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

ORI GIN AL ARTICLE

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

Massimiliano Agovino • Giuliana Parodi

Accepted: 30 December 2013� Springer Science+Business Media Dordrecht 2014

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 Ques-

tionnaire, where the data refer to 2009. In particular, we develop a multidimensional indi-

cator 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.

Keywords Quality of work �Multi-dimensional concept � Fuzzy set theory �Disabled people

1 Introduction and Review of the Literature

… it is important to bear in mind that preferences and choices underlying both

objective and subjective indicators of human well-being are vague (p. 1)… Fuzzy

M. Agovino (&) � G. ParodiDepartment of Economic Studies, University ‘‘G. d’Annunzio’’ of Chieti-Pescara, Chieti, Pescara, Italye-mail: [email protected]

G. Parodie-mail: [email protected]

123

Soc Indic ResDOI 10.1007/s11205-013-0568-4

sets theory permits a meaningful representation of ambiguous and vague objects or

outcomes. (p. 5) (Baliamoune-Lutz 2004).

This paper investigates the quality of work of disabled people in Italy, comparing it with

that of non-disabled people. We chose this topic as disabled people are likely to have

specific problems on the job, both related to the personal relationships with colleagues and

supervisors, and to the way in which tasks and equipment are organized; also, disabled

people are the target of specific employment policies (see for instance Law 68/1999), and it

is important to assess whether these specific employment policies succeed in creating jobs,

but also in creating good jobs; finally, we consider the emphasis on quality of work

particularly important for disabled people, for whom employment can be an empowering

aspect of self fulfilment, in addition to being a source of income. Our line of investigation

is innovative with respect to the existing literature on the quality of work, which so far has

not considered the quality of work of disabled people.

In what follows we provide a short review of the literature on job quality.

The concept of work quality has jumped to the forefront of institutional concern since

the 2000 Lisbon declaration, which advocated ‘‘sustainable economic growth with more

and better jobs’’ (UE 2000). Also, there are two main documents by international orga-

nizations on the theme of the quality of work; the European Commission’s document:

‘‘Employment and social policies: a frame work for investing in quality’’ (European

Commission 2001), based on objective aspects, and also the 2008 ILO Declaration on

Social Justice for a Fair Globalization, based on objective but also subjective character-

istics of job quality, which recommends the establishment of appropriate indicators to

monitor and evaluate the progress made in the implementation of the ILO Decent Work

Agenda (ILO 2012). Both documents are based on the multidimensional aspect of the

quality of work. The reasons of the interest on job quality are at least two fold: the first

deals with workers’ job satisfaction, and fulfilment on the job, and is therefore an important

tool to monitor the progress of the development of the labour market of a country (for a

review see Dahl et al. 2009); the second emphasizes the impact of quality work on

productivity, through the channel that job satisfaction increases commitment to the tasks

implemented. Economists have investigated ways of assessing the quality of work, by

developing indexes capable of measuring it well before the official acknowledgement of

international institutions. The questions debated by the literature focus on two issues, i.e.

unidimensional or multidimensional indicators; and objective or subjective definitions of it.

On the question of the dimensions of the indicators, a seminal work by Denison (1967)

concentrates on the multi-dimensional characteristics of the quality of work and develops

an index considering workers’education, hours of work, gender, and age.However, the

empirical literature of the 1980s and 1990s has defined it in unidimensional terms and has

highlighted only one aspect of such a complex phenomenon, using years of education as a

proxy of the quality of work (Warke 1986; Heywood 1986; Yang 1997; Sattinger 1980).

Only recently the multidimensional aspect of the quality of work has been investigated.

Once the multidimensional aspect is developed, the question arises of whether objective or

subjective elements should be taken into account. San et al. (2006) calculate an index of

the quality of work (SHH-LQI) at the level of manufacturing enterprises for Taiwan for the

period 1990–2000, based on an objective definition of it. The authors include in the

indicator seven factors: education, manpower training, labour productivity, structure of the

labour force, work safety and health of the workers, industrial relations and the work ethic,

labour-management models and the quality of the workers’ livelihood. Royuela and Su-

rinach (2012) consider two definitions of the concept of quality of work, one objective and

M. Agovino, G. Parodi

123

one subjective. The objective definition is based on the definition given by the already

quoted European Commission (2001), reflects the multidimensional aspect of the quality of

work and considers for its implementation different aspects of the phenomenon: the

objective characteristics of employment, the specific characteristics of the job, the sub-

jective evaluation of these characteristics by the individual worker (Royuela et al. 2009;

Royuela and Surinach 2012); the authors defend the relevance of the subjective element in

the assessment of the quality of work, despite the controversy surrounding it: Spector

(1997), Munoz de Bustillo Llorente and Fernandez Macıas (2005) are against it, while

Sheppard (1975), Clark (2005), Green (2004), Green and Tsitsianis (2005) and Kaiser

(2007) consider it essential. In recent years the use of subjective measures as key factors in

assessing the quality of work—in particular, job satisfaction—has become increasingly

common in empirical works conducted on European data. As Martel and Dupuis (2006)

argue, it is probably correct to consider both objective and subjective elements; this

approach is followed for instance by Sehnbruch’s (2008), who analyzes the quality of work

in the Chilean labour market using micro data, with a multidimensional approach.

There are few empirical studies on the quality of the work carried out on Italian data, in

addition, many of them have little economic content and have a mainly methodological

connotation (see Carpita (2003), Carpita and Golia (2008, 2012). Gallino (1993, 2001)

identifies four dimensions of quality of work, i.e. the economic dimension, the complexity

dimension, the organisational dimension of autonomy and control;,and the ergonomic

dimension); Carpita and Vezzoli’s study (2012), on a sample of workers in social coop-

eratives, show that non-monetary components (job satisfaction) have greater relevance in

the definition of a high quality of work. Addabbo and Solinas (2012) study the quality of

work of a sample of workers in the Modena province (Italy) and use the Gallino model,

augmented by two further dimensions, i.e. the social dimension and the work-life balance;

the results indicate a low level of quality of work (Addabbo and Solinas 2012). In our

analysis we follow Sehnbruch’s (2008) model, adapting it to the Italian context.

The rest of the paper is structured as follows: Sect. 2 illustrates the model used for the

analysis of the data, Sect. 3 illustrates the data, Sect. 4 presents the empirical findings, and

Sect. 5 concludes.

2 The Model

We propose to determine the quality of work in terms of multidimensional aspects. In a

multidimensional approach it is difficult to rank jobs in terms of their characteristics, and

therefore one can think of a gradual transition from low to high quality of work; in this

respect the technique of indices implemented via fuzzy sets theory can be of help (Lelli

2001; Zani et al. 2010, 2011). The fuzzy sets theory, conceptualized by Zadeh (1965) and

developed by Dubois and Prade (1980), is a suitable mathematical tool to analyze phe-

nomena that it is hard to place in a set. In his seminal paper, Zadeh defined fuzzy sets as ‘a

class of objects with a continuum of grades of membership’. While the early applications

of fuzzy logic were in science and engineering such as biology and artificial intelligence,

fuzzy-set theory has more recently been increasingly applied to many issues in various

social science and business fields. The use of this methodology in economics is quite new

and the best-known studies based on the fuzzy sets theory are multidimensional analysis of

poverty (see Cerioli and Zani 1990; Cheli and Lemmi 1995; Chiappero Martinetti 2000).

Identifying the Quality of Work by Fuzzy Sets Theory

123

Quality of work as part of human well-being can be considered as a vague concept1 and

may be defined, for example, as the capabilities and functionings generated by a job,

capabilities and functionings, which the individual has reason to value (Sehnbruch 2008 p.

567; Chiappero Martinetti 2008). In particular, let X be a set of elements x e X. A fuzzy

subset A of X is a set of ordered pairs:

½x; lAðxÞ� 8x 2 X ð1Þ

where lA (x) is the membership function of x to A in the closed interval [0, 1]. If lA (x) = 0

then x does not belong to A, while if lA (x) = 1 the x completely belongs to A. If 0 \lA

(x) \ 1 the x partially belongs to A and its membership to A increases according to the

values of lA (x). Let us assume that the subset A defines the position of each element with

reference to the achievement of the latent concept e.g. the quality of work of a sample of

workers. In this case, lA (x) identifies a situation of full achievement of the target (a worker

with highest quality of work), lA (x) = 0 denotes a total failure (a worker with the lowest

quality of work) and 0 \lA (x) \ 1 refers to a situation in between these two extremes.

In order to define the membership function for each variable it is necessary: to identify

the extreme situation such that lA (x) = 0 (non membership) and lA (x) = 1 (full mem-

bership); to define a criterion for assigning member function values to the intermediate

modalities of the variable (Cerioli and Zani 1990; Zani et al. 2010, 2011).

The notion of frequency has been considered helpful in offering a way out from the

issue of aprioristic choices (Lelli 2001). In particular, taking into account a set of n units

(individuals denoted by the subscript i) and assuming a non-linear and monotonic relation

between the p manifest variables Xs (s = 1,2,…, p) and the degrees of membership, and

ordering the modalities of Xs with respect to the quality of work associated to them, we

obtain the following membership function:

lA xið Þ ¼ 0 xi� l

lA xið Þ ¼ lA xi�1ð Þ þ F xið Þ�F xi�1ð Þ1�F xi lð Þð Þ l\xi\u

lA xið Þ ¼ 1 xi� u

8><

>:ð2Þ

where F(xsi) is the sampling cumulative function of the variable X and xi(l) is the highest

value xi B l. If l = x1 = min(xi) and u = xn = max (xi), the membership function (2)

corresponds to the totally fuzzy and relative approach suggested by Cheli and Lemmi

(1995). As Cheli and Lemmi (1995) outline, with this kind of specification any a prior and

arbitrary choice is avoided and membership functions are the mirror of the sample dis-

tributions (Chiappero Martinetti 2000).

Among the steps to construct a composite index, there is the definition of the weights to

be assigned to each component of the index, and finally of their aggregation. Sehnbruch

(2008) assigns equal weight to each component of the index of quality of work because this

method allows to reach an agreement when there is no consensus on the weight to be

assigned to each component. However, in this way, there is a risk of overestimating some

aspects of the quality of work, e.g. when several underlying variables measure the same

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


123

attribute (Freudenberg 2003). This situation is a serious problem2; in fact, it might seem

reasonable to believe that some variables have greater importance than others in deter-

mining the quality of work. For this reason, we can define membership values as follows

(Cerioli and Zani 1990):

lA ið Þ ¼Pp

s¼1 lA xsið ÞwsPp

s¼1 ws

i ¼ 1; 2; . . .; nð Þ ð3Þ

where ws denotes the weight assigned to variable Xs (s = 1,2,…, p).

In order to maintain an objective approach to measurement, a frequency-based

weighting system is appropriate, where the weighting structure is directly drawn from

reality (Brandolini and D’Alessio 1998; Chiappero Martinetti 2000). Specifically, the lit-

erature suggests a criterion for the determination of the weights considering for each

variable Xs the fuzzy proportion g(Xs) of the achievement of the target:

g Xsð Þ ¼1

n

Xn

i¼1

l xsið Þ ð4Þ

If Xs is binary, Eq. (4) coincides with the crisp3 proportion and in general it may be seen as

an index of the proportion of the units characterized by a total or partial latent phenomenon

Cheli and Lemmi 1995; Zani et al. 2010, 2011), The normalized weights may be deter-

mined as an inverse of g(Xs), in order to give higher importance to the rare features in the n

units. To avoid excessive weights to the variables with low value of g(Xs) we follow the

Cerioli and Zani’s formulation (1990):

ws ¼ ln1

g Xsð Þ

� �,Xp

s¼1

ln1

g Xsð Þ

� �

ð5Þ

Using formula (5), it is possible to assign to each variable a weight sensitive to the fuzzy

membership of the units to A. Zani et al. (2011) suggest to compare the solutions obtained

by different weighting criteria, ‘‘in order to gain insight into the stability of the pattern

highlighted by the different methods’’ (Zani et al. 2011 p. 446). For this reason, we

compute the fuzzy composite index with three weighting criteria:

• equal weight for each variable (w1);

• normalized weights as inverse functions of the fuzzy proportion of each variable

according to formula (5) (w2);

• normalized factor loadings applying PCA (Principal Component Analysis) on the srank correlation matrix (Zani et al. 2010; OECD 2008). This criterion will be

considered only in the case in which the first component explains more than 30 % of

the total variability (Zani et al. 2010, 2011)4 (w3).

2 It is not strictly necessary from a technical point of view that highly collinear variables be excluded. Forinstance, if two perfectly collinear variables were included in the composite, with weights w1 and w2, thenthe particular dimension of performance which they measure will be included in the composite with theweight (w1 ? w2). 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 whichtake into account their explanatory power. First we consider a number of principal components which takeinto account at least 95 % of the variance of each of the k initial variables, which imposes a minimal


123

3 The Data

For our analysis we use the information contained in the ISFOL PLUS (Participation,

Labour, Unemployment, Survey) 2010 Questionnaire, which refers to 2009 data. The data

are distributed by ISFOL (Institute for the Development of Vocational Training of

Workers).5 Using this Questionnaire allows to take advantage of the large quantity of

information available on issues related to the quality of work. The shortcoming of using

this Questionnaire is the small number of working disabled people covered by the inter-

view (see following Table 1). These small numbers forbid us to derive any kind of

inference, and we limit our analysis to descriptive statistics, in terms of fuzzy set analysis.

In this section we briefly explain our procedure to construct variables which allow us to

assess the quality of work of those in employment, following the line of investigation

developed by Sehnbruch’s (2008). using the answers to the ISFOL Questionnaire.

The questionnaire is organized in 10 modules, dealing with a preinterview, people at

work, inactive and looking for employment people, personal information, foreign people,

young people, over 50, generic information, job centers publicly run, and training; there are

also several submodules, with very detailed information on types of contract, and on the

quality of work. There are no direct questions on disability, but we identify as disabled

persons who report a continuing (not temporary) reduction in autonomy. This line of

approach is identical to the one followed by the literature on the economics of disability6

which uses EU-SILC data.7 Both the ISFOL and the EU-SHIW data are therefore based on

the subjective assessment of one’s own health.8 Table 1 shows the composition of the

sample of people at work interviewed in the questionnaire; it shows that both for disabled

and non-disabled persons the percentage of employees is around 80 % of all people in

employment.

We decided to limit our investigation to employees, because in Italy tax evasion is

widespread especially among the self employed, whose data on income are particularly

unreliable (cfr for instance Zizza and Marino 2008).

Footnote 4 continuedthreshold; second, we keep all the principal components whose eigen value is larger than 1; third, weobserve the screen plot of the eigen values as a function of the number of principal components; as eigenvalues are obtained in decreasing order, the graph will show a decreasing curve, with a kink in corre-spondence to the proper number of principal components. In particular, on the basis of the results of theanalysis, 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 ondaily 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 reductionin autonomy; (3) No particular problem.’’ It follows that the identification of disabled people with EU-SILCdata is in the spirit of the social model (Mitra 2008), for which disability derives from impairments affectingthe functioning and activity of the individual (whatever its origin: congenital, work accident, ageing, etc.), asstressed by the International Classification of Functioning, Disability and Health of the World HealthOrganization (WHO 2001).8 An extensive literature discusses the pros and cons of self reported data. For instance, for Bound andBurkhauser (1999), and for Gannon (2005), self reported data on disability are likely to be distorted becauseof 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 instanceKreider (1999); Kreider and Pepper (2007). The best defence in favour of the wide use of self reported dataon disability is their accurate predictive power.

.


123

http://www.isfol.it/temi/Lavoro_professioni/mercato-del-lavoro/plus

We develop the analysis closely following Sehnbruch’s (2008) investigation on the

quality of work of Chilean workers; she concentrates on some of the indicators mentioned

by the ILO manual, but she also takes into account some subjective elements of job

assessment. In particular she concentrates on adequate earnings, decent working time,

stability and security of work, health insurance, and professional training received. We

adapt Sehnbruch’s approach to the Italian context, as follows:

• Sehnbruck considers both employees and the self employed, while we concentrate on

employees only, and above we have justified our choice;

• as Italy has no minimum wage, in order to identify the quality of work in terms of

hourly wage we use a criterion based on the distribution of hourly wage, as later

discussed;

• stability of employment is by Sehnbruck linked to the time for which health insurance

is available. Given that in Italy health insurance is universal, we identify good or bad

situations of job stability in comparative terms, i.e. with reference to the distribution of

the duration of jobs in the sample.

Taking these aspects into account, we identify five categories of characteristics which,

according to their intensity, determine the quality of work. We maintain Sehnbruch’s

categories of professional position, income (i.e. hourly wage), employment stability,

training received, and we introduce the category of subjective job satisfaction, which

includes also Sehnbruck’s category of decent working time.

In order to develop our quantity analysis on job quality, we transform qualitative

assessments into quantitative ones. Following Sehnbruch (2008) we define three classes of

quality of work, i.e. low, medium, and high; in order to avoid subjectivity in attributing

scores to a job in terms of being ‘‘low’’, ‘‘medium’’, ‘‘high’’ with reference to a particular

attribute, we use the following criterion: we construct the distribution of jobs with refer-

ence to a particular attribute, and consider a job bad, average or good with respect to that

attribute according to the position taken by its own attribute in the distribution, assessed in

terms of quintiles. This procedure is used to attribute scores to employment stability, and to

hourly wage. For other attributes, for which the questionnaire itself offers a limited number

of choices among the answers, we attribute scores to these choices, ranging from 0,

indicating the worst situation, to 1, 2 (or 3 or 4) indicating the best situation. This pro-

cedure was used for professional position, for job satisfaction, and for training received.

The variables which express the quality of work are: professional position, income (i.e.

hourly wage), employment stability, training received, and subjective job satisfaction. To

each of these variables we attributed a grade in terms of job quality, as follows:

• professional position: We identify a poor, average or good professional position by

attributing a score which takes into account the type of contract, and whether the job is

paid. The grade ranges between 0, which corresponds to worst jobs, to 2, which

corresponds to best jobs. 8 groups of employees are included, and for each of them we

indicate in brackets the score we attribute to that group according to the type of

Table 1 Composition of people in employment

Self employed Employees Total

Total 3,314 13,273 16,587

Disabled persons 92 337 429

Non-disabled persons 3,222 12,936 16,158


123

contract: permanent contract (2), temporary contract (1), work and learning (1),

apprenticeship (1), job starting (1), agency work(1), job sharing (1), work on call (1);

we also decided to include among ‘‘employees’’ all those belonging to the ‘‘work and

training section’’, (school and work (1), stages (1), professional training (1), internships

(1)); and also the ones which the questionnaire defines as other employees (0).

• income, i.e. hourly wage: We obtain the hourly wage by transforming the weekly hours

of work into monthly hours, and by dividing the monthly salary by this figure. In order

to decide whether a particular job is characterized by a low, medium, or high wage, we

construct the distribution of hourly wage; we identify the quintiles; we eliminate

extreme wages falling into the bottom and the top quintile; we attribute value 0, 1 or 2

to hourly wages respectively falling into the second, third or fourth quintile.

• employment stability: We construct the distribution of the duration of the employees’

present jobs, and we partition it in quintiles. We attribute jobs a score ranging from 0 to

4 according to whether their duration belongs to the first, second, third, fourth or top

quintile of the duration distribution.

• training received: We classify the training received in terms of bad, average or good,

according to whether in the last 3 years people have received none, one, or more than

one training courses. To the three situations we attribute respectively score 0, 1, and 2.

• subjective job satisfaction: The questionnaire inquires about the overall level of job

satisfaction with reference to 8 items (working environmental, working hours, daily

workload, tasks and duties, safety at work, job prospects/career, remuneration, skills

development), with 4 different levels of satisfaction (low, medium low, medium high,

high); to each of these 4 levels of satisfaction we attribute a numerical score, ranging

between 0 and 3.

4 Some Empirical Evidence on Fuzzy Quality of Work in Italy

Considering observations with no missing values, we obtain a 8,379 9 12 data matrix. We

conduct our analysis by considering Eq. (2); in particular, if the variables are ordinal with

k categories, a suitable choice is the following: the membership function (m.f.) values of

the modalities up to the threshold l are put equal to 0 (absence of the phenomenon) and

those of the modalities Cu are put equal to 1 (complete presence); intermediate values of

m.f. are defined according to Eq. (2). In the Appendix 1, we report the result of m.f. for each

variable (Table 7).

Table 2 shows the correlation coefficients among fuzzy indicators obtained with dif-

ferent weights. The correlation between the pairs of indicators is very high (always greater

than 0.84): different weighting criteria have poor influence on the quality of work (Zani

et al. 2011) (we report in Appendix 1, Table 8, the weight matrix). In this section, we

report the indicator obtained with weighting criteria (5).

Here, we report the results of the index of quality of work for unit intervals, considering

the percentage value for class. We define Low quality work if the index is in the range

0 B xB 0.3, Medium quality work if the index is in the range 0.3 \ xB 0.7, High quality

work if the index is in the range 0.7 \ xB 1.9 The total sample size is 8,379, out of which

8,156 are non-disabled people and the rest are people with disabilities (223). Since the

9 The definition of these three classes of equal width is justified by the rather symmetric distribution of thescores of the index of the quality of work; in fact, the distribution of scores well approximates the normaldistribution (see Fig. 5, in Appendix 2); the approximation is not so good for people with disabilities


123

results of the whole sample are very similar to the ones of non-disabled people, below we

report only the results of non-disabled people and disabled people. We show the results on

the quality of work for disabled and non-disabled people overall, for gender difference, for

age brackets, and for educational levels. In showing the results we always follow the same

procedure, i.e. a Table shows the distribution of disabled and non-disabled people in the

three classes of low, medium and high quality of work; subsequently, a graph shows the

importance of each component in the quality of work index.

Table 3 analyses the overall quality of work for disabled and non-disabled people; it

shows that on average the index of the quality of work for non-disabled and disabled

persons is very similar (respectively, 0.53 and 0.52). 7.27 % of non-disabled people have a

low work quality, while the percentage for disabled people is tripled (21.07 %). 77.36 % of

non-disabled people are concentrated in the medium work quality class, while for disabled

people, the percentage is slightly lower (76,24); finally, 15.37 % of non-disabled people

are in the high quality work class, while the percentage of disabled people with a high

quality of work is seven times less than that of non-disabled people (2.69 %). The quality

of work for disabled people is worse than the one for non-disabled people, as Fig. 1 shows.

Only in terms of employment stability disabled people have an advantage compared to

non-disabled people; on the contrary, people with disabilities have deficiencies in terms of

job satisfaction with respect to working environment, safety at work, job prospects/career,

remuneration and skill development. In fact, even if disabled people declare themselves

unsatisfied more then non-disabled people in relation to remuneration, in reality in terms of

hourly wage (objective variable) they reach the same score.

Table 4 analyses the overall quality of work for disabled and non-disabled people by

gender; it shows that for non-disabled people, the percentage of women with a low quality

of work is very similar to that of men (respectively, 7.51 and 7.05); on the contrary, the

differences increase by gender among people with disabilities: the percentage of men with

disabilities with a low quality of work is nearly three times that of women with disabilities

(respectively, 8.74 and 21.66). 76.35 of non-disabled males have medium quality work, on

the contrary, the percentage for women is slightly higher (78.4); 78.33 % of disabled males

have a medium quality work, while 73.78 % of women with disabilities have a medium

quality work. Finally, 16.61 % of non disabled males have a high quality of work, while

for women the percentage is around 14 %. Furthermore, there are no disabled males with a

high quality of work; on the contrary, we observe that 17.47 % of women with disabilities

present a high quality of work.

In summary, the distinction by gender shows that for non-disabled people the gender

difference is very small and with a slight advantage in terms of high quality of work for

males; on the contrary, for disabled people the gender differences are substantial: males

with disabilities do not exceed the medium quality work class and many are concentrated

Table 2 Pairwise correlationsamong the fuzzy compositeindicators

w1 w2 w3

w1 1

w2 0.9404 1

w3 0.9648 0.8427 1

Footnote 9 continuedbecause of the relatively limited sample size, compared to non-disabled people (see Figure 6 in Appendix2).


123

Table 3 Quality of work index

The total sample size is 8,379,out of which 8,156 are non-disabled people and the rest arepeople with disabilities (223)

Quality of work Non-disabled people Disabled peopleClasses % %

Low

0 B x B 0.3 7.27 21.07

Medium

0.3 \ xss B 0.7 77.36 76.24

High

0.7 \ x B 1 15.37 2.69

Total 100 100

Mean 0.53 0.52

SD 0.16 0.16

Fig. 1 Quality of work index, non-disabled and disabled people

Table 4 Quality of work index by gender

Quality of work Non-disabled people Disabled people

Men Women Men WomenClasses % % % %

Low

0 B x B 0.3 7.05 7.51 21.66 8.74

Medium

0.3 \ x B 0.7 76.35 78.4 78.33 73.78

High

0.7 \ x B 1 16.61 14.09 0 17.47

Total 100 100 100 100

Mean 0.59 0.58 0.55 0.587

SD 0.163 0.16 0.15 0.167

Total: 4,288 men, 4,091 women; non-disabled people: 4,168 men; 3,988 women. Disabled people: 120 men;103 women


123

in the low quality work class; women with disabilities are more concentrated in the

medium quality work class and a large percentage in the high quality work class. This

result is probably justified on the grounds of lower expectations that disabled females

experience, because of double discrimination: females feel often at a disadvantage in

relation to males, for instance because of gender wage discrimination, widely documented

by the literature,10 therefore a disabled woman is likely to experience a double disad-

vantage. As a consequence, our results can be explained by Addabbo and Solinas (2012)

convincing considerations: ‘‘The more the worker is ‘‘fragile’’, the lower his/her expec-

tations of work are and the better his/her evaluation will be of the various dimensions [e.g.

job satisfaction] that characterize his/her work’’.

Fig. 2 Quality of work index by gender, non-disabled and disabled people

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


123

Figure 2 shows that for non-disabled people there are no big differences between males

and females in terms of each indicator. It is clear that the greater stability of male’s

employment and some differences in terms of job satisfaction, such as remuneration and

job prospects/career, determine a higher quality of work for men than for women. However

for disabled people gender differences are not significant; in particular, males have an

advantage over females in terms of hourly wages, professional position, employment

stability and wage; women enjoy an advantage for the remaining variables. In particular, it

appears that for disabled people the job satisfaction variables, i.e. the subjective variables,

are more important than the objective ones. This result is in line with papers in which

females have on average a higher level of satisfaction than males (Clark 1997; Sousa-Poza

and Sousa-Poza 2000).11

Table 5 considers the quality of work index for four different age groups. For non-

disabled people the percentage of people with a low score in the quality of work decreases

with age for the two extreme groups, from 9.40 to 4.44 % respectively for age group 18–34

to age group 55.64. For disabled people the situation is reversed, from 3.57 % for the

18–34 to 15.07 % for the 55–64.

Table 5 Quality of work index by age group

Quality of work 18–34 35–44 45–54 55–64Classes % % % %

Non-disabled people

Low

0 B x B 0.3 9.4 7.31 5.96 4.44

Medium

0.3 \ x B 0.7 81.67 77.08 75.38 71.1

High

0.7 \ x B 1 8.93 15.61 18.66 24.48

Total 100 100 100 100

Mean 0.55 0.58 0.60 0.635

SD 0.15 0.16 0.165 0.16

Disabled people

Low

0 B x B 0.3 3.57 3.13 10 15.07

Medium

0.3 \ x B 0.7 75 78.13 81.12 68.5

High

0.7 \ x B 1 21.43 18.75 8.89 16.44

Total 100 100 100 100

Mean 0.59 0.61 0.55 0.56

SD 0.16 0.14 0.15 0.176

The distribution of people by age group is the following one: total population: 3,230 aged 18–34; 1,537 aged35–44; 2,003 aged 45–54; 1,609 aged 55–64. Non-disabled population: 3,202 aged 18–34; 1,505 aged35–44; 1,913 aged 45–54; 1,536 aged 55–64. Disabled population: 28 aged 18–34; 32 aged 35–44; 90 aged45–54; 73 aged 55–64

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


123

The percentage of people with a medium score in the quality of work tends to decrease

with age for the two extreme groups, both for disabled and for non-disabled people, from

81.67 to 71.01 % for the non-disabled, and from 75 to 68.5 % for disabled people,

respectively for age groups 18–34 and 55–64.

The percentage of people with high score in the quality of work tends to increase for the

two groups of extreme age for the non-disabled, from 8.93 to 24.48 respectively for the age

groups 18–34 and 55–64; viceversa, it tends to decrease form 21.43 to 16.44 for disabled

people of the same age groups.

These results suggest a different mechanism for the variation in work quality between

generations. For non-disabled people the mechanism of seniority seems to explain the

strong increase in the percentage of people with a high score in the quality of work for the

two extreme groups of the young and the old; for disabled people the results can be

Fig. 3 Quality of work index by age group, non-disabled and disabled people


123

interpreted in terms of the effectiveness of Law 68/99 in affecting the percentage of

disabled people with different scores in terms of work quality.

As it is well known, Law 68/99 aims at the regulation and promotion of the employment of

persons with disabilities (Orlando and Patrizio 2006). More specifically, it pivots on the

concept of ‘‘targeted employment’’, so that the employment of disabled people is based on

quotas of compulsory hiring, but also on a careful assessment of their residual ability, on

providing, where necessary, training courses, internship and business mentoring, and on

special three sided employment contracts (Agovino and Rapposelli 2012, 2013a, b). As this

Law came into being in 1999, it benefits young generations, and this could explain our results:

Law 68/99 succeeded in reducing the incidence of jobs with a low quality score, and in

increasing the incidence of jobs with a medium and a high quality score. This explanation is

supported by the crossing of the question that provides information on disabled people who

have found a job with Law 68/99, with the question on the four age groups. In particular, we

observe that the percentage of disabled people who have found work by the Law 68/99 in age

group (18–34) are 2.5 times higher than ones in age group (55–64); respectively, 33 and 13 %.

In summary, for non-disabled people the quality of work increases from youth to maturity,

and Fig. 3 shows that this advantage depends on a better professional position, a higher hourly

wage, and employment stability. The situation is reversed for disabled people, as young

Table 6 Quality of work index by level of education

Quality of work No title Compulsoryschooling

Secondary schooldiploma

University degree,post graduate degrees

Classes % % % %

Non-disabled people

Low

0 B x B 0.3 40 12.51 6.95 4.84

Medium

0.3 \ x B 0.7 60 79.57 78.35 74.23

High

0.7 \ x B 1 0 7.91 14.71 20.93

Total 100 100 100 100

Mean 0.4 0.53 0.58 0.62

SD 0.19 0.16 0.16 0.158

Disabled people

Low

0 B x B 0.3 0 10.15 9.76 9.68

Medium

0.3 \ x B 0.7 0 81.16 73.98 70.97

High

0.7 \ x B 1 0 8.7 16.27 19.36

Total 0 100 100 100

Mean 0.54 0.57 0.62

SD 0.15 0.164 0.157

Total numbers of people with: no school title 5; with compulsory schooling 1,396; with secondary schooldiploma 4,615; with university degree 2,363. Number of non-disabled people: no school title 5; withcompulsory schooling 1,327; with secondary school diploma 4,492; with university degree 2,332. Numberof disabled people no school title 0; with compulsory schooling 69; with secondary school diploma 123;with university degree 31


123

people enjoy jobs with a higher quality score in comparison with mature persons. As Fig. 3

shows, despite a better professional position, a higher hourly wage, and more employment

stability, older people show a smaller job satisfaction than young people.

Table 6 shows remarkable differences in the effect of education on the distribution of

the score of work quality among non-disabled and disabled people. The distribution of non-

disabled people in the three levels of low, medium and high quality of work is polarized

according to the level of education; the incidence of a low score in the quality of work

decreases dramatically with increasing levels of education. This appears to be true also for

disabled people, even though the incidence of disabled people with a low index of job

quality is very small, whatever the level of education. Instead, for disabled people the level

of education does not appear to be particularly connected with a low score in terms of

quality of work, as those who are in low quality jobs are nearly equally distributed,

whatever their level of education. In other words, it seems that for disabled people

Fig. 4 Quality of work index by level of education, non-disabled and disabled people


123

education is irrelevant with respect to being in jobs with a low quality score, and that this

situation depends probably on variables, here not considered. Instead, for medium and high

scores of work quality the level of education plays for disabled people a role similar to that

for non-disabled: for both groups in fact increasing levels of education correspond to a

smaller incidence of a medium score of work quality, while for both groups increasing

levels of education correspond to an increased incidence of a high score of work quality; in

fact, the effect of a university degree in placing people in a high quality job is much more

pronounced for disabled than than for non-disabled people. This finding is in line with the

literature.12

Figure 4 shows how for both disabled and non-disabled persons the elements which

account for main differences in the total score of the work quality are hourly wage, training

received, and skill development; the graph also shows that these three elements are par-

ticularly connected with the level of education, as they increase with it. Degree holders,

and especially non-disabled ones, have a special advantage in this respect.

5 Conclusions

The paper assesses the quality of work of employees with disabilities in Italy using the

ISFOL PLUS 2010 questionnaire, comparing it with that of people without disability. In

particular, we have developed a multidimensional indicator of work quality, closely fol-

lowing an article by Sehnbruch (2008), adapted to the Italian context. The results of the

investigation show that on average the index of the quality of work for non-disabled and

disabled person is very similar; this average reflects a different distribution in the values of

the specific indicators, with disabled people scoring better with respect to job stability,

scoring equally with respect to hourly wage, and scoring worse with respect to other

characteristics.

Also, the results of the investigation show a different mechanism of determinants of

work quality for disabled and non-disabled people: while for these last ones seniority seem

to highly contribute to the score of work quality, institutional factors, like Law 68/99

appear to play a bigger role in the determination of the score for work quality for disable

people.

Differences between non-disabled and disabled people in the score of work quality

emerge also with respect to education. At low level of score of work quality, education

seems to play no role for disabled people, while it plays a substantial role for non-disabled

people; for jobs with high level of work quality score the incidence of people with

increasing education increases for both groups, with a very pronounced effect for disabled

people.

Substantial differences emerge with respect to gender among disabled people, where

women appear to be in higher work quality scores than men, but, as we mentioned above,

this may be due to lower expectations of disabled women, on the account of their

awareness of a possible double discrimination effect.

Finally, no substantial difference between genders emerges for non-disabled people.

Unfortunately, our analysis is based on descriptive evidence rather than on inference,

because of the limited number of disabled people in our sample, and this is the greatest

limit to our analysis. However, bearing this element of caution in mind, several important

policy considerations emerge from this analysis.

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


123

With respect to disabled people, our results show the relevance of institutional factors in

determining good quality of their work: younger disabled people, who could benefit from

Law 68/99, enjoy a higher quality of work than older disabled workers, who were less

supported by specifically favorable legislation. Also, our results show the great importance

of education for the quality of work which disabled people enjoy; our results show the very

important role played by higher education in placing disabled people in high quality jobs,

which is more relevant for disabled than for non-disabled people. These considerations

lead to encourage investments in higher education for disabled people, which are the

opposite of the repeated Governmental cuts on expenditures on education for disabled

people.

With respect to non disabled people, our results show that gender contributes to dif-

ferences in the quality of work. Also, the results show the overall relevance of seniority in

increasing the quality of work, with special reference to professional position, hourly wage

and employment stability. This suggests a certain degree of automatism linked to seniority

rather than merit in increasing the quality of work.

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

Appendix 1

See Tables 7 and 8.

Table 7 Membership function

Variable Scores (xi) ni F(xi) m.f.(xi)

1. Professional position (l1)

No contract 0 111 1.32 0

Atypical contract 1 1,199 15.63 0.145

Indefinite contract 2 7,069 100 1

2. Income (l2)

1/5 B x B 2/5 0 2,520 30.08 0

2/5 \ x B 3/5 1 3,151 67.68 0.538

3/5 \ x B 4/5 2 2,708 100 1

3. Job satisfaction

3.1 working environment (l31)

Low 0 446 5.32 0

Medium–low 1 1,179 19.39 0.149

Medium–high 2 4,381 71.68 0.701

High 3 2,373 100 1

3.2 working hours (l32)

Low 0 346 4.13 0

Medium–low 1 1,064 16.83 0.132

Medium–high 2 4,468 70.15 0.688

High 3 2,501 100 1

3.3 daily workload (l33)

Low 0 489 5.84 0


123

Table 7 continued

Variable Scores (xi) ni F(xi) m.f.(xi)

Medium–low 1 1,405 22.60 0.178

Medium–high 2 4,621 77.75 0.764

High 3 1,864 100 1

3.4 tasks and duties (l34)

Low 0 326 3.89 0

Medium–low 1 1,160 17.73 0.144

Medium–high 2 4,710 73.95 0.729

High 3 2,183 100 1

3.5 safety at work (Law 626) (l35)

Low 0 889 10.61 0

Medium–low 1 1,038 23 0.139

Medium–high 2 3,369 63.21 0.589

High 3 3,083 100 1

3.6 job prospects/career (l36)

Low 0 2,317 27.65 0

Medium–low 1 2,159 53.42 0.356

Medium–high 2 2,751 86.25 0.810

High 3 1,152 100 1

3.7 remuneration (l37)

Low 0 1,261 15.05 0

Medium–low 1 2,602 46.10 0.365

Medium–high 2 3,651 89.68 0.878

High 3 865 100 1

3.8 skills development

Low 0 855 10.20 0

Medium–low 1 1,917 33.08 0.255

Medium–high 2 4,145 82.55 0.806

High 3 1,462 100 1

4. Employment stability (l4)

x B 1/5 0 2,112 25.21 0

1/5 \ x B 2/5 1 1,419 42.14 0.226

2/5 \ x B 3/5 2 1,586 61.07 0.479

3/5 \ x B 4/5 3 1,706 81.43 0.751

x [ 4/5 4 1,556 100 1

5. Training received (l5)

None 0 5,276 62.97 0

One 1 978 74.64 0.315

More than one 2 2,125 100 1


123

Appendix 2

See Figs. 5 and 6.

Table 8 Value of the weights (w2, w3)

Variable W2 (Eq. (5)) * 100 W3 * 100

1. Professional position (l1) 2.247545 6.891034243

2. Income (l2) 9.923917 2.524951896

3. Job satisfaction:

3.1 working environment (l31) 6.161163 11.06316663

3.2 working hours (l32) 5.90076 9.569717553

3.3 daily workload (l33) 6.093292 10.55001185

3.4 tasks and duties (l34) 5.717802 11.77862404

3.5 safety at work (Law 626) (l35) 7.324107 3.570311337

3.6 job prospects/career (l36) 10.84157 11.01402092

3.7 remuneration (l37) 7.900983 8.939781893

3.8 skills development (l38) 7.088898 11.86310571

4. Employment stability (l4) 11.72646 10.86399859

5. Training received (l5) 19.0735 1.374079138

Tot 100 100

0.0

2.0

4.0

6.0

8

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1

Quality of employment

Fig. 5 Index of quality of work,non-disabled people

0.0

5.1

.15

Fra

ctio

n

.1 .2 .3 .4 .5 .6 .7 .8 .9

Quality of employment

Fig. 6 Index of quality of work,disabled people


123

References

Addabbo, T., & Solinas, G. (2012). Non-standard employment and quality of work: Towards new forms ofmeasurement. In T. Addabbo & G. Solinas (Eds.), Non-standard employment and quality of work,AIEL series in labour economics (pp. 233–260). Berlin: Physica. doi:10.1007/978-3-7908-2106-2_12.

Agovino, M., & Rapposelli, A. (2012). Employment of disabled people according to Law 68/1999. Amultidimensional analysis at regional level. Rivista Internazionale di Scienze Sociali, 1, 3–24.

Agovino, M., & Rapposelli, A. (2013a). Inclusion of disabled people in the Italian labour market: Anefficiency analysis of Law 68/1999 at regional level. Quality & Quantity, 47, 1577–1588.

Agovino, M., & Rapposelli, A. (2013b). Employment of disabled people in the private sector. An analysis atthe level of Italian Provinces according to article 13 of law 68/1999. Quality & Quantity. doi:10.1007/s11135-013-9851-3.

Baliamoune-Lutz, M. (2004). On the measurement of human well-being: Fuzzy set theory and Sen’scapability approach, UNU-WIDER, Helsinki.

Bound, J., & Burkhauser, R. V. (1999). Economic analysis of transfer programs targeted on people withdisabilities. In O. Ashenfelter & D. Card (Eds.), Handbook of labor economics volume 3C (pp.3417–3528). North Holland: Elsevier.

Brandolini, A., & D’Alessio, G. (1998). Measuring well-being in the functioning space. Rome: Bancad’Italia, Mimeo.

Carpita, M. (2003). Metodi per la costruzione di indicatori della qualita del lavoro: Un’applicazione alsettore dei servizi sociali. Statistica & Applicazioni, 1(2), 3–33.

Carpita M., & Golia S. (2008). Subjective measures of quality of work in the social cooperatives. Rapporti diricerca del Dipartimento Metodi Quantitativi, Universita di Brescia, Facolta di Economia, 312.

Carpita, M., & Golia, S. (2012). Measuring the quality of work: The case of the Italian social cooperatives.Quality & Quantity, 46(6), 1659–1685.

Carpita, M., & Vezzoli, M. (2012). Statistical evidence of the subjective work quality: The fairness driversof the job satisfaction. Electronic Journal of Applied Statistical Analysis, 5(1), 89–107.

Cerioli, A., & Zani, S. (1990). A fuzzy approach to the measurement of poverty. In C. Dagum & M. Zenga(Eds.), Income and wealth distribution. Inequality and Poverty (pp. 272–284). Berlin: Springer.

Cheli, B., & Lemmi, A. (1995). A totally fuzzy and relative approach to the multidimensional analysis ofpoverty. Economic Notes, 24(1), 115–134.

Chiappero Martinetti, E. (2000). A multidimensional assessment of well-being based on Sen’s functioningapproach. Rivista Internazionale di Scienze Sociali, 1008(2), 207–239.

Chiappero Martinetti E. (2008). Complexity and vagueness in the capability approach: Strengths orweaknesses? In F. Comin, M. Qizilbash & S. Alkire (Eds.), The capability approach. Concepts,measures and applications (pp. 268–309). Cambridge: Cambridge University Press.

Clark, A. E. (1997). Job satisfaction and gender: Why are women so happy at work? Labour Economics, 4,341–372.

Clark, A. E. (2005). Your money or your life: Changing job quality in OECD countries. British Journal ofIndustrial Relations, 43(3), 377–400.

Dahl, S., Nesheim, T., & Olsen, K. (2009). Quality of work: Concept and measurement. In A. Guillen &S. Dahl (Eds.), Quality of work in the European Union: Concept, Data and debates from a transna-tional perspective (pp. 19–40). Brussels: P.I.E. Peter Lang.

Denison, E. F. (1967). Why growth rates differ: Postwar experience in nine Western countries. Washington,DC: Brookings Institution.

Dubois & Prade (1980) Fuzzy sets and systems: Theory and applications. Academic Press, New York.European Commission. (2001). Employment in Europe 2001, Brussels.Freudenberg, M. (2003). Composite indicators of country performance: A critical assessment. OECD STI

Working Paper n. 16.Gallino, L. (1993). Dizionario di sociologia del lavoro. Utet: Torino.Gallino, L. (2001). Il costo umano della flessibilita. Laterza: Roma.Gannon, B. (2005). A dynamic analysis of disability and labour force participation 1995–2000. Health

Economics, 14, 925–938.Green, F. (2004). Work intensification, discretion, and the decline in well-being at work. Eastern Economic

Journal, 30(4), 615–625.Green, F., & Tsitsianis, N. (2005). An investigation of national trends in job satisfaction in Britain and

Germany. British Journal of Industrial Relations, 43(3), 401–429.Gunderson, M. (1989). Male-female wage differentials and policy responses. Journal of Economic Liter-

ature, 27(1), 46–72.


123

http://dx.doi.org/10.1007/978-3-7908-2106-2_12

http://dx.doi.org/10.1007/s11135-013-9851-3

http://dx.doi.org/10.1007/s11135-013-9851-3

Heywood, J. S. (1986). Labor quality and concentration-earnings hypothesis’. The Review of Economics andStatistics, 68(2), 342–346.

ILO. (2012). Decent work indicator, concepts and definitions (1st ed.). Geneva: ILO manual/InternationalLabour Office.

Jacobs, R., Smith, P., & Goddard, M. (2004). Measuring performance: An examination using compositeperformance indicators. University of York, Center for Health Economics Technical Paper n. 29.

Jones, M. K., & Sloane, P. (2012). Disability and social exclusion. In G. Parodi & D. Sciulli (Eds.), Socialexclusion. Short and long term causes and consequences (pp. 128–148). AIEL Series in LabourEconomics. Berlin: Physica-Verlag-Springer.

Kaiser, L. C. (2007). Gender-job satisfaction differences across Europe: An indicator for labor marketmodernization. International Journal of Manpower, 28(1), 75–94.

Kreider, B. (1999). Latent work disability and reporting bias. Journal of Human Resources, 34(4), 734–769.Kreider, B., & Pepper, J. V. (2007). Disability and employment: Re-evaluating the evidence in light of

reporting errors. Journal of the American Statistical Association, 102, 432–441.Kunze, A. (2000). The determination of wages and the gender wage gap: A survey. Discussion paper series,

IZA n. 193.Lelli, S. (2001). Factor analysis vs. fuzzy sets theory: Assessing the influence of different techniques on Sen’s

functioning approach. Public Economics CES Discussion Paper Series, 01.21, Katholieke UniversiteitLeuven.

Longhi, S., Nicoletti, C., & Platt, L. (2012). Interpreting wage gaps of disabled men: The roles of pro-ductivity and of discrimination. Southern Economic Journal, 78(3), 931–953.

Martel, J.-P., & Dupuis, G. (2006). Quality of work life: Theoretical and methodological problems, andpresentation of a new model and measuring instrument. Social Indicators Research, 77, 333–368.

Mitra, S. (2008). The recent decline in the employment of person with disabilities in South Africa 1998–2006. Fordham University, Department of Economics, discussion paper n. 2008–12.

Munoz de Bustillo Llorente, R., & Fernandez Macıas, E. (2005). Job satisfaction as an indicator of thequality of work. The Journal of Socio-Economics, 34, 656–673.

OECD. (2008). Handbook on constructing composite indicators, methodology and user guide. JRC,European Commission. http://www.oecd.org/std/clits/42495745.pdf.

Orlando, N., & Patrizio, M. (2006). Il collocamento mirato dei disabili: L’applicazione della Legge 68/1999nella Provincia di Bolzano. In G. Parodi (Ed.), Aspetti socioeconomici della disabilita (pp. 179–216).Roma: ARACNE editrice.

Rıo, C., Gradın, C., & Canto, O. (2011). The measurement of gender wage discrimination: The distributionalapproach revisited. Journal of Economic Inequality, 9(1), 57–86.

Royuela, V., Lopez-Tamayo, J., & Surinach, J. (2009). Results of a quality of work life index in Spain. Acomparison of survey results and aggregate social indicators. Social Indicators Research, 90(2),225–241.

Royuela, V., & Surinach, J. (2012). Quality of work and aggregate productivity. Social Indicators Research.doi:10.1007/s11205-012-0081-1.

San, G., Huang, T.-C., & Huang, L. H. (2006). The establishment and application of a labor quality index:The case of Taiwan’s manufacturing industry. Social Indicators Research, 79, 61–96.

Sattinger, M. (1980). The effect of labor quality on labor’s factor share. Atlantic Economic Journal, 8(2),33–43.

Sehnbruch, K. (2008). From the quantity to the quality of employment: An application of the capabilityapproach to the Chilean labour market. In F. Comin, M. Qizilbash & S. Alkire (Eds.), The capabilityapproach. Concepts, measures and applications (pp. 561–597). Cambridge: Cambridge UniversityPress.

Sheppard, H. L. (1975). Some indicators of quality of working life: A simplified approach to measurement.In L. E. Davis & A. B. Cherns (Eds.), The quality of working life (pp. 119–122). New York: Free Press.

Sloane, P. J., & Jones, M. K. (2011). Disability and social exclusion. In G. Parodi & D. Sciulli (Eds.), Socialexclusion. Short and long term causes and consequences. Heidelberg: Springer.

Sousa-Poza, A., & Sousa-Poza, A. A. (2000). Well-being at work: A cross-national analysis of the levels anddeterminants of job satisfaction. Journal of Socio-Economics, 29(6), 517–538.

Spector, P. E. (1997). Job satisfaction: Application, assessment, cause, and consequences. Thousand Oaks,CA: Sage.

UE. (2000). Lisbon European Council, March 23–24, 2000.Warke, T. W. (1986). International variation in labor quality. The Review of Economics and Statistics, 68(4),

704–706.WHO. (2001). International classification of impairments, disabilities, and handicaps. Geneva: World

Health Organization.


123

http://www.oecd.org/std/clits/42495745.pdf

http://dx.doi.org/10.1007/s11205-012-0081-1

Yang, D. T. (1997). Education in production: Measuring labor quality and management. American Journalof Agricultural Economics, 79(3), 764–772.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–343.Zani, S., Milioli, M. A., & Morlini, I. (2010). Fuzzy composite indicators: An application for measuring

customer satisfaction. Atti del XVL Convegno della Societa Italiana di Statistica, Padova, 16–18Giugno.

Zani, S., Milioli, M. A., & Morlini, I. (2011). Fuzzy methods and satisfaction indices. In R. S. Kennett & S.Salini (Eds.), Modern analysis of customer surveys (pp. 439–456). New York: Wiley.

Zizza, R., & Marino, M. R. (2008). L’evasione dell’Irpef: una stima per tipologia di contribuente. Pavia:XX riunione scientifica SIEP.


123

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

Documents

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