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Australia's import demand for clothing products: determinants and elasticities.
Abstract:
In this paper a model of Australia's import demand for clothing products is developed and estimated. The results show that, in the short-run, price of imports relative to domestic price of clothing and Australia's real income are the significant determinants of import demand. In the long run the significant determinants of import demand are relative price of imports, Australia's real income, and effective rate of assistance to Australia's clothing industries. The long run elasticity estimates indicate that a one percent increase in real income is associated with 2.58 per cent increase, a one per cent increase in the relative price results in a 0.41 per cent decline, and a one per cent decrease in the effective rate of assistance leads to 0.22 per cent increase in import demand for clothing products.

Keywords: Import Demand, Clothing Products, Australia.

Article Type:
Statistical data
Subject:
Clothing and dress (Imports)
Clothing and dress (Supply and demand)
Clothing and dress (Prices and rates)
Clothing industry (Imports)
Clothing industry (Prices and rates)
Authors:
Havrila, Inka
Gunawardana, Pemasiri J.
Pub Date:
01/01/2009
Publication:
Name: International Journal of Business Research Publisher: International Academy of Business and Economics Audience: Academic Format: Magazine/Journal Subject: Business, international Copyright: COPYRIGHT 2009 International Academy of Business and Economics ISSN: 1555-1296
Issue:
Date: Jan, 2009 Source Volume: 9 Source Issue: 1
Topic:
Event Code: 643 Imports; 600 Market information - general; 740 Commodity & service prices Computer Subject: Company pricing policy
Product:
Product Code: 2300000 Apparel & Related Products NAICS Code: 315 Apparel Manufacturing
Geographic:
Geographic Scope: Australia Geographic Code: 8AUST Australia
Accession Number:
208535066
Full Text:
1. INTRODUCTION

Australia's imports of clothing products (2) represent a small proportion of world imports of such products. However, Australia's share of world imports of clothing products increased from 0.66 per cent in 1965 to 2.9 percent in 2007. China, India, Indonesia, Fiji and Vietnam are among the major suppliers of Australia's imports of clothing products. The volume of Australia's imports of clothing products from the rest of the world, in real terms increased sharply from around US$182 million in 1965 to US$ 2,819 million in 2007, which represents an annual average increase of about 35 per cent from 1965 to 2007. Imports of clothing products contribute an insignificant share of Australia's total merchandise imports. During the period 1965 to 2007, average share of clothing products in Australia's total annual imports has varied between 0.6 per cent and 2.4 per cent. (3) The reduction of tariffs and quotas on clothing imports and the reduced effective assistance for domestic (import competing) manufacturing of clothing, especially since the early 1990's, gain prominence among the factors that have contributed to the increased imports of clothing products (Havrila and Gunawardana, 2008). For example, the nominal rate of assistance to Australia's clothing industries decreased from 90 per cent 1984-85 to 19 per cent in 2000-01, while the effective rate of assistance declined form 243 per cent in 1984-85 to 34 per cent in 2000-01 (IC, 1997 and PC, 2003, cited in Havrila, 2004, p. 26). Despite these trends in imports of clothing products and changes in policies towards the clothing sector, there has been no recent study that rigorously analyses the determinants of Australia's import demand for clothing products. This paper aims to fill this gap in research by developing and estimating a model of Australia's import demand for clothing products from the rest of the world. In so doing, we attempt to answer the questions: what are the major determinants of Australia's import demand for clothing products and what are the magnitudes of price and income elasticities of Australia's import demand for clothing products.

The remainder of the paper is structured as follows. In Section 2 presents a review of literature on import demand. A model of Australia's import demand for clothing is developed in Section 3. In Section 4, data and data sources are discussed. Section 5 focuses on the econometric procedure. Section 6 provides a discussion of results. Section 6 presents the conclusion.

2. REVIEW OF LITERATURE

International economic theory suggests that if an importing country accounts for an insignificant share of world imports a change in imports by that country (a "small country") is unlikely to influence the demand and price in the world market. A small country's import demand for a product is the difference between its domestic quantities demanded and supplied on the domestic market at the world price. (4) A shift in the importing country's domestic demand (due to changes in consumer income, price of substitutes, tastes) and/or domestic supply (due to the factors such as changes in weather, price of alternative products, technical change, R&D) will also shift the import demand. However, limitations of data on domestic consumption and production, as well as the difficulty of estimating domestic demand and supply functions, constrain the application of this approach to derive the import demand functions and price and income elasticities of import demand for traded commodities.

Thus, the empirical approach has been the estimation of import demand models directly, and then to derive the elasticities. Empirical literature points to numerous studies that estimated the import demand functions in many different countries, both developed and developing, using different data and different econometric techniques and obtaining various results.5 Houthakker and Magee (1969), Leamer and Stern (1970), Goldstein and Khan (1985) and Gafar (1988)) emphasised that the demand for imports is directly influenced by the level of domestic income and international competitiveness, shown by the prices of imported goods relative to the prices of domestically produced goods. Thus, in most empirical work (such as Khan and Ross, 1977; Kravis and Lipsey, 1978; Arize and Afifi, 1987; Boylan and Cuddy, 1987; Wilkinson, 1992; Athukorala and Menon, 1995; Menon, 1995; Carone et al, 1996; Bahmani-Oskooe and Niroomand, 1998) a general form of the import demand is usually specified as:

[M.sub.it] = f([P.sup.m/[P.sup.d], Y) (1)

where, M is the quantity of imports, [P.sup.m] is the import price index, [P.sup.d] is the domestic price index and Y is the consumer income.

Leamer and Stern (1970), and George et al. (1977) argued for the inclusion of a variable representing the level of foreign exchange reserve, or 'a proxy for the restraints on imports'. Silvapulle and Phillips (1985) tested this proposition, however, the variable was found insignificant in explaining import demand, or had the 'wrong' sign. Some other studies, including Miljkovic et al. (2002) reported a significant effect of exchange rates on Japanese import demand for U.S. beef and pork. Other studies found that the effect of the exchange rate was greater on export elasticities than on import elasticities (Kabir, 1988).

Since the late 1970s, some researchers have advocated a split-price specification (inclusion of [P.sup.m] and [P.sup.d] separately) instead of the price ratio (Murray and Ginman, 1976; Haynes and Stone, 1983; Arize, 1987; Gafar, 1988; Koo, Uhm et al., 1991; Deyak, et al., 1993a; Deyak, et al., 1993b; Carone, 1996). They argue that this form of specification could be useful in analysing changes in foreign prices and changes in the exchange rate or domestic trade barriers that would affect the import price only. In this specification, the relationship between imports and the respective variables can be expressed symbolically as in Equation 2. The definitions of the variables are as in Equation 1.

[M.sub.it] = f([P.sup.m], [P.sup.d], Y) (2)

Athukorala and Menon (1995) estimated import demand functions for Australian total manufactured imports as well as for nine subdivisions. To capture the cyclical effect on demand, Athukorala and Menon (1995) and Menon (1995) used the ratio of stocks to average sales volume to measure the general scarcity of domestic supplies. The relative price in the model was specified as the ratio of the tariff augmented import price and the price of the domestic competing commodity. An important finding relevant to this paper is that whereas the long-run price elasticity estimates for eight categories were in the range of -0.37 to -2.10 (for total imports -0.67), the estimate for clothing and footwear was not statistically different from zero.

A number of studies estimated the effect of trade liberalisation on the import demand elasticities and found various results. With regard to liberalisation, Melo and Vogt (1984) argue that an increase in the degree of import liberalisation and a country economic development would lead to higher income and price elasticities of import demand. Mah (1999) found a low and insignificant income elasticity of import demand for Thailand. Santos-Paulino and Thirlwall (2004) tested the impact of trade liberalisation on both imports and exports for 22 developing countries and found that the effect on import was greater than on exports. Income elasticities increased equally for imports and exports, however, the price elasticity of demand for imports was greater than for exports. Lopez (2005) observed that trade liberalisation positively affected import demand and the long-run income and price elasticities for imports were also significant. Estimated elasticities of import demand by Harb (2005) for forty developed and developing countries were higher for developing than for developed countries. Mehta and Parikh (2005) report an increase of price elasticities after trade liberalisation. Dutta and Ahmed (2000) examined a long-run import demand function for Bangladesh, using cointegration and error correction model and observed a long-run relationship among the variables. They also specified a dummy variable to account for import liberalisation policies. However, the results do not provide evidence of the effectiveness of liberalisation policies. Similar findings were obtained by Dutta and Ahmed (2006) for India.

Wijeweera et al. (2008) estimated import price and income elasticities for Bangladeshi bilateral trade on the basis of disaggregated data. They found that real income is not a significant determinant of imports, implying that imported goods and services from included countries are necessities, rather than luxuries. Similar results were reported by Rehman (2007) for the aggregate import demand function for Pakistan. Ho (2004) estimated both aggregated and disaggregated import demand functions for Macao and concludes that the disaggregate model is more appropriate the import demand in the long-run. The results indicate that different final demand components have different effects on import demand behaviour. Agbola and Damoense (2005) estimated import demand for pulses in India, and provide evidence that the response of import demand for pulses to key determinants differ substantially from product to product.

The study by Chiarlone (2000) focused on the estimation of sectoral import demand for Italy with European Union, Japan, Canada and the United States. Chiarlone attempted to include considerations of horizontal and vertical product differentiation as well as homogenous trade flows, using dummy variables. The findings indicate the strong response of imports to relative prices and income. The dummy variables for product differentiation suggest that imports perform differently with respect to various levels of product differentiation.

Based on the above review of empirical studies, it can be concluded that import demand is more sensitive to changes in real income than it is to import price changes.

3. A MODEL OF AUSTRALIA'S IMPORT DEMAND FOR CLOTHING PRODUCTS

Following the theoretical insights and the review of empirical studies, a model of Australia's imports for clothing is specified as:

MDC = f(RPC, YC, ERAC) (3)

where,

MDC = Australia's real imports of clothing

RPC = the relative price of imports of clothing (the ratio of import price of clothing to domestic price of clothing)

YC = Australia's real income represented by private final consumption expenditure for clothing

ERAC = the effective rate of assistance for clothing

An inverse relationship is expected between the level of imports and the relative prices of imports, RPC. It is expected that a rise in the relative price of imports (import prices, relative to the domestic prices) would result in a decrease in the quantity of imports demanded. As imports become more expensive, consumers are likely to turn to relatively cheaper domestically produced products. On the other hand, if the domestic price of clothing relative to price of imports rises, imports become relatively cheaper, leading to an increase in the demand for imported products. A hypothesised positive relationship between the variable representing income, YC (private final consumption expenditure for clothing) and import demand is based on the assumption that imported clothing products are normal goods. Thus, with rising incomes, consumers are assumed to increase the quantity demanded of imported clothing. Given historically high level of protection of the clothing industries relative to other industries (except motor vehicles), and extensive trade policy reforms in the 1990s, it was decided to include the average effective rate of assistance, (ERAC), to the clothing industries as an explanatory variable in the model. Other things being constant, an increase in the rate of assistance (ERAC) to Australia's domestic clothing industries leads to a reduction in imports. Therefore, a negative relationship is expected between ERAC and imports of clothing.

4. DATA AND SOURCES OF DATA

Econometric estimation in this paper is based on annual time series data for the period from 1970 to 2005, at the 2-digit SITC level of aggregation. Clothing products are categorised as SITC 84. A more detailed, three-digit level of disaggregation is: 841 Clothing not of fur and 842 Clothing made of fur. While it would have been desirable to estimate the models at a more disaggregated level within each category, it was not possible to obtain the data for all relevant variables at a disaggregated level. The data on imports of clothing were obtained from the International Economic Data Bank (IEDB) maintained by the Australian National University (ANU) and dxtime (Australian Bureau of Statistics, Time Series Plus Statistics). The effective rates of assistance data were provided by the Productivity Commission in Canberra. The real income variable used here is private final consumption expenditure deflated by the CPI for clothing, which also includes private final consumption expenditure on footwear and drapery. The data on private final consumption expenditure was obtained from dxtime (Australian Bureau of Statistics, Time Series Plus Statistics).

5. ECONOMETRIC PROCEDURE

The import demand model was estimated using the 'general to specific' approach, that is sometimes referred to as the Hendry method or the unrestricted error correction method or UECM (see Hendry and Mizon 1978; Mizon and Hendry 1980; Hendry and Richard 1982; Hendry 1995). This procedure avoids the need for testing for unit roots and two-stage estimation of the model. The procedure has been applied successfully in the empirical studies of Athukorala and Jayasuriya (1994), Gunawardana et al. (1995), Menon (1995), Athukorala and Rajapatirana (2000), Gunawardana and Vojvodic (2006) and Gunawardana, Havrila and Khorchurklang (2008). This approach minimises the likelihood of arriving at spurious regression estimations. The economic theory motivation is that, in the same model

both short-run responses and long-run adjustment of importers to changes in economic variables can be derived. It is particularly superior for small samples, as is the case in this analysis. First, the unrestricted equations are estimated using the OLS method. Taking into consideration the regression diagnostics, a more specific (parsimonious) model is gradually derived. Banerjee et al. (1993, p. 167) suggest that 'lagging' variables and including them as regressors often has the same effect as providing a cointegrated set of regressor variables and maintain that such a possibility is enhanced in a dynamic model as the probability of a cointegrated set being present is increased. Following Banerjee et al., the models were estimated with different lag structures. In order to evaluate the appropriateness of the regression results, a set of standard diagnostic tests was considered. These include testing for residual serial correlation (Godfrey 1978), normality (Jargue and Bera 1980; Bera and Jarque 1981) functional form misspecification (Ramsey 1969), and heteroskedasticity (White, 1980). The import demand model is estimated using log-log form due to the advantage that the estimated slope coefficients can be used to directly derive the elasticity values with respect to specified variables. The following log-log model is specified initially for Australia's import demand for clothing:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

where,

[MDC.sub.t] = Australia's real imports of clothing.

[RPC.sub.t] = the relative price of imports of clothing (the ratio of import price of clothing to domestic price of clothing).

[YC.sub.t] = private final consumption expenditure for clothing.

[ERAC.sub.t] = the effective rate of assistance to clothing

[[epsilon].sub.t] = error term

L = logarithm

t = the time period.

The hypothesised signs of parameter estimates can be symbolised as follows: [[empty set].sub.1]<0, [[empty set].sub.2]>0, [[empty set].sub.3 <0.

The UECM model was empirically estimated as:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where [Delta] is the first difference operator. The coefficients for the variables with [Delta] show the short-run relationships and the coefficients for the variables in level form indicate long run relationships with import demand.

6. DISCUSSION OF ESTIMATION RESULTS

The estimation results from a 'parsimonious' version of the model in Equation 5 are shown in Table 1. The initial OLS estimation was found to entail heteroskedasticity, hence the results shown in Table 1 are based on White heteroskedasticity-consistent standard errors and covariance. The findings show that, with the exception of the effective rate of protection, the short-run relationships in the demand for imports of clothing are statistically significant. However, the long-run relationships as well as the dynamic factor represented by the lagged dependent variable are statistically significant. Signs of the estimated coefficients are in accordance with theoretical expectations. The derived values of the long-run elasticities are indicative of an inelastic import demand for clothing with respect to relative price and effective rate of assistance. Respective absolute values of elasticities are 0.41 and 0.22. In contrast to the relative price and the effective rate of assistance, import demand for clothing appears to be highly responsive to changes in income represented by final consumption expenditure. With a positive and greater than one (2.58) coefficient of the long-run income elasticity, imported clothing can be classified as a luxury good. One percent change in income is likely to result in 2.58 percent change in import demand for clothing. The results indicate that the adjustment period of import demand to changes in relative price and effective rate of assistance is around two years, whereas the adjustment period of import demand to changes in income is about one year.

LMS --Lagrange multiplier test for serial correlation.

RESET --Ramsey's test for functional from misspecification.

JBN --Jarques-Bera test for the normality of residuals (based on the [chi square] distribution).

HSC --Heteroscedasticity test based on the regression of squared residuals on squared fitted values.

We now compare and contrast our findings with those of some of the previous empirical studies of import demand. Houthakker and Magee (1969) estimated the price and income elasticities of import demand for the United States by country of origin and by commodity class (for 5 major commodity classes). The results indicate generally higher income and price elasticity estimates for the United States imports. The price and income elasticities of import demand from Australia were -4.69 and 2.23 respectively. In comparison with the results in this study, while the price elasticity coefficient is distinctly higher, the income elasticity is rather close. The results in this study support the findings by Wilkinson (1992) who concludes that imports are more responsive to changes in economic activities than to the variation in relative prices. The long-run elasticity estimates (for Australia's aggregate imports) derived by Wilkinson are 1.9 with respect to economic activity and 0.5 with respect to relative price. O'Regan and Wilkinson (1997) derived the long-run price elasticity of import demand for clothing of 0.81, that is, relatively higher than the estimate in this study (0.41). Athukorala and Menon (1995) found that the long-run price elasticity estimates for the combined clothing and footwear category was not statistically different from zero. With regard to activity variable, Athukorala and Menon derived a unit long-run elasticity (1) for clothing. Menon (1995) examined the relationship between manufactured imports to Australia and relative prices and domestic economic activity over the period 1981 and 1992, based on quarterly data. The price elasticity estimates for individual categories ranged from 0.24 to 1.75, with almost two thirds of price elasticities of less than 1 and the remainder between 1 and 2. The relative price elasticity for total manufactured imports was estimated at 0.66. With respect to the activity elasticities, Menon found that most of the estimates were between 1 and 2. The activity elasticity for total imports was 1.87. The import demand for the category of textiles was found inelastic with respect to both price and activity variable. The respective elasticity estimates were -0.24 and 0.33. The import demand for apparel and clothing accessories was found unitary elastic with respect to price, and inelastic with respect to the activity variable (0.64). Thus, Menon's estimates for clothing differ rather significantly from the findings in our study.

7. CONCLUSION

The purpose of this paper was to identify the significant factors that determine Australia's import demand for clothing products and to estimate the elasticities of import demand. The model estimated and the hypotheses tested in this paper were developed on the basis of economic theory and previous empirical studies. The 'general to specific' unrestricted error correction modelling procedure was applied to estimate the short run relationships and long-run elasticities of import demand with respect to the specified variables. Prior to deriving a 'parsimonious' version of the model, the outcomes of the preliminary regressions and associated diagnostic tests were taken into account and the empirical models were re-specified accordingly. The findings of this study are expected to serve as an important indicator to retailers of how the demand at retail for imported clothing may change if various factors influencing the import demand vary. According to the results, Australia's import demand for clothing appears to be highly responsive to changes in Australia's income. The value of the long-run elasticity of income reveals that one percent increase in Australia's income is likely to result in 2.58 percent increase of Australia's import demand for clothing. However, the import demand for clothing is inelastic with respect to the relative price and the effective rate of assistance, both elasticities being less than one.

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Inka Havrila, Victoria University, Melbourne, Victoria, AUSTRALIA

Pemasiri J. Gunawardana, Victoria University, Melbourne, Victoria, AUSTRALIA

Notes:

(1.) Some parts of this paper are derived from Havrila (2004).

(2.) Standard International Trade Classification (SITC) Category 84, Articles of Apparel and Clothing Accessories.

(3.) In this section, trade data for 1965 are from NAPES ANU Database, while the trade data for 2007 are from World Trade Organisation, International Trade Statistics, 2008.

(4.) A detailed derivation of import demand functions and elasticities of import demand can be found in Kreinin (2002, Chapter 4, Appendix 4.1).

AUTHORS' PROFILES:

Dr. Inka Havrila earned her Ph.D. from Victoria University, Melbourne, Australia in 2004. Currently, she is a senior lecturer in economics at the school of economics and finance and a research associate of the centre of strategic economic studies, Victoria University, Melbourne, Australia.

Dr. Pemasiri J. Gunawardana earned his Ph.D. from Latrobe University, Melbourne, Australia in 1988. Currently he is a senior lecturer in economics at the school of economics and finance and a research associate of the centre of strategic economic studies, Victoria University, Melbourne, Australia.
TABLE 1
PARSIMONIOUS ESTIMATES OF IMPORT DEMAND MODEL FOR CLOTHING
(BASED ON WHITE HETEROSKEDASTICITY-CONSISTENT STANDARD ERRORS
AND COVARIANCE)

Dependent variable = [[Delta]MDC.sub.t]

Regresssor             Coefficient   t-ratio     Long-run     t-ratio
                       [Phi]                     Elasticity

Constant               1.93          1.20        -            -
[[Delta]LRPC.sub.t]    -0.43         -4.67 ***   -            -
[[Delta]LYC.sub.t]     1.02          1.82 *      -            -
[[Delta]LERAC.sub.t]   -0.10         -0.56       -            -
[LM DC.sub.(t-1)]      -0.46         -3.37 ***   -            -
[LRPC.sub.(t-2)]       -0.19         -2.54 **    -0.41        -1.31
[LYC.sub.(t-2)]        1.19          3.53 ***    2.58         5.04 ***
[LERAC.sub.(t-2)]      -0.10         -2.83 ***   -0.22        -2,04 ***

[R.sup.2] = 0.56   Adj. [R.sup.2] = 0.45   F = 4.17 ***   DW = 1.91
AIC = -0.75   LL = 20.76   SBC = -0.39

OLS Diagnostics:
LMS [F.sub.(1,25)] = 0.001   RESET [F.sub.(1,25)] = 0.0.032
JBN, [chi square](2) = 11.714   [HSC.sub.(1,32)] = 3.31

*** significant at the 1 % level; ** significant at the 5% level;
* significant at the 10% level.
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