In the past two decades, the number of social businesses has been increasing across Europe. Today more than 13.9 million employees in the EU work for social enterprises. Momentum is also building globally. The Schwab Foundation estimates that about 30 million jobs in the social economy were created in Sub-Saharan Africa in 2020 and forecasts the global impact investment market to increase to $1.8 trillion by 2030. Social enterprises are not only characterised by their aim to create social and economic value, but also by organisational peculiarities and structural complexities resulting from their work on the public-private nexus and the diversity of areas, tasks, and forms of cooperation.
Organisational and management studies provide important insights into specific social enterprises and startups and their characteristics. But there is still a shortage of comprehensive, comparative empirical data that reflects the economic situation and organisational and operational decisions made by these enterprises. Empirical methods in academia and evidence-based policymaking are becoming noticeable in the field of social entrepreneurship research. In practice, this improves the understanding of barriers and enabling factors for better policies and informed investment decisions.
Collecting empirical data on social enterprises
Some European countries like the UK, Germany, and the Netherlands have gathered data on social enterprise and startups and others are currently following. Internationally, the World Bank and the Global Entrepreneurship Monitor are providing databases and transnational research collaborations such as the “Social Enterprise as Force for more Inclusive and Innovative Societies” (SEFORÏS) project.
In 2019 the Euclid Network, together with its partners, launched the European Social Entrepreneurship Monitor (ESEM), a cross-country survey covering a range of questions from the characteristics of companies, their financial situation, and stakeholder integration to the impact of COVID-19 and the different perceptions of barriers and enabling factors. The survey aims to close the data gap in social entrepreneurship and provide information to researchers and policymakers.
Understanding barriers for social entrepreneurship
We used the ESEM 2021 data in a small research project combining different models and data types. We conceived the data collection, combination, and analysis as a learning effort, obtaining the following results:
Perceptions. Appreciations of institutional barriers, especially economic, financial, and network ones, are critical to how social entrepreneurs evaluate overall support for their work. However, these perceptions don’t seem to depend necessarily on their actual economic and financial situation. We need to understand better the variation of risk assessments between the sectors in which social enterprises are active, and how they are influenced by cultural contexts. To support social entrepreneurs, policymakers should focus on improving perceptions of social entrepreneurship. These aspects need further investigation, integrating both sociological and economic evidence,
Network. Social enterprises and startups are closely intertwined in a network of partners, clients, and beneficiaries. In theory, complex structures can also present barriers. However, qualitative studies, as well our data work, show that social enterprises are good at accelerating the power of networks. One of the interesting factors in this context is the extent to which these organisations involve their beneficiaries in the service delivery process. This underlines the specific nature of social entrepreneurship. By creating a good governance environment and by supporting networks financially and structurally, we can help improve the co-creation and service delivery of social enterprises.
Gender. The gender perspective is increasingly finding its way into science and policymaking. Data collection must be designed accordingly and gender-specific factors should be controlled for. In our small project, we used the percentage of women on the boards of social enterprises and startups and found potential differentiations. While more targeted data would have been useful, we find that gender influences perceptions. Along with other tailored questions, the voluntary query about gender in a survey is indispensable. Policymakers should make use of data to design targeted programmes to tackle the specific barriers women or LGBTQI+ entrepreneurs face, such as access to private capital, borrowing, and family support.
National contexts. When conducting cross-national and comparative analyses, it is important to stress the national context. Although social enterprises and startups can operate internationally, they are always initially subject to a specific jurisdiction and political framework, especially in the seed phase. The same applies to public funding options, which these organisations still heavily rely on, and which are often bound to regional investment programmes. Other national factors, such as the macroeconomic situation and the public recognition of social entrepreneurship, differ significantly. When working with cross-country data, at some point country subsets or dummies are used. While this strategy also made differences visible in our study, the goal is to look behind national categories. It would therefore be sensible to explore different categories, build clusters and feed these into larger structural equation models or other statistical methods. With this approach, we can detect policy similarities and differences better and improve sharing and learning between policymakers.
Learnings for better data collection
When working with different types of data, it is important to design surveys in a targeted and user-friendly way. Using the ESEM, Eurostat, or World Bank databases makes it clear that comparability is key. Panel data is valuable but challenging, as they provide comparable data on a relatively fixed sample over a period. It is crucial for both researchers and policymakers to invest time and energy in survey design and to develop international standards and categories that facilitate comparability.
The potential of data-driven policymaking for social entrepreneurship
The use of quantitative data for exploring complex contexts in the field of social entrepreneurship has great potential for policymaking. It creates large-scale comparability and reveals measurable differences within and between categories and countries. This provides more transparency and the opportunity to share best practices to reduce barriers to social entrepreneurship.
Quantitative data, especially panel data, help us draw initial conclusions about the performance of specific policies. The use of surveys also shows the possibility for targeted feedback, which can and should be enriched with qualitative insights. Without the expectation of representative samples, databases such as ESEM can be used to identify initial trends and integrate them into policymaking in a timely and targeted manner, without having one’s hands tied until the results of the next policy evaluation cycle.
Next to that, it is also relevant for other stakeholders, such as network partners and impact investors, to understand the barriers to social entrepreneurship better, and to improve network structures and funding opportunities.
