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The Annals of Regional Science

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01-08-2014 | Original Paper

Getting a full-time job as a part-time unemployed: How much does spatial context matter?

Authors: Joel Karlsson, Jonas Månsson

Published in: The Annals of Regional Science | Issue 1/2014

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Abstract

This paper investigates the extent to which differences in the probability to exit from part-time unemployment to a full-time job are accountable for by spatial contextual factors and individual characteristics. To correctly incorporate contextual effects, a multilevel analysis is applied using a mixed-effects model to explore whether contextual factors account for differences in the probability of transition to full-time employment between individuals with different characteristics. The results indicate that there is a contextual effect and that there are some spatial spill-over effects from neighbouring municipalities, and that the unemployment rate partly explains the context variability. Furthermore, the contextual effect is found to be especially large for individuals without a university degree.

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See Månsson and Ottosson (2011) for an overview.

The consequence of periods of part-time for future income is also a research area. Research has shown that it is not only during periods of part-time work an individual can expect lower income, but also after leaving part-time work (see e.g. O’Dorchai et al. 2007; Connolly and Gregory 2007; Wahlberg 2008; Manning and Petrongolo 2008; Bardasi and Gornick 2008; Burgoon and Dekker 2010).

There are seven other destinations that might be of relevance according to the Swedish Employment Service. These are in detail explored in a paper by Månsson and Ottosson (2011). Here, we limit the study to only include what in accordance with the written instructions and laws should be the objective for the Swedish Employment Service—getting a full-time, unsubsidised job on the open labour market.

For an in-depth discussion on the subject of multilevel statistical models (see e.g. de Leew and Meijer 2008; Hox and Roberts 2011).

Reviews are given in e.g., Anselin and Bera (1998) and Goodchild et al. (2000).

Applications using the multilevel framework are illustrated in Langford et al. (1999a, b), and Karlsson and Nilsson (2014).

The models are estimated by Stata and MLwiN (Browne 2009; Rasbash et al. 2009) using the runmlwin command (Leckie and Charlton 2011). See Draper (2008), Browne (1998) for an in-depth discussion about MCMC methods in multilevel modelling.

The Bayesian DIC for the two estimated models suggests that Model B is preferred to Model A.

We have tested for a potential endogeneity problem by including the average of each variable, at level 1 for each context (Mundlak 1978). We found that work-related disabilities to some extent are endogenous, however, excluding the variable that measures the average occurrence of individuals with work-related disabilities for each context do not alter the results.

Note, for the parameter of interest \(\theta \), we cannot test a two-sided hypothesis, \(H_0{:}\theta =\theta _0\), since the posterior probability of the point hypothesis will always be zero. Instead, we can calculate a 95 % credibility interval for the parameter. If \(\theta _0\) lies in the credibility interval, we cannot reject the null hypothesis, and \(\theta _0\) remains a credible value. That is we are not testing the ‘truth’ but rather its credibility.

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Title: Getting a full-time job as a part-time unemployed: How much does spatial context matter?
Authors: Joel Karlsson
Jonas Månsson
Publication date: 01-08-2014
Publisher: Springer Berlin Heidelberg
Published in: The Annals of Regional Science / Issue 1/2014
Print ISSN: 0570-1864
Electronic ISSN: 1432-0592
DOI: https://doi.org/10.1007/s00168-014-0623-3

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