Introduction

Much has been written on employment in the public and private sectors and the relative attributes of each one. To begin with, there exists a vast array of literature on public–private sector wage differentialsFootnote 1 (Bender, 1998; Mueller, 1998; Birch, 2006; Lucifora & Muers, 2006; Makridis, 2021; Sánchez-Sánchez & Fernández Puente, 2021), as well as job satisfaction (Ghinetti, 2007; Heywood et al., 2002). In addition, there is another area of research on employment in the public and private sectors that focuses on the returns to education that workers obtain when they are employed in both sectors. Most studies are empirical and have been done using surveys of mainly cross-sectional data for specific countries. However, within this line of research, there seems to exist a consensus that returns to education are higher in the private sector in most countries (Montenegro & Patrinos, 2022). That is to say, the return of workers in the private sector of the economy to further education is higher than for those working in the public sector (Psacharopoulos & Patrinos, 2018). According to Budria (2010), this is a worldwide phenomenon, although some authors have recently found exceptions to this stylized fact in a handful of countries such as India (Chen et al., 2022) and Turkey (Patrinos et al., 2019). Psacharopoulos (1979) explains that wages may exceed productivity in the public but not in the private sector. Furthermore, the overpayment of government workers with respect to their productivity can be mostly found at the lower end of the earnings distribution (Siminski, 2013). The public sector offers (relatively) high wages to unskilled workers and (relatively) low wages to the high-skilled (Budria, 2010). Psacharopoulos (1983) concludes that less qualified labor is better treated in the public than in the private sector.

Nonetheless, we could reasonably argue that it may not be the earnings that matter most to the workers, but the satisfaction they obtain from their working conditions and pay. Also, as far as we know, relatively less attention has been paid to returns to education in the public and the private sectors in terms of the workers’ well-being. Furthermore, there are several studies on job satisfaction in which both education and the sector (either public or private) are considered explanatory variables in job-satisfaction regressions, but the interaction between both variables and their effect on job satisfaction has not been analyzed yet.

In this paper, we analyze the self-reported levels of satisfaction with working conditions and pay of public and private sector workers who have attained different levels of education. In the context of Herzberg’s two-factor theory (Herzberg et al., 1959), our focus on satisfaction with working conditions and pay could be framed as an analysis of the hygiene or extrinsic factors of job satisfaction. In particular, we compare and rank the satisfaction with working conditions and pay of European workers who are employed in the private and public sectors. We intend to shed some new light on this topic by analyzing self-reported job satisfaction in a large sample of 43,850 workers from 35 countries, which is the last available wave of the European Working Conditions Survey (EWCS).

Since data on job satisfaction provided by surveys is based on subjective replies to an interviewer’s questions in some sort of categorical fashion, in our analysis we use the Balanced Worth (BW) procedure. The BW was developed from the concept of Net Difference (Lieberson, 1976) and incorporates the economic concepts of value and equilibrium. This methodology has been specifically designed to compare distributions of categorical data (Herrero & Villar, 2013, 2018) to analyze inequality among different population groups.

In recent years, the BW has been successfully used in different areas of the Social Sciences and has proven to be a suitable mechanism for researching happiness and well-being (Herrero & Villar, 2018). In particular, the BW procedure has been applied in studies on human capital and health (Herrero & Villar, 2013), education (Herrero et al., 2014), corporate responsibility (Gallén & Peraita, 2015), nationalist identity (Torregrosa, 2015), research (Albarrán et al., 2017), life satisfaction (Herrero & Villar, 2018), labor market (Herrero & Villar, 2019), happiness (Ravina-Ripoll et al., 2020), COVID-19 (Herrero & Villar, 2020), and income distributions (Herrero & Villar, 2021).

The paper proceeds as follows. We devote the next two sections to briefly summarizing the methodology and describing the data set. The following sections display our results regarding satisfaction with working conditions and payment in both sectors. The final section summarizes the conclusions of our empirical analysis.

The BW Procedure

Questions regarding job satisfaction are quite similar across surveys, as they ask workers to provide an overall assessment of their job satisfaction and working conditions, offering them several categorical responses. Score methods are commonly used when dealing with ordered categorical data, which consist of giving weights to each of the categories in an appropriate order and evaluating the groups according to their mean values. The problem with these methods is that the choice of weights introduces an exogenous cardinalization in the original information, leading to arbitrary results.

An alternative method is based on the concept of stochastic dominance. This procedure is robust, and only relies on categorical information, without using external weights. But this method provides neither a complete order nor cardinal information about the relative goodness of the distributions (Herrero & Villar, 2018). To amend these problems, Lieberson (1976) posed a procedure to compare any pair of distributions, based on the concept of probabilistic dominance. That is, how likely it is that an individual from one group chosen at random will belong to a higher category than an individual from any other group chosen at random.

Following Herrero and Villar (2013, 2018), let us consider the problem of comparing the distributions of g different groups over a set of k categories. The following table shows the information that we can draw from our data set, where the first row represents different categories. These categories are ordered from best to worst and the first column corresponds to the different groups and \({x}_{ij}\) denotes the relative frequency in which category j appears in group i, where \({\sum }_{j=1}^{k}{x}_{ij}=1, \forall i=\mathrm{1,2},\dots ,g.\)

 

\({c}_{1}\)

\({c}_{2}\)

…

\({c}_{k}\)

\({f}_{1}\)

\({x}_{11}\)

\({x}_{12}\)

…

\({x}_{1k}\)

\({f}_{2}\)

\({x}_{21}\)

\({x}_{22}\)

…

\({x}_{2k}\)

\(\vdots\)

    

\({f}_{g}\)

\({x}_{g1}\)

\({x}_{g2}\)

…

\({x}_{gk}\)

Our comparison problem consists of evaluating the relative dominance of these frequencies among them. This is an extension of the concept of Net Difference introduced by Lieberson (1976) for the case of two groups. Hence, let

$${p}_{ij}={x}_{i1}\left({x}_{j2}+{x}_{j3}+\dots +{x}_{jk}\right)+{x}_{i2}\left({x}_{j3}+\dots +{x}_{jk}\right)+\dots +{x}_{i\left(k-1\right)}{x}_{jk}$$

be the probability that a randomly chosen individual from group i belongs to a better category than a randomly chosen individual from group j. On the other hand, the probability that the randomly chosen individuals from both groups belong to the same category is

$${e}_{ij}={x}_{i1}{x}_{j1}+{x}_{i2}{x}_{j2}+\dots +{x}_{ik}{x}_{jk,}$$

where \({e}_{ij}={e}_{ji}\) and \({p}_{ij}+{p}_{ji}+{e}_{ij}=1\). The procedure posed by Herrero and Villar (2018) consists of choosing one individual from each group at random and comparing its category with that of a randomly chosen individual from all the other groups. In the pairwise comparison, when a randomly chosen individual of group i belongs to a higher category than a randomly chosen individual of group j, we say that the distribution of group i dominates the distribution of group j. If both individuals belong to the same category (with probability \({e}_{ij}\)), each group is declared dominant with a probability of 0.5. Hence, the probability of group i being declared not worse than group j is \({{q}_{ij}=p}_{ij}+\frac{1}{2}{e}_{ij}\). At this point, Herrero and Villar (2013, 2018) introduce the concept of the worth of group i, given by \({v}_{i}\), which could be interpreted as the value of not-being dominated by any group. To avoid problems of cycles that may arise in the pairwise comparisons between groups, Herrero and Villar (2018) propose to determine the groups’ worth by equating the expected worth of group i not being dominated by the rest of the groups with the expected worth of the rest of the groups not being dominated by group i. That is,

$${\sum }_{j\ne i}{q}_{ij}{v}_{j}={v}_{i}{\sum }_{j\ne i}{q}_{ji}.$$

Extending this criterion to all the groups, we can write the following homogeneous linear system \(Mv=0\), where

$$M=\left(\begin{array}{ccc}-\sum_{j\ne 1}{q}_{j1}& \begin{array}{cc}{q}_{12}& \dots \end{array}& {q}_{1g}\\ {q}_{21}& \begin{array}{cc}-\sum_{j\ne 2}{q}_{j2}& \dots \end{array}& {q}_{2g}\\ \begin{array}{c}\vdots \\ {q}_{g1}\end{array}& \begin{array}{c}\vdots \\ \begin{array}{cc}{q}_{g2}& \dots \end{array}\end{array}& \begin{array}{c}\vdots \\ -\sum_{j\ne g}{q}_{jg}\end{array}\end{array}\right)$$

and \({v}^{^{\prime}}=\left({v}_{1},{v}_{2},\dots ,{v}_{g}\right)\). Since the sum of each column of M equals zero, the vectors that conform it are linearly dependent, thus M is singular, and the homogeneous system has one degree of freedom. To amend the indetermination of the solution, Herrero and Villar (2018) add an additional equation associated with the normalization of the worth vector, say\(\sum_{1}^{g}{v}_{i}=g.\)Footnote 2 Herrero and Villar (2018) prove that the solution to this system exists, is unique, and is given by \({v}^{*}=\left({v}_{1}^{*},{v}_{2}^{*},\dots ,{v}_{g}^{*}\right)\) such that \(\sum_{1}^{g}{v}_{h}^{*}=g\), and

$$\begin{array}{cc}v_i^\ast=\frac{\sum_{j\neq i}q_{ij}v_j^\ast}{\sum_{j\neq i}q_{ji}},&i=1,2,\dots,g.\end{array}\\$$

We call \({v}^{*}\) the BW vector. The main contribution of the BW is that, according to the probability of the domination criterion, it ranks the different groups using a cardinal measure. In contrast to the score methods previously used to compare categorical distributions, the BW procedure does not only rank the dominance of a group, but it also provides endogenous information about the intensity of such dominance using the cardinality of its components. The BW has other desirable properties, such as anonymity, symmetry, and monotonicity (see Herrero & Villar, 2013, 2018).

The website of the Instituto Valenciano de Investigaciones Económicas (IVIE) provides the algorithm to calculate the BWV freely (http://www.ivie.es/balanced-worth/).

The Data

The data set that we use is the sixth wave of the European Working Conditions Survey (EWCS), which was conducted in 2015 by the European Foundation for the Improvement of Living and Working Conditions (Eurofound) and interviewed 43,850 workers whose answers provide an overview of working conditions in Europe. Using the cross-national weights given by the EWCS ensures that each country is represented in proportion to the size of its in-work population.

The EWCS is based on a questionnaire that was administered face-to-face to a random sample of 'persons in employment', both employees and self-employed, who work in the private sector, the public sector, or at other alternatives, such as joint private–public organizations or companies, the not-for-profit sector or non-governmental organizations. Among them, 68.47% of surveyed individuals declare to work in the private sector, while 23.06% declare to work in the public sector. For our purposes, we focus on the private and the public sectors, where 91.53% of workers are employed.

Regarding worker’s education, the EWCS classifies the attained educational levels of surveyed individuals according to the categories of the International Standard Classification of Education (ISCED). These categories and their percentages in the EWCS are the following: childhood education (0.58%), primary education (4.75%), lower secondary education (13.37%), upper secondary education (41.55%), post-secondary-non-tertiary education (7.02%), short-cycle tertiary education (9.42%), bachelor or equivalent (13.05%), master or equivalent (9.29%), and doctorate or equivalent (0.95%). For the sake of representativeness, we consider individuals that report levels of education from primary to master or equivalent.Footnote 3 Moreover, to keep a unimodal and symmetrical distribution, we gather the groups of post-secondary-non-tertiary education and short-cycle tertiary education in one group called post-secondary-tertiary education (16.43%).

Results

Let us first consider the educational attainment of workers employed in the private and public sectors using the BW procedure. Table 1 shows the distributions of groups for the private and the public sector according to the level of education of their workers and their corresponding Balance Worth Value (BWV).

Table 1 Distributions and BW for educational attainment by sectors
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The BW components in Table 1 indicate that a randomly chosen individual from the public sector is more likely to have a higher education level than a randomly chosen individual from the private sector. In other words, the concentration of educated workers is considerably higher in the public than in the private sector across European countries, as the frequency distributions and the subsequent BW calculation show. Notice the difference between both components of the BW vector, which suggests a high level of inequality in terms of the educational attainment of workers in both sectors. This will be our first finding.

Moreover, we have analyzed whether this result holds in each of the 35 countries of the EWCS. We have also calculated the difference between the BW components and ordered countries according to that difference. These calculations are shown in the Appendix. Figure 1 illustrates the differences in the BW components for the public and the private sectors for each country. As we can observe, in all the countries workers’ educational attainment is higher in the public than in the private sector with no exception. Nonetheless, there are differences across countries. The country with greater inequality in both sectors regarding the educational attainment of its workers is Albania, followed by Turkey and Greece. At the other end of the spectrum, we find The Netherlands, where the educational achievement of workers in the public and the private sector is more similar, remaining higher in the public than in the private sector, as in any other country in this data set.

Fig. 1
figure 1

Difference between workers’ educational attainment in the public and private sector BWV components by countries ranked from the lowest to the highest

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Working in the public sector has been related to higher levels of job satisfaction (Ghinetti, 2007; Heywood et al., 2002). Ghinetti (2007) indicates that public employees differ from private employees in the way they evaluate satisfaction with job security, consideration by colleagues, and safety and health job features. Sánchez-Sánchez and Fernández Puente (2021) conclude that public sector employees are more satisfied in terms of stability but not in terms of wages. According to Clark and Postel-Vinay (2009), permanent public jobs are perceived to be by and large insulated from labor market fluctuations and this may be the reason educated workers prefer to be employed in the public sector of the economy.

We now proceed to analyze and compare the levels of satisfaction with working conditions and payment reported by workers with different levels of educational attainment employed in both sectors.

Satisfaction with Working Conditions

Question Q88 of the EWCS enquires about the worker’s satisfaction with working conditions in the following terms: “On the whole, are you very satisfied, satisfied, not very satisfied, or not at all satisfied with working conditions in your main paid job?” Overall, 24.79% of workers report being very satisfied, 58.41% report being satisfied, 13.08% report being not very satisfied, 3.02% report being not at all satisfied, and 0.6% report no opinion or refuse to answer this question.Footnote 4

Tables 2 and 3 show the distributions and the satisfaction with working conditions for the different educational levels measured through the BW for the private and the public sectors, respectively.

Table 2 Distributions and BW for satisfaction with working conditions and educational attainment for the private sector
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Table 3 Distributions and BW for satisfaction with working conditions and educational attainment for the public sector
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Results radically differ in both sectors. While in the private sector, we can observe a monotonically increasing relationship between workers’ educational attainment and job satisfaction, in the public sector this monotonicity is lost. We can also observe how the BW components in the public sector are much closer to their mean value,Footnote 5 which suggests that the different educational groups are more similar in terms of job satisfaction than within the private sector, where the range of the BW components is larger. Neither lower nor upper secondary education is associated with higher job satisfaction than primary education. In a similar line, having a bachelor’s degree does not improve job satisfaction in the public sector. In general, we can assert that there is little difference in terms of satisfaction with working conditions among workers belonging to alternative educational groups in the public sector whereas job satisfaction clearly increases with education in the private sector.

To visualize these results and compare them across sectors, we have calculated the differences between each component of the BW vector and the component of the group of workers with primary education, which we denote as the primary-education normalized BW (PENBW). We have done this for both sectors and our findings are illustrated in Fig. 2. Clear differences can be observed between both sectors.

Fig. 2
figure 2

PENBW for satisfaction with working conditions by sectors

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Adopting an alternative perspective in terms of BW calculations, we can compare workers’ job satisfaction in the private and public sectors within each educational group. Table 4 displays these results. As we can observe, workers who have attained the highest levels of education, i.e., bachelor’s and master’s degrees, report being more satisfied with their working conditions if they work in the private sector whereas workers with lower educational attainment report being happier at work when they are employed in the public sector. Notice the relatively larger difference in the BW components for workers with primary education, which suggests that workers with the lowest level of educational attainment report being much more satisfied with their working conditions if they are employed in the public than in the private sector.

Table 4 BW for satisfaction with working conditions by sector for each level of educational attainment
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Satisfaction with Pay

We proceed to analyze whether workers employed in the private and public sectors feel satisfied with the pay that they receive. The EWCS poses the following questionFootnote 6: Considering all my efforts and achievements in my job, I feel I get paid appropriately, for which it offers the following options: Strongly agree; Tend to agree; Neither agree nor disagree; Tend to disagree; Strongly disagree. Overall, 97.15% of workers answer this question, and the distributions of replies for each level of educational attainment are depicted in Table 5 along with the BW calculation.

Table 5 Distributions and BW of education level for the whole sample
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The BW results indicate that the higher the level of education attained by workers, the higher the probability of them reporting they receive appropriate payment for their work. Indeed, there is a monotonic relationship between educational attainment and satisfaction with pay in the whole sample, but does this monotonicity hold in each sector?

To answer this question, in what follows, we undertake BW calculations segregating workers into two different subsamples, depending on whether they work for the private or the public sector. Results are shown in Tables 6 and 7, respectively. Figure 2 illustrates the associated PENBW calculations (Fig. 3).

Table 6 Distributions and BW of education level for the private sector
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Table 7 Distributions and BW of education level for the public sector
Full size table
Fig. 3
figure 3

PENBW for satisfaction with payment by sectors for each level of education

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Again, we find a completely different scenario in terms of the workers’ feelings toward their pay in the private and public sectors. Whereas workers in the private sector are more likely to report that the pay they receive is appropriate as their level of education increases, a nonmonotonic pattern can be observed for workers in the public sector. Moreover, in this sector, we find a paradoxical result, only the least educated (primary education) and the most educated (master or equivalent) are more likely to report being satisfied with the pay they receive. By contrast, workers with intermediate levels of educational attainment show lower degrees of satisfaction with their pay.

Finally, let us assess the BW calculation for workers' satisfaction with pay in the private and public sectors within each level of educational attainment. Table 8 depicts the results.

Table 8 BW for satisfaction with payment by sector for each level of educational attainment
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Only workers who have attained the lowest levels of education, i.e., primary or lower secondary, consider that they are more appropriately paid in the public sector than their private-sector counterparts. Workers with higher educational attainment feel more appropriately paid if they are employed in the private sector.

Although we are analyzing satisfaction with pay instead of monetary returns to education, we could say that our findings are in line with previous empirical evidence concerning the returns to education in terms of earnings in the public and private sectors. As we stated earlier, previous research has concluded that returns to education are higher for private-sector workers than for public-sector workers, at least in higher-income countries (Budria, 2010; Psacharopoulos, 1979, 1983). In our large sample of mainly European workers, we find that workers with higher educational attainment report higher satisfaction with their pay when they are employed in the private sector and workers with lower educational levels report to be more satisfied with their earnings if they work in the public sector. In this sense, it is not surprising to find that the analysis of subjective measures of workers’ well-being with their pay is coherent with earlier empirical studies regarding the relative -and objectively measured- monetary returns to education in both sectors.

Concluding Remarks

Understanding how to assess returns to education in the public and the private sectors of the economy should be crucial to implement policy decisions in public sector administration. Just as policymakers can learn from estimates of the monetary benefits of education, we argue that workers’ perceptions of returns to education in both sectors should also be considered. However, while there is an established consensus that education is more profitable -in terms of earnings- in the private than in the public sector, relatively little is known about satisfaction with working conditions and pay in both sectors. In this paper, we have analyzed both issues for public and private sector employees with different educational attainment by applying the BW procedure to a large sample of European workers. We conclude that:

  1. (a)

    Educational attainment is higher among workers employed in the public sector in each one of the 35 countries of the EWCS data set.

  2. (b)

    Higher levels of satisfaction with working conditions and pay are related to higher levels of education in the private sector but not in the public sector.

  3. (c)

    Public-sector workers with lower education levels report higher levels of satisfaction with working conditions and pay compared with their private-sector counterparts.

  4. (d)

    Private-sector workers with higher levels of educational attainment declare higher levels of satisfaction with working conditions and pay than their public-sector counterparts.

Given the relatively higher levels of educational attainment of workers employed in the public sector, despite nonincreasing satisfaction with working conditions and pay, other underlying factors, which have not been considered in this paper, must determine the preference -and prevalence- of educated workers for the public sector. While this analysis falls beyond the scope of this paper and more research is needed to unravel this puzzle, one lesson to be learned is that despite the lower levels of satisfaction reported by highly educated people with working conditions and pay in the public sector, the likelihood of finding educated workers is still higher in this sector than in the private sector. Nevertheless, public administration managers should be, in our view, aware of this fact to implement policies that prevent highly educated workers employed in the public sector from quitting their jobs and searching for jobs in the apparently more rewarding private sector.