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Financial Resilience, Income Dependence and Organisational Survival in UK Charities

• Research Papers
• Open access
• Published: 27 January 2021
• Volume 32 , pages 992–1008, ( 2021 )

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• Elizabeth Green 1 ,
• Felix Ritchie   ORCID: orcid.org/0000-0003-4097-4021 1 ,
• Peter Bradley 1 &
• Glenn Parry 2  

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The financial well-being of the charity sector has important social implications. Numerous studies have analysed whether the concentration of income in a few sources increases financial vulnerability. However, few studies have systematically considered whether the type of income (grants, donation, fund-raising activities) affects the survival prospects of the charity. We extend the literature by (a) explicitly modelling the composition of sources of income, (b) allowing for short-term volatility as well as long-term survival and (c) testing alternative specifications in a nested form. We show that the usual association between income concentration per se and financial vulnerability is a specification error. Greater vulnerability is associated with dependence on grant funding, not overall concentration. Previous studies showing that concentration of income per se is problematic are picking up a proxy effect. We also show that the volatility of income streams may be an important factor in the survival of charities, but that this also varies between income sources.

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Introduction

In the UK, the charitable sector generates income of about £40bn per year (2% of GDP; Keen 2015 ). The UK ‘Big Society’ programme (HM Government 2011 ) has resulted in the third sector playing a central role in the delivery of particular social and welfare services: one-fifth of UK charities report provision of social services as their primary form of activity income (Keen 2015 ; NCVO 2017 ).

Despite the critical role NPOs perform, there are concerns regarding their lack of long-term sustainability (Charities Aid Foundation 2017 ; Foundation for Social Improvement 2017 ). Bingham and Walters ( 2013 ) note that political support for the third sector does not necessarily translate into increased funding. Moreover, Wilsker and Young ( 2010 ) and Bingham and Walters ( 2013 ) both argue that the income stream affects the preferred delivery model; increased government support can therefore create an internal tension between the organisation’s mission and its funding source.

Much analysis has been carried out on the role that the revenue streams have on the financial vulnerability or survival prospects of the charity; the meta-analyses of Hung and Hager ( 2019 ) and Lu et al. ( 2019 ) list some 50 papers on this theme. However, the great majority of these focus on whether the concentration of income from a single type source, per se, creates instability in the organisations.

An important question, rarely tackled in the literature, is whether those different funding streams affect the financial viability of the organisation. Wilsker and Young ( 2010 ) argue that a lack of alignment between income and delivery streams increases the inefficiency and vulnerability of the organisation. Frumkin and Keating ( 2011 ) argue that concentrating on a small number of income streams increases efficiency. Qualitative analyses (e.g. Bingham and Walters 2013 ) show an awareness amongst managers of the importance of the social and political environment to funding streams. But while papers such as Myser ( 2016 ) and Duquette ( 2017 ) consider the type of funding, these are the exception rather than the rule.

This paper directly considers whether the type of funding affects survival prospects. NPOs receive income from three main sources: grants, which are fixed sums to achieve a specific outcome; income arising from the activities of the NPO, such as shop revenue, fees for paid-for services, or fund-raising events; and donations and legacies. These revenue streams have quite different implications for the organisation. For example, an organisation focused on day-to-day fund-raising activities is likely to have quite a different management structure from one that focuses on grant applications as its main income source. Kingston and Bolton ( 2004 ) argue that grant income is a poor funding mechanism as it is time-limited and with no guarantee of renewal. A particular interest in the UK is the expressed preference of the UK government 2010–2015 for the increased use of grant funding: did this create a systemic vulnerability in the sector?

There has also been relatively little research on the variability of incoming and expenditure streams, but this is clearly a related issue. As Kingston and Bolton ( 2004 ) note, grant income offers very high predictability during the period of the grant but uncertainty outside the grant award. Activity income is variable but predictable and somewhat under the control of the NPO. Donations and legacies are outside the control of the NPO but for long-established organisations can be highly predicable. It may be that the type of activity is itself less important than the effective forecasting of income.

This article seeks to extend understanding of the vulnerability of NPOs by examining the survival prospects of 153 UK charities, focusing particularly on the type and volatility of funding streams. We do this by nesting the concentration measure in a broader regression framework to allow complex factors to be distinguished, and for the different models to be compared for their explanatory power. Key findings are that (1) the type of income is more relevant than the concentration of revenue and (2) volatility of costs and income does indeed matter.

We take an empirical perspective, in line with most of the previous studies (Helmig et al. 2014 ); as Lu et al. ( 2019 ) note, there are good theoretical arguments for revenue concentration either increasing or decreasing survival prospects. Rather than considering all the determinants of financial vulnerability, we focus on testing the dominant finding that concentration of income is itself a risk factor. This is the stylised fact which our results challenge.

Literature Review: Financial Distress and Financial Vulnerability

There is a wide theoretical literature on what makes nonprofits operate in the way that they do; Helmig et al. ( 2014 ) provide a survey of how these models of management have been used to analyse financial vulnerability, distress and survival. However, there is a substantial identification problem: the same findings (for example, that nonprofits facing financial distress appear to stay in operation longer than for-profits) can be consistent with many different theories. In addition, many of the papers in this area appear prompted by the desire to give directly applicable advice to nonprofits, at least compared to other social science research articles. As a result, as Helmig et al. ( 2014 ) note, most studies ignore the link between theory and hypothesis in favour of identifying associations between nonprofit characteristics and outcomes.

Conceptual Framework

We continue this empirical approach. However, this still entails basic decisions about the concepts being examined. Compared to for-profit organisations, nonprofits have a different legal framework, a different set of motivations, and access to different sources of income. Analysts have therefore had to adapt the for-profit literature to the financial situation of charities and NPOs.

As Myser ( 2016 ) discusses, there is a substantial difference between strategic concerns (something in the organisation’s way of working that may lead to a catastrophic failure but which may not be causing current problems) and ‘financial distress’ (an ongoing management problem). Myser ( 2016 ) further splits the strategic problem into shorter-term ‘capacity’ and longer-term ‘sustainability’. Financial distress may be experienced by any organisation, but it seems likely that the particular characteristics of NPOs (in particular, the ‘mission’ of Abraham 2003 ) may lead to them operating with a level of strategic risk that a for-profit firm would not accept. For the purpose of this section, we adopt the term ‘financial vulnerability’ to cover the general prospects for the NPO, aware that its meaning is ambiguous.

Identifying financial distress and vulnerability in an NPO is not straightforward, particularly for charities. As Abraham ( 2003 , p. 1) notes: ‘Once [the mission of the NPO] is defined, an NPO often finds that it is unable to withdraw …Thus, the centrality of mission to the operation of an NPO may expose it to issues of financial sustainability that are not faced by organisations operating in other sectors.’ For example, Arya and Mittendorf ( 2014 ) argue that high programme expenditures, rather than administrative efficiency, become performance targets.

Tuckman and Chang’s ( 1991 ) framework for financial vulnerability of nonprofit organisations provides the starting point for most quantitative analysis in this field. They argue that four variables (strength of the equity base, concentration of income sources, share of administrative costs and net margin) provide useful indicators of an NPO’s vulnerability; we refer to these as the ‘vulnerability variables’. For 4700 nonprofits, each of these variables was ordered and then split into quintiles, where being in the bottom quintile was defined as being ‘vulnerable’. In their analysis, 42% of the NPOs studied were vulnerable on at least one metric and 1% on all four.

However, these metrics reflect relative performance (being in the bottom quintile) rather than an absolute measure of failure risk; hence, 20% of NPOs are always classed as ‘vulnerable’ even if overflowing with assets and income. In fact, if the four variables were distributed randomly among the NPOs, we would expect 41% to be vulnerable on one measure and 0.1% on all four. This implies very little correlation between metrics in the Tuckman and Chang dataset; indeed, Hager ( 2001 ) for the US and Thomas and Trafford ( 2013 ) for the UK report negligible bivariate correlation between metrics.

Despite the limited insight in the original paper, the idea that revenue concentration is an important indicator of financial vulnerability has proved popular. Tuckman and Chang’s ( 1991 ) proposals have been taken up by three groups of researchers: those who carry out descriptive analyses similar to Tuckman and Chang’s ( 1991 ) paper; those who compare the variables and metrics to other predictors of business performance; and those who use the variables in regression models to test the association with vulnerability. We review each of these groups in turn.

The first group, accepting the four rank-based metrics as indicators of relative vulnerability, use them to describe risk in particular sectors or organisations. Omar et al. ( 2013 ) and Thomas and Trafford ( 2013 ) both consider variations over sectors and time to identify changing vulnerability in particular clusters. Lohmann and Lohmann ( 2000 ) urge NPOs to accept the metrics as a measure of risk. Abraham ( 2003 ) applies the metrics to a single large charity to argue that, on these measures, the charity in question is unexpectedly vulnerable.

The second group have argued that the usefulness of the vulnerability variables can be tested against the models used to evaluate for-profit businesses. Keating et al. ( 2005 ), revised as Gordon et al. ( 2013 ), compare the performance of the vulnerability variables against two well-established business models (Altman 1968 ; Ohlson 1980 ) and find that no model has much predictive power. They then combine the variables from all models, plus two additional variables; in this model, the vulnerability variables are generally significant and with the expected signs. This suggests that it is the parsimony of the original Tuckman and Chang ( 1991 ) model that is at fault (that is, the metrics have too diffuse an impact to be detected in simple models). Tevel et al. ( 2014 ) compare the vulnerability variables, Ohlson ( 1980 ) and two ‘practitioner’ models. They argue that, if the variables have explanatory power, observing a ‘vulnerable’ NPO should be a good predictor for still finding it vulnerable some time later. On this measure, they find that the vulnerability variables are better predictors of long-term vulnerability. However, it could be argued that this finding merely reflects greater persistence of the vulnerability variables and the metrics based on them: a nonprofit may remain in the bottom quintile even if its absolute performance has improved.

The third, and largest, group of researchers use regression models to test the determinants of ‘financial vulnerability’ (defined in various ways), with the vulnerability variables included alongside others such as size or sector of the nonprofit. In these studies, the focus is usually on the coefficient associated with revenue concentration.

Greenlee and Trussel ( 2000 ) appear to be the first paper to do this, finding financial concentration associated with increased vulnerability, as are lower administrative costs and lower margins; equity is found to be insignificant. Hager ( 2001 ) applies the model to the arts sector; all four vulnerability variables have the expected signs, but statistical significance varies widely between different types of organisation. Trussel ( 2002 ) and Trussel and Greenlee ( 2004 ) include size of organisation as well as sector: larger organisations are found less likely to be financially vulnerable. Hu and Kapucu ( 2015 ) include management metrics and changes in the sources of funding. Prentice ( 2015 ) includes macroeconomic variables (state/national output) as explanatory variables and finds them to be significant. Myser ( 2016 ) uses a range of additional variables but not all of the vulnerability variables. Searing ( 2018 ) adds the age of the organisation, citing management studies showing both internal experience and external networks improve resilience. Unusually, Searing ( 2018 ) models ‘recovery from vulnerability’ rather than vulnerability itself, providing an opportunity to consider whether the routes into and out of vulnerability are the same.

Apart from Prentice ( 2015 ), most analysis uses probabilistic modelling of a binary outcome. However, where multiple observations over time on the same organisations are available, alternative specifications are possible; Hager et al. ( 2004 ) and Burde et al. ( 2017 ) apply survival analysis techniques to generate hazard functions for the probability of failure. Searing ( 2018 ) uses a panel data set with repeat periods of vulnerability, but treats the vulnerable periods as independent events rather than multiple events for the same body.

Most authors find that higher equity ratios and higher margins should be associated with higher survival prospects or less distress. There is more debate about the impact of administrative costs as a share of revenue. Tuckman and Chang ( 1991 ) proposed low administrative costs as indicators of vulnerability: an organisation with more ‘administrative fat’ to cut should survive any downturn better. Statistical studies generally support this view. However, Ecer et al. ( 2017 ) argue that financial efficiency is an indication of good management: resilient organisations adopt the same approach as for-profit firms. Thomas and Trafford ( 2013 ) find that administrative costs as a share of income appear to fall during a period of relative prosperity for the UK charity sector, suggesting that those charities do not use good times as a chance to ‘store fat’. This does not directly refute the argument that an ability to cut waste is important for staving off financial problems. Moreover, it is not clear how well the ‘pure’ administrative cost is measured: some activities may be easily allocated to ‘administration’ and ‘programme work’ but others, such as overarching management or estates costs, are much more difficult to allocate. Thomas and Trafford ( 2013 ) argue financial regulations give charities an incentive to under-report administrative expenditure.

Choice of Outcome Variable

One difficulty facing the multivariate analyses is the outcome variable. Strategic vulnerability could be approximated by failure, allowing for the fact that strategic vulnerability may not lead to failure, and failure may be due to reasons other than strategic vulnerability. However, the analysis of US NPOs dominates the field, and there is often no direct measure of failure as in the US it is not possible to force charities into bankruptcy or reorganisation. Good datasets on NPO failures are not widely available: four out of five papers with actual failure rates use data collected manually (Hager 2001 ; Hager et al. 2004 ; Fernandez 2008 ; Green et al. 2016 ).

Research to date therefore usually focuses on indirect measures of ‘distress’. Gilbert et al. ( 1990 ) suggest that three years’ worth of net losses indicates distress in for-profits. Greenlee and Trussel ( 2000 ) argue that distress in NPOs is better proxied by years of falling service expenditure. Greenlee and Trussel ( 2000 ) set the template for most subsequent studies, which tend to use similar measures. Hence, financial ‘vulnerability’ can often mean ongoing financial distress.

Studies with a ‘survival’ variable (Hager 2001 ; Hager et al. 2004 ; Fernandez 2008 ; Burde et al. 2017 ) argue that survival is the more relevant variable for NPOs. Myser ( 2016 ) carried out two separate analyses using ‘distress’ and ‘sustainability’ as outcome variables. Myser argues that the factors that underlie the two outcomes are significantly different, but also indicates that common variables have common impacts. This suggests the distinction between current and strategic problems is important but may not be crucial.

Some authors, rather than committing to a specific measure of financial health, have used multiple measures. Gordon et al. ( 2013 ), for example, use four different outcome measures. Searing ( 2018 ) compares insolvency and ‘financial disruption’ as alternative measures of vulnerability and finds statistically important differences in outcomes. Prentice ( 2015 ) argues that treating vulnerability as dichotomous is unnecessarily restrictive and ignores the interaction of financial indicators which might be in conflict. His analysis using a continuous composite index suggests that this can be a more effective proxy.

Findings on Revenue Concentration

Income concentration in these studies is calculated using a Herfindahl index. For organisation i , let I is be the income from source s , and T i total income; then, the concentration ratio c i is calculated as

The value of this ranges from 1/ s (income spread equally amongst sources) to 1 (all income from one source). Studies repeatedly show (Greenlee and Trussel 2000 ; Hager 2001 ; Hager et al.  2004 ; Trussel 2002 ; Trussel and Greenlee 2004 ; Carroll and Slater 2009 ; Hu and Kapucu 2015 ; Prentice 2015 ) that this is positively related to vulnerability: that is, more concentrated income is associated with the organisation suffering financial or strategic problems. Hung and Hager ( 2019 ) carry out a meta-analysis of 40 analyses and report an overall positive and statistically significant effect.

However, Hung and Hager ( 2019 ) note that the effect is small, as it is counterbalanced by a number of contrary or insignificant findings. For example, Chikoto and Neely ( 2014 ) and von Schnurbein and Fritz ( 2017 ) find that revenue concentration is positively associated with growth in funds and revenue, respectively, strengthening the financial base of the charity. Frumkin and Gordon et al. ( 2013 ) find that revenue concentration is strongly associated with greater efficiency and, by implication, long-term survival. Berrett and Holliday ( 2018 ) show that greater concentration of income is associated with a lower range of output goods and services, and therefore more specialisation, but this is not directly linked to survival. The meta-analysis of 23 papers in Lu et al. ( 2019 ) also questions the evidence for any relationship. Their review suggests that concentration has a negligible effect on financial vulnerability, although it does appear to be positively related to financial capability.

Table 1 summarises a selection of regression analyses on nonprofit survival or vulnerability. It highlights significant findings in respect of the four metrics commonly used, which approximate to the original Tuckman and Chang ( 1991 ) variables, including financial concentration. ‘+ve’ and ‘−ve’ indicate statistically significant positive or negative findings, respectively; ‘ns’ indicates a variable was included but was not found to be significant. Some articles with very similar models/findings are omitted; for a full review, see Hung and Hager ( 2019 ) or Lu et al. ( 2019 ).

Compared to the number of papers that include a concentration index in their analysis, very few authors have considered whether studying the components of income is more useful. Hager et al. ( 2004 ) find a negative association between the share of income from donations and the failure rate of NPOs. Myser’s ( 2016 ) analysis includes grant dependence as a separate explanatory variable and finds it to be insignificant; this contrasts with Green et al. ( 2016 ) who found it highly significant. This may reflect a US/UK split in the funding environment. Green et al. ( 2016 ) argue that grant funding in the UK is unpredictable, whereas in the USA Myser ( 2016 ) proposes that it should be more stable (or at least predictable) than other income sources. However, Hager et al. ( 2004 ) find that US government funding is associated with higher failure risk, though access to funding is measured as a simple dummy variable rather than a value. Duquette ( 2017 ), while not looking specifically at survival, notes that charities appeared to view grants, activity income and donations as qualitatively different types of revenue.

Apart from these four papers, few works directly analyse the type of funding. Carroll and Slater ( 2009 ) discuss it, but only analyse it via the concentration index. Hu and Kapucu ( 2015 ), Kim ( 2014 ) and Ecer et al. ( 2017 ) all analyse components of income, but not as direct indicators of vulnerability.

It is worth considering the mechanism through which different income streams matter. As Wilsker and Young ( 2010 ) and Kingston and Bolton ( 2004 ) note, different income streams have different predictability, and if one stream is dominant, this is likely to affect the management structure of the nonprofit. It may be that this is the factor which ultimately determines survival prospects. However, structure is hard to identify, although Hu and Kapucu ( 2015 ) provide proxies, and as such this is little explored.

This paper will extend this literature in three ways. First, we explicitly study the composition of sources of income. Second, we use a nested specification to allow the explanatory power of revenue concentration and revenue source to be compared. Third, we introduce measures of volatility in income and costs, as a way of exploring organisational flexibility.

Methods and Data

This paper focuses on UK charities using public financial data obtained from the Charities Commission website, where all UK charities must submit financial accounts for each accounting year. ‘Survival’ is determined by whether the charity is reported as operating or closed in 2015, having operated for at least four years previously. There appears to be no up-to-date list of UK charities that have ceased operations and so convenience sampling is used for non-continuing charities, and quota sampling for the matching set of continuing charities.

Charities identified as having ceased operations are identified through recent news articles within the year 2016–2017. Only recent closures could be studied as The Charities Commission stops publishing information for charities that have ceased operating; this leaves a short window between the announced closure of the charity and the removal of its financial information. Thirty ‘small’ charities (average income under £1 m per year) and 20 ‘large’ charities are selected. This oversamples large charities that are much less likely to close.

Continuing charities are selected randomly from the website to provide a quota sample with the same size distribution across surviving and non-continuing charities. With a larger population to choose from, we chose a larger sample size, identifying 52 small charities and 51 large charities.

In theory quota sampling could have used other criteria in addition to size, such as sector and financial status. However, matching samples by more characteristics reduces the opportunity to identify outcomes as a result of those characteristics.

Company accounts for the years 2010–2015 are examined. The start date was chosen to coincide with the new policy regime and to avoid over-sampling of long-lived charities. The end date was chosen as the last full year for which accounts would be reasonably available for all charities. A later start date would have increasing data points for surviving firms, but also increase the chances of a ‘survivor’ sample biased towards well-managed firms with good administrative processes. Table 2 shows the number of observations, whilst Table 3 shows the number of years worth of data available for each charity.

Most charities had either 5 or 6 years of data; for non-continuing charities, the data were most likely to be missing for 2015, the year of closure. It would have been feasible to only select continuing charities with a full 6 years of data. This was not a selection criterion as it was thought likely to bias the continuing sample towards stable charities with good record-keeping.

As noted in the literature review, most authors do not distinguish between financial vulnerability and financial distress. We use vulnerability in Myser’s ( 2016 ) definition of ‘strategic risk to operations’, identified through closure of the charity by 2015. The rationale for using this rather than ‘distress’ arises from our interest in income stream dependency as a strategic risk. Charities may close because of an extended period of financial distress, but they may also close because of a catastrophic loss of funding, which may not be preceded by any period of financial distress (for example, the closure of the UK charity Kids Company following withdrawal of its primary source of income, government grants).

The point of evaluation is either the last year before ceasing operations or (in the case of continuing charities) the year before the last observed period. For most charities, this means using the data reported for 2014, so that dead and surviving charities are assessed on the same basis. Four charities closed in 2014 but only accounts for 2010–2013 are available, so 2013 accounts were used.

It could be argued that taking data from the last year of operation misrepresents the true vulnerability of the charity as the event leading to the closure of the charity may already have taken place. If this is the case, the charity’s accounts will reflect an exceptional state, and the estimated coefficients will be biased towards zero. As a robustness check, we tried alternative specifications, including the use of average values over the period, discussed below.

The Basic Model

Our starting point is the standard model involving the four vulnerability variables:

Financial concentration, like the rest of the literature, is measured as a Hirschman–Herfindahl concentration index for income sources. Charity Commission income data is classified under four categories: grant funding, charitable activities, donations and ‘other’ income, which includes money from sources including investment or reimbursements. The latter category is very small, 2% of total income on average, and so this is excluded from the analysis. Grant income includes both government grants and grants from other charitable foundations.

Where no income information is supplied in one of the categories, we set this to zero on the basis that the company does not recognise this form of income. Income data supplied by charities do not equal the sum of the component parts. In 90% of cases, the difference is negligible (under 0.5%) and for 97% of cases under 10%. The larger errors are more likely to occur in live charities and, with one exception, do not appear in the year of closure; this seems to rule out a potential reason for the gap: charities ‘banking’ promised monies but not actually receiving it and so going into liquidation. We therefore recreate the total income for the organisation by summing the relevant components rather than using the reported ‘total’.

Equity is assessed by net asset value. This is assumed to be accurately reported. Three charities show negative net assets, but Framjee ( 2008 ) notes that this can occur for a number of reasons and does not necessarily imply anything about the financial state of the charity. It is calculated as a share of total income to normalise it across different-sized operations, in line with previous studies. We multiply by 12 to represent the months of income cover in the case of total loss of income, for ease of presentation in the figures below; this does not affect the estimates.

Margin is defined as income less costs, as a share of income. In our case, we model it equivalently as the total costs as a share of total income, which has the statistical advantage of restricting its range to positive values.

We do not have a direct measure of ‘administrative costs’ from these data. Previous papers assume that ‘administrative costs’ are non-programme costs, i.e. those not directly related to delivery of the service, such as fund-raising. As an attempt to proxy this, we have included staff costs as a percentage of income. The rationale is that organisations may find that staffing costs may give more or less scope for cost cutting in times of financial hardship, compared to other costs.

Costs (annual total and staff expenditure) and reserves are taken as reported, as setting these omitted variables to zero is implausible. One small charity was missing these data and is omitted, leaving 152 valid observations for the multivariate analysis from the original 153 identified.

Table 4 therefore lists our interpretation of the vulnerability variables.

Trussell (2002) was unusual including size as a determinant, but most recent studies (Prentice et al. 2015; Myser 2016 ; Searing 2018 ; Burde et al. 2017 ) include it as a cardinal variable. Discussions with funders and our own experience of working with large and small charities suggested a fundamental difference between large and small charities and therefore the use of a dummy variable approach. The dummy distinguished ‘large’ charities with average income over £1m per annum over the six-year period. Alternative specifications based on maximum or minimum income, or a different threshold value, made little difference, lending support to the idea that this is best treated as a classification issue, rather than a need for a scale variable. There was no a priori expectation on the sign. Carroll and Slater ( 2009 ) argue that larger firms should have a higher probability of survival, ceteris paribus, as the same absolute variation in income or costs will have less effect on a big charity compared to a small one. Trussel ( 2002 ) finds empirical support for this, although the recent studies find large size is more likely to lead to failure.

Extensions to the Basic Model

We extend the base model with two variations. As noted in the literature review, all models use some form of index for concentration of income sources, but it seems likely that grant funding, self-generated activities and donations have different characteristics.

Accordingly, we include two additional variables.

Grant income as a share of total income.

Donations as a share of total income.

Activity income is the residual (as grant plus activity plus donation shares must add up to 100%). A priori, we expect an increase in grant income, relative to activity income, to be negatively associated with survival: a higher dependence on successful bids to deliver discrete blocks of money is likely to increase the risk of failure. We have no a priori expectation on the sign of donations.

The second extension is to consider the volatility of the charity’s operating environment, which Carroll and Slater ( 2009 ) argue is an important component of overall vulnerability. No authors have included volatility in models based on the vulnerability variables, but Duquette ( 2017 ) does include it in his model of revenue allocation. We include volatility as an attempt to see whether structural rigidity is a significant factor in financial vulnerability. If greater income volatility is associated with greater survival probabilities, this would suggest that the variability builds ‘robustness’ in some way. In contrast, more income volatility leading to more failures would suggest that charities are not able to adapt well. Income volatility is negatively correlated with income share for each income type, suggesting it may proxy some form of institutional rigidity around that income stream.

We therefore include five volatility measures: one each for the activity, grant and donation shares of income, and each of the staff and total costs. These are calculated as the coefficient of variation for each measure for each charity, calculated using data for all the years available (5 or 6 years for most charities). We have no a priori view on sign.

Table 5 presents correlations between the income shares of types of income, costs, and assets, as well as the big/small dummy.

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.

Sources of income: means and medians shares as percentage of total income

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.

Proportion of charities dependent on a single type of income

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.

Costs and assets

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.

Months of income cover in assets, all years

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.

Number of months income cover, 2011–2014

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).

Volatility of income

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.

Our model advances the literature in two significant ways.

First, we take the widely reported finding that concentration of income sources per se has a negative effect on a charity’s survival prospects, and we demonstrate that this is not the case. The concentration measure is effectively a poor proxy for specific composition of income; that is, it loses its relevance when more appropriate measures of income dependence are included. In particular, in line with Hager et al ( 2004 ), Myser ( 2016 ) and Green et al. ( 2016 ), we find that dependence on grant funding is a much better explanatory factor. We also find that the share of donations has an even more positive impact on survival than activity income, despite donations being less under the control of the charity then income-generating activities.

Most importantly, we have estimated these variations as part of a nested model, allowing the impact of different specifications to be tested. The literature in this field is mostly composed of independent specifications particular to the paper. While several authors have run non-nested models, very few (Gordon et al. 2013 , being a notable exception) have run a hierarchy of models, testing multiple nested specifications on the same data. This provides us with strong evidence that the income concentration effect is a specification error, and not the result of different samples of variable construction.

Our second key contribution is to introduce volatility measures, which Carroll and Slater ( 2009 ) and Duquette ( 2017 ) argue is important, but which has not been statistically analysed before. Two hypotheses for the effect of volatility are considered: (1) instability in costs and income reduces survival prospects and (2) an unstable environment encourages charities to build in resilience—the ‘what doesn’t kill you makes you stronger’ argument. Our findings offer some support for the latter theory in the case of grant funding, but mostly support the former argument in the case of other income and costs. These results need to be treated with some caution, as the volatility measures are necessarily limited with at most 6 years’ worth of data.

Nevertheless, this does provide a consistent overall message: those charities with the greatest probability of survival have a high level of own-generated activity income and donations, and relative stability in that income and in costs. Charities with a high but stable grant income are more likely to fail.

At first glance, this seems perverse: how can more variation in a source of income improve a charity’s chances of survival? Green et al. ( 2016 ) propose that a stable level of grant funding can lead to dependency, so when grant funding is removed the charity is poorly placed to find other income streams. This is most likely to be the case where a charity has received the same or similar grant funding repeatedly, and where the funding counts for a large part of income. In contrast, an organisation that sees a large variation in its grant funding may place more emphasis on securing income from other sources. It may also be better placed to model the risk in its financial forecasts.

This in itself does not fully explain why grant funding volatility should have the opposite effect from activity and donation volatility. The missing part of the explanation may be that grant funding tends to come in large discrete blocks for fixed periods. In contrast, activity and donation income are more likely to be composed of a continuous, and continuously variable, stream of smaller amounts of income. Thus, even though the income stream may not be under the operational control of the charity, there is ample opportunity to observe and react to changing circumstances. Although Wright ( 2015 ) argues that only a relatively basic level of accounting knowledge is necessary for effective risk management, Ecer et al. ( 2017 ) suggest that the financial resilience of charities is limited by the lack of a for-profit ethos. Without the stimulus of uncertain income, charity management may not develop the necessary risk management skills. This reinforces the view of Hager ( 2001 ), Thomas and Trafford ( 2013 ) and Prentice (2017), that different indicators do not necessarily all point in the same direction for a charity.

In researching what causes charities to fail, there is one key finding: a diversified revenue stream per se increases financial resilience. By nesting this factor in a broader specification, we show that the basic model does not fully reflect the nuances of charity funding. In particular, we find that a dependence on grant funding is clearly associated with a higher risk of failure. We also argue that analyses that do not allow for the volatility of costs and income may be omitting crucial factors.

There are some limitations to the analysis. Sample sizes were limited by the need to identify closed charities in time for their information to be harvested. We have assumed that the primary reason for charity closure is financial, but we cannot rule out non-financial reasons. Using closure as a post-factum indication of vulnerability may include some charities that have undergone an extended period of financial distress, but it also identifies as ‘non-vulnerable’ charities that experienced financial distress but then recovered. Only three sources of funding were distinguished, whereas the meta-analyses of Lu et al. ( 2019 ) and Hung and Hager ( 2019 ) both suggested that number of funding sources affects the strength of the concentration effect. However, this may reflect more on the concentration measure, as more funding streams directly affects the variability of the measure; it is not clear that, for example, including multiple types of grant income, or distinguishing between donations and legacies, would necessarily change results significantly. Finally, we assume that the self-reported data are accurate, but there are inconsistencies in the data that suggest accounts are not being filed correctly. Regulators might want to consider the provision of information to the research community; it is noticeable that, with the exception of Burde et al. ( 2017 ), all the studies that employ actual survival rates were required to carry out their own data collection.

Despite these limitations, our analysis appears reasonably robust. Alternative specifications, with different variables and using different definitions, produced qualitatively similar results. Our results are not sensitive to the period used for estimation although, like Lu et al. ( 2019 ), we find that taking values over a longer period reduces the significance of effects. These findings are also consistent with findings on efficiency and survival from the for-profit sector.

This is an important finding for the UK, where social provision is increasingly tied to the health of the third sector, and vice versa. Chenhall et al. ( 2013 ) and Parry and Green ( 2017 ) note that there can be resistance to performance measures where this is seen to conflict with the ‘social’ objectives of the charity. However, it appears that a better understanding of cost ratios and of the dependency risk associated with different funding sources may offer trustees and regulators useful guidance on the long-term survival prospects for a charity.

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Acknowledgements

The authors would like to acknowledge the funding that made this work possible from British Academy Leverhulme Small Grant [SG142923], the Cabinet Office, Big Lottery Fund as part of the UWE Linkage project, and from the Faculty of Business and Law at UWE.

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Green, E., Ritchie, F., Bradley, P. et al. Financial Resilience, Income Dependence and Organisational Survival in UK Charities. Voluntas 32 , 992–1008 (2021). https://doi.org/10.1007/s11266-020-00311-9

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What Gives?: The science behind effective charitable giving

by Steph Guerra figures by Abby Burrus

December kicks off the Season of Giving. There are Salvation Army volunteers ringing bells outside of department stores, food drives for the hungry, and fundraising appeals from almost every charitable organization. With so many groups to choose from, is there actually a strategic way to choose the “right” or the “best” charity?

Indeed, a social movement known as Effective Altruism aims to help people make more informed decisions about where to donate their money by scientifically evaluating the effectiveness and impact of charities. Founded in the late 2000s, Effective Altruism aims to maximize the amount of good that charitable donations accomplish by using evidence to identify causes worthy of donations. Important steps in this process include: (1) finding a cause with the potential to make the most impact; (2) identifying the most effective solution; and (3) quantifying the desired impact.

Identifying a Cause

Identifying the right cause can be more important than selecting the right charity. Charitable causes can range from global health issues (such as vaccine deployment or end-of-life care) to economic development (such as supporting entrepreneurs in developing nations) to societal concerns (such as criminal justice reform). Some of the key features used by effective altruists to select worthy causes include the scale of a problem, how neglected it is, and how likely it is to be solved (see Figure 1):

1. Scale of the Problem

The scale of a problem refers to both the number of individuals affected by it and how deeply they are impacted by the problem. For example, if you are choosing between charities aimed towards curing blindness vs. HIV, you must consider two things: the prevalence of the two conditions and the potential quality of life improvement if either condition were resolved. According to the World Health Organization (WHO), approximately 36 million individuals are blind and 36.7 million individuals are living with HIV. Just considering the numbers alone, donating to either of these organizations appears equally cause-worthy. However, through extensive studies and surveys of afflicted individuals, the WHO determined that people living with untreated HIV exhibit a disease burden that is approximately 1.5 times higher than those living with blindness . Thus, if you want your money to make the most impact based purely on the problem’s scale, it makes sense to donate to charities that attempt to alleviate HIV/AIDS over blindness.

2. Neglected-ness

Neglected-ness is a measure of how many resources are currently devoted to a particular cause. Ideally, you want to identify a cause that is not already crowded by other philanthropists or foundations because focusing on a neglected, but still large-scale, problem means your donation will be more impactful. Donating a million dollars to a cause that only receives approximately $200,000 annually will have a bigger influence than donating to a cause that regularly receives $100 million annually. As an example of this principle, an effective altruist would consider disaster relief funding as an unwise investment because this cause already receives a large influx of donations. They would instead focus on a cause that receives little attention such as the field of artificial intelligence, which is viewed by many as a catastrophic threat to humanity , yet there are few resources and organizations devoted to its control.

3. Solvability

And lastly, when identifying the best cause, its solvability must be considered. This is perhaps one of the most important criteria because even if a cause is large-scale and neglected, it may not be worth donating to if there aren’t any clear or immediate solutions. The best causes are those that can be solved with an evidence-based intervention. A classic example of solvability is the prevention of malaria transmission in sub-Saharan Africa through the distribution of long-lasting insecticide treated bed nets (LLINs). Through a series of small- and large-scale randomized control trials, researchers established that the distribution and usage of these bed nets significantly decreased childhood mortality and other health outcomes related to malaria contraction. Distributing bed nets to prevent malaria transmission via the Against Malaria Foundation is an intervention that is large-scale, neglected (room for more funding), and solvable.

Identifying the Most Cost Effective Solution

After identifying the “right” cause, the next step for an effective altruist is to determine the most effective solution. The gold standard for deciding on the most effective solution is to conduct a randomized control trial (RCT). If the cause you are most interested in is improving educational achievement in the developing world, there are several charities that apply different solutions that you can consider for your donations. You could donate to an organization that builds new schools in remote and rural areas, to a group that trains local English teachers or to a group that sends school supplies to developing countries. To determine the most effective target for your charitable investment, you could conduct an RCT to investigate each intervention at different groups of schools. Each school group will either receive one intervention or no intervention at all. You can then track test scores and school attendance to see which group fares best over the course of the experiment (Figure 2).

Michael Kremer conducted a similar RCT experiment in the mid 1990s to evaluate the work of a Dutch charity called International Christian Support (ICS). Kremer wanted to improve educational achievement in Kenya and decided to evaluate local schools implementing key ICS interventions over time. His study showed that none of the interventions including more school supplies, newly trained teachers, or free school uniforms led to improved test scores or school attendance.

Undaunted, Kremer decided to test other strategies for improving education in Kenya. One novel strategy involved deworming children. Intestinal worms are parasitic infections that affect more than one billion people worldwide. While these infections often don’t kill, they do make children sick in countries like Kenya. In a new study, Kremer determined that treating intestinal worms led to improved educational outcomes, including decreased school absenteeism by 25 percent! This story illustrates the value of choosing interventions that have been adequately tested. If he had not tested different ways of increasing school performance, Kremer may have funded an organization that did little to improve his cause of interest (story adapted from McCaskill, 2015 ). Obviously, not everyone has the resources to conduct their own RCTs but, luckily, there are many charity evaluators (such as Give Well ) that consistently review academic studies and write up digestible reports for busy people to review.

Quantifying Impact

The final principle that powers the science behind effective altruism is calculating the actual impact each donated dollar provides. But how does one quantify impact? One key strategy is to calculate the number of life years saved by the solution. One quality adjusted life year (QALY) is considered one year of life lived in perfect health and is counted as 1 QALY. A year of life lived at less than perfect health will range anywhere from zero to 1 QALY and can be a result of chronic conditions such as lower back pain, blindness, or cancers. Large organizations, like the WHO, conduct surveys and research on populations living with various health outcomes to quantify QALY values across a spectrum of diseases. For example, one year of life lived with advanced cancer is equivalent to approximately half a year lived in perfect health.

Consider an intervention that saves the life of an infant from premature death. This infant has an average life expectancy of 80 years so by saving this infant’s life, the population has effectively gained eighty quality adjusted life years (QALYs). If your intervention strategy can save 100 infants lives for every $800,000 spent, that means that the cost of one QALY is only $100/QALY [$800,000/ (100 infants x 80 QALYs/infant)]. But as stated above, most people do not live in perfect health for their entire life. QALYs can account for more than just number of whole life-years gained, but also the improvement in the quality of life lived in those years.

Think about the average life and its quality over time (Figure 3). Let’s say we start at 100% quality of life at birth and that a typical infant and young child live at this same high quality. At certain points in life, a person may experience a severe dip in life-quality because of a major life trauma or illness. In addition, the average quality of life will slowly decrease over time due to aging and other chronic illnesses. Someone who is living at an average 80% quality of life could receive an altruistic intervention that extends her life by 20 years (see Figure 3). With this intervention, there is a net gain of 16 QALYs/person (20 years * .80). Now consider someone who will live for 60 years but with extreme chronic pain that lowers his average quality of life to 60%. If that person’s quality of life is improved by 30% over the course of a 60-year lifetime, then there is a net gain of 18 QALYs/person for that intervention (60 years * .30). QALYs allow researchers in public health and the effective altruism movement to estimate impacts of different solutions and ultimately survey their cost effectiveness.

The Effective Altruism movement aims to get the most bank for each buck donated to maximize impact. With this mindset, it becomes clear that certain causes should be prioritized over others. While this perspective might not fit well with the traditional spirit of giving, it has been gaining traction in recent years. Perhaps if we all thought more strategically about where we donated our money, the season of giving would be impactful well past December.

Steph Guerra is a PhD candidate in Karen Cichowski’s lab studying lung cancer therapeutics. She has given multiple talks for SITN and is a former co-director of the organization. Steph has also recently taken on the role of development director of the Philanthropy Advisory Fellowship organized by Effective Altruism at Harvard.

For more information:

Two related nonprofits, GiveWell and the Open Philanthropy Project , were established in the mid-2000s with the goal of conducting high-quality research on the effectiveness of individual charities and cause areas. If you are interested in giving to the most impactful causes, these websites have recommendations for their top-rated charities and more information on how they conduct their evaluations. Some of their top charities are those that have been mentioned throughout the article but include the Against Malaria Foundation, Give Directly, and Deworm the World Initiative.

‘Doing Good Better’ By William McCaskill

TEDTalk on Effective Altruism

How to be most altruistic in your career: 80,000 hours

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3 thoughts on “ What Gives?: The science behind effective charitable giving ”

As mentioned addressing the right cause is very important. The Means you choose to donate is also important to save yourself from scammers. Thanks for sharing a wonderful post.

Interesting! You make sense with the points you made. There is still definitely a great deal to learn about charitable giving. Thanks for sharing!

Nonprofit Fundraising

Charities belong in a nonprofit group of companies, they must not amass millions and millions of wealth hidden in every imaginable investment entities. It’s about time a new management paradigm is required for all charitable nonprofit organizations. I have written about an alternative way out for managing charities and it is applicable to all non-profit organizations. Please consider reading my research paper written some 23 years ago, titled “Helping those who help others “, published in Quality Progress magazine of the American Society for Quality, July 1997, pages 37-41. Should be available by searching on Google.

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Charitable Giving

Charitable giving is an increasingly important component of our national economy comprising over 2% of GDP in 2008. However, relatively little is known about what drives people to give to charities. This line of research includes large scale field experiments to investigate charitable giving.

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A Glimpse into the World of High Capacity Givers: Experimental Evidence from a University Capital Campaign

NBER Working Paper 22099 (2016)

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Toward an Understanding of why Suggestions Work in Charitable Fundraising: Theory and Evidence from a Natural Field Experiment

Journal of Public Economics, (2014), Volume: 114, pp. 1-13.

Exploring the origins of charitable acts: Evidence from an artefactual field experiment with young children

Economics Letters, (2013), 118(3), pp.431-434.

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How Can Bill and Melinda Gates Increase Other People’s Donations to Fund Public Goods?

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Testing for Altruism and Social Pressure in Charitable Giving

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Charitable Giving Around the World: Thoughts on How to Expand the Pie

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Charitable donations are more responsive to stock market booms than busts

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The Market for Charitable Giving

Journal of Economic Perspectives, 25(2) (2011), pp. 157–180

Small matches and charitable giving: Evidence from a natural field experiment

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The Role of Social Connections in Charitable Fundraising: Evidence from a Natural Field Experiment

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Is a Donor in Hand Better than Two in the Bush? Evidence from a Natural Field Experiment

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Rebate Rules in Threshold Public Good Provision

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Matching and challenge gifts to charity: evidence from laboratory and natural field experiments

Experimental Economics, (2008), 11(3), pp. 253-267.

A fundraising mechanism inspired by historical tontines: Theory and experimental evidence

Journal of Public Economics, (2007), 91(9), pp. 1750-1782.

Does Price Matter in Charitable Giving? Evidence from a Large-Scale Natural Field Experiment

American Economic Review, (2007), 97(5), pp. 1774- 1793.

Using Lotteries to Finance Public Goods: Theory and Experimental Evidence

International Economic Review, (2007), 48(3), pp. 901-927.

Toward an Understanding of the Economics of Charity: Evidence from a Field Experiment

Quarterly Journal of Economics, (2006), 121(2), pp. 747-782.

The impact of challenge gifts on charitable giving: an experimental investigation

Economics Letters, (2003), 79(2), pp. 153-159.

The Effects of Seed Money and Refunds on Charitable Giving: Experimental Evidence from a University Capital Campaign

Journal of Political Economy, (2002), 110(1), pp. 215-233.

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The impact generated by publicly and charity-funded research in the United Kingdom: a systematic literature review

Daniela gomes.

School of Health Sciences, City, University of London, Northampton Square, EC1V 0HB London, UK

Charitini Stavropoulou

Associated data.

All data generated or analysed during this study are included in this published article and its supplementary information files.

To identify, synthesise and critically assess the empirical evidence of the impact generated by publicly and charity-funded health research in the United Kingdom.

We conducted a systematic literature review of the empirical evidence published in English in peer-reviewed journals between 2006 and 2017. Studies meeting the inclusion criteria were selected and their findings were analysed using the Payback Framework and categorised into five main dimensions, namely knowledge, benefits to future research and research use, benefits from informing policy and product development, health and health sector benefits, and broader economic benefits. We assessed the studies for risk of selection, reporting and funding bias.

Thirteen studies met the inclusion criteria. The majority of the studies (10 out of 13) assessed impact at multiple domains including the main five key themes of the Payback Framework. All of them showed a positive impact of funded research on outcomes. Of those studies, one (8%), six (46%) and six (46%) presented a low, moderate and high risk of bias, respectively.

Conclusions

Empirical evidence on the impact of publicly and charity-funded research is still limited and subject to funding and selection bias. More work is needed to establish the causal effects of funded research on academic outcomes, policy, practice and the broader economy.

Electronic supplementary material

The online version of this article (10.1186/s12961-019-0425-2) contains supplementary material, which is available to authorized users.

Every year, public and charity funding bodies in the United Kingdom invest significantly in health research. In 2014 alone, the United Kingdom Clinical Research Collaboration reported an expenditure of approximately £3.01 billion in health research funded by public and charity sources [ 1 ], with the Medical Research Council, the National Institute of Health Research (NIHR) and the Wellcome Trust being ranked among the top public and philanthropic funders of health research worldwide [ 2 ]. Thanks to this investment, a number of achievements have been possible and the United Kingdom “ has developed some of the strongest and most productive clinical medicine research bases in the world ” [ 3 ].

Given the magnitude of the investment in health research, there has been a growing interest in exploring the impact it generates. However, defining and measuring research impact is not straightforward; it is a complex exercise and the growing international interest on this subject has led to the development of multiple appraisal frameworks. A recent literature review identified 24 different methodological research impact frameworks that have been used in various countries and settings [ 4 ], with the Payback Framework being the most commonly used [ 5 ] both in the United Kingdom and internationally [ 4 , 6 , 7 ]. The Payback Framework was developed by Buxton and Hanney [ 8 ] and measures impact in five dimensions, namely knowledge, benefits to future research and research use, benefits from informing policy and product development, health and health sector benefits, and broader economic benefits.

Using the Payback Framework, this study’s aim is to review, synthesise and critically assess the empirical evidence of the impact generated by publicly and charity-funded health research in the United Kingdom. We do so acknowledging that there is inherent uncertainty regarding the outcomes of health research, in particular blue-sky and preclinical investigation [ 9 ]. We also accept that different types of research are expected to have different impact, with implications in the final assessment of research.

Data sources and searches

To address the study’s aim we conducted a systematic literature review. The Electronic Databases considered in this review were DoPHER (Database of Promoting Health Effectiveness Reviews), EBSCOhost CINAHL (Cumulative Index to Nursing and Allied Health Literature), EBSCOhost MEDLINE, OVID Embase, CENTRAL (Cochrane Central Register of Reviews of Effects), CDSR (Cochrane Database of Systematic Reviews), DARE (Database of Abstracts of Reviews of Effects), and NHS EED (NHS Economic Evaluation Database). Searches were conducted using a combination of keywords and index terms (for example, MeSH terms) combined with Boolean operators, as part of an extensive search. These were adapted to the other relevant electronic databases using the synonyms used by each database. The search terms included combinations of different relevant terms, such as ‘budget’, ‘funding’, ‘investment’, ‘grant’, ‘research support’, ‘clinical research’, ‘health research’, ‘medical research’, and ‘impact’, ‘value’, or ‘research outcome’. A full list of search terms and an example of the search strategy of the three main databases (OVID Embase, EBSCOhost CINAHL and EBSCOhost MEDLINE) is available in Additional file  1 to enable replication. One author (DG) performed the search of the electronic databases.

In addition, citation tracking of relevant literature and reference lists of the identified studies and reports were searched for further studies by both authors. Hand searches were also conducted in the key journals Value in Health , The Journal of Health Economics and BMC Health Services Research .

Study selection

We included empirical studies that (1) explicitly analysed the impact generated by research funding provided by public and charity bodies in the United Kingdom, (2) analysed the impact generated in the United Kingdom or other countries, (3) were published between 2006 (the year the NIHR was lunched) and 2017, (4) were peer reviewed and (5) were written in English. We excluded speculative studies, opinion papers or editorials that discussed the potential impact generated by funded research in the United Kingdom and studies that did not focus on health-related research.

The selection of the relevant studies followed the steps identified by the PRISMA guidelines for systematic reviews and meta-analyses [ 10 ]. First, we screened all records on the basis of title and abstract. When it was not possible to determine whether a study met the inclusion/exclusion criteria based on title and abstract review, we included the study for full text review. Both authors screened all records, assessed the list of studies included for full text review and agreed on the final list.

Data extraction and data synthesis

Following the identification of the studies, data extraction and quality assessment was conducted using a standardised data extraction form. Extracted data included aims, programme assessed, methods, outcomes assessed and main findings. Given the diversity of the methodological approaches used in the identified studies, a quantitative analysis of the results was not possible. Overall, the results were synthesised into the five key domains of the Payback Framework, using the adaptation discussed in Donovan and Hanney [ 11 ], in which payback from research includes the following five main dimensions and definitions:

• Knowledge, including articles published in peer-reviewed journals, conference presentations, books, book chapters and research reports.
• Benefits to future research and research use, such as better targeting of future research, development of research skills, personnel and overall research capacity, a critical capacity to appropriately absorb and utilise existing research, including that from overseas, staff development, and educational benefits.
• Benefits from informing policy and product development, improved information bases for political and executive decisions, other political benefits from undertaking research, and development of pharmaceutical products and therapeutic techniques.
• Health and health sector benefits, including not only health improvements but also cost reduction in delivery of existing services, qualitative improvements in the process of delivery, and improved equity in service delivery.
• Broader economic benefits, wider economic benefits from commercial exploitation of innovations arising from research and development (R&D), and economic benefits from a healthy workforce and reduction in working days lost.

Risk of bias assessment

Given the heterogeneity of the studies included, we decided to develop a simple tool to assess the risk of bias. The tool focused on three main types of risk of bias, namely funding, selection and reporting bias. Funding bias refers to the potential tendency for the study to support the interests of the sponsor. Selection bias occurs when selected samples (including individuals, groups or data) are not representative of the population being reviewed. Reporting bias refers to the reported data favouring certain outcomes or key aspects of the study not being clearly described.

Each study was assessed and was given a score for each of the three types of bias using the risk of bias tool described in Table  1 . An overall score was then calculated in the following way – studies with no high-risk score in any of the three domains were classified as ‘low risk of bias’, those with two high-risk scores were classified as ‘moderate risk of bias’ and those with three high-risk scores were classified as ‘high risk of bias’.

Risk of bias assessment tool

a Overall rating for risk of bias: 1 (strong; no high-risk score); 2 (moderate: 1–2 high-risk scores); 3 (weak; 3 high-risk scores)

Study inclusion

The process of study selection is summarised in Fig.  1 and follows the PRISMA guidelines. From the main database searches (CINAHL n = 94, EMBASE n = 204 and MEDLINE n = 112) a total of 410 studies were identified. An additional 31 studies were identified by other searches, including CENTRAL, CDSR, DARE, DoPHER, Google Scholar, National Institute for Health and Clinical Excellence (NICE) and NHS EED, as well as key journals, citation tracking and the main funding websites. After removing 62 duplicates, we proceeded with the review of the title and abstract of the remaining 379 studies. At this stage, 331 studies were excluded and the remaining 48 were retrieved as full text for further review, which are listed in Additional file  2 .

Flow diagram of study selection following the PRISMA guidelines

Following full text review, 34 studies were excluded; 14 did not have a primary focus on the impact of public and charity funded research, 12 considered funding outside the United Kingdom, five did not provide empirical evidence, two were not peer-reviewed and one was published before 2006. One study was excluded to avoid duplication since it was a brief journal article [ 12 ] of an earlier report [ 13 ]. This resulted in 13 studies being included for analysis.

Study characteristics

The data extraction table can be found in Additional file  3 . The funding body mostly assessed in the studies included was the NIHR. Three studies focused on the NIHR’s Health Technology Assessment (HTA) Programme, one on its Service Delivery and Organisation (SDO) R&D Programme, one study looked at the NIHR’s Biomedical Research Centre at Oxford, one explored the impact of two NIHR-funded research networks, one analysed the NIHR-funded Cochrane Review Groups (CRGs) and, finally, one looked at the 10 years of the NIHR. Four studies looked at specialised charities including the United Kingdom Occupational Therapy Research Foundation, Asthma UK, the National Cancer Research Institute and leading funders of cancer research in the UK. Finally, one study took a wider approach looking at the general economic effect of government and charity research expenditure on pharmaceutical R&D.

Out of the 13 studies, seven (54%) used mixed methods, three (23%) used quantitative and three (23%) used qualitative methods. A number of different designs were used, including questionnaire surveys ( n = 4), qualitative interviews ( n = 5), case studies ( n = 3), document review ( n = 3), bibliometric analysis ( n = 3) and quantitative analysis using econometric techniques ( n = 3). The design of the studies was such that none of the studies could establish causality between research funding and various outcomes. Only one study explored the hypothesis that publicly and charity-funded research led to increased pharmaceutical R&D, but using appropriate econometric tests, it showed that there may be a dual causal relationship between the two. The authors therefore conclude that their results should be interpreted as a positive association between public expenditure and private R&D, and cannot claim causality [ 14 ].

Risk of bias

All reviewed studies had received funding, partly or exclusively, by the organisation they were assessing. High risk of selection bias was present in seven out of the 13 studies (54%), as they chose to analyse funded projects that were more likely to have shown positive impact. Finally, reporting bias was high in more than half of the studies (8/13; 62%), either because important information regarding the methods, funding sources or results was missing, or because the overall claims of the studies highlighted predominantly data showing positive impact of research. Overall, from the included studies only one presented a low risk of bias (8%). All of the remaining included studies presented a moderate (6/13; 46%) to high risk of bias (6/13; 46%), accounting for an estimated 92% of the included studies. The results are presented in Table  2 .

Data analysis

The results of the final 13 studies were synthesised in accordance to the Payback Framework domains.

Eight of the included studies report research impact as a form of knowledge generation [ 13 , 15 – 21 ]. Two of these studies focused on the NIHR’s HTA Programme. In 2007, Hanney et al. [ 13 ] assessed the impact of the first 10 years of the programme and showed that the mean number of publications per project was 2.93 (1.98 excluding the monographs). A more recent study by Guthrie et al. [ 16 ] reported that an estimated 96% of the NIHR-funded HTA Programme studies are published in the journal Health Technology Assessment ; the authors claimed that work funded by the HTA Programme is cited more than twice as frequently as would be expected on average.

Two more studies looked at knowledge generated by programmes funded by the Department of Health in the United Kingdom. Peckham et al. [ 19 ] assessed the impact of the first 5 years of the SDO Programme (2001–2006) and showed that, of the 23 research projects, a total of 39 papers had been published in peer-reviewed journals by early 2006, equivalent to 1.7 articles per project. In addition, there were 95 national and international conference presentations. Each of the 23 research projects produced an average of 6.7 citations. Another study by Bunn et al. [ 15 ] assessed the reviews published by 20 NIHR-funded CRGs, showing that 1502 out of 3187 (47%) new and updated reviews published on the Cochrane Database between 2007 and 2011 were published by the 20 CRGs. In addition, in a sample of 60 reviews (out of 1502), 27 presented 100 or more citations and five out of 60 were cited over 400 times in Google Scholar.

Two studies looked at knowledge generated by two charity funders. Hanney et al. [ 20 ] described the various impacts identified from a range of Asthma UK research by conducting a survey among investigators of funded projects. Of the 90 projects that were returned, they showed an average of four peer-reviewed journal articles per project. Four respondents did not record producing any articles. As the authors pointed out, the results may be biased as it is possible that those projects for which surveys were not returned had a much lower number of publications. Another study by Sainty [ 17 ] looked at the United Kingdom Occupational Therapy Research Foundation (UKOTRF) by sending a questionnaire form to 11 grant holders. They concluded that knowledge generation was the main self-reported outcome by previous grant holders; this outcome was measurable through reported scientific publications in peer-reviewed journals (publications n  = 6; submissions n  = 14), publication of books/book chapters ( n  = 2), Cochrane review citations (co-authorship of a Cochrane review n  = 1) and conference presentations (presentation at UKOTRF n  = 1; submission to annual conference and other national and international conferences n  = 39). They note that it is a requirement of the UKOTRF grant holders to submit an article to the British Journal of Occupational Therapy and an abstract to the organisation’s annual conference.

Sullivan et al. [ 18 ] compared the period before and after the launch of the National Cancer Research Institute in the United Kingdom in 2001, an initiative that brought together charity and public funders in the area of cancer research. They showed that UK cancer centres published just over one-eighth of all UK outputs (papers per year) in 1995 but almost a quarter by 2004.

Finally, Morgan Jones et al. [ 21 ] highlighted the impact of the NIHR in managing and sharing knowledge resources via the Journals Library and BioResource.

Benefits to future research/research use

Better targeting of future research was reported as an outcome of funded research in four of the reviewed studies [ 13 , 15 , 16 , 21 ]. In particular, the study by Morgan Jones et al. [ 21 ] that reviewed a number of NIHR cases gave a lot of emphasis on better targeting of future research as a result of NIHR-funded research. The report presented 10 cases showing collaboration with charities and the third sector, including, for example, the collaboration of NIHR with the Stroke Association in research that has resulted in earlier and more efficient diagnosis for stroke survivors with cognitive impairment. In their analysis of the NIHR-funded CRGs, Bunn et al. [ 15 ] showed that 13 out of 60 Cochrane reviews conducted by the CRGs were cited in protocols or primary research. In addition, respondents of the survey provided 40 examples where they felt their reviews influenced primary research. However, Bunn et al. [ 15 ] reported that most of these examples of impact relate to work conducted by the Cochrane reviewers themselves. Therefore, there is limited evidence of a broader impact.

Two studies analysing NIHR’s HTA showed the programme’s contribution to future research. Hanney et al. [ 13 ] showed that 61 (46%) HTA projects went on to receive further funding, yet it is not clear whether this funding was from the HTA or other bodies. A few years later, Guthrie et al. [ 16 ] showed that HTA funding contributed to the development of new research methods, by stimulating their research field more widely or by introducing new research priorities as a result of its findings. This evidence was provided in approximately 58% of the case studies, with examples of the studies provided as supporting evidence. Half of the studies were extended; however, there was little evidence that there was broader impact beyond those already holding HTA grants.

Six of the reviewed studies provided a series of examples through which funded research has contributed to the development of research skills, personnel and overall research capacity as well as enabling staff development and educational benefits [ 13 , 16 , 18 , 19 , 21 , 22 ]. Guthrie et al. [ 16 ] addressed the HTA Programme’s contributions to career development of researchers and overall research capacity through interviews of 20 stakeholders. Six out of the 20 interviewees reported contributions to career development and overall research capacity. The authors indicate that capacity-building is hard to establish, as most of the researchers receiving the grant were already established. In an earlier study, Hanney et al. [ 13 ] showed that 28 projects out of 133 completed questionnaires reported that qualifications had been gained or were expected to be gained from involvement in HTA projects. In addition, eight out of the 16 case studies they analysed reported that researchers involved in the funded projects progressed in their career through promotions, though the authors acknowledge that it is hard to tell this was the outcome of the funded project.

The report by Morgan Jones et al. [ 21 ] gave particular emphasis on the development of overall research capacity by developing or facilitating knowledge improvement and research teams in areas of research otherwise difficult to address. This was reported in three studies, namely (1) the Radiotherapy Trial Quality Assurance Team, supporting medical staff implementing research knowledge to practice; (2) the Hyper-acute Stroke Research Centres, enabling patient-informed decision and participation in treatment therapy trials to improve the delivery of better emergency care; and (3) the Enabling Research in Care Homes, involving the development of a research network at care homes. According to Morgan Jones et al. [ 21 ] the NIHR also recognises the relevance of retaining researchers following completion of funded research. The report referred to the Doctoral Research and Clinical Research Fellowships, the Leadership Programme and the Mentorship for Health Research Scheme. Furthermore, the report provided evidence that NIHR funding has contributed to the critical capacity to absorb and appropriately utilise existing research, including international research in five studies. The evidence was reflected in the centralisation of specialist cleft services and support in national and international trials; standardisation of children eczema treatment based on international guidelines; improvement of rehabilitation programme to stroke survivors; introduction of a personalised care approach to elderly patients with dementia; and collaboration in testing of artificial knee joints produced by foreign manufacturers.

McCrae et al. [ 22 ] examined the impact of two NIHR-funded research networks on a multi-centre randomised controlled trial of antidepressants in people with depression superimposed on dementia. The two networks are the Mental Health Research Network and the Dementia and Neurodegenerative Diseases Research Network. They showed that the Mental Health Research Network helped in gaining local ethics committee and NHS trust approvals, which can be a time-consuming process. Clinical study officers boosted a recruitment campaign and contributed to the monitoring and assessment of participants. The study mentioned a number of limitations of the networks, including potential problems of duplication or unclear roles and responsibilities, a degree of unrealistic expectation from principal investigators and additional bureaucratic burden.

The study by Sullivan et al. [ 18 ] showed that, following the establishment of the National Cancer Research Institute, there has been an increase in United Kingdom cancer centre collaborations with European (5–28% of all their outputs) and United States (6–21%) investigators. Peckham et al. [ 19 ], evaluating the first years of the SDO, argued that the projects demonstrate contributions to building the capacity of the workforce, as there are many examples from the bibliographic analysis and the case studies where the knowledge is used in teaching in universities. There was also some evidence that the research is stimulating user involvement in research.

In charity funding, Hanney et al. [ 20 ] showed that at least 62 higher degrees have been obtained or were expected, at least partly, as a result of Asthma UK’s project funding. In addition, 64% of the funded projects participating in the survey reported some career development, including promotion for principal investigators, further fellowships from major funders and recognition in the asthma field as a result of the Asthma UK project funding. Sainty [ 17 ] reported that funded research by the UKOTRF has not only contributed to promotion of the researcher’s profile and career progression but also to overall research capacity by enabling collaborative working with clinical organisations, universities and charitable partners. In addition, funding led to employer and other partner/host organisations contributions and attracted follow-on funding from external sources.

Benefits from informing policy and product development

Eight of the included studies assess the benefits of informing policy as a form of research impact [ 12 , 13 , 15 – 17 , 19 , 20 , 23 ]. The main methods used to measure the impact were self-reported evidence from recipients of grants, citations in policy documents and case studies.

Surveys were used in four studies [ 13 , 16 , 17 , 20 ]. Three out of the 11 respondents in the study by Sainty [ 17 ] reported that their findings were particularly relevant to inform discussions at a national policy event ( n  = 1) and national and regional dissemination forums ( n  = 2). Using a questionnaire survey, Hanney et al. [ 13 ] showed that 73% of the respondents claimed their study had an impact on policy to date, particularly for NICE projects. Hanney et al. [ 20 ], looking at Asthma UK, showed that 13% of the respondents claimed to have made an impact on policy, and 17% expected to do so in the future. Using Researchfish data, Guthrie et al. [ 16 ] provided evidence that 15% of the portfolio of studies analysed had an impact on policy, including participation in advisory committees and citations in clinical reviews, policy documents and guidelines.

Citations in policy documents were used as evidence of policy influence in two studies [ 15 , 18 ]. Bunn et al. [ 15 ] claimed that Cochrane reviews have contributed to informing policy, with an estimated 722 citations identified in 248 guidelines and 481 reviews cited at least once. They included several sets of developed guidance at a local ( n  = 10), national ( n  = 175) and international level ( n  = 62). Sullivan et al. [ 18 ] showed that, after the establishment of the National Cancer Research Institute in the United Kingdom, there has been an increase in the number of citations on clinical guidelines and the press (BBC).

Interestingly, the evaluation of the SDO by Peckham et al. [ 19 ] reveals differences in evidence of policy influence depending on the method used. The literature review they conducted did not identify any citations in the documents related to policy, but evidence from interviewees indicates other informal mechanisms in which the knowledge was transmitted to policy such as meetings with the Department of Health. This demonstrates that knowledge can be effectively transferred in different ways but these may be difficult to trace when building an understanding of knowledge flow and research output. It has been difficult to confirm the use of SDO-funded research by practitioners through case studies. The data on outputs show that there is potential for a wide range of practitioners to access the research.

Certain studies referred to specific policies or policy organisations mainly due to the nature and scope of the funding body assessed. Guthrie et al. [ 16 ] showed that funding by the HTA Programme enhanced the existent collaboration between the programme, NICE and the National Screening Committee, who represent powerful identities in the process of disseminating research findings through policy and guidelines. According to Guthrie et al. [ 16 ], 15% of the overall portfolio of HTA-funded studies reported having some impact on policy. HTA Programme research has the potential to constitute and improve the information base for policy development and redesign at both national and international levels; however, it is not primarily involved in the direct process of policy development. Guthrie et al. [ 23 ] presented similar findings in a later study. Earlier assessments of the HTA showed similar impacts. Hanney et al. [ 13 ] showed that the HTA’s Technology Assessment Reports for NICE had the clearest impact on policy in the form of NICE guidance. Other bodies where the projects had impact included the National Screening Committee, the National Service Frameworks, professional bodies or the Department of Health. The case studies they presented provided considerable detail about the exact names of the policy documents informed by specific HTA projects, and the precise issues in the documents that were influenced by the specific research.

Finally, evidence that publicly funded research leads to development of pharmaceutical products and therapeutic techniques was found in one of the reviewed studies only. Morgan Jones et al. [ 21 ] provide evidence on the development of therapeutic techniques which are “ safer, less invasive and more focused on patients’ quality of life ” by NIHR-funded research. They also provide evidence from seven funded studies, which led to more efficient and cost-effective treatments.

Health sector benefits

Regarding improved health outcomes, the majority of the studies provided self-reported evidence. In the study by Sainty [ 17 ], three out of 8 respondents reported that their research findings were being applied to local practice, yet no specific details are given. Respondents to the study by Bunn et al. [ 15 ], assessing the impact of NIHR-funded Cochrane reviews, reported that 19 out of 60 of the Cochrane reviews assessed indicated potential to lead to health and health sector benefits. However, the majority were unable to provide evidence if these have led to changes in practice, improved health, improved equity and quality in service deliver, or cost reduction in delivery of existing services. According to Hanney et al. [ 20 ], only a small minority (10%) of the respondents sponsored by Asthma UK claim to have already made an impact in any of the various forms this could take, with 6% believing they had made an impact specifically to health. In addition to the survey, three of the 14 case studies analysed by Hanney et al. [ 20 ] “ described health gains from Asthma’s UK-funded contributions to research on leukotriene receptor antagonists and on immunotherapy for allergic rhinitis, and the potential health gains from research on peptide immunotherapy ”. Case studies were also used by Peckham et al. [ 19 ] to demonstrate a range of ways in which NHS managers and policy-makers have used SDO-funded research to develop service delivery.

Morgan Jones et al. [ 21 ] provide more specific evidence on research findings that have contributed to improved health and improved equity in service delivery nationally and internationally. The evidence in the three studies provided include a reduction of people dying from traumatic injury bleed by administration of tranexamic acid, reduction of post-operative complications by using WHO’s Surgical Safety Checklist and a reduction of the number of people at risk of death by withdrawing co-proxamol.

Two studies, whose primary focus was to estimate the economic benefits of funded research, provided evidence of health gains. Glover et al. [ 24 ], analysing the economic returns from United Kingdom publicly and charity-funded cancer-related research, estimated that there were 5.9 million QALYs gained from the prioritised interventions from 1991 to 2010. Similarly, Guthrie et al. [ 23 ] estimate economic benefits as monetary gains over QALYs. However, a total number of QALYs gained is not provided.

There was one study that showed mixed findings. Lichten et al. [ 25 ] evaluated the impact of the Oxford Biomedical Research Centre, which is funded by the NIHR. They interviewed both research leaders and senior clinicians, and founded interesting differences between the two groups. The research leaders identified a wide range of beneficial impacts that they expected might be felt at local hospitals as a result of their research activity. The senior clinicians responsible for patient care at those hospitals presented a more mixed picture, identifying many positive impacts, but also a smaller number of negative impacts, from research activity, such as duplication of roles.

Four studies reported qualitative improvements in the process of delivery and cost reduction in delivery of existing services. The study by Sainty [ 17 ] suggested that the funded studies assessed have contributed to quality improvements and cost reduction in delivery of existing services. One respondent to the survey reported that work was already being carried out to translate the research findings into commissioning of services by the local trust, which is likely to contribute to more cost-effective service delivery. Another respondent claimed that the implementation of the research findings to clinical practice had the potential to lead to safer and more cost-effective services.

Morgan Jones et al. [ 21 ] also drew attention to the contributions of NIHR-funded research to quality improvements in the process of delivery and cost reduction in delivery of services in a diverse range of areas, including improvement to screening methods in newborn babies, improvement to stroke prevention and reduction of associated costs, implementation of new therapies by reduced cost of antipsychotic medication in dementia patients by implementation of cognitive stimulation therapy, improved health promotion, improved commissioning of services, and prevention of spreading of life-threatening communicable diseases.

According to the interviews they conducted, Guthrie et al. [ 16 ] showed that the HTA Programme is recognised as high-quality funded research and, as a result, more likely to be translated into clinical guidelines, applied to practice and ultimately lead to improved health. Case studies were also analysed and seven out of 12 studies provided evidence that the HTA Programme has contributed to health and health sector benefits, which included improved health, improved equity in service delivery and more cost-effective service delivery. Despite this, is it not possible to infer that health benefits resulted directly as a unique result of the HTA Programme. Some of the studies analysed failed to provide significant evidence on this matter, with the possibility that the final outcome has resulted from a combination of factors, including multiple studies’ findings applied to practice. Additionally, two of the studies concluded that their findings matched the current guidance already in place, limiting the interpretation of the value generated by their research.

Similar challenges were seen in the study by Hanney et al. [ 13 ]. Eight out of the 16 studies reported it was impossible, unlikely or unrealistic to show any health gains, or that the evidence was too limited to show any health improvements. Another six talked about potential health gains and two discussed health improvements.

Broader economic benefits

Six of the included studies assess the economic impact as a result of research findings. Glover et al. [ 24 ] assessed the monetary returns from publicly and charity-funded cancer-related research. They showed that, in 2011/2012 prices, the net monetary benefit of the 5.9 million QALYs gained from the prioritised interventions from 1991 to 2010 was £124 billion. Their estimated internal rate of return incorporated an estimated elapsed time of 15 years. The paper related 17% of the annual net monetary benefit estimated to be attributable to United Kingdom research (for each of the 20 years between 1991 and 2010) to 20 years of research investment 15 years earlier (that is, for 1976 to 1995); this produced a best-estimate internal rate of return of 10%.

Sussex et al. [ 14 ] inferred that there are wider economic benefits from public and charitable R&D expenditure, as well as a correlation between this and private R&D expenditure; every £1 of public research expenditure is associated with an additional £0.83–£1.07 of private sector R&D spend. They show that 44% of that supplementary private sector expenditure occurs within 1 year, with the remainder accumulating over decades. This spill-over effect implies a real annual rate of return (in terms of economic impact) to public biomedical and health research in the United Kingdom of 15–18% and, when combined with previous estimates of the health gain that results from public medical research in cancer and cardiovascular diseases, the total rate of return would be approximately 24–28%.

Morgan Jones et al. [ 21 ] provided evidence from seven studies on the wider economic benefits from commercial exploitation of innovations arising from R&D, through NIHR-funded research at the levels of attraction of private funding for public–private partnerships to further potential, investment in innovative research that leads to development of pioneering advanced prototypes, investment in quantifying impacts of new therapies that can improve patients’ lives whilst reducing current expenditure, and attracting foreign manufactures investment in the United Kingdom for trial of innovative drugs, devices and diagnostics.

Guthrie et al. [ 16 ], in their overall assessment of the HTA Programme, claimed that there is little overlap between HTA and industry and showed that half of the studies showed impact on industry. The team then went ahead to conduct a full economic analysis of the impact of the HTA Programme [ 23 ], finding significant economic impact and arguing that, if 12% of the potential net benefit of implementing the findings of this sample of 10 studies for 1 year was realised, it would cover the cost of the HTA Programme from 1993 to 2012.

Yet, the evidence was not always easy to show. Hanney et al. [ 20 ] provided limited evidence that Asthma UK-funded research brought benefits to the broader economy, though they do mention the development of two spin-out companies in the United Kingdom resulting from this. The study by Sainty [ 17 ] stated that only one out of eight respondents addressed economic impact by claiming that the submission of an economic paper to the British Journal of Occupational Therapy had the potential to generate economic benefits. Yet, there is no clear evidence on how this economic benefit would be generated and to whom or which services.

Funding is absolutely vital for research and funders and researchers are under pressure to show that money is well spent. Our study shows that the number of peer-reviewed papers that explore the impact of publicly and charity-funded research in the United Kingdom is limited, but it is growing. The majority of the studies reviewed (10/13) assessed impact at multiple domains of the Payback Framework. All studies argue that publicly and charity-funded research has a positive impact on most domains. Impact on knowledge was the easier dimension to quantify and measure, using bibliometric techniques such as number of publications and citations generated per project. In other domains, including impact on policy and practice, it is harder to demonstrate impact and evidence is mainly self-reported. However, it is expected that the Research Excellence Framework, a performance-based research funding system of higher education institutions in the United Kingdom [ 26 ], is likely to improve the way impact outside academia is assessed and measured and force researchers to devise better tools to achieve this. Yet, care should be given to avoid the selection and reporting bias that current Research Excellence Framework cases are likely to present when reporting impact.

The majority of the studies presented moderate to high risk of bias. All studies had received funding from the body they were assessing, raising concerns about the potential tendency of these studies, whether real or perceived, to support the interest of the funder. This is a concern raised by the authors of some of these studies [ 26 ], calling for more independent funding streams for research evaluation. In addition to funding bias and perhaps not unrelated to it, eight out of 13 studies showed high risk of reporting bias, highlighting the aspects or domains that were more likely to show high impact than those that did not. This may well reflect that certain domains are harder to measure, and should not undermine the fact that some of these studies put a significant amount of resources and effort in analysing cases, interviews and bibliometric databases and evidence of triangulation of different methodologies [ 13 , 16 , 21 ]. Finally, seven out of 13 studies had a high risk of selection bias, focusing purposively on cases, which were likely to create impact, therefore tending to overestimate the general impact of funded research. The use of purposive sampling may well be considered the best way of providing evidence of funded research “ which has generated benefits to and wider impacts on health ” [ 21 ] and the authors were very transparent in stating that in their reports. As Morgan Jones et al. clearly put it, their “ study was commissioned as a synthesis of impacts and benefits, not an evaluation ” [ 21 ] and acknowledge this as a limitation of their study.

Our review is not without limitations. We included in our analysis only peer-reviewed studies to ensure that the studies had been subject to criticism and met the standards for scientific publication. This means that we excluded studies that may be reporting on the impact of funded research but were not peer reviewed. Indeed, some funders themselves put significant effort to celebrate their successes, including, for instance, the Medical Research Council’s annual impact reports [ 27 ] and the Wellcome Trust’s system of tracking the career of its fellows [ 28 ]. We also acknowledge the challenge of extracting precise information from all studies, given the heterogeneity and nature of some of them. As an example, the study by Morgan Jones et al. [ 21 ] included 100 case studies showing impact on different aspects of the Payback Framework, which made the summary and synthesis of results difficult. In addition, we chose to look at the literature from 2006 onwards, as the launch of the NIHR that year was seen as a significant change in public funding of research in the United Kingdom. This choice means that we may have missed out on impact assessment studies that were conducted before 2006. Finally, we acknowledge that our choice of journals to hand-search was likely to be restricted and a wider range of journals could have been added.

Our results could be of potential interest to public and charity funders. The limited number of studies identified in this review highlights the need for a more systematic collection of data that will enable them to show that their investment offers value for money, in particular in areas where evidence is harder to establish such as policy development and wider societal effects. Significant effort towards that direction has been made in the last decade, including the introduction of Researchfish ® [ 29 ], a comprehensive digital platform to collect research outputs and other elements of wider impact produced by each research project funded by a large number of United Kingdom research organisations. These platforms are not without caveats, as they rely on self-reporting of outcomes, but they have certainly helped to embed the idea that at the end of a project impact should be both measured and reported. Other funders have developed spreadsheets to allow researchers to report specific outputs arising from research projects supported by the funder [ 28 ].

There is recognition that research activities have a degree of risk and unpredictability [ 30 ] and that scientific knowledge is often obtained in the process of trial and error. As a result, thorough assessment of health research impact can be encountered with some criticism and interpreted as “ neglecting the inherent value of science ” [ 31 ]. Nevertheless, some degree of evaluation of funded research is needed to ensure transparency and accountability as research funding entities are subject to growing pressure to demonstrate the impact generated by their funded research. Our study suggests that there is still space for improvement.

Additional files

Main databases search strategies. (DOCX 97 kb)

Full papers for review. (XLSX 12 kb)

Data extraction table. (DOCX 29 kb)

Acknowledgements

Not applicable.

DG addresses recognition and gratitude to Health Education North Central & East London (HENCEL) for their funding support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

Abbreviations, authors’ contributions.

CS conceived the study. Both authors contributed to the design. DG performed the search of all electronic databases. Both authors contributed to the writing of the manuscript, critically reviewed it and contributed important intellectual content. All authors have read and approved the final manuscript as submitted.

Ethics approval and consent to participate

Consent for publication, competing interests.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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