Back Matter

Author(s):
Mauro Mecagni, Juan Corrales, Jemma Dridi, Rodrigo Garcia-Verdu, Patrick Imam, Justin Matz, Carla Macario, Rodolfo Maino, Yibin Mu, Ashwin Moheeput, Futoshi Narita, Marco Pani, Manuel Rosales Torres, Sebastian Weber, and Etienne Yehoue
Published Date:
May 2015
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    References

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      Gulde-Wolf, Anne-Marie, DavidHoelscher, AlainIze, DavidMarston, and GianniDe Nicolò. 2004. “Financial Stability in Dollarized Economies,IMF Occasional Paper 230, International Monetary Fund, Washington.

      International Monetary Fund. 2012. Regional Economic Outlook for sub-Saharan Africa. Washington.

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      Kessy, Panteleo. 2011. “Dollarization in Tanzania: empirical evidence and cross-country experience,International Growth Centre Working Paper 11/0251, International Monetary Fund, Washington.

      Kokenyne, A., J.Ley, and R.Veryune. 2010. “Dedollarization,IMF Working Paper 10/188, International Monetary Fund, Washington.

      Levy-Yeyati, Eduardo. 2006. “Financial dollarization: evaluating the consequences,Economic Policy21(45): 61118.

      Marshall, D.A.1987. Inflation and Asset Returns in a Monetary Economy with Transactions Costs, Doctoral Dissertation, Carnegie-Mellon University.

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      Offenbacher, Akiva Edward, and RoyStein. 2003. “Dollarisation and Indexation in Israel’s Inflation and Disinflation: ‘There’s more than one way to skin a cat’.Comparative Economic Studies45(3): 278305.

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    Annex 1. World Dollarization Picture

    Figure A.1.Deposits Dollarization, 2001 and 2012

    (Deposits in Foreign Currency over Total Deposits)

    Source: International Financial Statistics (IFS) database, IMF.

    Figure A.2.Loans Dollarization, 2001 and 2012

    (Loans in Foreign Currency over Total Loans)

    Source: International Financial Statistics (IFS) database, IMF

    Table A.1.Dollarization, 2001–11(Average ratios of deposits/loans in foreign currency over totals)
    DepositsLoans
    2001–062007–11t-statistic2001–062007–11t-statistic
    Angola74.057.6(4.78) ***63.160.8(0.52)
    Benin0.00.0..0.00.0..
    Botswana0.00.0..0.00.0..
    Burkina Faso0.00.0..0.00.0..
    Burundi10.416.8(−5.01) ***......
    Cameroon0.00.0..0.00.0..
    Cape Verde5.05.0(−0.06)......
    Central African Rep.0.00.0..0.00.0..
    Chad0.00.0..0.00.0..
    Comoros0.90.2(2.09) *1.20.5(1.27)
    Congo, Dem. Rep. of83.288.2(−2.01) *65.088.5(−2.33) **
    Congo, Republic of0.00.0..0.00.0..
    Cote d’Ivoire0.00.0..0.00.0..
    Equatorial Guinea0.00.0..0.00.0..
    Eritrea17.916.1(2.54) **17.10.0(9.64) ***
    Ethiopia0.00.0..0.00.0..
    Gabon0.00.0..0.00.0..
    Gambia, The0.00.0..0.00.0..
    Ghana29.226.4(2.09) *19.027.3(−1.21)
    Guinea25.624.5(0.33)......
    Guinea-Bissau0.00.0..0.00.0..
    Kenya16.316.5(−0.26)14.416.0(−1.44)
    Lesotho0.00.0..0.00.0..
    Liberia86.887.7(−0.59)72.383.2(−6.69) ***
    Madagascar............
    Malawi19.013.1(3.31) ***......
    Mali0.00.0..0.00.0..
    Mauritius16.218.4(−1.05)0.00.0..
    Mozambique45.737.6(2.64) **47.028.1(3.81) ***
    Namibia0.30.7(−0.81)0.00.0..
    Niger0.00.0..0.00.0..
    Nigeria9.413.4(−2.21) *0.00.0..
    Rwanda25.919.4(4.02) ***0.00.0..
    Sao Tome & Principe54.361.4(−1.67)51.271.0(−1.53)
    Senegal0.00.0..0.00.0..
    Seychelles4.727.4(−6.86) ***9.531.7(−4.02) ***
    Sierra Leone28.435.4(−3.17) **0.00.1(−1.14)
    South Africa1.42.0(−1.80)0.60.6(−0.12)
    Swaziland0.00.0..0.00.0..
    Tanzania40.433.4(5.62) ***30.631.2(−0.40)
    Togo0.00.0..0.00.0..
    Uganda29.129.2(−0.05)22.725.0(−1.36)
    Zambia44.540.0(1.56)33.536.58(−0.39)
    Zimbabwe26.594.9(−10.38) ***......
    Albania32.544.9(−5.83) ***76.168.8(3.30) **
    Armenia68.754.3(1.99) *64.848.4(2.94) **
    Azerbaijan, Rep. of78.448.5(6.45) ***67.540.4(5.31) ***
    Bangladesh1.81.9(−0.79)0.00.0..
    Barbados12.38.1(1.35)0.00.0..
    Belarus50.048.6(0.19)45.232.0(3.07) **
    Belize5.84.0(4.08) ***0.98.4(−6.55) ***
    Bhutan4.04.1(−0.04)0.00.0..
    Bolivia83.544.9(6.68) ***93.657.8(4.55) ***
    Bosnia & Herzegovina52.748.9(1.55)1.88.5(−3.00) **
    Brazil......7.72.5(2.68) **
    Brunei Darussalam0.07.7(−1.83) ***0.00.5(−1.80)
    Cambodia96.197.0(−1.15)94.797.6(−2.74) **
    Canada26.941.3(−6.79) ***33.738.8(−1.51)
    Chile10.514.4(−2.71) **10.39.9(0.37)
    Colombia0.00.0..6.16.5(−0.43)
    Costa Rica44.945.4(−0.16)46.741.2(4.62) ***
    Croatia63.959.7(1.08)9.58.5(1.00)
    Dominica3.51.9(1.79)0.60.9(−0.66)
    Dominican Republic36.733.4(2.08) *21.915.9(2.07) *
    Egypt29.522.2(3.81) ***24.228.7(−3.18) **
    Fiji5.43.9(1.72)1.12.5(−2.23) *
    Georgia78.165.9(2.69) **79.168.0(4.71) ***
    Grenada6.56.7(−0.43)4.75.4(−0.76)
    Guatemala24.230.3(−1.01)31.635.8(−1.00)
    Guyana1.34.1(−4.81) ***0.00.0..
    Haiti48.054.6(−3.03) **50.054.2(−1.23)
    Honduras30.928.0(4.09) ***24.723.4(0.80)
    Indonesia16.715.1(1.62)24.617.9(2.37) **
    Iraq21.86.2(3.69) **0.40.3(0.76)
    Jamaica32.936.9(−1.65)25.128.6(−1.60)
    Japan0.70.9(−3.22) **0.60.9(−3.09) **
    Kazakhstan41.935.5(1.92) *49.340.1(3.31) **
    Korea, Republic of2.02.1(−0.55)5.27.1(−2.38) **
    Kosovo3.45.2(−4.36) **......
    Kuwait11.39.2(2.01) *1.78.2(−3.92) ***
    Kyrgyz Republic66.451.9(5.16) **71.063.2(7.55) ***
    Macedonia, FYR52.447.8(2.51) **21.224.2(−1.27)
    Malaysia2.24.1(−4.51) ***1.72.3(−1.69)
    Maldives57.953.4(1.76)57.569.5(−4.67) ***
    Mexico5.34.7(2.39) **8.76.7(1.83)
    Moldova46.244.3(0.97)40.243.2(−2.47) **
    Mongolia41.935.1(3.14) **40.032.3(4.45) ***
    Myanmar0.50.2(3.14) **0.80.4(3.68) ***
    Nepal7.26.5(1.44)1.11.9(−0.89)
    Nicaragua93.989.1(7.79) ***100.0100.0(1.33)
    Pakistan2.57.6(−2.92) **0.52.0(−2.13) *
    Papua New Guinea8.810.7(−1.59)6.94.7(1.87) *
    Paraguay58.834.5(6.20) ***45.134.1(3.90) ***
    Peru71.424.6(6.39) ***......
    Philippines28.121.9(7.02) ***2.212.9(−4.25) ***
    Qatar32.920.8(3.06) **23.628.9(−0.99)
    Romania36.935.4(0.64)56.658.2(−0.52)
    Samoa4.43.1(1.59)0.06.2(−15.35) ***
    Serbia, Republic of62.969.1(−2.15) *24.86.0(4.00) ***
    Solomon Islands2.55.2(−1.35)0.00.0..
    St. Kitts and Nevis21.918.9(1.81)4.84.9(−0.21)
    St. Lucia3.44.4(−0.74)9.820.6(−5.02) ***
    St. Vincent & Grens.2.44.6(−3.83) ***0.42.3(−6.80) ***
    Suriname45.554.2(−0.85)41.042.9(−0.21)
    Syrian Arab Republic11.723.7(−4.29) ***0.02.6(−3.59) ***
    Tajikistan57.961.6(−0.78)64.460.5(0.82)
    Thailand0.91.3(−2.48) **2.32.7(−0.37)
    Tonga8.94.5(2.47) **6.13.5(1.65)
    Trinidad and Tobago26.629.3(−1.09)8.320.6(−2.20) *
    Turkey42.030.2(2.99) **21.314.8(1.51)
    Ukraine34.341.5(−2.85) **41.348.1(−2.25) *
    Uruguay85.071.7(6.23) ***63.754.7(3.75) ***
    Uzbekistan30.622.4(5.09) ***76.240.8(8.95) ***
    Vanuatu63.544.9(4.14) ***12.315.6(−1.09)
    In parenthesis student t-stadistic for the null hyphothesis of difference between means equal zero***, ** and * correspond to 0.01, 0.05 and 0.1 significance level respectively
    Table A.2.Dollarization Levels by Region
    (average of ratios of deposits/loans in fx over totals)
    DepositsLoans
    2001–06S.D2007–11S.Dt-statistic2001–06S.D2007–11S.Dt-statistic
    Sub-Saharan Africa19.924.621.826.7(−0.74)14.223.216.626.2(−0.89)
    Latin America & the Caribbean29.830.625.224.8(1.31)26.329.124.625.0(0.48)
    East Asia and Pacific21.627.419.025.5(0.60)14.125.214.524.8(−0.10)
    Europe and Central Asia50.118.645.714.9(1.69) *50.623.640.720.1(2.86) ***
    High-income OECD members5.610.37.212.1(−0.53)9.914.35.411.3(1.17)
    High-income non OECD members24.521.221.018.7(0.74)7.29.812.610.9(−2.19) **
    Middle East and North Africa12.410.611.49.4(0.35)4.79.27.511.3(−0.93)
    South Asia17.125.124.028.1(−1.00)14.626.623.332.5(−1.14)

    Figure A.3.Loan and Deposit Dollarization by Region

    Annex 2. Measures to Mitigate Dollarization

    Durable de-dollarization relies on a credible disinflation plan and specific microeconomic (market-based) measures that facilitate financial sector development, increase the attractiveness of the local currency, and internalize risks associated with the use of foreign currencies, such as:

    Measures aimed at facilitating the development of the local financial market

    • Strengthening the payments system and enhancing the usability of the local currency. Dollarization often reflects the inadequacy of the domestic payments system in local currency. Addressing these deficiencies can encourage residents to enlarge their use of the local currency, build up credibility, and enhance confidence in the local currency.

    • Strengthening the central bank liquidity management and instruments. The use of the local currency can be made more attractive through a set of measures involving the central bank, which include: (1) imposing higher reserve requirements for foreign currency than for local currency; (2) strengthening the monetary transmission channels by introducing longer maturities, medium-term government bonds in local currency, and developing a benchmark yield curve; and (3) developing a foreign exchange market that reduces the need to hold precautionary foreign currency balances.

    • Developing domestic financial market and retail banking. Dollarization sometimes stems from the lack of liquid financial instruments (and markets) in local currency. In such situations, the issuance of local currency-denominated securities or assets should help develop domestic liquid monetary and capital and bond markets. Greater incentives to tap into retail banking should also enhance the potential for de-dollarization since most retail transactions are denominated in local currency.

    • Strengthening fiscal and public debt management. Fiscal prudence and consolidation, coupled with the issuance of local currency-denominated bonds, would help de-dollarize the government’s balance sheet by reducing the need for the government to borrow in foreign currency. Foreign aid ought to be absorbed in local currency and taxation designed so that it does not discriminate in favor of foreign currencies.

    Measures aimed at increasing the attractiveness of the local currency

    • Creating an interest rate wedge. The substitution between foreign currency and local currency can be encouraged by positive interest differentials that provide a higher remuneration to deposits in local currency; this has, however, in some cases, encouraged excessive and potentially destabilizing capital inflows.

    Measures aimed at internalizing the risks associated with the use of foreign currencies

    • Implementing effective supervision and prudential regulations. An enhanced regulatory compact can effectively encourage de-dollarization through the internalization of the risks of doing business in foreign currency. For instance, in many countries, banks lending in foreign currency to nonexporters have to abide by stricter regulatory requirements and to set up higher provisions.

    • Excluding foreign currency deposits from deposit insurance schemes.

    Concerning direct and administrative controls, some countries have tried different measures, including forced de-dollarization, such as:

    • Imposing higher reserve requirements on foreign currency. Measures aimed at either remunerating reserve requirements on local currency deposits at a higher rate or at imposing higher reserve requirements on foreign currency deposits may increase the deposit rate differential but also complicate banks’ management of net open positions (such as, Bolivia, Honduras, Israel, and Nicaragua).

    • Mandating the use of local currency in domestic transactions. This measure has been widely adopted in many countries, and can be either limited to tax payments and transactions involving the government and other public entities or can be applied to all transactions between, or involving, residents (such as, Israel, Lao P.D.R., and Peru).

    • Introducing regulatory discrimination against the use of foreign currency. Supervisory authorities can encourage the use of the local currency by imposing limits on foreign currency borrowing and lending (such as, Angola, Argentina, Israel, Turkey, and Vietnam).

    Forced de-dollarization

    • Mandating the conversion of foreign currency obligations and balances into domestic currency. Some countries (such as, Bolivia, Mexico and Peru) have attempted to use this measure but reversed it after it triggered capital flight and reduced financial intermediation.

    Gulde-Wolf and others (2004) suggest that dollarization could undermine financial stability through risks associated with liability dollarization, that is, when the increase in local currency value of dollar liabilities exceeds the value of the borrower’s income flow. Liability dollarization can often lead to sudden stops, and to corporate and banking crises, as witnessed during the East Asian crisis of 1997.

    Most estimates of the extent of dollarization in a country do not include foreign currency in circulation since it is a difficult concept to measure. A narrow version looks at the ratio of foreign currency deposits to total deposits or to broad money. Reinhart, Rogoff, and Savastano (2003) construct a broader measure of dollarization through an index of economic and financial indicators. Baliño, Bennett, and Borensztein (1999) consider an economy to be dollarized if the ratio of foreign currency deposits to broad money exceeds 30 percent. A model to estimate dollars in circulation in Cambodia is presented in de Zamaróczy and Sa (2003).

    To supplement these analyses, Annex 1.1 describes dollarization levels for households and firms in the SSA region, focusing on balance sheet analysis of dollarization by sectors and depicting restrictions on the use of foreign currency in SSA.

    CFA countries comprise WAEMU (Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, Togo) and CEMAC (Cameroon, Central African Republic, Chad, Republic of Congo, Equatorial Guinea, Gabon).

    The main source of these data to estimate dollarization levels is the IMF International Financial Statistics (IFS) database, which provides information on foreign currency denominated deposits or loans for an average of 100 countries during the period 2001–11. For the SSA region, the number of countries with available data in IFS is 30 for deposits and 29 for loans. In order to expand the coverage of countries in SSA, we used data from the African Department database. However, while IFS decomposes the information by sectors of the economy, the AFR database does not. Therefore, in the broad definition of dollarization used in this document, all sectors of the economy are included except for central bank and central government.

    Including all countries but Zimbabwe, the average of deposits dollarization was 14 percent in 2001 and 16 percent in 2012, while the average for loans was 7 percent in 2001 and 12 percent in 2012.

    Starting in 2003, deposits in FX in the Latin America and Caribbean region reached 36.3 percent and went down to 25.6 percent in 2012.

    The use of a common currency within a currency union including CEMAC and WAEMU (CFA francs) and Common Monetary Area (South African Rand) is not considered dollarization.

    It should be underscored that, firms in SSA are often owned by single individual families.

    Data in this table are sourced from the Standardized Report Forms (SRFs) provided by the country authorities to the Statistics Department (STA) of the IMF, which provide enough detail to distinguish between households from firms. This approach can be limited as countries have adopted the SRFs over time. For some countries data in the SRF format are compiled from pre-SRF data not based on the 2000 Monetary and Financial Statistics Manual (MFSM) methodology, leading to series breaks.

    As illustrated earlier, dollarization of credit and deposits appears not to change significantly in most countries during the period analyzed in SSA. To confirm this, the data is tested for stationarity, using the Levin-Lin-Chu test, which applies to balanced panels. The results confirm that over the period tested, 2000–11, that both deposit dollarization and credit dollarization are stationary, suggesting that we do not need to use panel-co-integration techniques (see Annex 2.2). Annex 2.1 presents further details of the estimation of cross-sectional averages in the spirit of Levy-Yeyati (2006), contrasting these results with a panel regression with robust standard errors, and accounting for the endogeneity and the bounded nature of the dependent variable. While the key findings remain robust throughout these regressions, significance levels vary, often due to the reduction in sample size implied by the various methods.

    We make use of two main measures of dollarization that are most widely available: deposit dollarization and loan dollarization. Deposit dollarization is the most frequently used concept since it is also the most widely available statistic. The main source for this data is the International Financial Statistics database. The extent of dollarization is the sum of “transferable deposits” and “other deposits” included in M2 of all sectors excluding the central bank and the central government. However, several countries do not fully report to IFS the level of dollarization. For these countries, and for countries where additional information is available, IMF country desk data is used to complement the data. The other measure we employ is the extent of loan dollarization. Again, not all countries full report the level of loan dollarization, where we again resort to IMF country desk data to complete this time series. Loan dollarization is measure as the ratio of loans in foreign currency to total loans across all sectors except for central bank and central government.

    One issue this chapter was not able to test is how prudential regulations have impacted dollarization in SSA, for lack of data. Evidence from Latin America and transition economies (for example, Kokenyne and others 2010) suggests that these measures—currency blind financial safety nets, implicit debtor guarantees—are crucial to provide incentives to de-dollarize.

    The sample excludes from the baseline specification countries that have zero deposit dollarization due to legal restrictions—WAEMU, CEMAC, Lesotho, and Swaziland—as their inclusion could bias the results. Furthermore, the analysis restricts some specifications to exclude all zero observations and other countries which potentially have legislation/practices in place that restrict the use of FX which we are not accounting for.

    Based on the work of Ize and Levy-Yeyati (1998), the minimum variation portfolio (MVP) model postulates that the choice of holding deposits in local currency or FX is determined by the relative volatility of the real exchange rate and inflation. If the real exchange rate depreciation is less volatile than inflation, then consumers would prefer to hold dollar deposit as it would be less risky. Here we focus on the expected return difference rather than the variance. Including the MVP would be a relevant extension.

    No full dataset on the actual remuneration of domestic foreign currency deposits is available. Thus, the return on dollar deposits is measured by the deposit rate in the United States adjusted by the expected exchange rate change.

    The results on Table 2.1 show countries that present some degree of dollarization and exclude zeros.

    For loan dollarization, the relationship may be of a different nature, as banks may use foreign debt to lend in foreign exchange (passing on the exchange rate risk). This nevertheless also implies an expected positive sign.

    SSA countries with de facto floats in selected years include South Africa, the DRC, Liberia, Madagascar, Malawi, Mauretania, Nigeria, Sierra Leone, and Zambia. Excluding South Africa would strengthen the finding. Note that the base category is intermediate regimes, which are associated often with the highest degree of dollarization. In some instances, the dummy for floating regimes drop since there are no sufficient observations.

    One may argue that FX deposits are a determinant of foreign loan extension. An extension could consider modeling the two variables in a system of equations.

    Note that we control for GDP per capita and financial development, so this effect does not just capture some degree of development.

    Kokenyne and others (2010) provide a summary of the key policies that encourage de-dollarization.

    Uruguay has also reduced dollarization levels, although more gradually than Bolivia.

    For instance, if the initial dollarization rate is 50 percent, a country is said to have de-dollarized if its dollarization rate has declined below 40 percent (that is, by 10 percentage points, or 20 percent of 50 percent).

    Reinhart and others (2003) used a stricter criterion, considering a country to have experienced a “large and lasting” reversal of dollarization if the share of foreign currency deposits on total deposits had declined by at least 20 percentage points, had settled below 20 percent immediately after the decline, and had remained below 20 percent until the end of the sample period.

    Excluding Canada, which had a dollarization rate slightly above 30 percent in 2001 but for which data on foreign currency deposits were available only until 2008.

    Of these, only two—Angola and Mozambique—were in SSA. In Angola, the share of foreign currency deposits on total deposits has declined from about 75 percent in 2001 to less than 50 percent in 2012; in Mozambique, the same share has declined from 56 percent in 2001 to 32 percent in 2012.

    The Polity IV democracy index, published by the Center for Strategic Peace as part of its INSCR (Integrated Network for Societal Conflict Research) database, measures institutional democracy on an 11-point scale (0–10) by combining indices that assign a quantitative measure (coding) to different features that qualify the competitiveness of political participation, the openness and competitiveness of executive recruitment, and constraints on the chief executive (such as, the presence of contested elections). The index takes large negative values (−66, −77, −88) to describe particularly fluid or volatile situations, such as foreign interventions, anarchy/interregnum, or transition between regimes.

    The word probability is used here to describe a statistical correlation, and not in a stochastic sense. We define the probability of success of a country satisfying a certain condition as the share of successful countries that met that condition.

    In sub-Saharan Africa, Angola and Mozambique (which de-dollarized) experienced a maximum appreciation of 14 percent and 23 percent, respectively, while Tanzania and Uganda (which did not de-dollarize) had a maximum appreciation slightly above 10 percent.

    All dollarized countries in SSA, except Angola, had an average current account deficit above 5 percent of GDP between 2001 and 2012, and all, except Angola, Sao Tomé and Príncipe, and Zambia, had an average fiscal deficit above 2 percent of GDP.

    Owing largely to the HIPC initiative, all dollarized countries in SSA, except Zimbabwe, experienced a significant reduction in external debt.

    Only four of these candidates (Angola, Mozambique, Tanzania, and Uganda) are countries in SSA. Angola and Mozambique are successful countries that significantly reduced their dollarization ratios between 2001 and 2012. Tanzania and Uganda started with lower dollarization rates (about 40 percent in Tanzania and 27 percent in Uganda) but failed to reduce them over time (in Uganda the rate of dollarization even increased, to 33 percent, by 2012). Five countries in SSA are not candidates; of these, three are outliers (the Democratic Republic of the Congo, Liberia, and São Tomé and Príncipe); for the other two, the public debt data are either missing (Zimbabwe) or show an initial level of public debt below 40 percent (Zambia).

    F.Y.R. of Macedonia.

    The two unsuccessful candidates in SSA (Tanzania and Uganda) had both an average inflation rates above 9 percent of GDP and an average fiscal deficit above 2 percent of GDP. By comparison, Mozambique had an inflation rate below (albeit close to) 9 percent and Angola kept an average fiscal surplus.

    About 70 percent of the variance of the dependent variable is across countries, and only 30 percent within countries. Panel data estimates obtained running the same model of Table 3.3 on annual data explains only about 25 percent of the total variance, and has a poor fit.

    Real GDP growth, inflation, and the fiscal balance explain 54 percent of the cross-country variance in nominal exchange rate appreciation after 2003.

    The model incorrectly predicts that Turkey’s dollarization rate would decline only marginally, from 50 percent in 2001–03 to 43–45 percent in 2010–12, whereas in fact it declined below 30 percent. Mozambique’s dollarization rate is projected to remain broadly constant at about 50 percent, or decline marginally to 44 percent, depending on the specification, while it actually declined to 34 percent.

    This group includes Nicaragua and Cambodia and, depending on the specification, Armenia, Bosnia and Herzegovina, Croatia, Macedonia, and Vanuatu. The dollarization rate in Cambodia and Nicaragua remained broadly constant at about 90 percent, whereas the model predicts a decline to 65–75 percent in both countries. In Vanuatu the model actually underpredicts the reduction in deposit dollarization; nevertheless, Vanuatu failed to meet our criterion for de-dollarization as its share of foreign currency loans on total bank loans increased over time. In the other countries, the dollarization rate declined more modestly than predicted by some specifications of the model.

    The model estimates the probability of success at 40 percent for Angola, 17 percent of Kazakhstan, 39 percent for Paraguay, and slightly less than 50 percent for Peru.

    Real GDP growth, inflation, and the fiscal balance explain 64 percent of the cross-country variance in the current account balance after 2003.

    Contrasting with the previous cases, Zambia presents a peculiar characteristic: a downward trend in deposits’ dollarization but an upward trend in loans’.

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