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The causal effect of informal job search on wage and job satisfaction: evidence from Egypt and Jordan using random forest method

Author

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  • Obbey Ahmed Elamin

Abstract

Purpose - This study aims to examine the impact of finding a job through family and friend contacts on employees' wages and job satisfaction. Design/methodology/approach - This study uses machine learning techniques in economics to estimate the causal effect of being hired through informal contacts on wages and job satisfaction using cross-sectional data from the Higher Education Graduates Survey 2012 (HEGS, 2012) in Egypt and Jordan. Findings - The author's results confirm that negative and positive consequences are likely to occur. In Egypt, a wage penalty of 28% is estimated in the starting wage, but the impact diminishes in the last wage. By contrast, in Jordan, no penalty is captured in the starting wage, but a premium of 10% is estimated in the last wage. Job satisfaction is negatively affected by the penalty in the starting wage. Social implications - Job market search methods that allow for professional job referrals, facilitate more efficient information transfer between job-seekers and employers and provide opportunities for job-seekers with weak social capital should be implemented to lower dependence on informal search methods. Originality/value - The research provides comprehensive evidence about finding a job through informal contact with employees' well-being. The consequences of finding a job using family and friends' contacts, whether positive or negative, cannot be ignored. Future research could benefit from the findings of this study. Peer review - The peer review history for this article is available at:https://publons.com/publon/10.1108/IJSE-05-2022-0318.

Suggested Citation

  • Obbey Ahmed Elamin, 2022. "The causal effect of informal job search on wage and job satisfaction: evidence from Egypt and Jordan using random forest method," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 50(4), pages 522-536, December.
  • Handle: RePEc:eme:ijsepp:ijse-05-2022-0318
    DOI: 10.1108/IJSE-05-2022-0318
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    More about this item

    Keywords

    Job search; Informal; Treatment effect; Machine learning; Causal forest; C01; C10; C21; J31; J64;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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