Technical efficiency in Australian textile and clothing firms: evidence from the business longitudinal survey

This paper estimates technical efficiency of Australian textile and clothing firms based on the data obtained from the Business Longitudinal Survey (BLS) conducted by the Australian Bureau of Statistics (ABS). Using a Cobb Douglas stochastic production frontier the paper examines firm level technical efficiency in the time varying inefficiency effect model with technical inefficiency effects assumed as an independently distributed truncated normal variable. Estimates of the production frontier revealed significant but small elasticities of labour and capital for textile and clothing firms, respectively, and a negative (but insignificant) Hicks neutral technical change for both. Estimated coefficients of the explanatory variables for inefficiency effects indicated that technical efficiency varied significantly according to firms’ age, size, capital intensity, proportion of non-production to total workers and type of legal status. Predicted firm specific efficiency varied from 16 per cent to 95 per cent and mean efficiency ranged between 30 to 70 per cent. In view of these results policies have been suggested to improve technical efficiency of the firms as well as productivity growth of the sub sectors.

Sources of productivity growth in Australian textile and clothing firms

This paper estimates the sources of productivity growth in Australian textile and clothing firms based on the Business Longitudinal Survey (BLS) from 1995 to 1998. Productivity growth estimates have been obtained for each sub-category of textile and clothing firms. Sources of growth in multifactor productivity (MFP) are examined with growth in technical efficiency and scale effects based on estimates of stochastic frontier production functions. Separate estimates of output growth have been compared with the productivity growth estimates for each of the product categories. MFP improved in all clothing firms and declined in textile firms over 1997–1998 by four-digit level of Australia New Zealand Standard Industrial classification Scheme (ANZSIC). MFP declined in most major categories of both textile and clothing firms in 1995–1997. Changes in technical efficiency mostly dominated scale effects in the overall direction of MFPG in both textile and clothing firms. The findings of the study provide evidence for policies for improving the firms' operative performance in the ongoing liberalised regime.

Sources of productivity growth in Australian textile and clothing firms

This paper estimates the sources of productivity growth in Australian textile and clothing firms based on the Business Longitudinal Survey (BLS) from 1995 to 1998. Productivity growth estimates have been obtained for each sub-category of textile and clothing firms. Sources of growth in multifactor productivity (MFP) are examined with growth in technical efficiency and scale effects based on estimates of stochastic frontier production functions. Separate estimates of output growth have been compared with the productivity growth estimates for each of the product categories. MFP improved in all clothing firms and declined in textile firms over 1997–1998 by four-digit level of Australia New Zealand Standard Industrial classification Scheme (ANZSIC). MFP declined in most major categories of both textile and clothing firms in 1995–1997. Changes in technical efficiency mostly dominated scale effects in the overall direction of MFPG in both textile and clothing firms. The findings of the study provide evidence for policies for improving the firms' operative performance in the ongoing liberalised regime.

An examination of Australian gold mining firms' exposure over the collapse of gold price in the late 1990s

Purpose – The purpose of this study is to examine the exposures of Australian gold mining firms in the highly volatile period from 1995 to 2000. This period has been characterized by significant changes in gold price due to bulk sale of gold by collective central banks. Specifically, the paper aims to investigate several firm-specific factors that are hypothesized to carry substantial influence on gold beta.

Design/methodology/approach – To estimate gold beta, we use the following multifactor model: R_g,_t = a+ß_gGPR_t + ß_xFXR_t + ß_mR_m,_t + _Et , where R_g,_t is the return on the gold stock Index at time t, GPR_t is the gold price return denominated in US dollar at time t, FXR_t is the foreign exchange return of Australian dollar in terms of US dollar at time t, R_m,t is the market return at time t, and _Et is the random error term at time t.

Findings – The paper finds that the values of gold beta are consistently greater than one, implying the sensitive nature of firms’ stock returns to gold price changes. This also suggests that investors holding gold mining stock would receive higher percentage increases in stock returns from a percentage increase in gold price returns, as opposed to investors holding gold bullion. Furthermore, these values have changed substantially over time with significant changes in gold price volatility. The most important and consistent relationship that we find is the impact of firms’ hedging behavior on their respective gold betas. This is consistent with Tufano’s study. It implies that firms, which hedge a greater proportion of their gold reserves, are less sensitive to movements in gold prices. The finding therefore supports the risk management theory that hedging increases shareholder’s wealth. However, cash operating costs, cash reserves and the level of gold production seem to influence very little on the firms’ exposure to gold price changes.

Originality/value – This study is of interest and important to the stock mining companies and investors because the extent of the effect of gold price movements on the stock returns of gold mining companies has significant impacts on returns for both firms and investors especially in their risk management and investment decisions, respectively.