The Political Economy of Speculative Attacks in the Developing World

The Political Economy of Speculative Attacks in the Developing WorldAuthor(s): David A. LeblangSource: International Studies Quarterly, Vol. 46, No. 1 (Mar., 2002), pp. 69-91Published by: Wiley on behalf of The International Studies AssociationStable URL: http://www.jstor.org/stable/3096119 .

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International Studies Quarterly (2002) 46, 69-91.

The Political Economy of Speculative Attacks in the Developing World

DAVID A. LEBLANG

University of Colorado

This paper examines the relationship between politics and speculative attacks in developing countries. While a burgeoning literature focuses on the economic determinants of speculative behavior, little attention has been paid to the importance of political factors. I examine the response of international capital markets to electoral and partisan changes in a sample of 78 developing countries using monthly data from January 1975 to December 1998. All other things being equal, the empirical evidence indicates that speculative attacks are more likely (1) under left rather than under right governments and (2) during the period after an election as compared with all other periods. The results suggest that models developed for OECD economies can be used to understand political-economic phenomena in developing countries.

Since the end of the Bretton Woods system of pegged exchange rates the international financial system has undergone numerous changes. The shift from fixed to floating and back to fixed by a large number of developed and devel-

oped economies, continued innovation in international financial instruments, and the tremendous growth of global capital markets has made international

capital a factor to be reckoned with by policymakers. Nowhere is this more obvious than when recounting the exchange rate crises that now seem to be a

regular feature of the international financial system. Speculative attacks have hit industrial and emerging markets with equal force and without prejudice. The crisis in the European Monetary System in 1992, the Mexican peso crisis of 1994-95, the Asian financial crisis of 1997, and crises in Brazil and in Russia in 1998 are but a handful of examples where international capital wreaked havoc with pegged exchange rate regimes. They also serve to bolster support for the idea that international capital should be considered a "structural characteristic of the international system, similar to anarchy" (Keohane and Milner, 1996:257).

While third image (or inside-out) approaches may have some merit in that

they identify the strength and importance of international capital, they do not

help us in understanding why speculative attacks occur. That is, given the existence, size, and strength of international capital markets, why do speculative attacks strike some economies and not others? Further, why do these attacks occur when they do? I argue that political factors play an important role in

Author's note: Thanks to William Bernhard, John Freeman, Jeff Frieden, Andy Sobel, and seminar participants at the University of Missouri, Washington University, Yale University, the School of International and Pacific Studies at UCSD and the Stern School of Business at New York University for comments on an earlier version of this paper. I am grateful to Geoff Garrett, Andy Rose and Patrick Walsh for providing some of the data used in this paper. Financial support from the University of Colorado and from National Science Foundation grant #SES-0096295 is gratefully acknowledged.

? 2002 International Studies Association. Published by Blackwell Publishers, 350 Main Street, Malden, MA 02148, USA, and 108 Cowley Road, Oxford OX4 IJF, UK.



The Political Economy of Speculative Attacks

understanding the causes of speculative attacks. If speculators anticipate that policymakers are going to devalue they will sell domestic currency and force the government to sell reserves in order to maintain the currency peg. Currency speculators form their expectations regarding currency devaluations not only from economic fundamentals but from political institutions and political events as well. Specifically I argue that speculators form their expectations by gleaning information about the timing of elections, the partisanship of government, and the structure of political institutions.

A focus on political and economic determinants of speculative attacks is war- ranted given the high frequency of these events. Figure 1 shows the number of monthly speculative attacks on exchange rate pegs over the period January 1975- December 1998 for a sample of 78 developing and transition economies. While the number of attacks rose through the 1980s as a result of the third world debt crisis, they did not disappear after that period. The Mexican peso crisis and its aftermath in 1994-95 and the 1997-98 crises in Asia, Russia, and Brazil are also prominent. The ease with which capital mobility leads to currency crises and the devastating consequences wrought by these episodes has led policymakers to consider drastic measures to limit (eliminate) their occurrence. Countries like Argentina have adopted currency boards as a solution while others (e.g., Ecua- dor) discuss adopting the US dollar as their national currency.

In addition, there is evidence that speculative attacks have real economic, social, and political consequences. Speculative attacks are "extremely worrying. They foster politically dangerous trade imbalances, thereby creating an environ- ment that may engender protectionist measures that distort and stifle trade" (Rose, 1999). Aside from disrupting international interactions, speculative attacks in Asia and elsewhere have resulted in bank failures, recessions, unemployment, and poverty that often disproportionately affects the poor (Lee and Rhee, 1998).

Given the frequency and severity of currency crises, it is extraordinary that we know so little about the factors that cause speculative behavior. What is also surprising is the disconnect between economic and political research and between research on OECD and non-OECD countries. For example, there is a volumi-

D[ Speculative Attack 32 -

.... .... ....i~.i~ ii.i .jii

'975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 199/ 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998

SPECULATIVE ATTACKS IN THE DEVELOPING WORLD

FIG. 1. Speculative Attacks

70



DAVID A. LEBLANG

nous theoretical and empirical literature from the economics profession examining the determinants of crises in developing countries (e.g., Frankel and Rose, 1996; Corsetti, Pesenti, and Roubini, 1998; Krugman, 1998). Its authors, however, ignore political factors in their theoretical models and political variables in their empirical work. Political scientists, on the other hand, fare no better. Where some cross-national quantitative work has incorporated political variables into crisis models, this work focuses solely on OECD economies (Eichengreen, Rose, and Wyplosz, 1995; Leblang and Bernhard, 2000). The scholarship that does examine developing countries is largely country or region specific (e.g., Hag- gard, 2000; Maclntyre, 2001).

In part, the present paper is an attempt to rectify this situation. This paper casts a wide net and examines the effect of political and economic factors in a sample of 78 developing countries. The "baseline" economic model employed in the empirical work is derived from the economic literature, and the political variables considered are those found to be theoretically important in case and regional studies and those that are incorporated into empirical models that focus on developed economies. The results suggest that many of the hypotheses developed in relatively narrow context are supported in a larger and very different sample.

The discussion in this paper is divided into four parts. Part one briefly reviews economic models of currency crises. These models identify policy credibility as the key to the maintenance of a pegged exchange rate but fall short of identifying what causes this credibility. The majority of part one draws on the political economy literature and develops hypotheses relating political institutions, elections, and ideology to currency crises. Part two of the paper details the sample, data, and methodology used to test these hypotheses while part three discusses the results. Part four concludes and offers suggestions for future research.

Political Economy of Speculative Attacks

Economic Models of Currency Crises

The Economics discipline has been studying the causes of speculative behavior for at least two decades. Paul Krugman's (1979) seminal paper demonstrates that a balance of payments crisis occurs when economic fundamentals deteriorate to a level that is inconsistent with the maintenance of a currency peg.2 This incon- sistency can arise, for example, if a government is financing a deficit by printing money.3 Under this (and other) circumstances, the result is a decline in confidence by domestic and foreign asset holders that the government is committed to the maintenance of the currency peg. Consequently, speculation against the peg increases and the government faces a continual loss of foreign exchange reserves. Krugman's model demonstrates that speculative attacks are predictable: market participants have full information about government policies and identify the point below which central bank reserves are insufficient to defend the currency peg.

First-generation models extend the Krugman framework to explain currency crises that occurred in countries such as Mexico (1973-82) and Argentina (1978- 81). However, models that focus only on deteriorating fundamentals and the

1 The discussion that follows is a generalization and simplification of a large and expanding field of research. For a more detailed discussion see Krugman (2000) and Obstfeld and Rogoff (1996).

2 The original model of a currency crisis is based on work by Salant and Henderson (1978) who show that an attempt by the government to peg the price of gold (based on government-held gold reserves) would lead to a speculative attack and would ultimately wipe out the gold reserves.

3 This is problematic because the rate of monetary expansion is inconsistent with the fixed exchange rate in the long run, and in the short run it will lead domestic currency holders to exchange their holdings for foreign assets.

71




reserve holdings of the central bank did not adequately explain or predict the crises that occurred either in the European Monetary System (1992) or in Mexico (1994-95). In these cases economic fundamentals were strong, the exchange rate was not overvalued, and government policy did not appear to be inconsistent with the peg. A new "second generation" of crisis models developed in an attempt to explain these speculative attacks. This generation of models argues that attacks on currency pegs can occur in spite of strong fundamentals and high levels of reserves.

Building on Diamond and Dybvig's (1983) model of a bank run, second- generation models by Obstfeld (1994, 1996), Dornbusch, Goldfajn, and Valdes (1995), and Sachs, Tornell, and Velasco (1996) provide different mechanisms by which government policies spur speculative behavior.4 At the heart of this generation of models is the idea that currency crises are the result of self-fulfilling beliefs on the part of currency holders. When actors anticipate a currency devaluation these beliefs lead them to convert domestic assets into foreign currency before the devaluation. If a sufficient quantity of domestic currency is converted, the central bank will run short of foreign exchange reserves and will be forced to devalue the domestic currency. These crises are self-fulfilling. Self-fulfilling crises occur, according to these models, even if the central bank is not in a vulnerable position: it has sufficient reserves to carry out day-to-day operations, but not enough reserves to prevent a run on the domestic currency.

First- and second-generation models neglect or overly simplify the role of policymakers and political institutions. Policymakers in first-generation models do not attempt to (1) alter their inconsistent policies, (2) borrow reserves, or (3) pursue other policies to defend the peg. Not only is it unrealistic to assume that policymakers behave in a passive manner when faced with exogenous economic shocks, it is incorrect to believe that policymakers are homogenous and ignore electoral and institutional incentives. Second-generation models fare no better. What causes a shift from an equilibria where the central bank has sufficient reserves (for day-to-day operations) and there is no speculation to one where the supply of reserves falls short of demand and the currency must be devalued? What leads to this shift? If it is a change in the credibility of policymakers, then what causes this change?

Politics and Speculative Attacks

Expectations about the behavior and credibility of policymakers, then, are the key to understanding the causes of speculative attacks. If speculators are uncer- tain about the government's commitment to the level of the nominal exchange rate, they may sell their holdings of the domestic currency and ultimately force a devaluation. Likewise, if there is uncertainty about the future course of government policies, then speculators have similar incentives. The question, then, is what information do currency traders (domestic and foreign holders of local currency) use to evaluate governmental objectives?

Currency traders, like other economic actors, observe the behavior of policymakers and understand that this behavior is influenced by electoral, ideological, and institutional factors. While there is some recent scholarship connecting political processes to exchange rate volatility (e.g., Freeman, Hayes, and Stix, 2000; Leblang and Bernhard, 2000), these papers, like most others studying

4 The Diamond-Dybvig model shows how a bank run can occur even when a bank is solvent. If depositors believe that the bank is insolvent (or close to insolvency) they will withdraw their money. A run on the bank occurs when other depositors observe this behavior and act in a similar manner in an attempt to salvage their deposits. The result is an equilibria where all depositors demand their deposits and the bank is forced to default. The

outcome is pareto inferior to a situation where all depositors leave their money in the bank.

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DAVID A. LEBLANG

politics and international capital, focus on industrial economies. They do, however, demonstrate that political uncertainty (operationalized in different ways) influences the behavior of traders in foreign exchange markets and causes exchange rate volatility. How does politics influence the behavior of traders in foreign exchange markets? In developing a model of speculative behavior hypotheses are gleaned from the literatures on political business cycles and partisan behavior.

There already exists a large literature relating elections to economic out- comes. This literature can be broken down into distinct but related strands: one focusing on uncertainty and the second examining the incentives of policymakers surrounding elections. The first is concerned with the behavior of economic actors under uncertainty. Elections that result in a change in president, prime minister, or governing coalition generate uncertainty because the new government may have different policy objectives than the incumbent. Even if an election does not lead to a change in leadership, the re-elected incumbent's policy preferences may change due to varying institutional, social, and/or political constraints. This policy uncertainty leads to speculative behavior because economic agents in global currency markets can easily alter their portfolios; selling the currency of a country where there is political risk and purchasing another, less risky asset. Lobo and Tufte (1998), Frieden (1999), and Leblang and Bern- hard (2000) all find that speculative behavior increases in the periods surrounding an election.5

A second argument relating electoral politics and speculative attacks is derived from the literature on political business cycles. Models of the political business cycle hold that politicians care about re-election and that voters judge incumbents based on the state of the economy (e.g., Nordhaus, 1975; Alesina, Roubini, and Cohen, 1997). In the period leading to elections incumbents pursue expan- sionary economic policies in an attempt to prime the pump and increase their probability of re-election. After elections policymakers have to reign in spending and fight inflation.

Manipulation of the economic for political purposes is easier in open economies because an "appreciation of the exchange rate immediately cuts inflation, raises the value in domestic prices of net exports and therefore boosts real income and aggregate demand" (van der Ploeg, 1989:854). Appreciating the currency prior to an election may be good policy for a re-election-maximizing incumbent because the benefits of appreciation are immediate and the costs- downward pressure on net exports, output, and employment-are not felt until after the election (van der Ploeg, 1989; Frieden, 2000). In fact, numerous studies have found that politicians often delay devaluations or the abandonment of an exchange rate peg until after an election (e.g., Edwards and Naim, 1997; Klein and Marion, 1997; Frieden, 1999; Frieden, Ghezzi, and Stein, 1999).

Since elections are visible and politically important events, currency traders understand when they occur and behave accordingly. All other things being equal, knowing that a devaluation is more likely to occur after an election should increase the probability of a speculative attack during the early part of the politician's term in office. However, if we assume that speculators have rational expectations, then they will anticipate the post-election devaluation and sell short their currency holdings prior to the election. If the majority of the market behaves in a similar fashion then a speculative attack occurs in the fashion described by second-generation models prior to an election.

Does the assumption of rational expectations lead to the conclusion that speculative attacks occur only prior to and not after an election? Would finding that speculative attacks occur after an election lead to the conclusion that spec-

5Eichengreen, Rose, and Wyplosz (1995) find no relationship between elections and speculative attacks in OECD countries.

73




ulators are myopic? There is good reason to believe that the answer to these

questions is "no." Recent research on emerging market crises by Calvo and Mendoza (2000a, 2000b) concludes that information costs have changed as the world has become more globalized. "Trading emerging market securities," they argue, "requires collecting detailed information about the countries involved. This information is costly and 'depreciates' quickly. Moreover, fixed information costs are large because assessing country risk requires gathering and processing information about all key macro and political variables on a recurrent basis, independently of investment size" (Calvo and Mendoza, 2000b:3). This cost may actually reduce a speculator's incentive to pay country-specific information costs (Calvo and Mendoza, 2000a).

These information costs may mean that speculators do not have complete information about the willingness or capability of the government to defend the

exchange rate peg. While rational expectations lead perfectly informed speculators to attack a currency prior to an election, it does not take into account the

probability that during this period policymakers are also more likely to defend their currency peg. Put differently, if policymakers want an appreciated currency in the run-up to an election, they also do not want to allow a devaluation to occur during that period. Thus, when confronted with a speculative attack, policymakers will use various policy tools (borrowing to supplement reserves, raising interest rates, imposing capital controls, etc.) to defend the exchange rate

prior to an election.6 Knowing this, either speculators will delay an attack until the government is less willing to defend the exchange rate peg (after the election) or a sufficient number of speculators will buy the local currency (due to either an anticipated appreciation or higher interest rates prior to the election) that a speculative attack will not be apparent.

Therefore, there are two hypotheses regarding the behavior of speculative markets surrounding elections. First, given uncertainty regarding the future course of government policy, currency traders will be more likely to convert their hold-

ings of domestic assets in the periods surrounding elections as compared with other non-electoral periods. Second, since policymakers have a larger incentive to defend the exchange rate parity prior to an election and put adjustment costs off until after the election, it is expected that speculative attacks will be more

likely after than before an election.

Hypothesis 1. Speculative attacks are more likely in the run-up to an election than during other non-electoral periods.

Hypothesis 2. Speculative attacks are more likely in the period after an election than in the period leading up to an election.

Currency traders also have information regarding the partisan composition of the policymakers in power. The literature on partisanship assumes that parties on the Left place more emphasis on employment and income distribution while

parties on the Right are more concerned with fighting inflation and maintaining price stability (Hibbs, 1987; Alesina, 1989). The implication is that policymakers from Left parties will have little credibility with international financial markets as far as exchange rate stability is concerned, and will face a greater risk of capital flight (Garrett, 1998). All things being equal, then, it is expected that speculative behavior will be a function of the partisan identity of the policymakers in power.

6 For a discussion of the economic and political issues surrounding an exchange rate defense see Drazen (1999) and Leblang (2001), respectively.

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DAVID A. LEBLANG

Hypothesis 3. Speculative attacks are more likely when Left governments are in power, as compared with governments from Center or Right parties.

However, as noted above, policymakers of any partisan stripe behave differently in periods surrounding elections. That is, it is expected that there will be an interaction between partisanship and the electoral cycle. There are two arguments here. First, because Left governments already have questionable credibility so far as price stability is concerned, they will be more likely to use all policy tools at their disposal to prevent an attack in the run-up to an election. That is, it is expected that policymakers from Left parties will be more likely than their counterparts from parties of the Right to stabilize the economy so as to enhance their re-election prospects. Speculative attacks, then, will be less likely prior to elections when parties of the Left are in power.

Second, when a Left government is up for re-election speculative behavior should decline because the outcome of the election cannot move the government further to the left; its partisanship can either remain the same or move to the Right. Likewise, if a Right government is in power, the period prior to an election should see increased speculative behavior because the outcome of the election will be either the status quo or a Left government that will implement less stable policies so far as the exchange rate is concerned.

Hypothesis 4. All things being equal, in the period prior to an election, speculative attacks are more likely when an incumbent from the Right is in power. After an election, speculative attacks are more likely when a Left government is in power.

First- and second-generation models of speculative attacks are incomplete in that they ignore or treat as exogenous the effect of politics. Political events and institutions provide information to currency traders. On the basis of this information traders may either change their expectations regarding the credibility of policymakers or shift from holding to selling the local currency. The task, then, is to test these hypotheses.

Sample, Data, and Methodology

Sample

The sample used to test the hypotheses relating political variables to speculative behavior comprises monthly data for 78 developing economies from January 1975 to December 1998.7 Not all countries are included for all time periods, however. Aside from limitations due to data availability, observations were excluded on the basis of two criteria: the lack of democratic political institutions and the absence of a pegged exchange rate.

Since the hypotheses outlined in the first section specify the effect of democratic events and institutions on the probability of speculative attacks, the sample is restricted to those countries and time periods where democratic institutions were in place. The determination of the existence of democratic institutions was based on multiple sources. The Database on Political Institutions (DPI) produced by the Development Research Group of the World Bank includes a variable called the Legislative Index of Electoral Competitiveness (Beck et al., 1999). This

7Industrial economies are excluded because there already exists significant empirical work on the political determinants of speculative attacks in OECD countries (e.g., Eichengreen, Rose, and Wyplosz, 1995; Leblang and Bernhard, 2000).

75




index, based on the work of Ferree and Singh (1999), codes legislative elections from one to seven. Countries that receive a score of five or greater are coded as being democratic.8 For this variable (and the others from DPI used below) annual observations were converted to a monthly frequency by the author. This entailed using sources such as POLITY III and POLITY IIId (Gurr, Jaggers, and Moore, 1990) which detail the dates of polity changes (the beginning of democracy) and sources such as the Europa Year Book (various years), Keesings Con- temporary Archive (various years), and the Political Handbook of the World (various years). These latter three sources were used (1) to identify the months of changes in the political variables utilized below, and (2) to update DPI through the end of 1998. Observations were excluded from the sample if they did not have democratic legislative institutions.

Second, since it only makes sense to speak about speculative attacks on pegged exchange rate regimes, countries that had a floating exchange rate were excluded from the sample. Rather than relying on the reported status of exchange rates that can be found in sources such as the International Monetary Fund's Annual Report on Exchange Arrangements and Exchange Restrictions (which does not report the months of exchange rate changes in a consistent form), a behavioral measure is employed. Following Kraay (1998), a country has a pegged exchange rate regime if the 12-month moving average of nominal exchange rate changes vis- a-vis the US dollar remains within a 2.5% band.9 This behavioral measure makes sense in that it captures countries that either have a formally stated pegged exchange rate regime and stick with it or have other types of stated exchange rate regimes but keep their currency relatively stable. In either case, it captures the fact that the government (monetary authority) is attempting to maintain a stable currency. These restrictions left a total sample of 17,547 monthly observations.10

Data In this section I describe the data and the way in which the dependent and independent variables are operationalized. Descriptive statistics are contained in Table 1.

Dependent Variable

Speculative attack. The measure of speculative attacks utilized in this paper follows directly from that proposed by Eichengreen, Rose, and Wyplosz (1995) and implemented by Kaminsky, Lizondo, and Reinhart (1997), Kaminsky and Reinhart (1996), Sachs, Tornell, and Velasco (1996), Corsetti, Pesenti, and Roubini

8 A score of five or greater indicates that multiple parties are legal and one or more parties won seats in the

legislature. A score of less than five indicates that either there is no executive or legislature, there is an unelected executive or legislature, or there is only one party.

9 The vast majority of countries in this sample are pegged either formally or informally to the US dollar; no countries during the period under investigation peg to the German or Japanese currency. An exception is the set of countries (1) that are members of the CFA franc zone and peg to the French franc or (2) that peg to a basket of currencies. I replicated the analyses presented below separating out those with franc or basket pegs and obtained almost identical results. I also assumed an equally weighted basket comprising the dollar, deutschmark, franc, and

yen; again, the results were almost indistinguishable. 10 The sample comprises the following countries: Algeria, Argentina, Bangladesh, Barbados, Belize, Benin,

Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde Islands, Central African Republic, Chile, Colombia, Comoros, Congo Republic, Costa Rica, Cote d'Ivoire, Djibouti, Dominican Republic, Ecuador, Egypt, El Salvador,

Equatorial Guinea, Ethiopia, Fiji, Gabon, Gambia, Ghana, Grenada, Guatemala, Guinea, Guinea Bissau, Guyana, Honduras, Hungary, India, Indonesia, Jamaica, Jordan, Kenya, Korea, Lebanon, Lesotho, Madagascar, Malawi,

Malaysia, Mali, Mauritania, Mauritius, Mexico, Mongolia, Morocco, Mozambique, Nepal, Nicaragua, Pakistan, Par-

aguay, Philippines, Senegal, Sierra Leone, Solomon Islands, South Africa, Sri Lanka, St. Lucia, Sudan, Tanzania, Thailand, Togo, Trinidad, Tunisia, Uganda, Uruguay, Venezuela, Yemen Arab Republic, Zambia, Zimbabwe.

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DAVID A. LEBLANG

TABLE 1. Descriptive Statistics

Variable Mean Std. Dev. Min Max

Speculative Attackt .0229099 .1496206 0 1

Speculative Attack(t_) .0173819 .1306933 0 1 Political Campaign Period .0600103 .2375126 0 1 Post Election Period .0454209 .2082313 0 1 Center Government .3883285 .4873838 0 1 Left Government .3656465 .4816247 0 1

Exchange Rate Overvaluation -.017775 .1061053 -3.878094 1.133631

Banking Crisis(t_ ) .0142165 .1671541 -6.910632 6.983481

Log(Reserves/Base Money)(,_ ) 19.3912 2.088439 8.326522 25.01653 Domestic Credit Growth -.0208316 5.000153 -655.6517 34.8862 Total Debt Service/GDP 15.57205 159.9631 .0121513 4427.391 Economic Openness 72.81729 38.08014 5.491547 258.1752

Foreign Interest Rate(t_l) 6.754702 2.611238 3 13.41667

Capital Openness(,_ ) .1335841 .3402146 0 1 Number of Prior Attacks 2.790961 2.998606 0 14

Contagion 3.999031 4.322852 0 28

Change in Real Interest Rate,t_ 0.1087 3.250698 -50.378 76.071

N = 17547 for all variables with the exception of Real Interest Rate where N = 13827.

(1998), and others. The motivation behind the index is that a government can respond to speculation against its exchange rate by (1) allowing the exchange rate to depreciate and/or (2) spending foreign currency reserves in international capital markets to buy up domestic currency. Exchange market pressure is measured as:

Asi t Ar, t

OA si ri

Here EMP is the index of exchange market pressure, s is the bilateral exchange rate of country i with the United States at time t, and r is the non-gold international reserves held by the central bank of country i at time t. Each compo- nent of the index is weighted by its respective standard deviation to prevent one variable from swamping the others.1 A high index indicates that there is pressure on a nation's currency. The rationale here is that an attack on a currency can be met by either a currency depreciation (an increase in s) or a loss in foreign exchange reserves (a decrease in r) by the central bank.12

Eichengreen, Rose, and Wyplosz (1995:278) argue that "speculative attacks are defined as periods when this index of speculative pressure reaches extreme values." I follow Kaminsky and Reinhart (1996) who identify the cut-off for a speculative attack as:

Speculative Attacki, t = 1 if EMPi,t > 2oEMPi + IEMPi

= 0 otherwise

] The discussion in footnote 9 regarding different anchor currencies applies as well to the calculation of this index as well. Calculating the index without the weights results in an exchange market pressure index that has a .96 correlation with the weighted one and almost no difference in the identification of speculative attacks.

12 Eichengreen, Rose, and Wyplosz (1995) also include changes in domestic interest rates in their index with the rationale that policymakers can fend off outward capital flows by raising the short-term interest rate. Interest rates are excluded here for two reasons. First, including interest rates would eliminate a large number of observations as a result of missing data for this variable. Second, in the final section of this paper I investigate the effect of changes in interest rates on the likelihood of speculative attacks; as such, I do not include the interest rate in the construction of the dependent variable.

77




Here, COEMP and / EMP are the country-specific mean and standard deviation of EMP, respectively. The cut-off of plus two standard deviations is selected so that extreme values of the exchange market pressure index should be identified as a

speculative attack.13 The empirical models below are unaffected if the cut-off for EMP is set at plus two or plus three standard deviations from the mean. There are 402 speculative attacks out of 17,547 observations (2.29%).

Independent Variables

Electoral period. Hypotheses 1, 2, and 4 state that speculative attacks are more

likely during the run-up to an election and the period after an election. The data for electoral periods were collected in two stages. First, using DPI it was deter- mined whether a country has a parliamentary or a presidential political system. Second, presidential and parliamentary election dates were gathered from DPI, Keesings Contemporary Archives, and the other sources listed above. An election is coded 1 by matching the date of an election with the appropriate system; that is, a country-month gets a 1 if there is a presidential election and it has a presidential system. Presidential elections in parliamentary systems are coded as zero.

One problem with coding the run-up to an election (or political campaign) is that it is difficult (1) to identify if an election has been called early or (2) to

identify the length of the electoral clock. As such, the political campaign is coded as the three months prior to an election and the election month itself. The post-election variable is coded as the three months following an election.'4

Partisanship. Partisan politics plays a role in hypotheses 3 and 4. The DPI includes variables identifying the political party of the prime minister and/or the

president, the three largest parties in the governing coalition and the largest party in the opposition (Beck et al., 1999). To collect this data, the DPI "asked whether the orientation of a party was immediately obvious from its name." Then, party orientation was cross-checked with a Web site maintained by Wil- fried Derksen and with information included in a number of publications.15 The DPI categorized parties by placing their preferences regarding state control of the economy on a standard left-right scale. Three variables, Left, Center, and Right, were then constructed. The variables Left and Center are used in the analysis below leaving Right as the left-out (comparison) category.

In addition to the political variables, a number of control variables are included in the speculative attack model as suggested by the theoretical and empirical literatures. Except where noted, all data are from the International Monetary Fund's International Financial Statistics CD-ROM (2000).

Real exchange rate (RER) overvaluation. Kaminsky, Lizondo, and Reinhart (1997) and Goldfajn and Valdes (1997) found overvaluation of the real exchange rate to be the most significant indicator of currency crises in the studies they surveyed. Observers of both the Mexican and Asian crises have argued that these attacks were the result of a rapidly appreciating domestic currency in real terms due to dramatic capital inflows (Sachs, Tornell, and Velasco, 1996; Radelet and Sachs, 1998). A currency overvaluation becomes unsustainable in the long run when it results in a loss of competitiveness and in large(r) current account imbalances. In

3 Selecting only extreme values of the EMP index as indicators of speculative behavior may reduce the number

of crises in the sample and may also decrease the correlation of crises with economic fundamentals. 14 The results presented below do not change and in some cases get stronger if the campaign and post periods

are extended to as many as six months. 15 Derksen's Web site is wwsv.agora.stm.it/elections/parties.htm. Other publications that were consulted included

East and Joseph (1993) and Szajkowski (1994).

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DAVID A. LEBLANG

the short term, an overvaluation renders a currency vulnerable to speculative attacks as economic agents attempt to profit by forcing the exchange rate back to its perceived value based on fundamentals.

Following Kaminsky, Lizondo, and Reinhart (1997) and Goldfajn and Valdes (1997) I measure overvaluation of the real exchange rate by using the residuals of a Hodrick-Prescott filtered real exchange rate series with lambda equal to 14400 for monthly data. The larger the residuals, the farther the real exchange rate is from its equilibrium value and the more likely is a currency crisis. The real exchange rate is calculated as the local currency per US dollar adjusted for wholesale prices in both the local country and in the United States.

Foreign interest rates. Currency crises are also more likely to occur when foreign interest rates are high. An increase in OECD interest rates, for example, has been identified as one of the key determinants of the debt crisis in 1982 as well as a prime reason why capital fled Mexico in late 1994 and early 1995 (Frankel and Rose, 1996). As interest rates in Germany, Japan, and the United States decline, capital flows into the developing world in search of a higher return. An increase in these interest rate triggers capital to flow out of developing and into the developed economies. Foreign interest rates are operationalized using the interest rate on short-term (90-day) deposits in the United States.16

Foreign exchange reserves. First-generation models suggest that currency crises are likely as central banks run short of international reserves. In fact, Krugman- type models argue that the quantity of reserves is the key variable when it comes to predicting the timing of a speculative attack on a fixed exchange rate regime. Using a variant of the Krugman-Flood-Garber model, Blanco and Garber (1986) estimate the probability that the Mexican peso would be devalued each quarter during the 1973-82 period. Cumby and van Wijnbergen (1989) use a similar model to explain attacks on Argentina's crawling peg in the early 1980s. These scholars, among others, find that the probability of devaluation in each country was closely linked to that country's holdings of international reserves. Thus, a ratio of foreign exchange reserves to the monetary base is included. The expec- tation is that the higher this ratio, the less likely are speculative attacks. To avoid problems with simultaneity, this variable is lagged by one month.

Banking sector crisis. Recent models of currency crises have focused on the twin crises: banking crises and currency crises (e.g., Demirguc-Kunt and Detragiache, 1997). Sachs, Tornell, and Velasco (1996), for example, argue that a rapid increase in commercial bank lending to the private sector indicates a greater risk of reversals of investor confidence. The quality of bank loans is likely to deteriorate significantly-and many are likely to become non-performing-when bank lending rises rapidly in a short period of time. Large lending means that banks are less able to effectively screen borrowers. This problem is exacerbated in the developing world where the ability and number of regulators is limited. The increase in bank lending is measured as the growth in claims on the private sector and is lagged by one period. As private sector claims increase, so does the likelihood of a banking and a currency crisis.

Domestic monetary policy. A variable measuring government monetary policy is also used as a control. Calvo (1995) and Sachs, Tornell, and Velasco (1996) include measures of domestic credit growth in their currency crisis models. The growth in domestic credit is straightforward: it indicates an increase in the

16 I also tried the model using Germany's interest rate or an average of the US, German, andJapanese interest rates. The substantive results did not change.

79




domestic money supply. As the money supply increases, there is more domestic currency in circulation that can be converted into foreign assets in the event that currency holders anticipate a devaluation. The higher the rate of domestic credit growth, the more likely it is that an (self-fulfilling) attack can be successful.

External debt and international openness. Recently, IMF policymakers and aca- demics have developed a "third generation" of speculative attack models. The motivation for this renewed effort is the fact that the "usual suspects" leading to a currency crisis were not evident in the East Asian crises over the 1996-97 period. These scholars focus on the moral hazard faced by international lending institutions, the composition of external debt, and capital controls (e.g., Frankel and Rose, 1996; Corsetti, Pesenti, and Roubini, 1998; Dooley, 1998; Krugman, 1998).

Three variables are included to take into account these international factors. First, a dummy variable indicating whether or not the capital account is open is included. All things being equal, the existence of capital controls should make it more difficult for domestic currency to leave, enable the domestic government to maintain a domestic interest rate that is different from the rest of the world, and avoid currency speculation (Leblang, 1997). The data are from the Inter- national Monetary Fund's Annual Report on Exchange Controls and Exchange Restric- tions (various years). Because this variable is reported annually, the lagged value is used.

International debt is measured by a country's debt service ratio. This variable measures the sum of principal and interest repayments in foreign currency paid on long-term debt, short- term debt, and repayments to the IMF. The amount is taken as a percentage of the total amount of exports. Debt service was selected rather than total long- or short-term debt (or other variables) because it is available for more countries and time periods than are other debt variables and because the correlation between debt service and external debt is above .80. A variable for international openness measured as imports plus exports as a percentage of gross domestic product is also included. Prior literature has argued that countries with higher levels of international debt (debt service) or international openness are more vulnerable to the whims of international capital. Thus, it is expected that countries with higher levels of these variables will be more likely to experience speculative attacks. Data for these two variables come from the World Bank's World Development Report on CD-ROM (2000).17

Contagion. There is a large literature that views currency crises as contagious events (e.g., Eichengreen, Rose, and Wyplosz, 1997). This argument suggests that as the number of countries experiencing a crisis at time t increases, the higher the probability that country i will also experience a crisis. A contagion variable is created by totaling the number of speculative attacks that occurred in all countries but country i in month t.

Other controls. I include two variables that indicate vulnerability of the country to prior crises. The first is a lagged endogenous variable to capture the fact that some speculative attacks may last longer than one month. It is anticipated that

17 It is important to note that these two variables are only available on an annual basis. The annual data was

interpolated using cubic spline routines to construct the monthly series. To check that this process was not causing the reported results, two alternative specifications were used. First, the variables were lagged by twelve periods. Second, the variables were held constant over the course of the year. The motivation for both of these alternatives is that currency traders have at least mid-term estimates about what the level of debt service and openness will be in the next six to twelve months. In neither specification were the results substantively different from those

reported below.

80



DAVID A. LEBLANG

the occurrence of a speculative attack at time t - 1 will make a speculative attack at time t more likely. A variable that counts the number of prior crises the country has experienced since January 1975 is also included to capture the country's overall vulnerability to speculative attacks.

Methodology

Since a speculative attack is defined as a dichotomous event (an attack occurs or it does not) it is appropriate to use a limited dependent variable technique such as logit or probit. Coefficients and predicted probabilities from standard logit or probit approaches, however, are biased when the observed outcome occurs only rarely in the data (King and Zeng, 2000). By construction a speculative attack cannot occur more than 2.5% of the time in the sample; thus logit or probit would be inappropriate. Fortunately, King and Zeng (2000) have derived a logit estimator for rare event data and Tomz, King, and Zeng (1999) have written suitable software to implement it. It is their rare events logit (relogit) procedure that is used here.

The second problem confronted by the present research design is that the sample comprises a pool of 78 cross-sections and up to as many as 278 time periods (if a country had a pegged exchange rate from January 1975 to Decem- ber 1998). The pooled nature of the sample necessitates the use of a statistical model to account for autocorrelated and heteroscedastic disturbances. Beck, Katz, and Tucker (1997) have developed an approach that begins with the assumption that binary panel data are grouped duration data. As such, problems such as serially correlated errors can be resolved by including a set of temporal dummy variables that take into account the length of time since the country's last "failure." In the present context, "time since prior failure" means the elapsed time since a country last experienced a speculative attack. These dummy variables can be interpreted as indicating whether the length of time since the last speculative attack makes a country more or likely to be at risk to experience an attack at time t. When there are a large number of time periods, Beck, Katz, and Tucker (1997) advocate the use of a set of cubic splines.18 In the models presented below a set of five splines was included. Heteroscedastic disturbances, or unequal variation across countries, are dealt with through the use of Huber/White robust standard errors.'9

Empirical Results

Table 2 contains the results using the rare-event logit model to test the hypotheses from the first section using the data and sample described in the second section. Standard parameter estimates and associated standard errors are not reported in Table 2; they are, however, contained in the Appendix. Rather, cell entries in Table 2 are the estimated percentage change in the probability of a speculative attack for a one standard deviation change in a continuous independent variable and for a one unit change for dichotomous variables holding all other variables at their respective means. Asterisks above these first differences

18 It is not obvious that the use of cubic splines is consistent with the rare events estimator of King and Zeng. King and Zeng's (2000) derivation of the rare events logit model appears to focus on corrections for the constant and thus has an effect on predicted probabilities. Including a set of temporal dummy variables or linear splines should not, in principle, introduce bias into the estimation of this constant. As a check I estimated the rare events logit model both with and without the splines and did not obtain results significantly different from those reported. 19 All statistical models were estimated using the relogit program written by Tomz, King, and Zeng (1999) and the btscs command written by Tucker (1999) and were implemented using STATA Version 6.0.

81



82 The Political Economy of Speculative Attacks

TABLE 2. Rare-Event LOGIT Estimates

Baseline Electoral Electoral Real Interest Model Model /Partisan Rate Model

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

*Speculative Attack(t_ ) 5.36* 5.61* 5.60* 8.76* Exchange Rate Overvaluation 2.30* 2.34* 2.33* 1.85* Banking Crisis(t l) 0.12* 0.17* 0.17* 0.16*

Log(Reserves/Base Money)(t_i) -0.56* -0.44* -0.36* -0.42* Domestic Credit Growth 1.44* 1.42* 1.41* 1.56* Total Debt Service/GDP -0.14 -0.09 -0.09 -0.04 Economic Openness -0.27 -0.38 -0.35* 0.40

Foreign Interest Rate(t_j) 0.27* 0.26* 0.24* 0.34*

'Capital Openness(t ) -0.05 0.07 0.05 0.27

Contagion 1.16* 1 1.171.17* 1.31* Number of Prior Attacks 0.32* 0.31* 0.25* 0.20*

"Political Campaign Period -0.03 -0.03 -0.09 4Post Election Period 1.13* 1.15* 0.90* 'Left Government 0.50* 0.52* *Center Government 0.40* 0.48* Real Interest Ratet_i -0.16*

Observations 17547 17547 17547 13827

Cell entries are the estimated percentage change in the probability of a speculative attack for a one standard deviation change in continuous independent variables and for a one unit (0 to 1) change in dichotomous independent variables holding all other variables at their means. Estimated cell values obtained via King and Zeng's (1999) rare events logit estimator. The models were estimated with a set of splines which are not reported. Obtained parameter estimates and standard errors are contained in the Appendix. *Indicates that the independent variable is dichotomous. *Indicates that the first difference is significantly different from zero at the 90% confidence level; two-tailed test.

indicate the statistical significance of the estimate using a 90% confidence interval and a two-tailed test.20

Column 1 of Table 2 is the "baseline" model of speculative attacks and includes the economic variables found to be theoretically and empirically important pre- dictors. The estimated results are generally in line with prior findings. The variables identified by first- and second-generation models are statistically significant and in the expected direction. Increasing an already overvalued real exchange rate, providing larger amounts of funding to the private sector (the indicator of

banking crises), expanding domestic credit, and higher foreign interest rates all tend to make the probability of a speculative attack more likely. It is also the case that a country that experiences a speculative attack at time t - 1 will be over 5% more likely to experience an attack at time t. This prior vulnerability is also

captured by the variable that measures the number of prior attacks: as the number of prior attacks increases, so does the probability that a country will be attacked during the present month. The argument that currency crises are con-

tagious is supported by the evidence from this 78-country sample: as the number of other countries experiencing speculative attacks increases from one to five, the probability that another will be attacked increases by a bit over 1%. Finally, and as anticipated by first-generation models (e.g., Krugman, 1979), a larger holding of foreign exchange reserves makes a speculative attack less likely. Note that this result is not entirely due to the fact that reserves are included in the measure of exchange market pressure that is the basis for the dichotomous

20 Standard errors and parameter estimates are reported in the Appendix so that readers can use the confidence interval of their choice.



DAVID A. LEBLANG

indicator of speculative attacks. The EMP measure uses the change in reserves while the foreign exchange reserves variable is a level. In addition, the bivariate correlation between the dependent variable and foreign exchange reserves is -0.0366 (p-value = 0.0000). Finally, the variables capturing various "new crisis models" (debt service, economic openness, and capital controls) are not statisti-

cally significant. Results from testing hypotheses relating the political variables to speculative

attacks are reported in columns 2 through 4 in Table 2. Hypotheses 1 and 2 focus on the relationship between electoral periods and speculative attacks. While a speculative attack is marginally lower during the campaign period (the election month and the three prior months) as compared to non-electoral periods, this effect is not statistically significant.21 However, the probability of a speculative attack increases by over 1% during the three-month period after an election as

compared with the rest of the year. This is as expected by hypothesis 2 and is consistent with the findings of Leblang and Bernhard (2000) for OECD countries and of Frieden, Ghezzi, and Stein (1999) and Klein and Marion (1997) for

developing countries. To get a clearer picture of the relationship between electoral periods and

speculative attacks I plot the unconditional frequencies of attacks surrounding elections in the top panel of Figure 2. This figure shows that the number of

speculative attacks decreases from two months prior to an election to the election month itself and then steadily increases. This is consistent with the argument that speculators may be deterred from launching an attack because they know it is during this period that policymakers are most likely to mount a defense. Figure 2 is also consistent with the results in Table 2 indicating a higher conditional probability of an attack during the post-election period. The bottom

panel of Figure 2 displays the raw (and unweighted) components of the exchange market pressure index: average changes in reserves and in exchange rates. This

figure highlights two phenomena: first, speculative attacks are not driven strictly by changes in either reserves or exchange rates (recall that an attack occurs when the exchange rate bars are positive-a depreciation-and reserves are negative). In addition, policymakers respond to attacks using a variety of policy measures that may not be captured by raw exchange rate and reserve changes. Second, the figure shows that the exchange rate depreciates in all the periods surrounding an election but that this depreciation increases after an election, providing support for the argument that governments put off the costs of monetary adjustment until after an election.

In column 3 of Table 2 are the variables capturing the ideological composition of the governing coalition. The Database of Political Indicators codes ideology in terms of Left, Center, and Right. I include Left and Center and use Right as the

comparison category. Note that both Left and Center governments are (statistically) significantly more likely to experience speculative attacks than governments of the Right. However, as evidenced in the Appendix, there is no discernible statistical difference between Left and Center governments. This finding lends support to hypothesis 3.

A conditional relationship between partisanship and speculative attacks is pos- ited in hypothesis 4. It is argued that speculative markets view partisanship differently according to the electoral period. As such, I re-estimated the equation in column 3 and included interactions between partisanship and the electoral period. That full model is not reported due to space (estimated parameters on the baseline variables remained unchanged), but the full set of first differ-

21 In other specifications I disaggregated this period and included separate dummy variables for the election month and the three months prior. The results were neither substantively nor statistically different from those reported in Table 2.

83




[] Speculative Attack 12-

-3 -2 -1 0 1 2 3 SPECULATIVE ATTACKS SURROUNDING ELECTIONS

E[] Change in Reserves E Change in Exchange Rate

58 -.... .. ...... ?.. ... ..~~~~~~~~~ ~ ~~ ~ ~~ ~ ~~~ ~ ~~~~~~~~........

iiii;iii!;ii!ii :iiiiiiii8 !!::ii : : : :::

::::8.:::: - : : ?:iiE '- '.

-

'.'. - '; .' '-:. ,

,

,.......... . . . . . . . . .

? ......-.... .- . . . . . . . . . ..

............ .... ........... . . . . . . . . .

~~~~~~~~~~~~~.:...:.:.:.:.:. . .... .... ..

..........., . . . . . . ................. ........... ..... . ... .... ... ..............

~~~~~~~~~~.-.-.-.'.- .......... ..... :!i!!!ii!!!- -....'.. ' ' '

.- -... --........

~s~:~~:~-~~:~,, ,.:,,,,,:..-: ,.,,::i ,============== :::::: ::::::: ii~?:iif-:: ::-:.:.: ~i:iiii: .-: ::::: .:+:.:.:+:.:.:.: ....... ..........-.......... ... . . . - . .....

-...--.,. . . . . . _ . , ........ .........,.. ........... ..........~~~~~~~~~~~.......... ~...,.,.-........... :-.:: ::: ..-....::::::::.-.-....,-.

. ..... . .... .... .. . . . . . . . . . ..

.0192r

-.025026 - -3 -.025026 -3 -2 -1 0 1 2 3 RESERVES AND EXCHANGE RATE SURROUNDING ELECTIONS

FIG. 2. Speculative Activity Surrounding Elections

ences and confidence intervals are contained in Table 3. The rows in Table 3

represent the electoral calendar broken down into three periods: the political campaign period (the election month and three prior months), the post election

period (the three months after an election), and the rest of the year (all months when campaign and post election periods are zero). Columns in Table 3 are

organized according to the partisan orientation of the party in power. The partisan arguments advanced in hypothesis 3 can be evaluated using the

information in the bottom row of Table 3. This row has predicted probabilities of

i.

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DAVID A. LEBLANG

TABLE 3. Interaction Between Electoral Periods and Partisanship

Partisan Orientation of Party in Power

Left Center Right

Political Campaign 1.18 1.75 3.00 (election and prior 3 months) (0.49, 2.30) (0.80, 3.09) (1.53, 5.00)

Post Election Period 4.16 2.65 1.63 (3 months after election month) (2.69, 6.00) (1.45, 4.08) (0.58, 3.64)

Rest of the Year 1.80 1.70 1.30

(non-campaign or post periods) (1.47, 2.20) (1.41, 2.00) (1.01, 1.60)

Cell entries represent the probability of a speculative attack, holding all other variables at their means, for the specific combination of partisan and electoral variables. The results are based on the model in Table 2, column 3 with interactions between electoral period and partisanship (campaign, post election, left, center) and are generated using the relogit and relogitq programs written by Tomz, King, and Zeng (1999). Cell entries are probability estimates with 90% confidence intervals in parentheses.

speculative attacks during all non-electoral periods broken down according to

partisanship. All things being equal, during non-electoral periods there is a

statistically significant difference in the probability of a speculative attack between Left and Right governments (although there is no difference between Left and Center or Right and Center governments). Left governments are one half of one

percent more likely to experience speculative attacks than are Right governments. We know from hypothesis 2 that speculative attacks are more likely in the

period after an election. That result is most likely driven by Left governments. When Left governments are in power, moving from the pre-election political campaign to the post election period increases the probability of a speculative attack by a statistically and substantively significant 3%.

Hypothesis 4 suggests that markets react to partisanship differently as the

political calendar changes. Leblang and Bernhard (2000) find that speculative attacks are more likely in the run-up to an election when a Right government is in power than when the incumbent is from the Left. That finding for OECD economies also holds for countries of the developing world. The top row of Table 3 indicates that the probability of a speculative attack during the pre- election political campaign period is almost 2% higher for Right than for Left

governments, holding all other variables at their means. In fact, during this

period, Left governments have the lowest probability of a speculative attack as

compared with all other periods. These results suggest that democratic politics and partisanship do have an

effect on speculative attacks. Knowing that, is it possible that politicians can

implement policies in order to diminish the probability that an attack will occur? This is especially important given the policy response of the International Mon-

etary Fund to the financial crises that occurred in Asia. Stanley Fischer, the IMF's First Deputy Managing Director, stated that "[t]he first order of business was, and still is, to restore confidence in the currency. To achieve this, countries have to make it more attractive to hold domestic currency, which, in turn, requires increasing interest rates temporarily.... Once confidence is restored, interest rates can return to more normal levels" (Fischer, 2001).

Recent empirical (e.g., Kraay, 1998) and theoretical models (e.g., Drazen, 1999; Lahiri and Veigh, 2000) examine the relationship between speculative attacks and interest rates. Kraay finds no empirical relationship between the level of interest rates and the outcome of a speculative attack. Drazen and Lahiri and Veigh, on the other hand, present models where changes in interest rates

85




induce speculators to hold domestic currency, rather than converting to assets denominated in foreign currency. In Drazen's (1999) model, the largest volume of speculative activity results from domestic speculation; he argues that increas-

ing interest rates makes the cost of borrowing to engage in speculation more

costly. In order to examine the effect of interest rates on the likelihood of a specu-

lative attack, I construct a measure of the real interest rate.22 Admittedly interest rate data for developing and emerging economies are error prone. With that caveat in mind, I calculated the change in the real interest rate and lagged it one

period in order to mitigate potential problems of endogeniety. The findings in column 4 of Table 2 provide statistical support for the argu-

ment that increasing real interest rates decreases the probability of a speculative attack. However, an examination of the magnitude of the first difference indicates that statistical significance does not necessarily entail substantive significance: increasing the real interest rate by one standard deviation decreases the

probability of a speculative attack by only two-tenths of one percent. Even a

strategy such as that implemented by Sweden during the 1992 ERM crisis (raising interest rates by 500%) would decrease the probability of a speculative attack by approximately 7.5%. Whether policymakers are willing to adopt this strategy is

certainly an open question. I also examined the interaction between interest rate policy and the partisan

nature of the incumbent. To do this I interacted the change in the real interest rate with the dummy variable for Left and Center governments and included all of these variables in the model in column 4 of Table 2. I do not report the

parameter estimates but show the conditional relationship between partisanship and interest rate policy in Table 4. Like Table 3, the cell entries are probabilities of speculative attacks for a given combination of partisanship and interest rate

policy,23 holding all other variables at their means. Two patterns are immediately obvious. First, for both Left and Center governments, increasing the rate of

change in the real interest rate decreases the probability of a speculative attack, while for Right governments the opposite occurs. Second, interest rate policy as carried out by Left and Right governments is only statistically different at extreme

high and low levels of interest rate changes. Finally, I examined the robustness of the empirical results in a number of

ways. First, I asked whether there was an interaction between interest rates, elections, and partisanship. In no case did I find a statistically significant interaction between electoral periods and interest rates, partisanship and interest rates or capital controls, electoral periods and interest rates. This suggests that markets respond differently to interest rate policy than they do to political information.

Second, I altered the estimation strategy. Given the current debate by scholars

investigating the democratic peace, I estimated the models in Table 2 using conditional (fixed-effects) logit. The inclusion of fixed country effects led to the

22 This variable was constructed as follows. Due to the lack of consistent definitions by local monetary author-

ities and large holes in some data series, and because I wanted to avoid stringing together different series for the

same country, I used the interest rate series that had the least number of missing values. My order of preference was to use series for the central bank's discount rate (IFS line 60), the money market interest rate (IFS line 60b), the

treasury bill rate (IFS line 60c), and the interest rate on deposits (IFS line 601). Again, I used the series that had the

most non-missing observations. I then took this series and deflated it by the lagged annual rate of inflation (IFS line

64x) to take into account that the real interest rate reflects inflation expectations; the best indicator of these

expectations is lagged actual inflation experience. 23 The values for the interest rate variable were obtained from the data. Ordering the interest rate data from

lowest to highest, -8% is the first percentile, -0.73% is the twenty-fifth percentile, 0.17% is the fiftieth percentile, 0.90% is the seventy-fifth percentile, and 8% is the ninety-ninth percentile.

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DAVID A. LEBLANG

TABLE 4. Interaction Between Interest Rates and Partisanship

Partisan Orientation of Party in Power

Change in Real Interest Rate(t- 1) Left Center Right

-8.00% 3.6 2.2 0.9 (2.4, 5.2) (1.5, 3.0) (0.4, 2.0)

-0.73% 2.0 1.9 1.4 (1.6, 2.4) (1.6, 2.3) (0.9, 2.7)

0.17% 1.9 1.9 1.4 (1.6, 2.2) (1.6, 2.4) (1.0, 1.7)

0.90% 1.7 1.9 1.5 (1.5, 2.1) (1.6, 2.4) (1.1, 1.9)

8.00% 1.0 1.7 2.3 (0.7, 1.5) (1.2, 2.3) (1.0, 4.2)

Cell entries represent the probability of a speculative attack, holding all other variables at their means, for the specific combination of partisan and interest rate variables. The results are based on the model in Table 2, column 4 with interactions between interest rates and partisanship and are generated using the relogit and relogitq programs written by Tomz, King, and Zeng (1999). Cell entries are probability estimates with 90% confidence intervals in parentheses.

loss of sixteen countries because these countries never experienced a speculative attack. That said, the substantive and statistical results from using conditional logit were, surprisingly, not much different from those reported in Table 2.

Finally, I checked to see if the electoral variables were endogenous to the occurrence of speculative attacks. I generated a variable measuring the number of months since the last election under the assumption that this variable should have a positive effect on the probability of an election in the present month. To this variable I added variables indicating whether a speculative attack occurred at time t or at time t - 1. If either (or both) of these variables is statistically significant then I conclude that elections may be caused by speculative attacks and are thus not exogenous. In columns 1 and 2 of Table 5 I present the results of this model using rare-events logit and conditional logit. In neither case are the attack variables statistically significant. In columns 3 and 4 I perform a test to see if elections are (at least weakly) exogenous. The procedure is as follows: (1) estimate a speculative attack model as in column 1 of Table 2; (2) calculate residuals; (3) include residuals in the election equation. These residuals are not statistically significant using either rare-events or conditional logit. I conclude that elections are at the very least weakly exogenous when it comes to understanding speculative attacks.

Conclusion

To what extent is the domestic political capacity of politicians constrained by international capital markets? This question has been the focus of a large and growing body of scholarship, some of which argues that financial integration holds governments hostage (e.g., Haggard and Maxfield, 1996:36) while other work suggests that capital mobility only tends to limit politicians' room to maneu- ver (Garrett, 1998).

The argument advanced in this paper is that markets do respond and react to political events and political information. Aside from economic fundamentals, markets take into account the timing of elections and the partisanship orienta-

87




TABLE 5. Endogeniety of Electoral Variables

TEST ONE TEST TWO

RELOGIT XTLOGIT RELOGIT XTLOGIT

Constant -4.616* -4.514* (0.079) (0.114)

Time Since Last Election 0.009* 0.028* 0.009* 0.028* (0.0008) (0.002) (0.0008) (0.002)

Speculative Attack, -0.358 -0.357 (0.507) (0.512)

Speculative Attack_ 1 0.199 0.170 (0.448) (0.468)

Residuals' -4.362 -4.89 (3.682) (3.029)

*Indicates that the coefficient is statistically significant at the 90% confidence level; two-tailed test. Test One regresses an election on the number of months since the last election and variables for speculative attacks at time t and at time t - 1. Test Two regresses an election on the number of months since the last election and the probability of a speculative attack at time t. "The residuals are residuals from rare-events logit model contained in column 1 of Table 2. RELOGIT is the rare-events logit model of King and Zeng (2000). XTLOGIT is conditional fixed effects logit and include a set of 77 country dummy variables (not reported).

tion of the government. Markets also respond to interest rate policy but the effect is not as large as one might expect. This does not answer the fundamental

questions about globalization, but it does indicate that markets respond to politics. On another level the results do answer a question asked at the beginning of this article regarding the transferability of models. Models such as that devel-

oped by Leblang and Bernhard (2000) for OECD economies produce similar results when applied to developing economies. What is now needed is to incor-

porate these broad cross-national findings into the detailed analysis provided by case studies (e.g., Haggard, 2000; MacIntyre, 2001) so that the dynamics of international financial integration can be more fully understood.

The findings in this paper pose interesting questions for future research. First, if Calvo and Mendoza (2000a, 2000b) are correct, then globalization reduces the incentives for international economic actors to gather information about emerging markets. In the event that this results in lower investment and/or trade between industrial and emerging markets then the welfare consequences need to be addressed. Further, it raises questions regarding the types of economic and

political institutions that provide cheap and transparent information to economic actors. Answers to these questions have implications for the design of both monetary and political institutions.

Second, the empirical results regarding speculative behavior surrounding electoral periods suggest that policymakers may have incentives to defend the exchange rate in spite of the fact that waging an exchange rate defense may be costly. A

mapping of the political incentives facing policymakers in open economies would be helpful in understanding the decision to defend a currency (Leblang, 2001). This is particularly timely given that members of the academic (e.g., Eichengreen, 2001) and policy (Fischer, 2001) communities have become more

skeptical about the continued viability of intermediate exchange rate arrangements (arrangements that fall between dollarization and a free float) in an era of

global capital.

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DAVID A. LEBLANG

Appendix Baseline Electoral Electoral/ Interest

Variable Model Model Partisan Rate

Constant -2.74 -2.77 -3.19 -3.35 (0.63) (0.62) (0.65) (0.77)

*Speculative Attack(t_ ) 1.53 1.53 1.54 1.89

(0.19) (0.19) (0.19) (0.20)

Exchange Rate Overvaluation 6.36 6.31 6.29 4.87

(1.10) (1.09) (1.09) (1.58) Banking Crisis(t_ 0.31 0.32 0.31 0.286

(0.09) (0.09) (0.09) (0.08) Log(Reserves/Base Money)(t_ ) -0.07 -0.06 -0.06 -0.06

(0.03) (0.03) (0.03) (0.33) Domestic Credit Growth 0.08 0.08 0.09 0.087

(0.03) (0.03) (0.03) (0.027) Total Debt Service/GDP -0.00 -0.00 -0.00 -0.00

(0.01) (0.01) (0.01) (0.01) Economic Openness -0.01 -0.01 -0.01 -0.003

(0.01) (0.01) (0.01) (0.002) Foreign Interest Rate(,-_) 0.04 0.04 0.04 0.051

(0.02) (0.02) (0.02) (0.024) *Capital Openness(,_ ) 0.03 0.03 0.02 0.134

(0.17) (0.17) (0.17) (0.190) Contagion 0.09 0.09 0.09 0.090

(0.01) (0.01) (0.01) (0.008) Number of Prior Attacks 0.04 0.04 0.03 0.02

(0.02) (0.02) (0.01) (0.02)

*Political Campaign Period -0.05 -0.05 -0.08 (0.25) (0.25) (0.27)

*Post Election Period 0.53 0.53 0.41 (0.20) (0.20) (0.23)

*Left Government 0.32 0.33 (0.16) (0.18)

*Center Government 0.27 0.31 (0.16) (0.18)

Change in Real Interest Ratet_, -0.03 (0.02)

Observations 17547 17547 17547 13827

Cell entries are the estimated rare-events logit estimate with robust standard errors in parentheses. Estimated cell values obtained via King and Zeng's (2000) rare-events logit estimator and include a set of spline which are not

reported. OIndicates that the independent variable is dichotomous.

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The Political Economy of Speculative Attacks in the Developing World