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Contextualizing spatiality of multidimensional poverty in rural and urban India: A geographical perspective

Contextualiser la structure spatiale de la pauvreté multidimensionnelle dans l’Inde rurale et urbaine : une perspective géographique
Soumyabrata Mondal, Saheli Kumar et Anand Prasad Mishra

Résumés

La présente étude tente d’estimer la structure spatiale de l’incidence, de l’étendue et de la gravité de la pauvreté multidimensionnelle dans l’Inde rurale et urbaine au cours de l’année 2021 en utilisant les données du rapport de NITI Aayog, basé sur l’ensemble de données NFHS-4. L’étude révèle que de grandes disparités entre les États persistent dans le modèle de pauvreté multidimensionnelle et son intensité dans les zones rurales et urbaines de l’Inde. L’étude tente également d’examiner les disparités interétatiques pour différents indicateurs de l’IPM rural et urbain, ce qui confirme que la gravité de la situation est aiguë pour la plupart des indicateurs de pauvreté dans les États les plus pauvres de l’Inde tels que le Bihar, le Jharkhand, l’Uttar Pradesh, le Madhya Pradesh, etc. Enfin, nous avons également effectué une analyse de décomposition pour identifier le contributeur le plus significatif à la pauvreté multidimensionnelle. Elle a révélé que dans l’espace rural et urbain, la dimension santé et les indicateurs de nutrition ont apporté la contribution la plus significative au score global de la pauvreté multidimensionnelle. Il conviendrait de mettre l’accent sur des politiques axées sur les objectifs dans les programmes d’éradication de la pauvreté. La présente étude peut être utile aux planificateurs du développement et aux décideurs politiques pour mieux comprendre le modèle de la pauvreté multidimensionnelle rurale et urbaine dans les États de l’Inde.

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Introduction

1Poverty is the underlying issue that, even in the early twenty-first century, is commonly acknowledged as one of the most persistent socioeconomic crises in many countries around the world (Khan et al., 2015). Poverty is a complex phenomenon that has been addressed in a variety of ways by various academics and development practitioners. Despite the fact that different definitions of poverty (World Bank, 1990; Lipton et al., 1995; World Bank, 2001; Chamhuri et al., 2004; Abdul-Mumin & Shamshiry, 2014) have been proposed throughout the years, there is no universally agreed definition of poverty due to its complexity. Poverty eradication in all of its forms remains one of the world’s most challenging issues and was designated as the United Nations Development Programme’s (UNDP) principal sustainable development goal. Although the number of people living in severe poverty reduced by more than half between 1990 and 2015, 783 million people around the world still live on less than $1.90 per day, and 1300 million people are multidimensionally poor (World Bank, 2018; OPHI, 2019; UNDP, 2019). Rapid economic growth has pulled millions of people out of poverty in countries like China and India, but progress has been inconsistent (Cano, 2019). India is now the world’s most populated country, with 176 million impoverished people in 2018 (World Bank, 2018), accounting for nearly a quarter of the world’s poor. Despite the fact that India reduced money-metric poverty from 37 percent in 2004-05 to 22 percent in 2011-12 (GOI, 2013), non-monetary poverty remains high. Stunted or underweight children account for about two-fifths of all children in India (IIPS & ICF, 2017). Premature mortality is on the rise since 1991 and basic health care is out of reach for the poor and underprivileged sections in rural India (Dubey & Mohanty, 2014; Iyengar & Dholakia, 2012). Despite an increase in primary school enrollment, the average number of years spent in school remains low (IIPS & ICF, 2017).

2In India, poverty has traditionally been assessed in terms of consumption and expenditure, which have a unidimensional constraint and fail to reflect various dimensions of deprivation (Das and Paria, 2018-19). Empirical studies have shown a considerable number of people who are multidimensionally impoverished even if they are not poor in terms of income and vice versa (Laderchi et al., 2003; Bradshaw & Finch, 2003; Alkire & Kumar, 2012) and ‘an increase in the economic well-being of households does not always imply a reduction in multidimensional poverty’ (Klasen, 2000; Wang et al., 2016; Suppa, 2018). Moreover, ‘income poverty levels and trends are not strongly connected to trends in other key determinants such as child mortality, primary school completion rates, undernourishment, and so on’ (Bourguignon et al., 2010). Eventually, to address the shortcomings of traditional poverty evaluation methodologies, in recent years, poverty has been considered as a multi-faceted issue and the notion of poverty is now encompassing poor health and nutrition, lack of education and skill, and insufficient livelihood and living conditions. Alkire & Foster (2007) presented a methodology for measuring multidimensional poverty that was comprehensive. Different studies (UNDP, 2010; Yu, 2013) have proven this paradigm to be particularly beneficial for regional and national poverty reduction planning. Multidimensional Poverty Index (MPI) takes into account both the occurrence and intensity of deprivation, making it more accurate than evaluating poverty just on the basis of income and consumption. The MPI also offers advantages over the Human Development Index (HDI), as HDI assesses well-being at the country level, whereas MPI uses data from individual households (Tripathi & Yenneti, 2020).

3When looking at poverty at a higher level, one of the biggest problems is that the variety within spatial units is typically greater than the variance between them (World Bank, 2006). There are significant variations in multidimensional poverty between urban and rural areas of any country (Trani, Biggeri & Mauro, 2013; Yu, 2013). Extreme poverty is primarily a rural issue. In 2013, four out of every five persons in rural areas stay below the international poverty threshold of $1.90 per day (Castaneda et al., 2018). Over the last few decades, rural poverty has reduced sharply, as a result of successful policies that promote economic opportunities for rural poor people and enhance social safety in rural areas (United Nations 2021). However globally poverty remains mainly a rural challenge as 80 per cent of poor people live in rural areas; many developing countries have a substantial rural population; 18 per cent of rural people lived in extreme poverty in 2013, compared to roughly 5 per cent of urban residents (Castaneda et al., 2018). In 2013 World Bank also specified that globally ‘more than three quarters of those living in extreme poverty are in rural areas and nearly two thirds of the extremely poor earn a living from agriculture’ (Olinto et al., 2013). According to the Global MPI 2014, ‘India’s rural areas are home of 86 per cent of multidimensionally poor individuals’ (Alkire et al., 2014). According to McAreavey & Brown (2019) ‘While poverty and deprivation exist throughout a nation, some aspects are more likely to affect rural people and communities than their urban counterparts. This is because certain demographic groups who are more likely to live in rural areas are more at risk of poverty, for example the elderly, farm workers and low income factory workers.’

4Although poverty is primarily a rural phenomenon, the proportion of poor people living in cities is increasing as a result of urbanisation. The urban poor suffer a variety of challenges, including limited access to income and employment, inadequate living conditions, poor infrastructure and services, vulnerability to natural disasters, environmental hazards, and health risks, spatial issues (mobility and transportation), and inequality. According to a study of worldwide poverty, the rate of monetary poverty reduction in urban regions is slower than in rural areas (Baker, 2008). Indeed, urban poverty in megacities in the Global South is a topic of interest in poverty study around the world. According to Rowntree (1901), urban poverty is defined as ‘a sort of poverty that occurs mostly in industrialised civilizations’, but also in the Global South, according to Mitlin & Satterthwaite (2012). Urban poverty is defined as ‘a set of economic and social problems that occur in industrialised cities as a result of a number of processes such as the establishment of comfortable living standards, the rise of individualism, social fragmentation processes, and the dualization of the labour market, which translates to social dualization’ (a divide between those who find themselves living within the conditions of well-being and those who remain on the margins, excluded) (Cano, 2019).

5Recent studies on urban poverty suggest that, in order to comprehend this reality today, it is necessary to broaden the scope of analysis, examining not only areas where poverty is chronic, but also the living conditions of groups in disadvantaged contexts, with a particular focus on impoverishment dynamics. To put it another way, new assessments of urban poverty must see it as a long-term, heterogeneous and multifaceted reality, with an emphasis on the processes that cause it, rather than just calculating the amount of poverty using pre-determined parameters (Cano, 2019). According to Mingione (1996; 2004), other variables such as the high cost of living in some cities, the lack of opportunities for individuals to meet their own needs, the negative impact of social instability, social isolation, or the tough competition between citizens and new immigrants in situations of poor and precarious labour conditions must be included in order to understand urban poverty in contemporary societies. Due to evictions, demolitions, and gentrification in the increasing metropolis, assessing poverty in urban India presents some typical complications (Coelho & Maringanti, 2012; Smets & Lindert, 2016). Under the official poverty line, people who live in overcrowded, very poor quality housing, insecure housing, lack of access to safe drinking water, open sewage, and open drainage, as well as a significant level of environmental health risk, are considered poor (Vakulabharanam & Motiram, 2012; Panda et al., 2016). India is exceptional in terms of urban poverty growth in that the proportion of urban poor has decreased over the last decade while the number of people living in slums has increased. The overall number of slum dwellers in India increased to 13.75 million in 2011 from 10.5 million in 2001, according to the Ministry of Housing and Urban Poverty Alleviation (Agarwal & Taneja, 2005; Subbaraman et al., 2012). According to the Oxford Poverty and Human Development Initiative’s most recent estimates (2018), around 9 per cent of the population in urban India is multidimensionally poor. In large cities (population of more than one million), male urban poverty was found to be 14 per cent, compared to 24 per cent and 20 per cent in small (less than 50,000 people) and medium (25,000 to 1 million people) cities, respectively (Ahmed, 2016).

6A number of studies have been conducted in India in relation to multidimensional poverty. Mohanty (2011) quantified poverty in multidimensional space and explored the relationship between multidimensional poverty and child survival using unit data from the NFHS-3. Sarkar (2012) developed the MPI and examined the poverty status in rural India using eight indicators such as household members’ highest educational attainment, mean per capita expenditure, protein intake, calorie intake, employment, land, electricity, and cooking fuel, as well as data from the National Sample Survey Organization (NSSO). Using NFHS dataset, Alkire & Seth (2013) scrutinized the variation in multidimensional poverty in India between 1999 and 2006. They discovered that some standard of living indicators, such as power, housing conditions, access to safe drinking water, and improved sanitation facilities, had a greater impact on national poverty reduction than other social indicators. Using the Indian Human Development Survey (IHDS) 2004-05, Dehury & Mohanty (2015) assessed and decomposed the multidimensional poverty dynamics in 84 natural regions of India and observed that roughly half of India’s population is multidimensionally poor, albeit there are significant regional differences. Kumar et al. (2015) discovered that among the states of India; Goa, Punjab, Himachal Pradesh, and Tamil Nadu are in a vulnerable stage; Kerala, on the other hand, is in a very excellent position in MPI, while the others are in very bad condition. Based on NFHS data from 1996 to 2006, Alkire & Seth (2015) revealed that while poverty levels in India have decreased, the drop is not uniform across all social parameters. Strotmann & Volkert (2018) investigated multidimensional poverty employing micro data from over 2300 people in four villages in rural Karnataka, and discovered favourable associations between the multidimensional poverty index and a lack of happiness. Roy et al. (2018) measured the incidence, depth, and severity of multidimensional poverty for rural families using a set of household level primary data from West Bengal. They also broke down the disparities in deprivation scores among and within socioeconomic, religious, and ethnic groups. Tripathi & Yenneti (2020) using National Sample Survey (NSS) data on Consumption Expenditure for the years 2004–2005 and 2011–2012 found that the multidimensional poverty head count in India has decreased from 62.2 percent in 2004–2005 to 38.4 percent in 2011–2012. Separate rural and urban regional analysis, on the other hand, clearly shows a large fall in rural poverty relative to urban poverty reduction. Alkire et al. (2021) investigated how poverty levels in India dropped between 2005-06 and 2015-16 among social categories. The study also looked at how poverty levels decreased over time for the poorest of the poor. Das et al. (2021) using NSSO data examined the extent of consumption-based deprivation and multidimensional poverty in India for the years 2004–2005 and 2011–2012. They discovered a significant drop in multidimensional and consumer poverty, though the decline was not uniform among Indian sub-groups. Based on NFHS data of 2005–2006 and 2015–2016 Das et al. (2021) examined the Eastern rural region has the greatest MPI of 0.43 (2005–2006) and 0.21 (2015–2016), while the Northern rural region has the lowest MPI of 0.14 and 0.03 correspondingly. In addition, the Northern area has the lowest MPI of all social subgroups. The findings also show a regional concentration of MPI, especially in India’s Central and Eastern regions. The study also finds that, while multidimensional poverty has decreased dramatically over the last decade, the fall has been regressive, which can be traced back to the pattern of the decline in the various deprivation indicators. Mothkoor & Badgaiyan (2021) analysed the trend of multidimensional poverty in India from 2014–15 to 2017–18 applying 71st and 75th round survey data of the NSSO. They calculated the MPI separately for rural and urban areas in order to better analyse policy flaws in these areas. According to them, the decline in deprivation is faster in rural India than in urban areas. Using the urban sample from the National Family Health Survey, 2015–16, Mohanty & Vasishtha (2021) estimated and decomposed the multidimensional poverty in urban India and found that in urban India, the probabilities of multidimensional poverty were higher among large families, female-headed households, widowed people, and scheduled tribes. Kaibarta et al. (2022) examined multidimensional poverty in eight slums in the Purulia Municipality of West Bengal based on mixed approach. According to their findings, the Scheduled Caste (SC) population is poorer across various socioeconomic groups and Scheduled Tribe (ST) households having the most limited access to health care. Despite the fact that a large number of studies on multidimensional poverty in India have been undertaken over time, no comparative analysis of rural and urban multidimensional poverty across Indian states has yet been conducted. Therefore, this study seeks to fill this gap using the latest data of NITI Aayog on MPI of India in 2021.

7The contributions of the paper are as follows: The first and foremost objective of the paper is to assess the spatial pattern of rural and urban multidimensional poverty across the states of India during 2021. Further we have tried to explore the spatial differentiation in absolute gap of rural and urban poverty among the states of India. The study also tries to depict how MPI, Headcount ratio (H) and Intensity (A) of poverty are related with each other in rural and urban context. Finally the study delves upon indicator wise rural and urban deprivation of MPI across the state of India during 2021 and contribution of each indicator in rural and urban MPI in India. This study will contribute to a better understanding of the current situation of multidimensional poverty, intensity, and deprivation in rural and urban India, as well as across states, which will aid in the creation of state-level policy.

8The present paper henceforth is designed as follows: ‘database and methodology’ section outlines the dataset and methodology used in the study; ‘spatial pattern of multidimensional poverty in rural India’ section highlights the spatial pattern of multidimensional poverty, headcount ratio and intensity of poverty across the rural areas of different states during 2021 in India; ‘spatial pattern of multidimensional poverty in urban India’ section depicts the spatial pattern of multidimensional poverty, headcount ratio and intensity of poverty across the urban areas of different states during 2021 in India; ‘spatial pattern of rural-urban gap in multidimensional poverty’ shows the pattern of existing gap between rural and urban multidimensional poverty across the states of India; ‘indicator wise deprivation in rural and urban MPI of India’ section explains indicator wise deprivation of rural and urban MPI across different states during 2021 in India; ‘decomposition of multidimensional poverty in rural and urban India’ section identifies the contribution of each indicator in the overall score of rural and urban MPI of India for 2021. Finally the ‘findings and concluding remarks’ section concludes.

Database and methodology

9The study is entirely based on secondary data. To assess spatial disparities in the pattern of multidimensional poverty in rural and urban India we have collected data from the baseline report of Multidimensional Poverty Index constructed by NITI Aayog (National Institution for Transforming India) in the year 2021 based on the unit data from the fourth round of the National Family Health Survey (NFHS‐4). NFHS is a nationwide cross-sectional Demographic Health Survey (DHS) undertaken on a regular basis by the Ministry of Health and Family Welfare of the Government of India. The fourth National Family Health Survey (NFHS-4) was conducted in 2015–16 and was aimed to produce estimates at the local, state, and national levels. NFHS‐4 applied a two‐stage stratified random sampling design. Rural and urban representative samples were obtained, and estimations were made. The 2021 MPI is based on Alkire and Foster (AF) methodology and takes into account three dimensions: health, education, and standard of living, with twelve indicators (three from health dimension, three related to education and rest of the seven from standard of living) (Fig. 1).

Figure 1. Dimensions and Indicators of National MPI for India 2021.

Figure 1. Dimensions and Indicators of National MPI for India 2021.

10The detailed account of the dimensions, indicators, the weights and the cut-off point has been given below in Table 1.

Table 1. Dimensions, indicators, deprivation cut-offs and weights of the multidimensional poverty.

Table 1. Dimensions, indicators, deprivation cut-offs and weights of the multidimensional poverty.

Source: National Multidimensional Poverty Index, 2021 based on NFHS-4 (2015-16), NITI Aayog

11The AF methodology employs dual cut-off thresholds, identifying the poor in each weighted indicator before aggregating the poor across all dimensions. ‘Equal weights were assigned to each dimension, and within each dimension, equal weights were given to each indicator’ (Alkire & Santos, 2010; Alkire & Seth, 2015; UNDP, 2015, 2019). The deprivation profile of a state is abridged in a deprivation score, which adds up the weights on each deprived indicator and displays the percentage of weighted deprivations experienced by the state’s population. Following Sen (1976), the next step is to determine who is poor using a poverty cut-off. They are classified as MPI poor if they experience one-third or more of the weighted deprivations. The next stage is to estimate the headcount ratio and the intensity of poverty after identifying multidimensionally poor people.

12Headcount ratio (H): it is the proportion of the multidimensionally poor to the total population and is defined as

13Where, q is the number of people who are multidimensionally poor and n is the total population.

14Intensity of poverty (A): It is the weighted average count of deprivation experienced by the multidimensionally poor and calculated as

15Where, Ci(k) is the deprivation score of multidimensionally poor individuals up to the ith individual and is the number of multidimensionally poor individuals.

16The Multidimensional Poverty Index (MPI) is the product of both the headcount ratio and the intensity of poverty. MPI is computed as

17Where, H is the headcount ratio, and A is the intensity of poverty.

18Besides we decomposed the MPI by dimensions and indicators to assess the contribution of the various dimensions or indicators to overall poverty. The contribution of a particular indicator to overall multidimensional poverty was computed as

19Where wi is the weight of the ith indicator (Table 1), CHi is the censored headcount ratio of the ith indicator and MPIc denotes the India’s national MPI.

20Various analytical tools like tabular analysis, correlation, regression, scatter plot and bubble chart etc. have been employed for the study. For better visual interpretation several maps and diagrams have also been prepared with the help of ArcGIS 10.5, SPSS 22, MS Excel 2010 software.

Spatial pattern of multidimensional poverty in rural India

21In any geographic space, identifying geographical patterns of poverty status provides new insights into the socio-spatial dynamics of poverty and poverty severity (Deinne & Ajayi, 2018). Despite the fact that the rate of rural poverty is decreasing at the national level day by day, major interregional inequalities in the pattern of multidimensional poverty continue in various parts of the country. The spatial pattern of multidimensional poverty, the headcount ratio, and the intensity of poverty in rural India in 2021 are depicted in the following section.

22To portray the interstate disparities in the Rural MPI we have categorized India in three categories – high (MPI>0.192), moderate (MPI 0.098-0.192) and low (MPI<0.098) (Fig. 2). According to the report of NITI Aayog on MPI, 2021 the value of national MPI of rural India is 0.155. It is witnessed from the Table 2 that high rural MPI (>0.192) persists in four states of India – Bihar (0.286), Jharkhand (0.246), Madhya Pradesh (0.219) and Uttar Pradesh (0.211). While low level of MPI (<0.098) is observed in states like Kerala (0.004), Goa (0.017), Sikkim (0.018), Punjab (0.028), Tamil Nadu (0.029), Himachal Pradesh (0.032), Haryana (0.066), Andhra Pradesh (0.067), Jammu & Kashmir (0.073), Karnataka (0.081), Telangana (0.088), Tripura (0.095), Uttarakhand (0.096) and Mizoram (0.098). Rest of the states bear moderate level of MPI in 2021 (Fig. 2). It should be mentioned that eight Indian states i.e. have overcome the national score of MPI that is 0.155 (Table 2). Fig. 2 also portrays spatial pattern of concentration of the multidimensional rural headcount ratio in India.

Table 2. State wise Pattern of Rural and Urban MPI, H and A.

Table 2. State wise Pattern of Rural and Urban MPI, H and A.

*Value of Jammu & Kashmir includes J & K and Ladakh UT

Source: National Multidimensional Poverty Index, 2021 Based on NFHS-4 (2015-16), NITI Aayog

23As reported in Table 2 in India during 2021, 32.75 per cent rural people are multidimensionally poor. The spatial pattern of Headcount ratio follows more or less similar pattern with MPI. Among the states, like MPI, the highest concentration of multidimensional rural poor is continued to stay in Bihar (56.01 per cent) followed by Jharkhand (50.93 per cent), Madhya Pradesh (45.96 per cent) and Uttar Pradesh (44.32 per cent). Lack of education, a high rate of illiteracy, a lack of infrastructure and industries, unequal land distribution, caste-based politics, a heavy population burden on natural resources, a problem with youth unemployment, and a lack of urbanization are the main causes of the high incidence of poverty in these states. On the other hand lowest multidimensional headcount ratio of the rural poverty is found in Kerala (0.95 per cent) followed by Sikkim (4.25 per cent), Goa (4.44 per cent) and Punjab (6.40 per cent). The crucial factor is that these states, particularly Kerala, which has the lowest percentage of the poor, have spent more than average on family welfare and health care. The states have spent nearly equal to the average budget allocation, which is crucial to lowering poverty levels in these states, aside from in areas such as agriculture, social security, roads and bridges, and minority welfare communities. According to Bhandari & Chakraborty (2015), Sikkim’s low poverty rate can be attributed in large part to Sikkim’s optimal growth in the mining and manufacturing sectors, increased electricity accessibility, quick expansion of the service sector, increased tourism, and other factors. The spatial pattern of rural headcount ratio entails that out of 29 states, four states fall under high poverty category, whereas 18 states come under low multidimensional poverty category and rest of the seven states belong to moderate headcount category (Fig. 2). It is also revealed from the Table 2 that nine states have crossed the national headcount ratio of multidimensional rural poverty in India during 2021 which needs special attention to the policy makers and future researchers. Effective distributive policies have to be formulated and implemented to benefit the rural populations of Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh, as these states suffer from higher multidimensional rural poverty. Special attention is also required in these states to transition from an agriculture-driven to an industry-driven economy through rapid urbanization. These states are also rich in natural resources such as coal, limestone, iron, nickel, cobalt, chromium, and marble. There is a paramount need to develop industries based on these local resources in order to ensure that local resources are utilized properly and that people get the maximum benefits. Additionally, these states need to promote institutions for basic to higher education to improve the quality of education. Industry-focused technical and vocational education must also be made available starting in the school system in order to create a labour force that complies with industry standards (Tripathi & Yenneti, 2020).

Figure 2. Spatial Pattern of Rural MPI, Headcount Ratio and Intensity of poverty.

Figure 2. Spatial Pattern of Rural MPI, Headcount Ratio and Intensity of poverty.

24Table 2 also highlights state wise pattern of intensity of multidimensional poverty in 2021 in India. Like MPI and headcount ratio among the states intensity of poverty is also highest in the state of Bihar i.e. 51.14 per cent followed by Meghalaya (48.37 per cent) and Jharkhand (48.27 per cent). Whereas intensity of rural poverty is lowest in state of Himachal Pradesh (39.28 per cent) followed by Goa (39.30 per cent) and Kerala (39.81 per cent). Jean Dreze, a noted development economist who has over several decades studied various facets of poverty in India, argues that the lower level of poverty intensity in states like Kerala, Himachal Pradesh, etc. is due to better implementation of social schemes (e.g., MGNREGA, the mid-day meal scheme, the public distribution system, etc.) in these states. It is revealed from the Table 2 that in India the national level of intensity of rural multidimensional poverty is 47.38 per cent. Geographical distribution of intensity of rural poverty entails that high intensity of rural poverty (> 45.39 per cent) persists in 11 states of India, which is quite serious for development perspective. The study reveals that high multidimensional rural poverty is mainly observed in the central part while moderate level of rural poverty mainly persists in the western and north eastern part whereas low level of poverty is concentrated among the northern and southern states in India during 2021. Notwithstanding real picture of poverty scenario can be depicted from the spatial pattern of intensity and severity of poverty.

25The pattern and interrelationship of MPI, H and A are significant for any spatial enquiry. To delve this we have done correlation and regression model.

Figure 3. Interrelationship between Rural MPI and Rural H.

Figure 3. Interrelationship between Rural MPI and Rural H.

26It is revealed that high correlation exists between rural MPI and H (0.9978). Further for better prediction we have computed R2 value and it comes 0.9956 (Fig. 3). Further we have tried to assess how rural MPI and rural A are associated with each other. It is revealed that like H, intensity of rural poverty (A) is highly correlated (0.9017) with rural MPI. Although it is revealed from the Table 2 and Fig. 4 that there are few states like Haryana, Punjab, Andhra Pradesh, Mizoram, Tripura where MPI is low but intensity of poverty is on the higher side. Hence we can state low MPI is not always associated with low intensity of poverty in different states of rural India.

Figure 4. Level of Rural MPI and Intensity of Poverty across Indian States in 2021.

Figure 4. Level of Rural MPI and Intensity of Poverty across Indian States in 2021.

27Further we have also computed correlation and regression between the two components of MPI i.e. H and A, and correlation value derived as 0.8931. It entails that both the components of MPI are well much interlinked with one another. Alkire & Santos (2014) in their study also observed that countries with a greater headcount ratio (H) tend to have higher poverty intensity. We have also calculated R2 value that appears 0.7977 (Fig. 5).

Figure 5. Interrelationship between Rural H and Rural A.

Figure 5. Interrelationship between Rural H and Rural A.

28It is obvious from the Table 2 that in several states of India like Kerala, Goa, Sikkim, Himachal Pradesh, Punjab, and Tamil Nadu where less than 10 per cent of rural people are multidimensionally poor but intensity of poverty is more or less to 40 per cent which needs proper investigation and research.

Spatial pattern of multidimensional poverty in urban India

29This section of our study draws attention to reveal the present regional disparities in the pattern of multidimensional poverty, headcount ratio and intensity of poverty in urban context as per latest report of NITI Aayog, 2021 in India. To reveal the interstate disparity in urban multidimensional poverty we have classified India into high (>0.079), moderate (0.041-0.079) and low (< 0.041) category. It is revealed from the Table 2 that during 2021, in India the national value of urban MPI is 0.040. Fig. 6 portrays the spatial dimension of urban MPI across the states of India. It is depicted that the highest score of MPI is achieved by Bihar (0.117) while the lowest score is obtained by the state Kerala (0.002). Except Bihar, high MPI is also observed in Uttar Pradesh (0.085). While as reported in Table 2 moderate MPI (0.041 to 0.079) is observed in several states like Jharkhand (0.067), Madhya Pradesh (0.062), Odisha (0.057), West Bengal (0.054), Rajasthan (0.052), Nagaland (0.048), Uttarakhand (0.046), Assam (0.044), Chhattisgarh (0.043), Manipur (0.042). In rest of the states urban MPI is low (less than 0.041). It is witnessed from the Fig. 6 that moderate to high urban MPI is mainly persisted among the central and eastern states while low urban MPI exists in the other parts of India. Unplanned urbanization, a lack of employment opportunities for the poor, a scarcity of affordable housing, overcrowding, unemployment, slower economic growth, unhygienic living conditions, limited or no access to services, high reliance on labour markets, complex social networks, and high levels of vulnerability are all factors that contribute to the persistence of urban poverty in these poverty-stricken states. When we examine the spatial distribution of urban headcount ratio, we see that it follows the same trend as India’s urban MPI. As reported in Table 2 in India during 2021 urban headcount ratio is 8.81 per cent. It is also depicted that among the states, highest concentration of multidimensionally urban poor is continued to stay in Bihar (23.91 per cent) followed by Uttar Pradesh (18.07 per cent) whereas lowest concentration of urban poor is noticed in Kerala (0.43 per cent) followed by Mizoram (1.42 per cent) and Himachal Pradesh (1.46 per cent). According to the data, 12 states in India have crossed the national headcount ratio of urban poverty, which is 8.81, necessitating special attention from policymakers in order to fulfil the sustainable development goals in the near future (Table 2). Figure 6 also depicts the spatial variation in the intensity of urban poverty in order to better comprehend the reality and severity of poverty in India’s states. As per latest estimate highest intensity of urban poverty is accomplished in Bihar with 49.00 per cent followed by Himachal Pradesh (48.24 per cent). Apart from these two states, high intensity of urban poverty may also be observed in five states of India like - Uttar Pradesh (47.06 per cent), Uttarakhand (46.82 per cent), Odisha (46.12 per cent), West Bengal (46.00 per cent) and Punjab (45.02 per cent). As per NITI Aayog estimates during 2021, six states in India have surpassed the national intensity of urban poverty i.e. 45.25 per cent while the remaining states fall below the national average. As witnessed in Fig. 6 low intensity (less than 41 per cent) of urban poverty persists only in two southern states of our country – Kerala (37.06 per cent) and Tamil Nadu (39.29 per cent). In comparison to the high MPI and headcount ratio in India, moderate to high intensity of urban poverty has a larger spatial extent, as shown in Fig. 6, which needs explanation from the experts.

30To reduce urban India’s multifaceted poverty, multiple sectors must work together to raise standards in education, make healthcare services more accessible, improve housing conditions, and provide for the necessities. Overcrowding in houses, growing slums, and unaffordable housing are major challenges facing urban India (Mohanty & Vasishtha, 2021). It is necessary to expand the current Prime Minister Urban Housing Scheme of the central government, as well as the housing schemes of the state governments and local bodies. Besides, providing capital at affordable interest rates can be helpful in reducing urban poverty. To lessen the severity of urban poverty, it is advisable to implement the practices of housing subsidies, slum rehabilitation, and slum development restrictions in all of India’s states.

Figure 6. Spatial Pattern of Urban MPI, Headcount Ratio and Intensity of poverty.

Figure 6. Spatial Pattern of Urban MPI, Headcount Ratio and Intensity of poverty.

31Now the question arises how MPI, headcount ratio (H) and intensity of poverty (A) are associated with each other in context to urban India? To address this conundrum we have calculated correlation and regression. From the analysis it is revealed that like rural India, in urban context also MPI and H are highly correlated (0.9975) with each other. From Fig. 7 it is revealed that R2 value comes 0.9951.

Figure 7. Interrelationship between Urban MPI and Urban H.

Figure 7. Interrelationship between Urban MPI and Urban H.

32When we look at the association between urban MPI and A, we find that the correlation value is 0.6766. It means that while MPI and A are favourably associated, their relationship isn’t as strong as the one between MPI and H. Further for better prediction we have computed regression model and R2 is obtained from the analysis as 0.4578 (Fig. 8).

Figure 8. Interrelationship between Urban MPI and Urban A.

Figure 8. Interrelationship between Urban MPI and Urban A.

33There are several states, such as Himachal Pradesh, Punjab, Telangana, Gujarat, Arunachal Pradesh, Haryana etc. those have low MPI scores yet significant poverty intensity and severity (Table 2).

Figure 9. Interrelationship between Urban H and Urban A.

Figure 9. Interrelationship between Urban H and Urban A.

34Similarly we have also compiled the relationship between two components of urban MPI i.e. urban H and urban A and correlation value obtained 0.6589. We have further calculated regression and R2 value comes as 0.4341. Therefore, only 43 per cent variance in intensity of urban poverty can be explained by the H of urban poverty (Fig. 9). According to Table 2, just 1.46 percent of the urban population in Himachal Pradesh is multidimensionally poor, but the intensity of poverty is 48.24 percent, which is somewhat interesting in terms of the poverty level in urban India.

Spatial pattern of rural – urban gap in multidimensional poverty

35It is well known that rural disadvantage and poverty are both prevalent in most development indicators. The majority of previous studies in this area haven’t looked at whether poverty persists differently in urban and rural locations across India’s states. With this in mind, we’ve attempted to explore the spatial distribution of existing rural-urban divides in multidimensional poverty across Indian states for 2021. Extensive evidence shows that in India ‘poverty is more manifested in rural areas compared to urban areas’. According to NITI Aayog’s recent predictions, the difference between rural and urban multidimensional headcount ratios is 23.94 in 2021, which is extremely considerable. When we look at the interstate pattern of multidimensional poverty in rural and urban areas, we see enormous inequalities (Fig. 10). The highest gap in rural and urban poverty is observed in Jharkhand (35.67). Besides Jharkhand the gap is also superfluous in states like Madhya Pradesh (32.14), Bihar (32.10) and Meghalaya (29.98). The wide disparity is caused by both these states’ inability to combat urban poverty as well as their high levels of rural poverty. By implementing policies to create jobs in rural areas, raise the real wages of rural labourers, and expand the rural non-farm sector, the government can reduce rural-urban poverty in these states.

Figure 10. Spatial Pattern of Rural Urban Gap in Multidimensional Poverty.

Figure 10. Spatial Pattern of Rural Urban Gap in Multidimensional Poverty.

36On the other hand from the Table 2 it is evident that the gap of rural and urban multidimensional poverty is minimal in states like Kerala (0.52), Goa (1.10), Sikkim (1.45), Punjab (2.08) etc. Since the poverty rate is so low in both rural and urban areas in these states, the difference is incredibly small. Except for a few southern and northern states, the gap between rural and urban poverty in India is more or less excessive, which should be taken into account before any plan is developed for a program to eradicate poverty. There are numerous explanations for why the disparity in rural-urban poverty persists in different regions of the nation. Some states have benefited directly from the policy, while others have lagged. Because economic policy and economic activity are not similar, it is clear that poverty is much more present in the States with the lowest levels of income. In such a situation, ideally, poverty reduction should bridge the rural-urban gap in poverty incidence to qualify as inclusive. Hence, one of the top priorities for India’s policymakers should be to bridge this gap in multidimensional poverty persistence through inclusive economic development. A well-thought-out policy should be created in order to persuade its political advisors to implement a clever economic plan for an equitable distribution of wealth, and resources, and an overarching vision of development that is inclusive, equitable, and sustainable.

Indicator wise deprivation in rural and urban MPI of India

37Though we have done a comprehensive analysis of the spatial pattern of multidimensional poverty in rural and urban contexts in the previous sections, it is still required to comprehend indicator-wise deprivation in rural and urban India across the states to realize the exact scenario of deprivation. As, unlike the headcount ratio, ‘the MPI can be broken down into its component indicators and therefore each indicator has a direct relationship with the MPI’s outcome’. Therefore, we look into indicator-by-indicator censored headcount ratios throughout India’s states, both rural and urban perspectives. The censored headcount ratios express the percentage of the population who are MPI poor and are deprived in each indicator. Fig. 11 portrays the national level of deprivation in India during 2021 as per NITI Aayog estimates.

Figure 11. Indicator wise Deprivation in Rural, Urban and All India Level.

Figure 11. Indicator wise Deprivation in Rural, Urban and All India Level.

38Table 3 and Fig. 12 provide a detailed description of the state wise censored headcount ratio in rural context of India in 2021. Table 3 reveals that out of the twelve indicators in several indicators like cooking fuel (31.47 per cent), sanitation (28.60 per cent), housing (28.09 per cent), nutrition (26.01 per cent) and maternal health (19.33 per cent) the scenario of deprivation is quite alarming in rural India. Although large interstate variation persists in deprivation of the indicators of MPI in India. Some states are well ahead in several indicators while some others are lagged far behind. Such as in nutrition the situation is dreadful in states like Bihar (44.94 per cent), Jharkhand (41.42 per cent), Madhya Pradesh (36.18 per cent), and Uttar Pradesh (35.94 per cent). Similarly in child and adolescent mortality the deprivation is highest in Uttar Pradesh (4.39 per cent) followed by Bihar (4.21 per cent), Jharkhand (3.33 per cent) and Madhya Pradesh (3.22 per cent). While in case of maternal health the situation is worst in Bihar (39.32 per cent) succeeded by Jharkhand (31.97 per cent), Uttar Pradesh (29.93 per cent) and Meghalaya (26.94 per cent). Like health in education dimension also states like Bihar, Jharkhand, Uttar Pradesh, Meghalaya and Madhya Pradesh are mostly deprived among the states of India in rural context.

Table 3. State wise Censored Headcount Ratio (%) of MPI Indicators (Rural).

Table 3. State wise Censored Headcount Ratio (%) of MPI Indicators (Rural).

Source: National Multidimensional Poverty Index, 2021 Based on NFHS-4 (2015-16), NITI Aayog * Value of Jammu & Kashmir includes J & K and Ladakh UT

Figure 12. Indicator Wise Rural Censored Headcount Ratio in States of India, 2021.

Figure 12. Indicator Wise Rural Censored Headcount Ratio in States of India, 2021.

39Among the indicators of standard of living in cooking fuel and sanitation situation is minacious in Bihar, Jharkhand, Madhya Pradesh and Uttar Pradesh (Table 3). While Madhya Pradesh (23.08 per cent) is mostly deprived in drinking water followed by Jharkhand (21.12 per cent). In electricity states like Bihar (31.89 per cent) and Uttar Pradesh (23.31 per cent) are lagged behind. In indicator like housing Bihar (51.58 per cent) is mostly deprived followed by Jharkhand (44.94 per cent) and Uttar Pradesh (40.85 per cent). Two north eastern states – Meghalaya (23.48 per cent) and Nagaland (23.31 per cent) fall behind in assets indicator. As reported in Table 3 even in the first half of the twenty first century in some states like Nagaland and Bihar, 21.84 and 21.01 per cent rural people are deprived in having bank account respectively. On the other hand states like Kerala, Goa, Sikkim, Punjab, Tamil Nadu and Himachal Pradesh are well ahead in all the indicators of multidimensional poverty in rural context than the other states of India. To reduce the level of multidimensional rural poverty in India, indicator-based special assistance programmes should be developed in backward states. Before initiating any developmental or poverty-eradication initiative, state governments should consider the current status of deprivation in their particular states.

40Like rural, in case of urban India large interstate disparities in indicator wise deprivation is observed during 2021. As per the latest estimate in 2021, among the indicators highest deprivation in urban context is portrayed in nutrition (7.10 per cent), followed by sanitation (6.09 per cent), cooking fuel (5.68 per cent) and maternal health (5.04 per cent). Table 4 and Fig. 13 depict the scenario of state wise censored headcount ratio in urban India during 2021. It is revealed from the Table 4 that highest deprivation in nutrition is continued to stay in Bihar (18.83 per cent) followed by Uttar Pradesh (13.99 per cent) and Jharkhand (12.95 per cent). Likewise in child and adolescent mortality the deprivation is maximum in Uttar Pradesh (2.08 per cent) followed by Bihar (1.91 per cent) and Madhya Pradesh (1.51per cent). It is also witnessed from the Table 4 that situation in maternal health is alarming in Bihar (17.32 per cent), Uttar Pradesh (11.29 per cent) and Jharkhand (9.76 per cent). In both of the indicators of education Bihar and Uttar Pradesh are the mostly deprived states of India (Table 4). While in cooking fuel highest deprivation is noticed in Bihar (19.45 per cent) followed by Jharkhand (13.87 per cent) and Odisha (11.07 per cent). In sanitation also the condition is worse in Bihar (18.36 per cent), Jharkhand (12.50 per cent) and Odisha (10.60 per cent).

Table 4. State wise Censored Headcount Ratio (%) of MPI Indicators (Urban).

Table 4. State wise Censored Headcount Ratio (%) of MPI Indicators (Urban).

* Value of Jammu & Kashmir includes J & K and Ladakh UT

Source: National Multidimensional Poverty Index, 2021 Based on NFHS-4 (2015-16), NITI Aayog

Figure 13. Indicator Wise Urban Censored Headcount Ratio in States of India, 2021.

Figure 13. Indicator Wise Urban Censored Headcount Ratio in States of India, 2021.

41In drinking water Manipur (7.41 per cent) is mostly deprived followed by Jharkhand (6.27 per cent). Likewise in electricity Bihar (7.59 per cent) and Uttar Pradesh (3.35 per cent) are lagged behind than the other states of India. Like electricity in housing also Bihar (16.51 per cent) and Uttar Pradesh (11.02 per cent) are the two most deprived states of India. In assets situation is worst in Bihar (9.88 per cent) and Odisha (4.85 per cent). Whereas in bank account Bihar (9.97 per cent) and Manipur (4.97 per cent) are much more deprived than other states. It is revealed from the Table 4 and Fig. 13 that the censored headcount ratio is minimal in the selected indicators of urban MPI in several states like Kerala, Mizoram, Tamil Nadu, Karnataka, Himachal Pradesh, Goa, Jammu and Kashmir, Punjab, Tripura, Telangana and Maharashtra. Thus from the preceding debate, we can conclude that deprivation is severe in India’s poorer states. To alleviate multidimensional poverty in urban India, special attention should be directed to backward states that lag far behind in several indicators of deprivation.

Decomposition of multidimensional poverty in rural and urban India

42Though all the selected indicators play crucial role in the ultimate score of MPI, still further explanation is required to know: which indicators are significantly contributing in the ultimate score of MPI in rural and urban context? To explore this decomposition of multidimensional poverty in both rural and urban perspective have been implemented. Understanding the contribution of each dimension and indicator in overall poverty is important for a better calibration of the MPI, as ‘the contribution of an indicator provides insight into the relative deprivation in a particular indicator based on the weight attached to that indicator’, which is necessary to get an idea of where interventions would lead to a reduction in the overall MPI.

Table 5. Indicator wise contribution to Rural and Urban MPI in India for 2021.

Table 5. Indicator wise contribution to Rural and Urban MPI in India for 2021.

Source: Compiled by authors based on NITI Aayog Report on MPI, 2021

43From the Table 5 it is revealed that in overall score of rural MPI in India, nutrition (27.94 per cent) has contributed most significantly. Except nutrition, among the indicators years of schooling (14.77 per cent) and maternal health (10.38 per cent) also contributed immensely. On the contrary, indicators like child-adolescent mortality (1.29 per cent) and bank account (2.10 per cent) have contributed least in the score of rural MPI of India (Fig. 14). If we try to assess the dimension wise contribution in rural MPI then among the three domains during 2021, highest contribution belonged to health dimension (39.61 per cent) and the least contribution made by education dimension (21.75 per cent). To improve the overall score of the rural MPI in India, it is necessary to reinforce developmental policies relating to rural health and education, with a focus on improving nutrition, maternal health, and years of schooling.

44While in context to urban India, nutrition (29.70 per cent) again contributed the highest in MPI. Besides nutrition, in health dimension maternal health (10.53 per cent) also significantly contributed in urban MPI. Likewise, both the indicators of education i.e. years of schooling (18.17 per cent) and school attendance (10.80 per cent) have also contributed significantly.

Figure 14. Contribution of Indicators to Rural and Urban MPI, India 2021.

Figure 14. Contribution of Indicators to Rural and Urban MPI, India 2021.

45While in standard of living dimension sanitation (7.27 per cent) has contributed most (Table 5). On the other hand indicators like electricity (1.54 per cent), child-adolescent mortality (1.67 per cent) and drinking water (1.92 per cent) are the least significant contributors in urban multidimensional poverty perspective (Fig. 14). In dimension wise contribution, health dimension (41.90 per cent) contributed most in overall urban MPI in India during 2021. This scenario clearly demonstrates that proper developmental policies should be formulated by development planners and policy makers as soon as possible in India, with a particular focus on nutrition, maternal health, years of schooling, school attendance and urban sanitation for the betterment of the multidimensionally urban poor.

46According to the study, the government should spend more money on improving citizens’ levels of nutrition, health, and educational attainment. Since education is a crucial component, its deficiency prevents both individuals and the affected households from realizing their fundamental rights (Wu & Qi, 2017). Similarly to this, health has both intrinsic and instrumental value (Alkire & Santos, 2010). This is because it affects many other capabilities; for example, being ill can limit a person’s ability to engage in social activities and prevent them from practicing their profession (Rippin, 2016).

Findings and concluding remarks

47Most of the poverty related studies have shown limitation to uncover the spatial pattern of differentiation in rural and urban multidimensional poverty in a country like India. Henceforth the present study is an attempt for investigation into the spatial pattern of multidimensional poverty in rural and urban India during 2021 using NITI Aayog report of MPI based on NFHS-4 dataset. The major findings of our study are as follows: First, the study depicts that high interstate disparities exist in the spatial concentration of multidimensional poverty and its intensity in rural India. According to the findings, high levels of rural multidimensional poverty persist in states such as Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh. Due to colonial and feudal relics left over from the past, stagnant economic activity, primarily brought on by the agrarian economy’s crisis, and concentrations of multidimensional poverty, these areas are more acute. These areas of extreme poverty have a rural society that is heavily burdened with social and cultural restrictions that limit the entrepreneurship of the poor population. Collectively, all these elements significantly contribute to the vicious cycles of poverty that affect many generations. Politically, these areas are trying to liberate themselves from various hurdles and social inequality through political unrest. Actually, the nature and dynamics of poverty in these areas are influenced by the socioeconomic environment of today as well as their historical roots. Whereas lower levels of multidimensional poverty persist in Kerala, Sikkim, Goa, and Punjab. Kerala is a state where the political participation of the various communities is ensured by highly vibrant democratic political and social practices that initiate the process of human resource development. This state also ensures maximum daily wages through various schemes for agricultural labourers and other service providers’ populations. In comparison to other states, it also guarantees access to health care, gender equality, educational advancement, skill development, and many other things. Punjab contributed in terms of production and capital formation as a result of agrarian reforms, technological advancements, and scientific input into the agriculture sector. The same is true for Goa, which develops its economy by promoting tourism and related activities involving other service providers. While Sikkim also benefits from tourism, high productivity, and profit from organic farming, which helps the state’s economy. The study also indicates that high intensity of rural poverty is more prone in Bihar, Meghalaya, Jharkhand, whereas it is less in states like Himachal Pradesh, Goa and Kerala. Hence it is assessed that central portion of India have high level of rural multidimensional poverty, while moderate level of rural poverty mainly persists in the western and north eastern part and low level of poverty is concentrated among the northern and southern states in India during 2021. Second, the study also reveals that high incidence of urban poverty is noticed in states like Bihar and Uttar Pradesh while it is minimal in Kerala, Mizoram and Himachal Pradesh. While urban poverty is more severe in several states like Bihar, Himachal Pradesh, Uttar Pradesh, Uttarakhand, Odisha, West Bengal and Punjab. Third, from the study it is explored that in context to rural India, both headcount ratio and intensity are highly correlated with MPI while in urban perspective though headcount ratio is highly correlated with MPI, intensity is not strongly related with MPI. Fourth, it is disclosed from the study that the large variation exists in the gap of rural and urban poverty in India. Among the states the rural-urban divide in multidimensional poverty is more in Jharkhand, Madhya Pradesh, Bihar and Meghalaya whereas it is less in Kerala, Goa, Sikkim, Punjab etc. The study ensures that except some southern and northern states, the gap in rural and urban poverty is spare which should be minimized through inclusive economic development. Fifth, the study also highlights the interstate deprivation in different indicators of rural and urban MPI. It is observed from the study that the magnitude of deprivation is acute in maximum indicators in the poorer states of India like Bihar, Jharkhand, Uttar Pradesh, Madhya Pradesh etc. in both rural and urban context whereas it is less in states like Kerala, Goa, Sikkim, Punjab, Tamil Nadu and Himachal Pradesh. To remove the multidimensional poverty in rural and urban India target based interventions should be paid on the backward states those are lagged much behind in different indicators of deprivation. The study contends that an urgent need exists for a comprehensive analysis of poverty in these states to identify its conditions, enhance the efficacy of evidence-based planning, and facilitate effective policy-making. This will enable better targeting of policy interventions while also accounting for the heterogeneities of states and regions. Finally the decomposition analysis of rural and urban MPI adds additional insights in the study and it is revealed that in both rural and urban perspective undernutrition contributed the highest to multidimensional poverty. While in dimension wise contribution among the three domains in 2021 highest contribution belonged to health dimension in both rural and urban MPI. Hence there is need for strengthening developmental policies related to health dimension with an emphasis on improving nutrition, maternal health, medical facilities etc. for the betterment of the overall score of rural and urban MPI in India. Apart from contributing to the growing literature on poverty studies in India, this study pinpoints the geographic dimension of multi-dimensional poverty across distinct spatial units such as rural and urban. When seen from a holistic viewpoint, the study also illustrates the basic fact that poverty can only be alleviated or minimised when weaker areas contribute to the overall growth process. The findings of this study should be useful to development planners and policymakers in order to improve policy interventions, and they should also incorporate spatial dimensions and spatial interventions into various development programmes in rural and urban India.

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Table des illustrations

Titre Figure 1. Dimensions and Indicators of National MPI for India 2021.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-1.png
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Titre Table 1. Dimensions, indicators, deprivation cut-offs and weights of the multidimensional poverty.
Crédits Source: National Multidimensional Poverty Index, 2021 based on NFHS-4 (2015-16), NITI Aayog
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-2.jpg
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URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-3.jpg
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URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-4.jpg
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URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-5.jpg
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URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-6.jpg
Fichier image/jpeg, 22k
Titre Table 2. State wise Pattern of Rural and Urban MPI, H and A.
Légende *Value of Jammu & Kashmir includes J & K and Ladakh UT
Crédits Source: National Multidimensional Poverty Index, 2021 Based on NFHS-4 (2015-16), NITI Aayog
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-7.jpg
Fichier image/jpeg, 462k
Titre Figure 2. Spatial Pattern of Rural MPI, Headcount Ratio and Intensity of poverty.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-8.jpg
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Titre Figure 3. Interrelationship between Rural MPI and Rural H.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-9.png
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Titre Figure 4. Level of Rural MPI and Intensity of Poverty across Indian States in 2021.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-10.png
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Titre Figure 5. Interrelationship between Rural H and Rural A.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-11.png
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Titre Figure 6. Spatial Pattern of Urban MPI, Headcount Ratio and Intensity of poverty.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-12.jpg
Fichier image/jpeg, 314k
Titre Figure 7. Interrelationship between Urban MPI and Urban H.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-13.png
Fichier image/png, 23k
Titre Figure 8. Interrelationship between Urban MPI and Urban A.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-14.png
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Titre Figure 9. Interrelationship between Urban H and Urban A.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-15.png
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Titre Figure 10. Spatial Pattern of Rural Urban Gap in Multidimensional Poverty.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-16.jpg
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Titre Figure 11. Indicator wise Deprivation in Rural, Urban and All India Level.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-17.png
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Titre Table 3. State wise Censored Headcount Ratio (%) of MPI Indicators (Rural).
Crédits Source: National Multidimensional Poverty Index, 2021 Based on NFHS-4 (2015-16), NITI Aayog * Value of Jammu & Kashmir includes J & K and Ladakh UT
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-18.jpg
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Titre Figure 12. Indicator Wise Rural Censored Headcount Ratio in States of India, 2021.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-19.png
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Titre Table 4. State wise Censored Headcount Ratio (%) of MPI Indicators (Urban).
Légende * Value of Jammu & Kashmir includes J & K and Ladakh UT
Crédits Source: National Multidimensional Poverty Index, 2021 Based on NFHS-4 (2015-16), NITI Aayog
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-20.jpg
Fichier image/jpeg, 444k
Titre Figure 13. Indicator Wise Urban Censored Headcount Ratio in States of India, 2021.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-21.png
Fichier image/png, 48k
Titre Table 5. Indicator wise contribution to Rural and Urban MPI in India for 2021.
Crédits Source: Compiled by authors based on NITI Aayog Report on MPI, 2021
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-22.jpg
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Titre Figure 14. Contribution of Indicators to Rural and Urban MPI, India 2021.
URL http://journals.openedition.org/belgeo/docannexe/image/59421/img-23.png
Fichier image/png, 68k
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Soumyabrata Mondal, Saheli Kumar et Anand Prasad Mishra, « Contextualizing spatiality of multidimensional poverty in rural and urban India: A geographical perspective »Belgeo [En ligne], 1 | 2023, mis en ligne le 04 mai 2023, consulté le 30 mai 2024. URL : http://journals.openedition.org/belgeo/59421 ; DOI : https://doi.org/10.4000/belgeo.59421

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Auteurs

Soumyabrata Mondal

Corresponding author, Department of Geography, Institute of Science, Banaras Hindu University, Varanasi – 221005, India
ORCID 0000-0002-6537-9387
soumyamondal1992@gmail.com

Saheli Kumar

Department of Geography, Institute of Science, Banaras Hindu University, Varanasi – 221005, India
ORCID 0009-0004-2115-6239
abhilasa.saheli@gmail.com

Anand Prasad Mishra

Department of Geography, Institute of Science, Banaras Hindu University, Varanasi – 221005, India
ORCID 0000-0002-1746-7892
adeepayan@gmail.com

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