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 selfsufficiency. 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 selfsufficient.' 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 selfsufficiency, 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.