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This document delves into the evolving landscape of third sector research, focusing on administrative data collected by the Charity Commission. It raises critical questions about representativeness, longitudinal perspectives, and the methods needed to study organizational change effectively. The authors, John Mohan and David Clifford from the Third Sector Research Centre at the University of Southampton, discuss the untapped potential of administrative data, highlight challenges such as data consistency and metadata limitations, and propose future directions for data sharing and enhanced analysis of charitable organizations.
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Guiding questions for session 1. Representativeness: what are we not seeing? 2. What are the challenges and opportunities provided by longitudinal perspectives? 3. How can we identify and think about different dimensions of organisational change? 4. Are specific methods needed for researching the third sector? 5. The future: what are the next steps relating to data and method?
Reflecting on the use of administrative data for research: challenges, opportunities and future directions John Mohan and David Clifford Third Sector Research Centre, University of Southampton
Context: administrative data • Exciting times for social science! • Flagship social surveys in UK, but increasingly also access to administrative data • Here: reflecting on TSRC’s experience of use of administrative data collected by Charity Commission
Context: Charity Commission data • Charities – charitable purpose and public benefit • Data collected as part of Charity Commission’s regulatory role • Register computerised since early 1990s • Financial information (detail depending on size), area of operation, what they do/ who they work with/ how they do it, etc.
Challenges • What are we not seeing? • Differences in administrative practice • Over time: changes in composition of charities on Register • Over space: Charity Commission vs OSCR • Lack of metadata (but P-ADLS..)
Opportunities 1. Individual organisations’ income and expenditure over time • Cross-sectional (snapshot) and longitudinal (following organisations through time) perspectives 2. Population of organisations (large numbers) • Detailed disaggregation of patterns possible: e.g. by organisation and area characteristics
Example of opportunities • Example of work on survival of charities • Which kinds of charities, in which kinds of areas, have a higher probability of dissolution? • Inactive organisations included in our data • Focus on those charities to have had at least £5,000 in income at some point since 1995 (around 140,000 charities) • Disaggregation by: initial income; type of charity; area
Probability of charity dissolution in a given year:charity and area characteristics
Probability of charity dissolution in a given year:by local authority
Future directions.. • Detailed financial information for a random sample of 10,000 charities (in collaboration with NCVO), e.g. income streams, income sources • Linking organisations (charities) and individuals (trustees)
Future directions.. 3. Other sources of administrative data: move towards data sharing (‘open data’) -collected by voluntary organisations: finance and funding records; information about where services are delivered; feedback and performance data -collected by government and Big Lottery Fund: organisations and projects funded
Future directions.. Challenges of using these datasets -consistency -representativeness -metadata (P-ADLS: collating this information) -recognising limits as well as opportunities
Acknowledgements • Steve Barnard, Peter Backus, Frida Geyne • Charity Commission • ADLS • National Council of Voluntary Organisations
For more information.. • www.tsrc.ac.uk • >Research>Quantitative Analysis
