Archive for category: Demographic and Social Economics

Data from the Census of Australia

Ian Manning, National Institute of Economic and Industry Research and
charter member, Brotherhood of St Laurence

Paper for the Australian Social Policy Conference, Social Policy Research Centre,
University of New South Wales

11 September 2019

Download the full paper (pdf)

Download the presentation (zip/pptx)

“Data is not information, information is not knowledge, knowledge is not understanding and understanding is not wisdom.”  (Clifford Stoll)

For two decades the National Institute of Economic and Industry Research has prepared a State of the Regions report for the Australian Local Government Association. Each annual report includes measures of inter-regional income inequalities based on National Accounts data. This paper first reviews the National Accounts indicators and then considers alternative indicators based on Census data, including the geographic patterns they reveal. The comparisons were made for each of the 544 LGAs listed in the Census but for purposes of exposition the results were aggregated to 67 regions.

The 2016 Census required respondents aged 15 and over to tick a box to denote their weekly income. The Census form included the comment: ‘information from this question provides an indication of living standards in different areas’. The Census income data are published as personal income and equivalised household income. The geographic patterns revealed in the Census data were described in the State of the Regions report for 2019-20 and are here further analysed, concentrating on the proportion of households, by region, reporting incomes in the bottom and top deciles of equivalised income.

The geographic distributions of income documented from the Census indeed throw light on living standards, but there are no conclusions, only further questions, such as the following.

• In what kinds of region do low personal incomes generate low equivalised incomes?
• Why is Sydney so income-segregated?
• What are the effects of fly-in fly-out and indigenous residence on living standards in remote areas?

National populations are a natural focus for studies of inequality, since national governments preside over taxation and public expenditure policies which directly affect the distribution of income and wealth. In Australia the national distribution of income, wealth and consumption expenditure, both between individuals and households, has been assiduously documented in a series of sample surveys, beginning with the ABS survey of income distribution for the Poverty Inquiry in 1973 and continued since, particularly in the ABS surveys of income and household expenditure and in the University of Melbourne HILDA survey. The surveys have been analysed in a search for trends, with the results somewhat dependent on the indicators of inequality chosen. The most definite results have included the association between income and employment (employed people generally receive higher incomes than not-employed), income and industry of employment (some industries pay better than others), income and gender (men tend to have higher individual incomes than women) and income and age (both young and old have lower incomes than the middle aged) (Productivity Commission 2018). The national distribution maps down to the regional level, so that regions where unemployment rates are low are expected to have high regional incomes, and likewise regions with middle-aged populations and regions where employment is concentrated in high-income industries. (One might add regions with masculine populations, but fortunately the sex ratio does not vary much between regions except as a consequence of age distribution.)

Though regional inequality of income can confidently be predicted from the national sample surveys, they cannot be proven from this source; neither can it be shown that regional inequality is greater or less than indicated by the factors identified at national level due to the influence of regional factors. The problem is simple: national sample surveys do not yield results which are statistically significant at the regional level.  Two main sets of indicators are in use to identify rich and poor regions. One set derives from administrative sources and is summarised in Gross Regional Product, the other set derives from the Census. This paper describes and assesses the available measures, limiting itself to recent estimates (the financial year 2018-19 and Census 2016). It does not attempt to quantify trends, but does attempt to identify the reasons why particular regions are currently rich or poor.