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Using Linked Longitudinal Administrative Data to Identify Social Disadvantage

Author

Listed:
  • Serena Pattaro

    (University of Glasgow)

  • Nick Bailey

    (University of Glasgow)

  • Chris Dibben

    (University of Edinburgh)

Abstract

Administrative data are widely used to construct indicators of social disadvantage, such as Free School Meals eligibility and Indices of Multiple Deprivation, for policy purposes. For research these indicators are often a compromise between accuracy and simplicity, because they rely on cross-sectional data. The growing availability of longitudinal administrative data may aid construction of more accurate indicators for research. To illustrate this potential, we use administrative data on welfare benefits from DWP’s National Benefits Database and annual earnings from employment from HMRC’s P14/P60 data to reconstruct individual labour market histories over a 5-year period. These administrative datasets were linked to survey data from the Poverty and Social Exclusion UK 2012. Results from descriptive and logistic regression analyses show that longitudinal measures correlate highly with survey responses on the same topic and are stronger predictors of poverty risks than measures based on cross-sectional data. These results suggest that longitudinal administrative measures would have potentially wide-ranging applications in policy as well as poverty research.

Suggested Citation

  • Serena Pattaro & Nick Bailey & Chris Dibben, 2020. "Using Linked Longitudinal Administrative Data to Identify Social Disadvantage," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(3), pages 865-895, February.
    • Handle: RePEc:spr:soinre:v:147:y:2020:i:3:d:10.1007_s11205-019-02173-1
    DOI: 10.1007/s11205-019-02173-1
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    References listed on IDEAS

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