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The effects of exchange rate volatility on export pricing decisions: evidence from Taiwan industries (1993-2003).


Export pricing decisions are more complicated than domestic pricing decisions. Exchange rate volatility can have major effects on export pricing decisions. Export pricing decisions and exchange rate risk have dominant and immediate impact on exporters' profitability and competitiveness. Exporters often have to compromise their profit margins in setting export prices in response to exchange rate changes. This study presents a conceptual framework of export pricing decision making from internal/external factors and export pricing methods, and examined the relationship between export pricing of international trade and exchange rate volatility in an empirical study.


Export pricing decisions are more complicated than domestic price decisions. There are many of variables involved in this issue: customer orientation, market competition, negotiation power, supply and demand position, exchange rate volatility, risk attitude, etc., (Katsikeas, Leonidou & Morgan, 2000; Reid, 1983).Even though pricing strategies and exchange rate risk have a dominant and immediate effect on exporters' profitability and competitiveness, the relationship between export pricing and exchange rate volatility for international trade is still a neglected research area.

Exchange rates can have a major effect on export pricing strategies. Currency appreciation (depreciation) can reduce (increase) exporters' competitiveness and profitability by changing margins causing firms to change prices in response to exchange rate changes. The consequence of exporters' reactions to exchange rate changes is the notion of exchange rate pass-through--the extent to which exporters pass along exchange rate-induced margin increases (decreases) by lowering (raising) prices in export market currency terms (Clark, Kotabe & Rajaratnam, 1999).

The objective of this paper is twofold: (1) to present conceptual framework of export pricing decisions making based on internal/external environment and export pricing methods, (2) to examine the relationship between export pricing of international trade and exchange rate volatility using an the empirical study.


Because of market integration, trade barriers cutback, technology innovation and globalization, exporters need to devote more emphasis on pricing decisions (Kublin, 1990). Existing studies, however, on export pricing were unbalanced because researchers emphasized domestic customer reaction to different pricing practices (Aulakh & Kotabe, 1993; Cavusgil, 1990; Cavusgil & Myers, 2000; Johanson & Arunthanes, 1995). Only a few empirical studies focused on export pricing strategy (Cavusgil, 1988; 1996; Nicholas & Bello, 1992; Stottinger, 2001; Tzokas, Hart, Argouslidis & Saren, 2000; Yang, 1996).

Studies on export pricing issues can be broadly divided into three basic dimensions. The first dimension includes internal factors, such as product variables, company variables and management attitudes. The second dimension includes, external factors, such as market related variables and industry related variables (Ahtiala & Orgler, 1995; Akintoye & Skitmore, 1992; Piercy, 1981; Thach & Axinn, 1991). FIGURE 1 presents a classification of these factors and variables.


The third dimension includes export pricing methods. Six pricing methods are suggested by Cannon and Morgan (1990), target-profit, cost-plus, perceived-value, going-rate, sealed-bid, and negotiated pricing. Cannon and Morgan (1990) presented a pricing strategy structure which conceptualizes pricing outcomes as a function of pricing strategies constrained by environmental factors. The framework was derived from the pricing literatures to explain and enhance pricing decisions making. A summary of six pricing methods is presented in the Table 1.

The extent to which exchange rate changes were reflected in the import/export prices has been termed "exchange rate pass-through". Empirical studies on exchange rate pass through are mixed, some studies show partial pass-through (Mann, 1986; Feenstra, 1987; Fisher, 1989; Kadiyali, 1997), others full pass-through (Rockerbie, 1992; Gagnon & knitter, 1995), and some opposite pass-through (Feenstra, 1989; Gross & Schmitt, 2000). Importantly, the degree of exchange rate pass-through can differ due to differences in model and methodology.


This study focused on the effects of exchange rate volatility on export pricing decisions with a two-stage estimation process. In the first stage, the exchange rate volatility is measured by using a moving sample standard deviation of growth rate as suggested by Chowdhury (1993) and Arize, Osang and Slottje (2000).

[EXRV.sub.t] = [[(1/m) [m.summation over (i=1)] [(log [EXR.sub.t+i-1] - log [EXR.sub.t+i-2]).sup.2]].sup.1/2] (formula 1)

where [EXR.sub.t] was the historical exchange rate. This time varying measure accounts for periods of high and low exchange rate uncertainty. In the second stage, Yang's (1996) regression model is modified by changing one of its dependent variable, the exchange rate, to the exchange rate volatility [EXRV.sub.t] from equation (1). The modified model in equation (2) shows a simple, standard long-run relationship among export price, exchange rate volatility and domestic price.

[DELTA]ln [EXP.sup.k.sub.t] = [[alpha].sub.1] [DELTA]ln [EXRV.sub.t] + [[alpha].sub.2] [DELTA]ln [DP.sup.k.sub.t] + [[alpha].sub.3] [DELTA]ln [EXRV.sub.t-1] + [[epsilon].sup.k.sub.t] (Formula 2)

where [EXP.sub.t] was the export price for industry k, [EXRV.sub.t] the exchange rate volatility, [DP.sub.t] the matching domestic price for industry k, [EXRV.sub.t-1] the lagged exchange rate volatility, and et the disturbance term. To estimate the exchange rate pass-through of export price, the export price was regressed against the exchange rate volatility, matching domestic price, and lagged exchange rate volatility. The domestic price was used to cover domestic market conditions. The lagged exchange rate volatility was used to capture time lag between price setting date and actual payment date.


The data used in this study were obtained from Taiwan Statistic Data Book (The Council of Economic Planning and Development in Taiwan) and database from DGBAS (Directorate-General of Budget, Accounting and Statistics in Taiwan). Monthly data for the sample periods from 1993/10 to 2003/10 was used in this estimation. All the data were divided into two periods, before and after Asian financial crisis October 1997. The export price and domestic price were approximated by the export price index and the consumer price index within six main categories (S.I.T.C. Code), which were most used in previous studies. There are several reasons to test the validity of exchange rate and export price for Taiwan market and two partitions for before and after Asian financial crisis.

First, Taiwan is a heavily export-dependent entity with the proportion of Export volumes in GDP at almost 50%. US dollar is the main quoting currency which dominated the Taiwan export industry. Second, during the Asian financial crisis, the exchange rate of Taiwan dollar underwent increased expectations of depreciation. In October 1997, Taiwan's central bank adopted a clean floating foreign exchange regime, leaving exchange rates to be determined by market forces. The Taiwan exchange rate was immediately depreciated to 30.5 against USD, compared to the previous level of approximately 28.62 under foreign exchange intervention. These unique characteristics might shade other noise variables which are not included in the above model and could improve the estimate accuracy and validity.

The estimation for before and after Asian financial crisis for each category, with t-stat in parentheses, are shown in Table 2 and Table 3.

The [x.sub.1] reports the exchange rate's elasticity to the export prices. The export prices are positively and statistically significant related to exchange rate in both before and after Asian Financial crisis, except for category 5, Electrical Products, before October 1997. Theoretically, the expected value of x1 was between one and zero; when exchange rate depreciated (appreciated), the exporters decreased (increased) their export price with different proportion of coefficient in each category. When [x.sub.1] approached to 0, the exporters tended to reducing profit margins and absorb the unfavorable exchange rate loss. For example, Category 4, Textile Products, and category 6, general goods, showed low x1 about 0.35 and 0.19 before October 1997. These two industries were the price takers within the buyers market. When x1 approached to 1, the exporters tended to pass-through the exchange rate difference to export price. The quoting price might fluctuate depending on exchange rate's volatility. Like Category 2, Base Metal, before October 1997, and Category 1, Agricultural Products, and Category 6, General Goods, after October 1997, the exporters became the price makers in sellers market.

In this study, however, there were two categories, Category 1 Agricultural Products and Category 3 Rubber and Plastic Products, with x1 larger than 1 before October 1997. The reason might be that the profit margins in these two industries were so low that exporters needed to achieve a greater mark-up under exchange rate uncertainty.

The domestic price, [x.sub.2], had positive and statistical significant relationship with export price in every category. In category 5, [x.sub.2] Electrical Products, increased from 0.22 to 1.45 after October 1997 which denoted that domestic market conditions became a major factor in export pricing. The lagged exchange rate, [x.sub.3], also had positive significant relationship to export price, except category 1,2,3,4, before October 1997. In category 6, the coefficient of lagged exchange rate volatility was larger than the matched exchange rate volatility in two different periods; 0.24 to 0.19 before October 1997 and 1.22 to 0.88 after October 1997. These results partially support the hypothesis that time lag had positive effects on export price. Furthermore, in a particular industry such as category 6, if the exchange rates were different in price setting and actual payment, time lagged exchange rate might have a higher coefficient than time matched exchange rate.

This modified model provides the evidences of the relationships between the export price, exchange rate volatility, domestic price, and time lagged exchange rate volatility. Generally, different industries had different pricing strategies, i.e., absorb, reflect or mark-up exchange rate pass-through to export price. Therefore, different pricing strategies against exchange rate volatility might keep on recurring due to the internal/external factors and pricing methods discussed above.


This study outlined three dimensions of export pricing issues that can affect exporters' pricing strategy: internal factors, external factors and six pricing methods. FIGURE 1 and Table 1 provide the insight of pricing decisions process and the concern of pricing methods from the literature review. The effects of exchange rate volatility on the export pricing in Taiwan's export market during the past decade showed that exchange rate volatility had a positive effect on export price. Using separate export price data in six main categories(by S.T.I.C code), this paper found that the exchange rate elasticity of export price fell within the range from 0.19 to 1.43, different from the theoretical model predicted value, between zero and one. There are several factors might cause this difference. For example, when products stand for very low profit margins, very strong seller's market, and very long payment terms, their exchange rate elasticity could higher than 1.

Overall, this study provided useful guidelines for both the theoretical framework and the empirical examination of the export pricing and exchange rate pass-through. However, further research in cross-countries and cross-industry are needed to provide a grater understanding on this field. As businesses contrive to globalize, the practicer must be aware of and deal with exchange rate pass-through since the ability to pass-through rate change increase can adversely affect profit margins.


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Yung Yen Chen, Nova Southeastern University
Table 1: Six Pricing Methods Approaches

Pricing Methods Definition Supportive Literature

Target-Profit Pricing with full cost and add Rich (1983)
 a target rate of return on
 capital employed

Cost-Plus Pricing with standard mark-up Monroe (1978)
 on unit costs, base on
 company or industry norms.

Perceived-Value Pricing with trial and error Curry and Riesz
 method to set the final (1988);
 price by customer's reaction Levin and
 Johnson (1984)

Going-Rate Pricing with the same price Kotler(1984);
 as main competitors, depending Porter (1985)
 on market strength

Sealed-Bid Pricing with the expectations Monroe(1979)
 from buyer's bidding.

Negotiated Pricing with negotiation with Schill (1985)
 an individual customer.

Table 2: Regression Result by S.I.T.C. category Before Oct 1997

Category [EXRV.sub.t] DP

1 1.340519 (2.477016) * 0.581152 (6.476851) *
2 0.835167 (5.040386) * 0.703813 (16.70284) *
3 1.433980 (5.550698) * 0.688103 (23.13267) *
4 0.348279 (2.340192) * 1.248061 (36.41185) *
5 0.517424 (1.243576) 0.221006 (1.771237) *
6 0.191857 (1.23451) * 1.157075 (33.86772) *

Category [EXRV.sub.t-1] [R.sup.2]

1 0.24153 (0.42541) ** 0.610845
2 0.04105 (0.24232) ** 0.936074
3 0.02906 (0.142857)** 0.933202
4 0.24019 (1.6646) 0.982556
5 0.4385 (1.0009) * 0.121817
6 0.2386 (0.94472) * 0.985605

Category DW

1 0.354414
2 0.552606
3 0.952842
4 0.812064
5 0.167128
6 1.10497

Notes: Figures in parentheses are the t-statistics. * means
Significant at the 5 per cent level. ** means

Significant at the 10 per cent level.

DW (Durbin-Watson Statistic) tests first order
residual autocorrelation

Category 1-6: Agricultural Products, Base Metal and Metal
Products, Rubber and Plastic Products, Textile Products,
Electrical Products, General Goods.

Table 3: Regression Result by S.I.T.C. category After Oct 1997

Category [EXRV.sub.t] DP

1 0.946648 (1.758875) * 1.65018 (13.85246) *
2 0.635931 (5.213115) * 0.854392 (23.99777) *
3 0.81165 (6.336519) * 0.544679 (14.45097) *
4 0.770469 (6.194107) * 0.922925 (13.51783) *
5 0.64187 (3.4711) * 3.457081 (38.62676) *
6 0.882684 (6.992196) * 0.78229 (5.58062) *

Category [EXRV.sub.t-1] [R.sup.2]

1 1.51281 (3.08353) * 0.775258
2 0.45313 (4.18919) * 0.933221
3 0.47921 (4.05655) * 0.833062
4 0.38606 (3.24005) * 0.831730
5 0.191739 (1.083064) * 0.966572
6 1.2248 (5.01721) * 0.536106

Category DW

1 0.890050
2 0.243653
3 0.415439
4 0.307215
5 0.768667
6 0.668153

Notes: Figures in parentheses are the t-statistics. * means
Significant at the 5 per cent level. ** means

Significant at the 10 per cent level.

DW (Durbin-Watson Statistic) tests first order residual

Category 1-6: Agricultural Products, Base Metal and Metal
Products, Rubber and Plastic Products, Textile Products,
Electrical Products, General Goods.
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Author:Chen, Yung Yen
Publication:Academy of Accounting and Financial Studies Journal
Date:Jan 1, 2005
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