Old Age, Gender and Physical Activity "Strength is Health”: George Barker Windship Health Through Sport How to Train the Body for Health and Poise Medicine, Science and Sport The Bicycle and Tricycle as Aids to Health and Recreation Passive Smoking What is Strength? Health Insurance Decisions What Makes Man a Sprinter? What to Fish for in February? Sport and its Economic and Social Importance Use of Weights for Health Does Ageing Drive Health Expenditures Medicare: Policy Issues Indoor Training for Outdoor Sport Starting and Starters Insured versus Not Insured Babylonian Astrology The Wrath of Heaven The Displaced Zodiac and the Beginnings of Astronomy The Duty of the Son of Heaven: Orientation Time Was Once Relative	Insured versus Not Insured There are, of course, numerous confounding relationships among cross-tabulations, including age, income, education, occupation and family status. This applies particularly in institutions such as those surveyed where there is a career-structured salary scale and qualification requirements for some positions (or, at least, great advantages in formal qualifications). Demographic Factors and Income There is a clear general relationship between age and insurance with a monotonic increase in the proportion of insured as age increases: 52% for those under 26 years to 92% for those 55 years and older. Similarly, married people were more likely to insure (79.5%) than single (57.4%). On the other hand, whether or not there were dependents had little effect and, in fact, the proportion of married/dependents was slightly lower than married/no dependents. When looking at income categories, three definitions of income were used: gross income, income after tax and discretionary income, all measured as household income. The last was defined as income after tax, superannuation, rent, mortgage, house and household insurances and council and water rates. The resulting figure would overstate the level of income over which the household did have discretion but we postulated that it would be a better indicator of income effects than the other two. Gross and after-tax income were highly correlated (Spearman Rho = .92) but the relationship between these two measures and discretionary income was substantially lower (Rho = .56 and .61 respectively). Therefore we used discretionary income for all analyses. As expected, the percentage of people insured increased with increasing income. The category below $10,000 p.a., in particular, had a much lower level of insurance (60%) than the next highest category (71%) but this relationship is confounded by the effects of, inter alia, age and marital status. Education level did not give much useful information. After collapsing into four categories to obtain reasonable cell numbers, the percentage of insured ranged from 67.7% to 74.2% but there was no logically explainable reason even for these differences. Occupation was dominated by "clerical" (about 46% of responses) with the only other large number in "lower management" and these categories probably overlap somewhat. However, to the extent that "clerical" and "semi-skilled" contain most of the lower income employees, and "middle" and "senior" management have the highest, it is not surprising that the former had the lowest insurance levels (both 64%) and the latter had the highest (almost 91% grouped).