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Empirical studies of gender segregation by occupation must be founded on rigorous measurement procedures. There appears to be a consensus that any index used in the analysis of time-series or international cross-section employment data must be either margin-free or decomposable to yield a margin-free component. On the other hand, Charles and Grusky (1995) advocate the use of multiplicative log models from which a margin-free odds ratio can be derived. In this paper, I contrast the construction and interpretation of the index of dissimilarity and the Karmel-MacLachlan index with the multiplicative modeling of gender segregation and the associated log index.

This paper continues our work focused on developing a new socio-economic geography for Australia such that the chosen spatial aggregation of data is based on an analysis of economic behaviour. The underlying hypothesis is that the development of a geographical classification based on underlying economic behaviour will provide new insights into critical issues of regional performance, including unemployment differentials, the impact of industry, infrastructure and changes in local public expenditure on local labour markets. As a precursor to detailed work on the 2006 Census of Population and Housing data, we establish the proof of concept in this paper of the Intramax methodology using 2001 Journey-to-Work data from the Australian Bureau of Statistics (ABS) for the state of New South Wales. The functional regionalisation generated by the Intramax method is then tested using ABS labour force data. We compare 2001 ABS Census of Population and Housing data aggregated by the ABS labour force regions to the same data aggregated using our functional regions. The results demonstrate the potential value of this technique for the development of a new geography. KEY WORDS spatial aggregation; functional economic areas; intramax algorithm; spatial clustering IntroductionThis paper continues our work focused on developing a new socio-economic geography for Australia such that the chosen spatial aggregation of data is based on an analysis of economic behaviour (see Watts et al. 2006). The underlying hypothesis that has motivated this work is that the development of a geographical classification based on an analysis of labour market behaviour will provide new insights into critical issues of regional performance, including unemployment differentials, the impact of industry, infrastructure and changes in local public expenditure on local labour markets. A systematic understanding of levels of interaction between neighbouring areas, will facilitate an assessment of the adequacy of the administrative geographical

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Persistent unemployment arises because free market ideology has overridden collective responsibilities. To restore full employment the government must provide permanent employment for all the unemployed in environmentally sustainable and useful jobs.