IDEAS home Printed from https://ideas.repec.org/p/cns/cnscwp/201911.html
   My bibliography  Save this paper

Incentivized Peer Referrals for Tuberculosis Screening: Evidence from India

Author

Listed:
  • J. Goldberg
  • M. Macis
  • P. Chintagunta

Abstract

We use a field experiment with 3,176 patients at 122 tuberculosis treatment clinics in India to test whether peer referrals increase screening and identification of patients with an infectious disease. Low-cost financial incentives considerably raise the probability that current patients refer prospective patients for screening and testing, resulting in the cost-effective identification of new tuberculosis cases. Incentivized referrals operate through two mechanisms - peers have private information about individuals in their social networks (beyond their immediate families) to target for outreach, and peers are more effective than traditional contact tracing by paid health workers in inducing these individuals to get tested.

Suggested Citation

  • J. Goldberg & M. Macis & P. Chintagunta, 2019. "Incentivized Peer Referrals for Tuberculosis Screening: Evidence from India," Working Paper CRENoS 201911, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  • Handle: RePEc:cns:cnscwp:201911
    as

    Download full text from publisher

    File URL: https://crenos.unica.it/crenos/node/7224
    Download Restriction: no

    File URL: https://crenos.unica.it/crenos/sites/default/files/wp-19-11.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Lori Beaman & Jeremy Magruder, 2012. "Who Gets the Job Referral? Evidence from a Social Networks Experiment," American Economic Review, American Economic Association, vol. 102(7), pages 3574-3593, December.
    2. Kugler, Adriana D., 2003. "Employee referrals and efficiency wages," Labour Economics, Elsevier, vol. 10(5), pages 531-556, October.
    3. Achim Ahrens & Christian B. Hansen & Mark E Schaffer, 2018. "PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference," Statistical Software Components S458459, Boston College Department of Economics, revised 24 Jan 2019.
    4. Fafchamps, Marcel & Islam, Asad & Malek, Mohammad Abdul & Pakrashi, Debayan, 2020. "Can referral improve targeting? Evidence from an agricultural training experiment," Journal of Development Economics, Elsevier, vol. 144(C).
    5. Fafchamps, Marcel & Islam, Asadul & Malek, Abdul & Pakrashi, Debayan, 2017. "Can Referral Improve Targeting? Evidence from a Vocational Training Experiment," CEPR Discussion Papers 12070, C.E.P.R. Discussion Papers.
    6. Balat, Jorge & Papageorge, Nicholas W. & Qayyum, Shaiza, 2017. "Positively Aware? Conflicting Expert Reviews and Demand for Medical Treatment," IZA Discussion Papers 10919, Institute of Labor Economics (IZA).
    7. Abhijit Banerjee & Arun G Chandrasekhar & Esther Duflo & Matthew O Jackson, 2019. "Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials," Review of Economic Studies, Oxford University Press, vol. 86(6), pages 2453-2490.
    8. Vivi Alatas & Abhijit Banerjee & Arun G. Chandrasekhar & Rema Hanna & Benjamin A. Olken, 2016. "Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia," American Economic Review, American Economic Association, vol. 106(7), pages 1663-1704, July.
    9. David Godes & Dina Mayzlin, 2009. "Firm-Created Word-of-Mouth Communication: Evidence from a Field Test," Marketing Science, INFORMS, vol. 28(4), pages 721-739, 07-08.
    10. Emily Oster & Rebecca Thornton, 2012. "Determinants Of Technology Adoption: Peer Effects In Menstrual Cup Take-Up," Journal of the European Economic Association, European Economic Association, vol. 10(6), pages 1263-1293, December.
    11. Yoko Laurence & Ulla Griffiths & Anna Vassall, 2015. "Costs to Health Services and the Patient of Treating Tuberculosis: A Systematic Literature Review," PharmacoEconomics, Springer, vol. 33(9), pages 939-955, September.
    12. Pope, Devin G., 2009. "Reacting to rankings: Evidence from "America's Best Hospitals"," Journal of Health Economics, Elsevier, vol. 28(6), pages 1154-1165, December.
    13. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," Review of Economic Studies, Oxford University Press, vol. 81(2), pages 608-650.
    14. Andreoni, James, 1990. "Impure Altruism and Donations to Public Goods: A Theory of Warm-Glow Giving?," Economic Journal, Royal Economic Society, vol. 100(401), pages 464-477, June.
    15. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2018. "Can Network Theory-based Targeting Increase Technology Adoption?," Papers 1808.01205, arXiv.org.
    16. Miller, Grant & Luo, Renfu & Zhang, Linxiu & Sylvia, Sean & Shi, Yaojiang & Foo, Patricia & Zhao, Qiran & Martorell, Reynaldo & Medina, Alexis & Rozelle, Scott, 2012. "Effectiveness of provider incentives for anaemia reduction in rural China: a cluster randomised trial," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 1-10.
    17. Shing-Yi Wang, 2013. "Marriage Networks, Nepotism, and Labor Market Outcomes in China," American Economic Journal: Applied Economics, American Economic Association, vol. 5(3), pages 91-112, July.
    18. Rebecca L. Thornton, 2008. "The Demand for, and Impact of, Learning HIV Status," American Economic Review, American Economic Association, vol. 98(5), pages 1829-1863, December.
    19. Mitchell Hoffman, 2017. "The value of hiring through employee referrals in developed countries," IZA World of Labor, Institute of Labor Economics (IZA), pages 369-369, June.
    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. Thomas Bossuroy & Clara Delavallade & Vincent Pons, 2019. "Biometric Tracking, Healthcare Provision, and Data Quality: Experimental Evidence from Tuberculosis Control," NBER Working Papers 26388, National Bureau of Economic Research, Inc.
    2. Ricardo Maertens & Alessandro Tarozzi & Kazi Matin Ahmed & Alexander van Geen, 2018. "Demand for Information on Environmental Health Risk, Mode of Delivery, and Behavioral Change: Evidence from Sonargaon, Bangladesh," Working Papers id:12934, eSocialSciences.

    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. Fafchamps, Marcel & Islam, Asad & Malek, Mohammad Abdul & Pakrashi, Debayan, 2020. "Can referral improve targeting? Evidence from an agricultural training experiment," Journal of Development Economics, Elsevier, vol. 144(C).
    2. Cátia Batista & Marcel Fafchamps & Pedro C. Vicente, 2018. "Keep It Simple: A Field Experiment on Information Sharing in Social Networks," NBER Working Papers 24908, National Bureau of Economic Research, Inc.
    3. Islam, Asadul & Ushchev, Philip & Zenou, Yves & Zhang, Xin, 2019. "The Value of Information in Technology Adoption," IZA Discussion Papers 12672, Institute of Labor Economics (IZA).
    4. Jacopo Bonan & Pietro Battiston & Jaimie Bleck & Philippe LeMay-Boucher & Stefano Pareglio & Bassirou Sarr & Massimo Tavoni, 2017. "Social Interaction and Technology Adoption: Experimental Evidence from Improved Cookstoves in Mali," Working Papers 2017.47, Fondazione Eni Enrico Mattei.
    5. Francesco Drago & Friederike Mengel & Christian Traxler, 2020. "Compliance Behavior in Networks: Evidence from a Field Experiment," American Economic Journal: Applied Economics, American Economic Association, vol. 12(2), pages 96-133, April.
    6. Rao, Neel, 2016. "Social effects in employer learning: An analysis of siblings," Labour Economics, Elsevier, vol. 38(C), pages 24-36.
    7. S Anukriti & Catalina Herrera‐Almanza & Praveen K. Pathak & Mahesh Karra, 2020. "Curse of the Mummy‐ji: The Influence of Mothers‐in‐Law on Women in India†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(5), pages 1328-1351, October.
    8. Afridi, Farzana & Dhillon, Amrita & Sharma, Swati, 2015. "Social Networks and Labour Productivity: A Survey of Recent Theory and Evidence," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 50(1), pages 25-42.
    9. Lena Hensvik & Oskar Nordström Skans, 2016. "Social Networks, Employee Selection, and Labor Market Outcomes," Journal of Labor Economics, University of Chicago Press, vol. 34(4), pages 825-867.
    10. Christian Dustmann & Albrecht Glitz & Uta Schönberg & Herbert Brücker, 2016. "Referral-based Job Search Networks," Review of Economic Studies, Oxford University Press, vol. 83(2), pages 514-546.
    11. Matilde Giaccherini & David H. Herberich & David Jimenez-Gomez & John A. List & Giovanni Ponti & Michael K. Price, 2019. "The Behavioralist Goes Door-To-Door: Understanding Household Technological Diffusion Using a Theory-Driven Natural Field Experiment," NBER Working Papers 26173, National Bureau of Economic Research, Inc.
    12. Amanda Pallais & Emily Glassberg Sands, 2015. "Why the Referential Treatment: Evidence from Field Experiments on Referrals," NBER Working Papers 21357, National Bureau of Economic Research, Inc.
    13. Glitz, Albrecht, 2017. "Coworker networks in the labour market," Labour Economics, Elsevier, vol. 44(C), pages 218-230.
    14. Z. K. Dong & D. S. Huang & F. F. Tang, 2014. "Information disclosure and job search: evidence from a social networks experiment," Applied Economics Letters, Taylor & Francis Journals, vol. 21(4), pages 293-296, March.
    15. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    16. Meta Brown & Elizabeth Setren & Giorgio Topa, 2016. "Do Informal Referrals Lead to Better Matches? Evidence from a Firm's Employee Referral System," Journal of Labor Economics, University of Chicago Press, vol. 34(1), pages 161-209.
    17. Kazushi Takahashi & Yukichi Mano & Keijiro Otsuka, 2018. "Spillovers as a Driver to Reduce Ex-post Inequality Generated by Randomized Experiments: Evidence from an Agricultural Training Intervention," Working Papers 174, JICA Research Institute.
    18. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020. "lassopack: Model selection and prediction with regularized regression in Stata," Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
    19. Jia Barwick, Panle & Liu, Yanyan & Patacchini, Eleonora & Wu, Qi, 2019. "Information, Mobile Communication, and Referral Effects," CEPR Discussion Papers 13786, C.E.P.R. Discussion Papers.
    20. Arimoto, Yutaka & Machikita, Tomohiro & Tsubota, Kenmei, 2018. "Broker versus social networks in adverse working conditions: cross-sectional evidence from Cambodian migrants in Thailand," IDE Discussion Papers 686, Institute of Developing Economies, Japan External Trade Organization(JETRO).

    More about this item

    Keywords

    tuberculosis; referrals; social networks; case finding; incentives; India; health;
    All these keywords.

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

    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:cns:cnscwp:201911. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CRENoS). General contact details of provider: https://edirc.repec.org/data/crenoit.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.