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Volume 55 Number 3 IMFstaffpapers 2008

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& 2008 by the International Monetary Fund ISBN 978-1-58906-724-0 International Standard Serial Number: ISSN 1020-7635

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ContentsVolume 55 Number 3 2008

Special Issue: IMF Eighth Jacques Polak Annual Research Conference

Mundell-Fleming Lecture: Exchange Rate Systems, Surveillance, and Advice Stanl ey FischerK 367

Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques for Infer ring Flexibil ity and Bas ket Weights Jeffrey Frankel and Shan g-JinK 384Wei

Exchange Rate Policy Attitudes: Direct Evidence from Survey Data J. Lawre nce Broz, Je ffr y Frieden, and Step K hen417 Weymo uth

Fear of Declaring: Do Markets Care What Countries Say About Their Exchange Rate Policies? Adolfo Barajas, Lennart Eri ckson, and RobertoK 445 St einer

A Micro-Empirical Foundation for the Political Economy of Exchange Rate Populism Irineu De Car valho Filho and Marcos K 481Chamo n

Global Rebalancing with Gravity: Measuring the Burden of Adjustment Robert Dekl e, Jonatha n Eaton, and SamuK 511el Kortum

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Mundell-Fleming Lecture: Exchange Rate Systems, Surveillance, and Advice

STANLEY FISCHERÃ

This paper draws together some lessons and questions about exchange rate systems and attempts to state what is known and what is not known about them. It begins by revisiting the bipolar issue with regard to exchange rates, restating the hypothesis and updating it in light of events of this decade, arguing that the bipolar view is fundamentally correct for emerging market and industrialized countries with open capital accounts. It also examines the choice of exchange rate regime for countries with capital accounts that are not open, and managed floating regimes and exchange market intervention for countries with open capital accounts. Concluding remarks provide comments and advice for IMF surveillance. [JEL F30, F31, F33] IMF Staff Papers (2008) 55, 367–383. doi:10.1057/imfsp.2008.12; published online 1 July 2008

he IMF Executive Board’s adoption on June 15, 2007, of the new T Decision on Bilateral Surveillance over Members’ Policies puts exchange rate policies at the center of the surveillance process. The IMF’s description of the new decision and the differences between it and the 1977 decision

ÃStanley Fischer is the governor of the Bank of Israel. This paper was presented as the Mundell-Fleming Lecture at the Eighth Annual Jacques Polak Research Conference at the IMF on November 14, 2007. The author is grateful to Thierry Tressel, Cigdem Akin, Meital Graham, Prachi Mishra, Inci O¨tker-Robe, and Jeromin Zettelmeyer for assistance, and to Mark Allen, Jeff Frankel, Morris Goldstein, Ken Rogoff, and Shakour Shaalan for helpful discussions.

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©International Monetary Fund. Not for Redistribution Stanley Fischer notes the following:

 ‘‘The new Decision introduces a concept of external stability as an organizing principle for bilateral surveillance,’’ where external stability ‘‘refers to a balance of payments position that does not, and is not likely to, give rise to disruptive exchange rate movements.’’  Article IV’s prohibition of exchange rate manipulation relates to policies directed at affecting the level of the exchange rate ‘‘in order to prevent effective balance of payments adjustment, or to gain an unfair competi- tive advantage over other members.’’ A member will be considered to be manipulating the exchange rate to gain an unfair competitive advantage if the IMF determines that the country is trying to increase net exports by promoting an undervalued exchange rate.  Members should ‘‘avoid exchange rate policies that result in external instability, regardless of their purpose.’’ A little over a month earlier, the board had discussed the Independent Evaluation Office’s Report ‘‘IMF Exchange Rate Advice’’ (IMF, IEO, 2007), which presented a critical view of the advice the IMF had offered in the period 1999–2005 on exchange rate systems, on the level of the exchange rate, and on the mechanics of exchange markets and of intervention. Although the criticisms are forceful and in many cases appear appropriate, they suffer from the lack of professional consensus on what the right advice should be. The IEO Evaluation Report struggles with this issue, but does not adequately resolve it, for example when noting (p. 28): ‘‘Of course, when there is little academic consensus on many points, the problem of distilling and establishing operational guidance is more challenging, but management oversight and the right internal structure are therefore all the more critical.’’ Similarly, in Chapter 5 on ‘‘Findings and Recommendations,’’ the report states (p. 37): ‘‘To improve assessments of the exchange rate level, the IMF should be at the forefront of developing the needed analytic framework. The genuine difficulty in doing this is no excuse for not making more progress.’’ (emphasis in original) In this lecture, I will try to draw together some lessons and questions about exchange rate systems, and attempt to state what is known and what is not known about them. I will also comment on problems of IMF surveillance. I start by revisiting the bipolar issue with regard to exchange rates, restating the hypothesis and updating it in light of events of this decade. I will argue that the bipolar view is fundamentally correct for emerging market and industrialized countries with open capital accounts—a qualification that was stated in Fischer (2004), but that was perhaps not adequately stressed. I then discuss managed floating regimes and exchange market intervention, for countries with open capital accounts. The following section is devoted to the

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©International Monetary Fund. Not for Redistribution EXCHANGE RATE SYSTEMS, SURVEILLANCE, AND ADVICE choice of exchange rate regime for countries whose capital account is not open, and I conclude with comments on IMF surveillance and advice.

I. The Bipolar Hypothesis The bipolar hypothesis about exchange rates has come in for serious criticism. For example, Frankel (2004) argues that there is no analytic rationale for the argument of the disappearing intermediate regime. The usual justification is the impossible trinity, but Frankel suggests that a variety of managed floats are fully consistent with the impossible trinity—that one can have half stability and half independence. In my 2001 article on the bipolar exchange rate approach, I tried to clarify the hypothesis, and I shall have to quote extensively (Fischer, 2004, p. 229):

‘‘Proponents of what is now known as the bipolar view—myself included—probably have exaggerated their point for dramatic effect. The right statement is that for countries open to international capital flows: (i) soft exchange rate pegs are not sustainable; but (ii) a wide variety of flexible rate regimes remain possible; and (iii) it is to be expected that policy in most countries will not be indifferent to exchange rate movements. To put the point graphically, if exchange rate arrangements lie along a line connecting hard pegs like currency unions, currency boards, and dollarization on the left, with free floating on the right, the intent of the bipolar view is not to rule out everything but the two corners, but rather to pronounce as unsustainable a segment of that line representing a variety of soft pegging exchange rate arrangementsy (emphasis in original)

‘‘For countries open to international capital flows, it [the bipolar view] includes as sustainable regimes both very hard pegs and a variety of floating rate arrangements, including managed floats. For countries not yet open to international capital flows, it includes the full gamut of exchange rate arrangementsy

‘‘The question that then arises is what exchange rate arrangements are excluded by the bipolar view. The answer is: for countries open to international capital flows, exchange rate systems in which the government is viewed as being committed to defending a particular value of the exchange rate, or a narrow range of exchange rates, but has not made the institutional commitments that both constrain and enable monetary policy to be devoted to the sole goal of defending the parity.’’ During the 1990s, it seemed that exchange rate systems were becoming bipolar in the sense defined above, as can be seen in the comparison for developed and emerging market countries1 between the data for 1991 and 1999

1I use the ‘‘developed and emerging market countries’’ category as an approximation for countries whose capital accounts are open, though in fact that is not true for all the countries listed as emerging (see Table 2), among them China.

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Figure 1. Advanced and Emerging Market Countries: Exchange Rate Regimes 1991, 1999, and 2006

1991 1999 2006 70 59% (38) 60 44% 47% 50 (28) (30) 33% 40 30% 28% 28% (21) (18) (19) (18) 23% 30 (15) 20 8% 10 (5) Percent of total countries Percent 0 Hard Peg Intermediate Float

Source: International Monetary Fund. Note: The number of countries is in parentheses. in Figure 1.2 Figure 1 also shows what has happened since then for the group of developed and emerging market countries. In brief, the shift toward bipolarity that was evident in the data for 1991–99 has continued, but at a reduced pace. The major changes in the distribution between 1991 and 1999 were due to the introduction of the euro and the emerging market financial crises of that decade. Nothing on a similar scale has happened in this decade, although the number of countries in the European Monetary Union (EMU) continues to grow, albeit slowly. As a reminder, the classification of exchange rate systems used in Figure 1, and in my previous paper, is that of the staff of the IMF, based on their evaluation of the de facto exchange rate arrangement in place at the time.3 Table 1 lists the categories of exchange rate regimes, with the

2The data in this paper differ somewhat from those in Fischer (2004), because in the former the exchange rate regimes as of each year relate to countries that were members of the IMF in that particular year, whereas in this paper the data for each year are those for countries that were IMF members in 2006. 3The approach is described in Bubula and O¨tker-Robe (2002). See also IMF (2003). As of the time of writing of this paper, the staff of the IMF is preparing a new classification of exchange rate regimes that will make more use of quantitative information on exchange rate behaviors and reserves, instead of qualitative information as in the currentde facto exchange rate classification. This new classification will change how currency unions are treated; for example, EMU countries will be classified as ‘‘independently floating.’’ Moreover, a new category of ‘‘tightly managed float’’ is being introduced. This new de facto classification, however, will not be retroactive, so there will be a break in the series in 2007. It is clear that for a member of the EMU, the exchange rate of the currency it uses is independently floating. But it is also clear that for almost all or all EMU members, the exchange rate of the currency they use is absolutely fixed against the currencies of the countries that account for the great bulk of their trade. This suggests a note of caution about the reclassification of these countries’ currency regimes.

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Table 1. De Facto Exchange Rate Classification

Category Consists of

Hard peg No separate legal tender Currency board Intermediate Other pegs Pegged rate in horizontal band Crawling peg Rates within crawling bands Float Managed float Independent float Source: IMF, Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) database.

Figure 2. (a) Advanced Countries: Exchange Rate Regimes, 1991, 1999, and 2006. (b) Emerging Market Countries: Exchange Rate Regimes, 1991, 1999, and 2006

64% 1991 70 (16) 1999 60 52% 52% 2006 (13) (13) 44% 50 40% (11) (10) 40 28% 30 (7)

20 8 % 8%

Percent of total countries Percent 4% 10 (2) (2) (1) 0 Hard peg Intermediate Float

56% 1991 60 (22) 49% 1999 46% 2006 50 (19) 41% (18) (16) 36% 36% 40 (14) (14)

30 15% 13% 20 (6) 8% (5)

Percent of total countries Percent 10 (3)

0 Hard Peg Intermediate Float

Source: International Monetary Fund. Note: The number of countries is in parentheses. intermediate grouping consisting of varieties of less than very hard pegs, include crawling pegs and bands. The floating category consists of ‘‘managed float’’ and ‘‘independent float,’’ the latter less managed than the former.

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Table 2. (a) Advanced Countries and (b) Emerging Market Grouped by Exchange Rate Arrangement (As of December 31, 2006)

Exchange Rate Regime (Number of countries) Countries

(a) No separate legal tender/ Austria, Belgium, Finland, France, Germany, Hong Kong currency board (13) SAR, Ireland, Italy, Luxembourg, Netherlands, Portugal, San Marino, Spain Other fixed pegs Pegged rate in horizontal band (1) Denmark Crawling peg Rates within crawling bands Managed float (1) Singapore Independent float (10) (*2) Australia, Canada, Iceland*, Japan, New Zealand, Norway*, Sweden, Switzerland, United Kingdom, United States

(b) No separate legal tender/ currency board (6) (*2) Bulgaria, Ecuador*, Estonia, Greece*, Lithuania, Panama Other fixed pegs (10) (*5) Argentina*, Egypt*, Jordan, Latvia, Morocco, Nigeria*, Pakistan, Qatar, Slovenia*, Venezuela* Pegged rate in horizontal band (3) (*2) Cyprus, Hungary*, Slovak Republic* Crawling peg (*1) China* Rates within crawling bands Managed float (10) (*6) Colombia*, Czech Republic, India, Malaysia*, Peru*, Philippines, Romania*, Russia, Sri Lanka*, Thailand* Independent float (9) (*3) Brazil, Chile, Indonesia, Israel*, Korea, Mexico, Poland*, South Africa, Turkey* Source: IMF Annual Report on Exchange Rate Arrangements and Exchange Restrictions database. Note: *indicates country whose exchange rate regime has changed since 1999.

Figure 2 breaks the ‘‘advanced and emerging market countries’’ grouping down into its two parts—for advanced (Figure 2a) and emerging market (Figure 2b) countries, respectively. Among the 25 advanced countries, bipolarity is almost complete, with only Denmark, which shadows EMU monetary policy, in the intermediate category. Among the 39 emerging market countries, a considerable number still remain in the intermediate category. Table 2a and b list the advanced and emerging market countries and their exchange rate regimes. Note that the intermediate category for the emerging market countries includes five countries that are in the process of joining the EMU, so that the shift to bipolarity for this country grouping is likely to continue in the coming years. It thus appears that the bipolarity view is broadly consistent with recent exchange rate regime developments, for the advanced and emerging market

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©International Monetary Fund. Not for Redistribution EXCHANGE RATE SYSTEMS, SURVEILLANCE, AND ADVICE countries grouping. I should, though, emphasize two qualifications. First, the bipolarity hypothesis is about countries open to international capital flows, which is why the data examined in Figures 1 and 2 are for the advanced and emerging market group—though, to be sure, some of the countries listed as ‘‘emerging market’’ have significant capital controls. Second, the ‘‘floating’’ group does not consist only of freely or almost freely floating exchange rate regimes, for it includes managed floats: indeed, for both the emerging market and ‘‘other’’ categories, there are more ‘‘managed’’ than ‘‘independent’’ floaters among the countries listed as having floating exchange rates. Further, even among the ‘‘independent’’ emerging market floaters, there are several countries that intervene frequently and significantly. The well-known difficulty of classifying exchange rate systems has led to a considerable literature, including articles by Levy Yeyati and Sturzenegger (2001), Reinhart and Rogoff (2004), Frankel (2004),4 Eichengreen and Razo- Garcia (2006), and Tavlas, Dellas, and Stockman (2006). The fundamental issue in the classification discussion relates to how to classify countries whose policies appear to be different than they are declared to be by the country (or by someone else’s classification scheme). For instance, how should one classify countries that declare they have a pegged exchange rate but that in practice change the peg frequently? Or, as in the case of the ‘‘freely falling’’ category of Reinhart and Rogoff, how should one classify rates that are flexible only because extremely high inflation prevents their being anything other than flexible? And, most important from the viewpoint of this paper, how should one classify heavily managed exchange rate regimes that are in principle flexible, but where the authorities intervene frequently and extensively?5 While this discussion is important, I believe the essential question is whether the authorities are committed to defending a particular exchange rate or narrow range of exchange rates, for these are the unstable exchange rate regimes when the capital account is open. Further, while there are countries whose currencies float freely, as noted in the quote from my 2001 paper above, I do not regard intervention as being inconsistent with an exchange rate that is defined as flexible. The data presented thus far relate to the proposition that as countries become more advanced and open their capital accounts, they also tend to leave the middle ground of soft pegs, and move toward either a hard peg or a floating (including managed floating) regime.6 Another way of examining this proposition is to focus on the empirical relationship between countries’ choice of exchange rate regime and the extent of their capital controls. The

4Frankel (2004, p. 96) notes: ‘‘Placing actual countries intoycategories is far more difficult than one who has never tried it would guess.’’ 5From the viewpoint of this paper, I should add that the grouping of countries into ‘‘advanced,’’ ‘‘emerging market,’’ and ‘‘other’’ categories is also not entirely obvious ex ante. 6While this is a statement about facts, I also believe—for reasons that will be explained below—that countries should move away from the soft center as they remove capital controls.

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IMF has an index of capital controls for which the latest year is 2005.7 It is available for only 90 of the 186 countries for which exchange rate regime data are available. A probit regression was run of the exchange rate regime (coded 1 if an intermediate regime, 0 otherwise) on the extent of capital controls, an index that runs from zero to 1 (for example, Japan and the United Kingdom are 0, the United States is 0.2, and India is 0.95). The estimated relationship is positive but weak,8 and significant only at the 15 percent level. On further examination of the data, it appears that there might be a bias with regard to the countries for which capital controls data are available: among the advanced countries, the capital controls index is available for 23 out of 25 countries; for the emerging market countries, the capital controls variable is available for 32 out of 39 countries; and for the ‘‘other’’ category (data on which are presented in Table 3), capital controls data are available for only 35 of 118 countries. It is quite likely that the extent of capital controls among the countries in the ‘‘other’’ group, for which the capital controls index is not available, is significantly higher than in the advanced and emerging market groupings. The frequency of intermediate regimes is also higher in the ‘‘other’’ group (47.5 vs. 4 percent for advanced countries and 35.9 percent for emerging market countries). I would like here to make two comments on the impossible trinity, in relation to Frankel’s (2004) statement that one can have half stability and half independence of monetary policy. In the first instance, although the impossible trinity is usually stated in terms of an independent monetary policy, it should more accurately be stated in terms of independent macroeconomic policy,for when a currency comes under serious pressure, typically both monetary and fiscal policy have to adjust if the exchange rate is to be maintained. This was, for instance, clear in the collapse of the Argentine currency peg. Second, an ‘‘independent’’ monetary policy in this context is one that is targeted at something other than the exchange rate. For many countries that have given up exchange rate pegging, the monetary regime switches to inflation targeting; in practice, typically flexible inflation targeting. For others, monetary policy is directed to a range of targets, including inflation and growth, sometimes also the real exchange rate, with trade-offs among them to be determined by the policymakers. Once the goals of monetary policy have been specified, monetary policy is no longer independent of the factors that move the economic variables that it is targeting. For instance, in setting the policy interest rate, the Bank of Israel, an inflation targeter, has to take into account changes in foreign interest rates that affect the exchange rate and through it the inflation rate.

7The capital controls data are from the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions database. 8The coefficient on the capital controls variable is 0.20, implying that a one-point increase in the capital controls variable—which runs from zero to 1—increases the probability that the exchange rate regime is intermediate only by 20 percent.

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Table 3. Other Countries Grouped by Exchange Rate Arrangement (As of December 31, 2006)

Exchange Rate Regime (Number of countries)

No separate legal tender/ Antigua and Barbuda, Benin, Bosnia, Brunei Darussalm, currency board (28) (*1) Burkina Faso, Cameroon, Central African Republic, Chad, Rep. of Congo, Cte dIvoire, Djibouti, Dominica, El Salvador*, Equatorial Guinea, Gabon, Grenada, Guinea-Bissau, Kiribati, Mali, Marshall Islands, Fed. States of Micronesia, Niger, Palau, Senegal, St Kitts and Nevis, St Lucia, St Vincent and the Grenadines, Togo Other fixed pegs (51) (*20) Islamic Rep. of Afghanistan*, Angola*, Aruba, The Bahamas, Bahrain, Barbados, Belarus*, Belize, Bhutan, Bolivia* Cape Verde, Comoros, Costa Rica*, Eritrea*, Ethiopia*, Fiji, Ghana*, Guyana*, Honduras*, Islamic Rep. of Iran, Kuwait Lebanon, Lesotho, Libya, FYR Macedonia, Maldives, Malta, Mauritania*, Mongolia*, Namibia, Nepal Netherlands Antilles, Oman, Rwanda*, Samoa, Saudi Arabia, Sierra Leone*, Solomon Islands*, Suriname, Swaziland, Syrian Arab Republic, Trinidad and Tobago, Tunisia*, Turkmenistan, Ukraine*, United Arab Emirates Uzbekistan*, Vanuatu, Vietnam*, Rep. of Yemen*, Zimbabwe Pegged rate in horizontal band (1) Tonga Crawling peg (4) (*3) Azerbaijan*, Botswana*, Iraq*, Nicaragua

Rates within crawling bands Managed float (33) (*13) Algeria, Armenia, Bangladesh*, Burundi, Cambodia, Croatia, Dominican Republic, The Gambia*, Georgia, Guatemala, Guinea, Haiti*, Jamaica, Kazakhstan, Kenya, Kyrgyz Republic, Lao PDR, Liberia*, Madagascar*, Malawi, Mauritius, Moldova*, Mozambique*, Myanmar, Papua New Guinea*, Paraguay, So Tom and Prncipe, Serbia*, Seychelles*, Sudan*, Tajikistan, Uruguay*, Zambia* Independent float (5) (*1) Albania, Dem Rep. of Congo*, Somalia, Tanzania, Uganda Source: IMF Annual Report on Exchange Rate Arrangements and Exchange Restrictions database. Note: *indicates country whose exchange rate regime has changed since 1999.

Thus, in practice, by giving up exchange rate pegging and shifting to inflation targeting, a central bank does not gain monetary independence in the sense that its monetary policy becomes independent of monetary policy— more generally of economic developments—in other countries. Rather it has switched from targeting one economic variable, the exchange rate, to another, namely the inflation rate, both of which depend to differing extents on economic developments abroad and at home. This appears to be the meaning of Frankel’s (2004) statement that a country can have half stability and half monetary policy independence of the exchange rate.

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Why then the general recommendation to countries to avoid the intermediate regimes if they are open to capital movements? Primarily because such regimes are crisis prone, in part because their policy dynamics are unstable: we are all familiar with the syndrome in which when the exchange rate is not under pressure, the country sees no need to change the regime; and when it is under pressure, the country is reluctant to change it because it is unclear by howmuchitwillhavetochangeifitisrepegged,orwhereitwillgoifitis allowed to find its own level. As they develop and open their capital accounts, some countries that have good reasons to do so—for instance those that meet the conditions for being part of an optimum currency area, or that expect to meet those conditions after joining the hard currency grouping—will choose to move toward harder pegs. Others will move toward more flexible exchange rates. As noted by Rogoff and others (2003), for advanced countries ‘‘free floats register faster growth than other regimes without incurring higher inflation.’’

II. Managed Floating As is evident from the fact that there are more managed than independent floaters among the floating group for the emerging market and ‘‘other’’ countries, very few countries, if any, are indifferent to the behavior of the exchange rate. For a strict inflation targeting country, the behavior of the exchange rate matters, but only to the extent that it affects inflation. For other countries, the nominal exchange rate may matter because changes in the nominal rate also lead to (partially or wholly temporary) changes in the real exchange rate and thus in net exports, which are targets of policy. Interest rate and/or fiscal policy can be used to try to affect the exchange rate; for instance, the Taylor rule for a central bank that is also targeting the exchange rate can include an additional term in which the interest rate responds to deviations of the real exchange rate from its target level. Monetary policy effects on the real exchange rate are likely to be temporary, but may be of sufficient duration to have a transitional effect on exports and imports. More fundamentally, changes in fiscal policy can be deployed to affect the real exchange rate over a longer period: by tightening fiscal policy and increasing national saving, the government can expect to generate a real depreciation and increase net exports. There is also the option of foreign exchange market intervention, which can be seen as introducing an extra policy instrument to deal with an extra target of policy.9 Some countries, such as the United States, Chile, Israel, and, more recently, Japan—as well as the European Central Bank—do not intervene in the exchange markets, or intervene very rarely. Even these central banks have not totally foresworn intervention; rather, they reserve the

9The early work on targets and instruments suggested the need for as many instruments as targets. In the presence of uncertainty, having an extra instrument could be useful even if there were not an extra target.

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©International Monetary Fund. Not for Redistribution EXCHANGE RATE SYSTEMS, SURVEILLANCE, AND ADVICE right to intervene if market conditions become disorderly, or in other extreme or emergency situations. If they intended never to intervene, then they would presumably not hold foreign exchange reserves.10 In the Israeli case, the central bank has not intervened since 1997, and Chile’s has intervened only twice since 1999. New Zealand’s had not intervened since 1985, but did so in 2007.11 A policy of nonintervention is, however, rare among emerging market countries, even those with flexible rates. Among countries listed as having flexible rates, Japan (with reserves of $544 billion), Russia ($263 billion), Korea ($136 billion), and India ($125 billion) have each accumulated more than $100 billion in reserves since the start of 2001—and these numbers pale by comparison with the accumulation of over a trillion dollars of reserves by China during that period. Countries may intervene to build up their reserves, as many did after the crises of the 1990s. But, clearly, the great bulk of the accumulation of reserves during the past few years is a result of the desire to prevent exchange rate appreciation. Ishii and others (2006) identify four circumstances under which countries intervene in the foreign exchange market:12 (1) to correct misalignments or to stabilize the exchange rate at a predetermined level—in other words, to try to set the exchange rate at a desired level, for example, one that will encourage exports; (2) to calm disorderly markets; (3) to accumulate reserves; and (4) to supply foreign exchange to the market—this occurs when the government is a major recipient of foreign exchange (for example, through royalty payments for mineral extraction).13 With regard to (2), the stabilization of disorderly markets: in extremis, the central bank may have to intervene to stabilize a disorderly market, but it needs to be aware that the more frequently and easily it intervenes, the more it will impede the development of a deep and robust market, in which it is possible to hedge against exchange rate changes without having to rely on

10A few years ago the Reserve Bank of New Zealand considered whether to stop holding reserves. This would have sent a powerful though not totally convincing signal that there would be no intervention, because intervention can also be financed by borrowing. 11The Bank of Israel intervened in the foreign exchange market in mid-March 2008, to stabilize a disorderly market. 12Included in Ishii and others (2006) is material on the mechanics of foreign exchange intervention, which can be seen as providing a partial answer to the IEO’s concerns that IMF staff are not equipped to give practical advice on intervention and other practical exchange rate issues, including how to develop the needed market infrastructure. In any case, it is not entirely clear that the staff should be experts in the nitty-gritty of exchange rate intervention, which is highly country and institution dependent. It is probably better in this area for the IMF to help the central bank of the concerned country to obtain technical assistance from a central bank that is or has been in a similar situation. This was attempted in the Indonesian program in 1997, but it became clear there that the forces moving the exchange rate were too powerful to be stemmed by intervention alone, however sophisticated it might be. 13I have avoided using the Calvo-Reinhart term ‘‘fear of floating,’’ preferring rather to try to explain why countries often intervene.

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©International Monetary Fund. Not for Redistribution Stanley Fischer government intervention.14 With regard to (3), the accumulation of reserves, the question is how best to do this. A variety of approaches has been used, including buying preannounced amounts of foreign exchange at a steady rate over a period of months. Other central banks have added to reserves on an opportunistic basis. Case (1) is most significant from the viewpoint of the new Decision on Bilateral Surveillance, for it relates to exchange rate manipulation. Why might such intervention work? Here the authors identify three channels: (1) a signaling channel—where the signal is one about future monetary policy or more generally the authorities’ preferences about the desired range of values of the exchange rate; (2) a portfolio balance channel—where the central bank is a sufficiently large player in the foreign exchange market to affect the exchange rate; and (3) a microstructure channel—where the central bank has sufficient information about the operation of the market and the forces active in it to be able to intervene in particular ways or at a particular time such that it can move the rate. In the face of significant capital flows, these channels are unlikely to be able to influence the rate for very long without supportive macroeconomic policy. That is why it has been so hard to find major effects of intervention on the exchange rates of the major industrialized countries. However, where capital flows are controlled or the country is not yet well integrated into the global capital markets, and provided policymakers are willing to intervene on a sufficiently large scale, the portfolio balance channel can operate to enable the country to have a sustained effect on the exchange rate, as the Chinese and the Russian cases illustrate. Interventions are not costless. Unless they are sterilized, they are likely to have an inflationary effect, and as they become larger, they become harder to sterilize. If domestic interest rates are higher than those abroad, monetary sterilization is expensive for the central bank; and if in any case the exchange rate will eventually have to appreciate against the currency in which the reserves are held, there will also be potentially large capital losses on the reserves on that account. Clearly, though, some countries regard the growth- and export-promoting effects of such actions as worth the cost, for the cost issues are well understood by the countries accumulating reserves on a massive scale. In cases where the export proceeds accrue to the government, they can be sterilized by being reinvested abroad through a sterilization fund. They can also be sterilized through running a larger fiscal surplus and building up a stabilization fund that may also invest abroad. Russia has used exchange market intervention and fiscal policy in the form of building up a

14Ishii and others (2006) describe the approach of the Mexican authorities, which involved selling options to sell foreign exchange if the exchange rate depreciated to a specified value. This approach may have encouraged the development of hedging instruments by the private sector.

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©International Monetary Fund. Not for Redistribution EXCHANGE RATE SYSTEMS, SURVEILLANCE, AND ADVICE stabilization fund, but nonetheless is paying a price for its sterilization in terms of inflation that is above the desired level. Why, despite the many reasons central banks may want to intervene in the foreign exchange markets, do some central banks do their best not to intervene? In the case of Israel, where there are essentially no capital controls, the nonintervention policy is based on the view that intervention is unlikely to have a sustained effect on the exchange rate, and that monetary policy decisions are more fundamental. Further, the central bank believes that the foreign exchange market works better when market participants do not expect the central bank to intervene except in extreme circumstances, and thus have to focus in their decisions on the underlying forces that determine the exchange rate. The pass-through from the exchange rate to prices is large and rapid in Israel—close to 0.3 within a quarter—and exchange rate movements are therefore taken into account in predicting inflation and the effects of interest rate decisions on prices. But this connection has not led the central bank to want to intervene in the foreign exchange market, a decision that I believe— in IMF language—has served the economy well.

III. Other Countries Turning to countries other than those defined as developed or emerging market countries, Figure 3 shows that although the evolution of exchange rate regimes between 1991 and 1999 was consistent with the bipolar hypothesis, the evolution of exchange rate regimes for this group of countries since 1999 is not. Rather, on balance there has been a move from the floating rate to the intermediate regimes. There has been a considerable amount of movement among exchange rate regimes, as shown in Figure 3. Fifteen countries moved into the inter- mediate regime from the floating regime and six moved from the intermediate

Figure 3. All Other Countries: Exchange Rate Regimes, 1991, 1999, and 2006

53% 60 (65) 46% 50 39% (56) 39% (48) (47) 40 31% 26% (38) 1991 30 22% 23% 1999 (32) 20% 2006 (27) (28) (25) 20

10 Percent of total countries Percent 0 Hard Peg Intermediate Float

Source: International Monetary Fund. Note: The number of countries is in parentheses.

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©International Monetary Fund. Not for Redistribution Stanley Fischer regime to floating. El Salvador moved from an intermediate regime to dollarization. Table 3 lists the countries in this group along with their exchange rate regimes; asterisks indicate changes in the exchange rate regime (including shifts among subcategories within the three major categories) between 1999 and 2006, with asterisked numbers in brackets indicating the number of countries in each of the seven categories in the table that were not in that category in 1999. The economics literature has not yet developed a strong position on which exchange rate system developing countries should adopt. Frankel (1999 and 2004) and Mussa and others (2000) emphasize that ‘‘no single currency regime is right for all countries or at all times.’’ Nonetheless, Rogoff and others (2003, p. 26) summarize their review of the evidence of the impact of the exchange rate regime on developing countries’ economic performance thus: ‘‘relatively rigid regimes—pegs and intermediate flexibility arrangements—appear to have enhanced policy credibility and thus helped achieve lower inflation at little apparent cost in terms of lost growth, higher growth volatility, or more frequent crises.’’ Mussa and others (2000) provide a list of factors that would favor a country pegging its rate: (1) low capital mobility, (2) a high share of trade with the country to which it is pegged, (3) facing shocks similar to those facing the country to which it pegs, (4) already relying extensively on its partner’s currency, (5) fiscal policy that is flexible and sustainable, (6) flexible labor markets, and (7) high international reserves. In other words, to sustain a pegged rate, a developing economy should have the capacity to perform well and flexibly, and maintain low inflation. Otherwise it would be advised to adopt a floating exchange rate regime, thereby allowing the exchange rate to act as an extra shock absorber. Of course, the requirements listed by Mussa and others (2000) are also those that, together with a strong financial system, would enable the country successfully to maintain a flexible exchange rate system. Mussa and others also note that as countries develop and become more financially sophisticated and more integrated into global markets, they should consider more flexible exchange rate regimes. This is far from a detailed manual or formula that tells each country what exchange rate system to adopt. Indeed, if the IMF were to develop a detailed manual it would be accused of having a cookie-cutter approach to the issue. It is not surprising that there is no such manual, for there is no way of specifying the right exchange rate system for a country without a careful analysis of its circumstances—and there is no question that among those circumstances is its exchange rate regime history. ‘‘If it ain’t broke, don’t fix it’’—unless it is likely to break soon, or is being held together only with the help of economically distortionary and costly measures—is good advice in this area too. The general presumption then is that as countries become more developed, they should be moving away from intermediate regimes, toward greater flexibility of the exchange rate—or in some cases toward a hard peg.

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IV. Exchange Rate Surveillance and Advice To return now to the new Decision on Bilateral Surveillance, the quotes with which this paper opens raise two questions. First, there seems to be a tension between the focus on an unsustainable balance of payments position and that on exchange rate manipulation, which relates to an undervalued exchange rate. From the viewpoint of what type of situation might give rise to disruptive exchange rate movements, a currency that is overvalued, or a balance of payments deficit that is so large as to be unsustainable, can give rise to rapid exchange rate movements. One example is the current account deficit of the United States in recent years. There was no exchange rate manipulation; nonetheless, by most measures the currency was overvalued. Possibly the overvaluation could be offset—though not fully, so long as other countries maintained their dollar pegs—through fiscal policy, thereby reducing the underlying threat to stability of the global exchange rate system. Which takes us to the second question: why does the decision relate to ‘‘exchange rate policies that result in external instability, regardless of their purpose y’’ rather than to ‘‘policies that result in external instability, regardless of their purpose?’’ (italics added). The failure to use fiscal policy to deal with an unsustainable current account deficit would be as deserving of staff and Board censure as would policies—such as sustained intervention—to manipulate the exchange rate to increase net exports. Presumably the emphasis on exchange rate policies was included to deal with the difficulty that if other countries peg to the dollar, then a significant part of the normal adjustment mechanism is neutralized— and that the pegging decision is not that of the United States. That is understandable, but should not have led to what appears to be a too narrow focus on exchange rate policies rather than overall economic policy. As a central banker from a small economy, I am also concerned that the new decision may focus too narrowly on external stability. For many countries, the IMF’s Article IV report is the most thorough and professional evaluation of the country’s economy and economic policies. As such, it provides a valuable service for the smaller members of the IMF. I hope the new decision does not significantly reduce the scope and depth of Article IV reports for such countries. There has been a great deal of criticism of the IMF for failing to censure the Chinese authorities more seriously over their exchange rate policies and huge and growing current account surplus. There are two questions to answer here. First, why are the Chinese pursuing these policies? The answer is that the undervaluation and export promotion strategy has been a highly successful growth strategy, not only for China but also for other countries. The policy works, spectacularly in the case of China. Second, why have the world’s entreaties and pressures—bilateral from the United States, in European-Chinese meetings, and via IMF surveillance—to revalue not had a substantial impact on Chinese policies? Presumably for the same reason, that the present policies have worked spectacularly for China.

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©International Monetary Fund. Not for Redistribution Stanley Fischer

The standard analysis of the costs and dangers to China and to the world economy of its massive rate of reserve accumulation is correct. So what should the IMF—equipped with the new decision on surveillance—be doing with its analysis? Mussa (2007) asserts that IMF management should have taken an increasingly tough stand, mentioning ‘‘the possibility of formal censure of a country’s policies by the Executive Board as a final resort for those rare and highly regrettable cases where all vigorous but less extreme efforts at persuasion have failed.’’ There is no question that the IMF staff should state its professional judgment clearly and unambiguously in all surveillance reports. There is also no question that in contacts with the authorities, the staff should be explaining its professional judgments and giving its advice more frankly than is possible in written reports. I am sure that at present, as in the past, very frank but confidential policy discussions take place at all levels with the authorities of member countries. I also believe that these discussions have an impact. But of course, the IMF’s capacity to influence a member’s policies is greater in the context of a program than in the context of consultations. One of my mentors once asked me, ‘‘Who do you listen to, your friends or your enemies?’’ The answer is that it is easier to accept advice from friends, who you believe support your objectives. But it is also necessary to listen to your enemies—sometimes they are right. Thus the IMF should be working very hard to gain the trust of its member countries (something that a cooperative organization should be doing in any case), should state its views firmly at all times, should press for policy changes that it believes are important through every effective channel, and reserve for very rare occasions a decision to enter into open conflict with a member. A final word—or rather two final stories—about advice. I was struck by the emphasis the IEO report on IMF exchange rate advice put on countries’ responses to questions about the quality of the advice they receive from the IMF. The mentor mentioned above used to say when asked for advice, ‘‘Tell me what advice you would like me to give you.’’ And, second, I fondly remember an aunt who lived in a small town in Zambia who twice a year used to visit my parents in the bigger town in Zimbabwe in which we lived. She had a significant weight problem. Before each visit she would ask my parents to arrange for her to see a doctor. They would ask whether she would like to see the same doctor as last time. She gave the same answer each time: ‘‘No, I need a different doctor, that doctor was no good, he told me to eat less.’’ Sometimes advice is valued less for its quality and more for its agreeableness, and that is simply a fact of life with which the IMF has to contend.

REFERENCES Bubula, Andrea, and Inci O¨tker-Robe, 2002, ‘‘The Evolution of Exchange Rate Regimes Since 1990: Evidence from De Facto Policies,’’ IMF Working Paper 02/155 (Washington, International Monetary Fund).

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Eichengreen, Barry, and Raul Razo-Garcia, 2006, ‘‘The International Monetary System in the Last and Next 20 Years,’’ Economic Policy, Vol. 21 (July), pp. 393–442. Fischer, Stanley, 2004, IMF Essays from a Time of Crisis: The International Financial System, Stabilization, and Development (Cambridge, Massachusetts, MIT Press). (Reprinted from Stanley Fischer, 2001, ‘‘Exchange Rate Regimes: Is the Bipolar View Correct?’’ Journal of Economic Perspectives, Vol. 15 (Spring), pp. 3–24.). Frankel, Jeffrey, 1999, ‘‘No Single Currency Regime Is Right for All Countries or at All Times,’’ Princeton Essays in International Finance No. 215 (Princeton, New Jersey, Princeton University). ______, 2004, ‘‘Lessons from Exchange Rate Regimes in Emerging Economies,’’ in Monetary and Financial Integration in East Asia: The Way Ahead, Vol. 2, ed. by Asian Development Bank (New York, Palgrave Macmillan Press), pp. 91–138. International Monetary Fund, 2003, ‘‘Developments and Issues in Exchange Rate Regimes,’’ Chapter II in Exchange Arrangements and Foreign Exchange Markets— Developments and Issues, World Economic and Financial Surveys (Washington). International Monetary Fund, Independent Evaluation Office (IEO), 2007, IMF Exchange Rate Policy Advice, Evaluation Report (Washington). Ishii, Shogo, Jorge Canales Kriljenko, Roberto Guimara˜es, and Cem Karacadag, 2006, Official Foreign Exchange Intervention, IMF Occasional Paper No. 249 (Washington, International Monetary Fund). Levy Yeyati, Eduardo, and Federico Sturzenegger, 2001, ‘‘Exchange Rate Regimes and Economic Performance, Exchange Rate Regimes and Economic Performance,’’ IMF Staff Papers, Vol. 47 (Special Issue), pp. 62–98. Mussa, Michael, 2007, IMF Surveillance over China’s Exchange Rate Policy (Washington, Peterson Institute for International Economics). Mussa, Michael, and others 2000, Exchange Rate Regimes in an Increasingly Integrated World Economy, IMF Occasional Paper No. 193 (Washington, International Monetary Fund). Reinhart, Carmen, and Kenneth Rogoff, 2004, ‘‘The Modern History of Exchange Rate Arrangements: A Reinterpretation,’’ Quarterly Journal of Economics, Vol. 119, pp. 1–48. Rogoff, Kenneth, and others 2003, ‘‘Evolution and Performance of Exchange Rate Regimes,’’ IMF Working Paper 03/243 (Washington, International Monetary Fund). Tavlas, George, Harris Dellas, and Alan Stockman, 2006, ‘‘The Classification and Performance of Alternate Exchange-Rate Systems’’ (unpublished; Athens, Bank of Greece).

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©International Monetary Fund. Not for Redistribution IMF Staff Papers Vol. 55, No. 3 & 2008 International Monetary Fund

Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques for Inferring Flexibility and Basket Weights

JEFFREY FRANKEL and SHANG-JIN WEI

This paper offers a new approach to estimate countries’ de facto exchange rate regimes, a synthesis of two techniques. One is a technique that the authors have used in the past to estimate implicit de facto weights when the hypothesis is a basket peg with little flexibility. The second is a technique used by others to estimate the de facto degree of exchange rate flexibility when the hypothesis is an anchor to the dollar or some other single major currency, but with a possibly substantial degree of flexibility around that anchor. Because many currencies today follow variants of band-basket-crawl, it is important to have available a technique that can cover both dimensions, inferring weights and flexibility. We try out the technique on some 20 currencies over the period 1980–2007. Most are currencies that have officially used baskets as anchors for at least part of this sample period. But a few are known floaters or known simple peggers. In general, the synthesis technique seems to work as it should. [JEL F31, F41] IMF Staff Papers (2008) 55, 384–416. doi:10.1057/imfsp.2008.18; published online 1 July 2008

Jeffrey Frankel is the James W. Harpel Professor of Capital Formation and Growth at the John F. Kennedy School of Government at Harvard University. Shang-Jin Wei is a professor of finance and economics, and N.T. Wang Professor of Chinese Business and Economy, at the Columbia University Business School. The longer working paper version of this paper includes all tables and is available at http://www.cid.harvard.edu/cidwp/157.html. Files of data, programs, and output, to aid any replication attempts, are posted at http://ksghome.harvard.edu/~jfrankel/ currentpubsspeeches.htm#On%20Exchange%20Rate%20Regimes. The print version of the longer working paper is available as NBER Working Paper 14016. The authors wish to thank Danxia Xie for excellent research assistance and Steve Kamin and other participants at the IMF Annual Research Conference for useful comments. They also thank the Mossavar-Rahmani Center of Business and Government and the Kuwait Fund of the Kennedy School of Government for support.

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xchange rate surveillance has moved back toward the top of the list of E the IMF’s mandates and priorities. Before one can evaluate whether a given country is following the right exchange rate regime, however, one must decide what the regime is that it is following. This seemingly simple task is far harder than one might think. Perhaps IMF staff could use some new analytical tools if it is to pursue this assignment conscientiously and persuasively. This paper proposes a technique to classify a de facto regime. It estimates simultaneously the implicit currency weights in the basket that anchors the home currency, and the degree of flexibility around that anchor.

I. De Facto vs. De Jure Classification of Exchange Rate Regimes It is by now well-known that there is a big difference between de facto exchange rate regimes, that is, the regimes that countries follow in practice, and de jure exchange rate regimes, that is, the regimes that national governments officially claim to be following and which, at least until 1997, were reproduced by the IMF almost unquestioningly in the table at the front of International Financial Statistics. The discrepancy between de facto and de jure is pervasive. In the first place, most countries that claim to ‘‘fix’’ are not, in fact, firmly fixed. Countries declaring a peg, often abandon it. Obstfeld and Rogoff pointed out in their 1995 article ‘‘The Mirage of Fixed Rates,’’ that only six major economies (leaving aside some with capital controls) had kept a peg longer than five years. In the second place, most countries that claim to ‘‘float,’’ are not in fact floating. Calvo and Reinhart (2002) coined the phrase ‘‘fear of floating.’’ They showed that the variability of foreign exchange reserves relative to the variability of the exchange rate was not only substantial for those who claimed to be floating (one might expect zero for a true pure floater), but that it tended to be fully as great as for those who are officially pegging. Many emerging market countries that claim to float, sometimes under an official monetary rule of inflation targeting, in fact have intervened heavily in recent years to dampen the appreciation of their currencies. In the third place, many countries that claim to be following one of the transparent intermediate regimes, namely a basket peg (or even a basket with a band), in fact do not. They keep the weights in the basket secret so that the government can surreptitiously depart from the official regime. When a country declares a basket peg with a band, it typically would take more than 100 observations for an observer to distinguish statistically whether it is in fact following this policy (Frankel, Schmukler, and Serve´ n, 2000). The national authorities are no doubt aware of this when they decide to keep the basket weights secret. There is no more topical illustration of this problem than the Chinese yuan. The Beijing authorities announced a change in exchange rate regime in July 2005, a switch away from a dollar peg and toward a more flexible regime with reference to a basket of 11 currencies, with a small (but cumulative)

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©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei band. They did not announce what were the weights on the currencies. As with so many other basket peggers, there is reason to suspect that the secrecy is not an accidental oversight. It is there deliberately to cloak a discrepancy between de jure and de facto. That the yuan is not just another currency—but lies at the heart of the disagreement to which the United States currently chooses to give top priority in its relations with China—makes it a particularly relevant example. There are by now many attempts to discern the true ‘‘de facto’’ exchange rate regimes that countries actually follow. Some of the more prominent include Ghosh, Gulde, and Wolf (2002); Levy-Yeyati and Sturzenegger (2003); Reinhart and Rogoff (2004); and Shambaugh (2004). Most of these classification schemes depart from the official classification in the direction of counting as de facto floating a country that has high variability of the exchange rate (or of the change in the exchange rate), relative to variability of reserves, and counting as fixed a country that has low variability of the exchange rate relative to reserves. A recent survey by Tavlas, Dellas, and Stockman (2006) that covers 11 studies divides them into two categories, viewed as

‘‘Pure de facto classifications becauseyassignment of regimes is based solely on statistical algorithms and/or econometric estimation’’ vs. ‘‘Mixed de jure-de facto classifications, because the self-declared regimes are adjusted by the devisers for anomalies.’’ One of the latter is the official product of the IMF’s former Monetary and Exchange Affairs Department (Bubula and O¨ tker-Robe, 2003) where the adjustment is accomplished both by consulting IMF economists and by looking at movements in reserves and exchange rates.

Although the discrepancy between the de jure regimes and any given attempt to determine de facto regime is well-known, it is less widely recognized that the various systems for classifying de facto regimes do not agree with each other. Calculations of correlations or correspondence across different classification schemes show that the de facto schemes hardly correspond any more closely to each other than to the official classification.1 There are various explanations for the variation in conclusions reached by the different classification schemes: differences in methodology, different choices as to where to draw the line between regimes, differences in timing of the data, and so forth. But perhaps the best way of summarizing the problem is that, apart from a relatively small number of countries that peg firmly (for example, countries with institutional commitments in the form of currency boards) and the handful that float freely (for example, the United States), most follow some messy intermediate regime that is not easily identified or

1See Table 1 in the working paper version of this paper, which also reports analogous tables. Computed by Shambaugh (2004) and Be´ nassy-Que´ re´ , Coeure´ , and Mignon (2006).

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©International Monetary Fund. Not for Redistribution ESTIMATION OF DE FACTO EXCHANGE RATE REGIMES unambiguously classified. This flies in the face of the famous ‘‘corners hypothesis’’ that rapidly became the conventional wisdom in the late 1990s, and has subsequently declined almost as rapidly. The corners hypothesis claimed that countries were abandoning the intermediate regimes in favor of the two corners: exchange rates fixed institutionally, as through a currency board, vs. freely floating exchange rates. But it is a fact that a majority of countries continue to follow some regime in between firm fixing and free floating (Masson, 2001). One intermediate regime is the basket peg. Often it comes combined with a managed float that ‘‘leans against the wind’’ to push the current exchange rate back in the direction of the central parity when it wanders afield, or more specifically with an announced band (target zone).2 In any case, the weights in the basket are often kept secret, as already noted. There exists a branch of the de facto classification literature that is different from and smaller than the research on flexibility vs. fixity cited above. The second approach applies especially to countries that are thought possibly to use baskets, for example because they say they do. This approach discerns from actual data the implicit weights placed on the constituent foreign currencies of the basket. The simple methodology was first developed in the early 1990s to test whether a country that announces a basket peg but does not reveal the exact weighting of the component currencies is acting in a manner consistent with its words: Frankel (1993) and Frankel and Wei (1994, 1995). The approach has since been used by others, including Be´ nassy-Que´ re´ (1999); Ohno (1999); Frankel, Schmukler, and Serve´ n (2000); Frankel and others (2001); and Be´ nassy-Que´ re´ , Coeure´ , and Mignon (2006).3 These two branches of the literature have hitherto remained separate, in splendid isolation. It could be argued that each has its own place. If one is confident that a country is following a basket peg, and is unsure only of the weights, one should then use the weight-inference technique. If one is confident of the major anchor currency (dollar, euro, etc.) in terms of which a government defines the value of its own currency, and is unsure only of the strength of the effort to stabilize, then one should use one of the methods of the larger branch of the literature, such as comparing the variability of the exchange rate (vis-a` -vis the dollar or euro, etc.) to the variability of reserves. The problem is that some countries do define a central parity in terms of weights but then allow variation around that parity. China since 2005 is a good example of a country that claims to be doing this, and Chile in the 1990s is a good example of a country that actually did it. And even if one suspects that a country is in truth following a simple peg, it is always better to have a meaningful alternative hypothesis within which the null hypothesis is

2Williamson (2001) proposes that many Asian countries should adopt combinations of baskets and bands (and perhaps crawls). 3More recently, it has been applied to China’s yuan: Eichengreen (2006); Shah, Zeileis, and Patnaik (2005); Yamazaki (2006, p. 8); and Frankel and Wei (2007).

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©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei nested. Thus we want to estimate basket weights, rather than presuming we know the anchor currency, but at the same time to allow for some variation around the central parity, as in a target zone or managed floating. Hence our proposal for a synthesis of the two types of techniques in use for estimating de facto regimes.

II. Inferring the De Facto Degree of Flexibility It is important when inferring the de facto flexibility of an exchange rate regime to look beyond the variability of the exchange rate in itself. The korona could show a higher degree of variability than the thollar, and yet this might be because the korona has been subject to larger shocks than the thollar, rather than because the authorities intervene less and allow a given shock to show up more in the form of price movement. Thus we want to look at the variability of the exchange rate relative to the variability of foreign exchange reserves, or the monetary base, or some other monetary aggregate. Figure 1 is a preliminary look at the data on exchange rate variability and reserve variability for 15 countries. We selected our sample to emphasize countries that claim to follow a basket peg, but added in a few that are known to be firm peggers and others that are known to be floaters, to be able to calibrate the results along the fixed-versus-floating dimension. We chose nine (small) countries that have been officially identified by the IMF as following basket pegs: Latvia, Papua New Guinea, Botswana, Vanuatu, Fiji, Western Samoa, Malta, and the Seychelles. We also added several known floaters: Australia, Canada, and Japan, and three peggers of special interest: Hong Kong, China, and Malaysia. The paper emphasizes commodity- exporting countries, such as Norway, in our list of currencies examined, for reasons that will be obvious later.4 Variances are computed for seven-year intervals, within the period 1980–2007. The aim in choosing this length of the interval is that it be long enough to generate reliable estimates of the parameters, and yet not so long as inevitably to include important changes in each country’s exchange rate regime. Here, as throughout this paper, we work with logarithmic changes in the exchange rate. An upward-sloping line runs from the origin. It runs through the point that represents the combination of average variance of (logarithmic change in) reserves and the average variance of (logarithmic change in) reserves. The points that lie well above this upward-sloping line represent countries that intervened actively in the foreign exchange market to stabilize their

4Before any readers hear faint alarm bells about the absence of a complete or random sample, we will note that we have no need of a random or complete sample. We have no need of a random sample because, although we are testing hypotheses about individual currencies, we are not testing any general hypotheses (such as ‘‘countries are at the corners’’). We have no need of a complete sample, because we are not attempting to offer a complete classification of IMF members as many of the other papers in the literature have done. Rather we are proposing a new technique. We have chosen to try it out on countries that we think are of interest for one reason or another.

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©International Monetary Fund. Not for Redistribution Figure 1. Comparison of Reserve Variability vs. Exchange Rate Variability

Variance of ΔReserves vs. Variance of ΔExchange Rate 1980–2007, in seven-year intervals

0.06

vuv80 REGIMES RATE EXCHANGE FACTO DE OF ESTIMATION scr80 png94

wst81 1 bwp80 0.04

log reserves scr94 Δ scr87 scr01 0.02 cad80 Variance of Variance

png80 aud80 png87 fiji80 cad87 vuv87 png01 fiji87 vuv94cad94 nok94 aud94 aud01 nok80 myr87myr80 lvl03wst88 aud87 nok87 0 vuv01wst95fiji01 fiji94 nok01 myr94 hkd94 bwp87 jpy87 hkd00cny15myr01 lvl96wst02 cad01mtl01mtl80bwp94 jpy01 mtl87 mtl94 jpy80 jpy94 bwp01

0 0.0005 0.001 0.0015 0.002 Variance of Δ log exchange rate (in US$)

Country from year t to year t + 6 Shocks line Floating/Fix line

1Intervention is computed by subtracting imputed interest earnings from reported change in reserves. Note: Currencies: Canadian dollar (cad), Hong Kong dollar (hkd), Malaysian ringgit (myr), Chinese yuan (cny), Japanese yen (jpy), Latvian lat (lvl), Papua New Guinea kina (png), Botswana pula (bwp), Vanuatu (vuv), Fiji dollar, Western Samoa tala (wst), Maltese lira (mtl), Seychelles rupee (scr), Norwegian kroner (nok), and Australian dollar (aud). Each data point represents a seven-year period, denoted in the suffix by the starting year of each period. 389 Periods of countries with yearly inflation rate higher than 40 percent have been eliminated.

©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei currencies during the period in question, for example, the Seychelles. The points that lie well below this upward-sloping line represent flexible-rate countries, where the authorities allow fluctuations in the demand for their currencies to show up primarily as movements in the exchange rate, for example, Japan. The downward-sloping line runs through the point representing the medians of reserve variability and exchange rate variability, and is also drawn so that half the points are above and to the right of the line, and the other half are below and to the left. The points in the first category represent countries where shocks tend to be large; this tends particularly to be the case with commodity producers, such as Botswana and Papua New Guinea. The points below and to the left of the downward-sloping lines represent countries where shocks tend to be small, for example, Hong Kong. Several interesting lessons emerge from the graph, even without further analysis. The first is the folly of judging a country’s exchange rate regime— specifically, the extent to which it seeks to stabilize the value of its currency— by looking simply at variation in the exchange rate. The 1980–86 Australian dollar shows a higher exchange rate variance than the 2001–07 Japanese yen. But this is not because the Australian dollar followed a more flexible exchange rate policy at that time. It is rather because Australia was hit by much larger shocks. One must focus on exchange rate variability relative to reserve variability to gauge where the country sits on the spectrum from fixed to floating. Perhaps this is obvious, but some have focused exclusively on exchange rate variability. The second interesting lesson to be drawn from the graph—though a less original observation than the first—is that countries that specialize in mineral products tend to have larger shocks, which presumably take the form primarily of volatility in their terms of trade. The third lesson, which was quite surprising when it was first noticed but should be familiar by now, is that even countries that float hold and use foreign exchange reserves in substantial magnitudes, sometimes more actively than countries that peg.5 An example in the figure is Canada in the 1980s. The fourth lesson is a counterpart to the third: a currency with a firm peg like the Hong Kong dollar can experience very low variability of reserves, because it has very low variability of shocks. It may in part be the absence of commodities in Hong Kong’s production portfolio that makes possible the low level of shocks. But the low variability in international demand for the Hong Kong dollar must also result in part from the stability and credibility that the currency board has itself achieved. After all, Hong Kong did experience large shocks in the late 1990s—the reversion of the territory from Britain to China and the East Asia crisis—and yet the shocks do

5In the early 1970s, when the international monetary system moved from fixed exchange rates to floating, the demand for international reserves did not fall as would have been predicted. Early references include Frenkel (1978) and Bilson and Frenkel (1979). Similarly, when many emerging market countries switched to more flexible exchange rate exchange rate regimes, or even to outright floating, in the currency crises of 1994–2002, the demand for reserves subsequently did not fall, but rather rose sharply (Rodrik, 2006).

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©International Monetary Fund. Not for Redistribution ESTIMATION OF DE FACTO EXCHANGE RATE REGIMES not show up in either the exchange rate or reserves. Reassuring nervous investors presumably was the goal in the first place, when the Crown Colony of Hong Kong adopted the currency board in 1983. It is for just such reasons that the classification schemes of Calvo and Reinhart (2002) and Levy-Yeyati and Sturzenegger (2003, 2005) do not look at exchange rate variability alone (prices), but rather compare it to variability in reserves or money supplies (quantities). The question is: when there is a shock that increases international demand for korona, to what extent do the authorities allow it to show up as an appreciation, and to what extent as an increase in reserves. In this paper, we frame the issue in terms of the exchange market pressure (EMP) variable, which is defined as the percentage increase in the value of the currency plus the percentage increase in reserves (or the monetary base, or M1).6 When this variable appears on the right-hand side of an equation and the percentage increase in the value of the currency appears on the left, a coefficient of 0 signifies a completely fixed exchange rate (no changes in the value of the currency), a coefficient of 1 signifies a freely floating rate (no changes in reserves), and a coefficient somewhere in between indicates a correspondingly flexible/stable intermediate regime. One possible limitation of these and other papers that estimate flexibility vs. stability of exchange rate regimes is that they sometimes have to make arbitrary judgments regarding what is the major currency in terms of which flexibility and stability are to be defined. The dollar is the most common choice. This may be fine for most Western hemisphere countries (though in fact not for Chile, and perhaps not Argentina and Brazil either). But for European countries, the euro is obviously more relevant. And for many others, particularly in Asia and the Pacific, and probably also the Middle East and parts of Africa, the relevant foreign currency is neither the dollar nor the euro, but some (possibly trade-weighted) basket. It would be better to let the data tell us what is the relevant anchor for a given country, especially for those that are not clearly in either the dollar or euro camp, rather than making the judgment subjectively or a priori.7

III. Inferring De Facto Weights So, on the one hand, the main branch of the regime classification literature has the drawback that—in its zeal to uncover the true degree of flexibility—it is unable to infer the relevant anchor. But, on the other hand, the smaller branch of the literature, which specializes in inferring the relevant anchor currency or basket under the null hypothesis of a perfect fit, equally omits to include anything to help make sense out of the error term under the alternative hypothesis that the country is not perfectly pegged to a major currency or to a basket. The equation is correctly specified to infer the

6The progenitor of the EMP variable, in a rather different context, was Girton and Roper (1977). 7Clearly, many of the authors of these papers are fully aware of the issue.

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©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei weights in the case of a perfect basket peg, with an R2 of 1, but is on less firm ground if the authorities allow even a relatively small band of flexibility around the central parity. Thus the contribution of this paper is to bring these two branches of the literature together to produce a complete equation suitable for use in inferring de facto regimes across the spectrum of flexibility and across the array of possible anchors. Assume that the value of the home currency is indeed determined by a currency basket. How does one then uncover the currency composition and weights in the basket? This is a problem to which ordinary least squares regression is unusually well suited. If we know the list of currencies in the basket, or a list that includes as a subset those that are used in the basket, then we regress changes in the log of H, the value of the home currency, against changes in the log values of the candidate currencies, X. This technique from Frankel and Wei (1994, 1995) has recently been applied to the current Chinese exchange rate regime.8 The reason to work in terms of changes rather than levels is the likelihood of nonstationarity. Concern for nonstationarity goes beyond the common refrain of modern time series econometrics, the inability to reject statistically a unit root. Working in changes, we can also include a constant term to allow for the likelihood of a trend appreciation (a key question of interest in the new renminbi regime) or trend depreciation (as in the crawling pegs popular in Latin American and elsewhere in the 1980s), whether against the dollar alone or a broader basket. Algebraically, if the value of the home currency H is pegged to the values of currencies X1, X2, y and Xn, with weights equal to w1, w2, y and wn, then X DlogHt ¼ c þ wð jÞ½DlogXð jÞt: (1Þ If the exchange rate is truly governed by a strict basket peg, then we should be able to recover the true weights, w( j), precisely, so long as we have more observations than candidate currencies, and the equation should have a perfect fit.

8Shah, Zeileis, and Patnaik (2005) adopted the implicit-weight methodology to study the Chinese currency basket after July 21, 2005 and found that the renminbi is still tightly pegged to the dollar and nothing else. However, they only consider four candidate currencies in the renminbi basket (the dollar, the yen, the euro, and the pound), probably unaware of the 11- currency disclosure made by the Chinese central bank. In addition, their sample was only the initial few months after July 21, 2005. Eichengreen (2006, pp. 22–25) had daily observations of data that ran from July 22, 2005, to March 21, 2006, and found a dollar weight around 0.9, but with no evidence of a downward trend in the weight, and no significance on nondollar currencies. Each of these three papers was too early to catch the evolution in 2006. Yamazaki (2006, p. 8) updated the estimation, and found some weight had shifted to the euro, yen, and won; but he estimated the equation in terms of levels rather than changes (risking nonstationarity), did not allow for a trend, did not allow for the other currencies on the list, and had a relatively small number of (bimonthly) observations. Frankel and Wei (2007) updated the estimation, ran the equation in monthly changes, included the full list of 11 candidate currencies, and allowed for gradual evolution during the sample period in both the basket weights and the trend term.

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One methodological question, before we turn to the new synthesis estimation specification. How do we define the ‘‘value’’ of each of the currencies? This is the question of the numeraire.9 If the exchange rate is truly a basket peg, the choice of numeraire currency is immaterial; we estimate the weights accurately regardless. If the linear equation holds precisely in terms of any one ‘‘correct’’ numeraire, then add the log exchange rate between that numeraire and any arbitrary unit to see that the equation also holds precisely in terms of the arbitrary numeraire. This assumes the weights add to 1, and there is no error term, constant term, or other noncurrency variable. In practice, few countries take their basket pegs literally enough to produce such a tight fit. One must then start to think about the nature of the error term and nonbasket factors in the regression (such as the trend term), and about whether they are better measured in terms of one numeraire or another. The introduction of reserves or the EMP variable as explanatory variables should soak up some of the error term and give better estimates: by including on the right-hand side of the equation percentage changes in total EMP (defined as percentage changes in the value of the currency plus percentage change in reserves), the test can allow for the fluctuations in demand for the currency that can push the exchange rate away from the central basket parity. The hope is that this approach may do a better job of answering the question to what extent the authorities intervene to stabilize the currency, not just the question what is the basket in terms of which the authorities define stability. If the true regime is more variable than a rigid basket peg, then the choice of numeraire does make some difference to the estimation. Some authors in the past have used a remote currency, such as the Swiss franc (for example, Frankel and Wei, 1994). But a weighted index such as the special drawing right (SDR) or a trade-weighted measure is probably more appropriate. Here is why. If the true regime is a target zone or a managed float centered around a reference basket, where the authorities intervene to an extent that depends on the magnitude of the deviation—and this seems the logical alternative hypothesis in which a strict basket peg is nested—then the error term in the equation represents shocks in demand for the currency that the authorities allow to be partially reflected in the exchange rate (but only partially, because they intervene if the shocks are large). Then one should use a numeraire that is similar to that used by the authorities in measuring what constitutes a large deviation. The authorities are unlikely to use the Swiss franc or Canadian dollar in thinking about the size of deviations from their reference point. They are more likely to use a weighted average of major currencies. If we use

9Frankel (1993) used purchasing power over a consumer basket of domestic goods as numeraire; Frankel and Wei (1995) used the SDR; Frankel and Wei (1994), Ohno (1999), and Eichengreen (2006) used the Swiss franc; Be´ nassy-Que´ re´ (1999) used the dollar; Frankel, Schmukler, and Serve´ n (2000) used a GDP-weighted basket of five major currencies; and Yamazaki (2006) used the Canadian dollar.

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©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei a similar measure in the equation, it should help minimize the possibility of correlation between the error term and the numeraire. Similarly, if there is a trend in the exchange rate equation (a constant term in the changes equation) representing deliberate gradual appreciation of the currency, then H should be defined in terms of whatever weighted exchange rate index the authorities are likely to use in thinking about the trend. These considerations suggest a numeraire that is itself composed of a basket of currencies. We choose here the SDR.

IV. Results of the Synthesis of Flexibility-Inference and Weight-Inference Techniques Our equation is X DlogHt ¼ c þ wð jÞDlogXð jÞt þ bfDemptgþut: (2Þ

One way to define the percentage change in total EMP10 is by

Dempt ¼ DlogEMPt ¼ DlogHt þ DlogRest: The w( j) coefficients capture the de facto weights on the constituent currencies. The coefficient b captures the de facto degree of exchange rate flexibility: b ¼ 1 means the currency floats purely, because there is no foreign exchange market intervention (no changes in reserves); b ¼ 0 means the exchange rate is purely fixed, because it never changes in value; and most currencies probably lie somewhere in between. Endogeneity is a possible problem, and is addressed below. We have tried estimating the equation without imposing a constraint on the sum of the weights in the basket. But there is a good argumentP for constraining the weights on the currencies to add up to unity: w( j) ¼ 1. However weak one thinks the link to the reference basket might be and however large or small the weight on the dollar, the authorities must view movements in the home currency through the metric of distance from some reference rate or effective exchange rate. There is no point throwing away the information represented by the summing-up constraint; we only have 48 observations per regression, and we need every degree of freedom we can get. The easiest way to implement the adding up constraint is to run the regressions with the changes in the log value of the home currency on the left- hand side of the equation transformed by subtracting off the changes in the log value of one of the currencies, say the British pound, and the changes in the values of the nonpound currencies on the right-hand side transformed in the same way.

10As noted, another way to define exchange market pressure is by expressing the change in reserves as fraction of the level of the monetary base rather than as a fraction of the level of reserves. Such regression results are reported subsequently.

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To see this, we repeat Equation (2), with some of the major currencies made explicit: X DlogHt ¼ c þ wð jÞ½DlogXtþbfDemptgþut

¼ c þ wð1ÞDlog$t þ wð2ÞDlog ht

þ wð3ÞDlogft þ wð4ÞDlogdt þ ...

0 þ bfDlogEMPtgþut: ð2 Þ We want to impose the adding up constraint w(4) ¼ 1w(1)w(2) w(3)y We implement it by running the regression Equation (3):

½DlogHt Dlogdt¼c þ wð1Þ½Dlog$t Dlogdt

þ wð2Þ½Dloght Dlogdt

þ wð3Þ½Dlogft Dlogdtþ...

þ bfDlogEMPtgþut: ð3Þ The results reported in the Appendix come from the estimation of this equation. One can recover the implicit weight on the pound by adding the estimated weights on the nonpound currencies, and subtracting the sum from 1. This coefficient estimate is reported in the last row of the table. Imposing the constraint sharpens the estimates a bit. Again the currencies are those that are hypothesized to have followed a basket peg, to try out the ability of the technique to infer the weights or reject the null hypothesis, plus some clear floaters and clear peggers thrown in to calibrate the inference of flexibility. Tables A1 through A9, respectively, present the individual results for nine currencies. In the longer working paper version (Frankel and Wei, 2008) we report results for several others as well: the Malaysian ringgit, Botswana pula, Fiji dollar, Papua New Guinea kina, Vanuatu vatu, West Samoan tala, and Indonesian rupiah.11 Endogeneity of the EMP variable is a possible concern. One would prefer to observe changes in the international demand for the home currency that are known to originate in exogenous shocks. In the case of countries that specialize in the production of mineral or agricultural products, there is a ready-made instrumental variable: changes in the terms of trade reflecting the price of the mineral or agricultural product on world markets. (This assumes

11We omit the yen and other very major currencies from the list of home countries. Such a currency is sufficiently large in world monetary markets that one cannot take the value of the other major currencies as exogenous as is necessary to estimate the weights on the right-hand side of our equation. For other smaller currencies, the assumption that the value of the dollar, euro, and other major currencies can be taken as exogenous seems reasonable.

395

©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei that the home country is too small to affect the world price, which is a reasonable assumption in all but a few cases, such as Saudi Arabia and oil.) Accordingly, Tables 3.1–3.21 in the working paper repeat the synthesis estimation technique, but for the commodity producers it uses changes in the world price of the commodity or commodities in question as an instrumental variable for changes in EMP. There is a good argument, when defining EMP, for computing the changes in reserves as percentages of the monetary base, rather than as percentages of the level of reserves itself. The problem with the latter approach is that for a country that holds relatively small levels of reserves, such as Canada and Australia, a change in reserves that is very small in absolute terms can look like a moderately large intervention in percentage terms, as we see in Figure 1. Accordingly, on the vertical axis of Figure 2 we express reserve changes as a percentage of the monetary base, rather than as a percentage of reserves themselves. Now Australia and Canada appear close to the bottom of the range of reserve variability where they belong, well below the upward-sloping line that demarcates the fixers from the floaters. Correspondingly, Tables 4 and 5 in the working paper version of this paper re-run the regressions with reserve changes defined as percentages of the monetary base. We begin with the estimated equations for two known peggers. Table A1 reports the results for the Chinese yuan or renminbi. It confirms earlier findings of a perfect peg to the dollar during 2001–04 (dollar coefficient ¼ 0.99, a flexibility coefficient insignificantly different from zero, and an R2 of 0.99). In 2005–07, the EMP coefficient suggests that only 90 percent of increased demand for the currency shows up in reserves rather than 100 percent; but the dollar weight and R2 are as high as ever. The Hong Kong dollar is covered in Table A2. As one would expect given the currency board arrangement, it is a simple tight peg to the US dollar: close to complete weight on the dollar, zero flexibility, and perfect fit.12 We now turn to some countries that are considered to have pegged or anchored to a basket for at least part of the sample period. Table A3 applies the synthesis estimation technique to the Chilean peso. The EMP coefficient shows an intermediate degree of flexibility, consistent with the proclaimed band of the 1980s and 1990s. The combination of a basket, band, and crawl seems able to explain most of the variation in the value of the peso in the 1990s (R2>0.9). The weight on the dollar is always high, but the yen also gets some weight in some years, until after 1999 when only the euro complements the dollar. There is a significant downward trend from 1980 to 1999. Of those countries to follow a band-basket-crawl regime in the 1980s and 1990s, Chile is one of the few that announced explicitly what

12Estimates in the middle column show the ill effects of near-perfect multilcollinearity between the US dollar and the Malaysian ringgit during this interval. We should adopt a rule that whenever the exchange rate between two potential regressors is virtually fixed, the smaller of the two currencies (in this case the ringgit) should be dropped from the regression equation.

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©International Monetary Fund. Not for Redistribution Figure 2. Comparison of Reserve Variability vs. Exchange Rate Variability (Changes in reserves are expressed as a percent of the monetary base)

1980–2007, in seven-year intervals

0.15 REGIMES RATE EXCHANGE FACTO DE OF ESTIMATION

kwd01

0.1

kwd87 Reserve)/MB in US$ Δ nok94 0.05 png01 kwd80 nok01 clp87 vuv87

Variance of ( Variance hkd97kwd94 wst94 nok87 wst01fiji80 nok80 fiji87 clp94 wst87 myr01 vuv94 scr01 scr80 fiji9clp014 aud01 cad94 lvl01vuv01fiji01 scr87 0 hkcnd04y05 lvl94scr94 cad01mtl0mtl810 jpy01 aud94 mtl87 jpy87mtl94 jpy80 jpy94

0 0.0005 0.001 0.0015 Variance of Δlog(exchange rate) in US$

Country from t to t + 6 Shocks line Floating/Fix line

Source: IMF, International Financial Statistics. Note: Reserve data: after subtracting off the imputed interest. 397

©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei were the parameters: basket weights, bandwidth, and rate of crawl.13 Our findings correspond to the official regime, but only rather roughly. The band was officially centered on the dollar alone in the 1980s, and was broadened to a basket starting in 1992. Our estimates qualitatively capture the shift in emphasis away from the dollar; but they find an apparently spurious weight on the yen in the mid-1980s and they miss entirely the 30 percent weight officially placed on the mark in the mid-1990s. Likewise, Chile officially moved to full floating in 1999. Our estimates qualitatively show the increase in the flexibility coefficient in 2000–03, but the estimates do not show the (loose) basket peg disappearing completely as the Chileans claim is the reality. Possible explanations for the lack of a close match between the official regime and the estimates include: (1) the common disjuncture between de facto and de jure (though it is much less likely to apply to Chile than for other countries), (2) endogeneity of the EMP variable, (3) some other shortcoming of the estimation technique, and (4) changes in the parameters that occur more frequently than the four-year subperiods examined here. For the years since the September 1999 move to floating, (5) the failure of the flexibility coefficient to approach 1.0 might possibly be explained by copper export earnings that add to reserves and yet are not considered by the authorities to constitute active foreign exchange intervention. Explanation (2) can be addressed by the instrumental variables technique, for which Chile is a natural candidate because copper is half its exports. Table 3.1 of the working paper uses the world copper price as the instrumental variable. At least the spurious significant coefficient on the yen in the mid-1980s disappears. But the German mark still does not make the dramatic appearance onstage in 1992–99 that one would expect from the official announcements. Proposition (4) may be the real explanation, and it is harder to address. The Chilean authorities announced 18 changes in regime parameters (basket weights, width of band, and rate of crawl) during the 18-year period 1982–99. One could imagine estimating each year separately, or matching the subsamples to the official announcements, or using more sophisticated econometric techniques that allow endogenously estimated breakpoints. The obstacle in all cases is that we have only monthly data, so it is not possible to estimate meaningful parameter values if they change every 12 months on average. The original Frankel-Wei technique required only exchange rate data, which allowed estimation at a daily frequency (or even intra-day). But the synthesis technique requires data on foreign exchange reserves, which are only available monthly for most countries. Indeed, the attempt to estimate six or more parameters on each set of 48 observations may already produce too much ‘‘estimation error.’’

13Details reported in the Appendix of Frankel and others (2001).

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The Latvian lat, shown in Table A4, is officially on a basket peg. Here the estimation technique appears to work well. The flexibility coefficient is low during the 1990s, and has disappeared altogether since 2000. The R2 exceeds 0.9 during 1996–2003. The combination of low flexibility coefficient and a high R2 during 2000–2003 suggests a particularly tight basket peg during these years. Initially, the estimated weights include 0.4 on the dollar and 0.3 on the yen, but both decline over time. There is a weight of 0.3 on the mark up until 1999, which is then transferred to the euro: 0.2 in 2000–03 and 0.5 in 2004–07. Latvia is preparing to enter the eurozone. Surprisingly, however, the estimation shows a coefficient of similar size on the Russian ruble popping up during 2004–07. The Maltese lira (Table A5) shows a tight peg during 1984–91 and 2004– 07 (low flexibility coefficient and high R2). The share of the dollar varies between 0.2 and 0.4 during 1980–2003. The European currencies garner 0.3– 0.4 during 1980–95, the pound perhaps 0.2–0.3 and the yen 0.1. At the end of the sample period, the weight on the euro rises almost to 0.9, with perhaps bit parts assigned to the dollar and pound. Malta is one of the 10 countries that joined the European Union in 2005 and one of two that joined the euro in 2008. For the Danish krone the EMP coefficient suggests that a very high share of fluctuations in currency demand are accommodated from foreign exchange reserves (Table 2.6 of the working paper). The weight on European currencies begins above 0.8, and rises to 1.0 with the advent of the euro in 1999. The R2 ranges from 0.85 to 0.99. In short the evidence is consistent with the known regime: Denmark remained behind in the 2¼ percent band, when its (non-Scandinavian) neighbors joined the euro. The Kuwaiti dinar shows a firm peg throughout most of the period: a near-zero flexibility parameter and R2 mostly above 0.9 (IV estimates in Table 3.5 of working paper). In the second half of the sample, the anchor was usually a simple dollar peg, although a small weight was assigned to other currencies in the 1980s basket. In a widely watched move, the Kuwaitis in 2007 abandoned the simple dollar peg that its partners in the Gulf Cooperation Council partners have been wedded to, and returned to a basket peg; but this shift is probably too recent to have had a substantial effect on any of these estimates. The Norwegian krone (Table A6) is one of the few basket peggers in the developed world. The estimates show heavy intervention, although the R2 never crosses 0.8. The weights are initially 0.3 on the dollar and 0.4 on European currencies (and perhaps a little weight on the yen and pound). But the weight on the European currencies rises at the expense of the dollar, until the latter part of the sample period shows full weight on the euro and none on the dollar. The results are similar to ordinary least squares when the world oil price is used as the instrumental variable for EMP (Table 3.8 of the working paper). The Russian ruble shows high intervention from the beginning (Table 2.13 in the working paper). There is evidence of an attempt to

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©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei stabilize around the dollar during 1996–2003, but the peg is loose; this no doubt reflects both the discrete devaluation of 1998 and the band that preceded it. More recently, the ruble has acted more like a basket peg, and has assigned somewhat less weight to the dollar. With use of the oil price as the instrumental variable, the flexibility parameter in the current decade drops more rapidly than under ordinary least squares (Table 5.10 in the working paper). The Seychelles rupee in Table A7 confirms its official classification as a basket pegger, particularly in 1984–95: not only is the flexibility coefficient essentially zero, but the R2 exceeds 0.97. The estimated weights are 0.4 on the dollar, 0.3 on the European currencies, 0.2 on the yen, and 0.1 on the pound. After 2004, however, the weight on the dollar suddenly shoots up to 0.9. Estimation for Thailand shows that the authorities intervened heavily in the 1980s and 1990s (Table 2.14 of the working paper). During 1988–95 they indeed adhered to a very tight basket peg (R2 ¼ 0.999). The weight on the dollar reached 0.75–0.88, but there was still a significant weight of 0.1 on the yen. That the weight on the dollar falls short of 1.0 may come as a surprise to those who, in the wake of the Thai crisis of 1997, received the impression that the baht had been explicitly pegged to the dollar. But the official policy had been a basket peg, not a dollar peg. In the early 1990s, observers had been surprised that the estimated weight on the dollar was so high, because the earlier orthodoxy had been that Southeast Asia was rapidly becoming a yen bloc.14 The flexibility parameter rises sharply in 2000–07, although there is still plenty of intervention.15 When the price of rice is used as an instrumental variable, once again the point estimates of the flexibility parameter rise, but the significance levels fall (Table 3.11 of the working paper). We now turn to a set of floaters. The estimated equation for the Australian dollar is reported in Table A8. The coefficient on EMP shows a lower degree of exchange rate flexibility than one would have expected, given that the currency is thought to have floated fairly freely throughout this period. The problem may be that reserves are measured as more variable than seems right. Or the problem may be endogeneity of the EMP variable. The Australian dollar is considered a commodity currency, so world commodity prices are a natural instrumental variable to correct for endogeneity. The IV estimation for Australia (Table 3.18 in the working paper) shows for each of the subperiods the estimated flexibility coefficient higher than it was under ordinary least squares; but they remain surprisingly low in magnitude and statistical significance. As noted regarding the definition of EMP, there is a good argument for computing the changes in reserves as a percentage of the monetary base,

14Frankel (1993); Frankel and Wei (1994), and references cited therein. 15Very high multicollinearity between the dollar and the Malaysian ringgit impedes the estimation. The won, Australian dollar, and ringgit probably ought to be dropped on a priori grounds.

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©International Monetary Fund. Not for Redistribution ESTIMATION OF DE FACTO EXCHANGE RATE REGIMES rather than as a percentage of the level of reserves itself. The lower half of Table A8, section b, uses the alternative definition of EMP, with changes in reserves expressed as percentages of the monetary base:

Dempt DlogHt þ DlogRest=MBt: As before, a coefficient of b ¼ 1 would means that the currency floats purely, because there are no changes in reserves and b ¼ 0 would mean the exchange rate is fixed. Now the estimated coefficient and significance level on the EMP variable are higher. This tends to confirm the value of this approach, as Australia is known to be a floater. Table 5.18 in the working paper applies instrumental variables to this same alternate definition of EMP, but it is little changed from IV with the first EMP definition. The Canadian dollar in Table A9 shows up as mostly flexible, though experiencing some intervention (the EMP coefficient ranges from 0.1 to 0.4), with the U.S. dollar usually receiving the largest weight in the basket that the authorities are treated as implicitly using as a reference. Again, when we switch to the alternative definition of EMP in the second half of the table, with reserve changes expressed as a percentage of the monetary base, the coefficient estimates and significance levels of EMP are generally a bit higher, as is consistent with the floating nature of the Canadian dollar. As with Australia, the other rich commodity-exporting floater, the IV estimates show estimates of the flexibility parameter in each subperiod that are higher than they were under ordinary least squares, but that are surprisingly insignificant statistically (Table 3.19 in the working paper). The Mexican peso shows a significant downward crawl throughout (until 2004), but it shows a peg to the dollar that is otherwise quite tight in 1988–91 (Table 2.20 in the working paper). Flexibility increases after the mid-point of the sample, which happens to be the peso crisis years of 1994–95. The flexibility parameter does not climb out of the range 0.3–0.4, indicating that reserve changes have remained substantial during the latter three subperiods, when the peso was supposedly floating. One likely explanation is that many monthly increases in reserves are associated with revenue earned by PEMEX oil exports, which the authorities leave in the form of dollar deposits, but which are not conventionally considered foreign exchange intervention. Using the oil price as an instrumental variable again produces flexibility parameters that are estimated higher in magnitude, but lower in significance (Table 3.20 in the working paper; 5.20 for the IV version where intervention is measured as a share of the monetary base).

V. Extensions In various extensions of the basic analysis, we have also

1. allowed coefficients to vary over time, even within the four-year sub-samples; 2. relaxed the constraint that log Ht and Dlog Rest enter with the same coefficient;

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3. entered the change in the interest rate alongside the change in reserves and the change in the exchange rate; 4. checked for robustness with respect to the numeraire unit used to define currency values; and 5. tried Monte Carlo studies on fabricated currencies to see if the new synthesis technique is giving us the right answer. The results in the extensions are generally in line with the results reported here. Using a different numeraire does not make too much difference, for example. Tables 6.1–6.10 of the working paper report the extension that allows the coefficients to vary over time, even within the subperiods (extension 1, above). We expand the specification of (3) to allow for trends in level and in the currency weights:

½DlogHt Dlogdt¼f ðtÞþwð1Þ½Dlog$t Dlogdt

þ wð2Þ½Dloght Dlogdt

þ wð3Þ½Dlogft Dlogdtþ...

þ bfDlogEMPðtÞg þ ut; ð4Þ where f(t) ¼ c0 þ c1 t. The time-dependent weight terms can be defined either using the exponential functional form for the weights w( j) so that they are automatically bounded by 0 and 1 or, for simplicity, linearly: w( j) ¼ b0( j) þ b1( j) t. The case of most interest is probably China (2005–07). There is no sign in this monthly data of a downward trend in the coefficient on the dollar: the estimated trend is 0.000. But there is a sign that the trend in the value of the yuan itself is rising over time: because the dependent variable is first differences, the statistically significant coefficient on ‘‘t’’ indicates an upward acceleration. The other results are as before: zero coefficients on nondollar currencies, zero coefficient on EMP, and an R2 of 1.00, all of which indicate a simple dollar peg holding during most of this period. Tables 7.1–7.8 of the working paper relax the constraint that Dlog H(t)and Dlog Res(t) enter EMP with the same coefficient (extension 2 above). The estimation takes into account that the variation of reserve changes is much larger than the variation of exchange rate changes (as can be seen in Figure 1), so that giving them equal weight means allowing the former to dominate in the estimates already reported. We define the new EMP variable as

D½logEMP ¼f½varD½logEx=ðvarD½logExþVarðD½logResÞg D½logRes

þfVarðD½logRes=ðvarD½logExþVarðD½logResÞg

D½logEx: ð5Þ

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Tables 8.1–8.5 of the working paper broaden the definition of EMP another step (extension 3 above). Here the change in the interest rate is entered alongside the change in reserves and the change in the exchange rate, as three alternative ways that the authorities can respond to a change in demand for their currency (Eichengreen, Rose, and Wyplosz, 1996). We define the new EMP variable as

D½logEMP¼fvarðD½logExÞ=VarðD½logResÞg D½logRes

þfvarðD½logExÞ=VarðDiÞg Di þ D½logExð6Þ

l ¼varðD½logExÞ=VarðD½logResÞ

g ¼varðD½logExÞ=VarðD½iÞ:

That is, D[log EMP] ¼ l D[log Res] þ g Di þ D[log Ex]. Table 9 of the working paper checks whether the results are robust with respect to the choice of numeraire, by using the Swiss franc as the standard by which currencies are valued, in place of the SDR. The results are similar. The choice of numeraire does not appear to make much difference. Last come the Monte Carlo exercises. We construct artificial exchange rate series under two regimes: managed float and band (Tables 10.1–10.4, and Tables 10.5–10.8, respectively, in the working paper). In the former case, we assume that a certain percentage of any change in EMP is absorbed in reserves and the rest in the exchange rate (‘‘leaning against the wind’’). In the latter case we restrict the width of the band or target zone to plus or minus 2 and ½ percent. Within the band we have tried a random walk subject to the restriction that the exchange rate cannot wander outside the band. One could consider other distributions. The Krugman theory of target zones provides a precise mathematical specification for the distribution within the band; but it assumes unrealistically that there is no intervention inside the band (only at the margins) and also that the band is 100 percent credible. In each case we try one version where the central parity is a basket that puts one-third weight on the dollar, one-third on the euro, and one-third on the yen, and we try another version where the central parity is simply pegged to the dollar. In the estimation, we constrain the weights to add to 1. Although the disturbances are drawn from a random normal distribution, the magnitude (variance of the distribution) is taken from real-world cases. We try two such cases: we take the Canadian dollar’s parameters as representative of a floating currency (a low-variance reserve case), and we take Papua New Guinea’s parameters as representative of a high-variance (attributable to commodity exports) small country with an intermediate regime. In most cases, the estimates correspond well with the parameters that were built in. For example, the difference between a band with sharp borders and a policy of consistently leaning against the wind turns out to be not all that important. In every case, the estimated weights are within one or two

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©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei standard errors of 1/3-1/3-1/3 for the basket case, and 1 for the dollar peg case. The technique tends to pick out the correct weights even though it is not designed for the specific statistical distribution of a band. The results are especially insensitive to the choice of numeraire, as between SDR and Swiss Franc. This is reassuring, because previously we have had to rely on the a priori theorem that the choice of numeraire makes no difference in the special case of a perfect peg. The technique seems equally robust when estimating the parameter that represents the degree of exchange rate flexibility. Typical R2’s tend to lie in the range 0.87–0.89. Our conclusion is that the technique works fairly well and robustly, when the true exchange rate regime is the one assumed (in this case leaning against the wind, around either a basket or the dollar). The technique may not always work as well in practice (of which one reflection may be lower R2’s in the tables reported in this paper), because in practice many countries do not in fact follow a regime such as a basket band for more than a few years in a row, without devaluing or otherwise changing the parameters. To allow for changes in parameters within the sample period one would have to take the technique to the next stage of econometric sophistication, and perhaps suffer nonetheless from data frequency insufficient to produce reliable estimates.

VI. Conclusion Intermediate exchange rate regimes remain alive and well. Some countries have announced basket regimes, often with an intermediate degree of flexibility that can be captured by some combination of a crawl, a band, or leaning-against-the-wind intervention. Most basket peggers keep the weights in the basket secret, which usually means they want to preserve a degree of freedom from prying eyes (whether to pursue a lower degree of de facto exchange rate flexibility, as with China, or a higher degree, as with others). The necessary task of distinguishing de facto from de jure exchange rate regimes has produced an active recent subliterature. But inferring de facto weights and inferring de facto flexibility are equally important, whereas most authors have hitherto done only one or the other. This paper’s main contribution is to propose a synthesis specification that allows estimation of true weights at the same time as estimation of the true tendency of monetary authorities to allow EMP to show up in the price, vs. the quantity, of foreign exchange. We have tried out the technique on some 20 currencies. The majority are countries reported by the IMF to have declared the use of baskets. But we have also included some floaters and some simple peggers. For the most part the synthesis technique seems to work as it should. Known floaters tend to score much higher flexibility parameters than known peggers, with the BBC countries in between. In some cases, the inferred behavior differs in some way from the de jure regime. For example China’s ‘‘basket’’ puts more weight on the dollar than the impression given by the government, but other declared basket peggers are not as firmly tied to the basket as they claim. Meanwhile,

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©International Monetary Fund. Not for Redistribution ESTIMATION OF DE FACTO EXCHANGE RATE REGIMES declared floaters often intervene heavily to dampen exchange rate fluctuations (fear of floating), but sometimes with reference to an anchor that is not a simple dollar parity as other authors may have assumed.

APPENDIX Estimating De Facto Exchange Rate Regimes This appendix estimates de facto exchange rate regimes for nine countries grouped as either known peggers (Tables A1 and A2), known basket peggers (Tables A3–A7), and known floaters (Tables A8 and A9). The dependent variable for the tables is changes in the value of local currency from January 1980 to June 2007. Values of all currencies are measured in terms of numeraire currency ¼ special drawings rights. Parameters are estimated by ordinary least squares: implicit weights in basket and degree of flexibility. For all of the tables, denotes statistically significant at the 0.10 level; statistically significant at the 0.05 level, and statistically significant at the 0.01 level.

Table A1. Known Peggers: Chinese Yuan (cny)

(1) (2)

(a) D log EMP defined as: Dlog rest+Dlog ExRt 2001–04 2005–08 USD 0.988*** 1.054*** (0.023) (0.110) JPY 0.006 0.039 (0.009) (0.035) Euro 0.054 0.080 (0.052) (0.081) D log EMP 0.035 0.107* (0.038) (0.051) KRW 0.061 (0.051) SGD 0.163 (0.104) MYR 0.025 (0.042) RUB 0.130 (0.146) AUD 0.026 (0.026) THB 0.036 (0.048) CAD 0.052 (0.031) Constant 0.001 0.000 (0.001) (0.002) Observations 48 23 R2 0.986 0.995 GBP 0.036 0.024

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Table A1 (concluded ) (1) (2)

(b)

D log EMP defined as: Drest/MBt1+Dlog ExRt cny 2001–04 2005–07 USD 0.916*** 1.029*** (0.079) (0.106) JPY 0.010 0.040 (0.017) (0.031) KRW 0.000 0.058 (0.010) (0.048) SGD 0.104 0.150 (0.091) (0.096) RUB 0.027 0.100 (0.034) (0.136) AUD 0.001 0.020 (0.011) (0.025) THB 0.054 0.036 (0.050) (0.044) CAD 0.017 0.052* (0.021) (0.028) EUR 0.061 0.062 (0.056) (0.077) Demp 0.069 0.103** (0.063) (0.039) MYR 0.027 (0.039) Constant 0.001 0.000 (0.001) (0.001) Observations 48 23 R2 0.989 0.996 GBP 0.028 0.030

Table A2. Known Peggers: Hong Kong dollar (hkd)

(1) (2) (3)

(a)

D log EMP defined as: Dlog rest+Dlog ExRt 1997–2000 2001–04 2005–08 JPY 0.000 0.005 0.007 (0.003) (0.011) (0.021) USD 0.987*** 326.676 1.009*** (0.016) (334.281) (0.039) KRW 0.003 0.001 0.007 (0.003) (0.014) (0.029) SGD 0.003 0.021 0.020 (0.009) (0.022) (0.049)

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Table A2 (concluded ) (1) (2) (3)

AUD 0.004 0.003 0.023 (0.008) (0.006) (0.020) MYR 0.005 327.678 0.009 (0.005) (334.278) (0.034) THB 0.002 0.018 0.024 (0.005) (0.018) (0.031) German mark 0.003 (0.008) Demp 0.001 0.027* 0.018 (0.005) (0.014) (0.032) Euro 0.004 0.003 (0.010) (0.033) Constant 0.000 0.000 0.000 (0.000) (0.000) (0.000) Observations 23 48 30 R2 0.998 0.998 0.997 GBP 0.012 0.011 0.017

(b)

D log EMP defined as: Drest/MBt1+Dlog ExRt hkd 1997–2000 2001–04 2005–07 JPY 0.000 0.001 0.009 (0.003) (0.012) (0.019) USD 0.987*** 0.990*** 1.016*** (0.013) (0.013) (0.032) KRW 0.003 0.001 0.012 (0.003) (0.014) (0.029) SGD 0.002 0.022 0.023 (0.009) (0.023) (0.047) AUD 0.004 0.001 0.023 (0.009) (0.006) (0.019) MYR 0.005 0.017 (0.005) (0.036) THB 0.001 0.017 0.017 (0.004) (0.018) (0.030) German mark 0.002 (0.007) Demp 0.000 0.006* 0.013 (0.001) (0.003) (0.010) Euro 0.004 0.007 (0.008) (0.030) Constant 0.000 0.000 0.000 (0.000) (0.000) (0.000) Observations 23 48 30 R2 0.998 0.997 0.997 GBP 0.012 0.012 0.024

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Table A3. Known Basket Peggers: Chile (clp)

(1) (2) (3) (4) (5) (6) (7)

(a) D log EMP defined 1980–83 1984–87 1988–91 1992–95 1996–99 2000–03 2004–07 as:

Dlog rest

+Dlog ExRt JPY 0.077 0.638** 0.046 0.201*** 0.108*** 0.172 0.031 (0.084) (0.255) (0.134) (0.057) (0.035) (0.108) (0.143) USD 0.642*** 0.609** 0.957*** 0.644*** 0.884*** 0.376** 0.755*** (0.232) (0.269) (0.112) (0.072) (0.079) (0.181) (0.152) French franc 0.421 0.101 0.233** (0.282) (0.442) (0.112) D log EMP 0.192** 0.513** 0.036 0.390*** 0.228*** 0.675*** 0.217*** (0.081) (0.222) (0.052) (0.062) (0.037) (0.100) (0.050) German mark 0.029 0.109* (0.091) (0.062) Euro 0.238** 0.584** (0.100) (0.259) Constant 0.006* 0.015** 0.010*** 0.006*** 0.004*** 0.001 0.002 (0.003) (0.007) (0.002) (0.002) (0.001) (0.003) (0.003) Observations 46 48 48 48 36 48 41 R2 0.681 0.768 0.886 0.907 0.911 0.769 0.692 GBP 0.014 0.146 0.230 0.127 0.101 0.214 0.307

(b)

Demp defined as: 1980–83 1984–87 1988–91 1992–95 1996–99 2000–03 2004–07

Drest/MBt1

+Dlog ExRt JPY 0.013 0.600 0.005 0.225** 0.089** 0.103 0.068 (0.102) (0.466) (0.103) (0.092) (0.041) (0.159) (0.159) USD 0.672** 1.245*** 1.040*** 0.870*** 0.998*** 0.617** 0.804*** (0.250) (0.330) (0.075) (0.101) (0.090) (0.261) (0.172) German mark 0.278 0.317 0.276** 0.150 0.065 (0.208) (0.475) (0.125) (0.141) (0.078) Demp 0.148* 0.101 0.014 0.053* 0.048*** 0.107* 0.090*** (0.073) (0.100) (0.012) (0.026) (0.011) (0.060) (0.028) Euro 0.024 0.613** (0.132) (0.295) Constant 0.010*** 0.023*** 0.008*** 0.006** 0.005*** 0.002 0.002 (0.003) (0.008) (0.002) (0.003) (0.002) (0.004) (0.003) Observations 46 48 48 48 36 48 41 R2 0.614 0.525 0.890 0.834 0.864 0.360 0.622 GBP 0.037 0.528 0.241 0.055 0.152 0.256 0.349

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Table A4. Known Basket Peggers: Latvian Lat (lvl)

(1) (2) (3) (4)

(a) Demp defined as: 1992–95 1996–99 2000–03 2004–07 Dlog rest+Dlog ExRt JPY 0.319*** 0.168*** 0.110*** 0.008 (0.079) (0.012) (0.026) (0.055) USD 0.427** 0.434*** 0.338*** 0.096 (0.169) (0.025) (0.088) (0.124) RUB 0.014 0.006 0.118 0.455*** (0.039) (0.006) (0.085) (0.154) German mark 0.260 0.270*** (0.148) (0.032) Demp 0.109** 0.024** 0.009 0.027 (0.045) (0.010) (0.013) (0.021) Euro 0.197*** 0.496*** (0.024) (0.092) Constant 0.004 0.001 0.000 0.003 (0.002) (0.001) (0.001) (0.002) Observations 16 36 48 42 R2 0.824 0.958 0.925 0.617 GBP 0.019 0.133 0.237 0.154

(b)

Demp defined as: 1993–96 1997–00 2001–04 2005–07

Drest/MBt1+Dlog ExRt JPY 0.234*** 0.152*** 0.056 0.003 (0.046) (0.019) (0.034) (0.144) USD 0.439*** 0.429*** 0.048 0.005 (0.069) (0.067) (0.124) (0.278) RUB 0.012 0.005 0.391*** 0.256 (0.024) (0.007) (0.118) (0.490) DEM 0.268*** 0.315*** (0.036) (0.057) Demp 0.081** 0.025 0.009 0.002 (0.032) (0.015) (0.014) (0.048) EUR 0.332*** 0.550 (0.055) (0.345) Constant 0.000 0.001 0.002 0.000 (0.001) (0.001) (0.001) (0.003) Observations 34 18 48 24 R2 0.894 0.948 0.799 0.517 GBP 0.072 0.110 0.173 0.202

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Table A5. Known Basket Peggers: Maltese Lira (mtl)

(1) (2) (3) (4) (5) (6) (7)

(a)

Demp 1980–83 1984–87 1988–91 1992–95 1996–99 2000–03 2004–07 defined as:

Dlog rest

+Dlog ExRt JPY 0.054* 0.163*** 0.011 0.085 0.178 0.041** 0.002 (0.030) (0.049) (0.033) (0.086) (0.143) (0.020) (0.032) USD 0.405*** 0.258*** 0.288*** 0.242*** 0.400** 0.212*** 0.055* (0.053) (0.036) (0.027) (0.077) (0.187) (0.034) (0.031) French franc 0.280*** 0.407*** 0.381*** (0.053) (0.064) (0.041) Demp 0.061** 0.083 0.034 0.212 0.540*** 0.073*** 0.029 (0.028) (0.089) (0.039) (0.162) (0.126) (0.026) (0.021) German mark 0.419*** 0.168 (0.088) (0.230) Euro 0.502*** 0.859*** (0.024) (0.054) Constant 0.001 0.001 0.000 0.003 0.002 0.000 0.000 (0.001) (0.001) (0.001) (0.002) (0.004) (0.001) (0.001) Observations 46 48 48 48 36 45 41 R2 0.875 0.918 0.933 0.592 0.724 0.934 0.894 GBP 0.261 0.171 0.321 0.253 0.253 0.245 0.089

(b)

Demp 1980–83 1984–87 1988–91 1992–95 1996–99 2000–03 2004–07 defined as:

Drest/MBt1

+Dlog ExRt JPY 0.054*** 0.090*** 0.003 0.071 0.165 0.039* 0.005 (0.020) (0.033) (0.029) (0.079) (0.157) (0.020) (0.032) USD 0.352*** 0.297*** 0.303*** 0.227*** 0.400** 0.207*** 0.049 (0.044) (0.024) (0.024) (0.079) (0.191) (0.034) (0.031) German mark 0.326*** 0.466*** 0.349*** 0.435*** 0.154 (0.034) (0.049) (0.034) (0.076) (0.257) Demp 0.023 0.004 0.051 0.175 0.480*** 0.057** 0.017 (0.014) (0.056) (0.034) (0.144) (0.129) (0.021) (0.015) Euro 0.505*** 0.867*** (0.024) (0.053) Constant 0.000 0.000 0.000 0.003 0.003 0.000 0.000 (0.001) (0.001) (0.001) (0.002) (0.004) (0.001) (0.001) Observations 46 48 48 48 36 45 41 R2 0.919 0.957 0.934 0.571 0.675 0.933 0.892 GBP 0.267 0.147 0.344 0.268 0.281 0.248 0.089

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Table A6. Known Basket Peggers: Norwegian Kroner (nok)

(1) (2) (3) (4) (5) (6) (7)

(a) Demp 1980–83 1984–87 1988–91 1992–95 1996–99 2000–03 2004–07 defined as:

Dlog rest

+Dlog ExRt JPY 0.117 0.119* 0.016 0.037 0.074 0.081 0.123 (0.073) (0.068) (0.058) (0.048) (0.051) (0.079) (0.168) USD 0.332*** 0.067 0.177*** 0.089** 0.260** 0.017 0.089 (0.089) (0.067) (0.033) (0.044) (0.114) (0.110) (0.144) French franc 0.391*** 0.699*** 0.578*** (0.092) (0.116) (0.066) Demp 0.017 0.106* 0.069** 0.050 0.076** 0.029 0.093** (0.026) (0.054) (0.029) (0.031) (0.035) (0.057) (0.044) German mark 0.776*** 0.873*** (0.066) (0.100) Euro 0.803*** 1.135*** (0.113) (0.220) Constant 0.003 0.005** 0.001 0.002 0.001 0.000 0.001 (0.002) (0.002) (0.001) (0.002) (0.002) (0.003) (0.003) Observations 46 48 48 48 36 48 42 R2 0.728 0.768 0.781 0.798 0.793 0.624 0.546 GBP 0.160 0.115 0.229 0.171 0.060 0.100 0.169

(b)

Demp 1980–83 1984–87 1988–91 1992–95 1996–99 2000–03 2004–07 defined as:

Drest/MBt1

+Dlog ExRt JPY 0.124* 0.055 0.007 0.049 0.099* 0.056 0.005 (0.063) (0.077) (0.054) (0.047) (0.051) (0.075) (0.304) USD 0.276*** 0.122* 0.185*** 0.076* 0.264* 0.008 0.048 (0.073) (0.069) (0.034) (0.039) (0.146) (0.108) (0.273) German mark 0.451*** 0.689*** 0.543*** 0.795*** 0.910*** (0.056) (0.133) (0.061) (0.073) (0.109) Demp 0.010 0.027 0.017* 0.012 0.015 0.009 0.010 (0.014) (0.018) (0.009) (0.011) (0.009) (0.012) (0.028) Euro 0.855*** 1.469** (0.114) (0.542) Constant 0.005** 0.006* 0.000 0.002 0.001 0.000 0.005 (0.002) (0.003) (0.001) (0.002) (0.002) (0.003) (0.008) Observations 46 48 48 48 36 48 11 R2 0.785 0.724 0.758 0.782 0.765 0.625 0.658 GBP 0.149 0.134 0.279 0.178 0.075 0.082 0.523

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Table A7. Known Basket Peggers: Seychelles Rupee (scr)

(1) (2) (3) (4) (5) (6) (7)

(a) Demp 1980–83 1984–87 1988–91 1992–95 1996–99 2000–03 2004-07 defined as:

Dlog rest

+Dlog ExRt JPY 0.068 0.230*** 0.178*** 0.201*** 0.030 0.321*** 0.073 (0.154) (0.033) (0.020) (0.016) (0.049) (0.115) (0.086) USD 0.391*** 0.417*** 0.370*** 0.396*** 0.586*** 0.367*** 0.934*** (0.099) (0.024) (0.021) (0.016) (0.113) (0.125) (0.080) French franc 0.430*** 0.250*** 0.312*** (0.135) (0.038) (0.028) Demp 0.015 0.002 0.002 0.004 0.013 0.011 0.007 (0.018) (0.003) (0.002) (0.002) (0.015) (0.017) (0.009) German mark 0.277*** 0.085 (0.018) (0.140) Euro 0.030 0.149 (0.126) (0.096) Constant 0.007 0.000 0.000 0.000 0.003 0.001 0.003** (0.006) (0.001) (0.000) (0.000) (0.002) (0.003) (0.001) Observations 46 48 48 48 36 48 42 R2 0.530 0.972 0.982 0.992 0.565 0.391 0.833 GBP 0.246 0.103 0.140 0.125 0.299 0.343 0.156

(b)

Demp scr 1980–83 1984–87 1988–91 1992–95 1996–99 2000–03 2004–07 defined as:

Drest/MBt1

+Dlog ExRt JPY 0.062 0.188*** 0.170*** 0.203*** 0.047 0.319*** 0.077 (0.154) (0.029) (0.016) (0.016) (0.048) (0.115) (0.087) USD 0.359*** 0.442*** 0.378*** 0.394*** 0.538*** 0.354*** 0.926*** (0.097) (0.018) (0.019) (0.017) (0.099) (0.127) (0.081) German mark 0.460*** 0.288*** 0.299*** 0.278*** 0.130 (0.131) (0.027) (0.028) (0.018) (0.106) Demp 0.044 0.004 0.006 0.006 0.144 0.053 0.016 (0.044) (0.005) (0.007) (0.005) (0.103) (0.041) (0.015) Euro 0.006 0.160 (0.120) (0.102) Constant 0.005 0.000 0.000 0.000 0.003 0.001 0.003** (0.005) (0.000) (0.000) (0.000) (0.002) (0.003) (0.001) Observations 46 48 48 48 36 48 42 R2 0.553 0.985 0.985 0.992 0.621 0.413 0.836 GBP 0.244 0.081 0.154 0.125 0.284 0.333 0.163

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Table A8. Known Floaters: Australian Dollar (aud)

(1) (2) (3) (4)

(a) D log EMP defined as: 1992–95 1996–99 2000–03 2004–07 Dlog rest+Dlog ExRt JPY 0.008 0.210 0.250** 0.160 (0.102) (0.135) (0.104) (0.150) USD 1.036*** 0.676*** 0.294 0.181 (0.070) (0.236) (0.201) (0.142) German mark 0.171 0.140 (0.141) (0.180) D log EMP 0.242*** 0.062 0.175*** 0.047 (0.043) (0.070) (0.043) (0.040) Euro 0.554*** 0.107 (0.123) (0.247) Constant 0.002 0.006 0.001 0.001 (0.002) (0.005) (0.003) (0.003) Observations 48 36 48 42 R2 0.833 0.327 0.538 0.171 GBP 0.143 0.254 0.098 0.552

(b)

Demp defined as: 1992–95 1996–99 2000–03 2004–07

Drest/MBt1+Dlog ExRt 0.017 0.208 0.251** 0.165 (0.095) (0.123) (0.103) (0.151) USD 0.996*** 0.647*** 0.269 0.177 (0.066) (0.227) (0.202) (0.144) German mark 0.148 0.111 (0.133) (0.200) D emp 0.300*** 0.175 0.178*** 0.032 (0.050) (0.128) (0.044) (0.028) Euro 0.542*** 0.099 (0.124) (0.243) Constant 0.003 0.006 0.001 0.001 (0.002) (0.004) (0.003) (0.003) Observations 48 36 48 42 R2 0.853 0.385 0.541 0.164 GBP 0.134 0.256 0.062 0.559

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Table A9. Known Floaters: Canadian Dollar (cad)

(1) (2) (3) (4) (5)

(a) D log EMP defined as: cad 1990– 1994–97 1998–01 2002–05 2006–09 Dlog rest+Dlog ExRt 93 JPY 0.046 0.018 0.035 0.324*** 0.162 (0.058) (0.080) (0.076) (0.092) (0.283) USD 0.902*** 0.966*** 0.321 0.449*** 0.543* (0.049) (0.095) (0.322) (0.112) (0.282) German mark 0.007 0.018 0.101 (0.066) (0.075) (0.313) D log EMP 0.106*** 0.067*** 0.124** 0.366*** 0.401** (0.014) (0.024) (0.034) (0.112) (0.129) Euro 0.337* 0.189 (0.179) (0.522) Constant 0.002* 0.002 0.008 0.004 0.005 (0.001) (0.002) (0.006) (0.002) (0.005) Observations 48 48 8 48 14 R2 0.958 0.790 0.776 0.708 0.684 GBP 0.046 0.002 0.543 0.110 0.430

(b)

Demp defined as: Drest/ cad 1990– 1994–97 1998–01 2002–05 2006–07 MBt1+Dlog ExRt 93 JPY 0.061 0.018 0.036 0.321*** 0.152 (0.060) (0.079) (0.078) (0.094) (0.273) USD 0.870*** 0.953*** 0.302 0.468*** 0.540* (0.058) (0.091) (0.321) (0.116) (0.270) German mark 0.017 0.026 0.118 (0.067) (0.076) (0.309) Demp 0.150*** 0.084*** 0.126** 0.355*** 0.433** (0.024) (0.028) (0.035) (0.099) (0.141) EUR 0.349* 0.182 (0.179) (0.483) Constant 0.002* 0.002 0.009 0.004* 0.005 (0.001) (0.002) (0.006) (0.002) (0.005) Observations 48 48 8 48 14 R2 0.957 0.796 0.777 0.713 0.709 GBP 0.052 0.003 0.544 0.138 0.430

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REFERENCES Be´ nassy-Que´ re´ , Agne` s, 1999, ‘‘Exchange Rate Regimes and Policies: An Empirical Analysis,’’ in Exchange Rate Policies in Emerging Asian Countries, ed. by Stefan Collignon, Jean Pisani-Ferry, and Yung Chul Park (London, Routledge). ______, Beno^ıt Coeure´ , and Vale´ rie Mignon, 2006, ‘‘On the Identification of De Facto Currency Pegs,’’ Journal of the Japanese and International Economies, Vol. 20 (March), pp. 112–27. Bilson, John, and Jacob Frenkel, 1979, ‘‘Dynamic Adjustment and the Demand for International Reserves,’’ NBER Working Paper 407 (Cambridge, Massachusetts, National Bureau of Economic Research). Burbula, Andrea, and Inci O¨ tker-Robe, 2002, ‘‘The Evolution of Exchange Rate Regimes Since 1990: Evidence from De Facto Policies,’’ IMF Working Paper 02/155, September (Washington, IMF). ______, 2003, ‘‘Exchange Arrangements and Foreign Exchange Markets: Development and Issues,’’ Monetary and Exchange Affairs Department, International Monetary Fund. Calvo, Guillermo, and Carmen Reinhart, 2002, ‘‘Fear of Floating,’’ Quarterly Journal of Economics, Vol. 117, No. 2 (May), pp. 379–408. Eichengreen, Barry, 2006, ‘‘China’s Exchange Rate Regime: The Long and Short of It,’’ revision of paper presented at a conference on Chinese money and finance, Columbia University, New York, February 2–3. ______, Andy Rose, and Charles Wyplosz, 1996, ‘‘Contagious Currency Crises,’’ NBER Working Paper 5681 (Cambridge, Massachusetts, National Bureau of Economic Research). Frankel, Jeffrey, 1993, ‘‘Is Japan Creating a Yen Bloc in East Asia and the Pacific?’’ in Regionalism and Rivalry: Japan and the U.S. in Pacific Asia, ed. by Jeffrey Frankel and Miles Kahler (Chicago, University of Chicago Press). ______, and Shang-Jin Wei, 1994, ‘‘Yen Bloc or Dollar Bloc? Exchange Rate Policies of the East Asian Economies,’’ in Macroeconomic Linkages: Savings, Exchange Rates and Capital Flows, ed. by Takatoshi Ito and Anne O. Krueger (Chicago, University of Chicago Press). ______, 1995, ‘‘Emerging Currency Blocs,’’ in The International Monetary System: Its Institutions and its Future, ed. by Hans Genberg (Berlin, Springer). ______, 2007, ‘‘Assessing China’s Exchange Rate Regime,’’ Economic Policy, No. 51 (July), pp. 575–14. ______, 2008, ‘‘Estimation of De Facto Exchange Rate Regimes: Synthesis of the Techniques for Inferring Flexibility and Basket Weights,’’ NBER Working Paper No. 14016 (May) (Cambridge, Massachusetts, National Bureau of Economic Research). ______, Sergio Schmukler, and Luis Serve´ n, 2000, ‘‘Verifiability and the Vanishing Intermediate Exchange Rate Regime,’’ in Brookings Trade Forum 2000, ed. by Susan Collins and Dani Rodrik (Washington, Brookings Institution). ______, Eduardo Fajnzylber, Sergio Schmukler, and Luis Serve´ n, 2001, ‘‘Verifying Exchange Rate Regimes,’’ Journal of Development Economics, Vol. 66, No. 2 (December), pp. 351–86. Frenkel, Jacob, 1978, ‘‘International Reserves Under Alternative Exchange Rate Regimes and Aspects of the Economics of Managed Float,’’ NBER Working Paper No. 287 (Cambridge, Massachusetts, National Bureau of Economic Research).

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©International Monetary Fund. Not for Redistribution Jeffrey Frankel and Shang-Jin Wei

Ghosh, Atish, Anne-Marie Gulde, and Holger Wolf, 2002, ‘‘Currency Boards: More than a Quick Fix?’’ Economic Policy, Vol. 31 (October), pp. 270–335. Girton, Lance, and Don Roper, 1977, ‘‘A Monetary Model of Exchange Market Pressure Applied to the Postwar Canadian Experience on Growth,’’ American Economic Review, Vol. 67, No. 4 (September), pp. 537–48. Levy-Yeyati, Eduardo, and Federico Sturzenegger, 2003, ‘‘To Float or to Trail: Evidence on the Impact of Exchange Rate Regimes on Growth,’’ American Economic Review, Vol. 93, No. 4 (September), pp. 1173–1193. ______, 2005, ‘‘Classifying Exchange Rate Regimes: Deeds vs. Words,’’ European Economic Review, Vol. 49, No. 6 (August), pp. 1603–35. Masson, Paul R, 2001, ‘‘Exchange Rate Regime Transitions,’’ Journal of Development Economics, Vol. 64, No. 2 (April), pp. 571–86. Ohno, Kenichi, 1999, ‘‘Exchange Rate Management in Developing Asia,’’ Working Paper No. 1 (January), Asian Development Bank Institute. Reinhart, Carmen, and Kenneth Rogoff, 2004, ‘‘The Modern History of Exchange Rate Arrangements: A Reinterpretation,’’ Quarterly Journal of Economics, Vol. 119, No. 1, pp. 1–48. Rodrik, Dani, 2006, ‘‘The Social Cost of Foreign Exchange Reserves,’’ NBER Working Paper No. W11952 (Cambridge, Massachusetts: National Bureau of Economic Research). Shah, Ajay, Achim Zeileis, and Ila Patnaik, 2005, ‘‘What is the New Chinese Currency Regime?’’ Research Report Series, Department of Statistics and Mathematics, (Vienna, Austria, Wirtschaftsuniversita¨ t Wien), No. 3, November. Shambaugh, Jay, 2004, ‘‘The Effects of Fixed Exchange Rates on Monetary Policy,’’ Quarterly Journal of Economics, Vol. 119, No. 1 (February), pp. 301–52. Tavlas, G., H. Dellas, and A. Stockman, 2006, ‘‘The Classification and Performance of Alternate Exchange-Rate Systems,’’ Bank of Greece, March. Williamson, John, 2001, ‘‘The Case for a Basket, Band and Crawl (BBC) Regime for East Asia,’’ in Future Directions for Monetary Policies in East Asia, ed. by David Gruen and John Simon (Sydney, Reserve Bank of Australia). Yamazaki, Kazuo, 2006, ‘‘Inside the Currency Basket,’’ Columbia University and Mitsubishi UFJ Trust and Banking Corp., December.

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Exchange Rate Policy Attitudes: Direct Evidence from Survey Data

J. LAWRENCE BROZ, JEFFRY FRIEDEN, and STEPHEN WEYMOUTH

Analyses of the political economy of exchange rate policy posit that firms and individuals in different sectors of the economy have distinct policy attitudes toward the level and stability of the exchange rate. Most such approaches hypothesize that internationally exposed firms prefer more stable currencies and that producers of tradables prefer a relatively depreciated real exchange rate. As sensible as such expectations may be, there are few direct empirical tests of them. This paper offers micro-level, cross-national evidence on sectoral attitudes about the exchange rate. Using firm-level data from the World Bank’s World Business Environment Survey, we find systematic patterns linking sector of economic activity to exchange rate policy positions. Owners and managers of firms producing tradable goods prefer greater stability of the exchange rate: in countries with a floating currency, manufacturers are more likely to report that the exchange rate causes problems for their business. With respect to the level of the exchange rate, we find that tradables producers—particularly manufacturers and export producers—are more likely to be unhappy following an appreciation of the real exchange rate than are firms in nontradable sectors (services and construction). These findings confirm theoretical expectations about the relationship between economic position and currency policy preferences. [JEL F30, F31, F33, F36, F41] IMF Staff Papers (2008) 55, 417–444. doi:10.1057/imfsp.2008.16; published online 17 June 2008

J. Lawrence Broz is an associate professor in the Department of Political Science at the University of California, San Diego; Jeffry Frieden is a professor in the Department of Government at Harvard University; and Stephen Weymouth is a Ph.D. student in the Department of Political Science at the University of California, San Diego. The authors thank Justin Wolfers and Barry Eichengreen for helpful comments. They also thank participants at the IMF’s Eighth Annual Jacques Polak Research Conference, Washington, November 15–16, 2007.

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s in other areas of public policy, governments’ choices of exchange rate A policies are conditioned by the preferences of their constituents. The nominal exchange rate regime and the level of the real exchange rate can have powerful effects on the private sector, and economic agents want government policies that favor them. A government that ignores its constituents’ concerns about the exchange rate will come under pressure to change course. Indeed, some of the most dramatic events in the history and recent experience of exchange rate policy have to do with the preferences of social groups: how they changed, conflict among them, how strong they are. The battle over gold in the late 19th and early 20th century—whether in the United States in the 1890s, the United Kingdom in 1925, or in Latin America throughout— was largely about which groups and sectors of the economy were likely to win, and lose, from being on the gold standard (Eichengreen, 1992; Simmons, 1994; Hefeker, 1995; Frieden, 1997; Broz, 1997). So too did the process of European monetary integration implicate powerful interests on both sides of the issue, as it continues to do in the accession countries of Eastern and Central Europe and the former Soviet Union (Eichengreen and Frieden, 1993; Frieden, 2002). Modern currency crises often begin with a government immobilized by contending demands to sustain a fixed rate and to devalue (Klein and Marion, 1997; Frieden, Ghezzi, and Stein, 2001; Leblang, 2002). Devising a politically viable policy response to exchange-market developments—including real appreciations and attacks on currencies—has proved extraordinarily difficult for governments from Argentina to Russia. Just as it is important for policymakers to address the attitudes of powerful constituents toward the exchange rate, it is important for scholars to understand what these attitudes are and how they are expressed. Nonetheless, there is very little systematic empirical work on the policy preferences of major economic agents. Although papers by Collins and Giavazzi (1992), Ga¨rtner (1997), Gabel (1998), and Scheve (2004) address related macroeconomic topics—differences in individual attitudes toward inflation, the euro, and global economic integration—there is little work that directly addresses the exchange rate attitudes of business owners operating in different sectors of the economy. By contrast, there is a large body of work that examines sectoral and factoral attitudes toward trade policy (Balistreri, 1997; Scheve and Slaughter, 2001; Beaulieu, 2002; and Mayda and Rodrik, 2005). This paper exploits a large cross-national survey, the World Bank’s World Business Environment Survey (WBES), to try to uncover the relationship between the economic activities of firms and their owners’ and managers’ attitudes toward the exchange rate (World Bank, 2000). The survey tells us only the extent to which corporate respondents regarded the exchange rate as ‘‘a problem,’’ which in itself is of limited use. However, we also know the currency regime prevailing in the country at the time of the survey, the level of the real exchange rate, and many things about the firms in question. By relating the prevailing exchange rate policy and the firm’s

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©International Monetary Fund. Not for Redistribution EXCHANGE RATE POLICY ATTITUDES: DIRECT EVIDENCE FROM SURVEY DATA economic characteristics to how ‘‘problematic’’ the currency is perceived to be, we can draw inferences about the sources of attitudes toward the exchange rate. We start with expectations from the theoretical literature on currency policy preferences. With respect to the exchange rate regime, we expect firms with substantial cross-border exposure to be particularly sensitive to currency volatility, and thus to be more satisfied with a fixed exchange rate. With respect to the level of the real exchange rate, we expect firms in the tradables sectors to be more satisfied when the currency is weak and more dissatisfied when it is strong. We find, indeed, that owners and managers of internationally oriented firms prefer greater stability of the exchange rate: in countries with a floating currency, manufacturers are more likely to report that the exchange rate causes problems for their business than are producers of nontraded goods and services, who typically do not require foreign exchange. With respect to the level of the exchange rate, we find that exporters and manufacturers are more likely to be unhappy following an appreciation of the real exchange rate than are firms in domestically oriented nontradable sectors (services and construction). These findings conform to expectations about the cross- sectoral distributional effects of exchange rates.

I. Exchange Rate Policy, Politics, and Policy Preferences The exchange rate is centrally important to economic activity, and govern- ment policy has a powerful impact on the currency. After all, the exchange rate is the single most important price in any economy, and it is a price that is routinely set, or at least targeted, by many governments. There is an enormous literature on appropriate currency policy, but unlike in the case of trade or fiscal policy, there is no simple welfare benchmark, so that debates typically involve different weightings of the trade-offs inherent in exchange rate policy choices. Supporters of flexibility confront opponents of volatility, but those who value the credibility and predictability of a fixed rate square off against those who dread its rigidity. A strong currency provides a powerful tool against inflation, and boosts national purchasing power; a weak currency gives national producers great incentives to sell into world markets. To paraphrase Jeffrey Frankel (1999), no single currency policy is right for all people. Exchange rate policy is constrained by contending economic interests and policy preferences, which makes it important to understand these interests and preferences. A theoretical literature deduces attitudes from the distributional consequences of various exchange rate regime arrangements predicted by economic theory (Frieden, 1991), while empirical analyses have imputed attitudes indirectly from actual currency policies and legislative and other voting behavior (Eichengreen, 1995; Frieden, 1997; Frieden, Ghezzi, and Stein, 2001; and Frieden, 2002). But scholars have rarely been able to

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find ways of directly mapping economic position to exchange rate policy preferences. There are two relevant dimensions of variation along which preferences may vary: on the regime by which the currency is managed, and on the level of the currency. In the first instance, the issue is whether to float or fix the exchange rate—and if to float, in which of the many possible ways.1 In the second instance, assuming the currency is not fixed, the question is the desired level of the real exchange rate.2 There is always the option to let the currency float completely freely, although developing countries have shown themselves reluctant to do this (Calvo and Reinhart, 2002; Levy Yeyati and Sturzenegger, 2005). On both dimensions, the attitudes of economic agents are likely to differ.

Attitudes Toward the Exchange Rate Regime: Stability and Credibility or Policy Flexibility? In an open economy, the main advantage of a fixed-rate regime is to lower exchange rate risk and transactions costs that can impede international trade and investment.3 Volatile exchange rates create uncertainty about international transactions, adding a risk premium to the costs of goods and assets traded across borders. In addition, an exchange rate peg can enhance monetary-policy credibility. Theory and evidence suggest that fixing the exchange rate to the currency of a low-inflation country both promotes international trade and investment and disciplines monetary policy by providing an observable nominal anchor.4 But fixing the exchange rate requires that the government sacrifice its capacity to run an independent monetary policy. A floating exchange rate, on the other hand, has the great advantage of allowing a government to pursue an independent monetary policy. This independence provides flexibility to accommodate foreign and domestic shocks, including changes in the terms of trade and world financial conditions, and to affect the competitiveness of (relative prices faced by) the tradable goods sector.

1Obviously, policymakers have a wide choice of regime, ranging from a completely free float to a variety of managed floats, degrees of fixity ranging from a target zone to a peg, and a currency board or dollarization. This discussion focuses on the extremes—hard pegs and pure floats—however, because the analysis of intermediate cases flows from the extremes, and the trade-offs described apply to the intermediate choices, albeit never as starkly as to the extremes. 2Under most regimes a government must decide whether it prefers a relatively appreciated or relatively depreciated currency. Free floats are rare, and by the same token, countries that opt for a pegged regime always have the choice of abandoning the peg. 3See Mundell (1961), McKinnon (1963), and Kenen (1969). A more recent survey is Tavlas (1994). 4See for example, the empirical results in Frankel (1995), Rose (2000), Ve´gh (1992), and Ghosh and others (1997).

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In an open economy, then, economic agents confront a trade-off between two competing sets of values. On the one hand, a fixed rate brings stability and credibility, and on the other, it sacrifices flexibility. The different valuation of this trade-off depends largely on the extent to which the economic actors are engaged in international economic activity (Frieden, 1991; Hefeker, 1997). Those heavily involved in foreign trade and investment—typically including exporters, international investors, the commercial and financial sectors, and those with substantial foreign currency liabilities—should favor exchange rate stability, because currency volatility is an everyday concern that makes their business riskier and more costly. By the same token, these groups care less about a loss of national monetary autonomy, because they typically do business in several countries, and can shift business or assets abroad if domestic conditions become unfavorable. By contrast, groups whose economic activity is confined to the domestic economy benefit from a floating regime. The nontradables sectors (services, construction, transport) belong in this camp. They are not required to deal in foreign exchange and so are free of the risks and costs of currency volatility. They are highly sensitive to domestic macroeconomic conditions and thus favor the national autonomy made possible by floating. Tradables producers may have reasons to oppose a fixed rate, as it eliminates the possibility of a depreciation to maintain or restore the competitiveness of tradables producers. This raises the second issue: the level of the real exchange rate.

Attitudes Toward the Level of the Exchange Rate The real exchange rate affects the relative price of traded goods in both local and foreign markets. There is no clear economic efficiency argument for or against any particular level. A strong (appreciated) currency gives residents greater purchasing power, but also entails a loss of competitiveness for tradables producers. A real appreciation benefits consumers of imports and harms producers of goods that compete with imports (and exporters). So tradable (import-competing and exporting) industries lose from a currency appreciation, but domestically oriented (nontradable) industries and domestic consumers gain. Of course, a real depreciation has the opposite effects, stimulating demand for locally produced tradable products, but raising the prices that consumers pay for foreign goods and services. Currency depreciations help exporting and import-competing industries at the expense of domestic consumers and producers of nontraded goods and services. Thus the level of the exchange rate, too, involves two competing goals—stimulating local tradables producers and raising local purchasing power. The benefit of increasing the competitiveness of national producers comes at the cost of reducing the real income of national consumers and vice versa.

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Two issues complicate theories of exchange rate policy preferences. First, exporters are likely to be torn between a concern for currency stability, on the one hand, and a concern for a favorable level of the exchange rate, on the other. These two concerns conflict, inasmuch as a fixed rate rules out adjusting the nominal exchange rate to improve the competitive position of exporters. Whether exporters favor stability over competitiveness, or vice versa, is likely to depend on such factors as the price sensitivity of consumers of exports, the ability of exporters to hedge against currency volatility, and so on. Second, and closely related, is the fact that tradable producers’ concern about currency movements depends upon how directly they are affected by changes in the exchange rate, which is a function of such things as pass- through, currency invoicing, and the importance of imported inputs (Campa and Goldberg, 1997 and 2005; Devereux and Engel, 2002). Generally, both exporters and import-competers in industries with high pass-through are more sensitive to the relative price effects of currency movements than those with low pass-through, because their prices respond more directly to changes in exchange rates. The policy preferences of exporters are thus likely to be contingent on a large number of factors. In both instances, the extent to which concern about volatility is more important than concern about the level of the exchange rate is largely an empirical question. A simplified picture of the expected preferences of firms would include:

Internationally exposed firms, including tradables producers, will prefer greater currency stability, hence a fixed exchange rate. Tradables producers will prefer greater competitiveness, hence a depreciated currency.

In what follows, we assess the empirical relevance and accuracy of these expectations in a large, cross-national survey of firm managers and owners.

II. Data and Methods To analyze attitudes toward the exchange rate, we use data from the World Bank’s WBES.5 The WBES was administered to owners and managers of over 10,000 firms in 80 countries in 1999, applying a common survey instrument to a representative sample of at least 100 firms in each country. We look at individual responses to the following question: ‘‘How problematic is the exchange rate for the operation and growth of your business?’’ Responses varied along the following ordered scale: 1 ¼ no obstacle, 2 ¼ minor obstacle, 3 ¼ moderate obstacle, 4 ¼ major obstacle. These individual responses represent the dependent variable of our analysis, which we will refer to as Exchange Rate Problem. The variable Exchange Rate Problem exhibits significant variation both across and within countries. The average value of Exchange Rate Problem is

5For a discussion of the WBES project, see Batra, Kaufmann, and Stone (2003).

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2.59, which indicates that the exchange rate represents a nontrivial con sideration for the average business. The average response in El Salvador of 2.58 is closest to the overall mean response. Hungary represents the lowest country average (1.61); managers in Ecuador, by contrast, were most likely to view the exchange rate as an obstacle to their business (3.74). This is understandable since Ecuador suffered a serious economic crisis in 1999. On the brink of hyperinflation and immersed in a deep financial crisis, Ecuador abandoned its currency in January 2000 and adopted the U.S. dollar as its legal tender. The average response in Thailand is also notably high at 3.63, which reflects the turmoil caused by the collapse of the baht in 1997 and the subsequent Asian financial and currency crises. The overall standard deviation of Exchange Rate Problem is 1.16, the between-country standard deviation is 0.60, and the within-country standard deviation is 1.00. Table 1 reports overall summary statistics. Tables 2a and 2b are the correlation matrices for our firm- and country-level data. We want to assess how the respondent firms’ sectors, conditioned by the country’s exchange rate regime and the level of the real exchange rate, affect attitudes toward the currency. For this purpose, we need to classify countries by exchange rate regime. We employ two widely used classifications of de facto exchange rate regimes: Levy Yeyati and Sturzenegger (2005) and Reinhart and Rogoff (2004). Although these classification schemes differ in details, both attempt to capture the actual behavior of the exchange rate. Levy Yeyati and Sturzenegger, henceforth ‘‘LYS,’’ categorize countries as floats or pegs according to observed changes in the nominal exchange rate, the volatility of these changes, and the volatility of international reserves.6 Reinhart and Rogoff, henceforth ‘‘RR,’’ exploit the conditional probability of the exchange rate staying within a given range over a rolling five-year window, and use information about parallel (dual market) exchange rates in determining whether a regime continues from one year to the next. Our results are largely robust to these alternative regime classification technologies. In Tables 3a and 3b, we report all our data by country according to the LYS ‘‘floating’’ and ‘‘pegged’’ exchange rate regime classifications. Table 3c provides a comparison of LYS and RR regimes in 1999, the year the WBES survey was administered. Note that RR classifies Indonesia as having a floating regime in 1999 but LYS do not. We drop Indonesia from the RR float sample because, in the aftermath of the currency crisis firms from all sectors would likely express dissatisfaction with the exchange rate. Indeed, the standard deviation of Exchange Rate Problem in Indonesia is 0.95, while its average value of 3.54 is one of the highest overall. Sectoral differences may be difficult to discern in this environment. A similar logic applies to Thailand and the Philippines, but because LYS and RR are consistent in their

6LYS include an ‘‘intermediate’’ category, which we omit from our analysis because we have no strong theoretical priors about business elites’ attitudes in these regimes.

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Table 1. Summary Statistics

Variable N Mean SD Min Max

Exchange Rate Problem 7596 2.585 1.160 1 4 Manufacturing 7061 0.386 0.487 0 1 Tradable 7061 0.454 0.498 0 1 Agriculture 7061 0.069 0.253 0 1 Exporter 7423 0.360 0.480 0 1 Government-Owned 7596 0.120 0.325 0 1 Size 7596 1.782 0.735 1 3 REER Appreciation 47 0.019 0.115 0.338 0.429 Log GDP/Capita 67 7.402 1.282 4.764 10.366 M3/GDP 67 39.680 25.110 0.002 119.487 Log FDI Stock/Capita 67 5.772 1.721 1.946 10.159

Table 2a. Correlation Matrix (Firm-level variables)

Exchange Rate Government- Problem Manufacturing Tradable Exporter Owned Size

Exchange Rate Problem 1 Manufacturing 0.0351* 1 Tradable 0.0464* 0.8676* 1 Exporter 0.0369* 0.3333* 0.3070* 1 Government- Owned 0.0515* 0.0594* 0.0862* 0.0699* 1 Size 0.0016 0.2175* 0.2385* 0.3072* 0.2243* 1 * indicates statistical significance at the 1 per cent level.

Table 2b. Correlation Matrix (Country-level variables)

Exchange Rate REER Log GDP/ M3/ Log FDI Stock/ Problem Appreciation Capita GDP Capita

Exchange Rate Problem 1 REER Appreciation 0.1325 1 Log GDP/ 0.2335 0.1282 1 capita M3/GDP 0.2015 0.1955 0.4619* 1 Log FDI Stock/ 1 Capita 0.2918 0.1766 0.8622* 0.4474* * indicates statistical significance at the 1 per cent level.

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©International Monetary Fund. Not for Redistribution Table 3a. Data by Country, Floating Exchange Rate Regime in 1999

(LYS classification) DATA SURVEY FROM EVIDENCE DIRECT ATTITUDES: POLICY RATE EXCHANGE Exchange Tradable Log Log FDI Rate (Manufacturing+ Government- REER GDP/ M3/ Stock/ Country Problem Manufacturing Agriculture) Agriculture Exporter Owned Size Appreciation Capita GDP Capita

Albania 2.543 0.323 0.331 0.008 0.198 0.126 1.315 — 6.896 55.672 4.934 Belarus 3.085 0.287 0.609 0.322 0.274 0.299 1.872 — 6.963 18.483 4.745 Canada 2.020 0.273 0.283 0.010 0.475 0.040 2.020 0.012 9.942 76.650 8.658 Chile 2.525 0.460 0.483 0.023 0.414 0.061 2.010 0.047 8.444 41.964 7.958 Colombia 3.293 0.381 0.412 0.031 0.394 0.020 2.343 0.093 7.633 34.217 5.781 Czech Republic 2.286 0.198 0.230 0.032 0.328 0.167 1.421 0.018 8.549 66.469 7.442 Guatemala 3.553 0.406 0.406 0.000 0.320 0.000 1.835 — 7.405 23.438 5.677 Haiti 2.882 0.429 0.494 0.065 0.216 0.029 1.686 — 6.135 32.835 3.258 Honduras 3.264 0.514 0.529 0.014 0.330 0.000 1.692 — 6.827 39.517 5.252 Hungary 1.614 0.277 0.376 0.099 0.394 0.198 1.535 0.022 8.290 47.059 7.727 Kenya 1.782 0.453 0.528 0.075 0.837 0.164 2.273 — 6.042 39.886 3.296 Madagascar 2.245 0.524 0.643 0.119 0.449 0.020 1.755 — 5.443 23.939 2.833 Mexico 3.182 0.525 0.525 0.000 0.394 0.000 2.020 0.093 8.573 32.450 6.673 Pakistan 2.904 0.505 0.516 0.011 0.478 0.064 1.851 0.068 6.248 46.733 3.951 Peru 3.000 0.386 0.482 0.096 0.245 0.009 2.066 — 7.611 28.100 5.948 Philippines 3.450 0.450 0.450 0.000 0.320 0.020 1.950 0.058 6.851 63.797 5.037 Poland 2.214 0.306 0.398 0.092 0.450 0.209 1.675 0.024 8.255 34.762 6.513 Russia 3.116 0.274 0.498 0.224 0.079 0.144 1.704 0.313 7.365 20.288 1.946 South 2.434 0.468 0.532 0.065 0.886 0.060 2.542 0.053 8.004 42.937 7.048 Africa Sweden 1.773 0.289 0.299 0.010 0.557 0.082 1.742 0.028 0.096 48.405 9.020 Tanzania 2.200 0.344 0.375 0.031 0.267 0.143 1.743 — 5.511 21.207 4.263 Thailand 3.634 0.535 0.576 0.041 0.645 0.006 1.744 0.036 7.611 101.974 6.236 Turkey 2.830 0.438 0.458 0.021 0.411 0.170 1.735 0.051 7.951 38.850 5.602 Ukraine 3.051 0.379 0.444 0.065 0.239 0.164 1.617 0.212 6.417 13.912 4.174 425 United 1.632 0.256 0.291 0.035 0.341 0.092 1.897 0.010 10.366 59.334 8.131 States

©International Monetary Fund. Not for Redistribution 426 Table 3b. Data by Country, Pegged Exchange Rate Regime in 1999 (LYS classification)

Exchange Tradable Rate (Manufacturing+ Government- REER Log GDP/ M3/ Log FDI Country Problem Manufacturing Agriculture) Agriculture Exporter Owned Size Appreciation Capita GDP Stock/Capita Weymouth Stephen and Frieden, Jeffry Broz, Lawrence J.

Argentina 1.811 0.322 0.356 0.034 0.295 0.032 1.874 0.089 8.955 25.988 7.436 Armenia 2.790 0.202 0.258 0.056 0.078 0.218 1.395 0.009 6.260 9.160 5.100 Belize 1.714 0.412 0.412 0.000 0.224 0.082 1.388 0.032 7.975 48.449 7.048 Bosnia and Herzegovina 1.211 0.500 0.563 0.063 0.606 0.316 1.747 — 6.764 25.231 4.205 Botswana 1.263 0.358 0.396 0.038 0.519 0.211 1.895 — 7.862 24.096 6.683 Bulgaria 2.402 0.513 0.650 0.137 0.345 0.342 1.573 0.025 7.274 47.415 5.697 Cameroon 2.297 0.378 0.486 0.108 0.611 0.054 1.973 0.013 6.450 14.178 4.277 China 1.793 0.609 0.620 0.011 0.359 0.239 1.804 0.054 6.649 116.640 5.011 Coˆte d’Ivoire 2.000 0.313 0.354 0.042 0.739 0.019 1.923 0.009 6.464 23.549 4.754 El Salvador 2.578 0.543 0.557 0.014 0.287 0.020 1.912 — 7.616 44.903 5.687 Estonia 1.808 0.375 0.392 0.017 0.595 0.208 1.650 0.040 8.119 28.245 7.490 Germany 1.695 0.189 0.200 0.011 0.358 0.074 1.916 0.032 9.983 73.334 7.959 Ghana 2.500 0.250 0.396 0.146 0.455 0.140 1.980 0.004 5.468 22.606 4.220 Lithuania 1.821 0.189 0.226 0.038 0.282 0.057 1.179 0.077 7.971 19.339 6.373 Malawi 2.273 0.318 0.364 0.045 0.650 0.091 2.000 0.017 5.010 19.058 3.296 Malaysia 1.977 0.506 0.529 0.023 0.379 0.034 1.667 0.009 8.214 119.487 7.686 Namibia 1.965 0.196 0.304 0.107 0.680 0.088 1.965 — 7.472 44.101 6.633 Panama 1.300 0.534 0.569 0.034 0.463 0.038 2.275 — 8.209 64.578 7.794 Senegal 2.167 0.136 0.182 0.045 0.500 0.000 1.333 — 5.998 23.228 4.331 Singapore 1.820 0.320 0.320 0.000 0.460 0.040 1.930 0.068 9.908 117.535 10.159 Slovak Republic 2.336 0.261 0.353 0.092 0.478 0.185 1.521 0.026 8.146 61.951 6.043 Trinidad and Tobago 2.396 0.458 0.514 0.056 0.426 0.069 1.644 0.017 8.613 56.635 8.506 Uganda 1.803 0.275 0.348 0.072 0.471 0.085 1.549 0.086 5.406 13.752 3.332 Zambia 1.950 0.216 0.351 0.135 0.342 0.100 1.750 0.005 5.710 17.726 5.361 Zimbabwe 3.000 0.343 0.457 0.114 0.632 0.014 2.000 — 6.476 41.335 4.443

©International Monetary Fund. Not for Redistribution Table 3c. Comparison of Levy-Yeyati and Sturzenegger (LYS) and Reinhart-Rogoff (RR) Regimes, 1999 XHNERT OIYATTDS IETEIEC RMSRE DATA SURVEY FROM EVIDENCE DIRECT ATTITUDES: POLICY RATE EXCHANGE LYS Floats RR Floats LYS Pegs RR Pegs

Country ER problem Country ER problem Country ER problem Country ER problem

Hungary 1.614 Hungary 1.614 Bosnia and Herzegovina 1.211 Bosnia and Herzegovina 1.211 United States 1.632 United States 1.632 Botswana 1.263 Panama 1.300 Sweden 1.773 Sweden 1.773 Panama 1.300 Germany 1.695 Kenya 1.782 Kenya 1.782 Germany 1.695 China 1.793 Canada 2.020 Uganda 1.803 Belize 1.714 Estonia 1.808 Tanzania 2.200 Singapore 1.820 China 1.793 Argentina 1.811 Madagascar 2.245 Tanzania 2.200 Uganda 1.803 Lithuania 1.821 Poland 2.214 Poland 2.214 Estonia 1.808 Malaysia 1.977 Czech Republic 2.286 Madagascar 2.245 Argentina 1.811 Coˆte d’Ivoire 2.000 South Africa 2.434 Czech Republic 2.286 Singapore 1.820 Senegal 2.167 Chile 2.525 Slovak Republic 2.336 Lithuania 1.821 Cameroon 2.297 Albania 2.543 South Africa 2.434 Zambia 1.950 Bulgaria 2.402 Haiti 2.831 Ghana 2.500 Namibia 1.965 El Salvador 2.578 Turkey 2.830 Chile 2.525 Malaysia 1.977 Egypt 2.742 Pakistan 2.904 Albania 2.543 Cote d’Ivoire 2.000 Peru 3.000 Peru 3.000 Georgia 2.767 Senegal 2.167 Zimbabwe 3.000 Ukraine 3.051 Nigeria 2.825 Malawi 2.273 Belarus 3.085 Turkey 2.830 Ceroon 2.297 Russia 3.116 Haiti 2.882 Slovak Republic 2.336 Honduras 3.264 Ukraine 3.051 Trinidad and Tobago 2.396 Mexico 3.182 Mexico 3.182 Bulgaria 2.402 Colombia 3.293 Colombia 3.293 Ghana 2.500 Guatemala 3.553 Indonesia 3.354 El Salvador 2.578 Philippines 3.450 Philippines 3.450 Armenia 2.790 Thailand 3.634 Thailand 3.634 Zimbabwe 3.000 Note: RR floats include cases of managed floating (which includes a de facto crawling band that is narrower than or equal to 75 percent and a moving band that is narrower than or equal to 72 percent), and freely floating. We drop Indonesia from the RR float sample because, in the aftermath of

427 the currency crisis, firms from all sectors found the exchange rate a problem in Indonesia in 1999, making it difficult to tease out any sectoral distinctions.

©International Monetary Fund. Not for Redistribution J. Lawrence Broz, Jeffry Frieden, and Stephen Weymouth classification of these countries as floating exchange rate regimes, inclusion of these countries will affect the two samples in the same way. Figures 1 and 2 provide preliminary comparisons of managers’ concerns about the exchange rate across these regimes. Overall, managers report more problems with the exchange rate in floating regimes than managers operating under pegged exchange rates. According to the summary data in Figure 1, the average response among managers in floats is 2.78 vs. 2.04 for managers in fixed regimes. Figure 2 shows that 36 percent of managers in floating regimes find the exchange rate to be a ‘‘major obstacle’’ to their businesses but only

Figure 1. Average Response of Firm Managers, by Exchange Rate Regime

4

2.78 3

2.04 2

Exchange rate problem 1

0 Floating Pegged Levy-Yeyati and Sturzenegger classifications, 1999

Figure 2. Respondents Reporting Problems with the Exchange Rate, by Exchange Rate Regime (In percent)

100 90 80 70 60 Major obstacle 50 40 Moderate obstacle 30 Minor obstacle

Percentage of respondents 20 No obstacle 10 0 Floating Pegged Levy-Yeyati and Sturzenegger classifications, 1999

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Figure 3. Manufacturing vs. Nonmanufacturing Average Responses, by Exchange Rate Regime

4

3 2.89 2.67

1.99 2.05 2

Manufacturing Nonmanufacturing

Exchange rate problem 1

0 Floating Pegged Levy-Yeyati and Sturzenegger classifications, 1999

13 percent of managers in pegged regimes report the same level of concern. These distributions reveal that, ceteris paribus, managers find pegged regimes less problematic than floats. We want to test the hypothesis that internationally exposed firms prefer greater currency stability, and hence a fixed exchange rate. To do so, we need firm-level proxies for ‘‘international exposure.’’ The WBES asks respondents several questions about the characteristics of their firms; most important for our purposes, firms are asked to identify the sector in which they operate: manufacturing, service, agriculture, or construction.7 In a separate inquiry, managers are asked whether their firms export (yes or no).8 We use these responses to create three unique proxies for international exposure. Manufacturing takes on a value of 1 if the firm operates in the manufacturing sector; Tradable is a dummy variable for firms in either manufacturing or agriculture; Exporter indicates that the firm exports at least a portion of its production. As noted in Table 2a, Exporter correlates significantly with Manufacturing at 0.33 and with Tradable at 0.31. A preliminary review of the data reveals variance across sectors in the degree to which exchange rate stability matters for firm managers. Figure 3 illustrates the average response among manufacturing firms vs. that of nonmanufacturing firms in floating and fixed exchange rate regimes. Manu- facturers, on average, express greater discontent under floating regimes: the average response among manufacturing firms is 2.89 compared with 2.67

7We drop all firms identifying themselves as ‘‘Other.’’ 8Respondents that indicated they were exporters were also asked to specify the percentage of exports to total sales. Unfortunately, most firms did not respond to this inquiry, and so we are constrained to use a dummy variable to indicate whether a firm exports or not.

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Figure 4. Manufacturing vs. Nonmanufacturing Responses in Floating Regimes (In percent)

100 90 80 70 60 Major obstacle 50 40 Moderate obstacle 30 Minor obstacle Percentage of respondents 20 No obstacle 10 0 Manufacturing Nonmanufacturing Levy-Yeyati and Sturzenegger floating regimes, 1999

for nonmanufacturers. Although the average response is lower for both groups in fixed-rate regimes, the difference between the two groups is much lower in fixed-rate regimes; in fact, manufacturers are slightly happier than nonmanufacturers in fixed regimes (average values of 1.99 and 2.05, respectively). Figure 4 compares the distribution of responses between manufacturing and nonmanufacturing firms operating in floating exchange rate regimes. Manufacturers express more dissatisfaction with exchange rates under floats than respondents operating in nonmanufacturing enterprises. Later, we use regression analysis to determine whether other factors contribute to these differences, but this preliminary evidence suggests that international exposure contributes to a preference for exchange rate stability among businesspeople. Our next hypothesis relates to the level of the exchange rate, which we measure by the variable REER Appreciation; the percentage change in the real effective exchange rate between 1999 and 1998.9 Positive values indicate a real appreciation of the currency. We expect firms in the tradables sectors to be more concerned about the exchange rate as the real exchange rate appreciates. To evaluate this conditional proposition, we make use of interactions between our sectoral dummies and REER Appreciation.

9The data are from the International Financial Statistics (IFS) and the Bank for International Settlements (BIS). The World Development Indicators provide data for Georgia, but coverage ends at 1998. Therefore, REER Appreciation for Georgia measures the percentage change in the real effective exchange rate between 1998 and 1997.

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Survey responses may reflect other firm- or country-level characteristics besides international exposure and sector of activity. We use the responses to two other WBES questions to control for firm-level factors: government ownership and firm size.10 We also include several country-level variables in our models: the log of GDP per capita, the log of foreign direct investment (FDI) stock per capita, and financial sector liabilities (M3/GDP). Our first set of models aims to identify firm owners’ concern with the stability of the exchange rate. We estimate the following equation:

ðExchange Rate ProblemÞij ¼ a þ b1ðSectorÞij þ b2ðFirmÞij

þ b3ðEconomyÞj þ eij; ð1Þ where the subscripts stand for firm i in country j. The dependent variable is Exchange Rate Problem, the response of firm i in country j. We divide the sample of responses according to the type of exchange rate regime under which the firms operate: floating or pegged. The variable of interest is Sector, which represents one of the following sectoral dummies: Manufacturing, Tradable,orExporter. The vectors Firm and Economy are firm- and country- level controls. To test our claims about sectoral attitudes toward the level of the exchange rate, we run interactions between the sectoral dummies and REER Appreciation. These models take the following form:

ðExchange Rate ProblemÞij ¼ a þ b1ðSectorÞij

þ b2ðSectorREER AppreciationÞj

þ b3ðFirmÞij þ b4ðEconomyÞj þ eij; ð2Þ where the subscripts stand for firm i in country j. The dependent variable is Exchange Rate Problem, the response of firm i in country j. As before, we divide the sample of responses according to the type of exchange rate regime under which the firms operate: floating or pegged. Sector represents one of the following sectoral dummies: Manufacturing, Tradable,orExporter. The variable of interest is the interaction Sector REER Appreciation. We expect internationally exposed firms to be more concerned about the exchange rate as the real exchange rate appreciates. Firm and Economy are vectors of firm- and country-level controls. Because our dependent variables are discrete, ordered responses, we estimate the equation with ordered probit models using standard maximum

10Government ownership is a dummy variable that takes the value of 1 if a respondent indicates state ownership. Firm size is an ordered response: 1 ¼ small (5–50 employees); 2 ¼ medium (51–500 employees); 3 ¼ large (>500 employees).

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©International Monetary Fund. Not for Redistribution J. Lawrence Broz, Jeffry Frieden, and Stephen Weymouth likelihood and heteroscedasticity-robust standard errors. Following Beck, Demirgu¨c¸-Kunt, and Levine (2006), who model a different WBES survey response as a function of firm- and country-level variables, we allow for possible correlation of the error terms among firms within the same country using Stata’s ‘‘cluster’’ command. This technique maintains the assumption of independent error terms across countries, while allowing for within- country correlation of the errors, perhaps due to factors such as common linguistic or cultural interpretations of the survey. To the extent that a government’s choice of exchange rate policy is influenced by the pressures of sectoral interest groups, as we believe, endogeneity bias is an obvious concern. For example, where manufacturing composes a large share of GDP, governments may be particularly responsive to manufacturers when setting exchange rate policy. But if the causal arrow runs unambiguously from firms’ preferences to exchange rate policies, the bias works against our finding sectoral differences in the data. That is, if manufacturers obtain the exchange rate policies they want, we should not observe a difference in the responses of manufacturing and nonmanufacturing firms. Thus, our results are likely to understate the effect of endogenous exchange rate policies on firms’ attitudes.

III. Results Tables 4–7 contain estimates of the covariates of exchange rate attitudes among firms operating in floating and in fixed exchange rate environments, respectively. Tables 4 and 5 use the LYS exchange rate regime classifications but Tables 6 and 7 replicate the analysis using RR’s regime classifications. Our expectation is that, in floating regimes, firm owners in internationally exposed sectors will express greater concern with exchange rates than other sectors. Moreover, this concern will be muted in fixed regimes, as currency risk is limited. We use sector dummies to proxy for ‘‘internationally exposed’’: Manufacturing, Tradable (manufacturing þ agriculture), and Exporter. In Models 1–4, we include the following variety firm- and country-level control variables:

Government-Owned (1 ¼ yes, 0 ¼ no) Size (1 ¼ small (5–50 employees); 2 ¼ medium (51–500 employees); 3 ¼ large (>500 employees) Log GDP/Capita (average 1997–99) M3/GDP (average 1997–99) Log FDI Stock/Capita (average 1997–99)

In Tables 4–7, the coefficients for our variables of interest largely confirm our priors: in floating regimes, manufacturers and tradables producers are more likely to express concern about the exchange rate than firms in other sectors. In fixed regimes, by contrast, the internationally exposed sectors are less likely to report that the exchange rate represents a problem for their

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Table 4. Exchange Rate Attitudes in Floating Regimes (LYS classification)

(1) (2) (3) (4)

Government-Owned 0.330** 0.293** 0.293** 0.313** (0.132) (0.127) (0.130) (0.137) Size 0.042 0.020 0.029 0.025 (0.066) (0.064) (0.065) (0.054) Log GDP/Capita 0.120 0.100 0.110 0.122 (0.098) (0.094) (0.094) (0.096) M3/GDP 0.006 0.007 0.007 0.006 (0.007) (0.007) (0.006) (0.007) Log FDI Stock/Capita 0.130*** 0.152*** 0.147*** 0.134*** (0.048) (0.045) (0.045) (0.051) Manufacturing 0.187*** (0.056) Tradable 0.102* (0.053) Exporter 0.092 (0.101)

Observations 3,108 2,918 2,918 3,049 Countries 25 25 25 25 Pseudo R2 0.034 0.041 0.039 0.035 Note: Ordered probit analysis of Exchange Rate Problem, a discrete, ordered dependent variable of firm managers’ responses to the question, ‘‘How problematic is the exchange rate for the operation and growth of your business?’’ (1=‘‘No Obstacle,’’ 2=‘‘Minor Obstacle,’’ 3=‘‘Moderate Obstacle,’’ 4=‘‘Major Obstacle’’). Robust standard errors, clustered by country, are in parentheses. ***, **, and * indicate statistical significance levels of 1, 5, and 10 percent, respectively.

businesses, although the estimates are not statistically significant in the fixed- regime sample. Note that nontradables are the complement of the set of tradables, so the value of the estimated coefficient on nontradables is the same as for Tradable, but with the opposite sign. Thus, nontradables (services þ construction) are less likely to be concerned about the exchange rates in floats. The results on Tradable are not significant because attitudes toward a floating exchange rate among firms in agriculture appear not to coincide with those of manufacturing firms. Run separately (but not reported), the dummy for Agriculture is negative and significant in floats and positive (though not significant) in fixed regimes, which is exactly the opposite of the results for Manufacturing. We speculate that firm owners in the agricultural sector are less concerned about the exchange rate in floats because agriculture has long been highly protected in most national economies. In addition, much farm output may be in traditional products or goods that do not enter readily into world trade (perishable or delicate produce, for example). Trade barriers and

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Table 5. Exchange Rate Attitudes in Fixed-Rate Regimes (LYS classification)

(1) (2) (3) (4)

Government-Owned 0.215 0.209 0.209 0.207 (0.154) (0.149) (0.147) (0.158) Size 0.031 0.004 0.009 0.034 (0.068) (0.069) (0.069) (0.066) Log GDP/Capita 0.159 0.161 0.161 0.149 (0.172) (0.163) (0.164) (0.170) M3/GDP 0.000 0.000 0.000 0.000 (0.002) (0.003) (0.003) (0.002) Log FDI Stock/Capita 0.026 0.016 0.016 0.027 (0.159) (0.148) (0.149) (0.157) Manufacturing 0.068 (0.062) Tradable 0.027 (0.066) Exporter 0.030 (0.119)

Observations 1,964 1,823 1,823 1,901 Countries 25 25 25 25 Pseudo R2 0.009 0.011 0.011 0.008 Note: Ordered probit analysis of Exchange Rate Problem, a discrete, ordered dependent variable of firm managers’ responses to the question, ‘‘How problematic is the exchange rate for the operation and growth of your business?’’ (1=‘‘No Obstacle,’’ 2=‘‘Minor Obstacle,’’ 3=‘‘Moderate Obstacle,’’ 4=‘‘Major Obstacle’’). Robust standard errors, clustered by country, are in parentheses.

other transport restrictions may thus insulate food producers from the vicissitudes of exchange rate variation in floating regimes. The negative and significant effect of Government-Owned implies state ownership reduces managers’ apprehensions about the exchange rate. It is difficult to see this variable as a proxy for the nontraded sector, however, because the simple correlation between Government-Owned and Services is small and negative (0.062). In fact, state ownership does not correlate strongly or positively with any other stand-in for ‘‘internationally sheltered’’ sector. Therefore, we treat it as a control. One interpretation of the finding that state-owned firms are less concerned than other firms about the exchange rate is that they have a privileged relationship with the government that protects them from such forces. They are ‘‘sheltered’’ not so much from exchange rate pressures but from market and political forces of any kind, as the government stands ready to subsidize and protect them. To test this interpretation, we estimated models of firm responses to a separate WBES question about the security of property rights. Our findings (not reported) indicate that state ownership has a large and negative influence on property

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Table 6. Exchange Rate Attitudes in Floating Regimes (RR classification)

(1) (2) (3) (4)

Government-Owned 0.350** 0.326** 0.335** 0.340** (0.149) (0.145) (0.147) (0.154) Size 0.096 0.064 0.071 0.062 (0.074) (0.074) (0.075) (0.068) Log GDP/Capita 0.009 0.009 0.007 0.018 (0.195) (0.183) (0.186) (0.198) M3/GDP 0.008 0.009 0.009 0.008 (0.006) (0.006) (0.006) (0.006) Log FDI Stock/Capita 0.212 0.207 0.209 0.225 (0.150) (0.147) (0.149) (0.152) Manufacturing 0.197*** (0.060) Tradable 0.144*** (0.055) Exporter 0.174 (0.120)

Observations 2,507 2,397 2,397 2,450 Countries 24 24 24 24 Pseudo R2 0.029 0.034 0.033 0.032 Note: Ordered probit analysis of Exchange Rate Problem, a discrete, ordered dependent variable of firm managers’ responses to the question, ‘‘How problematic is the exchange rate for the operation and growth of your business?’’ (1=‘‘No Obstacle,’’ 2=‘‘Minor Obstacle,’’ 3=‘‘Moderate Obstacle,’’ 4=‘‘Major Obstacle.’’). Robust standard errors, clustered by country, are in parentheses. ***, **, and * indicate statistical significance levels of 1, 5, and 10 percent, respectively.

rights concerns among firms, and that this result is robust to a battery of firm- and country-level controls. Our country-level control for the stock of FDI, Log FDI Stock/Capita, returns negative and (mostly) significant estimates in floating regimes. In other words, firms operating in floats with more FDI per capita are less likely to report that the exchange rate causes problems for their businesses. One interpretation of this result is that foreign investors are more diversified across countries and currencies and thus less sensitive to volatility in the local currency. In Figure 5, we used the Clarify software from Tomz, Wittenberg, and King (2003) to provide substantive meaning to the ordered probit estimates from Model 2 of Table 4. We simulated the difference in the predicted probabilities of manufacturing and nonmanufacturing firms’ responses while holding all other variables in at their means. In this way, we isolated the impact of ‘‘international exposure,’’ as proxied by being in the manufacturing sector, on firm managers’ concern with the exchange rate. The effect is

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Table 7. Exchange Rate Attitudes in Fixed-Rate Regimes (RR classification)

(1) (2) (3) (4)

Government-Owned 0.396* 0.372* 0.373* 0.405* (0.225) (0.214) (0.211) (0.230) Size 0.075 0.098 0.093 0.094 (0.084) (0.084) (0.087) (0.087) Log GDP/Capita 0.060 0.110 0.105 0.074 (0.247) (0.221) (0.222) (0.248) M3/GDP 0.001 0.001 0.001 0.001 (0.003) (0.003) (0.003) (0.003) Log FDI Stock/Capita 0.095 0.058 0.060 0.082 (0.269) (0.242) (0.243) (0.268) Manufacturing 0.035 (0.072) Tradable 0.010 (0.077) Exporter 0.100 (0.125)

Observations 1,311 1,201 1,201 1,281 Countries 16 16 16 16 Pseudo R2 0.014 0.014 0.014 0.014 Note: Ordered probit analysis of Exchange Rate Problem, a discrete, ordered dependent variable of firm managers’ responses to the question, ‘‘How problematic is the exchange rate for the operation and growth of your business?’’ (1=‘‘No Obstacle,’’ 2=‘‘Minor Obstacle,’’ 3=‘‘Moderate Obstacle,’’ 4=‘‘Major Obstacle’’). Robust standard errors, clustered by country, are in parentheses. ***, **, and * indicate statistical significance levels of 1, 5, and 10 percent, respectively.

meaningful and significant for all categories of the response except ‘‘moderate obstacle.’’ For example, in floating regimes, the simulated difference in the probability that a firm in the sheltered, nonmanufacturing sector says the exchange rate is a ‘‘major obstacle’’ is 7 percentage points lower than that of a manufacturing firm. Tables 8 and 9 test our claims about sectoral attitudes toward the level of the exchange rate. To do so, we make use of interactions between our sectoral dummies and REER appreciation. One of our goals is to test the proposition that internationally exposed sectors are more concerned with the exchange rate as the currency appreciates. In addition, we also hope to uncover the reason why producers in internationally exposed sectors express more dissatisfaction with exchange rates in floating regimes than in pegged regimes. The interactions allow us to determine the extent to which the concern in floating regimes involves the level of the exchange rate, as opposed to its volatility. Tables 8 and 9 report the estimates of Equation (2) for firms operating in floating regimes, using the LYS and RR samples, respectively. We estimate

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Figure 5. Substantive Effect of Sector (Manufacturing vs. Nonmanufacturing) (Clarify simulations run on Model 2, Table 4)

Levy-Yeyati and Sturzenegger Floating Regimes, 1999 0.45

0.40 0.39

0.35 0.32 0.30 0.26 0.25 0.25 0.22 0.20 0.20 Manufacturing 0.20 0.15 Nonmanufacturing 0.15

Predicted probabilities (model 2) 0.10

0.05 No Minor Moderate Major obstacle obstacle obstacle obstacle Exchange rate problem

the effects in floating exchange rate regimes because real appreciations are more likely in floats than in regimes classified as de facto pegs. Policymakers in de facto pegs have demonstrated a commitment to both nominal and real currency stability; indeed, the mean of REER Appreciation among countries coded as floating by LYS is 0.039, but it is just 0.002 among LYS pegs. We thus test the expectation that internationally exposed firms are more concerned about the exchange rate as the REER appreciates in floating regimes only. In Table 8, Models 1–4, the signs of our internationally exposed sector variables are positive in every model, as are the interaction terms: in LYS floating regimes, manufacturers, tradables producers, and exporters all express greater concern about the exchange rate as the real exchange rate appreciates. Currency appreciation makes tradable goods less competitive both at home and abroad, which accounts for the positive and significant results on the interaction terms for manufactures, tradable producers, and exporters. In Table 9, we estimate the same models using the RR sample and find additional evidence that sectoral attitudes are conditioned by currency appreciation. The interactions of manufacturing and tradables with real appreciation are positively signed and significant, indicating that firms in these internationally exposed sectors express greater concern with the currency as the REER appreciates. Although the same relationship holds for the interaction of exporter and REER appreciation, the conditional effect is not statistically significant in this sample. Figure 6 provides estimates of the magnitude of these interaction effects. Using Model 2 from Table 8, we simulated the change in the predicted

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Table 8. Real Appreciation and Exchange Rate Attitudes in Floating Regimes (LYS Classification)

(1) (2) (3) (4)

REER Appreciation (one year) 1.353 0.522 0.450 0.879 (1.134) (1.006) (1.038) (1.226) Government-Owned 0.361*** 0.353*** 0.358*** 0.340** (0.137) (0.131) (0.133) (0.144) Size 0.078 0.052 0.066 0.038 (0.075) (0.070) (0.071) (0.062) Log GDP/Capita 0.220** 0.230** 0.224** 0.230** (0.109) (0.102) (0.103) (0.111) M3/GDP 0.009* 0.010** 0.010** 0.009* (0.005) (0.005) (0.005) (0.005) Log FDI Stock/Capita 0.227*** 0.208*** 0.216*** 0.220*** (0.073) (0.065) (0.066) (0.080) Manufacturing 0.267*** (0.078) Manufacturing REER Appreciation (one year) 1.168*** (0.358) Tradable 0.212*** (0.063) Tradable REER Appreciation (one year) 1.249*** (0.330) Exporter 0.278*** (0.105) Exporter REER Appreciation (one year) 1.599*** (0.615)

Observations 2,323 2,258 2,258 2,276 Countries 16 16 16 16 Pseudo R2 0.078 0.084 0.083 0.082 Note: Ordered probit analysis of Exchange Rate Problem, a discrete, ordered dependent variable of firm managers’ responses to the question, ‘‘How problematic is the exchange rate for the operation and growth of your business?’’ (1=‘‘No Obstacle,’’ 2=‘‘Minor Obstacle,’’ 3=‘‘Moderate Obstacle,’’ 4=‘‘Major Obstacle’’). Robust standard errors, clustered by country, are in parentheses. ***, **, and * indicate statistical significance levels of 1, 5, and 10 percent, respectively.

probability of Exchange Rate Problem ¼ 4 (‘‘Major Obstacle’’) as REER appreciation moves from its minimum to its maximum value, holding all other variables at their means. The simulations were performed separately for manufacturing firms (left panel) and nonmanufacturing (right panel) firms using Clarify.11

11Similar effects (not reported) were obtained substituting the interactions of REER appreciation with exporter and tradables.

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Table 9. Real Appreciation and Exchange Rate Attitudes in Floating Regimes (RR classification)

(1) (2) (3) (4)

REER Appreciation (one year) 0.147 0.568 0.648 0.217 (0.969) (0.838) (0.860) (1.141) Government-Owned 0.435*** 0.422*** 0.436*** 0.431** (0.166) (0.154) (0.158) (0.168) Size 0.165** 0.130* 0.140** 0.118* (0.067) (0.067) (0.068) (0.066) Log GDP/Capita 0.052 0.068 0.074 0.061 (0.241) (0.227) (0.232) (0.244) M3/GDP 0.012** 0.013*** 0.013*** 0.012** (0.005) (0.005) (0.005) (0.005) Log FDI Stock/Capita 0.376** 0.395** 0.400** 0.386** (0.182) (0.169) (0.174) (0.185) Manufacturing 0.275*** (0.083) Manufacturing REER Appreciation (one year) 1.417** (0.599) Tradable 0.223*** (0.069) Tradable REER Appreciation (one year) 1.498*** (0.538) Exporter 0.279** (0.112) Exporter REER Appreciation (one year) 0.996 (0.841)

Observations 2,139 2,069 2,069 2,094 Countries 19 19 19 19 Pseudo R2 0.059 0.067 0.066 0.064 Note: Ordered probit analysis of Exchange Rate Problem, a discrete, ordered dependent variable of firm managers’ responses to the question, ‘‘How problematic is the exchange rate for the operation and growth of your business?’’ (1=‘‘No Obstacle,’’ 2=‘‘Minor Obstacle,’’ 3=‘‘Moderate Obstacle,’’ 4=‘‘Major Obstacle’’). Robust standard errors, clustered by country, are in parentheses. ***, **, and * indicate statistical significance levels of 1, 5, and 10 percent, respectively.

For manufacturing firms, the predicted probability that a respondent will report that the exchange rate is a ‘‘major obstacle’’ increases by 25 percentage points (from 0.24 to 0.49) as real appreciation moves from its minimum (0.31) to its maximum (0.09) value. By contrast, such a movement has far less influence on nonmanufacturing firms in floating regimes: the change in the probability of responding ‘‘major obstacle’’ is just 8 percentage points (from 0.27 to 0.35). There is also much more uncertainty around the point predictions for nonmanufacturing firms.

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Figure 6. Exchange Rate Appreciation: Manufacturing vs. Nonmanufacturing (Clarify simulations run on Model 2, Table 8)

LYS Floating Regimes, 1999 Manufacturing Firms Non-Manufacturing Firms 0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0 0 -0.3 -0.2 -0.1 0 0.1 -0.3 -0.2 -0.1 0 0.1 REER Appreciation REER Appreciation

predicted probability C.I. predicted probability C.I.

Notes: These figures illustrate the change in the predicted probability that Exchange Rate Problem ¼ 4 (a ‘‘major obstacle’’) as REER appreciation moves from its minimum to its maximum value, holding all other variables at their means. The simulations were performed separately for manufacturing (left panel) and nonmanufacturing (right panel) firms using the Clarify software (Tomz, Wittenberg, and King, 2003).

Our final concern involves the relative importance of volatility and appreciation to firm managers’ attitudes about the exchange rate. We found that in floating regimes internationally exposed firms are more concerned about the exchange rate than firms in sheltered sectors. We also found that internationally exposed firms are more concerned than sheltered, nonmanufacturing firms when the REER appreciates. But what we would like to know is whether the heightened concern among international exposed firms operating in floating regimes is caused by the inherent volatility of exchange rates in these regimes or by the tendency of floating regimes to experience real appreciations. Some evidence for the magnitude of each effect can be extracted from the Clarify results presented above in Figures 5 and 6. If we compare differences in the predicted probability of manufacturing and nonmanufacturing firms reporting that the exchange rate poses a ‘‘major obstacle’’ across these two sets of regressions, we can get some sense of the relative magnitudes of volatility and level, respectively. From Figure 5, we see that, without regard to the level of the real exchange rate, the difference in the predicted probability that a firm in the sheltered sector reports a ‘‘major obstacle’’ compared with that of a manufacturing firm is 7 percentage points. From Figure 6, we estimate that, as REER appreciation ranges from its minimum to its maximum, the probability that a manufacturing firm classifies the exchange rate as a ‘‘major obstacle’’ is 17 percentage points higher than for a nonmanufacturing firm. With the caveat that the interaction simulations are based on a

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©International Monetary Fund. Not for Redistribution EXCHANGE RATE POLICY ATTITUDES: DIRECT EVIDENCE FROM SURVEY DATA truncated sample and are subject to wide confidence intervals that diminish the statistical significance of the sectoral differences, it would appear that appreciation in floating exchange rate regimes is the more important problem for firms in the internationally exposed sectors. Our results provide support for existing arguments linking the economic activities of firms to their preferences over exchange rate policy. Manufacturers express greater uncertainty under floating exchange rates than do firms in sheltered, nontradable sectors. Manufacturers and exporters are also more likely to disapprove of a real appreciation than are firms in nontradable industries. Whether and how these preferences are translated into pressures on policymakers, and into policy, is a matter for further analysis.

IV. Conclusion Many analyses of the political economy of exchange rate policy start from the presumption that policymakers must take into account the exchange rate policy preferences of domestic economic agents. Such analyses typically posit that internationally oriented firms will be especially averse to the volatility associated with a floating rate, and will therefore prefer a fixed exchange rate. They also typically suggest that tradables producers will be particularly averse to the relative price effects of a real appreciation. Nontradables producers, conversely, are expected to prefer floating rates and an appreciated currency. The empirical evidence we present here supports these expectations. Owners and managers of manufacturing firms, inherently more affected by the international economy, are more likely to regard the exchange rate as a significant problem in floating-rate regimes than those in the sectors that are not so exposed to cross-border transactions. This implies a preference of these more internationally engaged firms for a fixed exchange rate. Similarly, firms in the manufacturing sector and those with export interests are particularly concerned by a real appreciation of the exchange rate; firms in nontradables sectors are less likely to demonstrate such concern. This implies a preference of tradables producers, including exporters, for a relatively depreciated real exchange rate. Although these results are hardly surprising, they do confirm theoretically grounded expectations about the policy preferences of important economic actors. Further work is needed to explore more nuanced and differentiated aspects of exchange rate policy preferences. For example, it would be important to know how such things as foreign currency liabilities, intra-firm trade, differential pass- through, and invoicing practices affect currency preferences. The evidence presented here is, nonetheless, a start toward a more rigorous and precise sense of the economic interests and attitudes with which policymakers contend.

REFERENCES Balistreri, Edward, 1997, ‘‘The Performance of the Hecksher-Ohlin-Vanek Model in Predicting Endogenous Policy Forces at the Individual Level,’’ Canadian Journal of Economics, Vol. 30, No. 1 (February), pp. 1–17.

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Batra, Geeta, Daniel Kaufmann, and Andrew W.H. Stone, 2003, ‘‘The Firms Speak: What the World Business Environment Survey Tells Us about Constraints on Private Sector Development,’’ in Pathways Out of Poverty: Private Firms and Economic Mobility in Developing Countries, ed. by Gary S. Fields and Guy Pfefferman (Norwell, Massachusetts, Kluwer Academic Publishers). Beaulieu, Eugene, 2002, ‘‘Factor or Industry Cleavages in Trade Policy? An Empirical Analysis of the Stolper-Samuelson Theorem,’’ Economics and Politics, Vol. 14, No. 2 (July), pp. 99–131. Beck, Thorsten, Asli Demirgu¨c¸-Kunt, and Ross Levine, 2006, ‘‘Bank Supervision and Corruption in Lending,’’ Journal of Monetary Economics, Vol. 53, No. 8 (November), pp. 2131–63. Broz, J. Lawrence, 1997, ‘‘The Domestic Politics of International Monetary Order: The Gold Standard,’’ in Contested Social Orders and International Politics, ed. by D. Skidmore (Nashville, Tennessee, Vanderbilt University Press). Calvo, Guillermo A., and Carmen M. Reinhart, 2002, ‘‘Fear of Floating,’’ Quarterly Journal of Economics, Vol. 107, No. 2 (May), pp. 379–408. Campa, Jose, and Linda S. Goldberg, 1997, ‘‘The Evolving External Orientation of Manufacturing: A Profile of Four Countries,’’ Federal Reserve Bank of New York Economic Policy Review, Vol. 3, No. 2 (July), pp. 53–81. ______, 2005, ‘‘Exchange Rate Pass-Through into Import Prices,’’ The Review of Economics and Statistics, Vol. 87, No. 4, pp. 679–90. Collins, Susan M., and Francesco Giavazzi, 1992, ‘‘Attitudes Towards Inflation and the Viability of Fixed Exchange Rates: Evidence from the EMS,’’ in A Retrospective on the Bretton Woods System: Lessons for International Monetary Reform, ed. by Barry Eichengreen and Michael Bordo (Chicago, University of Chicago Press). Devereux, Michael B., and Charles Engel, 2002, ‘‘Exchange Rate Pass-Through, Exchange Rate Volatility, and Exchange Rate Disconnect,’’ Journal of Monetary Economics, Vol. 49, No. 5, pp. 913–40. Eichengreen, Barry, 1992, Golden Fetters (Oxford, Oxford University Press). ______, 1995, ‘‘The Endogeneity of Exchange Rate Regimes,’’ in Understanding Interdependence: The Macroeconomics of the Open Economy, ed. by Peter Kenen (Princeton, New Jersey, Princeton University Press). ______, and Jeffry A. Frieden, 1993, ‘‘The Political Economy of European Monetary Unification: An Analytical Introduction,’’ Economics & Politics, Vol. 5, No. 2, pp. 85–104. Frankel, Jeffrey, 1995, ‘‘Monetary Regime Choice for a Semi-Open Economy,’’ in Capital Controls, Exchange Rates and Monetary Policy in the World Economy, ed. by Sebastian Edwards (New York, Cambridge University Press). ______, 1999, ‘‘No Single Currency Regime is Right for All Countries or at All Times,’’ Essays in International Finance No. 215 (Princeton, New Jersey, Princeton University Press). Frieden, Jeffry, 1991, ‘‘Invested Interests: The Politics of National Economic Policies in a World of Global Finance,’’ International Organization, Vol. 45, No. 4 (Autumn), pp. 425–51. ______, 1997, ‘‘Monetary Populism in Nineteenth-Century America: An Open Economy Interpretation,’’ Journal of Economic History, Vol. 57, No. 2 (June), pp. 367–95.

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______, 2002, ‘‘Real Sources of European Currency Policy: Sectoral Interests and European Monetary Integration,’’ International Organization, Vol. 56, No. 4 (Autumn), pp. 831–60. ______, Piero Ghezzi, and Ernesto Stein, 2001, ‘‘Politics and Exchange Rates: A Cross- Country Approach to Latin America,’’ in The Currency Game: Exchange Rate Politics in Latin America, ed. by Jeffry A. Frieden and Ernesto Stein (Baltimore, Johns Hopkins University Press). Gabel, Matthew, 1998, ‘‘International Economics and Mass Politics: Market Liberalization and Public Support for European Integration,’’ American Journal of Political Science, Vol. 42, No. 3 (July), pp. 936–53. Ga¨rtner, Manfred, 1997, ‘‘Who Wants the Euro—and Why? Economic Explanations of Public Attitudes towards a Single European Currency,’’ Public Choice, Vol. 93, No. 3, pp. 487–510. Ghosh, Atish, Anne-Marie Gulde, Jonathan D. Ostry, and Holger C. Wolf, 1997, ‘‘Does the Nominal Exchange Rate Regime Matter?’’ NBER Working Paper No. 5874 (January) (Cambridge, Massachusetts, National Bureau of Economic Research). Hefeker, Carsten, 1995, ‘‘Interest Groups, Coalitions and Monetary Integration in the Nineteenth Century,’’ Journal of European Economic History, Vol. 24, No. 3 (Winter), pp. 489–536. ______, 1997, Interest Groups and Monetary Integration: The Political Economy of Exchange Regime Choice (Boulder, Colorado, Westview Press). Kenen, Peter, 1969, ‘‘The Theory of Optimum Currency Areas,’’ in Monetary Problems in the International Economy, ed. by Robert Mundell and Alexander Swoboda (Chicago, University of Chicago Press). Klein, Michael W., and Nancy P. Marion, 1997, ‘‘Explaining the Duration of Exchange-Rate Pegs,’’ Journal of Development Economics, Vol. 54, No. 2 (December), pp. 387–404. Leblang, David, 2002, ‘‘The Political Economy of Speculative Attacks in the Developing World,’’ International Studies Quarterly, Vol. 46, No. 1 (March), pp. 69–91. Levy Yeyati, Eduardo, and F. Sturzenegger, 2005, ‘‘Classifying Exchange Rate Regimes: Deeds vs. Words,’’ European Economic Review, Vol. 49, No. 6 (August), pp. 1603–35. Mayda, Anna Maria, and Dani Rodrik, 2005, ‘‘Why Are Some People (and Countries) More Protectionist Than Others?’’ European Economic Review, Vol. 49, No. 6 (August), pp. 1393–430. McKinnon, Ronald I, 1963, ‘‘Optimum Currency Areas,’’ American Economic Review, Vol. 53, No. 4 (September), pp. 717–25. Mundell, Robert A, 1961, ‘‘A Theory of Optimum Currency Areas,’’ American Economic Review, Vol. 51, No. 4 (September), pp. 657–64. Reinhart, Carmen M., and Kenneth Rogoff, 2004, ‘‘The Modern History of Exchange Rate Arrangements: A Reinterpretation,’’ Quarterly Journal of Economics, Vol. 119, No. 1, pp. 1–48. Rose, Andrew, 2000, ‘‘One Money, One Market: Estimating the Effect of Common Currencies on Trade,’’ Economic Policy, Vol. 15, No. 30 (April), pp. 9–45. Scheve, Kenneth F, 2004, ‘‘Public Inflation Aversion and the Political Economy of Macroeconomic Policymaking,’’ International Organization, Vol. 58, No. 1 (Winter), pp. 1–34.

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______, Kenneth F, and Matthew J. Slaughter, 2001, ‘‘What Determines Individual Trade-Policy Preferences?’’ Journal of International Economics, Vol. 54, No. 2, pp. 267–92. Simmons, Beth, 1994, Who Adjusts? (Princeton, New Jersey, Princeton University Press). Tavlas, George, 1994, ‘‘The Theory of Monetary Integration,’’ Open Economies Review, Vol. 5, No. 2, pp. 211–30. Tomz, Michael, Jason Wittenberg, and Gary King, 2003, CLARIFY: Software for Interpreting and Presenting Statistical Results Version 2.1, Harvard University. Available via the Internet: http://gking.harvard.edu. Ve´gh, Carlos, 1992, ‘‘Stopping High Inflation: An Analytical Overview,’’ IMF Staff Papers, Vol. 39 (September), pp. 626–95. World Bank, 2000, World Business Environment Survey (Washington, World Bank).

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Fear of Declaring: Do Markets Care What Countries Say About Their Exchange Rate Policies?

ADOLFO BARAJAS, LENNART ERICKSON, and ROBERTO STEINER

Beginning with the papers by Calvo and Reinhart (2002) and Levy Yeyati and Sturzenegger (2001), there has been growing recognition of a disconnect between what emerging economies say they do in exchange rate policy (words), and what they do in practice (deeds). More specifically, a ‘‘fear of floating’’ behavior has been identified, whereby countries that classify themselves as floating exchange rate regimes intervene quite vigorously over time. While many persuasive arguments have been offered as to why countries intervene, the question remains as to why intervening countries continue to classify their regimes as floating. Thus, concurrently with fear of floating, there seems to be a ‘‘fear of declaring.’’ This paper examines one possible reason for fear of declaring: that international capital markets might reward countries that are classified toward the flexible end of the spectrum. Based on the JPMorgan Emerging Market Bond Index spread, we use a panel data approach that exploits both time and cross-country variability. With some qualifications, we find that spreads are lower in countries that have a fixed exchange rate regime, whether de jure or de facto, implying that there is no evidence that markets punish fear of floating. One possible explanation for this puzzle—that is, countries intervene but say that they do not, even though markets appear to be,

Adolfo Barajas is a senior economist and Lennart Erickson an economist with the IMF Institute; Roberto Steiner is a research associate with Fedesarollo in Colombia. This paper is dedicated to the memory of Taras Chernetsky. The authors thank the participants at the IMF 2007 Research Conference and at the IMF Institute seminar for discussions that led to key improvements in this study, most particularly the discussant for the paper, Carmen Reinhart. Thanks as well go to Radu Pa˜un for outstanding and patient research assistance. Finally, the authors thank the two anonymous readers for their thoughtful comments and suggestions. This paper is dedicated to the memory of Taras Chernetsky.

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©International Monetary Fund. Not for Redistribution Adolfo Barajas, Lennart Erickson, and Roberto Steiner at a minimum, indifferent to intervention—arises from the fact that there is evidence that de jure floating regimes may fare better in crisis situations. [JEL F31, F33, G15, O16] IMF Staff Papers (2008) 55, 445–480. doi:10.1057/imfsp.2008.14

ollowing a large body of theoretical work on the pros and cons of F adopting different exchange rate regimes, in the past decade a prominent literature arose that aimed to classify exchange rate regimes in a systematic manner and to explore empirically to what extent these classifications could be linked to economic outcomes. Although the IMF, in conjunction with its member countries, produces a taxonomy of exchange rate regimes since 1944, many researchers began to suspect that this official, or de jure, classification scheme did not always adequately represent what countries did in practice. Thus emerged attempts in the literature to use measurable information to produce de facto classifications that would more closely reflect the actual exchange rate policies of different countries. Early efforts at reclassifying exchange rate regimes included Obstfeld and Rogoff (1995) and Ghosh and others (1995). Also, starting in 1998 the IMF began to move away from a classification that relied solely on countries’ formal announcements and toward one that would take into account exchange rate variability as well as policy actions that affected the exchange rate. Bubula and O¨tker-Robe (2002) applied this methodology retroactively to 1990. More recently, two studies have produced comprehensive cross-country data sets of de facto exchange rate regimes, based on an analysis of actual policy behavior. These extensively cited studies are those of Levy Yeyati and Sturzenegger (LYS) (2002 and 2003), and Reinhart and Rogoff (RR) (2004). LYS provided a new classification of exchange rate regimes for 156 countries from 1974 to 2000. By use of cluster analysis, countries were grouped according to relative importance of the monthly changes in (1) the nominal exchange rate, (2) the variability of the nominal exchange rate, and (3) international reserves. Thus, regimes were classified into four main categories (flexible, dirty float, crawling peg, and fixed), in addition to a category for inconclusive results. RR also took into account the likely existence of parallel exchange rate markets, noting the distinction between dual (or multiple) markets, which are typically legal, and parallel markets, which may or may not be legal. Their taxonomy included 14 refined and five coarse classification regimes, with monthly observations for 153 countries over 1940–2001. Other notable studies aimed at producing de facto classifications include Shambaugh (2003), which used an intermediate methodology between RR— which based their classification solely on analyzing the behavior of the exchange rate—and LYS, which also looked at the evolution of international reserves. More recently, Dubas, Lee, and Mark (2005) modeled regimes as

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©International Monetary Fund. Not for Redistribution DO MARKETS CARE WHAT COUNTRIES SAY ABOUT THEIR EXCHANGE RATE POLICIES? the outcomes of a multinomial logit choice problem conditional on the level and volatility of both the exchange rate and international reserves. Observations (per country and per year for 1971–2002) were then assigned to that regime which exhibited the highest predictive probability. All of these studies of exchange rate regimes share one common result: a disconnect between de jure and de facto classifications, or between words and deeds. LYS (2002) note that, for any given country at any given point in time, de jure classifications coincide with the LYS de facto classification in only about half of the observations. RR express this idea slightly differently, saying that countries’ official classification of exchange rates are only ‘‘a little better than random’’ in terms of adequately reflecting the reality of exchange rate policy. Identified trends in exchange rate regimes over time differ depending on which classification is used. LYS (2002) point out that, while de jure classifications show a substantial drop since the mid-1970s in the share of developing countries choosing fixed rates, their de facto classification shows much greater stability in regimes over time, particularly since the 1980s. Finally, the ‘‘hollowing out’’ phenomenum, whereby intermediate regimes tend to disappear in favor of the fixed or flexible extremes, is not reflected in the LYS classification, which, on the contrary, shows that the share of intermediate regimes is relatively stable and, if anything, somewhat increases toward the end of the 1990s.1 Thus, disconnect between what countries say and what they do in terms of their exchange rate policy is now widely recognized and some studies have sought to explain how this behavior comes about. Focusing on emerging economies, the influential work of Calvo and Reinhart (2002) identified a particular type of disconnect, labeled ‘‘fear of floating,’’ whereby countries classify themselves as having floating exchange rate regimes, yet intervene quite vigorously over time, resisting market forces on the determination of their exchange rates. These authors argued that there are strong reasons to behave this way, related to the particularly high costs that exchange rate variability imposes on emerging economies. On the one hand, devaluations tend to lead to lost access to capital markets, highly disruptive balance sheet effects when key sectors of the economy have built up liabilities in foreign currency, pass-through effects on inflation, and possible adverse effects on trade (Hausmann, Panizza, and Stein, 2001). On the other hand, appreciations could cause Dutch disease-type of phenomena, which might hinder growth in the tradable sectors. Along these lines, LYS (2007) recently revisited exchange rate policy in the current-decade and updated their de facto classifications, finding that interventions to resist currency appreciations have become more prevalent than those aimed at preventing depreciations.

1It must be noted that discrepancies also arise among alternative de facto classification schemes. For example, the IMF (2006) has recently estimated a correlation coefficient of 0.54 between the RR and LYS classifications during 1990–2000, which is not much higher than the one observed (0.48) between RR and the IMF’s de jure classification. Dubas, Lee, and Mark (2005), on the other hand, report a 0.53 correlation between theirs and the RR classification.

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Furthermore, they found this policy to be relatively effective, in the sense that there was a measurable effect on the real exchange rate, and also preliminary evidence pointing to positive effects on economic growth. Although the fear of floating literature certainly explained why an emerging economy with the label of floater might opt to intervene under certain circumstances, it sheds no light on why a country that finds it in its best interest to intervene would insist on maintaining a label of floater. Alesina and Wagner (2003) came closer to addressing this issue, linking exchange rate policies to overall institutional quality. The premise of their analysis is that sustaining an exchange rate peg is extremely demanding and countries with strong institutions are more likely to be able to sustain a fixed exchange rate once they have announced it. By contrast, countries with weaker institutions would be more likely to announce a fixed regime and then be forced to float, in essence reneging on the promise of monetary stability. Thus, fear of floating arises as a signaling device, whereby countries intervene to raise credibility and distinguish themselves from the poor-institution countries that are forced into frequent and costly depreciations. Why not announce a fixed regime as well? The authors argued that floating provides some flexibility, particularly in calm times. A related argument was made by Genberg and Swoboda (2005) regarding the benefits of announcing a float while intervening actively. For the aforementioned reasons, a country might value exchange rate stability, and therefore see the need for frequent and sizable intervention. However, it may not be willing to make a commitment—to a specific value, or range for the exchange rate—and therefore, announces a float. Thus, this announcement should not be viewed as a commitment not to intervene, but rather, as a lack of commitment to a particular exchange rate. The announcement here also serves as a signaling device, intended to distinguish the country from those who commit to a fixed regime (and run the risk of having to abandon it in the future). If fear of floating is indeed a signaling device, then to whom is the signal directed? According to Alesina and Wagner, this behavior serves to ‘‘signal to an imperfectly informed market about some characteristics of that country,’’ namely its strong institutions and competent macroeconomic management. Thus, the next logical question is whether markets are indeed receiving this signal and interpreting it as countries would hope. Do markets in fact reward the countries that announce floating, regardless of whether flexibility is in fact allowed to prevail? Do they reward countries that tend to intervene more to resist exchange rate volatility? This is the starting point for our study, in which we estimate the impact of the de jure regimes and of policy actions— intervention—on sovereign spreads. Our study is closely related to an earlier literature concerning the international capital markets’ response to adherence to the gold standard in the late 19th and early 20th centuries. Based on a sample of nine ‘‘important peripheral countries,’’ Bordo and Rockoff (1996) examined whether those

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©International Monetary Fund. Not for Redistribution DO MARKETS CARE WHAT COUNTRIES SAY ABOUT THEIR EXCHANGE RATE POLICIES? who adhered more closely to the gold standard during 1870–1914 faced lower borrowing costs than those who did not. After controlling for monetary and fiscal policy, they found that, indeed, adherence was reflected in a lower spread of government bond interest rates over that of the U.K. Treasury. Thus, adherence acted as a ‘‘good housekeeping seal of approval.’’ In our study, however, several differences emerge. First, we focus on a sample of emerging economies, albeit those with ample access to international capital markets; second, our data set is richer, with quarterly information and a wider set of domestic policy controls; and third, the question itself has shifted, from one in which the initial suspicion was that markets rewarded fixed exchange rates (the gold standard) to one in which presumably markets now reward de jure floating exchange rates and might reward de facto intervention as well. Our focus on emerging markets is especially germane given that, although discrepancies between words and deeds can also occur in developed and in low-income countries, the notion that ‘‘fear of floating’’ can impact sovereign spreads is particularly relevant for countries that have access to international financial markets—which low-income countries generally do not—and in which issues such as liability dollarization and pass-through tend to be present which is generally not the case in developed countries. Furthermore, it is in emerging markets where signaling issues associated with ‘‘fear of floating’’ should become key. In short, we examine one possible reason why countries might fear to declare what their true exchange rate policy: that international capital markets reward countries that are classified toward the flexible end of the spectrum. In addition to the Alesina and Wagner arguments that rely on institutional quality, there may be other reasons why markets prefer floating regimes: (1) a ‘‘flexible’’ classification may signal to markets that the likelihood of an ill-advised defense of an unsustainable peg would be lower, and thus speculative attacks less likely; (2) markets might not always distinguish effectively between words and deeds, so countries that signaled a certain macroeconomic policy framework when they switched to a floating regime are keen to insist that this policy framework is still in place; and (3) although floating regimes per se may not have appreciable advantages over other regimes, markets could have a subjective bias against fixed exchange rates. Such bias might stem in part from the experience of the more recent international crises generally erupting in countries with fixed exchange rates, or from the current proliferation of inflation targeting strategies,2 which presuppose that the exchange rate is allowed to float.

2Rose (2007) reports that as of June 2006, inflation targeting countries accounted for over a quarter of world GDP. These include 14 of the 30 OECD countries, plus 10 developing countries.

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I. Empirical Approach and Data Description Our specification can be summarized as follows:

EMBIGit ¼ gi þ aDe Jureit þ lINTERVit þ bFUNDit

þ yVXOt þ dYEARt Thus, we regress the sovereign debt spreads against a set of explanatory variables that reflect exchange rate policy, domestic fundamentals, and global financial conditions. Exchange rate policy is represented by de jure exchange rate regimes (De Jure) and a measure of the extent of intervention (INTERV), both of which can vary by country and over time. We then incorporate a set of domestic fundamentals (FUND) commonly used in studies of sovereign spreads and/or credit ratings,3 which include inflation, the external current account balance, the general government balance, the level of international reserves (minus gold), and the external public debt ratio.4 We proxy global financial conditions with the VXO volatility index as well as with year dummies. Finally, we control for additional country-specific characteristics through country fixed effects. Our dependent variable is the spread of the Emerging Market Bond Index Global (EMBIG), which measures the premium above U.S. Treasury securities in basis points for dollar denominated sovereign debt. The period observed runs from 1997:Q4 to 2006:Q2. These spreads are observed, end of period, both quarterly and monthly. In order to match the frequency of the majority of the explanatory variables, we only make use of the quarterly observations. In the initial period the sample includes 22 countries, with further countries being added and dropped to the series over time, culminating in 31 countries at the end of the period.5 To capture de jure regimes, we use the IMF classification, which has evolved over time. In fact, six taxonomies have been in place since 1944. Prior to 1998, countries were classified according to the regimes they formally announced, whereas starting in 1998 the classification system began to move closer to a de facto set of criteria in which both actual exchange rate variability and policy actions affecting the exchange rate are considered.

3See Cantor and Packer (1996), Eichengreen and Mody (2000), Kamin and von Kleist (1999), Herrera and Perry (2000), Glennerster and Shin (2004), and Ciarlone, Piselli, and Trebeschi (2007), for studies of sovereign borrowing costs. Kaminsky and Schmukler (2002) and Powell and Martı´nez (2007) focus on the determinants of credit ratings. 4Unfortunately, we did not have access to comparable data on total public debt for our group of countries during the entire sample period. 5Countries included in the EMBIG are Algeria, Argentina, Brazil, Bulgaria, Chile, China, Colombia, Coˆte d’Ivoire, Croatia, Dominican Republic, Ecuador, Egypt, El Salvador, Greece, Hungary, Indonesia, Iraq, Korea, Lebanon, Malaysia, Mexico, Morocco, Nigeria, Pakistan, Panama, Peru, Philippines, Poland, Russia, Serbia, South Africa, Thailand, Tunisia, Turkey, Ukraine, Uruguay, and Venezuela. Several of these countries were dropped from our analysis due to lack of observations for certain key macroeconomic fundamentals that serve as explanatory variables.

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A country’s exchange rate regime is now frequently assessed based on both quantitative and qualitative analysis and, when in the opinion of the IMF there is a deviation between the prevalent classification and the actual exchange rate and/or the authorities’ intervention policy, a reclassification ensues. Once undertaken, reclassifications are communicated through a variety of channels, including the Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER).6 For each country in each quarter, we turn to two different IMF de jure classification systems. The first is the 1982 taxonomy (IMF, 1982), which comprised the following categories: (1) pegged to a single currency, (2) pegged to a composite (including the standard drawing right), (3) flexibility limited vis-a` -vis a single currency, (4) flexibility limited vis-a` -vis a cooperative arrangement, (5) adjusted according to a set of indicators, (6) other managed floating, and (7) independently floating. This system covered all countries in our sample from January 1982 to March 1998. The second system is the 1998 taxonomy and covers the period from June 1997 to the present (see IMF, 1997 and 1998). The categories for this system are (1) exchange arrangement with no separate legal tender, (2) currency board arrangement, (3) conven- tional pegged arrangement, (4) pegged exchange rate within horizontal bands, (5) crawling peg, (6) crawling band, (7) managed floating with no predetermined path for the exchange rate, and (8) independently floating. In addition, categories (1) and (2) are considered hard pegs, (4) to (6) are considered soft pegs, and the last two categories are considered floating regimes. This latter taxonomy was extended back to years prior to 1997, but the exercise did not cover the entirety of our sample. In order to have a consistent measure across the whole sample, we created three aggregated categories, assigning each category in the two taxonomies to one of three broader classifications. A Fixed category encompasses categories (1)–(5) of the 1982 taxonomy and categories (1)–(6) of the 1998 taxonomy. A Managed Floaters category encompasses category 6 in the 1982 taxonomy and category 7 in the 1998 taxonomy. Finally, a Free Floaters category encompasses category 7 in the 1982 taxonomy and category 8 in the 1998 taxonomy. As a proxy measure of policy intervention in the exchange market, we construct the index INTERV, which is closely related to the criteria used by different studies on de facto regimes. It must be emphasized that our intent is not to construct a new de facto classification, nor is it to provide a complete and precise measure of the full extent of exchange rate intervention. Rather,

6It is important to bear in mind that the revised taxonomy still does not amount to a de facto classification. The IMF might not always find itself in a position to actually reclassify a country’s regime, particularly if the member country has strong reasons to object to such re- classification. Furthermore, reclassifications, even if opportune, are by their nature more concerned with the present than with the past. Indeed, as Poirson (2001) shows, significant disconnect persists between the revised IMF classification and de facto measures based entirely on the relative variability of international reserves and nominal exchange rates.

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©International Monetary Fund. Not for Redistribution Adolfo Barajas, Lennart Erickson, and Roberto Steiner our goal is to construct a simple measure of intervention based on variables that are highly visible to markets on a timely basis,7 As in LYS and Poirson (2001), our index relies on the relative variabilities of international reserves and nominal exchange rates.

jðDIRiteitÞ=BMit1j INTERVit ¼ ; jDEit=Eit1jþjðDIRiteitÞ=BMit1j where IR are gross international reserves (denominated in U.S. dollars), BM is the base money, and e is the nominal U.S. dollar exchange rate for country i in month t. Thus, the numerator measures the monthly change in inter- national reserves converted to domestic currency and scaled by the previous end-month stock of base money, but the first term of the denominator is simply the monthly rate of (positive or negative) nominal depreciation of the exchange rate E expressed in relation to a reference currency. For many countries in the sample, the U.S. dollar served as the reference currency, but in some cases either the French franc, the German deutsche mark, or the euro were deemed more appropriate, according to the country’s exchange rate policies as described in the AREAER. The intervention index is thus a rough measure of the degree to which the monetary authorities intervene in the foreign exchange markets. In the case of a country with a completely pure float the numerator will be zero, and thus the index will be zero.8 Alternatively, a fully fixed regime should have zero variation in its exchange rate, and INTERV will equal one. All countries in the sample fall somewhere between these two extremes; the higher the index, the more intervention it reflects. INTERV is constructed on a monthly basis, and then incorporated into the regressions either as its average over the given quarter, or as the average over the previous eight quarters. As described earlier, FUND includes country-specific macroeconomic fundamentals that are presumed to be related to markets’ assessments of overall performance and repayment capacity. Included in this group are: year-on-year CPI inflation; real exchange-rate volatility, calculated as the standard deviation of monthly percentage changes in the real bilateral exchange rate vis-a` -vis the reference currency over the previous two years;9

7This index does not capture other modes of intervention, such as the use of capital controls, purchases or sales of foreign currency debt, operations in derivative markets, use of credit lines, and moral suasion on the banking system. While recognizing this limitation, we believe that due to profound problems in measuring these types of interventions on a cross- country sample and the potential difficulty of markets to perceive these interventions, our analysis is better served by using the relatively simple and clean measure described above. 8Strictly speaking, since IR can change because of valuation changes (other than those in the exchange rate vis-a` -vis the reference currency), it is likely that in practice even a fully floating exchange rate regime will in fact be characterized by an intervention index higher than zero. 9We also constructed real exchange rate volatility from multilateral real effective exchange rate measures, with similar results in the regressions. We report the results from the bilateral measures because these were available for a larger sample of countries.

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©International Monetary Fund. Not for Redistribution DO MARKETS CARE WHAT COUNTRIES SAY ABOUT THEIR EXCHANGE RATE POLICIES? and the ratios to GDP of the external current account, overall general government balance, international reserves minus gold, and external government debt. These variables were taken from the IMF’s International Financial Statistics (IFS), with the exception of debt, which was taken from the World Bank’s Global Development Finance database. It covers public and publicly guaranteed external debt, outstanding and disbursed, is in U.S. dollars and is recorded annually.10 In situations of extreme stress, in which spreads go up significantly, an emerging market economy might switch from one exchange regime to another—generally, from pegged to floating. In order to capture the possible effect of such extreme events on emerging market spreads without ascribing them to one exchange rate regime or another, we construct two alternative indicators of ‘‘crisis.’’ Following the methodology of Kaminsky, Lizondo, and Reinhart (1998) and Eichengreen, Rose, and Wyplosz (1994), we define a currency crisis as an extreme observation for exchange market pressure (EMP), a weighted average of reserve losses and real exchange rate depreciation. We consider two alternative EMP measures for the 1980– 2006 period:   qIR IR DREit DIRit EMPit ¼ a ð1 aÞ REit1 IRit1   qIR BM DREit DIRitEit EMPit ¼ a ð1 aÞ ; REit1 BMit1 where RE is the real exchange rate.11 The weights of the two components, a and 1–a, were chosen such that the sample variance of both is equalized: 2 2 2 2 2 2 2 2 a s(qRE/RE) ¼ (1a) s(qIR/IR) in the first case, and a s(qRE/RE) ¼ (1a) s(qIR/BM), in the second.12 We then constructed dummy variables CRISIS, which take a value of one if the respective EMP index exceeds a threshold of two standard deviations above the mean within a five-quarter window centered around quarter t. In other words, if the threshold is exceeded within two quarters before and two quarters after the current period, CRISIS takes a value of

10In order to include this variable in our quarterly regressions, we imputed the quarterly values of the debt stock as follows: the annual debt measured in dollars multiplied by the exchange rate prevailing end-of-quarter, then divided by annual GDP measured in national currency. 11We also calculated these indices using the nominal exchange rate, but in the regressions we only report those using the real exchange rate, as this measure deals more appropriately with episodes of hyperinflation. 12The variance, s2, may be calculated in two different ways. First, observations for all countries and periods can be considered together and the variance calculated on the entire sample. Second, different variances can be calculated over the sample period for each individual country, with as chosen as above, but each only applied to its corresponding country. For brevity, we only report estimations in which the variance is calculated for the entire sample.

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13 one. The variable CRISIS1 is constructed using EMPqIR/IR, while CRISIS2 is constructed using EMPqIR/BM. In consideration of the manner in which the CRISIS variable is constructed, the public debt variable is lagged. Specifically, the public debt ratio tends to be sensitive to the exchange rate, particularly so in times of turbulence. Therefore, it was necessary to separate the volume effect of the stock of public debt from that arising from exchange rate fluctuations. Indeed, for estimations in which debt and crises are measured contemporaneously, we find the two variables to be collinear and rarely both significant. Lagging public debt—outside the five-quarter window defining the crisis variable—would allow the stock effect to have an impact on spreads while reducing its sensitivity to current exchange rate fluctuations. Thus, the regressions reported in the following section use a lag of three quarters for public debt.14 Finally, we include variables that capture global conditions that vary over time only. Namely, we include the Chicago Board Options Exchange (CBOE) S&P 100 Volatility Index (VXO), which measures expectations of future volatility in U.S. equities markets, as a proxy for overall world market volatility,15 as well as year dummy variables.

II. Summary Statistics and Results Some general patterns emerge when observing the de jure exchange rate variables. Table 1 reports the percentage of observations in our sample which lie in each aggregated category: fixed, managed floaters, and free floaters. There appears to be a modest trend toward more flexible exchange rates during the sample period; from the 1990s to the current decade, the free floating category has gained about 4 percentage points at the expense of the other two categories. Table 2 reports the number of de jure regime switches, showing that switches in either direction—toward a more fixed or a more flexible regime—occurred with similar frequency throughout the past two decades, and that virtually all switches at a disaggregated level also occurred at the aggregate level. That is, countries very rarely made small changes in their regime that kept them within the same aggregate category (only five of the 68 switches observed since the 1980s). Table 3 reports the mean intervention indices, both over the whole sample period and by decade, for all of the countries in the sample, and ranks countries from lowest mean intervention (flexible) to highest (fix). Figure 1 plots the average intervention index of all the countries in each aggregated de

13It seems reasonable that spreads would begin to widen during the run-up to a currency crisis. Indeed, the crisis measures based on a centered window perform better in the regressions than the measures based solely on lagged episodes. 14In unreported estimations we also used longer lags (four quarters) for public debt, with similar results. 15For data and detailed description, see www.cboe.com/VXO.

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Table 1. Percentage of EMBIG Countries in Aggregated Exchange Rate Categories

1980s 1990s 2000s Whole Period

Fixers 41.11 30.25 28.50 32.18 Managed floaters 39.50 44.67 42.07 42.66 Free floaters 19.39 25.07 29.44 25.18 Note: This table displays the average percentage of countries falling into each of the de jure regime categories for each period. The sample of countries comprises 37 emerging economies for which the EMBIG is available.

Table 2. Switches Between Exchange Rate Regimes, 1985–2006

Switches at an Aggregate Level Switches at a Disaggregate Level

Toward flex Toward fix All Toward flex Toward fix All

1980s 11 6 17 12 7 19 1990s 16 17 33 18 18 36 2000s 7 6 13 7 6 13 Whole period 34 29 63 37 31 68 Note: This table displays the number of times that EMBIG countries switched from one type of regime to another, for each period indicated above. Switches at an aggregate level refer to moves from among the three larger categories (fixed, managed floating, or free floating), while switches at a disaggregate level refer to moves to and from any of the IMF’s disaggregated de jure categories.

jure category. As one would expect a priori, in our sample of countries the intervention index for the de jure fixers is consistently higher than that for the managed floaters, and that of the managed floaters tends to be consistently higher than that of the free floaters. However, as panels b and c show, the differences in mean intervention between categories exhibit considerable variability over time and, particularly when comparing managed floats to free floats, intersect and even cross in various periods. Thus, there are periods in which managed floaters intervene about the same on average as free floaters, and there are periods in which they intervene less. Comparing fixers and free floaters reveals that, while the former undoubtedly intervene more on average throughout the sample period, the positive difference between the two varies noticeably over time.

Basic Regression Results In Table 4 we report the results for the basic specifications. Overall, the controls for domestic fundamentals behave as expected. The general government balance, level of international reserves, public debt (lagged by

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©International Monetary Fund. Not for Redistribution 456 dloBrjs enr rcsn n oet Steiner Roberto and Erickson, Lennart Barajas, Adolfo

Table 3. Intervention Index

Whole period 1970s 1980s 1990s 2000s

Brazil 0.496 Turkey 0.508 Poland 0.211 Uruguay 0.467 South Africa 0.387 Turkey 0.547 Brazil 0.621 Brazil 0.395 Brazil 0.474 Brazil 0.494 Poland 0.587 Lebanon 0.646 Turkey 0.438 Algeria 0.562 Indonesia 0.563 Uruguay 0.589 Argentina 0.664 Algeria 0.461 Colombia 0.593 Dominican Republic 0.567 Colombia 0.632 Uruguay 0.717 Argentina 0.465 Ukraine 0.601 Croatia 0.611 Croatia 0.635 Colombia 0.725 Uruguay 0.491 Poland 0.602 Colombia 0.621 South Africa 0.636 Chile 0.729 Mexico 0.492 South Africa 0.611 Poland 0.629 Algeria 0.639 Algeria 0.741 Peru 0.544 Turkey 0.615 Turkey 0.652 Peru 0.690 Tunisia 0.745 Lebanon 0.576 Peru 0.658 Mexico 0.677 Indonesia 0.698 Malaysia 0.760 South Africa 0.581 Croatia 0.660 Chile 0.687 Argentina 0.702 Morocco 0.771 Colombia 0.582 Dominican Republic 0.665 Tunisia 0.710 Morocco 0.706 Peru 0.855 Morocco 0.629 Morocco 0.679 Thailand 0.719 Tunisia 0.707 Mexico 0.888 Nigeria 0.660 Indonesia 0.687 Uruguay 0.720 Mexico 0.717 Korea 0.892 Tunisia 0.667 Bulgaria 0.700 Peru 0.728 Hungary 0.746 South Africa 0.906 Pakistan 0.740 Russia 0.701 Korea 0.736 Lebanon 0.758 Philippines 0.907 Hungary 0.758 Tunisia 0.706 Philippines 0.737 Serbia 0.764 Nigeria 0.936 Malaysia 0.769 Ecuador 0.709 Hungary 0.755 Russia 0.784 Pakistan 0.948 Korea 0.787 Philippines 0.714 Morocco 0.757 Dominican Republic 0.785 Coˆte d’Ivoire 0.959 Indonesia 0.813 Hungary 0.726 Serbia 0.764

©International Monetary Fund. Not for Redistribution Chile 0.786 Egypt 0.984 Ecuador 0.815 Korea 0.741 Egypt 0.777 POLICIES? RATE EXCHANGE THEIR ABOUT SAY COUNTRIES WHAT CARE MARKETS DO Ukraine 0.788 Thailand 0.989 Philippines 0.827 Malaysia 0.771 Argentina 0.780 Korea 0.792 Ecuador 0.992 Chile 0.857 Venezuela 0.776 Pakistan 0.795 Philippines 0.797 Venezuela 0.997 Dominican Republic 0.858 Pakistan 0.791 Russia 0.834 Malaysia 0.805 Dominican Republic 1.000 China 0.863 Mexico 0.801 Algeria 0.853 Pakistan 0.819 El Salvador 1.000 Thailand 0.895 Thailand 0.816 Venezuela 0.875 Nigeria 0.841 Panama 1.000 Venezuela 0.964 Chile 0.838 Nigeria 0.887 Bulgaria 0.854 Egypt 0.983 Lebanon 0.850 Malaysia 0.955 Thailand 0.863 El Salvador 0.992 China 0.861 China 0.958 Ecuador 0.871 Coˆte d’Ivoire 1.000 Nigeria 0.897 Ukraine 0.961 Venezuela 0.905 Panama 1.000 Argentina 0.914 Bulgaria 0.974 China 0.919 El Salvador 0.923 Coˆte d’Ivoire 1.000 Egypt 0.927 Egypt 0.934 El Salvador 1.000 El Salvador 0.977 Coˆte d’Ivoire 1.000 Lebanon 1.000 Coˆte d’Ivoire 0.989 Panama 1.000 Ecuador 1.000 Panama 1.000 Panama 1.000 Note: This table displays the average intervention index (INTERV) for a given country and for each of the periods indicated above, and also ranks countries by their average intervention over each period. As defined in the text, INTERV is equal to the ratio of dIR/BM (the monthly percentage change in international reserves, scaled by base money) to the sum of dIR/BM and dE/E (the monthly percentage change in the nominal exchange rate). For each country, a single reference currency is chosen in order to calculate the nominal exchange rate. By construction, INTERV lies between 0 and 1, with 0 corresponding to a fully floating exchange rate, and a value of 1 corresponding to a completely fixed rate. 457

©International Monetary Fund. Not for Redistribution Adolfo Barajas, Lennart Erickson, and Roberto Steiner

Figure 1. Intervention Index Across De Jure Regime Categories

Mean Intervention Index for the De Jure Regime Categories 1.2 1 0.8 0.6 0.4 0.2 0 1997 1998 1999 2000 2001 2002 2003 2004 2005

Mean intervention index of fixers Mean intervention index of managed floaters Mean intervention index of free floaters

Difference Between the Mean Intervention Index of Fixers and of Free Floaters 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3 1997 1998 1999 2000 2001 2002 2003 2004 2005

Difference Between the Mean Intervention Index of Managed and Free Floaters 0.3

0.2

0.1

0

-0.1

-0.2

-0.3

-0.4 1997 1998 1999 2000 2001 2002 2003 2004 2005

three quarters), and global volatility conditions have an impact on sovereign spreads, which is statistically significant and of the expected sign. In particular, spreads decline with improvements in the fiscal balance and with the level of reserves, and increase with expectations of volatility in U.S. markets and with increases in the stock of debt. The two exceptions are inflation, which has the expected positive coefficient but is not significant, and the current account balance, which is paradoxically positively related to spreads, although not significantly so.16 The results also show that currency

16This result has arisen in previous studies. Powell and Martı´nez (2007), for example, interpret it is as a possible case of reverse causality: a country facing lower spreads (and hence, lower borrowing costs) could afford to sustain a weaker current account over time. Thus, a positive relationship between the two would emerge.

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Table 4. Determinants of EMBIG Spreads, All Countries Dependent Variable: EMBIG Spread

(1) (2) (3) (4) (5) (6) (7) (8)

Inflation 0.95 1.20 0.44 1.06 0.87 0.70 0.30 0.38 [0.54] [0.62] [0.23] [0.59] [0.53] [0.44] [0.18] [0.25] Current account 2,718.18 3,927.55 3,484.11 3,912.98 1,120.41 1,295.93 1,228.18 1,638.98 [0.93] [1.24] [1.19] [1.30] [0.54] [0.63] [0.62] [0.80] General government 7,576.78 6,927.93 7,228.15 6,531.38 6,215.77 5,930.04 5,471.81 4,764.67 balance [1.86]* [1.88]* [2.01]* [1.91]* [2.28]** [2.17]** [2.19]** [1.99]* Reserves minus gold 2,027.10 3,279.34 2,966.62 3,033.03 2,749.39 2,667.33 1,688.89 1,978.41 [1.62] [2.70]** [2.63]** [2.60]** [2.66]** [2.62]** [1.85]* [2.01]* Public debt (t–3) 2,955.10 2,441.14 2,128.19 2,438.56 1,495.71 1,598.75 2,708.90 3,004.07 [2.87]*** [3.83]*** [3.81]*** [4.11]*** [3.18]*** [3.32]*** [3.47]*** [3.52]*** Volatility index 14.60 10.18 10.15 10.84 10.05 10.43 8.80 9.51 [4.15]*** [3.90]*** [3.97]*** [4.23]*** [4.30]*** [4.37]*** [4.20]*** [4.43]*** De jure fixed 668.71 487.22 476.37 292.47 237.22 448.89 465.36 [2.23]** [1.72]* [1.75]* [1.51] [1.19] [1.87]* [1.86]* De jure managed floaters 3.03 118.00 73.80 416.70 417.67 79.02 24.03 [0.03] [0.87] [0.53] [1.78]* [1.72]* [0.75] [0.26]

Intervention index Average over previous 1,161.24 320.00 1,362.18 eight quarters [3.19]*** [0.76] [2.82]*** 459 Current quarter average 618.24 362.54 596.74 [2.68]** [2.93]*** [2.63]**

©International Monetary Fund. Not for Redistribution 460 Table 4 (concluded )

Real exchange rate 7,705.74 7,527.84 volatility [2.08]** [2.22]** CRISIS1 (t–2, t+2) 1,282.22 1,221.15

[2.33]** [2.23]** Steiner Roberto and Erickson, Lennart Barajas, Adolfo Constant 930.70 316.95 544.29 42.60 352.06 377.07 539.28 196.00 [1.79]* [0.86] [1.20] [0.13] [0.67] [1.11] [1.33] [0.65]

Joint significance test for year dummies (Ho: ct, for all t) Test statistic 2.33 2.03 2.49 2.24 2.56 2.35 1.73 1.21 p-Value 0.047 0.081 0.036 0.056 0.033 0.047 0.140 0.335 Number of observations 739 705 705 704 680 679 670 669 Number of countries 25 25 25 25 24 24 24 24 R2 0.38 0.43 0.45 0.44 0.49 0.5 0.53 0.52 Note: This table shows the results of fixed-effects regressions of country-specific EMBIG spreads for quarterly observations during the 1997–2007 period. The explanatory variables are the external current account, general government balance, and three-quarter lagged public debt (all expressed as a ratio to GDP), the average 12-month inflation rate, the VXO market volatility index, dummy variables for de jure fixed and managed floating regimes, an index of exchange intervention (as defined in the text), the standard deviation of the monthly percentage change in the bilateral real exchange rate over the previous eight quarters, and crisis dummy variables (see below). In addition, year dummies are included, for which only the results of a test for the joint significance are reported. The crisis dummy variable equals one when an exchange market pressure indicator (EMP) exceeds a threshold determined by its mean plus two standard deviations within a five-quarter window centered at t. That is, when EMP exceeds the threshold at any time between two quarters before and two quarters after the current period. Robust t-statistics are shown in brackets, with * denoting significance at 10 percent; ** at 5 percent; and *** at 1 percent.

©International Monetary Fund. Not for Redistribution DO MARKETS CARE WHAT COUNTRIES SAY ABOUT THEIR EXCHANGE RATE POLICIES? crises themselves cause spreads to rise above and beyond the levels explained purely by changes in domestic fundamentals, accounting for an additional increase of more than 1,200 basis points. Now we turn to the effect of exchange rate policy. Interestingly, spreads tend to be lower in countries with a de jure fixed regime, but de jure managed floating regimes have spreads that are roughly comparable to those in countries that claim to float. Similarly, the coefficient on intervention index is always negative, thereby reinforcing the view that, if anything, exchange rate fixity—either de jure or de facto—brings about lower spreads. Presumably, one main benefit of intervention from the viewpoint of capital markets is that it brings about greater stability in the real exchange rate. Indeed, real exchange rate volatility over the previous two years is seen to be positively related to spreads,17 and causes the impact of intervention over the same period to lose significance (column 5). However, intervention over the current quarter continues to have a negative significant effect even when real exchange rate volatility is included (column 6). Finally, one could argue that the negative relationship between de jure fixed regimes and spreads might simply be the result of countries exiting fixed regimes during periods of turmoil, and therefore the regressions could falsely attribute to floating regimes the higher spreads that arise during these difficult times. How- ever, when we control for these episodes with our CRISIS variable, we find that the negative impact of exchange rate fixity on spreads persists (columns 7 and 8)—that is, the negative relationship does not appear to be driven by exits from fixed to floating regimes in the aftermath of currency crises. The basic results also show that, once crisis episodes are accounted for, the year dummies cease to add explanatory power to the regressions. That is, there are no longer significant common time-varying effects that are not already being reflected in VXO or in crisis episodes.

Extensions and Robustness Checks Our previous results show that, ceteris paribus, countries that claim to be fixers and also intervene more heavily in the foreign exchange market by buying and selling international reserves will have lower spreads on average. However, although we control for crisis episodes, it may be that different regimes are not given equal treatment by markets when a crisis does erupt. Thus, in Table 5 we introduce interaction terms between the crisis dummies

17Some other recent empirical studies find support for potential benefits of exchange rate intervention. Powell and Martı´nez (2007) find that real exchange rate volatility tends to lower country ratings and these, in turn, have a significant upward effect on spreads. Using an asset pricing approach to jointly estimate the return on bank deposits, bonds, and equity in emerging markets, Diez de los Rı´os (2007) finds evidence that currency risk premia demanded by foreign investors is lower for countries that intervene more heavily in foreign exchange markets.

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©International Monetary Fund. Not for Redistribution Adolfo Barajas, Lennart Erickson, and Roberto Steiner and the de jure regimes. We find that, although spreads are generally higher for de jure floating regimes during tranquil or normal times, there is evidence that during crises these regimes are punished significantly less by capital markets. Depending on the precise measure of CRISIS used, either both fixed and managed floating regimes (CRISIS1, columns 1 and 3) or managed

Table 5. Determinants of EMBIG Spreads, All Countries

Dependent Variable: EMBIG Spread

(1) (2) (3) (4)

Inflation 0.34 0.46 0.94 1.00 [0.21] [0.32] [0.72] [0.83] Current account 1,935.87 730.58 530.37 647.26 [0.99] [0.43] [0.32] [0.38] General government balance 4,125.01 4,536.65 3,662.19 3,823.93 [2.20]** [2.04]* [2.36]** [2.01]* Reserves minus gold 2,010.46 1,261.72 1,813.44 1,092.21 [2.06]* [1.35] [1.98]* [1.24] Public debt (t3) 2,686.84 3,032.59 2,289.15 2,629.42 [2.95]*** [3.63]*** [2.85]*** [3.87]*** Volatility index 7.95 7.89 8.56 8.34 [4.05]*** [4.33]*** [4.62]*** [4.65]*** De jure fixed 692.37 395.18 527.15 239.54 [2.71]** [1.90]* [2.96]*** [1.22] De jure managed floaters 166.38 19.44 39.20 208.58 [1.41] [0.15] [0.26] [1.13]

Intervention index Average over previous 8 1,359.46 quarters [2.86]*** Current quarter average 439.95 448.51 [3.11]*** [3.09]*** Real exchange rate volatility 5,911.18 5,399.62 [2.52]** [2.35]** CRISIS1 (t2, t+2) 1,952.93 1,941.13 [6.47]*** [6.55]*** CRISIS2 (t2, t+2) 543.12 524.52 [0.84] [0.83]

Interactions between de jure regimes and the respective crisis definition De jure managed 99.64 1,424.26 170.41 1,203.83 Floaters CRISIS [0.18] [2.30]** [0.42] [1.98]* De jure floaters CRISIS 1,570.98 122.61 2,017.99 587.42 [4.80]*** [0.23] [5.66]*** [0.85] Constant 1,071.11 423.20 47.53 430.45 [2.72]** [0.99] [0.15] [1.14]

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Table 5 (concluded ) Dependent Variable: EMBIG Spread

(1) (2) (3) (4)

Joint significance test for year dummies (Ho: ct, for all t) Test statistic 2.68 3.82 2.15 2.12 p-Value 0.027 0.005 0.687 0.076 Number of observations 673 670 647 644 Number of countries 24 24 23 23 R2 0.56 0.56 0.59 0.58 Note: This table shows the results of fixed-effects regressions of country-specific EMBIG spreads for quarterly observations during the 1997–2007 period. The explanatory variables are the external current account, general government balance, and three-quarter-lagged public debt (all expressed as a ratio to GDP), the average 12-month inflation rate, the VXO market volatility index, dummy variables for de jure fixed and managed floating regimes, an index of exchange intervention (as defined in the text), the standard deviation of the monthly percentage change in the bilateral real exchange rate over the previous eight quarters, and crisis dummy variables (see below). In addition, year dummies are included, for which only the results of a test for the joint significance are reported. Finally, interaction terms are included between the crisis and de jure regime dummies. The crisis dummy variable equals one when an exchange market pressure indicator (EMP) exceeds a threshold determined by its mean plus two standard deviations within a five-quarter window centered at t. That is, when EMP exceeds the threshold at any time between two quarters before and two quarters after the current period. Robust t-statistics are shown in brackets, with * denoting significance at 10 percent; ** at 5 percent; and *** at 1 percent.

floating regimes only (CRISIS2, columns 2 and 4) are punished more harshly than floating regimes in the event of a crisis. In Table 6 we account for a sharp and discrete fall in Argentina’s reported EMBIG spread (from close to 5,000 to around 450), associated with what appears to be a recalculation of the spreads for Argentina in the wake of its debt restructuring and the issuing of new debt instruments. We do this in two ways: (1) by including a dummy variable for Argentina during 2005:2 (columns 1–4); and (2) and by excluding Argentina from the sample (columns 5–8). The dummy variable turns out to be highly significant, and the most salient results from Tables 4 and 5 continue to hold: among country fundamentals, public debt, reserves, and the government balance are all significant explanatory variables for spreads; the volatility index continues to have a positive and significant coefficient; de jure fixity is associated with lower spreads; and de facto fixity in the form of greater intervention is also associated with lower spreads. As before, once crises have been accounted for, year dummies cease to be significant. The results on fundamentals and on de jure and de facto fixity also continue to hold when Argentina is dropped from the sample.

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Table 6. Determinants of EMBIG Spreads, Accounting for Argentina, June 2005

Dependent Variable: EMBIG Spread

Including a dummy variable for Argentina June 2005 Excluding Argentina from the sample

(1) (2) (3) (4) (5) (6) (7) (8)

Inflation 1.15 1.45 0.28 0.29 1.84 1.93 1.03 1.01 [0.64] [0.72] [0.16] [0.17] [0.96] [0.94] [0.62] [0.61] Current account 3,215.62 4781.8 2673.35 2344.49 827.75 1616.32 83.02 202.75 [0.98] [1.29] [1.05] [0.89] [0.45] [0.84] [0.09] [0.24] General government 7,319.49 6,456.38 4,308.34 5,490.75 3,450.27 2,951.25 1,731.61 2,303.29 balance [1.90]* [2.07]** [2.15]** [2.41]** [1.88]* [2.16]** [2.32]** [2.80]** Reserves minus gold 1,879.97 3,546.56 2,478.91 1,893.92 2,645.89 2,784.30 2,815.79 2,457.27 [1.49] [2.59]** [2.49]** [2.05]* [1.78]* [2.19]** [2.54]** [2.44]** Public debt (t–3) 3,044.41 2,395.29 2,314.05 2,465.67 1,246.76 1,244.87 1,140.80 1,269.23 [2.86]*** [4.56]*** [4.21]*** [4.77]*** [3.60]*** [4.33]*** [2.75]** [2.98]*** Volatility index 14.33 9.67 7.61 8.28 15.47 11.21 9.51 9.91 [3.96]*** [3.34]*** [3.09]*** [3.42]*** [4.41]*** [4.50]*** [4.41]*** [4.73]*** Argentina June 2005 2,363.20 2,898.93 3,007.63 2,969.10 dummy [21.08]*** [8.39]*** [8.65]*** [8.91]*** De jure fixed 818.35 642.46 604.56 348.27 246.65 214.21 [1.86]* [1.71] [1.60] [2.14]** [1.89]* [1.75]* De jure managed floaters 60.66 157.04 162.98 132.52 87.18 80.26 [0.40] [1.05] [1.10] [1.10] [1.07] [0.95]

©International Monetary Fund. Not for Redistribution Intervention Index POLICIES? RATE EXCHANGE THEIR ABOUT SAY COUNTRIES WHAT CARE MARKETS DO Average over previous 1,938.28 1,777.87 623.95 536.14 eight quarters [2.45]** [2.38]** [1.97]* [1.75]* CRISIS1 (t–2, t+2) 1,361.82 748.81 [2.51]** [1.69] CRISIS2 (t–2, t+2) 1,163.71 652.64 [2.59]** [1.91]* Constant 975.06 248.29 1,710.63 974.56 140.89 6.20 636.96 398.92 [1.75]* [0.69] [2.56]** [1.76]* [0.55] [0.02] [2.66]** [1.41]

Joint significance test for year dummies (Ho: ct, for all t) Test statistic 2.62 2.04 1.55 1.64 2.53 3.69 10.13 7.76 p-Value 0.029 0.079 0.190 0.162 0.352 0.006 0.000 0.000 Number of observations 739 705 673 670 703 670 638 635 Number of countries 25 25 24 24 24 24 23 23 R2 0.45 0.53 0.65 0.64 0.4 0.42 0.5 0.5 Note: This table shows the results of fixed-effects regressions of country-specific EMBIG spreads for quarterly observations during the 1997–2007 period. The explanatory variables are the external current account, general government balance, and three-quarter lagged public debt (all expressed as a ratio to GDP), the average 12-month inflation rate, the VXO market volatility index, dummy variables for de jure fixed and managed floating regimes, an index of exchange intervention (as defined in the text), and crisis dummy variables (see below). In addition, year dummies are included, for which only the results of a test for the joint significance are reported. These regressions also control for a discrete fall in Argentina’s spread between May and June 2005, by including a dummy variable (columns 1–4) and by excluding Argentina from the sample (columns 5–8). The crisis dummy variable equals one when an exchange market pressure indicator (EMP) exceeds a threshold determined by its mean plus two standard deviations within a five-quarter window centered at t. That is, when EMP exceeds the threshold at any time between two quarters before and two quarters after the current period. Robust t statistics are shown in brackets, with * denoting significance at 10 percent; ** at 5 percent; and *** at 1 percent. 465

©International Monetary Fund. Not for Redistribution Adolfo Barajas, Lennart Erickson, and Roberto Steiner

So far, the results indicate that our measure of exchange rate intervention is associated with lower spreads. However, a related and key question is whether this is true for all de jure regimes. For de jure fixed regimes, intervention can be viewed as the action required to maintain whatever exchange rate commitment the authorities have made, whereas in floating regimes intervention can be viewed as a measure of disconnect between words and deeds, that is, a measure of fear of floating. It is quite possible that markets would view intervention differently in the two cases. For this reason, in Table 7 we show the results of regressions in which only the two de jure floating regimes are included (columns 1–4), or where only the free floating regimes are included (columns 5–7). Indeed, in both cases, although the relation between intervention and spreads continues to be negative, it ceases to be statistically significant. It then follows that only for de jure fixers does intervention produce lower spreads. However, it is also worth noting that intervention under de jure floating regimes—fear of floating—is not punished by markets. Finally, the results indicate that for the smaller sample of de jure free floating regimes, most country fundamentals cease to be significant, the sole exception being the current account, which now enters with the expected negative sign. Interestingly, the effect of the level of reserves on spreads continues to be significant when managed floating regimes are included, but not when the sample is restricted to free floating regimes only. As a further robustness check, we run regressions in which we include the Reinhart-Rogoff de facto classification scheme in place of INTERV. For each of the RR aggregated categories18 j, we create a dummy variable labeled RRj, with j ¼ 1.5, increasing with the degree of flexibility. We report these regressions in Table 8, for the full sample of countries (columns 1–3), de jure managed and free floating regimes (columns 4–5), and for free floating regimes only (columns 6–7). One consequence of this specification is that the sample size is reduced dramatically, as the RR regime classification is available only through 2001. The results are less clear than in previous estimations. For the full sample of countries, the coefficient signs on the de jure regimes are the opposite of what was observed earlier, with fixed and managed floating regimes now associated with higher spreads relative to free floating regimes. However, the RR regimes continue to reveal a preference by markets for fixity; in comparison to free floating (RR4), the full sample results show lower spreads for all three of the more fixed regimes, RR1–RR3, but the free falling category (RR5) displays the highest spreads of any category.

18RR initially created 14 separate regime classifications, which were then consolidated into six. In terms of our dummy variables (which equal one if the country-quarter belongs to a given category, and zero otherwise): RR1 encompasses four fixed categories, from no separate legal tender to a de facto peg; RR2 corresponds to three crawling peg or narrow band categories; RR3 includes four wider crawling or moving bands as well as managed floating; RR4 corresponds to freely floating; RR5 corresponds to freely falling; and RR6 refers to a dual market in which parallel market data are missing. In our sample there is a negligible number of observations for RR6, so this category is dropped.

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Table 7. Determinants of EMBIG Spreads, De Jure Floating Regimes Only Dependent Variable: EMBIG Spread

De jure managed floaters and free floaters De jure free floaters

(1) (2) (3) (4) (5) (6) (7)

Inflation 14.05 13.70 11.90 11.07 4.20 4.15 4.33 [1.37] [1.37] [1.30] [1.25] [1.05] [0.87] [1.09] Current account 217.59 71.35 931.06 850.26 3,100.08 3,107.92 3,195.40 [0.17] [0.05] [0.77] [0.78] [2.52]** [2.79]** [2.42]** General government balance 368.21 757.97 80.94 694.88 86.78 78.75 161.52 [0.62] [1.19] [0.11] [0.98] [0.05] [0.04] [0.08] Reserves minus gold 5,016.28 4,710.43 4,717.06 4,008.54 175.83 187.92 103.86 [5.91]*** [6.20]*** [6.11]*** [4.87]*** [0.19] [0.17] [0.09] Public debt (t–3) 745.01 692.10 894.13 1,181.30 801.57 808.53 825.26 [1.69] [1.58] [2.40]** [2.69]** [1.31] [1.42] [1.37] Volatility index 11.70 11.62 10.83 10.63 12.36 12.37 12.43 [3.58]*** [3.61]*** [3.91]*** [3.78]*** [2.35]** [2.41]** [2.36]** Argentina June 2005 dummy 4,359.33 4,349.96 4,286.70 4,212.40 [30.40]*** [31.18]*** [34.93]*** [30.93]***

Intervention Index Average over previous eight quarters 592.53 758.12 741.44 19.59 [1.28] [1.40] [1.41] [0.05]

467 Current quarter average 36.63 [0.46]

©International Monetary Fund. Not for Redistribution 468 Table 7 (concluded )

Real exchange rate volatility 368.64 [0.61] CRISIS1(t–2, t+2) 536.17

[1.92]* Steiner Roberto and Erickson, Lennart Barajas, Adolfo CRISIS2 (t–2, t+2) 662.14 [2.20]** Constant 185.73 611.63 900.51 728.41 152.00 166.64 132.28 [0.93] [1.99]* [2.82]** [2.46]** [0.59] [1.19] [0.53]

Joint significance test for year dummies (Ho: ct, for all t) Test statistic 4.07 3.84 4.50 3.43 65.75 29.77 76.73 p-Value 0.004 0.006 0.003 0.011 0.000 0.000 0.000 Number of observations 466 466 462 459 221 221 221 Number of countries 21 21 20 20 9 9 9 R2 0.77 0.78 0.83 0.84 0.50 0.50 0.50 Note: This table shows the results of fixed-effects regressions of country-specific EMBIG spreads for quarterly observations during the 1997–2007 period. The explanatory variables are the external current account, general government balance, and three-quarter lagged public debt (all expressed as a ratio to GDP), the average 12-month inflation rate, the VXO market volatility index, dummy variables for de jure fixed and managed floating regimes, an index of exchange intervention (as defined in the text), the standard deviation of the monthly percentage change in the bilateral real exchange rate over the previous eight quarters, and crisis dummy variables (see below). In addition, year dummies are included, for which only the results of a test for the joint significance are reported. These regressions also control for a discrete fall in Argentina’s spread between May and June 2005, by including a dummy variable. The sample is limited to those observations with de jure managed floating and free floating regimes (columns 1–4) and free floating regimes only (columns 5–7). The crisis dummy variable equals one when an exchange market pressure indicator (EMP) exceeds a threshold determined by its mean plus two standard deviations within a five-quarter window centered at t. That is, when EMP exceeds the threshold at any time between two quarters before and two quarters after the current period. Robust t-statistics are shown in brackets, with * denoting significance at 10 percent; ** at 5 percent; and *** at 1 percent.

©International Monetary Fund. Not for Redistribution Table 8. Determinants of EMBIG Spreads Using Reinhart-Rogoff Classifications of Exchange Rate Regimes POLICIES? RATE EXCHANGE THEIR ABOUT SAY COUNTRIES WHAT CARE MARKETS DO Dependent Variable: EMBIG Spread

All countries De jure managed and free floaters De jure free floaters

(1) (2) (3) (4) (5) (6) (7)

Inflation 0.23 0.14 0.17 13.30 8.36 5.50 2.00 [0.81] [1.00] [1.46] [1.80]* [0.98] [0.98] [0.29] Current account 5,260.16 707.62 527.62 930.09 821.42 1,818.70 1,538.09 [1.18] [0.75] [0.56] [1.03] [0.99] [0.88] [0.83] General government balance 2,838.52 3,274.13 3,421.80 1,769.83 2,124.82 3,029.51 2,892.38 [3.08]*** [2.16]** [2.21]** [1.21] [1.26] [1.81] [1.64] Reserves minus gold 3,909.86 4,011.02 4,088.03 6,546.42 6,654.66 494.60 336.77 [3.13]*** [3.36]*** [3.38]*** [6.80]*** [8.45]*** [0.78] [0.54] Public debt (t3) 2,430.97 1,798.38 1,841.24 1,595.75 1,945.02 [8.47]*** [3.65]*** [3.80]*** [3.33]*** [3.02]*** Volatility index 12.59 12.42 12.91 12.07 13.24 5.93 6.41 [6.44]*** [5.85]*** [6.23]*** [4.41]*** [4.49]*** [5.43]*** [4.79]*** De jure fixed 495.50 343.93 295.30 [1.90]* [2.28]** [1.99]* De jure managed floaters 283.88 318.12 263.82 [1.99]* [2.51]** [2.12]**

Reinhart-Rogoff de facto classification1 RR1 509.59 886.19 874.34 341.88 477.74 52.30 58.29 [4.31]*** [3.07]*** [3.02]*** [2.24]** [2.54]** [1.04] [1.09] RR2 309.76 759.02 736.05 116.02 246.99 0.00 0.00 [2.18]** [2.75]** [2.65]** [0.69] [1.25] [.] [.] RR3 226.92 270.29 76.65 176.81 94.70 49.66 [2.18]** [2.66]** [0.96] [2.09]* [1.28] [0.61] 469 RR5 379.09 106.94 17.53 [7.39]*** [1.12] [0.22]

©International Monetary Fund. Not for Redistribution 470 Table 8 (concluded )

Real exchange rate volatility 644.20 [0.45] CRISIS1 (t–2, t+2) 257.95 374.14 256.70

[2.84]** [4.72]*** [4.89]*** Steiner Roberto and Erickson, Lennart Barajas, Adolfo CRISIS2 (t–2, t+2) 248.72 290.76 185.22 [2.30]** [6.50]*** [5.41]*** Constant 88.46 471.84 480.83 570.86 661.35 173.01 164.74 [0.26] [1.79]* [1.85]* [2.54]** [2.91]** [1.08] [1.01]

Joint significance test for year dummies (Ho: ct, for all t) Test statistic 4.04 2.78 2.90 11.55 9.94 4.76 4.52 p-value 0.015 0.055 0.483 0.000 0.000 0.029 0.033 Number of observations 280 272 272 178 178 111 111 Number of countries 21 21 21 17 17 9 9 R2 0.41 0.64 0.63 0.72 0.70 0.36 0.31 Note: This table shows the results of fixed-effects regressions of country-specific EMBIG spreads for quarterly observations during the 1997–2007 period. The explanatory variables are: the external current account, general government balance, and three-quarter lagged public debt (all expressed as a ratio to GDP), the average 12-month inflation rate, the VXO market volatility index, dummy variables for de jure fixed and managed floating regimes, the Reinhart-Rogoff classifications (as defined in the footnote), the standard deviation of the monthly percentage change in the bilateral real exchange rate over the previous eight quarters, and crisis dummy variables (see below). In addition, year dummies are included, for which only the results of a test for the joint significance are reported. These regressions also control for a discrete fall in Argentina’s spread between May and June 2005, by including a dummy variable. The crisis dummy variable equals one when an exchange market pressure indicator (EMP) exceeds a threshold determined by its mean plus two standard deviations within a five-quarter window centered at t. That is, when EMP exceeds the threshold at any time between two quarters before and two quarters after the current period. Robust t-statistics are shown in brackets, with * denoting significance at 10 percent; ** at 5 percent; and *** at 1 percent. 1The Reinhart-Rogoff classification used above corresponds to a condensed version of the 14 regime categories originally created. RR1 encompasses four fixed categories, ranging from no separate legal tender to a de facto peg; RR2 corresponds to three crawling peg or narrow band categories; RR3 includes four wider crawling or moving bands as well as managed floating; RR4 corresponds to freely floating; RR5 corresponds to freely falling; and RR6 refers to a dual market in which parallel market data are missing. In our sample there is a negligible number of observations for RR6, so this category is dropped. As in the case of de jure classification, we drop the RR4 category and compare all regimes relative to free floating.

©International Monetary Fund. Not for Redistribution DO MARKETS CARE WHAT COUNTRIES SAY ABOUT THEIR EXCHANGE RATE POLICIES?

When focusing on the subsample comprising the de jure managed and free floating categories, the RR classification can also be used to measure the impact of fear of floating on spreads, and again there is evidence of a preference for fixity, as RR1 in particular is associated with significantly lower spreads. Once the sample is restricted to the de jure free floating regimes, however, the differences across RR regimes cease to be statistically significant. In these specifications the RR classifications replace INTERV as a measure of de facto exchange rate policy. To shed some light on the relationships between de jure and de facto classification schemes, as well as with INTERV, we correlate three variables: RR, which takes the values 1–5, going from less to more flexibility; De Jure, which varies between 1 and 3 and is also increasing in the degree of flexibility; and quarterly INTERV, which is decreasing in the degree of flexibility. The correlation matrix shown in Table 9 echoes the general observation from Figure 1 that de jure floating is generally associated with less intervention, although the correlation (0.435) is far from perfect. On the other hand, there is greater correlation between RR and De Jure (0.506) and between RR and INTERV (0.589). Thus, it is evident that the latter two variables, while both providing a measure of de facto exchange rate policy, are not equivalent. We also construct intervention indices that take account of domestic interest rates as a possible additional channel for intervention in the exchange market. Indeed, it is conceivable that in emerging economies some degree of defense of exchange rates is conducted through interest rates, without ever being reflected in movements in international reserves. Our working hypothesis is that these interventions would be manifested as deviations of domestic real interest rates from their long-run equilibrium levels. Thus, if a country’s real interest rate is driven above (below) its long-run level at any given time, we interpret this as intervention to prevent depreciation (appreciation). Using two alternative proxies for the long-run real interest rate rlr,19 its sample means and its Hodrick-Prescott trend, both over the 1995–2007 period, our ‘‘interest-rate-enhanced’’ intervention measure, INTERVI is then defined as

lr jðDIRiteitÞ=BMit1jþjrit ri j INTERVIit ¼ lr : jDEit=Eit1jþjðÞDIRiteit =BMit1jþjrit ri j When we included INTERVI in place of INTERV in our regressions, we found that the observed negative relationship between spreads and exchange rate intervention continued to hold. A final robustness check involves running regressions in which the dependent variable is the international investor credit rating (IICR), which consists of an overall score assigned to countries based on assessments by

19To measure the domestic real interest rate, we used the money market rate reported to the IFS and deflated it either by the past or forward 12-month CPI inflation rate. Both backward and forward-looking measures gave similar results.

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Table 9. Correlation between De Jure and RR Classifications, and INTERV

De jure RR INTERV

De jure 1.000 RR 0.506 1.000 [0.00] INTERV 0.435 0.589 1.000 [0.00] [0.00] Note: This table shows the correlation matrix between: De Jure, a variable denoting the IMF de jure classification varying from 1 (fixed) to 3 (free floating); RR, a variable which denotes the Reinhart-Rogoff classification, and which varies between 1 (fix) and 5 (free falling); and INTERV, the exchange market intervention index described in the text. p-values of the significance test on the correlations are shown in brackets.

economists and sovereign risk analysts of global banks and money management firms.20 These assessments cover not only economic and financial factors, but also political and institutional dimensions as well. Although the IICRs are reported semiannually21 rather than quarterly as is the case for the majority of variables in our data set, they do permit a wider coverage over time (with ample pre-1997 observations) and countries. After rescaling the IICR to facilitate comparability with our earlier regressions,22 we found that our main results continued to hold:23 ratings improved with strong fundamentals—in particular, with high levels of reserves and low levels of debt—and with lower real exchange rate variability, and, ceteris paribus, ratings were more favorable for de jure fixed regimes. We also found that these fixed regimes were punished most during crisis episodes. Finally, IICRs were less sensitive to INTERV than the EMBIG; although they did tend to improve with greater intervention, this relationship never achieved statistical significance.

Endogeneity We now consider whether the determinants of exchange rate regimes themselves might present endogeneity problems for our specifications. Exchange rate regimes may be affected by (1) other known variables, (2) omitted variables, which may also determine spreads, and (3) spreads

20This indicator is used extensively by the Reinhart, Rogoff, and Savastano (2003) study on debt intolerance. We are grateful to the authors for providing us with their data set of IICR. 21The IICR is reported in March and September of each year. 22The IICR ranges from zero to 100, with the latter denoting the lowest risk of default. We transformed it by subtracting it from 100. In this way, just as with the EMBIG, greater risk is associated with a higher value of the index. This rescaled index was found to have a significant correlation of 0.596 with the EMBIG. 23The results of the IICR regressions are available upon request.

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©International Monetary Fund. Not for Redistribution DO MARKETS CARE WHAT COUNTRIES SAY ABOUT THEIR EXCHANGE RATE POLICIES? themselves. We account for (1) by including a range of potential explanatory variables in our specifications. Should there exist omitted variables that affect both regimes and spreads (2), this might change our interpretation of our results, but (3) would present a more serious challenge. We turn first to de jure regimes. These tend not to change much over time, making it highly unlikely that endogeneity of type (3) holds, because spreads vary with high frequency. Of course, extreme movements in spreads may lead to a change in regime, but we have accounted for this effect with the CRISIS variables, both alone and interacted with the de jure regime. Nonetheless, it could be that the long-run average level of spreads could have a direct effect on long-run de jure regime choice. However, we find ourselves unable to imagine how such an effect could occur without operating through other variables, which leads us to examine potential long-run determinants of de jure regime. There exists a fairly extensive literature attempting to explain long-run de jure regimes, notably Poirson (2001). The literature models regime choice as a function of traditional optimum currency area criteria, other economic fundamentals (many of which we have included in our specifications) and institutional variables. The most complete survey of this empirical work is that of Juhn and Mauro (2002), which concludes that no variables appear to be robust predictors of de jure regimes. However, the authors note that the strongest candidate from a weak set of explanatory variables would be country size; larger economies are more likely to float. Thus, if de jure regime choice merely reflects country size, then our results showing a negative relation between fixed regimes and spreads would imply that markets prefer smaller countries. We do not find this interpretation to be particularly plausible. More recently, Singer (2008) has shown that regime choice may be influenced by remittances, with countries receiving greater remittances being more likely to adopt fixed exchange rates. Our results might then be interpreted as markets rewarding countries that receive more remittances. Although this is possible, it is difficult to imagine how remittances would affect spreads above and beyond their effect on the current account, for which we already control. The arguments are similar for actual intervention. The empirical studies cited above also attempt to explain some de facto regime choice—usually with a measure comparable to our measure of intervention, INTERV —and obtain similar results as with de jure regime choice. Therefore, we are equally confident that the relationship between spreads and INTERV is not contaminated with omitted variable bias. Of greatest concern would be a scenario in which there is a direct causal link from spreads to intervention. Suppose countries during specific periods experience ‘‘good times’’ in which markets perceive them to be of higher credit worthiness than during other periods. Suppose further that the monetary authorities take advantage of these good times to accumulate international reserves, believing that they can do so without jeopardizing the stability of their exchange rates. They may also take this action to avoid an

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©International Monetary Fund. Not for Redistribution Adolfo Barajas, Lennart Erickson, and Roberto Steiner appreciation of their currency: fear of floating in reverse, in the terminology of LYS (2007). The accumulation of reserves would, in our specifications, be reflected in greater measured intervention; that is to say, increasing INTERV. Thus, ‘‘good times’’ would lead to lower spreads, which would in turn lead to higher intervention. In the specifications discussed so far, the coefficient on the intervention index has been consistently negative, which we have interpreted as evidence that higher intervention leads to lower spreads. If the ‘‘good times’’ hypothesis is correct, then in fact lower spreads are causing greater intervention. However, the negative correlation between spreads and INTERV should only hold in good times; in times of adverse conditions, we would expect an increase in spreads simultaneously with an increase in INTERV, consistent with the classic fear of floating to prevent depreciation. It is important to emphasize that, in order to invalidate our results, this effect would almost certainly have to operate directly. It could not operate through one of our included control variables, nor could it operate through other variables that the studies cited above have rejected as drivers of de facto regime choice. To see if this ‘‘good times’’ hypothesis is driving our results, we need to identify when countries are experiencing such good times. To do so, we construct a variable that measures when the spreads were lowest during our sample period. We divide the observations of EMBIG for each country into quartiles. Because different countries have different underlying risk premia, we construct the quartiles country-by-country rather than over the whole sample of countries. We then construct a dummy that takes the value of 1 for those observations in which the EMBIG spreads fall into the lowest quartile (or lowest two quartiles) of the distribution and 0 otherwise. We include this new dummy variable (GTIMESQ for the lowest quartile, GTIMESH for the lowest two quartiles) in our specifications. If our results are driven by monetary authorities accumulating reserves when spreads are low, then they should not be robust to the inclusion of this variable. Specifically, if the ‘‘good times’’ hypothesis accounts for our results, then the coefficient on intervention should be negative only during ‘‘good times.’’ As seen in Table 10, this is not the case. The coefficient on intervention alone is negative and significant—that is, even during bad times—but the coefficient on the ‘‘good times’’ dummy interacted with intervention is positive and not significant. Thus, the observed negative relationship holds regardless of whether the country in question is experiencing good or bad times. As Table 10 also shows, these results are robust to including GTIMESH, referring to the two lowest quartiles of EMBIG spreads for each country.24 Also, note that the coefficients on both good times dummies alone are not

24We also tested whether the ‘‘good times’’ effect might be due to world market conditions not picked up by the year dummies or other control variables, by constructing a ‘‘good times’’ dummy that measures the lowest observations for world EMBIG spreads, with similar robust results. The results are available upon request.

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Table 10. Determinants of EMBIG Spreads, including a Dummy Variable Indicating ‘‘Good Times’’ Dependent Variable: EMBIG Spread

All countries De jure managed floaters and free floaters

(1) (2) (3) (4) (5) (6) (7) (8)

Inflation 0.58 0.18 0.52 0.18 13.61 11.82 13.86 11.99 [0.29] [0.10] [0.25] [0.10] [1.33] [1.27] [1.42] [1.33] Current account 4,046.17 2,528.06 3,863.16 2,275.26 141.10 988.08 132.96 920.91 [1.19] [0.98] [1.21] [0.96] [0.11] [0.80] [0.10] [0.68] General government 6,649.20 4,224.85 6,380.14 4,070.68 733.80 69.31 579.70 7.59 balance [2.35]** [2.19]** [2.34]** [2.13]** [1.13] [0.10] [0.99] [0.01] Reserves minus gold 2,717.88 2,216.63 2,442.77 2,058.15 4,645.93 4,685.66 4,385.34 4,520.39 [2.26]** [2.13]** [2.11]** [2.00]* [5.63]*** [6.01]*** [5.47]*** [6.07]*** Public debt (t3) 1,804.63 2,195.72 1,656.82 2,062.30 657.08 868.54 553.01 793.11 [5.57]*** [4.35]*** [4.59]*** [4.02]*** [1.49] [2.24]** [1.38] [2.25]** Volatility index 8.85 7.13 4.97 4.40 10.98 10.32 8.06 8.21 [2.95]*** [2.68]** [1.40] [1.46] [3.53]*** [3.88]*** [2.52]** [2.89]*** Argentina June 2005 3,296.71 3,133.89 3,495.51 3,303.36 4,409.83 4,333.04 4389.95 4315.06 dummy [7.24]*** [7.67]*** [7.12]*** [7.57]*** [29.33]*** [31.34]*** [34.74]*** [36.91]*** De jure fixed 597.31 645.53 456.58 529.23 [1.58] [1.74]* [1.42] [1.62]

475 De jure managed floaters 194.79 144.86 258.78 197.53 [1.07] [1.01] [1.38] [1.28]

©International Monetary Fund. Not for Redistribution 476 Table 10 (concluded )

Intervention Index Average over previous 1,462.18 1,873.42 1,288.30 1,726.82 641.57 804.07 593.67 799.22 eight quarters [2.76]** [2.55]** [3.33]*** [2.96]*** [1.30] [1.37] [1.31] [1.42] dloBrjs enr rcsn n oet Steiner Roberto and Erickson, Lennart Barajas, Adolfo ‘‘Good times’’ dummy variable Bottom quartile of spreads: 205.17 142.72 159.95 149.22 GTIMESQ [0.51] [0.40] [0.58] [0.57] Bottom half of spreads: 40.19 92.82 225.9 234.29 GTIMESH [0.13] [0.35] [1.13] [1.20] Interaction of GTIMES 509.99 343.5 429.62 246.43 89.04 104.12 27.98 115.08 with intervention index [0.85] [0.68] [0.76] [0.54] [0.23] [0.28] [0.10] [0.40] CRISIS1 (t–2, t+2) 1,337.07 1,272.09 535.62 [2.56]** [2.53]** [1.92]* Constant 914.37 1,755.18 994.14 1,804.09 687.70 960.68 831.56 1,088.50 [1.75]* [2.70]** [2.29]** [3.24]*** [2.19]** [2.73]** [2.84]** [3.32]***

Joint significance test for year dummies (Ho: ct, for all t) Test statistic 1.53 1.73 1.61 1.83 3.42 4.05 2.37 2.50 p-value 0.194 0.139 0.169 0.117 0.011 0.005 0.516 0.045 Number of observations 705 673 705 673 466 462 466 462 Number of countries 25 24 25 24 21 20 21 20 R2 0.57 0.65 0.59 0.66 0.78 0.83 0.79 0.84 Note: This table shows the results of fixed-effects regressions of country-specific EMBIG spreads for quarterly observations during the 1997–2007 period. The explanatory variables are the external current account, general government balance, and three-quarter lagged public debt (all expressed as a ratio to GDP), the average 12-month inflation rate, the VXO market volatility index, dummy variables for de jure fixed and managed floating regimes, an index of exchange intervention (as defined in the text), and crisis dummy variables (see below). In addition, year dummies are included, for which only the results of a test for the joint significance are reported. These regressions also control for a discrete fall in Argentina’s spread between May and June 2005, by including a dummy variable (columns 1–4) and by excluding Argentina from the sample (columns 5–8). The crisis dummy variable equals one when an exchange market pressure indicator (EMP) exceeds a threshold determined by its mean plus two standard deviations within a five-quarter window centered at t. That is, when EMP exceeds the threshold at any time between two quarters before and two quarters after the current period. Robust t- statistics are shown in brackets, with * denoting significance at 10 percent; ** at 5 percent; and *** at 1 percent.

©International Monetary Fund. Not for Redistribution DO MARKETS CARE WHAT COUNTRIES SAY ABOUT THEIR EXCHANGE RATE POLICIES? significant, indicating that all meaningful effects driving spreads to their low levels have already been incorporated in the set of explanatory variables.

III. Conclusion

This paper has examined why countries appear to exhibit a ‘‘fear of declaring,’’ that is, a disconnect between their declared exchange rate policy and their actual level of exchange-rate intervention. We have considered one possible reason for fear of declaring: that international capital markets might reward countries that are classified toward the flexible end of the spectrum. We have used the spreads of country’s sovereign debt over U.S. treasury bills as our measure of market perceptions of each country. Using a panel data approach that exploits both time and cross-country variability, we have considered the effect on these spreads of de jure regime choice and the actual degree of exchange rate intervention, employing a range of variables to control for underlying fundamentals. Our basic results reach the opposite conclusion; spreads tend to be lower in countries that have a fixed exchange rate regime, whether de jure or de facto. We find that above and beyond the performance of a country’s fundamentals, markets tend to punish countries that are approaching, experiencing, and coming out of a currency crisis, and that markets value real exchange rate stability. However, even when controlling for these preferences, we find the de jure fixed regimes exhibit lower spreads and that exchange rate intervention is associated with lower spreads as well. Importantly, when interacting the crisis variable with the de jure regimes, we find that free floating regimes tend to be punished the least in times of extreme turbulence. Our results are robust to the inclusion of alternative intervention indices that incorporate possible intervention through interest rates. Finally, we explore the implications of endogeneity of the exchange rate policy variables, focusing on a potentially serious type: that the negative relationship between spreads and intervention could simply reflect reverse causality during times of particularly favorable external conditions. We find that, for a reasonable specification of this ‘‘good times’’ hypothesis, our results do not appear to be driven by this type of reverse causality. However, our results also must be qualified as we incorporate extensions to the basic specifications. First, when we consider only de jure floating regimes, we find that intervention, while still associated with a reduction in spreads, is no longer significant. Second, when considering the RR classification as an alternative measure of actual exchange-rate policy, the results point more strongly to markets rewarding fear of floating; a preference for de jure floating combined with de facto (as measured by RR) fixing. However, we also observe that the RR classification tends to be more highly correlated with the de jure classification than is our INTERV measure. Thus, this paper identifies a puzzle: why are countries that intervene reluctant to openly declare that they are doing so, given that markets do not

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©International Monetary Fund. Not for Redistribution Adolfo Barajas, Lennart Erickson, and Roberto Steiner generally reward either de facto or de jure floaters? One possible reason may be reflected in our findings that de jure floating may be advantageous in times of crisis. It might be conceivable that countries opt for declaring flexibility even though it may entail costs during normal times in order to reap the benefits of lower spreads in turbulent times. Thus, flexibility may act as an insurance policy. Furthermore, once this ‘‘flexibility’’ is announced, there appears to be no punishment for fear of floating. In this regard, our results are consistent with the Alesina-Wagner hypothesis that through intervention emerging economies are sending imperfectly informed markets a signal about their good institutions and competent macroeconomic management, a signal that may not be completely contained in information regarding the macroeconomic fundamentals. Our study shows that, for the most part, markets are indeed receiving this signal and acting upon it.

REFERENCES Alesina, A., and A. Wagner, 2003, ‘‘Choosing (and Reneging on) Exchange Rate Regimes,’’ NBER Working Paper No. 9809 (Cambridge, Massachusetts, National Bureau of Economic Research). Bordo, Michael D., and Hugh Rockoff, 1996, ‘‘The Gold Standard as a Good Housekeeping Seal of Approval,’’ Journal of Economic History, Vol. 56, No. 2 (June), pp. 389–428. . Bubula, A., and I. O¨tker-Robe, 2002, ‘‘The Evolution of Exchange Rate Regimes Since 1990: Evidence from De Facto Policies,’’ IMF Working Paper 02/155 (Washington, International Monetary Fund). Calvo, G., and C. Reinhart, 2002, ‘‘Fear of Floating,’’ Quarterly Journal of Economics, Vol. 117, No. 1 (May), pp. 379–408. Cantor, R., and F. Packer, 1996, ‘‘Determinants and Impact of Sovereign Credit Ratings,’’ Economic Policy Review, Federal Reserve Bank of New York, pp. 37–53. Ciarlone, A., P. Piselli, and G. Trebeschi, 2007, ‘‘Emerging Markets Spreads and Global Financial Conditions,’’ Banca d’Italia Working Paper 637 (June). Diez de los Rı´os, Antonio, 2007, ‘‘Exchange Rate Regimes, Globalization, and the Cost of Capital in Emerging Markets,’’ Bank of Canada Working Paper 2007–29 (April). Dubas, Justin M., Byung-Joo Lee, and Nelson C. Mark, 2005, ‘‘Effective Exchange Rate Classifications and Growth,’’ NBER Working Paper No. 11272 (Cambridge, Massachusetts, National Bureau of Economic Research). Eichengreen, B., and A. Mody, 2000, ‘‘What Explains Changing Spreads on Emerging Market Debt: Fundamentals or Market Sentiment?’’ in The Economics of International Capital Flows, ed. by Sebastian Edwards (Chicago, University of Chicago Press). Eichengreen, B., A. Rose, and C. Wyplosz, 1994, ‘‘Speculative Attacks on Pegged Exchange Rates: An Empirical Exploration with Special Reference to the European Monetary System,’’ NBER Working Paper No. 4898 (Cambridge, Massachusetts, National Bureau of Economic Research). Genberg, Hans, and Alexander K. Swoboda, 2005, ‘‘Exchange Rate Regimes: Does What Countries Say Matter?’’ IMF Staff Papers, Vol. 52 (Special Issue), pp. 129–41.

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Ghosh, A., A.M. Gulde, J. Ostry, and H.C. Wolf, 1995, ‘‘Does the Nominal Exchange Rate Regime Matter?’’ IMF Working Paper 95/121 (Washington, International Monetary Fund). Glennerster, R., and Y. Shin, 2004, ‘‘Is Transparency Good for You?,’’ Paper presented at the Fifth IMF Jacques Polak Annual Research Conference, International Monetary Fund, Washington, November 4–5. Hausmann, R., U. Panizza, and E. Stein, 2001, ‘‘Why Do Countries Float the Way They Float?’’ Journal of Development Economics, Vol. 66, No. 2, pp. 387–14. Herrera, S., and G. Perry, 2000, ‘‘Determinants of Latin Spreads in the New Economy Era: the Role of U.S. Interest Rates and Other External Variables’’ (unpublished; Washington, World Bank). International Monetary Fund (IMF), 1982, Exchange Arrangements Maintained by Members, IMF Staff Memoranda, SM/06/358 (February 23). ______, 1997, Quarterly Report on Exchange Rate Arrangements, January l–March 31, 1997, IMF Staff Memoranda, SM/97/160 (June 24). ______, 1998, Developments and Issues in the International Exchange and Payments System, IMF Staff Memoranda, SM/98/172 (July 7). ______, 2006, Review of Exchange Arrangements, Restrictions and Markets, IMF Staff Memoranda, SM/06/358 (October 24). Juhn, G., and P. Mauro, 2002, ‘‘Long-Run Determinants of Exchange Rate Regimes: A Simple Sensitivity Analysis,’’ IMF Working Paper 02/104 (Washington, International Monetary Fund). Kamin, S., and K. von Kleist, 1999, ‘‘The Evolution and Determinants of Emerging Market Credit Spreads in the 1990s,’’ Bank for International Settlements Working Papers No. 68. Kaminsky, G., S. Lizondo, and C. Reinhart, 1998, ‘‘Leading Indicators of Currency Crises,’’ IMF Staff Papers, Vol 45, No. 1, pp. 1–48. Kaminsky, G., and S. Schmukler, 2002, ‘‘Emerging Market Instability: Do Sovereign Ratings Affect Country Risk and Stock Returns?’’ The World Bank Economic Review, Vol. 16, No. 2, pp. 171–95. Levy Yeyati, E., and F. Sturzenegger, 2001, ‘‘Exchange Rate Regimes and Economic Performance,’’ IMF Staff Papers, Vol. 47 (Special Issue), pp. 62–98. ______, 2002, ‘‘A De Facto Classification of Exchange Rate Regimes: Deeds vs. Words’’ (unpublished; Universidad Torcuato Di Tella). ______, 2003, ‘‘A De Facto Classification of Exchange Rate Regimes: A Metho- dological Note.’’ Appendix to ‘‘To Float or to Fix: Evidence on the Impact of Exchange Rate Regimes on Growth,’’ American Economic Review, Vol. 93, No. 4, pp. 1173–93. ______, 2007, ‘‘Fear of Floating in Reverse: Exchange Rate Policy in the 2000s’’ (unpublished; Washington, World Bank). Obstfeld, M., and K. Rogoff, 1995, ‘‘The Mirage of Fixed Exchange Rates,’’ Journal of Economic Perspectives, Vol. 9 (Fall), pp. 73–96. Poirson, He´le` ne, 2001, ‘‘How Do Countries Choose Their Exchange Rate Regime?’’ IMF Working Paper No. 01/46 (Washington, International Monetary Fund). Powell, A., and J.F. Martı´nez, 2007, ‘‘On Emerging Economy Spreads and Ratings’’ (unpublished; Washington, Inter-American Development Bank).

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Reinhart, C., and K. Rogoff, 2004, ‘‘The Modern History of Exchange Rate Arrangements: A Reinterpretation,’’ Quarterly Journal of Economics, Vol. 119, No. 1, pp. 1–48. Reinhart, C., K. Rogoff, and M. Savastano, 2003, ‘‘Debt Intolerance,’’ NBER Working Paper No. W9908 (Cambridge, Massachusetts, National Bureau of Economic Research). Rose, Andrew K., 2007, ‘‘A Stable International Monetary System Emerges: Inflation Targeting is Bretton Woods, Reversed,’’ Journal of International Money and Finance, Vol. 26, No. 5, pp. 663–81. Shambaugh, J., 2003, ‘‘The Effect of Fixed Exchange Rates on Monetary Policy,’’ Quarterly Journal of Economics, Vol. 119, No. 1, pp. 301–52. Singer, D.A., 2008, ‘‘Migrant Remittances, Financial Globalization, and Exchange Rate Regimes in the Developing World’’ (unpublished; Massachusetts Institute of Technology).

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©International Monetary Fund. Not for Redistribution IMF Staff Papers Vol. 55, No. 3 & 2008 International Monetary Fund

A Micro-Empirical Foundation for the Political Economy of Exchange Rate Populism

IRINEU DE CARVALHO FILHO and MARCOS CHAMON

Latin American countries have experienced cycles of expansionary policies, currency appreciation, and crises. The popularity of appreciations, through their effect on consumers’ purchasing power, has been an accepted assumption in the literature despite a dearth of studies on the distributional impact of exchange rate movements. This study computes the welfare effects of exchange rate movements at different points of the income distribution for Brazil and Mexico. It shows that the distributional effects of appreciations split both countries on a regional basis, instead of across income levels. In Brazil, appreciations are found to benefit less or harm more the rural areas; in Mexico, they benefit less or harm more the Northern border states. [JEL D31, F31, P16] IMF Staff Papers (2008) 55, 481–510. doi:10.1057/imfsp.2008.15; published online 1 July 2008

discussion of the political economy of exchange rates can be guided by A the following questions: Do politicians actually use it? Are the effects really there? Does it actually buy support? For the first question, there is indeed evidence that politicians in Latin America have a bias toward appreciating their currencies. Dornbusch and Edwards (1990) refer to the ensemble of expansionary policies with

Irineu de Carvalho Filho and Marcos Chamon are economists in the IMF Research Department. The authors are grateful to Rafael Oso´ rio, Sergei Soares, and Sarah Tarsila for sharing the code mapping the Brazilian expenditure survey data to consumer price index items. They also thank Allan Drazen, participants at the 2007 IMF Annual Research Conference, and seminar participants at PUC-Rio for helpful comments.

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©International Monetary Fund. Not for Redistribution Irineu de Carvalho Filho and Marcos Chamon distributive goals that have often generated real appreciation and periodic crisis in Latin America as ‘‘macroeconomic populism.’’ Frieden and Stein (2001) identify a conflict between external balance and domestic purchasing power in the decision to set the level of exchange rates. In a large cross-section of Latin American countries, they show that the currency tends to appreciate before and depreciate after elections.1 Bonomo and Terra (1999) base their discussion of the political economy of exchange rates in Brazil on the observation that real appreciations are politically popular. Although the ‘‘popularity’’ of appreciated currencies in the region can be reasonably taken for granted, there has been a dearth of studies of the distributional effects of appreciations. This paper skips the discussion on the specific channels through which a policymaker can affect the real exchange rate.2 Instead, we focus on the second question: are the effects really there? The main contribution of this paper is analyzing whether distributional effects can amplify the scope for appreciation-prone policies. We use household-level data from Brazil and Mexico (which account for over half of Latin America’s output and population) to examine how the exchange rate affects households at different points in the income distribution and with different characteristics. The goal is to uncover a compelling, economically significant, distributional effect that might serve as a foundation for a political economy theory of exchange rate populism, and to motivate further research on the third question: do appreciation policies buy political support?3 In a simplified general equilibrium model, this paper identifies two channels through which exchange rate movements can affect household welfare. For a given level of income, changes in exchange rates that are passed through to domestic prices affect the purchasing power of house- holds differentially, depending on how the prices of their consumption baskets respond to exchange rates. For instance, to the extent that poorer households spend a larger share of their consumption on tradable goods (food, a tradable good, tends to have a large weight on the poor’s budget) than richer households, the pass-through or consumption effect of an appreciation tends to be pro-poor. Exchange rate movements will also affect household welfare through a factor income channel. As exchange rate

1More recently, Aisen (2007) argued that political opportunism causes exchange rate– based stabilizations to be more likely launched before elections, because they create an initial consumption boom, as the real exchange rate appreciates. 2It seems reasonable to assume that there are a number of ‘‘levers’’ at the disposal of policymakers to affect exchange rates at least in the short term. One such option is through a tighter monetary policy. Other policy instruments include trade restrictions, fiscal policy (government spending tilted towards nontraded goods tend to appreciate the currency in the short run), financial sector regulation, capital controls, among others. 3There is a related literature that explores individual survey data to inquire on determinants of individual attitudes towards trade policy. See Balistreri (1997); Beaulieu (2002); Scheve and Slaughter (2001); and Mayda and Rodrik (2005).

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©International Monetary Fund. Not for Redistribution THE POLITICAL ECONOMY OF EXCHANGE RATE POPULISM movements affect the relative profitability of different sectors of the economy (for example, manufacturing relative to services), it also shifts the relative demand for labor of different skills, locations, and sector of employment, among other characteristics.4 In addition to this labor market channel, an appreciation can also affect incomes through its impact on profits, and on public and private transfers. Once we have computed the effect of exchange rate movements through these different channels, we can compute its net effect at different points of the income distribution and for household with different characteristics. It is beyond the scope of this paper to estimate the effect of exchange rate movements on average income. Instead we will focus on its distributional effects on income (taking the average income as given) and will normalize the average effect on income to equal zero. The focus on Brazil and Mexico is based on data availability, their large size, and the substantial differences in their export structures. Mexico has more opportunities for exporting labor intensive manufactures due to NAFTA and its proximity to the United States. As such, we should expect that a larger share of its population would be negatively affected by a real appreciation through the factor income channel. Our results indicate that the short-term effect of currency appreciation through the consumption channel benefits poorer households more than proportionately as expected. For Brazil, the consumption or pass-through effect of a 10 percent appreciation is equivalent in welfare terms to an increase in income of 1½ percent for the poorest households and only 1 percent for the richest ones. In Mexico, where the exchange rate pass-through is higher, the average of that effect is 4½ percent, and the difference between poorer and richer households is more muted (only about ¼ percent), with the effects peaking at middle income levels (but varying by less than ½ percent across the distribution). The evidence on income effects, however, shows sizable differences that cut through regional and sectoral divides in both countries, but less so across the income distribution. Finally, when total effects are calculated through the sum of pass-through and income effects, the shape of the distribution of welfare effects is dominated by the factor income effects.

I. Motivation: Some Regional Comparisons This section briefly documents the tendency for overvaluation spells among Latin American emerging market countries, in contrast to the experience of emerging Asian countries. The perception that some Asian countries, and in particular, China, have pursued active exchange rate policies has renewed interest on a debate about the merits of the neomercantilist view that an undervalued currency should

4In the short run, it is plausible that the impact of exchange rate appreciations, which are usually transitory, is dominated by their effects on current income, causing the political cleavages to be drawn on industry, instead of factor, lines.

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©International Monetary Fund. Not for Redistribution Irineu de Carvalho Filho and Marcos Chamon provide protection to domestic industries in order to stimulate the tradable sector (for example, Rodrik, 2006, 2007; Johnson, Ostry, and Subramanian, 2006). The flip side of this debate is the observation that Latin American countries have often allowed their currencies to appreciate, or maintained artificially appreciated official exchange rates in the presence of large black market premiums, and have achieved lower growth rates and been prone to periodic bouts of macroeconomic instability. Figure 1 traces the evolution of the real effective exchange rate (REER) and income in four Latin American and four East Asian countries. Two striking patterns emerge from these comparisons. Asian countries managed to grow steadily while preventing their currencies from appreciating substantially (most of the volatility in the East Asian REERs is concen- trated around the time of the Asian crisis). Latin American countries, on the other hand, have apparently experienced stagnating incomes, which has not prevented them from going through large swings in their REERs.5 Table 1 reports a measure of the frequency of overvaluation episodes for different economies. These episodes were defined based on the number of years when the REER exceeded its linear time trend by different thresholds. During 1980–2006, an overvaluation of 15 percent or more took place during 12 years in Argentina, 7 percent in Brazil, 8 percent in Colombia, 5 percent in Chile, and 4 percent in Mexico. There are far fewer and shorter episodes of overvaluation among Asian emerging markets. During that same period, the REER was above 15 percent of its trend during seven years in Indonesia. But among the other East Asian emerging markets the spells were relatively short: three years in Hong Kong SAR, two years in the Philippines, one year in Korea, Malaysia, and Thailand, and none in Taiwan Province of China. Most of the overvaluation spells mentioned above were related to the presence of fixed exchange rate regimes. Frieden and Stein (2001) show that, in a sample of Latin American countries, the choice of a fixed exchange rate is inversely related to the size of the manufacturing sector. Although differences in the choice of exchange rate regimes may be distorting the comparisons in Table 1, a similar point can be made by comparing black market premiums. A large black market premium is perhaps a clear sign of an artificially appreciated currency. Table 1 also reports the percentage of time during which the black market premium exceeded 15 percent over 1980– 98, based on the Reinhart and Rogoff (2004) de facto exchange rate classification. Argentina and Brazil experience those large black market premiums over half of the time in that sample: 52 and 62 percent, respectively. Chile, Colombia, and Mexico experienced large black market premiums during 40, 29, and 28 percent of the time, respectively. Among East Asian emerging markets, neither Thailand nor Malaysia experienced

5de Carvalho Filho and Chamon (2007) provide evidence that the apparent income stagnation in Brazil and Mexico is partly the result of measurement problems.

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Figure 1. Evolution of Real Effective Exchange Rate (REER) and Income in Selected Latin American and Asian Emerging Markets from 1980 to 2003

Argentina Brazil 200 200

150 150

100 100 REER REER

50 50

0 0 7.5 8 8.5 9 9.5 10 7.5 8 8.5 9 9.5 10 Log PPP GDP Per Capita Log PPP GDP Per Capita

Chile Mexico 200 200

150 150

100 100 REER REER

50 50

0 0 7.5 8 8.5 9 9.5 10 7.5 8 8.5 9 9.5 10 Log PPP GDP Per Capita Log PPP GDP Per Capita

Korea Malaysia 200 200

150 150

100 100 REER REER

50 50

0 0 7.5 8 8.5 9 9.5 10 7.5 8 8.5 9 9.5 10 Log PPP GDP Per Capita Log PPP GDP Per Capita

Taiwan Province of China Thailand 200 200

150 150

100 100 REER REER

50 50

0 0 7.5 8 8.5 9 9.5 10 7.5 8 8.5 9 9.5 10 Log PPP GDP Per Capita Log PPP GDP Per Capita

Sources: IMF (REER); and Penn World Table (PPP GDP per capita). Note: REER equal to 100 in 2000. An increase in the REER indicates an appreciation of the currency. PPP ¼ purchasing power parity.

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Table 1. Overvaluation Episodes and Large Black Market Premiums in Selected Emerging Markets Since 1980

Number of Years Overvalued

Relative to trend Share of Time With Black Market Premium Economy 15% or more 25% or more Above 15%

Argentina 12 10 0.52 Brazil 7 4 0.62 Chile 5 2 0.40 Hong Kong SAR 3 0 0.00 Colombia 8 2 0.29 Indonesia 7 5 0.18 Jamaica 6 3 0.62 Korea 1 0 0.07 Malaysia 1 0 0.00 Mexico 4 2 0.28 Paraguay 2 2 0.59 Peru 8 3 0.36 Philippines 3 0 0.09 Singapore 0 0 0.00 South Africa 3 0 0.14 Taiwan Province of China 0 0 NA Thailand 1 0 0.00 Turkey 5 2 0.13 Uruguay 8 2 0.30 Venezuela 9 6 0.41 Note: Real effective exchange rate (REER) data cover 1980–2006. Black market premium data from Reinhart and Rogoff (2004) cover 1980–98. Transition countries not reported due to concerns regarding pre-transition REER data. episodes of such large black market premia, but Korea and Indonesia experienced it 7 and 18 percent of the time, respectively. This tendency for Latin American countries to seek to appreciate their currencies has often been attributed to political economy considerations (Dornbusch and Edwards, 1990; Kaufman and Stallings, 1990). Latin America’s large inequality is thought to provide the grounds for strong short- term political gains from policies that redistribute rents from the exporting sector, and exchange rate appreciation figures prominently among such policies. In the rest of this paper, we quantify the strength of these distributional effects to check whether they could account for Latin America’s unusual exchange rate policies.

II. The Model This section derives the theoretical distributional effects of exchange rate movements. It follows Porto (2006), which adapted the small open economy

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©International Monetary Fund. Not for Redistribution THE POLITICAL ECONOMY OF EXCHANGE RATE POPULISM models of Dixit and Norman (1980) and Wooland (1982) to a multihouse- hold context in order to analyze the distributional effects of trade liberalization. We start from the expenditure function of a household j, which is the solution to the expenditure minimization problem for given prices and desired utility level. For the sake of simplicity, we assume it equals its income (which includes transfers).6 X j j j j j j e ðp; u Þ¼x0 þ wm þ k þ c ; (1Þ m where the expenditure function e j(.) depends on the vector of prices of consumption goods and required utility u j. Household income includes j exogenous income x0, the sum of labor income of each household member m, j j j wm, capital income, k , and government transfers, c . The production side of the economy is assumed to be competitive and face constant returns to scale. Firms producing good i use local factors of production N that are remunerated at factor prices w and imported intermediate inputs Z that are purchased at international prices p. Thus, the profit function can be calculated as the solution to the profit maximization problem of a representative firm producing all goods in the economy: X rðp; p ; E; v; fÞ¼MaxfNi;Zig fiðNi; ZiÞpi wN Ep Z; i where v is a vector of local factor endowments. The equilibrium in the goods market is determined by demand and supply equality. That is, X q q e jðp; u jÞ¼ rðp; p; E; v; fÞ; j qpi qpi where the demand for good i is given by the derivative of the expenditure function with respect to the price of that good (Shepard’s Lemma), whereas its supply is given by the own-price derivative of the GDP function (Hotelling’s Lemma). Given the nominal exchange rate, the international price of intermediate goods, the vector of utilities u, the equilibrium prices for the consumption goods is given by:

pi ¼ piðp ; E; v; f; uÞ: The assumption of perfect competition in this setting means that goods prices equal the unit costs:

pi ¼ ciðw; E; p ; fÞ:

6This simplifying assumption ignores the impact of exchange rate movements on savings.

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©International Monetary Fund. Not for Redistribution Irineu de Carvalho Filho and Marcos Chamon

The equalization of goods prices to unit costs allows us to solve for equilibrium factor prices:

w ¼ wðp; E; v; f; uÞ:

The distributional effects of exchange rate movements can be divided in two steps: the effect of exchange rate movements on consumption goods prices and its resulting effect on household welfare; and the effect of exchange rate movements on factor incomes. The welfare effect caused by the changes in the consumption goods are referred to as pass-through or consumption effects. The welfare effect channeled through changes in factor prices are referred to as factor income effects. The changes in household welfare are computed with measures of compensating variations, the income needed to compensate households for a change in the nominal exchange rate E. These compensations can be j measured with the change in exogenous income, x0, that would leave the household indifferent to the previous level of the exchange rate. Differentiating Equation (1) and assuming public transfers and capital income away, the compensating variation for a change in the nominal exchange rate is: ! j X X j dx0 j q ln pi j q ln wm j ¼ si ym d ln E; e i q ln E m q ln E

j j where si is the budget share spent on consumption good i by household j, ym is the share of the factor income of household member m on household j, and the equation describes the negative of the compensating variation that would be a revenue for the central planner setting the change in the nominal exchange rate if households were to transfer their gains from that change to the planner. The first term within the parentheses captures the consumption effect. The budget shares of each consumption good are data to be drawn from a household expenditure survey. The elasticity of consumption goods prices with relation to nominal exchange rate changes are exchange rate pass- through measures and are customarily estimated by vector auto regressions (VARs) (see McCarthy, 2007, for industrialized economies; and Belaisch, 2003, for Brazil). The second term within the parenthesis reflects the effect of nominal exchange rate movements on household income (factor income effects). This effect can be estimated based on a panel of cross-sections of income and employment surveys. As mentioned in the introduction, it is beyond the scope of this paper to estimate the effect of exchange rate movements on average income. Instead we will focus on its distributional effects on income (taking the average income as given), and instead of computing the compensating variations we will compute their distribution around a given mean.

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III. Estimation Consumption or Pass-Through Effects The estimation of the consumption or pass-through effects requires data on budget shares across the income distribution (or for groups of households based on observable characteristics), which are available from household expenditure surveys. To estimate the effect of exchange rate movements on consumer goods prices, we use the cumulative impulse response functions from a VAR with two endogenous variables, an individual consumer price index (CPI) subindex (for example, clothing and apparel) and the INS/IMF trade- weighted nominal effective exchange rate (NEER), while controlling for measures of international prices as exogenous variables. In this estimation, we restrict the sample to years with similar inflation levels, as the literature has shown that exchange rate pass-through tends to be higher in higher inflation environments (see Goldfajn and Werlang, 2000; and Ca’ Zorzi, Hahn, and Sa´ nchez, 2007). Thus, we drop Brazilian inflation data prior to the 1994 stabilization (due to hyperinflation in those years). We use Mexican inflation data beginning in 1990 (which includes some high-inflation years following the 1995 crisis, but nowhere near Brazilian hyperinflation levels). The set of international prices enters the VAR as exogenous variables and comprises the nonfuel commodities index and the average of three spot crude oil prices, both from the IMF’s World Economic Outlook (WEO), and the U.S. CPI (all items, urban average). To account for seasonal effects, the VAR was estimated with 12 lags of the endogenous variables. To avoid over-fitting the model, each exogenous variable was used only as 1 and 12-month changes. All variables enter the VAR in monthly log changes. Our preferred estimate of the nominal exchange rate pass-through is based on the cumulative impulse response function to shocks on the NEER. Because we are mainly concerned about short- and medium-term distributive effects, we focus on the pass-through over a 12-month horizon. We calculate the cumulative impulse response of prices and the NEER to shocks to the NEER. For the purposes of this paper, we define the 12-month pass-through as the ratio of the average cumulative impulse response of prices from three to 15 months after a shock to the NEER to the 12-month average cumulative own impulse response of the NEER. j Using the household expenditure survey, we compute the share si of each good i in the consumption basket of each household j. Based on these shares and the pass-through estimates, we can constructP a household-specific of the j j pass-through or consumption effect G ¼ isi (qln pi/qln E) of a change in the NEER. We estimate this effect at different points of the income distribution nonparametrically: G j ¼ f ðy jÞþe j; using a locally weighted regression with quartic kernel weights. To construct standard error bands for our estimates of the consumption effect, we

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©International Monetary Fund. Not for Redistribution Irineu de Carvalho Filho and Marcos Chamon bootstrap the sample of the household expenditure survey and our estimate of the pass-through to the price of each group of goods. Factor Income Effects The estimates of the income effects require time series data on income and employment for workers of different characteristics, which we construct by pooling different cross-sectional surveys. The equation estimated is:

ymt ¼ bmy:t þ amet þ Zmtg þ mm þ emt; (2Þ where ymt is the log of average household nominal income for group m in time t, y.t is the log of overall average nominal income in time t, et is a measure in logs of the exchange rate, Zmt is a vector of group characteristics that vary over time (for example, percent of households headed by a female; average family size), and mm is a group fixed effect. The coefficients bm capture the relative income of group m relative to the average but the coefficients am captures the distributional effects of exchange rates (that is, how group incomes vary with exchange rate movements, after controlling for overall average income). The latter is the main coefficient of interest. The estimation of the effect of exchange rates poses a few challenges. In the case of Brazil, nominal variables pre-1995 generally follow an I(2) process. However, since 1994, when the Plano Real brought average inflation rates to single digits, nominal variables are best characterized as an I(1) process. Because the estimation of factor income effects would benefit from a longer panel, our baseline specification spans from 1981 to 2006. For Brazil, we chose to use the log of the REER as our exchange rate variable in Equation (2) because this choice allows us to use a longer span of data, but for Mexico, we used the log of the NEER.7 Note that in our strategy, by including overall average nominal income in the year (y.t) as a regressor, we avoid the need to deflate incomes to constant price terms. This is useful, because de Carvalho Filho and Chamon (2007) estimate a sizable bias in the CPIs in Brazil and Mexico (more so for poorer households, and such bias could affect our distributional results). We do not intend to estimate the effect of exchange rate movements on the overall average income because the nominal exchange rate is endogenous to economy wide averages. It is arguable, however, that the nominal exchange rate is exogenous to the average income of specific groups of the population, once we control for overall average income. Hence all of our income estimates will focus on distributional effects, taking the average income as given. The final step is to construct expenditure-specific distributional a( y) effects based on a^m. We estimate:

j j a^j ¼ gðy Þþu ;

7Our results are robust to restricting the Brazilian sample only to the post-Plano Real period (1995–2006) and to the choice of NEER or REER to both Brazil and Mexico.

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using a locally weighted regression with quartic kernel weights, where a^j ¼ a^m for jAm. We are then ready to obtain the combined distributional effect of an exchange rate change for a household of expenditure y j: f ( y j) þ g( y j).

IV. Data Brazil We use the Pesquisa Nacional por Amostra de Domicı´lios (PNAD) to estimate the effect of the exchange rate on household income, and the Pesquisa de Orc¸amentos Familiares (POF) to obtain the household consumption shares of different groups of goods. The POF is a nationally representative expenditure survey. Its latest available round was the 2002/03 POF. It provides detailed income and expenditure data, used to construct the budget shares for 18 different groups of goods that match the groups of goods as reported in the I´ndice de Prec¸os ao Consumidor Ampliado (IPCA), which is considered Brazil’s official CPI. The groups considered are: food inside the household, food outside the household, home maintenance and fees, fuel and energy, furniture and appliances, repairs and maintenance, clothing, shoes and accessories, jewelry, fabrics, transportation, medicine and optical pro- ducts, health services, personal hygiene, recreation, communication, and education. The POF provides information on age and education of the head of household which, together with the household geographical location, is used to match the factor income effects estimated with data from the PNAD.8 The PNAD is a yearly household survey, with sample size equal to 1/500 of the Brazilian population (about 100,000 households per survey year), designed to produce a picture of the living conditions and economic life of the Brazilian population, rural and urban. It provides detailed infor- mation on demographic characteristics such as age and race, education attainment, school enrollment, income from different sources, housing and living arrangements, family structure, work, fertility, migration, and other topics. It provides several measures of labor participation and income, including hours of work, labor income, and income from sources other than labor. In both surveys, households can be grouped in terms of the age and education of the household head, and their geographical location. We consider six different geographical locations, based on whether the household is in a metropolitan area, other urban setting or in a rural area; and whether the household is in the North/Northeast region or not. We consider five educational levels: less than one year, some primary (2–5 years), some middle

8Ideally we would like to have information on sector of employment or activity at the POF, but that is not available.

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(6–9 years), some secondary (10–12 years), and some college. We use three age categories: 30 or below, between 31 and 50, and 51 or older. These categories group households into 6 5 3 ¼ 90 bins. For each of these bins we compute the average income in every survey from 1981 to 2005. We use the NEER and REER constructed by the IMF (which are based on trade-weighted averages). Because there is no apparent trend in the REER over 1981–2007, we use the level of the REER as the exchange rate variable for our baseline estimates of the factor income effects in Brazil.

Mexico We use the Encuesta Nacional de Ingresos y Gastos del Hogar (ENIGH) to estimate both the distributional effects on income and on relative prices. The ENIGH has national coverage, and is available for every even year in 1992–2006 plus 2005 (nine surveys in total). It provides detailed income and expenditure data, as well as a host of demographic characteristics of household members and living conditions. Our income estimates classify households based on region, education attainment, and sector of employment. We consider five regions: Distrito Federal (DF), urban and rural areas in entidades bordering the United States, and urban and rural areas elsewhere. We use fewer regions than in Brazil to the smaller size of the ENIGH. The particular focus on states bordering the United States is based on our prior that labor demand in those regions is more likely to be sensitive to movements in the exchange rate. We classify areas as urban or rural based on their localidade having more than 15,000 people.9 We consider five classifications of educational attainment of the household head: no formal education, some primary (1–6 years), some secondary (7–9 years), some preparatory (10–12 years), and some college. We use the same three age categories as in Brazil. Finally, we classify the employment as tradable if it is related to agriculture, mining, or manufacturing, and nontradable if it is anything else. This yields a total of 5 5 3 2 ¼ 150 bins in which households can be classified. The ENIGH provides information on income for each of the previous six months (so it has a panel component which is not present for example in the PNAD). Consumption expenditure shares are obtained for 17 groups: food (at home), alcohol and tobacco, clothing, footwear, clothing accessories and care, rent (including of owner-occupied housing), fuel and energy, other services related to housing, furniture and household equipment, home accessories and cleaning products, health, personal care, public transportation, private transportation, education, leisure, and other services (including food outside the home).

9ENIGHs are also available for 1984 and 1989, but we chose not to use these surveys because they did not provide this information on the size of localidades.

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V. Results Consumption Effects Table 2 presents the pass-through effects from the VARs for Brazil, estimated in a sample from July 1994 through December 2006. The elasticity of the CPI to changes in the NEER is 14 percent over 12 months and 17 percent over 24 months. Those results are similar to previous literature, such as Belaisch (2003), that finds that the pass-through of the Brazilian bilateral exchange rate with the U.S. dollar is 17 percent over 12 months and 23 percent in the long run. The results by groups of goods show higher pass- through for tradable goods (such as food at home, electronic appliances, personal care, and hygiene) than for nontradable ones (for example, personal services, communication). The only surprising pass-through coefficients are on clothing and shoes and accessories, which seem to behave as nontradable goods, likely due to high trade protection to that industry.10 The three groups of goods most sensitive to the exchange rate are: food inside the home, electronic appliances, and jewelry. All services-related groups exhibit very low pass-through (sometimes even slightly negative). Table 3 presents the pass-through effect for Mexico in a sample from 1990 to the present. The pass-through to the general inflation index is larger than in the case of Brazil, which likely reflects Mexico’s greater economic integration to the United States. The elasticity of the CPI to changes in the NEER is 46 percent over 12 months and 73 percent over 24 months. The results are also consistent with a dichotomy between tradable and nontradable goods. The three groups most sensitive to the exchange rate include: food, home accessories and cleaning products, and private transportation. Not surprisingly, education, a nontradable service, exhibits the lowest pass-through coefficients among all groups. The large pass-through for food, the consumption group accounting for the biggest expenditure share for poorer households, suggests that all else equal, an appreciation (depreciation) will help (hurt) the poor through the consumption channel. Figure 2a plots the distributional effects through the consumption channel of a 10 percent NEER appreciation in Brazil. As expected, the effect is stronger for poorer households, which experience a welfare improvement similar to a 1.6 percent increase in their expenditure. The strength of that effect steadily declines to about 1.3 percent as we move to the upper tail of expenditure distribution. Thus, the effects of a currency appreciation through the consumption channel are fairly evenly distri- buted across the expenditure distribution because of a combination of low exchange rate pass-through and limited discrepancies in the breakdown of consumption baskets between tradable and nontradable components across

10‘‘Protection through higher-than-average tariffs is provided to activities such as beverages, transport equipment, clothing and footwear.’’ See World Trade Organization (2004).

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Table 2. Brazil: Exchange Rate Pass-Through

%DP/DE %DP

Exchange Rate Pass-Through (in percent) 12 months 24 months 12 months 24 months

General index 14.1 17.1 0.8 0.9 Food at home 26.6 27.1 1.4 1.4 Food outside the home 7.3 6.6 0.4 0.3 Home maintenance and fees 9.3 13.9 0.7 0.8 Fuel and energy 13.9 11.9 1.1 0.5 Furniture 13.7 15.0 0.8 0.8 Electronic appliances 24.1 22.3 1.2 1.0 Repairs and maintenance 3.8 2.0 0.2 0.1 Clothing 1.5 0.1 0.0 0.0 Shoes and accessories 0.0 0.5 0.0 0.0 Jewelry 29.4 31.1 1.6 1.7 Fabrics 15.1 20.2 1.0 1.0 Transportation 20.7 19.7 0.9 0.8 Pharmaceutic and optical products 13.9 15.9 0.8 0.9 Health services 0.1 0.9 0.0 0.0 Personal care and hygiene 21.0 26.4 1.4 1.4 Personal services 3.8 6.4 0.3 0.3 Recreation and tobacco 6.6 5.7 0.3 0.3 Communication 6.9 1.4 0.3 0.2 Education and books 7.7 14.2 0.6 0.9 Note: Based on vector auto regression (VAR) model for log changes in prices and the nominal effective exchange rate (NEER), with monthly data from July 1994 to June 2006, including lags 1–12. The price data are from the Brazilian consumer price index (IPCA) and the exchange rate is the INS/IMF NEER. The regression controls for exogenous international prices: log changes in the nonfuel commodities index and the average of three spot crude oil prices, both from the IMF/WEO, and the U.S. consumer price index (all items, urban average; source: BLS) are included lagged 1 and 12 months. %DP/DE is the ratio of the cumulative impulse response of log prices to a shock in the log exchange rates equation to the cumulative impulse response of log exchange rates to its own shock. %DP is the cumulative impulse response of log prices to a shock in the log exchange rates equation. The 12(24)-month pass- through as the ratio of the average cumulative impulse response of prices between 3 and 15 (12–24) months after a shock to NEER to the 12-month average cumulative own impulse response of NEER.

the expenditure distribution. Figure 2b breaks down the consumption effects across type of location and shows that there are no relevant differences between households in metropolitan, other urban or rural areas, after controlling for total expenditure. Figure 3a plots the distributional effects of the consumption channel for Mexico. The results confirm the conjecture of stronger gains for the poorer households. But the effect is fairly flat, with the distributional effects of a 10 percent appreciation varying by less then ½ percent across the distribution. The level of that effect, however, is quite large, with a 10 percent appreciation raising purchasing power by 4½ percent, reflecting the stronger pass-through

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Table 3. Mexico: Exchange Rate Pass-Through

%DP/DE %DP

Exchange Rate Pass-Through (in percent) 12 months 24 months 12 months 24 months

General index 45.6 72.6 2.5 3.9 Food 53.1 83.0 3.0 4.4 Alcohol and tobacco 34.2 53.7 1.9 2.9 Clothing 41.3 73.5 2.4 4.1 Footwear 38.7 63.5 2.2 3.4 Clothing accessories and care 30.8 44.1 1.7 2.3 User cost of housing 37.7 59.8 2.1 3.3 Fuel and energy 40.3 59.4 2.4 3.2 Other services related to housing 19.1 39.2 1.2 2.4 Furniture and household equipment 51.5 77.0 2.9 4.1 Home accessories and cleaning products 58.6 86.3 3.2 4.5 Health 36.4 58.0 2.0 3.2 Personal care 53.4 82.8 3.0 4.4 Public transportation 46.3 63.0 2.4 3.2 Private transportation 60.6 83.5 3.0 4.1 Education 21.8 42.7 1.3 2.6 Leisure 36.0 58.0 1.9 3.1 Other services 34.7 56.7 1.9 3.1 Note: Based on vector auto regression (VAR) model for log changes in prices and the nominal effective exchange rate (NEER), with monthly data from January 1990 to September 2007, including lags 1–12. The price data are from the consumer price index (INPC) and the exchange rate is the INS/IMF NEER. The regression controls for exogenous international prices: log changes in the nonfuel commodities index and the average of three spot crude oil prices, both from the IMF/WEO, and the U.S. consumer price index (all items, urban average; source: BLS) are included lagged 1 and 12 months. The food category only includes food consumed at home. Food consumed outside the home (for example, restaurants) is included under the other services category. %DP/DE is the ratio of the cumulative impulse response of log prices to a shock in the log exchange rates equation to the cumulative impulse response of log exchange rates to its own shock. %DP is the cumulative impulse response of log prices to a shock in the log exchange rates equation. The 12(24)-month pass-through as the ratio of the average cumulative impulse response of prices between 3 and 15 (12–24) months after a shock to NEER to the 12-month average cumulative own impulse response of NEER. in Mexico. Figure 3b breaks down the consumption effects across type of location and shows that there are no relevant differences in those effects between households in the Distrito Federal, border states, or other states, after controlling for total expenditure.

Factor Income Effects First, we present results from a relatively parsimonious specification for Equation (2). This specification estimates only the first-level interactions between age, education, and location with the exchange rate variable. Hence the results can fit into a standard regression table and the unfolded patterns can be discussed.

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Figure 2. (a) Brazil: Pass-Through Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation; (b) Brazil: Pass-Through Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation, by Region of Residence

2.5

2

1.5

1

0.5

Pass-through effect in percent of expenditure 0

7 8 9 10 11 12 Ln(Expenditure)

Metropolitan Other urban Rural 2.5

2

1.5

1

0.5

Pass-through effect in percent of expenditure 0 7891011127 8 9 10 11 12 7 8 9 10 11 12 Ln(Expenditure)

Note: Data comprise the estimated pass-through for each household-specific consumption basket in the Brazilian POF household expenditure survey of 2002/03. Each household-specific pass-through is based on its shares of expenditure in 19 categories of goods and the estimated pass- through of the nominal effective exchange rate to each of those categories of goods for the 1994– 2007 period, as reported in Table 2. The equation estimated is:

G j ¼ f ðy j Þþe j ; where y j is the log of expenditure of household j and G j is its household-specific NEER pass- through. Nonparametric estimates were based on locally linear regressions with quartic kernel weights. Dashed lines are 95 percent bootstrap confidence intervals.

We present different estimates using alternately the NEER or REER as the exchange rate measure. In all specifications, we use interactions with the average nominal income in the period as controls. Those regressors act as an implicit deflator for the nominal income measures (again, we want to minimize the use of the CPIs in these countries because de Carvalho Filho

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Figure 3. (a) Mexico: Pass-Through Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation; (b) Mexico: Pass-Through Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation, by Region of Residence

6

5

4

3

2

1

0 Pass-through effect in percent of expenditure 6.5 7 8 9 10 11 Ln(Expenditure)

DF Border Other 6

5

4

3

2

1

Pass-through effect in percent of expenditure 0 6.5 7 8 9 10 11 6.5 7 8 9 10 11 6.5 7 8 9 10 11 Ln(Expenditure)

Note: Data comprise the estimated pass-through for each household-specific consumption basket in the Mexican ENIGH household expenditure survey of 2006. Each household-specific pass-through is based on its shares of expenditure in 17 categories of goods and the estimated pass- through of the nominal effective exchange rate to each of those categories of goods for the 1990– 2007 period, as reported in Table 2. The equation estimated is:

Gj ¼ f ðyjÞþej; where yj is the log of expenditure of household j and Gj is its household-specific NEER pass- through. Nonparametric estimates were based on locally linear regressions with quartic kernel weights. Dashed lines are 95 percent bootstrap confidence intervals. and Chamon (2007) have shown they are severely biased). To the extent that average nominal income plays the role of implicit deflator, the results should not depend on whether the exchange rate variable is the REER or NEER. Table 4 presents those results for Brazil. For each of the columns, the dependent variable is the log of the average total household income, measured in 90 cells (education location age) for each year and there are fixed effects for each cell.

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In column (1), the exchange rate variable is the REER and the coefficient on aggregate average income is constrained to one, so the left-hand side variable is the difference of group income to aggregate average income. The coefficients of interest are the interactions between the exchange rate variable and category characteristics. The base category are households whose head is under 30 years old, has 12 years or more of schooling, and lives in a metropolitan area. The results show that the elasticity of total household income to REER appreciation is smaller for household whose heads have less than one year of schooling, at 0.148, significant at 99 confidence (that is, a 10 percent real appreciation reduces the relative income of households at the lowest years of schooling category by 1½ percent relative to a household with similar age and location with 12 years of schooling). However, the interaction between the real exchange rate and educational levels is not monotonic. The relative incomes of households at the second-highest

Table 4. Brazil: Regression of Average Income by Household Group on Household Characteristics

Regression of Log of Total Household Income

(1) (2) (3) (4) Exchange Rate Variable Log(REER) Log(REER) Log(REER) Log(NEER) Sample 81-06 81-06 95-06 81-06

Schooling 0–1 yrs. exchange rate 0.148 0.045 0.115 0.039 [0.059] [0.035] [0.038] [0.029] Schooling 2–4 yrs. exchange rate 0.067 0.051 0.134 0.034 [0.059] [0.033] [0.037] [0.028] Schooling 5–8 yrs. exchange rate 0.004 0.122 0.153 0.051 [0.059] [0.034] [0.037] [0.029] Schooling 9–11 yrs. exchange rate 0.133 0.191 0.133 0.060 [0.059] [0.036] [0.038] [0.030] Other urban exchange rate 0.045 0.074 0.045 0.003 [0.046] [0.021] [0.024] [0.018] Rural exchange rate 0.074 0.207 0.134 0.109 [0.046] [0.027] [0.030] [0.022] South-SE-CW exchange rate 0.094 0.086 0.003 0.014 [0.059] [0.035] [0.038] [0.029] North-NE exchange rate 0.137 0.092 0.026 0.026 [0.059] [0.036] [0.039] [0.030] Age 30–50 exchange rate 0.032 0.018 0.004 0.001 [0.046] [0.024] [0.027] [0.020] Age 51-up exchange rate 0.074 0.093 0.142 0.088 [0.046] [0.026] [0.029] [0.021] % of female heads 1.031 0.859 0.275 1.030 [0.079] [0.082] [0.118] [0.081] Average family size 0.158 0.099 0.133 0.104 [0.013] [0.014] [0.028] [0.014]

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Table 4 (concluded ) Regression of Log of Total Household Income

(1) (2) (3) (4) Exchange Rate Variable Log(REER) Log(REER) Log(REER) Log(NEER) Sample 81-06 81-06 95-06 81-06

Aggregate effect constrained to 1 Yes No No No for all groups Observations 2068 2068 990 2068 R-squared 0.956 1.000 0.993 1.000 Root mean square error 0.178 0.089 0.066 0.092 Note: Data comprised of log of annual average total household income by three categories of age (A), six of region of residence (R) and five of schooling (S) (3 6 5=90 groups) from the Brazilian PNAD household surveys from 1981 to 2006. ! X X X ymt ¼ bsy:t þ bay:t þ bsy:t s2S a2A r2R ! X X X þ aset þ aaet þ aret þ Zmtg þ mm þ emt; s2S a2A r2R

where ymt is the log of average household nominal income for group m in time t, y.t is the log of overall average nominal income in time t, et is a measure in logs of the exchange rate, Zmt is a vector of group characteristics that vary over time (namely: percent of households headed by a female; average family size) and mm is a group fixed effect. For presentational purposes, the b., the coefficients on the interactions to the log of average total household income, and the m., the fixed effect for each of the 90 groups, are not reported in this table. Standard errors in brackets. Coefficients significant at the 10 percent level in bold. The exchange rate variable is Brazil’s nominal and real effective exchange rates (NEER and REER) from the INS/IMF.

education category, that is, 9–11 years of schooling for the head, increase by 1.3 percent relative to the highest education category in response to a 10 percent real appreciation. Relative incomes of households whose head is over age 50 tend to fall behind during appreciations (the coefficient is 0.074, marginally significant). Finally, a real appreciation is found to affect negatively the relative incomes of rural households (the coefficient is 0.074, statistically significant). In column (2), we introduce interactions of log average income with the cell characteristics. The pattern of results do not change: real appreciations negatively affect the relative incomes of the rural, the least educated, and the older headed households, while significantly increasing the relative incomes of households whose head are in the middle range of education, from five to 11 years of schooling. The negative coefficient on rural households is 0.207, stronger than the previous specification, but the coefficient on the lowest

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©International Monetary Fund. Not for Redistribution Irineu de Carvalho Filho and Marcos Chamon education category narrowed to 0.045 and became insignificant. The introduction of the interaction terms with log average income lowers the standard errors of the coefficients and also reduces the root mean square error by about 50 percent. In column (3), the sample is restricted to the period from 1995 to 2006, a period when inflation rates were lower. Perhaps reflecting lower measurement error, the root mean square error decreases further, but coefficient estimates are less precisely estimated because the sample size was reduced from 2,068 to 990 observations. For this subsample, households in rural (coefficient: 0.134) and other urban (coefficient: 0.045) areas are estimated to have a significant negative correlation to real appreciations, and so are older-headed households. The coefficients of the interactions with education, however, show that in recent years appreciations have been associated with an equalizing effect: they have increased the relative incomes of all households whose heads have less than 12 years of schooling. In column (4), we use the NEER as the exchange rate variable. The results are similar to the other columns (1) and (2) with sample spanning from 1981 to 2006, with appreciation hurting mainly the incomes of rural households, but the effect of appreciation on the interaction with the least educated household heads turned insignificant. Then, we move to the estimation of Equation (1) with interactions of each category with the exchange rate and the aggregate income variables (that is, we interact each age region education combination with the exchange rate and aggregate income). The results are in line with the parsimonious specification displayed in Table 4 and they are best summarized through snapshots of the estimated distributional effects through the factor income channel as in Figure 4a for Brazil. The relationship between the factor income effect of an appreciation and the household expenditure level has the opposite slope as the one for the consumption effect. A 10 percent appreciation has a distributional effect similar to that of lowering the income of the poorest households by 1 percent while raising that of the richest ones by about 0.25 percent. It is worth noting that the overall level of this effect is not estimated (only its shape along the expenditure distribution). The overall level is normalized to average to zero for the whole sample, but its average can be different from zero in the chart because the plot is conditional on the population composition as of the most recent expenditure survey. Figure 4b breaks down the sample by location: metropolitan, other urban and rural areas. It shows that whereas there is an inverted-U shaped curve relating the income factor effect to expenditure levels in metropolitan and other urban areas, factor income effects are uniformly negative in the rural areas (even though we do not estimate the aggregate level effect, our estimates do quantify this relative shift across regions). The negative effect at the poor tail of the distribution and the uniformly negative effect on rural areas likely reflects the concentration of households in activities whose profitability is lowered by an appreciation, because households in rural locations are often net producers of food (a tradable

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Figure 4. (a) Brazil: Factor Income Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation; (b) Brazil: Factor Income Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation, by Region of Residence

1

0.5

0

-0.5

-1 Factor income effect in percent of expenditure 7 8 9 10 11 12 Ln(Expenditure)

Metropolitan Other urban Rural 2

1

0

-1 Factor income effect in percent of expenditure

7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 Ln(Expenditure)

Note: The equation estimated is:

j j a^j ¼ gðy Þþu ; where yj is the log of expenditure of household j from the POF 2002/03 household expenditure survey and a^j is household j group-specific factor income effect. Nonparametric estimates were based on locally linear regressions with quartic kernel weights. Dashed lines are 95 percent bootstrap confidence intervals. Estimates of factor income effects am are based on estimation of Equation (2),

ymt ¼ bmy:t þ amet þ Zmtg þ mm þ emt; where data comprise log of annual average total household income by three categories of age (A), six of region of residence (R), and five of schooling (S) (3 6 5 ¼ 90 groups) from the Brazilian PNAD household surveys from 1981 to 2006 and et is the log of the real effective exchange rate (REER) from INS/IMF. To map from a nominal effective exchange rate (NEER) to the REER, a VAR model for log changes in the NEER and REER was estimated as in Table 2. The average factor income effect over the whole estimation sample is equal to zero by construction, since we are only measuring distributional effects (taking the average income as given), but slightly different than zero for the POF 2002/03 sample.

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©International Monetary Fund. Not for Redistribution Irineu de Carvalho Filho and Marcos Chamon good). This proposition can be taken to the data, as the PNAD provides information on sector of economic activity.11 Table 5 presents the estimates of the differential effect of the exchange rate variables on total household income across sectors of activity. As expected, households whose heads have been employed in agriculture in the last 12 months are the ones whose total income is more negatively related to appreciations. A 10 percent appreciation in the REER is associated to a reduction in the relative income of those households by about 2.1 percent, and that was statistically significant for all specifications. On the other hand, the finance and business services and the construction sectors showed positive coefficients for all specifications, albeit less precisely estimated. That is also expected, as those two sectors are eminently nontradable. Somewhat surprisingly, the relative incomes of households whose heads are in the public administration, health, and education sector are negatively related to appreciations. It would be interesting to investigate further why this is so. Unfortunately, information on sector of activity is not available in the Brazilian expenditure survey (POF) hence those sectoral effects are not included in the discussion of the total effects (as in Figures 4 and 6). Figure 5a plots the distributional effects through the income channel for Mexico (based on the regression reported in the first column of Table 6). Again, the income of poorer households fares worse following an appreciation. The distributional effects peak at the middle of the distribution, and then decline. All else equal, the effect of a 10 percent appreciation on the income of households will be about ½ percent lower for the poorest and ¼ lower for the richest households vis-a` -vis those in the middle of the distribution. Although income is a relevant dimension over which to separate households, as in the case of Brazil, additional insights can be gained by comparing households along other dimensions. We regress the average income in each household age region sector education group on dummies for each of those cells, and for the interactions of age, region, sector, and education on the average income in the period and the exchange rate.12 Table 6 reports the coefficients for the interactions with the exchange rate. Results are similar when we use the NEER or the REER as the exchange rate variable (the interaction of the dummies with aggregate income plays a role similar to that of a price deflator). All else equal, households working in the tradable sector (agriculture, mining, and manufacturing) experience a 1.1 percent decline in their income relative to those in the nontradable sector following a 10 percent NEER appreciation

11For those currently not employed, the survey inquires about their sector of activity, if any, in the last 12 months before the week of reference. 12We only interact one dimension at a time with the exchange rate and income in our estimates for Mexico, since the ENIGH sample is much smaller than the one in the PNAD, and we could not meaningfully estimate the interaction exchange rate and average income with each of the 150 combinations of age education region sector.

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Table 5. Brazil: Regression of Average Income by Household Group on Household Characteristics

Regression of Log of Total Household Income

(1) (2) (3) (4) Exchange Rate Variable Log(REER) Log(REER) Log(REER) Log(NEER) Sample 81-06 81-06 95-06 81-06

Agriculture exchange rate 0.211 0.179 0.190 0.136 [0.068] [0.048] [0.042] [0.040] Manufacturing exchange rate 0.059 0.006 0.022 0.011 [0.064] [0.046] [0.040] [0.038] Construction exchange rate 0.096 0.115 0.034 0.156 [0.071] [0.051] [0.044] [0.042] Commerce and services exchange rate 0.026 0.023 0.012 0.054 [0.060] [0.043] [0.036] [0.035] Public admin., health, education exchange rate 0.065 0.037 0.089 0.068 [0.062] [0.044] [0.038] [0.037] Finance, business services exchange rate 0.133 0.163 0.026 0.085 [0.070] [0.049] [0.042] [0.041] No activity exchange rate 0.070 0.060 0.166 0.095 [0.065] [0.045] [0.039] [0.038] 0–1 yrs. schooling exchange rate 0.019 0.029 0.092 0.061 [0.063] [0.044] [0.039] [0.037] 2–4 yrs schooling exchange rate 0.068 0.068 0.107 0.008 [0.060] [0.043] [0.037] [0.036] 5–8 yrs. schooling exchange rate 0.139 0.138 0.132 0.022 [0.062] [0.043] [0.037] [0.036] 9–11 yrs. schooling exchange rate 0.176 0.169 0.071 0.026 [0.063] [0.044] [0.038] [0.037] % of female heads 1.047 0.708 0.024 0.848 [0.111] [0.155] [0.213] [0.149] Average family size 0.071 0.017 0.12 0.061 [0.017] [0.032] [0.058] [0.030] Aggregate effect constrained to 1 for all groups Yes No No No Observations 805 805 385 805 R-squared 0.978 1.000 0.997 1.000 Joint significance, education exchange rate 0.002 0.000 0.009 0.049 (p-value) Joint significance, activity exchange rate 0.000 0.000 0.000 0.000 (p-value) Note: Data comprise log of annual average total household income by seven categories of occupation activity (O) and five of schooling (S) (7 5=35 groups) from the Brazilian PNAD household surveys from 1981 to 2006. ! X X ymt ¼ bsy:t þ boy:t s2 S o2O ! X X þ aset þ aoet þ Zmtg þ mm þ emt; s2S o2O where ymt is the log of average household nominal income for group m in time t, y.t is the log of overall average nominal income in time t, et is a measure in logs of the exchange rate, Zmt is a vector of group characteristics that vary over time (namely, percent of households headed by a female; average family size) and mm is a group fixed effect. For presentational purposes, the b., the coefficients on the interactions to the log of average total household income, and the m., the fixed effect for each of the 35 groups, are not reported in this table. Standard errors in brackets. Coefficients significant at the 10 percent level in bold. The exchange rate variable is Brazil’s nominal and real effective exchange rates (NEER and REER) from the INS/IMF.

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Figure 5. (a) Mexico: Factor Income Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation; (b) Mexico: Factor Income Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation, by Region of Residence

2

1

0

-1

-2 Factor income effect in percent of expenditure 6.5 7 8 9 10 11 Ln(Expenditure)

DF Border Other 2

1

0

-1

-2 Factor income effect in percent of expenditure 6.5 7 8 9 10 11 6.5 7 8 9 10 11 6.5 7 8 9 10 11 Ln(Expenditure)

Note: The equation estimated is:

j j a^j ¼ gðy Þþu ; where yj is the log of expenditure of household j from the ENIGH 2006 household expenditure survey and a^j is household j group-specific factor income effect. Nonparametric estimates were based on locally linear regressions with quartic kernel weights. Dashed lines are 95 percent bootstrap confidence intervals. Estimates of factor income effects am based on the regression in Table 6 (first column). The average factor income effect over the whole estimation sample is equal to zero by construction, since we are only measuring distributional effects (taking the average income as given), but slightly different than zero for the 2006 sample.

(or a 2.8 percent decline for a 10 percent REER appreciation).13 The regional dummies indicate that appreciations are associated to strong income declines in the northern border states. In our preferred specification (based on the

13Chiquiar (2008) finds that Stolper-Samuelson effects of NAFTA are stronger in the northern border states of Mexico than elsewhere in that country.

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Table 6. Mexico: Regression of Average Income by Household Group on Household Characteristics

Exchange Rate Variable Log(NEER) Log(REER) Sample 92-06 92-06

Schooling 1–6 yrs. exchange rate 0.065 0.047 [0.040] [0.071] Schooling 7–9 yrs. exchange rate 0.024 0.047 [0.046] [0.080] Schooling 10–12 yrs. exchange rate 0.100 0.011 [0.050] [0.089] College exchange rate 0.043 0.082 [0.050] [0.087] Tradable exchange rate 0.111 0.279 [0.027] [0.048] Border US urban exchange rate 0.295 0.395 [0.060] [0.107] Border US rural exchange rate 0.149 0.135 [0.048] [0.085] Other urban exchange rate 0.094 0.222 [0.045] [0.079] Other rural exchange rate 0.020 0.138 [0.044] [0.077] Age 30–50 exchange rate 0.006 0.067 [0.032] [0.056] Age 51-up exchange rate 0.066 0.000 [0.034] [0.060] % of female heads 0.090 0.100 [0.026] [0.026] Average family size 0.105 0.106 [0.005] [0.005]

Dummies for each cell, and for each group aggregate income Yes No Observations 11329 11329 R-squared 0.93 0.93 Note: Data comprise of log of annual average total household income by three categories of age (A), five of region of residence (R), five of schooling (S), and one for tradable or nontradable sector of employment (T): (3 5 5 2=150 groups) from the Mexican ENIGH household surveys from 1992 to 2006. ! X X X ymt ¼ bsy:t þ bay:t þ bsy:t þ bT yt s2S a2A r2R ! X X X þ aset þ aaet þ aret þ aT et s2S a2A r2R

þ Zmtg þ mm þ emt where ymt is the log of average household nominal income for group m in time t, y.t is the log of overall average nominal income in time t, et is a measure in logs of the exchange rate, Zmt is a vector of group characteristics that vary over time (namely: percent of households headed by a female; average family size) and mm is a group fixed effect. For presentational purposes, the b., the coefficients on the interactions to the log of average total household income, and the m., the fixed effect for each of the 150 groups, are not reported in this table. Standard errors in brackets. Coefficients significant at the 10 percent level in bold. The exchange rate variable is Mexico’s nominal and real effective exchange rates (NEER and REER) from the INS/IMF.

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NEER), the urban and rural areas near the U.S. border experience, respectively, a 3.0 and 1.5 percent decline in income relative to Mexico City. Note that this effect is in addition to the one related to working on the tradable sector. Incomes decline in other urban and rural areas relatively to Mexico City during periods of more appreciation. The combined differences in the factor income effect across regions (including the other factors, such as tradable sector employment) can be seen in Figure 5b. The results for the other characteristics indicate that less educated households and those in the

Figure 6. (a) Brazil: Total Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation; (b) Brazil: Total Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation, by Region of Residence

Pass-through Factor income Total

2

1

0 Total effect in percent of expenditure

-1 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 Ln(Expenditure)

Metropolitan Other urban Rural

3

2

1

0 Total effect in percent of expenditure

-1 7 8 9 10 11 12 7 8 9 10 11 12 7 8 9 10 11 12 Ln(Expenditure)

Note: Pass-through and factor income estimates are as in Figures 4a and 5a. Total effects are equal to the sum of pass-through and factor income effects for each household. Average level of the combined effect is given by the average level of the consumption pass-through effect (since average factor income effect is equal to zero by construction). Thus, one should focus on the distributional patterns, not on the absolute levels. Dashed lines are 95 percent bootstrap confidence intervals.

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30–50-year-old age group tend to do better during periods of more appreciation than the more educated and older households (although caution should be used due to the wide standard errors around those estimates).

Aggregating Consumption and Factor Income Effects The combined net effect can be quantified up to a constant, since whereas we estimate the level and the distribution of the consumption or pass-through

Figure 7. (a) Mexico: Total Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation; (b) Mexico: Total Effect of a 10 Percent Nominal Effective Exchange Rate Appreciation, by Region of Residence

Pass-through Factor income Total 6

4

2

0 Total effect in percent of expenditure

-2

6.5 7 8 9 10 11 6.5 7 8 9 10 11 6.5 7 8 9 10 11 Ln(Expenditure)

DF Border Other 6

5

4

3 Total effect in percent of expenditure

2 6.5 7 8 9 10 11 6.5 7 8 9 10 11 6.5 7 8 9 10 11 Ln(Expenditure)

Note: Pass-through and factor income estimates are as in Figures 5a and 7a. Total effects are equal to the sum of pass-through and factor income effects for each household. Average level of the combined effect is given by the average level of the consumption pass-through effect (since average factor income effect is equal to zero by construction). Thus, one should focus on the distributional patterns, not on the absolute levels. Dashed lines are 95 percent bootstrap confidence intervals.

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©International Monetary Fund. Not for Redistribution Irineu de Carvalho Filho and Marcos Chamon effect, only the relative distribution of factor income effects can be recovered (the average income effect is by construction zero for the entire sample). Figure 6a aggregates the consumption (pass-through) and factor income effects for Brazil. It shows that whereas pass-through effects are estimated with more uncertainty than factor income effects, the bulk of the distributional effects we find across the expenditure distribution is caused by the incomes of the poor (households whose head have less years of schooling, and located in rural areas) falling behind the rich during appreciations. This is confirmed by robustness checks. Figure 6b shows that this effect is driven by rural households doing substantially worse than everybody else, with their total effect falling short by 1–2 percentage points relative to those residing in metropolitan areas. In the case of Mexico (Figure 7a), the largest gains from appreciation remain in the lower middle classes, with a 10 percent appreciation improving their welfare relative to the very poor or very rich households by about ½ percent of expenditure. During appreciations, households in the northern border area also do significantly worse than those elsewhere in Mexico (Figure 7b).

VI. Conclusions This paper uses household-level data to estimate the distributional effects of exchange rate movements in Brazil and Mexico. Its main result is that the bulk of distributional effects of nominal appreciations seem to work through factor incomes instead of the pass-through to consumption prices. In the case of Brazil, the factor income effect generates a divide between metropolitan and rural areas. These differences are explained mostly by the sizable negative effect of appreciations on the incomes of workers in the agricultural sector and their concentration in rural areas. In Mexico, the divide is regional, with appreciation reducing the incomes of households located near the border to the United States relative to the rest of the country, which likely reflects the concentration of Mexico’s export sector in its northern border. These findings confirm Kaufman and Stallings (1990) observation that the political divisions in Latin American countries responsible for populist policies are based on sectors of economic activity, instead of class conflict. For both countries, appreciations, on average, tend to benefit households close to the median of the income distribution relatively to poorer or richer households. But, overall, the distribution of the relative welfare gains or losses from an appreciation is fairly uniform across expenditure levels. It is plausible that if incomes take longer to adjust than consumption prices, the pass-through effect to consumption could dominate voters’ perceptions of the welfare effect of nominal appreciations, at least in the short run. Or alternatively, income losses from appreciations could be concentrated on a few households experiencing unemployment. The combination of a limited distributional effect with a generous average

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©International Monetary Fund. Not for Redistribution THE POLITICAL ECONOMY OF EXCHANGE RATE POPULISM pass-through effect can actually contribute to appreciation pressures—if a sizable group of households stood to be particularly hit by an appreciation, that group might mobilize and voice its discontent over such policies. In summary, the evidence for Brazil and Mexico does not favor the view that Latin America’s large inequality is the main culprit for these countries’ propensity to let their currencies appreciate, because the distribution of relative welfare gains and losses of appreciations across the expenditure distribution is fairly flat. In contrast, the paper found significant differences in the welfare effects of appreciations across economic sectors and regions. The question on whether appreciations indeed buy political support across sector and region divides is worthy pursuing through future research.

REFERENCES Aisen, Ari, 2007, ‘‘Money-Based versus Exchange-Rate-Based Stabilization: Is There Space for Political Opportunism?,’’ IMF Staff Papers, Vol. 54, No. 2, pp. 385–417. Balistreri, Edward J, 1997, ‘‘The Performance of the Heckscher-Ohlin-Vanek in Predicting Endogenous Policy Forces at the Individual Level,’’ The Canadian Journal of Economics, Vol. 30, No. 1, pp. 1–17. Beaulieu, Eugene, 2002, ‘‘Factor or Industry Cleavages in Trade Policy? An Empirical Analysis of the Stolper-Samuelson Theorem,’’ Economics and Politics, Vol. 14, No. 2, pp. 99–131. Belaisch, Agne` s, 2003, ‘‘Exchange Rate Pass-Through in Brazil,’’ IMF Working Paper 2003/141 (Washington, International Monetary Fund). Bonomo, Marco, and Cristina Terra, 1999, ‘‘The Political Economy of Exchange Rate Policy in Brazil: 1964–1997’’ (unpublished; Rio de Janeiro, Fundac¸a˜o Getu´ lio Vargas). Ca’Zorzi, Michele, Elke Hahn, and Marcelo Sa´ nchez, 2007, ‘‘Exchange Rate Pass- Through in Emerging Markets,’’ ECB Working Paper Series No. 739 (Frankfurt, European Central Bank). Chiquiar, Daniel, 2008, ‘‘Globalization, Regional Wage Differentials and the Stolper- Samuelson Theorem: Evidence from Mexico,’’ Journal of International Economics, Vol. 74, No. 1, pp. 70–93. de Carvalho Filho, Irineu, and Marcos Chamon, 2007, ‘‘The Myth of Post-Reform Income Stagnation in Brazil,’’ IMF Working Paper 06/275 (Washington, International Monetary Fund). Dixit, Avinash, and V. Norman, 1980, Theory of International Trade: A Dual, General Equilibrium Approach (Cambridge, U.K., Cambridge Economic Handbooks). Dornbusch, Rudiger, and Sebastian Edwards, eds. 1990, The Macroeconomics of Populism in Latin America (Chicago, University of Chicago Press). Frieden, Jeffry, and Ernesto Stein, eds., 2001, The Currency Game: Exchange Rate Politics in Latin America (Washington, Inter-American Development Bank). Goldfajn, Ilan, and Se´ rgio R.C. Werlang, 2000, ‘‘The Pass-Through from Depreciation to Inflation: A Panel Study,’’ Texto para discussa˜o No. 423 (Rio de Janeiro, Departamento de Economia, PUC-Rio).

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Johnson, Simon, Jonathan Ostry, and Arvind Subramanian, 2006, ‘‘Levers for Growth,’’ Finance and Development, Vol. 43, No. 1, pp. 28–31. Kaufman, Robert, and Barbara Stallings, 1990, ‘‘The Political Economy of Latin American Populism,’’ in The Macroeconomics of Populism in Latin America, ed. by Rudiger Dornbusch and Sebastian Edwards (Chicago, The University of Chicago Press). Levy Yeyati, Eduardo, and Federico Sturzenegger, 2007, ‘‘Fear of Floating in Reverse: Exchange Rate Policy in the 2000s’’ (unpublished; Buenos Aires, Universidad Torcuato Di Tella). Mayda, Anna Maria, and Dani Rodrik, 2005, ‘‘Why Are Some People (and Countries) More Protectionist than Others?’’ European Economic Review, Vol. 49, No. 6, pp. 1393–430. McCarthy, J., 2007, ‘‘Pass-Through of Exchange Rate and Import Prices to Domestic Inflation in Some Industrialized Economies,’’ Eastern Economic Journal, Vol. 33, No. 4, pp. 511–37. Porto, Guido, 2006, ‘‘Using Survey Data to Assess the Distributional Effects of Trade Policy,’’ Journal of International Economics, Vol. 70, No. 1, pp. 140–60. Reinhart, Carmen, and Kenneth Rogoff, 2004, ‘‘The Modern History of Exchange Rate Arrangements: A Reinterpretation,’’ Quarterly Journal of Economics, Vol. 119, No. 1, pp. 1–48. Rodrik, Dani, 2006, ‘‘Industrial Development: Stylized Facts and Policies,’’ (unpublished; Cambridge, Massachusetts, Harvard University Kennedy School of Government). ______, 2007, ‘‘The Real Exchange Rate and Economic Growth: Theory and Evidence,’’ (unpublished; Cambridge, Massachusetts, Harvard University Kennedy School of Government). Scheve, Kenneth F., and Matthew J. Slaughter, 2001, ‘‘What Determines Individual Trade-Policy Preferences?’’ Journal of International Economics, Vol. 54, No. 2, pp. 267–92. Woodland, A.D., 1982, ‘‘International Trade and Resource Allocation,’’ in Advanced Textbooks in Economics, ed. by C.J. Bliss and M.D. Intriligator, Vol. 19 (Amsterdam, North-Holland). World Trade Organization, 2004, ‘‘Trade Policy Review—Brazil—Report by the Secretariat,’’ WT/TPR/S/140.

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Global Rebalancing with Gravity: Measuring the Burden of Adjustment

ROBERT DEKLE, JONATHAN EATON and SAMUEL KORTUM

This paper uses a 42-country model of production and trade to assess the implications of eliminating current account imbalances for relative wages, relative GDPs, real wages, and real absorption. How much relative GDPs need to change depends on flexibility of two forms: factor mobility and adjustment in sourcing of imports, with more flexibility requiring less change. At the extreme, U.S. GDP falls by 30 percent relative to the world’s. Because of the pervasiveness of nontraded goods, however, most domestic prices move in parallel with relative GDP, so that changes in real GDP are small. [JEL F30, F31, F12] IMF Staff Papers (2008) 55, 511–540. doi:10.1057/imfsp.2008.17; published online 24 June 2008

he United States’ chronic current account deficit will inevitably reverse, T and the reversal could be quite sudden. What would this reversal mean for the United States itself and for other countries? There are possible major effects on relative GDPs, real wages, and real absorption, not only across countries but also across individuals within countries. We explore this question using a gravity model of trade and production. Because it represents the major component of trade, we focus on manufactures, asking what happens if manufacturing is the sector that bears the burden of

Robert Dekle is professor of economics at the University of Southern California; Jonathan Eaton is professor of economics at New York University; and Samuel Kortum is professor of economics at the University of Chicago. The authors have benefitted from comments by our discussant, Doireann Fitzgerald. Eaton and Kortum gratefully acknowledge the support of the National Science Foundation.

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©International Monetary Fund. Not for Redistribution Robert Dekle, Jonathan Eaton, and Samuel Kortum rebalancing trade. We pursue this analysis using data for 2004 for the world, dividing it into 42 countries. Table 1 lists the countries, their GDPs, and three different deficit measures: the current account deficit, the overall trade deficit, and the deficit in manufactures.1 In 2004 the United States ran a current account deficit of $650 billion, nearly 6 percent of its GDP.2 Aggregating the surpluses of the three largest surplus countries, Japan, Germany, and China, gets us to only $370 billion, little more than half the U.S. deficit. Note that for each of these four countries with the largest imbalances, the manufacturing deficit is by far the largest component of the overall deficit. We build on previous work that integrates factor-market equilibrium into a model of international production and trade with heterogeneous goods and barriers to trade. Contributions include Eaton and Kortum (2002); Alvarez and Lucas (2007); and Chaney (forthcoming). We pursue a particular specification of gravity relationships, which we introduced in Dekle, Eaton, and Kortum (2007). Rather than estimating such a model in terms of levels, we specify the model in terms of changes from the current equilibrium. This approach allows us to calibrate the model from existing data on production and trade shares. We thereby finesse having to assemble proxies for bilateral resistance (for example, distance, common language, etc.) or inferring parameters of technology. A particular virtue is that we do not have to impose the symmetry in bilateral trade flows implied by these measures but spurned by the data. China, for example, runs the largest bilateral surplus with the United States, while running substantial deficits with some of its Asian neighbors, Japan in particular. Our approach recognizes and incorporates these bilateral asymmetries. Our earlier work considered the effect of eliminating current account deficits in a world in which factors could seamlessly move between manufacturing and other activities. Although this assumption might apply to the very long run, it probably fails to capture barriers to internal factor mobility that are likely to loom large for some time. Here we pursue the opposite extreme of treating factors as fixed in either manufacturing or nonmanufacturing activity. For comparison purposes, we present our results for the case of perfect factor mobility as well. In either case we allow adjustment to take the form of changes in the range of goods that countries exchange (the extensive margin) as well as changes in the amounts of each good traded (the intensive margin). But adjustment at the extensive margin may take time. Hence, to capture very short-run effects we consider a case in which both the allocation of labor and the extensive margin are fixed.

1We describe how we created this sample and where our data come from in Section II. 2This number is not only very large absolutely, it is also large relative to U.S. GDP. Australia, Greece, and Portugal have larger deficit-to-GDP ratios. Some small countries run current account surpluses that are much larger fractions of their GDP. The Bureau of Economics Analysis reports the U.S. current account deficit in 2006 as $857 billion, 6.1 percent of GDP.

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Table 1. GDP and Deficit Measures, 2004

Deficits

Country Code GDP Current account Trade Mfg

Algeria alg 85 11.2 7.2 11.8 Argentina arg 153 3.6 11.0 9.5 Australia aul 659 39.2 21.8 57.5 Austria aut 293 1.2 4.4 7.3 Belgium/Luxem bex 392 16.6 20.5 52.6 Brazil bra 604 12.5 26.1 8.8 Canada can 992 22.5 35.7 22.5 Chile chl 96 1.7 8.1 2.4 China/HK chk 2106 87.2 54.0 119.4 Colombia col 98 0.8 0.8 8.2 Denmark den 245 6.3 11.3 9.3 Egypt egy 82 4.0 0.8 1.1 Finland fin 189 9.9 9.6 17.1 France fra 2060 4.1 7.4 3.3 Germany ger 2740 105.4 122.9 278.3 Greece gre 264 13.1 13.9 29.2 India ind 689 7.8 14.5 11.9 Indonesia ino 254 1.9 10.1 25.1 Ireland ire 183 0.8 25.5 68.8 Israel isr 122 3.3 0.1 2.2 Italy ita 1720 13.4 4.0 46.6 Japan jap 4580 178.1 72.4 385.1 Korea kor 680 29.1 26.3 146.4 Ma/Phi/Sing mps 312 43.2 45.9 58.3 Mexico mex 683 5.8 17.8 20.2 Netherlands net 608 55.2 44.4 8.9 New Zealand nze 98 6.3 1.1 10.0 Norway nor 255 35.1 34.9 16.0 Pakistan pak 113 0.7 6.5 0.9 Peru per 70 0.1 1.6 2.5 Portugal por 178 12.7 14.3 9.8 Russia rus 592 59.4 69.6 11.7 South Africa saf 216 7.2 2.6 1.0 Spain spa 1040 53.5 44.8 61.7 Sweden swe 349 27.9 27.4 26.2 Switzerland swi 360 57.1 32.8 13.4 Thailand tha 161 7.1 6.0 21.1 Turkey tur 302 15.2 12.5 18.0 United Kingdom unk 2150 32.3 74.2 103.5 United States usa 11700 649.7 667.0 438.4 Venezuela ven 112 14.0 17.3 6.0 Rest of world row 3025 53.4 171.3 341.9 Note: All data are in billions of U.S. dollars. Negative numbers indicate surplus. Ma/Phi/ Sing is a combination of Malaysia, the Philippines, and Singapore.

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Both this paper and our previous one return to a venerable topic, the potential for a secondary burden of a transfer. A question we can answer is the extent to which the elimination of the giant U.S. current account deficit entails a loss in real resources beyond the loss of the transfer itself. Our model recognizes the importance of nontradability, so that it delivers Keynes’ prediction that the elimination of a transfer entails a worsened terms of trade. But as our model also incorporates nontraded goods whose prices decline, the burden of paying more for imports is mostly offset by the benefit of cheaper nontraded goods. With an active extensive margin, the offset is nearly complete. Our numbers thus come down on the side of Ohlin: the elimination of the transfer entails a loss in real absorption of virtually the same magnitude.3 This prediction emerges under either extreme assumption about factor mobility. But factor immobility introduces a major additional consideration: The internal redistribution of income implied by global rebalancing. We find that, with resource immobility, eliminating the current account deficit raises the returns to U.S. factors working in manufacturing to those working elsewhere by about 30 percent (with or without adjustment at the extensive margin). Obstfeld and Rogoff (2005) also employ a static trade model to examine the implications of eliminating current account imbalances. Their focus is on real exchange rates and the terms of trade, rather than real wages and welfare, our interest here. They employ a stylized three-region model. With labor mobility our results are closest to what Obstfeld and Rogoff call a ‘‘very gradual’’ unwinding, or a decade-long adjustment, but labor immobility (with or without an operative extensive margin) connects better with their baseline scenario.4

I. A Model of the World We consider a world of i ¼ 1, y, N countries. Country i is endowed with 5 M labor Li. Labor is allocated between two sectors, manufacturing Li and

3While our framework can quite handily deal with a multitude of countries, its analytic essence derives from the two-country model of trade and unilateral transfers of Dornbusch, Fischer, and Samuelson (1977). 4Corsetti, Martin, and Pesenti (2007) develop a symmetric two-country model in which adjustment can also occur across both the intensive and extensive margins. They examine the long-run consequences of the effects of improving net export deficits of 6.5 percent of GDP in one country to a balanced position. In the version of the model in which all adjustment takes place at the intensive margin, the authors find that closing the external imbalance requires a fall in long-run consumption (of the country undergoing the adjustment) by around 6 percent and a depreciation of the real exchange rate and the terms of trade by 17 and 22 percent respectively. When adjustment can also occur at the extensive margin, there is a much smaller depreciation in the real exchange rate and in the terms of trade, of 1.1 percent and 6.4, respectively. The changes in consumption and welfare under the two versions of the model, however, are similar. 5To generalize our analysis to incorporate multiple factors of production one may think of Li as a vector of factors.

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N nonmanufacturing Li , with M N Li þ Li ¼ Li: (1Þ Throughout we assume that all production is at constant returns to scale and that all markets are perfectly competitive.6

Income and Expenditure: Some Accounting We relate production and trade in manufactures to aggregate income, expenditure, and wages. We have to do some accounting to draw these connections. M We denote country i’s gross production of manufactures as Yi , of which a share bi is value-added. With perfect competition, value added corresponds M M M M to factor payments Vi ¼ wi Li , where wi is the manufacturing wage. N Similarly, wi is the nonmanufacturing wage, so that nonmanufacturing N N N value added is Vi ¼ wi Li and GDP is M N M M N N Yi ¼ Vi þ Vi ¼ wi Li þ wi Li : If we define the average wage as M M N N wi Li þ wi Li wi ¼ ; (2Þ Li then GDP is simply Yi ¼ wiLi. Our notation is designed to admit: (i) sectoral M N M N labor mobility, in which case Li and Li are endogenous with wi ¼ wi ¼ wi M N and (ii) immobile labor, in which case Li and Li are fixed with wages typically differing by sector. M We denote country i’s gross absorption of manufactures as Xi and its M M manufacturing deficit as Di . They are connected with Yi via the identity: M M M Yi ¼ Xi Di : (3Þ Manufactures have two purposes: as inputs into the production of manufactures and to satisfy final demand. We denote the share of manufactures in final demand as ai so that demand for manufactures in country i is M M Xi ¼ aiXi þð1 gÞð1 biÞYi ; (4Þ where Xi is final absorption, equal to GDP Yi plus the overall trade deficit Di, and g is the share of nonmanufactures (hence 1g the share of manufactures) in manufacturing intermediates.7

6See Eaton, Kortum, and Kramarz (2008) to see how the model could be respecified in terms of monopolistic competition with heterogeneous firms, as in Melitz (2003) and Chaney (forthcoming). 7 More precisely, the parameter ai captures both manufactures used in final absorption and manufactures used as intermediates in the production of nonmanufactures. For simplicity, we ignore this feedback from the manufacturing sector to the nonmanufacturing sector. As we discuss below, this feedback appears to be small.

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Combining Equations (3) and (4) we obtain M M aiðYi þ DiÞ¼½gð1 biÞþbiYi þ Di : Rearranging, we obtain equations for manufacturing production and absorption: M M aiðwiLi þ DiÞDi Yi ¼ ; (5Þ gð1 biÞþbi

M M aiðwiLi þ DiÞð1 gÞð1 biÞDi Xi ¼ : (6Þ gð1 biÞþbi These equations will allow us to connect world equilibrium in manufactures to various deficits.

International Trade Manufactures consist of a unit continuum of differentiated goods indexed by j. We denote country i’s efficiency making good j as zi(j). The cost of producing good j in country i is thus ci/zi(j), where ci is the cost of an input bundle in country i. Given the production structure introduced above,

M bi N gð1biÞ ð1gÞð1biÞ ci ¼ kiðwi Þ ðwi Þ pi ; (7Þ where pi is an index of manufacturing input prices in country i,tobe determined below. The term ki is a constant that depends on g, bi, and the productivity of labor in nonmanufacturing.8 We make the standard assumption of ‘‘iceberg’’ trade barriers, implying that to deliver one unit of a manufactured good from country i to country n requires shipping dniZ1 units, where we normalize dii ¼ 1. Thus, delivering a unit of good j produced in country i to country n incurs a unit cost:

cidni pniðjÞ¼ : ziðjÞ

Ricardian Specialization Here we set up the model assuming that buyers purchase any good from its lowest cost source, so that the extensive margin is active. We turn to what happens if this margin is shut off later in the paper. As in Eaton and Kortum (2002) country i’s efficiency zi (j) in making good j is the realization of a random variable Z with distribution:

y Tiz FiðzÞ¼Pr½Z z¼e ;

8 N If the unit cost function in nonmanufactures is wi /ai, reflecting productivity ai, then

gð1bi Þ bi gð1bi Þ ð1gÞð1biÞ ki ¼ðaiÞ bi ½gð1 biÞ ½ð1 gÞð1 biÞ .

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which is drawn independently across i. Here Ti>0 is a parameter that reflects country i’s overall efficiency in producing any good and y is an inverse measure of the dispersion of efficiencies. The implied distribution for pni (j)is cidni y y Pr½P p¼Pr Z ¼ 1 eTiðcidniÞ p : ni p Buyers in destination n will buy each manufacturing good j from the cheapest source at a price:

pnðjÞ¼min fpniðjÞg: i

The distribution Gn (p) of prices paid in country n is YN y Fnp GnðpÞ¼Pr½Pn p¼1 Pr½Pni p¼1 e i¼1 where: XN y Fn ¼ TiðcidniÞ : i¼1

The probability pni that country i is the cheapest source is its share of this sum:

y TiðcidniÞ pni ¼ : (8Þ Fn Invoking the law of large numbers, this probability becomes the measure of goods that country n purchases from country i. Thus pni is a bilateral trade share measured by numbers of goods. To obtain a trade share measured by expenditures we must specify demand.

Demand for Manufactures We assume that the individual manufacturing goods, whether used as intermediates or in final demand, combine with constant elasticity s>0. Spending in country n on good j is therefore ðs1Þ M pnðjÞ M Xn ðjÞ¼ Xn ; pn where pn is the manufacturing price index in country n, which appeared previously in expression (7) for the cost of an input bundle. We compute this price index by integrating over the prices of individual goods:

Z 1 1=ðs1Þ ðs1Þ 1=y pn ¼ p dGnðpÞ ¼ jFn ; (9Þ 0

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©International Monetary Fund. Not for Redistribution Robert Dekle, Jonathan Eaton, and Samuel Kortum where y ðs 1Þ 1=s1 j ¼ G y and G is the gamma function, requiring y>s1. We can express bilateral trade shares in expenditure terms mechanically as M XM p X p ¼ ni ¼ P ni ni ; (10Þ ni XM N M n k¼1 pnkXnk M where X ni is average spending per good in country n on goods purchased from i. M To compute X ni we need to know the distribution Gni( p) of the prices of goods that country n buys from country i because Z 1 ðs1Þ M M p Xni ¼ Xn dGniðpÞ: 0 pn As shown in Eaton and Kortum (2002), among the goods that n buys from i, the distribution of prices is the same regardless of source, so that M M Gni(p) ¼ Gn(p). It follows that X ni ¼ Xn and hence Equation (10) becomes

XM T ðc d Þy ni ¼ p ¼ p ¼ P i i ni : (11Þ XM ni ni N y n k¼1 TkðckdnkÞ The two measures of the bilateral trade share reduce to the same thing.

Trade Elasticities How do trade shares and prices respond to changes in input costs around the world? Say that the costs of input bundles in each country k move from ck to 0 0 ck . We can represent this change in terms of the ratio cˆk ¼ ck /ck.

Extensive Margin Operative We first consider the case in which a buyer can switch to any new source country that can deliver a good more cheaply. The resulting bilateral trade shares are

T ðc0d Þy T ðc d Þyc^y p0 ¼ P i i ni ¼ P i i ni i ni N 0 y N y y k¼1 TkðckdnkÞ k¼1 TkðckdnkÞ c^k p c^y ¼ P ni i : N y k¼1 pnkc^k The parameter determining how changes in costs translate into trade shares is y, which reflects the extent of heterogeneity in production efficiency. It captures how changes in costs bring about a change in international specialization in production and delivery to various markets, the extensive margin.

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We also need to consider how price indices adjust to a change in costs around the world. Starting from Equation (9), with the extensive margin active, the price index resulting from a change in costs is "#"# XN 1=y XN 1=y 0 0 y y pn ¼j TiðcidniÞ ¼ j Fnpnic^i i¼1 i¼1 "# XN 1=y y ¼pn pnic^i : ð12Þ i¼1 Note that s is nowhere to be seen. Extensive Margin Inoperative Say instead that after input costs change, countries are stuck buying each good from the same source as before, so that adjustment is only in how much is spent on each good, the intensive margin. To see what happens to trade shares, return to Equation (10), this time shutting down the extensive margin by fixing the pnk s. The price of any good that country n had bought from country i at price p now costs pcˆi. If country n goes on buying each good from its original source, the resulting bilateral trade shares (with a superscript SR to denote the short run) are p X0M ðpSRÞ0 ¼ P ni ni ni N 0M k¼1 pnkX nk R M 0 1 0 ðs1Þ pniX pcbi=p dGniðpÞ ¼ P n 0 R n : N M 0 1 0 ðs1Þ k¼1 pnkXn 0 pc^k=pn dGnkðpÞ Assuming that we started with a situation in which country n bought every good from the lowest cost source, so that Gni (p) ¼ Gn (p), the resulting trade shares simplify to p c^ðs1Þ pSR 0 P ni i : (13 ð ni Þ ¼ N ðs1Þ Þ k¼1 pnkc^k The parameter now determining how changes in costs translate into trade shares becomes s1, as in the Armington model. Because y>s1, the effective trade elasticity is lower when we shut down the extensive margin. Parallel to Equation (12) above, we also need an expression for the change in the price index in each country that results from a change in input costs. To derive this expression, recall that we can construct the price index from source-specific blocks: "# Z 1=ðs1Þ XN 1 ðs1Þ pn ¼ pni p dGniðpÞ : i¼1 0

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Therefore, in response to a change in costs: "# Z 1=ðs1Þ XN 1 SR 0 ðs1Þ ðpn Þ ¼ pni ðpc^iÞ dGniðpÞ i¼1 0 "# XN 1=ðs1Þ ðs1Þ ¼pn pnic^i : ð14Þ i¼1 The elasticity s1 again replaces y as the relevant parameter when we shut down the extensive margin. In all other ways, the analysis is exactly parallel. We will return to this result in our simulations where we interpret s1as the short-term trade elasticity. This interpretation is motivated by the dynamic two-country analysis of Ruhl (2005), in which firms choose not to adjust their extensive margin in response to temporary fluctuations in costs. In this case, all adjustment takes place via expenditure per good resulting from changes in prices and incomes.

Equilibrium The conditions for equilibrium in world manufactures are XN M M Yi ¼ pniXn : (15Þ n¼1 This set of equations determines relative wages across countries. To see how, plug in the expressions above for manufacturing production (5) and absorption (6) to obtain M aiðwiLi þ DiÞDi gð1 biÞþbi XN a ðw L þ D Þð1 gÞð1 b ÞDM ¼ p n n n n n n : ð16Þ ni g 1 b b n¼1 ð nÞþ n We obtain an expression for the trade shares by substituting Equation (7) into Equation (11): hi y M bi N gð1biÞ ð1gÞð1biÞ Ti kiðwi Þ ðwi Þ pi dni p hi: (17 ni ¼ P y Þ N M bk N gð1bkÞ ð1gÞð1bkÞ k¼1 Tk kkðwk Þ ðwk Þ pk dnk

From Equations (7) and (9), the price index for manufactures is ! hi1=y XN y M bi N gð1biÞ ð1gÞð1biÞ pn ¼ j Ti kiðwi Þ ðwi Þ pi dni : (18Þ i¼1

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The size of the nonmanufacturing sector (and hence of the manufacturing sector) is nailed down by

M N N N aiðwiLi þ DiÞDi Vi ¼ wi Li ¼ wiLi bi : (19Þ gð1 biÞþbi

M N World equilibrium is a set of wages and price levels wi , wi and pi and M N labor allocations Li and Li for each country i that solve Equations (1), (2), and (16)–(19) given parameters including labor endowments and deficits, Di M and Di . To complete the description of equilibrium, we have to take a stand on labor mobility. We consider the two extreme assumptions regarding internal labor market mobility. In the mobile labor case, which we take as reflecting the M N long run, the wage equilibrates between sectors, so that wi ¼ wi ¼ wi with M N Li and Li determined endogenously. In the immobile labor case, which we take as reflecting the short run, workers are tied to either manufacturing or M N nonmanufacturing. For this case we take Li and Li as given and solve for M N wi and wi separately. Our counterfactual experiments calculate the response of all endogenous variables to an exogenous change in deficits around the world.

II. Quantification Data We created our sample of 42 countries as follows. We began with the 50 largest as measured by GDP in 2000, and combined the others into a ‘‘country’’ labeled ROW. Incomplete data forced us to move Saudi Arabia, Poland, Iran, the United Arab Emirates, Puerto Rico, and the Czech Republic into ROW as well. Because of peculiarities in the data suggestive of entrepoˆt trade, which our approach here is ill-equipped to handle, we combined (1) Belgium and Luxembourg (which we pulled out of ROW), (2) China and Hong Kong SAR, and (3) Malaysia, Philippines, and Singapore into single entities. The result is 42 entities, which we refer to as countries, which constitute the entire world. To solve for the counterfactual, we need data on GDP (for Yi), M manufacturing value added (for Vi ), gross manufacturing production M M (for Yi ), overall and manufacturing trade deficits (Di and Di ), and M bilateral trade flows in manufactures (for Xni ), including purchases from M home Xii . Wherever possible we take data for 2004 with all magnitudes translated into U.S.$ billions. We take GDP Yi and manufacturing value added M Vi from the United Nations National Income Accounts Database (2007). We calculate value added in nonmanufacturing as a residual, N M Vi ¼ YiVi .

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The overall trade deficit in goods and services Di and current account deficits CAi, used for our counterfactual experiments below, are from the 9 IMF (2006). We calculate total final spending as Xi ¼ Yi þ Di. Our handling of production and bilateral trade in manufactures is more M involved. Our goal is a matrix of values Xni of the manufactures that country n buy from i. We begin with Comtrade data on bilateral trade from the United Nations Statistics Division (2006). We define manufactures as SITC trade codes 5, 6, 7, and 8. We measure trade flows between countries using reports of the importing country. We netted out trade within the three entities containing multiple countries. Bilateral trade data do not contain an entry for the value of manufactures M that country i purchases from local producers, Xii . We calculate these diagonal elements of the bilateral trade matrix as follows: (1) For each country i we calculate the share of value added in manufacturing bi as the ratio of value added in manufacturing to total manufacturing production for the most recent year for which each is available (and not imputed) from the United Nations Industrial Development Organization Industrial Statistics 10 M M M Database (2006). (2) We create a value of Yi for 2004 as Yi ¼ Vi /bi using M M M M M the 2004 value for Vi . (3) We calculateP Xii ¼ Yi Ei , where Ei is country M M i’s manufacturing exports Ei ¼ naiXni . With our bilateral trade matrix, we can calculate the trade deficit in M manufactures, Di . Except for the numbers used to calculate bi all data are for 2004, the most recent year for which we could get complete data.

Calibration In principle, computing the world equilibrium requires knowing the M N parameters dni, ki, ai, bi, g, Ti, Li (Li and Li separately in the case of factor immobility), and y (or s in the case with no extensive margin) as well 0 as the actual and counterfactual overall and manufacturing deficits Di, Di , M M0 Di , and Di . As explained below, however, because we only consider changes from the current equilibrium, all we need to know about dni, Ti, and ki is contained in the current trade shares pni but all we need to know about M N M N Li and Li is contained in value added Vi and Vi . We set y ¼ 8.28, the central value Eaton and Kortum (2002) report based on bilateral trade and cross-country product-level price data. We also report the implications of shutting down the extensive margin by replacing y with s1. There are a wide range of estimates of s that we might consider.

9We have to confront the problem that the data imply nonzero current account and trade balances for the world as a whole. Our procedures cannot explain this discrepancy so we allocated the deficits to countries in proportion to their GDPs. Because we use only importer data to measure bilateral trade in manufactures, world trade in manufactures balances automatically. 10For each country i other than ROW a measure of b is available in some year in the interval 1991–2003. Our measure of b for ROW is the simple average of the bs across countries not in ROW.

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Bernard Eaton, Jensen, and Kortum (2003) find that s ¼ 3.79 (and y ¼ 3.60) explains the size and productivity of advantage of U.S. plants that export. Ruhl (2005) finds that s ¼ 2.0 can reconcile the time-series data regarding the degree of adjustment in trade balances to temporary changes in relative costs. To create a sharper contrast with simulations in which the extensive margin is active, and because our approach of shutting down the extensive margin is inspired by Ruhl (2005), we go with the lower value. We calculate the share of nonmanufactures in manufacturing inter mediates g from input-output tables. We do not have enough input-output tables to calculate g for each country. Instead we calculate g ¼ 0.43 from the 1997 input-output use table of the United States, and apply this value for all countries (Organization for Economic Cooperation and Development, 2007).11 Using Equations (3) and (4), we calculate ai as

M M M Vi þ gð1 biÞYi þ Di ai ¼ : Xi

Table 2 presents the values of ai and bi for our 42 countries, along with data on the share of manufacturing value added in GDP and the share of exports in manufacturing gross production. Of our countries, Algeria has the smallest share of manufacturing value added (at 0.06) and China/Hong Kong (henceforth China) the largest (0.38). Argentina and Egypt have the least outwardly oriented manufacturing sector (10 percent exported), and Malaysia/Philippines/Singapore the most outwardly oriented (94 percent exported). The share of value added in manufacturing b averages about one- third, with India having the lowest value (0.19), and Brazil the highest (0.53). The calculated share of manufactures in final demand ranges from a low of 0.06 in Ireland to a high of 0.78 in China/Hong Kong. In spite of these outliers, the values of a are each typically between 0.25 and 0.50.

Counterfactual Deficits Our counterfactual is a world in which production and trade in manufactures has adjusted to eliminate all current account imbalances. Not modeling nonmanufacturing trade, we hold nonmanufacturing trade deficits at their 2004 level as a share of world GDP. We thus set for each country i

M 0 M Di ¼ Di þ CAi; where CAi is the 2004 current account surplus. We correspondingly set the new trade deficit at

0 M 0 M Di ¼ Di þ Di Di :

11As mentioned earlier, we do not take account of the use of manufactures as intermediates in the production of nonmanufactures. According to the 1997 input-output use table for the United States, the share of intermediates in the gross production of nonmanufactures is 8.5 percent.

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Table 2. Manufacturing Share of GDP, Export Share of Manufacturing, Share of Manufacturing in Final Demand (Alpha), and Share of Value Added in Manufacturing Gross Output (Beta)

Shares

Country Vmfg/GDP Exports/Ymfg Alpha Beta

Algeria 0.06 0.14 0.26 0.34 Argentina 0.22 0.10 0.52 0.33 Australia 0.11 0.15 0.26 0.39 Austria 0.17 0.57 0.34 0.35 Belgium/Luxembourg 0.15 0.83 0.48 0.27 Brazil 0.22 0.22 0.30 0.53 Canada 0.17 0.47 0.32 0.38 Chile 0.16 0.46 0.26 0.40 China/Hong Kong 0.38 0.23 0.78 0.27 Colombia 0.15 0.19 0.31 0.46 Denmark 0.12 0.68 0.23 0.45 Egypt 0.18 0.10 0.41 0.27 Finland 0.20 0.44 0.32 0.32 France 0.12 0.30 0.31 0.22 Germany 0.20 0.44 0.31 0.31 Greece 0.08 0.15 0.25 0.34 India 0.15 0.12 0.38 0.19 Indonesia 0.28 0.28 0.39 0.39 Ireland 0.24 0.93 0.06 0.34 Israel 0.14 0.72 0.23 0.35 Italy 0.17 0.27 0.35 0.26 Japan 0.21 0.25 0.29 0.36 Korea 0.26 0.64 0.23 0.38 Ma/Phi/Sing 0.27 0.94 0.44 0.29 Mexico 0.16 0.47 0.32 0.35 Netherlands 0.13 0.63 0.31 0.27 New Zealand 0.15 0.18 0.37 0.34 Norway 0.10 0.27 0.31 0.30 Pakistan 0.17 0.18 0.31 0.31 Peru 0.15 0.15 0.30 0.37 Portugal 0.14 0.35 0.32 0.28 Russia 0.16 0.26 0.28 0.38 South Africa 0.17 0.25 0.35 0.29 Spain 0.15 0.24 0.36 0.27 Sweden 0.18 0.58 0.27 0.34 Switzerland 0.19 0.66 0.30 0.39 Thailand 0.35 0.67 0.45 0.40 Turkey 0.20 0.32 0.39 0.38 United Kingdom 0.13 0.30 0.29 0.32 United States 0.13 0.22 0.22 0.48 Venezuela 0.17 0.16 0.34 0.51 Rest of world 0.15 0.45 0.42 0.34 Note: Vmfg is value added in manufacturing, Ymfg is gross production in manufacturing, beta is the share of value added in gross production, and alpha is the share of manufactures in final absorption. Ma/Phi/Sing is a combination of Malaysia, the Philippines, and Singapore.

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Table 3 reports the actual and counterfactual trade deficits both overall and in manufactures. Notice that the United States must run a surplus in manufactures of over two hundred billion dollars to balance its current account.

Formulation in Terms of Changes

As for Ti, ki, and dni, direct observations are hard to come by. Instead of attaching numbers to them, and to Li as well, we reformulate the model to express the equilibrating relationships in terms of aggregates of these parameters that are readily observable. We then solve for the proportional changes in wages and prices needed to eliminate current account deficits. We use x0 to denote the counterfactual value of variable x and xˆ to denote x0/x. We will repeatedly use the fact that factor payments correspond to value k0 k k k k k k added, so that wi Li ¼ wˆ i wi Li ¼ wˆ i Vi in each sector k ¼ M, N as well as in 0 the aggregate wi Li ¼ wˆ iwiLi ¼ wˆ iYi. Starting with the equation for the average wage (2), we have

M M N N w^i ¼ si w^i þ si w^i ; (20Þ M M N N where the sectoral shares are si ¼ Vi /Yi and si ¼ Vi /Yi. The goods market- clearing condition (16) becomes

0 M 0 aiðw^iYi þ DiÞDi gð1 biÞþbi XN a ðw^ Y þ D0 Þð1 gÞð1 b ÞDM 0 ¼ p0 n n n n n n : ð21Þ ni g 1 b b n¼1 ð nÞþ n The trade share Equation (17) becomes

M ybi N ygð1biÞ yð1gÞð1biÞ 0 pniðw^i Þ ðw^i Þ ðp^iÞ pni ¼ P : (22Þ N M ybk N ygð1bkÞ yð1gÞð1bkÞ k¼1 pnkðw^k Þ ðw^k Þ ðp^kÞ The price Equation (18) becomes: ! XN 1=y y M bi N gð1biÞ ð1gÞð1biÞ p^n ¼ pni½ðw^i Þ ðw^i Þ p^i : (23Þ i¼1 Finally, the sectoral share Equation (19) becomes 0 M 0 ^N 1 aiðw^iYi þ DiÞDi Vi ¼ N w^iYi bi : (24Þ Vi gð1 biÞþbi N N In the case of mobile labor, Vˆi ¼ Lˆi with

M N w^i ¼ w^i ¼ w^i : (25Þ

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Table 3. Actual and Counterfactual Trade Deficits (Overall and Manufactures)

Actual Deficit Counterfactual Deficit

Country Total Mfg Total Mfg

Algeria 7.24 11.80 4.00 23.03 Argentina 11.02 9.52 7.39 13.15 Australia 21.84 57.53 17.35 18.34 Austria 4.36 7.25 3.21 8.41 Belgium/Luxembourg 20.52 52.58 3.90 69.19 Brazil 26.12 8.84 13.58 3.71 Canada 35.70 22.53 13.23 45.00 Chile 8.05 2.43 6.34 0.71 China/Hong Kong 53.97 119.36 33.22 32.18 Colombia 0.80 8.21 0.01 7.40 Denmark 11.26 9.28 5.00 15.54 Egypt 0.79 1.12 4.82 5.15 Finland 9.56 17.08 0.39 7.13 France 7.41 3.27 3.34 7.34 Germany 122.90 278.28 17.47 172.85 Greece 13.86 29.18 0.71 16.03 India 14.46 11.87 22.22 4.10 Indonesia 10.13 25.14 8.23 23.25 Ireland 25.48 68.85 26.31 69.69 Israel 0.13 2.19 3.45 1.13 Italy 3.99 46.57 17.42 60.00 Japan 72.41 385.08 105.74 206.94 Korea 26.29 146.38 2.79 117.30 Ma/Phi/Sing 45.94 58.26 2.71 15.03 Mexico 17.79 20.16 12.00 14.37 Netherlands 44.38 8.90 10.84 64.12 New Zealand 1.07 9.99 5.26 3.67 Norway 34.91 15.96 0.14 51.01 Pakistan 6.52 0.93 5.85 1.60 Peru 1.62 2.47 1.54 2.55 Portugal 14.34 9.81 1.61 2.92 Russia 69.57 11.67 10.19 47.71 South Africa 2.64 1.01 4.52 6.15 Spain 44.79 61.73 8.69 8.24 Sweden 27.42 26.19 0.53 1.76 Switzerland 32.76 13.38 24.30 43.68 Thailand 5.98 21.06 1.09 13.99 Turkey 12.53 18.01 2.67 2.81 United Kingdom 74.19 103.50 41.87 71.18 United States 666.97 438.40 17.23 211.34 Venezuela 17.27 5.97 3.29 19.95 Rest of world 171.29 341.91 117.85 395.34 Note: All data are in billions of U.S. dollars. Ma/Phi/Sing is a combination of Malaysia, the Philippines, and Singapore.

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In the case of immobile labor, ^N N Vi ¼ w^i ; (26Þ N with Lˆi ¼ 0. In the case of immobile labor with no extensive margin we simply replace y with s1 in Equations (22) and (23). M N The parameters Ti, dni, ki, Li , and Li no longer appear. Instead we have M N manufacturing value added Vi , nonmanufacturing value added Vi , and manufacturing trade shares pni, not the counterfactual values but the actual M M M N M M (factual) ones, Xni /Xn . We can thus use data on Vn , Vn , and Xni /Xn , along with the parameters ai, bi, g, and y (or s with no extensive margin) to solve M N N the counterfactual equilibrium changes wˆ i , wˆ i , Vˆi , and pˆi that arise from 0 M0 moving to counterfactuals deficits Dn and Dn .

Computation Simple iterative procedures solve Equations (20)–(24) for changes in wages, employment, and prices, with Equations (26) and (25) employed appropriately for the case at hand. With 42 countries, a good quality laptop running GAUSS can deliver the solutions almost immediately. In this algorithm, world GDP is the nume´raire,

XN XN XN 0 w^iYi ¼ wiLi ¼ wiLi ¼ Y; i¼1 i¼1 i¼1 hence

XN Y iw^ ¼ 1: Y i i¼1 For each of our 42 countries we present the change in a set of outcomes, as the ratio of the counterfactual value to its original value. In the case of factor immobility, we present the change in manufacturing M N wage wˆ i , nonmanufacturing wage wˆ i , and the change in the overall wage wˆ i, using Equation (20). The change in the overall wage corresponds to 0 the change in GDP, because Yi ¼ wˆ iYi. With factor mobility we simply solve for wˆ i. Because we solve for the change in the manufacturing price index pˆi,we L ai N 1ai can calculate the change in the cost of living as p^i ¼ðp^iÞ ðw^i Þ . We can thus calculate the changes in real wages and real GDP. Taking into account the static gain or loss of the transfers themselves, we get the change in real absorption in country i as 0 0 ^ w^i 1 þ Di=Y i W i ¼ L : p^i 1 þ Di=Yi

The counterfactual bilateral trade share of country i in n, pni, can be constructed from the original shares using expression (22). The

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©International Monetary Fund. Not for Redistribution Robert Dekle, Jonathan Eaton, and Samuel Kortum counterfactual bilateral trade flow of n’s imports from i is 0 0 M 0 0 0 anðY n þ DnÞð1 gÞð1 bnÞDn X ni ¼ pni : gð1 bnÞþbn Finally, the change in the share of manufacturing value added in GDP is

^M N ^N ^ Vi 1 si ðVi =YiÞ ¼ N : Y^i 1 si We now turn to the results.

III. Results In discussing the results, we work backwards. Because it is conceptually simplest and relates to our earlier work, we start with the longest run in which both the allocation of labor and the extensive margin can adjust. We then look at a medium run in which labor is locked into its initial sector but the extensive margin still operates. We conclude with the very short run in which neither margin can adjust: labor is immobile and there is no change in the set of goods that countries buy from each other, only how much they buy. Our tables report all results in terms of relative changes, so that if a variable changed from x to x0 the table reports xˆ ¼ x0/x.12

Labor Mobility Table 4 reports the results for the mobile-factor case. With labor mobility, there is a single national wage whose change equals the change in GDP. The changes in wages are reported in the first column. As noted above, they are calculated so that world GDP remains the same. Note that relative wage changes are quite modest. Taking one of the largest swings, the U.S. wage (and hence GDP) falls relative to Japan’s by less than 8 percent. Because most goods are not traded, price indices, reported in the second and third columns, move in the same direction as wages, resulting in changes to real wages (equivalently real GDPs), reported in the fourth column, nearly always a fraction of a percent. In countries initially in deficit, labor shifts from nonmanufacturing to manufacturing. The change in the manufacturing share is shown in the fifth column. Note that the shifts can be substantial, with the share for the United States rising by almost 23 percent (about 3 percentage points). The manufacturing sector in Japan declines by 8 percent. The last column of Table 4 shows the change in real absorption. This change is dominated by the primary burden of eliminating the deficit. The United States experiences a 6 percent decline in real absorption but Japan’s and Germany’s rise by around 4 percent. The change in real absorption

12In the text, we refer to a percentage change in x as 100(xˆ1) and the percentage change in x2 relative to x1 as 100[(xˆ2/xˆ1)1].

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Table 4. Changes in Wages (GDP), Manufacturing Price Index, Aggregate Price Index, Real Wages (Real GDP), Manufacturing Share, and Real Absorption (Factor Mobility)

Price Indices Wage Real Wage Mfg Real Country (GDP) Mfg Aggregate (Real GDP) Share Absorption

Algeria 1.205 1.055 1.164 1.035 0.469 1.176 Argentina 1.020 1.016 1.018 1.002 0.978 1.029 Australia 0.946 0.969 0.952 0.994 1.235 0.935 Austria 1.015 1.016 1.015 1.000 0.993 1.004 Belgium/Luxembourg 1.021 1.013 1.017 1.004 0.944 1.049 Brazil 1.019 1.015 1.018 1.001 0.952 1.023 Canada 0.991 0.983 0.988 1.003 0.943 1.026 Chile 1.017 1.009 1.015 1.002 0.950 1.023 China/Hong Kong 1.015 1.015 1.015 1.000 0.989 1.042 Colombia 1.000 0.999 0.999 1.000 1.025 0.992 Denmark 1.033 1.022 1.031 1.003 0.901 1.030 Egypt 1.052 1.043 1.048 1.003 0.931 1.049 Finland 1.037 1.027 1.034 1.003 0.905 1.059 France 1.009 1.009 1.009 1.000 1.004 0.998 Germany 1.025 1.018 1.023 1.002 0.930 1.043 Greece 0.958 0.984 0.964 0.993 1.232 0.946 India 1.016 1.015 1.016 1.000 0.982 1.011 Indonesia 1.012 1.012 1.012 1.000 0.988 1.008 Ireland 1.006 1.003 1.006 1.000 1.005 0.996 Israel 1.011 1.008 1.010 1.001 0.916 1.028 Italy 1.007 1.010 1.008 0.999 1.013 0.991 Japan 1.033 1.027 1.031 1.002 0.920 1.040 Korea 1.023 1.015 1.021 1.002 0.915 1.046 Ma/Phi/Sing 1.049 1.008 1.031 1.018 0.860 1.184 Mexico 0.977 0.980 0.978 0.999 1.018 0.991 Netherlands 1.053 1.020 1.043 1.010 0.793 1.108 New Zealand 0.958 0.973 0.963 0.994 1.139 0.929 Norway 1.131 1.063 1.110 1.019 0.651 1.182 Pakistan 1.001 1.003 1.002 0.999 1.012 0.994 Peru 1.000 1.000 1.000 1.000 0.997 1.001 Portugal 0.975 0.991 0.980 0.995 1.168 0.929 Russia 1.097 1.066 1.088 1.008 0.756 1.124 South Africa 0.991 0.997 0.993 0.998 1.063 0.965 Spain 0.984 0.995 0.988 0.996 1.104 0.946 Sweden 1.050 1.030 1.044 1.005 0.818 1.092 Switzerland 1.079 1.027 1.063 1.015 0.667 1.186 Thailand 1.015 1.014 1.015 1.000 0.955 1.046 Turkey 0.991 1.002 0.996 0.996 1.089 0.948 United Kingdom 1.000 1.005 1.001 0.998 1.043 0.984 United States 0.955 0.973 0.959 0.996 1.228 0.944 Venezuela 1.104 1.052 1.086 1.017 0.725 1.170 Rest of world 1.017 1.015 1.016 1.001 0.972 1.020 Note: Simulation results are expressed as a ratio of the counterfactual to the actual value. Simulation based on y=8.28. Ma/Phi/Sing is a combination of Malaysia, the Philippines, and Singapore.

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Figure 1. Change in GDP, Mobile Labor

1.6

1.4

1.2 alg nor ven swi rus mps net swe fin 1 chk unk can mex saf por usa

0.8 Change in GDP (relative to world GDP)

0.6 -0.15 -0.1 -0.05 0 0.05 0.1 Current account deficit (fraction of GDP)

corresponds almost exactly to the change in the transfers involved in eliminating current account deficits. Quantitatively, then, Ohlin was right. There is no discernible secondary burden to eliminating the transfer. To what extent could we have predicted the changes in wages (and GDPs) from the size of the current account surplus that had to be eliminated? Figure 1 plots the change in the wage reported in the first column of Table 4 against the initial current account deficit as a share of GDP (with country codes as listed in Table 1). Note that there is a definite negative relationship. Mexico and Canada are a bit below other countries with similar deficits, reflecting their proximity to the United States whose relative GDP has declined substantially. There is also a systematic positive relationship between the initial deficit and the change in the size of the manufacturing sector. Figure 2 plots the change in the size of the manufacturing sector (column 5 of Table 4) against the initial current account as a share of GDP. These results closely match those in Dekle, Eaton, and Kortum (2007).

Labor Immobility Behind the mild price effects of eliminating the deficits just reported are big movements in labor across sectors. What if instead a worker is stuck in the sector where she is initially employed? The first two columns of Table 5 report the changes in relative wages that our model says are needed for M N manufacturing to balance current accounts, the results for wˆ i and wˆ i , respectively. Again, these changes leave world GDP unchanged. The third column indicates what happens to each country’s GDP.

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©International Monetary Fund. Not for Redistribution GLOBAL REBALANCING WITH GRAVITY: MEASURING THE BURDEN OF ADJUSTMENT

Figure 2. Change in Manufacturing Share, Mobile Labor

1.4

usa 1.2 por

saf unk mex 1 chk can fin mps swe 0.8 net rus ven

swi nor 0.6 Change in manufacturing share

alg 0.4 -0.15 -0.1 -0.05 0 0.05 0.1 Current account deficit (fraction of GDP)

Except for Canada, the GDP changes are always in the same direction as in the case of mobile labor, but the magnitudes of the changes are much larger. The United States shrinks relative to Japan by 22 percent (as opposed to 8 percent in the previous case). Figure 3 plots the change in GDP against the initial current account deficit as a share of GDP, using the same scale as Figure 1. Note that the relationship is again negative and about twice as steep as in the case of labor mobility. Hence eliminating countries’ ability to reallocate resources requires substantially more adjustment in relative GDPs. Nearly as systematic is the tendency of the wage in manufacturing relative to nonmanufacturing to rise in countries initially in deficit with the opposite in surplus countries. In the United States, the relative wage in manufacturing rises by 29 percent. The change for Australia, another large deficit country, is nearly as large. In Japan and Germany, the largest surplus countries, the relative wage of manufacturing workers declines by around 10 percent. Looking across countries, changes in nonmanufacturing wages contribute much more to changes in relative GDP. Figure 4 plots the change in the manufacturing share against the initial current account deficit as a share of GDP. Note the systematically positive relationship. Because of the pervasiveness of nontradedness, both the price index of manufactures (reported in the fourth column of Table 5) and the overall price index (reported in the fourth column of Table 6) move in line with relative GDP. As a consequence, changes in real GDP (reported in the third column of Table 6) are much smaller than the changes in relative GDP. Although the secondary burden of eliminating current account deficits is about twice what it was with labor mobility, it remains a tiny percentage of the initial deficit.

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Table 5. Changes in Wages, GDP, and Manufacturing Prices (Factor Mobility)

Wages Mfg Price Country Mfg Non-Mfg GDP Index

Algeria 0.951 1.402 1.378 1.059 Argentina 1.027 1.055 1.049 1.036 Australia 1.064 0.855 0.877 0.995 Austria 1.032 1.041 1.039 1.036 Belgium/Luxembourg 1.006 1.068 1.059 1.032 Brazil 1.019 1.083 1.069 1.033 Canada 0.977 1.043 1.032 1.003 Chile 1.011 1.073 1.063 1.028 China/Hong Kong 1.024 1.043 1.036 1.034 Colombia 1.030 1.006 1.010 1.021 Denmark 1.012 1.117 1.105 1.039 Egypt 1.023 1.113 1.096 1.062 Finland 0.992 1.128 1.101 1.045 France 1.033 1.028 1.028 1.030 Germany 0.997 1.096 1.076 1.037 Greece 1.081 0.888 0.904 1.010 India 1.022 1.043 1.040 1.035 Indonesia 1.025 1.042 1.037 1.032 Ireland 1.034 1.015 1.019 1.024 Israel 0.979 1.082 1.068 1.026 Italy 1.037 1.020 1.023 1.030 Japan 1.003 1.119 1.095 1.046 Korea 0.981 1.130 1.092 1.034 Ma/Phi/Sing 0.956 1.174 1.114 1.029 Mexico 1.002 0.982 0.985 1.001 Netherlands 0.936 1.181 1.150 1.037 New Zealand 1.048 0.909 0.930 0.997 Norway 0.943 1.322 1.283 1.071 Pakistan 1.028 1.012 1.015 1.023 Peru 1.018 1.022 1.022 1.020 Portugal 1.103 0.915 0.941 1.014 Russia 0.983 1.300 1.250 1.077 South Africa 1.052 0.975 0.988 1.018 Spain 1.070 0.954 0.971 1.017 Sweden 0.953 1.210 1.164 1.046 Switzerland 0.909 1.368 1.282 1.041 Thailand 1.005 1.080 1.054 1.034 Turkey 1.060 0.953 0.975 1.024 United Kingdom 1.044 0.997 1.003 1.026 United States 1.065 0.827 0.858 0.998 Venezuela 1.017 1.316 1.267 1.061 Rest of world 1.022 1.053 1.048 1.034 Note: Simulation results are expressed as a ratio of the counterfactual to the actual value. Simulation based on y=8.28. Ma/Phi/Sing is a combination of Malaysia, the Philippines, and Singapore.

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Figure 3. Change in GDP, Immobile Labor

1.6

1.4 alg

swi nor ven rus 1.2 swe net mps fin chk can 1 unk mex saf por usa 0.8 Change in GDP (relative to world GDP)

0.6 -0.15 -0.1 -0.05 0 0.05 0.1 Current account deficit (fraction of GDP)

Figure 4. Change in Manufacturing Share, Immobile Labor

1.4

usa 1.2 por

saf unk mex 1 chk can fin mps net swe 0.8 ven rus nor swi alg 0.6 Change in manufacturing share

0.4 -0.15 -0.1 -0.05 0 0.05 0.1 Current account deficit (fraction of GDP)

Although aggregate changes are small, the redistributional effects are substantial. Column 1 of Table 6 shows real gains to labor in the manufacturing sector in countries that are initially in deficit. In the United States, the real wage in manufacturing rises by 24 percent but declines by 4 percent outside manufacturing. In Japan, the real manufacturing wage declines by 9 percent with a 2 percent gain in nonmanufacturing. In every country the real wage moves in opposite directions in the two sectors.

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©International Monetary Fund. Not for Redistribution Robert Dekle, Jonathan Eaton, and Samuel Kortum

Table 6. Changes in Real Wages, Real GDP, Aggregate Price Index, and Real Absorption (Factor Immobility)

Real Wages Real Aggregate Real Country Mfg Non-Mfg GDP Price Index Absorption

Algeria 0.730 1.076 1.057 1.303 1.195 Argentina 0.983 1.010 1.004 1.045 1.032 Australia 1.197 0.961 0.987 0.889 0.926 Austria 0.993 1.002 1.000 1.039 1.005 Belgium/Luxembourg 0.958 1.017 1.008 1.051 1.054 Brazil 0.954 1.014 1.001 1.068 1.024 Canada 0.948 1.013 1.002 1.030 1.026 Chile 0.953 1.011 1.002 1.061 1.026 China/Hong Kong 0.988 1.007 1.000 1.036 1.042 Colombia 1.019 0.995 0.999 1.010 0.991 Denmark 0.921 1.016 1.005 1.099 1.034 Egypt 0.937 1.019 1.004 1.092 1.048 Finland 0.901 1.025 0.999 1.101 1.055 France 1.004 0.999 1.000 1.028 0.998 Germany 0.925 1.017 0.998 1.078 1.039 Greece 1.179 0.969 0.986 0.917 0.939 India 0.983 1.003 1.000 1.040 1.010 Indonesia 0.987 1.004 0.999 1.038 1.008 Ireland 1.018 0.999 1.004 1.015 1.002 Israel 0.916 1.012 0.999 1.069 1.024 Italy 1.013 0.997 1.000 1.024 0.992 Japan 0.913 1.020 0.997 1.098 1.035 Korea 0.886 1.020 0.986 1.107 1.030 Ma/Phi/Sing 0.863 1.061 1.006 1.107 1.171 Mexico 1.015 0.994 0.997 0.988 0.989 Netherlands 0.826 1.042 1.014 1.134 1.111 New Zealand 1.114 0.966 0.988 0.941 0.922 Norway 0.761 1.067 1.036 1.239 1.201 Pakistan 1.013 0.997 0.999 1.015 0.993 Peru 0.996 1.001 1.000 1.022 1.002 Portugal 1.166 0.968 0.995 0.946 0.930 Russia 0.797 1.054 1.013 1.234 1.133 South Africa 1.063 0.985 0.999 0.990 0.966 Spain 1.096 0.977 0.995 0.976 0.945 Sweden 0.819 1.040 1.001 1.164 1.087 Switzerland 0.721 1.085 1.018 1.260 1.178 Thailand 0.949 1.020 0.995 1.059 1.040 Turkey 1.082 0.973 0.995 0.980 0.947 United Kingdom 1.038 0.992 0.998 1.005 0.983 United States 1.237 0.960 0.996 0.861 0.944 Venezuela 0.831 1.076 1.036 1.223 1.196 Rest of world 0.978 1.007 1.003 1.045 1.024 Note: Simulation results are expressed as a ratio of the counterfactual to the actual value. Simulation based on y=8.28. MA/Phi/Sing is a combination of Malaysia, the Philippines, and Singapore.

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©International Monetary Fund. Not for Redistribution GLOBAL REBALANCING WITH GRAVITY: MEASURING THE BURDEN OF ADJUSTMENT

No Extensive Margin Sticking with a situation of labor immobility, we now take the further step of eliminating the extensive margin of adjustment. We interpret this case as applying to the very short run. Implementing this case amounts to replacing y with s1 in our solution algorithm described above. As mentioned, we follow Ruhl (2005) in setting s ¼ 2.0. There are thus two interpretations of what we are doing in this case. One is that the parameter y ¼ 8.28 is as above, but with no adjustment on the extensive margin, the parameter s ¼ 2 becomes the relevant one governing adjustment. Another interpretation is that we are simply repeating the immobile labor case, now using the much lower value of y ¼ 1. The results are shown in Tables 7 and 8. Focusing on relative GDP changes (in column 3 of Table 7), we see that they are magnified considerably when the extensive margin is inoperable. U.S. GDP falls by about 30 percent, but Japan’s rises by 26 percent relative to the world. Figure 5 plots the change in GDP against the initial deficit as a share of GDP, again using the same scale as Figure 1. Note that the relationship has become twice as steep again as that portrayed in Figure 3. Note also that U.S. neighbors Canada and Mexico have fallen further below the rest.

Table 7. Changes in Wages, GDP, and Manufacturing Prices (Factor Immobility, No Adjustment on Extensive Margin)

Wages Mfg Price Country Mfg Non-Mfg GDP Index

Algeria 1.382 1.561 1.551 1.177 Argentina 1.102 1.114 1.112 1.087 Australia 0.865 0.724 0.740 0.899 Austria 1.094 1.097 1.096 1.093 Belgium/Luxembourg 1.076 1.114 1.108 1.072 Brazil 1.074 1.139 1.125 1.075 Canada 0.876 0.957 0.943 0.911 Chile 1.062 1.125 1.115 1.050 China/Hong Kong 1.068 1.090 1.082 1.082 Colombia 1.030 1.008 1.011 1.014 Denmark 1.117 1.199 1.190 1.108 Egypt 1.226 1.311 1.295 1.226 Finland 1.133 1.310 1.274 1.152 France 1.064 1.059 1.060 1.060 Germany 1.085 1.215 1.188 1.105 Greece 0.935 0.807 0.817 0.970 India 1.071 1.096 1.093 1.083 Indonesia 1.073 1.102 1.094 1.085 Ireland 1.042 1.030 1.033 1.027 Israel 0.973 1.076 1.061 1.038 Italy 1.060 1.045 1.048 1.061

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©International Monetary Fund. Not for Redistribution Robert Dekle, Jonathan Eaton, and Samuel Kortum

Table 7 (concluded ) Wages Mfg Price Country Mfg Non-Mfg GDP Index

Japan 1.135 1.296 1.262 1.169 Korea 1.039 1.250 1.196 1.087 Ma/Phi/Sing 1.074 1.336 1.264 1.053 Mexico 0.861 0.852 0.853 0.903 Netherlands 1.081 1.309 1.280 1.093 New Zealand 0.884 0.798 0.811 0.908 Norway 1.336 1.573 1.549 1.250 Pakistan 1.007 0.988 0.991 1.013 Peru 1.000 1.008 1.007 1.006 Portugal 1.004 0.826 0.851 0.977 Russia 1.333 1.634 1.587 1.315 South Africa 1.007 0.930 0.943 0.996 Spain 0.995 0.890 0.905 0.992 Sweden 1.104 1.397 1.346 1.144 Switzerland 1.086 1.555 1.468 1.117 Thailand 1.050 1.141 1.110 1.084 Turkey 1.023 0.922 0.943 1.027 United Kingdom 1.039 0.994 1.000 1.039 United States 0.889 0.673 0.701 0.891 Venezuela 1.352 1.531 1.502 1.213 Rest of world 1.078 1.090 1.088 1.083 Note: Simulation results are expressed as a ratio of the counterfactual to the actual value. Simulation based on s=2. Ma/Phi/Sing is a combination of Malaysia, the Philippines, and Singapore.

As in the previous case, most of the GDP adjustment occurs through the nonmanufacturing wage. Figure 6 plots the change in the manufacturing share against the initial current account deficit. It looks very similar to Figure 4. Again, prices tend to move in line with relative GDP, so that changes in real GDP are small. They are, nonetheless, substantially larger than in the previous two cases. Note that U.S. real GDP falls by about 2 percent, about a third of the initial deficit. Hence with a very low response of trade shares to costs, a nontrivial secondary burden appears. Qualitatively the consequences of adjustment for real wages are much as in the previous case, with the manufacturing real wage rising in deficit countries and falling in surplus countries. For the United States, at least, the burden of the inability to adjust at the extensive margin is born by workers outside manufacturing. The increase in the manufacturing real wage is as in the previous case, but the decline in the nonmanufacturing wage is greater.

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Table 8. Changes in Real Wages, Real GDP, Aggregate Price Index, and Real Absorption (Factor Immobility, No Adjustment on Extensive Margin)

Real Wages Real Aggregate Real Country Mfg Non-Mfg GDP Price Index Absorption

Algeria 0.953 1.077 1.070 1.450 1.205 Argentina 1.002 1.013 1.011 1.100 1.042 Australia 1.129 0.946 0.965 0.766 0.901 Austria 0.999 1.001 1.001 1.095 1.006 Belgium/Luxembourg 0.984 1.019 1.014 1.094 1.060 Brazil 0.959 1.017 1.005 1.120 1.029 Canada 0.930 1.016 1.001 0.942 1.024 Chile 0.961 1.018 1.009 1.105 1.036 China/Hong Kong 0.986 1.006 0.998 1.084 1.039 Colombia 1.020 0.998 1.002 1.010 0.993 Denmark 0.949 1.018 1.010 1.178 1.040 Egypt 0.961 1.027 1.015 1.276 1.051 Finland 0.901 1.042 1.013 1.258 1.069 France 1.004 1.000 1.000 1.059 0.998 Germany 0.920 1.030 1.008 1.179 1.049 Greece 1.107 0.955 0.968 0.844 0.922 India 0.982 1.005 1.001 1.091 1.010 Indonesia 0.979 1.006 0.999 1.096 1.009 Ireland 1.012 1.000 1.003 1.030 1.003 Israel 0.912 1.008 0.995 1.067 1.021 Italy 1.009 0.995 0.997 1.051 0.990 Japan 0.902 1.030 1.003 1.258 1.038 Korea 0.857 1.032 0.987 1.211 1.031 Ma/Phi/Sing 0.893 1.111 1.052 1.202 1.225 Mexico 0.992 0.982 0.983 0.868 0.978 Netherlands 0.874 1.058 1.035 1.237 1.132 New Zealand 1.057 0.953 0.968 0.837 0.895 Norway 0.912 1.074 1.057 1.465 1.225 Pakistan 1.011 0.992 0.995 0.995 0.990 Peru 0.992 1.001 0.999 1.008 1.001 Portugal 1.152 0.948 0.976 0.872 0.913 Russia 0.867 1.062 1.032 1.538 1.156 South Africa 1.058 0.976 0.990 0.952 0.957 Spain 1.075 0.961 0.978 0.925 0.929 Sweden 0.833 1.055 1.016 1.325 1.104 Switzerland 0.771 1.104 1.042 1.408 1.200 Thailand 0.942 1.024 0.995 1.115 1.040 Turkey 1.064 0.959 0.980 0.962 0.932 United Kingdom 1.032 0.987 0.993 1.006 0.979 United States 1.243 0.940 0.980 0.716 0.929 Venezuela 0.956 1.083 1.062 1.414 1.231 Rest of world 0.991 1.003 1.001 1.087 1.023 Note: Simulation results are expressed as a ratio of the counterfactual to the actual value. Simulation based on s=2. Ma/Phi/Sing is a combination of Malaysia, the Philippines, and Singapore.

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Figure 5. Change in GDP, Immobile Sourcing

1.6 rus alg nor ven swi 1.4 swe

mps net fin 1.2

chk

1 unk can saf

mex por 0.8

Change in GDP (relative to world GDP) usa

0.6 -0.15 -0.1 -0.05 0 0.05 0.1 Current account deficit (fraction of GDP)

Figure 6. Change in Manufacturing Share, Immobile Sourcing

1.4

usa

1.2 por

saf unk mex 1 chk can algven fin nor mps rus net 0.8 swe swi

0.6 Change in manufacturing share

0.4 -0.15 -0.1 -0.05 0 0.05 0.1 Current account deficit (fraction of GDP)

IV. Conclusion We have revisited the question of the secondary burden of transfers using a 42-country gravity model of international production and trade in manufactures. Our motivation is to assess the implications for relative wages, relative GDPs, real wages, and real absorption in the major countries

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©International Monetary Fund. Not for Redistribution GLOBAL REBALANCING WITH GRAVITY: MEASURING THE BURDEN OF ADJUSTMENT of the world should the current transfers implied by existing current account deficits come to a halt. How much relative GDPs need to change depends on flexibility of two forms, factor mobility between manufactur- ing and nonmanufacturing, and the ability of trade to adjust at the extensive margin. With perfect mobility and an active extensive margin, the GDP of the United States (running the largest deficit) must fall about 8 percent relative to that of Japan (running the largest surplus). Without mobility, however, the decline is 22 percent. If there is no adjustment in supplier sourcing (the extensive margin) either, the decline is 44 percent. Because of the pervasiveness of nontraded goods, however, prices move largely in sync with relative GDPs so that aggregate real changes are much more muted. Regardless of the degree of labor mobility, the decline in U.S. real GDP is only 0.4 percent if the extensive margin is operative. Without an extensive margin, the drop rises to 2 percent of GDP. So only with extreme inflexibility does a secondary burden of eliminating the transfer inherent in the U.S. current account deficit show up. Although the overall real effects are small, with factor immobility redistributional effects are substantial. Regardless of whether the extensive margin is operative, eliminating current account deficits leads to a rise in the U.S. wage in manufactures relative to nonmanufactures of around 30 percent, reflecting a 24 percent real increase for manufacturing workers and a decline of around 5 percent for nonmanufacturing workers. In the long run in which labor is mobile, this wage difference induces an increase in the manufacturing share of employment of 23 percent.

REFERENCES Alvarez, Fernando, and Robert E. Lucas, 2007, ‘‘General Equilibrium Analysis of the Eaton-Kortum Model of International Trade,’’ Journal of Monetary Economics, Vol. 54, No. 6, pp. 726–68. Bernard, Andrew B., Jonathan Eaton, J. Bradford Jensen, and Samuel Kortum, 2003, ‘‘Plants and Productivity in International Trade,’’ American Economic Review, Vol. 93, No. 4, pp. 1268–90. Chaney, Thomas, forthcoming, ‘‘Distorted Gravity: Heterogeneous Firms, Market Structure, and the Geography of International Trade,’’ American Economic Review. Corsetti, Giancarlo, Philippe Martin, and Paolo Pesenti, 2007, ‘‘Varieties and the Transfer Problem: The Extensive Margin of Current Account Adjustment’’ (unpublished; Federal Reserve Bank of New York). Dekle, Robert, Jonathan Eaton, and Samuel Kortum, 2007, ‘‘Unbalanced Trade,’’ American Economic Review, Papers and Proceedings, Vol. 97, No. 3, pp. 351–5. Dornbusch, Rudiger, Stanley Fisher, and Paul Samuelson, 1977, ‘‘Comparative Advantage, Trade, and Payments in a Ricardian Model with a Continuum of Goods,’’ American Economic Review, Vol. 67, No. 4, pp. 823–9. Eaton, Jonathan, and Samuel Kortum, 2002, ‘‘Technology, Geography, and Trade,’’ Econometrica, Vol. 70, No. 5, pp. 1741–80.

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Eaton, Jonathan, Samuel Kortum, and Francis Kramarz, 2008, ‘‘An Anatomy of International Trade: Evidence from French Firms’’ (unpublished; CREST-INSEE, New York University, University of Chicago). International Monetary Fund (IMF), 2006, International Financial Statistics Yearbook (Washington, IMF). Melitz, Marc, 2003, ‘‘The Impact of Trade on Aggregate Industry Productivity and Intra- Industry Reallocations,’’ Econometrica, Vol. 71, No. 6, pp. 1625–95. Obstfeld, Maurice, and Kenneth Rogoff, 2005, ‘‘Global Current Account Imbalances and Exchange Rate Adjustments,’’ Brookings Papers on Economic Activity, Vol. 25, No. 1, pp. 67–146. Organization for Economic Cooperation and Development, 2007, ‘‘Structural Analysis Database,’’ Available on the Internet: www.oecd.org/document/15/0,2340,en_2649_ 201185_1895503_1_1_1_1,00.html. Ruhl, Kim, 2005, ‘‘The Elasticity Puzzle in International Economics’’ (unpublished; University of Texas at Austin). United Nations Industrial Development Organization, 2006 Industrial Statistics Database Available on the Internet: www.unido.org/index.php. United Nations Statistics Division, 2006 United Nations Commodity Trade Database Available on the Internet: http://comtrade.un.org/. ______, 2007 National Accounts Available on the Internet: http://unstats.un.org/unsd/ snaama.

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