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Empirically Examining the Individual-Level Determinants of Job Searching Outcomes: a Non-linear Analysis under the Case of Chile

Author

Listed:
  • Ruohan Wu

    (University of North Georgia)

  • Yuexing Lan

    (Auburn University at Montgomery)

  • Xueyu Cheng

    (Clayton State University)

Abstract

This paper empirically examines the determinants that can help job searchers successfully find jobs. We acquire individual-level data from the 2009 National Employment Survey of Chile and use an innovative non-linear estimation which examines the influences of two individual-level factors: job searchers’ searching efforts and their previous work experience. It is found that whether one searcher is actively searching and whether the searcher has been employed before both significantly affect the job searching outcomes. To be specific, actively searching for jobs with resumes and social networks as well as public assistance provides more opportunities to successfully find a job. Similar consequences may occur for someone without previous work experience. Furthermore, the disparities in the job searching results are also affected by multiple individual characteristics, such as age, gender, and education level. For example, active searching only significantly helps men but not women and those people with average instead of higher education backgrounds find jobs, while previous work experience can help women or those who receive higher education find jobs more easily. Relevant policy implications are also discussed.

Suggested Citation

  • Ruohan Wu & Yuexing Lan & Xueyu Cheng, 2018. "Empirically Examining the Individual-Level Determinants of Job Searching Outcomes: a Non-linear Analysis under the Case of Chile," Journal of Economics, Race, and Policy, Springer, vol. 1(1), pages 3-15, July.
    • Handle: RePEc:spr:joerap:v:1:y:2018:i:1:d:10.1007_s41996-018-0002-z
    DOI: 10.1007/s41996-018-0002-z
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    References listed on IDEAS

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    More about this item

    Keywords

    Job searching; Active searching; Previous working experience; Education; Gender; Age;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J10 - Labor and Demographic Economics - - Demographic Economics - - - General
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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