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1. Introduction
One in four Australian adults was obese in 2009 with another one-third being overweight [1]. Over the last two decades, there has been a steady shift in the Australian population towards the higher end of the body mass index (BMI), driven mainly by weight gain rather than by changes in height. The BMI, a simple index of weight for height, is commonly used to classify people as overweight and obese. It is defined as the weight in kilograms divided by the square of the height in metres (kg/m2) [2]. An Australian study suggests that excessive body weight is likely to be costly, with an estimated economic cost including direct health costs, productivity losses, and carer costs of Australian $60 billion dollars per year [3]. The increasing prevalence of obesity is linked to the onset of chronic diseases including type 2 diabetes, hypertension, coronary heart disease, elevated cholesterol levels, depression, and musculoskeletal disorders [4,5,6,7]. Other studies have demonstrated that obesity is associated strongly with a deterioration in health-related quality of life, including both the physical and mental health domains [8]. It has also been demonstrated that obesity negatively affects workforce participation and gives an increased risk of occupational injury [9,10,11]. This has resulted in a growing demand for research to better understand the factors that determine obesity [12] and the socio-economic impact of being overweight.
This paper explores those factors that influence the incidence of obesity among Australians by way of a random effects generalized ordered probit model. The paper utilizes data from the Household Income and Labour Dynamics in Australia (HILDA) Survey, a household-based annual panel survey. The HILDA is a survey of Australian representative households with an aim to provide longitudinal data on households and their members. The same households and their members are interviewed every year. It began in 2001 with a national probability sample of 7682 households, comprising 13,969 persons interviewed (aged 15 and over) and 4784 children under age 15. It has sample retention of approximately 95% from year to year. It also has new households formed from household members that split-off, such as children leaving home or couples separating [13].
A component of the survey is a self-completion questionnaire (SCQ) that is provided to all individuals...