Exchange Rate Regimes and Debt Maturity Structure*

International Monetary Fund
Published Date:
September 2005
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Do exchange rate regimes matter for macroeconomic performance? This is an old question of major importance for policy-makers which explains the persistent scientific debate on the topic. The recent empirical findings of Husain et al. (2004) suggest that exchange-rate regimes imply quite heterogeneous outcomes in countries with different economic development. In order to understand better this heterogeneity, this paper focuses on a specific interaction between exchange-rate regimes and economic performance which has been relatively unexplored so far. We investigate whether and how the exchange-rate regime affects the maturity mix of private debt in emerging markets that only have access to credit denominated in foreign currency.

The importance of credit in foreign currency in emerging markets can hardly be overemphasized. For example, Levy-Yeyati (2003) documents that more than 70% of bonded debt in emerging markets is denominated in foreign currency. At the same time many emerging markets have substantial maturity mismatches which are frequently identified as one major culprit of the Asian financial crisis of the 1990s (see, for instance, Chang and Velasco, 2000, Corsetti, Pesenti and Roubini, 1999, or Rodrik and Velasco, 1999).1 Yet, while many papers have explained how imbalances in the asset and liability structure of emerging markets can cause currency and financial crises, the factors that trigger such imbalances in the first place have received relatively little attention so far. In particular, few papers have considered the possibility that the exchange-rate regime influences the debt structure. Instead of the more prevalent view that maturity mismatches lead to volatile exchange-rates, we stress that highly flexible and volatile exchange rates may shift the debt profile towards short-term maturities, thereby increasing the vulnerability of emerging markets to liquidity crunches.

First, we provide a model which links exchange-rate regimes and maturity mismatch in open emerging market economies. We show theoretically how currency mismatch may lead to and exacerbate maturity mismatch in economies with flexible exchange rates resulting in higher output volatility. Compared with much of the literature on the subject, our model abstracts from asymmetric information and moral hazard (see, e.g., Diamond, 1991, Jeanne, 2000, and Tiróle, 2003), and focuses instead on the role of market incompleteness. Although we recognize that asymmetric information and moral hazard may be important, we show that such model ingredients are not necessary to explain the joint phenomena of currency depreciation and asset liquidation accompanied by high short-term debt ratios. Thus, the removal of market failures may not suffice to tilt the debt profile towards safer, long-term debt. Instead our model assigns a crucial role to the development of financial markets or instruments that allow agents to insure better against risk.

Second, we provide empirical results that support the predictions of the model for a set of 28 open emerging market economies. We add to the literature on exchangerate regimes and macroeconomic performance (see, for example, Husain et al., 2004, and their references) by analyzing the influence of exchange-rate regimes on the debt structure in some detail.2 In our analysis we take into account that the choice of the exchange-rate regime or the degree of intervention in the currency market is influenced by macroeconomic factors (the fear of floating discussed in Calvo and Reinhart, 2002). We use annual data from the World Economic Outlook (WEO) for macro-economic variables and the Bank of International Settlements (BIS) for debt variables. Our main finding is that countries with more flexible exchange rates hold a higher share of debt with short maturity. This maturity mismatch of foreign debt is associated with more volatile output.

The remainder of the paper is structured as follows. Section 2 presentw a simple model to show how exchange-rate regimes can change the debt-maturity structure and output volatility. We empirically test the main predictions of the model more formally in Section 3. Finally, we discuss policy implications and conclusions in Section 4.

A Simple Model

In this section we illustrate how flexible exchange rates might shift the debt structure towards short-term maturities.3 More specifically, we model how the exchange-rate regime influences solvency and the choice of debt maturity (the model draws on work in Bussière, Fratzscher and Koeniger, 2004). Forward-looking and impatient riskneutral agents choose whether to consume or to invest, financing their investment with short- or long-term foreign debt. We assume that debt (i) can only be obtained in the international capital market, (ii) is denominated in foreign currency and (iii) is constrained by solvency, which requires that agents can always repay.

Agents face a simple trade-off in their choice of debt maturity. Since flexible and volatile exchange-rates tighten solvency constraints relatively more for long-term debt, borrowers have an incentive to raise the share of short-term debt. However, shortterm debt is risky and the investment project can be liquidated before the investment return materializes so that agents have smaller collateral if they borrow short term As a consequence, a larger share of short-term debt raises the share of investment projects at risk. In our model, liquidation of the collateral, and a larger fraction of short-term debt are the result of optimal choices of individual agents.

We now present the structure of the model in more detail. Consider an economy with 3 periods. Impatient risk-neutral agents with endowment K either can invest K into a project or immediately consume it. If agents invest they can decide whether to finance consumption with short or long-term debt. Agents with short-term debt need to rollover their debt in the second period. The decision to invest yields an immediate flow fK, 1 > f > 0. Instead, the project income Y only becomes available in period 3.

All debt is denominated in foreign currency. This is realistic for emerging market economies in which most of the debt is in foreign currency (see Levy-Yeyati, 2003, or Corsetti, Pesenti and Roubini, 1999). The different currency denomination of investment returns and debt liabilities implies that exchange-rate flexibility influences the borrowing choices of investing agents. If financial markets are incomplete, borrowers will have to bear at least some of the exchange-rate risk. We assume as benchmark case that all risk is in fact borne by borrowers: markets are incomplete in that borrowers only have access to a risk-free asset with interest rate r. This implies that investing agents face constraints that ensure repayment of their debt in all states of the world. These constraints differ with respect to maturity and crucially depend on the pledgeable income and the maximum depreciation of the exchange rate. We now characterize these constraints for flexible and fixed exchange-rate regimes.

Solvency Constraints

Flexible Exchange Rates

Given the endowment K and project income Y, agents that invest and borrow short term cannot borrow more than K/(RX2) in the first period where R is the interest factor, X2 denotes the maximum possible depreciation of the exchange rate until period 2 and the exchange-rate in the first period is normalized to 1.

If the agent invests and borrows long-term instead he cannot borrow more than (K+Y)/(R2X3) in the first period where X3 denotes the maximum possible depreciation of the exchange rate until period 3. Note that access to credit becomes relatively tighter for long-maturity debt, the larger R and the larger X3 relative to X2. Since long-term debt needs to be repaid in period 3 the pledgeable income K+Y is worth less in present-value terms. This is especially so, if the maximum possible depreciation of the exchange rate increases over time. Instead, long-term debt increases the pledgeable income from K to K+Y, since the agent earns the project returns with certainty. Thus, more credit is available short-term if the difference in pledgeable income Y is relatively small compared with the additional possible depreciation of the exchange rate X3/X2. Note that if exchange rates are characterized by a stochastic process with increasing variance for longer time horizons, X3/X2 >1.

Fixed Exchange Rates

As a stylized comparison let us consider exchange rates that are fixed with certainty. In this case the pledgeable income is independent of debt maturity and agents cannot borrow more than (K + Y)/R2 in the first period where the exchange-rate in the first period is normalized to 1. Note that agents can always borrow more than K/R in the first period since arbitrage implies that the return to the project 1+y, where yY/K, needs to be larger than R2. That is, for agents to find it optimal to invest, projects have to yield a return at least as high as the risk-free asset over two periods. More importantly, more credit is available if exchange rates are fixed rather than flexible.

Optimal Maturity

Assuming that the debt roll-over in the second period implies an infinitesimally small transaction cost , all debt has long maturity if exchange rates are fixed with certainty. Instead, flexible exchange rates imply a non-degenerate choice of debt maturity. We analyze this choice focusing in particular on the role of different degrees of exchangerate volatility captured by the parameter X. This corresponds to investigating the effect of different exchange-rate regimes on the debt-maturity structure in the empirical section.

We assume that agents are impatient: the discount factor β < R-1. This together with risk neutrality implies that agents prefer to bring forward consumption as much as possible to the present. Thus, the solvency constraints determine the consumption profile of investing agents and also the optimal maturity choice.

We assume that in each period the exchange rate depreciates by factor X >1 with probability p and remains constant with probability 1-p. That is the worst possible exchange rate from the perspective of the borrower is X in period 2 and X2 in period 3. For simplicity we assume that the agent always needs to liquidate the project if the exchange rate depreciates and the agent borrows short-term4 This implies the following restriction on the parameter space: 1+ y < RX2.5

Table 1 summarizes the consumption profiles for the different choices of the agent. Using these consumption profiles we can easily spell out utility for the different choices as a function of the model’s parameters. Normalizing by K and denoting utility with uj, j = n, l, s, with the subscript n for no investment, l for investment financed with long-term debt and s for investment financed with short-term debt, we get

Table 1:Consumption profile
No debtLT debtST debt
Period 1KfK+K+YX¯2R2fK+KX¯R
Period 2
no depreciation0X¯1X¯2R(K+Y)(K+Y)X¯RKX¯
Period 3
no depr. inno depr. in period 20(11X¯)(K+Y)(11X¯)(K+Y)
period 1
depr. in period 200
depr. in period 1no depr. in period 2(11X¯)(K+Y)0
depr. in period 200



Note that əui/əy> 0 and əui/əX <0, i=l,s, as long as agents are impatient. Figure 1 plots utility as a function of X. Table 2 contains the parameter values that were chosen for illustration purposes.

Table 2:Parameter values
f =.2R = 1.05
β = (1.06)–1P =.5

Figure 1:Utilities as a function of X

In Figure 1, both us and ul decrease as the maximum possible depreciation X increases: more volatility of future exchange rates (a larger X) tightens credit constraints and shifts consumption of impatient agents into the future. Of course, un, the utility if the agent does not invest and borrow, does not depend on X and is flat at the normalization value 1. If X is not too high, however, it is optimal for the agent to invest: u1 > un and us > un. Interestingly, long-term debt is preferred for small X, u1> us, but higher X can shift debt towards short maturity: us > ul. Moreover, as mentioned above the scope for borrowing short-term becomes smaller for high project returns: in Figure 1, the region of the X’s in which the agent finds it optimal to borrow short-term becomes smaller as y increases from 0.1 to 0.15.

Linear utility implies that for given X and y the agent either borrows only long-term or only short-term if he invests. In order to generate predictions about the debt structure in the aggregate economy in a simple way, we add heterogeneity in the project return y. Consider an economy populated by a continuum of agents with heterogeneous project returns yi in the interval [0;y]. One can show that for each X there exists a critical value of y at which the agent finds it optimal to borrow longterm6 As the intuition obtained from the discussion of the solvency constraints and illustration in Figure 1 suggest, this critical value is higher for larger X so that the share of projects financed with short-term debt increases for larger X (see Bussière, Fratzscher and Koeniger, 2004, for an elaboration of this point). Of course, also the total number of projects decreases because investment becomes less attractive. However, relative to a benchmark with a fixed exchange rate, a flexible exchange-rate regime results not only in a smaller level of total debt but also in larger share of debt with short maturity. This increases volatility (as long as some projects are financed short-term): projects financed on a short-term basis are liquidated with probability p in which case the project return Y is lost.

This is the basic insight we want to convey: if a country adopts a flexible exchange-rate regime, additional macroeconomic volatility can result from an endogenous shift of the country’s debt structure towards short-term maturities. We now try to find some empirical support for this hypothesis.

Empirical Evidence

To test empirically whether the choice of the exchange rate regime has an impact on debt structure in emerging markets, we use financial and macroeconomic data for 28 emerging market economies: 9 in Asia, 8 in Latin America, 8 Central and Eastern European Countries (CEECs), as well as South Africa, Russia and Turkey. Time series on debt were taken from the BIS and start in the 1980s. The country sample was selected to include mostly open emerging markets (i.e., countries that opened up their financial account during or before the period under consideration), and based on data availability criteria. For CEECs, the first part of the 1990s had to be dropped due to data unavailability and because the first years of the transition to a market economy were characterized by very high volatility. Such high volatility can be considered as a one-off event, not representative of the mechanisms we aim at analyzing.

Data on exchange-rate regimes were provided by Reinhart and Rogoff (2004). This classification allows distinguishing 13 types of exchange-rate regimes, listed in Table 3 (the original classification included 15 categories but two of them never appeared in the sample)7. The Reinhart-Rogoff classification is a de facto classification in that the authors analyzed to what extent announcements of de jure regimes truly hold de facto. They find that many of the countries’ de jure regimes deviate significantly from the actual exchange-rate behavior.

Table 3:Exchange rate regime, distribution by period (%).
A. No separate legal tender0.43.2
B. Pre announced peg or currency board arrangement24.950.533.27.314.122.6
C. De facto peg5.
D. Pre announced crawling peg0.
E. Pre announced crawling band narrower than or equal to +/-2%
F. De facto crawling peg6.70.516.410.24.0
G. De facto crawling band narrower than or equal to +/-2%16.418.222.725.913.88.9
H. Pre announced crawling band that is wider than or equal to +/-2%
1. De facto crawling band narrower than or equal to +/-5%10.914.116.44.611.26.5
J. Managed floating11.715.96.414.14.925.8
K. Freely floating1.82.69.7
L. Freely falling15.14.113.623.622.73.2
M. Dual market in which parallel market data is missing2.
Number of observations1088220220220304124
Source: Reinhart and Rogoff, 2004. The sum of each column is by construction equal to 100%.
Source: Reinhart and Rogoff, 2004. The sum of each column is by construction equal to 100%.

Table 3 shows that for the countries in our sample, pre-announced pegs or currency board arrangements are the most common regimes over the entire period, representing nearly one fourth of the observations. However, the proportion of countries with such regimes fluctuates considerably over time: whereas it was above 50% in the 1960s, it fell to a third in the 1970s with the end of the Bretton Woods system, and less than 10% in the 1980s. Since then, the number of countries with a preannounced peg has increased again, to reach 22.6% of the country sample in the years 2000-2004. Conversely, the most extreme form of floating exchange rate (“freely falling”) has increased regularly between the 1960s and the 1980s, to become the most common category in the 1990s with 22.7% of the cases in the sample. After 2000 however, this category has fallen considerably to slightly above 3%, whereas managed floating exchange-rate regimes have become in the past four years the most common form of arrangement, with more than a fourth of the observations.

Figure 2:Share of pegged exchange rate regimes (categories A,B,C in Table 3) and de facto exchange-rate volatility in the sample.

Note: The scale is on the left and right vertical axis, respectively.

In addition to this classification, we also consider an alternative classification based on actual exchange-rate volatility.8 Such measure is in fact strongly correlated with the above mentioned de jure classification, as presented for instance on Fig. 29. To make the Reinhart and Rogoff measure amenable to econometric testing, we attribute values to the categories presented in Table 3 (1 for category A, 2 for B, etc, assuming that each increment is equal).10

In the Reinhart and Rogoff (2004) paper, two measures are presented: a socalled “fine measure” with the categories of Table 3, and a “coarse measure” with only six categories, each one aggregating two or three levels of the “fine measure”. By and large, these two measures yielded similar results in the specifications we have estimated (see below), suggesting that results do not depend on the mapping of the categories into numbers. A simple regression of the de facto volatility measure on the Reinhart-Rogoff measure using fixed effects - not reported here - shows a positive coefficient, significant at the 1% level, suggesting that more flexible exchange-rate regimes are linked to higher actual exchange-rate volatility. Clearly, the link between the Reinhart-Rogoff measure and the de facto volatility measures of exchange-rate regimes are more complex than this simple regression suggests. For instance, a fixed peg regime is likely to exhibit low exchange-rate volatility but this does not mean that exchange-rate uncertainty is equally low: economic agents may anticipate a speculative attack against the peg which would have the effect to transform the regime into a floating exchange rate, accompanied by higher exchange-rate volatility. To account for this phenomenon, we computed four different measures of de facto exchange-rate volatility, measuring the standard deviation of the exchange rate over (i) the past three years, (ii) the past year, (iii) the year ahead and (iv) the three years ahead.

Turning to the dependent variables, Table 4 presents key stylized facts, broken down by region and by time period. All debt variables presented in Table 4 are debt to banks, taken from the BIS database.11 This particular definition of debt is not without caveat as it excludes in particular other debt instruments which also play an important role as well; however, it has the advantage to be available for many countries over a long period of time. In addition, this source offers a convenient breakdown by maturity: short-term debt is defined as debt whose maturity is below one year. Interestingly, for Latin America and Asia a larger ratio of exchange-rate pegs over time is negatively correlated with the amount of short-term debt. For all emerging market economies this decrease is less pronounced but instead the higher ratio of pegged exchange-rate regimes is positively associated with total debt. This is suggestive for the mechanisms highlighted by the simple theoretical perspective. However, Table 4 also shows pronounced heterogeneity across regions. Overall, Latin American countries hold more debt, as a percentage of GDP, than Asian countries and new European Union Member States. Such differences do not seem to be directly linked to the above described measures of exchange-rate regimes. As we will see below in the econometric results, other factors such as fixed country effects and other (time varying) control variables have an impact on the total debt level of a country and need to be controlled for in the econometric analysis.

Table 4:summary statistics, total and regional breakdown
Lat. Am.AsiaNew EU M. S.all EMEs




< 19941994-



Pegged ER (% total)11.321.416.427.3NA51.112.327.1
Exchange rate volatility4.
Debt/GDP (%)53.248.938.538.3NA42.833.941.7
Short-term/total debt (%)17.614.819.015.9NA24.519.418.7
Growth volatility1/
Note: unweighted average over sample period.

Normalised to 1 for all EMEs, 1980-1989.

Note: unweighted average over sample period.

Normalised to 1 for all EMEs, 1980-1989.

To estimate the impact of exchange-rate volatility on the debt structure, we followed the GMM methodology developed by Arellano and Bond (1991) using lagged differences as instruments. The results reported below are robust to using a standard fixed-effect estimator (see Bussière, Fratzscher and Koeniger, 2004, for further discussion of the econometric methodology in our application). In order to control for other factors that may influence debt, we included on the right-hand side a set of control variables: real PPP adjusted GDP per head (as a proxy for catching up effects), the government budget balance (to account for the possible presence of non-Ricardian agents), investment (see Bleakley and Cowan, 2002), a dummy variable for capital account liberalization and a dummy variable for currency crises. The presence of the latter in the specification stems from the specific problem arising from currency crises, relatively frequent in our sample of emerging markets: as the debt ratios to GDP are computed using the exchange rate to convert foreign into national currency, such ratios mechanically jump up during crises times. Arguably, this variable is then not strictly exogenous. Likewise, the assumption that exchange-rate volatility is exogenously given is debatable and the endogeneity issue is only partly dealt with in the GMM methodology. Ideally, one would use instrumental variables in this context, that is, variables correlated with exchange-rate volatility but not with the debt ratios. However, we are not aware of a good instrument for our application. One should note however that if endogeneity plays a role, the link between exchange-rate volatility and debt that we measure in our regressions is actually a lower bound. If policy makers intervene in the foreign exchange markets to reduce to reduce exchange-rate volatility when the debt maturity structure is tilted towards short-term debt (fear of floating), the positive correlation between volatility and the share of short-term debt should be downward biased and thus less positive.

Table 5 reports the results. Each of the three columns corresponds to one of the three following dependent variables: short-term debt as a share of total debt, short-term debt as a percentage of GDP, and total debt as a percentage of GDP. According to the simple theoretical results presented in the previous section, more exchange-rate volatility (measured as either de jure or de facto) should have a positive impact on short-term debt (first two columns), and a negative one on total debt. The table is synthetic in that it reports only the coefficient in front of the exchange-rate regime variables and not the control variables (estimated in different regressions, one at a time). The full results are available upon request. Regressions including the de facto exchange-rate volatility measures are estimated over 304 to 336 observations (taking three years to compute lags implies losing some of the observations). Those including the Reinhart and Rogoff measure are estimated with around 350 observations. Overall, the results provide substantial evidence in favor of the model’s predictions. The coefficient of the Reinhart and Rogoff measures of exchange-rate volatility are positive and significant in the first two regressions, when the dependent variable is short-term debt. The de facto volatility measures are not as clear-cut, as many of the variables we used do not show up significantly, particularly in the second specification. However, interestingly, the de facto variable using the forward-looking one-year ahead volatility measure, which accordingly is the most relevant measure for debt with maturity under one year, enters the specification significantly and with a positive sign. Turning to the last column of Table 5, the impact of the de facto volatility measure on total debt is clearly negative and significant for three of the four measures, whereas it is negative but insignificant for the last one. This result is very much in line with the results of the theoretical section: more exchange-rate uncertainty is associated with lower total debt. More surprisingly, the impact of the Reinhart and Rogoff measure appears to be positive instead of negative. This result is counter-intuitive and seems to be driven by the currency crisis episodes: in the immediate aftermath of crises, countries often move to a more flexible exchange-rate regime but at the same time the debt ratio jumps up since the exchange rate depreciates and GDP falls. In fact, when currency crisis episodes are dropped from the sample, the impact is no longer significant. Although our currency crisis variable should pick up this effect, it does not completely solve the issue because the increase of the debt ratio often lasts a couple of years, whereas the crisis variable was set equal to one only at the time of the crisis.

Table 5:Exchange rate regime and debt
Dependent variable:S. T. debt

(% total debt)
S. T. debt

(% GDP)
Total debt

(% GDP)
Fine measure0.23 **0.32 ***0.82 ***
Coarse measure0.63 **

0.88 ***

2.28 ***

De facto definition
Three years lag−0.02−0.04−0.46 ***
One year lag0.020.01−0.29 ***
Three years lead0.14−0.24 *−0.46 *
One year lead0.17*−0.05−0.21
Standard errors in italics*, **, *** denote significance at the 10%, 5%, 1% level, resp.
Standard errors in italics*, **, *** denote significance at the 10%, 5%, 1% level, resp.

Finally, Table 6 reports results using output volatility as the dependent variable (again with one of the reported regressors at a time). The results suggest that more exchange-rate volatility is indeed correlated with larger output volatility, although it seems mostly through the impact on the debt maturity structure.

Table 6:Exchange rate regime and output volatility
Fine measure0.180.24
Coarse measure0.220.69
De facto definition
Three years lag−0.150.23
One year lag0.300.17*
Three years lead0.310.62
One year lead0.320.20
S.-T. debt (% tot. debt)2.850.98 **
S.-T. debt (% GDP)0.010.01
Total debt (% GDP)0.0040.01
Dependent variable is volatility of real output*, **, *** denote significance at the 10%, 5%, 1% level, respectively.
Dependent variable is volatility of real output*, **, *** denote significance at the 10%, 5%, 1% level, respectively.


This paper has investigated the role of exchange-rate regimes for macroeconomic stability, focusing in particular on the influence of the exchange-rate regime on the maturity structure of debt. A simple theoretical model suggests that a more flexible exchange-rate regime may shift the debt structure towards short-term debt, which in turn can increase output volatility. Taking the model to the data, we find broad support for the model. In particular, more exchange-rate uncertainty seems to be associated with a larger share of short-term debt.

A possible extension for future research would be to allow for the possibility of default in the model and to introduce moral hazard and asymmetric information, since these phenomena are important for capital markets in reality. However, better data are necessary to test predictions of such a model. Data on interest rates across different maturities for private debt would allow to analyze the interaction of credit prices and credit demand, instead of focusing only on credit supply and credit volumes.

Our model emphasizes the importance of market incompleteness and does not rely on asymmetric information or moral hazard to explain the debt structure and the inclination of emerging markets to be subject to financial crises and substantial real volatility in the economy. If market incompleteness is important, it is crucial to develop financial markets or instruments that allow agents to insure better against risk so that financial crises in emerging markets can be avoided. Concrete policy proposals to address this issue have started to emerge. Some of the proposals call for the development of domestic financial markets for local-currency substitutes to dollarized debt (Levy-Yeyati, 2003) or for the issuance of bond contracts denominated in units of a basket of emerging-market currencies (Eichengreen and Hausmann, 2003).


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We would like to thank for their very helpful comments Carmen Reinhart and seminar participants at the IMF-Banco de España conference on “Dollars, Debt, and Deficits - Sixty Years after Bretton Woods” in Madrid 2004. The views expressed in this paper are those of the authors and do not necessarily reflect those of the European Central Bank. This draft is as of October 2004.


Arteta (2002) establishes the additional result that floating regimes seem to increase currency mismatches in domestic financial intermediation.


For a more on the related literature see Bussière, Fratzscher and Koeniger (2004).


Of course, the choice whether to adopt flexible exchange rates might depend on the stock of short-term debt in the economy: countries with a large amount of short-term debt might fear to float the exchange rate. Since this endogeneity is extensively discussed in the literature (see Calvo and Reinhart, 2002), we focus entirely on the direct effect of flexible exchange rates on debt maturity in the theoretical part. Instead, the empirical estimation will try to account for the endogeneity of the exchange-rate regime.


In Bussière, Fratzscher and Koeniger (2004) we endogenize the probability of liquidation. This makes the algebra more cumbersome but does not change the basic insights.


In the second period the agent cannot roll-over the debt if K+YRX¯2RX¯KRX¯<0 which implies the condition in the text. Note that in the second period the pledgeable income is always K+Y since the project returns realize in period 3.


Of course, if this critical y is larger than y, no debt is borrowed long-term at all.


These categories were (a) pre announced horizontal band that is narrower than or equal to +/-2% and (b) moving band that is narrower than or equal to +/-2% (i.e., allows for both appreciation and depreciation over time).


We computed, for each year, the standard deviation of the first-differenced real effective exchange rate, using monthly data.


In Figure 2, pegged exchange rate correspond to the first three categories A, B and C of Table 3.


We tested this assumption using a set of 0/1 dummy variables, one for each of the categories represented in Table 3 (we took the first category as benchmark). Results suggested that the increment may actually be declining in the case of short-term debt: moving from regime A to regime B has a stronger effect on short-term debt than moving from B to C, etc. However, this effect is difficult to measure with precision given the degrees of freedom, which is why we resorted to the more straightforward linear specification.


We focus on private rather than government debt since the theoretical model outlined in the previous section applies to the former.

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