Digital Inclusion and Enhanced Multidimensional Poverty Assessment: Evidence from Low-Income Communities in Kuala Lumpur

Abstract

Malaysia’s aspiration to attain high-income status necessitates not only economic growth but also a deeper understanding of poverty that goes beyond financial indicators. The Multidimensional Poverty Index (MPI) for Malaysia is designed to be comprehensive, considering a wide range of factors relevant to the diverse population of the country. Unlike traditional income-based approaches, our study goes beyond money to capture how poverty affects households across multiple dimensions. The MPI reveals important insights that standard measures often miss—showing which families struggle with education, health, housing, or digital access, not just income. Therefore, this study aims to enhance the Multidimensional Poverty Index for the Malaysian context by identifying and incorporating new dimensions and indicators to better capture the complexity of poverty in the country based on an empirical study in Kuala Lumpur, Malaysia. The MPI represents a significant advancement, offering a multidimensional framework for poverty measurement. Based on results in Kuala Lumpur, 38.7% of households were found to be multidimensionally poor. This means that nearly 4 out of every 10 households in this study experienced deprivations in multiple basic needs, not just income. Household size also significantly influences the risk of multidimensional poverty, with households of more than six members being over three times more likely to be poor, primarily due to higher dependency ratios and greater consumption burdens.

1. Introduction

Poverty is defined as living on less than US$1.90 per day, or its equivalent of RM7.95 [1]. World Health Organization (WHO) categorizes individuals within this income bracket as poor, as they are unable to meet basic necessities. Ref. [2], on the other hand, attributes poverty to corrupt leadership and a regressive culture that impedes modernization and development. In the context of Malaysia, the Fifth Malaysia Plan [3] distinguishes between absolute poverty and relative poverty. Absolute poverty refers to the inability of individuals or households to afford basic daily necessities required for a minimum standard of living, including food, clothing, shelter, education, and healthcare [4]. Ref. [5] further explains that absolute poverty is measured using the Poverty Line Income (PLI), which is based on household income. A household is classified as poor when its income falls below the poverty threshold. The methodology for determining the PLI considers the minimum food and non-food requirements for each household member [6].

In Malaysia, economic growth alone does not guarantee poverty reduction. What matters more is how income is distributed and whether people can actually access jobs. When jobs are plentiful and income is fairly distributed, poverty rates drop. But during economic downturns or when income gaps widen, the opposite happens—more families fall into poverty. Beyond income-based measurements, poverty can also be assessed through the Multidimensional Poverty Index (MPI), which considers various dimensions such as education, healthcare, living conditions, and access to essential services. Economic factors—including unemployment rates, access to decent work, and the affordability of essential goods and services—significantly influence these dimensions and shape the overall poverty status in Malaysia. Ref. [7] highlights that poverty remains a persistent issue in Malaysia and, if left unaddressed, could hinder the nation’s progress toward achieving high-income status. While Malaysia has experienced rapid economic growth in recent decades, the country still faces structural challenges that need to be tackled. In this regard, the effectiveness of existing policies and programs aimed at improving the livelihoods of poor and vulnerable groups must be evaluated, and additional measures should be implemented to enhance their well-being and quality of life.

Poverty measurement is also closely linked to the Sustainable Development Goals (SDGs), a global framework adopted by Malaysia to promote sustainable development. The SDGs emphasize poverty eradication through inclusive economic growth, social protection systems, and equitable access to basic services. The economic impact on poverty measurements in Malaysia provides critical insights into the country’s progress toward achieving SDG targets related to poverty reduction, economic development, and social well-being. Economists now argue for a multidimensional approach to poverty assessment, recognizing that development extends beyond economic factors and encompasses various aspects of human well-being [8].

Malaysia’s Multidimensional Poverty Index (MPI) was officially introduced in 2015 as part of the Eleventh Malaysia Plan (2016–2020) to complement the income-based Poverty Line Income measure that had been used since the 1970s. However, the MPI has not featured prominently in policy formulation or public discourse, and there remains a notable absence of empirical studies that have substantively enhanced the MPI framework by incorporating emerging dimensions critical to upper-middle-income countries in transition. Digital inclusion, in particular, has become increasingly vital for Malaysia and the ASEAN region, where approximately 31% of the Southeast Asian population remains digitally excluded, and bridging this digital divide could unlock at least USD 15 billion annually across the region. This study addresses this gap by proposing an updated MPI framework that incorporates digital inclusion alongside traditional dimensions, with deprivation thresholds more appropriate for an upper-middle-income country such as Malaysia. Thus, the main objective of this empirical study is to enhance the MPI by selecting and including the new indicators in order to capture the complexity of poverty in Malaysia. It focuses mainly on low-income households in urban neighbourhoods in order to identify the intensity, composition and indicators of multidimensional deprivation within the population that are identified as economically vulnerable.

Therefore, the main objective of this enhanced MPI framework is to test and refine so that the framework is suitable for an upper-middle-income economy that is undergoing rapid urbanization and digital transformation. Thus, focusing on Kuala Lumpur, Malaysia, which is the most economically advanced and urbanized region in Malaysia, will provide comprehensive and critical assessments on whether the current MPI framework is sufficient enough to capture the deprivation issue in an urban setting environment.

However, this study has certain limitations that need to be acknowledged. First, the study area is focused on low-income households in Kuala Lumpur, and the outcome of the study may not be able to reflect the multidimensional poverty dynamics in other areas like rural areas or other states in Malaysia, which may have different socioeconomic conditions. Second, even though the enhanced MPI framework focuses on poverty measurement approaches, its empirical application is focused on an urban setting. Due to this, further validation on diverse datasets is necessary to assess its broader application and policy relevance. Third, the digital inclusion dimension only measures device ownership and internet access. These indicators capture basic access but do not reflect digital literacy, quality of connection, or effective usage. Therefore, the measure may not fully represent true digital inclusion. Future research should include more comprehensive indicators such as digital skills and usage quality.

2. Literature Review

Ref. [6] defines poverty as scarcity across multiple dimensions. Like many other nations, Malaysia faces complex socioeconomic challenges that cannot be adequately captured by a single economic indicator. Issues such as poverty, inequality, environmental degradation, healthcare, education, and social inclusion require a multidimensional lens to fully understand their interconnected nature and develop comprehensive solutions [8]. The evolution of poverty measurement in Malaysia has shifted from a single-dimensional approach, primarily based on income or consumption, to a multidimensional framework that considers various aspects of well-being [9]. This transition reflects a growing recognition that poverty is a complex phenomenon that cannot be sufficiently captured by a single measure. Many studies argue that relying on a single variable, such as income, does not fully reflect an individual’s standard of living or overall well-being. Today, human well-being depends not only on income but also on factors such as health, spirituality, education, and social inclusion. This perspective aligns with the view in [10] that poverty is inherently multidimensional.

Racial diversity and past economic dynamics are two socioeconomic and demographic elements that have an impact on poverty in Malaysia. The majority of Malaysia’s population is made up of Malays, indigenous groups, Chinese, and Indians, despite the country being multi-ethnic and multireligious. However, because of systemic and historical disparities in distribution of wealth, some racial groups are disproportionately more likely to live in poverty. In certain social and geographic situations, these inequalities have resulted in vulnerable populations due to differing levels of access to opportunities, resources, and social mobility. The Bumiputera policy, which attempts to rectify past injustices and enhance the socioeconomic development of Malays and other indigenous communities, is one of the affirmative action measures that the Malaysian government has put in place to address these issues [11]. It is essential to comprehend and measure poverty in these varied groups.

2.1. Unidimensional Poverty

Unidimensional poverty is typically measured using a single economic indicator, primarily focusing on income and production. This method may ignore other important facets of poverty and inequality since it solely takes economic factors into account when assessing poverty. In Malaysia, the income threshold below which people or households are deemed impoverished is established using the Poverty Line Income (PLI) as a baseline. A thorough examination of living expenses, household spending trends, and other pertinent socioeconomic variables is used to determine the PLI. Policymakers and researchers can assess the success of poverty reduction measures, analyze changes in poverty rates over time, and create focused interventions for people and households below the poverty line by establishing and tracking the PLI [12].

For instance, Malaysia revised its PLI in 2019 from RM980 to RM2208 per household per month, leading to an increase in the recorded poverty rate from 5.6% in 2019 to 8.4% in 2020 [6]. According to this update, over 400,000 households were below the poverty line in 2019, and another 234,400 households were below the poverty line in 2020 [13]. Broader factors were taken into account in the revised PLI, with a focus on the best possible food requirements as well as fundamental necessities like clothing, housing, utilities, transportation, communication, education, and healthcare. In order to provide a more accurate picture of absolute poverty, these factors are now measured in monetary terms as opposed to calorie-based estimates. According to this updated paradigm, one of the main causes of poverty is the denial of nourishing food and basic necessities [5]. This change implies that in order to give a more thorough picture of poverty in Malaysia, it could be required to include other aspects of basic requirements. Additionally, the growing number of impoverished households points to a worrying pattern, indicating that poverty is increasingly defined by a variety of deprivations rather than just a lack of money.

However, [14] points out that because the PLI is usually computed at the national level, it might not accurately reflect subpopulation, regional, or urban–rural differences. Applying a standard PLI may result in an underestimation or overestimation of poverty levels due to differences in living expenses and consumption habits among various regions and demographic groupings. Furthermore, the PLI does not specifically include non-income characteristics like access to social safety, healthcare, or education; instead, it concentrates exclusively on income. This restriction limits a thorough comprehension of poverty and could lead to an imprecise evaluation of people’s deprivation and well-being [15].

2.2. Multidimensional Poverty

Most poverty-related studies argue that measuring poverty using a single variable does not adequately reflect household well-being and living standards, thus providing a less accurate representation of poverty. Today’s human well-being is impacted by a number of other aspects, including social factors, health, education, and spirituality, in addition to income. This supports the idea put forth by [10] that poverty is essentially multifaceted. Ref. [16] looked at poverty from a health and nutrition perspective, claiming that low household income influences food intake and medical costs, which in turn causes hunger and malnutrition. Malnutrition happens when children, in particular, do not get enough nutrients from a balanced diet. According to a United Nations Children’s Fund study [17], 97% of households are impacted by price increases, which makes it more difficult for them to give their kids wholesome meals. Given that increased household spending or food aid frequently falls short of meeting basic dietary requirements, this shows that urban poverty has a substantial impact on children’s daily nutritional needs.

Ref. [18] argues that poverty is closely linked to education, as financial constraints often hinder educational attainment. A case study conducted in the Bachok District of Kelantan found that economic factors significantly impact primary education levels in rural areas. Children from low-income and low-educated families are more likely to experience educational disadvantages, and financial difficulties often result in gender disparities in schooling. Girls from impoverished families, for instance, are more likely to drop out due to familial responsibilities, such as caring for ill family members. A UNICEF [17] report highlights further challenges, noting that many children aged 5 to 6 do not attend preschool education, four out of ten do not have toys, three out of ten lack books, and most do not have a conducive learning environment. This underscores the heightened risk of poverty among children and youths in Malaysia.

Ref. [19] highlights the part that social variables play in sustaining poverty in addition to economic and educational ones. An individual’s capacity to overcome poverty is frequently hampered by limited access to social networks, which can promote economic mobility. Marriage, childbirth, and inheritance customs can also perpetuate poverty cycles by impeding the socioeconomic advancement of particular populations. Accordingly, the majority of academics concur that poverty is a multifaceted form of hardship that people in a society endure. The complexity of poverty may not be fully understood if income-based metrics are the only ones used to measure it [15].

The Alkire–Foster approach [20] developed by OPHI in 2007 and updated in 2018 with UNDP, offers a more practical solution. Instead of just measuring income, it looks at three key areas: education, health, and living conditions. This shift was important because researchers realized that a family might have some income but still lack access to clean water, healthcare, or decent schools. Ten indicators—two for health, two for education, and six for living standards—are further separated into these aspects. If a person or household’s deprivation score is 30% or more, they are considered multidimensionally poor [21,22]. This method takes into account several facets of well-being besides income, allowing for a more complex understanding of poverty.

By including non-income factors that are crucial to human well-being, the MPI encompasses a wider range of poverty and offers a more comprehensive evaluation [4]. The MPI gives [20,22] policymakers important insights into the nature of poverty and its underlying causes by identifying particular deprivations faced by people and households, in contrast to unidimensional poverty indicators. The MPI makes it easier to create focused initiatives that successfully reduce deprivation by acknowledging that poverty encompasses more than just a lack of money; it also involves a lack of access to essential services, healthcare, and education.

The MPI captures a broader spectrum of poverty and provides a more holistic assessment by integrating non-income dimensions essential for human well-being [4]. Unlike unidimensional poverty measures, the MPI identifies specific deprivations experienced by individuals and households, offering policymakers valuable insights into the nature of poverty and its root causes. By recognizing that poverty extends beyond income scarcity to include lack of access to basic services, education, and healthcare, the MPI facilitates the development of targeted interventions to alleviate deprivation effectively.

In general, the MPI offers a more thorough and pertinent framework for measuring poverty. It helps policymakers to create focused policies and monitor the advancement of poverty reduction by taking into consideration various factors and pinpointing particular areas of deprivation. The COVID-19 epidemic has, however, highlighted the necessity of updating and reevaluating the MPI to take into account the changing socioeconomic environment. Due to company closures, reduced operations, and layoffs, the pandemic resulted in significant revenue loss and job disruptions. According to [23], these economic shocks either caused a large number of households to fall into poverty or made poverty levels worse. Considering all factors, as income is a component of the MPI, these income shocks may have led to a rise in poverty rates. Interestingly, the economic consequences of the pandemic hindered access to vital services such healthcare, education, and social protection [24].

Therefore, it is crucial to refine the MPI to capture a wider range of deprivations experienced in the post-pandemic era. Updating the MPI can provide policymakers with more precise insights into emerging challenges and ensure that poverty reduction strategies remain effective in addressing the needs of affected populations.

3. Enhancing the Multidimensional Poverty Index

The global Multidimensional Poverty Index (MPI) was developed by [20,25] in collaboration with the United Nations Development Programme’s (UNDP) Human Development Report Office and was first introduced in the 2010 Human Development Report. The MPI was designed to complement traditional poverty indices based on income by incorporating multiple dimensions of deprivation. It measures acute multidimensional poverty across more than 100 developing countries, utilizing a carefully selected set of indicators within each dimension to capture various aspects of deprivation [26]. According to [26], these indicators are based on internationally comparable data and reflect the lived experiences of the poor by encompassing numerous dimensions and diverse aspects to determine and classify poverty levels. The selection process ensures the relevance and comparability of the MPI across different countries.

In 2018, the Oxford Poverty and Human Development Initiative (OPHI) and UNDP introduced an updated version of the MPI, which retained the Alkire–Foster method but was adapted to align with the Sustainable Development Goals (SDGs). The Global MPI 2018, based on data from 144 countries, measures poverty through three dimensions: health, education, and living standards [27,28]. These three dimensions form the foundation for every national MPI, which undergoes a rigorous process of determining its dimensions and indicators to suit the specific context of each country. The Global MPI further breaks these dimensions into ten indicators, in which two are for health, two are for education, and six are for living standards. The identification and finalization of these dimensions and indicators involve extensive collaboration among stakeholders, experts, and target groups. According to the Multidimensional Poverty Peer Network (MPPN), as of 2023, 38 countries, including Mauritania, Paraguay, Palestine, Thailand, Vietnam, Costa Rica, the Philippines, and the Dominican Republic, have officially adopted national MPIs.

3.1. Dimensions of MPI in Malaysia

Before the COVID-19 pandemic, Malaysia had made significant progress in reducing poverty and eliminating absolute poverty. However, the pandemic reversed these efforts, pushing many individuals and households into lower income brackets and increasing poverty levels. Given these changes, the MPI should be reassessed to ensure that all relevant elements are considered, particularly as an additional measurement alongside income, which remains crucial in the post-COVID-19 context [9]. The assessment of the MPI is necessary to maintain its relevance and address existing gaps in poverty measurement. The Alkire–Foster method provides flexibility, allowing different dimensions to be selected and adapted based on institutional and policy needs [29].

In the context of Malaysia, the Department of Statistics Malaysia [13] reports that the national Multidimensional Poverty Index (MPI) comprises four main dimensions and 11 indicators. The selection of these dimensions and indicators reflects the socioeconomic development priorities of Malaysian society. The four dimensions include education, health, living standards, and income. Each dimension consists of specific indicators with defined deprivation thresholds, ensuring a standardized method for determining exclusion or inclusion within the MPI framework. This national adaptation allows policymakers to assess poverty more comprehensively and implement targeted interventions to address multidimensional deprivations.

Following the publication of the 2019 Household Income Survey data, Malaysia’s Multidimensional Poverty Index (MPI) improved its income poverty dimension in July 2020. Nevertheless, the MPI’s non-income components remained unchanged at the time. The income poverty level in Malaysia was first set in 1977. Although the methodology for measuring poverty was improved in 2007 [30], the actual value of the national poverty line was relatively constant between 1977 and 2016. The poverty line threshold was raised from RM980 to RM2,208 per household per month in 2019 due to the 2019–2020 income dimension change. This change is seen as a first step in raising the income “floor” to reflect Malaysia’s present socioeconomic standing [31]. In order to better reflect modern living standards and new socioeconomic issues in the 2020s, the World Bank has advised updating Malaysia’s MPI’s non-income components [31]. Furthermore, according to [32], tackling multidimensional poverty may help lower inequality, which would increase the MPI’s efficacy as a policy instrument for poverty monitoring and intervention.

The World Bank has suggested two options for revising Malaysia’s MPI in order to enhance the framework and has modelled the possible effects of these modifications. The first option (Alternative 1), which focuses on extreme poverty and the most vulnerable groups, especially the poorest 5–10% of the population, is in line with Malaysia’s present framework for measuring poverty. Compared to the current MPI, this adjustment represents a small improvement. On the other hand, the second option (Alternative 2) takes a more aggressive stance, enacting greater minimum requirements that correspond to the aspirations of society in high- and upper-middle-income nations. Alternative 2 acknowledges that wealthier nations typically embrace higher standards of well-being for their citizens, which introduces a degree of relativism even though it does not use a relative poverty paradigm.

To improve the MPI framework, the World Bank has proposed two alternatives for updating Malaysia’s MPI and has simulated the potential impact of these changes. The first alternative (Alternative 1) closely aligns with Malaysia’s current poverty measurement framework, focusing on extreme poverty and the most vulnerable populations, particularly the bottom 5–10% of the population. This modification represents an incremental improvement over the existing MPI. Conversely, the second alternative (Alternative 2) adopts a more ambitious approach, establishing higher minimum standards that reflect societal expectations in upper-middle-income and high-income countries. Although Alternative 2 does not employ a relative poverty framework, it introduces a degree of relativism by acknowledging that richer countries generally adopt higher well-being standards for their populations. This viewpoint is incorporated into the World Bank’s global poverty monitoring program by taking into account both monetary poverty indicators, like the $2.15 international poverty line, and access to basic infrastructure and education [33].

3.2. Proposed Indicators and Dimensions for an Enhanced National MPI

Choosing pertinent indicators and dimensions that appropriately reflect the different facets of poverty within the Malaysian socioeconomic context is a crucial first step in improving the Multidimensional Poverty Index (MPI). Indicators ought to be contextually and culturally relevant, taking into account Malaysia’s particular development aims and problems, as was covered in earlier sections. In order to provide a thorough assessment of multidimensional poverty, ref. [34] states that indicators must be carefully chosen to prevent overlap and redundancy and to guarantee that each indicator represents a unique component of deprivation.

An improved MPI in Malaysia should cover a wide variety of deprivations that have a major influence on well-being. Internationally, education, health, standards of living, housing, work, and social inclusion are all common MPI aspects. However, several aspects need further attention in the Malaysian context, including access to quality education in rural areas; healthcare accessibility for vulnerable groups; and financial inclusion for marginalized communities.

Several scholars, including [4,9], have recommended that any new MPI indicators should involve calculating a composite index that aggregates the selected indicators to provide an overall measure of multidimensional poverty. The weighting of indicators, which establishes the relative significance of each dimension in causing overall poverty, is an essential component of this approach. The weighting process should be informed by statistical analysis and expert judgment to ensure balanced representation.

Furthermore, for the suggested indicators to be measured and analyzed properly, accurate and current data must be available. Policymakers need to make sure that the data sources they need are available, gathered in a methodical manner, and used efficiently.

Table 1. Potential dimensions and indicators and for an enhanced MPI.

Dimensions	Descriptions for Dimension and Indicators
Wealth	Income and consumption are widely recognized as essential dimensions of poverty measurement. For this study, wealth is used to capture economic well-being and material deprivation experienced by individuals and households. There are 3 indicators used in this dimension, including employment, savings and social protection.
Education	There are 2 indicators for this dimension. To capture educational deprivation, most national MPIs consider years of schooling and school attendance completed by adult household members.
Health	Health is critical dimension that reflects the overall well-being and physical capabilities of individuals. There are 3 indicators in this dimension, including nutrition, chronic disease/handicaps, and health spending.
Living Standards	This dimension focuses on the adequacy and quality of housing, as well as the overall living conditions of individuals and households. There are 6 indicators used in this dimension, including transportation, housing quality, overcrowding, treated water sources, food sufficiency and home furnishing assets.
Digital Inclusion	This dimension of digital inclusion examines the extent to which individuals have access to and use Information and Communication Technologies (ICTs). It comprises two indicators: access to a smartphone or laptop and access to the internet.

lists the possible indicators and dimensions for an improved MPI in Malaysia, keeping in mind that every national MPI framework is customized to meet the demands of its own nation.

Furthermore,

Table 2. Comparison of indicators of the MPI between UNDP, Malaysia, and the proposed indicators from this study, showing 5 dimensions with 16 indicators.

Dimensions MPI (2019)	Indicators in Malaysia MPI’s (2019)	Proposed Dimensions from This Study	Proposed Indicators from This Study	Deprivation Cut-Offs
Income	1. Household income	1. Wealth	1. Employed	If any household member unemployed at any time during the reference week worked at least one hour for pay, profit or family gain (as an employer, employee, own-account worker or unpaid family worker (2)).
			2. Saving	If the household did not have EPF (private sector) or pension (public sector).
			3. Social protection	If the household had no personal or group insurance (life and health).
Education	2. Years of schooling	2. Education	4. Years of schooling	If half of the household members (18 years old and above) have received education for less than 11 years or SPM or equivalent.
	3. School attendance		5. School attendance	If the household members (6–17 years old) did not attend school.
Health	4. Access to health facility	3. Health	6. Nutrition	If the household members did not consume a balanced diet (fat, protein, carbohydrate, fibre, vitamins, and water) for their daily intake.
	5. Access to clean water		7. Chronic disease/disability	If any household members were chronically ill (prolonged treatment/huge expenses/bed-ridden/required intensive care/dependent and handicapped) and could not afford the treatment/maintenance costs.
			8. Health spending	If the household members could not afford to get treatment at health facilities due to financial incapacity.
Living Standard	6. Conditions of living quarters	4. Living Standards	9. Transportation	If the household members did not own their own vehicle to facilitate movement (motorcycle/boat, etc.) or had no access to public transportation.
	7. Toilet facility		10. Housing quality	If the physical condition of the house was not suitable (unsound/dilapidated) for living. Types of materials are considered (roof, floor, walls).
	8. Overcrowding
	9. Garbage collection facility		11. Overcrowding	If household members share a bedroom with more than two (2) people in a room.
	10. Transportation		12. Treated water source	If the households did not have a source of treated water for 24 h a day.
	11. Access to basic communication tools		13. Food sufficiency	If the household was unable to eat due to having to save money/reduce food intake/stomach tie (not full) due to financial problems.
			14. Home furnishing assets	If the household did not have at least three basic household appliances.
		5. Digital Inclusion	15. Access to a smartphone or laptop	If the household did not have a smartphone or laptop (1).
			16. Access to the internet	If the household did not have its own internet access (with high-speed internet access) for at least the last 12 months (1).
Total	11	5	16

′s suggested methodology for assessing multidimensional poverty includes 16 variables across five major dimensions. These factors—wealth, living standards, health, education, and digital inclusion—are meant to offer a thorough evaluation of poverty. Three indicators are identified under the wealth dimension: social protection, which focuses on the lack of individual or group health or life insurance; savings, which takes into account the absence of an Employee Provident Fund (EPF) or pension; and employment, which is determined by whether at least one household member worked for pay or profit during the reference period. The education component includes the indicators school attendance, which is determined by whether children aged 6 to 17 are enrolled in school, and years of schooling, which is determined by whether half of the household members aged 18 or older have less than 11 years of formal education. Three indicators make up the health dimension: nutrition, which measures whether or not household members eat a balanced diet; chronic illness/disability, which determines whether a household has a member who is chronically ill or disabled and cannot afford treatment; and health spending, which indicates that treatment is out of reach because of financial limitations.

Transportation (not having a personal vehicle or using public transportation), housing quality (whether the house is sound or dilapidated), overcrowding (more than two people sharing a bedroom), treated water (not having around-the-clock access to treated water), food sufficiency (reducing food intake due to financial constraints), and home furnishing assets (not having at least three basic household appliances) are all indicators that fall under the living standard dimension. Last but not least, the lack of a laptop or smartphone in the home and the absence of high-speed internet connection during the previous 12 months are indicators that the digital inclusion dimension encompasses access to technology and connectivity. Appendix A has a full list of survey questions designed to capture these factors. These questions were specifically designed to collect data corresponding to each of the 16 indicators across the five dimensions, ensuring the framework’s practicality and alignment with the multidimensional poverty measurement objectives.

4. Methodology and Data Analysis

This study was conducted in Kuala Lumpur, Malaysia. This study adopts a quantitative, cross-sectional survey design to operationalize an enhanced the Multidimensional Poverty Index (MPI) tailored for the Malaysian context. The MPI framework in this study encompasses five key dimensions, namely wealth, education, health, living standards, and digital inclusion, comprising a total of 16 deprivation indicators. Data were collected from 300 households located in low-income urban areas of Kuala Lumpur, a city that reflects the pressures of rapid urbanization and socioeconomic disparity. The sample was selected using a stratified purposive sampling method, ensuring representation across demographic groups, including single mothers, the elderly, households with children, and individuals with disabilities. The sampling population was restricted intentionally to focus on low-income urban neighbourhoods in order to capture the assessment outcome of multidimensional deprivation among vulnerable households. According to the Alkire–Foster approach, the MPI can be used to assess specific subpopulations, but the result should be interpreted as a conditional estimation rather than representative of multidimensional poverty for all income groups in Kuala Lumpur.

A comprehensive questionnaire was designed, covering household demographics, education, work, health, living conditions, assets, and digital access (

Table 3. Construction of the enhanced MPI: dimensions and indicators.

Dimension	Indicators
Wealth	Employment, Savings, Social Protection
Education	Years of Schooling, School Attendance
Health	Nutrition, Chronic Illness/Disability, Health Spending
Living Standards	Housing Quality, Overcrowding, Treated Water, Food, Transport, Assets
Digital Inclusion	Access to Laptop/Smartphone, Access to the Internet

). Field teams conducted face-to-face interviews with 300 household heads, taking about 45–60 min per interview. Some households chose to use a digital version on tablets instead. All enumerators received training to ensure consistent data quality across all 300 households.

The deprivation status of each household was coded as binary (1 = deprived; 0 = not deprived) for each of the 16 indicators. Equal weights were assigned to each of the five dimensions (20%), and the weights were distributed equally among indicators within each dimension. For instance, the three indicators under the wealth dimension (employment, savings, and social protection) each received a weight of 0.05, while the two indicators under education (schooling and school attendance) received 0.0625 each. A household’s total deprivation score was calculated by summing the weighted indicators in which it was deprived. Households with a deprivation score equal to or exceeding 0.3 were classified as multidimensionally poor. Based on the literature, two extreme poverty cut-off points have been established. The intersection method classifies a household as multidimensionally poor if they are deprived of all indicators, while the union method designates a household as multidimensionally poor if they are found to be deprived of at least one indicator. Following this, the Alkire–Foster (AF) dual cut-off method provides a moderate option between the two extreme cut-off points, namely, the intermediate approach [35]. The study applied the intermediate approach, whereby a household is classified as multidimensionally poor if it is deprived of at least 30% of the total deprivation score.

5. MPI Construction: Analytical Procedures and Diagnostics

Methodologically, this study employs the Stata statistical software 17 to conduct rigorous inferential analyses of multidimensional poverty and its determinants. Unlike descriptive statistics which merely describe the characteristics of collected data, this study utilizes inferential statistical techniques, including regression modelling and hypothesis testing, to examine patterns and associations within the surveyed sample. This approach represents a methodological advancement over previous MPI studies in Malaysia, which have predominantly relied on descriptive analysis, thereby enabling more robust conclusions about the factors influencing multidimensional poverty. The construction and analysis of the Multidimensional Poverty Index (MPI) in this study involved a series of statistical steps and tests, all conducted using Stata 17. These steps were essential to ensure the robustness, accuracy, and interpretability of the MPI scores, as well as to explore the relationships between deprivation indicators and demographic factors. While the core of the MPI methodology is non-parametric (based on binary deprivation scores and aggregation), several descriptive, inferential, and diagnostic statistical tools were employed during the analysis. The analysis began with descriptive statistics to summarize the socio-demographic characteristics of the 300 surveyed households in Kuala Lumpur. Variables such as age, gender, household size, education level, employment status, and income were analyzed using mean, standard deviation, frequencies, and percentages. This provided a baseline understanding of the population profile and allowed for the identification of potential vulnerability patterns.

Using the Stata 17 software, the summarize and tabulate commands were executed in this step. Following this, 16 of the proposed MPI indicators were transformed into binary variables, which represent whether the households were deprived or not deprived by using (1) and (0) respectively. This binary method is one of the important steps to construct the deprivation matrix, especially using Alkire–Foster methods, because it allows for the computation of the deprivation scores by summing up the weightage. For instance, adults who have least that 11 years of schooling are coded as depr_schooling = 1, and households with no savings are coded as depr_savings = 1.

Each of the MPI indicators, which have five dimensions, i.e., wealth, education, health, living standards and digital inclusion, were equally weighted at 20% each. According to [35,36], indicators within a dimension will share the dimension’s weight equally. A household’s overall deprivation score was then computed as the sum of its weighted deprivation scores across all 16 indicators using linear addition (gen deprivation_score = ... in Stata). This score ranged from 0 (no deprivation) to 1 (deprived in all indicators). Households with a deprivation score of 30% or more (≥0.30) were classified as multidimensionally poor, consistent with international MPI standards. This binary classification (gen multidim_poor = (deprivation_score >= 0.3)) enabled the calculation of the core MPI statistics:

• The headcount ratio (H), which represents the percentage of households identified as multidimensionally poor.
• The intensity of poverty (A), which is the average proportion of deprivation among the poor.
• The MPI score, which is the product of H and A (MPI = H × A), providing a composite measure of both incidence and intensity of poverty.

To examine relationships between multidimensional poverty and socio-demographic factors, a bivariate analysis using chi-square tests was conducted. These tests assessed whether poverty status (poor vs. non-poor) was significantly associated with categorical variables such as gender, education level, employment status, or housing type. For example, the command tabulate multidim_poor gender, chi2 tested the independence of poverty status and gender. For deeper analysis, an optional logistic regression model (logit) was used to predict the likelihood of being multidimensionally poor based on multiple predictors. Variables included age, gender, household size, education, employment status, and dependency ratio. This multivariate technique provided insights into which factors significantly influenced poverty outcomes, adjusting for the effects of other variables. The model output included odds ratios and p-values, which helped identify high-risk groups. The robustness checks were performed through sensitivity analysis. This involved varying the poverty cut-off thresholds (e.g., 0.25 and 0.35) and reclassifying households to observe how poverty rates responded. New binary variables (gen poor_25 = (deprivation_score >= 0.25)) were created, and comparisons were made using tabulations. This step was crucial to ensure that the MPI results were not overly sensitive to the chosen threshold and to validate the consistency of findings under slightly different assumptions (

Table 4. Sensitivity analysis of MPI to alternative poverty cut-iffs.

Poverty Cut-Off (k)	Headcount Ratio (H)	Average Intensity (A)	Adjusted MPI (H × A)
0.25	0.550	1.300	0.715
0.30 (Baseline)	0.387	0.470	0.182
0.35	0.011	0.545	0.006

).

6. Result

The analysis used the survey data from 300 households using Stata 17 statistical software. The analysis approach combined three types of analysis: descriptive statistics to describe household characteristics, statistical tests to identify patterns, and regression models to understand which factors predict poverty. This approach allowed for not just describing poverty in Kuala Lumpur but also understanding what causes it. The analysis began with descriptive statistics to summarize the socio-demographic characteristics of the 300 surveyed households in Kuala Lumpur. Variables such as age, gender, household size, education level, employment status, and income were analyzed using mean, standard deviation, frequencies, and percentages. This provided a baseline understanding of the population profile and allowed for the identification of potential vulnerability patterns. In Stata, this step was executed using the summarize and tabulate commands. Next, each of the 16 proposed MPI indicators was transformed into binary (dummy) variables, representing whether a household was deprived (1) or not deprived (0) in that particular indicator. This step was essential for constructing the deprivation matrix based on predefined cut-off thresholds. For example, households with no savings were coded as depr_savings = 1, and those where no adult had completed at least 11 years of schooling were coded as depr_schooling = 1. The binary format is fundamental to the Alkire–Foster method, allowing for the computation of a deprivation score by summing weighted deprivations.

6.1. Profile of Respondents

The demographic analysis illustrates the complexity and diversity of urban poverty in Kuala Lumpur, shaped not only by income insufficiency but also by household size, education levels, employment instability, housing insecurity, and care responsibilities. These factors collectively contribute to the multidimensional deprivation patterns captured in the MPI results. A total of 300 households residing in various low-income areas across Kuala Lumpur participated in the survey. The demographic profile of the respondents (

Table 5. Profile of respondents (n = 300).

Aspects	Percentage (%)	Aspects	Percentage (%)
Gender		Employment
Male	52.0	Employed	67.0%
Female	48.0	Unemployed	21.3%
Status		Not in Labour Force	11.7%
Married	72.0%	Housing
Single	13.0%	Households Renting	64.5%
Widowed	9.7%	Households Owning	19.3%
Divorced/Separated	5.3%	Social Protection
		Receiving Social Protection	27.4%
Education
Education: <11 Years	38.5%	Post-secondary Education	18.3%
Completed SPM (Form 5)	33.7%	No Formal Education	9.5%
Households
Households with Elderly (60+)	34.0%	Households with Children <18	48.7%
Households with Disabled/Chronic Illness		12.3%
Average Age of Household Head		41.2 years
Average Household Size		4.8
Household Size
Household Size: 4–6 Members		62.3%
Household Size: >6 Members		21.7%

Source: Survey.

) provides critical insights into the socioeconomic composition of urban poor communities in Malaysia’s capital. The mean age of the heads of households was 41.2 years, with ages ranging from 19 to 73 years, indicating a relatively young to middle-aged population. In terms of gender distribution, 52.0% of respondents were male and 48.0% were female, reflecting a fairly balanced gender composition in household leadership roles. The average household size was 4.8 members, with the majority (62.3%) of households comprising four to six members, while about 21.7% had more than six members, suggesting that extended families or multi-generational living arrangements are still common among the urban poor in KL. Most respondents were married (72.0%), followed by single (13.0%), widowed (9.7%), and divorced or separated (5.3%). This marital composition reflects the prevalence of nuclear and dual-parent households, although a notable proportion of single-parent and elderly-headed households were also observed.

Regarding educational attainment, 38.5% of respondents had less than 11 years of formal education, with many ending their schooling at the lower secondary level (PMR or equivalent). About 33.7% had completed the SPM (Form 5), while only 18.3% reported having post-secondary qualifications such as diplomas or degrees. A small minority (9.5%) never attended school, indicating that basic literacy and skill-building programs may still be needed in some segments of the urban poor population. Employment patterns among respondents showed that 67.0% were employed at the time of the survey. Of these, a large proportion were engaged in informal or precarious employment, such as self-employment, gig work, or low-wage services. A total of 21.3% were unemployed, and the remaining 11.7% were homemakers, retirees, or unable to work due to illness or disability. The average reported monthly household income was RM1750.60, which falls below the revised 2022 national Poverty Line Income (PLI) for urban households of RM2208, indicating widespread income deprivation.

A breakdown of household types shows that 64.5% of respondents lived in rented properties, primarily low-cost flats or shared dwellings. Only 19.3% reported owning their homes, while the rest were living in temporary housing, squatter areas, or with relatives. In terms of access to government assistance, only 27.4% of households received regular social protection or financial aid, while the majority (72.6%) either did not qualify or had difficulty accessing such programs. The demographic composition also reveals a high dependency burden. A total of 34.0% of households had at least one elderly member (aged 60 and above), and 48.7% had children under the age of 18, with an average of 2.1 dependents per household. Additionally, 12.3% of households included persons with disabilities or chronic illnesses, often without access to sustained healthcare or insurance coverage.

6.2. Enhancing the Multidimensional Poverty Index: Empirical Findings

6.2.1. Descriptive Analysis

Overall, the findings show significant deprivation in several indicators among households. Within the wealth dimension, deprivation among the households was most acute in the social protection indicator, where more than 60% of households were not covered by any insurance policy (

Table 6. Percentage of deprivation faced by households.

Proposed Dimensions from This Study	Proposed Indicators from This Study	Deprivation Cut-Offs	Percentage (%)
1. Wealth	1. Employed	If any household member unemployed at any time during the reference week worked at least one hour for pay, profit or family gain (as an employer, employee, own-account worker or unpaid family worker (2)).	25.3
	2. Saving	If the household did not have EPF (private sector) or pension (public sector).	43
	3. Social protection	If the household had no personal or group insurance (life and health).	61.7
2. Education	4. Years of schooling	If half of the household members (18 years old and above) have received education for less than 11 years or SPM or equivalent.	38.5
	5. School attendance	If the household members (6–17 years old) did not attend school.	12.7
3. Health	6. Nutrition	If the household members did not consume a balance diet (fat, protein, carbohydrate, fibre, vitamins, and water) for their daily intake.	40.2
	7. Chronic disease/disability	If any household members were chronically ill (prolonged treatment/huge expenses/bed-ridden/required intensive care/dependent and handicapped) and could not afford treatment/maintenance costs.	21.6
	8. Health spending	If the household members could not afford to get treatment at health facilities due to = financial incapacity.	35.1
4. Living Standards	9. Transportation	If the household members did not own their own vehicle to facilitate movement (motorcycle/boat, etc.) or had no access to public transportation.	41.3
	10. Housing quality	If the physical condition of the house was not suitable (unsound/dilapidated) for living. Types of materials were considered (roof, floor, wall).	18.4
	11. Overcrowding	If household members shared a bedroom with more than two (2) people in a room.	36
	12. Treated water source	If the household did not have a source of treated water for 24 h a day.	9
	13. Food sufficiency	If the household was unable to eat because having to save money/reduce food intake/stomach tie (not full) due to financial problems.	29.5
	14. Home furnishing assets	If household did not have at least three basic household appliances.	32.8
5. Digital Inclusion	15. Access to smartphone or laptop	If the household did not have a smartphone or laptop (1).	17.6
	16. Access to the internet	If the household did not have its own internet access (with high-speed internet access) for at least the last 12 months (1).	24.3

). This is consistent with [37], whereby most of the vulnerable households were lacking life and health insurance protection. A significant number of people living in poverty face limited access to essential financial services, including health insurance and credit, mostly owing to the exorbitant pricing of these products, which exceed their financial means [38]. Subsequently, 43% of the households were deprived of the savings indicator, whereby many of them lacked an EPF or pension scheme. Employment deprivation was evident in 25.3% of households, highlighting the difficulty in obtaining steady income prospects. Collectively, these results indicate that financial vulnerability stems mainly from a lack of formal safety nets. Meanwhile, educational deprivation is mostly influenced by years of schooling, with 38.5% of households indicating that at least half of their adult members (aged 18 and older) did not complete 11 years of education (SPM or equivalent). Meanwhile, the indicator of school attendance, however, demonstrated lower deprivation at 12.7%. In the health dimension, households face the greatest deprivation in the nutrition indicator, with 40.2% failing to fulfil balanced dietary requirements. Subsequently, health expenditure reveals that 35.1% of households were unable to pay for healthcare services. Chronic disease or disability was present in 21.6% of families, underscoring the financial inability to provide long-term treatment or assistance for dependent family members. These findings indicate that health-related deprivations are associated not just with illness but also with insufficient resources for prevention and treatment.

The living standard dimension highlights pervasive material hardship, as households face deficits in transportation, housing quality, and essential amenities that directly affect their daily well-being. The most prevalent deprivation was transportation, with 41.3% of households lacking private or accessible public transport. Overcrowding was at 36%, home furnishing assets at 32.8%, and food sufficiency at 29.5%, all of which underscore deficiencies in fundamental living requirements. Housing quality constituted 18.4% of deprivation, while treated water supply signified the least deprivation within this category at 9%. In aggregate, these findings highlight structural and material deficiencies that undermine daily well-being. Digital deprivation was lower compared to other dimensions but remains a critical concern in the modern context. Lack of internet access accounted for 24.3%, while deprivation in terms of smartphone or laptop ownership affected 17.6% of households. This aligns with prior research by [39], which observed that students in Kuala Lumpur had issues of social isolation during e-learning. These findings reveal gaps in digital participation that may perpetuate inequalities in education, employment, and social mobility.

6.2.2. Multidimensional Poverty Status

By looking at the enhanced MPI framework employed in this study, from a total number of 300 households, 116 households reflected widespread and layered deprivation. Based on the survey results, 38.7% of the 300 households surveyed in this sample were identified as multidimensionally poor, and every 4/10 of the households experienced at least 30% deprivation from the mentioned weighted indicators. These findings accurately explained that among the 300 surveyed households, the data indicate income insufficiency, which leads to deficits in education, health, social protection, digital inclusion and social protection.

As shown in

Table 7. Calculation of multidimensional poverty index.

Measure	Value
Headcount Ratio (H)	38.7%
Intensity of Poverty (A)	47.0%
MPI Score	0.182

below, the intensity of poverty is 47%, which means that the multidimensionally poor households experience a number of concurrent disadvantages. The disadvantages faced by the households are food insecurity, poor housing conditions, limited access to digital tools and lack of savings and insurance coverage. In the context of the study, the resulting MPI value of 0.182 (MPI = 0.387 × 0.470) indicates a moderate degree of multidimensional poverty.

The MPI value should be understood in relation to the study’s specific sampling design. Malaysia’s official national MPI is much lower, showing overall conditions across different income levels and both urban and rural areas. However, the higher MPI recorded in this study is expected because the study concentrated only on low-income urban households living in high-end cost areas like Kuala Lumpur. Due to this, the MPI score here recorded here does not reflect the poverty conditions overall; instead, it just highlights the disadvantage that is experienced in terms of multidimensional deprivation among the urban population. This context emphasizes the importance of the improved MPI framework in uncovering areas of deprivation that national averages might hide.

6.2.3. Logistics Regression

Table 8. Logistic regression results: factors associated with multidimensional poverty in the sample.

Predictor	Coefficient (B)	Std. Error	Odds Ratio (Exp(B))	p-Value	Interpretation
Constant	−0.847	0.712	—	0.231	—
Age of HH Head	0.013	0.006	1.012	0.064	Not statistically significant
Female (ref: Male)	0.650	0.242	1.860	0.021 *	Female-headed households are 82% more likely
Education ≥ Secondary	−1.027	0.313	0.360	0.001 **	Education associated with lower poverty likelihood in this sample
Unemployed	1.352	0.472	3.890	0.001 **	Unemployed are 3.9× more likely to be poor
HH Size: 4–6 Members	0.584	0.265	1.801	0.024 *	Moderate household size increases risk
HH Size: >6 Members	1.177	0.416	3.223	0.003 **	Large households 3.2× more likely to be poor
Constant	−0.9367	0.672	—	0.444	—

** Significant at 0.01%; * significant at 0.05%.

demonstrates the logistic regression used to identify the determinants of multidimensional poverty, which reveal that the socioeconomic and demographic characteristics show statistical and meaningful effects on poverty status. Gender of the household head, education attainment, employment status, household size and income were found to be significantly associated with multidimensional poverty in this sample; however, the age of the household proved to have a limited role once structural factors were accounted for.

Female-headed households exhibit a significantly higher likelihood of experiencing multidimensional poverty. The positive coefficient (B = 0.650) and odds ratio of 1.863, significant at the 5% level (p = 0.021), indicate that female-headed households face approximately 86% higher odds of multidimensional poverty relative to male-headed households. This magnitude underscores the persistence of gender-based vulnerabilities, potentially reflecting unequal labour market participation, income instability, and disproportionate care responsibilities that translate into multiple forms of deprivation.

Educational attainment demonstrates a strong and statistically robust protective effect. Households where the head has at least secondary education show a negative and significant coefficient (B = −1.027, p = 0.001), corresponding to an odds ratio of 0.360. This implies a reduction of about 65% in the likelihood of multidimensional poverty compared to households with lower education levels. The size of this effect highlights education as a critical channel through which households mitigate deprivation across income, health, and living standards dimensions [40,41,42]

The employment status proved to be one of the most strongly associated factors in this sample. The coefficient for unemployment is positive at 1.352, with an odd ratio of 3.890, together with strong statistical significance. This finding reveals that unemployed household heads are nearly four times more likely to experience poverty than employed heads. The magnitude of this result highlights that having stable employment not only prevents income poverty but also non-monetary deprivation issues.

Furthermore, the size of the household also significantly shapes the poverty risk when the household size expands. For instance, households with four to six members show a positive coefficient of 0.584, which is significant at the 5% level, which indicates an 82% increase in poverty risk relative to smaller households. Additionally, the effects get stronger when size of the household increases to more than six members, and the coefficient increases to 1.177, with a probability of 0.003. This result explains the fact that a larger household size intensifies the dependency burden and resource dilution.

On the other hand, the age of the household head only reveals a marginal influence on poverty status. The coefficient is positive at 0.013; however, it is insignificant statistically at the 5% level. These findings reveal that the age-related life cycle is overshadowed by other socioeconomic factors like education, employment and income.

The findings also reveal that multidimensional poverty is affected by other disadvantages like gender, human capital, household dependency structure, labour market exclusion and financial capacity. The estimated coefficients’ magnitude and statistical significance values show that policy responses need to be more advanced and move beyond narrow income-based interventions and adopt strategies that promote educational attainment, employment opportunities, gender equity and targeted support for larger households. This approach is necessary and consistent with the multidimensional poverty perspective, which recognizes poverty as a complex and interconnected phenomenon [40,41,42].

7. Policy Recommendations and Conclusions

This analytical process illustrates how the enhanced MPI framework provides a more granular, multidimensional view of poverty than conventional metrics. It enables policymakers to identify not just who is poor but also how and where they are poor and whether it is through lack of health access, insecure jobs, poor housing, or digital exclusion. The framework also provides a robust foundation for longitudinal poverty tracking and policy targeting, particularly in rapidly urbanizing regions like Kuala Lumpur. The findings proved that households in Kuala Lumpur suffered deprivation across few indicators. The most deprived areas are as follows: social protection, transportation, nutrition and years of schooling. According to [43], these trends show the complex and overlapping aspects of poverty, where one shortfall in one area can spill over to others, and indicates the demand for comprehensive and cohesive policy interventions. Within this sample, the findings suggest that multidimensional poverty was associated with economic conditions as well as demographic factors, including the gender of the household’s head, educational attainment, employment status, household size, and income levels. Households led by females, those that were unemployed, and larger families showed a higher likelihood of multidimensional poverty, but further education and higher income serve as mitigating factors.

Limitations: This study has several limitations. First, the cross-sectional design precludes causal inference; the associations identified reflect patterns within this sample at a specific point in time and should not be interpreted as causal relationships. Second, the sample of 300 households was drawn using purposive sampling, which limits generalizability to the broader population. Third, self-reported data may be subject to recall bias. Future longitudinal research with probability-based sampling would provide stronger causal evidence.

Policy Suggestion and Future

Policies should thus include a comprehensive strategy that tackles both the prevalence (headcount) and the severity of poverty. This necessitates a transition from a solely financial aid paradigm to a comprehensive poverty alleviation framework, incorporating social protection, capacity enhancement, and infrastructure development. The research highlights an urgent necessity to broaden access to social safety programs. Policymakers could contemplate targeted subsidies or micro-insurance programs designed for low-income populations, who represent a vulnerable demographic. Furthermore, initiatives should focus on establishing sustainable job avenues through vocational training, entrepreneurial assistance, and reskilling programs, especially in industries experiencing increased demand. In terms of healthcare, policy interventions should prioritize access to affordable healthcare and food security. The proliferation of affordable community clinics, state-funded necessary pharmaceuticals, and nutritional support initiatives would immediately mitigate these issues. Collaborations with NGOs can augment outreach to marginalized families. By tackling overlapping deprivations in social protection, education, health, living standards, and digital access, policymakers can formulate more effective interventions that diminish both the extent and severity of poverty among households, thereby promoting resilience and sustained socioeconomic inclusion.