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Sourcebook on the Foundations of Social Protection Delivery Systems

Author

Listed:
  • Kathy Lindert
  • Tina George Karippacheril
  • Inés Rodriguez Caillava
  • Kenichi Nishikawa Chavez

Abstract

The Sourcebook synthesizes real-world experiences and lessons learned of social protection delivery systems from around the world, with a particular focus on social and labor benefits and services. It takes a practical approach, seeking to address concrete “how-to” questions, including: How do countries deliver social protection benefits and services? How do they do so effectively and efficiently? How do they ensure dynamic inclusion, especially for the most vulnerable and needy? How do they promote better coordination and integration—not only among social protection programs but also programs in other parts of government? How can they meet the needs of their intended populations and provide a better client experience? The Sourcebook structures itself around eight key principles that can frame the delivery systems mindset: (1) delivery systems evolve over time, do so in a non-linear fashion, and are affected by the starting point(s); (2) additional efforts should be made to “do simple well”, and to do so from the start rather than trying to remedy by after-the-fact adding-on of features or aspects; (3) quality implementation matters, and weaknesses in the design or structure of any core system element will negatively impact delivery; (4) defining the “first mile” for people interface greatly affects the system and overall delivery, and is most improved when that “first mile” is understood as the weakest link in delivery systems); (5) delivery systems do not operate in a vacuum and thus should not be developed in silos; (6) delivery systems can contribute more broadly to government’s ability to intervene in other sectors, such as health insurance subsidies, scholarships, social energy tariffs, housing benefits, and legal services; (7) there is no single blueprint for delivery systems, but there are commonalities and those common elements constitute the core of the delivery systems framework; (8) inclusion and coordination are pervasive and perennial dual challenges, and they contribute to the objectives of effectiveness and efficiency.

Suggested Citation

  • Kathy Lindert & Tina George Karippacheril & Inés Rodriguez Caillava & Kenichi Nishikawa Chavez, 2020. "Sourcebook on the Foundations of Social Protection Delivery Systems," World Bank Publications - Books, The World Bank Group, number 34044.
  • Handle: RePEc:wbk:wbpubs:34044
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    File URL: https://openknowledge.worldbank.org/bitstream/handle/10986/34044/9781464815775.pdf?sequence=9
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    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Blumenstock, Joshua & Aiken, Emily & Bellue, Suzanne & Udry, Christopher & Karlan, Dean, 2021. "Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance," CEPR Discussion Papers 16385, C.E.P.R. Discussion Papers.
    2. Costella, Cecilia & Diez, Ana & Beazley, Rodolfo & Alfonso, Mariana, 2023. "Shock-responsive social protection and climate shocks in Latin America and the Caribbean: Lessons from COVID-19," IDB Publications (Working Papers) 12699, Inter-American Development Bank.
    3. Emily Aiken & Guadalupe Bedoya & Joshua Blumenstock & Aidan Coville, 2022. "Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan," Papers 2206.11400, arXiv.org.
    4. Kamurase,Alex & Willenborg,Emma Schwirck, 2021. "Early Lessons from Social Protection and Jobs Response to COVID-19 in Middle East andNorth Africa (MENA) Countries," Social Protection Discussion Papers and Notes 167319, The World Bank.
    5. repec:wbk:hdnspu:193099 is not listed on IDEAS
    6. Aiken, Emily L. & Bedoya, Guadalupe & Blumenstock, Joshua E. & Coville, Aidan, 2023. "Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan," Journal of Development Economics, Elsevier, vol. 161(C).

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