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1. Social protection for Mozambique’s elderly: History, structure, and potential effectiveness

Author:
Sara Almeida, Hanna Berkel, Sam Jones, Patricia Justino, and Telça Massingue
Publication Date:
03-2025
Content Type:
Working Paper
Institution:
United Nations University
Abstract:
This study assesses the effectiveness of Mozambique's flagship social pension programme, the elderly component of the Programa de Subsídio Social Básico (PSSB). Focusing on three key criteria— programme targeting, reliability, and materiality—we combine administrative data on the universe of beneficiaries with the complete records of the 2017 Population and Housing Census. Merging these datasets at the localidade level, we provide a highly granular view of programme coverage rates and payment delivery regularity. We estimate that in the best case with zero targeting errors, the PSSB-Elderly programme reaches two-thirds of its target group. However, significant regional disparities persist, including undercoverage in poorer regions. Additionally, we document substantial delays in payments since 2023, as well as various inaccuracies in the beneficiary registry. These findings underscore the importance of simple and robust designs for social protection in resource-constrained contexts. They also suggest that expanding programme coverage towards a quasi-universal subsidy in high-poverty locations merits serious consideration.
Topic:
Poverty, Public Policy, Pension, and Payment Systems
Political Geography:
Africa and Mozambique

2. Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach

Author:
Can Verberi and Muhittin Kaplan
Publication Date:
01-2024
Content Type:
Journal Article
Journal:
Istanbul Journal of Economics
Institution:
Istanbul University Faculty of Economics
Abstract:
This study aims to investigate the effects of personality traits, in addition to basic financial literacy, private pension literacy and behavioural factors on Private Pension System (PPS) participation using machine learning algorithms. The PPS participation model was trained using both random forest and LightGBM algorithms, and the contributions of model inputs in the prediction of pension participation were interpreted using the Tree SHAP algorithms with swarmplots. The data employed in the empirical analysis is survey data collected from the Şırnak province of Türkiye with a sample size of 449. The findings of the study shows that: (i) PPS participation is more likely for females and middle-aged people; (ii) High basic financial literacy has a negative impact on PPS participation; (iii) Extraversion is the key personality trait affecting PPS participation; (iv) Advanced pension literacy has more impact on participation than simple pension literacy: (v) Present-fatalistic tendency is key behavioural factor and it negatively affects PPS; (vi) Present-hedonistic, conscientiousness, future-time orientation, and locus of control tendencies increase PPS participation. Furthermore, the distribution of colours in LightGBM has a greater degree of uniformity in both directions compared with the random forest algorithm. Finally, to increase PPS participation, the results of the study suggest the implementation of the following policy measures: Tailored pension literacy programmes can help to increase pension participation rates. Incentives should be created to prevent narrow-minded behaviour and establish a sense of protection and control around PPS, targeting middle-aged individuals and women.
Topic:
Economics, Behavior, Pension, Machine Learning, and Personality
Political Geography:
Turkey and Global Focus