Presentation from the online workshop: Statistical Needs for Research in Social Sciences, 29.03.2021

1. JOINT ANALYSIS OF EMPLOYER AND EMPLOYEE SURVEYS:
EWCS, ESENER AND LFS
IRENE HOUTMAN & IRIS EEKHOUT

2. 3
A JOINT VIEW OF EMPLOYER AND EMPLOYEE PERSPECTIVES
On request of EU-OSHA a ‘Joint analysis’ was performed using the
• European Survey in Enterprises on New and Emerging Risks (ESENER - employer level),
• Labor Force Survey & European Working Conditions Survey (LFS & EWCS - employee level)
Aim/challenge: to see if these three European surveys, collected in different ways from different sources,
could be combined in a statistically sound way, and provide additional answers to relevant questions in
the area of OSH risk awareness and OSH risk management, that could not be answered by analyzing
these datasets in isolation.
Main research questions related to:
• Different perspectives on risk awareness and their impact on risk management
• Impact of these perspectives on importance of drivers and barriers of risk management
• What does this mean for policy and practice?

3. 4
METHODOLOGICAL CHALLENGES
1) Different levels of observation: companies and employees
2) Employees are not sampled from the companies represented in the enterprise survey.
• There is only an indirect relation between the data sets: employee (EWCS) and employer/enterprise (ESENER)
Level LFS-2013 ESENER-2
Country x x
Sector x x
Company size x x
Enterprise x
Employee x

4. NESTED DATA STRUCTURE
Country (SE)
Sector A
Size 1
Observations
Size 2
Observations
Size 3
Observations
Sector B
Size 1
Observations
Size 2
Observations
Size 3
Observations
5

5. Multilevel model: generalization of linear regression model for grouped data
Each level describes the difference between the categories at that level with statistical parameters and relates
to the next level
Example:
at country level – the parameters describe how the various countries differ from each other in their OSH risk
measures
at the sector level – the parameters describe how sectors differ from each other within countries
at size level, the parameters describe how differently sized companies differ from each other within sectors,
within countries.
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MULTILEVEL MODEL TO JOINTLY ANALYSE

6. RELATION AT COUNTRY LEVEL (PSR)
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7. RELATION AT SECTOR LEVEL (MSD)
8

8. RELATION AT COMPANY SIZE LEVEL (OSH)
9

9. 10
RISK PERCEPTION (EMPLOYEE) AND RISK AWARENESS AND
RISK MANAGEMENT IN ENTERPRISE (EMPLOYER)
Note: * means signficant; correlations between brackets are at country level
ESENER
OSH risk MSD risk PSR
Awareness Management Awareness Management Awareness Management
EWCS
Environmental OSH
risk
0.71* 0.32*
Heavy lifting and
tiring positions
0.42* 0.49*
Repetitive
movements
0.31* 0.32*
Sitting –0.1 –0.19
Job demands
–0.1
(0.32*)
–0.14
(0.47*)
Job autonomy (high is
favourable)
0.04
(–0.57*)
0.05
(–0.47*)
Support –0.08 –0.1
Violence and
harassment
0.29
(0.54*)
0.30
(0.45*)
Job insecurity
–0.21
(–0.62*)
–0.24
(–0.60*)
Number of hours
worked
–0.15
(–0.27)
–0.19
(–0.34)

10. 11
PSYCHOSOCIAL RISK AWARENBESS AND MANAGEMENT VERY
DIFFERENT FOR DIFFERENT COUNTRY (CLUSTERS)
Source: ESENER 2014

11. 12
EXPLAINING RISK MANAGEMENT BY GENERAL-VS- SPECIFIC
RISKS AND BY WORK-RELATED HEALTH:
Risk management
Predictors (additive R2 ) OSH MSD PSR
(1) General occupational risks 0.03 0.19 0.11
(1) Specific occupational risks 0.11 0.26 0.19
(1) Work-related (specific) health
complaints
0.12 0.27 0.26
Source: ESENER, EWCS and LFS

12. The linkage is limited to the availability of common variables
in the different datasets
 Note that a small difference specification of these
common variables can hamper the joint analysis (e.g.
company size classifications; different/new sector
classifications)
Interpretations are limited to the common levels
Multilevel analysis provided logical and interpretable data.
Multilevel analyses allows regular statistical techniques (e.g.
correlations, regression analyses) to support hypothesis
testing and practical recommendations.
Multilevel analyses showed us that employee and employer
perspectives (at different levels in the organisation)
disagreed for some but not all topics. Resulting in different
practical implications.
LIMITATIONS
BENEFITS
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BENEFITS AND LIMITATIONS OF JOINT ANALYSIS

13. For data providers:
Harmonize common variables over the different data sources
Harmonize weight variables over different data sources, so that samples are comparable
Keep operationalizations consistent across data collection years
For researchers/users of the data:
(for researchers/users: Preferably join data sets in (about) the same time frame)
(for researchers:/users of the data: check the operationalization of common variables; e.g. routing errors)
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RECOMMENDATIONS

14. BEDANKT VOOR
UW AANDACHT
VRAGEN????

15. 16
RELATIE BEWUSTZIJN TUSSEN PSA RISCO DOOR MANAGER
EN WERKNEMERS VERTEGENWOORDIGER EN PSA AANPAK

16. 17
RELATIE ‘AWARENESS’ VAN ARBORISICO DOOR MANAGER EN
WERKNEMERSVERTEGENWOORDIGER EN AANPAK HIERVAN
Source: ESENER 1