The purpose of this paper is to examine the dynamics of labour market flows over the business cycle through a vacancy chain model. It provides a direct computation of vacancy chains using micro data, empirically investigates the relationship between chain length and the characteristics of jobs and workers initiating the chain, and finally assesses the wage progression of workers moving along the chain.
The paper draws on a longitudinal matched employer-employee database covering all employees in manufacturing in a large region of Italy. A transparent algorithm for vacancy chain computation is developed and standard econometric techniques are employed to analyze job-to-job transitions within identified chains.
Vacancy chains account on average for more than one-third of total hires, and both the number and the length of chains are clearly pro-cyclical. Chains set in motion by women workers, young, old, blue collars, or employed by small firms tend to be shorter. There is a well-defined wage progression from the tail to the head of the chain, revealing that workers are sorted along chains according to skill and/or bargaining power.
There is a limited possibility of identifying separately individual ability and bargaining power.
The vacancy chain methodology can increase the ability of policy makers to produce detailed maps of the labour market and identify worker profiles associated with poor outcomes and hence deserving special attention.
For the first time, this paper operationalizes the vacancy chain approach on a large scale, at a very high level of detail, and over a long-time span.
The authors thank Marco Valentini for skilful assistance in computing vacancy chain, Mario Padula and Frank Pyke for helpful discussions and two anonymous referees for their comments.
Disclaimer: The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.
Gianelle, C. and Tattara, G. (2014), "Vacancy chains and the business cycle. Stringing together job-to-job transitions in micro data", International Journal of Manpower, Vol. 35 No. 8, pp. 1212-1235. https://doi.org/10.1108/IJM-07-2012-0106
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