Poverty in Multidimensional Perspective: Policy Insights from Selected North Indian Districts

Abstract

This article examined the multidimensional aspects of poverty in selected North Indian districts using the Alkire–Foster counting method of estimating poverty incidence and intensity. Whereas more than half of the sample households are found to be vulnerable to poverty, about a quarter of households are observed to be in the grip of poverty in these districts. Among the dimensions of deprivations, education, standard of living and economic and social security are critical in contributing to multidimensional poverty and vulnerability. In these dimensions, people are mostly deprived of fuel for cooking, sanitation, ownership assets, informal jobs and social security measures. Therefore, the policymakers ought to be proactive in understanding the socio-economic structure of these districts to formulate inclusive distributive policies as appropriate area wise. However, policies such as urbanization, promotion of technical/vocational education, initiation of micro and small entrepreneurial activities completing and supplementing to farm activities and introducing measures of social protection can help people come out of the tragedies of poverty.

Keywords

Multidimensional poverty Alkire–Foster method North Indian districts

I. Introduction

People are both beneficiaries and drivers of development, in both developed and less developed economies (UNDP, 2010). Hence, it is essential to ensure an enabling environment in which they can live long, healthy, creative and with all dignity (UNDP, 1990). However, this objective is often eroded due to the persistence of poverty and inequality in societies. Particularly, poverty is that social malady which prohibits people from commanding over resources and thus, delimit their freedom of making optimal choices (Gordon, 2005; Kapila, 2009). Although poverty is commonly considered as a shortfall in income or consumption with reference to a threshold, called the poverty line, it is a multidimensional phenomenon in a broader perspective. Poverty is multidimensional as it counts for multiple deprivations in well-being such as health, education and standard of living. Because of its pervasive nature, it affects men, women and children of all ages across geographic regions and ethnic groups. It is so insidious that it brings into grip both rich and poor. It is income or consumption which may classify someone as rich or poor, but what if somebody is having a handsome income while regularly ailing, and what if somebody is having regular flows of income while difficulty in attaining standard educational degrees. In all these cases, the individual is termed multidimensionally poor, but not income poor. Perhaps the former is more dangerous as it creates several humps in a way towards societal optimality in production, consumption and distribution.

This multidimensional poverty makes people underprivileged in decision-making, getting quality and decent works, and limits their social participation, thereby increasing the chances of them being subject to exploitations and violence of varying degrees (World Bank, 2011). Precisely, multidimensional poverty is one of the reasons behind the social exclusion of masses in an emerging market economy like India. Probably, this may be one of the factors why poverty alleviation programmes in the country are not yet able to uplift people from the hell of income and/or consumption deprivations. Recent day’s observation of Indian societies reveals that a bulge of the working-age population becomes immediately vulnerable to poverty owing to their weak resilience to socio-economic and natural shocks. The recent outbreak of coronavirus pandemic is one of the novel examples to consider.

Therefore, the concept of poverty should be viewed in a multidimensional perspective, if the policymakers would like to see a developed society where people not only enjoying long and healthy lives but are socially participative, safe and secured against the shock of any kinds. Policy architects ought to work for removing policy impotency by understanding real ground-level dimensions and indicators in which people are mostly deprived and hence poor and vulnerable. In this direction and in continuation to several extant studies at the international, national, regional and local levels, this research work is an exploratory attempt to present the extent of multidimensional aspects of poverty in selected districts of North India while drawing out certain key policy insights. Adding two additional dimensions such as economic and social security to the conventional country-level basket of health, education and standard of living dimensions, this exploratory exercise found the poverty (multidimensional) headcount as 24.75 per cent and vulnerability (near poverty) as 53.21 per cent in selected North Indian districts. Additionally, it found the use of unhealthy fuel for cooking, lack of improved sanitation facilities, lack of ownership of household amenities, engagements in informal jobs and lack of health insurance as the major indicators of deprivations of well-being in these districts. Thus, this article can be an eye-opener for those who are working on instilling a decent socio-economic order in society.

In the remaining of the article, Section II reviews relevant literature to give a theoretical and empirical justification for the study; Section III briefly summarizes the materials and methodology used in the research work; Section IV examines the primary data from various angles to generate policy insights; and Section V concludes while pointing out the desired policy milestones.

II. Literature Review

Much before the role of human capital in the process of economic growth and development was emphasized by Lucas (1988), Romer (1986, 1990) and Rebelo (1991), the literature cites poverty as an important impediment of development (Nurkse, 1953). However, the nature and measurement of poverty have always been a moot point in the literature. Since the introduction of the concept of poverty in the arena of economics by Booth (1892) and Rowntree (1901), it has consistently been interpreted in terms of monetary (income or consumption) deprivations. Later, it has been realized that income alone can miss a lot—health, education and standard of living (Kolm, 1977; Mancero & Villatoro, 2013; Piachaud, 1987; Sen, 1993; Townsend, 1993). Thus, intellectuals started interpreting poverty as a multidimensional phenomenon as it embraces multiple human deprivations that people suffer throughout their lives. From the rudimentary appearance in the works of Lenoir (1974), Townsend (1979, 1993), Sen (1993), UNDP (1997), Tsui (2002), Bourguignon and Chakravarty (2003) till its basic form in Alkire and Foster (2007), the concept has been passed through several debates, criticisms and refinements. Finally, the concept of multidimensional poverty got international recognition with the publication of Multidimensional Poverty Index (MPI) for over 100 countries in the United Nations Development Programme’s (UNDP’s) Human Development Report 2010 based on the poverty measurement approaches suggested by Alkire and Santos (2010). Since then, the concept has evolved with several modifications and improvements to reach the form in which it has appeared in the UNDP-OPHI Global MPI Report 2019.

An improved methodology of measuring poverty in the multidimensional perspective was suggested by Alkire and Foster (2011a). It sums up a plurality of imperfectly overlapping deprivation indicators into a consistent parametric class of MPI based on the Foster–Greer–Thorbecke (FGT) concept of poverty measurement. It has become very popular for its ability to decompose the deprivations for population subgroups. For this purpose, we have also used this methodology in this study. In the multidimensional perspective, people face deprivations in their daily lives in nutrition, child mortality, years of schooling, school attendance, cooking fuel, sanitation, drinking water, electricity, housing and asset ownership, which restrict them from living a normal decent life (Alkire et al., 2017). So, this method recognizes human deprivations in health, education and standard of living dimensions. The fundamental logic is: ‘since growth can’t reduce poverty to the desired level, policy attention needs to focus on health and education’ (Bhagwati & Panagariya, 2012). While health is an enabling factor and education is a signalling device of ability or productivity, standard of living is the basis of decent living and source of social acceptance (Dotter & Klasen, 2014). Hence, the measures of multidimensional poverty include people who may not be income poor, but face deprivations in other areas of their lives (Alkire & Foster, 2011b). Even USA, a high-income country, has been registered to have multidimensional poverty, the deprivation index being 15.40 per cent in 2017 (Glassman, 2019).

At the global level, the apparent motives behind choosing health, education and standard of living dimensions of well-being for poverty measurement include the availability of country-level data and ease of interpretability of results. The measurement of multidimensional poverty simply does not ignore income, but it does so because the standard of living is a reflection of income. Thus, the concept of multidimensional poverty complements the concept of monetary poverty. Although additional dimensions can be included in the measurement of multidimensional poverty at the regional/national/state/district levels, there is no unanimity about which dimensions should be appropriately included. For instance, Santos and Villatoro (2018) constructed MPI for Latin American region by taking into consideration 13 indicators in 5 dimensions of well-being—housing (housing materials, overcrowding, housing tenure), basic services (improved water sources, improved sanitation, access to clean energy), living standard (monetary resources, durable goods), education (adult schooling achievement, children’s school attendance, children’s schooling gap) and employment and social protection (health insurance, social security or pension). In the Indian context, Das (2018) considered 9 indicators in 3 dimensions of well-being for analysing multidimensional poverty—education (years of schooling, school attendance), food and nutrition (food security, nutritional security) and living conditions (electricity, cooking fuel, own house, own land, assets). In a state-level study in India, Banerjee et al. (2017) included 10 indicators in 3 dimensions—health (nutrition, mortality), education (school attendance, years of school) and standard of living (water supply, sanitation, electricity, assets, main floor material, cooking fuel). In another state-level study, Tripathi and Yenneti (2019) used 9 indicators in 3 dimensions—income (MPCE), education (highest educational attainment in the household) and standard of living (employment, agricultural land, irrigated land, source of lighting, cooking fuels, dwelling unit, ration card).

Regarding the nature of multidimensional poverty, Burchi et al. (2019) mention the existence of poverty traps, the predominance of rural poverty and feminization of poverty at the global level. Aguilar and Sumner (2019) reveal the predominance of such poverty in agrarian-rural households, and mainly deprivations are in terms of undernutrition and lack of access to sanitation and clean water. Santos and Villatoro (2018) state the presence of wide disparities in multidimensional poverty between rural and urban areas, particularly in living standard and housing dimensions. The World Bank Report (2018) on poverty and shared prosperity states the prevalence of deprivations in access to adequate sanitation, which is more than income deprivation. Martinez and Perales (2017) state concentration of deprivations in health, education and material resources. And large-sized households are poverty-prone (Bautista, 2018). Goli et al. (2019) found that the primary human deprivations under multidimensional poverty include health and education. According to Giné-Garriga and Pérez-Foguest (2018), the key deprivations causing multidimensional poverty are sanitation and hygiene. Moreover, such deprivations in various dimensions of well-being overlap, making people mvulnerable during global pandemics such as the coronavirus disease outbreak. In this context, Alkire et al. (2020) predict that the deprivations in safe drinking water, nutrition and clean cooking fuel can put 5.7 billion people (60 million in India) at risk of multidimensional poverty across the globe due to coronavirus disease. Diwakar (2020) further adds that the limited access to water, sanitation, healthcare, school closures and constraints on livelihoods can impact the ability of households adversely and put people living at or near the poverty line at risk of new or deepened poverty.

The global MPI report 2018 shows that India is home to the largest number of multidimensionally poor people (364 million), but their spatial distribution is skewed (inequality among the poor is 0.014). In the Indian context, Bhuiya et al. (2007) found health, education, housing and clothing as significant contributors to multidimensional poverty; Alkire and Seth (2015) found housing, electricity, safe drinking water and sanitation as significant determinants of multidimensional poverty; Bhat (2013) found health, education, housing, sanitation and electricity as the significant determinants of multidimensional poverty in Jammu and Kashmir (J&K); Mishra and Shukla (2015) observed that rural multidimensional poverty predominates J&K; Dehury and Mohanty (2017) found health, sanitation, drinking water and cooking fuel as the significant contributors to multidimensional poverty; Mohanty et al. (2017) observed that health shocks to households are the main reasons behind multidimensional poverty; Banerjee et al. (2017) state that the major reason behind the concentration of multidimensional poverty in rural India is urban-biased policies of the government. In a district-level study, Mehta (2003) identified illiteracy, infant mortality, low levels of agricultural productivity and poor infrastructure as the primary causes of persistent deprivation leading to multidimensional poverty at the district level in India. In a village-level study, Unjum and Mishra (2018) observed that the deprivations in the form of low level of schooling, malnutrition, use of traditional fuel for cooking, bad sanitation facilities and informal employment mainly contribute to multidimensional poverty in Kashmir. Therefore, the incidence and intensity of multidimensional poverty can be reduced by improving the level of education (Berenger & Verdier-Chouchane, 2007), housing and income-generating employment standards (Bibi, 2004), and also by improving the level of nutritional intakes, safe drinking water supply, sanitation, hygiene and cooking fuel (Alkire & Seth, 2015; Dehury & Mohanty, 2017; Giné-Garriga & Pérez-Foguet, 2018; Unjum & Mishra, 2018).

It is inferred from the review of relevant studies that (a) there is a dearth of research works on multidimensional poverty at the district level in India; (b) there is no consensus in the consideration of dimensions and indicators of well-being; and (c) there is a wide spatial disparity in the causes of such poverty. At this crossroad, this article is an attempt to examine the multidimensional aspects of poverty in selected North Indian districts (see next section for details).

III. Materials and Methods

This article intends to draw policy insights from the analysis of multidimensional aspects of poverty in selected North Indian districts. The study adopted a quantitative survey design based on multi-purpose non-proportional purposive sampling. In designing the study, we have purposively chosen three North Indian territories, namely J&K, Punjab and Rajasthan. This selection comprises three crucial types of topography—J&K (mountainous), Punjab (fertile and alluvial plain) and Rajasthan (sandy and dry). In these three North Indian states, we have chosen seven districts based on convenience—one from J&K (Rajouri district), two from Punjab (Bathinda and Mansa) and four from Rajasthan (Bikaner, Churu, Ganganagar and Hanumangarh). The development status of these districts is presented in Table 1. It is revealed that deprivations are present in both income and non-income indicators irrespective of the level of human development in the selected districts of North India. This justifies the inclusion of these districts in the study of multidimensional poverty.

Table 1.
Development Status of Selected North Indian Districts

Selected Indicators J&K
Punjab
Rajasthan

Rajouri Bathinda Mansa Bikaner Churu Ganganagar Hanumangarh

Per capita income at current prices of 2016–2017 (₹) 14,569 112,387 108,781 95,281 65,398 117,506 107,244

Average literacy rate (%) 68.17 68.28 61.83 65.13 66.75 69.64 67.13

Coverage of full immunization of children (%) 44.50 92.60 91.90 56.00 57.40 79.90 62.10

Vitamin A supplement to children (%) 61.80 71.50 59.70 26.90 34.60 59.20 45.60

HH access to electricity (%) 94.30 99.50 99.80 91.50 95.30 94.10 94.80

HH access to safe drinking water (%) 66.40 96.70 98.40 86.20 83.60 92.10 96.60

HH access to toilet facilities (%) 39.60 85.00 75.00 64.80 73.30 73.40 55.20

HH access to clean fuel for cooking (%) 32.60 62.70 40.60 39.70 30.30 39.60 25.10

Labour force participation rate (%) 50.24 44.07 47.83 40.36 39.85 44.87 40.13

Urbanization rate (%) 8.14 36.00 21.00 33.90 28.30 27.20 19.70

HDI 0.450 0.674 0.633 0.779 0.606 0.809 0.761

USIPI District Development Index 0.390 0.530 0.450 0.360 0.340 0.420 0.380

Selected Indicators	J&K	Punjab	Rajasthan
Per capita income at current prices of 2016–2017 (₹)	14,569	112,387	108,781	95,281	65,398	117,506	107,244
Average literacy rate (%)	68.17	68.28	61.83	65.13	66.75	69.64	67.13
Coverage of full immunization of children (%)	44.50	92.60	91.90	56.00	57.40	79.90	62.10
Vitamin A supplement to children (%)	61.80	71.50	59.70	26.90	34.60	59.20	45.60
HH access to electricity (%)	94.30	99.50	99.80	91.50	95.30	94.10	94.80
HH access to safe drinking water (%)	66.40	96.70	98.40	86.20	83.60	92.10	96.60
HH access to toilet facilities (%)	39.60	85.00	75.00	64.80	73.30	73.40	55.20
HH access to clean fuel for cooking (%)	32.60	62.70	40.60	39.70	30.30	39.60	25.10
Labour force participation rate (%)	50.24	44.07	47.83	40.36	39.85	44.87	40.13
Urbanization rate (%)	8.14	36.00	21.00	33.90	28.30	27.20	19.70
HDI	0.450	0.674	0.633	0.779	0.606	0.809	0.761
USIPI District Development Index	0.390	0.530	0.450	0.360	0.340	0.420	0.380

Source: Compiled from Economic Survey of J&K (2017), Punjab (2019–2020), Rajasthan (2019–2020); Census of India District-wise Reports (2011); National Family Health Survey-4 (2015–2016); Periodic Labour Force Survey (2017–2018); Human Development Reports (2007); Kaul (2016); USIPI District Development and Diversity Index Report for India and Major States, Shariff (2015).

From these North Indian districts, 28 villages (see Table 2 for names of villages) have been selected purposively, and from each village, a fixed number of households were conveniently selected for survey (60 households from each village in Rajouri, 80 households from each village in Mansa, 50 households from each village in Bathinda and 50 households from each village in Rajasthan). Then the primary data were collected by administering a structured household-level socio-economic survey schedule, comprising items on health, education, standard of living, economic and social security dimensions of well-being, on 1,620 sample households during the period from July 2019 to October 2019. Table 2 depicts a brief account of this survey.

Table 2.

Brief Account of Primary Survey

North Indian States	Selected Districts	Sample Villages	No. of Sample Households
J&K	Rajouri	Khyore, Khablan, Nadian, Rehean, Ghalian, Kalar and Nah	420
Punjab	Bathinda	Ramniwas, Harnam Singh Wala, Bandi, Gurusar Joga, Kamalu, Karamgarh Sattran, Adampura and Nathana	400
	Mansa	Aklia, Kot Dharmu, Gehle, Ranghrial and Ahlupur	400
Rajasthan	Bikaner	Siyasar Chaugan and 17 Kyd	400
	Churu	Thailasar and Jaswantpura
	Ganganagar	Dungarsinghpura and Takhranwali
	Hanumangarh	6 BGP-A and Birkali
Total no. of sample households			1,620

Source: The authors.

Then the Alkire–Foster Method was used after augmenting it by including economic and social security dimensions, to analyse multidimensional aspects of poverty in these sample villages (Alkire & Foster, 2011a, 2011b). In this method, we have used 15 indicators in a framework of 5 dimensions of well-being (see Table 3 for details). Each of these five dimensions is assigned a weight of 1/5, which is equally distributed among the indicators within the same dimension; for example, the education dimension is given a weight of 1/5, and each of its two indicators is assigned a weight of 1/10 (years of schooling 1/10 and child school attendance 1/10).

Table 3.

Dimensions and Indicators of Multidimensional Poverty

Dimensions	Indicators	Household is Considered Deprived if…
Education	Years of schooling	No household member has completed 5 years of schooling.
Education	Child school attendance	Any school-aged child is not attending school up to the age at which they would complete class 8.
Health	Child mortality	Any child has died in the family in the last one year.
Health	Nutrition	Any adult or child for whom there is nutritional information is malnourished.
Standard of living	Electricity	The household has no electricity.
	Improved sanitation	The household’s sanitation facility is not improved. or it is improved but shared with other households.
	Improved drinking water	The household does not have access to improved drinking water or safe drinking water is more than a 30-min walk from home, roundtrip.
	Flooring	The household has a dirt, sand or dung floor.
	Cooking fuel	The household cooks with dung, wood, coal or charcoal
	Assets ownership	The household does not own more than one radio, TV, telephone, bike, motorbike or refrigerator and does not own a car or truck.
	Land holding	The household does not own any hectare of agricultural land.
Economic	Employment	No household member is employed in the formal sector.
Economic	Indebtedness	No household member is having any debt.
Social security	Health insurance	No household member has private or public sector health insurance.
Social security	Old age pension	No household member is enrolled for government pension plan or receives pension from the government.

Source: The authors.

Then we have calculated the incidence as well as the intensity of poverty in a multidimensional perspective in the selected North Indian villages. And, on the basis of these results, we have constructed the MPI for each village. The following formulae have been used for the purpose:

Incidence of multidimensional poverty: $H = \frac{q}{n}$ where H is the percentage of households which are multidimensionally poor, called incidence of multidimensional poverty, q is the total number of multidimensionally poor households and n is the total number of households under consideration.

Intensity of multidimensional poverty: $A = \frac{1}{q} \sum_{i = 1}^{q} c_{i}$ where A is the average percentage of dimensions in which households are deprived, called intensity of multidimensional poverty, $\sum_{i = 1}^{q} c_{i}$ is the sum of the proportion of total weighted deprivation that each household suffers and q is the total number of multidimensionally poor households.

Multidimensional Poverty Index: MPI = H × A

On the basis of the MPI, we have classified the households into four categories: (a) if 0 < MPI < 0.20, the household is multidimensionally not poor; (b) if 0.20 ≤ MPI < 0.33, the household is vulnerable to multidimensional poverty; (c) if 0.33 ≤ MPI < 0.50, the household is under multidimensional poverty; and (d) if 0.50 ≤ MPI ≤ 1, the household is under severe multidimensional poverty. Also, the taxonomy of the North Indian villages (see Table 4) is created on the basis of their MPI in terms of a fractile classification from the assumed distribution of the mean of MPI (Mishra, 2018; Narain et al., 2007). This taxonomy is significant in identifying the priority areas/regions for policy interventions for lifting people out of miseries of multidimensional poverty.

Table 4.

Taxonomy of Pattern of MPI in North Indian Villages

Criteria of Taxonomy	Category of MPI Villages
MPI less than or equal to mean – S.D.	Low level of multidimensional poverty
MPI in between mean – S.D. and mean	Low-middle level of multidimensional poverty
MPI in between mean and mean + S.D.	High-middle level of multidimensional poverty
MPI greater than or equal to mean + S.D.	High level of multidimensional poverty

Source: The authors.

In the next step, we have analysed the deprivation structure of people under such multidimensional poverty. Then we have obtained the headcount ratio per indicator under multidimensional poverty using the Alkire–Foster counting method. The following formula is used for calculating the deprivation structure under multidimensional poverty:

Households deprived in each dimension = $[\frac{1}{H_{s}} \sum_{i = 1}^{q} c_{i}] / M P I .$

It is expressed as a percentage where H_s is the total of household sizes in the village, $\sum_{i = 1}^{q} c_{i}$ is the sum of the proportion of total weighted deprivation that each household suffers and MPI is the Multidimensional Poverty Index.

In this exercise, we could know the contribution of each dimension to multidimensional poverty. Also, this exercise helped us in identifying the specific indicators in which people are mostly deprived and thus, multidimensionally poor. Finally, we have made result-specific policy recommendations for the eradication of such poverty from the selected districts of North India.

IV. Results and Discussion

At the outset, the mean distributions of the socio-economic characteristics of sample households in the sample villages of North Indian districts are calculated and are summarized in Table 5. The key observations are:

The average household size in sample villages centres around four, a fair household size, which does not make out the argument that bigger household size and poverty are positively correlated.

The average age of head of the household is around 50 years, a fairly productive age, which also does not imply that poverty is due to the higher age of head of the household.

The average year of schooling is around 6 years, which is short enough to imply that the deprivation in educational attainments might be the important indicator of multidimensional poverty in sample areas.

The average size of agricultural landholding is 1.115 ha in sample villages (with an outlier of 4.840 ha, the mean land holding of Harnam Singh Wala village in the Bathinda district of Punjab) infers that the households in these areas are marginal or small farmers. This characteristic is also indicative of multidimensional poverty in North Indian districts.

The average values of monthly per capita household income and consumption expenditure are ₹4,122.46 and ₹3,037.85 (₹137.41 and ₹101.26 per day income and consumption, respectively), which indicate that households in the sample villages are less deprived in the monetary dimensions. This is because the World Bank international income poverty line (US$1.90) equivalent was ₹133.76 at average exchange rate in 2019 (US$1 = ₹70.40 as per exchnagerates.org.uk).

Table 5.
Mean Distribution of Household Characteristics in North Indian Districts

Selected North Indian Districts Sample Villages Sample Size HH Size Age of HH Head (years) Years of Schooling Agriculture Land Holding (hectare) MPHI (₹) MPCE (₹)

Rajouri (J&K) Khyore 60 4 54 10 0.577 5272.78 1669.34

Khablam 60 5 55 8 0.546 4079.31 973.48

Nadian 60 4 55 7 0.588 6473.33 1850.72

Rehean 60 5 52 10 0.651 3740.64 1385.90

Ghalian 60 4 53 8 0.523 3086.67 1809.58

Kalar 60 4 53 10 0.651 4320.72 1724.09

Nah 60 4 50 10 0.581 3731.81 1736.33

Bathinda (Punjab) Ramniwas 50 4 53 5 0.932 4389.21 5961.97

Karamgarh Sattran 50 5 52 5 0.887 3886.11 5838.02

Bandi 50 4 51 6 6.232 4209.14 3699.05

Nathana 50 4 53 6 0.761 3338.43 3118.85

Gurusar Joga 50 4 52 7 0.968 4114.33 2999.44

Adampura 50 5 52 6 1.287 3286.36 2970.21

Kamalu 50 4 51 6 0.664 4445.59 4031.82

Harnam Singh Wala 50 4 49 7 4.840 3475.83 3144.39

Mansa (Punjab) Aklia 80 4 50 6 0.737 5053.26 4676.88

Kot Dharmu 80 5 56 5 0.766 3703.86 3046.52

Gehle 80 4 48 4 0.633 3545.93 3312.33

Ranghrial 80 4 51 4 0.568 3116.64 2955.07

Ahlupur 80 5 48 5 0.465 3356.98 2862.49

Bikaner (Rajasthan) Siyasar Chaugan 50 3 49 5 1.091 6078.52 4643.17

17 Kyd 50 3 47 4 0.478 3681.45 3410.71

Churu (Rajasthan) Thailasar 50 3 44 4 1.085 5279.66 3233.07

Jaswantpura 50 4 50 6 1.085 3647.00 2778.63

Ganganagar (Rajasthan) Dungarsinghpura 50 3 44 4 1.058 5379.67 4227.32

Takhranwali 50 4 46 7 0.923 3805.50 2614.16

Hanumangarh (Rajasthan) 6 BGP-A 50 3 48 4 0.835 3759.33 2262.71

Birkali 50 4 47 4 0.835 3170.95 2123.66

Selected North Indian Districts	Sample Villages	Sample Size	HH Size	Age of HH Head (years)	Years of Schooling	Agriculture Land Holding (hectare)	MPHI (₹)	MPCE (₹)
Rajouri (J&K)	Khyore	60	4	54	10	0.577	5272.78	1669.34
Khablam	60	5	55	8	0.546	4079.31	973.48
Nadian	60	4	55	7	0.588	6473.33	1850.72
Rehean	60	5	52	10	0.651	3740.64	1385.90
Ghalian	60	4	53	8	0.523	3086.67	1809.58
Kalar	60	4	53	10	0.651	4320.72	1724.09
Nah	60	4	50	10	0.581	3731.81	1736.33
Bathinda (Punjab)	Ramniwas	50	4	53	5	0.932	4389.21	5961.97
Karamgarh Sattran	50	5	52	5	0.887	3886.11	5838.02
Bandi	50	4	51	6	6.232	4209.14	3699.05
Nathana	50	4	53	6	0.761	3338.43	3118.85
Gurusar Joga	50	4	52	7	0.968	4114.33	2999.44
Adampura	50	5	52	6	1.287	3286.36	2970.21
Kamalu	50	4	51	6	0.664	4445.59	4031.82
Harnam Singh Wala	50	4	49	7	4.840	3475.83	3144.39
Mansa (Punjab)	Aklia	80	4	50	6	0.737	5053.26	4676.88
Kot Dharmu	80	5	56	5	0.766	3703.86	3046.52
Gehle	80	4	48	4	0.633	3545.93	3312.33
Ranghrial	80	4	51	4	0.568	3116.64	2955.07
Ahlupur	80	5	48	5	0.465	3356.98	2862.49
Bikaner (Rajasthan)	Siyasar Chaugan	50	3	49	5	1.091	6078.52	4643.17
17 Kyd	50	3	47	4	0.478	3681.45	3410.71
Churu (Rajasthan)	Thailasar	50	3	44	4	1.085	5279.66	3233.07
Jaswantpura	50	4	50	6	1.085	3647.00	2778.63
Ganganagar (Rajasthan)	Dungarsinghpura	50	3	44	4	1.058	5379.67	4227.32
Takhranwali	50	4	46	7	0.923	3805.50	2614.16
Hanumangarh (Rajasthan)	6 BGP-A	50	3	48	4	0.835	3759.33	2262.71
Birkali	50	4	47	4	0.835	3170.95	2123.66

Source: Authors’ calculation from Field Survey Data, 2019.

Note: HH—Household; MPHI: monthly per capita household income; and MPCE: monthly per capita household consumption expenditure.

In supplement to these household characteristics, it has been observed during the primary survey that the households in the sample villages are not developed to the desired level. Therefore, we investigated out the deprivations of these households in other dimensions including health, education, standard of living, economic and social security and the outcomes are summarized in Tables 6 and 7. The key observations are:

The poverty headcount ratio is highest in the Kalar village of Rajouri district of J&K and lowest in the Nadian village of the same district. The poverty intensity is highest in the Jaswantpura village of Churu district of Rajasthan and lowest in the Kot Dharmu village of Mansa district of Punjab.

MPI is highest for the Kalar village of Rajouri in J&K and lowest for the Nadian village of the same district.

In North Indian villages, about 24.75 per cent of households are multidimensionally poor/severely poor. It means that these many households are having deprivations in at least 33.33 per cent of the indicators of well-being.

In North Indian villages, about 53.21 per cent of households are vulnerable to multidimensional poverty. It means that these many households are not currently poor, but likely to fall into poverty in future. It indicates that these many households are having deprivations in at least 20.00 per cent but less than 33.33 per cent of the indicators of well-being.

Multidimensional poverty is fairly high in the Rehean village of Rajouri district of J&K; Nathana and Harnam Singh Wala villages of Bathinda district of Punjab; and 17 Kyd village of Bikaner and 6 BGP-A village of Hanumangarh districts of Rajasthan.

Multidimensional poverty is at high level in the Kalar village of Rajouri district in J&K; Gurusar Joga village of Bathinda district and Kot Dharmu, Ranghrial and Ahlupur villages of Mansa district in Punjab; and Siyasar Chaugan of Bikaner district in Rajasthan.

On comparison of the number of multidimensionally vulnerable households across the North Indian districts, we found that the highest number of vulnerable households is present in Bathinda district followed by Rajouri, Mansa, Churu and Ganganagar. And, the highest number of multidimensionally households is located in the Mansa district followed by Bathinda, Rajouri, Bikaner and Hanumangarh. Therefore, we observe that the problem of vulnerability to poverty is prominent in sample districts in contrast to the problems of multidimensional poverty. This finding has implications that the households in these regions are likely to fall into poverty in unfavourable circumstances; for example, the unprecedented outbreak of coronavirus pandemic and its adversities are likely to increase the number of poor households in the sample districts. Although another study following the end of pandemic can reveal the fact, the policy circle should keep in focus the development strategies for these districts. However, it is essential to identify the indicators in which masses are vulnerable, and this aspect has been left for further research.

Table 6.

Extent of Multidimensional Poverty in North Indian Districts

Selected North Indian Districts	Sample Villages	Not Poor (%)	Vulnerable (%)	Poor (%)	Severely Poor (%)	Headcount Ratio (%)	Poverty Intensity (%)	MPI
Rajouri (J&K)	Khyore	19 (31.67)	38 (63.33)	3 (5.00)	0 (0.00)	5.06	37.38	0.019
	Khablam	21 (35.00)	32 (53.33)	7 (11.67)	0 (0.00)	11.95	39.06	0.047
	Nadian	27 (45.00)	31 (51.67)	2 (3.33)	0 (0.00)	2.34	40.71	0.010
	Rehean	14 (23.33)	27 (45.00)	19 (31.67)	0 (0.00)	31.36	37.68	0.118
	Ghalian	14 (23.33)	37 (61.67)	9 (15.00)	0 (0.00)	14.23	38.89	0.055
	Kalar	6 (10.00)	25 (41.67)	28 (46.67)	1 (1.67)	48.77	39.84	0.194
	Nah	24 (40.00)	28 (46.67)	8 (13.33)	0 (0.00)	12.45	38.18	0.048
Bathinda (Punjab)	Ramniwas	16 (32.00)	23 (46.00)	11 (22.00)	0 (0.00)	20.83	34.22	0.075
	Karamgarh Sattran	14 (28.00)	26 (52.00)	9 (18.00)	1 (2.00)	20.33	40.38	0.082
	Bandi	13 (26.00)	28 (56.00)	8 (16.00)	1 (2.00)	15.20	38.34	0.058
	Nathana	2 (4.00)	36 (72.00)	12 (24.00)	0 (0.00)	22.89	41.46	0.095
	Gurusar Joga	7 (14.00)	24 (48.00)	17 (34.00)	2 (4.00)	38.28	40.04	0.153
	Adampura	6 (12.00)	36 (72.00)	7 (14.00)	1 (2.00)	13.08	39.40	0.052
	Kamalu	1 (2.00)	39 (78.00)	10 (20.00)	0 (0.00)	18.88	38.46	0.073
	Harnam Singh Wala	5 (10.00)	26 (52.00)	18 (36.00)	1 (2.00)	34.34	38.11	0.131
Mansa (Punjab)	Aklia	31 (38.85)	33 (41.25)	16 (20.00)	0 (0.00)	20.52	38.51	0.079
	Kot Dharmu	14 (17.50)	39 (48.75)	24 (30.00)	3 (3.75)	34.97	32.14	0.137
	Gehle	10 (12.50)	53 (66.25)	16 (20.00)	1 (1.25)	18.18	40.89	0.074
	Ranghrial	13 (16.25)	40 (50.00)	22 (27.50)	5 (6.25)	31.93	42.86	0.137
	Ahlupur	10 (12.50)	39 (48.75)	29 (36.25)	2 (2.50)	38.98	40.29	0.157
Bikaner (Rajasthan)	Siyasar Chaugan	13 (26.00)	14 (28.00)	14 (28.00)	9 (18.00)	37.72	43.54	0.164
	17 Kyd	10 (20.00)	19 (38.00)	15 (30.00)	6 (12.00)	26 .58	45.54	0.121
Churu (Rajasthan)	Thailasar	12 (24.00)	29 (58.00)	8 (16.00)	1 (2.00)	9.94	42.50	0.042
	Jaswantpura	15 (30.00)	32 (64.00)	2 (4.00)	1 (2.00)	2.46	48.00	0.012
Ganganagar (Rajasthan)	Dungarsinghpura	6 (12.00)	31 (62.00)	12 (24.00)	1 (2.00)	18.01	40.54	0.073
	Takhranwali	12 (24.00)	29 (58.00)	7 (14.00)	2 (4.00)	9.44	45.71	0.043
Hanumangarh (Rajasthan)	6 BGP-A	7 (14.00)	23 (46.00)	19 (38.00)	1 (2.00)	32.73	39.87	0.130
	Birkali	15 (30.00)	25 (50.00)	9 (18.00)	1 (2.00)	10.17	45.87	0.047

Source: Authors’ calculation from Field Survey Data, 2019.

Table 7.

Taxonomy of MPI Villages in North Indian Districts

Criteria of Taxonomy	Villages in Different Districts/States
Low level of multidimensional poverty (MPI ≤ Mean – S.D.)	1. J&K—Rajouri—Khyore, Nadian 2. Rajasthan—Churu—Jaswantpura
Low-middle level of multidimensional poverty (Mean – S.D. < MPI < Mean)	1. J&K—Rajouri—Khablam, Ghalian, Nah 2. Punjab—Bathinda—Ramniwas, Karamgarh Sattran, Bandi, Adampura, Kamalu 3. Punjab—Mansa—Aklia, Gehle 4. Rajasthan—Churu—Thailasar 5. Rajasthan—Ganganagar—Dungarsinghpura, Takhranwali 6. Rajasthan—Hanumangarh—Birkali
High-middle level of multidimensional poverty (Mean < MPI < Mean + S.D.)	1. J&K—Rajouri—Rehean 2. Punjab—Bathinda—Nathana, Harnam Singh Wala 3. Rajasthan—Bikaner—17 Kyd 4. Rajasthan—Hanumangarh—6 BGP-A
High level of multidimensional poverty (MPI ≥ Mean + S.D.)	1. J&K—Rajouri—Kalar 2. Punjab—Bathinda—Gurusar Joga 3. Punjab—Mansa—Kot Dharmu, Ranghrial, Ahlupur 4. Rajasthan—Bikaner—Siyasar Chaugan

Source: The authors.

Having known the extent of multidimensional poverty in the North Indian districts, we have calculated the contribution of each dimension of well-being to multidimensional poverty in these districts, and the results are presented in Table 8. The key observations are:

In the health dimension, Nah village (24.39%) in Rajouri district is most deprived followed by Ghalian village (20.00%) in the same district.

In the education dimension, Birkali village (43.60%) in Hanumangarh district is most deprived followed by Jaswantpura (41.67%) in Churu district and Takhranwali (41.18%) villages in Ganganagar district.

In the standard of living dimension, Harnam Singh Wala village (22.82%) in Bathinda district is most deprived followed by Gurusar Joga (22.75%) in Bathinda, Bandi (22.60%) in Bathinda, Ahlupur (22.16%) in Mansa, Kot Dharmu (21.94%) in Mansa, Kalar (21.75%) in Rajouri and Karamgarh Sattran (20.65%) in Bathinda district.

In the economic dimension, Ramniwas village (47.85%) in Bathinda district is the most deprived followed by Nathana (41.42%) in Bathinda and 6 BGP-A (41.34%) in Hanumangarh district.

In the social security dimension, Nadian village (49.12%) in Rajouri district is most deprived followed by Khablam (42.42%) in Rajouri, Khyore (42.36%) in Rajouri, Rehean (41.58% in Rajouri), Aklia (40.60%) in Mansa and Kalar (40.07%) in Rajouri district.

Table 8.

Deprivation Structure in North Indian Districts

Selected North Indian Districts	Sample Villages	Households Deprived in Each Dimension (%)
Selected North Indian Districts	Sample Villages	Health	Education	Standard of Living	Economic	Social Security
Rajouri (J&K)	Khyore	11.15	0.00	19.75	26.75	42.36
	Khablam	10.97	5.85	18.81	21.94	42.42
	Nadian	12.28	0.00	14.04	24.56	49.12
	Rehean	13.86	0.00	19.21	25.36	41.58
	Ghalian	20.00	0.00	15.71	25.71	38.57
	Kalar	15.40	1.05	21.75	21.72	40.07
	Nah	24.39	1.81	17.81	19.87	36.13
Bathinda (Punjab)	Ramniwas	0.00	4.29	17.18	47.85	30.67
	Karamgarh Sattran	7.08	4.04	20.65	32.85	35.38
	Bandi	5.05	11.78	22.60	31.97	28.61
	Nathana	5.24	6.82	11.39	41.42	35.13
	Gurusar Joga	1.25	4.68	22.75	34.66	36.84
	Adampura	3.27	8.19	17.31	38.48	32.45
	Kamalu	7.03	2.11	14.26	38.65	37.95
	Harnam Singh Wala	5.40	6.17	22.82	39.75	25.85
Mansa (Punjab)	Aklia	0.00	0.73	19.54	39.13	40.60
	Kot Dharmu	2.84	2.27	21.94	38.58	34.42
	Gehle	11.35	2.46	19.36	26.61	36.23
	Ranghrial	15.25	2.46	19.36	26.61	36.23
	Ahlupur	5.14	3.25	22.16	34.07	35.44
Bikaner (Rajasthan)	Siyasar Chaugan	3.28	16.77	19.06	32.81	28.07
Bikaner (Rajasthan)	17 Kyd	1.57	29.80	15.83	29.80	23.00
Churu (Rajasthan)	Thailasar	0.00	38.24	14.71	23.53	23.53
Churu (Rajasthan)	Jaswantpura	0.00	41.67	16.67	20.83	20.83
Ganganagar (Rajasthan)	Dungarsinghpura	1.70	17.86	14.09	28.07	38.27
Ganganagar (Rajasthan)	Takhranwali	0.00	41.18	12.50	24.45	21.88
Hanumangarh (Rajasthan)	6 BGP-A	0.00	17.65	15.93	41.34	25.08
Hanumangarh (Rajasthan)	Birkali	0.00	43.60	12.80	21.80	21.80

Source: Authors’ calculation from Field Survey Data, 2019.

It follows from the above discussion that the households in the sample villages of North Indian districts are multidimensionally poor due the deprivations mostly in education, standard of living, economic and social security dimensions. In these dimensions, interventions are required to reduce the level of such deprivations. However, such policy intervention requires the identification of the specific indicators in which deprivations are relatively higher. The outcomes of such explorative analyses are summarized in Tables 9–11.

In 28 sample villages of North Indian districts, a total of 6,646 number of individuals live in 1,620 surveyed households. Of these, the number of individuals deprived in the indicators of health and education dimensions are revealed from the following points (see Table 9):

In the ‘nutrition’ indicator, a total of 365 individuals are deprived (5.49%) of which, 73 are in Kalar village of Rajouri district followed by 69 in Ranghrial village of Mansa district and 47 in the Rehean village of Rajouri district. Furthermore, a total of 198 individuals are deprived in this indicator in the Rajouri district (1.98%) followed by 102 in the Mansa district (1.53%).

In the ‘child mortality’ indicator, only 55 individuals are deprived, of which 44 are located in the Mansa district.

In the ‘years of schooling’ indicator, a total of 205 individuals are deprived (3.08%), of which 137 individuals are in the sample districts of Rajasthan, Bikaner counting the highest 50.

In the ‘school attendance’ indicator, a total of 211 individuals are deprived (3.17%), of which 129 individuals are in the sample districts of Rajasthan, Bikaner counting the highest 53.

Table 9.

Number of Individuals Deprived in Health and Education Indicators

Selected North Indian Districts	Sample Villages	Health Dimension		Education Dimension
Selected North Indian Districts	Sample Villages	Nutrition	Child Mortality	Years of Schooling	School Attendance
Rajouri (J&K)	Khyore	5	0	0	0
	Khablam	15	0	6	2
	Nadian	3	0	0	0
	Rehean	47	0	0	0
	Ghalian	28	0	0	0
	Kalar	73	0	0	5
	Nah	27	0	0	2
Bathinda (Punjab)	Ramniwas	0	0	2	5
	Karamgarh Sattran	14	0	8	0
	Bandi	6	0	4	10
	Nathana	10	0	10	3
	Gurusar Joga	4	0	6	9
	Adampura	4	0	4	6
	Kamalu	7	3	0	3
	Harnam Singh Wala	9	5	12	4
Mansa (Punjab)	Aklia	0	0	2	0
	Kot Dharmu	0	15	0	6
	Gehle	22	4	7	7
	Ranghrial	69	6	5	7
	Ahlupur	11	19	6	13
Bikaner (Rajasthan)	Siyasar Chaugan	9	0	23	23
Bikaner (Rajasthan)	17 Kyd	0	3	27	30
Churu (Rajasthan)	Thailasar	0	0	16	10
Churu (Rajasthan)	Jaswantpura	0	0	5	5
Ganganagar (Rajasthan)	Dungarsinghpura	2	0	12	9
Ganganagar (Rajasthan)	Takhranwali	0	0	17	15
Hanumangarh (Rajasthan)	6 BGP-A	0	0	19	19
Hanumangarh (Rajasthan)	Birkali	0	0	18	18
Total		365	55	209	211

Source: Authors’ calculation from Field Survey Data, 2019.

In the multidimensional perspective of poverty, the standard of living dimension keeps a wide significance as it complements to the monetary measure of unidimensional poverty. The following are the key observations on deprivation pattern in this dimension (see Table 10):

In the ‘cooking fuel’ indicator, a total of 658 individuals (9.90%) are deprived in the sample villages of North Indian districts. The highest deprivations are observed in Ranghrial and Ahlupur villages of Mansa district of Punjab (1.49%). Overall, the deprivation rate in Mansa district is highest in the use of healthy cooking fuel.

In the ‘sanitation’ indicator, a total of 827 individuals are deprived in the selected districts of North India (12.44%). Such deprivation is highest in the Rehean village (2.86%) in Rajouri district followed by Kalar village (1.72%) in Rajouri and Kot Dharmu village (1.64%) in Mansa district. Overall, the deprivation rate in Rajouri district is highest in terms of unhealthy sanitation.

In the ‘drinking water’ indicator of standard of living, a total of 325 individuals (4.89%) are deprived of not getting safe drinking water facilities. Of this, only Mansa district has 167 individuals who are deprived of this indicator.

In the ‘electricity’ indicator, a total of 184 individuals (2.77%) are deprived of not getting fair advantages of electricity supply. Of this, Adampura village of Bathinda district has 68 individuals who are deprived of the uses of electricity.

In the ‘housing’ indicator, a total of 394 individuals (5.93%) are deprived in selected North Indian districts for not having good housing facilities. This problem is visible in the sample villages of Rajouri, Bathinda and Mansa districts.

The largest number of individuals (1,346 or 20.25%) are deprived in the ‘asset ownership’ indicator of the standard of living dimension. This problem spreads over all the districts, of which the Rajouri district is the most deprived (6.05%).

In the ‘agricultural land holding’ indicator, a total of 480 individuals are deprived in selected districts of North India (7.22%). This problem is highest in the Mansa district followed by Bathinda district of Punjab. Rajasthan also shows the presence of such deprivation.

Table 10.

Number of Individuals Deprived in Standard of Living Indicators

Selected North Indian Districts	Sample Villages	Cooking Fuel	Sanitation	Drinking Water	Electricity	Housing	Asset Ownership	Ag. Land Holdings
Rajouri (J&K)	Khyore	0	7	5	0	7	12	0
	Khablam	5	16	0	4	28	135	2
	Nadian	0	3	3	0	0	6	0
	Rehean	9	190	23	15	9	82	0
	Ghalian	9	18	0	0	14	36	0
	Kalar	14	114	33	28	9	102	61
	Nah	7	7	0	0	22	29	4
Bathinda (Punjab)	Ramniwas	45	0	0	0	0	45	8
	Karamgarh Sattran	6	28	13	31	4	49	12
	Bandi	15	18	7	0	8	31	15
	Nathana	0	0	0	0	17	46	13
	Gurusar Joga	57	43	23	4	26	80	20
	Adampura	22	22	13	68	8	0	3
	Kamalu	0	0	0	0	16	37	18
	Harnam Singh Wala	39	37	16	3	17	68	27
Mansa (Punjab)	Aklia	29	29	16	7	16	71	19
	Kot Dharmu	6	109	49	0	37	128	34
	Gehle	48	39	11	0	20	0	29
	Ranghrial	99	57	66	0	57	0	52
	Ahlupur	99	73	25	13	43	145	55
Bikaner (Rajasthan)	Siyasar Chaugan	45	10	22	7	17	63	19
	17 Kyd	30	5	0	0	5	42	24
Churu (Rajasthan)	Thailasar	7	0	0	1	3	16	8
Churu (Rajasthan)	Jaswantpura	3	0	0	1	0	5	5
Ganganagar (Rajasthan)	Dungarsinghpura	10	0	0	1	1	29	17
Ganganagar (Rajasthan)	Takhranwali	7	2	0	1	0	17	7
Hanumangarh (Rajasthan)	6 BGP-A	42	0	0	0	7	54	17
Hanumangarh (Rajasthan)	Birkali	5	0	0	0	3	18	11
Total		658	827	325	184	394	1,346	480

Source: Authors’ calculation from Field Survey Data, 2019.

The economic and social security dimensions of well-being are critical for ensuring capability and empowerment of individuals. Hence, deprivations in these dimensions can push individuals into the miseries of multidimensional poverty. The key observations on the deprivations in the indicators of these dimensions are as follows (see Table 11):

In the ‘employment’ indicator, a total of 1,391 individuals (20.93%) are deprived in the selected North Indian districts. The deprivation for not being employed in the formal sector is highest in the Mansa district followed by Bathinda, Rajouri and selected districts of Rajasthan.

In the ‘indebtedness’ indicator of economic dimension, a total of 516 individuals (7.76%) are deprived in the selected districts. This kind of deprivation is highest in Mansa district followed by Bathinda and Bikaner districts.

In the ‘health insurance’ indicator of social security dimension, a total of 1,436 individuals (21.61%) are deprived in the selected districts. The disadvantage of not having health insurance is highest in the Mansa district followed by Bathinda, Rajouri and Bikaner districts.

In the ‘old age pension’ indicator of social security dimension, a total of 607 individuals (9.13%) are deprived in selected districts. Such deprivation is highest in the Mansa district followed by Rajouri and Bathinda districts.

It is observed that the multidimensional deprivation is more on counts of social security (health insurance indicator) and economic (employment indicator) dimensions. Since a large deprivation count is seen for not being employed in the formal sector, the immediate implication is unstable income and insecure job profile. Such a distorted economic status forces people to leave their health security unattended, thereby challenging various aspects of their social security. Furthermore, the challenges of social security of people make them vulnerable to deprivations in health, education and living standards, which adversely affect the capacity to work, save and invest. Therefore, people become the victims of the vicious circle of poverty. Therefore, policy intervention is warranted to break this vicious circle. At this point in time, we can put forward two policy stances—first, it is essential to ensure the strict implementation of the minimum wage policy in the informal sector and, second, it is required to look at the effective implementation of the existing social security measures.

Table 11.

Number of Individuals Deprived in Economic and Social Security Indicators

Selected North Indian Districts	Sample Villages	Economic Dimension		Social Security Dimension
Selected North Indian Districts	Sample Villages	Employment	Indebtedness	Health Insurance	Old Age Pension
Rajouri (J&K)	Khyore	12	0	12	7
	Khablam	30	0	35	23
	Nadian	6	0	6	6
	Rehean	86	0	90	51
	Ghalian	36	0	36	18
	Kalar	103	0	117	71
	Nah	22	0	29	11
Bathinda (Punjab)	Ramniwas	43	35	35	15
	Karamgarh Sattran	49	16	49	21
	Bandi	31	7	31	3
	Nathana	46	33	46	21
	Gurusar Joga	69	42	80	38
	Adampura	31	16	31	9
	Kamalu	37	18	37	17
	Harnam Singh Wala	68	35	63	4
Mansa (Punjab)	Aklia	71	36	71	40
	Kot Dharmu	135	69	115	67
	Gehle	56	13	56	22
	Ranghrial	104	26	114	63
	Ahlupur	130	69	139	68
Bikaner (Rajasthan)	Siyasar Chaugan	49	41	63	14
Bikaner (Rajasthan)	17 Kyd	38	19	42	2
Churu (Rajasthan)	Thailasar	16	0	16	0
Churu (Rajasthan)	Jaswantpura	5	0	5	0
Ganganagar (Rajasthan)	Dungarsinghpura	29	4	29	16
Ganganagar (Rajasthan)	Takhranwali	17	2	17	0
Hanumangarh (Rajasthan)	6 BGP-A	54	35	54	0
Hanumangarh (Rajasthan)	Birkali	18	0	18	0
Total		1,391	516	1,436	607

Source: Authors’ calculation from Field Survey Data, 2019.

V. Conclusions

It is often argued that unidimensional monetary perspective of poverty does not capture many aspects of human deprivations, and therefore, the concept of poverty should be studied in the multidimensional perspective. Since monetary poverty does not capture many deprivations, policy interventions for alleviating poverty from society is often seen as a futile exercise. This entails formation and implementation of efficient inclusive distributive policies to come out of the clutches of multidimensional poverty. By examining different aspects of multidimensional poverty in selected North Indian districts, this article found that the households in the sample villages of these are multidimensionally poor primarily because of the deprivations in education, standard of living and economic and social security dimensions. Further decomposing these dimensions, we found that these households mostly deprived because of lack of access to clean fuel for cooking, non-availability of improved sanitation facilities, lack of ownership of household amenities for a decent living, underproductive engagements in informal jobs and lack of access to health insurance. The districts of Punjab and Rajasthan under the study are basically agrarian, and that of J&K is both horticulture- and tourism-dependent. Most importantly Indian agriculture, on average, is characterized by lower productivity, underemployment and lower labour wages. Hence, the households depending mainly on agriculture are unable to cope with poverty. Furthermore, the households of Rajouri district depending on horticulture and tourism are also facing many difficulties in managing poverty due to unfavourable topography and continuing rebellious activities. The solution lies in promoting non-farm activities, without substituting rather complementing and supplementing farm activities, through the implementation of micro and small entrepreneurial projects. However, this requires the urbanization of these districts. The data reveal that these states are poorly urbanized. Promoting urbanization can enable optimal use of labour productivities in non-farm activities and help to lift masses from the bottom of the pyramid. Moreover, a radical transformation in education is essential for promoting urbanization and non-farm activities. The desired skill formation can be ensured through the promotion of technical and vocation education starting from the school level. Here comes the question of affordability for such education, and the government can bridge the gap by advancing subsidized education and/or by providing returnable financial assistance, study loans, etc. In everything, households in multidimensional poverty are required to be effectively covered under the schemes and programmes of food and social securities. This can help the beneficiaries to fight against their vulnerabilities and useful for leading sustainable livelihoods. Overall, integrated village development programmes can go a long way in rescuing poor from the vicious circle of poverty. Despite the elegancy of the outcomes, this article is limited in purposefully selecting the sample units, and not determining the statistical significance of factors responsible for multidimensional poverty by using regression estimations.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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