In this paper, we analyse the differences in wage distributions between the UK and nine European countries in 2001. The data base is the European Community Household Panel (ECHP).
In recent research on labour markets, international comparative studies have been fruitfully exploited to highlight variations in labour market institutions, skill endowments and wage distributions (Blau and Kahn 1996 and 2003). Differences in wage distributions between the US and Canada have been studied by DiNardo and Lemieux (1997) and by Donald, Green and Paarsch (2000). Beaudry and Green (2003) analysed differences between the US and Germany focusing on changes in relative capital endowments.
We apply the approach suggested by Behr and Pötter (2006) which extends the pioneering approach of Donald et al. (2000). The decomposition of wage differences is based on a proportional hazards model for wages using a marginal likelihood for the regression part of the model. This allows to dispense with the arbitrary grouping of observations used by Donald et al. (2000), Fortin and Lemieux (1998), and DiNardo et al. (1996). Moreover, we use a general additive model based on splines for the effects of covariates which can capture any nonlinearities. Splines are generally more stable than the traditional use of polynomials.
This approach can also be compared to quantile regression methods popularised by Martins and Pereira (2004), Machado and Mata’s (2005) and others. In most of this literature, linear parametrisation of the quantile function are used. In contrast, the proportional hazards model implies a somewhat more complicated dependence on covariates and on a non-parametric component that nevertheless can be easily estimated (Dabrowska 2005).
The paper is structured as follows. In Section 2, we outline the decomposition approach based on the proportional hazards model. The data base is described briefly in Section 3. Section 4 contains an extensive descriptive analysis. In section 5 we provide the results of the decomposition analysis. Section 6 concludes.
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