How livelihood assets influence the multidimensional poverty status of mountainous farmers in Central Vietnam

Urban poverty research on African cities has focused mainly on megacities and large metropolitan areas. Despite their critical role in Africa's urbanization and development, secondary cities have received limited scholarly attention. Studies on secondary cities are unidimensional and rely mostly on secondary information from national census data. Using Wa, a secondary city in Ghana, this study assesses spatial patterns and determinants of multidimensional poverty, measured by two metrics: multidimensional poverty index (MPI) and multidimensional energy poverty index (MEPI). Spatially explicit primary data were collected from 775 households differentiated into core and fringe neighbourhoods. Mean MPI was significantly lower in core neighbourhoods than in fringe neighbourhoods. However, we found no statistically significant difference in MEPI between core and fringe neighbourhoods. The distribution of MPI is variably influenced by location and space but not MEPI. Although MPI was generally low, most areas with low MPI still had high MEPI, suggesting that energy access may be major problem even in secondary cities with low poverty. MPI was largely influenced negatively by household size and education. There was a significant negative relationship between MEPI and the food consumption score, suggesting a strong inverse relationship between food security and energy poverty. Together, the findings imply that scaling up the adoption of clean cooking energy in secondary cities can play a key role in addressing multidimensional urban poverty and advancing urban sustainability.

Getting rid of multidimensional poverty is both the people's wish and the direction of governance. Based on 2018 China Household Tracking Survey (CFPS) data, this paper identifies household multidimensional poverty in rural areas using a combination of the MPI index and the A-F method. The relationship between social capital and multidimensional poverty was also empirically analyzed using a Logit model. The results showed that: (1) 1599 multidimensional poverty households were identified, the incidence of multidimensional poverty was 24.94%, and the multidimensional poverty index was 0.103. In terms of poverty incidence, the three highest indicators of poverty incidence are adult education, health, and chronic diseases, reaching 42.06%, 37.65%, and 29.90%, respectively, and mainly concentrated in the education and health care systems. (2) Social capital can significantly reduce the probability of multidimensional poverty in rural households. Among them, social network significantly and negatively affects the occurrence of multidimensional poverty in rural households, social trust in neighbors has a significant negative effect on multidimensional poverty in rural households at the 1% level, and social prestige is positively related to multidimensional poverty in rural households. (3) Age of household head, household size, and income from working outside the home are significantly associated with multidimensional poverty in rural households. This paper expands the scope of social capital theory research and provides new perspectives and empirical evidence for alleviating multidimensional poverty.

Paper 2: Individual Specific Weighting Scheme to a Multidimensional Poverty Measure Abstract Weights in the estimation of multidimensional poverty have a central role by showing the relative importance of dimensions. This paper proposed two differentweighting schemes: endowment and distributional based weighting schemes.The later weighting scheme gives more weight to the dimension with small number of deprived individuals. On the other hand, the endowment based weighting scheme gives more weight to the dimension with large number of deprived individuals. The proposed weighting schemes considerboth distributional equity and simultaneous deprivations of indicators. Using the Ethiopian rural household survey (ERHS) data for the year 2004, the paper compared the proposed weighting schemes with the equal weighting scheme approach. The empirical result showed that, the multidimensional poverty index (MPI) estimation using endowment based equity weight has a lower estimate than the equal weighting approach. This happened because the indicators that have a small number of deprived individuals have got a higher weight under the equal weighting approach.

PurposeThis paper aims to examine the mismatch between multidimensional deprivation and monetary poverty in identifying the poor in Egypt and investigates their determinants empirically.Design/methodology/approachThe paper uses the Alkire-Foster multidimensional poverty measurement method using data from Egypt’s 2017/2018 Household Income, Expenditure and Consumption Survey (HIECS 2017/2018). Using a logistic regression model, the paper assesses the empirical relationship between multidimensional and monetary poverty and their determinants at the aggregate level and by dimension.FindingsThe paper demonstrates a significant mismatch between multidimensional and monetary poverty measures, underscoring their complementary nature. Statistics indicate that both measures overlap in classifying 35.81% of Egyptians, whereas monetary poverty ignores 63.12% of multidimensionally poor in at least one dimension. Regression estimates show a significant moderate negative association between expenditure per capita and multidimensional poverty and its dimensions. Moreover, they show that household head’s gender, age, education attainment, marital status, job proficiency, household size and location affect poverty mismatch and match in Egypt.Practical implicationsThis paper offers Egyptian policymakers the multidimensional poverty index that enables more efficient designing and targeting of poverty alleviation programs and assessing current poverty alleviation programs to modify them if needed.Originality/valueTo the best of the authors’ knowledge, this study is the first to examine the mismatch between both poverty measures in Egypt, using the recent full data set of HIECS 2017/2018. This paper confirms that depending only on monetary measures can send inaccurate insights for crafting effective social policies. Also, it offers policymakers a comprehensive insight into the country’s poverty landscape, which enable more efficient design, targeting of poverty alleviation programs and monitoring their effectiveness.

Eliminating extreme poverty and reducing poverty levels among marginalized groups are essential to achieving the Sustainable Development Goals. Despite broad national-level studies on multidimensional poverty, there is a lack of specific research focusing on marginalized groups, particularly ethnic minorities. This study aims to identify the multidimensional poverty status and determinants of poverty among ethnic farmers in Thua Thien Hue province, Central Vietnam, based on a survey of 283 respondents. The Alkire–Foster method was utilized to measure multidimensional poverty, and a binary logistic regression model was applied to identify the determinants of multidimensional poverty among farmers. The results showed that about 33.57% of households are in multidimensional poverty. The deprivation in the six poverty dimensions was inconsistent, with income at 68.23%, education and health at 9.69%, housing at 7.19%, information access at 3.65%, and living conditions at 1.56%. Nine factors significantly affect the multidimensional poverty of ethnic minority households, including the age of household heads, family size, number of shocks, labor, farm size, tropical livestock units (TLUs), customers, traded input values, and training participation. Diversifying livelihood activities, building the infrastructure system to have closer access to central markets and main roads, and enhancing the capacity of farmers to cope with shocks are necessary solutions to reduce the multidimensional poverty of ethnic minorities in Central Vietnam.

PurposeThe purpose of this paper is to analyze the effect of the different components of off-farm income on multi-dimensional poverty. Furthermore, the study aims to measure multi-dimensional poverty and also identify the determinants of multi-dimensional poverty in Nigeria. The paper reveals the different contributions of the dimensions of education, health and living standard.Design/methodology/approachThe study focuses on rural farm households in Nigeria. Data are obtained from the Nigeria General Household Survey, 2013. The survey covers both urban and rural areas of the 36 states of Nigeria. Owing to the interest of this study in the rural farm household’s sub-sector, a nationally representative sample of 836 rural farm households are selected for the study after the data merging process. Rural farm households in this paper earn 50 percent of their total income from crop and livestock production. The paper employs the Multi-dimensional Poverty Index (MPI) to measure multi-dimensional poverty across the six different geographical zones of Nigeria. The probit regression model is used to estimate and analyze the effect of off-farm income components on multi-dimensional poverty and also to identify the determinants of multi-dimensional poverty.FindingsThe results of the study show that among the off-farm income components, the non-farm wage income and non-farm self-employment income have negative association with multi-dimensional poverty. Findings show that multi-dimensional poverty is high in Nigeria with deprivations in health contributing the most. Northern Regions have a higher estimate. Results reveal that sex, age, number of adults, formal credit access, access to extension services and location characteristics are key determinants of multi-dimensional poverty. The MPI for Nigeria averaged 47 percent. Across regions, deprivation in the health dimension contributes about 44 percent to multi-dimensional poverty. Deprivation in living standards contributes 40.5 percent, while deprivation in education contributes 15.5 percent to multi-dimensional poverty.Research limitations/implicationsDue to the nature of the data used, the health indicators (nutrition and child mortality) are absent but proxies are used instead. Future research could introduce gender dimensions.Practical implicationsImproving the involvement of rural farm households in non-farm self-employment sector could improve their livelihoods and prevent migration to urban centers, especially among the youths.Social implicationsImproving the quality of health, education and living standards will lead to lower poverty levels in Nigeria. Farmers can best reduce their multi-dimensional poverty by engaging in more off-farm jobs.Originality/valueThis paper provides information to policy makers on the effect of different components of income from the off-farm sector on multi-dimensional poverty alongside with the determinants of multi-dimensional poverty at a national level for the rural farm households. By using MPI, the contribution of the different dimensions used in computing the MPI across the six geographical regions within the country is revealed. This provides policy makers with more information for development purposes.

Poverty and energy poverty are parts of the Sustainable Development Goals (SDGs). The former has been extensively covered in the literature, whereas the latter has not been given the necessary attention until the last decade. In this paper, we use multidimensional measures of energy poverty and poverty to ascertain whether energy‐poor households are also poor, and vice versa. In addition, the study seeks to examine the factors responsible for poverty and energy poverty. This paper uses data sets constructed from the two most recent nationwide household surveys in Ghana (GLSS VI and GLSS VII samples). We find substantial differences in the incidence and index of multidimensional energy poverty and multidimensional poverty by subgroup demographic characteristics. Further, we find that households belong to different categories of poverty and energy poverty status. Our multinomial regression results reveal that household size, marital status, educational background, residential area, age, employment, and gender significantly predict the household's multidimensional poverty and energy poverty status. We recommend massive investment in renewable energy, to make cleaner energy readily available for Ghanaians. In addition, the Ghanaian government should pursue sustainable employment and job creation avenues, directed toward an improved standard of living for the population.

Poverty is complex and multidimensional and particularly pronounced in children living in semi-arid West Africa. Unfortunately, few studies have addressed multidimensional child poverty in this region. Using novel data generated from 431 randomly selected households in the Boucle du Mouhoun region in Burkina Faso, the Alkire–Foster methodology was applied to estimate and decompose multidimensional poverty among adolescent children. Ten dimensions guided by the child poverty literature, data availability and the country’s SDGs were used. While deprivations in water and sanitation (89%), health (75%), nutrition (82%), and child labour (48.7%) were found to be more prevalent in the rural areas, child subjective well-being (73%) and child protection (61%) were more pronounced in the urban areas. Analysis of multiple overlaps in dimensions shows that all of the children suffer from deprivations in three or more dimensions simultaneously. Furthermore, when the poverty cut-off values were set at k = 20% (“Vulnerable to Poverty”), k = 30% (“Multidimensionally Poor”) and k = 50% (“Poverty Severity”), close to 95% of children are categorized as being in “Severe Poverty”, 68% as “Multidimensionally Poor”, and 38% as being “Vulnerable to Poverty”. Using binary logistic regressions, household size, age and marital status of household head, locality of child, income source, debt status, education, number of siblings, gender of child, adults and maternal health condition were found to be significantly correlated to poverty vulnerability, multidimensionally poor and poverty severity in the region. The implications of these findings for multidimensional child poverty targeted policies and interventions are discussed.

This study applied UNICEF’s Multiple Overlapping Deprivation Analysis (MODA) framework to adolescents (aged 11, 13 and 15) in 37 European countries and Canada using data from the 2013/14 Health Behaviour in School-aged Children survey. It is one of the first applications of MODA based entirely on data collected from adolescents themselves rather than from household reference persons on their behalf. Unlike most other multidimensional child poverty studies, the present analysis focuses on non-material, relational aspects of child poverty. Substantial cross-country variation was found in the prevalence of adolescent deprivations in nutrition, perceived health, school environment, protection from peer violence, family environment and information access. These single dimensions of poverty did not closely relate to national wealth and income inequality. However, when we looked at deprivation in three or more dimensions (i.e., multidimensional poverty), we found association with income inequality. In most countries, girls were at a higher risk of multidimensional poverty than boys. In addition, adolescents who lived with both parents in the household or reported higher family wealth were consistently less poor than other adolescents, in both single and multiple dimensions. The results of this study show the interconnectedness of social (family, school support) and psychological (health and violence) dimensions of poverty for adolescents in higher income countries. Children poor in the domains of family and school environment are also likely to be poor in terms of perceived health and protection from peer violence.

Multi-dimensional poverty approach is currently employed in research worldwide instead of the conventional monetary-based poverty one. The Sustainable Livelihoods Approach is closely related to multi-dimensional poverty concept in terms of using a complex set of socioeconomic indicators to reflect the accessibility to five livelihood assets of household or individual, that is, the human, social, natural, physical and financial assets. This study aims at exploring interrelations between monetary poverty and other socioeconomic characteristics of rural households in Vietnam relying on livelihood approach and searching relevant socioeconomic indicators for multi-dimensional poverty measurement. Various multivariate analysis methods as Principal Component Analysis, Multiple Correspondence Analysis and Cluster Analysis are applied. Data of 6,837 rural households extracted from VHLSS 2008 dataset are used in this study. The results confirm that multi-dimensional poverty of rural household is explained by at least ten dimensions representative of four livelihood assets. Several continuous and categorical variables are extracted as relevant indicators for multi-dimensional poverty measurement. Household classification by multi-dimensional poverty is likely more statistically efficient when homogeneity with group is improved in comparison to basing on expenditure per capita.

This study assessed multidimensional poverty in rural parts of Adamawa state, Nigeria. Specifically, the study objectives were to: describe the respondents’ socio-demographic characteristics, determine their multidimensional poverty status, and identify the determinants of multidimensional poverty in the sampled communities. Multi-stage cluster sampling technique was used to collect primary data from 480 household heads selected from 16 villages across the study area. Data collected were analysed using descriptive statistics, Multidimensional Poverty Analytical Tool (MPAT), and Binary Logistic regression model. The respondents’ socio-demographic characteristics described in the study showed that the mean age was 46.3 years, while the average household size was 7 persons. The study indicated that most (86.7%) of the respondents were male, who are mostly married (91.7%), and that majority (74%) are educated. The distribution of the respondents’ multidimensional poverty status revealed that majority (61.7%) of the households were poor. The study revealed that multidimensional poverty in the study area is influenced negatively by age, marital status, and household size. Similarly, gender, educational level, livelihood activities, farm size, livestock ownership, remittance, membership of group, and access to credit positively influence multidimensional poverty. Key among the recommendations of the study is the adequate provision of basic infrastructure in the area.

PurposeThe purpose of this study is to analyse the influence of household and community characteristics on multidimensional poverty in communities hosting public universities in Ondo state, Nigeria.Design/methodology/approachThe study constructs Global Multidimensional Poverty Index (MPI) using Alkire-Foster methodology and uses logistic regression to analyse the likelihood of experiencing multidimensional poverty.FindingsFindings from the study suggest that child schooling attendance, child mortality and asset ownership are the indicators in which households are mostly deprived in education, health and living standards consecutively. In addition, using logistic regression, the study finds multidimensional poverty reducing effects of education, age (before old), household size (having more economically active members), income and residing in urban areas. The study, however, documents that living far away from the universities increases the likelihood of experiencing multidimensional poverty in those communities.Research limitations/implicationsTo reduce multidimensional poverty in the communities of study, there is need to implement policies that will improve child schooling, reduce infant mortality, increase gainful employment and create enabling environment for asset ownership. This is in addition to upgrading infrastructure in those communities especially in their fringe areas so that development can spread, and multidimensional poverty reduction can follow in no distant future.Originality/valueUnlike capturing the effect of location on possibility of experiencing poverty using rural or urban dummy, the authors, in addition to that, incorporate distance to university variable on the premise of distance decay mechanism.

Good health affects productivity and income of the workers and this will further deepen the incidence of poverty and ill-health. This study examined the linkage between ill-health cost and multidimensional poverty of rural households in Ogun state, Nigeria. Multistage sampling was used to select 240 households for the study. Data collected were analysed with descriptive statistics, economic cost of illness, multidimensional poverty index and logistic regression model. The result revealed that majority (95%) of the households experienced malaria infestation, time cost of illness contributed most (92.6%) to the total economic cost. Result revealed that 69% of households are multidimensionally poor. Furthermore, marital status (p<0.01), off-farm income (p<0.01), financial cost (p<0.01), days forgone production (p<0.1), time cost (p<0.01) and area cultivated (p<0.1) positively, and significantly influence multidimensional poverty status while household size (p<0.01), cooperative membership (p<0.05), public health care services (p<0.1) and health extension contact (p<0.01) have negative, and significant effect. The study concluded that increase in out of pocket expenditure as a result of ill-health cost increases poverty status, availability and access to public health facilities reduces poverty status, it was therefore recommended that public health facilities should be located nearer to the people with minimum social stratification that might discourage poor masses from its usage, essential drugs should be provided at subsidized rates as this will go a long way in reducing financial cost thereby reducing poverty status.

This paper estimates the Multidimensional Energy Poverty Index (MEPI) for Pakistan and its provinces. It investigates its determinants using data from the most recent rounds of the Multiple Indicator Cluster Surveys (MICS). The results indicate that 39% of households in Pakistan experience multidimensional energy poverty (MEP). Provincially, the highest prevalence is in Baluchistan (50%), followed by Khyber Pakhtunkhwa (48%), Sindh (44%), and Punjab (29%). The likelihood of energy poverty rises with larger household sizes and limited access to water and sanitation facilities. Conversely, it declines with higher educational attainment among the household head and in upper wealth quintiles. Additionally, households receiving remittances are less likely to experience energy poverty. At the provincial level, significant heterogeneity exists in the determinants of MEP. Crucially, energy poverty is more common in rural areas. These findings support the achievement of Sustainable Development Goal 7 (SDG 7)—affordable and clean energy—by demonstrating significant disparities across provinces and among vulnerable households. Furthermore, the results align with SDG 6—clean water and sanitation—and SDG 10—reducing inequalities—by emphasizing the crucial roles of water and sanitation facilities, household wealth, and remittances in alleviating energy poverty. The study also advocates for improving educational quality (SDG 4) by emphasizing the positive influence of educational attainment on reducing energy poverty. Finally, it recommends targeted programs and province-specific policy actions to help improve energy access in Pakistan. • In Pakistan, only 52.6% of entire population have access to clean cooking fuels. • Probit Model is used to identify the factors impacting energy poverty. • Income, education have significant negative impact on energy poverty. • Household size shows positive impact on energy poverty. • Age and remittances have significant negative impact on energy poverty.

Analysis of incidence, intensity, and gender perspective of multidimensional urban poverty in Kenya