Research regarding subjective well-being (including life satisfaction and domain-specific satisfaction) is necessary, given the effects thereof on health, work performance, social relationships and ethical behaviour of employees.
This study aimed to investigate the relationships among life satisfaction, job satisfaction and wage satisfaction, as well as how these relationships related to gross wage category in a South African sample.
While research has shown that wage level and wage satisfaction are positively associated with both job and life satisfaction, the question arises whether wage level and satisfaction would compensate for the negative effect of a dissatisfying job on life satisfaction.
A cross-sectional design was used. A non-probability convenience sample (
Although job satisfaction and wage satisfaction were strongly related at a low level of wage satisfaction, fewer people were satisfied with their jobs at a high level of wage satisfaction level. Moreover, while job and life satisfaction were strongly related at a low level of job satisfaction level, relatively fewer people were satisfied with their lives at a high level of job satisfaction level. Wage dissatisfaction was associated with dissatisfaction with life but was more strongly associated with life satisfaction at a high level of wage satisfaction. Wage category and wage satisfaction did not interact with the job satisfaction level in affecting life satisfaction.
Managers should attend to the perceptions of wage dissatisfaction at low wage and wage satisfaction levels. Such dissatisfaction may have a negative impact on the job and life satisfaction of employees and result in detrimental effects on employees and organisations.
This study contributes to scientific knowledge regarding the relationships between wage, wage satisfaction, job dissatisfaction and life satisfaction.
Subjective well-being captures an individual’s subjective assessment of his or her own life (Diener & Seligman,
Financial concerns have a strong influence on employees’ work behaviour (Chapman, Uggerslev, Carroll, Piasentin, & Jones,
Past research focused primarily on the impact of pay on job satisfaction (Clark, Kristensen, & Westergård-Nielsen,
Job satisfaction is more closely related to satisfaction with one’s job or aspects of that job, and this may vary at different points in time (Diener, Suh, Lucas, & Smith,
In terms of direct income, the concept of
The theory of compensating wage differentials is based on utility theory and represents another attempt to explain the link between wages and satisfaction. According to this theory, individual utility is derived from both wages and non-financial job aspects (Böckerman, Ilmakunnas, & Johansson,
Not all researchers agree with the wages and life satisfaction link. Easterlin found that increasing individuals’ income did not necessarily increase their life satisfaction (Clark, Frijters, & Shields,
Research indicates that wage satisfaction involves both satisfaction with wage amount and the wage process (Brown & Huber,
The link between wages and job satisfaction is well researched, with wages generally being a predictor of job satisfaction (Carr & Mellizo,
Studies have shown that employed individuals (given that they receive a wage) experience higher life satisfaction than unemployed individuals, even those employed in ‘bad’ jobs, that is, jobs with low job quality (Arampatzi, Burger, & Veenhoven,
This study aimed to investigate the relationships among wage category (derived from a gross wage), wage satisfaction, job satisfaction and life satisfaction and to determine whether gross wage category and wage satisfaction compensate for the effect of low job satisfaction on life satisfaction.
This study utilised the South African WageIndicator (WI) survey data set and focused on individuals who were actively employed. As the data set contains data on a large spectrum of variables related to respondents’ work experiences, it was necessary to determine which variables were best suited to answering the research questions. The following variables were identified: job satisfaction, life satisfaction, wage satisfaction and gross wage. The data set was then cleaned to ensure that missing and indolent responses within the four variables under study were removed. Data cleaning resulted in a final sample of 763. The sample was not limited to a single organisation or geographic region and represented different genders, marital statuses, education levels, income levels, ages and racial groups.
Regarding wage distribution in the population (
Participants’ characteristics: Cleaned sample (
Category | Sample |
Population |
||
---|---|---|---|---|
Frequency | % | Frequency | % | |
< Grade 12 | 63 | 8.3 | 142 | 6.4 |
Grade 12 | 244 | 32.0 | 644 | 29.6 |
NTC I to III | 38 | 5.0 | 106 | 4.9 |
Diploma | 199 | 26.1 | 593 | 27.2 |
Bachelor’s degree | 120 | 15.7 | 387 | 17.8 |
Honours degree | 67 | 8.8 | 210 | 9.6 |
Master’s degree | 28 | 3.7 | 86 | 3.9 |
Doctorate | 4 | 0.5 | 10 | 0.5 |
Female | 471 | 61.7 | 1300 | 59.7 |
Male | 292 | 38.3 | 878 | 40.3 |
NTC, National Technical Certificate.
The WI data set contains both cross-sectional and longitudinal data regarding working conditions and wages across 65 countries. The WI survey has been active in South Africa since 2005, with the 2013 data set being the most recent available at the time of access. Access to the data set can only be obtained through a formal application process made to the International Data Service Centre (IDSC), and access is restricted to universities and research institutions.
Data collection for the WI data set takes place via an online multilingual survey hosted on dedicated sites in each of the countries covered by the survey. These dedicated sites provide users with a salary comparison tool and job-related information tailored to the specific country that the site covers. Visitors to these sites are invited to complete the WI survey. An incentive is offered for completion. The WI survey measures life satisfaction using a Likert scale, ranging from 1 (dissatisfaction) to 10 (satisfaction). Researchers have found that single-item life satisfaction measures exhibit similar criterion validity to multiple-item measures and do not produce systematically different correlations (Cheung & Lucas,
Job satisfaction is measured with a direct evaluation (‘How satisfied are you with your job?’). The survey utilises one item to evaluate an individual’s gross wage (‘What is your gross income?’) and how this is structured. The survey contains one item directly related to wage satisfaction (‘How satisfied are you with your pay?’). Both wage satisfaction and job satisfaction are measured using a five-point Likert rating scale, ranging from 1 (highly dissatisfied) to 5 (highly satisfied). Dolbier, Webster, McCalister, Mallon and Steinhardt (
To protect the confidentiality and privacy of respondents, no names, addresses or other direct identifiers were requested from respondents (Tijdens, Van Zijl, Hughie-Williams, Van Klaveren, & Steinmetz,
Access to the WI data set was obtained through a formal application process to the IDSC. On approval, the data set was downloaded in the form of an SPSS input file and stored locally. The latest available data set (2013) was selected and cleaned to remove missing or incomplete values. This was done using IBM SPSS 23 (IBM Corporation,
The data set was reviewed to determine the variables that had to be included in the study to answer the research question: Do wage category and wage satisfaction compensate for a dissatisfying job? The reasoning was to examine whether participants who received a high gross wage and were satisfied with their jobs would experience low job satisfaction and high life satisfaction; this would be most suited to answering the primary research question. This reasoning was supported by research indicating that two-way interactional relationships existed among wages (and wage satisfaction), job satisfaction and life satisfaction, with life satisfaction being a broader overarching concept (Boodoo et al.,
For the wage satisfaction, job satisfaction and life satisfaction variables, the decision was made to focus on contrast groups (high and low). Average scores were removed from the analyses to obtain more accurate estimates of the relations between variables when scores on the four variables were high and low. Groups were selected that scored either high or low (in terms of the upper and lower percentile) on wage satisfaction, job satisfaction or life satisfaction. These contrast groups were studied in relation to gross wage category. Four new variables were created in IBM SPSS, which divided the selected variables into three categories, namely, low (1), medium (2) or high (3). The medium group was removed for the satisfaction variables, leaving the following categories: high, medium and low gross wage category; high and low wage satisfaction; high and low job satisfaction; and high and low life satisfaction.
This allowed for the comparison of contrast groups among the different variables, for example, comparing gross wage with job satisfaction. The rationale for using contrast groups is that three categories of gross wages (high, medium, low) were retained, as these would allow the exploration of interactions between satisfaction and medium wage if the need were to arise. Gross wage was the only variable that contained continuous data, as individuals could enter any amount rather than select from a scale.
IBM SPSS 23 (IBM Corporation,
Cross-tabulation was used to produce a contingency table of the data. Therefore, the contingency table contained the number of cases that fell into each of the categories. This table was used to test whether the assumptions required for hierarchical log-linear analysis had been met, namely, no counts less than one and not more than 20% less than five (Field,
Hierarchical log-linear analysis was used to construct a model that predicted the data. Log-linear analysis allows for the expression of categorical data in the form of a linear model, provided that logarithmic values are used (Field,
The full model was proposed because there was no prior reason to remove any associations. Screening and model building were used to eliminate associations that did not contribute to observed cell frequencies (Field,
K-way and higher-order effects.
Effects | K | Likelihood ratio |
Pearson ( |
||
---|---|---|---|---|---|
K-way and higher-order effects |
1 | 23 | 681.624 | 0.000 | 713.896 |
2 | 18 | 465.223 | 0.000 | 623.545 | |
3 | 9 | 10.933 | 0.280 | 10.536 | |
4 | 2 | 0.529 | 0.767 | 0.506 | |
K-way effects |
1 | 5 | 216.401 | 0.000 | 90.352 |
2 | 9 | 454.289 | 0.000 | 613.009 | |
3 | 7 | 10.404 | 0.167 | 10.030 | |
4 | 2 | 0.529 | 0.767 | 0.506 |
, Tests that K-way and higher-order effects are zero;
, Tests that K-way effects are zero.
According to Tabachnick and Fidell (
Partial associations.
Effect |
|
Partial chi-square |
|
---|---|---|---|
Wage*Job satisfaction*Wage satisfaction | 2 | 0.202 | 0.904 |
Wage*Job satisfaction*Life satisfaction | 2 | 2.158 | 0.340 |
Wage*Wage satisfaction*Life satisfaction | 2 | 1.135 | 0.567 |
Job satisfaction*Wage satisfaction*Life satisfaction | 1 | 8.084 | 0.004 |
Wage*Job satisfaction | 2 | 5.853 | 0.054 |
Wage*Wage satisfaction | 2 | 24.523 | 0.000 |
Job satisfaction*Wage satisfaction | 1 | 85.612 | 0.000 |
Wage*Life satisfaction | 2 | 3.096 | 0.213 |
Job satisfaction*Life satisfaction | 1 | 54.871 | 0.000 |
Wage satisfaction*Life satisfaction | 1 | 52.564 | 0.000 |
Wage | 2 | 46.133 | 0.000 |
Job satisfaction | 1 | 8.615 | 0.003 |
Wage satisfaction | 1 | 159.858 | 0.000 |
Life satisfaction | 1 | 1.795 | 0.180 |
Significance
Stepwise selection by simple deletion of the model with six two-way terms was conducted with IBM SPSS HILOGLINEAR. The selection process stopped after the second step because the criterion probability (0.01) was reached. Each potential model generates a set of expected frequencies. The goal of the model selection is to find the smallest number of effects that still provides a fit between expected and observed frequencies. Criteria for the optimal model are that (1) it must have a non-significant likelihood ratio chi-square value and (2) the selected model should not be significantly worse than the next more complicated model (Tabachnick & Fidell,
The first model (Step 0) included six effects. The model was not significant, meaning that it provided an acceptable fit between expected and observed frequencies: χ2 = 10.934,
The likelihood ratio χ2 (14.030,
Cell counts and residuals.
Wage | Job satisfaction | Wage satisfaction | Life satisfaction | Observed |
Expected |
Standardised residuals | ||
---|---|---|---|---|---|---|---|---|
Count | % | Count | % | |||||
1 | 1 | 1 | 1 | 106.000 | 13.9 | 93.831 | 12.3 | 1.256 |
2 | 30.000 | 3.9 | 26.860 | 3.5 | 0.606 | |||
2 | 1 | 3.000 | 0.4 | 1.117 | 0.1 | 1.781 | ||
2 | 1.000 | 0.1 | 1.555 | 0.2 | -0.445 | |||
2 | 1 | 1 | 38.000 | 5.0 | 41.019 | 5.4 | -0.471 | |
2 | 32.000 | 4.2 | 44.290 | 5.8 | -1.847 | |||
2 | 1 | 6.000 | 0.8 | 4.854 | 0.6 | 0.520 | ||
2 | 23.000 | 3.0 | 25.474 | 3.3 | -0.490 | |||
2 | 1 | 1 | 1 | 107.000 | 14.0 | 113.417 | 14.9 | -0.603 |
2 | 31.000 | 4.1 | 32.467 | 4.3 | -0.257 | |||
2 | 1 | 6.000 | 0.8 | 3.014 | 0.4 | 1.720 | ||
2 | 1.000 | 0.1 | 4.193 | 0.5 | -1.559 | |||
2 | 1 | 1 | 53.000 | 6.9 | 49.581 | 6.5 | 0.485 | |
2 | 58.000 | 7.6 | 53.535 | 7.0 | 0.610 | |||
2 | 1 | 11.000 | 1.4 | 13.091 | 1.7 | -0.578 | ||
2 | 71.000 | 9.3 | 68.702 | 9.0 | 0.277 | |||
3 | 1 | 1 | 1 | 34.000 | 4.5 | 44.638 | 5.9 | -1.592 |
2 | 16.000 | 2.1 | 12.778 | 1.7 | 0.901 | |||
2 | 1 | 3.000 | 0.4 | 2.980 | 0.4 | 0.012 | ||
2 | 3.000 | 0.4 | 4.146 | 0.5 | -0.563 | |||
2 | 1 | 1 | 24.000 | 3.1 | 19.514 | 2.6 | 1.016 | |
2 | 24.000 | 3.1 | 21.070 | 2.8 | 0.638 | |||
2 | 1 | 9.000 | 1.2 | 12.944 | 1.7 | -1.096 | ||
2 | 73.000 | 9.6 | 67.930 | 8.9 | 0.615 |
Normal probability plot for the selected model.
Two types of information were useful in interpreting the model, namely, parameter estimates for the model and marginal observed frequency tables for all included effects. The log-linear parameter estimate, the
Parameter estimates.
Effect | Parameter | Estimate | SE | 95% confidence interval |
||
---|---|---|---|---|---|---|
Lower bound | Upper bound | |||||
Job satisfaction*Wage satisfaction | 1 | 0.574 | 0.069 | 8.310 |
0.439 | 0.710 |
Job satisfaction*Life satisfaction | 2 | 0.332 | 0.044 | 7.495 |
0.245 | 0.419 |
Wage satisfaction*Life satisfaction | 3 | 0.395 | 0.054 | 7.365 |
0.290 | 0.501 |
Wage satisfaction*Wage (1) | 4 | 0.421 | 0.070 | 6.00 |
0.283 | 0.559 |
Wage satisfaction*Wage (2) | 5 | 0.020 | 0.057 | 0.346 | -0.092 | 0.132 |
Job satisfaction | 6 | -0.492 | 0.068 | -7.216 |
-0.626 | -0.359 |
Wage satisfaction | 7 | 0.824 | 0.073 | 11.343 |
0.682 | 0.967 |
Life satisfaction | 8 | -0.102 | 0.054 | -1.891 | -0.207 | -0.004 |
Wage (1) | 9 | -0.237 | 0.070 | -0.370 | -0.374 | -0.099 |
Wage (2) | 10 | 0.354 | 0.057 | 6.195 |
0.242 | 0.466 |
,
SE, Standard error; z, z value
The strongest predictor of cell size was wage satisfaction (
Significance tests for the hierarchical model of life satisfaction.
Effect | Partial association ( |
90% confidence interval |
|
---|---|---|---|
Lower bound | Upper bound | ||
First-order effects | |||
Wage satisfaction | 159.858 | 120.967 | 204.155 |
Wage | 46.133 | 26.490 | 71.182 |
Job satisfaction | 8.615 | 1.664 | 20.976 |
Life satisfaction | 1.795 | 0.000 | 8.908 |
Job satisfaction*Wage satisfaction | 85.612 | 57.876 | 118.756 |
Job satisfaction*Life satisfaction | 54.871 | 33.209 | 81.945 |
Wage satisfaction*Life satisfaction | 52.564 | 31.488 | 79.232 |
Wage satisfaction*Wage | 24.523 | 10.118 | 42.361 |
While
Cross-tabulation of frequencies for two-way interaction effects (
Variable | Level | Statistic | Wage satisfaction |
Life satisfaction |
Wage satisfaction |
Total | |||
---|---|---|---|---|---|---|---|---|---|
1.00 | 2.00 | 1.00 | 2.00 | 1.00 | 2.00 | ||||
Job satisfaction | 1.00 | Count | 324.0 | 17.0 | - | - | - | - | 341.0 |
Expected count | 247.1 | 93.9 | - | - | - | - | 341.0 | ||
% within Job satisfaction | 95.0 | 5.0 | - | - | - | - | 100.0 | ||
% within Wage satisfaction | 58.6 | 8.1 | - | - | - | - | 44.7 | ||
2.00 | Count | 229.0 | 193.0 | - | - | - | - | 422.0 | |
Expected count | 305.9 | 116.1 | - | - | - | - | 422.0 | ||
% within Job satisfaction | 54.3 | 45.7 | - | - | - | - | 100.0 | ||
% within Wage satisfaction | 41.4 | 91.9 | - | - | - | - | 55.3 | ||
Job satisfaction | 1.00 | Count | - | - | 259.0 | 82.0 | - | - | 341.0 |
Expected count | - | - | 178.8 | 162.2 | - | - | 341.0 | ||
% within Job satisfaction | - | - | 76.0 | 24.0 | - | - | 100.0 | ||
% within Life satisfaction | - | - | 64.8 | 22.6 | - | - | 44.7 | ||
2.00 | Count | - | - | 141.0 | 281.0 | - | - | 422.0 | |
Expected count | - | - | 221.2 | 200.8 | - | - | 422.0 | ||
% within Job satisfaction | - | - | 33.4 | 66.6 | - | - | 100.0 | ||
% within Life satisfaction | - | - | 35.3 | 77.4 | - | - | 55.3 | ||
Wage satisfaction | 1.00 | Count | - | - | 362.0 | 191.0 | - | - | 553.0 |
Expected count | - | - | 289.9 | 263.1 | - | - | 553.0 | ||
% within Wage satisfaction | - | - | 65.5 | 34.5 | - | - | 100.0 | ||
% within Life satisfaction | - | - | 90.5 | 52.6 | - | - | 72.5 | ||
2.00 | Count | - | - | 38.0 | 172.0 | - | - | 210.0 | |
Expected count | - | - | 110.1 | 99.9 | - | - | 210.0 | ||
% within Wage satisfaction | - | - | 18.1 | 81.9 | - | - | 100.0 | ||
% within Life satisfaction | - | - | 9.5 | 47.4 | - | - | 27.5 | ||
Wage | 1.00 | Count | - | - | - | - | 206.0 | 33.0 | 239.0 |
Expected count | - | - | - | - | 173.2 | 65.8 | 239.0 | ||
% within Wage | - | - | - | - | 86.2 | 13.8 | 100.0 | ||
% within Wage satisfaction | - | - | - | - | 37.3 | 15.7 | 31.3 | ||
2.00 | Count | - | - | - | - | 249.0 | 89.0 | 338.0 | |
Expected count | - | - | - | - | 245.0 | 93.0 | 338.0 | ||
% within Wage | - | - | - | - | 73.7 | 26.3 | 100.0 | ||
% within Wage satisfaction | - | - | - | - | 45.0 | 42.4 | 44.3 | ||
3.00 | Count | - | - | - | - | 98.0 | 88.0 | 186.0 | |
Expected count | - | - | - | - | 134.8 | 51.2 | 186.0 | ||
% within Wage | - | - | - | - | 52.7 | 47.3 | 100.0 | ||
% within Wage satisfaction | - | - | - | - | 17.7 | 41.9 | 24.4 |
As far as the association between job satisfaction and wage satisfaction was concerned, most of the participants who reported job dissatisfaction (95%) also reported dissatisfaction with their wages. A slight majority of those who were satisfied with their jobs (54.3%) were dissatisfied with their wages. If wage satisfaction was high, only 45.7% of the participants showed high job satisfaction. Therefore, although job satisfaction and wage satisfaction were strongly related at a low level of wage satisfaction, fewer people were satisfied with their jobs at a high (compared to a low) wage satisfaction level.
Regarding the association between job satisfaction and life satisfaction, most participants who reported low job satisfaction (76%) also reported dissatisfaction with their lives. Although most of the participants (66.6%) who reported job satisfaction also reported satisfaction with their lives, 33.4% of those who were satisfied with their jobs were not satisfied with their lives. Therefore, although job and life satisfaction were strongly related at a low job satisfaction level, relatively fewer people were satisfied with their lives at a high job satisfaction level.
Concerning the association between wage satisfaction and life satisfaction, 65.5% of the participants who were dissatisfied with their wages were dissatisfied with their lives. At a high level of wage satisfaction, 81.9% of the participants were satisfied with their lives. Therefore, wage dissatisfaction was associated with dissatisfaction with life, but wage satisfaction was even more strongly associated with life satisfaction at a high level of wage satisfaction.
With regard to the relation between wage category and wage satisfaction, most of the participants in the low gross wage category (86.19%) reported low wage satisfaction. Only 13.8% of the participants in the low gross wage category reported high wage satisfaction. Furthermore, in the high wage category, 47.3% of the participants reported high wage satisfaction. However, it should be noted that 52.7% of the participants in the high wage category were dissatisfied with their wage.
The results of this study showed that gross wage was associated with dissatisfaction with wage at a low wage level. However, at a high wage level, this association was much weaker. Job dissatisfaction was associated with dissatisfaction with life at a low level of job satisfaction. However, at a high level of job satisfaction, the association between job and life satisfaction was substantially lower. Wage satisfaction was strongly associated with life satisfaction. Wage dissatisfaction was strongly associated with job dissatisfaction when the wage satisfaction level was low. However, at a high wage satisfaction level, job satisfaction was less strongly associated with wage satisfaction.
High wage and high wage satisfaction did not compensate for lower job satisfaction in affecting life satisfaction. The results showed that of the people who experienced low job satisfaction, 19 (of the expected 26 cases) experienced high life satisfaction when gross wage was high. A total of 31 people (compared to an expected number of 19 cases) who experienced low job satisfaction experienced high life satisfaction when gross wage was low. The hierarchical log-linear analysis also confirmed that interaction did not exist among gross wage, wage satisfaction, job satisfaction and life satisfaction.
This study aimed to examine the relationships among gross wage, wage satisfaction, job satisfaction and life satisfaction. The results provided support for the expected relationships between wage category and wage satisfaction (at a low wage category), wage satisfaction and job satisfaction (at a low level of job satisfaction), job satisfaction and life satisfaction (at a low level of job satisfaction) and wage satisfaction and life satisfaction (at a high level of life satisfaction). These results are not surprising, as previous studies found a link between wages, wage satisfaction, job satisfaction and life satisfaction (Boodoo et al.,
There is a gap in research examining the relationship between job satisfaction and wage satisfaction. Schreurs, Guenter, Van Emmerik, Notelaers and Schumacher (
The level of gross wage did play an important role in the statistical model but specifically in terms of its interaction with wage satisfaction. The results indicated that wages and wage satisfaction were related; therefore, wage satisfaction was likely to rise if wages increased. Lawler’s model supports the link between wages and wage satisfaction by conceptualising wage satisfaction as the discrepancy between the perceived wage that one should receive and the wage one receives (Dyer & Theriault,
It is unlikely that an individual would be more satisfied with his or her job purely because he or she is paid a higher wage. This relationship was based on the argument that wages predicted job satisfaction (Carr & Mellizo,
In terms of the two-factor theory of job satisfaction, wages are particularly linked to extrinsic job satisfaction, which reduces job dissatisfaction rather than promotes job satisfaction (Goetz et al.,
Judge et al. (
Life satisfaction among employees refers to their expectations and how well these are fulfilled, which again gives credence to the idea that an increase in wage satisfaction should result in an increase in life satisfaction (Clark et al.,
This study could not find evidence for three- or four-way effects between wage category, wage satisfaction, job satisfaction and life satisfaction. Inspection of the results confirmed that no such interaction effect exists, even when only wage category was included (i.e. when we did not consider wage satisfaction). Therefore, it seems that a high wage and wage satisfaction did not compensate for job dissatisfaction in impacting life satisfaction.
The results of this study showed that wage satisfaction, job satisfaction and life satisfaction were related. Therefore, if organisations want to reap the benefits of a satisfied workforce, they must focus on all three of these areas. This study, furthermore, showed that a wage that satisfied employees might affect employees’ life satisfaction. The findings of this study have implications for managers in organisations. Firstly, managers should attend to the perceptions of wage dissatisfaction at low wage levels. Such dissatisfaction may have a negative impact on the job and life satisfaction of employees. Secondly, managers should realise that employees in the high wage category are not necessarily satisfied with their wages.
The study utilised convenience sampling methods, and as such its results might not be generalisable to the general population. It did not examine how biographical factors might affect the specified model; future studies could focus on factors such as age, gender, education or geographic region that might affect the relationships among gross wage, job satisfaction and life satisfaction. Furthermore, this study did not consider job type or situational factors; it might be useful to examine whether the position an employee holds in a company would affect the relationship between gross wage and satisfaction. As with most studies examining wages and life satisfaction, this study could only comment on the correlations among variables. Future studies should make use of longitudinal designs to assess whether wage and wage satisfaction interact with job satisfaction to affect life satisfaction.
The author declares that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
J.A.D.C. wrote the literature review and interpreted the results. S.R. conducted the statistical analyses, reported the results and edited the manuscript. M.W.S. provided inputs for the literature study and the interpretation of the findings.