Purpose – The purpose of this paper is to provide the first large data-set regression analysis to test Kerlin’s (2013) macro-institutional social enterprise framework in relation to the country social enterprise models that flow from it. Kerlin (2013) offers a conceptual framework for country social enterprise models that allows countries to retain their unique understanding of social enterprise and better understand the factors influencing its development. Design/methodology/approach – This paper draws on the theory of historical institutionalism and multiple global datasets to test formal hypotheses on the relationship between macro-institutional factors and the size of the social enterprise sector across countries. Social enterprise data were obtained from the 2009 Global Entrepreneurship Monitor dataset. Hypotheses were tested using logistic hierarchical linear modeling. Findings – Results provide support for the framework at a significant level. Nearly half of the variance in the size of the social enterprise sector can be attributed to countries-level factors. We also find that the size of the social enterprise sector varies by economic competitiveness rank, size of the welfare state and collectivist cultural orientation. Research limitations/implications – The countries included in this study are not representative of the global landscape. Researchers are encouraged to test the framework with a more representative sample of countries, including those in the Global South. Practical implications – The paper includes implications for policy makers and researchers seeking to facilitate cross-regional dialogue, the transfer and support of social enterprises and research. Originality/value – This paper fulfills an identified need to advance the field of social enterprise by quantitatively testing established frameworks.

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Initiatives to map nonprofit organizations encompass efforts to define the boundaries of the sector and understand its scope and scale. As new technologies make it possible to digitize and analyze information in new ways, further questions about mapping civil society emerge. We integrate nonprofit scholarship, critical work on computational methods, and reflection on our experiences using machine learning to map nongovernmental organizations in Ghana, to develop a critical framework for mapping civil society in the digital age. The issues we raise about computational methods are embedded within greater concerns about the taken-for-granted assumptions in mapping civil society, and mapping as a tool to control, manage, and manipulate civil society. We are particularly attentive to the power within mapping as a mode of knowledge production.

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