Cost relationships and globalization in the Australian clothing industry

ABSTRACT

Australia developed a substantial clothing industry with protectionist international trade policies, including prohibitively high tariffs, quotas, and subsidies. The industry became an important source of jobs, with over 6.5 per cent of total manufacturing employment at the close of the 1960s. As Australia has moved towards freer trade, employment in clothing production has fallen substantially. The question of whether the clothing industry can remain viable in its new environment became an important issue. Here, we examine two crucial economic considerations relating to that question: economies of scale and relations (substitute/complementary) between the various inputs, both domestic and outsourced foreign. The findings strongly indicate the presence of economies of scale and that the industry has reduced its unit costs over the period of study. The results also suggest that most of the inputs are substitutes for one another, although only the estimated cross price elasticities between capital and labour are highly statistically significant in both models utilized in the study. To be successful in the future, the Australian clothing industry will likely need to find market niches where it can offer superior products and/or service as well as further reduce its unit costs.

KEYWORDS:

• Australia
• clothing industry
• globalization

JEL CLASSIFICATION:

• D24
• F14
• L67

Acknowledgement

The authors wish to thank an anonymous referee for helpful comments and suggestions for improvement. There was no external funding source for this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ See, for example, (Campbell 2015, 46–50; Larson 2015, 9–20; Einhorn and Roberts 2015, 13–15).

² Leather is often included in the TCF industry as well as textiles, clothing, and footwear. Sometimes, but not always, when leather is included, the industry is denoted by TCFL.

³ The Australian statistical reporting year is from July 1 of one calendar year through June 30 of the next year.

⁴ The Button plan provided for the gradual phasing out of tariffs and quotas and some adjustment packages for the TCF and motor vehicle industries (Industry Commission 1997, 3; Productivity Commission 1999, Vol. 1, 87).

⁵ In 1980, quotas protected approximately 90 per cent of the clothing industry (Productivity Commission 1999, Vol. 2, 81).

⁶ See Productivity Commission (1999, Vol. 2, 80) for a detailed timeline of some of these changes in assistance to the TCF industry. Also see Industry Commission (1997, I.12) for estimates of the nominal and effective rates of assistance to the clothing and textile industries from 1968–1969 to 1996–1997.

⁷ Data before 1992–1993 were from The Commission (1995). Another factor that makes difficult the comparison of export and import performance of recent years with that of earlier years is that, as a result of industry restructuring in the new environment of less assistance, some firms that were formerly classified as manufacturing firms are now classified by the Australian Bureau of Statistics as wholesale or retail firms, rather than manufacturing firms. For example, certain firms that have some manufacturing operations but also do wholesaling and retailing activities are now classified with the wholesale and retail sectors. Other firms now outsource some or all of their manufacturing activities (Productivity Commission 2003a, 7–10; Industry Commission 1997, Vol. 1, 54; Webber and Weller 2001, 399).

⁸ This programme provided TCF companies with import credits in exchange for exports of their products. It began on 1 July 1991, and ended on 30 June 2000 (Productivity Commission 1999, Vol. 2, 83, 2003a, 241).

⁹ See Jorgenson (2000, Chapter 4), Greene (2000, 640–644), Berndt and Christensen (1973); Christensen, Jorgenson, and Lau (1973); and Guilkey, Knox Lovell, and Sickles (1983, 615) for more detailed discussions of translog functions. Also see Binswanger (1974, 380); and Kohli (1991, 103–106) for a discussion of the technological change variable.

¹⁰ Technically, the estimation of this cost function requires that input markets be perfectly competitive, or at least that the firm views input prices as fixed. Although the input markets relevant to this study may not be perfectly competitive, prices such as award wage rates that do not change frequently in response to volume changes can perform a similar role for estimation purposes. The minimum requirements for the cost function to describe a ‘well-behaved’ technology are that it be (1) linearly homogeneous in input prices, (2) positive and monotonically increasing in input prices and output, and (3) concave in input prices. These regularity conditions for the cost function require the following restrictions on its parameters:(1) linearly homogeneous in input prices:

$\sum _{\mathrm{i}}{\beta }_{\mathrm{i}}=1,\sum _{\mathrm{i}}{\rho }_{\mathrm{i}\mathrm{Y}}=0,\sum _{\mathrm{i}}{\gamma }_{\mathrm{i}\mathrm{T}}=0,\mathrm{a}\mathrm{n}\mathrm{d}\sum _{\mathrm{i}}{\gamma }_{\mathrm{i}\mathrm{j}}=0\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{l}\mathrm{l}j,$

where i, j = K, L, D, F;(2) monotonically increasing in input prices and output:

$\frac{\partial \text{lnTC}}{\partial \text{lnP}}\text{and}\frac{\partial \text{lnTC}}{\partial \text{lnY}}>0$

(3) concavity in input prices.A sufficient condition for concavity of the cost function is that the Hessian matrix of second partial derivatives with respect to factor prices is negative semidefinite.Also, the symmetry condition that γ_ij equals γ_ji must be satisfied.

¹¹ The principal advantages of using a translog cost function rather than a translog production function are as follows: (1) the partial derivatives of a cost function with respect to input prices yield the corresponding input demand functions (Shephard’s Lemma), (2) the partial derivative of the cost function in logarithmic form with respect to factor prices yields the input cost shares, and (3) the partial derivative of the cost function in logarithmic form with respect to output yields the cost elasticity with respect to level of output (Binswanger 1974, 377; Jorgenson 2000, Chapter 1).

¹² If the data are normalized so that total cost, the output quantities, and the input prices are equal to one in the base period and if the translog cost function is exact, the logarithm of α₀ is equal to zero. Although a normalization procedure was followed and the variable means were used as the base, the estimated translog cost function was not assumed to be exact. Thus, α₀ may not be equal to zero. Separate stochastic error terms, to reflect errors in optimizing behaviour, were implicitly added to the estimated cost and share equations. The iterative Zellner-efficient method (IZEF, Zellner 1963) was used to obtain the parameter estimates.Barten (1969, 24–25) has shown that maximum-likelihood estimates of a set of share equations with one deleted are invariant to which equation is omitted. Kmenta and Gilbert (1968) and Ruble (1968, 279–286) have shown that iteration of the Zellner procedure until convergence yields maximum-likelihood estimates.One could argue that industry output is an endogenous variable and that an instrumental variable procedure should be used, since the regressor and the error terms may be correlated. Similar problems may arise with measurement errors; as a result, coefficient estimates may be inconsistent (Westbrook and Tybout 1993). However, using aggregate data for the United States, Appelbaum (1978, 94) compared the I3SLS results of Berndt and Christensen (1974) with those of his model using the maximum likelihood method and found they were similar. In addition, a potential problem with the instrumental variables methodology is that the results may be affected by the set of instrumental variables utilized.

¹³ As a result of the linearly homogeneous in prices assumption,

${\beta }_{\mathrm{F}}=\left(1-{\beta }_{\mathrm{K}}-{\beta }_{\mathrm{L}}-{\beta }_{\mathrm{D}}\right)$

${\gamma }_{\mathrm{F}\mathrm{F}}=\left[\left(1/2\right){\gamma }_{\mathrm{K}\mathrm{K}}+\left(1/2\right){\gamma }_{\mathrm{L}\mathrm{L}}+\left(1/2\right){\gamma }_{\mathrm{D}\mathrm{D}}+{\gamma }_{\mathrm{K}\mathrm{L}}+{\gamma }_{\mathrm{K}\mathrm{D}}+{\gamma }_{\mathrm{L}\mathrm{D}}\right]$

${\gamma }_{\mathrm{K}\mathrm{F}}=-\left({\gamma }_{\mathrm{K}\mathrm{K}}+{\gamma }_{\mathrm{K}\mathrm{L}}+{\gamma }_{\mathrm{K}\mathrm{D}}\right)$

${\gamma }_{\mathrm{L}\mathrm{F}}=-\left({\gamma }_{\mathrm{K}\mathrm{L}}+{\gamma }_{\mathrm{L}\mathrm{L}}+{\gamma }_{\mathrm{L}\mathrm{D}}\right)$

${\gamma }_{\mathrm{D}\mathrm{F}}=-\left({\gamma }_{\mathrm{K}\mathrm{D}}+{\gamma }_{\mathrm{L}\mathrm{D}}+{\gamma }_{\mathrm{D}\mathrm{D}}\right)$

${\rho }_{\mathrm{Y}\mathrm{F}}=-\left({\rho }_{\mathrm{Y}\mathrm{K}}+{\rho }_{\mathrm{Y}\mathrm{L}}+{\rho }_{\mathrm{Y}\mathrm{D}}\right)$

${\gamma }_{\mathrm{F}\mathrm{T}}=-\left({\gamma }_{\mathrm{K}\mathrm{T}}+{\gamma }_{\mathrm{L}\mathrm{T}}+{\gamma }_{\mathrm{D}\mathrm{T}}\right)$

¹⁴ The regularity conditions were not violated at any of the data points.The conventional single-equation Durbin-Watson statistic for the total cost function with the LNT time trend is 2.811, while that for the cost function with no time trend is 2.710. In both cases, these values are in the inconclusive range at the five per cent level of significance. See Durbin (1957), Malinvaud (1970, 509), and Berndt and Christensen (1973, 95) for a discussion of the Durbin-Watson statistic as a criterion for autocorrelation in the case of simultaneous equations.A Lagrange multiplier test for serial correlation was also done on the total cost equation using lagged values of the error term ranging from one to seven periods (see Godfrey 1988, 112–117; Greene 2000, 540–541). The null hypothesis of ρ = 0 could not be rejected at the 5 per cent level of significance for either model.

¹⁵ The Productivity Commission (2003a, 9–10) made similar observations.

¹⁶ Estimates of the direct price elasticity of demand for input i can be calculated using the estimated input shares and parameters of the cost function as

${\mathrm{E}}_{\mathrm{i}}=\frac{{\gamma }_{\mathrm{i}\mathrm{i}}+{{\mathrm{S}}_{\mathrm{i}}}^2-{\mathrm{S}}_{\mathrm{i}}\hspace{0.17em}}{\hspace{0.17em}{\mathrm{S}}_{\mathrm{i}}}$

Estimates of the cross price elasticities of demand (E_ij = ∂ln X_i/∂ln W_j) can be calculated as:

$\hspace{0.17em}{\mathrm{E}}_{\mathrm{i}\mathrm{j}}={\mathrm{S}}_{\mathrm{j}}+\frac{\hspace{0.17em}{\gamma }_{\mathrm{i}\mathrm{j}}\hspace{0.17em}}{\hspace{0.17em}{\mathrm{S}}_{\mathrm{i}}}$