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Effect of the Cash Support from the Vision Umurenge Programme on Household Financial Behaviour in Rwanda: The Case of Direct Support (DS)

Author

Listed:
  • Emmanuel Munyemana

    (African Centre of Excellence in Data Science, University of Rwanda, Kigali P.O. Box 428, Rwanda)

  • Charles Ruranga

    (African Centre of Excellence in Data Science, University of Rwanda, Kigali P.O. Box 428, Rwanda)

  • Joseph K. Mung’atu

    (Department of Statistics and Actuarial Sciences, College of Pure and Applied Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi P.O. Box 62000-00200, Kenya)

Abstract

This study aims to quantify the extent to which poor households receiving cash support from the Vision Umurenge Programme (VUP) allocate their income across major spending categories, mainly consumption, savings, household-level investment, and cash transfers for community participation. The analysis utilises a nationally representative panel dataset of 1642 respondents, collected between 2013 and 2017. A Maximum Likelihood Method (MLM) approach was employed to model four financial behaviours: (i) saving, (ii) consumption, (iii) investment, and (iv) social transfers as a proxy for community participation. The independent variables include the monetary benefits received by individuals over different periods, alongside demographic characteristics such as gender, age, education level, and area of residence (rural–urban), which were controlled in the analysis. The findings reveal a positive and statistically significant effect of the direct cash support provided by the VUP on increased consumption, and marginal effects on individual savings and investment behaviours. However, the data do not provide sufficient evidence to conclusively establish a relationship between participation in the VUP and cash transfers for community participation. The study recommends the intensification of efforts to engage in saving as way to build resilience, and further suggest a periodic increase in the VUP benefits’ size to cushion inflation effects.

Suggested Citation

  • Emmanuel Munyemana & Charles Ruranga & Joseph K. Mung’atu, 2024. "Effect of the Cash Support from the Vision Umurenge Programme on Household Financial Behaviour in Rwanda: The Case of Direct Support (DS)," Economies, MDPI, vol. 13(1), pages 1-23, December.
    • Handle: RePEc:gam:jecomi:v:13:y:2024:i:1:p:2-:d:1555046
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    References listed on IDEAS

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    1. Silvio Daidone & Benjamin Davis & Sudhanshu Handa & Paul Winters, 2019. "The Household and Individual-Level Productive Impacts of Cash Transfer Programs in Sub-Saharan Africa," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(5), pages 1401-1431.
    2. Christopher Gerry & Carmen Li, 2010. "Consumption smoothing and vulnerability in Russia," Applied Economics, Taylor & Francis Journals, vol. 42(16), pages 1995-2007.
    3. Maxine Molyneux & With Nicola Jones & Fiona Samuels, 2016. "Can Cash Transfer Programmes Have ‘Transformative’ Effects?," Journal of Development Studies, Taylor & Francis Journals, vol. 52(8), pages 1087-1098, August.
    4. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    5. Maurice Nirere, 2022. "Do social protection cash transfers reduce poverty in Rwanda? Evidence from an econometric analysis of Vision Umurenge Program Direct Support," African Development Review, African Development Bank, vol. 34(1), pages 114-126, March.
    6. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    7. Santiago Garganta & Leonardo Gasparini & Mariana Marchionni, 2017. "Cash transfers and female labor force participation: the case of AUH in Argentina," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-22, December.
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