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ItemMethodological aspects in the construction of a composite indicator of service delivery in Uganda(Makerere University, 2025)This study elaborates methodological aspects encountered in the building of composite indicators with application to service delivery in Uganda. This study amplified the three main stages, the selection of quality data, the building of the composite indicator itself and statistical approaches that may be utilized to model service delivery composite indicator (CI). This study formulated a data quality assessment framework (DQAF) to enhance the construction of a composite indicator. The DQAF was formulated with a dual orientation that prioritizes two user-oriented data quality components (DQCs) namely; relevance, and interpretability, and three producer-oriented DQCs of methodological soundness, accuracy, and statistical adequacy. The application of the DQAF to service delivery data resulted in the selection of 51 from a pool of 103 potential indicators, reflecting a 48.6% acceptability percentage. The composite indicator for statistical regions, which included five dimensions—education, health, water, agriculture, and roads—was developed utilizing official data from the 2021 National Service Delivery Survey conducted by the Uganda Bureau of Statistics, along with various sector performance reports from the Ministry of Health and the Ministry of Water and Environment. Additionally, the study developed an alternative composite indicator for district local governments, concentrating on the education, health, and water dimensions, which was modeled against potential covariates. The composite indicator for statistical regions indicated that Uganda achieved a score of 0.49 (0 ≤ composite indicator score ≤ 1) utilizing equal weighting, minimax transformation, and additive aggregation, whereas the score was 0.45 with equal weighting, distance-to-reference point transformation, and geometric aggregation. Min-max transformation yields higher scores compared to distance-to-reference point, attributable to the exogenously determined goalposts. Weights that are participatory determined were comparable with data-derived weights. Robustness tests demonstrated that the constructed composite indicator exhibited stability and can therefore be utilized. The absolute differences in ranks by region were observed, with Kampala and Lango exhibiting the lowest differences and Karamoja and Kigezi the highest, attributable to the presence of outliers and inequitable performance in the examined variables. The aggregation stage was the most sensitive accounting for nearly 60% of the total variance, primarily due to interaction with mainly the transformation stage; this underscores the necessity to cautiously select an aggregation method, as it greatly influences the robustness of the results. The absolute rank differences were highest in the education dimension at 2.00 and lowest in the roads and health dimension at 1.33, indicating the varying impact of excluding aspects from the composite index. In assessing the differentials of service delivery at local government level, the composite indicator scores ranged from 0.25 to 0.60, with a substantial portion of the density plot situated below 0.50, indicating inadequate service delivery levels. While beta regression adeptly models bounded data, random forest regression highlights the relative importance of predictors, and generalized additive model captures non-linear covariate effects. The comparable predictive accuracy of these methods, as evaluated using root mean square error, suggests their applicability to this investigation in accordance with the analytical objectives. It is recommended that data quality assessment frameworks should encompass producer and user data quality components that are developed collaboratively with potential users of the composite indicator, in addition to identifying and addressing redundancies among them. Given that the aggregation stage was the most sensitive, it is recommended to explore the use of penalization techniques to address the substitution of variables while maintaining official statistics.
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ItemMacroeconomic determinants of retail banking loan performance in Uganda: a case of ABSA Bank(Makerere University, 2025)This study examines the macroeconomic determinants of retail banking loan performance in Uganda, focusing on Absa Bank Uganda. Using quarterly secondary data from 2015 to 2023, the research investigates the influence of key macroeconomic variables GDP growth, inflation, unemployment, and exchange rate on loan performance. The data were analyzed using descriptive statistics, correlation analysis, and the Autoregressive Distributed Lag (ARDL) modeling technique. The findings reveal that the exchange rate has a statistically significant positive impact on loan performance, suggesting that depreciation of the Uganda Shilling is associated with improved loan recovery efforts, possibly due to stricter repayment enforcement or inflation induced repayment prioritization. In contrast, GDP growth, inflation, and unemployment were found to have no significant effect on loan performance during the study period. The model explained approximately 77.9% of the variation in loan performance, confirming a strong explanatory power. The study concludes that exchange rate fluctuations are a critical driver of retail loan performance in Uganda. It recommends that banks strengthen foreign exchange risk management and enhance credit risk assessment based on past repayment patterns. Subject keywords: Macroeconomic determinants; Retail banking; loan performance; Uganda; ABSA Bank
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ItemDeterminants of participation in informal and formal financial institutions in Uganda(Makerere University, 2025)This study aimed at identifying the determinants of participation in informal and formal financial institutions in Uganda. The objectives were to: establish the relationship between demographic factors and participation in informal and formal financial institutions; examine the relationship between socio-economic factors and participation in informal and formal financial institutions; assess the relationship between financial factors and participation in informal and formal financial institutions; and establish the relationship between demographic, socio-economic, and financial factors and the simultaneous participation in both informal and formal financial institutions. The study utilized nationally representative data from the Uganda National Household Survey (UNHS) 2019/20 and employed a multivariate probit model to estimate the key factors shaping financial institution participation choices. The empirical findings indicated that participation choices varied significantly across gender, education, marital status, residence, occupation, and income levels. Females, rural residents, older individuals, and low-income earners were more likely to use Informal Financial Institutions, while males, urban dwellers, professionals, and educated individuals tended to use Formal Financial Institutions. Borrowing behaviour and financial decision-making also had a notable influence, with joint decision-making linked to reduced reliance on Informal Financial Institutions. Simultaneous use of both Financial Institutions was more common among older, educated, and higher-income individuals, and less likely among the never-married and low-skilled workers. The study concludes that both structural and behavioural factors influence financial participation patterns in Uganda. To enhance sustainable and inclusive financial inclusion, the study recommends leveraging Informal Financial Institutions as gateways to formal inclusion, promoting joint household financial decision-making, simplifying access to formal credit, addressing gender-based disparities, expanding financial literacy, and scaling up formal financial infrastructure particularly in underserved regions. These policy recommendations provide actionable insights that align with Uganda’s National Financial Inclusion Strategy (2023–2028), which aims to expand access to quality and affordable formal financial services and promote poverty reduction through inclusive economic growth. Subject Key words: Informal financial institutions; formal financial institutions; Uganda
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ItemHealth-related quality of life, viral load suppression and survival among older persons living with hiv in Uganda(Makerere University, 2025)The population of older persons living with HIV (OPLHIV, aged ≥50 years) is steadily increasing, presenting an emerging public health challenge in low- and middle-income countries. Advances in ART have improved viral suppression, survival, and health-related quality of life (HRQoL), yet evidence on these outcomes among older adults in Uganda remains limited. This study assessed HRQoL, viral suppression, and survival among OPLHIV in Uganda. A cross-sectional survey involving 439 OPLHIV was conducted using a semi-structured questionnaire to collect socio-demographic, lifestyle, clinical, and non-clinical data. HRQoL was measured across seven domains and analyzed using ordinal regression. Viral load suppression (<200 copies/mL) was examined using binary logistic regression. Additionally, a retrospective cohort analysis of 30,758 electronic medical records from TASO Centres of Excellence (1987–2023) assessed survival using Cox proportional hazards regression. The mean age of participants was 58.0 years (SD ±7.4), and 42% reported good HRQoL. Multiple socio-demographic and clinical characteristics—such as age ≥60 years, education level, marital status, WHO clinical stage II, unemployment, TB history, multiple sexual partners, and viral load >200 copies/mL—were significantly associated with HRQoL domains (p<0.05). Viral suppression was achieved by 88% of respondents. Factors associated with viral suppression included unemployment (aOR=4.1; 95% CI 1.73–9.84), good adherence (aOR=16.6; 95% CI 1.91–145.60), spousal/family support (aOR=2.6; 95% CI 0.98–7.19), receiving food supplies (aOR=6.0; 95% CI 1.17–31.58), absence of recent opportunistic infections (aOR=0.28; 95% CI 0.07–1.09), and WHO stage II (aOR=0.14; 95% CI 0.04–0.53). Survival analysis showed that being female (aHR=1.19; 95% CI 1.15–1.22), married (aHR=0.99; 95% CI 0.77–0.80), separated/divorced (aHR=0.85; 95% CI 0.80–0.90), WHO stage II (aHR=1.66; 95% CI 1.62–1.73), viral load >200 copies/mL (aHR=1.49; 95% CI 1.44–1.54), and fair adherence (aHR=0.94; 95% CI 0.74–1.19) were significantly associated with mortality risk. Older persons living with HIV in Uganda face important challenges in health-related quality of life, viral suppression, and survival. While viral suppression was high, fewer than half achieved good overall HRQoL, with outcomes strongly influenced by age, education, marital and employment status, WHO clinical stage, viral load, and adherence. Survival was significantly associated with sex, marital status, disease stage, viral non-suppression, and adherence. These findings highlight the need for comprehensive, age-responsive HIV care that combines routine clinical monitoring with psychosocial and socioeconomic support to improve quality of life, sustain viral suppression, and enhance survival among older adults living with HIV.
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ItemDeterminants of maize commercialization among farmers in Uganda(Makerere University, 2025-12)Maize is a vital crop in Uganda, serving as both a staple food and source of income, majorly for smallholder farmers. Maize is also one of Uganda’s top ten agricultural export products, contributing up to 1.7% of the country’s total exports. Despite its importance, maize commercialization remains low, highlighting the need to investigate its determinants. Using secondary cross-sectional data from the 2020 Annual Agricultural Survey, descriptive statistics were employed to characterize the 3,464 farmers and Pearson correlation analysis to identify variables for further analysis. A Fractional Logistic Regression Model was fitted to examine the determinants of maize commercialization among farmers in Uganda, with all analyses conducted at a 5% level of significance. The findings revealed that 48.5% of maize produced by farmers is sold, indicating a moderate level of its commercialization in Uganda. The analysis further estimated that a minimum maize yield of 2.741 tonnes per hectare (95% CI: 0.060, 5.423) is required for a household to attain a Household Commercialization Index of 65%, the highest benchmark reported in recent Ugandan literature. In addition, a crop area of at least 4.120 hectares (95% CI: 1.851, 6.388) is required for a household to attain the same commercialization level. Results from the Fractional Logistic Regression Model showed that higher education (Average Marginal Effect (AME) = 0.030; P=0.044; 95% CI: 0.001, 0.058), use of irrigation (AME = 0.258; P= 0.000; 95% CI: 0.231, 0.284), fertilizer use (AME = 0.261; P= 0.003; 95% CI: 0.224, 0.297), use of modern agricultural machinery (AME = 0.039; P= 0.000; 95% CI: 0.021, 0.057), membership in a farmer group (AME = 0.027; P= 0.010; 95% CI: 0.014, 0.315), and access to agricultural extension services (AME = 0.150; P= 0.000; 95% CI: 0.124, 0.175) had statistically significant positive effects on maize commercialization. Subject keywords; Maize commercialization, Farmers, Uganda