Income Poverty in Nigeria: Incidence, Gap, Severity and Correlates

Edoumiekumo, Samuel Gowon; Karimo, Tamarauntari Moses; Tombofa, Steve S.

doi:10.11634/232907811402499

Cited by 13 publications

(14 citation statements)

References 6 publications

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“…For instance, the result revealed that, one unit increase in the household size will increase poverty incidence by reducing poor household's per capita income by N1445.41. The result corroborates with the research findings of [1,3,8,10,13,14,17,21,22,24].…”

Section: Determinants Of Poverty Among Rural Farming Households In Aksupporting

confidence: 92%

“…In the South-South region of Nigeria, S. Edoumiekumo, M. Tamarauntari and S. Steve [14] examined the incidence, depth and severity of poverty in Bayelsa state. Results from the logit regression showed that agriculture and household size increases the probability that a household will be poor while dwelling in the urban area; being headed by male, a naira increase in households per capita expenditure on education and per capita expenditure on health and a year's increase in the number of years spent in school by household head reduces the probability that a household will be poor.…”

Section: Literature Reviewmentioning

confidence: 99%

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Sustaining Small Scale Farming: Evidence of Poverty and income Disparity among Rural Farming Households in South-South Region of Nigeria

Akpan

Udoh

Patrick

2016

PoS

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The incidence of poverty is evidenced among rural farm households in developing societies. As a result of persistence poverty among rural farm households, there is sudden upsurge in agricultural livelihood diversification and rural-urban migration resulting in high rate of urban unemployment. To help generate suitable policy variables to help tackle this rampaging issue in the South-south region of Nigeria, this study specifically analyzes poverty and income inequality as well as identified determinants of poverty among rural farm households in Akwa Ibom State, Nigeria. Data were collected from 390 rural farm household heads spread across the rural areas of the State. Combination of sampling methods was employed to sample cross sectional data from respondents. The study used descriptive tools and regression analysis (Tobit regressions) to analyze information collected. The socioeconomic analysis reveals that, most farming household heads were male; an average of 12.3 years of formal was discovered; social capital formation was poor, while average age stood at 42.5 years. About 33.08 % of male headed households and 22.05 % of female headed households live below poverty line in the study area. Income inequality index revealed 0.4210 for male headed households and 0.4531 for female counterpart. The Tobit model estimates revealed that, household head farming experience, years in social organization, level of formal education, farm and non-farm income were negative drivers of rural poverty in the region. Household's age, household size, structure of land ownership and gender were positive drivers of poverty among rural farming households. It is recommended that sound family welfare packages should be implemented in the rural communities. Also, social capital formation should be promoted among rural farming households, while adult education policies should be re-visited. Government of the region should also improve educational facilities in the rural areas and make marginal lands available to less privileged farmers.

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Section: Determinants Of Poverty Among Rural Farming Households In Aksupporting

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Sustaining Small Scale Farming: Evidence of Poverty and income Disparity among Rural Farming Households in South-South Region of Nigeria

Akpan

Udoh

Patrick

2016

PoS

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“…Increase in education is often linked to increase in capital formation and adoption of risky farm exercise. The finding is in line with empirical results of Ogbonna (2012); Asogwa et al, (2012); Olawuyi and Adetunji (2013); Akinbode (2013); Adetayo (2014); Edoumiekumo et al, (2014) and Duniya and Rekwot (2015).…”

Section: Determinants Of Poverty Among Youth Farmers In Akwa Ibom Statesupporting

confidence: 91%

“…In the South-South region of Nigeria, Edoumiekumo et al, (2014) examined the incidence, depth and severity of poverty in Bayelsa state. Results from the logit regression showed that agriculture and household size increases the probability that a household will be poor while dwelling in the urban area; being headed by male, a naira increase in households per capita expenditure on education and per capita expenditure on health and a year's increase in the number of years spent in school by household head reduces the probability that a household will be poor.…”

Section: Literature Reviewmentioning

confidence: 99%

Level of Income Inequality and Determinants of Poverty Incidence among Youth Farmers in Akwa Ibom State, Nigeria

Akpan¹,

Patrick²,

Amama³

2016

JSD

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The prevalence of poverty among youths engaged in agricultural production is partly responsible for increase in agricultural diversification and rural-urban migration among other social vices in developing societies. To help generate suitable policies in order to tackle this rampaging issue, the study specifically analyzes poverty and income inequality as well as identified determinants of poverty among youths involved in agricultural production in the rural areas of Akwa Ibom State located in southern Nigeria. Data were collected from 300 youths spread across the rural areas of the State. Combinations of sampling methods were employed to sample cross sectional data from respondents. The study used descriptive tools and regression analysis (Logit regressions) to analyze information collected. The socio-economic analysis reveals that, most youth farmers were educated; social capital formation was poor, while land size averaged at 0.48ha per youth. About 45.1% of male youths and 72% of female youths live below poverty line in the study area. Income inequality index revealed 0.4009 for male youths and 0.3797 for female youths. The Logit model estimates revealed that, youths' years in social organization, level of formal education, age of youths; amount of non-farm income, farm size, agricultural extension activities and commercial purposes of agricultural production reduced the probability of poverty incidence among youth farmers in the State. Household size and dependent ratio were positive drivers of poverty among rural youths. Based on the research findings, it is recommended that poverty and income inequality among youths engaged in agricultural production in the rural area of the State can be successfully handled, if sound family welfare packages are implemented in the rural communities. Also, rural youth's groups should be strengthen to promote social capital formation; while farm enterprise development capacity should be encourage among youths as well as strengthening agricultural extension system in the State. Marginal lands should be developed and distributed to youth farmers in the area.

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“…This was achieved employing the monetary (income) indicator. Respondents were categorized into poor and nonpoor groups using two-third mean per-capita income as poverty line as used by Edoumiekumo et al (2014) and Igbalajobi et al (2013). Poverty line using income approach is defined as per-capita income (PCI) which is income per head per day.…”

Section: Relative Poverty Line Analysismentioning

confidence: 99%

Maize cob losses and their effects on the poverty status of maize farmers in Edo state, Nigeria

Okoror¹,

Ahmadu²

2018

Agro-Sci.

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This study analysed fresh maize cob losses and its effect on the poverty status of maize farmers in Edo State, Nigeria. The specific objectives were to estimate the physical and financial amount of fresh maize cob losses experienced by maize farmers, examine the effect of fresh maize cob losses on the respondents' poverty status, and determine the poverty status of the respondents. Multistage sampling technique was used in selecting 108 maize farmers. The data were analysed using descriptive statistics, budgeting technique, relative poverty line analysis, the Foster-Greer-Thorbecke (FGT) poverty measure, and t-test. The average profit without loss per maize farmer was N165, 599.15. However, with loss of about 185kg of fresh maize cobs per farmer, the profit reduced to N118, 931.17. The difference in the mean profit with and without loss was significant at 1%. Fresh maize cob losses increased the proportion of the poor respondents by 14%. The poverty incidence with and without maize cob loss was 0.64 and 0.50 respectively. The study concluded that maize farming was a profitable venture. However, maize cob loss resulted in significant reduction in the income of the farmers. This study therefore recommended that relevant bodies such as the government, farmers' cooperatives and non-governmental organizations should assist in addressing the problem of maize cob loss among farmers which would help alleviate their poverty.

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Income Poverty in Nigeria: Incidence, Gap, Severity and Correlates

Cited by 13 publications

References 6 publications

Sustaining Small Scale Farming: Evidence of Poverty and income Disparity among Rural Farming Households in South-South Region of Nigeria

Sustaining Small Scale Farming: Evidence of Poverty and income Disparity among Rural Farming Households in South-South Region of Nigeria

Level of Income Inequality and Determinants of Poverty Incidence among Youth Farmers in Akwa Ibom State, Nigeria

Maize cob losses and their effects on the poverty status of maize farmers in Edo state, Nigeria

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