Suitable statistical approaches for novel policies: spatial clusters of childcare’s services in Veneto, Italy

More and more often, policymakers face complex problems that require suitable information obtainable only from the "intelligence of data." This can be obtained by analyzing several data sets (many of high dimension) and adopting suitable, often "sophisticated," statistical models. Here we deal with policies for affordable and quality childcare, essential to balance work and family life, increase labor market participation, promote gender equality, and fight against fertility decline. Understanding the complex dynamics of demand and supply of childcare services is challenging due to the nature of the data: high-dimensional, complex, and heterogeneous nationwide. Considering the Italian case, this complexity and heterogeneity are partially due to the lack of governance at the regional level leading to immediate and effective new policies challenging. This paper aims to analyze the multidimensional aspect of the supply-demand of childcare services combination in the Veneto Italian region using a novel statistical approach and an innovative dataset. We apply the regionalization approach (a clustering method with spatial constraints) to give an immediate picture of childcare services' supply and demand variability. Our empirical findings confirm how the Veneto region is described by many "sub-regional models," providing a preliminary attempt to demonstrate how socio-demographic factors drive these patterns.