Abstract
This paper examines the impact of worker’s age on the consequences of occupational injuries. Using data from the Spanish Statistics on Accidents at Work for 2004–2010, a probit model is estimated in order to analyse the impact of the age on the probability of suffering a severe or fatal accident. Further, a duration model is used to assess the effect of worker’s age on the length of sick leave caused by occupational injuries. The analysis shows that the probability of suffering a severe or fatal accident, as well as the duration of the sick leave, increases with the worker’s age once personal, job, and accident characteristics are controlled for. From a policy perspective, the results point out that decisions about delaying the retirement age require additional measures, such as the occupational reallocation of these older workers towards tasks with lower incidence rates, in order to minimise these effects.
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Notes
The severity of the accident is determined by independent medical services, and depends on the type of injuries suffered. The accident is classified as “severe” if the worker needs external support to reach medical premises (e.g., an ambulance). If the worker is able to reach medical premises (after first aids have been undertaken) by his own, and there are no further physical consequences, the accident would be regarded as “minor”.
A probit model is estimated because the probabilistic regression is the more natural model when the outcome is 1 exactly when a hidden Gaussian variable Z_0=X^′ β_0+ε_0 exceeds a threshold c with ε~N(0,σ^2).
Although there might be some potential correlation between occupation and wages, both variables are included in the estimation because there are also important wage gaps among workers in the same occupation due to differences in other characteristics such as education or experience. The occupation variable proxies education level, and within each occupational group many different wage levels may be observed.
This strategy in turn is based on the identification strategy proposed by Levitt and Porter (2001) in the context of the study of seat belt and air bag effectiveness.
We also have estimated the duration models reported below using other parametric distribution functions such as the Weibull and the log-normal functions, as well as the semiparametric Cox regression models. In all cases, the results for our main variables of interest remain significantly robust to the different estimations carried out.
The main problem related to duration models is that of censured data. There are several types of censoring. Left censoring occurs when we do not know the starting moment of the event; right censoring occurs when the ending moment of the event in unknown; and interval censoring occurs when both are unknown. In our case we face right censoring. This may be caused by two different reasons. First, it may be the case that the analyst observes the duration before the transit occurs; second, it may be the case that the phenomenon under study ends before we observe the transit. Our dataset shows the first type of right censoring, given the annual structure of the register. There are injured workers in each year that at the end of that are still under sick leave, and these will not be present in the next year data, since the EAT registers occupational injuries along the year. In any case, we have full durations for 83.2 % of total observations. Additionally, our empirical approach makes use of the remaining 16.8 % for the estimation, as we describe next.
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Bande, R., López-Mourelo, E. The Impact of Worker’s Age on the Consequences of Occupational Accidents: Empirical Evidence Using Spanish Data. J Labor Res 36, 129–174 (2015). https://doi.org/10.1007/s12122-015-9199-7
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DOI: https://doi.org/10.1007/s12122-015-9199-7