Multi-dimensional poverty measurement and its determinants in peri-urban areas of Uganda.

Abstract

This study estimated the incidence and intensity of multidimensional poverty among peri-urban households in Uganda, examined its associated factors, and compared multidimensional and monetary poverty estimates. Secondary data from the 2016/17 Uganda National Household Survey were analysed. Multidimensional poverty was constructed using the Alkire–Foster counting approach and a predefined weighted deprivation threshold. Given by household i, the weighted deprivation score was cᵢ = Σⱼwⱼgᵢⱼ, and the binary outcome was ρₖ(cᵢ)=1(cᵢ≥0.40). The aggregate adjusted headcount was M₀ = H×A, where H is the weighted proportion classified as multidimensionally poor and A is their weighted mean deprivation intensity The analysis applied descriptive statistics, pairwise correlations, bivariate tests and Determinants were estimated using log[pᵢ/(1−pᵢ)] = β₀ + Xᵢβ. A survey-weighted linear probability model and design-adjusted Wald (F)-test were additionally used to compare multidimensional-poverty headcounts between peri-urban and gazetted urban households. The survey design incorporated sampling weights, 15 strata and 279 primary sampling units. Of the 1,945 peri-urban households in the analytic sample, 486 (25.0%) were multidimensionally deprived and 1,459 (75.0%) were not deprived. At the bivariate level, deprivation was significantly associated with the household head’s age, marital status, educational attainment and industry of employment, as well as distance to the nearest telephone service. Sex of the household head, geographical region and borrowing goods on credit were not significantly associated with deprivation. Correlations among deprivation indicators were generally weak to moderate, with the strongest association observed between housing and toilet deprivation ((r=0.578)), indicating no evidence of excessive inter-indicator correlation. In the adjusted logistic model, age, sex, region and borrowing status were not statistically significant. Households located 5–<8 kilometres from the nearest telephone service had 4.80 times the odds of multidimensional poverty compared with households located less than 3 kilometres away (OR=4.80; 95% CI: 2.47–9.31; (p<0.001)). Each additional income source increased the odds of deprivation by approximately 18% (OR=1.179; 95% CI: 1.019–1.363; (p=0.026)), suggesting that multiple income sources may reflect fragmented, seasonal or poorly remunerated livelihood activities rather than effective diversification. Relative to agriculture, forestry and fishing, employment in construction (OR=0.044; (p<0.001)), professional, scientific and technical activities (OR=0.163; (p=0.006)), administrative and support activities (OR=0.173; (p=0.040)), and education (OR=0.059; (p=0.002)) was associated with significantly lower odds of deprivation. The national survey-weighted comparison showed that the multidimensional-poverty headcount was 23.7% among peri-urban households (95% CI: 16.8%–32.4%) and 14.1% among gazetted urban households (95% CI: 11.9%–16.7%). The 9.7-percentage-point difference was statistically significant (95% CI: 1.4–12.1%; (F(1,264)=5.34), (p=0.022)); therefore, the null hypothesis of equal headcounts was rejected. The MPI also produced higher poverty estimates than the national monetary poverty-line approach, although MPI intensity and the monetary poverty gap measure conceptually different quantities and are not directly comparable. The study concludes that peri-urban poverty is strongly associated with infrastructure access and livelihood quality and recommends integrating multidimensional measures into poverty monitoring while prioritizing essential services and stable, adequately remunerated employment.