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Measuring Small Area Inequality Using Spatial Microsimulation: Lessons Learned from Australia

Author

Listed:
  • Riyana Miranti

    () (The National Centre for Social and Economic Modelling (NATSEM), Institute for Governance and Policy Analysis (IGPA), University of Canberra)

  • Rebecca Cassells

    () (Bankwest Curtin Economic Centre, Curtin University)

  • Yogi Vidyattama

    () (The National Centre for Social and Economic Modelling (NATSEM), Institute for Governance and Policy Analysis (IGPA), University of Canberra)

  • Justine Mc Namara

    () ((Previously) The National Centre for Social and Economic Modelling (NATSEM), Institute for Governance and Policy Analysis IGPA), University of Canberra)

Abstract

Measuring income inequality has long been of interest in applied social and economic research in the OECD countries including Australia. This includes measuring income inequality at the regional level. In this article, we have used spatial microsimulation techniques to calculate small area inequality in Australia using disposable income data which are not available at Measuring Small Area Inequality Using Spatial Microsimulation: Lessons Learned from Australia a small area level, drawing together data from the Australian Census and survey data. Using disposable income data increases the strength of the results, as a more accurate measure of income distribution is able to be obtained. We estimate inequality at a small area level for the two most populous states in Australia New South Wales and Victoria using conventional Gini coefficient methodology. We also examine the differences in inequality between the densely populated capital cities of each state and the balance of these states or rural areas. The results show that there are marked variations in inequality with distinct pockets of small areas with high income inequality in both states and their capital cities. The small area inequality estimation enables the policy maker to pinpoint pockets of inequality. This will be useful to identify regions that need better targeting/interventions.

Suggested Citation

  • Riyana Miranti & Rebecca Cassells & Yogi Vidyattama & Justine Mc Namara, 2015. "Measuring Small Area Inequality Using Spatial Microsimulation: Lessons Learned from Australia," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 152-175.
  • Handle: RePEc:ijm:journl:v:8:y:2015:i:2:p:152-175
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    File URL: http://www.microsimulation.org/IJM/V8_2/5_Miranti_Cassells_Vidyattama_McNamara.pdf
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    References listed on IDEAS

    as
    1. Wodon, Quentin & Yitzhaki, Shlomo, 2003. "The effect of using grouped data on the estimation of the Gini income elasticity," Economics Letters, Elsevier, vol. 78(2), pages 153-159, February.
    2. Tara Watson, 2009. "Inequality And The Measurement Of Residential Segregation By Income In American Neighborhoods," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(3), pages 820-844, September.
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    1. repec:ijm:journl:v10:y:2017:i:1:p:167-200 is not listed on IDEAS
    2. repec:ijm:journl:v109:y:2017:i:1:p:167-200 is not listed on IDEAS

    More about this item

    Keywords

    Income inequality; spatial microsimulation; Small area;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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