Advancing the Use of Administrative Data in Official Statistics

Project Details

Lead Supervisor: Natalie Shlomo
Contact Email:
Partners: University of Manchester
External Partners: ONS

Start Date: September 2019

The Administrative Data Research Programme led by Hannah Finselbach at the ONS have identified a series of tasks in a preliminary work plan to advance the use of administrative data in official statistics. Table 1 contains a summary of tasks based on the preliminary work plan. The final work plan will form the basis for a strategy document that will facilitate research and engagement with the academic community.

Working with the ONS, the project team (the student and supervisors) will select a set of tasks that are appropriate topics for PhD research in advancing the use of administrative data in official statistics.

This is an exciting opportunity for a forward thinking doctoral student with a background in social science, statistics or computer science, to build a research profile in an important emerging area.

Table 1: Summary of tasks on advancing the use of administrative data in official statistics which will inform the PhD research questions


Quality Measuring and communicating uncertainty of outputs including quality measures/intervals for estimates using administrative data and developing  the ‘Total Administrative Data Error’ framework


Linkage Linking multiple sources with possible  different hierarchies, developing methodology and tools  and measures of quality for linked data


Privacy and Confidentiality Generating synthetic data and assessing disclosure risk and data utility, new forms of data dissemination, investigating the potential of differential privacy


Estimation Comparison of administrative data sources with survey data and identifying and addressing under or over-coverage issues using case studies and applications


Statistical Data Editing


Editing and imputation,  data cleaning and harmonization  of administrative data sources


Transactional Data Transactional (streamed) data and dealing with time lags and combining aggregate data using time series approaches



Deadline 7th April 2019

Reference number MN33

Apply online here

Get in touch with the supervisor

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