One of the biggest problems facing humanity is the eradication of poverty in all of its manifestations. The extant literature amicably portrays that poverty is a "multidimensional phenomenon" and should therefore be measured by considering multiple indicators of wellbeing. According to the OPHI (2010), poverty has three dimensions and is made up of ten indicators. The first dimension is health, which has two indicators (nutrition and child mortality); the second dimension is education (indicated by years of schooling and school attendance); and living standards (cooking fuel, sanitization, water, electricity, floor, and assets) make up the last dimension. For example, Achenafi (2016) investigated the factors that contribute to urban poverty in Gondar City, Amhara Regional State, Ethiopia. The study made use of original information gained from 220 sample houses through semi-structured questionnaires, focused group discussions, and key informant interviews. It was discovered that the impoverished were more likely to be illiterate and lacked other essential amenities like water and health care. Monthly household income, household size, remittance, metered energy, and household health status significantly impacted the incidence of poverty from the hypothesised variables.
Esubalew (2006) conducted research on the factors that contribute to urban poverty in Debre Markos, Amhara Regional State, Ethiopia. A logistic regression model was used in the study, which used primary data from 260 household heads. The primary factors that affect household poverty include sex, household size, disease prevalence in the household, income, educational attainment, marital status, employment, age, tenure of housing, and water supply. And the study indicated that 172 (or 66%) of them were considered to be poor, with the head count, poverty gap, and severity index of the survey coming in at 0.66, 0.21, and 0.09, respectively.
Tesfaye (2018) conducted a study on "Determinants of Vulnerability to Poverty in Amhara Regional State, Ethiopia: Evidence from Rural Households of Gubalafto Woreda". The primary data came from a stratified random sample of 250 houses. The OLS and 3FGLS analytical models were used to determine the future poverty rate for the sampled families, which was determined to be 37.42%. Just 30.8 percent of the tested households in the study's early stages were unable to provide for their basic needs. According to the study, factors such as family size, wage employment, proximity to a primary market, and the Kolla agroecological dummy all have a positive effect on a person's vulnerability to poverty. On the other hand, oxen, land size, non-livestock assets, company ownership, access to financing, and the availability of extension services all significantly and adversely affect vulnerability to poverty.
Anteneh and Daniel (2019) conducted a study on "Determinants of Poverty In Rural Ethiopia: Evidence from Tenta Wereda Amhara Regional State," which was framed by a mixed research design and used a multistage sampling procedure to choose 196 representative samples. The results indicate that 67.3% of societies in the study region live below the national poverty level, which is 387.43 ETB per person per month and 4649.16 ETB per year. A rural household is more likely to overcome poverty if it has beehives, a large farm, oxen and small ruminant animals, and a male head of household. On the other hand, non-farming activity and family size increase the risk of poverty. Therefore, the criteria that determined rural poverty were the sex of the household head, the size of the farmland holding, the number of beehives owned, the number of oxen and small ruminants, the size of the household, and non-farm activities.
Desalegn (2019) studied "Determinants of Rural Poverty in Banja District of Awi Zone, Amhara Regional State, Ethiopia" using 190 households. Inferential statistics and an econometric model were used to analyse data on the existence and severity of poverty. Therefore, the Cost of Basic Needs method was used to determine the poverty line, which came out to be Birr 4301 per adult equivalent per year. 44% of sample homes were determined to be below the poverty line, while the poverty gap and severity of poverty were, respectively, 9% and 2%, according to the Foster, Greer, and Thorbeck measure of poverty. Thus, it was determined that it took poor households 3.35 years on average to escape poverty. The Tobit model's results showed that household size had a significant and positive impact on poverty, while having a negative impact on it were the number of cattle and oxen owned, the household head's educational level, the use of inputs, asset ownership, and credit usage in the research area.
Markew & Solomon (2020) studied "The determinants of household poverty: the case of Berehet woreda, Amhara regional state, Ethiopia". Using a stratified simple random selection technique, a sample of 384 homes was selected for the investigation. Foster Greer Thorbecke's Poverty Index was used to measure the intensity and breadth of poverty in the Woreda. According to their research, 36% of households in Woreda are considered to be below the poverty line, with a 12% average poverty gap and a 7% average severity of poverty. The binary logit model showed that the household dependence ratio, residential location, household education status, and access to credit were all statistically significant in predicting household poverty status.
Eshetu & Gian (2016) analysed "Determinants of farm household poverty status in South Wollo Zone, Amhara Regional State, Ethiopia", and 516 farm households were included in the study. According to the results of the probit model, while family size, dependency ratio, head's religion, and average distance to various services have positive associations with rural households' poverty status, educational level, use of irrigation, livestock ownership, participation in non-farm activities, size of farm land, and agro-climate zones have negative associations, The results of the probit model demonstrate that while family size, dependency ratio, the head's religion, and the average distance to various services have positive associations with rural households' poverty status, educational status, use of irrigation, livestock ownership, participation in non-farm activities, size of farm land, and agro-climate zones have negative associations.
Tadewos (2020) investigated "Magnitude and Determinants of Rural Household Poverty in Ebinat District of Amhara Regional State of Ethiopia", drawn from 367 households. The results of the logit estimation also indicated that the chance of poverty in a home was positively and significantly influenced by the size of the family and the price of fertiliser. It was observed that the following factors all had a theoretically consistent, statistically significant, and negative impact on poverty: saving culture, land size, livestock holding in TLU, farm and off-farm income per AE, family head's age, and educational level. With the overall poverty level being 4127.42 Birr per AE per year and the food poverty threshold being 3752.20 Birr, respectively, it was determined that 56.67 percent of the 367 households that were studied were poor. The FGT poverty index was used to determine the amount and severity of poverty. It is clear that 56.67% of the sample households live below the poverty line thanks to poverty gap and severity index values of 0.1817 and 0.0835, respectively.
The study "Determinants of Rural Household Poverty Across Agro-Ecology in Amhara Regional State, Ethiopia: Evidence from Yilmana Densa Woreda" by Birara (2018) examined 328 families. Additionally, the findings revealed that Yilmana Densa woreda had higher poverty head count ratio (62.3%), poverty gap (18.9%), and severity (5.8%) rates than the national and regional rates. The model's findings showed that household poverty was significantly and adversely correlated with factors such as level of education, cost of agricultural inputs, agro-ecology, ownership of land and livestock, preservation of culture, and area of rented land. But family size, health, inefficient utilisation of the work force, and poverty in the home all revealed a positive and substantial correlation. Kola's agriculture has higher poverty headcount ratios, gaps, and severity as compared to Dega and Woina-Dega agriculture.
A study on the "Determinants of Urban Poverty in the Case of Debre Birhan Town" was undertaken by Mulugeta (2019) using data from 203 randomly chosen sample families in the Amhara Regional State of Ethiopia. Using Food Energy Intake (FEI), 48 (or percent) of the 203 household heads that were questioned were classified as being poor. The binary logit regression model revealed that sex, married status, family size, education, income, health status, housing, and electricity are statistically significant predictors of poverty.
Ermiyas et al. (2019) investigated the "Determinants of Rural Poverty in Ethiopia: A Household Level Analysis in the Case of Dejen Woreda, Amhara Regional State, Ethiopia" by using 204 households. Accordingly, almost 49% of the examined rural households live below the poverty line, with an average poverty gap of 0.083 and a poverty severity gap of 0.065. The probit model was used to explore the primary drivers of rural poverty. The probit model study's findings show that household size, sex composition, dependence ratio, and livestock ownership are the primary determinants driving rural poverty. Poverty status is inversely correlated with the total number of animals a household owns and the gender of the household leaders (male dummies). On the one hand, there is a positive correlation between family size, the proportion of dependents, and household poverty.