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An Investigation into the Determinants of an Employees Overall
Satisfaction with their Job
By Josh Goodall
Applied Economic Analysis
1. Introduction
Over the years, research articles into the determinants of job satisfaction have taken
a vast variation of different forms. With job satisfaction being investigated across
several different disciplines and become a factor of great interest across social
sciences. Self-reported job satisfaction is a fascinating subjective variable, and this
motivation behind my choice of dependent variable. Self-reported job satisfaction
brings about model questioning due to people defining their own job satisfaction
rather than a concrete answer and this is part of the reasoning behind it being such a
highly debatable topic. Job satisfaction was initially investigated by (Herzberg, et al.,
1957), setting up the foundations for extended research into using job satisfaction as
a dependent variable. Inevitably stating that job satisfaction was a result of twofactors; satisfaction and motivation. ‘Satisfaction’ has been the key concept that has
been built on since then, specifically the ‘hygiene factors’ he mentions. Herzberg used
hygiene factors to ensure employees don’t become dissatisfied such as: salary,
security and working conditions. These are the factors more recent researchers have
focussed their investigations into and provided evidence to support the explanatory
power of these variables over job satisfaction.
However the current importance of job satisfaction is still unrecognised. Now in the
UK more people are working than ever before, the level of employment increased to
a record 30.2 million (ONS, January 2014) implying a high proportion of the UK are
actually employed. Satisfaction as an economic variable plays a central role in labour
market theories and in our ability to explain workers behaviours therefore having
implications on economic performance.
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As an employer, ideally they want their employees to be satisfied, since employee
satisfaction is closely related to their labour market behaviour. This is mainly due to
the fact that many experts believe that job satisfaction trends can affect behaviour
and influence work productivity, work ethic, employee absenteeism and staff
turnover (Diaz-Serrano and Cabral Viera, 2005). With many economists reporting that
job satisfaction is a positive respondent of workers productivity.
I intend to build and extend upon the research from past psychologists and economics
regarding the determinants of overall job satisfaction and specifically investigate the
relationship of certain factors in greater depth. Several economists specifically
investigate and analyse the effects of age. There are many conflicting arguments
regarding how age and job satisfaction are related, with the minority suggest it is
actually U shaped. This is one of the conclusions I intend to specifically focus on, in an
attempt to give evidence towards an individual conclusion. Conjoint with this union
membership has been modelled to reduce job satisfaction in North America however
there is low levels of evidence to suggest this is the case in the UK. Using UK data, I
aim to conclude whether union membership does also have the same negative
influence over workers or whether it serves the purpose it was designed for.
2. Background
Among the literature considered for this report, several economists’ findings are all
extremely similar and regard similar explanatory variables affecting job satisfaction. A
much-debated topic over the years has been gender inequality at work, with male’s
earnings exceeding women’s on most accounts. However (Clark, 1997) argues that
women are happier at work than men. With the reasoning behind his findings being
that higher job satisfaction reflects their lower expectations, resulting from the poorer
position they hold in labour market. This relationship is expected to erode over time,
as gender inequality reduces; gender expectations and therefore job satisfaction will
equal out. Nearly all economists or psychologists in their investigations use a form of
income, with low paid workers reporting lower levels of job satisfaction when
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compared with their higher paid counterparts. Instead a logarithm of labour income
– gross (LNY) will be used, a route that Clark does not pursue in his investigations, but
one that (Tansel and Gazioglu, 2006) include in a similar weekly format.
Age differences according to (Clark, 1996) play a greater part in determining overall
job satisfaction than those associated with gender, education, ethnicity or income.
This provides reasoning behind the levels of literature available with respect to age,
with many believing a linear relationship exists between age and satisfaction. Started
by (Herzberg, et al., 1957) and advanced on by (Clark et al., 1997), he has built
persuasive arguments and empirical evidence to suggest that the relationship
between job satisfaction and age is actually U-shaped. First, young employed may feel
satisfied with their job, due high youth unemployment and feel accomplished in
comparison with their peers. As this expectation rises towards middle age, more of
this reference group find attractive job opportunities consequentially declining their
satisfaction. In the latter of working life, job satisfaction rises could result from
reduced aspirations, due to recognition that few alternatives are available once their
career is established.
Remarkably the most unusual relationship studied in previous literature it that of job
satisfaction with the education levels. Previous studies have concluded that university
graduates actually have lower levels of job satisfaction than individuals with lower
graded qualifications (Clark, et al., 1996). Perceived to be because of expectations
differentials. A graduate has built higher expectations for the labour market
throughout their studies and when enter its not all it was made out to be.
Findings in literature on job satisfaction and martial status have been mixed, (Clark,
1996) reports married employees are more satisfied with their jobs. However (Tansel
and Gazioglu, 2006) report that married individuals have lower job satisfaction levels
than the unmarried, on all four measures of satisfaction. It is well known that married
individuals are happier in general, however working with these results; it indicates
that married or civil partnership couples are less satisfied with their jobs than single
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individuals.
Interestingly, another significant factor influencing overall job satisfaction is
temporary employment. Temporary employment is becoming increasingly important,
with growth in fixed-term employment increasing by 24.6% between 2000-2007.
Temporary employment is regarded as an important component of labour market
flexibility (Beckmann, 2009) however individuals on these contracts report lower
levels of job satisfaction. According to equity theory workers are inequality-averse and
compare their wages and job security, using permanent workers as a reference group.
However it is argued that temporary employment benefits job satisfaction, especially
in the modern economic climate as workers prefer the more limited commitments
associated with temporary work and do no seek long term jobs because they value
job mobility rather than job security (Guest and Clinton, 2006).
Unions play a large part in the labour market, and literature suggests that this is a
negative contributor to satisfaction within the workplace. (Hamermesh, 1977) and
(Meng, 1990) came to the conclusion that union members are generally less satisfied
than non-unionised workers. Unions are provided to workers in the labour market,
with their objectives characterised as an attempt to improve workers welfare and
wellbeing, i.e. satisfaction, through changes in working environment or wages. Yet still
economists have concluded that unionised workers are less satisfied. (Cappellari, et
al., 2004) goes in depth into endogeneity of unionisation decision jointly with job
satisfaction with his findings suggesting that union membership actually has no effect
on job satisfaction. Whereas (Borjas, 1979) concludes that the union effect on job
satisfaction was highly dependent on job tenure, with older union members reporting
the lowest levels of job satisfaction.
Health has been an influential positive factor towards overall job satisfaction
throughout all avenues of literature. (Gazioglu and Tansel, 2006) indicate that health
problems such as disability, long-standing illness or factors that limit your ability to
work or level of leisure time; have lower levels of job satisfaction. This is backed up by
(Clark, 1996); who show a similar relationship but indicated that good health has a
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strong positive relationship with job satisfaction. The work from (Gazioglu and Tansel,
2006) states that factors that limit your amount of leisure time inflict lower levels of
job satisfaction. Therefore the inclusion of satisfaction of amount of leisure time
seems relevant to this modelling; imposing the opposite effects. A good work-life
balance is important for every working individual; although there is no specific
literature that uses this variable its relevance is key. From relaxation to family time,
leisure is regarded as an important influence of overall well-being.
3. Data
The data used in this investigation was recorded in the second wave of ‘Understanding
Society = The UK Household longitudinal Survey’. This is a UK based survey, which
primarily took place between 2010 and 2012, aimed to build upon the BHPS survey.
The original survey consisted of an initial amount of observations, 54597.
From this survey there was a limited variables available to be incorporated into the
models. Due to the investigation of the job satisfaction of employees, I introduced a
filter to the dataset. This allowed me to restrict the dataset to employed and selfemployed workers only (Filter: b_jbstat <= 2), so unemployed and retired didn’t
interfere with my results. Research then allowed me to select variables that could be
included, the dataset was adjusted removing inapplicable answers and missing values,
and the qualitative responses were recoded into binary form; shown in figure 3 and
the descriptive statistics shown in appendix 1a.
The chosen dependent variable from the BHPS was b_jbsat, which was “job
satisfaction”. The answers generated form this variable were 1, completely
dissatisfied, 2; mostly dissatisfied, 3; somewhat dissatisfied, 4; neither satisfied nor
dissatisfied, 5; somewhat satisfied, 6; mostly satisfied and 7; completely satisfied. This
question provides qualitative responses and therefore was recoded into binary
format. The creation of a binary variable JobSat resulted in 5,6 & 7 being classed as
satisfied with their current job, 1 and dissatisfied with their current job, 2 including
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responses 1,2,3 &4. 4 was classified as dissatisfied as it allowed for more accurate
analysis as it made the observations more even distributed towards 1,2 & 3.
Figure 1: Dependent Variable Frequency Distribution
The inclusion of these explanatory variables has mostly been outlined in the
background section; all that haven’t been included will be expanded on.
Also (Clark, 1996) associates long working hour with low expectations, and finds a
strong relationship between WORKHRS and satisfaction with pay. This variable is not
included in our modeling, however he also shows a weaker negative relationship
between WORKHRS and overall job satisfaction. Other literature (Gazioglu and Tansel,
2006) use ln (WORKHRS) to take into account for non-linearity however this is not
apparent in my model and makes WORKHRS insignificant at a higher level.
Most literature including (Beckmann, 2009) use temporary employment as an
explanatory variable, however my sample has little variation with 93% of the
observations being in permanent employment and is negatively skewed around the
mean. Therefore used permanent dummy, to see if an opposing positive effect is
noticed.
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The other explanatory variables being used are number of dependent children living
at home (b_nchild_dv) and hours of commuting a day (COMMUTE). Both these
variables have no specific literature regarding their individual effects on job
satisfaction but are used throughout others work. However respectively both these
factors either effect the length of a working day or amount of leisure time an
employee receives therefore justifying their inclusion in the model.
As mentioned in literature, tenure is an important
explanatory variable. However problems arise when
using this dataset and the creation of tenure. Created
through (interview date – year started), it reduces the
sample size enormously as contains a significant amount of missing values (Figure 2);
therefore was excluded from my model.
Figure 3: Expected Values and Recoding
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4. Model
As my dependent variable was qualitative there is a choice between various binary
model structures, with ordinary least squares (OLS) being the simplest. However OLS
comes with its shortfalls and certain problems will arise with variables being used. The
problem with the OLS method is its inability to deal with a dependent variable in
binary form, this is because OLS can generate predicted values outside the range of 01 for outlying terms. Another problem with the simpler OLS method is that it assumes
a linear relationship between variables and due to the nature of the variables included
in my modelling the results could be misspecified as a consequence. For this reason
OLS will be used for my initial regressions as a base case comparison and then
alternative methods such as probit and logit models will be used for the main part of
empirical analysis. The problems of working with an OLS model are eliminated through
the use of logit and probit, as each variable is now based with a non-linear relationship
and models are limited to between 0-1.
This is the equation; I intend to test the empirical relationship of and the order in
which my empirical analysis will follow:
𝐽𝑂𝐵𝑆𝐴𝑇 = 𝛽0 + 𝛽1 𝐴𝐺𝐸 + 𝛽2 𝐹𝐸𝑀𝐴𝐿𝐸 + 𝛽3 𝐷𝐸𝐺𝑅𝐸𝐸 + 𝛽4 𝑀𝐴𝑅𝑅𝐼𝐸𝐷
+ 𝛽5 𝑏𝑣_𝑛𝑐ℎ𝑖𝑙𝑑_𝑑𝑣 + 𝛽6 𝑈𝑁𝐼𝑂𝑁 + 𝛽7 𝐶𝑂𝑀𝑀𝑈𝑇𝐸 + 𝛽8 𝑊𝑂𝑅𝐾𝐻𝑅𝑆
+ 𝛽9 𝐺𝑂𝑂𝐷𝐻𝐸𝐴𝐿𝑇𝐻 + 𝛽10 𝐿𝐸𝐼𝑆𝑈𝑅𝐸𝑆𝐴𝑇 + 𝛽11 𝑃𝐸𝑅𝑀𝐴𝑁𝐸𝑁𝑇
+ 𝛽12 𝐴𝐺𝐸2 + 𝛽13 𝐿𝑁𝑌 + 𝛽14 𝐴𝐺𝐸𝑈𝑁𝐼𝑂𝑁 + 𝑈𝑖
Initially after the testing the model using OLS technique, I will use sensitivity analysis
to allow for comparison. OLS results will be compared to the Logit model first and then
probit model; evaluated on the 1%, 5% and 10% significance levels. Throughout
diagnostic testing will be carried out to ensure that variables are significant through a
likelihood ratio test and goodness of fit test using a Hosmer-Lemeshow test.
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However previous work from (Clark, 1997) and (Tansel and Gazioglu, 2006) instead
used ordered probit and logit models respectively. Such methods are beyond the
scope of the current course and instead I shall compare the simpler logit and probit
formats.
5. Empirical Analysis
Initially before running any initial regressions, the inclusion of a correlation matrix
between my explanatory variables appears essential to eliminate any accounts of
multicollinearity before starting. Appendix 1b shows that there are two accounts
where the correlation between 2 variables is close to and for another greater than
0.95. It shows a strong correlation 0.986 between AGE and AGE2, which is not of any
surprise because one is a function of the other; the same applies for UNION and
AGEUNION.
First an initial regression was run, however for my first regression excluded
AGEUNION. The R2 suggest the model is a reasonable fit considering the amount of
binary variables; with 4.8% of the variation in job satisfaction being explained by
model. The results (Figure 4) show in regards to the UNION variable a significant
negative coefficient as found by previous literature (Meng, 1990). However unions are
in labour markets to objectively benefit employees therefore the result is
questionable. One explanation for this is causality; this is whether someone is satisfied
with his or her job and this affects their decision whether to join a union or not.
However the AGEUNION interaction term completely alters my results in union terms.
OLS (2) shows that the interaction terms changes the sign of UNION, meaning union
members are predicted to have higher levels of satisfaction than non-union members.
The interaction term shows a new concept no previous literature has shown. At the
start of careers union workers are perceived to have high job satisfaction but as age
increases the probability of job satisfaction falls at a faster rate for union members
than it does for non-unionised members. The latter supported by (Borjas, 1979) who
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reported that older workers in unions report lower levels of satisfaction than nonunion members.
Figure 4: OLS Regression Results (1 & 2):
After running these two regressions and noticing the effects of the interaction term
AGEUNION, the next step was to produce a logit model to allow comparison of
coefficients and significance levels. Paying attention to significance of the explanatory
variables it is clear that the variables included in the model looked initially to represent
the model well, with 9 variables significant to the 1% level and 2 variables to the 5%.
However resulting from both tests it’s also clear that there are 3 individually
insignificant.
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Figure 5: Significance of Explanatory Variables:
A likelihood-ratio test is carried out to see if these 3 insignificant variables are jointly
significant. The output of this test is shown in appendix 2c, and concludes that we
reject the null hypothesis of:
𝛽3 𝐷𝐸𝐺𝑅𝐸𝐸 = 𝛽8 𝑊𝑂𝑅𝐾𝐻𝑅𝑆 = 𝛽11 𝑃𝐸𝑅𝑀𝐴𝑁𝐸𝑁𝑇 = 0
therefore these 3 variables are kept in our model. This should be beneficial to the
model as they are all collectively significant and also have literature arguing they have
significant explanatory power.
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Figure 6: Logit, Coefficients and Odds Ratio
The JOBSAT equation for my model based on logit predicted values:
𝐽𝑂𝐵𝑆𝐴𝑇 = −0.324 − 0.049𝐴𝐺𝐸 + 0.353𝐹𝐸𝑀𝐴𝐿𝐸 + 0.006𝐷𝐸𝐺𝑅𝐸𝐸
+ 0.166𝑀𝐴𝑅𝑅𝐼𝐸𝐷 + 0.100𝑏𝑣_𝑛𝑐ℎ𝑖𝑙𝑑_𝑑𝑣 + 0.487𝑈𝑁𝐼𝑂𝑁
− 0.002𝐶𝑂𝑀𝑀𝑈𝑇𝐸 + 0.003𝑊𝑂𝑅𝐾𝐻𝑅𝑆 + 0.570𝐺𝑂𝑂𝐷𝐻𝐸𝐴𝐿𝑇𝐻
+ 0.822𝐿𝐸𝐼𝑆𝑈𝑅𝐸𝑆𝐴𝑇 − 0.024𝑃𝐸𝑅𝑀𝐴𝑁𝐸𝑁𝑇 + 0.001𝐴𝐺𝐸2
+ 0.181𝐿𝑁𝑌 − 0.014𝐴𝐺𝐸𝑈𝑁𝐼𝑂𝑁
The binary logit model, confirms the results from the less accurate OLS results.
The results are fairly consistent across all models in terms of sign and significance of
the coefficient estimates of each variable. The only noticeable changes are the
magnitude of the coefficients and the change of sign for COMMUTE, but this negative
coefficient is as originally predicted.
The deviations from prediction were as expected. UNION and MARRIED were
predicted to have a negative coefficient as stated by previous literature (Borjas, 1979),
however I suggested reasoning behind the change in sign previously for UNION and
the logit confirms this positive coefficient. With regards to MARRIED there was
conflicting argument regarding both relationships, and this confirms its positive
coefficient as (Clark, et al., 1996) found, plus being confirmed with significance at 1%
level. Other exceptions in the model are the differing signs for WORKHRS,
PERMANENT and DEGREE. All the predicted signs were as literature suggested,
however all incorrect in my models; although unproblematic in this case as all the
coefficients are individually insignificant as shown in appendix 2a.
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The logit results differed for the results above, however were in line with theory for
the other variables. The resulting coefficients for AGE and AGE2 matched (Clark, et al.
1996) as demonstrated opposing sings. The negative coefficient -0.049AGE and
positive coefficient 0.001AGE2 confirm the U-shape relationship (Herzberg, et al.,
1957) with job satisfaction and with p-values of 0.002 and 0.004 respectively we have
strong evidence to support this, also ruling out the positive linear relationship as
stated by others.
Heath and leisure satisfaction both have positive signs at 0.570 and 0.822 respectively
both at 1% significance level, which implies that individuals with good health and
individuals that are satisfied with their amount of leisure time were found to be more
satisfied with their current jobs. This is matched by the findings of (Clark, 1996) who
stated people with good heath have higher levels of satisfaction. Also as mentioned
(Gazioglu and Tansel, 2006) found that factors limiting amount of leisure time reduced
job satisfaction and the opposing affect was established through higher satisfaction
with leisure time resulting in higher overall job satisfaction.
Another significant variable is FEMALE, as predicted (Clark, 1997) the dummy variable
for females has a positive coefficient indicating that females are found to have higher
levels of job satisfaction than their male counterparts. Also another variable found to
have a positive coefficient was number of children, this was surprising as literature
doesn’t portray this information, therefore I’d discovered a variable in which had
positive explanatory over overall job satisfaction and it was significant at 1% level.
Using the odds ratio, we can interpret the odds in favour or against satisfaction with
your job. The greatest odds in favour with job satisfaction are LEISURESAT and
GOODHEALTH. With a 1 unit increase in the explanatory factors, there’s an increased
likelihood in favour of job satisfaction is 2.275x higher for LEISURESAT and 1.768x
higher for GOODHEALTH.
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Further analysis was applied using a probit model and its comparison with the logit as
its more versatile in terms of analysis; the results of the probit are shown in appendix
4. A comparison of the two binary regressions (Figure 7) allows for sensitivity analysis
of the model, with both regressions displaying the same signs and significance levels
therefore showing very little statistical difference between the two. This is proven also
through predicted probabilities in appendix 5, giving a difference of -0.042 between
the two models.
Figure 7: Logit and Probit Comparison
Misspecification of my model could have arisen for a number of reasons, the most
obvious being the omittance of relevant variables. Indeed, the exclusion of variables
such as job security, promotions opportunities & level of training have all been
significantly important in the previous studies cited, but are unavailable in the data
set. Testing misspecification in the model is through the Hosmer-Lemeshow test,
which assess the goodness of fit. The results shown in appendix 3 show that the model
is correctly specified.
Satisfied with the results of multiple regressions and the respective specification tests;
further analysis will be undertaken using specific variables. To add further prove to
the findings of Clark, the turning point of AGE2 was calculated. This confirms the Ushaped nature of the relationship between age and job satisfaction. However it
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interestingly concluding the age in which the job satisfaction was at a minimum was
40.31 years old.
This is significantly higher than (Herzberg, et al., 1957) who reported the turning point
to be low 30’s before job satisfaction started to rise again. Whereas (Clark, et al.,
1996) reports the minimum ages of 33, 36, 22 and 28, respectively of his 4 dependent
variables.
To enhance and support my findings on the U-shaped relationship, predicted
probabilities were calculated, appendix 5. I constructed values that I believed to
impersonate the average employee, with findings resulted in two curves for male and
female both displaying the controversial U-shape. It also confirms the findings of
(Clark, 1997) who cited that females observe higher levels of job satisfaction than
males. From this; it shows that for specific ages females have constantly higher
predicted probabilities of job satisfaction than males.
Figure 8: Effect of AGE/GENDER on Predicted Probabilities (JOBSAT)
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6. Conclusion
The objective of this report was to investigate a range of potential and possibly
discover determinants of an individuals overall satisfaction with their job. The models
created contained the reported variables regarding the determinants of job
satisfaction, which was confirmed by the high significant levels and realistic R 2 of
0.048. In comparison to other models, they all express higher R2 (Beckmann 2009) of
0.174, however this is unachievable because of larger sample sizes and a vast amount
of explanatory variables. My study provides a current outlook on overall job
satisfaction, which hasn’t been achieved by previous literature. However results
remain extremely similar with age, gender, good health, unions and leisure
satisfaction, all significant and having the largest effects on overall job satisfaction.
The main limitation that needs to be identified for comparison of this study, is
regarding the literature. The background literature researched uses partial variations
of the same dependent variable in comparison to my model analysis where I look at
its overall effect. However results of my study corroborate those of previous
literature, apart from minor differences as mentioned. This project can be improved
through the use of absent variables as already mentioned, and also through the
addition of an interaction term of WORKHRS2; a variable that’s not considered by
literature but could produce a possible inverted U with regards to job satisfaction.
Nevertheless these models have produced significant data in line with literature and
provided more insight into job satisfaction as a dependent variable as I had planned.
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References
Beckmann, M., Cornelissen, T. and Schauenberg, B. (2009), ‘Fixed-term employment,
work organization and job satisfaction: Evidence from German individual-level data’,
Faculty of Business and Economics, University of Basel, Working Papers 08/09
Borjas, GJ. (1979), ‘Job Satisfaction, Wages and Unions’, The Journal of human
Resources, Vol. 14: 21-40
Cappellari, L., Bryson, A. and Lucifora, C. (2004), ‘Does Union Membership Really
Reduce Job Satisfaction?’, British Journal of Industrial Relations, 42: 439–459
Clark, AE. (1997), ‘Job satisfaction and gender: Why are women so happy at work?’,
Labour Economics 4 341-372
Clark, AE., Oswald, A. and Warr, P. (1996), ‘Is job satisfaction U-shaped in age?’,
Journal of Occupational and Organizational Psychology, 69: Issue 1, 57-81
Diaz-Serrano, L. and Cabral Vieira, JA. (2005), ‘Low pay, higher pay and job
satisfaction within the European Union: Empirical evidence from fourteen countries’,
Discussion Paper No. 1558
Gazioglu, S. and Tansel A. (2006), ‘Job satisfaction in Britain: individual and job
related factors’, Applied Economics, 38, 1163-1171
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Guest, D. and Clinton, M. (2006), ‘Temporary employment contracts, workers’ wellbeing and behaviour: evidence from the UK’, Department of Management Working
Paper No. 38, King’s College, London
Hamermesh, DS. (1977), ‘Economic aspects of job satisfaction’, Essays in Labour
Market Analysis, New York: John Wiley (1977): 53-72
Herzberg, F., Mausner, B., Peterson, R.O. and Capwell, D.F. (1957), ‘Job attitudes:
Review of research and opinion’, Pittsburgh: Psychological service of Pittsburgh
Meng, R. (1990), ‘The relationship between unions and job satisfaction’, Applied
Economics, 22, 1635-1648
Office of National Statistics. (2014), ‘Labour Market Statistics, March 2014’ [Online]
Available from: http://www.ons.gov.uk/ons/dcp171778_354442.pdf [Accessed 19
March 2014]
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Appendix
1. Appendix 1a: Descriptive Statistics of Variables
JOBSAT
AGE
AGE2
LNY
FEMALE
DEGREE
MARRIED
b_nchild_dv
UNION
COMMUTE
WORKHRS
GOODHEALTH
LEISURESAT
PERMENENT
AGEUNION
Mean
Maximum
Minimum
0.7896
42.88
1969.7170
7.4180
0.5940
0.4843
0.5929
0.64
0.5924
25.75
32.9259
0.8861
0.5520
0.9503
26.2061
1
83
6889
9.62
1
1
1
5
1
515
84
1
1
1
76
0
16
256
0
0
0
0
0
0
0
1
0
0
0
0
Standard
Deviation
0.40763
11.441
982.89662
0.68031
0.49110
0.499
0.49132
0.932
0.49142
21.752
9.40821
0.31768
0.49732
0.21725
23.21075
Skewness
Observations
-1.421
-0.071
0.435
-1.074
-0.383
0.063
-0.378
1.332
-0.376
3.838
-0.618
-2.431
-0.209
-4.147
-0.033
9747
9747
9747
9747
9747
9747
9747
9747
9747
9747
9747
9747
9747
9747
9747
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Appendix 1b: Correlation Matrix:
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Appendix 2a: Logit 1
Dependent Variable: JOBSAT
Method: Binary Logistic
Total Observations: 9747
Observations with Dep (= 0) = 2051
Observations with Dep (= 1) = 7696
Variables in
B
S.E
Equation
AGE
-0.049
0.017
FEMALE
0.353
0.057
DEGREE
0.006
0.057
MARRIED
0.166
0.057
b_nchild_dv
0.100
0.033
UNION
0.487
0.207
COMMUTE
-0.002
0.001
WORKHRS
0.003
0.004
GOODHEALTH
0.570
0.072
LEISURESAT
0.822
0.053
PERMANENT
-0.024
0.121
AGE2
0.001
0.000
LNY
0.181
0.053
AGEUNION
-0.014
0.005
Constant
-0.324
0.430
Step
1
-2 Log likelihood
9570.351
Wald
Sig.
Exp(B)
8.290
38.585
0.011
8.497
9.242
5.527
4.119
0.511
63.064
241.953
0.041
9.525
11.574
9.114
0.566
0.004
0.000
0.918
0.004
0.002
0.019
0.042
0.475
0.000
0.000
0.840
0.002
0.001
0.003
0.452
0.952
1.423
1.006
1.181
1.105
1.628
0.998
1.003
1.768
2.275
0.976
1.001
1.199
0.986
0.724
Cox & Snell R Square
0.046
Nagelkerke R Square
0.072
Appendix 2b: Logit 2 (Insignificant Removed)
Dependent Variable: JOBSAT
Method: Binary Logistic
Total Observations: 9822
Observations with Dep (= 0) = 2067
Observations with Dep (= 1) = 7755
Variables in
B
S.E
Equation
AGE
-0.048
0.017
FEMALE
0.340
0.054
MARRIED
0.160
0.057
b_nchild_dv
0.091
0.032
UNION
0.511
0.206
COMMUTE
-0.002
0.001
GOODHEALTH
0.578
0.071
LEISURESAT
0.816
0.052
AGE2
0.001
0.000
LNY
0.203
0.041
AGEUNION
-0.015
0.005
Constant
-0.430
0.404
Step
1
-2 Log likelihood
9645.733
Wald
Sig.
Exp(B)
8.052
39.880
7.924
8.082
6.178
4.089
65.742
243.083
9.255
24.278
9.945
1.133
0.005
0.000
0.005
0.004
0.013
0.043
0.000
0.000
0.002
0.000
0.002
0.287
0.953
1.405
1.173
1.096
1.668
0.998
1.783
2.261
1.001
1.225
0.985
0.651
Cox & Snell R Square
0.046
Nagelkerke R Square
0.071
21
Applied Economic Analysis
100051160
Appendix 2c: Likelihood-Ratio Test
Individually insignificant variables: WORKHRS/ DEGREE/ PERMANENT
Number of restrictions: 3 (df = 3)
Unrestricted Model: P(Yi=1) =
exp(𝛽0 +𝛽1 𝑋1+𝛽2 𝑋2+𝛽3 𝑋3+𝛽4 𝑋4+𝛽5 𝑋5+𝛽6 𝑋6+𝛽7 𝑋7+𝛽8 𝑋8+𝛽9 𝑋9+𝛽10 𝑋10+𝛽11 𝑋11+𝛽12 𝑋12+𝛽13 𝑋13+𝛽14 𝑋14)
1+exp(𝛽0 +𝛽1 𝑋1+𝛽2 𝑋2+𝛽3 𝑋3+𝛽4 𝑋4+𝛽5 𝑋5+𝛽6 𝑋6+𝛽7 𝑋7+𝛽8 𝑋8+𝛽9 𝑋9+𝛽10 𝑋10+𝛽11 𝑋11+𝛽12 𝑋12+𝛽13 𝑋13+𝛽14 𝑋14)
Restricted Model P(Yi=1) =
exp(𝛽3 𝑋3+𝛽8 𝑋8+𝛽11 𝑋11)
1+exp(𝛽3 𝑋3+𝛽8 𝑋8+𝛽11 𝑋11)
H0: 𝛽3 𝐷𝐸𝐺𝑅𝐸𝐸 = 𝛽8 𝑊𝑂𝑅𝐾𝐻𝑅𝑆 = 𝛽11 𝑃𝐸𝑅𝑀𝐴𝑁𝐸𝑁𝑇 = 0
H1: H0 not true
LR = 2(LogLU – LogLR) = (-9570.351)-(-9645.733) = 75.38
Chi-Square critical value: χ2(3;0.1) = 6.25, χ2(3;0.05) = 7.82 and even at the 1% significance level: χ2(3;0.01) = 11.34
As LR> χ2 for all 1%, 5% and 10% significance levels, we can reject the null hypothesis.
Appendix 3: Hosmer-Lemeshow Test
Goodness-of-fit Test: Binary Logit
Contingency Table for Hosmer and Lemeshow Test
JOBSAT = .00
Observed
Expected
JOBSAT = 1.00
Observed
Expected
Total
S1
386
388.867
589
586.133
975
t2
e3
282
297.623
693
677.377
975
292
260.661
683
714.339
975
237
233.148
738
741.852
975
185
201.830
790
773.170
975
6
167
169.271
808
805.729
975
7
146
147.434
829
827.566
975
8
135
132.069
840
842.931
975
9
119
119.002
856
855.998
975
10
102
101.094
870
870.906
972
p
4
1
5
Hosmer-Lemeshow Test Results:
H0: Correctly Specified Model
22
Applied Economic Analysis
Hosmer and Lemeshow Test
Step
H1: Non-correctly specified model
Chi-square
1
8.349
df
100051160
Sig.
8
.400
H-L p-value: 0.400 > 0.1
Therefore we cannot reject the null hypothesis of a correctly specified model at all conventional levels of
significance (10%, 5% & 1%)
Appendix 4: Probit
Dependent Variable: JOBSAT
Method: Probit
Total Observations: 9747
Convergence Information: 25 Iterations
Variables in
Estimate
S.E
Equation
AGE
FEMALE
DEGREE
MARRIED
b_nchild_dv
UNION
COMMUTE
WORKHRS
GOODHEALTH
LEISURESAT
PERMANENT
AGE2
LNY
AGEUNION
Intercept
-0.028
0.201
0.008
0.093
0.058
0.270
-0.002
0.002
0.342
0.471
-0.012
0.000
0.105
-0.008
-0.174
0.010
0.033
0.032
0.033
0.019
0.117
0.001
0.002
0.043
0.030
0.069
0.000
0.031
0.003
0.248
Z
Sig.
-2.856
6.158
0.251
2.834
3.092
2.299
-2.195
0.745
7.907
15.648
-0.167
3.079
3.364
-2.999
-0.701
0.004
0.000
0.802
0.005
0.002
0.021
0.028
0.456
0.000
0.000
0.867
0.002
0.001
0.003
0.483
95% CI
Lower
Bound
-0.047
0.137
-0.056
0.029
0.021
0.040
-0.003
-0.003
0.257
0.412
-0.147
0.000
0.044
-0.013
-0.422
95% CI
Upper
Bound
-0.009
0.265
0.072
0.158
0.095
0.500
0.000
0.006
0.426
0.530
0.123
0.001
0.166
-0.003
0.074
23
Applied Economic Analysis
100051160
Appendix 5: Predicted
Probabilities, Logit v Probit
LOGIT
PROBIT
X's
AGE
FEMALE
DEGREE
MARRIED
b_nchild_dv
UNION
COMMUTE
WORKHRS
GOODHEALTH
LEISURESAT
PERMANENT
AGE2
LNY
AGEUNION
Constant
beta
-0.028
0.201
0.008
0.093
0.058
0.270
-0.002
0.002
0.342
0.471
-0.012
0.000
0.105
-0.008
-0.174
Xb
SUM(Xb):
X's
AGE
FEMALE
DEGREE
MARRIED
b_nchild_dv
UNION
COMMUTE
WORKHRS
GOODHEALTH
LEISURESAT
PERMANENT
AGE2
LNY
AGEUNION
Constant
-0.56
0.201
0.008
0.093
0
0.27
-0.002
0.08
0.342
0.471
-0.012
0
0.525
-0.16
-0.174
1.082
Values
20
1
1
1
0
1
1
40
1
1
1
400
5
20
1
X's
AGE
FEMALE
DEGREE
MARRIED
b_nchild_dv
UNION
COMMUTE
WORKHRS
GOODHEALTH
LEISURESAT
PERMANENT
AGE2
LNY
AGEUNION
Constant
betas
-0.049
0.353
0.006
0.166
0.100
0.487
-0.002
0.003
0.570
0.822
-0.024
0.001
0.181
-0.014
-0.324
Xb
-0.98
0.353
0.006
0.166
0
0.487
-0.002
0.12
0.57
0.822
-0.024
0.4
0.905
-0.28
-0.324
SUM(Xb):
2.219
PREDICTED PROBS
PROBIT
0.860373736
PREDICTED PROBS
LOGIT
0.901942789
DIFFERENCE
BETWEEN PROBIT
and LOGIT PROBS
-0.041569053
24