126 lines
4.2 KiB
YAML
126 lines
4.2 KiB
YAML
abstract: 'In no research domain has the application of accessibility been so vital
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as in the area of linking disadvantaged individuals to job
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opportunities. The inability to reach locations of employment and,
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therefore, partake in paid labor is considered to have severe
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consequences on an individual''s economic security and quality of life as
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well as society''s general level of welfare. Unfortunately, existing
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studies on job accessibility primarily apply aggregate measures that aim
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to link the population group of active, employed workers to pre-existing
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job locations. As a result, they fail to capture the person-specific
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labor-market opportunities for those individuals who are actually
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unemployed as well as the degree to which accessibility to opportunities
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is related to actual employment rates. The proposed paper answers this
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limitation by constructing a predictive model for long-term unemployment
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for job seekers in Flanders, Belgium, dependent on their access by
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private and public transport to job openings that correspond to their
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individual preferences and competences. In addition to accessibility,
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the predictive capacity was determined for various socio-demographics
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such as age, gender, migration background, educational background and
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preferred job type. The proposed regression model shows that job
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accessibility is negatively related to long-term unemployment. In
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addition, various inequities in long-term unemployment exist for the
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selected case study. Especially job seekers with a migration background
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and with higher age (55 years or older) have significantly higher
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probabilities of remaining unemployed. A conditional inference
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regression tree indicates that the most disadvantaged groups have a two
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to three times higher probability of being long-term unemployed.
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Moreover, higher accessibility levels prove to only benefit those who
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already are in a more advantaged position. These findings have important
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ramifications for policies focusing on improving employment rates, as
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they allow to specifically address those areas of research where major
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gains can be made.'
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affiliation: 'Fransen, K (Corresponding Author), Univ Ghent, Dept Ind Engn, Ctr Mobil
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\& Spatial Planning, Valentin Vaerwyckweg 1, B-9000 Ghent, Belgium.
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Fransen, Koos; Deruyter, Greta, Univ Ghent, Dept Ind Engn, Ctr Mobil \& Spatial
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Planning, Valentin Vaerwyckweg 1, B-9000 Ghent, Belgium.
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Fransen, Koos; Deruyter, Greta; De Maeyer, Philippe, Univ Ghent, Dept Geog, Res
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Grp Cartog \& GIS, Krijgslaan 281 S8, B-9000 Ghent, Belgium.
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Boussauw, Kobe, Vrije Univ Brussel, Dept Geog, Cosmopolis Ctr Urban Res, Pl Laan
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2,Room F4-55, B-1050 Brussels, Belgium.'
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author: Fransen, Koos and Boussauw, Kobe and Deruyter, Greta and De Maeyer, Philippe
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author-email: Koos.Fransen@UGent.be
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author_list:
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- family: Fransen
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given: Koos
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- family: Boussauw
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given: Kobe
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- family: Deruyter
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given: Greta
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- family: De Maeyer
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given: Philippe
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da: '2023-09-28'
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doi: 10.1016/j.tra.2018.01.023
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files: []
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issn: 0965-8564
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journal: TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
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keywords: Unemployment; Job accessibility; Social equity; Transport disadvantage
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keywords-plus: 'WELFARE-TO-WORK; TRAVEL BEHAVIOR; ACCESSIBILITY; EMPLOYMENT; OWNERSHIP;
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LOCATION; CHOICES'
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language: English
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month: JUL
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number-of-cited-references: '53'
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orcid-numbers: 'Fransen, Koos/0000-0002-8331-1968
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De Maeyer, Philippe A.M./0000-0001-8902-3855
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Deruyter, Greta/0000-0002-7258-125X
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Boussauw, Kobe/0000-0001-7619-2852'
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pages: 268-279
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papis_id: 14ac88e1af04d7f094b9caf727f10c09
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ref: Fransen2019relationshiptranspor
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researcherid-numbers: 'Fransen, Koos/AAL-3581-2020
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Fransen, Koos/AAP-5177-2020
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De Maeyer, Philippe A.M./F-2985-2011
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Deruyter, Greta/C-6389-2015
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'
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times-cited: '12'
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title: 'The relationship between transport disadvantage and employability: Predicting
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long-term unemployment based on job seekers'' access to suitable job openings in
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Flanders, Belgium'
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type: Article
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unique-id: WOS:000474333300022
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usage-count-last-180-days: '0'
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usage-count-since-2013: '22'
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volume: '125'
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web-of-science-categories: Economics; Transportation; Transportation Science \& Technology
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year: '2019'
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