For four decades, the spatial mismatch hypothesis has been used as a scientific framework for the understanding of spatially related mismatch issues on the labor market. Over time, the mismatch studies have encompassed a wider array of hypotheses including issues of gender and class. However, the validity of the hypotheses is sometimes contested, and almost always is the validity of the hypotheses questioned regarding the models of accessibility used to depict the labor market situation. In this article, ELMO, a new method for the computation of doubly constrained accessibility, is introduced and tested against other commonly used models of accessibility. Using a unique dataset containing coordinates and additional employment related data on all inhabitants and all jobs in a Swedish local labor market, the new method accomplishes to retain the doubly constrained nature even though over 20,000 jobs are included and over 24,000 employable individuals are included. The detailed nature of the model proves to be beneficiary to other models of accessibility, especially for use in mismatch related hypotheses.
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