Multidimensional Poverty Measurement Research Articles

Multidimensional child poverty in South Korea: developing measures to assess progress towards the sustainable development goals

This article presents the development of a measure to assess the prevalence and patterning of multidimensional child poverty in South Korea. The first goal of UN’s Sustainable Development Goals (SDGs) is to reduce poverty in all its dimensions, and countries are increasingly developing their own measures of multidimensional poverty. This flourishing of different measures presents challenges for international comparisons. The article applies an internationally-validated method of assessing multidimensional poverty to demonstrate its suitability for use in a high-income Asian economy. Multidimensional child poverty is assessed by combining data on child material deprivation with their household income. Using data from the 2018 Korean National Children Survey, we show that child material deprivation is higher (15%) than income poverty (12%). When measured using a combined measure of material deprivation and income, around one in every three children in Korea were found to be either poor or vulnerable to poverty. These findings show that the official monetary poverty measure on its own may understate the percentage of children unable to afford necessities in Korea, as envisioned by international targets like the SDGs. In terms of policy, analysis of individual deprivations suggests that a combination of in-kind benefits such as vouchers for leisure activities or education and asset-building programmes as well as cash transfers are needed for tackling child poverty.

Measurement and determinants of multidimensional urban poverty: Evidence from Shandong Province, China.

China eliminated rural poverty under current poverty standards in 2020. However, compared with rural poverty, urban poverty in China has been somewhat neglected. This paper aims to discover the changes and determinants of multidimensional urban poverty in Shandong Province, a representative province in Eastern China. Using a nationally representative panel dataset, the China Family Panel Studies, and the Dual Cutoff method, this study creates a multidimensional poverty index with four dimensions and 11 indicators to measure urban poverty in Shandong Province. This paper discovers that while the incidence of multidimensional urban poverty in Shandong Province decreased from 47.62% in 2010 to 36.45% in 2018, the intensity of multidimensional poverty only decreased from 41.27% to 37.25%, which indicates the inadequacy of urban anti-poverty efforts in Shandong Province. This paper also uses logistic regression to identify the determinants of multidimensional urban poverty. The findings suggest that income, health, drinking water, and durable goods are the main determinants of multidimensional urban poverty in Shandong Province. Based on these findings, this study provides targeted recommendations for future urban anti-poverty policies in Shandong Province.

Spatial Identification and Distribution Pattern of the Complexity of Rural Poverty in China Using Multisource Spatial Data

Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China’s counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns.

Spatial and temporal differences and convergence analysis of multidimensional relative poverty in ethnic areas.

Reducing multidimensional relative poverty is one of the important issues in the current global poverty governance field. This article takes 12 ethnic regions in China as the research object and constructs a multidimensional relative poverty measurement system. The calculated multidimensional relative poverty index is decomposed according to provinces, cities, dimensions, and indicators. Then, the Dagum Gini coefficient and convergence analysis are used to analyze spatiotemporal heterogeneity and convergence characteristics. The results show that the multi-dimensional relative poverty situation of various provinces in ethnic minority areas has improved from 2012 to 2021, among which Tibet province is the most serious and Shaanxi is the best. According to the analysis of convergence, it was observed that there is no σ-convergence of multidimensional relative poverty in ethnic areas in general, and there is absolute β-convergence in general and in the southwest and northwest regions, and there is no absolute β-convergence in the northeast region. Based on this, policy recommendations for reducing multidimensional relative poverty are proposed at the end of the article. Compared with previous studies, this article focuses on ethnic regions that are easily overlooked. Starting from the dimensions of economy, social development, and ecological environment, the poverty measurement system has been enriched.

Hybrid measures of multidimensional poverty

Hybrid measures of multidimensional poverty

Multidimensional Poverty Among B40 in Kuala Krai, Kelantan

Poverty is a phenomenon faced by an individual or a family without their own will. The poor can be identified through poverty measurement as a first step. The objective of this study is to identify the indicators that define the multidimensional poverty of the B40 group in Kuala Krai and examine the needs of the population in helping them to eradicate poverty. Poverty measurement is divided into two types: unidimensional poverty measurement, which shows individual poverty in terms of income only, and multidimensional poverty measurement, which is related to poverty in the holistic aspect of humanity. Usually, multidimensional poverty is experienced by B40 households. B40 households are low-income households, and a total of 2.7 million households still belong to the B40 group. This study was conducted on 153 households in Kampung Lubuk Simpul, Kuala Krai, by using questionnaires, indirect interviews, and observations. Quantitative methods such as factor analysis, the crosstab method, and frequency. The results of the study found that, in addition to economic factors, social and spatial factors are also the cause of B40 poverty. Therefore, in a situation where Malaysia is developing in line with other countries, the B40 household should not be marginalized to enjoy the opportunities resulting from the country's prosperity. To eradicate poverty, all parties need to look at poverty from a multi-dimensional perspective to identify the poor B40 group. Therefore, this study uses multidimensional poverty measurement because it can find out the indicators that cause the population to experience multidimensional poverty. The results of this study are not only to identify indicators that define multidimensional poverty among the population but also to study the needs of the population to eradicate multidimensional poverty. Therefore, this study can help the authorities improve the existing implementation methods to achieve the goals of work and the SDG to achieve zero poverty

From Nutritional Capability to Food Capability: Measurement of Multidimensional Food Poverty in Japan

Amartya Sen’s work has contributed to shifting our focus from food availability to food access and utilisation, together called ‘nutritional capability’. Existing food insecurity instruments have been informed partially by the capability approach, but remain focused on material deprivation and its economic sub-dimensions. This narrow focus has become problematic, particularly in high-income countries, where material deprivation is largely overcome and food poverty manifests itself differently from that in low-income countries. Food poverty in high-income countries should thus be approached from a ‘food capability’ perspective that fully recognises the multidimensionality of eating lives and the multiple factors that affect their dietary standards. To demonstrate the usefulness of this perspective, the Alkire-Foster method for multidimensional food poverty was utilised to analyse food poverty in Japan. The Alkire-Foster measure has a dual cut-off approach and its adapted method can produce a multidimensional food poverty index by setting reasonable food deprivation and poverty thresholds. The national data used for empirical demonstration was obtained from a web-based questionnaire conducted in Japan, to which 973 participants (aged 20–60 years) responded. The measurement identified a 20.6% food-poor population and inequalities in food capabilities among subgroups. Although economic deprivation was tightly linked to food poverty, the results suggested that gender- and age-based inequalities were larger than socioeconomic status-based ones, which reflected much wider societal problems in Japan than growing economic poverty, such as gender inequality and population ageing. The proposed measurement is effective for monitoring food policy impacts and complementing existing food insecurity assessment tools. The results will be useful for promoting social debate about what the minimal dietary standard should be in high-income societies.

Gender Differences in Multidimensional Poverty in Brazil: A Fuzzy Approach

AbstractThis study aims to improve the measurement of multidimensional poverty for the purpose of analyzing gender differences while considering the limitations of household surveys. To effectively analyze gender disparities, it addresses three issues that are often overlooked in the literature: disregard for within-household inequalities in household-level indicators; disregard for ineligible populations in indicators that represent only a specific group; and disregard for intermediate deprivation situations in cutoff-based poverty estimations. Using data from the Brazilian Consumer Expenditure Survey 2017–2018, we create two indexes with indicators that are key aspects in gender and feminist analyses. Applying a fuzzy approach and the Alkire–Foster method, we estimate multidimensional poverty and gender differences from three perspectives: intrahousehold, interhousehold, and intracouple. We also calculate inequality among the poor and intracouple gender gaps proposing fuzzy versions for these analyses. The main findings suggest that women are disadvantaged in terms of work and time quality, economic security, and access to resources—all of which are crucial components of agency or degree of empowerment.

Is Multidimensional Poverty Associated to Dementia Risk? The Case of Older Adults in Pakistan.

Multidimensional poverty is associated with dementia. We aimed at establishing this association in Pakistan. A cross-sectional study was conducted in Punjab and Sindh, Pakistan, between March 30, 2002, and August 22, 2022, among adults aged 50 and older. Multidimensional poverty measures were composed of 6 dimensions and 15 indicators. Poverty was compared between adults with and without dementia using the Rowland Universal Dementia Assessment Scale, adjusting for sex, age, marital status, and household size. Associations between dementia and poverty were investigated using a multivariate logistic regression model. We found that 594 (72.7%), 171 (20.9%), and 52 (6.4%) had no, mild, and moderate-to-severe dementia, respectively. More women than men had dementia (11.4% vs 2.9%). Approximately 40.4% of adults with dementia were found to be deprived in 4 or more dimensions compared to 8.9% without dementia, and the difference in multidimensional poverty between them was 348.6%. Education, health, living conditions, and psychological well-being were the main contributors to poverty. Poverty in 4 or more dimensions was strongly associated with dementia (odds ratio [OR], 5.02; 95% confidence interval [CI], 2.07-12.16) after adjusting for sex, marital status, age, and household size, with greater odds for older women (OR, 2.02; 95% CI, 1.41-2.90). Our findings suggest that early improvement in social determinants of health through targeted structural policies may prevent dementia later in life. Improving access to free, quality education, health care including mental health care and basic living standards, and employment should reduce the collective risk of dementia.

Exploratory Analyses of Existing Nexuses Between Cooperative Membership, Production, Wellbeing, And Determinants Among Layer Egg Production Enterprises: Evidences from South West Nigeria

Cooperatives have been evidently solicited to play a positively significant multifaceted role in the promotion of wellbeing among members hence, this study was conducted to examine the existing interrelationships between cooperative membership, farmers’ welfare, and farm production level along its determinants among layer egg producers in southwest Nigeria, using a multidimensional approach. A multistage sampling technique was employed in the collection of data from 185 poultry farmers; 94 Cooperators, and 91 Noncooperators, randomly from four local government areas, using well-structured questionnaires. Descriptive statistics, T-test, Variance inflation factor, Alkire-Foster multidimensional poverty measures, and multiple regression are employed in data analysis.: Many (54%) of the poultry farmers are medium scaled, within which a larger proportion of about 61% are cooperators. Also, the output of the cooperator category was found significantly higher than their non-cooperator counterpart by 128.1 % crates per day at 5 % probability level. Furthermore, there exists a veritable positive relationship between farmers’ multidimensional welfare and increased production scale, while years of farming experience, farming as a primary occupation, farm size, and cooperative membership, significantly influence increased output. The finding showed that farmers’ multidimensional poverty headcount indices are inversely proportional to their scale of production. There also exists a significant positive relationship between increased production scales, cooperative membership, and farmers’ welfare statuses.

An Econometric Approach to Design a Multidimensional Poverty Measure for Rural Himalayan Areas

This study is an effort to design a multidimensional poverty measure for rural Himalayan areas. It investigated the level of multidimensional poverty and the underlying causes. The study aimed to identify the major causes of multiple deprivations using indicators of Global MPI. Then, an econometric technique was used to identify the dominant causes for designing a robust area-specific Multidimensional Poverty Index to offer critical policy insights. The findings revealed that 13 per cent of the sampled households were multi-dimensionally poor. The prominent causes were income-related, infrastructure and facilities-related, household/domestic problems, health problems, education, awareness and expense problems. However, income-related problems had dominantly affected households in most of the indicators. The study investigated the antecedents using NLPCA and found that only six dimensions can be retained using these causes. Based on dominant grounds, it is recommended to use these retained indicators/dimensions for designing MPI to address the actual causes of deprivation for sustained poverty alleviation.

Three approaches to modeling the relationship among durable goods, academic achievement, and school attendance in Colombia1

In comparison to research on assets and their impact on school performance, less research has been devoted to household assets. This article studies the relationship among durable goods, academic achievement, and school attendance in Colombia. Durable goods are approximated through three different methodological approaches: inventory, attributional, and index approaches. Data come from the 2017 SABER test, a nationwide examination that assesses reading and math skills, for fifth- and ninth-grade students, (N = 621,218). Students with complete durable goods information (N = 364,436) were included. Multilevel modeling was used to test the effects of durable goods on academic achievement and school attendance. The results suggest that while the index approach provides a broad understanding of how durable goods relate to school performance, the inventory and the attributional approaches show that different types of durable goods have differential effects on educational outcomes. The index approach illustrates that the possession of durable goods is positively associated with academic achievement and school attendance. The inventory and the attributional approaches show that washing machines, computers, and internet access are positively related to academic achievement and school attendance. In contrast, TVs, video games, and cars are negatively related to school performance. This research added to the literature by using different methodological approaches to operationalize durable goods, and by expanding the geographic scope to Latin America. Findings from this study may contribute to reassessing current measures of multidimensional poverty in Colombia to include durable goods.

The effect and mechanism of farmland transfer on the multidimensional relative poverty of rural women

Introduction: Over the past 40 years of reform and opening up, China has made significant progress in poverty reduction in rural areas. China’s land system is different from that of other countries in the world because of its special national conditions and the influence of small farmers in large countries. The rural female labor force is the key force to reduce rural poverty and realize rural revitalization.Methods: This paper attempts to construct a multidimensional relative poverty measurement index system for rural women and estimates the multidimensional relative poverty of the rural female labor force using the data of China Family Panel Studies in 2018. This paper also uses propensity score matching to estimate the correlation between rural land transfer and rural female poverty.Results and discussion: Based on the aforementioned conclusions, this paper puts forward relevant policy suggestions from two aspects: encouraging the rational and effective transfer of agricultural land and breaking the inherent gender order 1) to encourage the rational and effective circulation of agricultural land, so as to reduce the degree of fragmentation and promote the appropriate scale management of contiguous plots; 2) to break the inherent gender order, so as to achieve the improvement of women’s economic income, and the alleviation of multidimensional relative poverty, so as to alleviate the poverty of families.

Financial inclusion's impact on energy poverty: Evidence from Indonesia

This study tests the association between financial inclusion and energy poverty in Indonesia. It adopts multi-dimensional energy poverty and financial inclusion measures employing the 2018 Statistics Indonesia data. The energy poverty measures cover six indicators, i.e., the primary cooking fuel, indoor air pollution, lighting, ownership of household appliances, and supporting essential services such as education, entertainment, and communication. On the other hand, the financial inclusion variable involves household access to banks, credit, and insurance. In testing the financial inclusion-energy poverty association, the present study performs the Two-Stage Least Square (2SLS) method, involving household distance to the nearest financial facilities as an instrument variable (IV) to overcome the potential endogeneity problem from the reverse causality between the financial inclusion and energy poverty. This analysis reports a robust negative and significant association between financial inclusion and energy poverty, bringing potential critical notes for Indonesia's policy formulation against energy poverty.

Multidimensional poverty and depression among with and without Disabilities

Abstract Background Traditionally, poverty has been measured based on an individual income, however it is now accepted that income gives too narrow a view of an individual's overall living standards and other indicators of living standards are needed. Multidimensional poverty measures are a useful tool for policymakers in order to identify target populations for policies aiming to improve equity and reduce chronic disadvantage. Methods We analyzed the Korea Welfare Panel Study, a nationally representative survey. Poverty variables were selected based on monetary and non-monetary categories. The six poverty dimensions were income, education, housing, medical care, social relationships, and employment. Depressive symptoms were measured using the Korean version of CES-D-11. Results All dimensions of poverty and depression had higher rates in people with disability than non-disabilities in the order of income (+33.1%p), education (+27.2%p), social relationships (+9.7%p), and medical care (+6.5%p). Depression rate was also higher for people with disability (p < .0001). The factors influencing depression of people with and without disability by multidimensional poverty were somewhat different. The disabled and non-disabilities with poverty of social relationships were equally more likely to have a depression (aOR=7.33, <.0001, aOR=3.85, <.0001), however, the non-disabled with medical care poverty were more likely to have a depression (aOR=6.76, <.0001) than without medical care poverty, and the disabled with employment poverty were about 3 times more depressed (aOR=3.67, <.0001) than with employment poverty. Conclusions This study suggest that access to various aspects of life, as well as income support, should be considered in policy making for resolving the poverty problems and mental health among people with disabilities. Key messages • There were disparities in the prevalence of multidimensional poverty and depression in the people with disabilities, especially those with severe disabilities. • The main factors influencing depression of people with disability by multidimensional poverty were social relationships and employment poverty.

Assessing reduction in multidimensional childhood poverty in India: a decomposition analysis

BackgroundEmpirically, the official measurement of multidimensional poverty often shows children as the poorest age group. According to Global Multidimensional Poverty Index report, Africa and South Asia bear the highest burden multidimensional child poverty (MCP). Around one-third of children aged 0–4 are multidimensionally poor in India. Policymakers in India must have appropriate information on child poverty to alleviate poverty. The purpose of this paper is to examine MCP trends and track efforts to reduce child poverty at the national level across geographic regions, castes, and religious groups.MethodsWe used the Alkire-Foster method to calculate the MCP index (MCPI) among children aged 0–4 using the latest two rounds of National Family Health Survey data (2015–16 and 2019–21). We applied the Shapley decomposition method to analyse the marginal contribution of incidence and intensity that lead to changes in MCPI.ResultsIn India, the incidence of child poverty reduced by more than 40% between 2015–16 and 2019–21 (46.6–27.4%) and the MCPI reduced by half (24.2–12.6%). The relative decline in MCPI has been largest for urban areas, northern regions, Other Backward Classes (OBCs) and Hindus. Children from rural areas, Scheduled Castes (SCs), Scheduled Tribes (STs), and Muslim households are the poor performers. When focusing exclusively on the poor child, we found all the population subgroups and geographic locations reduced the censored headcount ratios in all 14 indicators. Across places of residence, castes, religions, and regions the, indicators like electricity, birth registration, drinking water, assisted delivery, sanitation and cooking fuel made significant improvements between 2015–16 to 2019–21.ConclusionThe study indicates that by studying the MCPI over time, one can identify the priorities in policy development to achieve the Sustainable Development Goals.

Caste-based analysis of multidimensional early childhood poverty in India: Patterns and determinants

Children are among the most vulnerable groups worldwide, bearing a disproportionate share of the burden of poverty. One-third of children, compared to one-sixth of adults, are multidimensionally poor. Experiencing poverty in childhood can have long-term negative consequences across generations. As a result, multidimensional poverty measures have recently been investigated in the child poverty literature. This study analysed multidimensional poverty among children (0–59 months) in India using the Alkire-Foster’s counting approach and looked into how it differs across the country’s well-known social stratification, the Caste system. The determinants of multidimensional child poverty (MCP) have been determined using multiple logistic regression. By using National Family Health Survey, 2019–21 data, the study revealed that the incidence of MCP in India is 32 %; highest among Scheduled Tribes (STs) (50 %), followed by Scheduled Castes (SCs) (37 %), Other Backward Classes (OBCs) (30 %) and ‘Others’ (22 %). Across all the Castes highest rates of deprivation were recorded in the indicators of standard of living and early childhood development (ECD) dimensions; together, they contribute more than 50 % to the MCP index (MCPI). The contribution of the water, sanitation, and hygiene (WASH) dimension in MCPI of STs is also noticeably higher (20.2 %). Of the 15 indicators considered, availability of TV/Radio, followed by immunisation, sanitation, cooking fuel, housing condition, and stunting, are the leading contributors with a combined contribution of more than 55 %. A child being a male, of higher birth order and born to a mother with higher years of schooling was more likely to experience multidimensional poverty. The household head’s sex, age, and years of schooling were significantly associated with MCP. Across all castes, children from nuclear families, 7 + household members, higher number of children per woman, rural areas, central, eastern and north-eastern (except STs) regions were more likely to experience multidimensional poverty.

Patterns of Multidimensional Poverty in the United States

The accurate accounting of where and for whom deprivations occur is fundamental to addressing poverty. In the United States, the official poverty measure considers only a person’s income, although poverty is increasingly understood internationally as a set of multiple, interlinked deprivations. This article introduces a decomposable multidimensional poverty (MDP) measure that addresses these shortcomings by using thirteen American Community Survey microdata indicators to identify education, health, housing, and economic security deprivations for individuals. In 2017, the national poverty rate was 13.7 percent when measured using MDP and 13.1 percent using official poverty. Although similar at the national scale, Hispanic, Asian, and older persons had higher poverty rates using the multidimensional measure, whereas Black and young persons had higher rates when using official poverty. MDP tended to be higher than official poverty in dense urban areas, whereas official poverty tended to be higher in rural areas. Further, MDP was a stronger correlate with COVID-19 death rates than official poverty through the first three waves of the pandemic. The design of MDP recognizes that individuals can experience poverty in different ways and provides a more holistic view of people and places.

Optimizing development aid allocation: A data-driven approach using unsupervised machine learning and multidimensional indices

Development aid, often termed foreign assistance, involves the voluntary transfer of resources like goods, services, or capital from governments and international aid agencies to support the development of recipient countries or their people. This practice holds significant importance in international relations and the national economy of many nations. It is also a heavily researched area in economics due to its potential to create a safer, more equitable, environmentally sustainable, and prosperous world. However, it's observed that donor interests often take precedence over recipient needs in aid allocation decisions. Since these allocations are determined by national policies, there is limited room for external input. Consequently, the success of Sustainable Development Goals (SDGs) relies heavily on donor interests. To address this challenge, we propose data-driven approaches to ensure recipient needs are considered and aligned with SDGs. This involves balancing the legitimate interests of donors, recipients, and disadvantaged individuals. In our study, we utilize multidimensional poverty measures to better understand the hardships people face. This approach offers a more accurate representation than traditional monetary indicators, which can miss important dimensions of deprivation. We employ unsupervised machine learning to analyze this data objectively and recommend countries most in need of aid. Such systems can assist donor countries and agencies in efficiently allocating aid, ensuring that the needs of beneficiaries and targeted development goals remain well-aligned.

Can monetary poverty measurement detect multidimensionally deprived? Evidence from Egypt

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.