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Expanding social safety nets in the Covid-19 context - WFP

Sector: Government • Location: Africa NA

Source: KFW Bank aus Verantwortung

Project
Active

The aim of the project is to strengthen social security systems in Mali, Mauritania and Niger and thus reduce the socio-economic impact of the COVID-19 pandemic and the associated economic restrictions. National social security systems are being used and expanded to cushion the socio-economic consequences of the COVID-19 pandemic on poor and vulnerable population groups through transfer payments (

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Project Information

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The project “Expanding social safety nets in the Covid-19 context - WFP” is an infrastructure initiative in the Government sector, located in Africa NA. Taiyo aggregates data on it from KFW Bank aus Verantwortung.

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Participants

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active

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Description

Description

The aim of the project is to strengthen social security systems in Mali, Mauritania and Niger and thus reduce the socio-economic impact of the COVID-19 pandemic and the associated economic restrictions. National social security systems are being used and expanded to cushion the socio-economic consequences of the COVID-19 pandemic on poor and vulnerable population groups through transfer payments (cash transfers) and accompanying measures. Where possible, existing national social security systems will be expanded and adapted for the transfer payments. The focus will be on particularly vulnerable and affected groups in urban and rural regions. The program uses transfer payments to provide short-term support for the target group, while at the same time strengthening the national social security systems and the resilience of the target group in the long term. The target group of the project is around 270,000 households (around 1.76 million people) affected by the consequences of the COVID-19 restrictions. In addition, employees of government and non-governmental institutions and communities are the target group of the awareness-raising and training measures.

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Source

Source reliability

High

Data quality score

100%

Source

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URL

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More Details

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