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Social Indicators Research

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24-01-2021 | Original Research

Beyond Wage Gap, Towards Job Quality Gap: The Role of Inter-Group Differences in Wages, Non-Wage Job Dimensions, and Preferences

Authors: Marko Ledić, Ivica Rubil

Published in: Social Indicators Research | Issue 2/2021

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Abstract

Wage is not the only thing people care about when assessing the quality of their jobs. Non-wage job dimensions, such as autonomy at work and work-life balance, are important as well. Nevertheless, there is vast literature comparing groups of employed people that focuses on the inter-group wage gaps only. We go beyond the wage gap by proposing a framework for analysing inter-group gaps in multidimensional job quality. Job quality is measured by the so-called equivalent wage, a measure combining wage and multiple non-wage job dimensions in accordance with preferences over jobs as combinations of job dimensions. We derive a decomposition of the inter-group equivalent wage gap into three components: (1) the standard wage gap, (2) the gap in non-wage dimensions, and (3) inter-group preference heterogeneity. In an illustrative empirical application, we focus on the gender gap for recent university graduates using survey data from 19 countries. Men’s equivalent wages are substantially higher than women’s, and the equivalent wage gaps are significantly larger than the wage gaps. This is because the non-wage job dimensions are on average to men's advantage, and the preference heterogeneity is such that men care about the non-wage dimensions less than women do, and thus suffer less from having the non-wage dimensions at levels below the perfect level. This type of decompositions broadens information about labour market inequalities available to policy makers, but it is up to them to decide which of the three components of the equivalent wage gap are normatively relevant for them and whether they should aim to eliminate them.

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Fleurbaey and Schokkaert (2013) analyse the case of incomplete preferences.

The same would hold if we assumed multiplicative separability, i.e. \(u^{g} \left( {W,D,X} \right) = f^{g} \left( {W,D} \right)h^{g} \left( X \right)\), or any other form of weak separability.

Analogously: \(u^{g} \left( {W^{\prime},D{^{\prime}},\tilde{X}} \right) > u^{g} \left( {W^{\prime\prime},D{^{\prime\prime}},\tilde{X}} \right)\) if everyone with \(X = \tilde{X}\) strictly prefers \(\left( {W^{\prime},D{^{\prime}}} \right)\) to \(\left( {W^{\prime\prime},D^{\prime\prime}} \right)\); \(u^{g} \left( {W^{\prime},D{^{\prime}},\tilde{X}} \right) = u^{g} \left( {W^{\prime\prime},D{^{\prime\prime}},\tilde{X}} \right)\) if everyone with is indifferent between \(\left( {W{^{\prime}},D{^{\prime}}} \right)\) and \(\left( {W{^{\prime\prime}},D{^{\prime\prime}}} \right)\).\(X = \tilde{X}\)

This should not be confused with unselfish (other-regarding, altruistic) behaviour.

Note that the same would hold if we assumed multiplicative separability: for \(u^{g} \left( {W,D,X} \right) = f^{g} \left( {W,D} \right)h^{g} \left( X \right)\), \({u}^{g}\left(W,D,X\right)={u}^{g}({W}^{\mathrm{*}},{D}^{r},X)\) if and only if \(f^{g} \left( {W,D} \right) = f^{g} \left( {W^{*} ,D^{r} } \right)\). This would hold for any other form of weak separability, too.

Or, equivalently, by \(f^{{\text{A}}} \left( {W_{j} ,D_{j} } \right) = f^{{\text{A}}} \left( {W_{j}^{*} ,D^{r} } \right)\) and \(f^{{\text{B}}} \left( {W_{i} ,D_{i} } \right) = f^{{\text{B}}} \left( {W_{i}^{*} ,D^{r} } \right)\).

We stress that, although we assumed the same reference levels for all persons and dimensions, this assumption could be relaxed. For example, for some persons, or groups, or some non-wage dimensions, an imperfect level may be as good as the perfect one. In such cases, the normative reasoning above works for reference levels below the maximal ones. We did not opt for these person- or group- or dimension-specific reference levels because we did not find plausible arguments to do so.

\({\text{MRS}}_{{D_{k} ,\ln W}}^{g} = \varphi_{k}^{g}\) must be distinguished from the MRS between \({D}_{k}\) and wage \(W\). The latter is given as \({\text{MRS}}_{{D_{k} ,W}}^{g} = W\varphi_{k}^{g} = W \cdot {\text{MRS}}_{{D_{k} ,\ln W}}^{g}\).

\({\text{WTP}} = W \cdot \left[ {1 - \exp \left( {\mathop \sum \limits_{k = 1}^{K} \varphi_{k}^{g} \left( {D_{k} - D_{k}^{r} } \right)} \right)} \right]\)

For details on the Shapley value approach to decomposition, see, for example, Shorrocks (2013).

If they were at the perfect level, preferences would not matter at all, as the weight \(\overline{{{\mathbb{E}}\left[ {D_{k} - D_{k}^{r} } \right]}}\) would be zero, and thus the gap in preferences would be zero.

Unless the zero gap in non-wage dimensions is due to the averages of both groups being at the perfect level, in which case preferences do not matter at all.

How fine the breakdown can be will depend on the sample size.

Suppose that each of the two \(g\)-specific EW distributions is divided into \(Q\) quantile groups each. Then, \({\mathbb{E}}^{g} \left[ {\ln W^{*} } \right] = \left( {1/Q} \right)\mathop \sum \limits_{q = 1}^{Q} {\mathbb{E}}_{q}^{g} \left[ {\ln W^{*} } \right]\), where \({\mathbb{E}}_{q}^{g} \left[ \cdot \right]\) stands for the average over the quantile group \(q\) within group \(g\). The EW gap is then, \({\Delta }{\mathbb{E}}\left[ {\ln W^{*} } \right] = \left( {1/Q} \right)\mathop \sum \limits_{q = 1}^{Q} {\Delta }{\mathbb{E}}_{q} \left[ {\ln W^{*} } \right]\), and Eq. (8) can be used to decompose each \({\Delta }{\mathbb{E}}_{q} \left[ {\ln W^{*} } \right]\) into the wage gap, the gap in non-wage dimensions, and the gap in preferences pertaining to quantile group \(q\).

In the latter case, the contribution of the preference gap to the EW gap will arguably depend on the length of the period considered, as any significant preference changes take time to develop.

Like us, Decancq et al. (forthcoming) allow for preference heterogeneity just with respect to a binary characteristic (rural vs. urban residence).

The derivation is given in the Appendix A1.

We do not do that in this paper.

The transformation makes sense only if \(D_{k}\)’s are (treated as) cardinal; it makes no sense to apply this transformation to a binary variable.

The derivation is given in the Appendix.

The essence of the approach is described in Clarke and Oswald (2002). For a critical overview, see, for example, Dolan and Fujiwara (2016).

This example is from the data used in this paper, namely the HEGESCO/REFLEX surveys of recent graduates, described in Sect. 3.2.

For similar specifications, see Jara et al. (2019), Schokkaert et al. (2011), Schokkaert and Jara (2017), Defloor et al. (2017), Petrillo (2018), Ledić and Rubil (2019), and Decancq et al. (forthcoming). The only difference is that these authors allow for preference heterogeneity, modelled through interactions between job or life dimensions and certain personal or employer attributes.

From the first to the last, the questions are from, respectively, the German Socio-Economic Panel (GSOEP), the European Quality of Life Survey (EQLS), the British Household Panel Survey (BHPS), the International Survey for Higher Education Graduates (HEGESCO/REFLEX), and the European Working Conditions Survey (EWCS).

See also other papers that use the satisfaction approach to estimate prefereces: Schokkaert and Jara (2017), Petrillo (2018), Decancq et al. (2017, forthcoming), Decancq and Schokkaert (2016), Decancq and Neumann (2016), Defloor et al. (2017), Ledić and Rubil (2019), Mahler and Ramos (2019), Jara et al. (2019).

Or what amount of money would they be willing to receive to give up an amount of something they like.

However, some models of labour supply—the so-called latent job choice models (e.g., Dagsvik et al. 2014) – assume these job dimensions are part of the job ‘package’ and observed to the agent, but not to the econometrician.

For a comparison in a different context, see Benjamin et al. (2014).

REFLEX: Austria, Belgium, Czech Republic, Estonia, Finland, France, Germany, Italy, Japan, Netherlands, Norway, Portugal, Spain, Switzerland, United Kingdom. HEGESCO: Hungary, Lithuania, Poland, Slovenia, Turkey.

For more details on HEGESCO, see www.hegesco.org. Unfortunately, the web site for REFLEX does not exist any longer, but sufficient information is provided on HEGESCO’s web site, and on https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:34416/tab/1.

The unweighted average across the 19 countries is 54 percent for males and 50 percent for females. The shares vary across countries from 26 to 72 percent for males, and from 28 to 68 percent for females.

Some dimensions may seem very similar to one another. For example: ‘learn new things’ and ‘new challenges’; ‘time for leisure’ and ‘work and family’; ‘social status’ and ‘useful for society’. It might be that the number of dimensions could have been reduced using factor analysis. However, given the illustrative purpose of this empirical application, we decided to take each dimension as is. If different dimensions carried much the same information, that should be reflected in estimation results—the coefficients on ‘redundant’ dimensions would likely be statistically insignificant. Yet, according to our estimates, this does not seem to be the case (see Sect. 3.3).

The same question is asked for ‘high earnings’ as well, but it obviously is not a non-wage dimension, and thus we do not consider it.

It is recommended (e.g., Muñoz de Bustillo et al. 2011; OECD 2017) that job dimensions capture objective features of the job as experienced by workers, rather than workers’ subjective evaluations of these features. A worker’s own assessments of the extent to which a job dimension ‘applies’ to his job is in a sense a subjective evaluation. However, what is exactly meant by subjective evaluation is the worker’s assessment of how the level of a job dimension that he experiences on his job affects his subjective well-being—for example job satisfaction, or overall life satisfaction, or happiness. Judging by the wording of the questions on job dimensions in HEGESCO and REFLEX, it does not seem likely that respondents understood them as questions on the impact of job dimensions on their subjective well-being.

In a robustness check, we treat these variables as ordered categorical variables, and include them in the job satisfaction models as a set of four dummies. The results change little (see Sect. 3.4).

In a robustness check, we treat this variable as cardinal, and estimate the job satisfaction models using ordinary least squares. The results change very little (see Sect. 3.4).

This, and the previous one, could also be seen as job dimensions, but here we chose to specify job dimensions more generically.

Or, alternatively, introducing in the model additional 180 parameters (10 job dimensions × 18 country dummies).

Precisely, all statistically significant estimates of the coefficients on log-wage and the non-wage dimensions are positive, which is in line with the estimates from table 1, where preferences are equal across countries. In rare cases of negative estimates, these are very close to zero and statistically insignificant.

In fact, there is no multi-country application so far that estimated preferences separately for each country for the purpose of equivalent income computation. See Decancq and Schokkaert (2016), Petrillo (2018), and Ledić and Rubil (2019).

EW is also substantially more unequally distributed than wage, as measured by any commonly used inequality measure, such as the Gini coefficient or the Atkinson index. The estimates are not reported here but are available on demand. In the context of overall well-being, Decancq and Schokkaert (2016), Petrillo (2018), Decancq et al. (2017), and Ledić and Rubil (2019) found that equivalent income inequality is much higher than income inequality. For further analysis, see Decancq et al. (2017), who explored the role of preference heterogeneity for equivalent income inequality, and Ledić and Rubil (2019), who proposed a decomposition of the difference between equivalent income inequality and income inequality.

Some caution is in order, however, given the dependence of estimates on the analysts' choices concerning definitions, sample, and methods used (Weichselbaumer and Winter-Ebmer 2005).

Turkey is excluded because its wage gap is negative, although practically equal to zero.

See Fig. S4 for the results for all countries.

The ‘explained’ part is due to differences in various characteristics pertinent in wage formation, while the ‘unexplained’ part is attributed to the ‘returns’ on these characteristics, reflecting how each characteristic is valued in terms of pay. For an extensive overview of decomposition methods, see Fortin, Lemieux, and Firpo (2011).

https://ec.europa.eu/eurostat/databrowser/view/sdg_05_20/default/table?lang=en.

See the first paragraph of the present section.

For example, one might not identify with preferences arising from addictions or compulsions.

For reviews, see Roemer and Trannoy (2015, 2016) and Ramos and Van de gaer (2016).

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Title: Beyond Wage Gap, Towards Job Quality Gap: The Role of Inter-Group Differences in Wages, Non-Wage Job Dimensions, and Preferences
Authors: Marko Ledić
Ivica Rubil
Publication date: 24-01-2021
Publisher: Springer Netherlands
Published in: Social Indicators Research / Issue 2/2021
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-021-02612-y

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