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What can we learn from Small Area Income Estimates?

Cash Money
Published: December 2018

This project assesses existing estimates of income in England using new small area income estimates produced using consumer data. The Acxiom income estimates and CAMEO Postcode Classifications are compared with existing income estimates, produced by the Family Resources Survey and the Office for National Statistics. The aim of the project is to assess the reliability of estimates based on consumer data for analysis by social scientists.

Findings

This research finds substantial variation in the estimated income distribution in England, dependent on the estimates that are used. In particular:

  • ONS estimates of small area income appear to over-estimate average earnings, suggesting more work needs to be done to improve administrative data linkage before these sources can be used to produce National Statistics.
  • Consistent with prior research, the Family Resources Survey (FRS) has poor coverage of low-income households.
  • Each of the small area-income estimates considered (ONS estimates using administrative data, Acxiom, CAMEO Classifications) are for the most part consistent, although some inconsistencies exist in some regions.

The consumer data also facilitates new analysis of income inequality within smaller areas. For example, although average incomes are highest in London, income inequality is also higher in London than in any other region.

Methods

Local area income estimates are not commonly available. The UK Census has never included income questions because of fears about collecting accurate information and implications for response rates, while other surveys (such as The Family Resources Survey) are not sufficiently powered to conduct analysis in small geographical areas.

This research involves secondary analysis of 'Acxiom' and CAMEO Classifications', two consumer datasets. These datasets will be used to assess various measures of income in the UK at the local level, which will be presented spatially (for instance, through the use of maps).

Download the report