In total, data on 153 charities (815 individual observations) were collected. The charities are distributed as in Table 6.
There are substantial differences in sources of income between the surviving and closed charities. Figure 1 shows source of income from 2011 to 2014, the year of analysis before firms closed or survived, by mean and median.
The row of means shows that surviving charities depend on grants for less than 25% of their income, on average, with smaller firms likely to have a slightly higher dependence than larger firms; almost 50% of their income comes from donations and a third from revenue-generating activities. In contrast, closed charities depend on grants for 60% of income.
A similar story is told by the medians. In any year, more than half of the closed charities depend on grant funding for over 70% of their income. In contrast, in every year at least half of the surviving firms receive less than 10% of their income from grants.
There does appear to be a difference in 2011 for the ‘live, small’ firms, compared to later years for activity and donated income. It is not clear why this arises. One possibility is that this is a lagged response to the ‘Big Society’ programme introduced in 2010, increasing the proportion of grant income for those charities.
Closed charities are more likely to be dependent on a single source of income. Figure 2 shows the proportion of charities which depend on a single type of income for over 90% of their funding, across all years.
Thirty-two percentage of the large charities that had closed by 2015 rely on grant funding for over 90% of income; for small closed charities, the figure is 29%. In contrast, surviving charities are much less likely to be dependent on a single source for over 90% of their funding; where they do, it is activity income or donations.
Figure 3 shows cost ratios for the four types of charity.
Closed charities show higher staff-cost-to-income ratios across the period than operational charities. For non-staff costs, there is much less difference in the mean share of income accounted for by costs. It is notable that, on average, the closed small charities appear to be living beyond their means with total costs significantly more than 100% of income.
Closed charities have lower assets relative to income across the period on average, but the most striking feature of the data is the very low level of assets amongst the large closed charities. All other groups have assets worth at least 1.5 times annual income, but for the large closed firms, net assets only average 40% of annual income. Figure 4 shows asset cover for income, that is, how long missing income could be funded from assets, assuming all assets are fully liquid.
Twenty percentage of large closed charities appear to have negligible assets, whereas for the other groups this figure is nearer 5%. Eighty-five percentage of large closed charities and 60% of small closed charities have six months or less asset cover. In contrast, only 45% of charities (large and small) still operational in 2015 have less than 6 months of cover. It could be argued that this is as expected: charities on the brink of collapse would be expected to be running down their assets, particularly liquid ones. However, Fig. 5 shows the mean and median cover for each year 2011–2014, and the pattern is fairly stable.
Not all assets are liquid, and some are required for income-raising (for example, store premises). These figures therefore overstate the ability of charities to cover a significant shortfall in income. Nevertheless, they suggest that the successful charities have greater potential to mitigate the risk of loss of income.
Aside from income and costs, one potential risk factor for charities is the volatility of income and outgoings. Figure 6 shows the volatility of income measured as the absolute coefficient of variation (standard deviation relative to the absolute value of the mean).
The closed charities have greater volatility in activity and donation income, but in terms of volatility of grant funding, there is a more noticeable difference between large and small charities than between surviving and closed. This may reflect the ability of large charities to have multiple grant funds, whereas small charities are likely to receive grants sequentially: as each grant nears its end, new funding is bid for.
In summary, it appears that closed charities have higher staff costs, greater dependence on grant income, and fewer assets to call upon. The difference between large and small charities is much less notable, except for volatility of income.
These descriptive statistics suggest that there are factors that differentiate surviving and closed charities, but they cannot show how the different factors interact or their importance in determining outcomes. This paper uses a statistical model of the probability of a charity surviving to estimate the relative size of the different effects and the interactions of variables.
As noted above, this paper aims to assess the value of the concentration variable commonly used. Four models are estimated, each with observed survival as the outcome variable:
Model 0 survival is associated with the base variables: income concentration, and the other ‘vulnerability variables’ (margin; proxied by total costs; equity proxied by assets; administration costs, proxied by staff costs).
Model 1 survival is associated with the base variables and the proportion of income it receives from each type of funding.
Model 2 survival is associated with the base variables and the volatility of income and costs.
Model 3 survival is associated with the base variables, type of income, and volatility.
The inclusion of both staff and total costs raises the question of multicollinearity. As charities are primarily service organisations, there is a strong link between staff costs and total costs. However, as Fig. 3 shows, this relationship varies over organisation types. We therefore include both variables, as they are proxying different factors, but we note the possibility of multicollinearity in the results.
Size is also included as a control in all models. Results are presented in Table 7.
In terms of the ‘vulnerability’ variables, the income concentration ratio has the expected sign, but is only significant when the actual types of income (grants, donations) are not included. Equity (net assets) is only significant in the simplest model, although it does have the expected sign. Total costs as a share of income (margin) are significant at 10% but only in Model 1. The only ‘vulnerability’ variable that is always significant is the staff-costs-to-income ratio, with a negative sign. This is not easy to interpret. At first glance, it suggests that proportionately lower staff costs increase survival prospects, suggesting that Ecer et al.’s (2017) ‘high costs = organisational failure risk’ argument is correct. However, staff costs are the complement of non-staff costs, and so this could be interpreted as ‘high non-staff costs offer room to “cut the flab”’, as argued by Carroll and Slater (2009).
Distinguishing between sources of income (models 1 and 3) does substantially change the findings. The share of grant and donation income are highly significant, with the expected sign for grant income. (A higher proportion of grant income is associated with a lower probability of survival.) The significant and positive coefficient on donations suggests that a higher dependence on donations rather than one’s own activity is associated with a higher survival probability. This is despite the fact that donations are less likely to be under the control of the charity. However, greater volatility in donations is associated with a higher risk of failure. The implication is that the charity with the greatest probability of survival, ceteris paribus, is one with a large and predictable income from donations. As donation income is likely to be associated with longevity, this appears to contradict Searing’s (2018) finding that older nonprofits find it harder to recover from financial difficulties.
The other volatility measures have value in the basic if revenue concentration is the only measure of financial dependence (Model 2), volatility of grant funding has a positive coefficient, implying that more volatility is associated with a higher probability of success. One possible reason for this is that grant funding is, by its nature, unpredictable, and so greater volatility might help the charity to develop mechanisms for coping with uncertain income streams. However, when types of funding are included (Model 3), only the volatility of donation income remains significant.
Duquette (2017) finds that greater revenue volatility overall is associated with lower savings, in contrast to expectations. Our results suggest that this may be because overall revenue volatility is masking two opposing effects, from grants and donations. This is consistent with Duquette’s (2017) finding that the absolute size of the volatility effect is small.
The variable for whether a charity is large or not has no impact. However, this might be because the differences are more complex than a simple uplift in probability. To evaluate this, we ran separate probability models for large and small charities; see Table 8.
In terms of signs of coefficients, the results are broadly similar, but far fewer of the coefficients are significant; in other words, the model is struggling to identify clear determining factors. Net assets relative to income appear to be much more important for large charities, but staff costs are not; for small charities, the opposite is true. For the full model, the signs are as expected but very little is significant.
This is not surprising: probability models require many degrees of freedom, and the large/small split effectively halves the sample size for each estimate; hence, these results should be treated as indicative and interpreted with caution. A linear probability model, although only able to give indicative results, is less affected by low numbers of observation (although it is more likely to be affected by the multicollinearity between staff and total costs). Running a linear model on this data suggests that for large firms the key story is unchanged: a dependence of grant income lowers the probability of survival, and a high level of donations increases it. For small firms, the linear probability model supports the findings in Table 8: few factors are consistently associated with survival probability.
Finally, it was noted above that using data from the last year before failure might reflect charities in extremis and is therefore unrepresentative of their overall activity. To test this, we ran three alternative specifications:
Taking values from 2011, the first year data are available from all charities.
Taking values from 2013, the middle year of the period.
Averaging values across the three years prior to failure.
The volatility measures, being for the whole period, are unaffected by the choice of year. Table 9 presents the results for the full sample (not split by size), including the original model for comparison.
The findings show considerable robustness to alternative specifications. All coefficient signs are unchanged, and the coefficient values are generally within the same range. There have been some changes in significance: for example, the significance of the share of donations is more variable in the full model. The most notable variation is on costs: in Model 2 the size and significance of the total costs varies considerably; staff costs are highly significant in the final-year model but not others. It is not clear why this is the case. It may be something to do with the imminent failure of charities: staff costs may increase as redundancies are planned, and staff costs may be more difficult to reduce as income decreases. It may also be a result of the multicollinearity between the two costs measures, although this is difficult to determine in a nonlinear model.
When the results are split by large and small charity (not shown here for reasons of space, but available on request), the results are much the same: effect sizes are broadly consistent, although significance is much more variable because of the smaller sample sizes. There is an indication that significance is greater for smaller charities when using early years, suggesting that failure rates for small charities are predictable further in advance.