After the large exchange rate depreciations following the 1997 East Asian crisis, export volumes from East Asian countries responded with a notable lag. Two main explanations for this lag have been proposed: that contraction in domestic credit affected supply of exports and that “competitive depreciation” by other countries neutralized the effects on demand for exports. This paper considers the plausibility of these two mechanisms using a new monthly database of exports of selected industries. The results indicate that “competitive depreciation” played an important role in the propagation of the East Asian crisis through the trade channel, even at a monthly frequency. [JEL F1, F14]
During the financial crisis in 1997–98, export revenues of many East Asian countries did not increase in spite of massive depreciation by the afflicted economies.1 The depreciations led to sharp declines in dollar-denominated export prices with only modest increases in export volumes. The absence of a quick response of exports to depreciation played a key role in prolonging the East Asian crisis and is puzzling from an analytical point of view.
Several plausible factors could underlie the sluggish response of East Asian exports to the huge depreciations following the currency crisis. First, demand for Asian exports may have been price inelastic in the short run. Second, the contraction of credit to the private sector may have limited the supply of exports. Third, demand may have slowed down in response to an exogenous shift in world demand. Finally, demand for exports in a single country could have slowed because of “competitive depreciations” by others. These different hypotheses lead to very different interpretations of the Asian crisis, its propagation mechanisms, and the policy recommendations for recovery. This paper considers these alternative hypotheses. To address this question, a new monthly data set on price and quantity of exports for selected commodity groups is constructed. Using these data, demand and supply for Asian exports are analyzed within a vector cointegration framework of estimation.
The empirical results indicate that the demand for East Asian exports is very sensitive to prices—both own and competitors’—and to world growth rate. The supply prices of exports are generally insensitive to own quantities but very sensitive to nominal exchange rate changes. Typically, a nominal depreciation decreases the U.S. dollar-denominated export price, thereby increasing the demand for the depreciating country’s exports. However, depreciation of every export competitor’s currency weakens the positive demand effect of the initial depreciation such that the overall effect is a fall in export prices with a very modest increase in export volumes. In this context, evidence of a correspondence between export supply price and contraction of credit to the private sector is somewhat mixed.
The importance of trade in the transmission of the East Asian crisis has been studied both empirically and theoretically. Empirically, Glick and Rose (1999); Caramazza, Ricci, and Salgado (2000); and Van Rijckeghem and Weder (1999) look at market shares in trade for evidence of a contagion effect through the trade channel. These authors conclude that the trade shares are important in explaining currency crises in general (see, for instance, Glick and Rose, 1999) and the crisis in East Asia in particular.2 Abeysinghe (2001) uses a structural vector auto regression model during 1983–1998 at quarterly frequency to analyze the transmission of recessions across 12 Asian economies through their trade links. Thus, looking at trade shares constitutes an important first step in analyzing the role of trade in crises. However, for explicit comparisons of the alternative explanations behind export slowdown, it becomes necessary to estimate the underlying structural demand and supply equations, which is done in this paper. Moreover, this paper is among the few studies on the East Asian currency crisis that uses a unique database with countries’ disaggregated trade data at a monthly frequency.3 The use of high-frequency data permits the analysis of the relative speeds of adjustment of export volumes and prices in response to external shocks. Gerlach and Smets (1995) formalize the idea—in a theoretical model—that strong competition in the external sector can be responsible for the transmission of a currency crisis. This paper is an empirical validation of the same idea in the context of East Asia.
The sample covers monthly data between January 1990 and July 2002, with a few exceptions when data could not be retrieved. A complete description of the variables and the data sources is given below.
Price and volume of exports: Export prices are used to deflate the export revenues and obtain volumes of exports. For commodities disaggregated at the one-digit level, such as chemicals, manufactured items, and machinery, commodity-specific export price indices for Korea, Singapore, and Thailand are available. At the two- and three-digit levels, the best available country- and commodity-specific export price index is used. For instance, the export price index of SITC 7 (machinery) is used to obtain volumes of SITC 78 (road vehicles) and SITC 776 (semiconductors) and so on. For Indonesia and Malaysia, commodity-specific export prices could be retrieved and the unit value of exports is used to deflate all export revenues. Hong Kong SAR has export price indices for clothing and semiconductors. For other commodities, the unit value of exports is used. The use of alternative proxies for price indices when individual price information is missing is not uncommon. For instance, Muscatelli, Stevenson, and Montagna (1994) have used import (and sometimes export) price indices of the United States to obtain volumes of developing country manufacturing exports. However, there is a problem with this deflator. Ideally we would like to have Xij = Rij/Pij, where Rij is the export revenue earned by the jth country in the ith good. However, when Pij is not available, and a proxy like the U.S. import price index for i (denoted by Pi, USA) is used, a new variable,
Competitors’ export prices: For every commodity group geometric average weights are constructed (average of 1992–96) by taking the annual share of country j’s exports of commodity i (to the world) as a proportion of total Asian exports of that commodity. The weights are then used to obtain a geometric mean of export prices of the competitors. Thus, by construction,
where h is all the other Asian competitors of good i for country j. The term H refers to the five other competitors. The variable Xih is the total (annual) export of commodity i by country h. When country h does not have a commodity-specific export price, we simply use the overall export price, that is, the unit value of exports. The term K refers to the six countries in the sample. The weights are constructed with annual data obtained from the IMF’s Direction of Trade Statistics.
The country-specific sources on the prices and quantities of exports are as follows:
Hong Kong SAR: Data on export revenue of chemicals, manufactures, and machinery are from Hong Kong SAR’s Census and Statistic Department’s Monthly Digest of Statistics. Data on exports of road vehicles, clothing, and semiconductors come from the same department’s Trade Analysis Section (Hong Kong SAR’s External Trade). Unit-value index numbers for domestic exports (from the same source) are used to deflate export revenues of chemicals, manufactures, machinery, and road vehicles. The specific export price index is used for clothing, while that of electronic components is used for semiconductors. These price data are retrieved from the Census and Statistics Department. The price data are available from 1988:10–2002:01.
Indonesia: Export data are obtained from the Bank of Indonesia’s Economics and Statistics Department. The following data points for export revenue are missing in the sample of estimation for chemicals, manufactures, and machinery: 94:01–02; for road vehicles, clothing, and semiconductors: 1994:01–02; 1995:01–02; 1995:12; 1996:01–02; 1996:04–05. The unit value of the export index (in dollars) is used to obtain volumes of exports of these commodities. The source for the latter is the International Financial Statistics Database (IFS), series 74DZF. This series is available from 1980:01–1998:12 and is interpolated to obtain the missing values for the data points 1981:07–08 and 1987:01–02.
Malaysia: Export revenue data come from Malaysia’s Monthly External Trade Statistics, Department of Statistics, covering 1994:01–2002:07. Values for 2000:12 and 2001:12 are missing for all the series. The unit value of the export index is used to deflate export revenues and obtain volumes. The source for the latter is IFS (series 74DZF). The series is interpolated to obtain the missing data points between 1992:04 and 1993:06, and 1996:07 and 1998:02. After interpolation the series is complete only until 1999:03. In order to construct the graphs, we use the rate of growth of the export price from Indonesia. Given the data limitation we do not use Malaysia in the panel regressions.
South Korea: Export revenue data come from the Bank of Korea’s Monthly Bulletin and cover the period 1990:01–2002:10.
Singapore: Data on revenue and prices come from the Monthly Digest of Statistics, Singapore Department of Economics. Data on revenue and export prices cover the period 1989:01 to 2002:02 (missing between 1998:02 and 1998:06). The missing points are interpolated to complete the series.
Thailand: Monthly Bulletin, Bank of Thailand, is the source for exports of chemicals, manufactures, and machinery (available from 1989:01–2002:12). The following proxies were used (from the same source) for the two- and three-digit export commodities—line 18a (integrated circuits and parts) as a proxy for SITC 776 (semiconductors); line 2 (textile products) as a proxy for SITC 84 (clothing); and line 51a (passenger cars and parts) as a proxy for SITC 7812 (cars). Commodity-specific export prices are available during the same period. An aggregate export price index for Thailand is obtained from the IFS(series 74DZF).
United States import price index: The data for this index used to deflate export revenues for the alternative definition of volume come from the Bureau of Labor Statistics. For chemicals, manufactures, machinery, and clothing we retrieved quarterly series between 1990:03 and 1992:08, and monthly thereafter until 2003:01. For semiconductors, the data are quarterly from 1989:09 to 1993:12, and monthly thereafter (until 2003:01). The quarterly series for vehicles starts in 1989:09 and ends in 1993:12 and is monthly thereafter until 2003:01. All quarterly data are interpolated.
Scale variable: As discussed before, we first construct a trade-weighted world demand for each export commodity for the scale variable. However the use of this variable does not alter the performance of the estimated equations. This is because export data are highly trended, and therefore as long as we use a scale variable that is suitably trended, they perform well in the demand equation. Therefore we use world import demand for the estimation retrieved from the IFS database (series 71D). The world unit value of imports (series 75D) is used to deflate revenues and obtain volume of imports. The series for real world import is available from 1980:01–2002:09.
Domestic credit: The data source is IFS(domestic credit, based on claims on the private sector, series, 32DZF). This series (in domestic currency) covers the period 1980:01–2002:12 for all countries. For Hong Kong SAR, the series is annual between 1990 and 1993, quarterly between 1994:01 and 1995:12, and then monthly. Real domestic credit to the private sector is obtained by deflating with the country-specific consumer price index (CPI) data, which were also obtained from the IFS (series 64ZF), covering 1980:01–2002:12. For Hong Kong SAR, CPI data are available from 1990:01. Hong Kong SAR’s real domestic credit has to be interpolated for the co-integration tests. However, all estimations are carried out without interpolating this variable.
Input price: In the absence of wage prices at monthly frequency, the wholesale price index is used to proxy for input price. The series is retrieved from the IFS(line 63).
Nominal exchange rate: This monthly series comes from the IFS(period average market rate, series RFZF) and covers the period 1980:01–2003:01.
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Rupa Duttagupta is an Economist in the Exchange Regime and Debt and Reserve Management Division of the Monetary and Financial Systems Department, and Antonio Spilimbergo is a Senior Economist in the Expenditure Policy Division of the Fiscal Affairs Department at the International Monetary Fund, and a Research Fellow at the Centre for Economic Policy and Research and the William Davidson Institute. They wish to thank Fabio Canova, Enrica Detragiache, Kalpana Kochhar, Rachel Kranton, Gian Maria Milesi-Ferretti, Arvind Panagariya, Raymond Robertson, Francisco Rodriguez, Miguel Savastano, Robert Schwab, Peter Wickham, the participants at the seminar in the Asia and Pacific and Research Departments of the International Monetary Fund, and at the First Annual Research Conference at the IMF, and especially Susan Collins for many helpful suggestions. Young Kim provided excellent research assistance.
Henceforth “Asia” will refer to the following six economies in this sample: Hong Kong SAR, Indonesia, Malaysia, Singapore, South Korea, and Thailand. China, Philippines, and Taiwan Province of China could not be included in the sample because of lack of data.
However, not all economists agree that trade has played an important role. For instance, Kaminsky and Reinhart (2000) argue that the trade links between East Asian countries are not strong enough to explain the spread of the crisis.
Barth and Dinmore (1999) also study the movements of trade prices and aggregate volumes in East Asia during the crisis at monthly frequency. They find that although the export prices of the East Asian countries (Hong Kong SAR, Indonesia, Korea, Singapore, Taiwan Province of China, and Thailand) fell by 4.8 percent in 1997 and 9.1 percent in 1998, their aggregate export volumes went up by 8.8 percent in 1997 and only 0.7 percent in 1998.
In order to control for the seasonality of export revenues, both the actual monthly export revenues and a moving annual average are reported. The decline in exports from the first half of 2001 reflects in part a slowdown in world economic activity starting in 2001.
Several authors have explored the export slowdown at the end of 1995. Fernald, Edison, and Loungani (1999) show that the Chinese effective devaluation in 1994 did not change the trade shares in the rest of Asia and hence did not cause the Asian crisis. Corsetti, Pesenti, and Roubini (1998a and 1998b) suggest that the sharp appreciation of the U.S. dollar relative to the Japanese yen and European currencies since the second half of 1995 led to deteriorating cost-competitiveness in most Asian countries whose currencies were effectively pegged to the dollar. In addition, there was a price war in the electronic sector, which accounted for an important export share in several Asian countries. The weak economic growth in Japan and the overinvestment in these countries were the cause of the price war in 1995. This industry is included in this study in recognition of its importance in the development of the crisis. Finally, Chinn (1998) finds that while some Asian currencies, like those of Malaysia, Philippines, and Thailand, were overvalued before the crises, some others, like the Korean won, were not.
Moreover, as shown in Table 1 in the next section, the share of world import demand for the products that were exported by these countries in total world import demand did not decline during this period, implying that the Asian export decline did not result from a switch of world demand toward other commodities.
Muscatelli, Stevenson, and Montagna (1994) provide evidence of the increasing importance of manufacturing exports (relative to traditional or primary exports) in Southeast Asia in the 1990s.
For Korea and Thailand, data on road vehicles (SITC 78) could not be retrieved. Instead we included data on passenger cars (SITC 7812).
For similar reasons Fernald, Edison, and Loungani (1999) focused on semiconductors and clothing in their study of Chinese exports.
For a description of the highly competitive nature of the semiconductor industry, see Macher, Mowery, and Hodges (1999). This industry includes several products—for example, integrated circuits and memory devices. Memory devices are highly standardized and competition is mainly through price and timely delivery. The external market for memory devices has had three characteristic phases—the United States dominated this market prior to 1985, Japan dominated it between 1985 and 1990, and since 1990, the Newly Industrialized Economies have been increasing their market shares.
See Goldstein and Khan (1985) for a discussion on specifications of trade equations. Note that although in principle own and competitors’ prices should enter the demand equation as a ratio (standard assumption of price homogeneity), we enter them separately because
The empirical literature on whether nominal devaluation results in real devaluation is quite comprehensive. See Reinhart (1995) and the references therein.
The sign of the coefficient on private sector credit also tests the possibility that a credit crunch could have slowed down export supply during the crisis. While some authors (e.g., Ghosh and Ghosh, 1999; and Ferri and Kang, 1999) have analyzed the impact of a credit crunch on the entire economy, we focus on its effect on specific exports.
The Bayesian Information Criterion is used to determine the optimal lag length.
Notable exceptions are export volume of manufactures and clothing in Hong Kong SAR, chemicals and semiconductors in Indonesia, manufactures and clothing in Korea, vehicles and clothing in Malaysia, and chemicals and miscellaneous manufactures in Singapore. These series appeared to be trend-stationary.
The details of this methodology, including the critical values for the significance of co-integration, are in Maddala and Kim (1999). Note however that the sample spans only 11 years, which may be too short to establish a “long-run” relationship. Hence, the power of these tests would generally be low.
The exception is the case of Hong Kong SAR, which shows a negatively sloping demand curve.
Compared with these results, Muscatelli, Stevenson, and Montagna (1994) found, for a sample of Asian countries, that the long-run elasticity of export demand with respect to own price relative to competitors’ price is generally much greater than 1. Noting that estimation of the export commodities in this study is done at a more disaggregated level, own price and competitors’ price are allowed to enter the demand equation independently, although price homogeneity is tested as a robustness check (see below).
The Chow predictive test (Greene, 1997, Chapter 7) is used to check for the possibility of a structural break in demand or supply in July 1997, when the financial crisis started, and in December 1997, when Korea devalued. Andrews’ (1993) method of testing for a structural break (when the break point is unknown) is also used, restricting the breakpoint to between July 1997 and December 1997. The results indicate no structural break in demand or supply functions.
See Lane and Milesi-Ferretti (2000) for an application of panel dynamic ordinary least squares (DOLS).
The integer k denotes the number of lags (or leads) and is chosen in the following manner: starting with a reasonable upper bound of k, on estimation, if the variable (with the highest possible lag) is significant, then k is chosen to be the upper bound. If the variable is not significant, the lag length is reduced further until the last included lag is significant in the estimation. A similar method is used to choose the optimum lead length.
To achieve a balanced panel, we use the rate of growth of the export price from Singapore to obtain an additional six months of data for Hong Kong in 2002. Our results do not change if we do not include the last six months of observations for which we do not have data.
Using a Hausman test we could not reject a random effects model, that is, GLS is more efficient. Note that for road vehicles (SITC 78) the panel is imprecise, since the Korean and Thai data are for passenger cars (SITC 7812).
The panel on road vehicles has three countries (Indonesia, Korea, and Thailand) as Hong Kong SAR has an insignificant export of vehicles, and Singaporean data beyond the one-digit level could not be retrieved. For all other one-digit commodity groups we have five countries.
No obvious explanation can be provided for the perverse relationship between price and volume in the export supply equations for road vehicles and clothing. However, for clothing, the gradual shift in exports away from this sector may have led to a structural break in the export supply function that could not be captured by the standard export supply equation estimated here. Besides, Asia’s clothing exports were subject to quotas under the Multi Fiber Arrangement (MFA), which could have distorted the standard price-quantity relationship for the export supply equation.
Noting that only a small share of private domestic credit is disbursed to each of the specific industries considered in this sample, the possibility of private credit being endogenous to a specific commodity export is expected to be low.
The results also show that export supply price is responsive to domestic input costs, which likely decreased relative to imported costs following the nominal depreciation.
The only way to verify the depreciation explanation is by estimating demand and supply as is done here. Looking at changes in market shares cannot work because countries that engage in competitive devaluation could end up with the same market shares.