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References

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APPENDIX I

Data Construction and Sources

A. Sample

Industrial Countries (20):

  • Australia, Austria, Belgium, Canada, Denmark, Finland, France, Greece, Iceland, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, and United Kingdom

Emerging Market Countries (41):

  • Latin America (12): Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Jamaica, Mexico, Paraguay, Peru, Uruguay, and Venezuela.

  • Asia (13): Bangladesh, China, Hong Kong SAR, India, Indonesia, Korea, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan POC, and Thailand.

  • Countries in Transition (7): Bulgaria, Czech Republic, Hungary, Poland, Romania, Russia, and Slovak Republic.

  • Middle East and Africa (9): Egypt, Israel, Kenya, Morocco, Nigeria, Tunisia, Turkey, South Africa, and Zimbabwe.

B. Dependent and Explanatory Variables
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C. Measuring Trade Contagion

The simplest measure is the share of bilateral trade with the crisis country. This measure, however, does not take into account the substantial indirect trade effects via competition on third markets. Glick and Rose (1998) propose a measure of trade contagion which combines a direct bilateral linkage component and a third markets linkage component. The problem with the first component is that even if the bilateral trade flows are minimal it would indicate a very large direct trade linkage when bilateral trade is almost balanced. The problem with the second component is that it aggregates proxies for third markets competition which are biased; these proxies would indicate that the degree of competition faced by country 0 (say USA) from country I (say Uganda) in market K (say Germany) is the same as the degree of competition faced by country I (say Uganda) from country 0 (say USA) in market K (say Germany).

We build a new measure of trade contagion to avoid some shortcomings of measures previously used. Our proposed trade contagion variable averages the price and income effects induced by crises in other countries during the crisis window. A relative weight of one to two for the price and the income effect is chosen on the basis of the export elasticities estimated by Senhadji and Montenegro (1998) over a large sample of countries.

Ideally, the implied post-crisis real exchange rate appreciation is the expected loss of competitiveness for each country arising from exchange rate crashes in other countries (price effect) and should be calculated by adding all the effects of competitor-country devaluations, both via bilateral trade linkages and via competition in third markets, but should exclude own-country exchange rate changes. By construction, INS (IMF) data on real effective exchange rates (REER) account for both the direct and indirect effects of exchange rate movements of all partner countries. To neutralize the own-country real exchange rate effect, the data are adjusted by replacing the actual exchange rate changes and inflation of the specified country during the six-month crisis window with projections based on the trend in the three years prior to the crisis. This provides a proxy of the loss of competitiveness that the financial markets may have expected this country to suffer if its exchange rate and price levels maintained the pre-crisis path, while the rest of the world underwent the actual events.34

A similar methodology is used to measure the implied post-crisis export market growth - i.e., the expected output contraction of partner countries due to the crisis (income effect). It is a trade-weighted average of the slowdown in output growth of partner countries during the year after the crisis (from IMF World Economic Outlook projections for 1998 and 1999) with respect to the average growth rate during the three years before the crisis.35

APPENDIX II

Crisis Countries1,2

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E refers to ERM crisis, M to Mexican crisis, A to Asian crisis, and R to Russian crisis.

Countries without any crises in these indices - Industrial: Australia, Austria, Belgium, France, Japan, and Netherlands; Latin America: Chile, Costa Rica, and Jamaica; Asia: Bangladesh and China; Countries in Transition: Bulgaria; and Middle East and Africa: Egypt.

1

This paper builds on the empirical research underlying Chapter III of the May 1999 World Economic Outlook of the International Monetary Fund. The authors would like to thank participants in seminars at the “International Economics Conference,” Georgetown University, November 12-14, 1999 and at the IMF Research Department for useful comments and suggestions. The authors also thank Gretchen Byrne, Lisa Nugent, and Patricia Medina for excellent research and other assistance.

2

In this paper, contagion is defined broadly to include both spillover and “pure contagion” effects as described in Masson (2000).

4

For a taxonomy of the linkages explaining contagion, see Masson (2000). Early papers on contagion include, Agenor and Aizenman (1998); Eichengreen, Rose, and Wyplosz (1996); and Gerlach and Smets (1995).

5

The role of trade linkages in contagion has been explored by Eichengreen, Rose, and Wyplosz (1996) for industrial countries during the ERM crisis, and by Glick and Rose, (1998) more generally and especially for emerging market economies. See also Van Rijckeghem and Weder (1999a). For a theoretical analysis of the welfare effects of trade contagion, see Corsetti, Pesenti, and Roubini (1999).

6

The role of financial linkages in contagion has also been explored, inter alia, by IMF (1999), Baig and Goldfajn (1999), Bussiere and Mulder (1999), and Gelos and Sahay (1999). For the role of mutual funds as a channel of financial spillover, see Borenzstein and Gelos (1999), Levy-Yeyati and Ubide (1999), and Frankel and Schmukler (1996). For a theoretical model of financial linkages that could support the common creditor argument, see Allen and Gale (1999) and Lagunoff and Schreft (1998). For a model of financial contagion via rebalancing of portfolio for risk-management reasons, see Choueiri (1999).

7

See Calvo (1997, 1999); Sachs, Tornell and Velasco (1996); and Tornell (1998). For models on the information effects and financial contagion, see Huang and Xu (1998) and Calvo and Mendoza (1999). For a model of contagion due to political effects, see Drazen (1998).

9

For example, Frankel and Rose (1996) define a “currency crash” as a nominal depreciation of the currency of at least 25 percent in a year, along with a 10 percent increase from the previous year in the rate of depreciation. The latter condition is included so as to omit from currency crashes the large trend depreciations of high-inflation countries.

10

Among others, see Eichengreen, Rose, and Wyplosz (1996) and Kaminsky and Reinhart (1996). As in the latter, interest rates are excluded from the index because of the lack of comparable, market-determined interest rate data for many of the emerging market economies for the full sample period.

11

See Appendix I for the list of countries and more details on data construction and sources. The group of countries include 20 industrial countries and 41 emerging market economies. Germany and the United States serve as the reference countries for the European (other than Russia) and the non-European economies, respectively, and are therefore not included in the sample.

12

For the Russian crisis, the crisis period is only four months because the data sample ends in November 1998.

13

For CRIHIGH (CRILOW), the threshold is 1.96 (1.28) times the pooled standard deviation of the calculated index plus the pooled mean of the index, so that 2½ (10) percent of the monthly index values will exceed that threshold if the values are distributed normally. See Appendix I for details on the composition of the indices with additional financial market variables.

14

For a list of these countries, see Appendix II. Appendix II also lists countries which suffer from crises identified by using the alternative indices.

15

The Middle East and Africa region has experienced a larger incidence of crises than other regions because several of the countries in this region have relatively volatile international reserves. As a result, the incidence of crises for these economies may be biased somewhat upward.

16

See Appendix I for precise definitions of these variables.

17

Averages including only the ERM crisis for the industrial countries are not substantially different in general from the industrial country averages, and averages pooling all global crises for the emerging market economies or pooling only the Mexican and Asian crises for the emerging market economies are not substantially different in general from the emerging market country averages.

18

Univariate probit regressions of a crisis dummy variable on the variables discussed below yield almost identical results, in terms of statistical significance, to those obtained from differences between averages of variables of crisis and noncrisis countries.

19

Banking crises may only be a lagging indicator of banking sector problems. See IMF (1998) for a more complete discussion of this issue. The data set for banking crises is described in Aziz, Caramazza, and Salgado (1999) and in IMF (1998). It was augmented to cover the additional 10 countries included in the analysis of this paper.

20

Other studies, however, have found that other domestic macroeconomic variables may be significantly different in crisis and noncrisis countries. For example, Frankel and Rose (1996) found that public sector debt as a share of total debt helps to predict crises one year in advance, Edwards (1989) found that the fiscal deficit in the three years prior to a devaluation is higher for those countries that devalue than for a control group, and Tornell (1999) found that excess money growth plays a role in countries most affected by the Mexican and Asian crises.

21

Data on short-term and total external debt are not available for industrial countries.

22

These data are proxied by lending from Bank for International Settlements-reporting banks and are available only for the Mexican, Asian, and Russian crises and generally only for emerging market economies. The results for industrial countries rely on data from only 5 of the 20 countries. The common creditor in the Mexican crisis was the United States; in the Asian crisis, Japan; and in the Russian crisis, Germany. Replacing Germany with the United States as primary lender in the case of the Russian crisis yielded similar results.

23

CRIIND is built on the basis of the whole sample of 61 countries, which includes industrial countries. This corresponds to our belief that the definition of a crisis should be universal (a certain degree of exchange rate market pressure can be identified as a crisis both for emerging market and industrial countries). Making the definition specific to the emerging market countries by rebuilding the index based on the sample of countries used in the regressions, is equivalent to changing the threshold of the full sample index to 1.81, so that this new index would be somewhat in between CRIIND and CRIHIGH. We will address the robustness of results to changes in thresholds in Section IV. B.

24

The proxy of trade contagion (TC) combines the effects of the implied post-crisis real effective exchange rate appreciation and export market growth (see Appendix I. C).

25

They have been found relevant, however, in some empirical studies focusing on data at high frequency (for example, Baig and Goldfajn, 1999).

26

Other measures of total and private domestic credit growth (as listed in Appendix I) are also insignificant.

27

Introducing the growth rate in ICOR crowds out the significance of the current account variable, but all other variables remain significant. It however also reduces the sample by eliminating all transition economies, countries for which other regressions have shown that the current account is a particularly significant variable. Hence, we kept our benchmark model with the current account variable. It is interesting to note that most if not all the results we present in this paper would still hold if we replace the current account variable with ICOR, even though the correlation between the two is minimal (about 0.1).

28

Only the growth rate of the ratio of foreign to total liabilities is significant and crowds out the significance of short term debt to reserves. However, the former variable is not significant in the absence of the latter.

29

Also, when both BISA and BISB are introduced simultaneously in the regression, only BISB is significant.

30

The probability of a crisis when variables are at their mean value can be derived by calculating the Normal Cumulative Density Function (CDF) of the value of the right hand side (RHS) of the regression evaluated at mean values. Such probability is very small (0.071). As one would expect, a country with average characteristics faces a practically zero chance of entering a crisis. One can then approximate the effect of a given “deterioration” of a specific variable by adding to the RHS at mean values the given change in the variable multiplied by the “absolute value” of the corresponding estimated coefficient, and then applying the CDF to the new value for the RHS. The difference in the new CDF and the one at mean values approximate the probability effect of the change in that variable.

31

These dummies are equal to 1 in the case of a flexible exchange rate regime, and 0 otherwise. See Appendix I for the exact definition.

32

A similar dataset has also been used by Grilli and Milesi-Ferretti (1995) to study the effects and the determinants of capital controls for a large group of countries. We thank the authors for kindly providing us with the electronic version of the dataset until 1995. We have updated the dataset to 1998 on the basis of the aforementioned IMF publication.

33

It is interesting to note that if we exclude the transition economies from the sample, the current account variable is no longer significant. This may explain why previous studies that excluded the transition economies found no significant effect from this variable. We also find that trade contagion is almost significant when combined with an Asian countries dummy. This result, however, is due to the fact that Asian countries that suffered crises had worse current account balances than the average.

34

Some caveats: First, such a measure overestimates/underestimates the trade contagion effect from devaluations to the extent that financial markets expected the crisis to induce less/more severe devaluations than it actually did. Second, it underestimates by construction the relevance of trade contagion by biasing upwards the figures for noncrisis countries: it in fact measures for each of the noncrisis countries the competitiveness effect from all crisis countries, but for each of the crisis countries it captures the effect of competition from all crisis countries minus the own effect. Third, it automatically incorporates actual events which may not have been expected or may not be ascribed to the crisis. (It is virtually impossible to control for global events, as it is very difficult to assess whether some global effects during the crisis, such as changes in the dollar exchange rate, are or are not related to the crisis.)

35

This measure is subject to similar caveats as mentioned above for the indicator of price effects.

Trade and Financial Contagion in Currency Crises
Author: Mr. Ranil M Salgado, Mr. Luca A Ricci, and Mr. Francesco Caramazza