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Job Satisfaction in the “Big Four” of Europe: Reasoning Between Feeling and Uncertainty Through CUB Models

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The paper offers a comparative investigation of objective and subjective driving forces behind the satisfaction that people feel in their job in four representative countries of Western Europe. The main element of this work’s novelty is its linking the research of cross-country similarities and differences in the leading determinants of global job satisfaction to methodological issues that arise when responses to survey questions are detected on a rating scale through self-evaluation. In particular, this paper is one of the first attempts to test the potentialities of CUB models on EWCS data in a broader conceptual framework in which the response on overall job satisfaction depends on some psychological dynamics of the evaluation process. Although overall job satisfaction is significantly higher for British and German employees, the subjective factors—the amount of socio-economic security embodied in a job, the working conditions and the aspects of work–life balance—are the most relevant in shaping job satisfaction, disregarding the myth that considers earnings as the dominant factor.

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Fig. 1


  1. 1.

    The shifted Binomial is preferred because its support coincides to the choice set {1, 2,…, m}, which is more common than the Binomial support that starts with 0.

  2. 2.

    In rating analysis that expresses a direct evaluation on the item, \(\left( {1 - \xi } \right)\) increases with agreement towards the item. In ranking analyses that put at first place the best item, \(\xi\) increases with the expressed preference.

  3. 3.

    The extreme values of \(\pi_{i}\) are associated to complete uncertainty (\(\pi = 0)\), and the mixture resolves to a discrete Uniform random variable where any category has the same probability to be chosen, and to no uncertainty (\(\pi = 1)\) when the choice is completely determined by feeling.

  4. 4.

    Another extension of CUB models, not implemented in this analysis, considers the shelter effect that allows modelling the presence of a sort of “refuge” category (Corduas et al. 2009; Iannario 2012a). A more Generalised class of CUB models (GeCUB) is also defined if covariates are included into a CUB model with shelter effect (Iannario and Piccolo 2012b, 2016). Capecchi and Piccolo (2016) proposed a Combination of a discrete Uniform random variable with a SHelter effect (CUSH models). Iannario (2012b, 2014) designed CUBE models as a Combination of a Uniform and a BEta-binomial distribution, which allow capturing a possible over-dispersion; its specific case (IHG) is applied when the data generating process follows an Inverse Hyper-Geometric distribution, which is adequate if the mode is an extreme value of the support. Varying Uncertainty in CUB models (VCUB) is the most recent generalisation to consider the uncertainty component differently from the discrete Uniform distribution (Gottard et al. 2016).

  5. 5.

    EWCS is a questionnaire-based survey with interviews conducted face-to-face to a random sample of persons in employment, both employees and self-employed, which is representative of the entire working population in each European country. The sampling strategy is based on a multi-stage design where each country is divided into sections based on region and degree of urbanisation, in each of which a number of PSUs is drawn randomly. A random sample of households is then drawn in each PSU, and in each household, the interviewee is the worker who has the birthday next. In general, in 2010, the total number of completed interviews was 43,816 on 34 countries and around 1.000 in most countries. Precisely, in Germany it was 2.133, in Italy 1.500, in the UK 1.575, and in France 3.046. Further technical details can be found in the 2010 Technical Report (www.eurofound.europa.eu) where official documents that provide a complete and rigorous description of EWCS variables are available.

  6. 6.

    A restriction has been made omitting the “do not know” and “refusals” on the question concerning the overall job satisfaction, whose incidence (0.7% on the entire dataset) is somewhat negligible. Precisely, on the restricted dataset to each country, the shares are: 0.93% for France, 0.23% for Germany, 0.47% for Italy, and 0.99% for the United Kingdom.


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Correspondence to Gennaro Punzo.


Appendix 1

See Tables 3, 4, 5, 6 and 7.

Table 3 List of variables
Table 4 Weighted descriptive statistics: France and Germany
Table 5 Weighted descriptive statistics: Italy and the United Kingdom
Table 6 Multivariate frequency distribution of job satisfaction by subgroups: France and Germany
Table 7 Multivariate frequency distribution of job satisfaction by subgroups: Italy and the UK

Appendix 2

See Figs. 2, 3, 4 and 5.

Fig. 2

CUB(0, 0) of global job satisfaction by country

Fig. 3

Estimated CUB(0, 0) models of global job satisfaction by country for gender, age groups, earnings groups, and enterprise size

Fig. 4

Estimated CUB(0, 0) models of components of socio-economical dimension by country

Fig. 5

Estimated CUB(0, 0) models of components of working condition dimension by country

Appendix 3

Data Quality: Multiple Imputation Through Amelia II

Item non-responses (don’t know and refusal) are ubiquitous in most quantitative research studies, and the 2010 EWCS dataset is not immune to the problem. The percentages of item non-responses by country are quite negligible—1.4% for France, less than 1% for Germany, 1.6% for Italy and the United Kingdom. However, if each unit with at least one missing value had been deleted, a large number of observations would have been lost—33.8% for France, 24.9% for Germany, 40.6% for Italy and 45.3% for the United Kingdom—with unavoidable effects on the conclusions that could be drawn from the data.

Therefore, after having removed the very few missing values (less than 1% for each country) on the question concerning the global job satisfaction (see footnote 2), we take up the strategy of multiple imputation (Honaker and King 2010) through the Amelia II’s EMB (Expectation–Maximization with Bootstrapping) algorithm implemented by Honaker et al. (2015). Multiple imputation (Rubin 1987; Little and Rubin 2002)—known as the gold standard of treating missing data (Baraldi and Enders 2010; Cheema 2014)—assures data quality without losing too many observations and avoids biases, inefficiencies and incorrect uncertainty estimates that can result from the deletion instead.

The imputation model in Amelia II assumes that the complete dataset D (both observed D obs and unobserved D mis) has:

  • A multivariate normal distribution:

    $$D \sim N_{k} \left( {\mu , \varSigma } \right)$$
  • The unobserved data are missing at random (MAR).

The first hypothesis is often an approximation to the true distribution of data, even though this method works as well as other more complicated models even in the face of categorical or mixed data (Schafer 1997; Schafer and Olsen 1998). The second one means that the pattern of missingness only depends on the observed part (\(D^{obs}\)), not the unobserved data \((D^{mis} )\). Let M be the binary missingness matrix, which indicates the presence of missing values with 1 (and 0 otherwise), the MAR assumption is:

$$P\left( {M |D} \right) = P(M|D^{obs} )$$

The vector of parameters \(\theta = \left( {\mu ,\varSigma } \right)\) determines the data distribution that, under the MAR assumption, it can be factorised as follows:

$$P\left( {D^{obs} ,M |\theta } \right) = P\left( {M |D^{obs} } \right) P (D^{obs} |\theta )$$

The parameters \(\theta\) are estimable through the likelihood function:

$$L\left( {\theta ,D^{obs} } \right) \propto P (D^{obs} |\theta )$$

Supposing a flat prior on \(\theta\), the posterior function is rewritten as:

$$P\left( {\theta ,D^{obs} } \right) \propto P (D^{obs} |\theta ) = \smallint P\left( {D|\theta } \right)dD^{mis}$$

This posterior function is solved through the EMB algorithm that combines the classic EM algorithm (Dempster et al. 1977) with a bootstrap approach to take draws from this posterior. From the posterior of the complete-data parameters, imputations are made by drawing values of \(D^{mis}\) from its distribution conditional on \(D^{obs}\) and the draws of \(\theta\), which is a linear regression with parameters that can be calculated directly from \(\theta\) (Honaker et al. 2015).

In brief, what Amelia does is imputing m values for each missing data and creating m five new complete datasets. As suggested by Honaker et al. (2015), being a few missing data, we chose \(m = 5\), and without affecting generality, one of the five datasets (the fourth) was selected randomly to avoid the complexity of merging imputed values from the five datasets given that most are categorical variables.

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Punzo, G., Castellano, R. & Buonocore, M. Job Satisfaction in the “Big Four” of Europe: Reasoning Between Feeling and Uncertainty Through CUB Models. Soc Indic Res 139, 205–236 (2018). https://doi.org/10.1007/s11205-017-1715-0

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  • Job satisfaction
  • CUB models
  • European countries

JEL Classification

  • C25
  • J28
  • O52