Social sector closures: Can we use financial data to help others steer clear of the cliff edge?

Could a social sector organisation's area of work, salary spend, level of reserves or type of investment help determine its likely financial sustainability? Sarah Thelwall, founder of financial benchmarking specialist MyCake, says crunching the numbers from the non-profits and social enterprises that have previously closed down could help others to predict – and prevent – financial fragility. 

When a charity or social enterprise closes for good it means job losses, the cessation of life-changing services and the loss of networks, assets and hope for change. These are tragedies and the fact that we’ve been studying the data on closures is no consolation for those who’ve been affected.

What we can do, however, is learn lessons about why they failed and translate this into ways of identifying financial fragility so that organisations can be better supported, business models improved and financial cliff edges avoided in future.

At MyCake we have identified four questions to which we think financial data could provide answers – answers that could support policy and decision making among grant funders, social investors and government departments:

  1. What is the ‘natural’ closure rate of non-profits in the UK and how does this vary by sector and turnover level?
  2. What are the warning signs of a worsening financial position that, if not heeded, increase the risk of closure?
  3.  What is the relationship between use of external capital influence (community shares, debt, equity) and closure rates?
  4. Does the ratio of grant funding to trading income have a connection to closure rates?

 

1. What is the ‘natural’ closure rate of non-profits?

Data from the UK’s Charity Commission, the body that regulates charities, can tell us what profiles of organisations have an underlying higher rate of closure. If we consider that closure means that the organisation has failed (not universally true as organisations can close simply because they change the type of legal entity that they operate), then higher closure rates = higher failure rates, ie, higher risk.

My Cake figure 1

Above: Annual closure rates for non-profits which report to the Charity Commission by sector, where the reason for closure is thought to be financial failure

 

What do we know so far? Some sectors and turnover bands have a higher level of churn of organisations ie higher levels of new organisations as well as higher levels of cessation.

For example, nurseries and playgroups close far more often than religious organisations.

Nurseries and playgroups close far more often than religious organisations

If we want to understand the extent to which the voluntary and community sector ecosystem in a given place is at risk, then understanding closure rates in one area by comparison to another may help us better understand the fragility or robustness of the ecosystem.

What could the analysis be used to inform?

  • Priorities for support by sector which take into account sectors/geographies/deprivation levels where failure rates are higher or lower;
  • Priorities for support by stage of growth, as we’d have a better understanding of the failure rates by turnover bands, age of organisation and levels of growth over time;
  • The setting of markers of risk and evaluation of what constitutes high/medium/low risk for each indicator. This in turn would inform a wider discussion about where taking risk is a positive element of innovation and change, versus risks to be avoided/mitigated to improve resilience.

 

2. What are the financial warning signs?

The development of robust analysis which enables us to identify which pieces of data in a profit & loss or balance sheet are forerunners to closure would allow us to move from analysing the past to improving our prediction of the future.

What do we know so far? We have identified a cohort of organisations where we are confident that the reason for closure is financial failure. We’ve then picked three sectors of interest and acquired detailed financial data on the last year of full trading.

Even before we get into a detailed statistical analysis of our cohort of some closed organisations for which we have extensive data, a few things stand out:

  • For all years from 2015 onwards, the median contribution to reserves is negative in this cohort of ceased organisations. That is to say expenditure exceeds income as the norm – not surprising if the reason for closing is financial failure.
  • For all years from 2015 onwards, levels of working capital are low with a median of about 20% of turnover. On average non-profits hold three months of funds in reserves (25%) although the latest research by NCVO indicates that approximately one quarter of all charities hold no reserves at all.

MyCake_figure 2

My Cake figure 3

Above: Contribution to reserves as a percentage of turnover; differences between spend on salary in live versus ceased organisations

  • For all years from 2015 onwards, median spend on salaries exceeds 60% of turnover. The norm nationally is around 40-45% of turnover. It should be noted that very young organisations and/or those which don’t maintain buildings will naturally (and healthily) spend a higher proportion of turnover on salaries, so this metric alone is not a marker of financial difficulties. However, our analysis suggest that it is one of the most important predictors.

What could such analysis inform? We are working on an algorithm which takes the results of this analysis of differences between closed and live organisations as the basis for a ‘resilience rating’. The intention is a predictive tool where outliers can be identified.

The outliers could be those which show greatest resilience and are examples of the best in class or at the other end of the spectrum those whose profile indicates higher risk of failure.

MyCake_AP4

Above: An example of a resilience rating changing over a five-year period

 

3. How does external capital influence closure rates?

Cross-referencing organisational closures to data sets on organisations which have run community share offers, accessed debt finance from a social investor or accessed capital grant funding monies would allow us to look at whether closure rates in these cohorts are higher or lower than the natural rate of closure.

What do we know so far? Work undertaken by the Community Shares Unit suggests that closure rates in organisations which have undertaken a community share offer are lower than in the rest of the non-profit population – a total 8% of organisations which ran a community share offer have ceased to trade over a period of roughly five to seven years, while closure rates in the Charity Commission data set are around 3% each year.

Closure rates in organisations which have undertaken a community share offer appear to be lower than in the rest of the non-profit population

What could such analysis inform? While it is unlikely that this information could be used as a simple proxy for risk and resilience (as there are only about 500 organisations which have run a community share offer but over 150,000 charities in the UK) there would no doubt be things to learn by slicing the data according to the type of financial investment. In theory, an injection of either debt or equity should enable faster revenue growth because assets which can be monetised into revenue income have been acquired sooner than organic growth alone would enable.

Ultimately it would be useful to see whether financial failure is more or less common in these cohorts by comparison to the sector norms.

 

 

4. Does the ratio of grant funding to trading income influence closure rates?

The corollary to exploring the relationship between types of capital injection and failure rates is to look at the relationship between the types of revenue income and the resilience and robustness of business models.

Why would we want to know? The last decade has seen a focus on encouraging a greater proportion of income to be derived from trading activities rather than grants on the basis that trading is more likely to enable growth than grant funding.

Has a focus on use of resources to support trading income growth been at the expense of maintaining sufficient working capital and the development of a healthy level of reserves? Does this mean that, while we have business models which are sustainable provided there are no substantial changes to external factors, that we have a sector full of organisations which are not financially resilient to external shocks?

What do we know already? We have anecdotal evidence of Covid-19-induced financial default, but it will take some time before the data comes through into the annual accounts data in the Charity Commission. (For other research on the impact of Covid-19 on charities see here, here, and here.) 

What could such analysis inform? Ultimately we’d like to understand whether some business models are inherently more resilient than others and where the sectoral and financial ‘sweet spots’ are. When does income diversity help or hinder resilience?

 

Progress on questions as to what leads to or prevents financial failure is important as it could help local and national government policy makers, social investors, grant funders and philanthropists consider the scale of risk they want to take and their definitions of successful provision of monies and support. MyCake plans to address some of the questions raised in this paper over the coming months and years and would value feedback from readers as to which of these questions they see as priorities, any other questions they would ask in this area and any additions to the thinking that they may have.

This article is an extract from a longer paper published here; the full version includes more details on the methodology and further ideas. Header image by Nicholas Raymond / Creative Commons

  • Sarah Thelwall is the founder of MyCake.

 

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