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Conditional vs unconditional cash transfers: a study of poverty demographics in Pakistan

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  • Ayesha Afzal
  • Nawazish Mirza
  • Fatima Arshad

Abstract

This paper aims to provide a detailed demographic description of poverty in Pakistan with an attempt to highlight those segments of the poor who can be aided to transition out of extreme poverty through appropriate policy measures. Data are collected from the Household Integrated Economic Survey (HIES) for the years 1985–2016 and captures falling poverty, gender-wise division of the employed and unemployed, type of employment (self-employed, unpaid workers, employers, employees) by gender, labour participation of vulnerable age groups, as well as unemployed widows. The paper discusses the effectiveness of conditional (CCT) and unconditional (UCT) cash transfer programs across the world and using data indicators, highlights the appropriate target groups in need of such intervention in Pakistan. The existing components of BISP are discussed, with policy recommendations targeted to enhance its impact by focusing UCTs on the most vulnerable segments. CCTs can be used to improve health and education outcomes; given Pakistan’s lagging performance, illiteracy among youth, infant and maternal health are of particular consideration. Cash transfers can be made conditional, subject to regular health checkups for mothers and children and mandatory school attendance to improve these outcomes. The paper further suggests an extension of the program to provide short-term financial relief to the temporarily unemployed.

Suggested Citation

  • Ayesha Afzal & Nawazish Mirza & Fatima Arshad, 2019. "Conditional vs unconditional cash transfers: a study of poverty demographics in Pakistan," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 3366-3383, January.
  • Handle: RePEc:taf:reroxx:v:32:y:2019:i:1:p:3366-3383
    DOI: 10.1080/1331677X.2019.1661006
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    Cited by:

    1. Patrick Rehill & Nicholas Biddle, 2024. "Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning," Papers 2401.07075, arXiv.org, revised Mar 2024.

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