Abstract
Worker perceptions of job-related health risk are a little-studied dimension of heterogeneity in the labour market. According to information from the European Working Conditions Survey (EWCS), one out of three European workers considers that her health and safety is at risk because of work. Not surprisingly, risk perceptions are influenced by objective risk factors such as hazardous working conditions, onerous job characteristics and by the probability to be affected by occupational accidents and illnesses. This paper explores also the role played by personal characteristics and household structure for the explanation of risk perceptions. After controlling for job characteristics, workplace hazards, job satisfaction and health outcomes, I find that risk perceptions are strongly correlated with gender, age, and household structure. Lone parents as well as older and more experienced workers have a higher propensity than other categories to consider their health at risk because of work. The same seems to hold true for better educated workers, especially for those who have completed tertiary education. Further results suggest that the relationship between household structure and risk perception is stable across gender.
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Notes
Another instance is represented by the fact that the majority of drivers tend not to equate their own traffic risk with that of the average person, because they believe themselves to be more skilful and safer than average (see Matthews and Moran 1986).
For a synthetic overview see Bouyer et al. (2001).
The exact wording of the question is: “Do you think that your health and safety is at risk because of your work?”.
I have constructed variables for different household typologies, accounting for the presence of children below the age of 18 and differentiating between singles, couples without children, families and lone parents.
Each dummy variable takes the value of 1 if the respondent stated that he or she is exposed to the condition “around half of the time” or more, and of 0 otherwise.
Although the table displays three aggregated age cohorts, the age variables included in the model are expressed in years. In order to construct the household variables, dependents up to the age of 18 who live with one or both of their parents were counted as children. The educational dummies are based on the international ISCED classification.
A comparison with the third wave of the EWCS, where only the EU15 were surveyed, shows that this share has remained virtually unchanged between 2000 and 2005 (see European Foundation 2005).
This calculation includes Romania and Bulgaria among the new EU member states, although technically they were not part of the Union at the moment when the survey was carried out.
According to the statistical agency of the European Union, in 2005 the incidence rate of deadly work accidents for every 100,000 workers was 2.3 in the EU15 and 2.6 in the EU27.
I present the results in this form because it corresponds to the regression output provided by STATA to zero-inflated count data models.
On the other hand, it cannot be entirely ruled out that the observed variables fail to account fully for gender differences in occupational sorting, i.e. for the fact that women might sort into safer jobs even when they work in the same industry and occupation as men and are exposed to similar working conditions.
Since the difference between actual and predicted risk perception lies in the range between −1 and 1, I implement the following transformation: When the difference (i.e. predicted value minus actual value) is larger than 0.5 I assume that there is a low risk perception on the part of the individual; when the difference is below −0.5 I assume a high risk perception; values between −0.5 and 0.5 represent the baseline of the subsequent analysis, as I assume that the individual response coincides with what is predicted by the model.
In this specification I do not include any other control variables. Inclusion of control variables (country, industry, occupation and firm size) and of the variables for personal characteristics and satisfaction leads to results which are almost identical to those displayed in Table 4. I take this as evidence that results are robust to different specifications and transformations of the information on working conditions and job characteristics.
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Acknowledgments
I would like to thank René Böheim, Martin Falk and Andrea Weber as well as two anonymous referees for very useful comments to earlier drafts of this article. Responsibility for the final product lies entirely with me. I am also grateful to participants of the NoeG 2009 conference in Linz for their comments and to the European Foundation for the Improvement of Living and Working Conditions for providing the EWCS data.
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Appendices
Appendix 1
See Table 5.
Appendix 2
See Table 6.
Appendix 3
3.1 Principal component analysis
Scree plot after PCA for working conditions and job characteristics
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Leoni, T. What drives the perception of health and safety risks in the workplace? Evidence from European labour markets. Empirica 37, 165–195 (2010). https://doi.org/10.1007/s10663-010-9129-0
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DOI: https://doi.org/10.1007/s10663-010-9129-0