Human Development and the Determinants of the Incidence of Civilian Disability Pensions in Italy: A Spatial Panel Perspective

Abstract

The aim of this paper is to study factors which may affect the incidence of civilian disability pensions in Italy from a macroeconomic point of view, taking into account all age groups, and introducing spatial effects by taking into account possible interaction among the policies of individual regions. The analysis is developed on a panel of the 20 Italian regions over the period 2002–2008. Education, life expectancy, and the environment appear to reduce the incidence of civilian disability pensions; spillovers in the policies of neighboring regions are connected with the ageing of the population, and with the environment. Policy recommendations are developed.

Appendices

Appendix 1

See Table 4.

Table 4 Contiguity matrix

Appendix 2

2.1 HDI: Construction and Some Results

In order to calculate the HDI we proceed in two steps (Saisana and Tarantola 2002; Freudenberg 2003; Jacobs et al. 2004):

• in the first step, we normalize the variables which make up each indicator; in particolar, for each variable we calculate the ratio between the observed value of each variable and its minimum with respect to its range of variation, i.e. difference between the maximum and minimum, of each variable (Boyle and McCarthy 1997; Mazumdar 1999; Marchante et al. 2006; Marselli and Vannini 2006):

$$Z_{ij} = \frac{{X_{ij} - \hbox{min} X_{i} }}{{\hbox{max} X_{i} - \hbox{min} X_{i} }}$$

where X indicates the original values observed for each variable, Z are the normalized variables, j and i indicate respectively the regions and years for which we normalize; in this way the variables are transformed into an indicator ranging between 0 and 1.

• In the second step, we calculate the HDI by combining the three normalized indexes above mentioned, after weighing each of them by 1/3, and aggregating by the geometric mean.²⁰ (Klugman et al. 2011):

$$HDI_{ij} = I_{{life_{ij} }}^{{{1 \mathord{\left/ {\vphantom {1 3}} \right. \kern-0pt} 3}}} * I_{{education_{ij} }}^{{{1 \mathord{\left/ {\vphantom {1 3}} \right. \kern-0pt} 3}}} * I_{{income_{ij} }}^{{{1 \mathord{\left/ {\vphantom {1 3}} \right. \kern-0pt} 3}}}$$

Analysing the correlations among the three variables, INCOME, EDUCATION, LIFE and the HDI a high correlation emerges (higher than 0.70) with available income per capita (Table 5). This indicates problems of redundancy (McGillivray 1991; McGillivray and White 1993), so that HDI will appear to be led mainly by the variable income. In order to overcome this problem, in the regression analysis we consider separately the three components of HDI, so catching the impact of each of them on ICDP.

Table 5 Correlation matrix

Appendix 3

3.1 Granger Causality Test for Panel Data

The procedure used to test the causal relationship in a panel data set was proposed by Holtz-Eakin et al. (1988). The Granger causality test for panel data is presented as follows:

$$y_{it} = \alpha_{0} + \sum\limits_{j = 1}^{m} {\alpha_{j} } y_{it - j} + \sum\limits_{j = 1}^{m} {\delta_{j} } x_{it - j} + f_{i} + \varepsilon_{it}$$

(i)

where i = 1,…,N are the observation units and t = 1,…,m is the time index. The model in differences allows us to eliminate the FE (f _i )

$$y_{it} - y_{it - 1} = \alpha_{0} + \sum\limits_{j = 1}^{m} {\alpha_{j} } \left( {y_{it - j} - y_{it - j - 1} } \right) + \sum\limits_{j = 1}^{m} {\delta_{j} } \left( {x_{it - j} - x_{it - j - 1} } \right) + \left( {\varepsilon_{it} - \varepsilon_{it - 1} } \right)$$

(ii)

This specification introduces a simultaneity problem because the error term is correlated with y _{it  −} y _it-j−1. In this case a consistent estimate can be obtained using the two-stage instrumental variables method (2SLS).

In order to verify if x causes y it will be necessary to test the joint hypothesis H ₀:δ ₁ = δ ₂ = ⋯ = δ _m  = 0. If the null hypothesis is rejected then x Granger causes y.

Below we report the Granger test to verify the direction of causality, in the version for panel data (Holtz-Eakin et al. 1988).

The Granger²¹ test performed on ICDP and LIFE shows that LIFE Granger-causes ICDP (regression 1, Table 6). In particular, we observe that the null hypothesis of the Granger test which assumes that LIFE does not cause ICDP is rejected at 1 %.

Table 6 Granger Causality Test

Conversely, when we verify the opposite hypothesis which ICDP Granger-causes LIFE, the Granger test does not reject the null hypothesis; consequently, it is clear that ICDP does not cause LIFE (regression 2, Table 6).