Does employment protection reduce the demand for unskilled labour?

Abstract

We propose a model in which workers with little education or in the tails of the age distribution – the inexperienced and the old – have more chance of job failure (mismatch). Recruits' average education should then increase and the standard deviation of starting age decrease when strict employment protection raises hiring and firing costs. We test the model using annual distributions of recruits' characteristics from a 1975–1995 panel of plants in Belgium, the Netherlands, Italy, the UK and the US. The model's predictions are supported using the Blanchard–Wolfers index of employment protection as well as our alternative index.

Keywords:

• Employment protection
• labour demand
• unskilled workers
• firm panel data

Acknowledgements

The authors have benefited from comments from John Addison, Clive Belfield, John Heywood, Winfried Koeniger and Xiangdong Wei, and from the comments of seminar participants at the University of Birmingham, KDI School of Public Policy and Management, and Lingnan University. Support from the Joseph Rowntree Foundation for this study is gratefully acknowledged. We retain responsibility for any remaining errors.

Notes

¹In a similar vein, Koeniger et al. (2004), propose a model in which employment protection reduces the firm's outside option, so permitting unions to negotiate higher wages. Hence, if higher employment protection shelters costs for unskilled more than skilled workers, strict employment protection should help unions to reduce the skilled/unskilled wage differential – which they find. Our model provides a rationale for greater employment protection effects for unskilled workers.

²There is also a time-varying measure of the strictness of employment protection legislation based on employer views (Di Tella & McCulloch, 2002), but this series is short, 1984–1990. Alternatively, there is a series on product market regulation (Nicoletti & Scarpetta, 2003) – which is related to labour regulation but, of course, is not the same.

³Admittedly, an increase in capital usage over time, in response to employment protection, could also account for recruitment of more skilled workers. Our empirical findings for the employment protection variable would then simply have to be interpreted as reduced forms.

⁴An alternative dispersion measure is the proportion of workers in the tails of the hiring distribution, which involves somewhat arbitrary cut-off points. Hence we choose the standard deviation.

⁵A recursive model may be consistently estimated using equation-by-equation ordinary least squares (Greene 2003: 397), but not if the covariance matrix of the equation disturbances is non-diagonal, as appears to the case for some of our specifications.

⁶Our index is constructed based on the OECD (1999) index for individual dismissal of workers with regular contracts, applying OECD weights. It includes scores for procedural inconveniences (procedures and delay to start notice) notice and severance pay for no-fault individual dismissal and difficulty of dismissal (definition of unfair dismissal, trial period, compensation and reinstatement). It is then combined with an index of the strictness of regulation of temporary employment, again based on OECD (1999), and smoothed over time.

⁷For an early discussion of how supply shifts may affect relative wages and employment, see Perlman (1958).

⁸For consistency over time, we concentrate on permanent males, defining ‘permanent’ to include workers whether hired on a temporary basis or not, who became subsequently employed on an open-ended basis within a year. Where such hires fell below 2 in any year, we recorded a missing observation.

⁹However, an exception is the Italian distilling plant, which has starting age averaging only 23.7. Special factors seem to be operative in this plant, which recruits extensively among relatives of current employees. Such extra knowledge of applicants could allow age and education criteria to be lowered. Again, we rely on the fixed effects term in equation (8) to control for these special factors.

¹⁰The instrument we used for education was the school-leaving age variable, which can reasonably be excluded from the age equation. The instrument we used for starting age was the average age of the company's worker stock which, for its part, can reasonably be excluded from the education equation.