Spatial variability of total fertility rate and crude birth rate in a low-fertility country: Patterns and trends in regional and local scale heterogeneity across Italy, 2002–2018

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)

“…Nevertheless, for an analysis conducted at national level it may represent a serious limitation in understanding the intimate relationship between population density and fertility. This is because both indicators are characterised by a very deep and important geographical variability that needs a control at a microscale territorial level (Burillo et al 2020;Salvati et al 2020). With regard to the second aspect concerning the results attained, all studies on this topic agree in finding a negative effect of population density on fertility and therefore, they confirm the importance of using this variable as a crucial determinant in modelling fertility processes and dynamics (Lutz et al 2005(Lutz et al , 2006Lutz and Qiang 2002;Testa 2003).…”

“…Most of the literature on this topic has examined spatial variations in fertility on different geographical scales, highlighting the diffusion of its low level in Europe (Arpino and Tavares 2013;Burillo et al 2020;Campisi et al 2020). Some authors have focused on the determinants of a decline in birth rates, pointing to the role played across space by economic (Salvati et al 2020), socio-cultural and political factors (Campisi et al 2020;Vitali and Billari 2017). Conversely, the research on the possible predictors of fertility at the ecological level remains inconsistent.…”

“…In both studies, the territorial level of analysis was the provincial one, that is to say a territorial level intermediate between the local (municipality) and the regional. Another recent work using Morans' indices to measure spatial fertility dependence at different territorial levels (Salvati et al 2020) suggests that the spatial variation of fertility reflects its response at the local level to economic expansion and recession over the last decade. This study was conducted both at provincial and municipal (local) levels but without any 'causal' approach, i.e.…”

Impact of Covid‐19 on the convergence of GDP per capita in OECD countries

“…However, there are other contexts that have deserved the consideration of academics who have carried out studies related to spatial dynamics, such as those from the following countries: Russia (Balash et al, 2020), Belarus (Celbis et al, 2018), Mexico (German-Soto & Brock, 2015, Romania (Goschin, 2017), China (He et al, 2017), Great Britain (Henley, 2005), Tunisia (Labidi, 2019), the Iberian Peninsula , the United States (Rey & Montouri, 1999), Colombia (Royuela & Adolfo Garcia, 2015) and Brazil (Silveira-Neto & Azzoni, 2006). Spatial autocorrelation approaches have also been considered in other assessments, such as the following: personal insolvency (Bishop, 2013), ripple effect on housing values (de la Paz et al, 2017), technical efficiency (Ezcurra, Iraizoz, & Rapun, 2008), transport infrastructures (Gao et al, 2019), economic growth efficiency with low carbon (Ju & Zhang, 2020), pollutant emissions (Li et al, 2018), food inflation (Liontakis & Kremmydas, 2014), eco-efficiency , rental housing , transport efficiency (Ma, Wang, Sun, Liu, & Li, 2018), sulphur dioxide emissions (Nan et al, 2020), fertility rate (Salvati et al, 2020), homicides and personal damages (Santos-Marquez & Mendez, 2020), interregional migration (Sardadvar & Rocha-Akis, 2016), diabetes incidence/ prevalence (Shrestha et al, 2016), carbon emissions (Su, 2020), educational standards (Tselios, 2008) and energy efficiency (Zhang et al, 2017). Some of the analyses related to spatial issues found increasing returns to scale (Dall'Erba et al, 2008), based on the developments associated with the Verdoorn law (Angeriz et al, 2008), and identify processes which promote spatial asymmetries (Cracolici et al, 2007) through agglomeration or polarization dynamics.…”

“…Spatial autocorrelation approaches have also been considered in other assessments, such as the following: personal insolvency (Bishop, 2013), ripple effect on housing values (de la Paz et al, 2017), technical efficiency (Ezcurra, Iraizoz, & Rapun, 2008), transport infrastructures (Gao et al, 2019), economic growth efficiency with low carbon (Ju & Zhang, 2020), pollutant emissions (Li et al, 2018), food inflation (Liontakis & Kremmydas, 2014), eco‐efficiency (Liu et al, 2020), rental housing (Liu et al, 2020), transport efficiency (Ma, Wang, Sun, Liu, & Li, 2018), sulphur dioxide emissions (Nan et al, 2020), fertility rate (Salvati et al, 2020), homicides and personal damages (Santos‐Marquez & Mendez, 2020), interregional migration (Sardadvar & Rocha‐Akis, 2016), diabetes incidence/prevalence (Shrestha et al, 2016), carbon emissions (Su, 2020), educational standards (Tselios, 2008) and energy efficiency (Zhang et al, 2017).…”