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Datafying anti-poverty programmes: implications for data justice

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journal contribution
posted on 10.04.2019, 13:06 by Silvia Masiero, Soumyo Das
This paper seeks to illuminate the significance of datafication for anti-poverty programmes, meaning social protection schemes designed specifically for poor people. The conversion of beneficiary populations into machine-readable data enables two core functions of social protection, those of recognising entitled beneficiaries and assigning entitlements connected to each anti-poverty scheme. Drawing on the incorporation of Aadhaar, India’s biometric population database, in the national agenda for social protection, we unpack a techno-rational perspective that crafts datafication as a means to enhance the effectiveness of anti-poverty schemes. Yet at the same time, narratives collected in the field show multiple forms of data injustice on recipients, underpinned by Aadhaar’s functionality for a shift of the social protection agenda from in-kind subsidies to cash transfers. Based on such narratives the paper introduces a politically embedded view of data, framing datafication as a transformative force that contributes to deep reform of existing anti-poverty schemes.

History

School

  • Business and Economics

Department

  • Business

Published in

Information, Communication and Society

Volume

22

Issue

7

Pages

916-933

Citation

MASIERO, S. and DAS, S., 2019. Datafying anti-poverty programmes: implications for data justice. Information, Communication and Society, 22 (7), pp.916-933

Publisher

Taylor & Francis (© Informa UK Limited)

Version

AM (Accepted Manuscript)

Publisher statement

This is an Accepted Manuscript of an article published by Taylor & Francis in Information, Communication and Society on 13 May 2019, available online: https://doi.org/10.1080/1369118X.2019.1575448

Acceptance date

11/01/2019

Publication date

2019-05-13

Copyright date

2019

ISSN

1369-118X

eISSN

1468-4462

Language

en