An interesting study from the Swedish Institute of Future Studies, about the links between the age of the workers and employment, on a long time period (data 1986-2002). This article has been written by Marie Gartell (Institute for Futures Studies, Stockholm University, Department of Economics), Ann-Christin Jans (Swedish Public Employment Service) and Helena Persson(The Swedish Confederation of Professional Associations) and could inspire people dealing with ageing workers policies.

Abstract
Using employer-employee data covering the whole Swedish economy over a uniquely long time period from 1986 to 2002, we examine how job and worker flows have been distributed across age groups. We find that job and worker flows vary by age groups, not only with respect to magnitude and variation, but with respect to direction as well. The differences between the age groups are mainly driven by the job creation rates. Further, estimating a multinomial logistic model, we investigate the importance of age for leaving, changing or entering a new employment. Even though controlling for a number of factors, estimated age effects are substantial.

Conclusions
Job and worker flows are lower for older than for younger workers. Job reallocation rates decreases with age which is mainly explained by large differences in job creation rates while job destruction rates are much more similar across age groups. Both hiring and separation rates decreases with age which means that also worker reallocation rates are considerably lower for older than younger workers.
The results of job and worker flows suggest that the matching process between jobs and workers are important, something that is shown in the high flow rates found for the youngest workers. In the middle age group most workers are likely to have made a good match, a match that suits both the employer and the worker well, resulting in lower flow rates. The lower rates found for the elderly workers are probably closely linked to the Swedish institutions that regulate and influence the employment protection on the labor market, and not only the result of a good matching. Further, the results correspond to those found in data from the Netherlands.
Some previous studies have found job reallocation to be countercyclical, suggesting that downturns are periods of restructuring the establishment. Our results confirm those previous studies on an aggregate level and support the model by Mortensen and Pissarides (1994) predicting job reallocation to be countercyclical. According to Garibaldi ( 1998 ) this countercyclical pattern implies that firing costs (i.e. separation costs) are low. However, when examining the correlations between different age groups the countercyclical behavior was only found among the oldest workers. For the youngest and the middle-aged workers job reallocation rates were found to be acyclical. Contrary to expected this implicate that firing costs are lower for older than for younger workers, which might be explained by that older workers are overpaid and/or have a different skill set than younger workers (Lazear, 1995; Abowd et al., 2007).
While the reallocation of jobs is found to be larger during downturns on the aggregate level, this does not hold for reallocation of workers. Instead worker reallocation exhibits an acyclical pattern. The number of people hired is larger during upturns while the number of people leaving displays no cyclical pattern. One interpretation is that people are very careful not to leave their jobs during bad times and only leave when they have to, preferring to quit
during upturns to find better jobs. There are some differences between age groups. Worker reallocation for the youngest workers shows a strong procyclical pattern due to both more hirings and separations during up-turns. The oldest workers, on the other hand, have significantly more separations during downturns.
The effects found for different age groups may at least partly depend on their educational level, something that is hard to separate for in these flows. Consequently multinomial logistic models were estimated to separate effects of age and education. Even when controlling for educational level, among various other variables, the estimated age effects are substantial.

The full article