Abstract
The recent rash of international currency crises has generated considerable interest in the role that exchange rate regimes have played in contributing to these crises. Many economists have argued that efforts to operate adjustably pegged exchange rate regimes have been a major contributor to “the unstable middle” hypothesis and some have argued that this unstable middle is so broad that only the two corners of hard fixes or floating rates will be stable in a world of high capital mobility—the two corners or bipolar hypothesis. Two recent empirical studies by researchers at the International Monetary Fund reach opposing conclusions on these issues. We examine the issue further and show that conclusions can be quite sensitive to how exchange rate regimes are grouped into categories and the measures of currency crises that are used. In general we find that the dead center of the adjustable peg is by far the most crisis prone broad type of exchange rate regimes, but that countries need not go all the way to freely floating rates or hard fixes to substantially reduce the risks of currency crises.
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Notes
Note that Tavlas himself does not subscribe to the bipolar hypothesis, however. He argues that sound macro fundamentals, among other things, are essential for a sustainable exchange-rate regime.
A third approach is to examine transitions of exchange rate regimes over time. Masson (2001) uses a Markov chain model of exchange rate transition to test for the two corners hypothesis. He argues that neither fixes nor floats are absorbing states or that fixing and floating together form a closed set. For an absorbing state, there are no transitions away from the regime itself, while for a closed set; there can be transitions from intermediate regimes to fixes or floats. He concludes that intermediate regimes would continue to constitute an important fraction of actual exchange rate regimes.
In this study, we use the new IMF regime classifications compiled by BOR (2002). The IMF also published a set of de facto exchange rate arrangements for all member countries in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (2004). The data provides eight categories of classification: (1) Exchange arrangement with no separate legal lender, (2) Currency board arrangement, (3) Conventional pegged arrangement, (4) Pegged exchange rate within horizontal bands, (5) Crawling peg, (6) Crawling band, (7) Managed floating with no pre-announced path for the exchange rate, (8) Independently floating. This classification scheme closely corresponds to BOR’s regime classification, except that BOR also distinguish between forward and backward crawling pegs/bands under the crawling category and tightly managed floating versus other managed floating under the managed floating category, which results in a more detailed thirteen categories of exchange rate regime classification (see Appendix 2).
Neither the new IMF (2004) nor R–R’s classification systems adequately classify countries that have nominally free floating exchange rate regimes but engage in heavy intervention in practice. For example, the IMF and R–R classify Japan and Korea as independent and free floats, respectively, but it is known that both countries intervened heavily in their foreign exchange markets, particularly after the Asian crisis. See Willett and Kim (2006) and Willett et al. (2006).
This methodology is used by BOR (2003). We also check the robustness of our results by controlling for country fixed effects, which allows us to capture the difference across different countries in the sample. The limitation of controlling for the country fixed effects is that countries not having experienced any currency crisis are removed from the sample. This results in a loss of degrees of freedom in the estimation.
See Appendix 1 for the list of countries.
These reported results will allow us to both compare the predicted probabilities of crises across all six types of regimes and analyze whether those differences are statistically significant.
We suspect that BOR do not fine statistically significances among the various intermediate regime classification because we expect there to be little functional difference in crisis proneness among some of the intermediate regimes and that in some cases clear dividing lines cannot be determined. In addition, for many of the fine classifications of the regimes there are quite limited numbers of observations.
The number in parentheses is BOR’s 13-category regime classification, see Appendix 2.
See Willett (2007).
We also planned to compare the crisis dates with Bordo et al. (2001), but there wasn’t a large enough overlap of the samples to make this analysis fruitful.
See Angkinand et al. (2006) for more discussions on the measures of currency crises and how periods of crises are sensitive to selected criteria and weighting schemes.
The difference of frequency for some regimes, particularly under a conventional fixed peg to a single currency, is due to our use of annual data instead of the monthly data as used by BOR. The use of annual data is necessary for the use of crisis dates from the other studies.
Hard pegs and tightly managed floats are excluded from the regressions. There are no identified currency crises among European countries after they adopted the Euro currency in 1999. This leads to the prediction failure from the logit estimations under the hard pegs. There is only one industrial country (Norway) classified as adopting a tightly managed regime and there is no crisis during its period of use (1993–1996).
At first glance the positive coefficient, although not significant, of crawls offers very surprising results [column (7), Table 4]. This may suggest that the probability of crises under crawls is higher than under adjustable parities. A little digging, however, explains this anomaly. The only two industrial countries adopting crawling pegs/bands are Greece and Portugal (1990–1998). We suspect that whether or not these results are good predictors of what would happen if say Canada adopted a crawling peg.
Note that where we are dealing with necessary policy choices, useful statistical information is not confined only to statistically significant relationships. Clearly a government would not be wise to switch from a successfully operating regime to another based on estimates that there was only a 60–40 chance of improvement. But for that matter they would also be unlikely to switch in the face of a 95% probability of improvement, if that expected improvement was quite small. On the other hand, if a crisis has forced a country off of one regime and it is temporarily floating, than it must decide whether to continue floating or adopt some other regime. In such a case there is little reason for a policy biased toward the status quo. Given equal expected gains and losses, a 60% probability of picking the better regime is better than a 40% probability. Thus if we find substantial differences in frequencies of crises across different regimes, this is useful information for policy makers even if the differences are not statistically significant at the traditional levels.
We report the results only for the overall sample and emerging market economies in the sensitivity tests, since as discussed the latter sample is more relevant to our analysis. In addition, in some specifications (e.g. using GH’s crisis measure) the total observations are dramatically reduced for separate regressions of industrial countries and developing countries.
See Willett et al. (2006) for R–R’s and LY–S’ classifications of exchange rate regimes and their limitations.
If we followed Reinhart and Rogoff’s strategy of putting high inflation flexible rate countries into a separate category of freely falling rates, than the remaining “free floating” regimes should perform much better.
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Acknowledgments
Financial assistance is gratefully acknowledged from the Freeman Foundation Program in Asian Political Economy at the Claremont Colleges and the National Science Foundation. We are grateful for the comments received from Arthur Denzau, Nancy Neiman Auerbach, Pierre Siklos, and the participants at the 2004 Claremont-IIE workshop on the Political Economy of Exchange Rate Regimes, the 2005 International Studies Association Annual Meeting, and the 2005 Western Economic Association Annual Meeting. We also would like to thank Bubula and Otker-Robe for sharing their exchange rate regimes data.
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Appendices
Appendix 1 Sample Countries
1.1 Sample for 21 Industrial countries
Australia | Denmark | Iceland | Netherlands | Spain |
Austria | Finland | Ireland | New Zealand | Sweden |
Belgium | France | Italy | Norway | Switzerland |
Canada | Greece | Japan | Portugal | United Kingdom |
1.2 Sample for 42 Emerging Market Economies:
Argentina | Ecuador | Kenya | Pakistan | Sri Lanka |
Bangladesh | Egypt | Korea | Peru | Thailand |
Botswana | Estonia | Kuwait | Philippines | Turkey |
Brazil | Hong Kong | Latvia | Poland | Ukraine |
Bulgaria | Hungary | Lithuania | Russia | Uruguay |
Chile | India | Malaysia | Singapore | Venezuela |
China | Indonesia | Mexico | Slovakia | Zimbabwe |
Colombia | Israel | Morocco | Slovenia |
|
Czech Republic | Jordan | Nigeria | South Africa |
|
1.3 Sample for 27 Developing Countries:
Algeria | Côte d’Ivoire | Lebanon | Romania | Uzbekistan |
Bahrain | El Salvador | Macedonia, FYR | Saudi Arabia | Vietnam |
Belarus | Ghana | Nepal | Syria | Yemen, Republic of |
Bolivia | Georgia | Oman | Tanzania |
|
Cameroon | Iraq | Panama | Tunisia | |
Costa Rica | Kazakhstan | Paraguay | United Arab Emirates | |
Appendix 2 Variable Descriptions
2.1 Currency Crisis Index
We obtain the data for currency crisis episodes from three sources. The first source is from Bubula and Otker-Robe, BOR, (2003). They measure the exchange market pressure (EMP) from a weighted average of the monthly percentage change in exchange rate vis-à-vis the anchor country and the monthly variation in percentage points in the domestic interest rate. Currency crises are identified if the EMP index exceeds three times standard deviations from its country-specific sample mean.
The second source is from Glick and Hutchison, GH, (1999). They construct the EMP index from a weighted average of monthly real exchange rate changes and international reserve loss. A crisis is identified when the EMP index exceeds two times country-specific standard deviation plus country-specific mean. The crisis window (whether the large value of the EMP is counted as the same or new crisis) is 24 months.
For the third source, we construct an alternative EMP index following the methodologies of Eichengreen et al. (1995) and Kaminsky and Reinhart (1999). The EMP index is calculated from a weighted average of the depreciation of the domestic currency vis-à-vis the anchor country, the loss of international reserves, and the increase in interest rates (we therefore call this index as a Three-Component Currency Crisis Index). For weighting scheme, we use the pooled precision weights (the weights attached each of these three components are calculated from the inverse of their respective standard deviations calculated from the total sample). Currency crises are identified if the EMP index exceeds two standard deviations from its country-specific sample mean. The crisis window is 12 months.
2.2 Exchange Rate Regimes
This paper uses the classification of exchange rate regimes from three sources. The main source is the IMF de facto exchange rate regime classifications, compiled by Bubula and Otker-Robe (2003). The exchange rate regimes are divided into thirteen categories: (1) dollarization, (2) currency unions, (3) currency boards, (4) conventional fixed peg to a single currency, (5) conventional fixed peg to a basket, (6) horizontal band, (7) forward looking crawling peg, (8) backward looking crawling peg, (9) forward looking crawling band, (10) backward looking crawling band, (11) tightly managed floating, (12) other managed floating with no predetermined exchange rate path, and (13) freely floating rates.
Based on these thirteen categories, we use a six-way grouping in this paper: hard pegs (1–3), adjustable parities (4–6), crawls (7–10), tightly managed floats (11), other managed floats (12), and floats (13).
Alternatively, we use a four-way grouping: hard pegs (1–3), adjustable parities (4–6), crawls (7–11), and floats (12–13).
The second source of regimes data is from Reinhart and Rogoff, R–R, (2004). Their fourteen categories of exchange rate regimes are: (1) No separate legal tender, (2) Pre announced peg or currency board arrangement, (3) Pre announced horizontal band that is narrower than or equal to ±2%, (4) De facto peg, (5) Pre announced crawling peg, (6) Pre announced crawling band that is narrower than or equal to ±2%, (7) De facto crawling peg, (8) De facto crawling band that is narrower than or equal to ±2%, (9) Pre announced crawling band that is wide than or equal to ±2%, (10) De facto crawling band that is narrower than or equal to ±5%, (11) Moving band that is narrower than or equal to ±2% (i.e., allows for both appreciation and depreciation over time), (12) Managed floating, (13) Freely floating, and (14) Freely falling.
In this paper, we group R–R’s fine fourteen regimes into seven regimes: hard pegs (1–2), adjustable parities (3–4), crawls (5–10), moving bands (11), managed floats (12), freely floats (13), and freely falling (14).
The third source of regimes data is from Levy-Yeyati and Sturzenegger, LY–S, (2005). They divide exchange rate regimes into five categories: fixed, crawls, dirty floats, and floats. The fifth category is “inconclusive” category. We use their original classification.
2.3 Economic Control Variables
Lending Boom: domestic credit growth as a percentage of GDP—the change in private lending to the banking sector from IFS line 32d.
M2/Reserve: the ratio of M2 over reserves from World Development Indicators (WDI).
CA/GDP: the ratio of current account balance over GDP from World Development Indicators (WDI).
REER: the real effective exchange rate index. It is calculated from nominal effective exchange rate (a measure of the value of a currency against the weighted average of several foreign currencies) divided by a price deflator or index of costs. Data is obtained from both JP Morgan and World Development Indicators (WDI).
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Angkinand, A., Chiu, E.M.P. & Willett, T.D. Testing the Unstable Middle and Two Corners Hypotheses About Exchange Rate Regimes. Open Econ Rev 20, 61–83 (2009). https://doi.org/10.1007/s11079-007-9066-0
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DOI: https://doi.org/10.1007/s11079-007-9066-0