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Childhood and capability deprivation in Italy: a multidimensional and fuzzy set approach

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Abstract

In the last decades, starting with the United Nations Convention on the Rights of the Children in 1989, children’s quality of life have received a growing attention in scientific research, as well as in politics. This work aims to gain insights into Italian children’s living conditions and deprivation of capabilities using the Capability Approach, an alternative normative framework for the evaluation of human development, well-being and freedom by thinking in terms of human functionings and capabilities. From a methodological point of view we present an approach based on a fuzzy methodology applied to data from the EU-SILC 2009 ad-hoc module on children. The use of this methodology makes it possible to preserve the richness of the data available from the EU-SILC survey, that include both monetary and non monetary aspects of children deprivation. To get more insides into Italian children living conditions we also combine the fuzzy methodology with the capability approach at a disaggregated level of analysis by three social economic factors (single parent household, household educational level, macro-region of residence). Besides the well-known Italian North/South disparity of financial indicators—confirmed also for households with children—our findings suggest a new duality for Italian children quality of life, given by the multidimensional domains of deprivation internal or external to children’s households.

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Notes

  1. The focus lies on the NUTS-1 level according to the Nomenclature of territorial units for statistics, which may provide useful insights into living conditions.

  2. All capabilities together correspond to the overall freedom to lead the life that a person has reason to value (Sen 1999; Robeyns 2003, p. 63).

  3. For Nussbaum’s list of basic human capabilities see Nussbaum (2011, pp. 33–34).

  4. Nussbaum’s effort to define specific capabilities has been a subject of criticism which has led to the development of alternative potential lists suitable for capabilities’ measurement (see Biggeri et al. 2004; Robeyns 2003; Di Tommaso 2007; Terzi 2007; Wolff and De-Shalit 2007; De Neubourg et al. 2012). Although all these efforts are interesting to review, it cannot be disputed that all lists end up with similar capabilities to the ones Nussbaum has on her’s. Considering the minor differentiations, the aforementioned studies constitute a verification of Nussbaum’s core capabilities list.

  5. This methodology was also implemented in the S.A.M.P.L.E. (Small Area Methods for Poverty and Living Condition Estimates) project, funded by the European Commission (see http://www.sample-project.eu/).

  6. The traditional poverty approach is characterized by a simple dichotomization of the population into poor and non poor defined in relation to some chosen poverty line that represents a certain percentage (generally 50, 60 or 70 %) of the mean or the median of the equivalised income distribution.

  7. Qizilbash and Clark (2005) express their concerns on the use of methodology proposed by Cheli and Lemmi (1995). Chiappero Martinetti (1994; 2000) has used this approach in the Italian context many times.

  8. The weights \(w_{hj}\) are composed by two factors: the dispersion of each elementary item j and its correlation with the other items in the given dimension h.

  9. It is worth noting that \(s_{j,i}\) is a score function, thus it is defined in a reversed order with respect to the membership function previously defined.

  10. Although the inclusion of the financial domain in our analysis may raise some criticism, we believe that to be able to deal with the unexpected dues at household level can give a more comprehensive picture of children’s well-being in Italy. Studies on the financial capability are gradually gaining attention (i.e. Johnson and Sherraden 2007; Zakaria and Sabri 2013). “It is crucial in modern society that people have the ability to understand, assess and act in their best financial interests” (Johnson and Sherraden 2007). For this reason, we include also a financial dimension in our analysis. This choice does not affect results for the other capabilities. It means that the findings remain the same if we drop this dimension.

  11. Two items (not affording (if want) a personal car and accessibility of public transport) related with the capability of being able to move freely from place to place had to be excluded due to the low Cronbach’s alpha value (<0.05) (Chronbach 1951).

  12. This choice is reasonable to the extent that households without children in particular age classes are not deprived referring to specific items that involve only children in such specific age classes.

  13. Measuring items at the household level implies that the respective achievements are, in principle, accessible to all members of the household and recognises the difficulty of inferring differences in access to household resources using household survey data (Klasen 2000).

  14. To quantify the degree of intercorrelations among the items and the appropriateness of factor analysis, the Measure of Sampling Adequacy (MSA) and partial correlations among items were calculated (Hair et al. 2009). Overall MSA was 0.90 and the MSA of single items ranged between 0.70 and 0.93, justifying the application of factor analysis. The result was validated by the low values observed in the partial correlations among items that were all much lower than 0.3. Small partial pairwise correlations between the variables after checking all other variables compared to the original correlations confirmed that some underlying structure existed in the set of selected items.

  15. The two items are “Internet connection” and “Invite friends round to play & eat from time to time”.

  16. However the final domains, where these two items have been eliminated, had also the second highest factor loading according to EFA.

  17. Families are classified based on the major income earner (the person with the highest income in the household). The household educational level has been defined through the International Standard Classification of Education (ISCED). Individuals whose attained educational level is lower than the ISCED level 3 are classified as low-educated while individuals whose ISCED level is greater than 3 are classified as high-educated.

  18. Data from dati.istat.it.

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Acknowledgments

This work was supported by the European Union FP7 project InGRID "Inclusive Growth Research Infrastructure Diffusion", GA no. 312691, http://www.inclusivegrowth.be.

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Authors and Affiliations

  1. Faculty of Human and Social Sciences, Bergische University of Wuppertal, Gausstrasse 20, 42119, Wuppertal, NRW, Germany

    Antoanneta Potsi

  2. Department of Management and Quantitative Sciences, Parthenope University of Naples, Via Generale Parisi 13, 80132, Naples, Italy

    Antonella D’Agostino

  3. Department of Economics and Management, University of Pisa, Via Ridolfi 10, 56124, Pisa, Italy

    Caterina Giusti

  4. ISTAT-Regional Office-Florence, Lungarno Colombo, 54, 50136, Florence, Italy

    Linda Porciani

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  1. Antoanneta Potsi
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Potsi, A., D’Agostino, A., Giusti, C. et al. Childhood and capability deprivation in Italy: a multidimensional and fuzzy set approach. Qual Quant 50, 2571–2590 (2016). https://doi.org/10.1007/s11135-015-0277-y

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