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?
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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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