Abstract
ABSTRACTAn evolutionary approach to the delimitation of labour market areas: an empirical application for Chile. Spatial Economic Analysis. Labour market areas (LMAs) are argued to represent a more appropriate policy framework than administrative units for the analysis of spatial labour market activity. This article develops LMAs for Chile by applying an evolutionary computation approach. This innovative approach defines LMAs through an optimization process by maximization of internal cohesion, subject to restrictions of minimum levels of self-containment and population. To evaluate the appropriateness of the LMAs, comparative analyses are performed between alternative delimitations based on different parameter configurations of the proposed method versus administrative boundaries and the most widely used method for official LMA delimitation, the travel-to-work areas method.
Highlights
Labour market areas (LMAs) constitute a type of functional region (Brown & Holmes, 1971) that captures the extent of commuting fields of residents and catchment areas of firms from a particular geographical area
Our results indicate that the grouping evolutionary algorithm (GEA)-based LMAs provide a more appropriate spatial framework to analyse labour market activity in Chile than communes, provinces and travel-to-work areas (TTWAs)-LMAs
To improve public management of these resources, the OECD recommends moving towards a territorial policy approach that capitalizes on the opportunities and needs of the country’s diverse geography
Summary
Labour market areas (LMAs) constitute a type of functional region (Brown & Holmes, 1971) that captures the extent of commuting fields of residents and catchment areas of firms from a particular geographical area They are, argued to provide a more appropriate spatial framework to capture the interplay between labour demand and supply than administrative geographical units (Goodman, 1970; Organisation for Economic Co-operation and Development (OECD), 2002; Smart, 1974). This section proposes two improvements to the GEA methodology: (1) a modification of the interaction index used in the problem’s fitness function; and (2) the adoption of a spatially structured population model in the evolutionary algorithm. Together, these refinements improve the efficiency of GEA and the quality of LMA delimitations.
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