SJA_41_3_1356-1374

Introduction

Poverty, a multifaceted and persistent challenge, remains a stark reality for a significant portion of the global population, particularly in developing countries (Ferdous and Ullah, 2023; Araujo et al., 2024). It extends beyond mere income deficiency, encompassing deprivations in health, education, living standards, and social inclusion (Elliott and Golub, 2022; Diaz-Bonilla et al., 2024). In these nations, where economic structures are often fragile and social safety nets are weak, poverty manifests in diverse forms, shaped by historical legacies, political instability, and environmental vulnerabilities (Bernards, 2022). The lack of access to necessities like clean water, sanitation, and healthcare perpetuates cycles of deprivation, hindering human capital development and limiting economic opportunities (De-Schutter et al., 2023), coupled by unequal distribution of resources, often exacerbated by corruption and weak governance, further deepens the divide between the affluent and the impoverished (Prince et al., 2023).

The vulnerability of rural populations in developing countries is particularly pronounced (Birkmann et al., 2022). Agricultural dependence, coupled with limited access to land, credit, and technology, exposes these communities to the vagaries of climate change and market fluctuations (Sinha et al., 2022). The degradation of natural resources, driven by unsustainable agricultural practices and deforestation, further compounds their vulnerability (De-Schutter et al., 2023). In urban centers, rapid and unplanned urbanization leads to the proliferation of slums, characterized by overcrowding, inadequate infrastructure, and exposure to environmental hazards (Bernards, 2022). These conditions create breeding grounds for social unrest and exacerbate existing inequalities (Sinha et al., 2022). The impact of globalization, while offering potential avenues for economic growth, can also exacerbate poverty through the displacement of traditional industries and the exploitation of vulnerable labor (Bernards, 2022). The volatility of global commodity prices and the influence of international financial institutions can further destabilize fragile economies, leaving the poor more susceptible to economic shocks (Elliott and Golub, 2022; World Bank, 2024).

The sustainable development goals (SDGs) recognize multidimensional poverty as a critical global challenge, extending beyond income to encompass access to essential services and needs. Eradicating poverty, encapsulated in SDG-1, is central to achieving the interconnected SDGs, including health, education, gender equality, and sanitation, thus forming a foundational requirement for equitable and sustainable development. However, there is no consensus on its definition. One early definition viewed poverty as a threshold representing the level of minimum bare subsistence and the introduction of a poverty line concept is necessary to measure poverty accurately. Over the last few decades, many authors like (Alkire, 2013; Terzi et al., 2021; Muzerengi and Gudyani, 2023) have developed theoretical approaches to the issue of poverty, acknowledging its multidimensional nature, which cannot be fully captured by a single indicator based on income or consumption. Therefore, addressing the multidimensional nature of poverty is crucial for the success of the SDGs agenda-2030.

Multidimensional poverty measures the various dimensions of poverty that are not solely based on income (Alkire, 2007; Thorbecke, 2013; Zimmerman et al., 2022). The multidimensional approach to assessing poverty and human well-being is not limited to determining poverty status alone (Alkire, 2013; Salecker et al., 2020). It considers various factors that contribute to an individual’s overall well-being and these factors include the possessions that a person has, their prospects for the future, and their position in society, whether advantaged or disadvantaged (Reeves et al., 2016), as the multidimensional approach provides a more comprehensive understanding of an individual’s quality of life (Alkire, 2013; Salecker et al., 2020). This approach is an effective strategies for addressing poverty and inequality, emphasizing the significance of considering all these aspects instead of just material possessions (Reeves et al., 2016), with the goal to provide a more thorough and inclusive assessment of the extent of poverty and deprivation picture of the complexity and severity of poverty in a society (Alkire, 2013).

The comprehension of poverty has significantly improved and deepened over the last three decades largely, due to the groundbreaking capabilities approach work of amartya sen (Alkire, 2005; Comim et al., 2008). The capability approach emphasizes that it’s not enough for individuals to have the freedom to pursue a particular kind of life, but that they must also have the necessary capabilities and resources to do so (Bilgeri, 2023). However, despite the remarkable methodological advancements in poverty analysis, there are still several conceptual and measurement issues that require further clarification.

Poverty is a complex problem that often manifests itself in a multidimensional framework, with individuals suffering from different deprivation profiles depending on their geographical context (Handastya and Betti, 2023; Lyons et al., 2023). In developing countries, there may be fundamental problems with access to water, healthcare, and education, which are more widely available in the developed world (Ayoo, 2022; Pandey et al., 2022). According to Fields and Kanbur (2023), research on distributional issues in economics and development economics over the past thirty years can be divided into two periods: The 1970s to the mid-1980s and the mid-1980s to the end of the last century. The former fifteen years were characterized by great conceptual leaps and ferment, while the latter period was marked by consolidation, application, and fierce policy debate. Recent methodological contributions entering a period of resurgence in research aimed at refining and expanding the understanding of poverty. The findings suggest that in the field of multidimensional poverty, it is important to measure the different dimensions and factors involved and is crucial to ensure that individuals have achieved the minimum acceptable levels of these attributes (Sen, 1993).

Within this global context, Pakistan presents a complex and challenging case study. With a large and rapidly growing population, the country grapples with persistent poverty, particularly in its rural areas (Ahmad et al., 2024). Agriculture, the backbone of the Pakistani economy, employs a significant portion of the workforce, yet productivity remains low due to factors such as outdated farming techniques, limited access to irrigation, and vulnerability to climate change (Dogar, 2023). The skewed distribution of land ownership and the prevalence of feudal structures perpetuate inequality, leaving smallholder farmers and landless laborers in a precarious position (Ayaz and Mughal, 2023; Gazdar, 2023). The lack of access to quality education and healthcare, particularly for women and girls, further limits human capital development and reinforces social disparities (Jamal et al., 2023).

The impact of political instability, including periods of military rule and internal conflict, has also contributed to Pakistan’s poverty challenges (Rashid and Rashid, 2024). The diversion of resources towards security concerns and the erosion of institutional capacity have hindered efforts to address poverty and promote sustainable development. Moreover, the country’s vulnerability to natural disasters, such as floods and earthquakes, further exacerbates poverty, displacing communities and destroying livelihoods. The effects of climate change, including increased frequency and intensity of extreme weather events, threaten to undermine agricultural productivity and exacerbate water scarcity, further jeopardizing the livelihoods of vulnerable populations.

The dynamics of poverty in Pakistan are also influenced by social and cultural factors (Saddique et al., 2023). Deep-rooted social norms and discriminatory practices, particularly against women and marginalized communities, limit their access to education, employment, and political participation (Mirza and Lodhi, 2024). The prevalence of child labor and bonded labor further perpetuates cycles of poverty, denying children the opportunity to break free from deprivation. The informal sector, which employs a large portion of the workforce, is characterized by low wages, precarious working conditions, and a lack of social protection. This leaves workers vulnerable to exploitation and economic insecurity. Thus, addressing poverty in Pakistan requires a comprehensive and multifaceted approach that tackles its root causes and addresses its multidimensional nature. Acknowledging the preceding discourse and the inherent multidimensionality of poverty, this study adopts a comprehensive design structured around the subsequent specific objectives, aimed at thoroughly examining the complexities of rural poverty in the Pakistani context.

Objectives of the research

The specific objectives include:

To measure the multidimensional poverty across five key dimensions: Well-off status, indoor dwelling, asset ownership, social programs, and coping capacity in Pakistani farm HHs.
To study evidence-based insights into the specific deprivations experienced by Pakistani households, drawing.
To pinpoint a comprehensive understanding of the multidimensional nature of poverty and its spatiotemporal patterns in rural Pakistan.

Hypothesis

There is no significant relationship between non-income dimensions and rural poverty levels in Pakistan.
The spatiotemporal patterns of rural poverty in Pakistan are not independent of the interrelationships among various poverty dimensions and indicators.

Materials and Methods

At the time of conducting a poverty study in a society, it is important to consider the incidence, distribution, and intensity of poverty measures (Walker, 2019; Cutillo et al., 2020; Wang et al., 2021). The Multidimensional Poverty Index (MPI) based on the Alkire-Foster approach is a measure of poverty that captures multiple dimensions of deprivation. MPI is the product of the percentage of the population who are poor and the average percentage of weighted indicators that the poor experience. The first component, the percentage of the population who are poor, provides an estimate of the incidence of poverty. It is calculated based on the proportion of individuals who lack access to the indicators of a particular dimension. The second component, the intensity of poverty, is measured as the average percentage of weighted indicators that poor people experience. This research employs the MPI methodology, adapted to encompass expanded poverty dimensions, leveraging its inherent flexibility in dimension and indicator selection. The weighted indicators within this framework precisely quantify the severity of deprivation across these diverse dimensions. The Alkire-Foster (AF) methodology for the Multidimensional Poverty Index (MPI) offers significant advantages over other poverty measurement methods by explicitly capturing the multifaceted nature of poverty beyond income, encompassing health, education, and living standards. Its flexibility allows for context-specific dimension and indicator selection, enabling customization and adaptability to evolve regional priorities. The methodology provides clear identification of the multidimensionally poor, robust aggregation into a single index, and transparent decomposability for targeted policy interventions. By focusing on simultaneous deprivations, it accurately reflects the intensity of poverty, offering actionable insights for policymakers and facilitating SDG monitoring. Furthermore, it aligns ethically with the capabilities approach, allows for comparability across regions, and enables sensitivity analysis. In contrast to income-based measures, headcount ratios, poverty gap indices, and the HDI, the MPI provides a granular and comprehensive understanding of poverty, essential for effective poverty reduction strategies.

A questionnaire, encompassing qualitative inquiries derived from established poverty literature, was used to survey approximately 6,000 HHs (with 1000 from each provinces and trattorias were selected) across Pakistan via face to face interviews. The final analysis included 5,775 HHs due to non-response from the remainder. The Pakistani farm HHs multidimensional poverty index can be represented mathematically as follows:

MPI=A × H ….(1)

Let there be “n” HHs in a particular area of Pakistan and there be “m” indicators used to measure multidimensional poverty, denoted by I1, I2, ……, Im, Xi1, Xi2, ..., Xim be the values of the m indicators for the ith HH. Let Wi1, Wi2,…., Wim be the weights assigned to each indicator, reflecting their relative importance in measuring poverty. The multidimensional poverty index (MPI) for the ith HH can be calculated as:

MPIi = ∑j = 1 to m (wij × h (xi, j)) ….(2)

where h(xi,j) is the function that assigns a score of 1 or 0 to the jth indicator of the ith HH, depending on whether the HH meets a certain threshold or not. if the threshold or deprivation cut-off for the particular indicator is having at who has a particular indicator then then h(xi, j) = 1 if xi,j ≥ threshold and h(xi,j) = 0 otherwise. The MPI for the entire area can be calculated as the average of the MPIs for all HHs:

MPI = (1/n) ∑j = 1 to n MPIi ….(3)

The development of the Pakistani farm HHs MPI employs a hybrid approach, integrating statistical analyses with qualitative insights to construct a robust measure of poverty. This index transcends traditional income-based assessments by encompassing the diverse dimensions of deprivation prevalent within rural Pakistani households. Specifically, Table 1 presents the chosen dimensions, their corresponding indicators, and the assigned weights, all determined in accordance with the established Alkire-Foster methodology for multidimensional poverty measurement. This structured framework ensures a comprehensive and nuanced evaluation of poverty, reflecting the complex realities faced by these communities.

Table 1: Dimensions, indicators, weight, deprivation cut-off of multidimensional poverty in Pakistan.

Dimensions (Weight)	Indicators	Weight	Depravation cut-off	References
Well-off status (1/5)	HHs have a high income compared to the average income in area	1/30	if not	(Kumar et al., 2019)
	HHs have property/savings	1/30	if not	(Berhanu et al., 2022)
	HHs can afford/purchase goods and services	1/30	if no	(Wondimu et al., 2022)
	HHs have access to quality education, healthcare services	1/30	if no	(Cole and Nguyen, 2020)
	HHs have access to social and cultural opportunities	1/30	if no	(Sugiharti et al., 2022)
	HHs afford a meal with meat, chicken/ fish weekly	1/30	if not	(Hoteit et al., 2022)
In-door dwelling (1/5)	HHs dwelling is considered overcrowded	1/30	if yes	(Cant et al., 2019)
	HHs dwelling is poorly ventilated	1/30	if yes	(Howden-Chapman et al., 2022)
	HHs lacks basic amenities i.e. running water and electricity	1/30	if yes	(Sharma et al., 2019)
	HHs having poor quality housing (leaking roofs, damp walls	1/30	if yes	(Divringi et al., 2019)
	HHs lack access to basic sanitation facilities i.e., toilets and handwashing	1/30	if yes	(Ruggiero et al., 2020)
	HHs have inadequate space for living, cooking, and sleeping	1/30	if yes	(Ruggiero et al., 2020)
Assets ownership (1/5)	HHs having home ownership	1/25	if not	(Haque et al., 2020)
	HHs have crop related assets	1/25	if not	(Khayyam, 2020)
	HHs have livestock related assets	1/25	if yes	(Khayyam and Munir, 2022)
	HHs have farm structures, and water/irrigation related assets	1/25	if no	(Aravindakshan et al., 2020)
	HHs have a washing machine/color television	1/25	if no	(Mostafavi et al., 2019)
Social programs (1/5)	Know about the social services for poverty reduction	1/15	if not	(Karki and Poudyal, 2021)
	Benefited from social assistance program	1/15	if not	(Lee et al., 2021)
	Knows social policies, legislation/regulation	1/15	if not	(Karki and Poudyal, 2021)
Coping capacity (1/5)	HHs have education and skills training	1/25	if not	(Artiga et al., 2020)
	HHs have social networks and support systems	1/25	if not	(Sentell et al., 2020)
	HHs have access to financial resources and credit	1/25	if not	(Sugiharti et al., 2022)
	HHs have entrepreneurial skills	1/25	if no	(Artiga et al., 2020)
	HHs voice listens for policy changes that address the root causes of poverty	1/25	if no	(Vazquez et al., 2022)

Dimensions and indicators of multidimensional poverty

The selected dimensions and indicators collectively depict the multidimensional poverty status of HHs, elucidating the complex interplay of factors that shape their overall living conditions. Specifically, ‘well-off status’ assesses financial security, encompassing income, savings, purchasing power, and access to essential services like quality education and healthcare, alongside social and cultural opportunities. ‘In-door dwelling’ evaluates the quality of housing, considering factors such as overcrowding, ventilation, and access to basic amenities. Assets ownership examines the household’s material possessions, including homes, agricultural assets, and domestic appliances. ‘Social programs’ gauges awareness and participation in poverty reduction initiatives, alongside knowledge of relevant policies. The coping capacity assesses the HHs resilience in the face of adversity, encompassing skills, social networks, financial access, entrepreneurial abilities, and advocacy potential. These dimensions, analyzed together, provide a holistic understanding of the multidimensional nature of poverty to improve HH well-being.

Results and Discussion

The sustainable development Goal-1, targets the eradication of extreme poverty by 2030, emphasizing the interconnected domains of people, planet, prosperity, peace, and partnership. Therefore, addressing poverty requires a holistic approach, encompassing financial status, living conditions, asset ownership, social programs, and coping capacity, to develop effective interventions that enhance HH well-being.

Descriptive statistics of the dimensions and indicators

The Table 2, data presents a detailed analysis of multidimensional poverty within Pakistan, based on a sample of 5,775 HHs, focusing on the well-off status dimension. This dimension is assessed through indicators such as HH income relative to the local average, property and savings ownership, affordability of goods and services, access to quality education and healthcare, access to social and cultural opportunities, and the ability to afford a weekly meal with meat, chicken, or fish. The data reveals a significant prevalence of financial vulnerability, with substantial proportions of HHs reporting limited income, lack of savings, and inability to afford basic goods and services. For example, only 23.9% of HHs reported high income relative to their area, while 54.8% reported not having this status. Similarly, the majority (69.8%) could not afford essential goods and services. Access to critical services like quality education and healthcare was also severely limited, with 75.4% of HHs reporting a lack of access. The data also indicated that 56.1% of HHs lack access to social and cultural opportunities. Regarding food security, 57.2% of HHs reported being unable to afford a weekly meal with meat, chicken, or fish. The Chi-square tests demonstrated strong associations between each indicator and poverty, with p-values of 0.0002 or 0.00, confirming the statistical significance of these relationships. These findings underscore the multidimensional nature of poverty in Pakistan, highlighting the widespread deprivation across various indicators of financial stability and access to essential services and opportunities.

Table 2: Dimensions/indicators and descriptive statistics of multidimensional poverty in Pakistan (n= 5775).

Dimensions	Indicators	Yes (%)	No (%)	DK (%)	S. Dev.	C.V	Chi2	p value
Well-off status	HHHs have a high income compared to the average income in area!	23.9	54.8	21.3	1069.7	55.6	1188.8	0.0002
	HHs have property and savings	25.1	58.5	16.4	1277.8	66.4	1696.3	0.0000
	HHs have ability to afford and purchase goods and services	20.6	69.8	9.6	1858.9	96.6	3590.1	0.0000
	HHs have access to quality education, healthcare services	17.1	75.4	7.6	2135.0	110.9	4735.9	0.0000
	HHs have access to social and cultural and opportunities	32.7	56.1	11.2	1382.2	71.8	1984.8	0.0000
	HHs afford a meal with meat, chicken/ fish weekly	36.8	57.2	6.0	1486.0	77.2	2294.4	0.0000
In-door dwelling	HHs dwelling is considered overcrowded	48.1	45.5	6.4	1354.7	70.4	1906.7	0.0000
	HHs dwelling is poorly ventilated	31.6	63.3	5.2	1696.2	88.1	2989.1	0.0000
	HHs lack basic amenities like running water, electricity, or heating	31.2	61.6	7.2	1599.1	83.1	2656.9	0.0000
	HHs having poor quality of housing i.e., leaking roofs, damp walls, and broken windows	20.6	69.8	9.6	1858.9	96.6	3590.1	0.0000
	HHs lack access to basic sanitation facilities i.e., toilets and handwashing	17.1	75.4	7.6	2135.0	110.9	4735.9	0.0000
	HHs have inadequate space for living, cooking, and sleeping	32.7	56.1	11.2	1382.2	71.8	1984.8	0.0000
Assets ownership	HHs having home ownership	49.1	45.4	5.6	1400.6	72.8	2038.2	0.0000
	HHs have crop related assets	68.6	26.8	4.6	1886.5	98.0	3697.7	0.0000
	HHs have livestock related assets	21.3	42.4	36.3	704.5	36.6	515.7	0.0004
	HHs have farm structures, and water/irrigation related assets	31.2	61.6	7.2	1599.1	83.1	2656.9	0.0000
	HHs have a washing machine/color television	30.9	59.7	9.5	1534.2	79.7	2445.6	0.0000
Social programs	Know about the social services for poverty reduction	21.7	56.5	21.9	1434.1	74.5	2136.8	0.0000
	Benefited from social assistance program	38.9	44.6	16.6	1220.2	63.4	1546.8	0.0000
	Knows social policies, legislation/regulation	34.6	39.5	25.8	511.0	26.5	271.3	0.0006
Coping capacity	HHs have education and skills training	29.6	54.6	15.9	1113.6	57.9	1288.5	0.0004
	HHs have social networks and support systems	21.1	59.1	19.8	868.4	45.1	783.4	0.0002
	HHs have access to financial resources and credit	10.2	68.6	21.2	1431.9	74.4	2130.3	0.0000
	HHs have entrepreneurial skills	17.1	75.4	7.6	2135.0	110.9	4735.9	0.0000
	HHs voice listens for policy changes that address the root causes of poverty	10.2	68.6	21.2	1431.9	74.4	2130.3	0.0000

The in-door dwelling dimension of spatiotemporal multidimensional poverty in Pakistan reveals significant housing deprivations. Overcrowding affects 48.1% of HHs, while 31.6% experience poorly ventilated housing, and 31.2% lack basic amenities like water and electricity. Poor housing quality, including leaks and dampness, impacts 20.6%, and 17.1% lack basic sanitation. Inadequate living space affects 32.7%. High standard deviations and coefficients of variation across these indicators highlight substantial variability in housing conditions, indicating uneven poverty distribution. The significant Chi-square and p-values of 0.00 confirm a strong statistical association between these housing deprivations and poverty status.

Analysis of asset ownership, the third dimension of spatiotemporal multidimensional poverty in Pakistan, reveals substantial disparities. Home ownership is split, with 49.1% owning homes, and a high variation (SD: 1400.6, CV: 72.8%) indicating uneven distribution, strongly associated with poverty (p=0.00). Crop-related asset ownership is higher (68.6%), but also exhibits significant variation (SD: 1886.5, CV: 98%), again strongly linked to poverty (p=0). Livestock asset ownership is low (21.3%), with moderate variation (SD: 704.5, CV: 36.6%) and a significant association with poverty (p=0.0004). Farm structure and irrigation asset ownership is also low (31.2%), showing high variation (SD: 1599.1, CV: 83.1%) and a strong poverty association (p=0). Similarly, ownership of washing machines/televisions is limited (30.9%), with high variation (SD: 1534.2, CV: 79.7%) and a strong link to poverty (p=0). These findings highlight a clear correlation between limited asset ownership and poverty, particularly for livestock, farm structures, and appliances, underscoring the multidimensional nature of deprivation.

Social programs, the fourth dimension of Pakistan’s multidimensional poverty assessment, revealed significant disparities: only 21.7% of HHs were aware of poverty reduction services, 38.9% benefited from assistance programs, and 34.6% had knowledge of social policies, each showing substantial variability and a strong correlation with poverty status (p<0.001 for service awareness and assistance benefit, p=0.0006 for policy knowledge).

The coping capacity dimension reveals a pervasive deficit across all measured indicators: education/skills, social networks, financial access, entrepreneurial skills, and policy voice. Consistently low “Yes” percentages, coupled with high “No” and “Don’t Know” responses, highlight significant vulnerability and a lack of awareness among HHs. The standard deviations and coefficients of variation indicate considerable inequality in coping capacities. The highly significant chi-squared values (p<0.001) demonstrate a strong association between these indicators and poverty status, especially concerning entrepreneurial skills.

Pakistan multidimensional poverty index

The Figure 1 data presents the MPI for the provinces and territories within Pakistan. Analyzing the head count ratio (H), intensity (A), and the resulting MPI values understand the complex landscape of poverty across the country. The MPI for Pakistan is 0.26, with a head count ratio of 0.45 and an intensity of 0.58, signify the regional disparities, highlighting the importance of examining province-specific data. The KP exhibits the highest MPI value (0.39), indicates a significant level of multidimensional poverty. The high head count ratio (0.54) signifies that a substantial proportion of the population in KP is multidimensionally poor. The high intensity (0.72) suggests that those who are poor experience a large number of deprivations simultaneously. The prolonged impact of the war on terror, displacement of populations, and limited infrastructure development have significantly contributed to the high MPI in KP. The province of Punjab demonstrates the lowest MPI (0.15), relatively lower head count ratio (0.39) and intensity (0.38) indicate that while poverty exists, it is less prevalent and less severe. This may be due to the relatively better infrastructure, industrialization, and agricultural productivity that have contributed to lower poverty levels in Punjab. The Sindh presents a high head count ratio (0.57), the highest of all regions, implying that a large portion of its population is multidimensionally poor. However, the intensity (0.52) is lower than KP’s, resulting in an MPI of 0.3. This highlights a situation where a large number of people are poor, but the depth of their deprivations is somewhat less severe than in KP. The Balochistan data reveals the head count ratio is relatively low (0.18), the intensity is alarmingly high (0.83), leading to an MPI of 0.16, which is still substantial. This indicates that while a larger proportion of the population is poor, those who are poor experience extremely severe deprivations across multiple dimensions. This may be due to the sparse population, arid climate, and poor infrastructure, combined with a lack of resources and governance issues contribute to the high intensity of poverty. Azad Jammu and Kashmir (AJK) and Gilgit-Baltistan (GB) exhibit similar MPI values (0.27 and 0.26, respectively). Both regions have moderate head count ratios (around 0.5) and intensities (around 0.5), suggest that a significant portion of their populations faces multidimensional poverty, with moderate levels of deprivation. This may be because of geographic isolation, limited infrastructure, and vulnerability to natural disasters contribute to multidimensional poverty in these regions.

Spatiotemporal distribution of Pakistan multi-dimensional poverty

The Figures 2 and 3 presents the multidimensional spatial distribution of poverty vulnerability status of HHs across provinces and territories of Pakistan by categorizes HHs into five vulnerability levels: extremely high, high, medium, small, and very small, based on their multidimensional poverty indicators. The national figures reflect the combined picture, with the highest numbers in the high (3163) and extremely high (1583) categories, followed by medium (847). The overwhelming majority of HHs fall within the high and extremely high categories, indicating for addressing systemic issues related to governance, resource distribution, and access to basic services. The KP Shows a significant concentration of HHs in the extremely high (483) and high (452) vulnerability categories, indicating a severe poverty situation. This severe vulnerability in the extremely high and high categories reflects the impact of conflict, displacement, and limited infrastructure development has undoubtedly contributed to this severe poverty situation. The province of Punjab has the highest number of HHs in the extremely high (571) category and high (429) categories, suggesting a stark division with no households in the lower vulnerability brackets. The concentration of HHs solely in the extremely high and high could be due to unequal access to resources, land ownership disparities, and concentrated industrial and agricultural development that benefits some while marginalizing others. The Sindh province shows the significant numbers in the high (439), and medium (257) categories. The presence of HHs in all categories suggests that while significant poverty exists, there are also pockets of relative prosperity that may be due to the factors such as landlessness, water scarcity, and limited access to education and healthcare contribute to this vulnerability. The province of Balochistan has a high vulnerability category (728), extremely high (124) category, underscores the province’s deep-seated poverty. The vast, arid landscape, coupled with limited infrastructure and governance challenges shows the vulnerability categories reflect the province’s overall fragility. The AJK also shows a significant concentration in the high vulnerability category (690), followed by extremely high (121), while the GB presents significant numbers in the high (425) and medium (312) categories. The AJK and GB, concentration in the high and extremely high categories may be due to mountainous terrain, limited connectivity, and vulnerability to natural disasters.

Khyber Pakhtunkhwa MPI and vulnerability scale

The data presented in Figure 4 provides, the MPI within KP, for six districts: Upper Dir, Shangla, Torghar, Hangu, Mohmand, and Khyber. The KP shows an MPI of 0.41, with a head count ratio of 0.54 and intensity of 0.72, pointed out significant disparity at the district level. The data reveal that the district Torghar exhibits the highest MPI (0.63), accompanied by the highest head count ratio (0.78), indicates that a significant majority of Torghar population is multidimensionally poor, and those who are poor experience a high level of deprivation across multiple dimensions. The district of Shangla also displays a very high MPI (0.59), with a high head count ratio (0.68) and intensity (0.86), reinforces the pattern of acute multidimensional poverty in these mountainous districts. The district Hangu, having a slightly lower head count ratio (0.61) compared to Torghar and Shangla, shows the highest intensity (0.92), implies that although a smaller proportion of the population is poor, those individuals experience an extremely high degree of deprivation. The high intensity in Hangu suggests that while the population might be smaller, the deprivations are severe, possibly due to limited resources and poor access to basic services. The districts like Torghar and Shangla, located in mountainous regions, face challenges related to accessibility. Rugged terrain hinders infrastructure development, limiting access to essential services such as education, healthcare, and transportation contributes to higher poverty levels. Khyber district’s MPI (0.41) matches the KP provincial average, with a head count ratio (0.62) and intensity (0.66). This indicates that the district’s poverty situation aligns with the broader provincial trend, though still exhibiting significant poverty. Upper Dir displays a considerably lower MPI (0.21) compared to the other districts, with a head count ratio (0.41) and intensity (0.52), suggests relatively better conditions but still indicates a notable level of poverty. The district of Mohmand stands out with the lowest MPI (0.07) and head count ratio (0.14), suggests that multidimensional poverty with the intensity (0.53) is still significant, indicating that those who are poor experience considerable deprivation and the poor still face significant challenges.

The Figure 5 depicts the multidimensional poverty vulnerability status of HHs across six districts of KP by categorizing HHs into extremely high, high, medium, small, and very small, multidimensional

poverty index vulnerability levels. The KP data reflects a significant concentration of HHs in the extremely high (483) and high (452) categories, suggests that a significant portion of the population in the region is vulnerable to multidimensional poverty. The district Upper Dir shows that a significant number of HHs in the high (88) category, closely followed by extremely high (46). Shangla district presents a high concentration of HHs in the high (84) and extremely high (67) categories. The district of Torghar shows a high concentration in the high (104) and extremely high (56) categories. The Hangu district presents the highest number of HHs in the extremely high category (73) among the listed districts, followed closely by the high category (91). The district of Mohmand presents a unique distribution, with a significantly high number of HHs in the extremely high category (151) and a very low number in the high category (15), while the district Khyber shows the extremely high (90) and high (70) categories. The data reveals significant disparities in multidimensional poverty vulnerability across the districts of KP. The concentration of households in the extremely high and high categories across most districts highlights the pervasive nature of poverty in the region. The consistent presence of large numbers of HHs in the extremely high and high categories across all districts, particularly in Shangla, Torghar, and Hangu, indicates a severe and widespread poverty problem, suggests that a significant portion of the population in these districts experiences multiple deprivations across various dimensions.

Punjab MPI and vulnerability scale

Figure 6 presents the MPI for four districts of Punjab, i.e., Khushab, Mianwali, Bahawalnagar, and Lodhran. The provincial MPI of Punjab is 0.15, with a head count ratio of 0.39 and an intensity of 0.37, suggest that Punjab exhibits a relatively low level of multidimensional poverty. The districts of Khushab and Bahawalnagar both exhibit an MPI of 0.15, with a head count ratio (H) of 0.39 and an intensity (A) of 0.38, while Bahawalnagar has a slightly higher head count ratio of 0.41 but a slightly lower intensity of 0.37, suggests a similar proportion of the population is multidimensionally poor in both districts. The Mianwali district shows the lowest MPI, 0.12 with a head count ratio of 0.31 and an intensity of 0.37, indicates that a smaller proportion of Mianwali’s population is multidimensionally poor. The district of Lodhran presents the highest MPI among the four districts, at 0.17, with a head count ratio (0.45) is the highest, and its intensity (0.38), implies that a larger percentage of population faces multidimensional poverty.

The data reveals that while Punjab generally exhibits a relatively low level of multidimensional poverty compared to other provinces in Pakistan, there are still notable variations among its districts. The MPI values, ranging from 0.12 to 0.17, indicate that poverty, though less severe, is still a significant issue in these regions. The head count ratios, ranging from 0.31 to 0.45, suggest that a substantial portion of the population in each district experiences multidimensional deprivations. The intensity values, which are relatively consistent across all districts, indicate that the depth of deprivations faced by the poor are somewhat similar, suggesting that the kinds of deprivations across the districts are likely similar. Mianwali’s lower MPI indicates relatively better conditions, likely due to a lower proportion of its population experiencing multidimensional poverty, could be attributed to factors such as better access to basic services, more robust economic opportunities, or a more equitable distribution of resources within the district. On the other hand, Lodhran’s higher MPI and head count ratio suggest that it faces greater challenges in addressing multidimensional poverty and this could be due to a higher concentration of vulnerable populations, limited access to essential services, or economic disparities. The differences in head count ratios, despite similar intensity, suggest that the prevalence of poverty varies more than the depth of poverty.

Figure 7 presents the vulnerability status of HHs across four districts in Punjab. The Figure 7 reveal that the Punjab reflects with 571 HHs in the extremely high category and 429 in the high category, and none in the lower vulnerability categories. The district of Khushab reports 141 HHs in the extremely high category and 109 in the high category followed by the Mianwali has 162 HHs in the extremely high category and 88 in the High category, showing a higher concentration in the most severe vulnerability level. Bahawalnagar has 128 HHs in the extremely high category and 122 in the high category, indicating a relatively even distribution between the two highest vulnerability levels. Lodhran reports 140 HHs in the extremely high category and 110 in the high category. Mianwali’s higher count of HHs in the extremely high category compared to other districts indicates a potentially more acute poverty situation. The Bahawalnagar’s relatively even distribution between the extremely high and high categories suggests that a significant portion of its population is highly vulnerable, requiring comprehensive poverty reduction strategies. Khushab and Lodhran exhibit similar distributions, indicating comparable levels of severe vulnerability.

Sindh multi-dimensional poverty index and vulnerability scale

The Figure 8 data presents the Sindh province MPI values in the four districts (Kashmoor, Tando Muhammad Khan (T.M. Khan), Sanghar, and Thatta). The Sindh shows an MPI of 0.30, with a head count ratio of 0.57 and an intensity of 0.52, reflecting the diverse poverty landscape within the province. The districts of Kashmoor and Thatta exhibit the highest MPI values, both at 0.38. Kashmoor has a head count ratio (H) of 0.72 and an intensity (A) of 0.53, while Thatta has a slightly higher head count ratio of 0.77 but a slightly lower intensity of 0.50, indicates that a substantial proportion of the population in both districts experiences multidimensional poverty, with Thatta having a higher prevalence of poverty, and Kashmoor having a slightly higher depth of deprivations. The Sanghar district shows an MPI of 0.25, with a head count ratio of 0.50 and an intensity of 0.50, indicates a moderate level of multidimensional poverty compared to the other districts and the provincial average, with both prevalence and intensity being at the median. The district of T.M. Khan displays MPI 0.17, with intensity (0.55) is the highest, while its head count ratio (0.31) is the lowest, suggests that while a smaller proportion of T.M. Khan’s population is multidimensionally poor, those who are poor experience a higher degree of deprivation compared to the other districts. The data reveals significant disparities in multidimensional poverty across Sindh’s districts. Kashmoor and Thatta, with their high MPI values and head count ratios, indicate severe poverty situations, suggests that a substantial proportion of the population in these districts lacks access to essential services and experiences multiple deprivations. The variations in MPI values across Sindh’s districts can be attributed to a combination of socioeconomic, geographical, and governance factors. Factors such as agricultural productivity, access to education and healthcare, infrastructure development, and social inequalities likely play a role. Kashmoor and Thatta, which often experience natural disasters like flooding and are characterized by poor infrastructure, are examples of regions that face significant challenges. T.M. Khan, despite its lower poverty prevalence, is likely facing a lack of targeted resources, and significant inequality.

Figure 9 describes the vulnerability status of HHs across four districts in Sindh (Kashmoor, Tando Muhammad Khan (T.M. Khan), Sanghar, and Thatta) by categorizes HHs into five extremely high, high, medium, small, and very small, multidimensional poverty vulnerability levels. The data reveal, with 172 HHs in the extremely high, 439 in the high and 257 in the medium categories, shows that the highest proportion of HHs in Sindh are in the high vulnerability category. The district of Kashmoor distribution across the vulnerability categories shows, with 40 HHs in the extremely high, 69 in the high and 80 in the medium category, suggests a diverse poverty profile, with HHs spread across a wider range of vulnerability levels. The district of T.M. Khan displays HHs in the extremely high (89) and high (97) categories, which indicates a significant prevalence of high vulnerability. The Sanghar district shows HHs concentration in the high category (192), suggests that while a substantial proportion of Sanghar’s HHs are highly vulnerable, the prevalence of extreme vulnerability is relatively low. The district of Thatta presents a high concentration in the medium category (122), followed by the high category (81), which indicates that Thatta has a large number of HHs in the medium vulnerability range. The data reveals significant variations in the distribution of HH vulnerability across Sindh districts. Kashmoor relatively balanced distribution indicates a more diverse poverty landscape, with households spread across a wider range of vulnerability levels.

Balochistan MPI and vulnerability scale

Figure 10 exhibits the MPI values for four districts in Balochistan i.e., Zhob, Qilla Abdullah, Nasirabad, and Qalat. The data reveal that the Balochistan MPI of 0.17, with a head count ratio of 0.18 and an intensity of 0.82, reflecting the diverse poverty landscape within the province. The district Zhob exhibits the lowest MPI value at 0.09, accompanied by a head count ratio of 0.14 and an intensity of 0.69, signifies that while a relatively small proportion of Zhob’s population is multidimensionally poor, those who are poor experience a considerable degree of deprivation. The Qilla Abdullah, presents the highest MPI at 0.16, with the the extremely low head count ratio of 0.06, coupled with an exceptionally high intensity of 0.93, indicates that a minuscule percentage of the population is poor, but those who are poor suffer from severe deprivations across multiple dimensions. The districts of Nasirabad and Qalat display MPI values of 0.19 and 0.25, with a head count ratio of 0.23, 0.29 and an intensity of 0.84 each respectively, shows that Qalat has the highest incidence of poverty, and both districts experience a very high intensity of poverty.

The Qilla Abdullah’s exceptionally low head count ratio but extremely high intensity suggests a highly concentrated form of severe deprivation. This could be attributed to specific factors affecting a very small segment of the population, such as limited access to essential services due to geographical isolation, targeted social exclusion, or the impact of localized environmental disasters. The high intensity implies that these individuals experience a multitude of deprivations simultaneously, potentially related to healthcare, education, and living standards. Zhob, with its low head count ratio and moderate intensity, presents a different scenario, suggests a broader, albeit less severe, poverty problem affecting a larger portion of the population. This could be due to factors such as limited economic opportunities, inadequate infrastructure, or less effective social service delivery. Nasirabad and Qalat, with their higher head count ratios and intensities, indicate a more widespread and severe poverty situation. The high intensity values suggest that the poor in these districts face significant challenges across multiple dimensions, potentially related to healthcare, education, and living standards. The higher count ratios point to a larger proportion of the population being affected by these deprivations.

Figure 11 presents the vulnerability status of HHs across four districts in Balochistan. The Figure 11 shows that 734 HHs in the high, 124 in the extremely high, 57 in the medium categories, indicates a dominant presence of high vulnerability across the province, with a moderate number of HHs experiencing extreme vulnerability. The district Zhob shows a significant number of HHs in the high category (178), followed by the extremely high category (58), suggests a significant prevalence of high vulnerability, with a moderate number of HHs experiencing extreme vulnerability. The Qilla Abdullah, presents a highly concentrated vulnerability pattern, with 204 HHs in the high category and 46 in the extremely high category, indicates a stark polarization, where HHs are exclusively classified as either high or extremely high vulnerability. The Nasirabad district displays a similar pattern to Qilla Abdullah, with a dominant number of HHs in the high category (213) and a smaller number in the extremely high category (10). The district of Qalat shows a concentration of HHs in the high category (139), followed by the extremely high category (10), and then the medium category (21). The data reveals significant variations in the distribution of HH vulnerability across Balochistan districts. Qilla Abdullah and Nasirabad display a stark bimodal distribution, with HHs concentrated exclusively in the high and extremely high categories, suggests a severe and pervasive poverty situation, where a substantial portion of the population faces significant multidimensional deprivations.

Azad Jammu and Kashmir MPI and vulnerability scale

The data in Figure 12 presents AJandK MPI values for four districts i.e. Neelum, Bagh, Muzaffarabad, and Sudhnoti. The data reveals that the AJK MPI of 0.27, with a head count ratio of 0.5 and an intensity of 0.54, reflecting the diverse poverty landscape within the region. The districts of Neelum and Sudhnoti exhibit the highest MPI values, both at 0.33. Neelum has a head count ratio (H) of 0.61 and an intensity (A) of 0.54, while Sudhnoti has a slightly higher head count ratio of 0.64 and a slightly lower intensity of 0.51, indicates that a significant proportion of the population in both districts experiences multidimensional poverty, with Sudhnoti having a higher prevalence of poverty, and Neelum having a slightly higher depth of deprivations. The district of Bagh shows an MPI of 0.3, with a head count ratio of 0.47 and an intensity of 0.62, indicates multidimensional poverty, with a moderately high prevalence and a relatively high intensity. The Muzaffarabad district displays the lowest MPI among the four districts, at 0.12, head count ratio of 0.26 and an intensity of 0.48, suggests that a smaller proportion of Muzaffarabad’s population is multidimensionally poor compared to the other districts, and the intensity, while still significant, is the lowest. The data reveals significant variations in multidimensional poverty across AJK’s districts. Neelum and Sudhnoti, with their high MPI values and head count ratios, indicate severe poverty situations. The Bagh MPI indicates a significant level of poverty with high intensity suggests that those who are poor experience considerable deprivations. Muzaffarabad’s low MPI indicates relatively better conditions compared to the other districts with the lower head count ratio suggests that a smaller proportion of its population is poor, and the lower intensity implies that the depth of deprivations is also less severe.

Figure 13 describes the vulnerability status of farm HHs across four districts (Neelum, Bagh, Muzaffarabad, and Sudhnoti) in Azad Jammu and Kashmir (AJK), into five vulnerability levels: Extremely high, high, medium, small, and very small.

The AJK data reflects 690 HHs in the high, 121 in the extremely high and 161 in the medium categories, indicates a dominant presence of high vulnerability across AJK, with a significant number of HHs experiencing moderate and extreme vulnerability. The district of Neelum exhibits that HHs in the high category (178), followed by the extremely high category (34), and then the medium category (36), suggests a significant prevalence of high vulnerability. Bagh district shows a pattern, with the HHs in the high (202), followed by the medium (22), and then the small (26) categories, indicates a dominant presence of high vulnerability, with a significant number of HHs exhibiting moderate vulnerability. The district of Muzaffarabad presents a distribution with a dominant number of HHs in the high (167), followed by the extremely high (69) categories, suggests a significant prevalence of high vulnerability, with a moderate number of HHs experiencing extreme vulnerability. Sudhnoti district displays the highest concentration of HHs in the high category (143), followed by the medium category (89). The data reveals significant variations in the distribution of HHs vulnerability across AJK districts. Bagh stands out with the absence of HHs in the extremely high category, suggesting a unique context where extreme vulnerability is less prevalent. Muzaffarabad, exhibits a significant concentration in the high and extremely high categories, suggests that even in the relatively more developed areas of AJK. Neelum and Sudhnoti both show a large amount of HHs in medium vulnerability, suggests that there are many HHs that are close to the high vulnerability range.

Gilgit-Baltistan MPI and vulnerability scale

Data in Figure 14 depicts the Gilgit-Baltistan (GB), MPI for four districts, viz. Gilgit, Hunza, Diamer, and Ghanche. The data reveal the GB MPI of 0.25, with a head count ratio of 0.49 and an intensity of 0.49, reflecting the diverse poverty landscape within the region. The Gilgit district exhibits an MPI of 0.23, with a head count ratio (H) of 0.46 and an intensity (A) of 0.51, indicates a moderate level of multidimensional poverty, with a significant proportion of the population experiencing deprivations. The district of Hunza presents a higher MPI of 0.35, accompanied by a head count ratio of 0.68 and an intensity of 0.52, suggests a more severe poverty situation compared to Gilgit, with a larger proportion of the population facing deprivations. The district Diamer MPI of 0 and a head count ratio of 0, but an intensity of 0.45, implies that all HHs in Diamer are classified as multidimensionally poor according to the index, faced a moderate level of deprivation. The Ghanche district has the highest MPI among the four districts, at 0.41, with a high head count ratio of 0.82 and an intensity of 0.50, indicates a significant level of multidimensional poverty, affecting a large portion of the population. The data reveals significant variations in multidimensional poverty across Gilgit-Baltistan’s districts. Ghanche stands out with the highest MPI and head count ratio, indicating a severe poverty situation. Hunza, while having a lower MPI than Ghanche, also exhibits a high head count ratio, indicating a significant prevalence of poverty. Gilgit displays a moderate level of multidimensional poverty, with both prevalence and intensity being within a middle range. Diamer’s unique result, with an MPI and head count ratio of 0, but a non-zero intensity, shows saver poverty.

Figure 15 presents the vulnerability status of HHs across four districts in GB. The GB data reflects with 329 HHs in extremely high, 309 in the high and 249 in the medium categories, shows that the highest proportion of HHs in GB are within the extremely high, high, and medium vulnerability categories. The Gilgit district exhibits a distribution with 96

in the high category, and 46 each in the extremely high and medium categories, suggests a significant prevalence of both high and extreme vulnerability, with a moderate number of HHs in the medium vulnerability range. The district of Hunza presents a more diverse distribution, with 28 HHs in the extremely high category, 105 in the high category, 90 in the medium category, indicates a wider range of vulnerability levels within Hunza, with a significant portion of HHs experiencing high and medium vulnerability. The Diamer district showing 244 HHs in the extremely high with no households recorded in the high, small, or very small categories, indicates a highly concentrated form of extreme vulnerability, with a near-absence of households in other vulnerability levels. The Ghanche district displays shows extremely high 108 in the high 112 category, suggests a relatively balanced distribution across the high, medium, and small vulnerability levels, with a lower prevalence of extreme vulnerability. The data reveals significant variations in the distribution of HH vulnerability across Gilgit-Baltistan’s districts. Diamer’s unique distribution, with a highly concentrated form of extreme vulnerability, suggests that specific factors are driving severe deprivation in this district. This could be due to limited access to essential services, geographical isolation, or the impact of localized environmental disasters. Gilgit and Ghanche exhibit a more diverse distribution, with HHs spread across the high, medium, and small vulnerability levels, suggests a more nuanced poverty profile, where some households have a degree of resilience against severe poverty. Hunza, with its relatively balanced distribution across the high, medium, and small categories, indicates a wider range of vulnerability levels within the district.

The analysis of farm HH poverty in Pakistan reveals a multifaceted issue, extending beyond mere income deficiency. It encompasses deficits in financial stability, living conditions, asset ownership, access to social programs, and coping capacity, demonstrating a strong correlation between poverty and deficiencies in these areas. The lower asset ownership, limited awareness of social services, and inadequate coping mechanisms significantly increase a HHs vulnerability. These findings are supported by studies Padda and Hameed (2018) showing high rates of rural poverty, characterized by poor sanitation, substandard housing, and limited economic resources, and the observed rise in multidimensional poverty of HH assets in central Pakistan. The findings of Khan et al. (2020) indicates a decrease in Punjab’s MPI, particularly in its northern regions, and other supporting research, highlight the persistent high vulnerability of Southern Punjab, and the overall high levels of multidimensional poverty in Khyber Pakhtunkhwa, Balochistan, and Sindh. The necessity of using the MPI is highlighted, as it provides a comprehensive understanding of poverty beyond income, proving invaluable for policymakers in identifying and addressing regional disparities. The MPI measures poverty across multiple dimensions and is useful for developing countries where income poverty is not the only measure of poverty. In Pakistan, the MPI reveals that Khyber Pakhtunkhwa, Balochistan, and Sindh have higher levels of poverty compared to Punjab and Gilgit-Baltistan. Balochistan has the highest headcount ratio, while Khyber Pakhtunkhwa has the highest intensity value. The findings of Nawab et al. (2023) is contradictory with our finds that in Punjab, the MPI has decreased at an average rate of 1.1% per year, dropping from 10% of the HHs in 2007 to 6.7% in 2018, while in line with our finds that Southern Punjab is comparatively poorer. The multidimensional poverty vulnerability scale across Pakistan highlights the significant regional variations in poverty levels. Punjab and Sindh exhibit the highest numbers of HHs classified as extremely high and high vulnerability, while Balochistan shows a dominant concentration of HHs in the high vulnerability category. The importance of accurately identifying vulnerable HHs through multidimensional poverty measures is reinforced by previous studies in Punjab, highlighting the effectiveness of these tools in directing resources and interventions to those most in need.

Conclusions and Recommendations

The MPI and the spatial mapping of vulnerability levels were used to assess the extent of multidimensional Poverty in the selected districts of Pakistan. The findings underscore the complex and pervasive nature of poverty, revealing that deprivation extends beyond mere income to encompass various dimensions, including financial access, education, healthcare, housing, asset ownership, social program awareness, food security, and community networks. The study highlights significant regional disparities, with Khyber Pakhtunkhwa, Balochistan, and Sindh exhibiting higher MPI values compared to Punjab and Gilgit-Baltistan. Particularly alarming is Balochistan high poverty intensity, indicating that those experiencing poverty in this province face severe and multifaceted deprivations. The analysis of HH vulnerability status reveals a substantial number of HHs across all provinces classified as extremely high or high vulnerability, demonstrating the widespread impact of multidimensional poverty. The regional disparities, especially the high intensity of poverty in Balochistan, call for urgent to identify and prioritize areas requiring immediate attention and resource allocation, ensuring that interventions are both effective and equitable. The spatial distribution of this vulnerability underscores the need for targeted interventions that acknowledge the unique challenges faced by different regions. This research emphasizes that effective poverty alleviation strategies must adopt a holistic approach, simultaneously addressing multiple dimensions of deprivation. This includes expanding financial access and education, improving healthcare and housing, promoting skills and asset ownership, enhancing social program awareness and food security, strengthening community networks, and empowering policy participation. These multifaceted interventions are crucial for achieving the SDGs and fostering sustainable development in Pakistan.

Acknowledgements

The authors gratefully acknowledge the financial support provided by the Higher Education Commission (HEC), Islamabad, Pakistan, under the Thematic Research Grant No. HEC/ACAD/TRGP/2018/000412 for the conduct of this study.

Novelty Statement

This study pioneers the integration of multidimensional poverty indices and spatiotemporal analysis to assess rural poverty dynamics in Pakistani farm households, addressing gaps in geographically contextualized assessments of poverty.

Author’s Contribution

Dawood Jan: Led conceptualization and methodology.

Muhammad Israr: Conducted data analysis and manuscript preparation.

Shahzad Khan: Contributed to proofreading and data compilation;

Shakeel Ahmad: Managed GIS mapping, software, visualization.

Nafees Ahmad: Supported review and data compilation.

Generative AI or AI-assisted Technology Statement

The author(s) declare that no Genrative AI was used in the creation of this manuscript.

Conflict of interest

The authors have declared no conflict of interest.

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