Dead-end jobs or steppingstones? Precarious work in Albania. A new GLO Discussion Paper by GLO Affiliate Elvisa Drishti and Fiona Carmichael.

A new GLO Discussion Paper finds that the research is more supportive of the dead-end hypothesis than the idea that a lower quality job can be a steppingstone into a better job.

Elvisa Drishti

GLO Discussion Paper No. 1011, 2022

Dead-end jobs or steppingstones? Precarious work in Albania Download PDF
by Drishti, Elvisa & Carmichael, Fiona

GLO Affiliate Elvisa Drishti

Author Abstract: Purpose: This study asks whether lower quality forms of employment lead to career transitions into higher quality forms of employment acting as steppingstones, or bridges or, whether instead they lead to dead-ends, or traps, in which workers move between unstable jobs with low prospects for upward mobility and unemployment. Design/methodology/approach: This study uses a unique dataset recording retrospective monthly employment states over 3 years for 373 individuals in the Albanian city of Shkoder. The analysis uses sequence and regression analysis to investigate whether people employed in lower quality, more precarious jobs remain in these kinds of jobs or instead are able to transition into higher quality, permanent and füll-time employment. Findings: In line with previous evidence for the region and Europe, the analysis confirms the precarization of many working lives particularly for women, young people and those with lower educational attainment. This evidence is more supportive of the dead-end hypothesis than the idea that a lower quality job can be a steppingstone into a better job. Originality: This study contributes to the limited knowledge of labour market functioning in developing post-socialist Western Balkans countries. Recent flexicurity policies have generated an increased prevalence of more precarious employment arrangements in Albania. This investigation addresses previous research limitations regarding point-in-time transitions and unobserved heterogeneity using retrospective recall data and controlling for personality traits.

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