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| Source: | Find a Tender Service (FTS) |
| Notice Type: | Pipeline / planning |
| Buyer: | Liverpool John Moores University |
| Main Category: | Services |
| Procurement Method: | — |
| Tender Status: | Planned |
| Estimated Value (ex. VAT): | £40,000 |
| Release Date: | 6 September 2023 |
| Application Deadline: | — |
| Procurement ID (OCID): | ocds-h6vhtk-03fa53 |
| Notice Reference: | 026282-2023 |
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The Middle East and North African (MENA) region faces significant challenges related to high unemployment rates. A research team, led by Moustafa Haj Youssef at Liverpool John Moores University, is embarking on a comprehensive survey project to create a unique dataset focused on labor market trends and business ownership in the MENA region. The study will encompass six countries: Egypt, Jordan, Kingdom of Saudi Arabia, Lebanon, Tunisia, and the United Arab Emirates. The dataset will include a wealth of information on various socio-economic and demographic attributes of individuals, such as age, education, income, marital status, labor market outcomes, household characteristics, and parental background. Additionally, the survey will delve into respondents' work experiences, offering insights not readily available in other MENA region datasets. A section of the survey will also explore respondents' personality traits and their sense of agency in their lives, contributing valuable data that is scarce not only in the MENA region but in many other regions as well. This project aims to categorise workers into different groups, including self-employed and paid workers. Researchers plan to analyse the socio-economic and demographic characteristics of each group while considering the influence of cultural, social, religious, and institutional factors unique to the countries under study. The project will also distinguish between "sustained self-employed" and "dabblers self-employed," a differentiation not previously explored in the MENA region. Furthermore, the research team intends to estimate the returns to education for various worker groups and capture the diversity of responses to the COVID-19 pandemic among these groups.
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Not addedContract imported automatically · AI writes the response
Estimated Value
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