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14 January 2019

19 Pages

Ambiguity in the Attribution of Social Impact: A Study of the Difficulties of Calculating Filter Coefficients in the SROI Method

,
and
Department of Business Management, Faculty of Economics and Business, Universidad Nacional de Educación a Distancia, UNED, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.

Abstract

In order to analyse, manage, and compare social projects we need, among other things, to be able to measure their impact. One of the methodologies currently used to measure and manage social impact is Social Return on Investment (SROI). However, not all the results calculated by the SROI method are directly attributable to the project, and, therefore, to determine the real impact it is necessary to filter out the changes that the project has not produced. Filter coefficients perform this function. However, the theoretical logic on which the chain is constructed that converts the outputs into impacts is ambiguous. In this study, we will analyse twenty-five real cases where SROI was used to measure social projects. We will identify the difficulties of isolating and measuring impacts by performing a comparative study of the procedures that entities develop to calculate the filters. This allows us to calculate the impacts from the outputs. We will then propose the improvements needed to overcome these shortcomings.

1. Introduction

In recent years, Third Sector of Social Action organisations are facing ever-increasing demands for information about the social and environmental impact they generate. The various groups affected by the activity of these organisations, including investors, consumers, funders, employees and the public administration, are aware that in order to assess entities in the Third Sector of Social Action (TSSA from now on), they need information that provides a holistic picture of their activity that is not limited to financial indicators. These interested parties have started to put pressure on the entities to disclose information about their social and environmental performance [1,2].
The various interest groups of TSSA organisations can exercise this control in a formal and direct way, through evaluation requirements included in contracts, or indirectly, through rules that individuals and organisations must follow in order to gain approval and the verification that their activity meets the established requirements [3]. Therefore, TSSA organisations are conditioned by the influence of the logics and rules of other groups [4].
These demands have led to an increase in sustainability reports by TSSA organisations. Therefore, does this increase mean an improvement in the transparency of TSSA entities? Previous research has concluded that the content and depth of sustainability reports varies significantly between entities [5]. There are even studies that reflect a lack of quality in these reports [6,7,8,9].
There is a need for a standardised methodology for monitoring and evaluation that integrates, in plain and easily understood language, the internal information needs for learning and development in the organisation, with the information demanded by external interest groups to account for their performance [10,11,12].
One of the external groups that applies the most pressure is the funders [13], meaning that in order to obtain the necessary resources to develop their activity, TSSA organisations need to communicate and account for what they do and how they do it [14,15]. It is not enough to show information that is favourable to the organisation or project; the institutional pressures exerted on the third sector mean that the demand for transparency is very high [15,16,17].
In parallel, and at an internal level, these entities, in addition to meeting the economic, social and environmental objectives required of them, must prepare, process and communicate a large quantity of information that meets specific requirements, both to practise effective management, and to control their results and possible deviations [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18].
This explains the relevance of the dependence of TSSA entities on their main financial backers, and how this dependence entails providing information to backers on their activities and results in a particular format. Increasingly, funders require formal methodologies for measuring social impact that generate objective metrics with an empirical basis, with the aim of improving the standardisation, verifiability and accountability of these metrics [19,20,21,22].
However, the measurement of social impact is an extensively questioned practice, owing to, among other reasons, the ambiguity that methodologies for measuring impact suffer from. A frequent object of criticism is the lack of logic of the underlying assumption within most methodologies of a causal chain or logic model through which the activities of the entity are translated into results, changes and impacts for each of the stakeholders [2,23]. In contrast, practitioners with experience in implementing such formal methodologies often stress the causal ambiguity of this chain; they contend that impacts are difficult to understand with precision, much less calculate [2,24,25].
One of the methodologies currently used to measure and manage social impact is the Social Return on Investment method, or SROI, which, based on traditional cost-benefit analysis, quantifies social value through indicators associated with the results achieved. SROI is a process of understanding, measurement, management and communication of the social, environmental and economic values generated by an organisation. Its purpose is to examine, qualitatively and quantitatively, the project’s process of generating social value.
Where impact is caused by a range of factors, it is difficult to determine the amount of social value that can be attributed to the activity of the project that is being measured. To cope with this and determine the real impact of the project, filter coefficients are introduced. However, their estimation has some shortcomings [4,26,27]. Even though the filters are perfectly defined within the SROI methodology, when we fix them at a certain percentage, we cannot find a method or reference that enables these filters to be calculated with precision and ease [28,29,30]. We can see that these coefficients have a different accuracy depending on the person who conducts the study of measurement, their interests and ability to access the information. Since the function of these filter indexes is the correction of the result to reach the impact, their effect on the impact is key. This is why improving the construction of the coefficients that filter the outcomes to calculate the impact is essential for accuracy in the measurement of social value.
In this study we investigate the following research questions: what are the difficulties in calculating the filter coefficients of the SROI and how to develop the necessary improvements to overcome these difficulties. We explore these questions through a comparative case of real SROI analysis of organisations from different sectors and geographical areas.
This article critiques the legitimacy of the SROI method to measure the social impact created by organisations, calling into question the underlying theoretical logic between outputs and impacts. While outputs can be managed by organisations, exogenous factors that escape their control influence results and impacts. Therefore, the attribution of results to a particular actor can be very difficult [2,24,31]. Through the analysis of the reports of SROI measurement of twenty-three social projects, and the identification of the gaps in the underlying theoretical logic between outputs and impacts, we will study the calculation of the filter coefficients of social impact and identify their shortcomings.
We will consider an impact to be those results that can be attributed directly to the organisation or project in question. To arrive at this concept, it is necessary to filter the changes, taking away that which has not been produced by this project, so that the organisation only claims what it has created itself as a social impact. To do this, we must introduce these elements: deadweight, displacement, attribution and drop-off [27] (p. 54).
Deadweight deals with quantifying whether the project has really influenced society or whether, on the contrary, the result we see is a product of other external interventions. In other words, this coefficient responds to the question of the extent to which the result would have been achieved if the project being evaluated had not taken place [27] (p. 56). Displacement studies what percentage of the change achieved has displaced other changes. If the project consisted in helping people at risk of social exclusion to get a job, displacement would seek to quantify whether these jobs were obtained to the detriment of another worker. If so, the quantification of this circumstance has to be removed, which will diminish the impact of our initiative [27] (p. 57). Attribution measures the percentage of changes that is not attributable to carrying out the project. It indicates the need to deduct, in the calculation of the impact, the percentage of the result that has been caused by the contribution of other organisations or people [27] (p. 59). Drop-off is the decline in the results of a project over time, which could be compared to the depreciation on a fixed asset due to use. In other words, this coefficient analyses how long the results have lasted [27] (p. 61). It is logical to think that in the years after completing the project, the quantity of the result obtained will diminish, and therefore the attribution to this project will diminish [18].
The next section outlines the methodology that was followed throughout the study. The third section is a comparative report of the cases studied during the research. The fourth and last section identifies the deficiencies detected in the study. This is the central body of our research because it allows us, through the above analysis, to draw conclusions on the results. We will warn of the shortcomings that must be addressed in order to eliminate the ambiguity of the causal chain of logic between outputs and impacts, and also provide a measurement that contains the real value that a project generates, integrating both social and economic impacts to give a result that can be standardised, verifiable, accountable, comparable and understandable to others.

2. Materials and Methods

An ISI web of knowledge and Scopus online literature search was conducted for the years 2007–2016 for articles and conference papers on SROI analysis. Key words used included: “SROI”, “Social return on investment” in the subject area “Social Sciences”. We searched in all fields of the articles using the above-mentioned key words separately on the date of 31st October 2016. We obtained the following results: 23 results for “SROI”; 36 results for “Social Return on Investment”.
In addition, we conducted a search by entering the above terms in “Google Scholar” and the documents, reports, book chapters, articles and practical guides were also checked, and relevant documents included in the review. We decided to include non-academic material in the study material, as SROI is a methodology that is debated more frequently in professional environments than in academic ones. Where relevant to the discussion on suitable theories, articles and papers based on the methodological approach of literature review [32,33,34,35] were also added, given that the empirical studies and surveys are still limited, especially in the scientific literature.
The most relevant documents were examined and on that basis we chose the most important ones for the study. In particular, with reference to the selection process, we verified the presence of a consideration of the filter coefficients of the SROI method and its importance for the objective of the analysis. In total, twenty-three case studies from this set have been analysed to identify possible irregularities in the calculation of the filter coefficients:
  • A program aimed at combating loneliness and isolation of the elderly in Scotland.
  • Conservation of the natural environment surrounding the Greenlink area in Scotland.
  • A project providing access to water to families who do not have access to other water sources in Stockholm.
  • Promotion of healthy lifestyle habits for overweight people in Bristol.
  • The Solvatten project provides household water treatment in developing countries (Kenya).
  • Benefits of interaction with the rural environment for people with problems of socialisation or behaviour in England.
  • Benefits of Tai Chi, in the forest, in patients of the Hospital Firth Valley in Scotland.
  • A rehabilitation program based on an intensive program of activities for people with bone marrow injury in Australia.
  • A programme for the development of adolescent women in areas where unemployment, poverty and lack of education are rife in Northern Ireland.
  • A program that informs local women about health issues in an informal way in Northern Ireland.
  • A project to provide a minimum income for rural families living in semi-arid areas in Ghana.
  • A project to support, through job creation, people with mental health problems in Glasgow.
  • Helping parents who care for children with disabilities develop their skills, endurance, and confidence in the UK.
  • Make organically grown food accessible to the citizens of Sydney.
  • Use art as a catalyst to bring about positive and lasting change in people’s lives in North Ayrshire.
  • A family intervention project that works with families with children and in situations of high vulnerability in Northamptonshire (England).
  • Evaluation of the social impact of a “social initiative protected employment centre”.
  • A project to create a cafeteria for 30 elderly or disabled persons who are provided with transportation to the cafeteria in England.
  • The Oxford Castle Renovation Project.
  • Respond to complaints about antisocial behaviour in the area caused by the increasing volume of unsupervised and unauthorised bicycles in Scotland.
  • Restoration of gardens, with the participation of volunteers from the municipality and students in Scotland.
  • Use of a natural space for outdoor activities with the mentally ill in Scotland.
  • Evaluation of the social return of investment in the creation of the Ecoclub school in Scotland.
The cases belong to different sectors and geographical areas to avoid the inaccuracies that the sample selection bias could generate in the conclusions. They are briefly described in Appendix A. Through a comparative study of the procedures followed to calculate the coefficients in each one of the projects, we have identified the shortcomings of SROI methodology in this area. The study ends with a proposal for improving the construction of these filter coefficients that make up for the shortcomings identified and respond to the needs for accuracy in the measurement.

3. Results

Before the SROI calculation can be finalised a decision has to be made as to how long the changes produced by the analysed project will last. In an SROI analysis the length of time changes endure is considered so that their future value can be assessed. The question to be answered is “if the activity stopped tomorrow, how much of the value would still be there?” This is dealt with by assuming that the value will reduce or drop off a percentage each year. As well as considering how long changes will last, it is necessary to take account of other factors that may also have impacts. The recorded change might have happened regardless of the activity, something else may have made a contribution to it or the activity may have displaced changes taking place elsewhere. In considering the influence these factors exert, a realistic approach should be adopted. The aim is to be pragmatic about the benefits actually provided by the project and to recognise that the value it creates is influenced by other factors. The SROI methodology does this by reducing the value calculated for each outcome on a percentage basis, applying several filter coefficients: deadweight, attribution and displacement.
A reduction for deadweight reflects the fact that a proportion of an outcome might have happened without any intervention. Attribution recognises that external factors, or the contribution of others, may have a part to play in the changes that are identified and displacement applies when one outcome is achieved but at the expense of another outcome, or another stakeholder is adversely affected.
This section presents an analysis that compares each of the filter coefficients of the social projects that, when analysed, produced the most significant results. There are four tables in total: deadweight (Table 1), displacement (Table 2), attribution (Table 3) and drop-off (Table 4). Our objective is to make the study of the various ways of calculating these coefficients more visual, and to allow for a more exhaustive comparison.
Table 1. Deadweight.
Table 2. Displacement.
Table 3. Attribution.
Table 4. Drop-off.

4. Discussion

The analysis allows us to observe that the main shortcomings of the filter coefficients are related to the heterogeneity in the estimation. Even though all the cases studied share the same conception of these filters, the way in which the percentage is obtained in each case is very different, as summarised in Table 5.
Table 5. Filters coefficients comparison (I: Collection of information C: Calculation of filters).
One of the obstacles we encountered is that the influence of the subjectivity of the people responsible for the identification and subsequent quantification of information is very high. The estimates are approximations, which calls into question the accuracy of the evaluation of impact. Accuracy in measurement is one of the aims of the social impact evaluations. However, the estimates of the filter coefficients involve a wide margin of error and can produce erroneous interpretations and uses of the ratios.
In most of the cases considered in this analysis, the information that is used to make these estimates comes from interviews with the project’s interest groups. In these interviews, which normally use questionnaires, the participants report on an individual basis, in accordance with their personal assessment. Therefore, the information that is incorporated into the measurement is fundamentally based on value judgements and not on objective data.
The calculation of the coefficients also supports various possibilities. Some cases associate, to each result for each interest group, a percentage of deadweight; other cases associate a percentage for all the results of each group, and others make a general estimate of the whole project. However, the majority of the cases arrive at these percentages through logical deduction or reasoning, which generates an added difficulty, since this reasoning does not always follow the same pattern.
If the quantity of the filter coefficients is high, it is assumed that the project is not of great benefit on its own, which is why, in the majority of cases, analysts set small percentages. Several of the cases studied allocate a deadweight figure (Table 1 and Table 5) of under 10%; one of them (case 5) even considers that there is no deadweight for any of its results. This means that the project recognises that without its activity, none of the results that participants have experienced would have been achieved. On the other hand, given the absence of certainty, some projects try to avoid this problem and establish a representative percentage. Cases 6 and 11, for example, use this methodology. Some projects consider that the changes observed would not have been possible if the project had not taken place, but assign a 5% deadweight figure to avoid an overestimation of the impact.
Other cases follow a different methodology to perform these calculations and establish references to comparison groups or benchmarks. For example, case 2 bases its estimates on official surveys. In this case, the calculations offer greater objectivity, since the same method could be applied for similar programs. This would make such projects directly comparable with each other.
In the analysis of deadweight there is another estimation system that consists of calculating an average of the information obtained in individual interviews with the interest groups (case 15 as example). That is to say, the individual results are added up and then divided by the number of participants. This system is based on the assumption that the groups that participate in the same programme tend to have similar personality profiles, which is why, to avoid errors of subjectivity, an average of all the information obtained is taken.
Another important element in the calculation of impact is displacement (Table 2 and Table 5). Again, the main shortcoming that this coefficient presents is that it is based on estimates. Most of the projects do not take into account the displacement that their activity can generate. This claim is based on the idea that from among the twenty-three cases analysed, only twelve of them consider a certain percentage of this indicator, while the remaining eleven do not identify any type of displacement.
The fact that this coefficient does not exist in most of the projects is a limitation to homogenizing the calculation of impact. Of the cases that contemplate the existence of displacement, a small portion of them consider its effects to be minimal, and use a percentage of below 20%. Cases 2 and 4 go over a 50% displacement rate in some of their results; however, their estimate is based on the discussions held with participating agents. Considering whether a project displaces its results to other activities is not very complex; the problem lies in what percentage it is best to assign to this displacement.
Thirdly, we analysed attribution (Table 3 and Table 5). All the cases studied allocated a percentage for each of the results, and estimates in the twenty-three cases were based on the declarations of the interest groups.
The last correction factor is drop-off (Table 4 and Table 5), although some projects do not regard it as such. The drop-off indicator is used to record whether the result obtained is maintained, or not, over time, and is only calculated for results that last for more than a year. In the study we observe a number of different procedures to calculate it. One of them is to deduct a fixed percentage each year after the completion of the project. Another procedure used is to arrange an interview with the interest groups some time after the completion of the project. However, this system can imply a self-selection bias because the people who will be willing to be interviewed are likely to be the people who are the most satisfied with the results of the project.
Within the cases analysed, we would like to highlight, for their originality, cases 8 and 14. Both cases unify their assessment criterion of the filter coefficients, dividing each result into five categories. In this way they get the interest groups to identify more clearly which percentage of deadweight, displacement, attribution or drop-off is more in line with the results that they perceive.

5. Conclusions

In our analysis we have highlighted some of the difficulties in standardising calculations of filter coefficients, since the social benefits generated by projects are primarily intangible, and difficult to estimate. The main objective of this study has been to reflect the differences in unifying criteria, the shortcomings of the methodology and the difficulty in achieving more consistent estimates. If we could obtain more homogeneous filter coefficients, it would be possible to compare the return on investment of several social projects and know which of them generates greater value.
Along these lines, we perceive clearly that an effort is required to homogenise the calculation of filter coefficients in order to: (1) Minimise, as far as possible, the degree of subjectivity that they support; (2) reinforce their quantitative character against other qualitative interpretations; (3) provide them with a universal character; by standardising them they can be used in a general way and similarly in other projects and (4) incorporate the time value in their estimation and analysis to check their development, make comparisons over time and corroborate their diagnosis. In this way they would become even more useful tools for making financial and social decisions.
In view of the results obtained, we propose some ways forward or recommendations that, as well as improving measurement and evaluation, constitute new avenues of research, to continue refining and improving these coefficients and thus obtain more valuable measurements:
(1)
Create evaluation bands or quantification stages, forcing the filters to take discrete values (in bands or stages). A good example is case 8, which, as we have seen, structures its percentages on a scale that goes from lowest to highest in terms of the influence of the project on the result obtained. By establishing standard bands, we can reduce variation and subjectivity and get more easily comparable values. Intermediate values can be included within the bands, establishing the characteristic of the band, and adjusting once within it.
(2)
Stipulate some standard or average values for certain common or standard situations. This would help spread uniform and unambiguous criteria to be used in the various analyses. For example, adjust deadweight to a certain percentage when dealing with individual exceptions (case 9 is a good example, where individual recoveries, not attributable to the project, were given a deadweight figure of 5%).
(3)
Identify comparison populations, like in case 11, where a comparison group or control group was introduced. The data collected for these groups represent the deadweight for the SROI. It was a set of stakeholders outside the project’s area of influence, since they did not participate in the project’s activities, but had equivalent characteristics.
(4)
Homogenise, as far as possible, the timeline for the project. Projects with a duration of under six months make it very difficult to quantify the coefficients appropriately. Durations of over three years are equally difficult to quantify, given the high probability of exogenous events and spurious variations beyond the control of the analysis. Setting durations or timelines for analysis of 6, 12, 24 and 36 months (allowing intermediate periods of 6 months) seems a sensible approach, with time periods that allow results to be compared and analysed better. In this way we will get filters with more accurate and comparable results, and in particular, the calculation of the drop-off will have much greater value and significance.
(5)
Identify reference estimates, benchmarks, of universal validity as the basis for calculating the filters. This happened in case 2, where to filter the result of the project for the volunteers, they used the rate of volunteers in North Lanarkshire as a deadweight percentage according to a survey of households in Scotland.
(6)
Define standard rules in the valuation of the coefficients of certain interest groups. For example, groups that provide funding. Establishing certain rules in the quantification of the filters and their importance in these groups, not being the main stakeholders, can facilitate their homogeneous, common quantification.
Applying these recommendations to one of the analysed cases, the Craft Café case, results that allow a proper comparation with to other SROI cases can be obtained. The recommendations guarantee a unified assessment criterion of the filter coefficients, and the percentages of deadweight, displacement, attribution or drop-off are more in line with other analysis, as detailed in Table 6. In any case, this re-evaluation has only illustrative purposes, as the case does not provide all the information from the interviews and the whole process should be re-done to guarantee the accuracy of the re-evaluation. In our analysis we have taken an in-depth look at filter coefficients in the calculation of SROI through an exhaustive review of twenty-three relevant projects in the social sphere.
Table 6. Craft Café case. Re-evaluation.
For the drop-off, the case recognised that they do not have research data available to establish the drop-off rate. Therefore, they have assumed a drop off percentage of 33% for this SROI analysis. The drop-off should be re-evaluated as 50%. Regarding the displacement, there is no evidence for this that justifies an inclusion of displacement in the impact results. As conclusion. the level of standardisation in the evaluation has been increased.
By virtue of its analysis, this article is a contribution to research in this field, and provides different contributions for both scholars and practitioners. Specifically, we have managed to: (1) make known the cases themselves, presenting a panoply of examples of successful social projects that can serve as models of good practice and inspiration for starting similar projects promoting the social economy; (2) study these projects in a systematic way, considering the methodology, results and study of the filter coefficients, in a concise and accessible way; (3) systematise the review of the calculation and comparison of the filter coefficients, allowing a comprehensive view, through the summary and analysis of these results; (4) observe some relevant facts and significant trends in the form and results of the calculation of these filter coefficients, identifying gaps and problems in their evaluation, and, finally, (5) suggest, in view of the situation observed, some recommendations and improvements in the road towards homogenising the method of calculation of the filters, as a necessary step for their implementation and success, which in turn open up new avenues of research.
The quantitative assessment of social impact, despite the conceptual and methodological difficulties that we have seen, is of great value for society. Its fixing and dissemination are important tasks, but there is still a long way to go. This work hopes to make a small contribution to this field by taking an in-depth look at the calculation and improvement of filters, which are, ultimately, coefficients defined to make the calculation of social value more accurate, precise, credible and usable.

Author Contributions

Conceptualisation, M.S.-G.; Data curation, J.N.-M.; Investigation, M.S.-G.; Methodology, M.S.-G. and J.N.-M.; Supervision, M.S.-G.; Writing—original draft, M.S.-G.; Writing—review & editing, L.M.R.-G.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Caption.

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