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Information, Mobile Communication, and Referral Effects

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  • Panle Jia Barwick
  • Yanyan Liu
  • Eleonora Patacchini
  • Qi Wu

Abstract

We use the universe of de-identified and geocoded cellphone records for 1.8 million individuals from a major Chinese telecommunication provider to examine the role of information exchange in urban labor markets. Information flow, as measured by call volume, correlates strongly with worker flows at different levels of geographic aggregation. Having a referral in a location increases by close to four times the likelihood that a job switcher moves there. Different from the communication pattern between nonreferral pairs, communication between referral pairs exhibits a distinct inverted-U shape that peaks prior to the job switch. Compared with our referral measure, the commonly-used social network proxies deliver a lower bound estimate of the referral effect. We supplement the phone records with administrative data on firm attributes and auxiliary data on job postings and residential housing prices. Referred jobs are associated with higher monetary gains, a higher likelihood to transition from part time to full time, reduced commuting time, and a higher probability of entering desirable jobs. Referral information is more valuable for young workers, people switching jobs from suburbs to the inner city, and those changing their industrial sectors. Firms receiving referred workers are associated with more successful recruits, higher matching rates and faster growth.

Suggested Citation

  • Panle Jia Barwick & Yanyan Liu & Eleonora Patacchini & Qi Wu, 2019. "Information, Mobile Communication, and Referral Effects," NBER Working Papers 25873, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:25873
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    3. Bailey, Michael & Farrell, Patrick & Kuchler, Theresa & Stroebel, Johannes, 2020. "Social connectedness in urban areas," Journal of Urban Economics, Elsevier, vol. 118(C).
    4. Zhou, Di & Zha, Fangjing & Qiu, Wenyan & Zhang, Xiaoru, 2024. "Does digital literacy reduce the risk of returning to poverty? Evidence from China," Telecommunications Policy, Elsevier, vol. 48(6).
    5. 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.
    6. Büchel, Konstantin & Ehrlich, Maximilian v., 2020. "Cities and the structure of social interactions: Evidence from mobile phone data," Journal of Urban Economics, Elsevier, vol. 119(C).
    7. Tian, Yuan & Caballero, Maria Esther & Kovak, Brian K., 2022. "Social learning along international migrant networks," Journal of Economic Behavior & Organization, Elsevier, vol. 195(C), pages 103-121.
    8. Ruochen Dai & Dilip Mookherjee & Kaivan Munshi & Xiaobo Zhang, 2024. "Community Networks, Entrepreneurship and the Process of Economic Development," Cowles Foundation Discussion Papers 2406, Cowles Foundation for Research in Economics, Yale University.
    9. Virág Ilyés & István Boza & László Lőrincz & Rikard H Eriksson, 2023. "How to enter high-opportunity places? The role of social contacts for residential mobility," Journal of Economic Geography, Oxford University Press, vol. 23(2), pages 371-395.
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    12. Kirchberger, Martina, 2021. "Measuring internal migration," Regional Science and Urban Economics, Elsevier, vol. 91(C).
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    JEL classification:

    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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