Abstract
Following the rapid and intense decrease after the “babyboom”, today Italy is one of the nations with the lowest fertility rate, In recent time the Total Fertility Rate (TFR) was equal to 1.35, very close to the so-called “lowest low fertility” threshold. These low values of fertility not only impact on the population’s age structure, but also on social welfare systems since the increasing share of elderly needs more financial aid while the support from the working age population decreases. But is this decline at the national level? Do some determinants of the TFR act more in a specific territory?
As we know the role of the determinates on fertility is largely unexplored at the territorial micro-level in Italy. So in this paper, through the use of a Semi-parametric Geographically Weighted Regression (S-GWR) model, we try to investigate the impact of the socio-economic determinates on the provincial TFR in Italy.
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Notes
- 1.
We recall that most fertility in Italy occurs in religious marriage.
- 2.
- 3.
In this analysis we take only formal unions (civil and religious marriages).
- 4.
We recall that Moran’s I ranges from −1 to +1 and tends to 0 if there is no relationship between the value of a certain location and the average value of neighboring location. A positive Moran’s I indicates high spatial autocorrelation, which implies that values in neighboring positions tend to cluster together. A low negative Moran’s I is an indication that high and low values are interspersed. The values of p-value (p) indicate the significance level of the null hypothesis “no spatial autocorrelation” (see Anselin 1988).
- 5.
Although there are several options for the estimation methods of bandwidth, the fixed kernel function was employed because it fits the best to this model.
- 6.
In fact, the Moran’s I test on the residuals after fitting OLS model suggests that there is strong signal of spatial autocorrelation among the residuals (Moran’s I = 0.41; p-value < 0.00; Spatial Weight Matrix = inverse distance). So the independence assumption of the error term appears to be violated.
- 7.
Positive value of GVT suggests no spatial variability in terms of model selection criteria (see Nakaya 2015 for more details).
- 8.
The variables utilized in the OLS model have low values of multicollinearity. The Variance Inflation Factor (VIF) for all the variables is about 2.
- 9.
This process is generally referred to as the postponement of childbearing which is the central focus of timing studies in fertility research. We know that OECD countries have witnessed a rising mean age at first birth since the 1970s, coupled with an increasing proportion of births among mothers at advanced ages (Balbo et al. 2013).
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Mucciardi, M. (2021). Local and Global Analysis of Fertility Rate in Italy. In: Popkova, E.G., Sergi, B.S. (eds) "Smart Technologies" for Society, State and Economy. ISC 2020. Lecture Notes in Networks and Systems, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-030-59126-7_52
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