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Studying geographical distributions of household income data is extremely valuable when researching poverty and its associated harms. However, such distributions are rarely available to researchers due to access and privacy challenges. The generation of synthetic income data is a promising approach to overcome real-world income data collection issues. We draw on several existing income data resources to examine likely income parameters for smaller geographical areas (namely, the UK’s ‘Lower Super Output Areas’: LSOAs), and develop a direct sampling approach to generate synthetic household income distributions in the case city of Nottingham, UK. The resulting LSOA-level household income distributions (Synthetic Nottingham Homes), can aid various poverty studies. The synthetic data are then pseudo-validated to evaluate their proximity to real-world conditions. Subsequently, to demonstrate the utility of such synthetic data, we present an applied example of the Synthetic Nottingham Homes in the context of fuel (energy) poverty estimations.

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Generating Experimental Area-Level Synthetic Income Datasets for Use in Poverty Research

  • 会议论文

  • 发表日期：

    2026年

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  • Torran Semple,
  • John Harvey,
  • Lucelia Rodrigues,
  • Grazziela Figueredo,
  • Mark Gillott

摘要

Studying geographical distributions of household income data is extremely valuable when researching poverty and its associated harms. However, such distributions are rarely available to researchers due to access and privacy challenges. The generation of synthetic income data is a promising approach to overcome real-world income data collection issues. We draw on several existing income data resources to examine likely income parameters for smaller geographical areas (namely, the UK’s ‘Lower Super Output Areas’: LSOAs), and develop a direct sampling approach to generate synthetic household income distributions in the case city of Nottingham, UK. The resulting LSOA-level household income distributions (Synthetic Nottingham Homes), can aid various poverty studies. The synthetic data are then pseudo-validated to evaluate their proximity to real-world conditions. Subsequently, to demonstrate the utility of such synthetic data, we present an applied example of the Synthetic Nottingham Homes in the context of fuel (energy) poverty estimations.