Spatial inequality in the Australian youth labour market: The role of neighbourhood composition

Australia has experienced a polarization of income and labour market outcomes over the past 20 years (GREGORY and HUNTER, 1995; HARDING, 1996). This has taken an increasingly spatial dimension (HUNTER. 1995a, 1995b), giving rise to concerns that the spatial pooling of disadvantage may hamper the labour market outcomes of youth growing up in poorer residential areas. This paper explores the role that the differential neighbourhood 'quality' of an individual's residential area at age 16 has on their labour market outcomes at age 18 and age 21. Evidence is found that youth who live in poorer quality neighbourhoods face an increased likelihood of being unemployed at both the age of 18 and 21, even after controlling for personal and family characteristics.

A simulation analysis of the market effect of the Australian Broadcasting Corporation

In this paper we utilise a stochastic address model of broadcast oligopoly markets to analyse the Australian broadcast television market. In particular, we examine the effect of the presence of a single government market participant in this market. An examination of the dynamics of the simulations demonstrates that the presence of a government market participant can simultaneously generate positive outcomes for viewers as well as for other market suppliers. Further examination of simulation dynamics indicates that privatisation of the government market participant results in reduced viewer choice and diversity. We also demonstrate that additional private market participants would not result in significant benefits to viewers.

Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.