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Identifying the Quality of Work by Fuzzy Sets Theory: A Comparison Between Disabled and Non-disabled Workers

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Abstract

The paper assesses the quality of work of people with and without disabilities in Italy using the ISFOL PLUS (Participation, Labour, Unemployment, Survey 2010 Questionnaire, where the data refer to 2009. In particular, we develop a multidimensional indicator of quality of work within the fuzzy set theory. The results of the investigation show a different mechanism of determinants of quality of work for disabled and non-disabled people: while for these last ones seniority seem to highly contribute to the score of quality of work, institutional factors, like Law 68/99, whose aim is the regulation and promotion of the employment of persons with disabilities, appear to play a bigger role in the determination of the score for quality of work for disabled people. For medium and high levels of score of quality of work, education appears to play a similar role for disabled and non-disabled people, as the incidence of people with high quality jobs corresponds to people with a high level of education. However, for disabled people who are in low quality jobs the level of education appears to be irrelevant. Substantial differences emerge with respect to gender among disabled people, where women appear to be in higher quality of work scores than men; no substantial difference between genders emerges for non-disabled people.

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Notes

  1. Chiappero Martinetti (2000) distinguishes between vagueness and ambiguity, defining vagueness as a concept associated with the difficulty of making sharp distinctions in some domain of interest, and ambiguity as a concept related to situations in which the choice between two or more alternatives (that are well defined) is left unspecified. The difficulty of making sharp distinctions makes the fuzzy theory suited to the solution of problems characterized by imprecision (in the sense of vagueness).

  2. It is not strictly necessary from a technical point of view that highly collinear variables be excluded. For instance, if two perfectly collinear variables were included in the composite, with weights w 1 and w 2, then the particular dimension of performance which they measure will be included in the composite with the weight (w 1 + w 2). This is not problematic if the weights have been chosen correctly (Jacobs et al. 2004, pp. 34–35).

  3. A crisp set traditionally assigns a value of either 1 or 0 to each element in the universal set, discriminating, in this way, between members and non-members of the crisp set (Chiappero Martinetti 2000).

  4. The choice of the proper number of principal components takes place on the basis of three criteria which take into account their explanatory power. First we consider a number of principal components which take into account at least 95 % of the variance of each of the k initial variables, which imposes a minimal threshold; second, we keep all the principal components whose eigen value is larger than 1; third, we observe the screen plot of the eigen values as a function of the number of principal components; as eigen values are obtained in decreasing order, the graph will show a decreasing curve, with a kink in correspondence to the proper number of principal components. In particular, on the basis of the results of the analysis, we choose only three principal components.

  5. For more details go to: http://www.isfol.it/temi/Lavoro_professioni/mercato-del-lavoro/plus.

  6. For a review of the literature see for instance Sloane and Jones (2011).

  7. The EU-SILC data do not have a specific question to identify disability, but they provide information on daily activity limitations, as answer to this specific question of the EU Questionnaire:

    “What is your state of health? (1) Temporary or partial reduction in autonomy, (2) Continuing reduction in autonomy; (3) No particular problem.” It follows that the identification of disabled people with EU-SILC data is in the spirit of the social model (Mitra 2008), for which disability derives from impairments affecting the functioning and activity of the individual (whatever its origin: congenital, work accident, ageing, etc.), as stressed by the International Classification of Functioning, Disability and Health of the World Health Organization (WHO 2001).

  8. An extensive literature discusses the pros and cons of self reported data. For instance, for Bound and Burkhauser (1999), and for Gannon (2005), self reported data on disability are likely to be distorted because of possible systematic interactions between health, disability, and the situation in the labour market. Econometric models have been proposed trying to overcome these potential problems (see for instance Kreider (1999); Kreider and Pepper (2007). The best defence in favour of the wide use of self reported data on disability is their accurate predictive power.

    .

  9. The definition of these three classes of equal width is justified by the rather symmetric distribution of the scores of the index of the quality of work; in fact, the distribution of scores well approximates the normal distribution (see Fig. 5, in Appendix 2); the approximation is not so good for people with disabilities because of the relatively limited sample size, compared to non-disabled people (see Figure 6 in Appendix 2).

  10. On gender wage differential see for instance Gunderson (1989), Kunze (2000) and Río et al. (2011); specifically on wage discrimination and disability see Longhi et al. (2012).

  11. For works related to different factors affecting the job satisfaction of men and women see for instance Kaiser (2007), Addabbo and Solinas (2012).

  12. For a comprehensive survey of the literature on this point, see Jones and Sloane (2012).

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Acknowledgments

This paper is part of the 2009 PRIN project “Measuring human development and capabilities in Italy: methodological and empirical issues” financed by the Italian Ministry of Education, University and Research.

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  1. Department of Economic Studies, University “G. d’Annunzio” of Chieti-Pescara, Chieti, Pescara, Italy

    Massimiliano Agovino & Giuliana Parodi

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  1. Massimiliano Agovino
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Correspondence to Massimiliano Agovino.

Appendices

Appendix 1

See Tables 7 and 8.

Table 7 Membership function
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Table 8 Value of the weights (w 2 , w 3)
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Appendix 2

See Figs. 5 and 6.

Fig. 5
figure 5

Index of quality of work, non-disabled people

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Fig. 6
figure 6

Index of quality of work, disabled people

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Agovino, M., Parodi, G. Identifying the Quality of Work by Fuzzy Sets Theory: A Comparison Between Disabled and Non-disabled Workers. Soc Indic Res 119, 1627–1648 (2014). https://doi.org/10.1007/s11205-013-0568-4

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