Analyzing Multidimensional Poverty and Its Determinants in Ethiopia: A Micro-level Analysis at Debre Tabor City
Yismaw Ayelign Mengistu1, Bantalem Sinishaw Mekonen2, Nega Assefa Aynalem3
1Assistant Professor, Economics, Debre Tabor University, Debre Tabor, Amhara, 272, Ethiopia.
Background:
Poverty remained pervasive in affecting households particularly in developing countries like Ethiopia.
Purpose:
The main target of this study was to estimate the multidimensional poverty index of households in Debre Tabor city along with the analysis of determinants of multidimensional poverty.
Method:
The researchers selected 394 households from six Kebeles using a stratified random sampling technique. The Alkire and Foster dual cut-off method of measuring poverty was employed to estimate the multidimensional poverty index and ordered logistic regression was used to analyze the effects of determinants of multidimensional poverty. Five dimensions of poverty namely, income, education, health, living standards, and energy use were incorporated in measuring it.
Results And Conclusion:
The result revealed that 59% of households were multidimensional poor with a poverty cut-off of 33%. The multidimensional poor households were underprivileged in 54% of weighted indicators. The energy use dimension was a serious contributor to the incidence and severity of multidimensional poverty. In addition, poverty incidence by illiterate-headed household heads, with single marital status, and casual wage worker-headed households was higher relative to their counterparts.
Policy Recommendation:
We strongly recommended that education must be extensively provided not only to children but also to adults; females must be promoted to participate in business activities, credit must be expanded with an excellent follow-up, and households should participate in existing cooperatives.
Related Concept Videos
01:50Cross-Sectional Research
01:21Dimensions of Health and Illness
01:25Statistical Methods for Analyzing Epidemiological Data
01:10Central Tendency: Analysis
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
02:01Factorial Design
01:25Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...