[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/p82fk.html
   My bibliography  Save this paper

Information About Vacancy Competition Redirects Job Search

Author

Listed:
  • Bhole, Monica
  • Fradkin, Andrey
  • Horton, John
Abstract
Job seekers typically do not know the degree of competition they face for a particular vacancy. As a result, they may unwittingly send applications to vacancies with a lot of competition and may overlook vacancies with little competition. We study how providing information about competition for a vacancy redirects applications. To do so, we conduct three field experiments on a large online job platform in which treated job searchers are shown information about the number of prior applicants to a vacancy. This information increases overall applications and redirects applications to vacancies with few prior applications. Applications are sent to vacancies that receive fewer cumulative applications but result in similar outcomes to control applications. We use a complementary treatment to show that job seekers also use the age of the vacancy to direct search towards newer vacancies with relatively little competition. Our results are consistent with a model in which searchers have imperfect information about competition for a vacancy and redirect their search towards less competitive vacancies when they receive an improved signal.

Suggested Citation

  • Bhole, Monica & Fradkin, Andrey & Horton, John, 2021. "Information About Vacancy Competition Redirects Job Search," SocArXiv p82fk, Center for Open Science.
  • Handle: RePEc:osf:socarx:p82fk
    DOI: 10.31219/osf.io/p82fk
    as

    Download full text from publisher

    File URL: https://osf.io/download/606b9847f6585f01c361b403/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/p82fk?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Christopher A. Pissarides & Barbara Petrongolo, 2001. "Looking into the Black Box: A Survey of the Matching Function," Journal of Economic Literature, American Economic Association, vol. 39(2), pages 390-431, June.
    2. Scott R. Baker & Andrey Fradkin, 2017. "The Impact of Unemployment Insurance on Job Search: Evidence from Google Search Data," The Review of Economics and Statistics, MIT Press, vol. 99(5), pages 756-768, December.
    3. Marinescu, Ioana, 2017. "The general equilibrium impacts of unemployment insurance: Evidence from a large online job board," Journal of Public Economics, Elsevier, vol. 150(C), pages 14-29.
    4. James Albrecht & Bruno Decreuse & Susan Vroman, 2023. "Directed Search With Phantom Vacancies," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(2), pages 837-869, May.
    5. R. Jason Faberman & Marianna Kudlyak, 2019. "The Intensity of Job Search and Search Duration," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(3), pages 327-357, July.
    6. Arnaud Cheron & Bruno Decreuse, 2017. "Matching with Phantoms," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(3), pages 1041-1070.
    7. Charles Hodgson & Gregory Lewis, 2020. "You Can Lead a Horse to Water: Spatial Learning and Path Dependence in Consumer Search," Cowles Foundation Discussion Papers 2246, Cowles Foundation for Research in Economics, Yale University.
    8. John J. Horton, 2017. "The Effects of Algorithmic Labor Market Recommendations: Evidence from a Field Experiment," Journal of Labor Economics, University of Chicago Press, vol. 35(2), pages 345-385.
    9. Jason Abaluck & Giovanni Compiani, 2020. "A Method to Estimate Discrete Choice Models that is Robust to Consumer Search," NBER Working Papers 26849, National Bureau of Economic Research, Inc.
    10. Kory Kroft & Devin G. Pope, 2014. "Does Online Search Crowd Out Traditional Search and Improve Matching Efficiency? Evidence from Craigslist," Journal of Labor Economics, University of Chicago Press, vol. 32(2), pages 259-303.
    11. Peter Kuhn & Hani Mansour, 2014. "Is Internet Job Search Still Ineffective?," Economic Journal, Royal Economic Society, vol. 124(581), pages 1213-1233, December.
    12. Laura Gee, 2014. "The More you Know: Information Effects in Job Application Rates by Gender in a Large Field Experiment," Discussion Papers Series, Department of Economics, Tufts University 0780, Department of Economics, Tufts University.
    13. Ioana Marinescu & Daphné Skandalis, 0. "Unemployment Insurance and Job Search Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 136(2), pages 887-931.
    14. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2012. "Optimal Search for Product Information," Management Science, INFORMS, vol. 58(11), pages 2037-2056, November.
    15. Laura K. Gee, 2019. "The More You Know: Information Effects on Job Application Rates in a Large Field Experiment," Management Science, INFORMS, vol. 67(5), pages 2077-2094, May.
    16. Laura K. Gee & Jason Jones & Moira Burke, 2017. "Social Networks and Labor Markets: How Strong Ties Relate to Job Finding on Facebook’s Social Network," Journal of Labor Economics, University of Chicago Press, vol. 35(2), pages 485-518.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Horton, John J. & Johari, Ramesh & Kircher, Philipp, 2024. "Sorting through Cheap Talk: Theory and Evidence from a Labor Market," LIDAM Discussion Papers CORE 2024013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Horton, John J. & Johari, Ramesh & Kircher, Philipp, 2021. "Cheap Talk Messages for Market Design: Theory and Evidence from a Labor Market with Directed," LIDAM Discussion Papers CORE 2021033, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Altmann, Steffen & Glenny, Anita Marie & Mahlstedt, Robert & Sebald, Alexander, 2022. "The Direct and Indirect Effects of Online Job Search Advice," IZA Discussion Papers 15830, Institute of Labor Economics (IZA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suguru Otani, 2024. "Nonparametric Estimation of Matching Efficiency and Elasticity on a Private On-the-Job Search Platform: Evidence from Japan, 2014-2024," Papers 2410.17011, arXiv.org.
    2. Fabo, B., 2017. "Towards an understanding of job matching using web data," Other publications TiSEM b8b877f2-ae6a-495f-b6cc-9, Tilburg University, School of Economics and Management.
    3. Morgan Raux, 2019. "Looking for the "Best and Brightest": Hiring difficulties and high-skilled foreign workers," AMSE Working Papers 1934, Aix-Marseille School of Economics, France.
    4. Lichter, Andreas & Schiprowski, Amelie, 2021. "Benefit duration, job search behavior and re-employment," Journal of Public Economics, Elsevier, vol. 193(C).
    5. Brenčič, Vera, 2024. "Terms of use and network size: Evidence from online job boards and CV banks in the U.S," Information Economics and Policy, Elsevier, vol. 67(C).
    6. R. Jason Faberman & Marianna Kudlyak, 2016. "What Does Online Job Search Tell Us about the Labor Market?," Economic Perspectives, Federal Reserve Bank of Chicago, issue 1, pages 1-15.
    7. Michèle Belot & Philipp Kircher & Paul Muller, 2022. "How Wage Announcements Affect Job Search—A Field Experiment," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 1-67, October.
    8. Michèle Belot & Philipp Kircher & Paul Muller, 2019. "Providing Advice to Jobseekers at Low Cost: An Experimental Study on Online Advice," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(4), pages 1411-1447.
    9. Hensvik, Lena & Le Barbanchon, Thomas & Rathelot, Roland, 2021. "Job search during the COVID-19 crisis," Journal of Public Economics, Elsevier, vol. 194(C).
    10. He, Chuan & Mau, Karsten & Xu, Mingzhi, 2021. "Trade Shocks and Firms Hiring Decisions: Evidence from Vacancy Postings of Chinese Firms in the Trade War," Labour Economics, Elsevier, vol. 71(C).
    11. Karahan, Fatih & Mitman, Kurt & Moore, Brendan, 2019. "Individual and Market-Level Effects of UI Policies: Evidence from Missouri," IZA Discussion Papers 12805, Institute of Labor Economics (IZA).
    12. James Albrecht & Bruno Decreuse & Susan Vroman, 2023. "Directed Search With Phantom Vacancies," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(2), pages 837-869, May.
    13. Ioana Marinescu & Ronald Wolthoff, 2020. "Opening the Black Box of the Matching Function: The Power of Words," Journal of Labor Economics, University of Chicago Press, vol. 38(2), pages 535-568.
    14. Fatih Karahan & Kurt Mitman & Brendan Moore, 2019. "Micro and Macro Effects of UI Policies: Evidence from Missouri," Staff Reports 905, Federal Reserve Bank of New York.
    15. Laurel Wheeler & Robert Garlick & Eric Johnson & Patrick Shaw & Marissa Gargano, 2022. "LinkedIn(to) Job Opportunities: Experimental Evidence from Job Readiness Training," American Economic Journal: Applied Economics, American Economic Association, vol. 14(2), pages 101-125, April.
    16. Marinescu, Ioana & Skandalis, Daphné & Zhao, Daniel, 2021. "The impact of the Federal Pandemic Unemployment Compensation on job search and vacancy creation," Journal of Public Economics, Elsevier, vol. 200(C).
    17. Leonardo Fabio Morales & Carlos Ospino & Nicole Amaral, 2021. "Online Vacancies and its Role in Labor Market Performance," Borradores de Economia 1174, Banco de la Republica de Colombia.
    18. Harald Mayr, 2022. "Cheap search, picky workers? Evidence from a field experiment," Economics Bulletin, AccessEcon, vol. 42(4), pages 2079-2087.
    19. Stef Garasto & Jyldyz Djumalieva & Karlis Kanders & Rachel Wilcock & Cath Sleeman, 2021. "Developing experimental estimates of regional skill demand," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-02, Economic Statistics Centre of Excellence (ESCoE).
    20. Camille Landais & Pascal Michaillat & Emmanuel Saez, 2018. "A Macroeconomic Approach to Optimal Unemployment Insurance: Applications," American Economic Journal: Economic Policy, American Economic Association, vol. 10(2), pages 182-216, May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:p82fk. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.