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

The term disability pensions covers a variety of forms of income support to people with disability, which differ according to institutional arrangements within the various countries. We specify in paragraph 4 which data we use to investigate the situation in Italy.

Throughout the paper we generically talk about government to indicate intervention of the public sector, without specifying the geographical level of the public intervention, which may vary among countries.

The Human Development Report (UNDP 1990) presents the Human Development Index (HDI) as a three-dimensional indicator of well being. Throughout this paper we use the three components of the HDI separately, as for Italy the component Income is highly correlated and dominates the other two. See “Appendix” 2 for evidence.

For the importance of environmental factors in affecting the employment of disabled people see Agovino and Rapposelli (2012, 2013a, b).

We are interested in the regional distribution of the incidence of civilian disability pensions, not in the expenses for it. But the spatial link among regions about public expenditures is likely to exist among regions about the incidence of civilian disability pensions.

“Everything is correlated, but nearer things are more correlated than things further apart” (Tobler 1970).

In “Appendix” 1 we show the contiguity matrix used in the econometric estimates.

The region-specific variable, time-invariant and activated by regional dummy, captures how each region deviates from the average structural relationship common to all regions (the regional fixed effects).

The time-specific variable, activated by time dummies, is useful to clear the structural relationship, which is common to all regions, from cyclical variations that are also common to all regions.

The Lagrange Multiplier (LM) tests check for a spatially lagged dependent variable and for spatial error autocorrelation; the robust LM tests check for the existence of one type of spatial dependence conditional on the other. A mathematical derivation of these tests for a spatial panel data model with spatial fixed effects can be found in Debarsy and Ertur (2010). These tests are based on the residuals of the non-spatial model with spatial fixed effects and follow a Chi squared distribution with one degree of freedom. If a non-spatial model is estimated without any fixed effects or a non-spatial model with both spatial and time-period fixed effects, the residuals of these models can be used instead (Elhorst 2010c).

Both tests follow a Chi-squared distribution with K degrees of freedom. For further clarifications on the building of Wald tests see Elhorst (2010c).

Following the literature, we only concentrate on secondary and tertiary education, as literacy and primary education do not seem variable applicable to developed economies. Also following the literature we attribute a higher weight, equal to 2/3, to tertiary education, and a lower weight, i.e. 1/3, to secondary education (Marchante et al. 2006).

In some disadvantaged areas of the USA the impact of crime on health is very strong, and considered one of the main causes of ill health (Minkler 1992).

In paragraph 5.2 we test for possible endogeneity between this variable and the dependent variable.

We list Southern Italy regions: Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia, Sicilia and Sardegna. Other regions belong to the North of Italy (Emilia-Romagna, Friuli-Venezia Giulia, Liguria, Lombardia, Piemonte, Trentino-Alto Adige, Valle d'Aosta e Veneto) and to the Centre of Italy (Lazio, Marche, Toscana e Umbria).

The choice to use a contemporary MIGRATION variable was determined by the difficulty of identifying the correct temporal lags which may have provided significant estimates in the econometric analysis. We could have proceeded by trial and error, but this procedure would have been methodologically rough. More properly, we could have used a P-VAR (Vector Autoregressive model for Panel data), which, through the impulse response analysis, would have allowed us to identify the years in which the impact of the MIGRATION variable on ICDP, the dependent variable, would have been statistically significant. After that we would have run a regression analysis with the appropriate lags for MIGRATION. This exercise would have complicated too much the analysis in this paper, but we may consider it in future work.

The problem with this hypothesis is to link disability only to the concept of “disability at birth”, omitting the presence and the action of environmental factors; consequently, it would exclude the causes of disability arising during the lifetime.

We use a set of instrument variable L(X, WX, W ² X,), that is, regressing WICDP on L(X, WX, W ² X,) and ICDP _(t-1) on L(X, WX, W ² X,); where, X are the regressors (except LIFE) with W² we indicate the second-order contiguity matrix (Anselin 1988). In the case of temporally lagged dependent variable, ICDP_(t-1), we use as additional instrument the temporal lag of second order of dependent variable (ICDP _(t-2) ). Even though the Granger test has verified the non endogeneity of the variable LIFE, we instrument this variable with the same instruments used for WICDP and ICDP _(t-1) (see Hsiao 2003).

The consistency of the GMM estimator requires that there is no serial correlation of the second order in the differenced error term.

Initially, HDI was calculated adopting the arithmetic mean of the three indicators. More recently, the geometric mean has been adopted; this produces lower index values, with the largest changes occurring in countries with uneven development across dimensions. The geometric mean has only a moderate impact on HDI rankings. The HDI based on the geometric mean takes into account differences in achievement across dimensions. Poor performance in any dimension is now directly reflected in the HDI, which captures how good a country’s performance is across the three dimensions. That is to say, a low achievement in one dimension is not any more linearly compensated for by high achievement in another dimension. The geometric mean reduces the level of substitutability between dimensions and at the same time ensures that a 1 % decline in index of, say, life expectancy at birth has the same impact on the HDI as a 1 % decline in the education or income index. Thus, as a basis for comparisons of achievements, this method is also more respectful of the intrinsic differences across the dimensions than an arithmetic average.

The test have been conducted for a different number of lags. Here we report the results in which the regressors are significant.