Icon Our Work‘I feel more loved’: Autonomy, self-worth and Kenya’s universal pension

This paper presents the findings of a qualitative research study conducted in order to observe the impacts of Kenya’s social pension on one rural community. This unique research required the researchers to live with a family and actively engage in cultural gatherings and meals, and through this, they were able to gain in-depth insights into how the pension impacted on the lives of beneficiaries, their families, and their community.

The study found that the pension has already had significant outcomes on older persons’ autonomy and self-worth. Older persons no longer need to rely on others for basic support, and therefore feel less like a burden to their family members. They are able to earn money in a way that is more appropriate for their age, which affords them more freedom to realise their ideal roles in society as elders.

This Development Pathways’ project is a multi-year study. The paper presents findings from the first two visits, but an endline study will be conducted in 2020/2021.

Find a link to the summary report here.

Find a link to the full report here.


  • This comment is in relation to the first comment: I am aware of the means-tested program refered to by the first commenter in Zimbabwe. The program has a multiple levels of targeting: 1- geographical: communities with poverty rate of 98 % (extreme poverty of 68%- 2013 statistics). 2- complex means testing that has so many criteria to establish who is poor (love to share the zigza so many steps of verification and reverifiction…), 3- capacity constrained individual (PWD).

    In the first round of reports shared (2013), here are few points that are quite ‘interesting’:
    1- what I found fascinating was that exclusion error amounted to about 28% (same for inclusion, but i am not interested in where poverty is universal)!!!! I want you to remember that the program is geographically targeted to communities with 98 % poverty rate. so how it was possible to reach that exclusion error (a random assigning of benefits would certainly have much lower error of 2%)
    2- Funny enough, most recipients (two out of three) were senior citizens. so instead of wasting resources for a very complex targeting, just cover senior citizens in those targeted poor communities. you will get much better results at much lower administrative cost and less corruption (many reports of exchanging favors were shared).
    3- As always, there is a complexity biased that instead of using a common sense simple approach, we resort to let us ‘evaluate’ them. Here is the irony: an evaluation was commissioned with a price tag of 1.3 million. let me do the math for you. the program at that moment (2013) was covering 20 000 households, so the cost of the evaluation per capita was about $68 whereas the benefit amount was $10!!!. so you waste $68 to ‘investigate’ how the poor spend $10!! does it take a genius to know how the poor will spend the little money? of course the evaluation would repeat the same old story that nutrition improved, local economy benefited from multiplier….and then through the usual soft criticism of more M&E and change KPIs etc (i felt reading the evaluation report that i read it so many times as the language, the findings …are pretty much the same).

    I did not want to sound disrespectful, but sometimes we need to share our two cents straightforward.

  • The impact of social pensions for very poor elderly persons are in many ways positive. We know this since more than 20 years. Helpage has published many studies on this topic. What has not been researched is the impact of a universal social pension on the social assistance system as a whole. Looking just at one specific program is a silo approach. How much does the pension cost in percent of the total social assistance budget?
    In Eswatini the universal pension absorbs 67 % of the social assistance budget leaving little for other most needy persons and households. Lesotho sin a similar situation. Below is the abstract of my findings from Eswatini.

    This paper compares two approaches to establish or improve social assistance systems in low and lower-middle income countries. Using the examples of Eswatini and Lesotho it provides quantitative evidence on the social protection impact of social assistance systems that are based on categorical programs and are dominated by universal Old Age Grants. Both systems fail to provide social assistance to large sections of the poorest and most vulnerable households. An alternative approach has been used in Malawi and in a number of other African countries like Zimbabwe and Ethiopia. The Social Support System of Malawi uses a systemic and inclusive social assistance approach consisting of means-tested programs tailored to cover the social assistance needs of the poorest and most vulnerable households.

    The comparison leads to conclusions with regard to how low income and lower middle-income countries should design or redesign their social assistance systems. The design process should start with a quantitative poverty and vulnerability assessment leading to a detailed identification of social assistance needs of different categories of poor and vulnerable households. The design should further be based on an assessment to what extent the identified social assistance needs are covered by existing programs. The comparison of social assistance needs with the coverage of these needs by existing programs leads to the identification of social protection gaps. The results of the gap analysis provide the base for social assistance policy decisions and priority setting. When planning how best to close the prioritized social assistance gaps within the financial space available, different program options have to be assessed with regard to their impact on the performance of the social assistance system as a whole. The guiding principle should be to harmonize the system in such a way that the combined impact of system components (programs) improves the living conditions of all extremely poor and vulnerable households and achieves a maximal welfare impact.



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