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Revisiting the Concept of Dollarization

Author(s):
Nkunde Mwase, and Francis Kumah
Published Date:
January 2015
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I. Introduction

This paper proposes a new approach for analyzing dollarization that abstracts from the value-effect of exchange rate movements. The literature has focused on explaining the dollarization ratio in nominal terms, where foreign currency deposits (FCDs) are converted to local currency using the exchange rate and deflated by either broad money or total deposits (all in local currency terms). However, a key drawback of this approach is that exchange rate movements could lead to large changes in the dollarization ratio even with an unchanged stock of FCD. Indeed, to better understand economic agents’ demand for FCD, it is important to remove the effect of exchange rate movements.

Deposit dollarization is defined in this paper as the share of foreign currency deposits in total domestic deposits in the banking system. To prevent an exchange-rate bias, we remove the effect of exchange rate changes on the share of foreign currency deposits in total deposits and derive “real” deposit dollarization. A “real” deposit dollarization index is computed by converting both foreign currency deposits and bank deposits to dollars and multiplying both (back to domestic currency) by a fixed base-year nominal exchange rate.

We then examine recent trends in ‘real’ deposit dollarization with a particular focus on demand for FCDs in low-income countries (LICs) during the recent Global Financial Crisis. The standard argument would be that during periods of volatility, deposit dollarization tends to increase as agents seek to hold FCDs as these are typically perceived as safer assets–the typical “flight to quality” argument. This would suggest that, although economic agents in many developing countries were wary about potential spillover or second-round effects of the recent global financial crisis they still opted to increase FCDs; in this case, flight to quality concerns outweigh home asset preference.

We investigate the channels through which global financial stress affects dollarization, and the extent to which policy interventions (including capital controls and other prudential measures, when used) can limit or dampen dollarization. Understanding what drives surges in dollarization is important, given that dollarization could weaken the monetary policy transmission mechanism. Since monetary instruments mainly impact domestic liquidity, high dollarization reduces the capacity of central banks to control liquidity that could fuel consumer price inflation, particularly as monetary policy instruments, in this event, principally affect only a shrinking share of domestic currency holdings.

Given that central banks cannot act as lender of last resort (LOLR) in foreign currency, high dollarization also reduces the capacity of central banks to stem a liquidity crisis, and exposes the financial system to liquidity and solvency risks. For domestic currency deposits, the central bank could step in as LOLR since it can create domestic currency in the event of emergency, albeit with possible inflationary effects if non-sterilized. For FCDs, strictly limited international reserves are the only buffer that exists to stem a liquidity crisis. So bank runs on foreign currency deposits have much more serious consequences.

The paper focuses on LICs using monthly data spanning 2006–09. Trends in dollarization in LICs vary depending on whether it is defined in nominal or real terms. While dollarization in nominal terms surged in LICs during the Global Financial Crisis, it maintained a downward trend in real terms. For Emerging Markets (EMs), dollarization generally surged during the crisis but was higher in nominal terms. Overall, these findings suggest that exchange rate movements play an important role, with domestic currency depreciations raising the dollarization ratio even before economic agents change behavior in response to the depreciation.

Analysis of recent trends suggests that beyond the variance of inflation and depreciation (as suggested by recent literature) the level of inflation and size of depreciation also affect dollarization. This suggests that it is important to examine the level of inflation and depreciation to better understand the evolution of dollarization.

We estimate a simple linear relationship between our real dollarization index and identified factors, applying a panel system GMM estimator. We also apply the fixed effects estimator with autoregressive disturbances. Further, we estimate impulse response functions and derive variance decompositions to identify the dynamic response of (changes in) dollarization due to shocks from identified variables to quantify the relative importance of each of the shocks as a source of variation in dollarization. The results from the system GMM and the fixed effects (with AR (1) disturbances) estimators are broadly similar, indicating that our estimates do not suffer from explanatory variable endogeneity bias under the latter estimator.

Specifically, we find that deposit dollarization among LICs during the sample period was driven largely by changes in relative prices, exchange rate movements, global volatility (measured by Chicago Board Options Exchange Volatility Index, the VIX) and institutional factors. Exchange rate depreciation is associated with a surge in dollarization, while a rise in VIX helps contain it. Our findings on the effect of the exchange rate regime are statistically insignificant but consistent with the Calvo-Reinhart-type fear-of-floating effects. These findings are broadly robust to alternative estimation approaches, with the exception of the VIX. We find that an increase in global risk aversion reduces real dollarization, albeit with low economic impact using the fixed effects estimator, while the results from the system GMM estimator are not statistically significant. Impulse response analysis, using panel vector autoregression (PVAR), yielded results that indicate that shocks to VIX, relative prices and exchange rate have strong, significant but short-lived impact on real dollarization. The findings of a positive, albeit short-term, relationship between the VIX and dollarization suggests that in the face of global financial volatility, domestic residents increased their dollar holdings due to uncertainty about the spillover effects of the crisis—flight-to-quality outweighs home asset preference.

The rest of the paper is organized as follows. Section II discusses the measurement of deposit dollarization, and Section III compares deposit dollarization using the traditional approach and the new concept of “real” dollarization, and examines recent trends in dollarization. Section IV reviews the literature on dollarization and outlines the contributions of this paper to the literature. Section V outlines the theoretical model that informs our approach—a version of the portfolio balance model, while section VI presents the estimated panel econometric models and summarizes our empirical results. Section VII concludes the paper with a summary of the findings and a discussion of policy implications.

II. Measuring Deposit Dollarization

Dollarization can take various forms, including transacting, storing financial assets/liabilities and/or indexing prices in foreign currency—usually in dollars. Since monetary authorities are usually not able to perfectly monitor foreign currency circulating in their economies, it is difficult to obtain an accurate measure of foreign currency transactions (“payments dollarization”).

The literature has typically computed the deposit dollarization ratio in nominal terms as a ratio of FCDs to broad money or total deposits.2 Data on FCDs and other monetary aggregates including broad money are typically compiled by the Central Bank and presented in local currency terms. However, unlike domestic currency or deposits that are in local currency, the FCDs are in different foreign currencies but are converted to local currency terms. However, exchange rate movements could lead to big swings in the dollarization ratio even with constant stock of FCDs in foreign currency terms. For example, if there are 70 units of FCD and 30 units of local currency deposits (LCD) and the currency depreciates by 10 percent, the FCD goes up from 70 to 77 units in local currency terms. Since the LCD remain unchanged, there are now 107 units and the share of FCD in total is 77/107 = 72 percent, although nothing has happened to the amount of dollar foreign exchange holdings. This simple example supports the need to adjust foreign exchange holdings for exchange rate changes.

We argue that dollarization should be measured in “real” terms by abstracting exchange rate movements, and that it is important to consider the impact of identified drivers of the exchange rate adjusted FCD ratio. The “real” deposit dollarization indicator is derived as a constant-exchange rate indicator, adj.FCDt(adj.FCDt+LCDt) where adjusted FCD is derived as (FCDt/NERt*NERt==2000); NER is the nominal exchange rate (local currency per dollar). While the usage of the term “real” dollarization differs from its usage by Ize and Yeyati (2003) as “the extent to which prices and wages are denominated in foreign currency and as measured by the pass-through coefficient of exchange rate changes on prices, being moderate,” it is consistent with Garcia-Escribano and Sosa (2011) who compute deposit and credit dollarization at constant exchange rates.

In addition, we argue that the dollarization ratio should be measured using deposits as opposed to broad money. Estimates of deposit dollarization using broad money do not adequately measure economic agents’ preference for foreign currency. For example, using broad money makes it difficult to ascertain whether the reported dollarization ratio reflects preference for deposits or currency. It also suffers from likely measurement errors, due to paucity of data on foreign currency in circulation.3 In contrast, with our measure (using total deposits as the denominator)4 a high share of deposit dollarization implies that residents consider the relative preference for holding deposits in foreign currency versus domestic.

III. Recent Trends In Deposit Dollarization

Analysis of the trends in deposit dollarization ratio indicate differing patterns depending on whether it is computed in “real” or nominal terms (Figure 1). Specifically, there was a surge in the dollarization ratio in nominal terms during the global financial crisis. In contrast, the ratio computed in real terms points to a continued decline in dollarization in LICs while a smaller increase than in nominal terms is observed for EMs. At the same time, “real” dollarization was less volatile (i.e. showing lower swings) than when measured in nominal terms (Figure 3). While the observed decline in “real” dollarization in LICs was more accentuated, the increase in EMs was relatively dampened/moderated (Figure 1). The list of countries included in the analysis is presented in Appendix Table A1.

Figure 1.Trends in Deposit Dollarization, 2006m1-2009m12

(In percent)

Source: IMF International Financial Statistics (IFS) database.

Figure 2.Ghana and Zambia: Trends in Deposit Dollarization, 2006m1-2009m12

(In percent)

Source: IMF International Financial Statistics (IFS) database.

Figure 3:Surge in Deposit Dollarization, 2006m3-2009m11

(Percent change, three-month moving average)

Source: IMF International Financial Statistics (IFS) database.

The finding that dollarization (both in levels and percent changes) is lower in “real” terms is attributable to the large exchange rate depreciations that some countries experienced during the crisis. In many of the countries considered, despite the gradual correction, the exchange rates remain more depreciated and volatile than in the pre-crisis period, resulting in the exchange rate being typically lower than in the base year. For example, the large increase in dollarization ratios in Ghana and Zambia during the global financial crisis is partly explained by large exchange rate movements (Figure 2).

Further, visual analysis suggests that regional characteristics and institutional factors could help explain the deposit dollarization. Sub-Saharan Africa (SSA) and Middle East and Central Asian LICs have the lowest deposit dollarization ratios (real and nominal) but also the largest declines during the financial crisis (Figure 4).5

Figure 4.LICs: Deposit Dollarization, by Region, 2006m1-2009m12

Source: IMF International Financial Statistics (IFS) database and author’s estimates.

Scatter plots suggest that the surge in dollarization in LICs is negatively correlated with global volatility, the VIX (Figure 5). Specifically, Figure 5 provides a comparison of the relationship between dollarization growth (both real and nominal) and the VIX for LICs and EMs. While there is a negative correlation for the entire LIC sample, a closer analysis of the relationship for select LICs (e.g., Zambia, Nigeria, Kenya and Ghana) suggests some heterogeneity across countries, even for those that had greater access to capital markets (Figures 5 and 6). There is no clear relationship between dollarization and the VIX in emerging market economies (Figure 5).

Figure 5.LICs and EMs: Deposit Dollarization Growth and Global Risk Aversion, 2006m1-2009m12

Source: IMF International Financial Statistics (IFS) database, Chicago Board Options Exchange (CBOE) Volatility Index, and author’s estimates.

Figure 6.Selected Countries: Deposit Dollarization Growth and Global Risk Aversion, 2006m1-2009m12

Source: IMF International Financial Statistics (IFS) database, Chicago Board Options Exchange (CBOE) Volatility Index, and author’s estimates.

The negative relationship suggests that when global volatility increases, deposit dollarization declines, which somewhat seemingly contradicts conventional wisdom of flight to quality but could be due to heightened home asset preference during global uncertainty. However, many LICs were not directly exposed to the global financial crisis and have limited financial integration. Another plausible explanation for this finding could be that domestic economic agents had less information about spillover effects of the financial crisis and thus did not adjust their balances. However, this explains the absence of a positive relationship and not of the negative. This could reflect the impact of global shortage of U.S. dollars and could have contributed to outflows. The impact took many channels including curtailing of private capital flows (e.g., in Uganda and Zambia); capital flight and portfolio inflow reversals in foreign-owned banks—even in countries with capital account restrictions—and reflecting this, a drop in trade of local debt by foreign investors, a collapse in trade financing and stock markets.6

Changes in prudential measures during the global financial crisis had significant effects on dollarization in low-income countries (Figure 7). While dollarization ratios declined in both LICs that introduced changes exchange rate-related measures and those that changed other prudential measures, there was a bigger decline in real terms for those that introduced changes in the former. However, this is an instantaneous relationship and does not capture the dynamic effects of changes in prudential measures; neither does it distinguish between an increase or decrease in trend.7 Many developing countries introduced controls on lending locally in foreign currency and raised reserve requirements at the onset of the global financial crisis to build resilience against these external financial shocks, but most countries removed these controls shortly thereafter (Figure 8). Indeed, examination of country practices regarding current and capital controls indicates that changes to these, as well as controls of foreign exchange accounts permitted domestically, were the most commonly adopted during the period.

Figure 7.LICs: Deposit Dollarization Growth and Change in Prudential Measures, 2006m1-2009m12

Source: IMF International Financial Statistics (IFS) and IMF ARAER databases and authors' estimates.

Figure 8.Current and Capital Account Controls, 2005-2011

(Percent of Countries)

Sources: IMF AREAER database; and staff estimates

Visual analysis suggests that beyond the variance of inflation and depreciation (as suggested by the literature) the level of inflation and size of depreciation also affect (nominal) dollarization (Figure 9). Countries that had high inflation or depreciation rate prior to the global financial crisis experienced an increase in the variance of dollarization during the crisis. In contrast, countries that went into the crisis with lower inflation tended to experience a reduction in the variance of dollarization. The findings for low dollarization suggest that it remains broadly unchanged except for a few outliers. In addition, the analysis suggests that countries that started off with high depreciation or inflation tended to experience a bigger increase in the variance of dollarization. This suggests that it is important to examine the level of inflation and depreciation to better understand the evolution of dollarization.

Figure 9:Variance of Nominal Dollarization, by inflation and depreciation

(Standard deviation of dollarization)

Source: IFS database, IMF, and author calculations

Notes: Standard deviation is computed by country for pre-crisis (2006-07) and crisis (2008-09) periods.

1/A country is included in the high inflation (depreciation rate) sample if its median monthly inflation (depreciation) in 2006 is higher than the sample median.

2/ In the low inflation case and also low depreciation rate: Armenia is excluded from the chart, it is an outlier with a deviation of about 10 percent pre-crisis and about 15 percent therafter.

IV. Literature Review

The causal factors discussed in the literature that relate to the short-run drivers of dollarization could be grouped into five broad categories:

  • Price movements that affect store of value: Concerns about loss in value of financial assets lead residents to hold a large proportion (or all) of these assets in foreign currency. A variety of models have been developed to explain this economic behavior. The main thrust of the results from these models is that inflation and/or exchange rate depreciation reduce the real value of financial assets, and therefore if residents (households/banks) expect either of these to occur they may choose to hedge this risk by holding their assets in foreign currency.8 Ize and Levy-Yeyati (2003, 2005) highlight the importance of the relative volatility of inflation and the exchange rate.
  • Relative rates of return: Some studies have pointed to the role that spreads between domestic and foreign interest rates could play in economic agents’ decision to switch demand between local and foreign currency holdings. However, to the extent that these differentials partly reflect expectations of exchange rate movements, these two factors (interest rate spreads and exchange rate movements) could be interrelated, as consistent with uncovered interest parity.9
  • Institutions and policy credibility: Concerns about potential collapse of the financial system and exchange rate regime as well as the possibility of debt default could increase dollarization. Ize and Parrado (2002) note that the preference for holding foreign currency could be determined by expectations about how monetary policy would be conducted in the event of a collapse of a fixed exchange rate regime regardless of the probability of the collapse occurring.10 Institutional factors including political, the legal and even the cultural environment as well as the level of fiscal discipline are also be important factors.11 Concerns about fiscal sustainability could affect the expected probability of a devaluation arising from plausible monetization of the fiscal deficit.12 In addition, if residents believe that the government would provide a bailout in the event of a sharp depreciation of the domestic currency they may borrow in foreign currency and not internalize the currency risk, thus leading to higher dollarization.13
  • Macroprudential tools: More recently, the literature has explored the role of macroprudential measures to reduce the incentive for banks to borrow externally when domestic interest rates are raised (e.g., capital controls, reserve requirements and other prudential measures).14

V. The Theoretical Model

Models of currency substitution or dollarization (particularly, of deposit dollarization) have as a basis some aspect of a portfolio balance model as discussed for example in Kouri (1976), Calvo and Rodrguez (1977) and Branson and Hendersen (1985). Following the lead of these researchers, suppose wealth can be held in the form of domestic money, domestic bonds and foreign bonds denominated in foreign currency, such that the demand for foreign bonds is an implicit demand for foreign currency, then dollarization (deposit dollarization) would be driven by factors similar to those that drive the choice of an optimal portfolio. These factors include (i) expected movements in the value of the domestic currency, which may be sparked by fiscal and (or monetary) policy or by changes in expectations or confidence, (ii) perceptions of changes in sovereign risk, and (iii) real/technology shocks.

Following the portfolio balance approach, we discuss dollarization in the context of a simple rational expectations form of the model in equations (1)(5) below.

Equations (1)(5) above show that the net wealth of the private sector could be expressed as the sum of domestic money (Mt), bonds (Bt), and foreign bonds (Ft) denoted in foreign currency; st is the domestic price of foreign exchange. By definition, the current account balance denotes the rate of accumulation of foreign assets over time (equation 5). Domestic and foreign interest rates are denoted by rt and rt*, respectively, Est] is the expected depreciation of the domestic currency, while ρt is used here to represent sovereign risk that may arise as a result of increased uncertainty about economic policy actions generally or simply from increases in risk as reflected in higher domestic interest rates. The variables are all in levels. In equation (5), domestic net savings (n(.)) is specified to be a function of the real exchange rate (Stpt) and real/technology shocks (Zt).

As specified, real exchange depreciations increase net savings (or the current account balance); just as real shocks do. The solution of this rational expectations specification yields, among others, the result that with constant exchange rate expectations—i.e., for Est] = 0—expansive monetary policy and exchange rate depreciations increase demand for foreign assets – and hence the demand for foreign currency. Positive real or technology shocks that appreciate the domestic currency also reduce the demand for foreign assets – and reduce dollarization. Further, increases in sovereign risk (ρt) feed a flight to quality, increasing demand for foreign assets. These results can be obtained by deriving equilibrium demand schedules for the three assets in an exchange rate-foreign assets plane as follows.

Holding rt* and ρt constant and totaling differentiating equations (2) and (4) yields differential equations for the shares of money and foreign currency as

which can be solved for dst and drt as functions of relative shares of domestic and foreign assets in agents’ portfolio. Thus, from the solution of equations (1) - (4), we can express the expected change in the exchange rate as a function of relative shares of assets:

Equations (5) and 7) are dynamic equations that can be solved for the equilibrium levels and F (foreign currency) and s (the exchange rate). To obtain slopes of the respective loci for F and s in (F, s) space, we totally differentiate these equations, given F˙=0 and Est] = 0 as follows:

Given these slopes and the behavior of foreign assets holdings and the exchange rates off their respective loci, saddle point equilibrium requires that — st/Ft be less than rt*/ns/p—this is the Marshall-Lerner condition, requiring that stns/p/n be greater than one.

The equilibrium paths for F and S (given that the Marshall-Lerner condition holds) are depicted in Figures 10 and 11 below.

Figure 10.Saddle Path in S-F Space

Figure 11.Exchange rate depreciation induces foreign asset demand

Note that, while exchange rate depreciation (i.e. when the domestic value of foreign currency declines) increases foreign currency demand (i.e. a movement along the F˙=0 schedule),

  • (i) Positive real shocks would shift the foreign currency demand schedule upwards, reducing demand for foreign exchange;
  • (ii) Unanticipated expansionary monetary policy that shifts the expected depreciation schedule upwards – appreciating the domestic currency, initially – would require an offsetting exchange rate depreciation (or an increase in foreign asset holdings or dollarization) to hold Est] constant (Figure 11); and
  • (iii) Heightened uncertainty (i.e. increases ρt) either about the course of short-term economic policies or about general prospects of the economy drives foreign currency demand as a safe haven for preserving wealth.

In addition to the factors explained above, it is noteworthy from the literature review that the economic policy environment (the monetary policy, prudential measures and exchange rate regime) and quality of government institutions matter in the overall economic agent decision to hold foreign currency denominated assets; these variables are included in our empirical estimations in the sections that follow.15

VI. The Empirical Approach and Results

Drawing on the theoretical model outlined above, we specify a dynamic linear model of the form

where yit denotes our real deposit dollarization index, as defined above; RRit denotes expected real returns (or an indicator, such as inflation, that affects real returns) on domestic assets relative to dollar-denoted assets; Sit denotes the exchange rate (defined as US dollar per domestic currency, so that an increase denotes domestic currency appreciation); VIXit is a measure of global risk aversion or uncertainty; Xit is a set of controls (including exchange rate regime, prudential requirements, and quality of institutions); ηi is country-specific, unobservable, fixed effects; and vit is serially uncorrelated errors.

The baseline model is the fixed effects estimator with autoregressive disturbances in a first estimation of the model, which is complemented with a system GMM estimator and panel Vector AutoRegression (PVAR). The fixed effects model is simpler and outperforms other models, assuming that normality conditions are satisfied.16 However, since tests suggest that the dependent variable could be correlated with the error term (i.e., implying endogeneity of explanatory variables), the results from the fixed effects model could be biased. To address this potential endogeneity bias, we apply the Arellano and Bond (1991) system GMM estimator using as instruments appropriately lagged levels of the dependent variable and the predetermined variables. To investigate the short-term dynamics of the dependent variable to perturbations in the predetermined variables, we run panel VARs and estimate impulse-response functions and variance decompositions.

A description of the explanatory variables used in equation (9) above is presented in five broad categories in Box 1 below. Variable descriptions and summary statistics for LICs are available in Appendix Tables A2 and A3, respectively. The empirical analysis focuses on the LICs (see Appendix Table A1).17

Box 1.Determinants of Surge in Deposit Dollarization

  • ❖ Macroeconomic variables
    • ➢ Returns/Price differential or relative returns/prices
    • ➢ Exchange rate: depreciation (level and volatility), exchange market pressure
  • ❖ Institutions
    • ➢ Bureaucracy quality (e.g., International Country Risk Guide (ICRG))
    • ➢ Entrepreneurial quality (e.g., Kauffman index of entrepreneurial activity (KIEA))
  • ❖ Prudential measures
    • ➢ Reserve requirements
    • ➢ Net open positions and other exchange related measures
    • ➢ Other prudential requirements.
  • ❖ Flight to quality:
    • ➢ Global risk aversion (VIX)
  • ❖ Controls:
    • ➢ Level of dollarization
    • ➢ Exchange rate regime

The empirical results presented in Table 1 below suggest the following18:

Table 1.Panel Econometric Estimates of Real Dollarization in Low-Income Countries, 2006-09
Dependent variable: log. of real dollarization indicator
Explanatory variables:Fixed EffectsSystem GMM
(with AR(1) disturbances)(t-2)
1. Macroeconomic variables
Log. relative prices0.1638**0.1405*
(0.0320)(0.0680)
Log. exchange rates−0.2226***−0.0946***
2. Institutions(0.0000)(0.0020)
Kauffman’s entrepreneurial activity indicator (KIEA)0.1547***0.2576***
(0.0050)(0.0000)
International Country Risk Guide (ICRG)0.0106***0.0181***
3. Prudential measures(0.0000)(0.0000)
Exchange rate related measures−0.02050.8230
4. Flight to quality(0.7220)(0.1280)
Log. global risk aversion indicator (VIX)−0.0108**−0.0221
5. Controls(0.0110)(0.4570)
Crisis time dummy0.0642*0.0638
(0.0570)(0.5550)
Exchange rate regime−0.0725−0.0562
(0.4760)(0.6550)
Constant0.2319***
(0.0000)
Number of observations944989
R-squared0.4749
ρ—AR(1)0.8354
F-ratio100.74501.49
Prob > F0.00000.0000
Sargan Test of Overidentification (χ2)352.9
Prob > (χ2)1.0000
Arellano-Bond AR(1) Test in first differences (z)−0.6200
Prob >z0.5320
Arellano-Bond AR(2) Test in first differences (z)0.3300
Prob >z0.7440
Source: IMF World Economic Outlook (WEO) database; and authors’ estimations using panel methods in Stata.Notes: All variables are expressed in logarithms. Figures in parenthesis are probability values, with ***, **, and * indicating statistical significance at 1 percent, 5 percent, and 10 percent respectively.
Source: IMF World Economic Outlook (WEO) database; and authors’ estimations using panel methods in Stata.Notes: All variables are expressed in logarithms. Figures in parenthesis are probability values, with ***, **, and * indicating statistical significance at 1 percent, 5 percent, and 10 percent respectively.
  • Exchange rate appreciation tends to moderate deposit dollarization (Table 1). Specifically, a one-percent appreciation in the exchange rate is associated with a 0.09–0.2 percent decrease in the real deposit dollarization ratio. These results are strongly significant and robust to model specifications and estimation approaches. A related finding supports the Calvo-Reinhardt fear-of-floating effect; specifically, countries that operated more flexible exchange rate regimes tended to experience lower dollarization, although the elasticity is not statistically significant, possibly because the effects of exchange rate regime is captured by exchange rate changes.
  • Increase in global risk aversion reduces real dollarization. A ten percent increase in the VIX index induces a statistically significant 0. 1 percent decline in real dollarization. However, its low economic impact suggests that it is not a very important determinant of real dollarization. Moreover, the results are sensitive to the estimation strategy and are insignificant under system GMM but significant in the PVAR (see related discussions below).
  • Increases in sovereign risk and entrepreneurial activity increase real dollarization. Economies with high sovereign risk classification or perception (as reflected in the ICRG score) tend to have higher real deposit dollarization. Similarly, economies with relatively high entrepreneurial activity (as reflected in the Kauffman entrepreneurial activity indicator), unsurprisingly, have higher real dollarization. In addition, increasing inflation (or volatility of inflation), which increases sovereign risk, also fuels real dollarization; this finding is corroborated by earlier work by Ize and Levy-Yeyati (2003) on financial dollarization.
  • Strengthening prudential measures during an upsurge does not moderate dollarization; it may actually worsen the situation. Although statistically insignificant, the estimated elasticities point to a mixed impact of exchange-rate-related prudential measures on real deposit dollarization, once other macroeconomic variables are included in the regression.

To assess the dynamic responses of real deposit dollarization to the determinants of discussed above, we specify and estimate a panel VAR of the form

where the variables are as defined in equation (9) above, with the variables RR, S and VIX assumed to follow autoregressive processes with lag lengths k, l, m, and n as specified, and the optimal lag length is determined using statistical methods. The linear GMM estimator is used to estimate this equation in first differences; a simple Choleski decomposition scheme is used to identify the various shocks. One standard deviation shocks to global uncertainty (VIX), relative prices (RR), and exchange rates (S) have a strong and significant impact on real dollarization surge (Appendix Figures 7 and 8); whereas results presented in Appendix Figure 7 reflect an ordering of the variables with relative prices preceding exchange rates (in which case, relative prices determine exchange rate movements, in a purchasing power parity (PPP) fashion), those in Appendix Figure 8 are based on an ordering where the exchange rate precedes relative prices (in an exchange rate pass-through to prices fashion). The estimated results turned out to be robust to between these two specifications, although the transmission mechanisms differ. Generally, the estimated responses to a shock to one of these variables (VIX, RR, and S) tend to be large but short-lived, lasting up to about 2 months. These results are corroborated by estimated variance decompositions (Appendix Figures A4 and A5). The detailed results from the PVAR estimations are discussed below.

A one standard deviation innovation in global volatility index (VIX) increases domestic prices (and relative prices) significantly during the following five months, the domestic currency depreciates (in a PPP fashion) and real dollarization surges (first column of Appendix Figure 7). Similar results are obtained for shocks to relative prices (column 2) and shocks to exchange rates (column 3). In Appendix Figure 8, the transmission mechanism is slightly different, but the results are basically the same. Shocks to the global volatility index (VIX), or uncertainty, depreciate the domestic currency, increase domestic prices (and hence relative prices, in an exchange rate pass-through fashion), and drive up real dollarization (column 1). On the other hand, exchange rate appreciations reduce relative prices and real dollarization, while an increase in relative prices have similar results on real dollarization as when global uncertainty increases (column 3). These results are broadly consistent with our theoretical framework and empirical results presented in Table 1 above, with the exception of the VIX. The findings of a positive, albeit short-term, relationship between the VIX and dollarization suggests that in the face of global financial volatility, domestic residents may increased their “dollar” holdings due to uncertainty about the spillover effects of the financial crisis. In addition, the findings could reflect the fact that many LICs were not adversely affected by the global financial crisis.

VII. Conclusion and Policy Implications

This paper uses an approach to measuring deposit dollarization that adjusts foreign exchange deposits for fluctuations in exchange rates in order to capture actual changes in these deposits that are due exclusively to changes in behavior of economic agents. Since exchange rate movements affect the domestic currency value of foreign currency holdings or deposits, exchange rate movements could bias any measure of dollarization if not corrected for these movements. Indeed, as this paper shows, exchange rate changes (or more appropriately, expectations about these) could affect economic agent behavior ex ante. The import of our measure is to remove the ex post effects of exchange rate movements on foreign currency holdings or deposits due to measurement effects of exchange rates. Using this new definition of dollarization provides a different assessment of the trends in dollarization in LICs. Specifically, instead of a surge in dollarization, we find a continued decline in real dollarization ratio in LICs. In addition, the paper demonstrates that beyond the variance of inflation and depreciation, the level of inflation and size of depreciation also matter for dollarization.

This paper examines, using panel econometric methods, the responses of real dollarization in low-income countries to innovations in underlying factors, such as relative prices, exchange rates, global (as well as country-specific sovereign) risk or uncertainty. The results from the first two (fixed effects and system GMM) estimators suggest that (i) countries that had higher sovereign risk and entrepreneurial activity (as reflected in the Kauffman entrepreneurial activity indicator) exhibited higher surges in real dollarization; and (ii) exchange rate appreciation and declines in relative prices had dampening effects on real dollarization. However, exchange-rate-related macroprudential measures did not have statistically significant effects on dollarization, once other macroeconomic and institutional factors were controlled for.

A rise in global financial stress had a positive, albeit economically-small impact on real dollarization, based on results from the fixed effects estimator, but yielded statistically insignificant results under the system GMM estimator. Subsequent panel impulse-response functions reveal that shocks to global uncertainty or risk (as measured by the VIX indicator), exchange rates and relative prices have strong, albeit short-lived, impact on real deposit dollarization. The findings of a positive, albeit short-term, relationship between the VIX and dollarization suggests that in the face of global financial volatility, domestic residents increased foreign currency holdings due to uncertainty about spillover effects from the crisis. These results are broadly in line with conventional economic wisdom, particularly regarding investor flight to quality (and in some instances, home asset preference), and household asset value preservation in the face of adverse shocks.

The findings of this paper suggest that, while there could be scope for macroprudential measures in dampening dollarization, policy efforts should focus primarily on macroeconomic stability to contain inflationary pressures and to anchor exchange rate expectations. Macroeconomic stability and prudent debt management to avoid debt distress and taper sovereign risk would help better integrate low-income economies into the international financial market and dampen the impact and amplitude of the identified drivers of dollarization.

Appendix Figures

Figure 1.Estimated Panel Impulse-Response Functions.

(Based on estimated panel VAR(1) of [log. VIX, log. Rel P., log. Exrate, log. Rdol])

Source: IMF World Economic Outlook (WEO) database; and authors’ panel VAR estimates.

Notes: The dashed lines are the estimated responses of (a) relative prices, (b) exchange rates, and (c) real dollarization index to one standard deviation shocks to global volatility index (col. 1), relative prices (col. 2), and exchange rates(col. 3).

Figure 2.Estimated Panel Impulse-Response Functions

(Based on estimared Panel VAR(1) of [log. VIX, log. Exrate, log. Rel. P., log. Rdol])

Source: IMF World Economic Outlook (WEO) database; and authors’ panel VAR estimates.

Notes: The dashed lines are the estimated responses of (a) exchange rates, (b) relative prices, and (c) real dollarization index to one standard deviation shocks to global volatility index (col. 1), exchange rates (col. 2), and relative prices (col. 3).

Appendix Tables
Table A1.Country List and Average Deposit Dollarization, January 2006–December, 2009
nominalreal
EconomyCountryDOL1RDOL1
EMAlbania40.5435.84
LICArmenia51.7748.60
EMAzerbaijan, Rep. of59.6356.21
LICBangladesh2.092.20
EMBelarus41.9151.27
LICBhutan3.933.59
LICBolivia65.5559.34
LICBurundi15.8216.81
LICCambodia97.8094.47
LICCape Verde6.295.40
LICComoros0.280.25
LICDominica1.441.44
LICEritrea17.7017.70
LICGeorgia65.6459.75
LICGhana27.5840.93
LICGrenada6.096.09
LICGuyana2.933.00
LICHaiti52.9353.83
LICHonduras28.9628.96
EMKazakhstan38.6241.22
LICKenya16.4416.14
LICMaldives55.0855.08
LICMoldova45.9936.40
LICMongolia39.0844.81
LICMozambique42.0741.18
LICMyanmar0.280.26
LICNepal7.647.43
LICNicaragua90.85102.64
LICNigeria12.0513.00
EMPakistan7.349.63
LICPapua New Guinea9.958.96
LICSamoa3.173.05
LICSao Tome & Principe63.5779.41
LICSierra Leone32.3234.79
LICSolomon Islands7.137.43
LICSt. Lucia6.276.27
LICSt. Vincent & Grens.4.064.06
LICSudan20.0621.67
LICTanzania36.0335.23
LICTonga4.904.92
LICUganda28.5628.13
EMUkraine39.2056.66
EMUzbekistan26.0029.63
LICVanuatu53.1548.72
LICZambia39.6147.03
Source: IMF IFS and staff estimates
Source: IMF IFS and staff estimates
Table A2:Variable Description and Definition
VariableDescriptionDefinitionSource
yReal dollarization indexForeign currency deposits/domestic deposits in deposit -taking deposit-taking institutions that are included in the broad money Adjusted for exchange rate movementsIFS
RRRelative pricesDomestic price index/US consumer price indexIFS
SExchange ratesNominal exchange rates (US dollars per domestic currencyIFS
VIXGlobal risk aversionChicago Board Options Exchange (CBOE) Volatility IndexCBOE
Crisis_dumCrisis dummyAugust 2008-December 2009=1, zero otherwiseAuthors
KauffInstutional quality indicatorKauffman Entrepreneurial Activity IndexKauffman Indicator
ICRGInstutional quality indicatorInternational Country Risk Guide (ICRG)ICRG
Er_RegExchange Rate RegimeExchange rate regime dummy (higher more flexible)IMF ARAER
Prud_ERExchange rate related prudential indicatorPrudential measures undertakenIMF ARAER
Table A3:Summary Statistics of Monthly Panel Data, 2006-09
VariablesObservationsMeanStd. Dev.Min.Max.
Log. Real dollarization indexoverall4,5592.671.46−3.376.32
between971.42−2.324.62
within470.310.824.79
Log. Relative pricesoverall35044.710.134.485.37
between730.114.535.07
within480.084.345.04
Log. Exchange ratesoverall3534−4.562.50−24.960.09
between742.47−9.70−0.06
within480.53−21.300.10
Log. Global volatility indexoverall40702.131.38−0.364.63
between1850.002.132.13
within221.38−0.364.63
Crisis dummy (august 2008=1, zero otherwise)overall88800.350.4801
between1850.00.350.35
within480.4801
Kauffman Entrepreneurial Activity Indexoverall36964.481.631.248.13
between771.641.338.04
within480.134.075.09
International Country Risk Guide (ICRG)overall369641.9630.51080.00
between7730.64074.87
within482.0330.3247.89
Exchange rate regime dummy (higher more flexible)overall36962.140.5013
between770.3913
within480.321.163.10
Exchange rate related prudential indicatoroverall36960.030.1701
between770.0700.35
within480.15−0.331.01
Source: IMF World Economic Outlook (WEO) database; and authors’ estimations using panel methods in Stata.
Source: IMF World Economic Outlook (WEO) database; and authors’ estimations using panel methods in Stata.
Table A4:Estimated Variance Decompositions(Based on estimated panel VAR(1) of [log. VIX, log. Rel P., log. Exrate, log. Rdol])
Explained by a shock to:
Global volatility indexRelative pricesExchange rates
Variations in:k=10k=20k=10k=20
(a) Relaive prices8.556.7635.9324.981.312.11
(b) Exchange rates1.481.466.496.2126.9324.96
c) Real Dollarization1.881.8315.9215.2031.1529.77
Source: IMF World Economic Outlook (WEO) database; and authors’ estimations using panel methods in Stata.Note: Numbers presented in the table indicate percent of variation in (a) relative prices, (b) exchange rates, and (c) rea dollarization, k- periods after a one standard deviation shock to global volatility index, relative prices and exchange rates, using ordering of the variables indicated in the sub-title.
Source: IMF World Economic Outlook (WEO) database; and authors’ estimations using panel methods in Stata.Note: Numbers presented in the table indicate percent of variation in (a) relative prices, (b) exchange rates, and (c) rea dollarization, k- periods after a one standard deviation shock to global volatility index, relative prices and exchange rates, using ordering of the variables indicated in the sub-title.
Table A5.Estimated Variance Decompositions: Alternative of Variable for Choleski Decomposition(Based on estimated panel VAR(1) of [log. VIX, log. Exrate, log. Rel P., log. Rdol])
Explained by a shock to:
Global volatility indexExchange ratesRelative prices
Variations in:k=10k=20k=10k=20
(a) Exchange rates14.8314.5932.1529.981.271.18
(b) Relaive prices8.556.7615.4513.5621.7813.52
c) Real Dollarization1.881.8345.7543.711.311.26
Source: IMF World Economic Outlook (WEO) database; and authors’ estimations using panel methods in Stata.Note: Numbers presented in the table indicate percent of variation in (a) exchange rates, (b) relative prices, and (c) real dollarization, k- periods after a one standard deviation shock to global volatility index, relative prices and exchange rates, using ordering of the variables indicated in the sub-title.
Source: IMF World Economic Outlook (WEO) database; and authors’ estimations using panel methods in Stata.Note: Numbers presented in the table indicate percent of variation in (a) exchange rates, (b) relative prices, and (c) real dollarization, k- periods after a one standard deviation shock to global volatility index, relative prices and exchange rates, using ordering of the variables indicated in the sub-title.
References

    Aizenman, J., K.Kletzer, and B.Pinto,2005, “Sargent-Wallace Meets Krugman-Flood-Garber, or: Why Sovereign Debt Swaps do not Avert Macroeconomic Crises,” The Economic Journal, Vol. 115, No. 2, pp. 343–367.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Aghion, P, P.Bacchetta, and A.Banerjee.,2001, “Currency Crises and Monetary Policy in an Economy with Credit Constraints,” European Economic Review, Vol. 45(7), pp. 1121–1150.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Baba, N., R. N.McCauley, and S.Ramaswamy,2009, “U.S. Dollar Money Market Funds and Non-U.S. Banks,” Bank for International Settlements Quarterly Review (March), pp. 65–81.

    • Search Google Scholar
    • Export Citation

    Basso, H. S., O.Calvo-Gonzalez, and M.Jurgilas,2011, “Financial Dollarization: The Role of Foreign-Owned Banks and Interest Rates,” Journal of Banking & Finance, Vol. 35(4), pp. 794–806.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Branson, W. and D.Henderson,1985, “The Specification and Influence of Asset Markets” in Handbook of International Economics, ed. by Jones and Kenen (Amsterdam: North Holland).

    • Search Google Scholar
    • Export Citation

    Burnside, C., M.Eichenbaum, and S.Rebelo,2001, “Prospective Deficits and the Asian Currency Crisis,” Journal of Political Economy, Vol. 109, pp. 155–97.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Burnside, C., M.Eichenbaum, and S.Rebelo,2004, “Government Guarantees and Self-Fulfilling Speculative Attacks,” Journal of Economic Theory, Vol. 119, pp. 31–63.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Calvo, G. A. and P.Guidotti,1989, “Credibility and Nominal Debt: Exploring the Role of Maturity in Managing Inflation,” IMF Working Paper 89/73 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    Calvo, G. A. and C. A.Rodriguez,1977, “A Model of Exchange Rate Determination under Currency Substitution and Rational Expectations,” Journal of Political Economy, Vol. 85(3), pp. 617–25.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Cetorelli, N. and L. S.Goldberg,2009, “Globalized Banks: Lending to Emerging Markets in the Crisis,” Staff Report No. 377, Federal Reserve Bank of New York.

    • Search Google Scholar
    • Export Citation

    Chamon, M.,2001, “Foreign Currency Denomination of Foreign Debt: Has the Original Sin Been Forgiven but Not Forgotten?” (unpublished; Massachusetts: Harvard University).

    • Search Google Scholar
    • Export Citation

    Doblas-Madrid, A.,2009, “Fiscal Trends and Self-Fulfilling Crises,” Review of International Economics, Vol. 17, No. 1, pp. 187–204.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Dooley, M.,2000, “A Model of Crises in Emerging Markets,” The Economic Journal, Vol. 110 (460), pp. 256–272.

    Garcia-Escribano, M. and S.Sosa,2011, “What is Driving Financial De-Dolarization in Latin America?,” IMF Working Paper 11/10 (Washington: International Monetary Fund).

    • Crossref
    • Search Google Scholar
    • Export Citation

    Honig, A.,2009, “Dollarization, Exchange Rate Regimes, and Government Quality,” Journal of International Money and Finance, Vol. 28(2), pp. 198–214.

    • Crossref
    • Search Google Scholar
    • Export Citation

    IMF, 2011a, Global Financial Stability Report, September 2011: Toward Operationalizing Macroprudential Policies: When to Act?

    IMF, 2011b, “Global Financial Stability Report, March 2011: The Implications of the Global Financial Crisis for LICs.

    Ize, A. and E.Parrado,2002, “Dollarization, Monetary Policy, and the Passthrough,” IMF Working Paper 02/188 (Washington: International Monetary Fund).

    • Crossref
    • Search Google Scholar
    • Export Citation

    Ize, A. and E.Levy-Yeyati,2005, “Financial De-dollarization: Is it for Real?” IMF Working Paper 05/187 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    Ize, A. and E.Levy-Yeyati,2003, “Financial Dollarization,” Journal of International Economics, Vol. 59, pp. 323–347.

    Jeanne, O.,2003, “Why Emerging Markets Borrow in Foreing Currency,” CEPR Discussion Paper No. 4030.

    Kouri, P.,1976, “The Exchange Rate and the Balance of Payments in the Short-run and in the Long Run: A Monetary Approach,” The Scandinavian Journal of Economics, No. 2, Vol. 78, pp. 280–304.

    • Crossref
    • Search Google Scholar
    • Export Citation

    McKinnon, R. and H.Pill,1999, “Credible Economic Liberalizations and Overborrowing,” American Economic Review, Vol. 87, pp. 189–193.

    • Search Google Scholar
    • Export Citation

    Nicolo, D., P.Honohan, and A.Ize,2005, “Dollarization of the banking system: Causes and Consequences,” Journal of Banking and Finance, Vol. 29, pp. 697–1727.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Rennhack, R. and M.Nozaki,2006, “Financial Dollarization in Latin America,” in Financial Dollarization: The Policy Agenda, ed. by Armas, Ize, Levy (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    Reinhart, C., R.Rogoff, and M.Savastano,2003, “Addicted to Dollars,” NBER Working Paper No. 10015 (Cambridge, Massachusetts: National Bureau of Economic Research).

    • Search Google Scholar
    • Export Citation

    Sahay, R. and C.Vegh,1996, “Dollarization in Transition Economies: Evidence and Policy Implications,” in The Macroeconomics of International Currencies: Theory, Policy, and Evidence, ed. by Mizen and Pentecost (UK: Cheltenham).

    • Search Google Scholar
    • Export Citation

    Savastano, M.,1996, “Dollarization in Latin America: Recent Evidence and Some Policy Issues,” IMF Working Paper 96/4 (Washington: International Monetary Fund).

    • Crossref
    • Search Google Scholar
    • Export Citation

    Schneider, M. and A.Tornell,2004, “Balance Sheet Effects, Bailout Guarantees and Financial Crisis,” Review of Economic Studies, Vol. 71, pp. 883–913.

    • Crossref
    • Search Google Scholar
    • Export Citation

    Terrier, G., R.Valdes, C. E.Tovar, J. C.Chan-Lau, C.Fernández-Valdovinos, M.García-Escribano, C.Medeiros, M.Tang, M.Vera Martin, and C.Walker,2011, “Policy Instruments to Lean Against the Wind in Latin America,” IMF Working Paper 11/159 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
1

The authors would like to thank Catherine Pattillo, Charles Yartey, and Jean Noah for their comments; Sibrabata Das and Lamin Njie for excellent research assistance; and Nazma Nunhuck and Dilcia Noren for production.

2

See for example Honohan et. al. (2005) and Reinhart et. al. (2003).

3

Since we do not have an accurate estimate of foreign currency circulating in an economy at any point in time, the denominator is likely to be biased as well. Further, the ratio will be biased by other factors, including domestic agents’ preference to hold currency and therefore not reflect a clear picture of preference for holding foreign currency deposits over domestic deposits. See Savastano (1996) for more details.

4

In this definition, only deposits captured in broad money, and therefore expected to affect aggregate demand, are included.

5

The average surge in deposit dollarization is computed by taking the average during periods when the dollarization growth rate was increasing (i.e., greater than zero percent).

6

The Emerging Markets Trading Association (EMTA) reports that between Q2 and Q3 of 2008, local debt trade by foreign investors declined by 71 percent in LICs compared to only 22 percent for EMs. In addition, LICs volume of trade financing dropped by 18 percent in the last quarter of 2008, while the Merrill Lynch Africa Lions index, which tracks 15 African countries, declined by almost 70 percent during March–December 2008. See IMF, 2009.

7

Reserve requirements require banking institutions to hold a fraction of their deposit liabilities at the Central Bank in the form of cash or highly liquid sovereign paper. The regulation usually specifies the size of the requirement according to currency denomination (domestic or foreign currency) and maturity.

8

See Rennhack and Nozaki (2006) on the role of asymmetric exchange rate policy in asset portfolio choice.

10

See also Jeanne (2003) and Calvo and Guidotti (1989) for additional examples of implications of lack of monetary credibility on dollarization.

11

For example, Nicolo, Honohan, and Ize (2005) suggest that shifting from highly restrictive to completely unrestricted or liberal political system increases uncertainty and risk, and induces shifts in agent portfolios in favor of foreign assets.

14

For a detailed overview of recent experiences with prudential policies, see IMF (2011a) and Terrier et al (2011).

15

Indeed, Ize and Yeyati (2005) admit that “at any rate, minimum variance portfolio (MVP) explains only a limited share of dollarization in cross-country estimates of financial dollarization”; and this statement probably applies to the portfolio balance model, and perhaps all other models, as well.

16

We assume the following: (i) explanatory variables (xit), so that E[xitvit] = 0 ; (ii) correlated individual effects, such that E[xitηi] ≠ 0; and (ii) strict exogeneity, such that E[xitvis ] = 0, for all s, t.

17

Only countries that report data on dollarization are included; countries with no deposit dollarization (e.g., Benin and Cameroun) are excluded. LICs are identified from the list of countries deemed eligible for concessional financing from the IMF’s Poverty Reduction and Growth Trust (PRGT) as at April 8, 2013.

18

For brevity, only the final results from the fixed effects and system GMM are presented. Alternative model specifications (e.g., including lagged dependent variable in levels and results from other prudential measures) are not shown.

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