Modelling geographical variations in fertility and population density of Italian and foreign populations at the local scale: a spatial Durbin approach for Italy (2002–2018)

Abstract

Studies on fertility determinants have frequently pointed to the role that socio-economic, cultural and institutional factors play in shaping reproductive behaviours. Yet, little is known about these determinants at an ecological level, although it is widely recognised that demographic dynamics strongly interact with ecosystems. This research responds to the need to enhance the knowledge on variations in fertility across space with an analysis of the relationship between fertility and population density of Italians and foreigners in Italy at the municipal level for the period 2002–2018. Using global and local autocorrelation measures and a spatial Durbin model, we show that there is a negative association between the fertility and population density of the Italian population, while the density of foreigners is correlated with higher fertility. This second result poses new insights on the relationship between space and fertility. Moreover, we find that the features of neighbouring areas, measured by population density, contribute significantly to explaining spatial fertility variation, confirming the importance of the study of spatial diffusion in demographic processes.

Neighbourhood effects and determinants of population changes in Italy: A spatial perspective

“…It is important to clarify that these spatial lag models are designed to produce indirect evidence of diffusion in cross-sectional data (as in our case). This type of model has recently been used in other demographic studies that investigated the geographical variation in fertility and international migration in Spain (Sabater and Graham, 2019); the diffusion patterns of fertility in Italy at the sub-regional level (Benassi and Carella, 2022;Vitali and Billari, 2017); and the geographical variation of mortality rates in the United States (Yang et al, 2015).…”

“…We chose this type of spatial weighting matrix for several reasons. First, the use of this matrix is very common, and it is often adopted when statistical units refer to a local scale (Benassi and Carella, 2022;Iglesias-Pascual et al, 2022;Salvati et al, 2020;Yang et al, 2015). Moreover, contrary to other approaches to spatial weighting, such as the K-Nearest Neighbour (K-NN), we did not have to decide on an arbitrary number of neighbouring municipalities.…”

“…Moreover, contrary to other approaches to spatial weighting, such as the K-Nearest Neighbour (K-NN), we did not have to decide on an arbitrary number of neighbouring municipalities. It should be noted that this kind of spatial weighing matrix is the same as that applied in two previous studies on the same topic (e.g., Benassi et al, 2021;Benassi and Carella, 2022), which can be considered the progenitors of this paper. Therefore, using the same spatial matrix guarantees the coherence and the comparability of the results of these studies.…”

A Multiversal Model of Vibration of Effects of the Equitable and Sustainable Well-Being (BES) on Fertility

“…The first two are already mentioned in Section 3.2: frgn rate and marrg rate. The rate of foreigners is consistently positively correlated to an increase in fertility in Italy, to the point that the literature suggests that foreigners sustain a healthier age pyramid in Italy (Strozza et al, 2016;Benassi and Carella, 2022). The relationship between marriage and newborns is more ambiguous because is routed in cultural schemes that may change over time.…”

The Local Regression Approach as a Tool to Improve Place-Based Policies: The Case of Molise (Southern Italy)

“…With reference to Italy, especially in recent years, several papers have appeared that approach demographic processes as spatial phenomena and, therefore, directly consider the spatial dimension in the measurement and interpretation of such processes (Benassi & Carella, 2023;Mazza & Punzo, 2016;Salvati et al, 2020;Vitali & Billari, 2017). Some of these contributions have focused in particular on the study of the factors that influence population change on a local scale (Benassi et al, 2022(Benassi et al, , 2023a, some with reference to the depopulation process (Reynaud et al, 2020).…”