Access to insurance for European SMEs: Patterns, clusters, and policy implications

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Type of the article: Research Article

Abstract
The study aims to explore the insurance profiles of SMEs and to identify access gaps across different firm categories, using the Ipsos European Public Affairs survey dataset, which consists of 8,187 SMEs from Europe. This dataset was analyzed using multiple correspondence analysis, cluster analysis, and discriminant analysis. Results show clear evidence of ownership concentration in a few insurance products: commercial motor (64.3% of SMEs own such an insurance), general liability (54.4%), and workers’ compensation (46.1%). On the other hand, uptake is lowest for cyber insurance (15.3%), non-damage business interruption (14.5%), and commercial insurance with business interruption (20.3%); notably, 8.2% report no insurance product ownership. Furthermore, three behavioral clusters were identified: minimally insured (n = 3,604; mean 1.58 policies), moderately insured (n = 2,603; mean 5.22), and broadly insured (n = 1,980; mean 6.73). Also, portfolios exhibit structured “baskets” with frequent co-ownership of general liability and motor (40%). Findings document systematic, demography-linked disparities and actionable access gaps. The study concludes that persistent disparities in access are linked to firm size, age, and turnover, underscoring the need for tailored policy measures and market solutions to address inclusion gaps. The practical value of this study lies in providing evidence-based insights for insurers, regulators, and policymakers seeking to expand SME risk protection.

Acknowledgment
We gratefully acknowledge EIOPA for providing access to the SME insurance dataset used in this study. 

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    • Figure 1. Correlation between policies and principal dimensions in multiple correspondence analysis
    • Figure 2. Policy space and quality of representation (cos2) of policy categories (yes or no), in multiple correspondence analysis
    • Figure 3. Cluster membership in a two-dimensional space of the correspondence analysis
    • Table 1. Sample demographics: age, total turnover, SME category
    • Table 2. Insurance products that SMEs own, or do not own but consider important
    • Table 3. Proportion (%) of SMEs that own each insurance product, within the SME categories, Age and Total Turnover; number of owned policies (in total) across categories
    • Table 4. Proportion of SMEs (%) with two specific insurance products, along with the number of SMEs with 0, 1, …, 12 policies (in total)
    • Table 5. 40 most owned combinations of three or four insurance products
    • Table 6. Number of owned products (in total), across clusters
    • Table 7. Clusters and insurance products
    • Table 8. Clusters vs demographics
    • Table A1. Two specific questions of the questionnaire used in this study
    • Conceptualization
      Nikolaos Daskalakis
    • Project administration
      Nikolaos Daskalakis
    • Writing – original draft
      Nikolaos Daskalakis, Fotios S. Milienos
    • Writing – review & editing
      Nikolaos Daskalakis, Fotios S. Milienos
    • Data curation
      Fotios S. Milienos
    • Methodology
      Fotios S. Milienos