### 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 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.

Original language | English |
---|---|

Pages (from-to) | 152-175 |

Number of pages | 24 |

Journal | International Journal of Microsimulation |

Volume | 8 |

Issue number | 2 |

Publication status | Published - 2016 |

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*International Journal of Microsimulation*,

*8*(2), 152-175.

}

*International Journal of Microsimulation*, vol. 8, no. 2, pp. 152-175.

**Measuring small area inequality using spatial microsimulation : Lessons learned from Australia.** / MIRANTI, Riyana; Cassells, Rebecca; VIDYATTAMA, Yogi; McNamara, Justine.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Measuring small area inequality using spatial microsimulation

T2 - Lessons learned from Australia

AU - MIRANTI, Riyana

AU - Cassells, Rebecca

AU - VIDYATTAMA, Yogi

AU - McNamara, Justine

PY - 2016

Y1 - 2016

N2 - 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 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.

AB - 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 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.

KW - Income inequality

KW - Small area

KW - Spatial microsimulation

UR - http://www.scopus.com/inward/record.url?scp=85006043280&partnerID=8YFLogxK

M3 - Article

VL - 8

SP - 152

EP - 175

JO - International Journal of Microsimulation

JF - International Journal of Microsimulation

SN - 1747-5864

IS - 2

ER -