Today we publish our third deal-level data release, this time up to December 2016. It includes Big Society Capital’s deals as well as social investment deals made by other investors and arranged by intermediaries which have not received our investment.
In our last publication, we had c.700 transactions from 30 contributors and have now increased that to c.900 transactions from 33 contributors. In addition, over 1,000 transactions from Key Fund’s pre-2016 loan book are included. We recognise that all of the 1,970 transactions listed here brings together the considerable work of intermediaries, charities and social enterprises, and the additional resource requirement of providing data. We are grateful for their combined efforts to increase broader understanding of how social investment is being used.
It's worth noting there are still some fields with incomplete data. In future deal-level data releases, we will aim to fill in these gaps as well as add new data points. We would be interested to hear what you’d like to see in future data releases. We think turnover and asset data for the frontline organisations receiving investment would be helpful additions, as well as repayment and return data from an investor perspective.
As with previous data releases, there are a number of caveats one needs to take into account when looking at the data. These include but are not limited to:
- Data gaps: There are some data gaps as organisations might not capture information as per the data fields in this dataset.
- Overlap and duplicates: As we are capturing transactions from a social investor into or that benefits a charity or social enterprises, there could be several transactions involved in a deal. There will also be some overlap between some transactions, as the activity of arrangers (such as ClearlySo) are counted separately to the activity of fund investors. Of the 1,970 transactions listed, 293 are duplicates.
- Repayment: In this publication we have not captured the transactions that have been repaid but will be doing so in future publications
- Investment date: We have asked fund managers, banks and other intermediaries to provide the contract signing date to capture all investments that are available to charities and social enterprises to access. This is not always the date captured by the fund managers and other intermediaries and therefore the data contains a mix of committee approval dates and contract signing dates.
- Value: We have chosen to capture the value of the investment commitment at the point of investment committee approval. In some investments the value drawn down by the charity or social enterprise varies from the initial commitment and we were unable to capture that information.
- Outcomes Matrix: A number of our funds, banks and arrangers do not classify the loans they have made according to Big Society Capital’s Outcome Matrix and we have therefore had to match with those categories which may not be a perfect alignment. Where organisations were unable to provide the outcome areas and the names of investees were provided we made our best attempts at identifying the outcome areas.
- Direct investments into intermediaries that would classify themselves as social enterprises are included in this dataset (e.g. our £15m commitment to Charity Bank) and therefore is likely to impact the numbers.
In addition to the deal level data we are also publishing the metrics investees are using to measure their impact. We ask this of all our investees, and by making the data public, we hope it will be used by organisations addressing similar social challenges to learn from their peers. The publication of this data will also create the framework for us to report on organisations’ impact in the future.
There is still a long way to go in improving the availability and quality of data in social investment and we look forward to working with our stakeholders to continue making progress on the challenge of data in the market.
In the next few weeks, we will publish a visualisation of this data, and that of our own portfolio, to help increase understanding and to highlight some of the interesting stories that the data tells.