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This paper aims to examine the mismatch between multidimensional deprivation and monetary poverty in identifying the poor in Egypt and investigates their determinants empirically.

The 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.

The 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.

This 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.

To 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.

At present, the scale of China's floating population has reached 376 million people. Compared with the local inhabitants, the poverty problem of the floating population is more complex, and this problem should also attract the attention of all sectors of society. This paper aims to measure and analyze the multidimensional poverty of the floating population in China.

The data used in this paper are the data of the China Migrants Dynamic Survey (CMDS) in China. This survey is a large-scale national migratory population sampling survey organized by the China National Health Commission, covering 31 provinces (autonomous regions and cities) and other autonomous regions. This paper uses the dynamic monitoring and Alkire and Foster (A-F) method to study the multidimensional poverty problem of the floating population.

This study finds that income poverty is no longer the main type of poverty faced by the floating population. The multidimensional poverty of the floating population mainly occurs in the social security and education dimensions, of which social security has become the most severe poverty dimension of the floating population. From the perspective of group differences, compared with the floating population in urban areas, the multidimensional poverty of migrant workers is more serious. However, the poverty of migrant workers is mainly concentrated in one-dimensional poverty and two-dimensional poverty.

In the future, the authors should focus on the social security of the floating population in the place of influx and the education of the floating population.

Through the review of the existing literature, the authors find that the current research on the multidimensional poverty of the floating population is mainly concentrated on the migrant worker groups that move from rural areas to urban areas. However, insufficient attention is paid to the urban floating population groups moving between cities.

The 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.

The study constructs Global Multidimensional Poverty Index (MPI) using Alkire-Foster methodology and uses logistic regression to analyse the likelihood of experiencing multidimensional poverty.

Findings 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.

To 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.

Unlike 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.

This article aims to analyse the spatial and temporal evolution of multidimensional poverty in Brazil's North Region and its relationship with territorial, economic and population dynamics.

A multidimensional poverty index (MPI) was calculated using the Alkire-Foster method and a spatial econometric model was estimated. The data come from population censuses conducted by the Brazilian Institute of Geography and Statistics (IBGE) for the years 1991, 2000 and 2010.

The results show a decrease in multidimensional poverty over the period analysed. However, they show this reduction occurred in a heterogeneous way in time and space, with emphasis on microregions in which, despite a reduction in the rate, the percentage of the population considered multidimensionally poor remained high during the 30 years of the study.

The quality of available data.

It is possible to point out that public policies focused on improving infrastructure in medium-sized locations tend to have two positive effects: first, making production and consumption more accessible and sustainable for local communities, given that currently the cost of transport and logistics are factors that hinder socioeconomic development; second, to reduce the pressure of demand that this population of small and medium-sized locations exerts on public services currently offered only in larger locations. Although the logic of concentrating most public services in the largest cities makes sense in terms of economic efficiency, the particularities of the northern region and the recognition of its environmental importance point to the need for incentives for more spatially distributed economic activity.

From the paper results, it is possible to think about more local public policies which are able to improve people's lifes without to damage the environment.

This is the first study on multidimensional poverty that covers the entire North region of Brazil (Amazon region) and that contemplates both the temporal and spatial dynamics of poverty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2021-0699.

This paper evaluates the impact of socio and demographic factors on the multidimensional poverty of smallholder arable crop farming households in Nigeria.

Data were drawn from the second wave of the LSMS-Integrated Surveys on Agriculture General Household Survey Panel 2012/2013. The methods adopted in analysing the data were descriptive statistics, Alkire and Foster Method (AFM) and logit regression model.

The result shows that 84.34% of the households were headed by a male while 80.26% of the respondents were married with a mean household size of seven persons. The multidimensional poverty of arable crop farm households in Nigeria is 0.60, while the adjusted headcount ratio (MPI) is 0.27, with an average intensity of 0.45. We found that deprivation in the dimension of living standard accounted for 45.5% of the overall multidimensional poverty index (MPI). The result of the logistic regression indicates that household location, gender, household size and non-farm income are negatively correlated to poverty. The factors that increase poverty among households are the age of the household head and access to extension services.

The study presents an alternative means of assessing poverty among smallholder arable crop farming households in Nigeria. This study recommends that policymakers should focus more on improving the living standard of arable crop farming households to reduce poverty in rural areas. Similarly, concerted efforts should be made towards providing adequate health care and improved sanitation, supply of electricity and educational training that goes beyond primary education for farming household members.

The purpose of this paper is to assess the existence of critical tipping points for explanatory variables (age, government grants, education and household size) – in particular, household debt service-to-income on multidimensional poverty.

The paper applies a generalized additive model (GAM) using regression splines on National Income Dynamics Study data to establish threshold effects of the explanatory variables on multidimensional poverty.

The results show that the tipping point at which debt is associated with improved household welfare is 42.5 percent (level of debt service-to-income). With significant findings, household heads younger than 60 years of age and more children are associated with lower multidimensional poverty. Government grants may suffer from fungibility as they do not seem to be an effective tool for multidimensional poverty eradication. The ideal household size with negative significant correlation to multidimensional poverty is less than four members. And lastly, education proves to be the best instrument for households to escape multidimensional poverty.

High household indebtedness is a severe social problem. Its effects include deteriorating physical and mental health, relationship difficulties and breakdown. Significant social costs arise such as medical treatment and indirectly, reduction of productivity. Further effects on society include rising criminal behavior, children dropping out of school thereby transferring poverty to succeeding generations. Non-performing loans increase and in turn lead to reduced credit availability. The overall health of the economy is impacted due to reduced aggregate demand.

Macro studies have demonstrated the presence of thresholds on debt analyses. However, such is not known in micro analyses, this paper attempts to bridge this knowledge gap by applying GAM for analysis of debt-poverty nexus at the micro level.

The purpose of this paper is to compare the assessment of poverty/deprivation using different conceptions of this phenomenon including the traditional money‐metric measure and different forms of multidimensional constructs.

The data were drawn from a household survey conducted in Nsukka, Nigeria. Interviewer‐administered questionnaires were used in data collection from about 410 households across urban and rural localities. The counting and FGT methodologies were used to assess impoverishment, while regression analyses were used to assess the determinants of deprivation across different constructs.

Between 70 per cent and 78 per cent of the study population were identified as poor/deprived. However, more than 11 per cent of those living on less than USD1.25/day were classified as non‐poor using different measures of multidimensional poverty. Similarly, more than 62 per cent of individuals who live on more than 1.25USD/day (i.e. non‐poor) are classified as poor using different measures of multidimensional deprivation. There is some level of correlation between measures, some inevitably stronger than others. The major determinants of deprivation across the various constructs of deprivation include large family size, low level of education, poor employment, rural location, and poor health.

This paper uses novel datasets that incorporate variables relating to the capability approach in understanding deprivation. Specifically, it analyses the so‐called missing dimensions of poverty. It also applies a new methodology for the assessment of impoverishment and deprivation. It highlights the importance of the capability approach in explaining poverty.

The purpose of this paper is to evaluate the impact of credit access on income and multidimensional poverty by providing an econometric framework.

The study is conducted in Assam, India and uses a quasi-experiment design to gather primary data. Econometric tools like Heckit procedure, Tobit selection equation and probit model are used for empirical purpose.

The paper finds that the level of individual welfare is influenced by equivalent factors. In addition, the study observes a larger incidence of poverty among treatment households of semiformal and informal borrowers. The study argues that formal sources are more effective in reducing the number of poor households by lifting those who are closest to the poverty line.

The study indicates a vicious circle of income and multidimensional poverty among semiformal and informal borrowers. By tradition, as rural Assam gets a dominant role of traditional community-based financial institutions, we should develop the banking structure by involving these institutions. The study excludes other probable explanatory variables while evaluating the impact of credit access on income and multidimensional poverty, and this limitation is left to future research.

This is probably the first empirical paper in Assam showing the impact of credit access on multidimensional poverty by adjusting for endogeneity and selection bias.

In recent years, there is growing recognition of the importance of geography and space in the analysis of economic convergence by focusing on the dynamics of monetary indicators. The analysis of spatial convergence based on socio-economic indicators are rare. These variables present a complement to understand the spatial dynamics of territorial units. The purpose of this paper is, first, to analyzes and describes trends in multidimensional poverty in Morocco and second it explores the convergence hypothesis.

Data are driven from HCP (2017). It concerns 75 provinces over the period 2004 and 2014. In addition to the availability of data, this period corresponds to significant changes in public policy. The nature of the observations necessitates the use of the spatial analysis techniques.

The results show that poverty is a geographical phenomenon with low speed of convergence. The paper propose some solutions to help policymakers implement an effective targeting policy aimed at reducing spatial inequalities in terms of multidimensional poverty in Morocco.

The analysis of spatial convergence based on socio-economic indicators are rare. This paper will focus on the convergence of the poverty index for a developing country.

The impact of cash transfers on improving the living conditions of children and reducing early-life deprivations and vulnerabilities are crucial to safeguarding equality of opportunities and achieving sustainable, equitable and inclusive growth within the Sustainable Development Goals. The study aims to examine the change in deprivation rate among children aged 0–17 years between 2010 and 2012, as well as the impact of cash transfer on multidimensional child poverty in Ghana using the global Multidimensional Poverty Index (MPI).

The study used the Ghana Livelihood Empowerment Against Poverty Impact Evaluation Survey data, which has the baseline data collected in 2010, and the follow-up was collected in 2012. The authors used the difference-in-difference estimation technique to assess the impact of the cash transfer program on the MPI of pre-school (0–5 years) and school-aged (5–17 years) children, and compared the results with that of Propensity Score Matching.

The deprivation trend reveals that deprivation among pre-school children increased for nutrition, water and sanitation. The estimated result shows that cash transfer significantly reduces MPI of pre-school and school-aged in beneficiary households by 10.5 and 1.3% relative to non-beneficiary children, respectively.

For cash transfer programs to efficiently alleviate child poverty in Ghana, the paper recommends that the conditionality aspect of the program that has been neglected by managers of the program should be enforced. Also, the program should be supplemented with food nutrients for children to reduce the deprivation of nutrition.