A multilevel study of area socio-economic status and food purchasing behaviour

Objective: This study examined the association between area socioeconomic status (SES) and food purchasing behaviour.----- Setting: Melbourne city, Australia, 2003.----- Participants: Residents of 2,564 households located in 50 small areas.----- Design: Data were collected by mail survey (64.2% response rate). Area SES was indicated by the proportion of households in each area earning less than Aus$400 per week, and individual-level socioeconomic position was measured using education, occupation, and household income. Food purchasing was measured on the basis of compliance with dietary guideline recommendations (for grocery foods) and variety of fruit and vegetable purchase. Multilevel regression examined the association between area SES and food purchase after adjustment for individual-level demographic (age, sex, household composition) and socioeconomic factors.----- Results: Residents of low SES areas were significantly less likely than their counterparts in advantaged areas to purchase grocery foods that were high in fibre and low in fat, salt, and sugar; and they purchased a smaller variety of fruits. There was no evidence of an association between area SES and vegetable variety.----- Conclusions In Melbourne, area SES was associated with some food purchasing behaviours independent of individual-level factors, suggesting that areas in this city may be differentiated on the basis of food availability, accessibility, and affordability, making the purchase of some types of foods more difficult in disadvantaged areas.

Value-mapping for major economic infrastructure projects

The establishment of corporate objectives regarding economic, environmental, social, and ethical responsibilities, to inform business practice, has been gaining credibility in the business sector since the early 1990’s. This is witnessed through (i) the formation of international forums for sustainable and accountable development, (ii) the emergence of standards, systems, and frameworks to provide common ground for regulatory and corporate dialogue, and (iii) the significant quantum of relevant popular and academic literature in a diverse range of disciplines. How then has this move towards greater corporate responsibility become evident in the provision of major urban infrastructure projects? The gap identified, in both academic literature and industry practice, is a structured and auditable link between corporate intent and project outcomes. Limited literature has been discovered which makes a link between corporate responsibility; project performance indicators (or critical success factors) and major infrastructure provision. This search revealed that a comprehensive mapping framework, from an organisation’s corporate objectives through to intended, anticipated and actual outcomes and impacts has not yet been developed for the delivery of such projects. The research problem thus explored is ‘the need to better identify, map and account for the outcomes, impacts and risks associated with economic, environmental, social and ethical outcomes and impacts which arise from major economic infrastructure projects, both now, and into the future’. The methodology being used to undertake this research is based on Checkland’s soft system methodology, engaging in action research on three collaborative case studies. A key outcome of this research is a value-mapping framework applicable to Australian public sector agencies. This is a decision-making methodology which will enable project teams responsible for delivering major projects, to better identify and align project objectives and impacts with stated corporate objectives.

Socio-environmental factors and suicide in Queensland, Australia

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

A review of the economic consequences of a policy of universal leucodepletion as compared to existing practices

Leucodepletion, the removal of leucocytes from blood products improves the safety of blood transfusion by reducing adverse events associated with the incidental non-therapeutic transfusion of leucocytes. Leucodepletion has been shown to have clinical benefit for immuno-suppressed patients who require transfusion. The selective leucodepletion of blood products by bed side filtration for these patients has been widely practiced. This study investigated the economic consequences in Queensland of moving from a policy of selective leucodepletion to one of universal leucodepletion, that is providing all transfused patients with blood products leucodepleted during the manufacturing process. Using an analytic decision model a cost-effectiveness analysis was conducted. An ICER of $16.3M per life year gained was derived. Sensitivity analysis found this result to be robust to uncertainty in the parameters used in the model. This result argues against moving to a policy of universal leucodepletion. However during the course of the study the policy decision for universal leucodepletion was made and implemented in Queensland in October 2008. This study has concluded that cost-effectiveness is not an influential factor in policy decisions regarding quality and safety initiatives in the Australian blood sector.

Econometric analysis of stuctural systems with permanent and transitory shocks

This paper considers the implications of the permanent/transitory decomposition of shocks for identification of structural models in the general case where the model might contain more than one permanent structural shock. It provides a simple and intuitive generalization of the influential work of Blanchard and Quah [1989. The dynamic effects of aggregate demand and supply disturbances. The American Economic Review 79, 655–673], and shows that structural equations with known permanent shocks cannot contain error correction terms, thereby freeing up the latter to be used as instruments in estimating their parameters. The approach is illustrated by a re-examination of the identification schemes used by Wickens and Motto [2001. Estimating shocks and impulse response functions. Journal of Applied Econometrics 16, 371–387], Shapiro and Watson [1988. Sources of business cycle fluctuations. NBER Macroeconomics Annual 3, 111–148], King et al. [1991. Stochastic trends and economic fluctuations. American Economic Review 81, 819–840], Gali [1992. How well does the ISLM model fit postwar US data? Quarterly Journal of Economics 107, 709–735; 1999. Technology, employment, and the business cycle: Do technology shocks explain aggregate fluctuations? American Economic Review 89, 249–271] and Fisher [2006. The dynamic effects of neutral and investment-specific technology shocks. Journal of Political Economy 114, 413–451].

The creative sector in New Zealand : mapping and economic role : report to New Zealand Trade and Enterprise

The New Zealand creative sector was responsible for almost 121,000 jobs at the time of the 2006 Census (6.3% of total employment). These are divided between • 35,751 creative specialists – persons employed doing creative work in creative industries • 42,300 support workers - persons providing management and support services in creative industries • 42,792 embedded creative workers – persons engaged in creative work in other types of enterprise The most striking feature of this breakdown is the fact that the largest group of creative workers are employed outside the creative industries, i.e. in other types of businesses. Even within the creative industries, there are fewer people directly engaged in creative work than in providing management and support. Creative sector employees earned incomes of approximately $52,000 per annum at the time of the 2006 Census. This is relatively uniform across all three types of creative worker, and is significantly above the average for all employed persons (of approximately $40,700). Creative employment and incomes were growing strongly over both five year periods between the 1996, 2001 and 2006 Censuses. However, when we compare creative and general trends, we see two distinct phases in the development of the creative sector: • rapid structural growth over the five years to 2001 (especially led by developments in ICT), with creative employment and incomes increasing rapidly at a time when they were growing modestly across the whole economy; • subsequent consolidation, with growth driven by more by national economic expansion than structural change, and creative employment and incomes moving in parallel with strong economy-wide growth. Other important trends revealed by the data are that • the strongest growth during the decade was in embedded creative workers, especially over the first five years. The weakest growth was in creative specialists, with support workers in creative industries in the middle rank, • by far the strongest growth in creative industries’ employment was in Software & digital content, which trebled in size over the decade Comparing New Zealand with the United Kingdom and Australia, the two southern hemisphere nations have significantly lower proportions of total employment in the creative sector (both in creative industries and embedded employment). New Zealand’s and Australia’s creative shares in 2001 were similar (5.4% each), but in the following five years, our share has expanded (to 5.7%) whereas Australia’s fell slightly (to 5.2%) – in both cases, through changes in creative industries’ employment. The creative industries generated $10.5 billion in total gross output in the March 2006 year. Resulting from this was value added totalling $5.1b, representing 3.3% of New Zealand’s total GDP. Overall, value added in the creative industries represents 49% of industry gross output, which is higher than the average across the whole economy, 45%. This is a reflection of the relatively high labour intensity and high earnings of the creative industries. Industries which have an above-average ratio of value added to gross output are usually labour-intensive, especially when wages and salaries are above average. This is true for Software & Digital Content and Architecture, Design & Visual Arts, with ratios of 60.4% and 55.2% respectively. However there is significant variation in this ratio between different parts of the creative industries, with some parts (e.g. Software & Digital Content and Architecture, Design & Visual Arts) generating even higher value added relative to output, and others (e.g. TV & Radio, Publishing and Music & Performing Arts) less, because of high capital intensity and import content. When we take into account the impact of the creative industries’ demand for goods and services from its suppliers and consumption spending from incomes earned, we estimate that there is an addition to economic activity of: • $30.9 billion in gross output, $41.4b in total • $15.1b in value added, $20.3b in total • 158,100 people employed, 234,600 in total The total economic impact of the creative industries is approximately four times their direct output and value added, and three times their direct employment. Their effect on output and value added is roughly in line with the average over all industries, although the effect on employment is significantly lower. This is because of the relatively high labour intensity (and high earnings) of the creative industries, which generate below-average demand from suppliers, but normal levels of demand though expenditure from incomes. Drawing on these numbers and conclusions, we suggest some (slightly speculative) directions for future research. The goal is to better understand the contribution the creative sector makes to productivity growth; in particular, the distinctive contributions from creative firms and embedded creative workers. The ideas for future research can be organised into the several categories: • Understanding the categories of the creative sector– who is doing the business? In other words, examine via more fine grained research (at a firm level perhaps) just what is the creative contribution from the different aspects of the creative sector industries. It may be possible to categorise these in terms of more or less striking innovations. • Investigate the relationship between the characteristics and the performance of the various creative industries/ sectors; • Look more closely at innovation at an industry level e.g. using an index of relative growth of exports, and see if this can be related to intensity of use of creative inputs; • Undertake case studies of the creative sector; • Undertake case studies of the embedded contribution to growth in the firms and industries that employ them, by examining taking several high performing noncreative industries (in the same way as proposed for the creative sector). • Look at the aggregates – drawing on the broad picture of the extent of the numbers of creative workers embedded within the different industries, consider the extent to which these might explain aspects of the industries’ varied performance in terms of exports, growth and so on. • This might be able to extended to examine issues like the type of creative workers that are most effective when embedded, or test the hypothesis that each industry has its own particular requirements for embedded creative workers that overwhelms any generic contributions from say design, or IT.

Holding cost model for Greenfield Housing developments

Presentation provided to a PhD Colloquium between two Australian and one Malaysian University providing the opportunity to inform and critique progress of students concerning their selected topic. This presentation essentially involves "The conceptualisation, sensitivity and measurement of holding costs and other selected elements impacting housing affordability" as provided by Gary Owen Garner of QUT, with research objectives thus: 1. To establish the nature and composition of holding costs over time, as related to residential property in Australia, and internationally. 2. To examine the linkages that may exist between various planning instruments, the length of regulatory assessment periods, and housing affordability. 3. To develop a model that quantifies the impact of holding costs on housing affordability in Australia, with a particular focus on the consequences of extended assessment periods as a component of holding costs. Thus, provide clarification as to the impact of holding costs on overall housing affordability.

Economic issues in funding and supplying public sector information [Presentation slides]

PowerPoint presentation by Dr John S Cook at the Spatially Enabled Government Summit 2009, Mapping the Future of Interoperability, Data Collection & Data Management for Operational Excellence within Australian Government, held on 24-27 August, 2009 at the Marque Hotel, Canberra, ACT

Yield-factor volatility models

The term structure of interest rates is often summarized using a handful of yield factors that capture shifts in the shape of the yield curve. In this paper, we develop a comprehensive model for volatility dynamics in the level, slope, and curvature of the yield curve that simultaneously includes level and GARCH effects along with regime shifts. We show that the level of the short rate is useful in modeling the volatility of the three yield factors and that there are significant GARCH effects present even after including a level effect. Further, we find that allowing for regime shifts in the factor volatilities dramatically improves the model’s fit and strengthens the level effect. We also show that a regime-switching model with level and GARCH effects provides the best out-of-sample forecasting performance of yield volatility. We argue that the auxiliary models often used to estimate term structure models with simulation-based estimation techniques should be consistent with the main features of the yield curve that are identified by our model.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

Did the goodwill accounting standard impose material economic consequences on Australian acquirers?

This research explores the empirical association between takeover bid premium and acquired (purchased) goodwill, and tests whether the strength of the association changes after the passage of approved accounting standard AASB 1013 in Australia in 1988. AASB 1013 mandated capitalization and amortization of acquired goodwill to the income statement over a maximum period of 20 years. We use regressions to assess how the association between bid premium and acquired goodwill varies in the pre-AASB and post-AASB 1013 periods after controlling for confounding factors. Our results show that reducing the variety of accounting policy options available to bidder management after an acquisition results in a systematic reduction in the strength of the association between premium and goodwill.

Forecasting time-varying value-at-risk

In this thesis we are interested in financial risk and the instrument we want to use is Value-at-Risk (VaR). VaR is the maximum loss over a given period of time at a given confidence level. Many definitions of VaR exist and some will be introduced throughout this thesis. There two main ways to measure risk and VaR: through volatility and through percentiles. Large volatility in financial returns implies greater probability of large losses, but also larger probability of large profits. Percentiles describe tail behaviour. The estimation of VaR is a complex task. It is important to know the main characteristics of financial data to choose the best model. The existing literature is very wide, maybe controversial, but helpful in drawing a picture of the problem. It is commonly recognised that financial data are characterised by heavy tails, time-varying volatility, asymmetric response to bad and good news, and skewness. Ignoring any of these features can lead to underestimating VaR with a possible ultimate consequence being the default of the protagonist (firm, bank or investor). In recent years, skewness has attracted special attention. An open problem is the detection and modelling of time-varying skewness. Is skewness constant or there is some significant variability which in turn can affect the estimation of VaR? This thesis aims to answer this question and to open the way to a new approach to model simultaneously time-varying volatility (conditional variance) and skewness. The new tools are modifications of the Generalised Lambda Distributions (GLDs). They are four-parameter distributions, which allow the first four moments to be modelled nearly independently: in particular we are interested in what we will call para-moments, i.e., mean, variance, skewness and kurtosis. The GLDs will be used in two different ways. Firstly, semi-parametrically, we consider a moving window to estimate the parameters and calculate the percentiles of the GLDs. Secondly, parametrically, we attempt to extend the GLDs to include time-varying dependence in the parameters. We used the local linear regression to estimate semi-parametrically conditional mean and conditional variance. The method is not efficient enough to capture all the dependence structure in the three indices —ASX 200, S&P 500 and FT 30—, however it provides an idea of the DGP underlying the process and helps choosing a good technique to model the data. We find that GLDs suggest that moments up to the fourth order do not always exist, there existence appears to vary over time. This is a very important finding, considering that past papers (see for example Bali et al., 2008; Hashmi and Tay, 2007; Lanne and Pentti, 2007) modelled time-varying skewness, implicitly assuming the existence of the third moment. However, the GLDs suggest that mean, variance, skewness and in general the conditional distribution vary over time, as already suggested by the existing literature. The GLDs give good results in estimating VaR on three real indices, ASX 200, S&P 500 and FT 30, with results very similar to the results provided by historical simulation.