary autonomy. Likewise, in the wake ofdifficulties associated with fixed exchange rates, questions are being raised, yet again, about thedesirability of limited exchange rate flexibility. The results presented suggest one should consider these
middle-ground steps with some caution. We present some evidence that soft pegs allow for greater
scope over monetary policy than pegs in particular in emerging and developing countries. But there isless evidence that narrowly targeted, temporary capital controls, like those advocated in the theoretical
literature (typically for reasons of financial stability) enable monetary authorities to gain autonomy
when exchange rates are pegged. Episodic controls are only effective if they are targeted to a wide setof assets, and even here, their effects are uncertain. That is, gates only work if they are much like walls,
but, of course, broad capital controls may introduce costly distortions. The main message or our paper isthe re-affirmation of the standard result from international macroeconomics; the simplest and mostcertain means for achieving some measure of monetary autonomy is to allow the exchange rate to float.
23

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Miniane, Jacques and John H. Rogers, (2007), “Capital Controls and the International Transmissionof U.S. Money Shocks,” Journal of Money, Credit and Banking, Vol. 39, No. 5 (August 2007)
Obstfeld, Maurice, Jay C. Shambaugh, and Alan M. Taylor, (2004) “Monetary Sovereignty, Exchange
Rates, and Capital Controls: The Trilemma in the Interwar period," IMF Staff Papers, Special Issue, 51,
75-108.
Obstfeld, Maurice, Jay C. Shambaugh, and Alan M. Taylor, (2005) “The Trilemma in History: Tradeoffs
among Exchange Rates, Monetary Policies, and Capital Mobility," Review of Economics and Statistics,
87 (3), 423-38.
Obstfeld, Maurice, Jay C. Shambaugh, and Alan M. Taylor, (2010) Financial Stability, the Trilemma,
and International Reserves, American Economic Association Journal – Macroeconomics vol. 2, no. 2,
April 2010, pp. 57-94.
Popper, Helen, Alex Mandilaras, and Graham Bird, 2012, “A New Measure of the Stability of
Exchange Rate Arrangements in the Context of the Trilemma”
Reinhart, Carmen M., and Kenneth S. Rogoff. 2004. The Modern History of Exchange Rate
Arrangements: A Reinterpretation. Quarterly Journal of Economics, 119:1, 1-48.
Schindler, Martin. 2009. “Measuring Financial Integration: A New Data Set.” IMF Staff Papers 56, no1: 222–38.
Schmitt-Grohe, Stephanie and Martin Uribe, (2012), “Prudential Policy for Peggers,” NBER Working
Paper no. 18031.
Shambaugh, Jay, C., (2004) “The Effect of Fixed Exchange Rates on Monetary Policy,” Quarterly
Journal of Economics, vol. 119 no.1, p. 301-352.
25

Figure 1: The Open Economy Policy Trilemma

Capital Mobility

A: Float &Monetary
Autonomy

Monetary
Autonomy

B: Peg &CapitalMobilityPeg
C: Monetary
Autonomy &Peg26

Figure 2: Chinn-Ito Capital Account Indicators
4

Values and Ranges for 3 Dummy Variables

Mid-Range

Closed01

Density
2

3

Open

-1.5

-.79

0
.46
Chinn Ito Indicator

1.5

27

-10

-10

-5

-5

e( downr | X )0e( downr | X )
0

5

5

10

10

Figure 3: scatters of change in own interest rate and change in base interest rate across samples

-4

-2

0
e( dbaser | X )

2

-10

4

-5

0

5

0

5

e( dbaser | X )coef = .40166567, (robust) se = .05831013, t = 6.89

coef = .68027474, (robust) se = .07611179, t = 8.94

closed pegs

-10

-10

-5

-5

e( downr | X )0e( downr | X )
0

5

5

10

10

Open pegs-50e( dbaser | X )coef = .23359537, (robust) se = .10195539, t = 2.29

Open non pegs

5

-10

-5
e( dbaser | X )coef = .09259407, (robust) se = .05187828, t = 1.78

closed nonpegs

28

Chinn-Ito Data Set
1973 – 2011
Gates Walls Data Set
1995 – 2011

Table 1A: Exchange Rate Categorization
Peg
Soft Peg
Float
1,404
803
919
45%
26%
29%
484
399
393
38%
31%
31%

Total
3,126
1,276

Table 1B: Capital Control Indicators
Chinn-Ito Indicators (country-year)
Open
Mid-Range
Closed
1,014
961
1,151
32%
31%
37%
Gates-Walls Indicator (country)
Open
Gates
Walls
376
610
290
29%
48%
23%

Table 1C: Comparison of Two Capital Control Indicators (1995 – 2011)
Chinn-Ito Indicators
Open
Mid-Range
ClosedGates-Walls Open355
19
0Indicators
Gates Country Obs.
309
202
95
● Of which● Open Gates
113
190● Narrow Gates
151
49
15
● Wide Gates
45
144
80
Walls
1
81
208
665
302
303

374
606
215
269
290

29

Table 2: 2 x 2 Classification of Exchange Rate and Capital Control Regimes (OLS)
Open vs Non-Open
Peg
Non-Peg
Open0.68** 435(0.08) [0.28]
0.40** 969(0.06) [0.14]
0.33**

Non-Open
Peg vs. Non-Peg

0.23*(0.10)
0.09+(0.05)-0.27**

579[0.02]
1,143[0.00](0.07)(0.06)KEY Coef. N(s.e.) [R2]
Chinn-Ito trivariate classification for Peg, Closed. 1973-2011
2x2 entries based on subsample regressions, entries in marginal column and row based on
an interaction regression** p<0.01, * p<0.05, + p<0.10
Table 3: 3 x 3 Classification of Exchange Rate and Capital Control Regimes (OLS)vs. Mid-Open vs. Closed
Peg
Soft Peg
Float
Open

Mid-Open
Closedvs. Soft Peg

vs. Float

0.68** 435(0.08) [0.28]

0.31* 300
(0.13) [0.05]

0.17 279(0.14) [0.01]

0.54** 439(0.06) [0.22]
0.25** 530(0.07) [0.07]

0.38** 273(0.08) [0.05]
0.18+ 230(0.10) [0.01]

0.07 249(0.08) [0.00]-0.06 391
(0.11) [0.00]

0.19**

0.22** 3,126(0.07)(0.08) [0.07]

0.41**-0.06-0.29**(0.08)(0.09)

0.22**(0.06)

KEY(0.07)

Coef. N(s.e.) [R2]

Capital control / exchange rate regime subsamples. 1973-2011
Entries in marginal column and row based on an interaction regression.
** p<0.01, * p<0.05, + p<0.1030

Table 4: Peg Subsamples by Duration of Spell and Years in Spell
All Pegs
Long
Short
Very
Later
Early
Pegs
Pegs
Short
Years of Years of
Pegs
Long
Pegs
Pegs
0.459**
0.459**
0.476**
0.451**
0.473**
0.465**∆Ri, t(s.e.)(0.049)(0.052)(0.118)(0.138)(0.054)(0.085)Constant -0.153** -0.092** -0.474*-0.276-0.013-0.515**(s.e.)(0.040)(0.030)(0.190)(0.215)(0.026)(0.127)
R2
0.168
0.216
0.081
0.072
0.252
0.099
N
1,404
1,181
223
127
1,000
381
** p<0.01, * p<0.05, + p<0.10
Long Pegs – peg spells with duration greater than or equal to 5 years
Short Pegs – peg spells with duration less than or equal to 4 years
Very Short Pegs – peg spells with duration less than or equal to 2 years
Later Years of Long Pegs – Years 5 and following for Long Peg Spells
Early Years of Pegs – First 4 years of Peg Spells
Very Early Years of Pegs – First 2 years of Peg Spells

Table 5: Gates, Walls, Open: Subsample Regressions
Open
Open
Any Closed Narrow
Gates
Gates
Gates
∆Ri,t (peg)
0.710**
0.608**
0.704**
0.862**(s.e.)(0.083)(0.204)(0.145)(0.117)∆Ri, t (non0.107
0.133
0.026
0.020
peg)
(0.190)(0.272)(0.119)(0.170)(s.e)
Dif. peg and 0.603**
0.475
0.678**
0.842**
non-peg(0.208)(0.340)(0.185)(0.236)
2
R
0.070
0.046
0.030
0.066
N
376
123
487
215
** p<0.01, * p<0.05, + p<0.10

Very
Early
Years ofPegs
0.509**(0.102)-0.712**(0.180)
0.101
245

Broad
Gates
0.534*(0.236)0.010(0.157)

Walls

0.523+(0.267)
0.014
272

0.141
(0.232)
0.016
290

0.286
(0.194)0.145(0.193)31

Table 5A: Gates, Walls, Open: Comparison of Coefficients Across Capital Control
Categories from an Interaction Regression
Open
Open Gates
Any Closed
Walls
Gates
Open
-0.112-0.006-0.400*(0.210)(0.162)(0.199)Open Gates
0.030
0.106
-0.288(0.321)(0.251)(0.265)
Any Closed Gates -0.082-0.113-0.394+(0.215)(0.287)(0.230)Walls
0.017-0.0130.100(0.268)(0.330)(0.229)** p<0.01, * p<0.05, + p<0.10
Results in upper diagonal are associated with peg country sample while results in lower
diagonal are associated with non-peg country sample.
Table 5B: Narrow Gates, Broad Gates, Walls, Open: Comparison of Coefficients AcrossCapital Control Categories from an Interaction Regression
Open
Open Gates
Narrow Gates Broad
Walls
Gates
Open
-0.1120.165-0.185-0.400*(0.209)(0.139)(0.245)(0.199)Open Gates
0.030
0.277
-0.073-0.288(0.321)(0.234)(0.310)(0.265)
Narrow Gates-0.103-0.132-0.350-0.565*(0.245)(0.309)(0.254)(0.214)
Broad Gates-0.088-0.1180.014-0.215(0.239)(0.306)(0.223)(0.294)Walls
0.017-0.0130.119
0.105(0.268)(0.330)(0.258)(0.251)** p<0.01, * p<0.05, + p<0.10
Results in upper diagonal are associated with peg country sample while results in lower
diagonal are associated with non-peg country sample.

32

Table 6A: Autonomy for What? Advanced Country Subsamples
Open Peg
Open Soft Peg∆Ri,t1.046**
0.715**(0.051)(0.114)
∆πt-1
0.100
0.140
(0.095)(0.086)∆lnYt-1-5.11910.256+(3.074)(5.215)F-stat
2.74+
4.51*
N
155
97
R2
0.643
0.451
Mid PegLess than 20
observations

Mid Soft Peg
1.016*(0.413)-0.025(0.163)25.645+(13.140)
2.02
31
0.471

Mid Float
0.663*(0.242)0.344(0.257)-12.994(48.624)
1.01
26
0.257

Closed Peg

Closed Soft Peg

Closed Float

Less than 20
observations

Less than 20
observations

Less than 20
observations

∆Ri,t
∆πt-1
∆lnYt-1

Open Float
0.487**(0.143)0.062(0.141)15.603**(4.165)9.86**
96
0.267

F-stat
N
R2
∆Ri,t
∆πt-1
∆lnYt-1

F-statN
R2** p<0.01, * p<0.05, + p<0.10

33

Table 6B: Autonomy for What? Non-Advanced Country Subsamples
Open Peg
Open Soft Peg
Open Float∆Ri,t0.566**-0.099-0.515+(0.097)(0.182)(0.277)
∆πt-1
0.008
0.060
-0.106(0.033)(0.067)(0.086)∆lnYt-1
5.523*
6.123
13.086(2.183)(5.030)(8.436)
F-stat
3.57*
1.26
1.74
N
202
138
89
R2
0.260
0.025
0.061
∆Ri,t
∆πt-1
∆lnYt-1
F-statN
R2

Mid Peg
0.450**(0.110)0.021(0.029)4.099(5.126)
0.59
167
0.106

Closed Peg∆Ri,t0.215*(0.094)∆πt-1-0.003(0.012)∆lnYt-1-2.047(2.191)
F-stat
0.44
N
356
R2
0.048
** p<0.01, * p<0.05, + p<0.10

Mid Soft Peg
0.329+(0.187)0.122+(0.065)27.364*(10.748)
9.38**
145
0.125

Mid Float
0.124(0.173)-0.091+(0.051)26.828**(8.227)
7.81**
146
0.128

Closed Soft Peg
0.379(0.248)0.058+(0.034)3.658(6.293)
1.48
123
0.036

Closed Float-0.292(0.205)0.126*(0.047)
11.459+(6.624)
3.68*
204
0.077

34

APPENDIX TABLES
Appendix Table 2a:
2x2 Classification of Exchange Rate and Capital Control Regimes: Year Fixed Effects
Open vs Non-Open
Peg
Non-PegOpen

Non-Open
Peg vs. Non-Peg

0.64** 435(0.10) [0.45]

0.02 579(0.10) [0.16]

0.36** 969(0.07) [0.26]
0.29**-0.06 1,143(0.08) [0.09]-0.27**(0.07)(0.06)KEY Coef. N(s.e.) [R2]
Chinn-Ito trivariate classification for Peg, Closed. 1973-2011
Marginal variables refer to an interaction regression** p<0.01, * p<0.05, + p<0.10

Fixed Effects

Appendix Table 3a: 3 x 3 Classification of Exchange Rate and Capital Control Regimes
Year Fixed Effects
Peg
Soft Peg
Float
vs. Midvs. Closed
Open
-0.11
-0.31**
Open
0.64** 435
0.07 300-0.15 279
(0.11) [0.40] (0.15) [0.22](0.17) [0.24](0.08)(0.09)
Mid-Open
Closedvs. Soft Peg

vs. Float

0.41** 439(0.08) [0.42]
0.38** 530(0.07) [0.19]

0.18 273(0.14) [0.34]-0.14 230
(0.16) [0.16]-0.31 249
(0.23) [0.22]-0.08 391
(0.13) [0.14]

0.16*

0.21*

(0.07)(0.08)

0.37**(0.07)

Capital control / exchange rate regime subsamples. 1973-2011
Marginal variables refer to an interaction regression** p<0.01, * p<0.05, + p<0.10

0.20**(0.07)

KEY

Coef. N(s.e.) [R2]

Fixed Effects3536