Market Shares and Pricing-to-Market: Evidence from Disaggregated ...

Generated by Foxit PDF Creator © Foxit Software http://www.foxitsoftware.com For evaluation only.

INTERNATIONAL ECONOMICS & FINANCE JOURNAL

VOL.3,NO.2, 2008

Market Shares and Pricing-to-Market: Evidence from Disaggregated Trade Data

MoonJoong Tcha1 Minsoo Lee2

Abstract Previous studies frequently found that exchange rates have driven systematic fluctuation in the price of commodities traded. As this pricing mechanism in the foreign market is closely related to market organization, market shares of both exporters and importers are expected to affect the extent of exchange rate pass-through. While the market shares of exporters have been analyzed by some previous studies, the effect of importers’ market share has not been explored. This paper presents some key findings of these phenomena, using very disaggregated data collected from wool trade data. The results indicate that both exporters and importers who have “market power” are frequently found to practice their market power in pricing (pricing-to-market) when exchange rates fluctuate. Key Words: Exchange rate, prices, market shares, pricing-to-market 1

Senior Research Fellow, Korea Development Institute, Seoul, Korea. Corresponding Author: Professor of Economics, HSBC School of Business, Peking University, China. Email: [email protected]

2

Acknowledgment We are greatly indebted to Ken Clements for his constructive and helpful comments from an earlier stage of drafting this paper. We are also grateful to Nazrul Islam and John Stanton for their professional advice on wool. Substantial parts of this research were done while the first author was affiliated with the University of Western Australia. Excellent research assistance by Stephane Verani and the financial support by the Australian Research Council Large Grant are gratefully acknowledged.

1



VOL.3,NO.2, 2008

1. Introduction Recent fluctuation of exchange rates and rapid growth of international trade have highlighted the importance of the mechanism through which exchange rate changes affect prices of exported commodities. An analysis of this mechanism, labeled as exchange rate pass-through (ERPT), also received wide attention as a microfoundation for more macro-phenomena such as the problem of current account imbalance. In particular, when the exporter has a substantial market share and practicing its market power in a destination market, the pricing behavior to maximize profit given exchange rate fluctuation is labeled as pricing-to-market (PTM). If the market is perfectly competitive, all the fluctuation in exchange rate should be transferred to the destination price or the elasticity of ERPT should be one. This full transfer of changes in exchange rate to changes in commodity prices is also called “complete ERPT”; when transfer is partial, called “incomplete ERPT”; and called “perverse ERPT” when the commodity price is influenced in an unexpected way. Each of the above ERPTs can be either symmetric or asymmetric. “Symmetry” indicates that the rate of transfer to a commodity price remains the same in both the appreciation and depreciation periods of the currency. Otherwise, it becomes asymmetric. The extent of ERPT or PTM depends on how much of the exchange rate shock can be absorbed by changes in cost and changes in mark-up. As the mark-up can be determined by the market power of the exporters, the degree of ERPT or PTM depends on the market power of exporters. However, considering that it is possible for importers to affect the market condition, the degree of ERPT or PTM may also depend on the market power of the importers. This study aims to investigate the relationship between the export price of commodities and fluctuations in exchange rates, with special interest in market powers. This study has two related aims: (i) To analyze how the exchange rate shock is absorbed by the exporter’s price and the importer’s price, and (ii) how origin specific and destination specific variables affect the extent of the impact of exchange rate changes on the export price.

2



VOL.3,NO.2, 2008

2. Background of the Topics Research on the effect of exchange rate shocks on commodity prices received impetus with the free fluctuation of exchange rates since the post-Bretton Woods era. Since then it is frequently observed that changes in the exchange rates are not fully transferred to commodity prices, or changes in the exchange rate influence commodity prices in unexpected directions (called perverse pass-through). In general, researchers attribute this type of influence to the profit or market share maximization behavior of 1

the producers operating in imperfectly competitive markets.

Empirical studies conducted for such large economies as the US, Germany, and Japan support the incomplete pass-through or PTM phenomena. Marston (1990) finds that Japanese manufacturing firms pass through their export prices to the United States in only 50 to 60 percent of the exchange rate movements. Menon (1995) reports at least 50 percent of exchange rate pass-through to the import prices for the US, Germany and Japan. Employing the Phillips-Loretan procedure for long-run equilibria because of the non-stationarity of the variables, Feenstra, Gagnon and Knetter (1996) find strong evidence of incomplete pass-through combined with different degrees of ERPT across markets (pricing-to-market or PTM) in the automobile industry from Germany, the UK and the US. Small open economies face an exogenously determined export price in foreign currency, which implies the complete pass-through. However, several empirical studies find that export producers in small open economies with some market power have ability to affect prices. Lee and Tcha (2005) empirically examine the exchange rate pass-through elasticity, using sheep meat exports from the two major exporters, Australia and New Zealand. Their results show the coexistence of incomplete and complete pass-through in the international sheep meat industry. The Australian sheep meat exporters have a relatively smaller market share than New Zealand and are not able to exercise monopoly power. New Zealand producers, on the other hand, can increase their mark-ups in those destination countries where they have a large market share. Dwyer, Kent, and Pease (1994) find that ERPT is complete for the prices of

1 For example, see Dornbush, 1987; Gagnon & Knetter, 1995; Krugman, 1987; Tivig, 1996; Varangis and Duncan, 1993 among many.

3



VOL.3,NO.2, 2008

imports and manufactured exports in the long run; however, in the short run it is incomplete. Tongzon and Menon (1993) show that at the aggregate level the passthrough elasticity is close to 0.7 but it is in general lower at the more disaggregate level. Lee (1997) illustrates that on average only 62 per cent of exchange rate fluctuations are passed through to Korean imports and the degree of pass-through is affected by a domestic market concentration in Korea. For Swedish exports of machinery and transport equipment exporters absorb about 26 percent of an exchange rate change in their profit margin (Athukorala and Menon, 1995). Griffith and Mullen (2001) have discoverd that the Ricegrowers’ Cooperative Limited in New South Wales, Australia, has been able to exercise its monopoly market power by varying mark-ups over different markets. The major theoretical approaches to PTM are the adjustment cost’s explanation and monopolistic competition from the supply side and the market share argument from the demand side.

The source of mark-up variations can be explained by the

importance of incomplete market structure and the size of the market share (Feenstra et al., 1996).

If the markets are segmented and the industry is imperfectly competitive,

then monopolistic firms will find that charging different prices across markets is profitable.

Exporters have an incentive to reduce the mark-up to the importers whose

market shares are relatively large when importers’ currencies have depreciated against the exporter’s currency (Lee and Tcha 2005).

If one exporter maintains his mark-up as

the importer’s currency depreciates while his competitors moderately reduce the destination price, then the importers are likely to substitute other varieties for the relatively more expensive variety, which, in turn, will affect the market share. Therefore, exporters will stabilize prices in terms of the buyer’s currency to maintain their market shares in the presence of competitors who are exporting the similar but differentiated products (Krugman 1987). The PTM elasticity varies across industries as explained above due to such different environment as market shares, substitutability of differentiated products, and level of competition. For example, Tivig (1996), and Gross and Schmitt (2000) prove and develop the possibility of the perverse pass-through of exchange rates in the context of dynamic oligopoly competition. While it is expected that the destination price would increase when its currency depreciates, an exporter operating in an imperfect market

4



VOL.3,NO.2, 2008

may strategically decrease its destination price in the current period. That is, the current perverse pass-through is the strategy to take a large market share, and practice the market power in the next period to maximize the inter-temporal profit, at the expense of the first period’s profit loss. The PTM behavior of the firms under imperfect competition has significant implications in terms of the monetary policy of a small open economy (Shahnawaz, Lee and Gan, 2007a; 2007b). For instance, the monetary authority of a small open economy may implement a tight monetary policy to curb domestic inflation in the face of domestic currency depreciation; however, the policy will prove quite ineffective if the exporting firms increase the importer’s currency prices proportionately. Hence, in the presence of monopolistically competitive exporting firms that are practicing PTM, the ERPT appears incomplete, thus making it difficult for the domestic monetary authority to control inflation. Notwithstanding some unexpected outcomes such as perverse movement or no pass-through of commodity prices, most studies that utilized disaggregated data (such as 4-digit country specific industry data) reported the existence of PTM behavior. However, the extent of pass-through was partial and differentiated by periods and market structure, across regions and products (for example, Feenstra, Gagnon & Knetter, 1996; Gagnon & Knetter, 1995; Knetter, 1989, Marston, 1990).

3. The Data The main purpose of this research is to find how exporters’ and importers’ market shares affect PTM behavior and accordingly the degree of ERPT. While theoretical and empirical frameworks in this study can be applied to more general cases of goods traded internationally, this study uses wool trade data in Australia. More specifically, this empirical study used data extracted from the Australian Bureau of Statistics (ABS) export database and international trade data provided by the Department of Agriculture in Western Australia (DAWA), giving a total of 72 periods (monthly data for six financial years from July 1995 to June 2001) for the value and quantity of four types of wool exported from each state to each destination. This data

5



VOL.3,NO.2, 2008

from DAWA was also used in Tcha (2004, 2005)2 . Tcha (2005) emphasizes the strength of this data in ERPT study, presenting four reasons. First, the reliability and quality of data is credential. The Department of Agriculture, Western Australia (DAWA), collated the relevant data in a very disaggregated level, classified by the exporting ports and destinations. Second, once disaggregated to a reasonable level, wool in the same category is completely homogeneous. There is no more differentiation as can be found in manufacturing goods (such as a car with/without sun-roof or different alloy wheels), hence, we can minimize the level of noise coming from differentiation of goods in the same category. Third, wool is raw material and usually free from trade restriction. This characteristic of wool can exclude disturbance coming from trade barriers. Fourth, Australia is a major producer of wool, and practicing its market power in some markets, showing typical pricing-to-market behavior. This is helpful in investigating the relationship between ERPT and market shares in particular with homogenous goods. Furthermore, the Australian dollar showed reasonable fluctuation with major currencies during the period of observation, in the late 1990s and early 2000s, and sudden changes of exchange rate that might incur hysterical response can be excluded. The database holds two variables for a variety of wool exported from each Australian state to destinations: the quantity of export and the value of export for each period. The quantity is given in kilograms while the value is given in current Australian Dollars. The unit price of wool was computed simply by dividing the value by the quantity (AUD/kg). On some occasions, no trade took place between one exporting state and an import destination. No trade implies a null quantity. Therefore the price for this period cannot be inferred. Since this study is focusing on the effect of fluctuation of the exchange rate on the fluctuation of domestic price, we needed to undertake some transformations that enabled us to overcome the missing-observation problem without losing significant information contained in the original database. Furthermore, minimizing the number of missing observations is crucial since the method used is a 2 While Tcha (2005) used wool data and analysed ERPT, his study mainly concentrated on asymmetric response of prices to appreciation/depreciation of exchange rate. This study explores the relationship between ERPT elasticities and market shares.

6



VOL.3,NO.2, 2008

3

system estimation whereby a missing observation in one series cancels all the data of the system for this particular point in time. The three main ports for wool exports from Australia are Sydney, Melbourne, and Fremantle. Since the trade for wool is an auction, taking place at each of the three ports, it is possible to group the six states in three ports linking each exporting state to its closest port. Figure 1 illustrates the grouping. The prices for Sydney and Melbourne are calculated as a quantity weighted average price based on the export of two or three states. In consequence this initial grouping will yield more continuous sequences for Sydney (SYD) and Melbourne (MEL) but will leave Fremantle (FRE) unchanged. After this first transformation, some variables remained with too many missing values forcing the transformation from monthly data to quarterly. For each quarter a quantity weighted average price was computed. A complete series was therefore made up of 24 observations, and Table 1 summarizes the state of the variables after the two initial transformations described so far. Three kinds of Greasy wool (HS 51011110 Greasy shorn wool (incl. fleece-washed wool), not carded or combed, 19 μm and finer; HS 51011120 Greasy shorn wool (incl. fleece-washed wool), not carded or combed, 20 μm to 23 μm; HS 51011130 Greasy shorn wool (incl. fleece-washed wool), not carded or combed, 24 μm to 27 μm) and one Scoured wool (HS 51211130 Degreased shorn wool, not carbonized, carded or combed, 20 μm to 23 μm) were selected as the number of missing values is too large for other kinds of wool. These Greasy raw wools were labeled as RAW 1, RAW 2 and RAW 3 respectively throughout the paper.

Figure 1. Grouping of states by ports. STATES

PORTS

New South Wales Sydney Queensland Tasmania Victoria

Melbourne

3 This is discussed further Southin Australia Section 4. Western Australia

Fremantle 7



VOL.3,NO.2, 2008

Table 1 shows many of the variables are now available as a continuous sequence of 24 observations. The series with darker shading were discarded from the analysis since the number of missing observations remained too large. On the other hand, series highlighted with light shading could be filled by extrapolation. The criterion for selecting the series to be extrapolated was a minimum of 21 observations (i.e. a maximum of three missing observations). This extrapolation allowed us to include a wide range of importing countries, which will increase the quality of the system estimation. Note that the export of scoured wool from Sydney to the United Kingdom presents an exception where a series with less than 21 observations was considered for extrapolation. Since the missing values for this particular series were dispersed sufficiently, it may be possible to make a sensible extrapolation and again may add some information when estimating the system of equation described later. Table 1. Number of Observations in the Time Series of Interest Destination Wool

Sydney

Melbourne

Fremantle

24 23 24

23 24 24

21 22 24

24 23 24 24 24 24 22 23 24 22 23 24

24 24 24 24 24 24 24 24 24 24 24 24

24 24 24 24 24 24 18 22 24 24 24 23

24 24 23

24 24 24

22 24 24

Greasy (Raw) Wool 51011110 China France Italy 51011120 China Czech Republic France Germany India Italy Japan Spain Taiwan Turkey United Kingdom United States 51011130 China India Spain

8



VOL.3,NO.2, 2008

Scoured Wool 51012120 China Germany India Italy Japan Korea, Republic of Malaysia Spain Taiwan Thailand Turkey United Kingdom United States

24 10 24 24 24 24 22 3 23 22 13 19 13

24 24 24 24 24 24 24 22 24 24 24 24 24

19 24 24 24 22 21 17 23 17 24 23 23 22

When transformed from monthly to quarterly, prices from the different exporting ports are characterized by smoother fluctuations and a strong positive correlation. Therefore, it is possible to regress one series on another and to use the estimated relationship between the two series to infer the missing values as suggested by Dagenais (1975). As shown by Table 1, Melbourne has the greatest number of continuous time series (24 observations) and thus was used most of the time to conduct the extrapolation for the two other ports. Data and analysis methods used in this study are introduced in the following sections.

4. Pricing-to-Market - The Model 4.1 Demand Side Importers have a choice between the two differentiated goods which are imported from Australia (or a port in Australia) and the rest of the world. They decide an optimal consumption of each good to maximize their utility: u = u ( x, s) , where x is the quantity of good from Australia and s is the quantity of good from the rest of the world. The constant elasticity of substitution (CES) utility function that allows substitution between these products is most suitable (Bodnar, Duman and Marston, 2002) in this case. Agents in each destination will maximize their utilities,

9



[

u ( x, s ) = α xη + (1 − α ) s η

(2)

]

VOL.3,NO.2, 2008

1

η

subject to the budget constraint, y = p( x) x + p( s ) s . The α and η are the parameters for preference and substitutability, y the total expenditure on x and s , and p( k ) the import price denoted in destination currency

( k = x , s ) . It is conventionally assumed that α ∈ (0,1), and η ≤ 1 . The elasticity of substitution between x and s , µ is equal to

1 ≥ 0 . The inverse demand 1 −η

functions for x and s denoted in importer’s currency are in the following form: (2A)

α x (η −1) y p ( x) = α xη + (1 − α ) s η

(2B)

p ( s) =

(1 − α ) s (η −1) y . α xη + (1 − α )s η

The regular demand functions for x and s can be derived as follows: −1

(3A)

1  (1−η )  η 1 − 1 α    x = y  p( x) + ( p( x)) (1−η ) ( p(s ) ) (η −1)    α   

(3B)

1  η 1  1 − α  (η −1)  ( 1 − η ) ( 1 − η )  . ( p( s ))  s = y  p ( s ) + ( p ( x) )   α   

−1

From 2A and 2B, we can derive the partial elasticities of demand as functions of market share φ and substitutability parameter η ,

(4A)

 ∂ ln p( x)  ∂ ln x  ∂ ln p ( s)   ∂ ln x

∂ ln p( x)  ∂ ln s  = η (1 − φ ) − η (1 − φ ) and ∂ ln p ( s )   − η φ η φ − 1   ∂ ln s 

(4B)

 ∂ ln x  ∂ ln p ( x)   ∂ ln s  ∂ ln p ( s)

∂ ln x  1 η φ − 1 η (1 − φ )  ∂ ln p (s )  , = ∂ ln s  1 − η  η φ η (1 − φ ) − 1 ∂ ln p (s ) 

where market share

φ=

α xη y p ( x) x = . y α xη + (1 − α ) s η

10



VOL.3,NO.2, 2008

4.2. Supply Side According to Froot and Klemperer (1989) and Knetter (1986), profit maximizing export producers from Australia sell wool to n foreign destination markets, indexed by j, and the market segmentation does not allow any arbitrage condition.

We

assume that each producer believes that the other will not change the price that it is quoting. An exporter in Australia selling the product of x will maximize its profit in t: (5)

  ∑ xi , r( e AU max ∑ e AU p ( x ) x c − j j j j  p j( x ) j  i

 )  , j = 1,L , n  

subject to x j = d x ( p j ( x), p j (s ), y j : α ,η ) , where p j ( x ) is the destination price in the jth destination market (i.e., in Korean won), x j = d x ( p j ( x), p j ( s ), y j : α ,η ) the quantity demanded by the destination market j. The

exchange rate e j AU is defined as Australian dollar price per unit of foreign currency (i.e., AUD/won). The total cost function c(⋅) depends on the quantity demanded by the destination market and input price r denoted in the exporter’s currency.

The first order condition for the exporter’s profit maximization problem of equation (5) is (6)

mc( e AU )  ∂ ln x j ∂ ln p j ( x )  j   , j = 1,L , n , pj( x ) = AU  1 + ∂ ln x j ∂ ln p j ( x )  ej  

where mc is the marginal cost which can depend on the exchange rate e AU and j [ ∂ ln x j ∂ ln p j ( x ) ] is the price elasticity of demand for x in the jth destination.

The

solution for the exporter (6) is re-expressed using 4(B) as the following: (7)

mc(e AU j )  φ j − (1 η j )   . p j ( x) =  φ −1  e AU j j  

Equation (7) explains the PTM behavior of the exporter: the price the exporter charges to country j depends on marginal cost, exchange rate, market share and substitutability. The PTM elasticity, or the extent of pass-through can be expressed from

11



VOL.3,NO.2, 2008

equation (7) by taking a derivative of p j ( x ) with respect to e AU j (in logarithmic values) and assuming that the marginal cost also depends on the exchange rate: (8) ∂ ln p j ( x ) ∂ ln e AU j

 ∂ ln mc φ j ( 1 −η j ) ∂ ln φ j   φ j (η j − 1 ) ∂ ln φ j  = −1+  1 +  AU AU ( φ j − 1 )( η j φ j − 1 ) ∂ ln e j   ( φ j − 1 )( η j φ j − 1 ) ∂ ln p j ( x )   ∂ ln e j 

−1

The changes in the marginal cost are common to all destination countries but they vary over time since the commodity exported from a source country is assumed to be identical across destination markets (Lee and Tcha 2005). The changes in an exchange rate will lead to the changes in an exporter’s price and part of these changes can be caused by changes in the mark-up in each destination which is country specific. The magnitudes of the changes in mark-ups resulting from the exchange rate fluctuations are determined by the exchange rate elasticity of market share and price elasticity of market share since

∂ ln K j ∂ ln e AU j

=

φ j ( 1 −η j ) ∂ lnφ j ∂ ln K j φ j (η j − 1 ) ∂ lnφ j = and , AU ( φ j − 1 )(η j φ j − 1 ) ∂ ln e j ∂ ln p j ( x ) ( φ j − 1 )(η j φ j − 1 ) ∂ ln p j ( x )

where Kj is the mark-up in the jth destination charged by exporters, i.e.,

Kj =

∂ ln x j ∂ ln p j ( x ) 1 + ∂ ln x j ∂ ln p j ( x )

.

Therefore, the PTM elasticity of equation (8) can also be represented by the import price elasticity of mark-up and exchange rate elasticity of mark-up: (9)

∂ ln p j ( x ) ∂ ln e AU j

 ∂ ln mc ∂ ln K j 1 = − + ∂ ln e AU  ∂ ln e AU j j

−1  ∂ ln K j   1 −  .   ∂ ln p j ( x ) 

If the demand functions were derived from the CES utility specification with a large elasticity of substitution between two varieties, the optimal mark-up set by the exporter would fall when the importer’s currency depreciates and the exporter would raise the optimal mark-up when there is a depreciation of the exporter’s currency. This in turn would lead the exchange rate pass-through to be incomplete.

4.3. Empirical Model

12



VOL.3,NO.2, 2008

The first order condition derived above, equation (7), shows that the price is determined by the exchange rate, marginal cost, market share, and price of substitute. Therefore, the following function is to be estimated: (10)

AU AU ln p jt ( x ) = F ( e AU ), jt , p jt ( s ), φ jt , mct

j = 1,L , n; t = 1,L ,T .

While function (10) reveals the effect of exchange rate on the destination price, estimating ERPT needs more modification. First, the data used in this study does not provide the destination price but provides the export price at port. Therefore, in empirical specification, the port price is used instead of destination price, and exchange rate in the right side of (10) changes to the value of destination j’s currency in terms of Australian dollars. Second, the price of the product from the rest of the world pjt(s) for each destination is not available, especially in the form of quarterly data. Third, market share of the importing country in this commodity is added as this variable reflects the limitation of the exporter’s pricing-to-market scheme. Finally, it is difficult to find marginal cost, and wage in each state is used as a proxy. In consequence, the price from port i to destination j at time t (for simplicity, subscript t for time is not included in the equation) is expressed in terms of the wage and exchange rate, where the coefficient for exchange rate is hypothesized to be affected by port i's market share in country j (= sij) and country j's market share in the port i's total export (=μi j). Using double-log, the equation is presented as ln Pij AU = α 0 + α 1 ln w i + λij ln e j + u i j

(11)

= α 0 + α1 ln wi + ( β 0 + β1sij + β 2 sij2 + γ 1µ ji + γ 2 µ 2ji ) ln ej + u ij

where i = Fremantle, Melbourne and Sydney, and j is the major destination for each type of wool exported from each port. Exporter’s and importer’s market shares are included as quadratic functions following conventional consideration for exporter’s market share as found in Feenstra, Gagnon and Knetter (1996). If neither market share affects PTM elasticities, the coefficient for exchange rate will be estimated to be a constant, β0. The seemingly unrelated regression (SUR) method is used to estimate equation (11). The price of the same kind of wool exported from different ports would be influenced by certain variables which were not directly included in the equation (such as

13



VOL.3,NO.2, 2008

drought), and consequently the residuals might be correlated with one another. Accordingly,

the

contemporaneous

correlation

between

cross-sections

4

and

heteroscedasticity are allowed in the model. As there are only 24 quarterly observations for 6 years, the stationarity of variables were not considered as advised by Enders (1998). However, it is suspected that the error term has a first-order autoregressive error structure such as u

ijt

= ϕ ij u

i j t-1

+εi j t . The Durbin-Watson statistics from initial

estimation suggested the possibility of autocorrelation. Therefore, first order autocorrelation is also allowed. When we denote cross-section observations by subscript h (h = 1, 2, 3…, H) and time series observations by t (t = 1, 2, 3…, T), the contemporaneous correlation between cross-sections and heteroscedasticity are presented as E(uht ukt)= σ hk2 and

E(u 2ht )= σ hh2 . The following assumptions are also

imposed for the error disturbance term: E( ε ht )=0, E(u h ,t −1 ε ht )=0, E (ε ht ε kt ) = φhk , E (ε ht ε ks ) = 0 (for s≠t), and E(uht ukt)= σ hk2 = φhk /(1 − ϕ hϕ k ) , as suggested by Parks(1967).

5. Estimation and Discussion While the PTM elasticity defined as the percent change in destination price by 1 percent change in exchange rate is

∂ ln P ∂ ln e

AU

, P AU and e are used in this study.

Therefore, the coefficient obtained from empirical estimation and PTM elasticity have the following relation: λ =

∂ ln P AU ∂ ln P = − 1− . We will modify the results of ∂ ln e ∂ ln e AU

estimation accordingly. 4 The SUR estimation method will increase efficiency of the estimator if the errors of the different equations in the system to be estimated are contemporaneously correlated. We can test for contemporaneous correlation using a simple LM test constructed as follows:

λ = T ∑ rij2 ij

2 ij

where r is the squared correlation of the estimator of the variance obtained using OLS on each equation of the system. This test is distributed as χ2(n), where n is the number of squared correlations term. For all the system estimated, the test statistic of no contemporaneous correlation is rejected at less than 1% significance level, justifying the use of the SUR estimation. Furthermore, it is well known that if the error is not subject to contemporaneous correlation, SUR will be equivalent to standard GLS estimation.

14



VOL.3,NO.2, 2008

From equation (11), the percent change in export price with respect to 1 percent appreciation of exporter’s (Australia’s) currency is:

∂ ln pij AU

(12)

∂ ln e j

= λij = β 0 + β 1sij + β 2 sij 2 + γ 1µ ij + γ 2 µ ij2

Analyses of ERPT are carried out in four steps, and more detailed discussions on the ERPT and the influence of market powers estimated are presented below.

■ Step 1. Estimating the Coefficients First of all, the coefficients β ' s and γ ' s are estimated for the four kinds of wool – RAW1 to RAW3 and Scoured wool - using the SUR method with AR(1). As the presentation of the results is rather tedious and the main focus of this paper is not to understand transaction of each and every kind of wool (from different ports to different countries), the results of these estimations and relevant discussions will be presented in Appendix. Following steps show how these results are used for further analysis with focus. ■ Step 2. Estimating Elasticities λ =

∂ ln P AU ∂ ln e

Based on the findings in Tables A1 to A4, the elasticity or each case for each type of wool is constructed, by using the export and import shares for each trade partner in given time, such as:

λijt = βˆ 0 + βˆ 1 sijt + βˆ 2 sijt2 + γˆ 1µ ijt + γˆ 2 µijt2 As discussed previously, we have four types of wool under examination, which are exported from the three Australian ports. For RAW1 and RAW3, there are 3 major destinations, and for RAW2 and Scoured wool, 9 to 12 major destinations for each port. This altogether produces 83 trade cases. Since the elasticity using the above equation is for each period (quarter), we have 24 elasticities for each of 83 cases. In this paper, average elasticity (over 24 quarters) is computed for each case, using the average export and import shares. This is discussed in Step 3.

■ Step 3. Computing Average Elasticity The average elasticity for each type of wool from each port i to each destination

15



VOL.3,NO.2, 2008

j is computed where the bar means the average over 24 quarters, such as:

λ ij = βˆ 0 + βˆ 1 sij + βˆ 2 sij2 + γˆ 1µ ij + γˆ 2 µ ij2 . It will be helpful to investigate the structural relationship between the magnitude of the elasticity of export and that of import, as some previous research concentrates on changes in destination prices, while this study, due to the availability of data, uses the export price. Suppose the transport cost and other transaction cost such as tariff are 5

ignored. The type of elasticity is categorized and summarized in Table 2. Table 2. The Export Elasticity and Phase of Pass-Through Magnitude of Export Elasticity 0 0) is roughly similar for both kinds of wool.

Figure 3. Frequency of ERPT - RAW2 30 24

25

Frequency

20 15 10

7

5

3 1

0

0

0

-3

-2

-1

0 0

1

2

3

Figure 4. Frequency of ERPT – Scoured Wool 16

15

14

Frequency

12 10 8

7

6 4 4 2 2

1 0

0

-4

-3

1 0

0 -2

-1

0

1

2

3

4

■ Step 4. Market Power and ERPT It has been in the centre of the ERPT or PTM studies whether market share

21



VOL.3,NO.2, 2008

matters and, if so, what the systematic mechanism that the market share influences the ERPT is. Table 4 summarizes the relationship between market shares and the elasticity, based on Tables A1 to A4. The types of ERPT (normal, perverse, and excessive) are determined by using the average elasticity computed in Step 3. The third column lists the cases where neither exporter’s nor importer’s market share affects the ERPT elasticity. Alternatively, it is the collection of the cases where complete pass-through is observed, or incomplete but constant pass-through is observed. The table summarizes findings in this section. For example if one is interested in the ERPT for Fremantle price to Italy for RAW1 (Greasy 51011110), the table shows that the ERPT is excessive, and Italy’s (importer’s) market power is practiced. Table 3 more accurately reports the magnitude of the ERPT for Melbourne price to Italy for the same type of wool. It is shown that Italy’s market power also works in Melbourne when ERPT is normal. Table 3 shows the magnitude is –0.15. In contrast, the export price from Sydney to Italy is not affected by either’s market share and the ERPT is normal.

22



VOL.3,NO.2, 2008

Table 4. Effects of Market Share on ERPT

Type of Wool

Exporting Port

Greasy Fremantle (51011110) Melbourne

Sydney

No Effect

China

Destination Exporter’s Importer’s market market power power France (E)

Italy (E) Italy China

Italy

Czech Greasy Fremantle Republic France (E) (51011120) Spain Taiwan UK

Both

Total Cases 3

France

3

China (E)

France (E)

3

China India Turkey

Germany (E) Italy (E) USA (E)

11

Czech

Melbourne Republic(E) France Japan Spain Turkey Sydney

Greasy Fremantle (51011130) Melbourne

France Taiwan UK

Germany (E) China Italy USA (E) Taiwan (E) UK

Turkey

Spain

India

Scoured Fremantle (51012120)

Japan

UK

23

12

China Germany (E) Czech Republic India Japan (E) Italy Spain (E) USA

12

China (E) India (P)

3

China

Sydney

India

Spain (E)

3

India (P)

China (E) Spain (P)

3

Germany Italy (E) Korea (E) Spain (E) Thailand (E)

India USA (E)

9



Total Cases Excessive Perverse Normal

VOL.3,NO.2, 2008

Melbourne

Germany (P) Malaysia India Italy Thailand (P) Japan Taiwan (P) Korea Spain Turkey (E) UK (P)

USA (E)

12

Sydney

UK

India Italy Malaysia Thailand (E)

9

16 6 1 9

83 28 8 47

China (P) Japan (E)

26 3 2 21

Korea (E) Taiwan

17 5 3 9

24 14 2 8

Note: (E) and (P) represent Excessive and Pervasive ERPT respectively.

6. Summary and Conclusions This study develops a theoretical framework for pricing-to-market when market shares matter, together with conventional variables explaining ERPT, and then performs empirical estimation. The extensive wool price database is used to carry out an econometric analysis of the relationship between prices and exchange rate changes, which quantify the degree of the fluctuation of export price due to exchange rate changes. This analysis in particular takes into account the effect of the exporter’s and importer’s market powers on the extent of price changes due to exchange rates. The major contributions of this study include the analysis of ERPT considering symmetry response cases, comparison of ERPT across different ports and across major destinations, and investigation of the effect of market shares. The elasticity of export prices for four kinds of wool, exported from three major ports in Australia to major destinations, are examined in this study. Altogether, 83 trade relationships are investigated using 24 quarterly observations that cover the period of 1995 to 2001. Major implications from this study can be briefly summarized in what follows. First, while normal ERPT is dominant, the other two kinds of responses to

24



VOL.3,NO.2, 2008

exchange rate changes - perverse, and excessive – are also found when symmetric response is assumed. The exporter’s and the importer’s market shares turned out to be important for selected cases. Using the wool trade data, the normal ERPT was found for 47 trade cases (or 57%) out of 83 cases for four types of wool investigated, from all the three ports to all major destinations. All the excessive responses were found from the export price of scoured wool. Therefore this phenomenon, excessive response, is more likely commodity-specific. Also, it was found that even for the same kind of product exported from the same country to the same destination, price elasticities with respect to exchange rate could be different. This finding confirms that exporters with different market powers or facing importers with different market powers, have different pricing strategies. Secondly, it was generally found that the origin specific variable, such as an origin’s wage rate, has a positive effect on the ERPT; however, some negative effects are also found. This implies that as the marginal cost increases, the export price varies too. As all the origins in this study are the three ports in Australia, the same exchange rate is applied to them for each destination. In this regard, the exchange rate is a destination-specific variable. The exchange rate was found to be a significant determinant of the export price in many cases. Thirdly, the frequencies of irregular and unexpected results were different across products. In addition, even for the exactly homogeneous product from the same exporting country, ERPTs are different by exporting ports or importing countries. For instance, the export prices of Scoured wool to Turkey and India exhibit substantially different movements from the others. A limited number of observations, or some noise created from aggregation (from monthly to quarterly), may generate data problems. In fact, large fluctuation of quantity traded was revealed for this type of wool, which is consistent with Goldberg and Knetter’s (1997) observation for a variety of manufacturing goods trade. It is worthwhile to repeat their explanation regarding the reasons of the large fluctuation in quantity: • A fairly high fraction of export shipments go unrecorded, introducing a great deal of noise into quantities; • Industry specific reasons such as a great fluctuation in inventories, especially for

25



VOL.3,NO.2, 2008

durable goods; • Shipping pattern change and goods are routed to intermediate destinations; • When the markets involved are new or in transition phase. Buyers are learning about new products, but sellers have not yet established their position, and the set of competitor’s changes dramatically. While the data we used are of very high quality, it is still possible that some observations are not accurate owing to aforementioned reasons of Goldberg and Knetter’s (1997). This needs further investigation.

26



VOL.3,NO.2, 2008

REFERENCES Athukorala, P. and Menon, J. (1995). “Exchange Rates and Strategic Pricing: The Case of Swedish Machinery Exports.” 57(4), pp.533-546. Bondar, G., Dumas, B., and Marston, C. (2002). “Pass-through and Exposure.” Journal of Finance, 57(1), pp.199-231. Dagenais, M.G. (1975). “The Use of Incomplete Observations in Multiple Regression Analysis: A Generalized least squares Approach.” Journal of Econometrics, 1, pp.317-328. Dornbush, R. (1987). “Exchange Rates and Prices.” American Economic Review, 77, pp. 93-106. Dwyer, J., Kent, C., and Pease, A. (1994). “Exchange Rate Pass-through: Testing the Small Country Assumption for Australia,” Economic Record, 70(211), pp.408423. Enders, W. (1998). Applied Econometrics Time Series. John-Wiley and Sons, NewYork. Feenstra, R.C., Gagnon. J.E, and Knetter, M.M. (1996), “Market Share and Exchange Rate Pass-through in the World Automobile Trade.” Journal of International Economics, 40, pp.187-207. Feenstra, R.C., J. Gagnon, and Knetter, M. (1996). “Market Share and Exchange Rate Pass-through in World Automobile Trade.” Journal of International Economics, 40, pp.187-207. Froot, K.A. and Klemperer, P.D. (1989). “Exchange Rate Pass-through When Market Share Matters.” American Economic Review, 79, pp.637-654. Gagnon, J.E. and Knetter, M. (1995). “Markup Adjustment and Exchange Rate Fluctuations: Evidence from Panel Data on Automobile Exports.” Journal of International Money and Finance, 14, pp.289-310. Goldberg, P.K. and Knetter, M. M. (1997). “Goods Prices and Exchange Rates: What We have Learned?” Journal of Economic Literature, 35, pp.1243-1272. Griffith, G. and J. Mullen (2001). “Pricing-to-market in NSW Rice Export Markets,” Australian Journal of Agricultural and Resource Economics, 45(3), pp.323-334. Gross, D.M. and Schmitt, N. (2000). “Exchange Rate Pass-Through and Dynamic Oligopoly: An Empirical Investigation.” Journal of International Economics, 52, pp.89-112. Knetter, M. (1989). “Price Discrimination by U.S. and German Exporters.” American Economic Review, 83(3), pp.473-86. Krugman, P. (1987). “Pricing-to-market When Exchange Rate Changes.” In S.W. Arndt and J.D. Richardson, eds., Real Financial Linkages Among Open Economies. Cambridge, Mass: MIT Press. Lee, J. (1997). “The Response of Exchange Rate Pass-through to Market Concentration in a Small Economy: The Evidence from Korea,” Review of Economics and Statistics, 79(1), pp.142-145. Lee, M. and Tcha, M. (2005), “Pass-Through Elasticity, Substitution and Market Share: The Case for Sheep Meat Exports,” Journal of International Trade and Economic Development, Vol.14, No.2, pp.209-228. Marston, R. (1990). “Pricing to Market in Japanese Manufacturing.” Journal of International Economics, 29, pp. 217-23.

27



VOL.3,NO.2, 2008

Menon, J. (1995) “Exchange Rate Pass-through,” Journal of Economic Survey, 9(2), pp.197-231. Parks, R (1967). “Efficient Estimation of a System of Regression Equations When Disturbances are both Serially and Contemporaneously Correlated.” Journal of the American Statistical Association, 62, pp.500-509. Shahnawaz, K., Lee, M. and Gan, C. (2007a) “Real Effects of Monetary Policy in New Zealand,” Australian Economic Review, Vol.40, No.4, pp.385-401. _____________________________________ (2007b) “Exchange Rate Dynamics of New Zealand,” Journal of Economic Policy Reform, Vol.10, No.3, pp.241-260. Tcha, M. (2004). The World Wool Trade and Australia, Main Report (draft), Economic Research Centre, The University of Western Australia, Crawley. _______. (2005). “Exchange Rate Pass-Through, Asymmetric Responses and Market Shares.” Korea Development Review, 27(1), pp.185-210. Tivig, T. (1996). “Exchange Rate Pass-through in Two-period Duopoly.” International Journal of Industrial Organization, 14, pp.631-645. Tongzon, J.L. and J. Menon (1993). “Exchange Rates and Export Pricing in a Small Open NIC: The Singaporean Experience,” Singapore Economic Review, 38(2), pp.201-211. Varangis, P.N. and Duncan, R.C. (1993). “Exchange Rate Pass-through: An Application to US and Japanese Steel Prices.” Resources Policy, 19, pp.30-39.

DATA SOURCES

Bilateral Exchange Rate Data for the period 1995-2001 are collected from: International Financial Statistics of the International Monetary Fund Financial Statistics of the Federal Reserve Board

Wage Rate Data for the period 1995-2001 are collected from: Australian Bureau of Statistic: (6302.0) “Average Weekly Earnings”

Wool Trade Data for the period 1995-2001 are provided by: Department of Agriculture Western Australia

28



VOL.3,NO.2, 2008

Appendix: The Results of Estimation and Discussions RAW1 (Greasy Shorn Wool, not carded or combed, 19μm and finer, 51011110) Table A1 reports the result of estimation for RAW1. For this type of wool, exports from the three ports to three destinations are examined. For only two out of nine cases (Sydney-Italy and Fremantle-China), no coefficient for export and import market shares turns out to be significant. This result implies that the fluctuation of exchange rate between Australia and Italy does not change the export price of this kind of wool from Sydney to Italy. Also that between Australia and China does not affect the export price of this kind of wool exported from Fremantle to China. As the export price does not change while the bilateral exchange rate changes, all the fluctuation in the exchange rate is incidental on the price at destination. In other words, there is complete ERPT. For the other cases, the ERPT elasticity is found to be a function of some variables such as the export and import market share, or nonzero constant. This implies that a certain degree of incomplete, excessive or perverse pass-through exists. Also, depending on the significance and magnitude of each coefficient, the effects of exporter’s market share and import market turn out to be different across ports, destinations, and type of wools. For example, export prices from Sydney to China are found to be significantly affected by the port’s share of the destination’s import, while those from Sydney to France and Melbourne to Italy are significantly affected by the buyer’s share in the port. Export prices from Melbourne to France are affected by both factors. For export price from Fremantle, the importer’s market share is found to be significant for wool to Italy and the exporter’s market share is found to be significant for wool to France, although both of them are marginally significant. More discussions on the ERPT exchange rate are presented in Step 2.

29



VOL.3,NO.2, 2008

Table A1. Results of Estimation - RAW1: α0 α1 Constant Ln(W)

β0 Ln(E)

Β1 β2 γ1 γ2 φ s*Ln(E) s2*Ln(E) μ*Ln(E) μ2 *Ln(E) AR(1)

Fremantle China -20.50 (17.94)

3.49 (2.61)

-0.06 (0.91)

0.23 (0.64)

-1.02 (1.14)

0.51 (0.65)

-1.17 (1.03)

0.34* (0.18)

France -30.29*** (5.92)

4.42*** (0.89)

2.81*** (0.54)

-0.97* (0.58)

1.13 (0.87)

0.79 (1.09)

0.32 (2.97)

0.08 (0.18)

Italy -33.95*** (5.78)

3.61*** (0.67)

1.87*** (0.40)

0.16 (0.29)

-0.77 (1.05)

-0.31 (0.19)

0.22* (0.13)

0.18 (0.16)

-0.85 (0.77)

0.71*** (0.13)

Melbourne China 11.80 (11.99)

-1.36 (1.80)

-0.90 (0.62)

0.03 (0.33)

-0.06 (0.34)

0.74* (0.46)

France -30.52*** (5.93)

4.94*** (0.90)

0.62 (0.48)

-1.81*** (0.57)

1.77*** (0.47)

6.06** -22.26 (2.47) (16.27)

-0.10 (0.19)

Italy -21.26*** (5.03)

3.43*** (0.65)

0.35 (0.32)

-0.25 (0.31)

0.33 (0.48)

-0.42*** (0.15)

0.28** (0.11)

0.41*** (0.11)

Sydney China 32.72*** (12.19) France

5.11 (3.28)

Italy -21.76*** (5.57)

-4.48*** (1.74)

-1.03 (0.64)

-1.57*** (0.37)

1.47*** (0.47)

1.11 (0.94)

-1.58 (3.15)

0.28 (0.16)

-0.76 (0.49)

1.13*** (0.37)

-0.68 (0.44)

0.29 (0.59)

11.72** -261.92* (6.28) (139.26)

-0.10 (0.10)

3.58*** (0.67)

0.34 (0.44)

-0.12 (0.23)

0.20 (0.21)

-0.75 (0.60)

0.44*** (0.12)

Note: Estimation Method: Seemingly Unrelated Regression (SUR) with AR(1) Sample: 1995-4 2001-2 Total system observations: 207 Standard error given below the coefficients ***, **, *: Significant at 1%, 5%, 10% Level of Significance respectively LM Test for Contemporaneous Correlation: λ = 134.57***

30

0.52 (0.34)



VOL.3,NO.2, 2008

RAW2 (Greasy Shorn Wool, not carded or combed, 20μm to 23μm, 51011120) The results of estimation for RAW2 are summarized in Table A2. In case of Fremantle, its export price to four economies (Czech Republic, Spain, Taiwan, and the UK) was found to be completely independent of exchange rate. As discussed for RAW1, this implies that as the export price does not change when exchange rate changes, all the changes in exchange rate are completely transferred to the destination price, namely complete ERPT is revealed. In other words, Australian exporters in the port are completely risk-safe from the fluctuation of exchange rate with these economies, receiving fixed price in Australian dollar, regardless of bilateral exchange rate. This complete pass-through is also found for two cases from Melbourne (to Japan and Turkey) and three cases for Sydney (to France, Taiwan, and the UK). It is noteworthy that the export price to Taiwan and the UK does not change (or the complete ERPT) from the two ports. While Turkey experiences the same phenomenon for export from one port only (Melbourne), for the other two ports, the level of significance of coefficients is at margin (10%).

31



VOL.3,NO.2, 2008

Table A2. Results of Estimation - RAW2 α0 α1 Constant Ln(W)

β0 Ln(E)

β1 β2 γ1 γ2 φ S*Ln(E) s2*Ln(E) Μ*Ln(E) μ2*Ln(E) AR(1) Fremantle

China

-4.71 (18.19)

1.17 (2.71)

2.09 (1.37)

Czech Republic

-1.23 (5.96)

0.34 (0.86)

0.15 (0.32)

0.01 (0.11)

4.54*** (0.93)

2.34*** (0.71)

France -30.40*** (6.01)

-17.73** 28.02** (7.85) (12.72)

-0.08 (1.16)

0.43 (1.69)

-0.04 (0.10)

-0.78 (0.97)

7.94 (10.18)

-1.55 (1.90)

3.23 (2.36)

0.42 (1.55)

-2.50 (3.92)

0.21 (0.17) 0.52*** (0.09) 0.10 (0.15)

Germany

-5.39 (5.90)

1.05 (0.92)

0.02 (0.26)

-1.88 (2.10)

2.48 (3.69)

9.37* (5.44)

-36.49 (31.03)

0.67*** (0.10)

India

4.33 (6.81)

-0.10 (0.92)

-0.56 (0.38)

-0.83** (0.26)

0.92*** (0.28)

0.43 (0.45)

-2.06 (1.82)

0.69*** (0.11)

3.62*** (0.58)

1.09** (0.43)

0.39 (0.37)

-0.65 (0.75)

1.06* (0.62)

-6.50* (3.56)

0.17 (0.17)

Italy -29.90*** (4.95) Spain

-5.89 (7.50)

1.60 (1.03)

-0.48 (0.49)

0.11 (0.11)

-0.28 (0.24)

2.79 -125.96 (3.87) (231.05)

0.52** (0.18)

Turkey

-5.61 (8.71)

1.28 (1.54)

-0.08 (0.12)

-0.09* (0.05)

0.11* (0.07)

0.49 (0.67)

-5.34 (14.33)

0.56** (0.18)

Taiwan

3.58 (12.72)

0.21 (1.79)

-1.15 (0.68)

-2.24 (1.49)

9.07 (5.66)

1.16 (1.39)

-9.40 (9.92)

0.73*** (0.10)

United Kingdom

-6.51 (8.29)

1.31 (1.33)

0.60 (0.43)

-0.08 (1.41)

0.04 (4.77)

-9.20 67.15 (10.21) (171.66)

0.34** (0.15)

United States

-7.21 (8.53)

1.34 (1.36)

0.19 (0.39)

-1.18 (1.92)

1.95 (4.23)

-27.10*** 162.68** (10.20) (82.60)

-0.12 (0.14)

Total system observations: 253 LM Test for Contemporaneous Correlation: λ = 132.97***

32



α0 α1 Constant Ln(W)

β0 Ln(E)

VOL.3,NO.2, 2008

β1 β2 γ1 γ2 φ s*Ln(E) s2 *Ln(E) μ*Ln(E) μ2*Ln(E) AR(1) Melbourne

-9.15 (6.11)

1.66* (0.92)

-0.12** (0.39)

1.07 (1.26)

-1.21 (1.42)

-0.76** (0.39)

0.87* (0.48)

0.68*** (0.07)

Czech Republic -11.72 (7.59)

1.90* (1.16)

0.29*** (0.22)

-0.14 (0.15)

0.21 (0.24)

0.94 (1.36)

-17.64 (22.40)

0.75*** (0.07)

-9.44** (4.40)

1.72*** (0.67)

0.00*** (0.40)

0.50 (1.02)

-1.11 (1.47)

-3.45 (2.77)

19.95 (13.28)

0.42*** (0.12)

Germany

-3.94 (6.36)

0.84 (0.99)

0.11 (0.65)

-4.21 (2.97)

6.36* (3.61)

12.40 (9.51)

-76.00 (54.67)

0.65*** (0.09)

India

-6.51 (4.35)

1.40** (0.64)

-0.29 (0.27)

-0.40* (0.21)

0.58** (0.29)

-0.97 (0.80)

15.43** (7.75)

0.86*** (0.04)

Italy

-5.00 (5.64)

0.67 (0.76)

0.43 (0.29)

-0.68*** (0.18)

1.02*** (0.25)

-0.39 (0.63)

1.67 (3.19)

0.53*** (0.12)

Japan -18.19* (11.25)

2.58 (1.69)

0.71 (0.50)

0.19 (0.17)

-0.10 (0.14)

-1.28 (2.29)

30.41 (34.77)

0.30 (0.18)*

Spain

-0.57 (5.61)

1.05 (0.80)

-1.01** (0.41)

-0.19 (0.13)

0.13 (0.11)

0.29 -42.25 (3.27) (155.17)

0.27 (0.18)

Turkey

5.08 (9.29)

-0.85 (1.62)

0.15 (0.14)

0.00 (0.07)

-0.02 (0.10)

0.67 (1.25)

-3.79 (41.50)

0.64*** (0.13)

2.98*** (0.72)

-0.75 (0.24)

-0.64** (0.30)

0.70** (0.33)

0.76 (0.65)

-2.43 (2.32)

0.85*** (0.05)

4.00*** (1.09)

-2.59 (1.23)

6.63** (2.98)

-5.44*** (2.13)

-9.33 (7.67)

73.52 (46.68)

1.00*** (0.06)

1.22 (1.26)

0.08** (0.37)

-0.65 (0.74)

0.46 (0.57)

4.21 (3.45)

-71.72** (37.37)

0.79*** (0.09)

China

France

Taiwan -15.43*** (4.57) United -38.14 Kingdom (228.52) United States

-6.49 (8.06)


33



α0 α1 Constant Ln(W)

VOL.3,NO.2, 2008

β0 Ln(E)

β1 β2 γ1 γ2 φ s*Ln(E) s2*Ln(E) μ*Ln(E) μ2*Ln(E) AR(1)

China

5.27 (5.65)

-0.64 (0.89)

-0.69* (0.38)

Sydney -0.03 (2.56)

7.70 (5.64)

-37.73*** 242.41* (8.88) (144.34)

Czech Republic

5.85 (4.13)

-0.78 (0.65)

-0.42 (0.25)

-0.75 (0.79)

2.35 (2.37)

-14.86*** 74.03 (5.95) (100.22)

-1.43** (0.73)

-0.04 (0.52)

0.29 (0.89)

-0.42 (1.05)

-0.92 (1.00)

France 10.74* (5.83)

1.29 (3.15)

Germany

-3.54 (4.08)

1.00 (0.69)

-0.12 (0.04)

-0.13*** (0.04)

0.12* (0.06)

2.59*** -42.73*** (0.67) (17.36)

India

0.93 (3.00)

0.01 (0.44)

0.03 (0.24)

-0.24*** (0.07)

0.42*** (0.11)

9.50*** -360.49*** (1.55) (59.03)

5.13*** (1.45)

0.57 (0.55)

0.22 (0.19)

-0.48** (0.23)

2.06*** (0.72)

-5.31** (2.70)

-0.58** (0.25)

-0.03 (0.23)

-0.12 (0.20)

-0.09 (0.25)

-0.61*** (0.25)

2.16*** (0.81)

-2.13*** (0.45)

-1.73*** (0.44)

-0.76 (0.51)

1.34 (1.31)

4.27** (2.07)

-50.34* (28.36)

Italy -34.13*** (10.06) Japan

6.09*** (2.37)

Spain 20.88*** (3.77) Turkey

4.92 (3.95)

-0.59 (0.60)

0.15 (0.29)

-0.26* (0.15)

0.20 (0.19)

-0.52 (0.72)

4.64 (4.45)

Taiwan

-2.25 (2.51)

0.50 (0.38)

-0.26 (0.39)

3.59 (2.25)

-6.53 (4.19)

1.61 (1.63)

-2.84 (6.92)

7.72*** (2.11)

-0.96*** (0.33)

-0.43 (0.56)

-3.92 (2.80)

3.90 (3.77)

5.19 (5.41)

27.47 (28.11)

-2.82** (0.81)

-0.88** (0.30)

4.86*** (1.72)

-9.86*** (3.58)

-0.80 (0.77)

2.16* (1.31)

United Kingdom

United 20.20*** States (5.50)

Total system observations: 288 LM Test for Contemporaneous Correlation: λ = 248.08*** Note: Estimation Method: Seemingly Unrelated Regression (SUR) with AR(1) For Fremantle and Melbourne and SUR only for Sydney. Standard error given below the coefficients ***, **, *: Significant at 1%, 5%, 10% Level of Significance respectively

34



VOL.3,NO.2, 2008

RAW3 (Greasy Shorn Wool, not carded or combed, 20μm to 23μm, 51011120) Table A3 summarizes the results of the estimation for RAW3. For two cases out of nine, (Fremantle-Spain) and (Melbourne-India), the export price is not affected by the exchange rate. For the other cases, export market shares and/or import market shares were found to affect the export price. In particular, Australia’s wool exporters do not practice any market power as found from the results that the coefficient for linear and quadratic terms of the export market share is always insignificant, regardless of the exporting port. In fact, the coefficients for India’s importing share turn out to be significant for export price at Fremantle and Sydney, implying that it practices market power to Australian exporters.

Scoured Wool (Degreased Shorn Wool, not carbonized, carded or combed, 20μm to 23μm, 51012120) Among the nine destinations from Fremantle, Japan is the only market where the complete ERPT is found (Table A4). When the bilateral exchange rate between Australia and Japan fluctuates, it is found that the export price at Fremantle in Australian dollars does not change, which implies that, all other things being equal, the price in the Japanese market fluctuates by the same proportion. For Italy, while market shares do not appear to be significant, the constant ERPT is found to be 0.83. The US is the only market where both exporter’s and importer’s market shares are related to the ERPT elasticity. For the remaining cases, either exporter’s or importer’s market share does matters. Melbourne has 12 major markets, and the complete pass-through is found from 5 cases, to Germany, Italy, Japan, Korea, and the UK. For Spain and Turkey, the constant pass-through, -0.82 and –0.32, are observed respectively. For Malaysia, Thailand, and Taiwan, only Australia’s market power works, and India is the only country where only the importer’s market power matters. Both exporter’s and importer’s market power are found significant for this port’s export to the US. Only one complete pass-through is found for scoured wool export from Sydney, to the UK. For India, Italy, Malaysia, and Thailand, the two market shares matter. For China and Japan, Sydney’s ERPT is affected by its market share in their imports, and for Taiwan, importer’s market share turns out to be significant.

35



VOL.3,NO.2, 2008

Table A3. Results of Estimation - RAW3

α0 α1 β0 β1 β2 γ1 γ2 φ Constant Ln(W) Ln(E) s*Ln(E) s2*Ln(E) μ*Ln(E) μ2*Ln(E) AR(1) Fremantle to China

-2.03 (11.11)

0.73 (1.64)

-0.70 (0.56)

-0.26 (0.41)

India 32.01*** -3.61*** -2.40*** -0.33 (3.12) (0.41) (0.34) (0.31)

-0.25 (0.21)

0.39* (0.23)

0.66*** (0.10)

1.14 0.64*** -1.31*** -0.51*** (0.75) (0.18) (0.35) (0.16)

Spain

-7.61 (7.20)

1.44 (1.01)

-0.14 (0.36)

-0.06 (0.07)

0.01 (0.06)

0.05 (0.06)

0.74*** (0.12)

Melbourne to Italy

2.23 (5.78)

-0.05 (0.86)

0.09 -1.08** 0.72** (0.41) (0.53) (0.37)

-0.25 (0.65)

0.23 (0.46)

0.65*** (0.09)

India

8.99 (8.38)

-0.86 (1.19)

-0.70 (0.60)

-0.11 (0.29)

0.10 (0.22)

0.20 (0.26)

-0.49 (0.76)

0.83*** (0.10)

Spain -16.67** 2.66** (7.49) (1.15)

0.09 (0.34)

0.19* (0.10)

-0.26** (0.12)

0.32* (0.16)

Sydney to China

-1.17 (3.86)

0.50 (0.54)

0.06 (0.06)

0.49 (0.81)

-0.42 -6.70*** 24.50*** 1.51*** -1.16*** -0.46*** (0.26) (0.94) (3.64) (0.22) (0.23) (0.16)

India 50.51*** -5.56*** -4.23*** 0.40 (8.32) (0.90) (1.01) (0.37) Spain 8.07** (3.38)

-0.81** 0.76*** (0.36) (0.09)

-0.33 -1.08*** 0.28* (0.42) (0.34) (0.15)

-0.52 (0.98) -0.45** (0.22)

Note: Estimation Method: Seemingly Unrelated Regression (SUR) with AR(1) Sample: 1995-4 2001-2 Total system observations: 207 Standard error given below the coefficients ***, **, *: Significant at 1%, 5%, 10% Level of Significance respectively LM Test for Contemporaneous Correlation: λ = 107.34***

36

1.20* -4.43*** 0.12 (0.38) (1.25) (0.24) 0.06 (0.09)

-0.15* (0.09)

0.28 (0.21)



VOL.3,NO.2, 2008

Table A4. Results of Estimation – Scoured Wool α0 Constant

α1 Ln(W)

β0 Ln(E)

β1 s*Ln(E)

β2 γ1 γ2 s2*Ln(E) μ*Ln(E) μ2*Ln(E)

φ AR(1)

Fremantle Germany 13.92*** (4.63)

-1.91*** (0.73)

-0.18 (0.61)

-0.43 (2.24)

0.10 (2.33)

-10.75* (6.49)

39.86** (18.94)

0.47*** (0.17)

India 19.58*** (5.72)

-2.62*** (0.73)

-0.29 (0.55)

-0.70** (0.34)

1.06* (0.62)

0.82* (0.46)

-1.26 (2.15)

0.08 (0.18)

Italy

-6.91* (4.05)

0.42 (0.50)

0.83** (0.31)

-0.03 (0.15)

-0.15 (0.18)

0.15* (0.09)

-0.04 (0.12)

0.17 (0.16)

Japan

2.75 (16.74)

-0.65 (2.36)

0.75 (0.64)

-0.61 (0.61)

1.04 (1.44)

1.02 (1.33)

-3.96 (5.72)

0.34* (0.18)

Korea

1.61 (9.78)

-1.17 (1.59)

1.16*** (0.35)

-0.81 (0.61)

2.29 (2.18)

3.63*** -30.12*** (1.42) (9.40)

-0.29 (0.18)

Spain -18.18 (19.66)

2.24 (2.92)

1.20 (0.69)

-0.15 (0.10)

0.03 (0.09)

6.69*** -77.79** (2.03) (31.40)

0.76*** (0.11)

-9.91 (8.61)

1.52 (1.33)

0.63** (0.23)

-0.17 (0.31)

0.35 (0.56)

0.94*** (0.35)

0.72*** (0.09)

United 37.05*** Kingdom (11.98)

-5.69*** (1.93)

-1.48** (0.57)

4.19*** (0.81)

-5.05*** (1.18)

-2.45 (5.63)

United -19.95*** States (6.20)

3.42*** (0.99)

0.75** (0.33)

2.38*** (0.77)

-0.46 (1.70)

Thailand


37

-2.88*** (0.96) -1.87 (35.43)

-21.65*** 37.94*** (1.76) (10.63)

0.40** (0.16) -0.63*** (0.13)



VOL.3,NO.2, 2008

Table A4. Continued α0 α1 Constant Ln(W)

β0 Ln(E)

β1 β2 γ1 γ2 φ s*Ln(E) s2*Ln(E) μ*Ln(E) μ2*Ln(E) AR(1) Melbourne

Germany

7.18 (6.77)

-0.84 (1.06)

India

3.41 (5.01)

Italy

-0.57 (2.92)

0.55 (3.00)

-15.90 (39.76)

464.28 (890.89)

0.43** (0.18)

1.06 -2.63*** (0.74) (0.38)

0.12 (0.14)

-0.04 (0.12)

-0.99*** (0.30)

3.03*** (0.97)

0.94*** (0.07)

-3.17 (7.11)

0.79 (1.01)

0.06 (0.31)

0.06 (0.11)

-0.04 (0.12)

0.06 (0.16)

0.09 (0.52)

0.72*** (0.09)

Japan

-7.43 (6.49)

1.34 (0.98)

0.19 (0.24)

-0.09 (0.12)

0.08 (0.09)

0.22 (0.35)

-0.68 (0.91)

0.68*** (0.11)

Korea

-15.84* (8.21)

2.96** (1.27)

-0.17 (0.17)

-0.04 (0.07)

0.04 (0.05)

-0.03 (0.19)

0.25 (0.71)

0.80*** (0.08)

Malaysia

6.62 (7.19)

-0.69 (1.12)

-0.65* (0.34)

1.70*** (0.65)

-1.59*** (0.54)

0.65 (1.90)

0.37 (7.59)

0.54*** (0.20)

-1.89 (1.30)

-0.82* (0.46)

-0.01 (0.08)

-0.04 (0.07)

-1.56 (3.98)

Spain 17.68** (8.61)

0.23 (0.90)

154.56 0.50*** (173.42) (0.11)

Thailand

5.39 (4.03)

-0.75 (0.64)

0.49*** (0.12)

-0.46** (0.23)

0.53* (0.29)

0.60 (0.58)

-2.83 (2.55)

0.62*** (0.07)

Turkey

-25.61* (14.79)

4.91* (2.58)

-0.32** (0.14)

-0.03 (0.06)

0.01 (0.05)

-0.81 (0.79)

11.88 (14.28)

-0.38* (0.20)

Taiwan

14.21 (10.48)

-1.48 (1.50)

-0.91 (0.55)

-0.31** (0.15)

0.29** (0.13)

-0.67 (1.30)

11.83 (11.63)

0.56*** (0.10)

United Kingdom

-9.62 (7.95)

1.74 (1.25)

-0.09 (0.33)

-0.10 (0.67)

0.29 (0.48)

-9.04 (6.35)

52.04 (62.35)

0.67*** (0.09)

United States

-11.29 (10.13)

2.02 (1.60)

-1.33 (0.88)

6.92*** (2.04)


38

-4.94*** -24.62*** 124.55*** 0.67*** (1.36) (4.72) (32.64) (0.16)



VOL.3,NO.2, 2008

Table A4. Continued α0 Constant

α1 Ln(W)

β0 Ln(E)

β1 β2 γ1 γ2 φ s*Ln(E) s2 *Ln(E) μ*Ln(E) μ2*Ln(E) AR(1) Sydney

China 16.68** (7.45)

-1.99* (1.08)

-1.34*** (0.37)

1.23** (0.54)

-2.14*** (0.76)

0.74 (1.88)

-0.23 (0.17) 0.49*** (0.18)

India 23.38*** (4.10)

-2.86*** (0.53)

-0.94* (0.53)

-0.33*** (0.12)

0.65*** 0.66*** (0.19) (0.15)

-0.57 (0.43)

Italy 23.80*** (6.23)

-3.50*** (0.73)

0.11 (0.48)

-0.47** (0.20)

1.18** (0.56)

0.46 (0.35)

-3.28** (1.44)

0.01 (0.17)

0.57** (0.87)

0.30 (0.28)

0.32*** (0.13)

-0.32** (0.16)

-0.31 (0.19)

0.60* (0.33)

0.69*** (0.12)

-1.64 (1.01)

-0.42 (0.28)

-0.07 (0.10)

0.06 (0.13)

0.43* (0.26)

-1.51 (1.04)

0.57*** (0.14)

6.95*** 8.03*** -17.59*** 0.56*** (1.28) (1.32) (3.57) (0.17)

Japan

-3.18 (6.42)

Korea 15.31** (7.11)

Malaysia

6.93* (3.84)

-0.80 (0.60)

0.31 (0.21)

-4.55*** (0.83)

Thailand

0.11 (2.06)

0.11 (0.33)

0.32 (0.05)

1.18*** -1.04*** -2.09*** (0.19) (0.24) (0.20)

Taiwan 38.82*** (5.69) United Kingdom Note:

-12.28 (8.87)

8.53 (7.44)

-4.82*** -1.92*** (0.67) (0.49)

-0.09 (0.21)

0.06 (0.24)

-1.16 (1.19)

2.95 (3.08)

-4.29 (9.19)

-0.98* (0.53)

Estimation Method: Seemingly Unrelated Regression (SUR) with AR(1) Sample: 1995-4 2001-2 Total system observations: 207 Standard error given below the coefficients ***, **, *: Significant at 1%, 5%, 10% Level of Significance respectively LM Test for Contemporaneous Correlation: λ = 79.38***

39

-2.08* (1.15)

3.86*** 0.65*** (0.42) (0.09) 24.80*** -0.41** (9.26) (0.20)

-9.75 -4.49 (25.67) (687.10)

0.60*** (0.16)