Workplace job satisfaction: a multilevel analysis
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Transcript Workplace job satisfaction: a multilevel analysis
Workplace job satisfaction: a
multilevel analysis
WERS 2004 Users Group
Meeting, NIESR
March 16, 2007
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Outline of presentation
Introduction
Data
Methodology
Results
Summary of findings
Further work
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Introduction
Job satisfaction as a theme of research in
economics
The link between JS and economic
outcomes
Determinants of JS: The evidence thus far
Should we explore JS and its determinants
further?
How is this study different, is it?
3
Data
The data used is WERS 2004
The most comprehensive of the WERS
series of surveys
Nationally representative survey of British
workplaces
Use is made of data from the management
and employee surveys
SEQ: 22,451 (61% response rate)
MQ: 2,295 workplaces (64% response)
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Data (cont’d)
Eight different facets of JS have been
monitored in wers2004
An ‘overall’ JS indicator has also been
generated
Each of the five scale JS indicators have
been collapsed into a dummy (1 if ‘very
satisfied’ or ‘satisfied’ & 0 otherwise
A range of exogenous variables (employee
& establishment) has been used
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Data (Descriptive stat)
All
Overall
Sense of ach
Scope
Influence
Training
Pay
Security
Decision
The work
No. of obs
Mean
0.712
0.696
0.712
0.572
0.497
0.352
0.630
0.395
0.714
20692
St.err.
0.005
0.005
0.004
0.005
0.006
0.006
0.006
0.005
0.005
Men
Mean St.err.
0.680
0.007
0.672
0.007
0.708
0.006
0.569
0.007
0.453
0.008
0.336
0.007
0.590
0.008
0.396
0.008
0.676
0.007
9620
Women
Mean
St.err.
0.740
0.006
0.717
0.006
0.716
0.006
0.575
0.006
0.537
0.007
0.365
0.007
0.665
0.007
0.395
0.006
0.749
0.005
11072
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Data (corr. Matrix)
1. Overall
2. Sense of
3. Scope
4. Influence
5. Training
6. Pay
7. Security
8. Decision
9. The work
1
1.00
0.55
0.54
0.53
0.38
0.30
0.42
0.40
0.53
2
3
4
5
6
7
8
9
1.00
0.51
0.46
0.26
0.19
0.25
0.32
0.57
1.00
0.58
0.24
0.18
0.23
0.34
0.43
1.00
0.28
0.22
0.28
0.42
0.41
1.00
0.24
0.27
0.26
0.27
1.00
0.25
0.25
0.22
1.00
0.24
0.30
1.00
0.28
1.00
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Methodology
The methodology employed exploits the
data structure
No account has been made for possible
endogeneity problems yet
Accounts for unobserved heterogeneity,
unlike most in the literature
This version, focuses on unmeasured
heterogeneity in overall response
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Methodology (cont’d)
Following Hammermesh (1977) &
Freeman (1978), utility from work or
aspects of work is given as
U u( y, h, ind, job, emp, workplace)
This is modelled using the basic 2-level
ML model that is specified as
Sij 0 j β1 xij β2 x j ij
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Methodology (cont’d)
Unobserved heterogeneity component is
modelled as
0 j 00 0 j
so that
Sij 00 1 xij β2 x j 0 j ij
We’ve binary JS indicators » need for a link
function given by
Prob(S ij ) f (1 xij 2 x j j ) 1 exp ()
1
with
j ~ N (0, )
2
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Results
Please see results in the handout!
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Summary of results
That firm-level unobserved heterogeneity
is important for the most part
Important/significant employee & employer
effects, particularly
availability of training opportunity (+)
Union membership (-)
Flexible work arrangement (+)
Skills mismatch (-)
Industry of employment (education (+), health (+))
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Further work
Refining/reducing the correlates
Investigate whether different results if
using the ordinal indicators of satisfaction
Introducing higher levels (‘astatus’ for eg)
Random coefficient models
Account for possible endogeneity
~~~~
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