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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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    2. Bailey, Michael & Farrell, Patrick & Kuchler, Theresa & Stroebel, Johannes, 2020. "Social connectedness in urban areas," Journal of Urban Economics, Elsevier, vol. 118(C).
    3. 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).
    4. 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.
    5. 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.
    6. Maxim Ananyev & Michael Poyker & Yuan Tian, 2021. "The safest time to fly: pandemic response in the era of Fox News," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(3), pages 775-802, July.
    7. Büchel, Konstantin & Ehrlich, Maximilian V. & Puga, Diego & Viladecans-Marsal, Elisabet, 2020. "Calling from the outside: The role of networks in residential mobility," Journal of Urban Economics, Elsevier, vol. 119(C).
    8. Kirchberger, Martina, 2021. "Measuring internal migration," Regional Science and Urban Economics, Elsevier, vol. 91(C).
    9. Li, Teng & Barwick, Panle Jia & Deng, Yongheng & Huang, Xinfei & Li, Shanjun, 2023. "The COVID-19 pandemic and unemployment: Evidence from mobile phone data from China," Journal of Urban Economics, Elsevier, vol. 135(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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