Early childhood services are characterized by substantial variability in Italy, and notable differences can be observed between and within regions. Traditionally, the level of service coverage is measured by the ratio of available places to potential resident users. However, this metric can be biased, as it does not account for citizen mobility or cross-territorial agreements, such as shared management partnerships among municipalities. For instance, in zones without day-cares, parents may be required to enroll their children in facilities outside their residential area, and some municipalities may fund services provided in neighboring areas. As a consequence, the conventional raw coverage rate is often underestimated. This paper proposes a methodology that adjusts for user flows between different territories, refining the assessment of the number of individuals who can access the service. Adjusted data are incorporated into a spatial Poisson regression model to estimate service coverage levels across representative Italian regions, providing a more accurate fit of service supply and demand.
Zanotto, L., Aliverti, E., Caldura, F., Campostrini, S. (2025). Service coverage level considering user mobility: spatial modelling of Italian day-cares. STATISTICAL METHODS & APPLICATIONS, Online first (04 November 2025), 1-17 [10.1007/s10260-025-00814-z].
Service coverage level considering user mobility: spatial modelling of Italian day-cares
Zanotto L.
;
2025
Abstract
Early childhood services are characterized by substantial variability in Italy, and notable differences can be observed between and within regions. Traditionally, the level of service coverage is measured by the ratio of available places to potential resident users. However, this metric can be biased, as it does not account for citizen mobility or cross-territorial agreements, such as shared management partnerships among municipalities. For instance, in zones without day-cares, parents may be required to enroll their children in facilities outside their residential area, and some municipalities may fund services provided in neighboring areas. As a consequence, the conventional raw coverage rate is often underestimated. This paper proposes a methodology that adjusts for user flows between different territories, refining the assessment of the number of individuals who can access the service. Adjusted data are incorporated into a spatial Poisson regression model to estimate service coverage levels across representative Italian regions, providing a more accurate fit of service supply and demand.| File | Dimensione | Formato | |
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