# A Spatial Analysis of Disposable Income in Ireland: A GWR Approach

**Authors:** Paul Kilgarriff, Martin Charlton  
**arXiv:** [2008.11720](https://arxiv.org/abs/2008.11720)  
**Date:** 2020-08-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2008.11720v1/

## Key points

- The paper "A Spatial Analysis of Disposable Income in Ireland: A GWR Approach" explores the spatial distribution of income in Ireland.
- Median gross household disposable income data from the Central Statistics Office (CSO) at the Electoral Division (ED) level is used to explore the spatial variability in income.
- Geary's C highlights the spatial dependence of income, indicating that income distribution is not random across space and is influenced by location.
- Using a global Ordinary Least Squares (OLS) regression will lead to biased results due to spatial autocorrelation.
- Geographically Weighted Regression (GWR) is used to examine the spatial heterogeneity of income and the impact of local demographic drivers on it.
- Demographic drivers have varying levels of influence on income across locations, with lone parent having a stronger negative impact in the Cork commuter belt than it does in the Dublin commuter belt.
- The relationship between household income and the demographic context of an area is complicated, and this paper attempts to examine these relationships while acknowledging the impact of space.
- This study provides valuable insights into how different factors affect household incomes across different regions in Ireland.
- By using a GWR approach instead of a global OLS regression, Kilgarriff and Charlton are able to account for spatial autocorrelation and provide more accurate estimates for how demographics impact incomes at a local level.
- This research can inform policymakers' decisions regarding regional economic development policies aimed at reducing disparities in household incomes across Ireland's various regions.

## Summary

In their paper "A Spatial Analysis of Disposable Income in Ireland: A GWR Approach," authors Paul Kilgarriff and Martin Charlton delve into the spatial distribution of income in Ireland. Using median gross household disposable income data from the Central Statistics Office (CSO) at the Electoral Division (ED) level, they explore the spatial variability in income. They find that Geary's C highlights the spatial dependence of income, indicating that income distribution is not random across space and is influenced by location. The presence of spatial autocorrelation means that using a global Ordinary Least Squares (OLS) regression will lead to biased results. To overcome this issue, they use Geographically Weighted Regression (GWR) to examine the spatial heterogeneity of income and the impact of local demographic drivers on it. The GWR results reveal that demographic drivers have varying levels of influence on income across locations. For instance, lone parent has a stronger negative impact in the Cork commuter belt than it does in the Dublin commuter belt. The relationship between household income and the demographic context of an area is complicated, and this paper attempts to examine these relationships while acknowledging the impact of space. Overall, this study provides valuable insights into how different factors affect household incomes across different regions in Ireland. By using a GWR approach instead of a global OLS regression, Kilgarriff and Charlton are able to account for spatial autocorrelation and provide more accurate estimates for how demographics impact incomes at a local level. This research can inform policymakers' decisions regarding regional economic development policies aimed at reducing disparities in household incomes across Ireland's various regions.