1. Using administrative data to study the financial vulnerability of charities in Scotland Diarmuid McDonnell Doctoral Researcher School of Social Science University of Stirling 21/04/2016

2. The sad reality

3. Introduction Collaborative research project between University of Stirling and OSCR. Focus on the ways in which risk is operationalized by the regulator and experienced by charities. Mixed methods but with more of a focus on quantitative analysis of large administrative datasets. October 2013 – December 2016 (in a perfect world).

4. Regulatory context Independent regulator for Scottish charities since 2006. Key mandate is to protect and enhance public confidence in the sector. Recent shift in regulatory approach: targeted regulation. Intervening in the right charities, at the right time, and in the right way.

5. Admin Data Lots of uses of administrative data for civil society research: Sampling frame for conducting surveys with civil society organisations (CSOs); State of the sector reports (SCVO/NCVO, Scottish Government); Sources and concentration of income (TSRC); Studying misconduct in the sector; Financial vulnerability of CSOs;

6. Topic Long academic and practitioner tradition of measuring and predicting financial vulnerability in CSOs (Tuckman & Chang, 1991; Trussel, 2002; Keating et al, 2005; Greenlee et al, 2013). Understanding and predicting vulnerability is thought to be useful for a number of stakeholders: regulators, managers, researchers, trustees, funders, government and the public. My research takes an alternative look at vulnerability: the regulator’s perspective.

7. Financial exceptions

9. Data

11. Sample description The median organisation doesn’t receive any income from government funding or trading activities, spends £230,391 on conducting its charitable activities and £4,200 on governance costs, has £129,909 in unrestricted funds (reserves), and has been in existence for 21 years. In contrast the mean charity receives £1,039,762 and £135,133 in income from government funding and trading activities respectively, spends £2,044,046 on conducting its charitable activities and £17,306 on governance costs, has £2,056,464 in unrestricted funds (reserves), and has been in existence for 31 years.

12. Findings 61.45 percent of charities (41.73 percent of observations) in the sample triggered at least one financial exception over the period 2007-2013. The mean number of exceptions per charity is 9 (s.d. 10) and the median is 8; the mean number of exceptions per charity per annum is 2 (s.d. 1) and the median is 1.

13. Findings

15. Discussion What is the purpose of exception monitoring by OSCR –an early warning system, legitimacy of the regulator? Should CSOs care about these measures of accountability and financial vulnerability – what can they feasibly do with this information? Do the public care about these measures? Do they capture the actual vulnerability of CSOs? If not where can we get better measures?

16. Why ‘Peer under the hood’ of civil society. Historical, cross-sectional and longitudinal research questions (Mohan et al, 2010). Definitions/measurements have largely remained consistent over the period 2006-2014; comparing like-with-like over time (reliability). All charities are included in OSCR’s data; no hidden subpopulations (N=All). Generally high quality data; often the best we have.

17. Why not Administrative data is not collected for your benefit; extensive data management involved (make friends with SPSS/Stata). Not much data into the internal operations of charities; need to make do with poor-quality proxies (for example, the number of trustees as a measure of governance). Significant lags in the reporting of the data by charities (makes longitudinal/causal analysis difficult). Concerns surrounding the use of charity accounts (Morgan, 2011).

18. Reflection As voluntary sector researchers and practitioners, what opportunities and challenges do we face when using administrative data on the sector? Powerful source of data for a range of research purposes but there are two crucial points: Data management ‘perfection’ Choosing the right research questions