Suchergebnisse

This paper describes linkage of Homelessness Case Level Collection (H-CLIC) data to Census 2021, allowing researchers to better understand households experiencing and at risk of homelessness. This fits within a wider portfolio of Better Outcomes Through Linked Data (BOLD) pilot projects, developing research possibilities and partnerships within the homelessness research sphere.
To mitigate impact from limited and lower quality variables, linkage was performed in two phases: individual-level linkage and associative matching. Individual-level linkage combined deterministic and probabilistic linkage methods, supported using Splink, with two stages of probabilistic linkage with differing extents of geographical blocking. Clerical-focused associative matching was employed following individual linkage, using UPRN and household make-up.
The project took a pipeline approach, with consideration to future-proofing the pipeline to facilitate annual updates to the H-CLIC spine. Also considered in detail are the quality implications of differing temporal data in linking dynamic H-CLIC data to static Census data.
The linkage resulted in construction of a research-ready dataset, which enables H-CLIC applicants to be joined to their Census records. To compensate for limited address information and support best use of processing resources, blocking was used to limit search spaces in the probabilistic linkage. Households with missing links between individual members were successfully identified and passed through associative matching methods. Though matching was successful, the remaining residuals were challenging to deal with.
Error introduced by the linkage methodology is presented via linkage quality metrics and bias analysis. Bias introduced by the linkage processing may be of particular concern, particularly considering coverage of these sample data. Within the fifty-three current Local Authorities included, a skew was observed toward smaller, southernly areas with lower homelessness rates.
The linked dataset enables research into households vulnerable to homelessness, their movements across England, and their exposure to repeated homelessness. The data facilitates research into this important topic, supporting evidence-based policymaking. Furthermore, such use is anticipated to encourage further local authorities to provide their data, broadening its reach and our understanding.

ObjectivesADR-UK supplied the Wage and Employment Dynamics (WED) research team with Pay as You Earn (PAYE) data linked to Annual Survey of Hours and Earnings (ASHE) data. This presentation explains the methodology taken to link PAYE and ASHE databases together, along with quality assurance and analysis of the linked data.
MethodsThe data were primarily linked using deterministic linkage methods on an encrypted National Insurance Number (NINo) variable. A total of four Pay as You Earn tables were linked to the ASHE data.
Where a PAYE table contained an encrypted NINo variable, the PAYE table was linked to the ASHE database using exact matching on NINo. Where a PAYE table did not contain an encrypted NINo variable, the table was linked to another PAYE table with an existing NINo variable first and then linked to the ASHE data.
ResultsAs multiple HMRC tables were linked to the ASHE database, the linkage rate for each table varies. However, at least 0.93% of each PAYE table linked to the ASHE database. As the ASHE data is based on a 1% sample of employee jobs taken from HMRC's PAYE records, these linkage rates are considered relatively high.
To compensate for loss of variables needed to calculate linkage quality, the data was filtered into categories on the 'sex' and 'age' variables and the linkage rates analysed. Bias analysis associated to records with a missing NINo value was also investigated. When the records with a missing NINo value were excluded from calculations, the linkage rate for each table rose to 1%.
ConclusionThis data will be used to assist research outputs that focus on low-pay labour markets and wage progression, wage inequalities and employment. The presentation will also discuss challenges overcome and lessons learnt from this project, with suggestion of how this information can be used to improve ADR-UK projects going forward.

ObjectivesADARC is a partnership of six government departments and three research institutions across ADR-UK aiming to build four Research Ready Datasets about farming households. A|ARC requires government data linking experts to work closely with agricultural statisticians and academics across traditional boundaries pooling a wide range of data and expertise.
MethodsThe ADARC partnership aims to build a model of collaborative working for administrative data research. ADARC brings together those collecting data with those linking and analysing de-identified data and eventually those using results to develop future policies. The data is from several different sources at individual, household and business levels requiring new methodologies developed by this collaboration. ADARC is also working between all four ADR centres to share learning across the ADR network building relationships for future projects beyond agriculture. The linkage process has been different in all four nations but this has offered new learning for all involved.
ResultsDue to the Covid-19 pandemic, Phase 1 of the project has been slower than hoped. However, ADARC has shown that it is possible to conduct a truly UK-wide four nation project. Data linking is underway in all four nations bringing together data on individuals, households and businesses and analysis has begun in Wales and England. The project to date has faced the usual challenges of data acquisition and data quality issues which the partnership approach has been vital for overcoming. Despite this, we've demonstrated that there's a huge level of enthusiasm for working together across organisational barriers. ADARC has just been funded for Phase 2 to expand the datasets in scope and explore the feasibility of linking in spatial data.
ConclusionADARC has created a model which demonstrates that partnership working is essential for fully achieving the objective of ADR UK of unlocking the potential of administrative data. The lessons of ADARC also provides a powerful case study for future cross-UK projects far beyond agriculture.

ObjectivesDevelop administrative linkages within a national Trusted Research Environment (TRE) that hosts Longitudinal Population Study (LPS) data for over 20 LPS. We will describe the methodological development carried out to enable linkage to administrative datasets. These linked administrative data will support research for public good, informing policy and practice.
MethodsThe first sets of administrative data under consideration in this Feasibility Study are from the Department of Work and Pensions (DWP), the Department for Education (DfE) and HM Revenue and Customs (HMRC). Working with UK Government departments through a Task & Finish group we have gathered input from DWP, HMRC and DfE and Office for National Statistics (ONS) data sharing experts. The Task & Finish group identified three pragmatic data linkage and data sharing models, that would enable data to be linked via a newly designed secure data pipeline in a legal, secure, and trustworthy manner for all stakeholders.
ResultsTo encourage sustainability and acceptability, a model designed to be maintained over a long period is based on the re-use of Departmental Personal Identifiable Information (PII) – i.e., name, date of birth, gender, National Insurance number - and attribute data already deposited by the Departments into ONS. ONS will develop for the linkage and extraction of ONS Data into the TRE a system which conducts, and quality assesses the linkage; minimises the Departmental data to participants within the TRE only and the variables specified in the agreements; and, de-identifies the data to their DEA processing standards. The minimised and functionally anonymous data extract will be securely transferred for ingest and integration into the TRE enabling researchers to address a wider range of questions for public benefit.
ConclusionThis is a model for efficient and low-burden linkages to inform cross cutting research. It will form part of a responsive UK data science capability which can inform government research needs and be used to meet future crisis e.g. new pandemics, the impacts of climate change or economic shocks.