An Inverse U-Shape Relationship between Rate of Unemployment and Level of Education: Some Evidences from Pakistan

Mehar, Ayub Khan

doi:10.1177/0019466219950409

Cited by 1 publication

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“…9 The structural dimensions alluded to include minimum wages, trade union activity, inadequate information flows, and mechanisms to bridge the supplydemand chasm. Potentially, this could even include factors like gender, location, parents' education, household land-ownership, caste, class and family income (Mathew 1995, Heyneman 2014, Majumder and Mukherjee 2013, Biyu 2009, Topel 1997, Mehar 1995.…”

Section: Literature Reviewmentioning

confidence: 99%

Employment and Unemployment amongst Educated Youth in Bangladesh: An Exploratory Analysis

Murshid¹,

Mahmood²,

Shashi³

2021

BDS

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This is an exploratory exercise that attempts to identify the potential for carrying out online socio-economic surveys in Bangladesh, taking the question of “educated unemployment” as a test case. The topic is of great interest not just in Bangladesh but also throughout South Asia and beyond, where the issue is of particular concern in the context of rapid growth and rising aspirations amongst young people. Most studies depend either on own data generation or periodic national level surveys like the Labour Force Surveys (LFS). The former consists of small datasets, while the latter have a limited number of relevant variables available for analysis. Thus, easier access to larger datasets with better coverage of variables would be a highly welcome additional resource for researchers and policymakers. It was, in fact, possible to rapidly generate a large volume of data using an online platform (Facebook) for this exercise. The data validation approach used here is to compare findings with those reported in the wider literature. In general, the results obtained from the online survey appear both reasonable and defensible. The estimates of educated unemployment are consistent with other available estimates. The relationship of unemployment to education, gender and location is similar to those reported in the literature. The effect of “control” variables like family size, age and family income was as expected. In particular, family income (reflecting family influence) emerged as a powerful predictor. The study was also able to throw light on two other aspects of the labour market, including duration of unemployment and salary levels.

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Section: Literature Reviewmentioning

confidence: 99%