Segmenting citizens according to their self-sufficiency: A tool for local government

Identifying subgroups of citizens with varying levels of self-sufficiency in a large local or regional population provides local government with essential input for providing matching services and well-grounded spending of health and well-being expenditures. This paper identifies self-sufficiency levels of citizens by segmenting a broad adult population. We used data from a citizen survey based on a randomly selected response group containing questions on a wide range of topics, including finances, health and living conditions, and complemented these data with registration data, including information on housing type and household composition. We conducted a latent class cluster analysis using six indicators: perception of making ends meet, perceived health, quality of life, self-efficacy, access to social support and social network. High scores on the indicators translate to high levels of self-sufficiency. We used a biased-adjusted, three-step approach to characterise the segments. Six meaningful segments were identified and labelled as ‘highly self-sufficient,’ ‘self-sufficient – medium access to social support,’ ‘self-sufficient – medium self-efficacy,’ ‘moderately self-sufficient – low self-efficacy & high social network,’ ‘moderately self-sufficient – low access to social support/social network & high perceived health’ and ‘not self-sufficient.’ At a macro level, perception of making ends meet and quality of life have discriminating value in assessing self-sufficiency. For a more detailed differentiation between groups with similar levels of self-sufficiency, perceived health, self-efficacy, access to social support, and social network are valuable indicators. Overall, this study introduces a comprehensive tool to assess self-sufficiency in larger groups of citizens by using a parsimonious number of indicators. Local and regional governments can apply this tool to effectively assess the self-sufficiency levels of their population and signal potentially vulnerable groups. In this way, the tool makes the identification of self-sufficiency levels of larger populations more feasible and more efficient and can be widely adopted in different contexts.