Interfacing a CGE Labour Market Model with the E3ME Multi-Sector Macroeconomic Model

In recent years, a series of European labour market forecasts have been produced on behalf of, and have been published by, the European Centre for the Development of Vocational Training (CEDEFOP). These forecasts were generated using a modular modelling approach containing two major components: * a multi-sector macroeconomic model (E3ME) for 29 European countries, primarily developed and operated by Cambridge Econometrics, and * a labour market extension (WLME), primarily developed and operated by the Institute for Employment Research at the University of Warwick. The countries are treated as an integrated system in E3ME but the extension is applied to each country separately. Forecasts of employment by industry are determined by E3ME; forecasts of employment by occupation and qualification are determined by the extension. The two components rely mainly on time series econometric techniques to generate their forecasts. This paper describes how WLME can be replaced with an alternative extension (MLME) which incorporates a computable general equilibrium model. The CGE model has been developed primarily at the Centre of Policy Studies at Monash University. Compared to WLME, MLME relies less on time series extrapolation and more on explicitly modelled economic behaviour. This approach introduces a range of behavioural and technical parameters which offer more scope for modelling developments in the labour market which impact on occupations and skills rather industries. Forecasts produced using the new E3ME-MLME system are reported for the United Kingdom, Greece and the Netherlands, and compared with the corresponding forecasts produced using the existing E3ME-WLME system. The focus of the comparison is on qualitative differences in the way the two sets of forecasts are to be interpreted. In particular, the sense in which explicit specification of technical change and economic behaviour (in the new system) can be substituted for time series extrapolation techniques (in the existing system) is carefully considered. The primary objective of the paper, therefore, is to demonstrate the empirical feasibility of the alternative methodology rather than to produce robust alternative forecasts.